Biology subjects

Nasrallah, I. M.

Publications and source records attributed to Nasrallah, I. M..

3 recordsLinked to original sources

bioRxiv2025

Postmortem brain MRI reveals differential associations of subcortical and limbic volumes with cortical thinning and neuropathology patterns

Structured AbstractO_ST_ABSIntroductionC_ST_ABSThe impact of different neuropathologies on deep brain structures remains to be understood. We examine subcortical and limbic volumetry in neurodegenerative diseases involving p-tau, -synuclein and TDP-43. MethodsWe acquired neuropathological measures and brain segmentations from postmortem analysis of 132 donors with Alzheimers disease (AD), Lewy body disease (LBD), Frontotemporal Lobar Degeneration with TDP-43 (FTLD-TDP) and FTLD-Tau. ResultsLBD had the least subcortical, limbic and cortical atrophy compared to AD, FTLD-TDP and FTLD-Tau. In donors with both AD and LBD pathologies, primary LBD was associated with less atrophy than primary AD. While AD had cortico-subcortical and cortico-limbic morphometric associations, LBD had more limited parieto-occipital cortico-limbic associations. FTLD-TDP had cortico-subcortical while FTLD-Tau had cortico-subcortical and cortico-limbic associations. In AD and FTLD-Tau, hippocampal volumes correlated with p-tau burden, neuron loss and gliosis. In LBD, thalamic -synuclein severity was associated with subcortical/limbic volumes. DiscussionPostmortem neuroimaging reveals disease- and region-specific structure-pathology relationships.

neuroscience↗

bioRxiv2024

pNet: A toolbox for personalized functional networks modeling

Personalized functional networks (FNs) derived from functional magnetic resonance imaging (fMRI) data are useful for characterizing individual variations in the brain functional topography associated with the brain development, aging, and disorders. To facilitate applications of the personalized FNs with enhanced reliability and reproducibility, we develop an open-source toolbox that is user-friendly, extendable, and includes rigorous quality control (QC), featuring multiple user interfaces (graphics, command line, and a step-by-step guideline) and job-scheduling for high performance computing (HPC) clusters. Particularly, the toolbox, named personalized functional network modeling (pNet), takes fMRI inputs in either volumetric or surface type, ensuring compatibility with multiple fMRI data formats, and computes personalized FNs using two distinct modeling methods: one method optimizes the functional coherence of FNs, while the other enhances their independence. Additionally, the toolbox provides HTML-based reports for QC and visualization of personalized FNs. The toolbox is developed in both MATLAB and Python platforms with a modular design to facilitate extension and modification by users familiar with either programming language. We have evaluated the toolbox on two fMRI datasets and demonstrated its effectiveness and user-friendliness with interactive and scripting examples. pNet is publicly available at https://github.com/MLDataAnalytics/pNet.

neuroscience↗

bioRxiv2019

Harmonization of large multi-site imaging datasets: Application to 10,232 MRIs for the analysis of imaging patterns of structural brain change throughout the lifespan

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,232 structural brain MRI scans from participants without known neuropsychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive normative age trends of brain structure through the lifespan (3 to 96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this normative reference of brain development and aging, and to examine deviations from normative ranges, potentially related to disease.

bioinformatics↗