Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning
CJ Mellema, KP Nguyen, A Treacher, A Montillo - Scientific reports, 2022 - nature.com
Autism spectrum disorder (ASD) is the fourth most common neurodevelopmental disorder,
with a prevalence of 1 in 160 children. Accurate diagnosis relies on experts, but such …
with a prevalence of 1 in 160 children. Accurate diagnosis relies on experts, but such …
Mapping the heterogeneous brain structural phenotype of autism spectrum disorder using the normative model
Background Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder
characterized by substantial clinical and biological heterogeneity. Quantitative and …
characterized by substantial clinical and biological heterogeneity. Quantitative and …
Functional connectivities are more informative than anatomical variables in diagnostic classification of autism
Abstract Machine learning techniques have been implemented to reveal brain features that
distinguish people with autism spectrum disorders (ASDs) from typically developing (TD) …
distinguish people with autism spectrum disorders (ASDs) from typically developing (TD) …
Predicting autism spectrum disorder using domain-adaptive cross-site evaluation
R Bhaumik, A Pradhan, S Das, DK Bhaumik - Neuroinformatics, 2018 - Springer
The advances in neuroimaging methods reveal that resting-state functional fMRI (rs-fMRI)
connectivity measures can be potential diagnostic biomarkers for autism spectrum disorder …
connectivity measures can be potential diagnostic biomarkers for autism spectrum disorder …
Predictive models for subtypes of autism spectrum disorder based on single-nucleotide polymorphisms and magnetic resonance imaging
Y Jiao, R Chen, X Ke, L Cheng, K Chu, Z Lu… - Advances in medical …, 2011 - Elsevier
Purpose Autism spectrum disorder (ASD) is a neurodevelopmental disorder, of which
Asperger syndrome and high-functioning autism are subtypes. Our goal is: 1) to determine …
Asperger syndrome and high-functioning autism are subtypes. Our goal is: 1) to determine …
Diagnosis of autism spectrum disorders using regional and interregional morphological features
This article describes a novel approach to identify autism spectrum disorder (ASD) utilizing
regional and interregional morphological patterns extracted from structural magnetic …
regional and interregional morphological patterns extracted from structural magnetic …
The triple network model, insight, and large-scale brain organization in autism
V Menon - Biological psychiatry, 2018 - biologicalpsychiatryjournal.com
Autism is characterized by significant heterogeneity in the degree of social and emotion
impairments. Addressing sources of variability in the expression of clinical symptoms …
impairments. Addressing sources of variability in the expression of clinical symptoms …
[HTML][HTML] Identification of neural connectivity signatures of autism using machine learning
G Deshpande, LE Libero, KR Sreenivasan… - Frontiers in human …, 2013 - frontiersin.org
Alterations in interregional neural connectivity have been suggested as a signature of the
pathobiology of autism. There have been many reports of functional and anatomical …
pathobiology of autism. There have been many reports of functional and anatomical …
A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework
Abstract Autism Spectrum Disorder (ASD) is characterized as a neurodevelopmental
disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical …
disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical …
Crowdsourced validation of a machine-learning classification system for autism and ADHD
Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) together
affect> 10% of the children in the United States, but considerable behavioral overlaps …
affect> 10% of the children in the United States, but considerable behavioral overlaps …