Deep learning based pipeline for fingerprinting using brain functional MRI connectivity data

In this work we describe an appropriate pipeline for using deep-learning as a form of improving the brain functional connectivity-based fingerprinting process which is based in functional Magnetic Resonance Imaging (fMRI) data-processing results. This pipeline approach is mostly intended for neuroscientists, biomedical engineers, and physicists that are looking for an easy form of using fMRI-based Deep-Learning in identifying people, drastic brain alterations in those same people, and/or pathologic consequences to people’s brains. Computer scientists and engineers can also gain by noticing the data-processing improvements obtained by using the here-proposed pipeline. With our best approach, we obtained an average accuracy of 0.3132 ± 0.0129 and an average validation cost of 3.1422 ± 0.0668, which clearly outperformed the published Pearson correlation approach performance with a 50 Nodes parcellation which had an accuracy of 0.237.Thanks to Eduarda Sousa for support. NFL was supported by a fellowship of the project MEDPERSYST - POCI-01-0145-FEDER-016428, funded by Portugal’s FCT. This work was also supported by NORTE-01-0145-FEDER-000013, and NORTE 2020 under the Portugal 2020 Partnership Agreement through the FEDER, plus it was funded by the European Commission (FP7) “SwitchBox - Maintaining health in old age through homeostasis” (Contract HEALTH-F2-2010-259772), and co-financed by the Portuguese North Regional Operational Program (ON.2 – O Novo Norte), under the QREN through FEDER, and by the “Fundação Calouste Gulbenkian” (Portugal) (Contract grant number: P-139977; project “TEMPO - Better mental health during ageing based on temporal prediction of individual brain ageing trajectories”). We gratefully acknowledge the support of the NVIDIA Corporation with their donation of a Quadro P6000 board used in this research. This work was also supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio