Clifford R. Jack

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.

The paper develops an automated method combining MRI contrasts to improve tissue segmentation and perivascular space detection using data from aging and Alzheimer's studies.

The paper identifies that diffusion MRI biomarkers show microstructural damage up to 16 years before white matter hyperintensities in cerebral small vessel disease.

The paper introduces dGAMLSS, a distributed algorithm for fitting GAMLSS models to construct population reference charts without sharing patient data.

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.

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 investigates whether hemispheric asymmetry in tau pathology in Alzheimer's disease is linked to reduced inter-hemispheric connectivity or asymmetric amyloid-beta distribution, finding a strong association with amyloid-beta laterality.

This study identifies two ASD subgroups with distinct brain morphology using cross-cultural datasets, highlighting key brain regions associated with subgroup characteristics and autistic traits.

The paper shows longer scans improve prediction accuracy in brain-wide association studies while reducing costs, with 30-minute scans being the most cost-effective.

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.

The paper conducts GWAS on brain volumes, identifies genetic loci, and develops polygenic scores with cross-ancestry predictive ability and causal links to neuropsychiatric disorders.

This study identifies 67 genetic loci associated with head size, finding strong overlaps with cancer pathways and genes, suggesting shared genetic mechanisms between head growth and cancer risk.

This paper evaluates the polygenic risk score for Alzheimer's disease in Japanese individuals, showing its utility despite genetic differences from European populations.

This study shows hub disruption in ADAD follows a targeted attack pattern, detectable pre-clinically and linked to amyloid beta changes and neuronal activity.

The paper quantifies Aβ production from PSEN1 variants and links it to clinical and biomarker progression in ADAD, showing strong associations with disease trajectories.

This study assesses white matter hyperintensity severity using multimodal MRI in Alzheimer's Disease by analyzing signal measures from different MRI contrasts and their associations with clinical variables.

The paper introduces brain charts as a standardized resource for benchmarking brain morphology across the human lifespan using MRI data from a large and diverse cohort.

The paper describes MRI and PET protocols used in the DIAN study to investigate preclinical Alzheimer's disease and reports key imaging findings.

This study uses an epidemic spreading model to show that tau spreads through neuronal connections in Alzheimer's disease, with beta-amyloid accelerating this process in some brain regions.

This study compares whole hippocampus and subfield biomarkers for Alzheimer’s disease detection and prediction using multimodal MRI data, finding subiculum biomarkers improve prediction accuracy.

This study identifies genetic loci specific to periventricular and deep white matter hyperintensities, revealing distinct genetic architectures and potential biological mechanisms.

This paper develops a blood-based machine learning model to predict cerebral spinal fluid Aβ1-42 levels, showing high accuracy in identifying Alzheimer's disease risk.

The paper analyzes diffusion MRI data from ADNI3 to assess how different protocols affect diffusion indices and their association with cognitive impairment and age.

This paper introduces SuStaIn, a machine learning method that identifies disease subtypes and their temporal progression from cross-sectional data, improving prediction of diagnostic conversion in neurodegenerative diseases.

The paper examines the heritability of regional brain volumes using genetic and neuroimaging data from multiple cohorts, finding high heritability and consistent patterns across populations.