Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Stroke patients vary considerably in terms of outcomes: some patients present ‘natural’ recovery proportional to their initial impairment (fitters), while others do not (non-fitters). Thus, a key challenge in stroke rehabilitation is to identify individual recovery potential to make personalized decisions for neuro-rehabilitation, obviating the ‘one-size-fits-all’ approach. This goal requires (i) the prediction of individual courses of recovery in the acute stage; and (ii) an understanding of underlying neuronal network mechanisms. ‘Natural’ recovery is especially variable in severely impaired patients, underscoring the special clinical importance of prediction for this subgroup. Fractional anisotropy connectomes based on individual tractography of 92 patients were analysed 2 weeks after stroke (TA) and their changes to 3 months after stroke (TC − TA). Motor impairment was assessed using the Fugl-Meyer Upper Extremity (FMUE) scale. Support vector machine classifiers were trained to separate patients with natural recovery from patients without natural recovery based on their whole-brain structural connectomes and to define their respective underlying network patterns, focusing on severely impaired patients (FMUE
Dimitri Nestor Alice Van De Ville, Elvira Pirondini, Cyprien Alban Félicien Rivier
Dimitri Nestor Alice Van De Ville, Maria Giulia Preti, Elvira Pirondini, Cyprien Alban Félicien Rivier
Dimitri Nestor Alice Van De Ville, Friedhelm Christoph Hummel, Gabriel Girard, Takuya Morishita, Elena Beanato, Lisa Aïcha Mireille Julie Fleury, Maximilian Jonas Wessel, Philipp Johannes Koch, Philip Egger, Andéol Geoffroy Cadic-Melchior