Open AccessIdentifying individuals using fNIRS-based cortical connectomes
Author(s) -
Júlia de Souza Rodrigues,
Fernanda L. Ribeiro,
João Ricardo Sato,
Rickson C. Mesquita,
Claudinei Eduardo Biazoli
Publication year - 2019
Publication title -
biomedical optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.10.002889
Subject(s) - connectome , resting state fmri , pairwise comparison , computer science , principal component analysis , identification (biology) , pattern recognition (psychology) , human connectome project , artificial intelligence , neuroimaging , neuroscience , functional connectivity , psychology , biology , botany
The fMRI-based functional connectome was shown to be sufficiently unique to allow individual identification (fingerprinting). We aimed to test whether a fNIRS-based connectome could also be used to identify individuals. Forty-four participants performed experimental protocols that consisted of two periods of resting-state interleaved by a cognitive task period. Connectome identification was performed for all possible pairwise combinations of the three periods. The influence of hemodynamic global variation was tested using global signal regression and principal component analysis. High identification accuracies well-above chance level (2.3%) were observed overall, being particularly high (93%) to the oxyhemoglobin signal between resting conditions. Our results suggest that fNIRS is a suitable technique to assess connectome fingerprints.