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Cognitive Dysfunction

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Zoom-In Neural Network Deep-Learning Model for Alzheimer's Disease Assessments.

Sensors (Basel, Switzerland)
Deep neural networks have been successfully applied to generate predictive patterns from medical and diagnostic data. This paper presents an approach for assessing persons with Alzheimer's disease (AD) mild cognitive impairment (MCI), compared with n...

Predicting cognitive impairment in chronic kidney disease patients using structural and functional brain network: An application study of artificial intelligence.

Progress in neuro-psychopharmacology & biological psychiatry
OBJECTIVE: To develop and validate artificial intelligence models for the prediction of cognitive impairment in chronic kidney disease (CKD) patients using structural and functional brain network.

Multi-stage classification of Alzheimer's disease from F-FDG-PET images using deep learning techniques.

Physical and engineering sciences in medicine
The study aims to implement a convolutional neural network framework that uses the 18F-FDG PET modality of brain imaging to detect multiple stages of dementia, including Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI)...

Deep learning signature of brain [F]FDG PET associated with cognitive outcome of rapid eye movement sleep behavior disorder.

Scientific reports
An objective biomarker to predict the outcome of isolated rapid eye movement sleep behavior disorder (iRBD) is crucial for the management. This study aimed to investigate cognitive signature of brain [F]FDG PET based on deep learning (DL) for evaluat...

Early diagnosis of Alzheimer's disease and mild cognitive impairment based on electroencephalography: From the perspective of event related potentials and deep learning.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology
Alzheimer's disease (AD), a neurodegenerative disorder characterized by progressive cognitive decline, is generally prevalent in elderly people with significant disability and mortality. There is no effective treatment for AD currently, but the early...

Generalizable deep learning model for early Alzheimer's disease detection from structural MRIs.

Scientific reports
Early diagnosis of Alzheimer's disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based on 3D deep convolutional neural networks to accurately differentiate mild Alzheimer's disease demen...

Interpretable deep learning of myelin histopathology in age-related cognitive impairment.

Acta neuropathologica communications
Age-related cognitive impairment is multifactorial, with numerous underlying and frequently co-morbid pathological correlates. Amyloid beta (Aβ) plays a major role in Alzheimer's type age-related cognitive impairment, in addition to other etiopatholo...

Predicting conversion to Alzheimer's disease in individuals with Mild Cognitive Impairment using clinically transferable features.

Scientific reports
Patients with Mild Cognitive Impairment (MCI) have an increased risk of Alzheimer's disease (AD). Early identification of underlying neurodegenerative processes is essential to provide treatment before the disease is well established in the brain. He...

Mapping the association between tau-PET and Aβ-amyloid-PET using deep learning.

Scientific reports
In Alzheimer's disease, the molecular pathogenesis of the extracellular Aβ-amyloid (Aβ) instigation of intracellular tau accumulation is poorly understood. We employed a high-resolution PET scanner, with low detection thresholds, to examine the Aβ-ta...

Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia.