Abstract
Neuropsychiatric disorders have been ranked as the leading causes of living on disability in the world. To date, the diagnosis of most neuropsychiatric disorders has largely been based on self-reported symptoms and clinical signs. Recently, magnetic resonance imaging (MRI) has attracted increasing attention for mapping human brain connectome function and dysfunction. In this talk, I briefly review the progress of my lab in the brain pattern analysis (also termed multivariate pattern analysis, MVPA) based on MRI and clinical applications, mainly focusing on the methodologies including the classification feature extraction, dimensionality reduction, and classifier design, as well as MVPA applications in neuropsychiatric disorders.
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References
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Acknowledgments
This work was supported by the National Science Foundation of China (61420106001, 91420302, and 61375111).
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Hu, D. (2016). Brain Pattern Analysis Based on Magnetic Resonance Imaging. In: Wang, R., Pan, X. (eds) Advances in Cognitive Neurodynamics (V). Advances in Cognitive Neurodynamics. Springer, Singapore. https://doi.org/10.1007/978-981-10-0207-6_8
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DOI: https://doi.org/10.1007/978-981-10-0207-6_8
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