Biology subjects

Hussain, S. J.

Publications and source records attributed to Hussain, S. J..

8 recordsLinked to original sources

bioRxiv2026

Micro-offline gains do not drive implicit motor sequence learning

Motor sequence performance improves during and between brief practice bouts (micro-online and offline gains). We compared both metrics across two groups: one exposed to an implicit motor sequence, and one not. Micro-online gains drove sequence-specific learning and positively correlated with overall skill. However, micro-offline gains were comparable between groups and did not track sequence-specific learning. We conclude that implicit motor sequence learning is driven by micro-online rather than micro-offline gains.

neuroscience↗

bioRxiv2026

Resting bilateral sensorimotor mu rhythm suppression facilitates ipsilesional M1 excitability after stroke

ObjectiveStroke-related corticospinal tract (CST) disruption causes lasting hand impairments, but many stroke survivors retain some residual CST connections. In neurotypical adults, motor cortex (M1) TMS interventions can strengthen CST transmission when coupled to EEG brain states reflecting heightened M1 excitability. Because stroke alters the relationship between these brain states and cortical excitability, we aimed to identify poststroke brain states that accurately capture ipsilesional M1 excitability. We hypothesized that heightened ipsilesional M1 excitability would be represented by a common, group-level EEG pattern and a participant- specific, personalized pattern. MethodsWe acquired single-pulse TMS-EEG-EMG datasets in 15 chronic stroke survivors with residual CST connections. We then identified group-level and individual-specific EEG power patterns that distinguished between high and low ipsilesional M1 excitability states. ResultsAt the group level, bilateral sensorimotor mu power was significantly suppressed during high versus low excitability states, but this suppression did not correlate with hand impairment severity or trait-level ipsilesional M1 excitability. At the individual level, spatiotemporally varied EEG activity patterns distinguished between excitability states, but these patterns were only present in 60% of individuals. Conclusion and SignificanceThis study is the first to systematically characterize poststroke EEG brain states reflecting ipsilesional M1 excitability. Findings suggest that individual-specific EEG patterns may inconsistently index ipsilesional M1 excitability and instead identify bilateral sensorimotor mu power suppression as a group-level excitability marker that is present across the full spectrum of poststroke hand impairment. HighlightsO_LIWe analyzed TMS-EEG-EMG to identify group and individual level ipsilesional motor cortical excitability states in chronic stroke C_LIO_LIBilateral sensorimotor mu suppression marked heightened ipsilesional motor cortical excitability across hand impairment severity C_LIO_LI60% participants had individual level scalp patterns linked to motor cortical excitability states, challenging their reliability C_LI

neuroscience↗

bioRxiv2026

Incorporating motor preparation time transforms micro-offline gains into micro-offline losses

During explicit sequence learning (ESL), micro-offline gains (MOGS) occur during brief rest periods. MOGS are calculated as the difference in keypresses-per-second (KPS) between the first sequence of one trial and the last sequence of the preceding trial. To date, all studies evaluating MOGS have calculated KPS from the motor execution time (MET) that occurs between keypresses, but this approach ignores potential contributions from motor preparation which occur prior to the first keypress. Given that ESL relies on both pre-movement motor planning and subsequent motor execution, we hypothesized that ignoring motor preparation time (MPT) neglects a critical component of skill acquisition, potentially misrepresenting the true magnitude of MOGS. To test this, we calculated MOGS with and without MPT in thirty adults who performed an ESL task. Our results show that including MPT flipped MOGS from positive to negative and significantly increased the positive correlation between early learning and a gold-standard ESL metric: the number of correct sequences performed. Our results suggest that MPT should be incorporated into MOGS calculations and that excluding it overestimates micro-offline learning.

neuroscience↗

bioRxiv2021

Decoding individualized motor cortical excitability states from whole-brain electroencephalography

Brain state-dependent transcranial magnetic stimulation (TMS) requires real-time identification of cortical excitability states. Current approaches deliver TMS during brain states that correlate with motor cortex (M1) excitability at the group level. Here, we hypothesized that machine learning classifiers could successfully discriminate between high and low M1 excitability states in individual participants using information obtained from low-density electroencephalography (EEG) signals. To test this, we analyzed a publicly available dataset that delivered 600 single TMS pulses to the right M1 during EEG and electromyography (EMG) recordings in 20 healthy adults. Multivariate pattern classification was used to discriminate between brain states during which TMS evoked small and large motor-evoked potentials (MEPs). Results show that personalized classifiers successfully discriminated between low and high M1 excitability states in 80% of tested participants. MEPs elicited during classifier-predicted high excitability states were significantly larger than those elicited during classifier-predicted low excitability states in 90% of tested participants. Personalized classifiers did not generalize across participants. Overall, results show that individual participants exhibit unique brain activity patterns which predict low and high M1 excitability states and that these patterns can be efficiently captured using low-density EEG signals. Our findings suggest that deploying individualized classifiers during brain state-dependent TMS may enable fully personalized neuromodulation in the future.

neuroscience↗

bioRxiv2019

Voluntary movement initiation is not coupled to optimal sensorimotor oscillatory phases

Voluntary movement requires motor commands to be released from motor cortex (M1) and transmitted to spinal motoneurons and effector muscles. M1 activity oscillates between brief excitatory and inhibitory states that correlate with single neuron spiking rates. Here, we asked if the motor commands needed to produce voluntary, self-paced finger movements are preferentially released from M1 during restricted phases of this ongoing sensorimotor oscillatory activity. 21 healthy adults performed a self-paced finger movement task while EEG and EMG signals were recorded. For each finger movement, we identified the individual sensorimotor mu (8-12 Hz) and beta (13-35 Hz) oscillatory phase at the estimated time of motor command release from M1 by subtracting individually-defined MEP latencies from EMG-determined movement onset times. We report that motor commands were preferentially released at ~120{degrees} along the beta cycle but were released uniformly across the mu cycle. These results suggest that motor commands are preferentially released from M1 near optimal peak phases of endogenous beta rhythms.

neuroscience↗

bioRxiv2019

Identifying site- and stimulation-specific TMS-evoked EEG potentials using a quantitative cosine similarity metric

The ability to interpret transcanial magnetic stimulation (TMS) evoked EEG potentials (TEPs) is limited by artifacts, such as auditory evoked responses produced by the TMS coil. TEPs generated from direct cortical stimulation should vary in spatial distribution with stimulation site and from responses to sham stimulation. Responses that do not show these effects are likely to be artifactual. In 20 healthy volunteers, we delivered active and sham TMS to right prefrontal, left primary motor, and left posterior parietal cortex and compared the waveform similarity of TEPs between stimulation sites and active and sham TMS with a cosine similarity-based analysis method. We looked for epochs after the stimulus when the spatial pattern of TMS-evoked activation had greater than random similarity between stimulation sites and sham vs. active TMS, indicating presence of a dominant artifact, such as the auditory brain response. We calculated and binarized the derivatives of the TEPs recorded from 30 EEG channels and calculated cosine similarity between conditions at each time point with millisecond resolution. Only TEP components occurring before approximately 80 ms differed across stimulation sites and between active and sham, indicating site and condition-specific responses. TEP components before about 80 ms can be safely interpreted as stimulation location-specific responses to TMS, but components beyond this latency should be interpreted with caution due to high similarity in their spatial distribution.

neuroscience↗

bioRxiv2019

Beta rhythm events predict corticospinal motor output

The beta rhythm (15-30 Hz) is a prominent signal of sensorimotor cortical activity. This rhythm is not sustained but occurs non-rhythmically as brief events of a few (1-2) oscillatory cycles. Recent work on the relationship between these events and sensorimotor performance suggests that they are the biologically relevant elements of the beta rhythm. However, the influence of these events on corticospinal excitability, a mechanism through which the primary motor cortex controls motor output, is unknown. Here, we addressed this question by evaluating relationships between beta event characteristics and corticospinal excitability in healthy adults. Results show that the number, amplitude, and timing of beta events preceding transcranial magnetic stimulation (TMS) each significantly predict MEP amplitudes. However, beta event characteristics did not explain additional MEP amplitude variance beyond that explained by mean beta power alone, suggesting that conventional beta power measures and beta event characteristics similarly capture natural variation in human corticospinal excitability. Despite this lack of additional explained variance, these results provide first evidence that endogenous beta oscillatory events shape human corticospinal excitability.

neuroscience↗

bioRxiv2018

Offline coil position denoising enhances detection of TMS effects

OBJECTIVETranscranial magnetic stimulation (TMS) is extensively used in basic and clinical neuroscience. Previous work has shown substantial residual variability in TMS effects even despite use of on-line visual feedback monitoring of coil position. Here, we aimed to evaluate if off-line denoising of variability induced by neuronavigated coil position and orientation deviations can enhance detection of TMS effects.\n\nMETHODSRetrospective modeling was used to denoise the impact of common neuronavigated coil position and rotation deviations during TMS experimental sessions on motor evoked potentials (MEP) to single pulse TMS.\n\nRESULTSNeuronavigated coil deviations explained approximately 44% of total MEP amplitude variability. Offline denoising led to a 136.71% improvement in the signal to noise ratio (SNR) of corticospinal excitability measurements. CONCLUSIONS: Offline modeling enhanced detection of TMS effects by removing variability introduced by neuronavigated coil deviations.\n\nSIGNIFICANCEThis approach could allow more accurate determination of TMS effects in cognitive and interventional neuroscience.\n\nHIGHLIGHTSO_LICoil deviations impact TMS effects despite use of on-line neuronavigation feedback.\nC_LIO_LIOffline denoising of coil deviation impacts on TMS effects significantly reduced variability at trial level.\nC_LIO_LIOffline denoising also significantly improved overall SNR of TMS effects.\nC_LI

neuroscience↗