Biology subjects

Kinany, N.

Publications and source records attributed to Kinany, N..

6 recordsLinked to original sources

bioRxiv2025

Spinal cord structural and functional architecture and its shared organization with the brain across the adult lifespan

The spinal cord connects the brain to peripheral systems. Yet its integration with cerebral networks remains a key neuroscience question. Capturing structural and functional central nervous system (CNS) changes throughout the lifespan is essential for characterizing healthy and pathological aging. Leveraging a unique multimodal dataset combining spinal and cerebrospinal imaging, we jointly mapped the spinal cord structural and functional architecture across adulthood. Our results revealed age-related changes across these modalities and identified organizational principles shared with the brain. These changes were most pronounced in the somatosensory pathway, with microstructural decline coupled to shifts in functional connectivity and local spontaneous activity as aging progresses. Extending analyses to the brain uncovered convergent CNS-wide aging mechanisms, including gray matter loss, functional dedifferentiation, and increased spontaneous activity, highlighting shared neural aging trajectories. Together, our findings provide a systems-level view of alterations with age and lay the groundwork for early biomarkers of sensorimotor decline.

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

Cerebro-spinal somatotopic organization uncoveredthrough functional connectivity mapping

Somatotopy, the topographical arrangement of sensorimotor pathways corresponding to distinct body parts, is a fundamental feature of the human central nervous system (CNS). Traditionally, investigations into brain and spinal cord somatotopy have been conducted independently, primarily utilizing body stimulations or movements. To date, however, no study has probed the somatotopic arrangement of cerebro-spinal functional connections in vivo in humans. In this study, we used simultaneous brain and cervical spinal cord functional magnetic resonance imaging (fMRI) to demonstrate how the coordinated activities of these two CNS levels at rest can reveal their shared somatotopy. Using functional connectivity analyses, we mapped preferential correlation patterns between each spinal cord segment and distinct brain regions, revealing a somatotopic gradient within the cortical sensorimotor network. We then validated this large-scale somatotopic organization through a complementary data-driven analysis, where we effectively identified spinal cord segments through the connectivity profiles of their voxels with the sensorimotor cortex. These findings underscore the potential of resting-state cerebro-spinal cord fMRI to probe the large-scale organization of the human sensorimotor system with minimal experimental burden, holding promise for gaining a more comprehensive understanding of normal and impaired somatosensory-motor functions.

neuroscience↗

bioRxiv2023

Lumbosacral spinal cord functional connectivity at rest: From feasibility to reliability

In the past decade, exploration of spontaneous blood-oxygen-level-dependent (BOLD) signal fluctuations has expanded beyond the brain to include the spinal cord. While most studies have predominantly focused on the cervical region, the lumbosacral segments play a crucial role in motor control and sensory processing of the lower limbs. Addressing this gap, the aims of the current study were two-fold: first, confirming the presence and nature of organized spontaneous BOLD signals in the human lumbosacral spinal cord; second, systematically assessing the impact of various denoising strategies on signal quality and functional connectivity (FC) patterns. Given the susceptibility of spinal cord fMRI to noise, this step is pivotal to ensure the robustness of intrinsic FC. Our findings uncovered bilateral FC between the ventral horns. Importantly, these patterns were consistently observed across denoising methods and demonstrating fair to excellent reliability. Conversely, no other significant connectivity patterns were identified across the remaining horns. Importantly, the evaluation of diverse denoising strategies highlighted the efficacy of PNM-based pipelines in cleaning the signal while preserving the strength and reliability of connectivity estimates. Together, our results provide evidence of robust FC patterns in the lumbosacral spinal cord, thereby paving the way for future studies probing caudal spinal activity.

neuroscience↗

bioRxiv2019

Motor improvement estimation and task adaptation for personalized robot-aided therapy: a feasibility study

BackgroundIn the past years, robotic systems have become increasingly popular in both upper and lower limb rehabilitation. Nevertheless, clinical studies have so far not been able to confirm superior efficacy of robotic therapy over conventional methods. The personalization of robot-aided therapy according to the patients individual motor deficits has been suggested as a pivotal step to improve the clinical outcome of such approaches.\n\nMethodsHere, we present a model-based approach to personalize robot-aided rehabilitation therapy within training sessions. The proposed method combines the information from different motor performance measures recorded from the robot to continuously estimate patients motor improvement for a series of point-to-point reaching movements in different directions and comprises a personalization routine to automatically adapt the rehabilitation training. We engineered our approach using an upper limb exoskeleton and tested it with seventeen healthy subjects, who underwent a motor-adaptation paradigm, and two subacute stroke patients, exhibiting different degrees of motor impairment, who participated in a pilot test.\n\nResultsThe experiments illustrated the models capability to differentiate distinct motor improvement progressions among subjects and subtasks. The model suggested personalized training schedules based on motor improvement estimations for each movement in different directions. Patients motor performances were retained when training movements were reintroduced at a later stage.\n\nConclusionsOur results demonstrated the feasibility of the proposed model-based approach for the personalization of robot-aided rehabilitation therapy. The pilot test with two subacute stroke patients further supported our approach, while providing auspicious results for the applicability in clinical settings.\n\nTrial registrationThis study is registered in ClinicalTrials.gov (NCT02770300, registered 30 March 2016, https://clinicaltrials.gov/ct2/show/NCT02770300).

bioengineering↗