Modulating Emotional and Cognitive Circuits in Major Depressive Disorder via Temporal Interference Stimulation

Modulating Emotional and Cognitive Circuits in Major Depressive Disorder via Temporal Interference Stimulation | 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 Modulating Emotional and Cognitive Circuits in Major Depressive Disorder via Temporal Interference Stimulation Chencheng Zhang, Yishu Chen, Junyi Yan, Min Li, Shupei Zhou, Simeng Zhang, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7957625/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Major depressive disorder (MDD) often leads to cognitive impairments such as impaired inhibitory control, involving complex circuits in both cortical and subcortical regions. We applied non-invasive temporal interference (TI) stimulation to specific neural circuits to explore its modulatory effects on the abnormal neural networks in MDD. In a randomized, crossover study, 15 patients received TI stimulation targeting the left DLPFC, left sgACC, right amygdala, left VS and sham stimulation delivered in a random order. Functional connectivity and behavioral changes from before and after stimulation were assessed using rs-fMRI and an emotional Stroop task. TI stimulation of the sgACC reduced the FC between the sgACC and the frontal cortex, while stimulation of the right amygdala enhanced the FC between the amygdala and bilateral hippocampi. These FC changes were accompanied by an improvement in Stroop task reaction time, along with significant clinical symptom improvement. This study is one of the first to demonstrate that TI can effectively modulate MDD-related network activity, providing a new approach to understanding and regulating cognitive-emotional functions at the neural circuit level. Biological sciences/Neuroscience/Cognitive neuroscience/Cognitive control Health sciences/Diseases/Psychiatric disorders/Depression Major Depressive Disorder (MDD) Temporal Interference (TI) Functional connectivity (FC) rs-fMRI Emotional Stroop Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders globally 1 . Patients with MDD experience a wide spectrum of symptoms, including depressed mood, anhedonia, and feelings of worthlessness or guilt. These symptoms are often accompanied by severe cognitive dysfunction, such as impaired inhibitory control, which further prevents patients from processing negative emotions, creating a vicious cycle 1 , 2 . Evidence suggests that inhibitory control relies on a widely distributed brain network, including multiple deep brain regions, rather than a single frontal lobe area 3 , 4 . Patients with MDD show significant dysfunction in these related brain areas 2 , 5 , 6 , for example, reduced activity in the left dorsolateral prefrontal cortex (DLPFC) 7 , 8 or enhanced activity in deep brain regions like the amygdala (Amg) 9 . Traditional non-invasive neuromodulation techniques often cannot stimulate deep brain regions non-invasively 10 , which severely limits the understanding of the widespread neural network mechanisms involved in MDD and restricts the development of interventions for these disorders. Therefore, there is an urgent need for a tool of precisely and non-invasively modulating neural activity in both cortical and subcortical areas. Temporal interference (TI) stimulation is a novel brain stimulation technique known for its non-invasive nature and ability to reach deep brain regions 11 . In recent years, TI has been applied in cognitive neuroscience research as an alternative to deep brain stimulation (DBS) and transcranial magnetic stimulation (TMS). These applications include disrupting motor skill consolidation, enhancing episodic memory, and improving motor skill learning by effectively targeting and interfering with neural activity in the striatum or hippocampus 12 – 14 . TI stimulation works by applying two or more high-frequency (> 2 kHz) electric fields via scalp electrodes, which individually, are too high to affect neural activity. These fields intersect at the target location, generating an interference pattern—an envelope amplitude waveform modulated at a difference frequency, which is sufficient to influence neural activity. This technique offers new possibilities for understanding and modulating abnormal brain network activity in MDD. To validate TI's modulatory effect on key brain regions within the cognitive-emotional circuit, we examined four key brain regions closely associated with MDD symptoms. The dorsolateral prefrontal cortex (DLPFC) is a critical target for brain stimulation in MDD, and its functional connectivity (FC) with the subgenual anterior cingulate cortex (sgACC) has been well studied 15 , 16 . Research has shown that the DLPFC-sgACC connection is crucial for emotion regulation and cognitive-emotional integration, potentially contributing to inhibitory control 17 . The amygdala (Amyg) is one of the most critical brain regions involved in MDD 9 , responsible for emotional processing, decision-making, reward processing, attention, and perception 18 . Given its significant role in regulating emotion and cognition, the Amyg is thought as a potential component of the inhibitory control network. The ventral striatum (VS), a key component of the reward system, shows abnormal activity in MDD is closely linked to the motivational deficits in MDD, making it an important target for neuromodulation research 19 . We adopted a randomized, within-subject design to apply TI stimulation to 15 MDD patients in five, randomly presented conditions (left DLPFC TI stimulation, left sgACC TI stimulation, right Amyg TI stimulation, right VS TI stimulation, and a sham control). Each condition was administered separately, with a one-week washout period to avoid carryover effects. Before and after each stimulation session, the participant underwent an eight-minutes rs-fMRI scan and performed an emotional Stroop test. The difference between congruent/incongruent response time (RT) of the emotional Stroop test was defined as a marker of inhibitory control 5 . We hypothesized that TI stimulation of each target could enhance the function of inhibitory control in participants, which in turn would be associated with changes in functional connectivity (FC) change related to the underlying mechanisms of inhibitory control and improvement of MDD symptoms. Materials and Methods Participants Fifteen participants (18–53 years, mean age 35.1 ± 10.9 years, 2 males) recruited at Shanghai Pudong Mental Health Center took part in this study. The inclusion criteria were: (1) adults aged between 18 and 65 years, both male and female; (2) participants were diagnosed with MDD based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the diagnosis was made by an experienced clinician; (3) a score of 17 or higher on HAMD-17; (4) participants must not have changed their antidepressant medication regimen from 30 days prior to and throughout the study. The exclusion criteria were: (1) participants with a history of other psychiatric disorders (e.g., schizophrenia, dementia) that, in the judgment of the investigator, could affect the results of the study ; (2) participants with a history of neurologic disorders, such as seizures; (3) participants with implants, such as the presence of metallic foreign bodies in the skull or metallic implants in the heart; (4) participants that have received neurostimulation therapies in the past three months; (5) a high risk for suicide assessed by an experienced clinician; (6) pregnant or breastfeeding women. All participants gave written informed consent prior to study inclusion. This study was approved by the Institutional Review Board of Shanghai Pudong New Area Mental Health Center and Shanghai Jiao Tong University School of Medicine Ruijin Hospital (PDJWLL2021057). Study design A randomized crossover design was implemented to evaluate TI stimulation efficacy of the four selected targets (Fig. 1 a). All participants went through five sessions of 20-minute TI stimulation over a period of five weeks, where four targets and one control stimulation were randomly assigned to each week for each participant. One week interval was chosen to minimize carry-over effects. The sequence of TI stimulation sessions was sequence-balanced and participants were not aware of the sequence (blinded). At the baseline assessment, T1 weighted structural MRI was obtained. Before each weekly session of TI stimulation and one to two weeks after the final session, HAMD (Hamilton Depression Rating Scale) and HAMA (Hamilton Anxiety Rating Scale) scores were collected. Before and after each TI stimulation session, an 8-minute resting-state fMRI scan was conducted, followed by an emotional Stroop test to assess inhibitory control. TI stimulation Five stimulation patterns including four targets and one control stimulation were investigated. The four targets included left DLPFC, which was stimulated with a frequency of 10 Hz, and left sgACC, right Amyg and right VS, which were stimulated with a frequency of 100 Hz. The left DLPFC was selected as a stimulation target due to its widely reported hypoactivity in MDD 20 , 21 ; 10 Hz stimulation was applied to enhance its activity, consistent with conventional rTMS protocols 22 , 23 . The sgACC and VS, which often exhibit hyperactivity in MDD, were stimulated at 100 Hz to inhibit neural activity, in line with prior deep brain stimulation studies 19 , 24 . The Amyg was included as a fourth target given its significant role in MDD pathophysiology and historical difficulty in direct targeting, but its frequency selection followed the same inhibitory rationale (100 Hz) due to its frequently observed hyperactivation 9 , 25 . For the sham stimulation, two currents of the same frequency (2 kHz) were applied at an arbitrary electrode location. By leveraging the properties of TI stimulation, a 0 Hz difference frequency produces no envelope-modulated amplitude waveform and therefore should not drive neural activity, And the electrodes were placed at arbitrary locations. Since the stimulation target was different each time and currents were always delivered, participants could not distinguish between the real and sham stimulation. The initial current intensity was set to 1 mA, with the predefined ratio between electrode pairs maintained. If the participant reported no discomfort, the intensity was increased incrementally by 0.2 mA steps until reaching a maximum of 4 mA. Once the final intensity was determined, a 20-minute TI stimulation session was administered. To ensure participant comfort and avoid abrupt current changes, 2-second ramp-up and ramp-down periods were applied at the beginning and end of the stimulation. Emotional Stroop test To evaluate inhibitory control of the MDD participants, we applied an emotional Stroop test before and after each session of TI stimulation. In line with a previous study, we utilized the difference in RT on congruent vs. incongruent conditions as a marker of inhibitory control 5 . Specifically, the emotional Stroop test consisted of 80 trials in total with a randomized order (Fig. 1 b). In each trial, a human face with an emotional word in red was displayed simultaneously on the screen for a duration of 1,000 ms, followed by a blank screen with a cross “+” in the center for a random duration between 3,000–5,000 ms. In congruent trials, the emotion expressed by the face was consistent with that of the word, whereas they contradicted with each other in incongruent trials. Upon appearance of the cross, the participant was instructed to press a button as fast as possible to categorize the emotional valence of a word as either positive or negative. Non-responses were treated as error trials and were neglected in the analysis. RT of each trial was recorded. MRI acquisition Structural and functional images were acquired using a 3T UIH uMR 780 scanner. A 3D GRE sequence was utilized to acquire T1w images with a repetition time (TR) = 7 ms, echo time (TE) = 3 ms, flip angle = 9°, number of slices = 160, voxel size = 1 × 1 × 1 mm, and field of view (FOV) = 256 mm. Resting-state functional images were acquired using an EPI sequence with the following parameters: TR = 2 s, TE = 30 ms, flip angle = 80°, number of slices = 36, voxel size = 3.59 × 3.59 × 3.50 mm, FOV = 230 mm. MRI data processing Preprocessing and denoising were performed on neuroimaging data from all sessions which generated significant improvement in emotional Stroop test and the control session in CONN version 22a ( https://web.conn-toolbox.org/ ). The detailed steps are as follows: Functional and anatomical data underwent preprocessing using a modular pipeline, which encompassed realignment, outlier detection, indirect segmentation, normalization to MNI space, and smoothing. Functional data were coregistered to a reference image (the first scan of the first session) utilizing a least squares approach and a 6-parameter (rigid body) transformation, followed by resampling with b-spline interpolation. Potential outlier scans were identified using ART, specifically those with framewise displacement exceeding 0.9 mm or global BOLD signal changes greater than 5 standard deviations. For each participant, a reference BOLD image was computed by averaging all scans, excluding outliers. Both functional and anatomical images were then coregistered and normalized into the standard MNI space, segmented into gray matter, white matter, and cerebrospinal fluid (CSF) tissue classes, and resampled to 2 mm isotropic voxels following an indirect normalization procedure using the SPM unified segmentation and normalization algorithm with the default IXI-549 tissue probability map template. Finally, functional data were smoothed using spatial convolution with a Gaussian kernel of 6 mm full width at half maximum (FWHM).Furthermore, the functional images were denoised with a standardized pipeline, which incorporated the regression of potential confounding variables, such as white matter time series (5 CompCor noise components), CSF time series (5 CompCor noise components), linear and quadratic motion parameters alongside their first-order derivatives (24 factors), outlier scans (less than 102 factors), and linear trends (2 factors) within each functional run. This was followed by a bandpass frequency filtering of the BOLD time series, targeting frequencies between 0.008 Hz and 0.09 Hz. The CompCor noise components within white matter and CSF were estimated by calculating the mean BOLD signal and the largest principal components that are orthogonal to the BOLD mean, motion parameters, and outlier scans within each participant's eroded segmentation masks. Based on the number of noise terms integrated into this denoising approach, the effective degrees of freedom of the BOLD signal were estimated to vary from 529.9 to 733.8 (average: 704.6) after denoising across all participants. Statistical analysis All statistical analyses were conducted using R for Windows 4.2.3 via R Studio. For the RTs in the Stroop test, only the RTs in correct trials were included in the analysis. To test the effects of TI stimulation on RT, we performed a 2 (time: pre-test, post-test) * 5 (regions: L DLPFC, L sgACC, R amyg, R VS, control stimulation) repeated measures ANOVA, and four 2 (time: pre-test, post-test) * 2 (conditions: target, control stimulation) repeated measures ANOVAs. Details will be explained in the Results section. For clinical data (HAMD/HAMA), the Wilcoxon signed-rank test and the Friedman test were employed due to the non-normal distribution of the data. Image visualization was conducted using MRIcron ( https://www.nitrc.org/projects/mricron/ ). We performed first-level and group-level analyses on preprocessed and denoised resting-state functional MRI data, comparing scans before and after TI stimulation. This analytical procedure was applied consistently across four predefined regions of interest (ROI). For the left sg ACC, two seed regions were defined based on the Brainnetome Atlas (BNA) ( https://atlas.brainnetome.org ) to match the precision of TI stimulation: BNA 179 (pregenual cingulate gyrus, Brodmann area 32) and BNA 187 (subgenual ACC). The selected ROI for R Amyg is the combination of BNA 212 (R medial amygdala) and 214 (R lateral amygdala). For R VS, BNA 224 (R nucleus accumbens) was selected. BNA 21 was selected as L DLPFC. The results were thresholded at p < 0.005, uncorrected, at the voxel level, and significant clusters were reported only when they also survived at p < 0.05, cluster-level correction for multiple comparisons. Because we had no hypothesis on potential differential functions of the medial and lateral amygdala, we combined these subregions as one region. Seed-based connectivity maps (SBCs) were estimated to characterize the spatial pattern of FC with a seed region. The strength of FC was represented by Fisher-transformed bivariate correlation coefficients derived from a weighted general linear model (weighted-GLM), which was estimated separately for each seed region and target voxel, modeling the relationship between their BOLD signal time series. Individual scans were weighted using a boxcar signal that characterized each individual task or experimental condition, convolved with an SPM canonical hemodynamic response function (HRF), and rectified. Group-level analyses were conducted utilizing a General Linear Model (GLM). For each individual voxel, a separate GLM was estimated, with first-level connectivity measures at that voxel serving as dependent variables (one independent sample per subject and one measurement per task or experimental condition, where applicable), and groups or other subject-level identifiers acting as independent variables. Hypotheses at the voxel level were evaluated using multivariate parametric statistics, incorporating random effects across subjects and sample covariance estimation across multiple measurements. Inferences were made at the level of individual clusters (groups of contiguous voxels). Cluster-level inferences relied on parametric statistics grounded in Gaussian Random Field theory. Results were thresholded at p < 0.001, uncorrected, at the voxel level, and significant clusters were reported only when they also survived at p < 0.05, cluster-level correction for multiple comparisons. Results Neuroimaging results Because the purpose of this study was not to compare the therapeutic effects or neural activity differences resulting from stimulating four different targets, we analyzed the neuroimaging changes induced by each of the four targets using a 2 (Time: pre- and post-stimulation) x 2 (Stimulation: real and control stimulation) ANOVA. No significant neuroimaging change was identified in R VS analysis, consistent with the behavioral result. For BNA 179 (L pgACC), we observed a significant interaction, indicating that the FC strength between L pgACC and L superior frontal gyrus (SFG) and middle frontal gyrus (MFG) were reduced after TI stimulation (lower connectivity strength in the post-test than pre-test), compared to the control stimulation condition (Fig. 2 a, Table 1 ). For BNA 187 (L sgACC), we also found a significant interaction, with the FC strength between L sgACC and R SFG attenuated after stimulation (lower connectivity strength in the post-test than pre-test), compared to the control stimulation condition (Fig. 2 b). For the ROI of R Amyg (BNA 212 and BNA 214 combined), we found a significant interaction, with the FC strength between R Amyg and bilateral hippocampus enhanced after TI stimulation (greater connectivity strength in the post-test than pre-test), compared to the control stimulation condition (Fig. 2 c). In contrast to the behavioral results, we found that after TI stimulation, the left DLPFC (BNA 21) showed increased FC strength with the left precentral gyrus, putamen, and bilateral supplementary motor areas (post-test connectivity strength was higher than pre-test) (Fig. 2 d). Table 1 Brain regions showing significant FC with each seed region Cluster regions BA Size (voxels) Max Z MNI coord (mm) x y z BNA 179 (L sgACC) L SFG 10 77 4.44 -20 68 8 L MFG 46,47 81 3.9 -26 42 14 BNA 187 (L sgACC) R SFG 46 89 4.03 28 44 14 BNA R Amyg R HIP 30 238 4.21 18 -22 -12 L HIP 30 188 3.75 -18 -22 -12 BNA 21 (DLPFC) L preCG 4 130 4.26 -54 2 42 L PUT 75 4.18 -26 -18 8 SMA 6 126 3.89 -6 -12 56 Note: L = left; R = right; SFG = Superior frontal gyrus; MFG = Middle frontal gyrus; HIP = Hippocampus; preCG = Precentral gyrus; PUT = Putamen; SMA = Supplementary motor area Behavioral results We found a significant interaction in the 2*5 ANOVA on the RTs, F (4, 11) = 3.784, p = 0.036, η 2 = 0.579, but no significant main effects of time or regions, with both Fs < 1. Although a significant interaction was observed, the primary objective of this study was not to compare differences among the various targets, but rather to individually verify whether stimulation at each target site could elicit significant specific changes. Therefore, we performed four 2 (time: pre-test, post-test) * 2 (conditions: target, control stimulation) repeated measures ANOVAs by comparing each target region with the control stimulation condition. There was a significant interaction in the L sgACC region, F (1, 14) = 14.059, p = 0.002, η 2 = 0.501, but no significant main effects of time or conditions, with both Fs < 1. We also found a significant interaction in the R Amyg region, F (1, 14) = 11.573, p = 0.004, η 2 = 0.453. For the L DLPFC region, there was no significant interaction, F (1, 14) = 1.168, p = 0.224, η 2 = 0.104, and no significant main effects of time, F (1, 14) = 2.022, p = 0.177, η 2 = 0.126, or conditions, F (1, 14) = 1.175, p = 0.297, η 2 = 0.077. For the R VS region, we did not find a significant interaction, F (1, 14) = 1.149, p = 0.302, η 2 = 0.076, nor significant main effects of time, F (1, 14) = 1.492, p = 0.242, η 2 = 0.096, or conditions, F (1, 14) = 2.187, p = 0.161, η 2 = 0.135. The significant results are shown in Fig. 3 in terms of the pre-test vs. post-test RT differences. TI stimulation of the L sgACC and R Amyg produced shorter RTs in the post-test than pre-test, compared to the control stimulation condition. As the error rate in each condition was very low (< 5%), we did not perform an analysis of the response accuracy. We then conducted a correlation analysis between changes in reaction time and changes in functional connectivity strength. We found that following TI stimulation to the left sgACC, the change in reaction time was significantly and negatively correlated with the FC strength change between L sgACC and L SFG, r (13) = -0.52, p = 0.047 (Fig. 3 ). This indicates that a smaller reduction in FC induced by TI stimulation was associated with a greater degree of RT improvement. Clinical results For clinical outcomes, significant HAMD/HAMA improvement was observed (HAMD p < 0.001, HAMA p < 0.001, Fig. 4 ), aggregating five-week stimulation sessions, notwithstanding the randomization order and the inclusion of a control session. To investigate the effect induced by specific target, Friedman test was performed for both HAMD and HAMA changes. Though no significant results were observed (HAMD P = 0.41, HAMA P = 0.053) considering the relatively limited effect size induced by single stimulation session, pairwise comparisons were still performed to identify any potential therapeutic effect. All targets but sgACC and control showed a significant decrease of HAMD (one-tailed Wilcoxon signed-rank test, L DLPFC V = 98, p = 0.0047; L sgACC V = 76.5, p = 0.14; R Amyg V = 112.5, p = 0.003; R VS V = 107.5, p = 0.0074; control V = 73.5, p = 0.2). DLPFC and Amygdala showed a significant decrease of HAMA (one-tailed Wilcoxon signed-rank test, L DLPFC V = 94, p = 0.0099; L sgACC V = 74, p = 0.44; R Amyg V = 112.5, p = 0.0031; R VS V = 47, p = 0.23; control V = 68, p = 0.26). Discussion In this study, by applying TI stimulation to modulate neural activity in specific brain regions, we assessed the extent to which it can modulate brain regions related to cognition and emotion regulation, thereby potentially improving inhibitory control in MDD patients. Our behavioral and functional connectivity findings indicate that TI stimulation of the left sgACC significantly reduced FC between the left ACC and frontal cortex, while stimulation of the right Amyg enhanced FC between the amygdala and bilateral hippocampi. These circuit-specific changes in FC were accompanied by an improvement in emotional Stroop task performance, suggesting that impaired inhibitory control was restored or that its deterioration was prevented. Furthermore, although we did not find a behavioral improvement after TI stimulation of the DLPFC, we did find an increase in FC with the left precentral gyrus, putamen, and bilateral supplementary motor areas. Further details will be discussed as following. Stimulation of both the sgACC and Amyg targets facilitated inhibitory control, yet likely through distinct neural pathways. TI stimulation of the left sgACC led to a decrease in FC with frontal regions, which may reflect the normalization of hyperconnectivity in the cognitive control circuit 26 . In contrast, TI stimulation of the right Amyg enhanced FC between the amygdala and bilateral hippocampi, which may be related to the modulation of emotional responses systems. Previous studies have shown that the amygdala-hippocampal circuit plays a key role in memory formation, especially under stress 27 , suggesting that improved emotional regulation could underlie the enhanced inhibitory control observed after right Amyg TI stimulation. Similarly, the finding that stimulating the L DLPFC, while not improving inhibitory control, did lead to increased FC with the precentral gyrus, putamen, and SMA, may provide a different perspective on the role of the DLPFC in the MDD inhibitory control network. A significant negative correlation was observed between changes in FC strength of sgACC and L_SFG and RT changes, indicating that a smaller reduction in FC strength following TI stimulation was associated with a greater improvement in RT. It has been proved that MDD patients exhibit baseline neural hyperactivation, especially the hyperconnectivity between sgACC and related brain regions, which contributes to circuit dysfunction 7 , 28 , 29 . As previously discussed, TI stimulation may promote normalization of this hyperconnectivity, thereby restoring abnormally increased FC to a typical range. Meanwhile, the reduction in RT after TI stimulation reflects a decrease in Stroop interference effects and an enhanced ability to inhibit task-irrelevant information 30 . Thus, the observed negative correlation may suggest that improvement in inhibitory control is associated with a reduction in pathologically heightened FC induced by TI stimulation. Depression significantly disrupts inhibitory control, a cognitive process essential for regulating automatic responses and achieving goal-directed behavior 31 . Impairments in inhibitory control are closely linked to persistent low mood, a core feature of MDD. Individuals with MDD consistently exhibit deficits in inhibitory control compared to healthy subjects 32 . Notably, stronger baseline inhibitory control has been associated with more favorable therapeutic outcomes in psychotherapy, emphasizing its importance as a treatment target 32 . Our findings align with previous research, showing that MDD patients have reduced activity in key regions involved in inhibitory control 33 , 34 . Moreover, this study demonstrates that TI stimulation can effectively modulate both brain activity and behavioral performance, offering a promising non-invasive approach to improving inhibitory control in MDD. Although TI stimulation of the left DLPFC did not yield significant behavioral improvements in inhibitory control, it specifically enhanced FC between the left DLPFC and the precentral gyrus, putamen, and bilateral SMA. This dissociation suggests that the modulatory effect of DLPFC stimulation may operate through circuit-level preparatory changes rather than immediate behavioral expression 35 . Specifically, the strengthened connectivity with the SMA—a key region involved in response inhibition and motor planning 36 —and the putamen, which translates cognitive commands into action, indicates a facilitation of the neural substrates underlying executive control 37 . Thus, the absence of behavioral effects may reflect that a single stimulation session was insufficient to fully overcome the long-term cognitive deficits associated with MDD, though it may potentially establish a neurophysiological foundation for future behavioral improvements. Our findings imply that the role of the DLPFC in MDD may be more indirect than conventionally presumed 38 , whereas the left sgACC and right Amyg may play more critical roles in modulating cognitive-emotional dysfunction in this patient cohort. It is noteworthy that MDD is a highly heterogeneous disorder 39 , and the patient sample examined in our study may represent a specific subtype in which the contribution of the DLPFC to inhibitory control is less prominent 40 , or its modulation requires alternative stimulation parameters. This does not preclude the importance of the DLPFC in other MDD populations or under different stimulation protocols 20 , 21 . The discrepancies between our results and previous studies emphasizing the role of DLPFC-related circuits may be attributable to differences in sample characteristics. Future studies with larger, more precisely phenotyped samples are needed to identify which patient subtypes are most likely to benefit from DLPFC-targeted neuromodulation 41 . The ACC (sgACC and pgACC) is a key node of the salience network, which is responsible for detecting behaviorally relevant stimuli and coordinating the switch between other large-scale networks (e.g., the executive control network and the default mode network) to guide behavior 27 , 42 . The decreased connectivity between the ACC and the frontal cortex following TI stimulation may reflect a restoration of balance within the salience network, potentially leading to more efficient resource allocation and better inhibitory control 43 . Similarly, the enhanced FC between the right amygdala and the bilateral hippocampus—a circuit crucial for emotional regulation and memory formation—may represent another facet of this network-level modulation. The salience network, which includes the amygdala, plays a vital role in assigning emotional significance to stimuli. Therefore, the TI-induced changes in both the ACC and the amygdala-hippocampus circuit collectively suggest that the stimulation effectively engages and modulates distributed neural systems underlying emotional and cognitive processing. These findings are consistent with recent evidence that inhibitory control deficits in MDD are distributed across multiple neural networks beyond the DLPFC 4 . Moreover, they provide initial evidence for using TI stimulation to study the causal neural mechanisms of cognitive behavior from a broader network perspective. Besides cognitive improvement, clinical improvement measured in HAMD/HAMA was also significant and relatively persistent after five-session stimulation, though target-specific analysis based on single-session stimulation yielded limited improvement. Due to the small sample size, we cannot draw definitive conclusions about TI’s efficacy in improving MDD symptoms, but our findings provide valuable evidence of its potential, warranting more in-depth exploration in future research. Furthermore, to examine in greater detail the neural mechanism of a specific cognitive function, more experimental data are also required. One key limitation of our study concerns the stimulation parameters. Although TI stimulation of the left sgACC and right amygdala yielded positive behavioral effects, the parameters used for the left DLPFC—such as frequency, intensity, or duration—may not have been optimal for effectively modulating this region. TI stimulation depends on the precise intersection of electric fields to target deep brain structures, and even minor adjustments to these parameters can substantially influence the outcomes. Previous rTMS studies have shown that the DLPFC can be successfully modulated under specific stimulation conditions 44 Although we used a frequency of 10 Hz—consistent with common rTMS protocols—other parameters such as intensity and duration differed. The selection of stimulation parameters inherently involves a trade-off: while flexible parameterization allows broader exploration of possible effects, it also expands the parameter space that must be systematically narrowed through iterative experimentation guided by prior knowledge. In summary, our findings demonstrate that TI stimulation can effectively modulate the activity of specific cognitive-emotional circuits in MDD patients, including cortical and subcortical regions such as the DLPFC, sgACC, and Amygdala. This modulation leads to improvements in impaired inhibitory control and, to some extent, alleviates the clinical symptoms of MDD. Furthermore, this study not only validates TI's capability to effectively stimulate deep brain sites but also provides a referential paradigm for future systematic investigations into the neural mechanisms of cognitive behaviors involving deep brain regions. Future TI stimulation research can effectively perturb and analyze multiple neural circuits within a single study, potentially offering highly robust and compelling evidence for how complex neural activity is organized for specific cognitive functions. Declarations Funding This study was supported by Shanghai Municipal Science and Technology Major Project, Shanghai Pudong New Area Health Committee Excellent Young Medical Talents Training Program Project (PWRq2021-31), and Medical Discipline Construction Project of Pudong Health Committee of Shanghai (Grant No.: PWYgy2021-02). Author contributions Conceptualization, C.Y.P., Y.L., H.F.G., Y.R.F., J.Z., C.C.Z.; Data Curation, J.Y.Y., J.J.W., F.W.; Formal Analysis, Y.S.C., J.Y.Y., M.L., L.L., X.Q.Z., T.L., L.Y., L.K.R., I.R.V., U.Z.; Investigation, S.P.Z., S.M.Z., W.L., F.W., Q.Q.L., J.W.X.; Methodology, Y.S.C., J.Y.Y., M.L., J.J.W.; Project Administration, H.F.G., Y.R.F., J.Z., C.C.Z.; Resources, S.P.Z., W.L., F.W.; Supervision, Y.L., H.F.G., Y.R.F., C.C.Z.; Writing - Original Draft, Y.S.C., J.Y.Y., M.L.; Writing – Review & Editing, Y.S.C., J.Y.Y., C.Y.P., W.L., F.W., Q.Q.L., J.W.X., L.L., X.Q.Z., T.L., Y.L., I.R.V., U.Z., Y.F., H.F.G., Y.R.F., J.Z., C.C.Z.; Funding acquisition, H.F.G., Y.R.F., J.Z., C.C.Z. Competing interest Long Li, Xiaoqi Zhu, and Tian Liu, employees of Neurodome, were responsible for simulating the TIS electric fields but did not participate in data analysis, or interpretation of results. The remaining authors have no competing interests to declare in relation to this study. 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A., Ćurčić-Blake, B., Aleman, A. & Sommer, I. E. Efficacy of non-invasive brain stimulation on cognitive functioning in brain disorders: a meta-analysis. Psychol. Med. 50 , 2465–2486 (2020). Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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15:18:50","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138849,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7957625/v1/2a80931cf78230250c5b79a6.html"},{"id":97460081,"identity":"7c72e691-6eef-426a-adaf-5e62dc65cc42","added_by":"auto","created_at":"2025-12-04 15:18:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":119395,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of the study and an overview of the emotional Stroop test\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7957625/v1/761928c374080ad9ed9c83b9.png"},{"id":97460078,"identity":"4998c994-d8cd-4828-b658-579fe8e7bfa0","added_by":"auto","created_at":"2025-12-04 15:18:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":425665,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003emap of FC strength from the stimulation target BNA 179 (L pgACC, in green) to the L SFG/MFG (in red). The plot shows FC strength differences between the pre-test and post-test in each cluster in the TI stimulation and control conditions, respectively; b, map of FC strength from the stimulation target BNA 187 (L sgACC, in green) to the R SFG (in red); c, map of FC strength from the stimulation target R Amyg (BNA 212 and 214, in green) to bilateral hippocampus (in red). d, map of FC strength from the stimulation target L DLPFC (BNA 21, in green) to the L preCG, L PUT and SMA (in red). The plot showed FC strength differences between the pre-test and post-test in the cluster in the TI stimulation and control conditions, respectively. Color scale indicates t value of connectivity strength\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7957625/v1/04bb015df1cfe76eed666632.png"},{"id":97667635,"identity":"12589f25-7fa9-4fad-bca0-09b6367a9ea3","added_by":"auto","created_at":"2025-12-08 09:23:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51489,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea, \u003c/strong\u003eThe RT Difference (post-test minus pre-test) in the emotional Stroop task before and after TI stimulation in four conditions; \u003cstrong\u003eb,\u003c/strong\u003e TI stimulation applied to the sgACC resulted in a significant negative correlation between pre-to-post changes in sgACC–L_SFG FC and changes in RT (r\u003csub\u003e(13) \u003c/sub\u003e= -0.52. p=0.047);\u003cstrong\u003e b\u003c/strong\u003e, A significant negative correlation was observed between the change in reaction time on the Stroop task preceding TI stimulation and the change in resting-state FC under sgACC stimulation., r\u003csub\u003e(13)\u003c/sub\u003e = -0.52, p = 0.047.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7957625/v1/f33d5b8be3fda717c49d7b85.png"},{"id":97460079,"identity":"1e2f92da-18e2-4657-abb0-49d00fbe39fb","added_by":"auto","created_at":"2025-12-04 15:18:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":191357,"visible":true,"origin":"","legend":"\u003cp\u003eClinical scales change. \u003cstrong\u003ea\u003c/strong\u003e, trajectories of weekly assessed HAMD-17 for each participant (left) and box plot of HAMD-17 before and after 5-week treatment (right). Significant decrease of HAMD-17 was observed after treatment (one-tailed Wilcoxon test, P \u0026lt; 0.001). Average HAMD-17 scores are depicted by black bold line, colored lines indicate individual participants. \u003cstrong\u003eb\u003c/strong\u003e, trajectories of weekly assessed HAMA for each participant (left) and box plot of HAMA before and after 5-week treatment (right). Significant decrease of HAMA was observed after treatment (one-tailed Wilcoxon test, P \u0026lt; 0.001). Average HAMA scores are depicted by black bold line, colored lines indicate individual participants.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7957625/v1/8ce3e5a4a692227c3456e506.png"},{"id":97677588,"identity":"47e3096d-9f94-4982-87cd-2e3710957b91","added_by":"auto","created_at":"2025-12-08 09:53:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1486853,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7957625/v1/5586e1cc-8f76-4844-bbba-312c43041c7d.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Modulating Emotional and Cognitive Circuits in Major Depressive Disorder via Temporal Interference Stimulation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMajor depressive disorder (MDD) is one of the most prevalent psychiatric disorders globally\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Patients with MDD experience a wide spectrum of symptoms, including depressed mood, anhedonia, and feelings of worthlessness or guilt. These symptoms are often accompanied by severe cognitive dysfunction, such as impaired inhibitory control, which further prevents patients from processing negative emotions, creating a vicious cycle\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Evidence suggests that inhibitory control relies on a widely distributed brain network, including multiple deep brain regions, rather than a single frontal lobe area\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Patients with MDD show significant dysfunction in these related brain areas\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, for example, reduced activity in the left dorsolateral prefrontal cortex (DLPFC)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e or enhanced activity in deep brain regions like the amygdala (Amg)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Traditional non-invasive neuromodulation techniques often cannot stimulate deep brain regions non-invasively\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, which severely limits the understanding of the widespread neural network mechanisms involved in MDD and restricts the development of interventions for these disorders. Therefore, there is an urgent need for a tool of precisely and non-invasively modulating neural activity in both cortical and subcortical areas.\u003c/p\u003e\u003cp\u003eTemporal interference (TI) stimulation is a novel brain stimulation technique known for its non-invasive nature and ability to reach deep brain regions\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In recent years, TI has been applied in cognitive neuroscience research as an alternative to deep brain stimulation (DBS) and transcranial magnetic stimulation (TMS). These applications include disrupting motor skill consolidation, enhancing episodic memory, and improving motor skill learning by effectively targeting and interfering with neural activity in the striatum or hippocampus\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. TI stimulation works by applying two or more high-frequency (\u0026gt;\u0026thinsp;2 kHz) electric fields via scalp electrodes, which individually, are too high to affect neural activity. These fields intersect at the target location, generating an interference pattern\u0026mdash;an envelope amplitude waveform modulated at a difference frequency, which is sufficient to influence neural activity. This technique offers new possibilities for understanding and modulating abnormal brain network activity in MDD.\u003c/p\u003e\u003cp\u003eTo validate TI's modulatory effect on key brain regions within the cognitive-emotional circuit, we examined four key brain regions closely associated with MDD symptoms. The dorsolateral prefrontal cortex (DLPFC) is a critical target for brain stimulation in MDD, and its functional connectivity (FC) with the subgenual anterior cingulate cortex (sgACC) has been well studied\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Research has shown that the DLPFC-sgACC connection is crucial for emotion regulation and cognitive-emotional integration, potentially contributing to inhibitory control\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The amygdala (Amyg) is one of the most critical brain regions involved in MDD\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, responsible for emotional processing, decision-making, reward processing, attention, and perception\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Given its significant role in regulating emotion and cognition, the Amyg is thought as a potential component of the inhibitory control network. The ventral striatum (VS), a key component of the reward system, shows abnormal activity in MDD is closely linked to the motivational deficits in MDD, making it an important target for neuromodulation research\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe adopted a randomized, within-subject design to apply TI stimulation to 15 MDD patients in five, randomly presented conditions (left DLPFC TI stimulation, left sgACC TI stimulation, right Amyg TI stimulation, right VS TI stimulation, and a sham control). Each condition was administered separately, with a one-week washout period to avoid carryover effects. Before and after each stimulation session, the participant underwent an eight-minutes rs-fMRI scan and performed an emotional Stroop test. The difference between congruent/incongruent response time (RT) of the emotional Stroop test was defined as a marker of inhibitory control\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. We hypothesized that TI stimulation of each target could enhance the function of inhibitory control in participants, which in turn would be associated with changes in functional connectivity (FC) change related to the underlying mechanisms of inhibitory control and improvement of MDD symptoms.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eFifteen participants (18\u0026ndash;53 years, mean age 35.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9 years, 2 males) recruited at Shanghai Pudong Mental Health Center took part in this study. The inclusion criteria were: (1) adults aged between 18 and 65 years, both male and female; (2) participants were diagnosed with MDD based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the diagnosis was made by an experienced clinician; (3) a score of 17 or higher on HAMD-17; (4) participants must not have changed their antidepressant medication regimen from 30 days prior to and throughout the study.\u003c/p\u003e\u003cp\u003eThe exclusion criteria were: (1) participants with a history of other psychiatric disorders (e.g., schizophrenia, dementia) that, in the judgment of the investigator, could affect the results of the study ; (2) participants with a history of neurologic disorders, such as seizures; (3) participants with implants, such as the presence of metallic foreign bodies in the skull or metallic implants in the heart; (4) participants that have received neurostimulation therapies in the past three months; (5) a high risk for suicide assessed by an experienced clinician; (6) pregnant or breastfeeding women.\u003c/p\u003e\u003cp\u003e All participants gave written informed consent prior to study inclusion. This study was approved by the Institutional Review Board of Shanghai Pudong New Area Mental Health Center and Shanghai Jiao Tong University School of Medicine Ruijin Hospital (PDJWLL2021057).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy design\u003c/h3\u003e\n\u003cp\u003eA randomized crossover design was implemented to evaluate TI stimulation efficacy of the four selected targets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). All participants went through five sessions of 20-minute TI stimulation over a period of five weeks, where four targets and one control stimulation were randomly assigned to each week for each participant. One week interval was chosen to minimize carry-over effects. The sequence of TI stimulation sessions was sequence-balanced and participants were not aware of the sequence (blinded).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt the baseline assessment, T1 weighted structural MRI was obtained. Before each weekly session of TI stimulation and one to two weeks after the final session, HAMD (Hamilton Depression Rating Scale) and HAMA (Hamilton Anxiety Rating Scale) scores were collected. Before and after each TI stimulation session, an 8-minute resting-state fMRI scan was conducted, followed by an emotional Stroop test to assess inhibitory control.\u003c/p\u003e\n\u003ch3\u003eTI stimulation\u003c/h3\u003e\n\u003cp\u003eFive stimulation patterns including four targets and one control stimulation were investigated. The four targets included left DLPFC, which was stimulated with a frequency of 10 Hz, and left sgACC, right Amyg and right VS, which were stimulated with a frequency of 100 Hz.\u003c/p\u003e\u003cp\u003eThe left DLPFC was selected as a stimulation target due to its widely reported hypoactivity in MDD\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e; 10 Hz stimulation was applied to enhance its activity, consistent with conventional rTMS protocols\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The sgACC and VS, which often exhibit hyperactivity in MDD, were stimulated at 100 Hz to inhibit neural activity, in line with prior deep brain stimulation studies\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The Amyg was included as a fourth target given its significant role in MDD pathophysiology and historical difficulty in direct targeting, but its frequency selection followed the same inhibitory rationale (100 Hz) due to its frequently observed hyperactivation\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor the sham stimulation, two currents of the same frequency (2 kHz) were applied at an arbitrary electrode location. By leveraging the properties of TI stimulation, a 0 Hz difference frequency produces no envelope-modulated amplitude waveform and therefore should not drive neural activity, And the electrodes were placed at arbitrary locations. Since the stimulation target was different each time and currents were always delivered, participants could not distinguish between the real and sham stimulation. The initial current intensity was set to 1 mA, with the predefined ratio between electrode pairs maintained. If the participant reported no discomfort, the intensity was increased incrementally by 0.2 mA steps until reaching a maximum of 4 mA. Once the final intensity was determined, a 20-minute TI stimulation session was administered. To ensure participant comfort and avoid abrupt current changes, 2-second ramp-up and ramp-down periods were applied at the beginning and end of the stimulation.\u003c/p\u003e\n\u003ch3\u003eEmotional Stroop test\u003c/h3\u003e\n\u003cp\u003e To evaluate inhibitory control of the MDD participants, we applied an emotional Stroop test before and after each session of TI stimulation. In line with a previous study, we utilized the difference in RT on congruent vs. incongruent conditions as a marker of inhibitory control\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Specifically, the emotional Stroop test consisted of 80 trials in total with a randomized order (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). In each trial, a human face with an emotional word in red was displayed simultaneously on the screen for a duration of 1,000 ms, followed by a blank screen with a cross \u0026ldquo;+\u0026rdquo; in the center for a random duration between 3,000\u0026ndash;5,000 ms. In congruent trials, the emotion expressed by the face was consistent with that of the word, whereas they contradicted with each other in incongruent trials. Upon appearance of the cross, the participant was instructed to press a button as fast as possible to categorize the emotional valence of a word as either positive or negative. Non-responses were treated as error trials and were neglected in the analysis. RT of each trial was recorded.\u003c/p\u003e\n\u003ch3\u003eMRI acquisition\u003c/h3\u003e\n\u003cp\u003eStructural and functional images were acquired using a 3T UIH uMR 780 scanner. A 3D GRE sequence was utilized to acquire T1w images with a repetition time (TR)\u0026thinsp;=\u0026thinsp;7 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;3 ms, flip angle\u0026thinsp;=\u0026thinsp;9\u0026deg;, number of slices\u0026thinsp;=\u0026thinsp;160, voxel size\u0026thinsp;=\u0026thinsp;1 \u0026times; 1 \u0026times; 1 mm, and field of view (FOV)\u0026thinsp;=\u0026thinsp;256 mm. Resting-state functional images were acquired using an EPI sequence with the following parameters: TR\u0026thinsp;=\u0026thinsp;2 s, TE\u0026thinsp;=\u0026thinsp;30 ms, flip angle\u0026thinsp;=\u0026thinsp;80\u0026deg;, number of slices\u0026thinsp;=\u0026thinsp;36, voxel size\u0026thinsp;=\u0026thinsp;3.59 \u0026times; 3.59 \u0026times; 3.50 mm, FOV\u0026thinsp;=\u0026thinsp;230 mm.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMRI data processing\u003c/h2\u003e\u003cp\u003ePreprocessing and denoising were performed on neuroimaging data from all sessions which generated significant improvement in emotional Stroop test and the control session in CONN version 22a (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://web.conn-toolbox.org/\u003c/span\u003e\u003cspan address=\"https://web.conn-toolbox.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The detailed steps are as follows:\u003c/p\u003e\u003cp\u003eFunctional and anatomical data underwent preprocessing using a modular pipeline, which encompassed realignment, outlier detection, indirect segmentation, normalization to MNI space, and smoothing. Functional data were coregistered to a reference image (the first scan of the first session) utilizing a least squares approach and a 6-parameter (rigid body) transformation, followed by resampling with b-spline interpolation. Potential outlier scans were identified using ART, specifically those with framewise displacement exceeding 0.9 mm or global BOLD signal changes greater than 5 standard deviations. For each participant, a reference BOLD image was computed by averaging all scans, excluding outliers. Both functional and anatomical images were then coregistered and normalized into the standard MNI space, segmented into gray matter, white matter, and cerebrospinal fluid (CSF) tissue classes, and resampled to 2 mm isotropic voxels following an indirect normalization procedure using the SPM unified segmentation and normalization algorithm with the default IXI-549 tissue probability map template. Finally, functional data were smoothed using spatial convolution with a Gaussian kernel of 6 mm full width at half maximum (FWHM).Furthermore, the functional images were denoised with a standardized pipeline, which incorporated the regression of potential confounding variables, such as white matter time series (5 CompCor noise components), CSF time series (5 CompCor noise components), linear and quadratic motion parameters alongside their first-order derivatives (24 factors), outlier scans (less than 102 factors), and linear trends (2 factors) within each functional run. This was followed by a bandpass frequency filtering of the BOLD time series, targeting frequencies between 0.008 Hz and 0.09 Hz. The CompCor noise components within white matter and CSF were estimated by calculating the mean BOLD signal and the largest principal components that are orthogonal to the BOLD mean, motion parameters, and outlier scans within each participant's eroded segmentation masks. Based on the number of noise terms integrated into this denoising approach, the effective degrees of freedom of the BOLD signal were estimated to vary from 529.9 to 733.8 (average: 704.6) after denoising across all participants.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted using R for Windows 4.2.3 via R Studio. For the RTs in the Stroop test, only the RTs in correct trials were included in the analysis. To test the effects of TI stimulation on RT, we performed a 2 (time: pre-test, post-test) * 5 (regions: L DLPFC, L sgACC, R amyg, R VS, control stimulation) repeated measures ANOVA, and four 2 (time: pre-test, post-test) * 2 (conditions: target, control stimulation) repeated measures ANOVAs. Details will be explained in the Results section. For clinical data (HAMD/HAMA), the Wilcoxon signed-rank test and the Friedman test were employed due to the non-normal distribution of the data. Image visualization was conducted using MRIcron (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nitrc.org/projects/mricron/\u003c/span\u003e\u003cspan address=\"https://www.nitrc.org/projects/mricron/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe performed first-level and group-level analyses on preprocessed and denoised resting-state functional MRI data, comparing scans before and after TI stimulation. This analytical procedure was applied consistently across four predefined regions of interest (ROI). For the left sg ACC, two seed regions were defined based on the Brainnetome Atlas (BNA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://atlas.brainnetome.org\u003c/span\u003e\u003cspan address=\"https://atlas.brainnetome.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to match the precision of TI stimulation: BNA 179 (pregenual cingulate gyrus, Brodmann area 32) and BNA 187 (subgenual ACC). The selected ROI for R Amyg is the combination of BNA 212 (R medial amygdala) and 214 (R lateral amygdala). For R VS, BNA 224 (R nucleus accumbens) was selected. BNA 21 was selected as L DLPFC. The results were thresholded at p\u0026thinsp;\u0026lt;\u0026thinsp;0.005, uncorrected, at the voxel level, and significant clusters were reported only when they also survived at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, cluster-level correction for multiple comparisons. Because we had no hypothesis on potential differential functions of the medial and lateral amygdala, we combined these subregions as one region.\u003c/p\u003e\u003cp\u003eSeed-based connectivity maps (SBCs) were estimated to characterize the spatial pattern of FC with a seed region. The strength of FC was represented by Fisher-transformed bivariate correlation coefficients derived from a weighted general linear model (weighted-GLM), which was estimated separately for each seed region and target voxel, modeling the relationship between their BOLD signal time series. Individual scans were weighted using a boxcar signal that characterized each individual task or experimental condition, convolved with an SPM canonical hemodynamic response function (HRF), and rectified.\u003c/p\u003e\u003cp\u003eGroup-level analyses were conducted utilizing a General Linear Model (GLM). For each individual voxel, a separate GLM was estimated, with first-level connectivity measures at that voxel serving as dependent variables (one independent sample per subject and one measurement per task or experimental condition, where applicable), and groups or other subject-level identifiers acting as independent variables. Hypotheses at the voxel level were evaluated using multivariate parametric statistics, incorporating random effects across subjects and sample covariance estimation across multiple measurements. Inferences were made at the level of individual clusters (groups of contiguous voxels). Cluster-level inferences relied on parametric statistics grounded in Gaussian Random Field theory. Results were thresholded at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, uncorrected, at the voxel level, and significant clusters were reported only when they also survived at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, cluster-level correction for multiple comparisons.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eNeuroimaging results\u003c/h2\u003e\u003cp\u003eBecause the purpose of this study was not to compare the therapeutic effects or neural activity differences resulting from stimulating four different targets, we analyzed the neuroimaging changes induced by each of the four targets using a 2 (Time: pre- and post-stimulation) x 2 (Stimulation: real and control stimulation) ANOVA. No significant neuroimaging change was identified in R VS analysis, consistent with the behavioral result. For BNA 179 (L pgACC), we observed a significant interaction, indicating that the FC strength between L pgACC and L superior frontal gyrus (SFG) and middle frontal gyrus (MFG) were reduced after TI stimulation (lower connectivity strength in the post-test than pre-test), compared to the control stimulation condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For BNA 187 (L sgACC), we also found a significant interaction, with the FC strength between L sgACC and R SFG attenuated after stimulation (lower connectivity strength in the post-test than pre-test), compared to the control stimulation condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). For the ROI of R Amyg (BNA 212 and BNA 214 combined), we found a significant interaction, with the FC strength between R Amyg and bilateral hippocampus enhanced after TI stimulation (greater connectivity strength in the post-test than pre-test), compared to the control stimulation condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). In contrast to the behavioral results, we found that after TI stimulation, the left DLPFC (BNA 21) showed increased FC strength with the left precentral gyrus, putamen, and bilateral supplementary motor areas (post-test connectivity strength was higher than pre-test) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBrain regions showing significant FC with each seed region\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCluster regions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSize (voxels)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMax Z\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMNI coord (mm)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ey\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBNA 179 (L sgACC)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL SFG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL MFG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46,47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBNA 187 (L sgACC)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR SFG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBNA R Amyg\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR HIP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL HIP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBNA 21 (DLPFC)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL preCG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL PUT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: L\u0026thinsp;=\u0026thinsp;left; R\u0026thinsp;=\u0026thinsp;right; SFG\u0026thinsp;=\u0026thinsp;Superior frontal gyrus; MFG\u0026thinsp;=\u0026thinsp;Middle frontal gyrus; HIP\u0026thinsp;=\u0026thinsp;Hippocampus; preCG\u0026thinsp;=\u0026thinsp;Precentral gyrus; PUT\u0026thinsp;=\u0026thinsp;Putamen; SMA\u0026thinsp;=\u0026thinsp;Supplementary motor area\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eBehavioral results\u003c/h2\u003e\u003cp\u003eWe found a significant interaction in the 2*5 ANOVA on the RTs, F\u003csub\u003e(4, 11)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.784, p\u0026thinsp;=\u0026thinsp;0.036, η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.579, but no significant main effects of time or regions, with both Fs\u0026thinsp;\u0026lt;\u0026thinsp;1. Although a significant interaction was observed, the primary objective of this study was not to compare differences among the various targets, but rather to individually verify whether stimulation at each target site could elicit significant specific changes. Therefore, we performed four 2 (time: pre-test, post-test) * 2 (conditions: target, control stimulation) repeated measures ANOVAs by comparing each target region with the control stimulation condition. There was a significant interaction in the L sgACC region, F\u003csub\u003e(1, 14)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;14.059, p\u0026thinsp;=\u0026thinsp;0.002, η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.501, but no significant main effects of time or conditions, with both Fs\u0026thinsp;\u0026lt;\u0026thinsp;1. We also found a significant interaction in the R Amyg region, F\u003csub\u003e(1, 14)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;11.573, p\u0026thinsp;=\u0026thinsp;0.004, η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.453. For the L DLPFC region, there was no significant interaction, F\u003csub\u003e(1, 14)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.168, p\u0026thinsp;=\u0026thinsp;0.224, η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.104, and no significant main effects of time, F\u003csub\u003e(1, 14)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.022, p\u0026thinsp;=\u0026thinsp;0.177, η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.126, or conditions, F\u003csub\u003e(1, 14)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.175, p\u0026thinsp;=\u0026thinsp;0.297, η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.077. For the R VS region, we did not find a significant interaction, F\u003csub\u003e(1, 14)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.149, p\u0026thinsp;=\u0026thinsp;0.302, η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.076, nor significant main effects of time, F\u003csub\u003e(1, 14)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.492, p\u0026thinsp;=\u0026thinsp;0.242, η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.096, or conditions, F\u003csub\u003e(1, 14)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.187, p\u0026thinsp;=\u0026thinsp;0.161, η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.135. The significant results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e in terms of the pre-test vs. post-test RT differences. TI stimulation of the L sgACC and R Amyg produced shorter RTs in the post-test than pre-test, compared to the control stimulation condition. As the error rate in each condition was very low (\u0026lt;\u0026thinsp;5%), we did not perform an analysis of the response accuracy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe then conducted a correlation analysis between changes in reaction time and changes in functional connectivity strength. We found that following TI stimulation to the left sgACC, the change in reaction time was significantly and negatively correlated with the FC strength change between L sgACC and L SFG, r\u003csub\u003e(13)\u003c/sub\u003e = -0.52, p\u0026thinsp;=\u0026thinsp;0.047 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This indicates that a smaller reduction in FC induced by TI stimulation was associated with a greater degree of RT improvement.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eClinical results\u003c/h2\u003e\u003cp\u003eFor clinical outcomes, significant HAMD/HAMA improvement was observed (HAMD p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HAMA p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), aggregating five-week stimulation sessions, notwithstanding the randomization order and the inclusion of a control session. To investigate the effect induced by specific target, Friedman test was performed for both HAMD and HAMA changes. Though no significant results were observed (HAMD P\u0026thinsp;=\u0026thinsp;0.41, HAMA P\u0026thinsp;=\u0026thinsp;0.053) considering the relatively limited effect size induced by single stimulation session, pairwise comparisons were still performed to identify any potential therapeutic effect. All targets but sgACC and control showed a significant decrease of HAMD (one-tailed Wilcoxon signed-rank test, L DLPFC V\u0026thinsp;=\u0026thinsp;98, p\u0026thinsp;=\u0026thinsp;0.0047; L sgACC V\u0026thinsp;=\u0026thinsp;76.5, p\u0026thinsp;=\u0026thinsp;0.14; R Amyg V\u0026thinsp;=\u0026thinsp;112.5, p\u0026thinsp;=\u0026thinsp;0.003; R VS V\u0026thinsp;=\u0026thinsp;107.5, p\u0026thinsp;=\u0026thinsp;0.0074; control V\u0026thinsp;=\u0026thinsp;73.5, p\u0026thinsp;=\u0026thinsp;0.2). DLPFC and Amygdala showed a significant decrease of HAMA (one-tailed Wilcoxon signed-rank test, L DLPFC V\u0026thinsp;=\u0026thinsp;94, p\u0026thinsp;=\u0026thinsp;0.0099; L sgACC V\u0026thinsp;=\u0026thinsp;74, p\u0026thinsp;=\u0026thinsp;0.44; R Amyg V\u0026thinsp;=\u0026thinsp;112.5, p\u0026thinsp;=\u0026thinsp;0.0031; R VS V\u0026thinsp;=\u0026thinsp;47, p\u0026thinsp;=\u0026thinsp;0.23; control V\u0026thinsp;=\u0026thinsp;68, p\u0026thinsp;=\u0026thinsp;0.26).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, by applying TI stimulation to modulate neural activity in specific brain regions, we assessed the extent to which it can modulate brain regions related to cognition and emotion regulation, thereby potentially improving inhibitory control in MDD patients. Our behavioral and functional connectivity findings indicate that TI stimulation of the left sgACC significantly reduced FC between the left ACC and frontal cortex, while stimulation of the right Amyg enhanced FC between the amygdala and bilateral hippocampi. These circuit-specific changes in FC were accompanied by an improvement in emotional Stroop task performance, suggesting that impaired inhibitory control was restored or that its deterioration was prevented. Furthermore, although we did not find a behavioral improvement after TI stimulation of the DLPFC, we did find an increase in FC with the left precentral gyrus, putamen, and bilateral supplementary motor areas. Further details will be discussed as following.\u003c/p\u003e\u003cp\u003eStimulation of both the sgACC and Amyg targets facilitated inhibitory control, yet likely through distinct neural pathways. TI stimulation of the left sgACC led to a decrease in FC with frontal regions, which may reflect the normalization of hyperconnectivity in the cognitive control circuit\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In contrast, TI stimulation of the right Amyg enhanced FC between the amygdala and bilateral hippocampi, which may be related to the modulation of emotional responses systems. Previous studies have shown that the amygdala-hippocampal circuit plays a key role in memory formation, especially under stress\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, suggesting that improved emotional regulation could underlie the enhanced inhibitory control observed after right Amyg TI stimulation. Similarly, the finding that stimulating the L DLPFC, while not improving inhibitory control, did lead to increased FC with the precentral gyrus, putamen, and SMA, may provide a different perspective on the role of the DLPFC in the MDD inhibitory control network.\u003c/p\u003e\u003cp\u003eA significant negative correlation was observed between changes in FC strength of sgACC and L_SFG and RT changes, indicating that a smaller reduction in FC strength following TI stimulation was associated with a greater improvement in RT. It has been proved that MDD patients exhibit baseline neural hyperactivation, especially the hyperconnectivity between sgACC and related brain regions, which contributes to circuit dysfunction\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. As previously discussed, TI stimulation may promote normalization of this hyperconnectivity, thereby restoring abnormally increased FC to a typical range. Meanwhile, the reduction in RT after TI stimulation reflects a decrease in Stroop interference effects and an enhanced ability to inhibit task-irrelevant information\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Thus, the observed negative correlation may suggest that improvement in inhibitory control is associated with a reduction in pathologically heightened FC induced by TI stimulation.\u003c/p\u003e\u003cp\u003eDepression significantly disrupts inhibitory control, a cognitive process essential for regulating automatic responses and achieving goal-directed behavior\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Impairments in inhibitory control are closely linked to persistent low mood, a core feature of MDD. Individuals with MDD consistently exhibit deficits in inhibitory control compared to healthy subjects\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Notably, stronger baseline inhibitory control has been associated with more favorable therapeutic outcomes in psychotherapy, emphasizing its importance as a treatment target\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Our findings align with previous research, showing that MDD patients have reduced activity in key regions involved in inhibitory control\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Moreover, this study demonstrates that TI stimulation can effectively modulate both brain activity and behavioral performance, offering a promising non-invasive approach to improving inhibitory control in MDD.\u003c/p\u003e\u003cp\u003eAlthough TI stimulation of the left DLPFC did not yield significant behavioral improvements in inhibitory control, it specifically enhanced FC between the left DLPFC and the precentral gyrus, putamen, and bilateral SMA. This dissociation suggests that the modulatory effect of DLPFC stimulation may operate through circuit-level preparatory changes rather than immediate behavioral expression\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Specifically, the strengthened connectivity with the SMA\u0026mdash;a key region involved in response inhibition and motor planning\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e\u0026mdash;and the putamen, which translates cognitive commands into action, indicates a facilitation of the neural substrates underlying executive control\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Thus, the absence of behavioral effects may reflect that a single stimulation session was insufficient to fully overcome the long-term cognitive deficits associated with MDD, though it may potentially establish a neurophysiological foundation for future behavioral improvements. Our findings imply that the role of the DLPFC in MDD may be more indirect than conventionally presumed\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, whereas the left sgACC and right Amyg may play more critical roles in modulating cognitive-emotional dysfunction in this patient cohort. It is noteworthy that MDD is a highly heterogeneous disorder\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, and the patient sample examined in our study may represent a specific subtype in which the contribution of the DLPFC to inhibitory control is less prominent\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, or its modulation requires alternative stimulation parameters. This does not preclude the importance of the DLPFC in other MDD populations or under different stimulation protocols\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The discrepancies between our results and previous studies emphasizing the role of DLPFC-related circuits may be attributable to differences in sample characteristics. Future studies with larger, more precisely phenotyped samples are needed to identify which patient subtypes are most likely to benefit from DLPFC-targeted neuromodulation\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe ACC (sgACC and pgACC) is a key node of the salience network, which is responsible for detecting behaviorally relevant stimuli and coordinating the switch between other large-scale networks (e.g., the executive control network and the default mode network) to guide behavior\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The decreased connectivity between the ACC and the frontal cortex following TI stimulation may reflect a restoration of balance within the salience network, potentially leading to more efficient resource allocation and better inhibitory control\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Similarly, the enhanced FC between the right amygdala and the bilateral hippocampus\u0026mdash;a circuit crucial for emotional regulation and memory formation\u0026mdash;may represent another facet of this network-level modulation. The salience network, which includes the amygdala, plays a vital role in assigning emotional significance to stimuli. Therefore, the TI-induced changes in both the ACC and the amygdala-hippocampus circuit collectively suggest that the stimulation effectively engages and modulates distributed neural systems underlying emotional and cognitive processing. These findings are consistent with recent evidence that inhibitory control deficits in MDD are distributed across multiple neural networks beyond the DLPFC\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Moreover, they provide initial evidence for using TI stimulation to study the causal neural mechanisms of cognitive behavior from a broader network perspective.\u003c/p\u003e\u003cp\u003e Besides cognitive improvement, clinical improvement measured in HAMD/HAMA was also significant and relatively persistent after five-session stimulation, though target-specific analysis based on single-session stimulation yielded limited improvement. Due to the small sample size, we cannot draw definitive conclusions about TI\u0026rsquo;s efficacy in improving MDD symptoms, but our findings provide valuable evidence of its potential, warranting more in-depth exploration in future research.\u003c/p\u003e\u003cp\u003eFurthermore, to examine in greater detail the neural mechanism of a specific cognitive function, more experimental data are also required. One key limitation of our study concerns the stimulation parameters. Although TI stimulation of the left sgACC and right amygdala yielded positive behavioral effects, the parameters used for the left DLPFC\u0026mdash;such as frequency, intensity, or duration\u0026mdash;may not have been optimal for effectively modulating this region. TI stimulation depends on the precise intersection of electric fields to target deep brain structures, and even minor adjustments to these parameters can substantially influence the outcomes. Previous rTMS studies have shown that the DLPFC can be successfully modulated under specific stimulation conditions\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e Although we used a frequency of 10 Hz\u0026mdash;consistent with common rTMS protocols\u0026mdash;other parameters such as intensity and duration differed. The selection of stimulation parameters inherently involves a trade-off: while flexible parameterization allows broader exploration of possible effects, it also expands the parameter space that must be systematically narrowed through iterative experimentation guided by prior knowledge.\u003c/p\u003e\u003cp\u003eIn summary, our findings demonstrate that TI stimulation can effectively modulate the activity of specific cognitive-emotional circuits in MDD patients, including cortical and subcortical regions such as the DLPFC, sgACC, and Amygdala. This modulation leads to improvements in impaired inhibitory control and, to some extent, alleviates the clinical symptoms of MDD. Furthermore, this study not only validates TI's capability to effectively stimulate deep brain sites but also provides a referential paradigm for future systematic investigations into the neural mechanisms of cognitive behaviors involving deep brain regions. Future TI stimulation research can effectively perturb and analyze multiple neural circuits within a single study, potentially offering highly robust and compelling evidence for how complex neural activity is organized for specific cognitive functions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Shanghai Municipal Science and Technology Major Project, Shanghai Pudong New Area Health Committee Excellent Young Medical Talents Training Program Project (PWRq2021-31), and Medical Discipline Construction Project of Pudong Health Committee of Shanghai (Grant No.: PWYgy2021-02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, C.Y.P., Y.L., H.F.G., Y.R.F., J.Z., C.C.Z.; Data Curation, J.Y.Y., J.J.W., F.W.; Formal Analysis, Y.S.C., J.Y.Y., M.L., L.L., X.Q.Z., T.L., L.Y., L.K.R., I.R.V., U.Z.; Investigation, S.P.Z., S.M.Z., W.L., F.W., Q.Q.L., J.W.X.; Methodology, Y.S.C., J.Y.Y., M.L., J.J.W.; Project Administration, H.F.G., Y.R.F., J.Z., C.C.Z.; Resources, S.P.Z., W.L., F.W.; Supervision, Y.L., H.F.G., Y.R.F., C.C.Z.; Writing - Original Draft, Y.S.C., J.Y.Y., M.L.; Writing \u0026ndash; Review \u0026amp; Editing, Y.S.C., J.Y.Y., C.Y.P., W.L., F.W., Q.Q.L., J.W.X., L.L., X.Q.Z., T.L., Y.L., I.R.V., U.Z., Y.F., H.F.G., Y.R.F., J.Z., C.C.Z.; Funding acquisition, H.F.G., Y.R.F., J.Z., C.C.Z.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLong Li, Xiaoqi Zhu, and Tian Liu, employees of Neurodome, were responsible for simulating the TIS electric fields but did not participate in data analysis, or interpretation of results. The remaining authors have no competing interests to declare in relation to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analyzed during the current study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMalhi, G. S. \u0026amp; Mann, J. J. Depression. \u003cem\u003eThe Lancet\u003c/em\u003e \u003cstrong\u003e392\u003c/strong\u003e, 2299\u0026ndash;2312 (2018).\u003c/li\u003e\n \u003cli\u003eCulpepper, L., Lam, R. W. \u0026amp; McIntyre, R. S. Cognitive Impairment in Patients With Depression: Awareness, Assessment, and Management. \u003cem\u003eJ. Clin. Psychiatry\u003c/em\u003e \u003cstrong\u003e78\u003c/strong\u003e, 1383\u0026ndash;1394 (2017).\u003c/li\u003e\n \u003cli\u003eKang, W., Hern\u0026aacute;ndez, S. P., Rahman, Md. S., Voigt, K. \u0026amp; Malvaso, A. 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A., Ćurčić-Blake, B., Aleman, A. \u0026amp; Sommer, I. E. Efficacy of non-invasive brain stimulation on cognitive functioning in brain disorders: a meta-analysis. \u003cem\u003ePsychol. Med.\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 2465\u0026ndash;2486 (2020).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Major Depressive Disorder (MDD), Temporal Interference (TI), Functional connectivity (FC), rs-fMRI, Emotional Stroop","lastPublishedDoi":"10.21203/rs.3.rs-7957625/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7957625/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMajor depressive disorder (MDD) often leads to cognitive impairments such as impaired inhibitory control, involving complex circuits in both cortical and subcortical regions. We applied non-invasive temporal interference (TI) stimulation to specific neural circuits to explore its modulatory effects on the abnormal neural networks in MDD. In a randomized, crossover study, 15 patients received TI stimulation targeting the left DLPFC, left sgACC, right amygdala, left VS and sham stimulation delivered in a random order. Functional connectivity and behavioral changes from before and after stimulation were assessed using rs-fMRI and an emotional Stroop task. TI stimulation of the sgACC reduced the FC between the sgACC and the frontal cortex, while stimulation of the right amygdala enhanced the FC between the amygdala and bilateral hippocampi. These FC changes were accompanied by an improvement in Stroop task reaction time, along with significant clinical symptom improvement. This study is one of the first to demonstrate that TI can effectively modulate MDD-related network activity, providing a new approach to understanding and regulating cognitive-emotional functions at the neural circuit level.\u003c/p\u003e","manuscriptTitle":"Modulating Emotional and Cognitive Circuits in Major Depressive Disorder via Temporal Interference Stimulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 15:18:45","doi":"10.21203/rs.3.rs-7957625/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7264a098-859a-4bd9-aed2-642cba3b989b","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58015443,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Cognitive control"},{"id":58015444,"name":"Health sciences/Diseases/Psychiatric disorders/Depression"}],"tags":[],"updatedAt":"2026-04-01T11:13:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 15:18:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7957625","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7957625","identity":"rs-7957625","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}