  Heterogeneity of brain diseases is a challenge for precision
diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised
cLustEring-Generative Adversarial Network), a novel semi-supervised
deep-clustering method, which dissects neuroanatomical heterogeneity, enabling
identification of disease subtypes via their imaging signatures relative to
controls. When applied to MRIs (2 studies; 2,832 participants; 8,146 scans)
including cognitively normal individuals and those with cognitive impairment
and dementia, Smile-GAN identified 4 neurodegenerative patterns/axes: P1,
normal anatomy and highest cognitive performance; P2, mild/diffuse atrophy and
more prominent executive dysfunction; P3, focal medial temporal atrophy and
relatively greater memory impairment; P4, advanced neurodegeneration. Further
application to longitudinal data revealed two distinct progression pathways:
P1$\rightarrow$P2$\rightarrow$P4 and P1$\rightarrow$P3$\rightarrow$P4. Baseline
expression of these patterns predicted the pathway and rate of future
neurodegeneration. Pattern expression offered better yet complementary
performance in predicting clinical progression, compared to amyloid/tau. These
deep-learning derived biomarkers offer promise for precision diagnostics and
targeted clinical trial recruitment.
