Neda Jahanshad

The paper maps human hippocampal perfusion and microvascular pulsatility across age using MRI and integrates these with histological and transcriptomic data, revealing distinct spatial organization and age-related differences.

This paper evaluates automated white matter lesion segmentation methods in Parkinson's disease and healthy controls, finding U-Net-pgs to be the most accurate and reliable approach across multiple metrics and datasets.

This study uses quantitative susceptibility mapping to analyze choroid plexus calcifications in UK Biobank participants, finding age-related increases and associations with various health conditions.

The paper introduces CCSI, a metric linking cortical blood flow and cellular architecture, showing its correlation with metabolic and structural features across brain regions.

Nipoppy standardizes neuroimaging data workflows to enable reproducible and comparable multi-center data sharing.

FastEBM improves disease progression modeling by using a diffusion-based Markov framework, achieving better accuracy and speed than existing methods in both synthetic and real-world data.

The paper compares three harmonization methods for multisite diffusion MRI data to reduce protocol-related variability while preserving associations with cognitive impairment and PET biomarkers.

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.

The paper develops and evaluates the Regional Vulnerability Index (RVI-ASD) as a neuroimaging biomarker for autism, showing its stability, heritability, and associations with cognition and social outcomes.

This paper introduces an asymmetry-aware tractometry framework to quantify left-right hemispheric asymmetries in white matter tracts and applies it to Alzheimer's Disease data to differentiate cognitive states.

SPECTRA improves white matter microstructure mapping by incorporating spatial resolution and adaptive error control in tractometry analysis.

This study analyzes structural brain network changes in bipolar disorder, finding widespread connectivity disruptions linked to illness severity and medication use.

This study evaluates how sample size and resolution affect tractometry's sensitivity and specificity in detecting dementia and amyloid-related effects in Alzheimer's disease.

This study uses diffusion MRI tractometry to identify localized white matter microstructural changes associated with stimulant misuse, finding distinct patterns for amphetamine use compared to cocaine use.

The paper examines how childhood maltreatment affects brain structure in 3,711 individuals with MDD and PTSD, finding age- and sex-specific brain morphometric differences.

The paper examines how increasing training sample size improves the calibration of diffusion tensor imaging normative models using hierarchical Bayesian regression.

This study investigates brain age differences in youth with conduct disorder compared to controls, finding accelerated brain maturation in those with adolescence-onset CD.

This paper introduces SDNorm, a method to reduce variability in diffusion MRI streamline reconstructions, improving reproducibility across different acquisition protocols.

This study uses machine learning to show that integrating brain connectome data with amyloid-beta PET imaging improves prediction of cognitive decline in older adults.

This study uses BUAN tractometry to assess how amyloid positivity affects white matter tracts in Alzheimer's disease, revealing localized microstructural changes associated with amyloid pathology.

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.

The paper compares DTI and NODDI models using dMRI data from ADNI participants to assess their ability to detect AD-related biomarkers like cognitive impairment and amyloid-beta/tau pathology.

The paper uses GWAS to identify genetic variants associated with corpus callosum morphometry and links them to cortical structure and neuropsychiatric disorders.

IBMMA improves neuroimaging analysis by handling missing data and large datasets, revealing new brain region findings overlooked by traditional methods.

This paper meta-analyzes longitudinal diffusion MRI studies to show how white matter fractional anisotropy changes with age and identifies genetic influences on these changes.

The paper examines thalamic volume differences in trauma-exposed individuals with PTSD, MDD, and controls, finding specific volume reductions and associations with symptom severity and childhood trauma.

This study identifies lifestyle, early-life, and genetic factors associated with brain age gaps in individuals with major depressive disorder compared to controls.

The paper creates lifespan reference curves for white matter microstructure using diffusion MRI data from multiple datasets and validates them with ApoE4 risk factor analysis.

This paper applies a spherical harmonics-based deep learning model to MRI-derived brain shape metrics to predict age, sex, and Alzheimer's disease diagnosis, showing strong performance in classification and regression tasks.