Paul M. Thompson

The paper predicts how preprocessing affects fMRI connectivity estimates using prior scans, showing personalized models outperform average predictions.

The paper evaluates temporal complexity measures in resting-state fMRI to identify disease-related changes, finding that real data show greater outliers than randomized controls.

This study compares hidden Markov models (HMMs) and vector autoregressive (VAR) models in resting-state fMRI to determine if apparent state transitions reflect discrete dynamics or continuous activity, finding that VAR often outperforms HMMs in continuous cases.

The paper examines how dementia severity affects resting-state fMRI network responses to preprocessing, finding that greater severity correlates with reduced temporal concentration after nuisance regression.

The paper examines if temporal variability in brain connectivity improves prediction of two-year clinical severity in mild cognitive impairment compared to static connectivity measures.

This paper investigates why performance plateaus in 3D MRI transformers for brain disease classification and finds that representation collapse, not task difficulty, causes the plateau.

This study analyzes fMRI data from OCD patients and controls to identify age-specific differences in brain activation during inhibitory control and error processing.

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 evaluates how sample size affects machine learning performance in classifying dementia stages using subcortical brain features from 2,511 ADNI samples.

This paper identifies systematic prediction bias in aging clocks and introduces a constrained optimization framework to improve calibration and downstream inference.

The paper uses polygenic scores from GWAS to identify spatial genetic effects on brain structure across multiple scales in MRI data.

This paper identifies widespread gray and white matter volume differences in autism using a large voxel-based morphometry analysis of 3,051 participants.

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

This study examines how the spectral distribution of discriminative signals in MRI transformers affects their scaling behavior with increasing training data, finding that distributed representations improve more with larger datasets.

The paper maps the focal and generalized effects of common genetic variants on human brain structure using polygenic scores and MRI 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.

The paper introduces a 3D brain geometry toolkit for multisite neuroimaging analysis, showing subcortical shape measures improve diagnostic and treatment prediction compared to traditional volume features.

This study identifies genetic loci and pathways associated with limbic white matter microstructure in aging and Alzheimer's Disease using multi-cohort diffusion MRI and bulk RNA-seq data.

This paper introduces a Hodge-Laplacian framework to analyze higher-order topology in OCD brain networks, identifying loop-level abnormalities not detectable by conventional functional connectivity analysis.

This study examines sex differences in structural brain alterations in common epilepsies using neuroimaging data from a large international cohort, finding some regional differences but no significant sex-by-diagnosis interactions.

The ENIGMA MEG pipeline automates spectral analysis of multi-site resting state MEG data, demonstrating compatibility with large-scale processing.

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.

The paper uses machine learning to predict autopsy-confirmed neuropathology from clinical, neuroimaging, and CSF biomarkers, finding that different modalities best predict specific pathologies.

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 improves modeling of amyloid-β spread in Alzheimer's Disease using a gene-modulated network diffusion framework, showing better performance in correlation and replicability across cohorts.

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

BundleWarp improves white matter tractometry by aligning tracts while preserving anatomical features, enhancing sensitivity to disease-related changes in conditions like Alzheimer's and Parkinson's.