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Publications

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Decentralized Mixed Effects Modeling in COINSTAC.

Basodi S. et al, (2024), Neuroinformatics

MRI economics: Balancing sample size and scan duration in brain wide association studies.

Ooi LQR. et al, (2024), bioRxiv

FEMA: Fast and efficient mixed‐effects algorithm for large sample whole‐brain imaging data

Parekh P. et al, (2024), Human Brain Mapping, 45

Reciprocal relationships between stress and depressive symptoms: the essential role of the nucleus accumbens.

Ma Y. et al, (2023), Psychol Med, 1 - 12

Neuroscience Needs Network Science.

Barabási DL. et al, (2023), J Neurosci, 43, 5989 - 5995

Cardiovascular and metabolic health is associated with functional brain connectivity in middle-aged and older adults: Results from the Human Connectome Project-Aging study.

Rashid B. et al, (2023), Neuroimage, 276

A scalable approach for continuous time Markov models with covariates.

Hatami F. et al, (2023), Biostatistics

Liver disease is a significant risk factor for cardiovascular outcomes - a UK Biobank study.

Roca-Fernandez A. et al, (2023), J Hepatol

Integrating at-scale health data into doctoral training (Preprint)

Nichols TE. et al, (2023)

Association of gout with brain reserve and vulnerability to neurodegenerative disease

Topiwala A. et al, (2023), Nature Communications, 14

Image-Based Biological Heart Age Estimation Reveals Differential Aging Patterns Across Cardiac Chambers.

Salih AM. et al, (2023), Journal of magnetic resonance imaging : JMRI

Reply to: Multivariate BWAS can be replicable with moderate sample sizes.

Tervo-Clemmens B. et al, (2023), Nature, 615, E8 - E12

The effect of d-cycloserine on brain processing of breathlessness over pulmonary rehabilitation: an experimental medicine study.

Finnegan SL. et al, (2023), ERJ Open Res, 9

Brain deficit patterns of metabolic illnesses overlap with those for major depressive disorder: A new metric of brain metabolic disease.

Hatch KS. et al, (2023), Hum Brain Mapp

Erratum: Separating Clinical and Subclinical Depression by Big Data Informed Structural Vulnerability Index and Its impact on Cognition: ENIGMA Dot Product (Pacific Symposium on Biocomputing (2022) (133-143) DOI: 10.1142/9789811250477_0013)

Kochunov P. et al, (2023), Pacific Symposium on Biocomputing

Erratum: Separating Clinical and Subclinical Depression by Big Data Informed Structural Vulnerability Index and Its impact on Cognition: ENIGMA Dot Product

Kochunov P. et al, (2023), Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 28

Multi-Task Cooperative Learning via Searching for Flat Minima.

Wu F. et al, (2023), CoRR, abs/2309.12090

Partitioning variance in cortical morphometry into genetic, environmental, and subject-specific components using a large adolescent sample

Smith D. et al, (2023)

Telomere length and brain imaging phenotypes in UK Biobank.

Topiwala A. et al, (2023), PLoS One, 18

Multi-organ imaging demonstrates the heart-brain-liver axis in UK Biobank participants

McCracken C. et al, (2022), Nature Communications, 13

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