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Updated: Jul 10, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
The role of neuroimaging in mild cognitive impairment
1Department of Nuclear Medicine, Saitama Medical School Hospital, Saitama, Japan. matsudah@saitama-med.ac.jp
Abstract:
The main purposes of neuroimaging in Alzheimer's disease (AD) have been moved from diagnosis of advanced AD to diagnosis of very early AD at a prodromal stage of mild cognitive impairment, prediction of conversion from mild cognitive impairment (MCI) to AD, and differential diagnosis from other diseases causing dementia. Structural MRI studies and functional studies using F-18 fluorodeoxyglucose-positron emission tomography (FDG-PET) and brain perfusion single-photon emission computed tomography (SPECT) are widely used in diagnosis of AD. Outstanding progress in diagnostic accuracy of these neuroimaging modalities has been obtained using statistical analysis on a voxel-by-voxel basis after spatial normalization of individual scans to a standardized brain-volume template instead of visual inspection or a conventional region of interest technique. In a very early stage of AD, this statistical approach revealed gray matter loss in the entorhinal and hippocampal areas and hypometabolism or hypoperfusion in the posterior cingulate cortex and precuneus. These two findings might be related in view of anatomical knowledge that the regions are linked through the circuit of Papez. This statistical approach also offers prediction of conversion from MCI to AD. Presence of hypometabolism or hypoperfusion in parietal association areas and entorhinal atrophy at the MCI stage has been reported to predict rapid conversion to AD.
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