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• W2498176809 abstract "Functional and structural neuroimaging have made progress in our understanding of schizophrenia. However, there is still uncertainty about how the clinical symptoms of schizophrenia relate to the neurobiology of the human brain. This prompted the current drift toward more data-driven (ie, fewer assumptions) and higher-dimensional (ie, more features per observation) neuroimaging analyses on larger samples of neuroimaging data (ie, more observations). Coordinate-based meta-analysis by activation likelihood estimation (ALE) may be used to perform quantitative large-scale aggregation of neuroimaging findings. However, emerging machine-learning (ML) approaches allow the automatic detection and prediction of diagnosis and treatment-response patterns in massive datasets. Coordinate-based meta-analysis and machine learning are introduced and exemplified regarding their potential in computational psychiatry research on schizophrenia." @default.
• W2498176809 created "2016-08-23" @default.
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• W2498176809 date "2016-01-01" @default.
• W2498176809 modified "2023-09-27" @default.
• W2498176809 title "Statistical Learning of the Neurobiology of Schizophrenia" @default.
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• W2498176809 doi "https://doi.org/10.1016/b978-0-12-801829-3.00027-6" @default.
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