Functional Connectivity Changes in Human Brain Networks from 2 Hz Rhythmic Muscle Contraction to the Hand: A pilot study

Functional Connectivity Changes in Human Brain Networks from 2 Hz Rhythmic Muscle Contraction to the Hand: A pilot study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Functional Connectivity Changes in Human Brain Networks from 2 Hz Rhythmic Muscle Contraction to the Hand: A pilot study William Stauber, Tyler McGaughey, Nick Evans, Alyssa Chaffin, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4548047/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract For patients undergoing prolonged bed rest, inactivity results in a decline in multiple physiological systems that can be attenuated by physical exercise in the hospital such as walking. In addition, non-voluntary activation of skeletal muscles can produce some benefits similar to walking. We hypothesize that rhythmical muscle stimulation of small muscles of the hand, in contrast to sensory stimulation of the skin, will lead to patterns of functional connectivity in the brain that reflect central mechanisms behind some of the physiological benefits afforded by exercise. Using a 2x2 design, healthy participants (age 21 to 31) underwent resting-state functional magnetic resonance imaging (rsfMRI) immediately before and after a45 minute treatment with either muscle stimulation (2 Hz) or skin stimulation (100 Hz) to the left hand. Six of eight participants responded to the rhythmical muscle contractions in a manner consistent with endorphin release. Functional connectivity data were analyzed using CONN toolbox software. Relative to skin stimulation, rhythmic muscle stimulation led to significant differences in connectivity with regions associated with the autonomic and limbic systems, including the hypothalamus, amygdala, periaqueductal grey, thalamus, basal ganglia, plus insulae and cingulate cortices. In addition, the rhythmic muscle stimulation led to changes in several previously identified resting state networks. In conclusion, distinct networks of the human central nervous system appear to play roles in the outcomes reported for therapeutic use of rhythmical muscle stimulation of hand muscles. These outcomes support the use and future development of similar treatment protocols for bedridden patients or people unable to engage in daily exercise. Biological sciences/Biological techniques/Imaging Biological sciences/Biological techniques/Imaging/Functional magnetic resonance imaging Biological sciences/Physiology/Neurophysiology Exercise bedrest patient autonomic nervous system (ANS) hypothalamus resting state functional magnetic resonance imaging (rsfMRI) rhythmic hand muscle stimulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Physical activity is generally accepted as a requirement for optimal health. In people subjected to prolonged bedrest confinement, inactivity results in declines in multiple physiological systems (Convertino et al., 1997 ) that can be attenuated by physical exercise even while in bed (Greenleaf, 1997 ). Dynamic exercise and electrical stimulation of somatic afferents in skeletal muscles are known to produce post-activation reductions in blood pressure (Chen and Bonham, 2010 ) and reductions in pain (Song et al., 2022 ) that often outlasts the time of muscle contractions by many hours. For example, post-exercise hypotension results from changes in brainstem nuclei involved in blood pressure regulation responding to skeletal muscle afferent input (Chen and Bonham, 2010 ). Electrical muscle stimulation has also been applied to critically ill patients, revealing that such procedures have an effect on microcirculation (Gerovasili et al., 2009 ). In addition, bioactive growth hormone (bGH) is released from the pituitary (muscle afferent-pituitary axis) in response to exercise or proximal stimulation of severed somatic nerves (McCall et al., 2001 ), revealing that muscle contractions can produce multiple desired responses that could enhance health resulting from somatic afferent input. Patients who walk during hospitalization are generally reported to have better outcomes (Cohen et al., 2019 , Fisher et al., 2010 ); however this activity may require physical assistance by trained staff, walking spaces may be limited by equipment and qualified personnel resources (King et al., 2021 ), or the patient may simply not be ambulatory. Since walking involves rhythmically active skeletal muscles, somatic afferent activity from skeletal muscles seems likely to play a role in enhanced recovery. For example, stimulation of the hand muscles at low frequencies, similar to walking cadence, has been shown to enhance ulcer healing (Kaada, 1983 ), increase vasodilation in Raynaud’s patients (Kaada, 1982 ), and increase plasma endorphins (Facchinetti et al., 1986 ). Some of these outcomes also outlast the activity by several hours and may be mediated by factors that result from activating central nervous systems (CNS) regions involved in autonomic control: These regions include the hypothalamus, periaqueductal gray, nucleus tractus solitarius (NTS; in the dorso-lateral medulla) (Knigge and Joseph, 1984 , Veening et al., 2012 ), and cortical regions implicated in healing and homeostasis, such as the insula plus limbic system structures including the cingulate cortex (Cottam et al., 2018 , Craig, 2009 , Kobayashi and Koitabashi, 2016 , Macey et al., 2012 , Voigt et al., 2022 ). Given the above rationale for CNS involvement in exercise benefits, the present study sought to identify the effects in the brain resulting from 45 minutes of non-voluntary, rhythmical muscle stimulation of small muscles of the hand (at 2 Hz), compared to 45 minutes of continuous sensory stimulation of the skin (at 100 Hz) over the same area as a critical control, using resting-state functional magnetic resonance imaging (rsfMRI). Measurements of whole brain rsfMRI allows for an objective measure of potential CNS network processing changes, and has been applied for assessing therapeutic interventions such as walking (Morris et al., 2022 ): This method is non-invasive and only requires participants to lie still in the scanner (Lv et al., 2018 ), and enables the assessment of synchronous activations between brain areas and networks that occur during the absence of any specific cognitive task. From the present study, a better understanding of any CNS mechanisms that may underlie the putative health effects of non-voluntary, rhythmical muscle stimulation of small muscles of the hand will likely lead to improvements in therapeutic protocols involving such treatments, especially for bedridden patients or people unable to engage in daily exercise. Materials and Methods Participants Although the therapeutic approach is ultimately expected to be applied to relatively older populations, to demonstrate feasibility for this pilot study we recruited a closely matched set of eight (8) right-handed young adult participants (average age 26 years; four females, aged 21, 24, 25 and 28, and four males aged 23, 27, 27, and 31). Participants were screened to ensure their likely availability and compliance with completing two MRI scanning sessions over the course of one to four weeks (six participants’ sessions were 1 week apart, one was 2 weeks and another was 4 weeks). Handedness was assessed based on 12 questions from the Edinburgh Handedness Inventory (Oldfield, 1971). All participants had higher education and were either students or worked in their professions. All could perform activities of daily living independently, had a self-reported normal range of hearing and vision, and none had any previous history of major neurological or psychiatric disorders. Informed consent was obtained for all participants, and all methods were performed in accordance with guidelines approved by the West Virginia University Institutional Review Board (Association for the Accreditation of Human Research Protection Programs, Inc., (AAHRPP) Accredited). Experimental Design Using a 2x2 single blinded design, participants were informed that the purpose of this study was to examine how the brain responds to different potentially therapeutic effects of electrical stimulation therapy to the left hand using a commonly available transcutaneous electrical neural stimulation (TENS) unit (ComfyTENS, TENSproducts Inc., Grand Lake, CO 80447). The participants were told that there would be two scanning sessions on different weeks, including one session with “treatment A” (Condition A) and the other with “treatment B” (Condition B): This order was counterbalanced across participants. The first session used condition A-B (2Hz then 100Hz) for participants 2,4,5, and 7), and scanning personnel were single blinded as to which treatment condition was being administered. Upon a given visit, each participant was seated in a comfortable chair outside the MRI scanning room (waiting room) where they watched 10 minutes of a video DVD (“Planets” by National Geographic), which served to help normalize participants’ arousal level and state of mind prior to resting state functional magnetic resonance imaging (rsfMRI), as pre-scanning task effects have been reported to be potential factors that could bias rsfMRI outcomes(Hasson et al., 2009, Lewis et al., 2009, Van Dijk et al., 2010). Heart rate and blood pressure was collected during this time. Participants then underwent a baseline “pre-stimulation” MRI scanning session that included three consecutive rsfMRI scans plus an anatomical scan, as detailed below. They were instructed to keep as still as possible and have their eyes closed during rsfMRI scanning(Agcaoglu et al., 2019). Participants came out of the scanner and back to their chair in the waiting room where vital signs were taken again. Gel electrodes were placed on their hand (Figure 1A) and electrical stimulation of either Condition A or Condition B was set at a comfortable level for 45 minutes while they sat resting. Vital signs were taken again after the electrical stimulation treatment. For Condition A, the hypothesized treatment condition that would lead to physiological health benefits, the left hand was stimulated with a symmetrical biphasic current (duration of 100 microsec) at 2 Hz frequency, and at an amplitude set to elicit visible rhythmic muscle contractions of the hand, and still be deemed comfortable by the participant. Condition B was a control condition, which consisted of an amplitude setting that stimulated the skin to only produce sensation at 100 Hz and no muscle movement. Next, participants went back into the MRI scanner for a post-stimulation rsfMRI scanning session (three rsfMRI plus one anatomical scan) using identical scanning parameters as for the pre-stimulation scanning. Vital signs were again collected at the end of this MRI scanning session. Figure 1 near here Data acquisition The imaging was conducted on a 3 Tesla Siemens Verio MRI scanner using a 12-channel head coil. We acquired whole-head brain volumes using a resting state design (36 axial interleaved slices at 4 x 4 x 4 mm 3 isometric voxel resolution). Blood oxygen-level dependent (BOLD) signals were collected using a continuous acquisition echo planar pulse sequence (ep2d: TR = 2.210 sec, TE = 27 msec, FOV = 256 mm, 75 degree flip angle). Three sequential rsfMRI scans were collected, each lasting ~6 min (164 measurements per scan), typically with only a very short break between scans (< 30 sec) to check the status of the participant. Whole brain T1-weighted anatomical MR images were collected using a magnetization-prepared rapid acquired gradient-echo (MPRAGE) pulse sequence (1.5 mm sagittal slices, 0.625 x 0.625 mm 2 in-plane resolution, FOV = 195 mm, TI = 1100 msec, 5 min 13 sec). Data preprocessing Functional and structural images were analyzed using a Matlab/SPM-based software CONN (version 22.a) (Morfini et al., 2023, Nieto-Castanon and Whitfield-Gabrieli, 2022, Whitfield-Gabrieli and Nieto-Castanon, 2012). Data were preprocessed using a standard pipeline in Statistical Parametric Mapping software (SPM12, Wellcome Department of Cognitive Neurology, University College London) (Penny et al., 2007)running under Matlab Release 2021b (The MathWorks, Inc., Natick, MA, United States) and Unix running on an Macintosh computer. Our hypothesis was that autonomic systems would be involved in physiological changes induced by the 2 Hz rhythmic muscle stimulation paradigm: Thus, in addition to the 132 brain regions of interest (ROIs), and identified resting state networks provided in the CONN atlas, we created five additional ROIs related to the autonomic system using the MNI152 brain atlas as the template (see Introduction). Guided by drawings from a neuroanatomy atlas (Haines, 2019), this included a left and right hypothalamus, left and right dorso-lateral ROIs of the medulla oblongata that would encompass the solitary nucleus and tracts (aka. nucleus tractus solitarius, NTS), plus a bilateral periaqueductal gray (PAG) volume. These volumetric ROIs (see Table 1) are available for download as NIFTI files (see journal website). Raw-level MRI anatomical images (all slices) and rsfMRI functional runs (all slices and all scans) were visually inspected across all participants using the CONN toolbox. Anatomical data were segmented into grey matter, white matter, and CSF tissue classes using SPM unified segmentation and normalization algorithm(Hallquist et al., 2013, Nieto-Castanon, 2020)with the default IXI-549 tissue probability map template. The three functional runs were concatenated into one file (NIFTI; *.nii format) using AFNI software plugin 3dTcat (Cox, 1996). All raw data were then uploaded into the CONN toolbox for subsequent analyses. Denoising In addition, functional data were denoised using a standard denoising pipeline (Nieto-Castanon, 2020)including the regression of potential confounding effects characterized by white matter timeseries (5 CompCor noise components), CSF timeseries (5 CompCor noise components), session effects and their first order derivatives (2 factors), and cubic trends (4 factors) within each functional run, followed by bandpass frequency filtering of the BOLD timeseries between 0.008 Hz and 0.09 Hz. CompCornoise components within white matter and CSF were estimated by computing the average BOLD signal as well as the largest principal components orthogonal to the BOLD average within each subject's eroded segmentation masks(Behzadi et al., 2007, Chai et al., 2012). From the number of noise terms included in this denoising strategy, the effective degrees of freedom of the BOLD signal after denoising were estimated to range from 344.7 to 345 (average 345) across all subjects(Nieto-Castanon, 2020). First-level analyses Seed-based connectivity maps (SBC) and ROI-to-ROI connectivity matrices (RRC) were estimated characterizing the patterns of functional connectivity with either (A) 21 ROIs related to autonomic system (see Table 1), or (B) ROIs for analyses utilizing the complete CONN atlas and networks (132 ROIs plus 5 manually created ROIs, see above). This method included predefined shape and locations derived from the human connectome project (HCP) atlas (Nieto-Castanon, 2020) adjusted to each volume, and were used to assess eight resting state networks (RSNs) including Default Mode, Cerebellar, Fronto-Parietal, Language, Salience, SensoriMotor, Dorsal Attention, and Visual networks. Functional connectivity strength was represented by Fisher-transformed bivariate correlation coefficients from a weighted general linear model (weighted-GLM), defined separately for each pair of seed and target areas, modeling the association between their BOLD signal timeseries. In order to compensate for possible transient magnetization effects at the beginning of each series of scans, individual scans were weighted by a step function convolved with an SPM canonical hemodynamic response function and rectified. Group-level analyses Group-level (Second-level) analyses applied to each of the above first-level analyses were performed using a General Linear Model (GLM)(Nieto-Castanon, 2020). For each individual voxel a separate GLM was estimated, with first-level connectivity measures at this voxel as dependent variables (one independent sample per subject and one measurement per task or experimental condition, if applicable), and groups or other subject-level identifiers as independent variables. Voxel-level hypotheses were evaluated using multivariate parametric statistics with random-effects across subjects and sample covariance estimation across multiple measurements. Inferences were performed at the level of individual clusters (groups of contiguous voxels). Cluster-level inferences were based on parametric statistics from Gaussian Random Field theory(Nieto-Castanon, 2020, Worsley et al., 1996). Primary results were derived by thresholding using a combination of a cluster-forming p < 0.01 voxel-level threshold plus a familywise error corrected p-FWE< 0.01 cluster-size threshold (Chumbley et al., 2010).Cluster-level inferences based on Threshold Free Cluster Enhancement analyses(Smith and Nichols, 2009) aim at removing the dependency of other cluster-level inference methodologies on the choice of an a priori cluster-forming height threshold. Results Behavioral examination of effects of 2 Hz rhythmic muscle stimulation After receiving 2 Hz stimulation to the hand for 45 min that induced rhythmical muscle contractions (Condition A), six of eight participants reported the feeling of being relaxed or sleepy, consistent with endorphin release (Facchinetti et al., 1986, Veening and Barendregt, 2015). Notably, this effect was not reported after the 100 Hz skin stimulation condition by any of the eight participants. No significant differences in vital signs across sessions were observed (data not shown). This may be reflective of a floor effect since all participants were placed into a relatively relaxed condition in the MRI waiting room area during the pre-scanning time periods (see Methods). Examination of 2 Hz rhythmic muscle stimulation on autonomic systems The region of interest (ROI) selection for analyses included the 132 atlas ROIs provided by the CONN software, the pre-defined resting state networks therein, plus the five additional ROIs created manually (Figure 1B). To refine our search to autonomic nervous systems, we also analyzed an a priori set of 21 ROIs (Table 1) of predefined shape and location based on reported involvement in autonomic and limbic system functions (see Introduction). For Condition A (2 Hz muscle stimulation), the ROI-to-ROI functional connectivity analysis using the spatial pairwise clustering (SPC) method revealed significant changes in interconnectivity between several regions, including the thalamus, basal ganglia, insula and anterior cingulate cortex (Figure 2A, Table 2A; p < 0.01, FWE-corrected, two-sided). For Condition B (100 Hz skin stimulation), the analysis revealed significant changes between the left putamen and left insula (Figure 2B, Table 2B; p < 0.01, FWE-corrected, two-sided). Moreover, as a critical control, there were significant differences between the 2 Hz muscle stimulation relative to changes occurring after the 100 Hz skin stimulation condition as the baseline (Figure 2C-D;Table 2C).This differential analysis for the 2 Hz rhythmic muscle activation condition additionally revealed significant involvement of the left and right hypothalamus ROIs, right amygdala, right hippocampus, and PAG. Table 1 near here Figure 2 near here Table 2 near here Examination of the hypothalamus through Seed-to-Voxel analyses The left and right hypothalamus ROIs were separately selected as seed regions to reveal connectivity patterns between the ROIs in the CONN atlas plus additional ROIs we created (see Methods). With the significance threshold dropped to voxel-wise p < 0.05, and a cluster size correction of p < 0.05, this analysis for the after (vs before) 2 Hz muscle stimulation revealed a number of brain regions showing change in interconnectivity. These changes were greater for the left hypothalamus as the seed region (Figure 3, Table 3A; 2,793 mm 3 ) than for the right hypothalamus (data not illustrated, Table 3B; 1,891 mm 3 ). Figure 3 near here Table 3 near here Examination of Predefined Network Changes Significant differences in functional connectivity network patterns after (vs before) 2 Hz muscle stimulation (Condition A; Figure 4A, Table 4A;p < 0.01, FWE-corrected, two-sided) included the Default Mode, Cerebellar, and Fronto-Parietal networks. In contrast, patterns after (vs before) 100 Hz skin stimulation (Condition B; Figure 4B, Table 4B;p < 0.01, FWE-corrected, two-sided) led to changes in the Fronto-Parietal, Language, Salience, and SensoriMotor networks. Significant differences in functional connectivity patterns after (vs before ) 2 Hz muscle stimulation (Condition A) relative to patterns after (vs before) 100 Hz skin stimulation (Condition B) as a baseline control was also evident (data not shown), involving all of the eight a priori networks provided by the CONN toolbox (Default Mode, Cerebellar, Fronto-Parietal, Language, Salience, SensoriMotor, Dorsal Attention, and Visual networks). However, exploration of the significance of these global network changes was beyond the scope of the present study. Figure 4 near here Table 4 near here Discussion The main finding of the present study was that 2 Hz rhythmic muscle stimulation to the hand, in contrast to 100 Hz skin (sensory) stimulation to the same region as a critical control, led to significant functional connectivity pattern changes in autonomic and limbic systems of the central nervous system (CNS) in humans. This included the hypothalamus, amygdala, PAG, thalamus, basal ganglia, plus insulae and anterior cingulate cortices. These changes were generally expected given studies of animal models (see Introduction). The 2 Hz muscle stimulation also led to changes in several predefined rsfMRI networks. Our results provide objective evidence that CNS changes immediately accompany physical therapy treatments when using ~ 2 Hz stimulation that elicits rhythmic muscle activity. These outcomes are addressed next in the context of network connectivity patterns followed by potential physiological mechanisms. Connectivity changes after 2 Hz rhythmic muscle stimulation. In our study of humans, rhythmic muscle stimulation (2 Hz) to the hand led to significant changes in autonomic and limbic systems of the CNS not seen from sensory stimulation (100 Hz) of the skin. Thus, specific CNS network connectivity changes as revealed by rsfMRI analyses occurred because of non-voluntary, rhythmic muscle contractions but not skin stimulation. Of particular note was involvement of the right insula after 2 Hz muscle stimulation (Fig. 2 A) and left insula after 100 Hz skin stimulation (Fig. 2 B), both interacting with the left putamen. The insular cortices have also been shown to be responsive to autonomic challenges (Macey et al., 2012 ). Moreover, the dorsal-anterior portions of the insula (right more so than left) are proposed to function as levers for network switching, serving as a critical gatekeepers to executive controls by integrating internal information (interoceptive, autonomic states) with external (exteroceptive) multisensory stimuli (Craig, 2005 , Molnar-Szakacs and Uddin, 2022 ). This network resting state change may explain why changes in the eight major resting states were significantly altered after 2 Hz muscle stimulation, reflecting down-stream changes in cognitive states that occur during the somnolent phase after exercise. Conversely, modulation of subcortical limbic and paralimbic structures, including the hypothalamus, amygdala, hippocampus, caudate, putamen and insula may constitute the steps by which multiple physiological systems, and the resulting diverse therapeutic effects of physical therapies, may be regulated, as addressed next. Physiological changes with rhythmic muscle stimulation Major public health benefits of physical activity have been reported (Macera et al., 2003 ). Most of these physical activities involve rhythmical or intermittent activations of skeletal muscles for periods of around 30 minutes or longer, performed multiple times a week. Active skeletal muscles can release compounds called myokines (Lee and Jun, 2019 ); some have remote actions on tissues such as IL-6 for liver and adipose tissue (Febbraio and Pedersen, 2002 )and irisin for the hippocampus (Jodeiri Farshbaf and Alvina, 2021 ). These myokines are likely involved with the some of the positive effects of exercise on health. However, there are also somatic nerves from active muscles that independently connect to pathways in the brain. For example, somatic interventions uniquely stimulate the release of bioassayable growth hormone (bGH) from the pituitary(Hymer et al., 2020 )in response to hypothalamic input. Growth hormone action would increase bone density and skeletal muscle mass. Our study aimed to explore some of the connections in the brain, including the hypothalamus that respond to strong, but not painful, rhythmical contractions of hand muscles known to activate somatic afferent nerves (Group III) (Kenney and Seals, 1993, Sjolund and Eriksson, 1979), which are also part of the interoception network (Craig, 2009 ). Interoception results from afferent input responding to alterations in the physiological condition of the body(Craig, 2009 ), in our case, changes resulting from contracting hand muscles. In contrast, exteroception results from stimuli from the external world such as skin stimulation ( e.g. , touch) to direct attention prior to any executive response (Cottam et al., 2018 ). With the exception for fatiguing contractions (Noakes, 2012 ), the role of afferent nerve activity from skeletal muscles to correct imbalances in homeostasis has received little attention. Yet, in response to skeletal muscle afferents, neuroendocrine pathways from the thalamus control energy balance (Ibeas et al., 2021 ) and support growth of muscle by the release of bioassayable GH by the pituitary (McCall et al., 2000). In addition, the nervous system senses and responds to signals from skeletal tissue ( i.e. skeletal/bone interoception) - crucial for maintaining bone homeostasis (Xiao et al., 2023 ). Surprisingly, rhythmical activation of hand muscles appears to have enhanced healing effects on chronic diabetic ulcers in the feet(Kaada, 1983 ). This outcome is possibly mediated by increased global vasodilation due to the actions of vasoactive intestinal peptide (VIP)(Henning and Sawmiller, 2001 ) and/or release of bGH or other factors. Thus, long-term increased circulation of these factors would probably help maintain healthy tissues(Dvorakova, 2005 ) even in the absence of voluntary physical activity. Normally, the onset of physical exercise causes an increase in blood pressure and heart rate, mediated by reflexes involving muscle afferents that inhibit baroreflexes so that increased blood pressure can result (Chen and Bonham, 2010 ). For example, moderate-intensity rhythmic handgrip exercise resulted in increased blood pressure in young healthy men and women (Tarumi et al., 2021 ). However, from our protocol of non-voluntary, rhythmical hand muscle contractions, blood pressure and heart rates did not change in our normotensive subjects. In contrast, 4 patients in our intensive care unit (ICU) with elevated vitals responded to our hand stimulation protocol with decreases in heart rate and mean blood pressure during and immediately after treatment (Stauber and Swisher, unpublished observations). In another study using a similar protocol, patients with mild to moderate hypertension experienced a decrease in blood pressure and heart rates after hand stimulation(Kaada et al., 1991 ). Additionally, following 2 weeks of daily hand stimulation, the same patients had decreased baseline blood pressures on the day of examination ( ibid ), providing indirect evidence for a training effect – a possible resetting of the autonomic nervous system by the peripheral conditioning stimulus ( i.e. , non-voluntary, rhythmic hand muscle activation) acting on the brain. Most chronic diseases have increased sympathetic tone (Porzionato et al., 2020 ) including heart failure and some neurological diseases (Aimo et al., 2021 ). Exercise training and electrical stimulation of skeletal muscles appear to be viable options to reset the balance of the ergoreflexes ( ibid ) involved in autonomic control. Therefore, in those patients unable to perform physical exercise or walking, our therapy of daily hand muscle stimulation therapy probably resets the system back to normal mediated by the hypothalamus among other brain regions. Future Directions Reduction in the length of stay and improved patient outcomes are important both for improving quality of life, reducing mortality, and lowering cost of hospitalization. Preliminary studies by our group have revealed that this protocol can be applied effectively to the most severely affected patients in the ICU at Ruby Memorial Hospital by our clinical collaborators. We anticipate that these and similar studies will support the use of non-invasive treatments similar to that used in the present study to decrease the length of stay of patients in the ICU, reduce time on ventilators or assisted ventilation, and improve the quality of life for survivors of intensive care. On a technical note, our study paradigm construct was able to reveal relatively small effect sizes in brain network changes using only 8 participants (before and after conditions). Thus, the neuroimaging approaches used in the present study may be adapted for a wide variety of other rsfMRI studies that entail comparison of a standard 2x2 designs (treatment versus sham/placebo). Limitations While three 6 min rsfMRI scans were collected for each MRI session, they were not contiguous scans. One study revealed that a longer scan length, of 13 minutes or longer, would allow one to capture more low frequency fluctuations (Birn et al., 2013 ). Thus, correlations at these frequencies will have been missed. Also the sizes of the ROI volumes were highly variable, with, for example, the dorso-lateral medulla being relatively small (Table 1 ). These differences in size and definition of ROIs will influence detectable interconnectivity patterns(Song et al., 2016 ). Imaging at higher spatial resolution (smaller voxels and/or higher field strength) would be ideal for future studies addressing brain stem regions to relate the circuitry of humans to various animal models. Collecting blood samples for assays of various biomarkers (such as bGH) would also be important for objectively refining treatment protocols. Declarations Author Contribution JWL and WS wrote the main manuscript text. All other authors contributed to data collection, analyses, and reviewed the manuscript. ACKNOWLEDGEMENTS This work was supported by WVU Centers for Neuroscience NIGMS NIH COBRE grants E15524 and GM103503, plus affiliated WVU Summer Undergraduate Research Internships, and by NIH Grant P20GM103434 to the West Virginia Idea (WV-IDeA) Network of Biomedical Research Excellence (WV-INBRE), and gift from Drs. Susan & Jerry Dorsch. Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Volumetric brain region of interest files (in .nii format) are available as Supplementary Materials online. References Agcaoglu O, Wilson TW, Wang YP, et al. 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Interoceptive regulation of skeletal tissue homeostasis and repair. Bone Res 2023;11(1):48; doi: 10.1038/s41413-023-00285-6. Tables Table 1 . Talairach atlas coordinates (MNI152 atlas) for the 21 selected ROI regions related to the limbic system. Gray cells depict regions manually created and added to the CONN atlas. MNI Atlas Coordinates ROI # ROI name Side x y z Volume 1 Accumbens Left -9.5 -11.5 -7.2 886 2 Accumbens Right 9.4 -12.2 -6.5 668 3 Amygdala Left -23.0 4.9 -17.7 2606 4 Amygdala Right 23.1 4.0 -17.7 2709 5 Caudate Left -12.8 -9.0 9.7 4303 6 Caudate Right 13.3 10.0 10.5 4165 7 Cingulate, Anterior bilateral 0.8 -18.3 24.3 20844 8 Cingulate, Posterior bilateral 0.8 36.6 30.0 19228 9 Hippocampus Left -25.2 23.2 -13.8 6127 10 Hippocampus Right 26.5 21.0 -14.3 5625 11 Hypothalamus Left -4.0 4.8 -9.7 1049 12 Hypothalamus Right 4.0 4.8 -9.7 1049 13 Insular cortex Left -36.4 -1.2 0.1 10648 14 Insular cortex Right 37.4 -2.5 -0.2 10801 15 Medulla, lateral Left -4.0 43.7 -54.4 112 16 Medulla, lateral Right 4.0 43.7 -54.4 112 17 PAG bilateral 0.1 31.1 -8.6 388 18 Putamen Left -24.9 -0.5 0.3 6974 19 Putamen Right 25.5 -1.8 0.3 6469 20 Thalamus Left -10.0 19.2 6.3 10781 21 Thalamus Right 10.8 18.3 6.6 10238 Table 2 . Statistical cluster analyses of the ROI-to-ROI changes associated with the limbic system (n=21 ROIs) for ( A ) After versus Before 2 Hz muscle stimulation, ( B ) After versus Before 100 Hz skin stimulation. Each entry corresponds to a given curve in the connectome ring of Figure 2. A. After > Before 2 Hz stimulation Statistic p-unc p-FDR p-FWE Cluster 1 TFCE = 387.15 0.000081 0.001163 0.002000 Thalamus, R Thalamus, L T(7) = 8.58 - 0.000058 0.008511 Caudate, L Thalamus, L T(7) = 6.57 - 0.000312 0.013092 Thalamus, L Putamen, R T(7) = 5.79 - 0.000671 0.014084 Thalamus, R Putamen, R T(7) = 5.08 - 0.001427 0.018539 Cluster 2 TFCE = 328.99 0.000093 0.001630 0.003000 Caudate, R Caudate, L T(7) = 7.92 - 0.000097 0.008511 Cingulate, Anterior Caudate, R T(7) = 7.65 - 0.000122 0.008511 Cluster 3 TFCE = 243.56 0.000258 0.001936 0.007000 Insular cortex, R Insular cortex, L T(7) = 6.27 - 0.000417 0.013275 Cluster 4 TFCE = 231.35 0.000310 0.001936 0.009000 Thalamus, L Insular cortex, L T(7) = 5.94 - 0.000577 0.013452 B. After > Before 100 Hz stimulation Cluster 1 TFCE = 255.17 0.000341 0.007103 0.009000 Putamen, L Insular cortex, L T(7) = 8.32 - 0.000071 0.009721 C. 2 Hz vs 100 Hz baseline stimulation differences Cluster 1 TFCE = 228.06 0.000033 0.000379 0.001000 Caudate, L Caudate, R T(14) = 7.06 - 0.000006 0.000923 Thalamus, R Thalamus, L T(14) = 6.79 - 0.000009 0.000923 Thalamus, L Caudate, L T(14) = 6.23 - 0.000022 0.001151 Thalamus, L Putamen, R T(14) = 5.38 - 0.000098 0.002738 Thalamus, R Putamen, R T(14) = 5.17 - 0.000142 0.002990 Thalamus, R Caudate, L T(14) = 4.58 - 0.000431 0.006957 Caudate, L Putamen, R T(14) = 4.13 - 0.001016 0.014230 Caudate, R Putamen, R T(14) = 3.95 - 0.001450 0.018564 Thalamus, L Insular cortex, R T(14) = 3.93 - 0.001503 0.018564 Caudate, R Cingulate, Anterior T(14) = 3.51 - 0.003490 0.030292 Cluster 2 TFCE = 176.01 0.000045 0.000379 0.001000 Putamen, L Insular cortex, L T(14) = 6.51 - 0.000014 0.000959 Insular cortex, R Insular cortex, L T(14) = 5.73 - 0.000052 0.002185 Putamen, R Insular cortex, R T(14) = 5.38 - 0.000097 0.002738 Putamen, R Insular cortex, L T(14) = 5.09 - 0.000165 0.003146 Cingulate, Anterior Insular cortex, L T(14) = 3.58 - 0.002989 0.028531 Insular cortex, R Putamen, L T(14) = 3.55 - 0.003200 0.029221 Putamen, R Putamen, L T(14) = 3.45 - 0.003895 0.030292 Cluster 3 TFCE = 84.93 0.000100 0.000432 0.002000 Thalamus, L Insular cortex, L T(14) = 4.65 - 0.000375 0.006564 Cluster 4 TFCE = 73.96 0.000165 0.000614 0.005000 Hippocampus, R Thalamus, R T(14) = 5.34 - 0.000104 0.002738 Hippocampus, R Thalamus, L T(14) = 3.61 - 0.002844 0.028437 Hippocampus, R PAG T(14) = 3.10 - 0.007849 0.043876 Amygdala, R PAG T(14) = 2.80 - 0.014053 0.059167 Cluster 5 TFCE = 73.79 0.000189 0.000615 0.006000 Hypothalamus, R Hypothalamus, L T(14) = 5.21 - 0.000133 0.002990 Hippocampus, R Hypothalamus, R T(14) = 3.89 - 0.001634 0.019060 Amygdala, R Hypothalamus, R T(14) = 3.34 - 0.004873 0.035288 Amygdala, R Hippocampus, R T(14) = 3.23 - 0.005995 0.040361 Table 3 is available in the Supplementary Files section. Table 4 . Statistical cluster analyses of network changes in predefined resting state networks for ( A ) After versus Before 2 Hz muscle stimulation, and ( B ) After versus Before 100 Hz skin stimulation. Each entry corresponds to a given curve in the connectome ring of Figure 4. A. After > Before 2 Hz stimulation Statistic p-unc p-FDR p-FWE Cluster 1 TFCE = 344.98 0.000091 0.008033 0.007000 Cerebellar.Anterior Cerebellar.Posterior T(7) = 10.10 - 0.000020 0.009924 FrontoParietal.LPFC, R DefaultMode.MPFC T(7) = 2.33 - 0.052340 0.168577 Cerebellar.Posterior DefaultMode.MPFC T(7) = 1.14 - 0.292978 0.504573 B. After > Before 100 Hz stimulation Cluster 1 TFCE = 637.13 0.000024 0.001870 0.001000 Salience.RPFC, L FrontoParietal.LPFC, L T(7) = 11.80 - 0.000007 0.003533 Cluster 2 TFCE = 570.14 0.000042 0.001870 0.003000 SensoryiMotor.Lateral, L FrontoParietal.LPFC, L T(7) = 10.35 - 0.000017 0.004233 SensoryiMotor.Lateral, L Language.IFG, L T(7) = 8.80 - 0.000049 0.004882 SensoryiMotor.Lateral, L Language.pSTG, L T(7) = 6.26 - 0.000419 0.017540 C. 2 Hz vs 100 Hz baseline stimulation differences Cluster 1 TFCE = 285.74 0.000000 0.000000 0.000000 DorsalAttention.IPS, R SensoriMotor.Lateral, R T(14) = 8.02 - 0.000001 0.000220 DorsalAttention.IPS, L SensoryiMotor.Lateral, L T(14) = 7.66 - 0.000002 0.000282 DorsalAttention.IPS, L Salience.SMG, L T(14) = 5.75 - 0.000050 0.001544 DorsalAttention.IPS, R Salience.SMG, R T(14) = 4.67 - 0.000360 0.006475 DorsalAttention.IPS, R SensoriMotor.Superior T(14) = 4.41 - 0.000591 0.008624 DorsalAttention.IPS, L SensoriMotor.Superior T(14) = 3.70 - 0.002361 0.018980 DorsalAttention.FEF, R DorsalAttention.FEF, L T(14) = 2.82 - 0.013588 0.063579 DorsalAttention.IPS, R Salience.SMG, L T(14) = 2.60 - 0.021130 0.084097 DorsalAttention.IPS, R SensoryiMotor.Lateral, L T(14) = 2.47 - 0.027136 0.100130 DorsalAttention.IPS, L SensoriMotor.Lateral, R T(14) = 2.44 - 0.028528 0.102534 DorsalAttention.IPS, L Salience.SMG, R T(14) = 2.09 - 0.055574 0.156133 Cluster 2 TFCE = 261.88 0.000000 0.000000 0.000000 Salience.RPFC, L FrontoParietal.LPFC, L T(14) = 8.87 - 0.000000 0.000201 Salience.RPFC, L Language.IFG, L T(14) = 3.41 - 0.004227 0.029456 Salience.RPFC, L FrontoParietal.PPC, L T(14) = 2.88 - 0.012191 0.061699 Cluster 3 TFCE = 248.00 0.000000 0.000000 0.000000 SensoryiMotor.Lateral, L FrontoParietal.LPFC, L T(14) = 7.49 - 0.000003 0.000288 SensoryiMotor.Lateral, L Language.IFG, L T(14) = 6.65 - 0.000011 0.000595 SensoryiMotor.Lateral, L Language.pSTG, L T(14) = 5.61 - 0.000065 0.001782 Salience.SMG, L Language.IFG, L T(14) = 4.33 - 0.000688 0.009477 Cluster 4 TFCE = 243.23 0.000000 0.000000 0.000000 Visual.Lateral, L Visual.Occipital T(14) = 6.99 - 0.000006 0.000528 Visual.Lateral, R Visual.Occipital T(14) = 6.66 - 0.000011 0.000595 Visual.Lateral, R Visual.Medial T(14) = 5.99 - 0.000033 0.001270 Visual.Medial Visual.Occipital T(14) = 5.91 - 0.000038 0.001337 Visual.Lateral, L Visual.Medial T(14) = 5.83 - 0.000044 0.001445 Visual.Lateral, R Visual.Lateral, L T(14) = 5.64 - 0.000061 0.001782 Cluster 5 TFCE = 181.54 0.000014 0.000212 0.001000 Cerebellar.Anterior Cerebellar.Posterior T(14) = 8.11 - 0.000001 0.000220 FrontoParietal.LPFC, R DefaultMode.MPFC T(14) = 2.94 - 0.010780 0.057577 Cerebellar.Posterior DefaultMode.MPFC T(14) = 1.39 - 0.186074 0.361277 Cluster 6 TFCE = 163.10 0.000019 0.000239 0.001000 Salience.SMG, R Language.pSTG, R T(14) = 6.59 - 0.000012 0.000595 Salience.AInsula, R Language.IFG, R T(14) = 4.57 - 0.000436 0.007452 Cluster 7 TFCE = 128.35 0.000045 0.000492 0.002000 SensoryiMotor.Lateral, L Salience.AInsula, L T(14) = 6.53 - 0.000013 0.000599 Cluster 8 TFCE = 123.34 0.000051 0.000495 0.003000 Salience.AInsula, R Salience.AInsula, L T(14) = 6.78 - 0.000009 0.000595 Cluster 9 TFCE = 105.77 0.000099 0.000846 0.007000 DorsalAttention.IPS, L FrontoParietal.LPFC, L T(14) = 5.25 - 0.000123 0.003177 DorsalAttention.IPS, L Language.IFG, L T(14) = 5.15 - 0.000149 0.003508 DorsalAttention.IPS, L Language.pSTG, L T(14) = 4.37 - 0.000637 0.009026 DorsalAttention.IPS, L FrontoParietal.PPC, L T(14) = 3.89 - 0.001641 0.015071 Visual.Occipital Language.pSTG, L T(14) = 3.81 - 0.001920 0.016437 DorsalAttention.IPS, L FrontoParietal.PPC, R T(14) = 3.42 - 0.004139 0.029456 Additional Declarations No competing interests reported. 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Webster","email":"","orcid":"","institution":"West Virginia University (WVU)","correspondingAuthor":false,"prefix":"","firstName":"Paula","middleName":"J.","lastName":"Webster","suffix":""},{"id":319512543,"identity":"4f11ce7f-d813-4dd4-90ba-4fced80995fb","order_by":6,"name":"Gina Sizemore","email":"","orcid":"","institution":"West Virginia University (WVU)","correspondingAuthor":false,"prefix":"","firstName":"Gina","middleName":"","lastName":"Sizemore","suffix":""},{"id":319512545,"identity":"eb126223-10af-4f7f-b407-b26ee57b605e","order_by":7,"name":"Julie A. Brefczynski-Lewis","email":"","orcid":"","institution":"West Virginia University (WVU)","correspondingAuthor":false,"prefix":"","firstName":"Julie","middleName":"A.","lastName":"Brefczynski-Lewis","suffix":""},{"id":319512546,"identity":"edda452f-cd09-4931-97a2-f55e776b9722","order_by":8,"name":"James W. Lewis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIie3NsQqCQBjA8ZMbWi5cjUBf4RPHhl6lI8jlBEcnMQRdfIADh17BLdqMW4VWxyRoamnOIbWhJs+x4f5wB3d8Pz6EVKo/TItwhBAgE75/GxnRBuJMJz3qb1pMJpjT/Y34oXvUxRXPE4H0GYNRonEaOwSEd+I7GMgie0hITpMlg9IraoIwqwSCWrYlp+mLQejCpfqQ9QSSYAZ4AyXrSNBtMWTk0MTLFoRd1DsQbeASo7r7o8TOxPnJ29CCi2gaDitTT7fFOIl+HmV3yOh4nyWdUKlUKtUbBApHMoAbyrgAAAAASUVORK5CYII=","orcid":"","institution":"West Virginia University","correspondingAuthor":true,"prefix":"","firstName":"James","middleName":"W.","lastName":"Lewis","suffix":""}],"badges":[],"createdAt":"2024-06-07 20:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4548047/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4548047/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59606786,"identity":"70f424ca-3977-4fbc-b51c-89948e2ded30","added_by":"auto","created_at":"2024-07-03 18:54:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57743,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Example of electrode placement. (B) Illustration of manually created ROIs (from Table 1), including hypothalamus (left and right), dorso-lateral medulla (left and right), and periaqueductal gray (PAG).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4548047/v1/9eb827bb0f1be41f1703f8cf.jpg"},{"id":59606781,"identity":"08674cb1-88ef-4ad3-813d-e25f919aea12","added_by":"auto","created_at":"2024-07-03 18:54:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39555,"visible":true,"origin":"","legend":"\u003cp\u003eMean rsfMRI functional connectivity network changes after versus before electrical stimulation applications, illustrating (\u003cstrong\u003eA\u003c/strong\u003e) Condition A; After versus Before 2 Hz rhythmic muscle activation, (\u003cstrong\u003eB\u003c/strong\u003e) Condition B; After versus Before 100 Hz sensory/skin stimulation, and (\u003cstrong\u003eC\u003c/strong\u003e) significant differences of Condition A (muscle)with Condition B (skin) differences as the baseline. All data at p \u0026lt;0.01 voxel threshold, FWE corrected (two-sided),the t-value scale applies to panels A-C. (\u003cstrong\u003eD\u003c/strong\u003e) Illustration of brain ROIs showing significant differences corresponding to panel C.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4548047/v1/db1394222c98781b914d5c8e.jpg"},{"id":59607088,"identity":"365c253f-b342-46a6-8f57-ae22b8a50d16","added_by":"auto","created_at":"2024-07-03 19:02:39","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32871,"visible":true,"origin":"","legend":"\u003cp\u003eSeed-to-Voxel results using the left hypothalamus ROI as a seed region, revealing significantly greater average connectivity after (vs before) 2 Hz muscle stimulation (Condition A; at p \u0026lt; 0.05 uncorrected voxel threshold, and p \u0026lt; 0.05 p-FDR cluster threshold correction (two-sided).Refer to Table 3 for cluster locations and laterality differences.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4548047/v1/9a27bca0101c0f14e2dd31c7.jpg"},{"id":59606783,"identity":"394df871-10a2-482a-87e4-be2aaa178c1a","added_by":"auto","created_at":"2024-07-03 18:54:39","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":37996,"visible":true,"origin":"","legend":"\u003cp\u003eMean functional connectivity changes in predefined CONN resting state networks due to (\u003cstrong\u003eA\u003c/strong\u003e) Condition A; After versus Before 2 Hz rhythmic muscle activation, (\u003cstrong\u003eB\u003c/strong\u003e) Condition B; After versus Before 100 Hz skin stimulation. All data at p \u0026lt;0.01 voxel threshold, FWE corrected (two-sided), scale applies to all panels. Note small text denote CONN standardized output ROI names evident in Table 3, while larger text denotes the major network clusters.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4548047/v1/e386788feb575037ba10d437.jpg"},{"id":65812498,"identity":"ee31d89b-99ec-49b6-9cf6-d495500de2a6","added_by":"auto","created_at":"2024-10-03 04:39:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1345639,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4548047/v1/fc7e8f1b-854d-48dd-aee7-1de2185ab238.pdf"},{"id":59606782,"identity":"ff9e58e1-602a-4b5a-9431-664632f7c87b","added_by":"auto","created_at":"2024-07-03 18:54:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14533,"visible":true,"origin":"","legend":"","description":"","filename":"msTENS240611aTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-4548047/v1/b80603e305fd9df5c6be6149.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Functional Connectivity Changes in Human Brain Networks from 2 Hz Rhythmic Muscle Contraction to the Hand: A pilot study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePhysical activity is generally accepted as a requirement for optimal health. In people subjected to prolonged bedrest confinement, inactivity results in declines in multiple physiological systems (Convertino et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) that can be attenuated by physical exercise even while in bed (Greenleaf, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Dynamic exercise and electrical stimulation of somatic afferents in skeletal muscles are known to produce post-activation reductions in blood pressure (Chen and Bonham, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and reductions in pain (Song et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) that often outlasts the time of muscle contractions by many hours. For example, post-exercise hypotension results from changes in brainstem nuclei involved in blood pressure regulation responding to skeletal muscle afferent input (Chen and Bonham, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Electrical muscle stimulation has also been applied to critically ill patients, revealing that such procedures have an effect on microcirculation (Gerovasili et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In addition, bioactive growth hormone (bGH) is released from the pituitary (muscle afferent-pituitary axis) in response to exercise or proximal stimulation of severed somatic nerves (McCall et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), revealing that muscle contractions can produce multiple desired responses that could enhance health resulting from somatic afferent input.\u003c/p\u003e \u003cp\u003ePatients who walk during hospitalization are generally reported to have better outcomes (Cohen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Fisher et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e); however this activity may require physical assistance by trained staff, walking spaces may be limited by equipment and qualified personnel resources (King et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), or the patient may simply not be ambulatory. Since walking involves rhythmically active skeletal muscles, somatic afferent activity from skeletal muscles seems likely to play a role in enhanced recovery. For example, stimulation of the hand muscles at low frequencies, similar to walking cadence, has been shown to enhance ulcer healing (Kaada, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1983\u003c/span\u003e), increase vasodilation in Raynaud\u0026rsquo;s patients (Kaada, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), and increase plasma endorphins (Facchinetti et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Some of these outcomes also outlast the activity by several hours and may be mediated by factors that result from activating central nervous systems (CNS) regions involved in autonomic control: These regions include the hypothalamus, periaqueductal gray, nucleus tractus solitarius (NTS; in the dorso-lateral medulla) (Knigge and Joseph, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1984\u003c/span\u003e, Veening et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and cortical regions implicated in healing and homeostasis, such as the insula plus limbic system structures including the cingulate cortex (Cottam et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Craig, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Kobayashi and Koitabashi, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Macey et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Voigt et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the above rationale for CNS involvement in exercise benefits, the present study sought to identify the effects in the brain resulting from 45 minutes of non-voluntary, rhythmical muscle stimulation of small muscles of the hand (at 2 Hz), compared to 45 minutes of continuous sensory stimulation of the skin (at 100 Hz) over the same area as a critical control, using resting-state functional magnetic resonance imaging (rsfMRI). Measurements of whole brain rsfMRI allows for an objective measure of potential CNS network processing changes, and has been applied for assessing therapeutic interventions such as walking (Morris et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e): This method is non-invasive and only requires participants to lie still in the scanner (Lv et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and enables the assessment of synchronous activations between brain areas and networks that occur during the absence of any specific cognitive task. From the present study, a better understanding of any CNS mechanisms that may underlie the putative health effects of non-voluntary, rhythmical muscle stimulation of small muscles of the hand will likely lead to improvements in therapeutic protocols involving such treatments, especially for bedridden patients or people unable to engage in daily exercise.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAlthough the therapeutic approach is ultimately expected to be applied to relatively older populations, to demonstrate feasibility for this pilot study we recruited a closely matched set of eight (8) right-handed young adult participants (average age 26 years; four females, aged 21, 24, 25 and 28, and four males aged 23, 27, 27, and 31). Participants were screened to ensure their likely availability and compliance with completing two MRI scanning sessions over the course of one to four weeks (six participants\u0026rsquo; sessions were 1 week apart, one was 2 weeks and another was 4 weeks). Handedness was assessed based on 12 questions from the Edinburgh Handedness Inventory (Oldfield, 1971). All participants had higher education and were either students or worked in their professions. All could perform activities of daily living independently, had a self-reported normal range of hearing and vision, and none had any previous history of major neurological or psychiatric disorders. Informed consent was obtained for all participants, and all methods were performed in accordance with guidelines approved by the West Virginia University Institutional Review Board (Association for the Accreditation of Human Research Protection Programs, Inc., (AAHRPP) Accredited).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExperimental Design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing a 2x2 single blinded design, participants were informed that the purpose of this study was to examine how the brain responds to different potentially therapeutic effects of electrical stimulation therapy to the left hand using a commonly available transcutaneous electrical neural stimulation (TENS) unit (ComfyTENS, TENSproducts Inc., Grand Lake, CO 80447). The participants were told that there would be two scanning sessions on different weeks, including one session with \u0026ldquo;treatment A\u0026rdquo; (Condition A) and the other with \u0026ldquo;treatment B\u0026rdquo; (Condition B): This order was counterbalanced across participants. The first session used condition A-B (2Hz then 100Hz) for participants 2,4,5, and 7), and scanning personnel were single blinded as to which treatment condition was being administered. Upon a given visit, each participant was seated in a comfortable chair outside the MRI scanning room (waiting room) where they watched 10 minutes of a video DVD (\u0026ldquo;Planets\u0026rdquo; by National Geographic), which served to help normalize participants\u0026rsquo; arousal level and state of mind prior to resting state functional magnetic resonance imaging (rsfMRI), as pre-scanning task effects have been reported to be potential factors that could bias rsfMRI outcomes(Hasson et al., 2009, Lewis et al., 2009, Van Dijk et al., 2010). Heart rate and blood pressure was collected during this time. Participants then underwent a baseline \u0026ldquo;pre-stimulation\u0026rdquo; MRI scanning session that included three consecutive rsfMRI scans plus an anatomical scan, as detailed below. They were instructed to keep as still as possible and have their eyes closed during rsfMRI scanning(Agcaoglu et al., 2019). Participants came out of the scanner and back to their chair in the waiting room where vital signs were taken again. Gel electrodes were placed on their hand (Figure 1A) and electrical stimulation of either Condition A or Condition B was set at a comfortable level for 45 minutes while they sat resting. Vital signs were taken again after the electrical stimulation treatment. For Condition A, the hypothesized treatment condition that would lead to physiological health benefits, the left hand was stimulated with a symmetrical biphasic current (duration of 100 microsec) at 2 Hz frequency, and at an amplitude set to elicit visible rhythmic muscle contractions of the hand, and still be deemed comfortable by the participant. Condition B was a control condition, which consisted of an amplitude setting that stimulated the skin to only produce sensation at 100 Hz and no muscle movement. Next, participants went back into the MRI scanner for a post-stimulation rsfMRI scanning session (three rsfMRI plus one anatomical scan) using identical scanning parameters as for the pre-stimulation scanning. Vital signs were again collected at the end of this MRI scanning session. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1 near here\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData acquisition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe imaging was conducted on a 3 Tesla Siemens Verio MRI scanner using a 12-channel head coil. We acquired whole-head brain volumes using a resting state design (36 axial interleaved slices at 4 x 4 x 4 mm\u003csup\u003e3 \u003c/sup\u003eisometric voxel resolution). Blood oxygen-level dependent (BOLD) signals were collected using a continuous acquisition echo planar pulse sequence (ep2d: TR = 2.210 sec, TE = 27 msec, FOV = 256 mm, 75 degree flip angle). Three sequential rsfMRI scans were collected, each lasting ~6 min (164 measurements per scan), typically with only a very short break between scans (\u0026lt; 30 sec) to check the status of the participant. Whole brain T1-weighted anatomical MR images were collected using a magnetization-prepared rapid acquired gradient-echo (MPRAGE) pulse sequence (1.5 mm sagittal slices, 0.625 x 0.625 mm\u003csup\u003e2\u003c/sup\u003e in-plane resolution, FOV = 195 mm, TI = 1100 msec, 5 min 13 sec).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData preprocessing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFunctional and structural images were analyzed using a Matlab/SPM-based software CONN (version 22.a) (Morfini et al., 2023, Nieto-Castanon and Whitfield-Gabrieli, 2022, Whitfield-Gabrieli and Nieto-Castanon, 2012). Data were preprocessed using a standard pipeline in Statistical Parametric Mapping software (SPM12, Wellcome Department of Cognitive Neurology, University College London) (Penny et al., 2007)running under Matlab Release 2021b (The MathWorks, Inc., Natick, MA, United States) and Unix running on an Macintosh computer. Our hypothesis was that autonomic systems would be involved in physiological changes induced by the 2 Hz rhythmic muscle stimulation paradigm: Thus, in addition to the 132 brain regions of interest (ROIs), and identified resting state networks provided in the CONN atlas, we created five additional ROIs related to the autonomic system using the MNI152 brain atlas as the template (see Introduction). Guided by drawings from a neuroanatomy atlas (Haines, 2019), this included a left and right hypothalamus, left and right dorso-lateral ROIs of the medulla oblongata that would encompass the solitary nucleus and tracts (aka. nucleus tractus solitarius, NTS), plus a bilateral periaqueductal gray (PAG) volume. These volumetric ROIs (see Table 1) are available for download as NIFTI files (see journal website).\u003c/p\u003e\n\u003cp\u003eRaw-level MRI anatomical images (all slices) and rsfMRI functional runs (all slices and all scans) were visually inspected across all participants using the CONN toolbox. Anatomical data were segmented into grey matter, white matter, and CSF tissue classes using SPM unified segmentation and normalization algorithm(Hallquist et al., 2013, Nieto-Castanon, 2020)with the default IXI-549 tissue probability map template. The three functional runs were concatenated into one file (NIFTI; *.nii format) using AFNI software plugin \u003cem\u003e3dTcat \u003c/em\u003e(Cox, 1996). All raw data were then uploaded into the CONN toolbox for subsequent analyses. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDenoising \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn addition, functional data were denoised using a standard denoising pipeline (Nieto-Castanon, 2020)including the regression of potential confounding effects characterized by white matter timeseries (5 CompCor noise components), CSF timeseries (5 CompCor noise components), session effects and their first order derivatives (2 factors), and cubic trends (4 factors) within each functional run, followed by bandpass frequency filtering of the BOLD timeseries between 0.008 Hz and 0.09 Hz. CompCornoise components within white matter and CSF were estimated by computing the average BOLD signal as well as the largest principal components orthogonal to the BOLD average within each subject\u0026apos;s eroded segmentation masks(Behzadi et al., 2007, Chai et al., 2012). From the number of noise terms included in this denoising strategy, the effective degrees of freedom of the BOLD signal after denoising were estimated to range from 344.7 to 345 (average 345) across all subjects(Nieto-Castanon, 2020).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFirst-level analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSeed-based connectivity maps (SBC) and ROI-to-ROI connectivity matrices (RRC) were estimated characterizing the patterns of functional connectivity with either (A) 21 ROIs related to autonomic system (see Table 1), or (B) ROIs for analyses utilizing the complete CONN atlas and networks (132 ROIs plus 5 manually created ROIs, see above). This method included predefined shape and locations derived from the human connectome project (HCP) atlas (Nieto-Castanon, 2020) adjusted to each volume, and were used to assess eight resting state networks (RSNs) including Default Mode, Cerebellar, Fronto-Parietal, Language, Salience, SensoriMotor, Dorsal Attention, and Visual networks. Functional connectivity strength was represented by Fisher-transformed bivariate correlation coefficients from a weighted general linear model (weighted-GLM), defined separately for each pair of seed and target areas, modeling the association between their BOLD signal timeseries. In order to compensate for possible transient magnetization effects at the beginning of each series of scans, individual scans were weighted by a step function convolved with an SPM canonical hemodynamic response function and rectified.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGroup-level analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGroup-level (Second-level) analyses applied to each of the above first-level analyses were performed using a General Linear Model (GLM)(Nieto-Castanon, 2020). For each individual voxel a separate GLM was estimated, with first-level connectivity measures at this voxel as dependent variables (one independent sample per subject and one measurement per task or experimental condition, if applicable), and groups or other subject-level identifiers as independent variables. Voxel-level hypotheses were evaluated using multivariate parametric statistics with random-effects across subjects and sample covariance estimation across multiple measurements. Inferences were performed at the level of individual clusters (groups of contiguous voxels). Cluster-level inferences were based on parametric statistics from Gaussian Random Field theory(Nieto-Castanon, 2020, Worsley et al., 1996). Primary results were derived by thresholding using a combination of a cluster-forming p \u0026lt; 0.01 voxel-level threshold plus a familywise error corrected p-FWE\u0026lt; 0.01 cluster-size threshold (Chumbley et al., 2010).Cluster-level inferences based on Threshold Free Cluster Enhancement analyses(Smith and Nichols, 2009) aim at removing the dependency of other cluster-level inference methodologies on the choice of an \u003cem\u003ea priori\u003c/em\u003e cluster-forming height threshold.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eBehavioral examination of effects of 2 Hz rhythmic muscle stimulation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAfter receiving 2 Hz stimulation to the hand for 45 min that induced rhythmical muscle contractions (Condition A), six of eight participants reported the feeling of being relaxed or sleepy, consistent with endorphin release (Facchinetti et al., 1986, Veening and Barendregt, 2015). Notably, this effect was not reported after the 100 Hz skin stimulation condition by any of the eight participants. No significant differences in vital signs across sessions were observed (data not shown). This may be reflective of a floor effect since all participants were placed into a relatively relaxed condition in the MRI waiting room area during the pre-scanning time periods (see Methods). \u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003eExamination of 2 Hz rhythmic muscle stimulation on autonomic systems\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe region of interest (ROI) selection for analyses included the 132 atlas ROIs provided by the CONN software, the pre-defined resting state networks therein, plus the five additional ROIs created manually (Figure 1B). To refine our search to autonomic nervous systems, we also analyzed an \u003cem\u003ea priori\u003c/em\u003e set of 21 ROIs (Table 1) of predefined shape and location based on reported involvement in autonomic and limbic system functions (see Introduction). For Condition A (2 Hz muscle stimulation), the ROI-to-ROI functional connectivity analysis using the spatial pairwise clustering (SPC) method revealed significant changes in interconnectivity between several regions, including the thalamus, basal ganglia, insula and anterior cingulate cortex (Figure 2A, Table 2A; p \u0026lt; 0.01, FWE-corrected, two-sided). For Condition B (100 Hz skin stimulation), the analysis revealed significant changes between the left putamen and left insula (Figure 2B, Table 2B; p \u0026lt; 0.01, FWE-corrected, two-sided). Moreover, as a critical control, there were significant differences between the 2 Hz muscle stimulation relative to changes occurring after the 100 Hz skin stimulation condition as the baseline (Figure 2C-D;Table 2C).This differential analysis for the 2 Hz rhythmic muscle activation condition additionally revealed significant involvement of the left and right hypothalamus ROIs, right amygdala, right hippocampus, and PAG. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 near here\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2 near here\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 near here\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003eExamination of the hypothalamus through Seed-to-Voxel analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe left and right hypothalamus ROIs were separately selected as seed regions to reveal connectivity patterns between the ROIs in the CONN atlas plus additional ROIs we created (see Methods). With the significance threshold dropped to voxel-wise p \u0026lt; 0.05, and a cluster size correction of p \u0026lt; 0.05, this analysis for the after (vs before) 2 Hz muscle stimulation revealed a number of brain regions showing change in interconnectivity. These changes were greater for the left hypothalamus as the seed region (Figure 3, Table 3A; 2,793 mm\u003csup\u003e3\u003c/sup\u003e) than for the right hypothalamus (data not illustrated, Table 3B; 1,891 mm\u003csup\u003e3\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3 near here\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 near here\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003eExamination of Predefined Network Changes \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSignificant differences in functional connectivity network patterns after (vs before) 2 Hz muscle stimulation (Condition A; Figure 4A, Table 4A;p \u0026lt; 0.01, FWE-corrected, two-sided) included the Default Mode, Cerebellar, and Fronto-Parietal networks. In contrast, patterns after (vs before) 100 Hz skin stimulation (Condition B; Figure 4B, Table 4B;p \u0026lt; 0.01, FWE-corrected, two-sided) led to changes in the Fronto-Parietal, Language, Salience, and SensoriMotor networks. Significant differences in functional connectivity patterns after (vs before ) 2 Hz muscle stimulation (Condition A) relative to patterns after (vs before) 100 Hz skin stimulation (Condition B) as a baseline control was also evident (data not shown), involving all of the eight \u003cem\u003ea priori \u003c/em\u003enetworks provided by the CONN toolbox (Default Mode, Cerebellar, Fronto-Parietal, Language, Salience, SensoriMotor, Dorsal Attention, and Visual networks). However, exploration of the significance of these global network changes was beyond the scope of the present study. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4 near here\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 near here\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe main finding of the present study was that 2 Hz rhythmic muscle stimulation to the hand, in contrast to 100 Hz skin (sensory) stimulation to the same region as a critical control, led to significant functional connectivity pattern changes in autonomic and limbic systems of the central nervous system (CNS) in humans. This included the hypothalamus, amygdala, PAG, thalamus, basal ganglia, plus insulae and anterior cingulate cortices. These changes were generally expected given studies of animal models (see Introduction). The 2 Hz muscle stimulation also led to changes in several predefined rsfMRI networks. Our results provide objective evidence that CNS changes immediately accompany physical therapy treatments when using\u0026thinsp;~\u0026thinsp;2 Hz stimulation that elicits rhythmic muscle activity. These outcomes are addressed next in the context of network connectivity patterns followed by potential physiological mechanisms.\u003c/p\u003e \u003cp\u003e \u003cem\u003eConnectivity changes after 2 Hz rhythmic muscle stimulation.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn our study of humans, rhythmic muscle stimulation (2 Hz) to the hand led to significant changes in autonomic and limbic systems of the CNS not seen from sensory stimulation (100 Hz) of the skin. Thus, specific CNS network connectivity changes as revealed by rsfMRI analyses occurred because of non-voluntary, rhythmic muscle contractions but not skin stimulation. Of particular note was involvement of the right insula after 2 Hz muscle stimulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and left insula after 100 Hz skin stimulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), both interacting with the left putamen. The insular cortices have also been shown to be responsive to autonomic challenges (Macey et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Moreover, the dorsal-anterior portions of the insula (right more so than left) are proposed to function as levers for network switching, serving as a critical gatekeepers to executive controls by integrating internal information (interoceptive, autonomic states) with external (exteroceptive) multisensory stimuli (Craig, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Molnar-Szakacs and Uddin, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This network resting state change may explain why changes in the eight major resting states were significantly altered after 2 Hz muscle stimulation, reflecting down-stream changes in cognitive states that occur during the somnolent phase after exercise. Conversely, modulation of subcortical limbic and paralimbic structures, including the hypothalamus, amygdala, hippocampus, caudate, putamen and insula may constitute the steps by which multiple physiological systems, and the resulting diverse therapeutic effects of physical therapies, may be regulated, as addressed next.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePhysiological changes with rhythmic muscle stimulation\u003c/h2\u003e \u003cp\u003eMajor public health benefits of physical activity have been reported (Macera et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Most of these physical activities involve rhythmical or intermittent activations of skeletal muscles for periods of around 30 minutes or longer, performed multiple times a week. Active skeletal muscles can release compounds called myokines (Lee and Jun, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); some have remote actions on tissues such as IL-6 for liver and adipose tissue (Febbraio and Pedersen, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e)and irisin for the hippocampus (Jodeiri Farshbaf and Alvina, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These myokines are likely involved with the some of the positive effects of exercise on health. However, there are also somatic nerves from active muscles that independently connect to pathways in the brain. For example, somatic interventions uniquely stimulate the release of bioassayable growth hormone (bGH) from the pituitary(Hymer et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)in response to hypothalamic input. Growth hormone action would increase bone density and skeletal muscle mass. Our study aimed to explore some of the connections in the brain, including the hypothalamus that respond to strong, but not painful, rhythmical contractions of hand muscles known to activate somatic afferent nerves (Group III) (Kenney and Seals, 1993, Sjolund and Eriksson, 1979), which are also part of the interoception network (Craig, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInteroception results from afferent input responding to alterations in the physiological condition of the body(Craig, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), in our case, changes resulting from contracting hand muscles. In contrast, exteroception results from stimuli from the external world such as skin stimulation (\u003cem\u003ee.g.\u003c/em\u003e, touch) to direct attention prior to any executive response (Cottam et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). With the exception for fatiguing contractions (Noakes, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), the role of afferent nerve activity from skeletal muscles to correct imbalances in homeostasis has received little attention. Yet, in response to skeletal muscle afferents, neuroendocrine pathways from the thalamus control energy balance (Ibeas et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and support growth of muscle by the release of bioassayable GH by the pituitary (McCall et al., 2000). In addition, the nervous system senses and responds to signals from skeletal tissue (\u003cem\u003ei.e.\u003c/em\u003e skeletal/bone interoception) - crucial for maintaining bone homeostasis (Xiao et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSurprisingly, rhythmical activation of hand muscles appears to have enhanced healing effects on chronic diabetic ulcers in the feet(Kaada, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). This outcome is possibly mediated by increased global vasodilation due to the actions of vasoactive intestinal peptide (VIP)(Henning and Sawmiller, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and/or release of bGH or other factors. Thus, long-term increased circulation of these factors would probably help maintain healthy tissues(Dvorakova, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) even in the absence of voluntary physical activity.\u003c/p\u003e \u003cp\u003eNormally, the onset of physical exercise causes an increase in blood pressure and heart rate, mediated by reflexes involving muscle afferents that inhibit baroreflexes so that increased blood pressure can result (Chen and Bonham, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For example, moderate-intensity rhythmic handgrip exercise resulted in increased blood pressure in young healthy men and women (Tarumi et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, from our protocol of non-voluntary, rhythmical hand muscle contractions, blood pressure and heart rates did not change in our normotensive subjects. In contrast, 4 patients in our intensive care unit (ICU) with elevated vitals responded to our hand stimulation protocol with decreases in heart rate and mean blood pressure during and immediately after treatment (Stauber and Swisher, unpublished observations).\u003c/p\u003e \u003cp\u003eIn another study using a similar protocol, patients with mild to moderate hypertension experienced a decrease in blood pressure and heart rates after hand stimulation(Kaada et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Additionally, following 2 weeks of daily hand stimulation, the same patients had decreased baseline blood pressures on the day of examination (\u003cem\u003eibid\u003c/em\u003e), providing indirect evidence for a training effect \u0026ndash; a possible resetting of the autonomic nervous system by the peripheral conditioning stimulus (\u003cem\u003ei.e.\u003c/em\u003e, non-voluntary, rhythmic hand muscle activation) acting on the brain.\u003c/p\u003e \u003cp\u003eMost chronic diseases have increased sympathetic tone (Porzionato et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) including heart failure and some neurological diseases (Aimo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Exercise training and electrical stimulation of skeletal muscles appear to be viable options to reset the balance of the ergoreflexes (\u003cem\u003eibid\u003c/em\u003e) involved in autonomic control. Therefore, in those patients unable to perform physical exercise or walking, our therapy of daily hand muscle stimulation therapy probably resets the system back to normal mediated by the hypothalamus among other brain regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFuture Directions\u003c/h2\u003e \u003cp\u003eReduction in the length of stay and improved patient outcomes are important both for improving quality of life, reducing mortality, and lowering cost of hospitalization. Preliminary studies by our group have revealed that this protocol can be applied effectively to the most severely affected patients in the ICU at Ruby Memorial Hospital by our clinical collaborators. We anticipate that these and similar studies will support the use of non-invasive treatments similar to that used in the present study to decrease the length of stay of patients in the ICU, reduce time on ventilators or assisted ventilation, and improve the quality of life for survivors of intensive care. On a technical note, our study paradigm construct was able to reveal relatively small effect sizes in brain network changes using only 8 participants (before and after conditions). Thus, the neuroimaging approaches used in the present study may be adapted for a wide variety of other rsfMRI studies that entail comparison of a standard 2x2 designs (treatment versus sham/placebo).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWhile three 6 min rsfMRI scans were collected for each MRI session, they were not contiguous scans. One study revealed that a longer scan length, of 13 minutes or longer, would allow one to capture more low frequency fluctuations (Birn et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Thus, correlations at these frequencies will have been missed. Also the sizes of the ROI volumes were highly variable, with, for example, the dorso-lateral medulla being relatively small (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These differences in size and definition of ROIs will influence detectable interconnectivity patterns(Song et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Imaging at higher spatial resolution (smaller voxels and/or higher field strength) would be ideal for future studies addressing brain stem regions to relate the circuitry of humans to various animal models. Collecting blood samples for assays of various biomarkers (such as bGH) would also be important for objectively refining treatment protocols.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJWL and WS wrote the main manuscript text. All other authors contributed to data collection, analyses, and reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e \u003cp\u003eThis work was supported by WVU Centers for Neuroscience NIGMS NIH COBRE grants E15524 and GM103503, plus affiliated WVU Summer Undergraduate Research Internships, and by NIH Grant P20GM103434 to the West Virginia Idea (WV-IDeA) Network of Biomedical Research Excellence (WV-INBRE), and gift from Drs. Susan \u0026amp; Jerry Dorsch.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request. Volumetric brain region of interest files (in .nii format) are available as Supplementary Materials online.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgcaoglu O, Wilson TW, Wang YP, et al. Resting state connectivity differences in eyes open versus eyes closed conditions. Hum Brain Mapp 2019;40(8):2488-2498; doi: 10.1002/hbm.24539.\u003c/li\u003e\n\u003cli\u003eAimo A, Saccaro LF, Borrelli C, et al. The ergoreflex: how the skeletal muscle modulates ventilation and cardiovascular function in health and disease. Eur J Heart Fail 2021;23(9):1458-1467; doi: 10.1002/ejhf.2298.\u003c/li\u003e\n\u003cli\u003eBehzadi Y, Restom K, Liau J, et al. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 2007;37(1):90-101; doi: 10.1016/j.neuroimage.2007.04.042.\u003c/li\u003e\n\u003cli\u003eBirn RM, Molloy EK, Patriat R, et al. The effect of scan length on the reliability of resting-state fMRI connectivity estimates. 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Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J Neurophysiol 2010;103(1):297-321; doi: 10.1152/jn.00783.2009.\u003c/li\u003e\n\u003cli\u003eVeening JG, Barendregt HP. The effects of beta-endorphin: state change modification. Fluids Barriers CNS 2015;12:3; doi: 10.1186/2045-8118-12-3.\u003c/li\u003e\n\u003cli\u003eVeening JG, Gerrits PO, Barendregt HP. Volume transmission of beta-endorphin via the cerebrospinal fluid; a review. Fluids Barriers CNS 2012;9(1):16; doi: 10.1186/2045-8118-9-16.\u003c/li\u003e\n\u003cli\u003eVoigt K, Andrews ZB, Harding IH, et al. Hypothalamic effective connectivity at rest is associated with body weight and energy homeostasis. Netw Neurosci 2022;6(4):1316-1333; doi: 10.1162/netn_a_00266.\u003c/li\u003e\n\u003cli\u003eWhitfield-Gabrieli S, Nieto-Castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect 2012;2(3):125-141; doi: 10.1089/brain.2012.0073.\u003c/li\u003e\n\u003cli\u003eWorsley KJ, Marrett S, Neelin P, et al. A unified statistical approach for determining significant signals in images of cerebral activation. Hum Brain Mapp 1996;4(1):58-73; doi: 10.1002/(SICI)1097-0193(1996)4:1\u0026lt;58::AID-HBM4\u0026gt;3.0.CO;2-O.\u003c/li\u003e\n\u003cli\u003eXiao Y, Han C, Wang Y, et al. Interoceptive regulation of skeletal tissue homeostasis and repair. Bone Res 2023;11(1):48; doi: 10.1038/s41413-023-00285-6.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. \u0026nbsp;Talairach atlas coordinates (MNI152 atlas) for the 21 selected ROI regions related to the limbic system. Gray cells depict regions manually created and added to the CONN atlas.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"478\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003eMNI Atlas Coordinates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003eROI #\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eROI name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eSide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003ey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003eVolume\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eAccumbens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e-11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e886\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eAccumbens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e-12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eAmygdala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e2606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eAmygdala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e2709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eCaudate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e-9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e4303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eCaudate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e4165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eCingulate, Anterior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003ebilateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e-18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e24.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e20844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eCingulate, Posterior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003ebilateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e36.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e19228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eHippocampus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-25.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e6127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eHippocampus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e5625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eHypothalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e1049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eHypothalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e1049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eInsular cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-36.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e10648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eInsular cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e-2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e10801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eMedulla, lateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e43.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-54.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eMedulla, lateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e43.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-54.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003ePAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003ebilateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003ePutamen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e6974\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003ePutamen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e6469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e-10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e10781\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.626834381551364%\" valign=\"bottom\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.20545073375262%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.578616352201259%\" valign=\"bottom\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.51362683438155%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.062893081761006%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.949685534591195%\" valign=\"bottom\"\u003e\n \u003cp\u003e10238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. \u0026nbsp;Statistical cluster analyses of the ROI-to-ROI changes associated with the limbic system (n=21 ROIs) for (\u003cstrong\u003eA\u003c/strong\u003e) After versus Before 2 Hz muscle stimulation, (\u003cstrong\u003eB\u003c/strong\u003e) After versus Before 100 Hz skin stimulation. Each entry corresponds to a given curve in the connectome ring of Figure 2.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"676\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.00738552437223%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA. After \u0026gt; Before 2 Hz stimulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.86115214180207%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.44165435745938%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.896602658788774%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-unc\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.896602658788774%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-FDR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.896602658788774%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-FWE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 387.15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000081\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001163\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(7) = 8.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.008511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eCaudate, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(7) = 6.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.013092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003ePutamen, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(7) = 5.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.014084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003ePutamen, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(7) = 5.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.018539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 328.99\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000093\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001630\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eCaudate, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eCaudate, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(7) = 7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.008511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eCingulate, Anterior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eCaudate, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(7) = 7.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.008511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 243.56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000258\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001936\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eInsular cortex, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eInsular cortex, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(7) = 6.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.013275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 231.35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000310\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001936\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eInsular cortex, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(7) = 5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.013452\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.00738552437223%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eB. After \u0026gt; Before 100 Hz stimulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.86115214180207%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.44165435745938%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.896602658788774%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.896602658788774%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.896602658788774%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 255.17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000341\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007103\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003ePutamen, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eInsular cortex, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(7) = 8.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.009721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.8685376661743%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC. 2 Hz vs 100 Hz baseline stimulation differences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.44165435745938%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.896602658788774%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.896602658788774%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.896602658788774%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 228.06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000379\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eCaudate, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eCaudate, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 7.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 6.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eCaudate, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 6.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003ePutamen, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003ePutamen, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eCaudate, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.006957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eCaudate, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003ePutamen, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 4.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.014230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eCaudate, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003ePutamen, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.018564\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eInsular cortex, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 3.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.018564\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eCaudate, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eCingulate, Anterior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.003490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.030292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 176.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000379\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003ePutamen, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eInsular cortex, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000959\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eInsular cortex, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eInsular cortex, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003ePutamen, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eInsular cortex, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003ePutamen, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eInsular cortex, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 5.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.003146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eCingulate, Anterior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eInsular cortex, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.028531\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eInsular cortex, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003ePutamen, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.003200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.029221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003ePutamen, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003ePutamen, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.003895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.030292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 84.93\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000432\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eInsular cortex, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.006564\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 73.96\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000165\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000614\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eHippocampus, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 5.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eHippocampus, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eThalamus, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.028437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eHippocampus, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003ePAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.007849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.043876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eAmygdala, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003ePAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.014053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.059167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 73.79\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000189\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000615\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eHypothalamus, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eHypothalamus, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 5.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eHippocampus, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eHypothalamus, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.019060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eAmygdala, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eHypothalamus, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.004873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.035288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.6224188790560472%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.48082595870206%\" valign=\"bottom\"\u003e\n \u003cp\u003eAmygdala, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.828908554572273%\" valign=\"bottom\"\u003e\n \u003cp\u003eHippocampus, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421828908554572%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.005995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.882005899705014%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.040361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 is available in the Supplementary Files section.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e. \u0026nbsp;Statistical cluster analyses of network changes in predefined resting state networks for (\u003cstrong\u003eA\u003c/strong\u003e) After versus Before 2 Hz muscle stimulation, and (\u003cstrong\u003eB\u003c/strong\u003e) After versus Before 100 Hz skin stimulation. Each entry corresponds to a given curve in the connectome ring of Figure 4.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"675\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.629629629629626%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA. After \u0026gt; Before 2 Hz stimulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-unc\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-FDR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-FWE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 344.98\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000091\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eCerebellar.Anterior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eCerebellar.Posterior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(7) = 10.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.009924\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eFrontoParietal.LPFC, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eDefaultMode.MPFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(7) = 2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.052340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.168577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eCerebellar.Posterior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eDefaultMode.MPFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(7) = 1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.292978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.504573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.333333333333336%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eB. After \u0026gt; Before 100 Hz stimulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 637.13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001870\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eSalience.RPFC, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eFrontoParietal.LPFC, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(7) = 11.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.003533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 570.14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001870\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eSensoryiMotor.Lateral, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eFrontoParietal.LPFC, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(7) = 10.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.004233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eSensoryiMotor.Lateral, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eLanguage.IFG, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(7) = 8.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.004882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eSensoryiMotor.Lateral, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eLanguage.pSTG, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(7) = 6.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.017540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.333333333333336%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC. 2 Hz vs 100 Hz baseline stimulation differences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 285.74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eDorsalAttention.IPS, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eSensoriMotor.Lateral, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 8.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eDorsalAttention.IPS, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eSensoryiMotor.Lateral, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 7.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eDorsalAttention.IPS, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eSalience.SMG, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 5.75\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eDorsalAttention.IPS, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eSalience.SMG, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.006475\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eDorsalAttention.IPS, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eSensoriMotor.Superior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 4.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.008624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eDorsalAttention.IPS, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eSensoriMotor.Superior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.018980\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eDorsalAttention.FEF, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eDorsalAttention.FEF, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.013588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.063579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eDorsalAttention.IPS, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eSalience.SMG, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 2.60\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.021130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.084097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eDorsalAttention.IPS, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eSensoryiMotor.Lateral, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.027136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.100130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eDorsalAttention.IPS, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eSensoriMotor.Lateral, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.028528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.102534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eDorsalAttention.IPS, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eSalience.SMG, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.055574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.156133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 261.88\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eSalience.RPFC, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eFrontoParietal.LPFC, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 8.87\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eSalience.RPFC, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eLanguage.IFG, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.004227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.029456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eSalience.RPFC, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eFrontoParietal.PPC, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.012191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.061699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 248.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eSensoryiMotor.Lateral, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eFrontoParietal.LPFC, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 7.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eSensoryiMotor.Lateral, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eLanguage.IFG, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 6.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eSensoryiMotor.Lateral, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eLanguage.pSTG, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 5.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eSalience.SMG, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eLanguage.IFG, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.009477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 243.23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eVisual.Lateral, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eVisual.Occipital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 6.99\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000528\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eVisual.Lateral, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eVisual.Occipital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 6.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eVisual.Lateral, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eVisual.Medial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 5.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001270\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eVisual.Medial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eVisual.Occipital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 5.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eVisual.Lateral, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eVisual.Medial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 5.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001445\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eVisual.Lateral, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eVisual.Lateral, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 5.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 181.54\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000212\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eCerebellar.Anterior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eCerebellar.Posterior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eFrontoParietal.LPFC, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eDefaultMode.MPFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.010780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.057577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eCerebellar.Posterior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eDefaultMode.MPFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.186074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.361277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 163.10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000239\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eSalience.SMG, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eLanguage.pSTG, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 6.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eSalience.AInsula, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eLanguage.IFG, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 4.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.007452\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 128.35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000492\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eSensoryiMotor.Lateral, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eSalience.AInsula, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 6.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000599\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 123.34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000051\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000495\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eSalience.AInsula, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eSalience.AInsula, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTFCE = 105.77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000099\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000846\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eDorsalAttention.IPS, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eFrontoParietal.LPFC, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.003177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eDorsalAttention.IPS, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eLanguage.IFG, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.003508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eDorsalAttention.IPS, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eLanguage.pSTG, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.009026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eDorsalAttention.IPS, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eFrontoParietal.PPC, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.015071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eVisual.Occipital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eLanguage.pSTG, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.016437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5925925925925926%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.03703703703704%\" valign=\"bottom\"\u003e\n \u003cp\u003eDorsalAttention.IPS, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.703703703703702%\" valign=\"bottom\"\u003e\n \u003cp\u003eFrontoParietal.PPC, R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003eT(14) = 3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.004139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.481481481481481%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.029456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Exercise, bedrest patient, autonomic nervous system (ANS), hypothalamus, resting state functional magnetic resonance imaging (rsfMRI), rhythmic hand muscle stimulation","lastPublishedDoi":"10.21203/rs.3.rs-4548047/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4548047/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFor patients undergoing prolonged bed rest, inactivity results in a decline in multiple physiological systems that can be attenuated by physical exercise in the hospital such as walking. In addition, non-voluntary activation of skeletal muscles can produce some benefits similar to walking. We hypothesize that rhythmical muscle stimulation of small muscles of the hand, in contrast to sensory stimulation of the skin, will lead to patterns of functional connectivity in the brain that reflect central mechanisms behind some of the physiological benefits afforded by exercise. Using a 2x2 design, healthy participants (age 21 to 31) underwent resting-state functional magnetic resonance imaging (rsfMRI) immediately before and after a45 minute treatment with either muscle stimulation (2 Hz) or skin stimulation (100 Hz) to the left hand. Six of eight participants responded to the rhythmical muscle contractions in a manner consistent with endorphin release. Functional connectivity data were analyzed using CONN toolbox software. Relative to skin stimulation, rhythmic muscle stimulation led to significant differences in connectivity with regions associated with the autonomic and limbic systems, including the hypothalamus, amygdala, periaqueductal grey, thalamus, basal ganglia, plus insulae and cingulate cortices. In addition, the rhythmic muscle stimulation led to changes in several previously identified resting state networks. In conclusion, distinct networks of the human central nervous system appear to play roles in the outcomes reported for therapeutic use of rhythmical muscle stimulation of hand muscles. These outcomes support the use and future development of similar treatment protocols for bedridden patients or people unable to engage in daily exercise.\u003c/p\u003e","manuscriptTitle":"Functional Connectivity Changes in Human Brain Networks from 2 Hz Rhythmic Muscle Contraction to the Hand: A pilot study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-03 18:54:34","doi":"10.21203/rs.3.rs-4548047/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4114eb1a-d991-414d-9dca-b9c6d17ce7c4","owner":[],"postedDate":"July 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33783628,"name":"Biological sciences/Biological techniques/Imaging"},{"id":33783629,"name":"Biological sciences/Biological techniques/Imaging/Functional magnetic resonance imaging"},{"id":33783632,"name":"Biological sciences/Physiology/Neurophysiology"}],"tags":[],"updatedAt":"2024-10-03T04:38:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-03 18:54:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4548047","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4548047","identity":"rs-4548047","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}