Methodology for high-yield acquisition of functional near-infrared spectroscopy data from alert, upright infants

Abstract

Abstract. Functional near-infrared spectroscopy (fNIRS) research to date has tended to publish group-averaged rather than individual infant data due to normative basic research goals. Acquisition of individual infant time courses holds interest, however, both for cognitive science and particularly for clinical applications. Infants are more difficult to study than adults as they cannot be instructed to remain still. In addressing this, upright infants pose several associated complications for the researcher. We identified and optimized the factors that affect the quality of fNIRS data from individual 6-to 9-month-old infants exposed to a visual stimulation paradigm. The fNIRS headpiece was reconfigured to reduce inertia, increase comfort, and improve conformity to the head, while preserving fiber density to avoid missing the visual cortex activation. The visual-stimulation protocol was modified to keep the attention of infants throughout the measurement, thus helping to reduce motion artifacts. Adequate optical contact was verified by checking power levels before each measurement. By revising our experimental process and our data rejection criteria to prioritize good optical contact, we report for the first time usable hemodynamic data from 83% of infants and that two-thirds of infants produced a statistically significant fNIRS response.

Inquiry-based environments for bio-signal processing training in engineering education

“…Different acquisition paradigms for the study of the prefrontal cortex via f-NIRS have been proposed in the literature. [38][39][40][41][42][43][44][45] The participants to the learning path collected from the literature different procedures for the stimulation (auditory, visual, visuospatial-auditory, visuospatial-visual, auditory-visual and visuospatial). After long discussions about the benefits and limitations of the mainly used acquisition procedures, they decided to investigate the effects of memory tasks, since it has been shown that they elicit hemodynamic responses in the brain.…”

Robust and automated motion correction for real infant fNIRS data

“…b) Rapid and large (> 0.1 au) signal changes produced by motion artifacts. rate amplitude and that they typically take at least 2 s from start to peak (Goodwin et al, 2016;Arichi et al, 2012), motion artifacts were defined as any change in signal passing an amplitude threshold of at least 0.1 OD units (i.e. five times the heart rate amplitude) within a 1 s interval (Figure 2b).…”

“…Translating these general strategies into specific implementations that perform well (i.e. remove artifacts while retaining hemodynamic responses) across participants and samples has nonetheless turned out to be a difficult challenge (Brigadoi et al, 2014;Cooper et al, 2012;Goodwin et al, 2016;Behrendt, Firk, Nelson & Perdue, 2018;Di Lorenzo et al, 2019;Hu et al, 2015;Barker, Aarabi & Huppert, 2013;Reyes, Bohache, Wijeakumar & Spencer, 2018).…”

“…occurring over 1 s or less) changes in the optical density (OD) form of the data that are 5 or more times greater than the typical amplitude of OD changes produced by the participant's heartbeat. Changes of this amplitude are common for even modest motion artifacts, but exceed the amplitude of most hemodynamic responses, which are typically between 2 and 5 times larger than the heartbeat oscillation (Goodwin et al, 2016), and also typically take more than 1 s to develop from start to peak (Arichi et al, 2012;Issard & Gervain, 2018).…”

Comparing fixed-array and functionally-defined channel of interest approaches to infant functional near-infrared spectroscopy data

“…Whereas the cortical source of EEG is difficult to definitively determine, as the electrical activity measured by EEG propagates and sums across the scalp, physical constraints on light propagation and detection through underlying tissue reduce source localization estimates in NIRS data to a few centimeters around a given data channel's (light source-detector pair) scalp placement (Cui et al, 2011;Luck, 2005;Minagawa-Kawai, 2008). The increase in use and application of fNIRS has been accompanied by advances in hardware, software, and head probes to meet data collection and data processing challenges (Aasted et al, 2015;Gervain et al, 2011;Goodwin et al, 2016;Huppert et al, 2009;Lloyd-Fox et al, 2010;Santosa et al, 2018;Ye et al, 2009;Yücel et al, 2017;Saikia et al, 2019;Zimmermann et al, 2019). However, parallel advances in data analysis are needed for fNIRS to become and remain a premier method for studying brain function in infants (Gervain et al, 2011;Powell et al, 2018, Gemignani & Gervain, 2021a, 2021b).…”