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

• Published in: Neurophotonics (Print) Vol. 10; no. 1; p. 013510
• Main Authors: Novi, Sergio L., Carvalho, Alex C., Forti, R. M., Cendes, Fernado, Yasuda, Clarissa L., Mesquita, Rickson C.
• Format: Journal Article
• Language: English
• Published: United States Society of Photo-Optical Instrumentation Engineers 01.01.2023; SPIE; S P I E - International Society for
• Subjects: Brain mapping; Classification; Confounding (Statistics); Digitization; Fingerprinting; Functional magnetic resonance imaging; Hemodynamics; Hemoglobin; Infrared spectroscopy; Magnetic resonance imaging; Medical imaging; Neural networks; Neuroimaging; Physiology; Reproducibility; Special Section Celebrating 30 Years of Functional Near Infrared Spectroscopy (Part I); Time series; resting-state functional connectivity; brain fingerprinting; functional near-infrared spectroscopy; functional magnetic resonance imaging; subject identification
• ISSN: 2329-423X; 2329-4248
• DOI: 10.1117/1.NPh.10.1.013510

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. 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. 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. 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. 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.