AIMC Topic: Neuropsychological Tests

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Neuropsychological test using machine learning for cognitive impairment screening.

Applied neuropsychology. Adult
OBJECTIVES: Neuropsychological tests (NPTs) are widely used tools to evaluate cognitive functioning. The interpretation of these tests can be time-consuming and requires a specialized clinician. For this reason, we trained machine learning models tha...

Machine learning detects altered spatial navigation features in outdoor behaviour of Alzheimer's disease patients.

Scientific reports
Impairment of navigation is one of the earliest symptoms of Alzheimer's disease (AD), but to date studies have involved proxy tests of navigation rather than studies of real life behaviour. Here we use GPS tracking to measure ecological outdoor behav...

Fine motor impairment in children with epilepsy: Relations with seizure severity and lateralizing value.

Epilepsy & behavior : E&B
Motor skill deficits are common in epilepsy. The Grooved Pegboard Test (GPT) is the most commonly used fine motor task and is included in the NIH Common Data Elements Battery for the assessment of epilepsy. However, there are limited data on its util...

Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques.

Scientific reports
Many studies report predictions for cognitive function but there are few predictions in epileptic patients; therefore, we established a workflow to efficiently predict outcomes of both the Mini-Mental State Examination (MMSE) and Montreal Cognitive A...

A combination of support vector machine and voxel-based morphometry in adult male alcohol use disorder patients with cognitive deficits.

Brain research
Cognitive performance deteriorates with drinking. However, the neural basis of cognitive deficits in alcohol use disorder (AUD) is still incompletely understood. Here we examined the relationship between overall drinking, brain structural alterations...

A Real-Time Clinical Decision Support System, for Mild Cognitive Impairment Detection, Based on a Hybrid Neural Architecture.

Computational and mathematical methods in medicine
Clinical procedure for mild cognitive impairment (MCI) is mainly based on clinical records and short cognitive tests. However, low suspicion and difficulties in understanding test cut-offs make diagnostic accuracy being low, particularly in primary c...

Deep recurrent model for individualized prediction of Alzheimer's disease progression.

NeuroImage
Alzheimer's disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years, with no treatments or available medicines. In this regard, there have been efforts to identify the risk of developin...

A machine learning approach to screen for preclinical Alzheimer's disease.

Neurobiology of aging
Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on F-florbetapir and F-fluorodeox...

Deep Learning Corpus Callosum Segmentation as a Neurodegenerative Marker in Multiple Sclerosis.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
BACKGROUND AND PURPOSE: Corpus callosum atrophy is a sensitive biomarker of multiple sclerosis (MS) neurodegeneration but typically requires manual 2D or volumetric 3D-based segmentations. We developed a supervised machine learning algorithm, DeepnCC...

Robotic technology quantifies novel perceptual-motor impairments in patients with chronic kidney disease.

Journal of nephrology
BACKGROUND: Neurocognitive impairment is commonly reported in patients with chronic kidney disease (CKD). The precise nature of this impairment is unclear, due to the lack of objective and quantitative assessment tools used. The feasibility of using ...