Bhismadev Chakrabarti

The paper investigates associations between gut microbiota composition, brain and blood GABA levels, and related features in healthy adults.

The paper identifies patterns of neural variability in autism using fMRI across tasks, finding greater global connectivity deviations and similar spatial patterns in autistic individuals compared to controls.

The paper identifies a multimodal brain pattern associated with autism by integrating gray and white matter morphology data and linking it to clinical symptoms.

This study examines brain structural correlates of autistic traits in a mixed sample of adults with and without clinical autism diagnoses, finding grey matter and white matter microstructure differences associated with higher autistic traits.

The paper identifies atypical brain asymmetry in autism as a potential neurophenotype for stratification, showing individualized patterns linked to symptom severity and language delay.

The paper shows that the Hurst exponent from fMRI BOLD signals indexes synaptic excitation-inhibition ratio, revealing sex-specific E:I imbalance in autistic mice medial prefrontal cortex.

This study investigates whether social and non-social autism symptom domains are genetically distinct by analyzing genetic correlations between systemizing and autism traits.

This study compares neural and behavioral measures of placebo analgesia against a real analgesic to estimate the true size of the placebo effect in acute pain.

This study conducts the first genome-wide association study (GWAS) of self-reported empathy, identifying genetic correlations with autism, schizophrenia, anorexia nervosa, and extraversion.

This study examines how perceived closeness and autistic traits affect vocal modulation across three cultural samples, finding cultural differences in the relationship between autism traits and articulation.

The paper performs a genome-wide meta-analysis of cognitive empathy, identifies genetic associations, and finds heritability and correlations with sex, neuropsychiatric conditions, and brain anatomy.

The paper identifies 5 distinct subgroups within autism spectrum conditions using clustering analysis on cognitive datasets, revealing varying levels of mentalizing impairments.