Temporal Generalization in fNIRS-Based Autism Classification: A Cross-Time-Window Transfer Benchmark

2026-08-03

AI Summary

This study addresses the challenge of cross-temporal-window distribution shift in functional near-infrared spectroscopy (fNIRS)-based autism classification, caused by inter-individual variability in hemodynamic response delays. The work formalizes this temporal transfer problem for the first time and establishes a benchmark encompassing varying window lengths and shifts. Leveraging fNIRS topographic maps processed through vision-based models, the authors systematically evaluate eight strategies—including zero-shot inference, adversarial domain adaptation, and self-supervised learning—under settings with no target data or minimal fine-tuning. Results reveal that zero-shot performance remains limited (54–69%), underscoring individual variability as a critical bottleneck. However, fine-tuning on merely ~5% of target subjects restores accuracy to 90–96%, while unsupervised domain adaptation achieves 78–90%, demonstrating that even short 2.5-second windows retain sufficient discriminative information.

Abstract

Functional near-infrared spectroscopy (fNIRS) is a promising modality for autism spectrum disorder (ASD) classification, yet existing approaches assume temporally aligned evaluation. In practice, the optimal observation window varies across subjects due to differences in hemodynamic delay and neurovascular coupling, creating a temporal distribution shift that degrades performance. We formalize this as a \textit{cross-time-window transfer problem}, introducing a protocol that varies window length (2.5--10\,s) and offset within biological motion trials. Using topographic map representations of fNIRS recordings, we benchmark three vision architectures under two zero-shot baselines and eight adaptation strategies under leave-one-subject-out cross-validation ($N{=}124$). Key findings: (1) zero-shot cross-window accuracy is near chance (54--69\%); (2) ${\approx}5\%$ subject-specific fine-tuning recovers 90--96\%, while a subject-specific upper bound reaches 97--100\%, identifying inter-subject variability as the dominant barrier; (3) domain-adversarial and self-supervised strategies achieve 78--90\% without target-subject data; and (4) discriminative information is recoverable from windows as short as 2.5\,s. These findings provide a practical roadmap for deploying fNIRS-based ASD classifiers under realistic temporal variability.

Problem

• temporal generalization
• fNIRS
• autism spectrum disorder
• cross-time-window transfer
• distribution shift

Innovation

• cross-time-window transfer
• temporal generalization
• fNIRS-based ASD classification
• domain adaptation
• topographic map representation