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Neuropsychological Tests

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Accelerated Aging of the Amygdala in Alcohol Use Disorders: Relevance to the Dark Side of Addiction.

Cerebral cortex (New York, N.Y. : 1991)
Here we assessed changes in subcortical volumes in alcohol use disorder (AUD). A simple morphometry-based classifier (MC) was developed to identify subcortical volumes that distinguished 32 healthy controls (HCs) from 33 AUD patients, who were scanne...

Identification of the Neural Circuit Underlying Episodic Memory Deficit in Amnestic Mild Cognitive Impairment via Machine Learning on Gray Matter Volume.

Journal of Alzheimer's disease : JAD
Based on whole-brain gray matter volume (GMV), we used relevance vector regression to predict the Rey's Auditory Verbal Learning Test Delayed Recall (AVLT-DR) scores of individual amnestic mild cognitive impairment (aMCI) patient. The whole-brain GMV...

An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the Clock Drawing Test.

Journal of Alzheimer's disease : JAD
BACKGROUND: Widespread dementia detection could increase clinical trial candidates and enable appropriate interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia-related disorders, it can be leveraged to deve...

Short-Term Memory Binding Distinguishing Amnestic Mild Cognitive Impairment from Healthy Aging: A Machine Learning Study.

Journal of Alzheimer's disease : JAD
BACKGROUND: Amnestic mild cognitive impairment (aMCI) is the most common preclinical stage of Alzheimer's disease (AD). A strategy to reduce the impact of AD is the early aMCI diagnosis and clinical intervention. Neuroimaging, neurobiological, and ge...

Screening for Early-Stage Alzheimer's Disease Using Optimized Feature Sets and Machine Learning.

Journal of Alzheimer's disease : JAD
BACKGROUND: Detecting early-stage Alzheimer's disease in clinical practice is difficult due to a lack of efficient and easily administered cognitive assessments that are sensitive to very mild impairment, a likely contributor to the high rate of unde...

Deep Learning and Risk Score Classification of Mild Cognitive Impairment and Alzheimer's Disease.

Journal of Alzheimer's disease : JAD
BACKGROUND: Many neurocognitive and neuropsychological tests are used to classify early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD) from cognitive normal (CN). This can make it challenging for...

Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment.

Journal of Alzheimer's disease : JAD
BACKGROUND: Amyloid-β (Aβ) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer's disease. However, Aβ evaluation through Aβ positron emission tomography (PET) is limited due to high cos...

Digitalisation of the Brief Visuospatial Memory Test-Revised and Evaluation with a Machine Learning Algorithm.

Studies in health technology and informatics
The disease multiple sclerosis (MS) is characterized by various neurological symptoms. This paper deals with a novel tool to assess cognitive dysfunction. The Brief Visuospatial Memory Test-Revised (BVMT-R) is a recognized method to measure optical r...

Neuropsychiatric symptoms as predictors of conversion from MCI to dementia: a machine learning approach.

International psychogeriatrics
OBJECTIVES: To use a Machine Learning (ML) approach to compare Neuropsychiatric Symptoms (NPS) in participants of a longitudinal study who developed dementia and those who did not.