rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis
Abstract Autism Spectrum Disorder (ASD) diagnosis is still based on behavioral criteria
through a lengthy and time-consuming process. Much effort is being made to identify brain …
through a lengthy and time-consuming process. Much effort is being made to identify brain …
Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed
Collaborative networks and data sharing initiatives are broadening the opportunities for the
advancement of science. These initiatives offer greater transparency in science, with the …
advancement of science. These initiatives offer greater transparency in science, with the …
Contrastive machine learning reveals the structure of neuroanatomical variation within autism
Autism spectrum disorder (ASD) is highly heterogeneous. Identifying systematic individual
differences in neuroanatomy could inform diagnosis and personalized interventions. The …
differences in neuroanatomy could inform diagnosis and personalized interventions. The …
An open MRI dataset for multiscale neuroscience
Multimodal neuroimaging grants a powerful window into the structure and function of the
human brain at multiple scales. Recent methodological and conceptual advances have …
human brain at multiple scales. Recent methodological and conceptual advances have …
Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects
approximately 1% of the population and causes significant burdens. ASD's pathogenesis …
approximately 1% of the population and causes significant burdens. ASD's pathogenesis …
Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging
Brain imaging research enjoys increasing adoption of supervised machine learning for
single-participant disease classification. Yet, the success of these algorithms likely depends …
single-participant disease classification. Yet, the success of these algorithms likely depends …
Insights from an autism imaging biomarker challenge: promises and threats to biomarker discovery
MRI has been extensively used to identify anatomical and functional differences in Autism
Spectrum Disorder (ASD). Yet, many of these findings have proven difficult to replicate …
Spectrum Disorder (ASD). Yet, many of these findings have proven difficult to replicate …
Alterations in connectome dynamics in autism spectrum disorder: a harmonized mega-and meta-analysis study using the autism brain imaging data exchange dataset
Background Neuroimaging studies have reported functional connectome aberrancies in
autism spectrum disorder (ASD). However, the time-varying patterns of connectome …
autism spectrum disorder (ASD). However, the time-varying patterns of connectome …
[HTML][HTML] CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation
Quantifying the volume of the cerebellum and its lobes is of profound interest in various
neurodegenerative and acquired diseases. Especially for the most common spinocerebellar …
neurodegenerative and acquired diseases. Especially for the most common spinocerebellar …
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 …