Identifying individuals using fnirs-based cortical connectomes

Júlia De Souza Rodrigues; Fernanda Lenita Ribeiro; João Ricardo Sato; Rickson Coelho Mesquita; Claudinei Eduardo Biazoli Júnior

doi:10.1364/BOE.10.002889

Identifying individuals using fnirs-based cortical connectomes

Júlia De Souza Rodrigues^*, Fernanda Lenita Ribeiro, João Ricardo Sato, Rickson Coelho Mesquita, Claudinei Eduardo Biazoli Júnior

^*Corresponding author for this work

Computer Science

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	2889-2897
Number of pages	9
Journal	Biomedical Optics Express
Volume	10
Issue number	6
DOIs	https://doi.org/10.1364/BOE.10.002889
Publication status	Published - 1 Jun 2019

Bibliographical note

Funding Information:
São Paulo Research Fundation (FAPESP) (2018/21934-5); National Council for Scientific and Technological Development (CNPQ); Coordination for the Improvement of Higher Education Personnel (CAPES).

Publisher Copyright:
© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.

ASJC Scopus subject areas

Biotechnology
Atomic and Molecular Physics, and Optics

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10.1364/BOE.10.002889

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abstract = "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.",

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AU - Sato, João Ricardo

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AU - Biazoli Júnior, Claudinei Eduardo

N1 - Funding Information: São Paulo Research Fundation (FAPESP) (2018/21934-5); National Council for Scientific and Technological Development (CNPQ); Coordination for the Improvement of Higher Education Personnel (CAPES). Publisher Copyright: © 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.

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AB - 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.

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Identifying individuals using fnirs-based cortical connectomes

Abstract

Bibliographical note

ASJC Scopus subject areas

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