Biology subjects

Cotton, F.

Publications and source records attributed to Cotton, F..

3 recordsLinked to original sources

bioRxiv2022

Upper limb rehabilitation after stroke: constraint versus intensive training. A longitudinal case-control study correlating motor performance with fMRI data

BackgroundThe reproducible beneficial effect of constraint-induced movement therapy (CIMT) in hemiparetic stroke patients makes it a good model to study brain plasticity during rehabilitation procedures. ObjectiveAssess the functional brain reorganization induced by each of the two components of CIMT: (i) non-affected upper-limb constraint and (ii) intensive training of the paretic arm. MethodsBrain activity of a right hemiparetic chronic stroke patient and of 10 healthy controls was recorded with a functional magnetic resonance imaging (fMRI) during a finger opposition task. For the patient, a total of 8 assessments were performed, before and after each component of CIMT. At each time point, brain activity during movement was compared with rest. Patients results were first compared to the control group and then correlated to motor performance across sessions. ResultsConstraint-therapy-related improvement was correlated with a decrease of cerebral activity in sensory-motor regions of both the affected and the non-affected hemispheres. Intensive-therapy-related improvement was correlated with the recruitment of pre-motor cortices and cerebellum in both hemispheres. ConclusionsTwo different patterns of brain activity underlie the effects of intensive training and constraint which could account for the respective effect of each component of the therapy.

neuroscience↗

bioRxiv2019

Modeling individual disability evolution in multiple sclerosis patients based on longitudinal multimodal imaging and clinical data

BackgroundThe individual disease evolution of multiple sclerosis (MS) is very different from one patient to another. Therefore, the prediction of long-term disability evolution is difficult based on only clinical information. Magnetic resonance imaging (MRI) provides a very efficient tool to distinguish between healthy and abnormal brain tissue, monitor disease evolution, and help decision-making for personalized treatment of MS patients.\n\nObjectiveWe aim to develop a patient-specific model to predict individual disease evolution in MS, using demographic, clinical, and imaging data that were collected at study onset.\n\nMethodsThe study included 75 patients tracked over 5 years. The latent class linear mixed model was used to consider individual and unobserved subgroup variability. First, the clinical model was established with demographic and clinical variables to predict clinical disease evolution. Second, the imaging model was built using the multimodal imaging variables. Third, the imaging variables were added one by one, two by two, and all three together to investigate their contribution to the clinical model. The clinical disability is measured with the Expanded Disability Status Scale (EDSS). The performances of the clinical, imaging, and the combined models were compared mainly using the Bayesian Information Criterion (BIC). The mean of the posterior probabilities was also given as the secondary performance evaluation criterion.\n\nResultsThe clinical model gave higher BIC value than imaging and any combined models. The means of the posterior probabilities given by the three models were over 0.94. The clinical model clustered the patients into two latent classes: stable evolution class (n=6, 88%) and severe evolution class (n=9, 12%).\n\nConclusionThe latent class linear mixed model may provide a well-fitted prediction for the disability evolution in MS patients, thus giving further information for personalized treatment decisions after thorough validation with a larger and independent dataset.

neuroscience↗

bioRxiv2018

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-the-art algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, ...), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.

neuroscience↗