Post-Movement Beta Rebound in Sensorimotor Cortex Endures One Week After Three Days of Practice

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Sisti, Amarnath Amarnath, Rebecca Balcha, Gabriel Freitas, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4768967/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The post-movement beta rebound (PMBR) is the tightly coupled increase in beta power that occurs in the sensorimotor cortex upon movement termination. It is a potential biomarker of motor control; abnormal responses could signal disease. With respect to its interaction with learning, both decreases and increases have been observed. In this study, we examined the effect of two types of practice schedules, blocked and randomized, on memory retention one week later. A blocked schedule leads to better performance during acquisition but poorer performance during long-term retention, a phenomenon known as the contextual interference effect. The aim of the present study is two-fold: (1) test the contextual interference effect using a visuomotor bimanual tracking task (2) determine whether three days of practice leads to a decreased PMBR at retention test one week later. We hypothesized that learning with either schedule would lead to decreased PMBR. Our data demonstrated no main effect of practice schedule. It is most likely that the task variants were not sufficiently different to induce the contextual interference phenomenon. Further, the PMBR was not attenuated by learning. It was evident before and after three days of practice. This has important implications for its putative role as a biomarker. Biological sciences/Psychology Biological sciences/Psychology/Human behaviour Figures Figure 1 Figure 2 Figure 3 Introduction The implementation of biomarkers for detecting pathological states has been a key factor in the acceleration of personalized medicine (Selleck et al., 2017). Within the last six years, the first biomarker for traumatic brain injury (TBI) was approved by the FDA (U.S. Food and Drug Administration, 2018; Nishimura et al. 2022). The vast majority are constrained to “wet lab” environments, such as blood-borne pathogens or metabolites of cerebrospinal fluid (CSF) (Kraus et al., 2011; Toader et al., 2023). Research using non-invasive, neuroimaging approaches, such as electroencephalography (EEG), can predict neurodegenerative conditions such as dementia and autism (Bosl et al., 2018; Jiao et al., 2023). However, the use of EEG ‘neural signatures’ as biomarkers has not yet been widely adopted in clinical practice. EEG has experienced a resurgence in recent years, because it is non-invasive; it is portable compared with magnetic resonance imaging (MRI); it has superior temporal resolution compared with MRI. It is the mode of choice for understanding seizures, sleep disorders, and brain computer interface (BCI) applications for the severely disabled. EEG captures neurophysiological responses on the order of milliseconds with a range of electrodes anywhere from two to 256 (high-density) in 3-dimensional space. Several brain frequencies, most notably alpha (8-12 Hz) and beta (13-30 Hz), are reliably linked to overt, fundamental behaviors, such as closing the eyes (alpha) or the cessation of movement (beta). The present study focuses on beta, because of its potential role as a putative biomarker of motor control dysfunction (Cohan et al., 2019; Barone and Rossiter, 2021; Ulanov and Shtyrov, 2022). The post-motor beta rebound (PMBR) refers to the temporary increase of power of the beta band (12 - 30 Hz) in the sensorimotor cortex which coincides with movement cessation (Pfurtscheller and Lopes, 1999; Pfurtscheller and Solis-Escalante, 2009; Gaetz et al., 2010). The increase in beta power reflects GABA-ergic inhibitory activity along the thalamo-cortical pathway (Heinrichs-Graham et al., 2017). In neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS) and cerebral palsy, there is a decrease in the PMBR (Bizovicar et al., 2014; Hinton et al., 2024). Learning has also been correlated with a decreased PMBR (Tan et al., 2014, 2016; Torrecilloset al., 2015; Espenhahn et al., 2020, Korka et al., 2023; Coleman et al. 2024). These somewhat paradoxical findings speak to the challenges of mapping neural oscillations onto cognitive processes. The use of a unique EEG pattern to predict a diseased state with subclinical threshold requires a robust, consistent response over time. Espenhahn and colleagues (2017) demonstrated consistency of PMBR over time, remarkably, up to seven weeks. Participants performed a visually-cued unimanual wrist-flexion task. The task did not require extensive training; participants demonstrated stable kinematic measures from the first session through the final, sixth session, which notably, was up to 50 days later (Espenhahn et al., 2017). Other research on PMBR and learning includes the use of a Bayesian model to consider the roles of both error-correction and prior knowledge on a trial-by-trial basis; using this approach, a negative correlation between PMBR and error was demonstrated (Tan et al., 2014, 2016). In ecological studies, real-world motor learning skills, such as playing billiards, both increases and decreases of PMBR were observed (Haar and Faisal, 2020). Taken together, it is clear that additional studies are needed to determine the interaction of the PMBR with learning. In the present study, we hypothesized that learning would attenuate the PMBR. To test this, participants practiced a visuomotor bimanual tracking (VBT) task for 3 days. Then, one week later a retention test was given. A robust learning phenomenon known as the contextual interference effect (CIE) was applied to distinguish between two practice conditions. The CIE refers to the impact of practice organization on long-term memory (Shea and Morgan, 1979; Graser et al., 2019; Farrow and Buszard, 2017). It compares two types of practice schedules: blocked vs. random. In the blocked schedule, a specific task is performed repeatedly before moving on to the next one. For instance, only Task A is practiced on Day 1, then Task B is practiced on Day 2, and Task C is practiced on Day 3 (AAA, BBB, CCC). In the randomized schedule, all three tasks are practiced each day, but in a different sequence: CAB on Day 1, BCA, on Day 2, and CBA on Day 3, et cetera . The blocked schedule results in better performance than the randomized schedule during acquisition. However, surprisingly, long-term retention is superior in the group that practiced using a randomized schedule, despite the initial differences in acquisition (Pauwels et al., 2014). In our study, we aimed to both: (1) observe the CIE using a visuomotor bimanual tracking task and (2) determine whether learning using either schedule would result in a decreased PMBR. Materials and Methods Participants Participants (n=36) were 18 years of age or older and were students of Norwich University. All completed the Oldfield (1971) handedness questionnaire, as well as questions regarding previous history with activities that require bimanual coordination, including video games, driving manual transmission automobiles, or playing a musical instrument. Participants were fitted with EEG QuikCap (Compumedics, Neuroscan) during Pre-Test and Post-Test only. EEG data were not collected during the 3 practice sessions. All procedures were approved by the NU Institutional Review Board Ethics Committee and in accordance with the 1964 Declaration of Helsinki (revised in 2008). All participants signed the Informed Consent form. Apparatus and task description Participants were comfortably seated at a table in front of a computer monitor with both lower arms resting on two custom-made adjustable ramps. At the end of each ramp, a dial was mounted and could be rotated by holding an upright peg between the thumb and index finger (Sisti et al., 2011, 2012). The two dials controlled movement of a red cursor (a flexible line segment 1 cm long) on the monitor. When the left dial was rotated clockwise, the cursor moved up; when it was rotated counterclockwise, the red cursor moved down; and vice versa. The right dial controlled horizontal movement. The objective of the task was to use the two dials simultaneously to track the moving target. The target was a white dot that moved along a stationary path, either a curved line or a jagged line. For pre- and post-test, the path was a curved line and during practice, the path was similar except it was a jagged line. Participants from both blocked and randomized groups had the same pre- and post-test. The 3 practice sessions included a similar path, with the exception that it was a jagged line rather than curved. This was done to control for temporal interval differences between end of training and retention test between blocked and randomized schedules, i.e. blocked group would have only practiced Task A on Day 1 and would have recently practiced Task C on Day 3, whereas randomized group would have practiced all 3 tasks on Day 3. Tasks differed by altering the starting position of the jagged line. The entire experiment included 5 sessions, beginning with a pre-test, followed by 3 consecutive practice sessions each on a different day, and finally a post-test, which occurred one week after the last practice session. Each session included 20 trials. In the randomized practice schedule (n=15), the starting position was randomized within a training day. In the blocked practice schedule (n=21), the starting position remained constant each day. The sample sizes across the two conditions were unequal, because the blocked practice schedule was counterbalanced to control for any sequence of practice effects. Improvement within a session was determined by calculating the difference between task performance on the first and last trial. Neurophysiological and pre-processing methods Before pre-test and post-test sessions, participants were fitted with a 32-electrode QuikCap (Compumedics, Neuroscan). The central electrode, Cz, was placed at the midpoint between nasion and anion. Soluble electrolytic gel was inserted into each electrode using a blunt-edge syringe. Gel was inserted until the impedance values were less than 5 kΩ. Once impedance values were less than 5 kΩ, instructions for the behavioral task were administered. Neuroscan Curry8 was used for signal acquisition and offline processing. Triggers were manually delivered to signal the start (keyboard press 1) and end (keyboard press 2) of every trial. Manual triggers are marked in the software with both a time stamp and a visual cue, i.e. a purple vertical line in the data acquisition window. EEG data were collected with a sampling rate of 1,000 Hz. The following steps were applied to each participant’s data in the same sequence: bandpass filter of 1.0 –30 Hz to remove non-physiological artifacts; ocular artifacts removal using Independent Component Analysis; removal of other residual artifacts (e.g., muscular) using a voltage threshold (+100 µV, +200 ms). We restricted our analysis to the sensorimotor cortices because this is where the beta rebound is most evident. This region corresponds with C3 and C4 electrodes, left and right hemisphere, respectively (Figure 1). Signals were averaged across the 20 trials for the pre-test and the post-test. Conversion of signals from the time to the frequency domain was done by applying the Fast Fourier Transform (FFT) using Curry 8 software. A two-second interval was selected during the last two seconds of movement and the first two seconds of the intertrial interval. Mean values across subjects were calculated for each condition. Data were then exported as .txt files to MS Excel and formatted for statistical analysis in SPSS. The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Statistical Analysis Task performance was assessed by calculating the Euclidean distance between the participant’s cursor and the target cursor at the end of each trial, referred to as the Finish Offset (FO). A value of zero indicates the participant finished the trial precisely on the target; FO units are arbitrary. Improvement on task performance, interpreted as learning, was determined by calculating the difference between the first and last trial. The effect of practice schedule on learning was analyzed using a 2 x 5 ANOVA, in which the first factor was practice condition (blocked or randomized) and the second factor was practice session (Pre-Test, Session 1, 2, 3, Post-Test). To determine the effect of PMBR on learning, a 2 x 2 ANOVA was calculated; the first factor was movement termination (before vs after movement) and the second factor included the electrodes corresponding with the sensorimotor cortex (C3 and C4). Results Task performance improved in both groups from pre-test to post-test following 3 practice sessions [F(4)=13.51, p<.001] (Figure 2). Mean FO for the blocked group (n=21) was 9.41 + 0.87 at Pre-Test and 4.86 + 0.76 at Post-Test. Mean FO for the randomized group (n=15) was 7.80 + 1.21 at Pre-Test and 3.41 + 0.58 at Post-Test. There was no main effect of practice condition (F=2.09, p=.14). There was no significant interaction between practice condition and practice session [F(4)=1.67, p=.15]. Beta power of the sensorimotor cortex before and after movement termination, i.e. the end of the trial, was analyzed using a 2 x 2 ANOVA; the first factor was Movement Termination (Before vs After) and the second factor was the electrode corresponding with the left and right sensorimotor cortex (C3 and C4). This was done for both Day 1 and Day 4. The previous behavioral analysis revealed no effect of practice condition, therefore, blocked and randomized groups were collapsed. There was a main effect of Movement Termination (F=7.85, p=0.006); no main effect of electrode (F=1.02, p=0.31) and no significant interaction of Movement Termination by Electrode (F=0.065, p=0.79). Because there was no significant effect of electrodes, C3 and C4, were collapsed. A 2 x 2 ANOVA of Movement Termination (Before vs After) x Day (Pre-Test vs Post-Test) was computed. There was a significant main effect of Movement Termination (F=16.43, p<.001); no main effect of Day (F=0.04, p=0.83) and no significant interaction (F=.013, p=0.91). The PMBR was evident before 3 days of practice and one week later at the retention test (Figure 3). Discussion The PMBR may serve as an index of motor control, where an abnormal response could signal dysregulation (Espenhahn et al., 2017). Biomarkers, sometimes referred to as ‘neural signatures’ in the context of EEG, hold tremendous potential for patient care. However, the etiology of any neural signature must be clearly understood. With this in mind, we sought to test whether the PMBR would be attenuated by learning. Further, we included two types of practice schedules to test the robustness of the CIE. Neither hypothesis was supported. Each is discussed in turn. Because of its implications for human performance, the CIE has been extensively studied in both laboratory and applied settings (for a comprehensive review of CIE and related learning phenomena, see Raviv et al., 2022). In the present experiment, we used a visuomotor bimanual tracking across several days in young adults. To create unique tasks, the starting position was altered. This is a relatively subtle change and is the most likely rationale for the absence of the CIE. To observe the CIE, that is the superior performance of the blocked schedule during acquisition, but inferior performance at retention, it may be necessary to include a task that differs in more significant ways. This would then require greater attentional resources during acquisition and may result in the differences of acquisition and retention. Importantly, while no CIE was detected, both groups of participants demonstrated significant improvement overtime and a clear retention of the task one week later. Regarding the absence of a learning-induced attenuation of the PMBR, there are several possible explanations. There are key differences between the present experiment and previous research demonstrating learning-induced attenuation of PMBR (Tan et al., 2014; 2016) Most notably, in the experiments by Tan et al., 2014; 2016, learning occurred within a single session. The session consisted of 350 trials. Our experiment included about one-third as many trials distributed across several days, as well as a retention test one week after practice. It is possible that the attenuation from a single session of many trials reflected short-term neuroplasticity, or perhaps fatigue from cognitive load and sensorimotor processing. The fact that the PMBR was stable over time is consistent with other studies (Espenhahn et al., 2017). Further, the divergent findings regarding learning-attenuation are not necessarily contradictory to other reports (Tan et al.,2016). The difference in both time scale and training requirements between experiments are significant. Additional studies are needed to further characterize the PMBR in both short- and long-term memory. Mapping neural processes to cognitive and behavioral processes is a central goal of neuroscience. In this experiment, we examined a single neural oscillation to determine how learning might alter it. While this experiment did not induce changes, other studies that include many training trials on a single day did (Tan et al., 2014). Elucidating the factors that drive these differences will be important in the development of EEG biomarkers. Embracing machine learning techniques to augment human interpretation of data will facilitate the ability to examine the full range of neural oscillations and how they respond to cognitive and behavioral treatments. The idea that a noninvasive, neural signature can signal health or disease in subthreshold conditions, i.e. the early stages before persistent symptoms manifest, warrants further investigation. Declarations Author Contribution HS - Conceived experimental design, conducted analysis, wrote manuscriptAA, RB, GF, EV - Recruited and tested participants, pre-processed data, formatted data Data Availability The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. References Barone, J., & Rossiter, H. E. (2021). Understanding the Role of Sensorimotor Beta Oscillations. Front Syst Neurosci, 15, 655886. doi: 10.3389/fnsys.2021.655886 Bizovicar, N., Dreo, J., Koritnik, B., & Zidar, J. (2014). Decreased movement-related beta desynchronization and impaired post-movement beta rebound in amyotrophic lateral sclerosis. Clin Neurophysiol, 125(8), 1689–1699. doi: 10.1016/j.clinph.2013.12.108 Bosl, W.J., Tager-Flusberg, H. & Nelson, C.A. EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach. Sci Rep 8, 6828 (2018). https://doi.org/10.1038/s41598-018-24318-x Cohan R, Bearss KA, DeSouza JFX. Frequency-Specific Biomarkers in Neurodegenerative Disorders: Implications of Alpha and Beta Oscillations in Motor Behaviour. J Neurol Neuromed (2019) 4(3): 18–24 Coleman, S. C., Seedat, Z. A., Pakenham, D. O., Quinn, A. J., Brookes, M. J., Woolrich, M. W., & Mullinger, K. J. (2024). Post-task responses following working memory and movement are driven by transient spectral bursts with similar characteristics. Hum Brain Mapp, 45(7), e26700. doi: 10.1002/hbm.26700 Erbil, N., & Ungan, P. (2007). Changes in the alpha and beta amplitudes of the central EEG during the onset, continuation, and offset of long-duration repetitive hand movements. Brain Res, 1169, 44–56. doi: 10.1016/j.brainres.2007.07.014 Espenhahn, S., de Berker, A. O., van Wijk, B. C. M., Rossiter, H. E., & Ward, N. S. (2017). Movement-related beta oscillations show high intra-individual reliability. Neuroimage, 147, 175–185. doi: 10.1016/j.neuroimage.2016.12.025 Espenhahn, S., Rossiter, H. E., van Wijk, B. C. M., Redman, N., Rondina, J. M., Diedrichsen, J., & Ward, N. S. (2020). Sensorimotor cortex beta oscillations reflect motor skill learning ability after stroke. Brain Commun, 2(2), fcaa161. doi: 10.1093/braincomms/fcaa161 Gaetz, W., Macdonald, M., Cheyne, D., & Snead, O. C. (2010). Neuromagnetic imaging of movement-related cortical oscillations in children and adults: age predicts post-movement beta rebound. Neuroimage, 51(2), 792–807. doi: 10.1016/j.neuroimage.2010.01.077 Graser, J. V., Bastiaenen, C. H. G., & van Hedel, H. J. A. (2019). The role of the practice order: A systematic review about contextual interference in children. PLoS One, 14(1), e0209979. doi: 10.1371/journal.pone.0209979 Haar, S., & Faisal, A. A. (2020). Brain Activity Reveals Multiple Motor-Learning Mechanisms in a Real-World Task. Front Hum Neurosci, 14, 354. doi: 10.3389/fnhum.2020.00354 Heinrichs-Graham, E., et al. (2017) The Functional Role of Post-Movement Beta Oscillations in Motor Termination. Brain Structure and Function, 222, 3075–3086. https://doi.org/10.1007/s00429-017-1387-1 Hinton, E. H., Busboom, M. T., Embury, C. M., Spooner, R. K., Wilson, T. W., & Kurz, M. J. (2024). Adults with cerebral palsy exhibit uncharacteristic cortical oscillations during an adaptive sensorimotor control task. Sci Rep, 14(1), 10788. doi: 10.1038/s41598-024-61375-x Jiao, B., Li, R., Zhou, H. et al. Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer’s disease using EEG technology. Alz Res Therapy 15, 32 (2023). https://doi.org/10.1186/s13195-023-01181-1 Korka, B., Will, M., Avci, I., Dukagjini, F., & Stenner, M. P. (2023). Strategy-based motor learning decreases the post-movement beta power. Cortex, 166, 43–58. doi: 10.1016/j.cortex.2023.05.002 Pauwels L, Swinnen SP, Beets IAM (2014) Contextual Interference in Complex Bimanual Skill Learning Leads to Better Skill Persistence. PLoS ONE 9(6): e100906. https://doi.org/10.1371/journal.pone.0100906 Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999;110(11):1842-57. doi: 10.1016/s1388-2457(99)00141-8 . PMID: 10576479. Pfurtscheller, G., & Solis-Escalante, T. (2009). Could the beta rebound in the EEG be suitable to realize a "brain switch"? Clin Neurophysiol, 120(1), 24–29. doi: 10.1016/j.clinph.2008.09.027 Raviv, L., Lupyan, G., & Green, S. C. (2022). How variability shapes learning and generalization. Trends Cogn Sci, 26(6), 462–483. doi: 10.1016/j.tics.2022.03.007 Shea, J. B., & Morgan, R. L. (1979). Contextual interference effects on the acquisition, retention, and transfer of a motor skill. Journal of Experimental Psychology: Human Learning and Memory, 5(2), 179–187. https://doi.org/10.1037/0278-7393.5.2.179 Sisti HM, Geurts M, Clerckx R, Gooijers J, Coxon JP, Heitger MH, Caeyenberghs K, Beets IA, Serbruyns L, Swinnen SP. Testing multiple coordination constraints with a novel bimanual visuomotor task. PLoS One. 2011;6(8):e23619. doi: 10.1371/journal.pone.0023619 . Epub 2011 Aug 17. PMID: 21858185; PMCID: PMC3157395. Tan, H., Jenkinson, N., & Brown, P. (2014). Dynamic neural correlates of motor error monitoring and adaptation during trial-to-trial learning. J Neurosci, 34(16), 5678–5688. doi: 10.1523/JNEUROSCI.4739-13.2014 Tan, H., Wade, C., & Brown, P. (2016). Post-Movement Beta Activity in Sensorimotor Cortex Indexes Confidence in the Estimations from Internal Models. J Neurosci, 36(5), 1516–1528. doi: 10.1523/JNEUROSCI.3204-15.2016 Torrecillos, F., Alayrangues, J., Kilavik, B. E., & Malfait, N. (2015). Distinct Modulations in Sensorimotor Postmovement and Foreperiod beta-Band Activities Related to Error Salience Processing and Sensorimotor Adaptation. J Neurosci, 35(37), 12753–12765. doi: 10.1523/JNEUROSCI.1090-15.2015 Toader C, Dobrin N, Brehar FM, Popa C, Covache-Busuioc RA, Glavan LA, Costin HP, Bratu BG, Corlatescu AD, Popa AA, Ciurea AV. From Recognition to Remedy: The Significance of Biomarkers in Neurodegenerative Disease Pathology. Int J Mol Sci. 2023;24(22):16119. doi: 10.3390/ijms242216119 . PMID: 38003309; PMCID: PMC10671641. Ulanov M, Shtyrov Y. Oscillatory beta/alpha band modulations: A potential biomarker of functional language and motor recovery in chronic stroke? Front Hum Neurosci. 2022;16:940845. doi: 10.3389/fnhum.2022.940845 . PMID: 36226263; PMCID: PMC9549964. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4768967","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":342467536,"identity":"e3b1c3e2-c2e4-4b30-a0f7-dc1f2d8d21d9","order_by":0,"name":"Helene M. 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Activity in the left and right sensorimotor cortex is captured by electrodes C3 and C4, respectively (center).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4768967/v1/66e14d20b39ea2009495ee6e.png"},{"id":62935369,"identity":"13506913-fa5e-4b40-bc53-1a041a741136","added_by":"auto","created_at":"2024-08-21 08:32:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65677,"visible":true,"origin":"","legend":"\u003cp\u003eParticipants were randomly assigned to either a blocked or randomized schedule to test the contextual interference effect. Both groups demonstrated significant improvement from Pre-Test to Post-Test, which occurred one week after the end of training.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4768967/v1/3fdd521a3fd28d1ac13854c5.png"},{"id":62935371,"identity":"b317bf7c-ab07-4b79-94d4-affa7bca36d7","added_by":"auto","created_at":"2024-08-21 08:32:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":332245,"visible":true,"origin":"","legend":"\u003cp\u003eThe PMBR was evident at both the pre-test and post-test. Training using either schedule did not alter the PMBR one week later.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4768967/v1/5e635a9b49048571123a2344.png"},{"id":64053393,"identity":"f0373efc-697f-4954-8d78-77dc5ff2e82c","added_by":"auto","created_at":"2024-09-05 17:15:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1032423,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4768967/v1/9e2a200e-0bc3-460a-b425-632cb4afb2d4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Post-Movement Beta Rebound in Sensorimotor Cortex Endures One Week After Three Days of Practice","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe implementation of biomarkers for detecting pathological states has been a key factor in the acceleration of personalized medicine (Selleck et al., 2017). Within the last six years, the first biomarker for traumatic brain injury (TBI) was approved by the FDA (U.S. Food and Drug Administration, 2018; Nishimura et al. 2022). The vast majority are constrained to \u0026ldquo;wet lab\u0026rdquo; environments, such as blood-borne pathogens or metabolites of cerebrospinal fluid (CSF) (Kraus et al., 2011; Toader et al., 2023). Research using non-invasive, neuroimaging approaches, such as electroencephalography (EEG), can predict neurodegenerative conditions such as dementia and autism (Bosl et al., 2018; Jiao et al., 2023). However, the use of EEG \u0026lsquo;neural signatures\u0026rsquo; as biomarkers has not yet been widely adopted in clinical practice.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEEG has experienced a resurgence in recent years, because it is non-invasive; it is portable compared with magnetic resonance imaging (MRI); it has superior temporal resolution compared with MRI. It is the mode of choice for understanding seizures, sleep disorders, and brain computer interface (BCI) applications for the severely disabled. EEG captures neurophysiological responses on the order of milliseconds with a range of electrodes anywhere from two to 256 (high-density) in 3-dimensional space. Several brain frequencies, most notably alpha (8-12 Hz) and beta (13-30 Hz), are reliably linked to overt, fundamental behaviors, such as closing the eyes (alpha) or the cessation of movement (beta). The present study focuses on beta, because of its potential role as a putative biomarker of motor control dysfunction (Cohan et al., 2019; Barone and Rossiter, 2021; Ulanov and Shtyrov, 2022).\u003c/p\u003e\n\u003cp\u003eThe post-motor beta rebound (PMBR) refers to the temporary increase of power of the beta band (12 - 30 Hz) in the sensorimotor cortex which coincides with movement cessation (Pfurtscheller and Lopes, 1999; Pfurtscheller and Solis-Escalante, 2009; Gaetz et al., 2010). The increase in beta power reflects GABA-ergic inhibitory activity along the thalamo-cortical pathway (Heinrichs-Graham et al., 2017). In neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS) and cerebral palsy, there is a decrease\u003cem\u003e\u0026nbsp;\u003c/em\u003ein the PMBR (Bizovicar et al., 2014; Hinton et al., 2024). Learning has also been correlated with a decreased PMBR (Tan et al., 2014, 2016; Torrecilloset al., 2015; Espenhahn et al., 2020, Korka et al., 2023; Coleman et al. 2024). These somewhat paradoxical findings speak to the challenges of mapping neural oscillations onto cognitive processes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe use of a unique EEG pattern to predict a diseased state with subclinical threshold requires a robust, consistent response over time. Espenhahn and colleagues (2017) demonstrated consistency of PMBR over time, remarkably, up to seven weeks. \u0026nbsp;Participants performed a visually-cued unimanual wrist-flexion task. The task did not require extensive training; participants demonstrated stable kinematic measures from the first session through the final, sixth session, which notably, was up to 50 days later (Espenhahn et al., 2017). Other research on PMBR and learning includes the use of a Bayesian model to consider the roles of both error-correction and prior knowledge on a trial-by-trial basis; using this approach, a negative correlation between PMBR and error was demonstrated (Tan et al., 2014, 2016). In ecological studies, real-world motor learning skills, such as playing billiards, both increases and decreases of PMBR were observed (Haar and Faisal, 2020). Taken together, it is clear that additional studies are needed to determine the interaction of the PMBR with learning.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the present study, we hypothesized that learning would attenuate the PMBR. To test this, participants practiced a visuomotor bimanual tracking (VBT) task for 3 days. Then, one week later a retention test was given. A robust learning phenomenon known as the contextual interference effect (CIE) was applied to distinguish between two practice conditions. The CIE refers to the impact of practice organization on long-term memory (Shea and Morgan, 1979; Graser et al., 2019; Farrow and Buszard, 2017). It compares two types of practice schedules: blocked vs. random. In the blocked schedule, a specific task is performed repeatedly before moving on to the next one. For instance, only Task A is practiced on Day 1, then Task B is practiced on Day 2, and Task C is practiced on Day 3 (AAA, BBB, CCC). In the randomized schedule, all three tasks are practiced each day, but in a different sequence: CAB on Day 1, BCA, on Day 2, and CBA on Day 3, \u003cem\u003eet cetera\u003c/em\u003e. The blocked schedule results in better performance than the randomized schedule during acquisition. However, surprisingly, long-term retention is superior in the group that practiced using a randomized schedule, despite the initial differences in acquisition (Pauwels et al., 2014). In our study, we aimed to both: (1) observe the CIE using a visuomotor bimanual tracking task and (2) determine whether learning using either schedule would result in a decreased PMBR.\u003c/p\u003e"},{"header":"Materials and Methods ","content":"\u003cp\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParticipants (n=36) were 18 years of age or older and were students of Norwich University. All completed the Oldfield (1971) handedness questionnaire, as well as questions regarding previous history with activities that require bimanual coordination, including video games, driving manual transmission automobiles, or playing a musical instrument. Participants were fitted with EEG QuikCap (Compumedics, Neuroscan) during Pre-Test and Post-Test only. EEG data were not collected during the 3 practice sessions. \u0026nbsp;All procedures were approved by the NU Institutional Review Board Ethics Committee and in accordance with the 1964 Declaration of Helsinki (revised in 2008). All participants signed the Informed Consent form. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eApparatus and task description\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were comfortably seated at a table in front of a computer monitor with both lower arms resting on two custom-made adjustable ramps. At the end of each ramp, a dial was mounted and could be rotated by holding an upright peg between the thumb and index finger (Sisti et al., 2011, 2012). The two dials controlled movement of a red cursor (a flexible line segment 1 cm long) on the monitor. When the left dial was rotated clockwise, the cursor moved up; when it was rotated counterclockwise, the red cursor moved down; and vice versa. The right dial controlled horizontal movement. The objective of the task was to use the two dials simultaneously to track the moving target. The target was a white dot that moved along a stationary path, either a curved line or a jagged line. For pre- and post-test, the path was a curved line and during practice, the path was similar except it was a jagged line. Participants from both blocked and randomized groups had the same pre- and post-test. \u0026nbsp;The 3 practice sessions included a similar path, with the exception that it was a jagged line rather than curved. This was done to control for temporal interval differences between end of training and retention test between blocked and randomized schedules, i.e. blocked group would have only practiced Task A on Day 1 and would have recently practiced Task C on Day 3, whereas randomized group would have practiced all 3 tasks on Day 3. Tasks differed by altering the starting position of the jagged line.\u003c/p\u003e\n\u003cp\u003eThe entire experiment included 5 sessions, beginning with a pre-test, followed by 3 consecutive practice sessions each on a different day, and finally a post-test, which occurred one week after the last practice session. Each session included 20 trials. In the randomized practice schedule (n=15), the starting position was randomized within a training day. In the blocked practice schedule (n=21), the starting position remained constant each day. The sample sizes across the two conditions were unequal, because the blocked practice schedule was counterbalanced to control for any sequence of practice effects. Improvement within a session was determined by calculating the difference between task performance on the first and last trial.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNeurophysiological and pre-processing methods\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBefore pre-test and post-test sessions, participants were fitted with a 32-electrode QuikCap (Compumedics, Neuroscan). The central electrode, Cz, was placed at the midpoint between nasion and anion. Soluble electrolytic gel was inserted into each electrode using a blunt-edge syringe. Gel was inserted until the impedance values were less than 5 kΩ. Once impedance values were less than 5 kΩ, instructions for the behavioral task were administered. Neuroscan Curry8 was used for signal acquisition and offline processing. Triggers were manually delivered to signal the start (keyboard press 1) and end (keyboard press 2) of every trial. Manual triggers are marked in the software with both a time stamp and a visual cue, i.e. a purple vertical line in the data acquisition window. EEG data were collected with a sampling rate of 1,000 Hz. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe following steps were applied to each participant\u0026rsquo;s data in the same sequence: bandpass filter of 1.0 \u0026ndash;30 Hz to remove non-physiological artifacts; ocular artifacts removal using Independent Component Analysis; removal of other residual artifacts (e.g., muscular) using a voltage threshold (+100 \u0026micro;V, +200 ms). We restricted our analysis to the sensorimotor cortices because this is where the beta rebound is most evident. This region corresponds with C3 and C4 electrodes, left and right hemisphere, respectively (Figure 1). Signals were averaged across the 20 trials for the pre-test and the post-test. Conversion of signals from the time to the frequency domain was done by applying the Fast Fourier Transform (FFT) using Curry 8 software. \u0026nbsp;A two-second interval was selected during the last two seconds of movement and the first two seconds of the intertrial interval. Mean values across subjects were calculated for each condition. Data were then exported as .txt files to MS Excel and formatted for statistical analysis in SPSS. The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTask performance was assessed by calculating the Euclidean distance between the participant\u0026rsquo;s cursor and the target cursor at the end of each trial, referred to as the Finish Offset (FO). A value of zero indicates the participant finished the trial precisely on the target; FO units are arbitrary. Improvement on task performance, interpreted as learning, was determined by calculating the difference between the first and last trial. The effect of practice schedule on learning was analyzed using a 2 x 5 ANOVA, in which the first factor was practice condition (blocked or randomized) and the second factor was practice session (Pre-Test, Session 1, 2, 3, Post-Test). To determine the effect of PMBR on learning, a 2 x 2 ANOVA was calculated; the first factor was movement termination (before vs after movement) and the second factor included the electrodes corresponding with the sensorimotor cortex (C3 and C4).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTask performance improved in both groups from pre-test to post-test following 3 practice sessions [F(4)=13.51, p\u0026lt;.001] (Figure 2). Mean FO for the blocked group (n=21) was 9.41 \u003cu\u003e+\u003c/u\u003e 0.87 at Pre-Test and 4.86 \u003cu\u003e+\u003c/u\u003e 0.76 at Post-Test. Mean FO for the randomized group (n=15) was 7.80 \u003cu\u003e+\u003c/u\u003e 1.21 at Pre-Test and 3.41 \u003cu\u003e+\u003c/u\u003e 0.58 at Post-Test. There was no main effect of practice condition (F=2.09, p=.14). There was no significant interaction between practice condition and practice session [F(4)=1.67, p=.15].\u003c/p\u003e\n\u003cp\u003eBeta power of the sensorimotor cortex before and after movement termination, i.e. the end of the trial, was analyzed using a 2 x 2 ANOVA; the first factor was Movement Termination (Before vs After) and the second factor was the electrode corresponding with the left and right sensorimotor cortex (C3 and C4). This was done for both Day 1 and Day 4. The previous behavioral analysis revealed no effect of practice condition, therefore, blocked and randomized groups were collapsed. There was a main effect of Movement Termination (F=7.85, p=0.006); no main effect of electrode (F=1.02, p=0.31) and no significant interaction of Movement Termination by Electrode (F=0.065, p=0.79). Because there was no significant effect of electrodes, C3 and C4, were collapsed. A 2 x 2 ANOVA of Movement Termination (Before vs After) x Day (Pre-Test vs Post-Test) was computed. There was a significant main effect of Movement Termination (F=16.43, p\u0026lt;.001); no main effect of Day (F=0.04, p=0.83) and no significant interaction (F=.013, p=0.91). The PMBR was evident before 3 days of practice and one week later at the retention test (Figure 3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe PMBR may serve as an index of motor control, where an abnormal response could signal dysregulation (Espenhahn et al., 2017). Biomarkers, sometimes referred to as \u0026lsquo;neural signatures\u0026rsquo; in the context of EEG, hold tremendous potential for patient care. However, the etiology of any neural signature must be clearly understood. With this in mind, we sought to test whether the PMBR would be attenuated by learning. Further, we included two types of practice schedules to test the robustness of the CIE. Neither hypothesis was supported. Each is discussed in turn.\u003c/p\u003e\n\u003cp\u003eBecause of its implications for human performance, the CIE has been extensively studied in both laboratory and applied settings (for a comprehensive review of CIE and related learning phenomena, see Raviv et al., 2022). \u0026nbsp;In the present experiment, we used a visuomotor bimanual tracking across several days in young adults. To create unique tasks, the starting position was altered. This is a relatively subtle change and is the most likely rationale for the absence of the CIE. To observe the CIE, that is the superior performance of the blocked schedule during acquisition, but inferior performance at retention, it may be necessary to include a task that differs in more significant ways. This would then require greater attentional resources during acquisition and may result in the differences of acquisition and retention. Importantly, while no CIE was detected, both groups of participants demonstrated significant improvement overtime and a clear retention of the task one week later.\u003c/p\u003e\n\u003cp\u003eRegarding the absence of a learning-induced attenuation of the PMBR, there are several possible explanations. There are key differences between the present experiment and previous research demonstrating learning-induced attenuation of PMBR (Tan et al., 2014; 2016) Most notably, in the experiments by Tan et al., 2014; 2016, learning occurred within a single session. The session consisted of 350 trials. Our experiment included about one-third as many trials distributed across several days, as well as a retention test one week after practice. It is possible that the attenuation from a single session of many trials reflected short-term neuroplasticity, or perhaps fatigue from cognitive load and sensorimotor processing. The fact that the PMBR was stable over time is consistent with other studies (Espenhahn et al., 2017). Further, the divergent findings regarding learning-attenuation are not necessarily contradictory to other reports (Tan et al.,2016). The difference in both time scale and training requirements between experiments are significant. Additional studies are needed to further characterize the PMBR in both short- and long-term memory.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMapping neural processes to cognitive and behavioral processes is a central goal of neuroscience. In this experiment, we examined a single neural oscillation to determine how learning might alter it. While this experiment did not induce changes, other studies that include many training trials on a single day did (Tan et al., 2014). Elucidating the factors that drive these differences will be important in the development of EEG biomarkers. Embracing machine learning techniques to augment human interpretation of data will facilitate the ability to examine the full range of neural oscillations and how they respond to cognitive and behavioral treatments. The idea that a noninvasive, neural signature can signal health or disease in subthreshold conditions, i.e. the early stages before persistent symptoms manifest, warrants further investigation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHS - Conceived experimental design, conducted analysis, wrote manuscriptAA, RB, GF, EV - Recruited and tested participants, pre-processed data, formatted data\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarone, J., \u0026amp; Rossiter, H. E. (2021). Understanding the Role of Sensorimotor Beta Oscillations. Front Syst Neurosci, 15, 655886. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnsys.2021.655886\u003c/span\u003e\u003cspan address=\"10.3389/fnsys.2021.655886\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBizovicar, N., Dreo, J., Koritnik, B., \u0026amp; Zidar, J. (2014). Decreased movement-related beta desynchronization and impaired post-movement beta rebound in amyotrophic lateral sclerosis. Clin Neurophysiol, 125(8), 1689\u0026ndash;1699. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clinph.2013.12.108\u003c/span\u003e\u003cspan address=\"10.1016/j.clinph.2013.12.108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBosl, W.J., Tager-Flusberg, H. \u0026amp; Nelson, C.A. EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach. Sci Rep 8, 6828 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-018-24318-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-018-24318-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohan R, Bearss KA, DeSouza JFX. Frequency-Specific Biomarkers in Neurodegenerative\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDisorders: Implications of Alpha and Beta Oscillations in Motor Behaviour. J Neurol Neuromed (2019) 4(3): 18\u0026ndash;24\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColeman, S. C., Seedat, Z. A., Pakenham, D. O., Quinn, A. J., Brookes, M. J., Woolrich, M. W., \u0026amp; Mullinger, K. J. (2024). Post-task responses following working memory and movement are driven by transient spectral bursts with similar characteristics. Hum Brain Mapp, 45(7), e26700. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hbm.26700\u003c/span\u003e\u003cspan address=\"10.1002/hbm.26700\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErbil, N., \u0026amp; Ungan, P. (2007). Changes in the alpha and beta amplitudes of the central EEG during the onset, continuation, and offset of long-duration repetitive hand movements. Brain Res, 1169, 44\u0026ndash;56. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.brainres.2007.07.014\u003c/span\u003e\u003cspan address=\"10.1016/j.brainres.2007.07.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEspenhahn, S., de Berker, A. O., van Wijk, B. C. M., Rossiter, H. E., \u0026amp; Ward, N. S. (2017). Movement-related beta oscillations show high intra-individual reliability. Neuroimage, 147, 175\u0026ndash;185. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2016.12.025\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2016.12.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEspenhahn, S., Rossiter, H. E., van Wijk, B. C. M., Redman, N., Rondina, J. M., Diedrichsen, J., \u0026amp; Ward, N. S. (2020). Sensorimotor cortex beta oscillations reflect motor skill learning ability after stroke. Brain Commun, 2(2), fcaa161. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/braincomms/fcaa161\u003c/span\u003e\u003cspan address=\"10.1093/braincomms/fcaa161\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaetz, W., Macdonald, M., Cheyne, D., \u0026amp; Snead, O. C. (2010). Neuromagnetic imaging of movement-related cortical oscillations in children and adults: age predicts post-movement beta rebound. Neuroimage, 51(2), 792\u0026ndash;807. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2010.01.077\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2010.01.077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraser, J. V., Bastiaenen, C. H. G., \u0026amp; van Hedel, H. J. A. (2019). The role of the practice order: A systematic review about contextual interference in children. PLoS One, 14(1), e0209979. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0209979\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0209979\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaar, S., \u0026amp; Faisal, A. A. (2020). Brain Activity Reveals Multiple Motor-Learning Mechanisms in a Real-World Task. Front Hum Neurosci, 14, 354. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnhum.2020.00354\u003c/span\u003e\u003cspan address=\"10.3389/fnhum.2020.00354\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeinrichs-Graham, E., et al. (2017) The Functional Role of Post-Movement Beta Oscillations in Motor Termination. Brain Structure and Function, 222, 3075\u0026ndash;3086. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00429-017-1387-1\u003c/span\u003e\u003cspan address=\"10.1007/s00429-017-1387-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHinton, E. H., Busboom, M. T., Embury, C. M., Spooner, R. K., Wilson, T. W., \u0026amp; Kurz, M. J. (2024). Adults with cerebral palsy exhibit uncharacteristic cortical oscillations during an adaptive sensorimotor control task. Sci Rep, 14(1), 10788. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-024-61375-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-61375-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiao, B., Li, R., Zhou, H. et al. Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer\u0026rsquo;s disease using EEG technology. Alz Res Therapy 15, 32 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13195-023-01181-1\u003c/span\u003e\u003cspan address=\"10.1186/s13195-023-01181-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKorka, B., Will, M., Avci, I., Dukagjini, F., \u0026amp; Stenner, M. P. (2023). Strategy-based motor learning decreases the post-movement beta power. Cortex, 166, 43\u0026ndash;58. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cortex.2023.05.002\u003c/span\u003e\u003cspan address=\"10.1016/j.cortex.2023.05.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePauwels L, Swinnen SP, Beets IAM (2014) Contextual Interference in Complex Bimanual Skill Learning Leads to Better Skill Persistence. PLoS ONE 9(6): e100906. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0100906\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0100906\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999;110(11):1842-57. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s1388-2457(99)00141-8\u003c/span\u003e\u003cspan address=\"10.1016/s1388-2457(99)00141-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 10576479.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePfurtscheller, G., \u0026amp; Solis-Escalante, T. (2009). Could the beta rebound in the EEG be suitable to realize a \"brain switch\"? Clin Neurophysiol, 120(1), 24\u0026ndash;29. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clinph.2008.09.027\u003c/span\u003e\u003cspan address=\"10.1016/j.clinph.2008.09.027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaviv, L., Lupyan, G., \u0026amp; Green, S. C. (2022). How variability shapes learning and generalization. Trends Cogn Sci, 26(6), 462\u0026ndash;483. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tics.2022.03.007\u003c/span\u003e\u003cspan address=\"10.1016/j.tics.2022.03.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShea, J. B., \u0026amp; Morgan, R. L. (1979). Contextual interference effects on the acquisition, retention, and transfer of a motor skill. Journal of Experimental Psychology: Human Learning and Memory, 5(2), 179\u0026ndash;187. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0278-7393.5.2.179\u003c/span\u003e\u003cspan address=\"10.1037/0278-7393.5.2.179\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSisti HM, Geurts M, Clerckx R, Gooijers J, Coxon JP, Heitger MH, Caeyenberghs K, Beets IA, Serbruyns L, Swinnen SP. Testing multiple coordination constraints with a novel bimanual visuomotor task. PLoS One. 2011;6(8):e23619. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0023619\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0023619\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2011 Aug 17. PMID: 21858185; PMCID: PMC3157395.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan, H., Jenkinson, N., \u0026amp; Brown, P. (2014). Dynamic neural correlates of motor error monitoring and adaptation during trial-to-trial learning. J Neurosci, 34(16), 5678\u0026ndash;5688. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1523/JNEUROSCI.4739-13.2014\u003c/span\u003e\u003cspan address=\"10.1523/JNEUROSCI.4739-13.2014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan, H., Wade, C., \u0026amp; Brown, P. (2016). Post-Movement Beta Activity in Sensorimotor Cortex Indexes Confidence in the Estimations from Internal Models. J Neurosci, 36(5), 1516\u0026ndash;1528. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1523/JNEUROSCI.3204-15.2016\u003c/span\u003e\u003cspan address=\"10.1523/JNEUROSCI.3204-15.2016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorrecillos, F., Alayrangues, J., Kilavik, B. E., \u0026amp; Malfait, N. (2015). Distinct Modulations in Sensorimotor Postmovement and Foreperiod beta-Band Activities Related to Error Salience Processing and Sensorimotor Adaptation. J Neurosci, 35(37), 12753\u0026ndash;12765. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1523/JNEUROSCI.1090-15.2015\u003c/span\u003e\u003cspan address=\"10.1523/JNEUROSCI.1090-15.2015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToader C, Dobrin N, Brehar FM, Popa C, Covache-Busuioc RA, Glavan LA, Costin HP, Bratu BG, Corlatescu AD, Popa AA, Ciurea AV. From Recognition to Remedy: The Significance of Biomarkers in Neurodegenerative Disease Pathology. Int J Mol Sci. 2023;24(22):16119. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms242216119\u003c/span\u003e\u003cspan address=\"10.3390/ijms242216119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 38003309; PMCID: PMC10671641.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUlanov M, Shtyrov Y. Oscillatory beta/alpha band modulations: A potential biomarker of functional language and motor recovery in chronic stroke? Front Hum Neurosci. 2022;16:940845. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnhum.2022.940845\u003c/span\u003e\u003cspan address=\"10.3389/fnhum.2022.940845\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 36226263; PMCID: PMC9549964.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4768967/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4768967/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe post-movement beta rebound (PMBR) is the tightly coupled increase in beta power that occurs in the sensorimotor cortex upon movement termination. It is a potential biomarker of motor control; abnormal responses could signal disease. With respect to its interaction with learning, both decreases and increases have been observed. In this study, we examined the effect of two types of practice schedules, blocked and randomized, on memory retention one week later. A blocked schedule leads to better performance during acquisition but poorer performance during long-term retention, a phenomenon known as the contextual interference effect. The aim of the present study is two-fold: (1) test the contextual interference effect using a visuomotor bimanual tracking task (2) determine whether three days of practice leads to a decreased PMBR at retention test one week later. We hypothesized that learning with either schedule would lead to decreased PMBR. Our data demonstrated no main effect of practice schedule. It is most likely that the task variants were not sufficiently different to induce the contextual interference phenomenon. Further, the PMBR was not attenuated by learning. It was evident before and after three days of practice. This has important implications for its putative role as a biomarker.\u003c/p\u003e","manuscriptTitle":"Post-Movement Beta Rebound in Sensorimotor Cortex Endures One Week After Three Days of Practice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-21 08:32:13","doi":"10.21203/rs.3.rs-4768967/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"57dc2ab3-6e99-43d1-8929-3917fbb4f15d","owner":[],"postedDate":"August 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":36280321,"name":"Biological sciences/Psychology"},{"id":36280322,"name":"Biological sciences/Psychology/Human behaviour"}],"tags":[],"updatedAt":"2024-09-05T17:07:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-21 08:32:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4768967","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4768967","identity":"rs-4768967","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}