Predicting cortical-thalamic functional connectivity using functional near-infrared spectroscopy and graph convolutional networks

Abstract Functional near-infrared spectroscopy (fNIRS) measures cortical hemodynamic changes, yet it cannot collect this information from subcortical structures, such as the thalamus, which is involved in several key functional networks. To address this drawback, we propose a machine-learning-based...

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Hlavní autoři:	Lingkai Tang, Lilian M. N. Kebaya, Homa Vahidi, Paige Meyerink, Sandrine de Ribaupierre, Soume Bhattacharya, Keith St. Lawrence, Emma G. Duerden
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Nature Portfolio 2024-11-01
Edice:	Scientific Reports
Témata:	Functional near-infrared spectroscopy Functional magnetic resonance imaging Functional connectivity Machine learning Graph convolutional network
On-line přístup:	https://doi.org/10.1038/s41598-024-79390-3
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