Biology subjects

Mobarak-Abadi, M.

Publications and source records attributed to Mobarak-Abadi, M..

3 recordsLinked to original sources

bioRxiv2026

Assessing the Functional-Structural Reorganization of Connector Hubs in Temporal Lobe Epilepsy

Temporal lobe epilepsy (TLE) is associated with altered functional connectivity, yet its effects on connector hubs, regions supporting long-range and inter-network communication, remain poorly understood. We used SParsity-based Analysis of Reliable k-hubness to characterize connector hub reorganization in unilateral TLE compared with healthy controls. Patients exhibited significant widespread reductions in k-hubness involving the occipital, cingulate, and precuneus regions, ipsilateral hippocampus, and the bilateral pallidum. Compared to the distribution of k-hubness in healthy controls, the hub disruption index revealed rapid loss of k-hubness alongside emergence of new hubs in contralateral homologous regions. To investigate microstructural substrates of these functional changes, apparent diffusion coefficient and fractional anisotropy from diffusion magnetic resonance imaging were examined. The strongest associations with k-hubness were observed in subcortical structures and the hippocampus. These findings demonstrate that TLE is characterized by widespread, asymmetric connector hub reorganization extending beyond the epileptogenic temporal lobe, revealing distinct patterns of network disruption and reorganization across the brain.

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↗