John P. John

This paper develops a lifespan normative model of brain white matter microstructure using diffusion MRI data from 54,583 individuals, enabling detection of microstructural deviations in diseases like Alzheimer's and 22q11.2 deletion syndrome.

This study compares deterministic and probabilistic tractography methods for the left arcuate fasciculus across four datasets, finding probabilistic methods yield higher inter-subject shape similarity but may obscure subtle pathological variations.

This paper generates synthetic diffusion MRI scalar maps from T1-weighted images using RevGAN, showing they retain microstructural features and perform well in downstream tasks.

This study examines non-duality in advanced meditators using EEG-based intrinsic neural timescales and psychological scales, finding that advanced meditators show reduced distinction between internal and external attention.

This study introduces MINT, a framework that jointly models white matter microstructure and macrostructure to better detect anomalies in MCI and dementia, showing improved sensitivity compared to traditional diffusion metrics.

This study compares explainable AI models for Alzheimer's disease classification using MRI scans, evaluating interpretability methods and generalization across different populations.

This study investigates the neural correlates of equanimity in advanced meditators using EEG data and finds shorter intrinsic neural timescales correlate with higher equanimity.

This paper introduces DNT, a variational autoencoder framework for detecting white matter abnormalities in Alzheimer's disease using macro- and microstructural data.

This paper evaluates CNN models for brain age prediction and dementia classification using diffusion MRI, showing improved performance after dataset harmonization across Indian and North American cohorts.

BundleCleaner denoises and subsamples diffusion MRI tractography data, improving alignment with atlas bundles and enabling efficient tractometry analysis in Alzheimer's disease studies.

This study uses whole exome sequencing in families with multiple affected individuals to identify rare variants in genes associated with both Mendelian and complex neuropsychiatric syndromes.

This study determines the minimum sample size needed for multisite harmonization using structural brain MRI features and shows how sample size requirements increase with site variability.

The paper analyzes whole exome sequencing data from individuals with severe mental illness to identify genes under positive selection and their biological functions.

The paper describes MRI acquisition, quality assurance, and quality check protocols for a longitudinal study on brain disorders using stem cells, showing consistent and reliable data acquisition.

The paper examines the relationship between adverse childhood experiences (ACEs) and the age of onset of psychiatric diagnoses in individuals from families with multiple affected members.

This study investigates brain dynamics in schizophrenia using ERP and fMRI, finding reduced EEG dynamics and altered functional connectivity in specific brain networks during gestalt-perception and corollary-discharge tasks.

The paper develops a database of coding variations from the Indian population to aid in understanding genotype-phenotype correlations and disease mechanisms.