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Machine learning has been increasingly used to obtain individualizedneuroimaging signatures for disease diagnosis, prognosis, and response totreatment in neuropsychiatric and neurodegenerative disorders. Therefore, ithas contributed to a better understanding of disease heterogeneity byidentifying disease subtypes that present significant differences in variousbrain phenotypic measures. In this review, we first present a systematicliterature overview of studies using machine learning and multimodal MRI tounravel disease heterogeneity in various neuropsychiatric and neurodegenerativedisorders, including Alzheimer disease, schizophrenia, major depressivedisorder, autism spectrum disorder, multiple sclerosis, as well as theirpotential in transdiagnostic settings. Subsequently, we summarize relevantmachine learning methodologies and discuss an emerging paradigm which we calldimensional neuroimaging endophenotype (DNE). DNE dissects the neurobiologicalheterogeneity of neuropsychiatric and neurodegenerative disorders into a lowdimensional yet informative, quantitative brain phenotypic representation,serving as a robust intermediate phenotype (i.e., endophenotype) largelyreflecting underlying genetics and etiology. Finally, we discuss the potentialclinical implications of the current findings and envision future researchavenues.

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning