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Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders.
Subtyping brain diseases from imaging data
The imaging community has increasingly adopted machine learning (ML) methods to provide individualized imaging signatures related to disease diagnosis, prognosis, and response to treatment.
Disentangling Alzheimer's disease neurodegeneration from typical brain aging using machine learning
First, a subset of AD patients and CN adults were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus AD).
Deep Learning and Bayesian Deep Learning Based Gender Prediction in Multi-Scale Brain Functional Connectivity
Hence, in this study we propose to predict gender from multiple scales of brain FC with deep learning, which can extract full FC patterns as features.
