Saâd Jbabdi

The paper integrates 3D light-sheet microscopy with diffusion MRI to predict cellular features from MRI data across multiple mouse strains.

This paper presents high-resolution diffusion MRI techniques for in vivo and ex vivo macaque brain imaging at 10.5 Tesla, enabling detailed connectivity studies.

This paper introduces a framework to model brain connectivity by separating functional segregation and gradients, showing it better approximates the connectome than alternative approaches.

This paper develops a deep learning model using domain adaptation to predict high-resolution fibre orientations from diffusion MRI, informed by microscopy data, improving connectivity mapping in human brains.

This paper develops a scalable PS-OCT system for whole-brain imaging in rhesus macaques, achieving high-resolution volumetric data with multi-terabyte datasets.

This study identifies hierarchical neural signatures of language by linking semantic and phonological processing to brain networks using fMRI, myelin measures, and AI models.

The paper introduces a dynamical ATN model to simulate and predict the spatiotemporal progression of Aβ, tau, and neurodegeneration in Alzheimer's disease using multimodal imaging data.

The paper introduces a multi-modal Monte Carlo MRI simulator that models tissue microstructure's impact on various MRI sequences and validates it against established techniques.

This paper evaluates the diffusion-weighted steady-state free precession (DW-SSFP) sequence for ex vivo macaque brain imaging at 10.5T, showing improved SNR efficiency and geometric distortion reduction.

This paper introduces standardized tractography protocols for mapping cortico-subcortical tracts in human and macaque brains, showing robustness and applicability across species.

This study compares white matter connectivity in two anthropoids and one strepsirrhine using diffusion MRI, finding greater dorsal prefrontal innervation in anthropoids.

This study uses diffusion-weighted MRS to track cell-specific developmental trajectories in the cerebellum and thalamus of rat neonates, revealing age-related changes in metabolite diffusion properties.

The paper develops a mechanistic model of tau protein dynamics using PET data to predict regional tau accumulation and disease progression stages in Alzheimer's patients.

The paper compares white matter organization between humans, chimpanzees, and macaques, identifying uniquely human connectivity profiles in areas related to language and abstract reasoning.

This paper develops a hybrid diffusion MRI-microscopy tractography method to improve 3D brain connectivity reconstruction by combining high-resolution microscopy with diffusion MRI data.

The paper applies the BENCH framework to link diffusion MRI signal changes to microstructural features in brain tissue using histology data.

This paper introduces a hierarchical framework for fitting crossing fibre models to diffusion MRI data, enabling consistent and comparable fibre-specific parameters across subjects.

This study compares hippocampal structure and function across humans and macaques to show conservation of micro- and macro-structure but evolutionary reconfiguration in functional connectivity.

This paper introduces a transfer learning method to accurately localise the Vim in clinical-quality MRI data by transferring anatomical information from high-quality datasets.

The paper introduces a universal dynamic MRS toolbox that simultaneously fits dynamic spectra, reducing parameter uncertainty and replicating prior studies.

This study uses interleaved fMRS and fMRI to examine how visual contrast affects neurometabolite levels and BOLD responses in the human brain, finding non-linear relationships and consistent glutamate increases.

This commentary critiques Pang et al.'s 2023 study, arguing that their geometric mode approach to brain dynamics lacks specificity to brain shape and orientation, and does not sufficiently establish geometry as a more fundamental constraint than connectivity.

The paper introduces the BigMac dataset, combining multi-contrast MRI and microscopy data from a macaque brain, enabling multimodal analysis across scales and tissue states.

The paper uses neuroimaging data from the UK Biobank to analyze functional connectivity between interoceptive brain regions and links it to health-related domains using canonical correlation analysis.

The paper introduces an automated pipeline for registering stand-alone histology sections to whole-brain MRI data with sub-voxel accuracy.

This paper introduces an automated pipeline to extract quantitative histological metrics for voxelwise MRI-histology comparisons and finds activated microglia strongly predicts DTI FA, suggesting diffusion MRI sensitivity to neuroinflammation.

This paper introduces a framework to study brain ontogeny and phylogeny by integrating connectivity maps across species and developmental stages.

This paper estimates axial diffusivity in the NODDI model using high b-value data and shows the impact of noise assumptions on parameter estimates.

This paper introduces BENCH, a Bayesian framework to infer parameter changes in biophysical models despite parameter degeneracies, applied to diffusion MRI data.

The paper improves predictions of individual differences in task-evoked fMRI activity using a sparse ensemble learner and shows better performance than previous methods.