Philip A. Cook

ComBat-Predict improves the generalizability of neuroimaging models to new sites by harmonizing site effects and reducing bias in cortical thickness predictions.

The paper introduces LAMNr flows for multimodal data analysis, enabling shared latent subspaces and cross-view imputation, with applications in biological imaging.

The paper presents a curated dataset of processed diffusion MRI data from the ABCD Study, showing that specific diffusion metrics are sensitive to adolescent white matter development and robust to image quality variation.

The paper describes improvements in diffusion MRI processing for the HBCD study, introducing new software and observing developmental patterns in brain data.

This study examines changes in brain connectivity and neurovascular dynamics during dexmedetomidine-induced unconsciousness and recovery, finding widespread connectivity decreases and cerebellum-specific increases in structure-function coupling.

This study evaluates parameter-efficient fine-tuning methods for vision transformers in neuroimaging, showing they outperform full fine-tuning and training from scratch with fewer parameters.

The paper shows thalamocortical connections develop along a sensorimotor-association gradient in youth, linking structural connectivity to cortical plasticity and environmental sensitivity.

This study evaluates head motion correction methods in diffusion MRI, showing both Eddy and SHORELine perform well but are affected by denoising and sampling schemes.

This study identifies white matter fiber covariance networks in youth, showing their development aligns with executive function and hierarchical cortical maturation patterns.

This study compares anatomical and diffusion MRI for Parkinson's disease detection using deep convolutional neural networks, finding that dMRI-based models show promise for classification.

This study evaluates six compressed sensing diffusion spectrum imaging (CS-DSI) schemes, finding they provide accurate and reliable white matter structure measures with up to 80% reduced scan time compared to full DSI.

This paper shows that removing scanner effects in mean and variance is insufficient for multivariate pattern analysis and proposes a method to harmonize covariance across scanners.

This paper proposes a 3D CNN for classifying Alzheimer's and Parkinson's disease using T1-weighted MRI, achieving higher ROC-AUC than random forest on multiple datasets.

FlywheelTools provides software for reproducible data curation and manipulation on the Flywheel platform, addressing challenges in converting neuroimaging data to BIDS format.

The paper models cortical brain deviations from normative neurodevelopment to predict psychiatric symptoms, finding that transdiagnostic psychopathology dimensions better predict symptoms than specific disorder measures.

The paper examines how different psychopathology dimensions affect various scales of brain functional connectivity using multiple modeling approaches.

QSIPrep is a software platform for preprocessing diffusion MRI data that supports multiple sampling schemes and ensures reproducibility through standardized workflows and quality measures.

The paper introduces longitudinal ComBat to harmonize multi-scanner imaging data, showing it improves detection of longitudinal changes and controls type I error better than existing methods.

This study examines how structure-function coupling in brain networks develops during youth, finding it aligns with cortical hierarchies and supports cognitive improvements.

This study evaluates DTI, NODDI, and MAPL in youth to assess their associations with age and motion, finding that multi-shell models offer advantages for neurodevelopmental studies despite motion sensitivity.

The paper introduces an ANTs-based longitudinal cortical thickness pipeline and demonstrates its effectiveness in analyzing ADNI-1 data for detecting structural brain changes over time.

The paper identifies brain-based dimensions of psychopathology using sCCA in youths, finding four dimensions linked to functional dysconnectivity that replicate in an independent dataset.

This study links accelerated cortical thinning in structural brain networks to irritability in youth, highlighting associations in emotion-related brain regions.

The paper examines the association between cortical thickness and impulsive choice in adolescents, finding reduced cortical thickness in specific brain regions linked to impulsive decision-making.

The paper develops an automated method to assess structural brain image quality using Euler number and validates its effectiveness in identifying low-quality images and its relationship with cortical thickness and age-related associations.

ComBat, an empirical Bayes location-and-scale model from genomics, removes additive and multiplicative scanner effects from regional cortical thickness across 11 scanners while preserving age associations.

This paper evaluates how in-scanner head motion affects structural connectivity estimates from diffusion tractography in a developmental cohort, showing motion biases connectivity strength in a consistency and length-dependent manner.