Deep learning identifies robust gender differences in functional brain organization and their dissociable links to clinical symptoms in autism

K Supekar, C de Los Angeles, S Ryali…�- The British Journal of�…, 2022 - cambridge.org
Background Autism spectrum disorder (ASD) is a highly heterogeneous disorder that affects
nearly 1 in 189 females and 1 in 42 males. However, the neurobiological basis of gender�…

[HTML][HTML] Network-specific sex differentiation of intrinsic brain function in males with autism

DL Floris, MC Lai, T Nath, MP Milham, A Di Martino�- Molecular autism, 2018 - Springer
Background The male predominance in the prevalence of autism spectrum disorder (ASD)
has motivated research on sex differentiation in ASD. Multiple sources of evidence have�…

[HTML][HTML] Sex differences in cortical volume and gyrification in autism

M Schaer, J Kochalka, A Padmanabhan, K Supekar…�- Molecular Autism, 2015 - Springer
Background Male predominance is a prominent feature of autism spectrum disorders (ASD),
with a reported male to female ratio of 4: 1. Because of the overwhelming focus on males�…

Robust, generalizable, and interpretable artificial intelligence–derived brain fingerprints of autism and social communication symptom severity

K Supekar, S Ryali, R Yuan, D Kumar…�- Biological�…, 2022 - Elsevier
Background Autism spectrum disorder (ASD) is among the most pervasive
neurodevelopmental disorders, yet the neurobiology of ASD is still poorly understood�…

Exploring the structural and strategic bases of autism spectrum disorders with deep learning

F Ke, S Choi, YH Kang, KA Cheon, SW Lee�- Ieee Access, 2020 - ieeexplore.ieee.org
Deep learning models are applied in clinical research in order to diagnose disease.
However, diagnosing autism spectrum disorders (ASD) remains challenging due to its�…

[HTML][HTML] Large-scale brain functional network integration for discrimination of autism using a 3-D deep learning model

M Yang, M Cao, Y Chen, Y Chen, G Fan…�- Frontiers in human�…, 2021 - frontiersin.org
Goal Brain functional networks (BFNs) constructed using resting-state functional magnetic
resonance imaging (fMRI) have proven to be an effective way to understand aberrant�…

Machine learning methods for brain network classification: Application to autism diagnosis using cortical morphological networks

I Bilgen, G Guvercin, I Rekik�- Journal of neuroscience methods, 2020 - Elsevier
Background Autism spectrum disorder (ASD) affects the brain connectivity at different levels.
Nonetheless, non-invasively distinguishing such effects using magnetic resonance imaging�…

[HTML][HTML] Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards

M Plitt, KA Barnes, A Martin�- NeuroImage: Clinical, 2015 - Elsevier
Objectives Autism spectrum disorders (ASD) are diagnosed based on early-manifesting
clinical symptoms, including markedly impaired social communication. We assessed the�…

Contrastive machine learning reveals the structure of neuroanatomical variation within autism

A Aglinskas, JK Hartshorne, S Anzellotti�- Science, 2022 - science.org
Autism spectrum disorder (ASD) is highly heterogeneous. Identifying systematic individual
differences in neuroanatomy could inform diagnosis and personalized interventions. The�…

The link between autism and sex-related neuroanatomy, and associated cognition and gene expression

DL Floris, H Peng, V Warrier…�- American Journal of�…, 2023 - Am Psychiatric Assoc
Objective: The male preponderance in prevalence of autism is among the most pronounced
sex ratios across neurodevelopmental conditions. The authors sought to elucidate the�…