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

1. Introduction

Functional neuroimaging has empowered the understanding of brain function continuously and noninvasively under several conditions since the early 1990s. In particular, blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS) share similar hemodynamic origins and reveal brain function by relying on the neurovascular coupling to infer neural activity and extract functional connectivity (FC) maps.^1–5 However, the low signal-to-noise ratio (SNR) intrinsic to both modalities ultimately leads to a lack of intra-subject reproducibility, which is enlarged by several confounding factors, including motion artifacts and physiology from noncortical tissues.^6–11 To overcome this limitation, researchers have often opted to perform group analysis by gathering data from different subjects based on their shared features. Although this approach increases the overall SNR and eases group comparisons, collapsing data from participants ignores unique individual features that may hold great potential for subject discrimination—a relevant feature that is valuable for several purposes, including clinical applications in which individualized treatments could benefit patient recovery and outcome.^12–14

The individual analysis ultimately depends on data reproducibility, which can be quantitatively assessed with test-retest protocols (i.e., experimental designs in which data are acquired from the same participant several times within days or a few weeks). To this end, several test-retest protocols have been previously applied to characterize the variability of fMRI and fNIRS data.^{10,12,15–22} Because healthy brain function is not expected to change significantly at the macroscale within a few days, repeated runs/sessions can help to develop optimal approaches for data acquisition (such as fMRI sequences and fNIRS probe positioning) and for analysis pipelines to yield more reproducible results at the intra-subject level.^9,10,22–26 Thanks to these efforts, recent methodologies have increased the intra-subject reproducibility to a level that it is possible to accurately identify individuals based on their FC maps, i.e., brain fingerprinting.

Since the pioneering work by Finn et al., novel approaches have improved brain fingerprinting with FC patterns measured with BOLD-fMRI.¹² For example, Amico and Goñi explored principal component analysis (PCA) for assessing and optimizing individual identification and showed that unique features at the intra-subject level could be better reconstructed in the connectivity domain.¹⁴