Biology subjects

Vahdat, S.

Publications and source records attributed to Vahdat, S..

4 recordsLinked to original sources

bioRxiv2025

Spine-Prints: Transposing Brain Fingerprints to the Spinal Cord

Functional connectivity (FC) patterns in the human brain form a reproducible, individual-specific "fingerprint" that allows reliable identification of the same participant across scans acquired over different sessions. While brain fingerprinting is robust across healthy individuals and neuroimaging modalities, little is known about whether the fingerprinting principle extends beyond the brain. Here, we used multiple functional magnetic resonance imaging (fMRI) datasets acquired at different sites to examine whether a fingerprint can be revealed from FCs of the cervical region of the human spinal cord. Our results demonstrate that the functional organisation of this region also exhibits individual-specific properties, suggesting the potential existence of a spine-print within the same acquisition session. Although the spine-print scores are not directly comparable to those observed in the brain, this discrepancy may in part reflect the intrinsic limitations of imaging this region with fMRI, where where the signals are more susceptible to noise and effective resolution relative to structure size, and tSNR are markedly lower than in the brain.This study provides the first evidence of a spinal cord connectivity fingerprint, underscoring the importance of considering a more comprehensive view of the entire central nervous system. Eventually, these spine-specific signatures could contribute to identifying individualized biomarkers of neuronal connectivity, with potential clinical applications in neurology and neurosurgery.

neuroscience↗

bioRxiv2025

EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data

Functional magnetic resonance imaging (fMRI) of the spinal cord is relevant for studying sensation, movement, and autonomic function. Preprocessing of spinal cord fMRI data involves segmentation of the spinal cord on gradient-echo echo planar imaging (EPI) images. Current automated segmentation methods do not work well on these data, due to the low spatial resolution, susceptibility artifacts causing distortions and signal drop-out, ghosting, and motion-related artifacts. Consequently, this segmentation task demands a considerable amount of manual effort which takes time and is prone to user bias. In this work, we (i) gathered a multi-center dataset of spinal cord gradient-echo EPI with ground-truth segmentations and shared it on OpenNeuro https://openneuro.org/datasets/ds005143/versions/1.3.0, and (ii) developed a deep learning-based model, EPISeg, for the automatic segmentation of the spinal cord on gradient-echo EPI data. We observe a significant improvement in terms of segmentation quality compared to other available spinal cord segmentation models. Our model is resilient to different acquisition protocols as well as commonly observed artifacts in fMRI data. The training code is available at https://github.com/sct-pipeline/fmri-segmentation/, and the model has been integrated into the Spinal Cord Toolbox as a command-line tool.

neuroscience↗

bioRxiv2022

DeepRetroMoCo: Deep neural network-based Retrospective Motion Correction Algorithm for Spinal Cord functional MRI

There are unique challenges in the preprocessing of spinal cord fMRI data, particularly voluntary or involuntary movement artifacts during image acquisition. Despite advances in data processing techniques for movement detection and correction, there are challenges in extrapolating motion correction algorithm developments in the brain cortex to the brainstem and spinal cord. We trained a Deep Learning-based convolutional neural network (CNN) via an unsupervised learning algorithm, called DeepRetroMoCo, to detect and correct motions in axial T2*-weighted spinal cord data. Spinal cord fMRI data from 27 participants were used for training of the network (135 runs for training and 81 runs for testing). We used average temporal signal-to-noise-ratio (tSNR) and Delta Variation Signal (DVARS) of raw and motion-corrected images to compare the outcome of DeepRetroMoco with sct_fmri_moco implemented in the spinal cord toolbox. The average tSNR in the cervical cord was significantly higher when DeepRetroMoco was used for motion correction compared to sct_fmri_moco method. Average DVARS was lower in images corrected by DeepRetroMoco than those corrected by sct_fmri_moco. The average processing time for DeepRetroMoco was also significantly shorter than sct_fmri_moco. Our results suggest that DeepRetroMoCo improves motion correction procedures in fMRI data acquired from the cervical spinal cord.

neuroscience↗

bioRxiv2021

Changes in resting state functional connectivity associated with dynamic adaptation of wrist movements

Dynamic adaptation is an error-driven process of adjusting planned motor actions to changes in task dynamics (1). Adapted motor plans are consolidated into motor memories that contribute to better performance on re-exposure to the same dynamic condition. In parallel, dynamic perturbations can be compensated for by alternate motor control processes, such as co-contraction, that contribute to error reduction (2). Whether these control strategies share the same neural resources for memory formation is unclear. To address this gap in knowledge, we used a novel fMRI-compatible wrist robot, the MR-SoftWrist (3), to identify neural processes specific to dynamic adaptation and subsequent memory formation. Using the MR-SoftWrist, we acquired fMRI during a motor performance and a dynamic perturbation task to localize brain networks of interest. Resting state fMRI scans were acquired immediately before and after task performance to quantify changes in resting state functional connectivity (rsFC) within these networks. Twenty-four hours later, we assessed behavioral retention of training. A variance decomposition analysis was used to isolate behavior associated with adaptation versus alternate error reduction strategies. Immediately after the dynamic perturbation task, rsFC significantly increased within the corticothalamic-cerebellar network of the trained wrist and decreased interhemispherically within the cortical sensorimotor network. These changes were associated to behavioral measures of initial acquisition and retention, indicative of memory formation. Variance decomposition analysis revealed that increases within the cortico-thalamic-cerebellar network were associated with adaptation, while interhemispheric decreases in rsFC within the sensorimotor network were associated with alternate error reduction processes. Significance StatementMotor memory formation processes have been studied using resting state functional connectivity (rsFC) before and after exposure to adaptation tasks. However, due to technical limitations, previous studies have not investigated rsFC within brain regions localized during task execution, nor quantified rsFC within minutes after task performance. The present study used an fMRI-compatible wrist robot to localize relevant brain regions during dynamic adaptation within the cortico-thalamic-cerebellar network and bilateral sensorimotor network. Immediately following dynamic adaptation, we measured significant changes in rsFC within these networks, that were associated with adaptation behavior and with gains in behavior assessed 24 hours later, indicative of memory formation. Variance decomposition analysis identified distinct rsFC networks associated with adaptation specific processes, and with alternate motor control strategies.

bioengineering↗