Connectomics underlying motor functional outcomes in the acute period following stroke

Connectomics underlying motor functional outcomes in the acute period following stroke

Blog Article

ObjectiveStroke remains the number one cause of morbidity in many developing countries, and while effective neurorehabilitation strategies exist, it remains difficult to predict the individual trajectories of patients in the toyo proxes st iii 305/40r22 acute period, making personalized therapies difficult.Sophisticated and data-driven methods are necessary to identify markers of functional outcomes.MethodsBaseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 79 patients following stroke.Sixteen models were constructed to predict performance across six tests of motor impairment, spasticity, and activities of daily living, using either whole-brain structural or functional connectivity.

Feature importance analysis was also performed to identify brain regions and networks associated with performance in each test.ResultsThe area under the receiver operating characteristic curve ranged from 0.650 to 0.868.

Models utilizing functional connectivity tended grundfos scala1 3-45-1 115v 60 hz 99631742 to have better performance than those utilizing structural connectivity.The Dorsal and Ventral Attention Networks were among the top three features in several structural and functional models, while the Language and Accessory Language Networks were most commonly implicated in structural models.ConclusionsOur study highlights the potential of machine learning methods combined with connectivity analysis in predicting outcomes in neurorehabilitation and disentangling the neural correlates of functional impairments, though further longitudinal studies are necessary.