AIMC Topic: Neuropsychological Tests

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Hidden Markov modeling of frequency-following responses to Mandarin lexical tones.

Journal of neuroscience methods
BACKGROUND: The frequency-following response (FFR) is a scalp-recorded electrophysiological potential reflecting phase-locked activity from neural ensembles in the auditory system. The FFR is often used to assess the robustness of subcortical pitch p...

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows.

BMC medical informatics and decision making
BACKGROUND: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patient...

Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children.

Environmental pollution (Barking, Essex : 1987)
Data-driven machine learning methods present an opportunity to simultaneously assess the impact of multiple air pollutants on health outcomes. The goal of this study was to apply a two-stage, data-driven approach to identify associations between air ...

Diagnosis of Dementia by Machine learning methods in Epidemiological studies: a pilot exploratory study from south India.

Social psychiatry and psychiatric epidemiology
BACKGROUND: There are limited data on the use of artificial intelligence methods for the diagnosis of dementia in epidemiological studies in low- and middle-income country (LMIC) settings. A culture and education fair battery of cognitive tests was d...

Ordinal convolutional neural networks for predicting RDoC positive valence psychiatric symptom severity scores.

Journal of biomedical informatics
BACKGROUND: The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) provided a set of 1000 neuropsychiatric notes to participants as part of a competition to predict psychiatric symptom severity scores. This paper summarizes ou...

Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data.

BMC medical informatics and decision making
BACKGROUND: Feature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classification, yet little is known about how feature selection methods perform in diffusion tensor images (...

Discriminating cognitive status in Parkinson's disease through functional connectomics and machine learning.

Scientific reports
There is growing interest in the potential of neuroimaging to help develop non-invasive biomarkers in neurodegenerative diseases. In this study, connection-wise patterns of functional connectivity were used to distinguish Parkinson's disease patients...

Dynamic neural architecture for social knowledge retrieval.

Proceedings of the National Academy of Sciences of the United States of America
Social behavior is often shaped by the rich storehouse of biographical information that we hold for other people. In our daily life, we rapidly and flexibly retrieve a host of biographical details about individuals in our social network, which often ...

Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study.

Behavioural neurology
Subjects with Alzheimer's disease (AD) show loss of cognitive functions and change in behavioral and functional state affecting the quality of their daily life and that of their families and caregivers. A neuropsychological assessment plays a crucial...