AIMC Topic: Alzheimer Disease
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Operationalizing postmortem pathology-MRI association studies in Alzheimer's disease and related disorders with MRI-guided histology sampling.
Acta neuropathologica communications
May 28, 2025
Postmortem neuropathological examination, while the gold standard for diagnosing neurodegenerative diseases, often relies on limited regional sampling that may miss critical areas affected by Alzheimer's disease and related disorders. Ultra-high reso...
Radiomic and proteomic signatures of body mass index on brain ageing and Alzheimer's-like patterns of brain atrophy.
EBioMedicine
May 27, 2025
BACKGROUND: The impact of high body mass index (BMI) states and associated proteomic factors on brain ageing and Alzheimer's disease (AD) remains unclear.
Identification of shared mechanisms between Alzheimer's disease and atherosclerosis by integrated bioinformatics analysis.
European journal of medical research
May 22, 2025
Alzheimer's disease (AD) and atherosclerosis (AS) are two interacting diseases mostly affecting aged adults. AD is characterized by the deposition of neuritic plaques mainly consisting of Aβ, and AS is defined by the formation of atheromatous plaque ...
Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer's disease.
Scientific reports
May 17, 2025
The recent approval of anti-amyloid pharmaceuticals for the treatment of Alzheimer's disease (AD) has created a pressing need for the ability to accurately identify optimal candidates for anti-amyloid therapy, specifically those with evidence for inc...
Early detection of Alzheimer's disease progression stages using hybrid of CNN and transformer encoder models.
Scientific reports
May 14, 2025
Alzheimer's disease (AD) is a neurodegenerative disorder that affects memory and cognitive functions. Manual diagnosis is prone to human error, often leading to misdiagnosis or delayed detection. MRI techniques help visualize the fine tissues of the ...
EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models.
Scientific reports
May 7, 2025
In the context of lifestyle changes, stress and other environmental factors have resulted in the sudden hike in dementia globally. This necessitates investigations with respect to every horizon of the due cause for it; further on, the diagnosis and t...
Enhancing Neurodegenerative Disease Diagnosis Through Confidence-Driven Dynamic Spatio-Temporal Convolutional Network.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 7, 2025
Dynamic brain networks are more effective than static networks in characterizing the evolving patterns of brain functional connectivity, making them a more promising tool for diagnosing neurodegenerative diseases. However, existing classification met...
Predicting and Evaluating Cognitive Status in Aging Populations Using Decision Tree Models.
American journal of Alzheimer's disease and other dementias
May 5, 2025
To improve the identification of cognitive impairment by distinguishing normal cognition (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD). A recursive partitioning tree model was developed using ARMADA data and the NIH Toolbox, a...
Classification of Neuropsychiatric Disorders via Brain-Region-Selected Graph Convolutional Network.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 5, 2025
For the classification of patients with neuropsychiatric disorders based on rs-fMRI data, this paper proposed a Brain-Region-Selected graph convolutional network (BRS-GCN). In order to effectively identify the most significant biomarkers associated w...
Ensemble Learning-Based Alzheimer's Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images.
Sensors (Basel, Switzerland)
May 2, 2025
Ensemble learning (EL), a machine learning technique that combines the results of multiple learning algorithms to obtain predicted values, aims to achieve better predictive performance than a single learning algorithm alone. Machine learning techniqu...
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