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Updated: Jan 20, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Association between brain amyloid accumulation and neuropsychological characteristics in elders with depression and
HyunChul Youn1, Suji Lee2, Changsu Han3
1Department of Psychiatry, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
Objectives:
To investigate the presence of cerebral amyloidopathy and its associations with performances on neurocognitive tests and clinical features in depressed elders with mild cognitive impairment (MCI).
Methods/Design:
In total, 94 older adults with concomitant MCI and some depressive symptoms were included in this study. Cerebral amyloidopathy was evaluated using 18 F-florbetaben-positron emission tomography. A standardized neurocognitive test battery and brain magnetic resonance imaging (MRI) were administered to all subjects. We examined the Apolipoprotein E genotype using a polymerase chain reaction-based method.
Results:
Among the 94 initial participants, seven participants were excluded because of failure to undergo MRI or complete the neuropsychological battery. Among 87 subjects, 45 elders (51.7%) had cerebral amyloidopathy and were classified as the concomitant depression and MCI with cerebral amyloid-accumulation-positive (CDAP) group; others were classified as the concomitant depression and MCI with cerebral amyloid-accumulation-negative (CDAN) group. Poorer performances on word list recall and constructional recall were observed in the CDAP group than in the CDAN group.
Conclusions:
The results indicate that around half of older adults with concomitant MCI and some depressive symptoms might be prone to have Alzheimer dementia. Results of neurocognitive tests possibly aid in discerning the presence of cerebral amyloidopathy.
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