Revealing the spatiotemporal requirements for accurate subject identification with resting-state functional connectivity: a simultaneous fNIRS-fMRI study

Sergio Luiz Novi Junior; Alex De Castro Carvalho; Rodrigo Menezes Forti; Fernado Cendes; Clarissa L. Yasuda; Rickson C. Mesquita

doi:10.1117/1.NPh.10.1.013510

3 February 2023 Revealing the spatiotemporal requirements for accurate subject identification with resting-state functional connectivity: a simultaneous fNIRS-fMRI study

Sergio Luiz Novi Junior, Alex De Castro Carvalho, Rodrigo Menezes Forti, Fernado Cendes, Clarissa L. Yasuda, Rickson C. Mesquita

Author Affiliations +

Neurophotonics, Vol. 10, Issue 1, 013510 (February 2023). https://doi.org/10.1117/1.NPh.10.1.013510

Abstract

Significance

Brain fingerprinting refers to identifying participants based on their functional patterns. Despite its success with functional magnetic resonance imaging (fMRI), brain fingerprinting with functional near-infrared spectroscopy (fNIRS) still lacks adequate validation.

Aim

We investigated how fNIRS-specific acquisition features (limited spatial information and nonneural contributions) influence resting-state functional connectivity (rsFC) patterns at the intra-subject level and, therefore, brain fingerprinting.

Approach

We performed multiple simultaneous fNIRS and fMRI measurements in 29 healthy participants at rest. Data were preprocessed following the best practices, including the removal of motion artifacts and global physiology. The rsFC maps were extracted with the Pearson correlation coefficient. Brain fingerprinting was tested with pairwise metrics and a simple linear classifier.

Results

Our results show that average classification accuracy with fNIRS ranges from 75% to 98%, depending on the number of runs and brain regions used for classification. Under the right conditions, brain fingerprinting with fNIRS is close to the 99.9% accuracy found with fMRI. Overall, the classification accuracy is more impacted by the number of runs and the spatial coverage than the choice of the classification algorithm.

Conclusions

This work provides evidence that brain fingerprinting with fNIRS is robust and reliable for extracting unique individual features at the intra-subject level once relevant spatiotemporal constraints are correctly employed.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Citation Download Citation

Sergio Luiz Novi Junior, Alex De Castro Carvalho, Rodrigo Menezes Forti, Fernado Cendes, Clarissa L. Yasuda, and Rickson C. Mesquita "Revealing the spatiotemporal requirements for accurate subject identification with resting-state functional connectivity: a simultaneous fNIRS-fMRI study," Neurophotonics 10(1), 013510 (3 February 2023). https://doi.org/10.1117/1.NPh.10.1.013510

Received: 27 July 2022; Accepted: 10 January 2023; Published: 3 February 2023

ACCESS THE FULL ARTICLE

JOURNAL ARTICLE
16 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY