John C. Morris

This study evaluates how biomarker variability affects individualized amyloid time estimation in Alzheimer's disease, finding that inter-individual variability is the main source of error.

This study finds that a history of traumatic brain injury with loss of consciousness (TBI-LOC) increases late-life amyloid PET burden, especially in APOE ε4 carriers with no clinical dementia.

The paper investigates cell-to-cell communication in Alzheimer's disease to characterize disease biology and suggest new avenues for therapeutic intervention.

This study uses multimodal normative modeling on ATN biomarkers to analyze Alzheimer Disease heterogeneity, finding greater intra-group variation in more severe stages and a correlation between disease severity index and cognitive decline.

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 maps sleep oscillatory events as biomarkers for early Alzheimer's disease by analyzing EEG and CSF biomarkers in aging adults.

This study finds that preserved aerobic glycolysis in the brain is linked to resilience against Alzheimer's disease pathology, while white matter hyperintensity burden is associated with reduced youthful AG patterns.

This study examines how brain age estimates from different MRI modalities relate to Alzheimer's disease biomarkers and cognition in early stages.

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 integrates multi-omics data from Alzheimer's disease cohorts to identify molecular profiles linked to disease progression and severity.

The paper identifies 617 novel genomic loci and pathways associated with structural covariance patterns in the human brain using a stochastic multivariate factorization method.

This study investigates the association between Klotho heterozygosity and Alzheimer's disease risk in APOE4 carriers using multiple AD endophenotypes from large cohorts.

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 identifies increased circPSEN1 levels in ADAD brains compared to sporadic AD and controls, suggesting a potential role in neuroinflammatory processes.

This study identifies metabolomic and lipidomic signatures in Alzheimer's disease brains with different genetic risk factors and finds associations with disease subtypes and progression.

The paper uses GWAS, PRS, and MR to show a genetic link between Parkinson's disease and CSF Aβ42 and APOE locus.

This paper identifies five new genetic risk loci for Lewy body dementia through whole-genome sequencing and shows shared risk profiles with Alzheimer’s and Parkinson’s disease.

The paper integrates genomic and proteomic data across multiple tissues to identify protein quantitative loci (pQTL) and their implications for neurodegenerative diseases.

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.

The paper develops a model to predict brain aging from resting state fMRI and finds accelerated brain aging in preclinical Alzheimer's disease, independent of amyloid-beta pathology.

The paper harmonizes 10,232 structural brain MRIs from 18 studies to create a normative reference of brain development and aging across 3 to 96 years.

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 uses single-nuclei RNA-seq to compare cell proportions and gene expression in Mendelian and sporadic AD brains, identifying reduced excitatory neurons in a Mendelian carrier and proposing best practices for analysis.

This study estimates the heritability of dementia with Lewy bodies (DLB) and finds it to be higher than previous estimates, with low variance explained by polygenic risk scores from Parkinson's or Alzheimer's disease.

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.

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.

This study identifies how genetic risk variants for Alzheimer's disease affect cerebral cortex cell-type proportions in bulk RNA-seq data using digital deconvolution.

This study evaluates gene-based methods for family-based analyses of familial late-onset Alzheimer disease, identifying novel candidate genes.