Assessing the Validity and Reliability of HD-DOT TD-fNIRS Resting-State Measurements in Rapid Succession Data Collection Settings

Abstract

Functional magnetic resonance imaging (fMRI) has long been a cornerstone in the study of brain activity, but its high operational costs, limited availability, and restricted practical applicability have led researchers to seek alternative neuroimaging technologies. Recent advancements in high-density diffuse optical tomography (HD-DOT) and Time-Domain (TD) functional near-infrared spectroscopy (fNIRS) have emerged as promising solutions, offering the ability to generate detailed tomographic maps of hemodynamic fluctuations associated with neural activity. In this study, using the Kernel Flow device, we assess the performance of HD-DOT TD-fNIRS in terms of signal validity and reliability, particularly in rapid succession data collection settings. We conducted a multiple test-retest experiment involving fNIRS recordings from three participants across 20 ten-minute sessions of eyes-open resting-state brain activity over six days. Our findings indicate that HD-DOT TD-fNIRS systems like the Kernel Flow can reproduce hemodynamic patterns identified by fMRI, albeit with less spatial detail, and can detect resting state networks overall, though some individual network detections are not significant. The system performs consistently over days, with more variability within the time of day, and can capture subject-specific patterns with high accuracy as identified through FC fingerprinting analysis. We conclude that the new generation of HD-DOT TD-fNIRS systems holds significant promise for enhancing and expanding the measurement of functional brain data in both clinical and more naturalistic research settings. This study represents an important step towards a comprehensive understanding of the data quality and consistency achievable with these innovative neuroimaging devices.