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Updated: Aug 14, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
PET network abnormalities and cognitive decline in patients with mild cognitive impairment
Davangere P Devanand1, Christian G Habeck, Matthias H Tabert
1Department of Biological Psychiatry, College of Physicians and Surgeons, New York State Psychiatric Institute, Columbia University, New York, NY 10032, USA. dpd3@columbia.edu
Abstract:
Temporoparietal and posterior cingulate metabolism deficits characterize patients with Alzheimer's disease (AD). A H(2)(15)O resting PET scan covariance pattern, derived by using multivariate techniques, was previously shown to discriminate 17 mild AD patients from 16 healthy controls. This AD covariance pattern revealed hypoperfusion in bilateral inferior parietal lobule and cingulate; and left middle frontal, inferior frontal, precentral, and supramarginal gyri. The AD pattern also revealed hyperperfusion in bilateral insula, lingual gyri, and cuneus; left fusiform and superior occipital gyri; and right parahippocampal gyrus and pulvinar. In an independent sample of 23 outpatients with mild cognitive impairment (MCI) followed at 6-month intervals, the AD pattern score was evaluated as a predictor of cognitive decline. In this MCI sample, an H2(15)O resting PET scan was carried out at baseline. Mean duration of follow-up was 48.8 (SD 15.5) months, during which time six of 23 MCI patients converted to AD. In generalized estimating equations (GEE) analyses, controlling for age, sex, education, and baseline neuropsychological scores, increased AD pattern score was associated with greater decline in each neuropsychological test score over time (Mini Mental State Exam, Selective Reminding Test delayed recall, Animal Naming, WAIS-R digit symbol; Ps<0.01-0.001). In summary, a resting PET covariance pattern previously reported to discriminate AD patients from control subjects was applied prospectively to an independent sample of MCI patients and found to predict cognitive decline. Independent replication in larger samples is needed before clinical application can be considered.
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