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P3‐078: MACHINE LEARNING DIFFERENTIATES EARLY STAGES OF ALZHEIMER'S DISEASE FROM NORMAL AGING: A BRAIN MORPHOMETRY STUDY
Author(s) -
Zhao Weina,
Luo Yishan,
Zhao Lei,
Mok Vincent C.T.,
Sun Yu,
Shi Lin,
Han Ying
Publication year - 2018
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2018.06.1434
Subject(s) - atrophy , support vector machine , temporal lobe , cognitive impairment , receiver operating characteristic , cognition , amygdala , alzheimer's disease , medicine , psychology , artificial intelligence , neuroscience , disease , computer science , epilepsy
methanol). Interestingly, we identified several metabolites which were disproportionately affected in mild AD cases than severe, the majority of which turned out to be lipids. Conclusions: Combining two complementary analytical techniques offers a more holistic view of the brain metabolome. Brain metabolic responses differ according to disease severity providing clues about how the disease pathology develops. Future studies should investigate the disease mechanisms and the reproducibility of the metabolite biomarkers discovered here.