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Functional Connectome-Based Predictive Modeling in Autism
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DOI：10.1016/j.biopsych.2022.04.008.png)
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Abstract
En 中文
Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.
Keywords:
SPECTRUM DISORDER
DEFAULT MODE
FMRI DATA
BRAIN
CONNECTIVITY
STATE
CLASSIFICATION
NETWORK
ATTENTION
CHILDREN
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