Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review
Abstract Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective
rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) …
rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) …
[HTML][HTML] Open and reproducible neuroimaging: From study inception to publication
Empirical observations of how labs conduct research indicate that the adoption rate of open
practices for transparent, reproducible, and collaborative science remains in its infancy. This …
practices for transparent, reproducible, and collaborative science remains in its infancy. This …
Four distinct trajectories of tau deposition identified in Alzheimer's disease
Alzheimer's disease (AD) is characterized by the spread of tau pathology throughout the
cerebral cortex. This spreading pattern was thought to be fairly consistent across individuals …
cerebral cortex. This spreading pattern was thought to be fairly consistent across individuals …
Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer's disease
Graphs are widely used as a natural framework that captures interactions between
individual elements represented as nodes in a graph. In medical applications, specifically …
individual elements represented as nodes in a graph. In medical applications, specifically …
[HTML][HTML] BrainStat: A toolbox for brain-wide statistics and multimodal feature associations
Abstract Analysis and interpretation of neuroimaging datasets has become a
multidisciplinary endeavor, relying not only on statistical methods, but increasingly on …
multidisciplinary endeavor, relying not only on statistical methods, but increasingly on …
Classification of brain disorders in rs-fMRI via local-to-global graph neural networks
H Zhang, R Song, L Wang, L Zhang… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Recently, functional brain network has been used for the classification of brain disorders,
such as Autism Spectrum Disorder (ASD) and Alzheimer's disease (AD). Existing methods …
such as Autism Spectrum Disorder (ASD) and Alzheimer's disease (AD). Existing methods …
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
Abstract Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the promise
to reveal functional biomarkers of neuropsychiatric disorders. However, extracting such …
to reveal functional biomarkers of neuropsychiatric disorders. However, extracting such …
Benchmarking functional connectome-based predictive models for resting-state fMRI
Functional connectomes reveal biomarkers of individual psychological or clinical traits.
However, there is great variability in the analytic pipelines typically used to derive them from …
However, there is great variability in the analytic pipelines typically used to derive them from …
Metric learning with spectral graph convolutions on brain connectivity networks
Graph representations are often used to model structured data at an individual or population
level and have numerous applications in pattern recognition problems. In the field of …
level and have numerous applications in pattern recognition problems. In the field of …
[HTML][HTML] Optimising network modelling methods for fMRI
A major goal of neuroimaging studies is to develop predictive models to analyze the
relationship between whole brain functional connectivity patterns and behavioural traits …
relationship between whole brain functional connectivity patterns and behavioural traits …