Training the Surviving Neural Network to Create New Synaptic Connections That Can Maximize Restoration of Motor Functions After Stroke
Training the Surviving Neural Network to Create New Synaptic Connections That Can Maximize Restoration of Motor Functions After Stroke
Multimodal imaging of stroke patients. Patients included in the study will be investigated with multiparametric imaging tools including MRI and EEG imaging. Improvements in motor skills will be monitored clinically.
We aim to (i) quantify the size of the brain lesion and assess the surviving functional neural network connections; (ii) Analyze the type and degree of inflammation initiated by the stroke-induced brain lesion. We will stratify inflammation according to lesion volume, surviving functional neural network connections, age, NIHSS and Modified Rankin Scale (mRS) and analyze how inflammation markers change during rehabilitation; (iii) Train the surviving neural network to create new synaptic connections that can maximize restoration of motor functions; (iv) Apply and develop advanced mathematical and computational methods to model and predict how stroke rehabilitation can rewire the neural network connections to best facilitate restoration of lost motor function.
Inclusion Criteria:
Exclusion Criteria:
axel.sandvig@ntnu.no+46 72575620