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Beaton, S.

Publications and source records attributed to Beaton, S..

2 recordsLinked to original sources

Characterising longitudinal dynamics of infant neurodevelopment using functional change point detection

SignificanceHabituation is an early-developing cognitive process linked to learning, typically reflected in functional near-infrared spectroscopy (fNIRS) studies as a reduction in evoked hemodynamic response amplitude. Conventional fNIRS analyses rely on linear time-invariance assumptions, potentially limiting insight into how responses evolve across repeated stimulus presentations. AimTo determine whether functional change point (FCPt) detection can characterize trial-specific changes in the infant hemodynamic response and reveal developmental differences in habituation timing. ApproachFunctional data analysis (FDA) treats entire response curves as statistical objects. Within this framework, FCPt detection identifies statistically significant structural shifts in the mean response across trials. FCPt detection with wild binary segmentation was applied to infant fNIRS data (n = 204) collected from infants living in rural Gambia at 5-, 8-, and 12-months of age during a habituation and novelty detection paradigm. ResultsSignificant changes were identified within auditory cortical regions. A high proportion of detected change points corresponded to decreases in response magnitude at 8- and 12-months. Ordinal regression revealed an age-related shift toward earlier occurrence of decreasing change points, indicating that older infants completed habituation sooner within the trial sequence. ConclusionFCPt detection provides temporal information on the infant hemodynamic response unavailable to conventional analysis. This additional information has revealed a previously overlooked developmental change in the habituation response during the first year of infancy.

neuroscience↗

Investigating the effect of channel pruning on functional near-infrared spectroscopy data collected from children aged 5-24 months

SignificanceInfant functional near-infrared spectroscopy (fNIRS) data are particularly vulnerable to noise; participant behaviour can result in motion artefacts and reduced set-up times can cause poor optode coupling. Accurate channel pruning is therefore essential but approaches vary and often use adult-derived thresholds, risking unnecessary data loss. AimThis work systematically compared pruning approaches and parameter choices to evaluate their effects on data quality and retention in infant fNIRS. ApproachData from 5-24 month-old infants were collected across two cohorts, using two paradigms. Channel pruning was performed using the coefficient of variation (CV) and the Quality Testing of Near Infrared Scans (QT-NIRS) tool, varying key thresholds. Multilevel models assessed effects of pruning method, parameter choice, age, motion, and testing site on signal-to-noise ratio (SNR) and channels retained. ResultsQT-NIRS produced significantly higher SNR than CV pruning across nearly all age, task, and cohort combinations, when matched for data retention. Higher QT-NIRS thresholds improved quality but reduced retention. Motion prevalence strongly reduced both SNR and retention; testing site and age had smaller but notable effects. ConclusionsQT-NIRS offers a better balance of data quality and retention than CV pruning. Lower QT-NIRS thresholds than adult defaults are recommended for infant data. These findings provide practical guidance for preprocessing pipelines in developmental fNIRS research.

neuroscience↗