Biology subjects

Fischl, B. R.

Publications and source records attributed to Fischl, B. R..

3 recordsLinked to original sources

bioRxiv2025

Spatial Image Gradient Estimation from the Diffusion MRI Profile

PurposeIn the course of diffusion, water molecules experience varying values for the relaxation-time properties of the underlying tissue. This factor, which has rarely been accounted for in diffusion MRI (dMRI), is modeled in this work, allowing for the estimation of the gradient of the relaxation-time properties from the dMRI signal. MethodsWith the aim of mining the dMRI data for information about the spatial variations in the tissue relaxation-time properties, a new mathematical relationship between the diffusion signal and the spatial gradient of the image is derived, enabling the estimation of the latter from the former. The hypothesis was validated on human brain dMRI images from three datasets: the public Human Connectome Project Young Adults database, 10 healthy volunteers and 1 ex vivo sample scanned in-house with stimulated-echo diffusion encoding and a long diffusion time of 1 second (which will be made publicly available), and 3 subjects from the public Multi-TE database. The effects of the confounding factor of "fiber continuity" were furthermore measured. ResultsThe image spatial gradient estimated from the diffusion signal was compared to the gold-standard spatial gradient approximated through the finite difference. The former gradient was significantly related to the latter in all datasets (i.e., with a difference significantly smaller than chance), with an effect distinct from fiber continuity. ConclusionThe results support the hypothesized relationship between within-voxel dMRI signal and image gradient, with an effect that was not explainable by the confounding factor of fiber continuity.

bioengineering↗

bioRxiv2024

Automatic Geometry-based Estimation of the Locus Coeruleus Region on T1-Weighted Magnetic Resonance Images

The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly available imaging databases for training automatic LC segmentation models take advantage of specialized contrast-enhancing (e.g., neuromelanin-sensitive) MRI. Segmentation models developed with such image contrasts, however, are not readily applicable to existing datasets with conventional MRI sequences. In this work, we evaluate the feasibility of using non-contrast neuroanatomical information to geometrically approximate the LC region from standard 3-Tesla T1-weighted images of 20 subjects from the Human Connectome Project (HCP). We employ this dataset to train and internally/externally evaluate two automatic localization methods, the Expected Label Value and the U-Net. We also test the hypothesis that using the phase image as input can improve the robustness of out-of-sample segmentation. We then apply our trained models to a larger subset of HCP, while exploratorily correlating LC imaging variables and structural connectivity with demographic and clinical data. This report contributes and provides an evaluation of two computational methods estimating neural structure.

neuroscience↗

bioRxiv2023

Exploratory Correlation of The Human Structural Connectome with Non-MRI Variables

INTRODUCTIONDiscovery of the associations between brain structural connectivity and clinical and demographic variables can help to better understand the vulnerability and resilience of the brain architecture to neurodegenerative diseases and to discover biomarkers. METHODSWe used four diffusion-MRI databases, three related to Alzheimers disease, to exploratorily correlate structural connections between 85 brain regions with non-MRI variables, while stringently correcting the significance values for multiple testing and ruling out spurious correlations via careful visual inspection. We repeated the analysis with brain connectivity augmented with multi-synaptic neural pathways. RESULTSWe found 85 and 101 significant relationships with direct and augmented connectivity, respectively, which were generally stronger for the latter. Age was consistently linked to decreased connectivity, and healthier clinical scores were generally linked to increased connectivity. DISCUSSIONOur findings help to elucidate which structural brain networks are affected in Alzheimers disease and aging and highlight the importance of including indirect connections.

neuroscience↗