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

• Published in: Scientific reports Vol. 14; no. 1; pp. 29794 - 17
• Main Authors: Tang, Lingkai, Kebaya, Lilian M. N., Vahidi, Homa, Meyerink, Paige, de Ribaupierre, Sandrine, Bhattacharya, Soume, St. Lawrence, Keith, Duerden, Emma G.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 30.11.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/116/1925; 631/114/1305; 692/700/1421/65; Adult; Brain Injuries - diagnostic imaging; Brain Injuries - physiopathology; Brain injury; Brain mapping; Cerebral Cortex - diagnostic imaging; Cerebral Cortex - physiology; Female; Functional connectivity; Functional magnetic resonance imaging; Functional near-infrared spectroscopy; Graph convolutional network; Humanities and Social Sciences; Humans; Infant, Newborn; Infrared spectroscopy; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; multidisciplinary; Neonates; Neural networks; Neural Networks, Computer; Neuroimaging; Prediction models; Science; Science (multidisciplinary); Spectroscopy, Near-Infrared - methods; Spectrum analysis; Thalamus; Thalamus - diagnostic imaging; Thalamus - physiology; Young Adult; Functional connectivity; Functional magnetic resonance imaging; Graph convolutional network; Functional near-infrared spectroscopy; Machine learning

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 approach...