Biology subjects

Khatibi, A.

Publications and source records attributed to Khatibi, A..

8 recordsLinked to original sources

bioRxiv2026

A transition to a more efficient attentional strategy facilitates motor learning in the presence and absence of movement-evoked experimental pain. A cross-sectional experimental study.

Pain demands attention and can disrupt task-related goals. Attention allocation is a key cognitive process supporting motor learning and disruption of internal schemas associated with attentional control during motor learning can result in interference in improvements in performance. Movement-contingent pain is an important characteristic of persistent musculoskeletal pain. Despite this, research exploring pain interference with motor learning and attention has exclusively utilised tonic pain paradigms. Understanding the impacts of movement-contingent pain on motor learning and attention may provide important insights into the interaction between pain and motor learning. The aim of this study was to; 1) explore the robustness of a movement-contingent pain paradigm across an extended period of training, 2) explore the impact of movement-contingent pain on improvements in performance and attentional allocation during motor learning. Three groups (healthy non-pain, healthy experimental-pain and persistent pain experimental-pain) completed ten trials of a motor sequence learning task while experiencing a movement-contingent electrical stimulation. Three task performance measures and five gaze indices, previously associated with attentional control, were collected. Results showed that; 1) low frequency electro-cutaneous stimulation could produce a valid and consistent pain experience across a sustained period of training, 2) attentional allocation becomes more efficient across learning, accompanied by improvements in task performance, 3) changes in task performance and attentional measures across training were similar in all groups despite the presence of pain, 4) movement-contingent experimental pain enhanced spatial performance at all time points in healthy participants but was not accompanied by a different pattern of attentional allocation. This study demonstrates that the impact of movement-contingent pain on motor learning is comparable to the impacts of tonic experimental pain and provides interesting insights into patterns of attentional allocation across time but little evidence that these attentional allocations are impacted by the presence of pain or a past history of pain.

neuroscience↗

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↗

bioRxiv2024

The brain selectively allocates energy to functional brain networks under cognitive control

Network energy has been conceptualized based on structural balance theory in the physics of complex networks. We utilized this framework to assess the energy of functional brain networks under cognitive control and to understand how energy is allocated across canonical functional networks during various cognitive control tasks. We extracted network energy from functional connectivity patterns of subjects who underwent fMRI scans during cognitive tasks involving working memory, inhibitory control, and cognitive flexibility, in addition to task-free scans. We found that the energy of the whole-brain network increases when exposed to cognitive control tasks compared to the task-free resting state, which serves as a reference point. The brain selectively allocates this elevated energy to canonical functional networks; sensory networks receive more energy to support flexibility for processing sensory stimuli, while cognitive networks relevant to the task, functioning efficiently, require less energy. Furthermore, employing network energy, as a global network measure, improves the performance of predictive modeling, particularly in classifying cognitive control tasks and predicting chronological age. Our results highlight the robustness of this framework and the utility of network energy in understanding brain and cognitive mechanisms, including its promising potential as a biomarker for mental conditions and neurological disorders.

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↗

bioRxiv2022

Pattern of frustration formation in the functional brain network

The brain is a frustrated system that contains conflictual link arrangements named frustration. The frustration as a source of disorder prevents the system from settling into low energy states and provides flexibility for brain network organization. In this research, we tried to identify the pattern of frustration formation in the brain at the levels of region, connection, canonical network, and hemisphere. We found that frustration formation has not a uniform pattern. Some subcortical elements have an active role in frustration formation, despite many low contributed cortical elements. Frustrating connections are mostly between-network types and triadic frustrations are mainly formed between three regions from three distinct canonical networks. Although there were no significant differences between brain hemispheres. We also did not find any robust differences between the frustration formation patterns of various lifespan stages. Our results may be interesting for those who study the organization of brain links and promising for those who want to manipulate brain networks.

neuroscience↗

bioRxiv2021

Topological impact of negative links on the stability of resting-state brain network

Stability is a physical attribute that stands opposite the change. However, it is still unclear how the arrangement of links called topology affects network stability. In this study, we tackled this issue in the resting-state brain network using structural balance. Structural balance theory employs the quality of triadic associations between signed links to determine the network stability. In this study, we showed that negative links of the resting-state network make hubs to reduce balance-energy and push the network into a more stable state compared to null-networks with trivial topologies. In this regard, we created a global measure entitled tendency to make hub to assess the hubness of the network. Besides, we revealed nodal degrees of negative links have an exponential distribution that confirms the existence of negative hubs. Our findings indicate that the arrangement of negative links plays an important role in the balance (stability) of the resting-state brain network.

neuroscience↗

bioRxiv2020

Evaluation and Optimization of Motion Correction in Spinal Cord fMRI Preprocessing

Motion correction is an essential step in the preprocessing of functional magnetic resonance imaging (fMRI) data, improving the temporal signal to noise ratio (tSNR) and removing unwanted variance. Because of the characteristics of the spinal cord (non-rigidity, surrounded by moving organs), motion correction becomes especially challenging. We compared the efficiency of different motion correction protocols and suggest a preferred method for spinal cord fMRI data. Here we acquired gradient-echo echo-planar-imaging axial lumbar spinal cord fMRI data during painful mechanical stimulation of the left lower extremity of 15 healthy volunteers on a 3T scanner. We compared multiple motion correction techniques: 2D and 3D FLIRT realignment with and without slice-wise regulation, SliceCorr (implemented in the Spinal Cord Toolbox) and proposed a method 3D FLIRT in addition to Slice Regulation (SLiceReg) along the spinal cord. TSNR, image entropy, DVARS, image Sum of Absolute Differences and number of activated voxels in the spinal cord from GLM analysis to evaluate the performance of multiple motion correction procedures. The tSNR and DVARS 3D FLIRT + SLiceReg were significantly improved over other realignment methods (p<0.001). In comparison, tSNR=14.20{+/-}0.02 and DVARS=165.77{+/-}1.54 were higher than other methods. Additionally, the number of activated voxels of the statistical map in our suggested method was higher than the other realignment methods (p<0.05). Our results illustrated the proposed motion correction algorithm that integrated 3D motion correction and 2D slicewise regularization along spinal cord curvature could improve subject-level processing outputs by reducing the effects of motions. Our proposed protocols can improve subject-level analysis, especially in lumbar region that suffers from involuntary motions and signal loss due to susceptibility effect more than other spinal cord regions.

neuroscience↗