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Updated: Mar 24, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
[Comprehensive early diagnosis of cognitive impairment]
1Kirov Military Medical Academy, St. Petersburg, Russia.
Objective:
To study cognitive function, CSF biochemical markers of amyloidosis and neurodegeneration, cerebral metabolism using PET, cerebral volumetric changes and metabolic content of the cingular gyrus using magnetic resonance spectroscopy (MRS) in patients with amnesic and neurodynamic variants of mild cognitive impairment (MCI).
Material And Methods:
Authors examined 369 patients with cognitive impairment of different severity. All patients underwent neuropsychological examination. To deter-mine the levels of β-amyloid and tau-protein,a lumbar puncture was performed in 125 patients and 20 controls. 18-Fluorine deoxyglucose positron emission tomography (FCSRT) was performed in 77 patients. Assessment of metabolite levels by MRS and volumetric parameters by magnetic resonance morphometry was done in 92 patients.
Results And Conclusion:
The «free and cued selective reminding test» and «trail making test» showed the best diagnostic value in the early differential diagnosis of amnesic and neurodynamic variants of MCI. A decrease in beta-amyloid-42 protein and an increase in tau-protein in CSF are early markers of neurodegenerative dementia as well as of the additional involvement of the neurodegeneration in cerebrovascular disease. Specific areas of glucose hypometabolism on preclinical stages of dementia were identified using FCSRT. Spectroscopy and morphometry based on magnetic resonance can predict neurodegeneration in the cingular cortex, frontal and temporal brain.
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