[HTML][HTML] Improving the detection of autism spectrum disorder by combining structural and functional MRI information
Abstract Autism Spectrum Disorder (ASD) is a brain disorder that is typically characterized
by deficits in social communication and interaction, as well as restrictive and repetitive …
by deficits in social communication and interaction, as well as restrictive and repetitive …
BolT: Fused window transformers for fMRI time series analysis
Deep-learning models have enabled performance leaps in analysis of high-dimensional
functional MRI (fMRI) data. Yet, many previous methods are suboptimally sensitive for …
functional MRI (fMRI) data. Yet, many previous methods are suboptimally sensitive for …
Adversarial learning based node-edge graph attention networks for autism spectrum disorder identification
Graph neural networks (GNNs) have received increasing interest in the medical imaging
field given their powerful graph embedding ability to characterize the non-Euclidean …
field given their powerful graph embedding ability to characterize the non-Euclidean …
Enhancing diagnosis of autism with optimized machine learning models and personal characteristic data
Autism spectrum disorder (ASD) is a developmental disorder, affecting about 1% of the
global population. Currently, the only clinical method for diagnosing ASD are standardized …
global population. Currently, the only clinical method for diagnosing ASD are standardized …
Explainable deep learning for personalized age prediction with brain morphology
Predicting brain age has become one of the most attractive challenges in computational
neuroscience due to the role of the predicted age as an effective biomarker for different brain …
neuroscience due to the role of the predicted age as an effective biomarker for different brain …
Topological properties of resting-state fMRI functional networks improve machine learning-based autism classification
A Kazeminejad, RC Sotero - Frontiers in neuroscience, 2019 - frontiersin.org
Automatic algorithms for disease diagnosis are being thoroughly researched for use in
clinical settings. They usually rely on pre-identified biomarkers to highlight the existence of …
clinical settings. They usually rely on pre-identified biomarkers to highlight the existence of …
Classifying ASD based on time-series fMRI using spatial–temporal transformer
As the prevalence of autism spectrum disorder (ASD) increases globally, more and more
patients need to receive timely diagnosis and treatment to alleviate their suffering. However …
patients need to receive timely diagnosis and treatment to alleviate their suffering. However …
Diagnostic prediction of autism spectrum disorder using complex network measures in a machine learning framework
Objective imaging-based biomarker discovery for psychiatric conditions is critical for
accurate diagnosis and treatment. Using a machine learning framework, this work …
accurate diagnosis and treatment. Using a machine learning framework, this work …
TE-HI-GCN: An ensemble of transfer hierarchical graph convolutional networks for disorder diagnosis
Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life
for patients and potentially supports the development of new treatments. Graph …
for patients and potentially supports the development of new treatments. Graph …
Controlling for effects of confounding variables on machine learning predictions
Machine learning predictive models are being used in neuroimaging to predict information
about the task or stimuli or to identify potentially clinically useful biomarkers. However, the …
about the task or stimuli or to identify potentially clinically useful biomarkers. However, the …