期刊
BIOMEDICAL OPTICS EXPRESS
卷 6, 期 3, 页码 1074-1089出版社
Optica Publishing Group
DOI: 10.1364/BOE.6.001074
关键词
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资金
- Stanford Institute for Neuro-Innovation and Translational Neurosciences (SINTN) [S10 RR024657, P41 EB015891]
- Albert Yu and Mary Bechmann Foundation and Autism Speaks
Functional near-infrared spectroscopy (fNIRS) is an increasingly popular technology for studying brain function because it is non-invasive, non-irradiating and relatively inexpensive. Further, fNIRS potentially allows measurement of hemodynamic activity with high temporal resolution (milliseconds) and in naturalistic settings. However, in comparison with other imaging modalities, namely fMRI, fNIRS has a significant drawback: limited sensitivity to hemodynamic changes in deep-brain regions. To overcome this limitation, we developed a computational method to infer deep-brain activity using fNIRS measurements of cortical activity. Using simultaneous fNIRS and fMRI, we measured brain activity in 17 participants as they completed three cognitive tasks. A support vector regression (SVR) learning algorithm was used to predict activity in twelve deep-brain regions using information from surface fNIRS measurements. We compared these predictions against actual fMRI-measured activity using Pearson's correlation to quantify prediction performance. To provide a benchmark for comparison, we also used fMRI measurements of cortical activity to infer deep-brain activity. When using fMRI-measured activity from the entire cortex, we were able to predict deepbrain activity in the fusiform cortex with an average correlation coefficient of 0.80 and in all deep-brain regions with an average correlation coefficient of 0.67. The top 15% of predictions using fNIRS signal achieved an accuracy of 0.7. To our knowledge, this study is the first to investigate the feasibility of using cortical activity to infer deepbrain activity. This new method has the potential to extend fNIRS applications in cognitive and clinical neuroscience research. (C) 2015 Optical Society of America
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