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Updated: Feb 23, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Neurocognitive and Behavioral Indexes for Identifying the Amnestic Subtypes of Mild Cognitive Impairment
Susana Cid-Fernández1, Mónica Lindín1, Fernando Díaz1
1Laboratorio de Neurociencia Cognitiva, Facultade de Psicoloxía, Departamento de Psicoloxía Clínica e Psicobioloxía, Universidade de Santiago de Compostela, Galiza, Spain.
Abstract:
Early identification of amnestic mild cognitive impairment (aMCI) subtypes is important for early diagnosis and prognosis of Alzheimer's disease. Healthy, single-domain (sdaMCI) and multiple-domain aMCI (mdaMCI) participants performed an auditory-visual distraction-attention task. Event-related brain potentials (ERPs) were recorded while the participants performed the task to evaluate Go/NoGo N2 and P3 ERP components. The results showed the expected behavioral and cognitive decline in mdaMCI participants relative to controls (fewer hits, longer reaction times [RTs], slightly smaller Go-N2 and NoGo-N2 amplitudes), while sdaMCI participants showed some decline (slightly longer RTs, smaller Go- and NoGo-N2 amplitudes) along with some unexpected results (a late positive slow wave, PSW) and good levels of execution. In addition, some of these parameters proved to be useful markers. Thus, the number of hits was the best marker for diagnosing mdaMCI participants (distinguishing them from controls, from sdaMCI participants, and from both groups together), while the PSW amplitude was the best marker for diagnosing sdaMCI participants (distinguishing them from controls, and from control & mdaMCI participants).
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