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Neuroimaging

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Artificial intelligence and stroke imaging.

Current opinion in neurology
PURPOSE OF REVIEW: Though simple in its fundamental mechanism - a critical disruption of local blood supply - stroke is complicated by the intricate nature of the neural substrate, the neurovascular architecture, and their complex interactions in gen...

A proficient approach for the classification of Alzheimer's disease using a hybridization of machine learning and deep learning.

Scientific reports
Alzheimer's disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the nervous system, affecting the cognitive ability of the human brain. Over the past two decades, neuroimaging data from Magnetic Resonance Imaging (MRI)...

segcsvd: A Convolutional Neural Network-Based Tool for Quantifying White Matter Hyperintensities in Heterogeneous Patient Cohorts.

Human brain mapping
White matter hyperintensities (WMH) of presumed vascular origin are a magnetic resonance imaging (MRI)-based biomarker of cerebral small vessel disease (CSVD). WMH are associated with cognitive decline and increased risk of stroke and dementia, and a...

MCBERT: A multi-modal framework for the diagnosis of autism spectrum disorder.

Biological psychology
Within the domain of neurodevelopmental disorders, autism spectrum disorder (ASD) emerges as a distinctive neurological condition characterized by multifaceted challenges. The delayed identification of ASD poses a considerable hurdle in effectively m...

Fusion of brain imaging genetic data for alzheimer's disease diagnosis and causal factors identification using multi-stream attention mechanisms and graph convolutional networks.

Neural networks : the official journal of the International Neural Network Society
Correctly diagnosing Alzheimer's disease (AD) and identifying pathogenic brain regions and genes play a vital role in understanding the AD and developing effective prevention and treatment strategies. Recent works combine imaging and genetic data, an...

Classifying Alzheimer's Disease Using a Finite Basis Physics Neural Network.

Microscopy research and technique
The disease amyloid plaques, neurofibrillary tangles, synaptic dysfunction, and neuronal death gradually accumulate throughout Alzheimer's disease (AD), resulting in cognitive decline and functional disability. The challenges of dataset quality, inte...

A systematic review of deep learning in MRI-based cerebral vascular occlusion-based brain diseases.

Neuroscience
Neurological disorders, including cerebral vascular occlusions and strokes, present a major global health challenge due to their high mortality rates and long-term disabilities. Early diagnosis, particularly within the first hours, is crucial for pre...

Multimodal multiview bilinear graph convolutional network for mild cognitive impairment diagnosis.

Biomedical physics & engineering express
Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease (AD) and can serve as an important indicator of disease progression. However, many existing methods focus mainly on the image when processing b...

Convolutional Neural Networks for the segmentation of hippocampal structures in postmortem MRI scans.

Journal of neuroscience methods
BACKGROUND: The hippocampus plays a crucial role in memory and is one of the first structures affected by Alzheimer's disease. Postmortem MRI offers a way to quantify the alterations by measuring the atrophy of the inner structures of the hippocampus...

Diagnosis of Alzheimer's disease using FusionNet with improved secretary bird optimization algorithm for optimal MK-SVM based on imaging genetic data.

Cerebral cortex (New York, N.Y. : 1991)
Alzheimer's disease is an irreversible central neurodegenerative disease, and early diagnosis of Alzheimer's disease is beneficial for its prevention and early intervention treatment. In this study, we propose a novel framework, FusionNet-ISBOA-MK-SV...