Biology subjects

Tatz, J.

Publications and source records attributed to Tatz, J..

3 recordsLinked to original sources

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↗

bioRxiv2025

A Practical Preprocessing Pipeline for Concurrent TMS-iEEG: Critical Steps and Methodological Considerations

Transcranial magnetic stimulation combined with intracranial EEG (TMS-iEEG) has emerged as a powerful approach for probing the causal organization and dynamics of the human brain. Despite its promise, the presence of TMS-induced artifacts poses significant challenges for accurately characterizing and interpreting evoked neural responses. In this study, we present a practical preprocessing pipeline for single pulse TMS-iEEG data, incorporating key steps of re-referencing, filtering, artifact interpolation, and detrending. Using both real and simulated data, we systematically evaluated the effects of each step and compared alternative methodological choices. Our results demonstrate that this pipeline effectively attenuated various types of artifacts and noise, yielding cleaner signals for the subsequent analysis of intracranial TMS-evoked potentials (iTEPs). Moreover, we showed that methodological choices can substantially influence iTEPs outcomes. In particular, referencing methods might strongly affect iTEP morphology and amplitude, underscoring the importance of tailoring the referencing strategy to specific signal characteristics and research objectives. For filtering, we recommend a segment-based strategy, i.e., applying filters to data segments excluding the artifact window, to minimize distortion from abrupt TMS-related transients. Overall, this work represents an important step toward establishing a general preprocessing framework for TMS-iEEG data. We hope it encourages broader adoption and methodological development in concurrent TMS-iEEG research, ultimately advancing our understanding of brain organization and TMS mechanisms. HighlightsO_LIWe presented a practical preprocessing pipeline for single pulse TMS-iEEG data, incorporating key steps of re-referencing, filtering, artifact interpolation, and detrending. C_LIO_LIThe pipeline effectively attenuates multiple types of artifacts and noise, enabling accurate characterization of evoked neural responses. C_LIO_LIMethodological alternatives for each preprocessing step were evaluated using real and/or simulated datasets. C_LIO_LIRe-referencing substantially affects the morphology and amplitude of intracranial TMS-evoked potentials and requires careful consideration. C_LIO_LIA segment-based filtering strategy is recommended to better minimize distortion from TMS-related artifacts. C_LI

neuroscience↗

bioRxiv2021

Towards a two-stage model of action-stopping: Attentional capture explains motor inhibition during early stop-signal processing

The ability to stop an already initiated action is paramount to adaptive behavior. Most scientific debate in the field of human action-stopping currently focuses on two interrelated questions. First: Which mental and neural processes underpin the implementation of inhibitory control, and which reflect the attentional detection of salient stop-signals instead? Second: Why do physiological signatures of inhibition occur at two different latencies after stop-signals (for visual signals, either before or after [~]150ms)? Here, we address both questions via two pre-registered experiments that combined transcranial magnetic stimulation, electromyography, and multi-variate pattern analysis of whole-scalp electroencephalography. Using a stop-signal task that also contained a second type of salient signal that did not require stopping, we found that both signals induced equal amounts of early-latency inhibitory activity, whereas only later signatures (after 175ms) distinguished the two. These findings resolve ongoing debates in the literature and strongly suggest a two-step model of action-stopping.

neuroscience↗