Biology subjects

Sareen, E.

Publications and source records attributed to Sareen, E..

5 recordsLinked to original sources

bioRxiv2026

Concurrent TMS and Spinal Cord fMRI Reveal Intensity-Dependent Modulation of Spinal Circuits

The spinal cord serves as a crucial relay for motor commands, yet the role of its local circuitry in sensorimotor integration remains poorly understood. Most non-invasive cortical stimulation studies, rely on electrophysiological readouts or inferred spinal function from corticospinal anatomy, leaving the downstream impact of cortical stimulation on spinal circuitry largely uncharted in vivo. Advances in spinal cord functional MRI (SC-fMRI) now enable spatially resolved imaging of segmental gray and white matter and their interactions with descending cortical inputs. Here, we introduce a multimodal framework that combines single-pulse transcranial magnetic stimulation (TMS) of the primary motor cortex with SC-fMRI to probe TMS-evoked spinal activity in humans. Using graded TMS intensities, we examined blood oxygenation level-dependent (BOLD) responses in the cervical spinal cord and asked how spinal activation depends on effective engagement of the descending motor system. Our findings reveal robust, intensity-dependent spinal BOLD responses aligned with descending pathways, with activation concentrated in expected territories such as the lateral corticospinal tract and ventral horn at segments innervating the stimulated hand muscle. By linking peripheral output to segment- and pathway-resolved spinal signals, these results demonstrate that concurrent TMS-SCfMRI can capture, in vivo, how cortical drive is expressed within human spinal circuitry and provide a new framework to measure spinal contributions to sensorimotor control with spatial specificity beyond traditional peripheral readouts. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=198 HEIGHT=200 SRC="FIGDIR/small/740278v1_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@119f96eorg.highwire.dtl.DTLVardef@46aa73org.highwire.dtl.DTLVardef@1609045org.highwire.dtl.DTLVardef@9fbf70_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

Joint subject-identification and task-decoding from inferred functional brain graphs via a multi-task neural network

Functional connectivity (FC) between brain regions as manifested via fMRI entails signatures that can be used to differentiate individuals and decode cognitive tasks. In this work, we use methods from graph structure inference to estimate FC, which is in contrast to the conventional approach of deriving FC via correlation. Moreover, we infer FC graphs from seed-based co-activation patterns instead of raw fMRI data. We also propose a multi-task neural network architecture to jointly perform subject-identification and taskdecoding from inferred functional brain graphs. We validate the developed model on data from the Human Connectome Project across eight fMRI tasks. Most importantly, our results show the superior task-decoding performance of FC graphs inferred from seed-based activity maps over graphs inferred from raw fMRI data. Furthermore, via gradient-based back-projection, we derive a significance score for inputs to the neural network, and present results showing the differential role of brain connections in subject-identification and task-decoding.

neuroscience↗

bioRxiv2022

Task matters: individual MEG signatures from naturalistic and neurophysiological brain states

The discovery that human brain connectivity data can be used as a "fingerprint" to identify a given individual from a population, has become a burgeoning research area in the neuroscience field. Recent studies have identified the possibility to extract these brain signatures from the temporal rich dynamics of resting-state magnetoencephalography (MEG) recordings. However, to what extent MEG signatures constitute a marker of human identifiability when engaged in task-related behavior remains an open question. Here, using MEG data from naturalistic and neurophysiological tasks, we show that identification improves in tasks relative to resting-state, providing compelling evidence for a task dependent axis of MEG signatures. Notably, improvements in identifiability were more prominent in strictly controlled tasks. Lastly, the brain regions contributing most towards individual identification were also modified when engaged in task activities. We hope that this investigation advances our understanding of the driving factors behind brain identification from MEG signals.

neuroscience↗

bioRxiv2021

Exploring MEG brain fingerprints: evaluation, pitfalls and interpretations

Individual characterization of subjects based on their functional connectome (FC), termed "FC fingerprinting", has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction, frequency bands, and behavioral significance. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. We assess the behavioral significance of FC across connectivity measures and imaging modalities using partial least square correlation analyses. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprints in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention, and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. Finally, the multivariate correlation analyses show that MEG connectomes have strong behavioral significance, which however depends on the considered connectivity measure and temporal scale. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identification offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.

neuroscience↗

bioRxiv2019

Studying functional brain networks from dry electrode EEG set during music and resting states in neurodevelopment disorder

There has been an emerging interest in the study of functional brain networks in cognitive neuroscience in order to better understand brain responses to different stimuli. Such studies can help in understanding brain connectivity alterations that arise in neurodevelopmental disorders such as intellectual disability (ID). This research contributes to this body of knowledge by studying alterations in brain connectivity in ID compared to the typically developing controls (TDC). Electroencephalography (EEG) data of subjects with ID and TDC is collected through limited channel dry electrode system. Data was analyzed for the auditory and rest state processing along with the study of intra-network connectivity of the brain via clustering coefficients. Research findings indicate evidences for links between the sensory deficits and social impairment in ID individuals.

neuroscience↗