Dimensional Neuroimaging Endophenotypes: Neurobiological Representations
  of Disease Heterogeneity Through Machine Learning
Junhao Wen Mathilde Antoniades Zhijian Yang Gyujoon Hwang Ioanna Skampardoni Rongguang Wang Christos Davatzikos
Machine learning has been increasingly used to obtain individualized
neuroimaging signatures for disease diagnosis, prognosis, and response to
treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it
has contributed to a better understanding of disease heterogeneity by
identifying disease subtypes that present significant differences in various
brain phenotypic measures. In this review, we first present a systematic
literature overview of studies using machine learning and multimodal MRI to
unravel disease heterogeneity in various neuropsychiatric and neurodegenerative
disorders, including Alzheimer disease, schizophrenia, major depressive
disorder, autism spectrum disorder, multiple sclerosis, as well as their
potential in transdiagnostic settings. Subsequently, we summarize relevant
machine learning methodologies and discuss an emerging paradigm which we call
dimensional neuroimaging endophenotype (DNE). DNE dissects the neurobiological
heterogeneity of neuropsychiatric and neurodegenerative disorders into a low
dimensional yet informative, quantitative brain phenotypic representation,
serving as a robust intermediate phenotype (i.e., endophenotype) largely
reflecting underlying genetics and etiology. Finally, we discuss the potential
clinical implications of the current findings and envision future research
avenues.