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Cognitive Dysfunction

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Deep Learning for Alzheimer's Disease Classification using Texture Features.

Current medical imaging reviews
BACKGROUND: We propose a classification method for Alzheimer's disease (AD) based on the texture of the hippocampus, which is the organ that is most affected by the onset of AD.

Alzheimer's Disease Classification Based on Multi-feature Fusion.

Current medical imaging reviews
BACKGROUND: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer's Disease (AD).

Optimizing Machine Learning Methods to Improve Predictive Models of Alzheimer's Disease.

Journal of Alzheimer's disease : JAD
BACKGROUND: Predicting clinical course of cognitive decline can boost clinical trials' power and improve our clinical decision-making. Machine learning (ML) algorithms are specifically designed for the purpose of prediction; however. identifying opti...

Episodic-Memory Performance in Machine Learning Modeling for Predicting Cognitive Health Status Classification.

Journal of Alzheimer's disease : JAD
BACKGROUND: Memory dysfunction is characteristic of aging and often attributed to Alzheimer's disease (AD). An easily administered tool for preliminary assessment of memory function and early AD detection would be integral in improving patient manage...

Automated Rating of Multiple Sclerosis Test Results Using a Convolutional Neural Network.

Studies in health technology and informatics
This work concerns methods for automated rating of the progression of Multiple Sclerosis (MS). Often, MS patients develop cognitive deficits. The Brief Visuospatial Memory Test-Revised (BVMT-R) is a recognized method to measure optical recognition de...

Multivariate Pattern Analysis of Genotype-Phenotype Relationships in Schizophrenia.

Schizophrenia bulletin
Genetic risk variants for schizophrenia have been linked to many related clinical and biological phenotypes with the hopes of delineating how individual variation across thousands of variants corresponds to the clinical and etiologic heterogeneity wi...

Isometry Invariant Shape Descriptors for Abnormality Detection on Brain Surfaces Affected by Alzheimer's Disease.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Alzheimer's disease (AD), a progressive brain disorder, is the most common neurodegenerative disease in older adults. There is a need for brain structural magnetic resonance imaging (MRI) biomarkers to help assess AD progression and intervention effe...

Detection of Early Stage Alzheimer's Disease using EEG Relative Power with Deep Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Electroencephalogram (EEG) signal based early diagnosis of Alzheimer's Disease (AD), especially a discrimination between healthy control (HC) and mild cognitive impairment (MCI) has received remarkable attentions to complement conventional diagnosing...

Multi-task fused sparse learning for mild cognitive impairment identification.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Brain functional connectivity network (BFCN) has been widely applied to identify biomarkers for the brain function understanding and brain diseases analysis.