Ariel Rokem

This study benchmarks four structural MRI foundation models for age, sex, and BMI prediction, finding that 3D-Neuro-SimCLR and AnatCL outperformed baselines overall.

This paper develops brain-age models using diffusion MRI data from full- and preterm infants, showing accurate age prediction but limited sensitivity to clinical complications.

The paper introduces a protocol for assessing and controlling MRI data quality using MRIQC software, focusing on anatomical, functional, and diffusion MRI scans of the human brain.

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.

This study identifies two axes of white matter development in human cortico-cortical tracts using diffusion MRI across three datasets, revealing non-uniform maturation patterns.

The paper introduces Reproducible Brain Charts (RBC), an open data resource integrating brain development data from 6,346 youth, showing the benefits of harmonized phenotypes and reproducible processing for studying psychopathology.

This study shows that gray and white matter myelination are synchronized in early infancy, with synchrony linked to gestational age and motor outcomes.

The paper trains a deep learning model to quantify brain vascular morphology from standard MR images, showing it captures aging and health-related features not found in traditional metrics.

This study examines the effects of two-week reading training on preschoolers' letter knowledge, decoding skills, and white matter structure, finding skill improvements without significant white matter changes.

This paper shows that free water elimination improves tractometry reliability and accuracy in aging brains using a large cohort and provides open-source software for this technique.

The study links longitudinal changes in the left arcuate fasciculus's white matter to reading development gains, but not math, using diffusion MRI and academic scores.

The paper examines how educational opportunity affects white matter development in adolescents, finding it correlates with accelerated development in academic-related tracts after controlling for socioeconomic factors.

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.

The paper examines the relationship between white matter properties and reading skill development using longitudinal and cross-sectional data, finding that reading skill gains correspond to longitudinal white matter changes but not individual differences in white matter.

This study links developmental changes in alpha rhythm to visual white matter maturation and visual detection performance using dMRI-EEG data.

The study examines age-related changes in white matter properties of optic radiations carrying foveal, macular, and peripheral visual field information using diffusion MRI data from the UK Biobank.

The paper finds that myelin growth slows after birth, particularly in preterm infants, and links this delay to long-term developmental consequences.

This study evaluates deep learning improvements for brain-age modeling using diffusion MRI data in children and adolescents, finding small but notable gains with recurrent neural networks.

This study uses diffusion MRI and a CNN to show glaucoma has a unique non-linear effect on optic radiation tissue properties, unlike linear effects on other brain connections.

This paper introduces Non-Stationary Dynamic Mode Decomposition (NS-DMD) to analyze time-varying spatiotemporal data, demonstrating its application to neural recordings from a non-human primate.

This study evaluates the reliability of dMRI-based tractometry by assessing reproducibility, test-retest reliability, and robustness using software and datasets from the Human Connectome Project and clinical-grade measurements.

This paper investigates how numerical uncertainty in brain imaging pipelines affects connectome stability and shows that induced variance can improve network reliability and classification of individual differences.

The paper shows that maturation of optic radiations in childhood influences the speed and amplitude of visual cortex responses to high contrast stimuli.

This paper implements Diffusional Kurtosis Imaging (DKI) in the DIPY project, demonstrating its utility for microstructural analysis and tractography in brain imaging.

This paper introduces a sparse group lasso method to analyze diffusion MRI data, identifying informative white matter features for ALS classification and brain age prediction.

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

This study evaluates DWI-derived scalar images for T1-DWI co-registration, finding that anisotropic power (AP) images provide the most consistent and reliable registration across modalities.

This study predicts retinal implant users' phosphenes using a model simulating individual retinal ganglion axon pathways, accurately replicating percept shapes.

The paper combines citizen science and deep learning to scale expert decision making in neuroimaging by amplifying labels and training models to emulate expert quality control.