Jonathan Chang

Autism Mutations Preferentially Target Distributed Brain Circuits and Cell Types Associated with Sensory Integration and Decision Making

This study identifies brain circuits and cell types affected by ASD mutations using a computational approach integrating genetic data with spatial transcriptomics and connectome data.

Rare mutations implicate CGE interneurons as a vulnerable axis of cognitive deficits across psychiatric disorders

The paper identifies CGE-derived GABAergic interneurons as a key target of rare mutations linked to cognitive deficits in ASD and SCZ using human and mouse models.

Supervised deep machine learning models predict forelimb movement from excitatory neuronal ensembles and suggest distinct pattern of activity in CFA and RFA networks

The paper uses supervised deep learning models to decode forelimb movement from neuronal ensembles in CFA and RFA, finding distinct activity patterns between the two areas.

Exons as units of phenotypic impact for truncating mutations in autism

The paper shows that truncating mutations affecting the same exon in autism lead to similar intellectual phenotypes, suggesting exons, not genes, are units of phenotypic impact.

Non-driver somatic alteration burden confers good prognosis in non-small cell lung cancer

This study shows that high non-driver somatic alteration burden in NSCLC is linked to better prognosis, including improved survival and immune response.

Crowdsourced geometric morphometrics enable rapid large-scale collection and analysis of phenotypic data

This study introduces a crowdsourced method for collecting morphometric data and shows it performs as well as expert morphologists on ray-finned fish families.