AIMC Topic: Cognitive Dysfunction

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Alzheimer's disease classification using a hybrid deep learning approach with multi-layer U-net segmentation and XAI driven analysis.

PloS one
Alzheimer's disease (AD) is a neurodegenerative illness causing a significant decrease in cognitive function, and early, accurate diagnosis is of great therapeutic and diagnostic value. Currently, there is promising potential for applying various typ...

Diabetic retinopathy as the primary predictor of mild cognitive impairment in type 2 diabetes: Insights from machine learning models.

PloS one
Mild cognitive impairment (MCI) is a significant and increasingly recognized problem in individuals with type 2 diabetes mellitus (T2DM). This study aims to develop a machine-learning model to predict MCI in patients with T2DMThe dataset was obtained...

The long-term neuroprotective effect of MIND and Mediterranean diet on patients with Alzheimer's disease.

Scientific reports
Alzheimer's disease is a progressive neurodegenerative disorder with no cure, making preventive strategies crucial. Dietary interventions, particularly the Mediterranean (MeDi) and MIND diets, have been associated with reduced cognitive decline, but ...

A novel hybrid deep learning model for segmentation and uzzy Res-LeNet based classification for Alzheimer's disease.

Neurogenetics
Alzheimer's disease (AD) is a progressive illness that can cause behavioural abnormalities, personality changes, and memory loss. Early detection helps with future planning for both the affected person and caregivers. Thus, an innovative hybrid Deep ...

Cognitive impairment assessment using eye-tracking: multilevel saccade paradigms with differential analysis and attention-based neural networks.

Physiological measurement
. The accurate assessment of cognitive impairment plays a vital role in more targeted treatments for Dementia. Eye movement analysis is a non-invasive and objective method that offers fine-grained insight into cognitive functioning, complementing con...

Handwriting in Mild Cognitive Impairment: Reliability Assessment and Machine Learning-Based Screening.

JMIR aging
BACKGROUND: Mild cognitive impairment (MCI) is a precursor of dementia. Therefore, MCI identification and monitoring are crucial to delaying dementia onset. Given the limits of existing clinical tests, objective support tools are needed.

Linking dynamic connectivity states to cognitive decline and anatomical changes in Alzheimer's disease.

NeuroImage
Alterations in brain connectivity provide early indications of neurodegenerative diseases like Alzheimer's disease (AD). Here, we present a novel framework that integrates a Hidden Markov Model (HMM) within the architecture of a convolutional neural ...

Cognitive prediction using regional connectivities and network biomarkers in Alzheimer's disease.

Neuroscience
Achieving a deep understanding of brain mechanisms requires multi-scale perspectives to capture the architecture of complex networks. In this study, we focused on patients with cognitive impairment and constructed individual brain networks from neuro...

AlzFormer: Video-based space-time attention model for early diagnosis of Alzheimer's disease.

Neuroscience
Early and accurate Alzheimer's disease (AD) diagnosis is critical for effective intervention, but it is still challenging due to neurodegeneration's slow and complex progression. Recent studies in brain imaging analysis have highlighted the crucial r...

Evaluating the Feasibility of a Dyadic, Touch-Based Multimedia Tablet Intervention and Its Effects on the Caregiver-Patient Relationship Among Individuals With Mild Cognitive Impairment: Qualitative Triangulation Study.

JMIR aging
BACKGROUND: Approximately 20% of the global population is affected by mild cognitive impairment (MCI), with around 15% progressing to dementia within 2 years. Touch-based multimedia applications can support cognitive, social, and physical functioning...