Paul M. Thompson

The paper compares deterministic and probabilistic tractography methods in ADNI data, finding bundle-level differences in white matter microstructure metrics between the two approaches.

CALM-VLM improves confidence and reliability in brain MRI classification by calibrating vision-language models and enabling selective prediction.

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.

MAVeRiC-AD improves Alzheimer's disease classification from MRI by combining multiple models with demographic data, achieving higher accuracy across different sites.

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

This study compares deep learning methods to predict cognitive decline using brain MRI and clinical data over two years, showing the value of integrating image and tabular data.

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 identifies a shared genetic factor underlying brain age gaps across 30 cohorts, finding 19 SNPs and associations with health traits.

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 paper generates synthetic brain MRI data using a Diffusion Transformer to augment neuroimaging datasets and improve dementia classification.

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.

This paper generates synthetic diffusion MRI scalar maps from T1-weighted images using RevGAN, showing they retain microstructural features and perform well in downstream tasks.

This study evaluates deep learning models that predict multiple dementia-related neuropathologies using MRI, clinical, and genetic data, showing the potential of multimodal AI for non-invasive profiling.

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 introduces DeepTaskGen, a deep learning method that generates synthetic task-based contrast maps from resting-state fMRI data, outperforming benchmarks and enabling biomarker development for cognitive tasks.

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.

The paper introduces a scalable toolkit for 3D surface-based brain analysis, demonstrating its utility in mapping subcortical shape variations across 21 international samples.

This study identifies small but consistent anatomical correlates of negative symptom dimensions in schizophrenia, linking them to prefrontal cortical thickness and limbic subcortical volumes.

This study analyzes fMRI data from OCD patients and controls to identify neural activation patterns during executive control tasks, finding reduced activation in frontoparietal regions and increased default mode network activity in OCD.

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 associations between epilepsy-related polygenic risk scores and brain morphology in children, finding PRS-related cortical thinning linked to structural patterns in TLE-HS patients.

This study uses MRI volumetrics to analyze cerebellar and brainstem degeneration in SCA3, finding consistent atrophy patterns and reduced cerebrocerebellar covariance.

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 paper introduces a framework using a vision-language model with natural text supervision to perform MRI retrieval, captioning, classification, and visual question answering in Alzheimer's disease research.

ComBatLS improves multi-site image harmonization by preserving biological variance in features, outperforming existing methods in sex-imbalanced site scenarios and reducing sex biases.