BACKGROUND
The functional MRI (fMRI) is an essential tool for the presurgical planning of brain tumor removal, allowing the identification of functional brain networks in order to preserve the patient’s neurological functions. One fMRI technique used to identify the functional brain network is the resting-state-fMRI (rsfMRI). However, this technique is not routinely used because of the necessity to have a expert reviewer to identify manually each functional networks.
OBJECTIVE
We aimed to automatize the detection of brain functional networks in rsfMRI data using deep learning and machine learning algorithms
METHODS
We used the rsfMRI data of 82 healthy patients to test the diagnostic performance of our proposed end-to-end deep learning model to the reference functional networks identified manually by 2 expert reviewers.
RESULTS
Experiment results show the best performance of 86% correct recognition rate obtained from the proposed deep learning architecture which shows its superiority over other machine learning algorithms that were equally tested for this classification task.
CONCLUSIONS
The proposed end-to-end deep learning model was the most performant machine learning algorithm. The use of this model to automatize the functional networks detection in rsfMRI may allow to broaden the use of the rsfMRI, allowing the presurgical identification of these networks and thus help to preserve the patient’s neurological status.
CLINICALTRIAL
Comité de protection des personnes Ouest II, decision reference CPP 2012-25)
Optimization and classification of developmental brain diseases using machine learning of functional brain networks
