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[HTML][HTML] Application of deep learning for prediction of Alzheimer's disease in PET/MR imaging
Y Zhao, Q Guo, Y Zhang, J Zheng, Y Yang, X Du… - Bioengineering, 2023 - mdpi.com
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of
people worldwide. Positron emission tomography/magnetic resonance (PET/MR) imaging is …
people worldwide. Positron emission tomography/magnetic resonance (PET/MR) imaging is …
[HTML][HTML] Machine learning and graph signal processing applied to healthcare: A review
Signal processing is a very useful field of study in the interpretation of signals in many
everyday applications. In the case of applications with time-varying signals, one possibility is …
everyday applications. In the case of applications with time-varying signals, one possibility is …
Multipattern graph convolutional network-based autism spectrum disorder identification
The early diagnosis of autism spectrum disorder (ASD) has been extensively facilitated
through the utilization of resting-state fMRI (rs-fMRI). With rs-fMRI, the functional brain …
through the utilization of resting-state fMRI (rs-fMRI). With rs-fMRI, the functional brain …
Preserving specificity in federated graph learning for fMRI-based neurological disorder identification
Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive
approach to examining abnormal brain connectivity associated with brain disorders. Graph …
approach to examining abnormal brain connectivity associated with brain disorders. Graph …
A plug-in graph neural network to boost temporal sensitivity in fmri analysis
Learning-based methods offer performance leaps over traditional methods in classification
analysis of high-dimensional functional MRI (fMRI) data. In this domain, deep-learning …
analysis of high-dimensional functional MRI (fMRI) data. In this domain, deep-learning …
[HTML][HTML] Decoding visual fMRI stimuli from human brain based on graph convolutional neural network
Brain decoding is to predict the external stimulus information from the collected brain
response activities, and visual information is one of the most important sources of external …
response activities, and visual information is one of the most important sources of external …
High‐accuracy machine learning techniques for functional connectome fingerprinting and cognitive state decoding
The human brain is a complex network comprised of functionally and anatomically
interconnected brain regions. A growing number of studies have suggested that empirical …
interconnected brain regions. A growing number of studies have suggested that empirical …
Utilizing graph convolutional networks for identification of mild cognitive impairment from single modal fMRI data: a multiconnection pattern combination approach
J He, P Wang, J He, C Sun, X Xu, L Zhang… - Cerebral …, 2024 - academic.oup.com
Mild cognitive impairment plays a crucial role in predicting the early progression of
Alzheimer's disease, and it can be used as an important indicator of the disease …
Alzheimer's disease, and it can be used as an important indicator of the disease …
Explainable deep-learning framework: decoding brain states and prediction of individual performance in false-belief task at early childhood stage
Decoding of cognitive states aims to identify individuals' brain states and brain fingerprints to
predict behavior. Deep learning provides an important platform for analyzing brain signals at …
predict behavior. Deep learning provides an important platform for analyzing brain signals at …
An Adaptively Weighted Averaging Method for Regional Time Series Extraction of fMRI-based Brain Decoding
Brain decoding that classifies cognitive states using the functional fluctuations of the brain
can provide insightful information for understanding the brain mechanisms of cognitive …
can provide insightful information for understanding the brain mechanisms of cognitive …