Biology subjects

Hillary, F. G.

Publications and source records attributed to Hillary, F. G..

3 recordsLinked to original sources

bioRxiv2026

NeuroFLAME: A Scalable, Privacy-Preserving Federated Framework for Secure, Reproducible, and Multi-Site Neuroimaging Analysis

Federated analysis offers a scalable approach to multi-site neuroimaging research by enabling distributed statistical modeling, machine learning, decomposition, harmonization, and validation without exchanging sensitive individual-level data. However, a significant implementation gap exists, as the majority of federated clinical neuroimaging studies remain technical proofs of concept, struggling to fully navigate the rigorous data-sharing regulations and security constraints of real-world clinical and research environments. Compounding this, existing federated analysis frameworks are typically exposed as command-line tools and configuration files, imposing a considerable setup burden that demands specialized technical expertise from neuroscience researchers. To address these barriers, we present NeuroFLAME, an enterprise-grade, open-source federated neuroimaging platform built upon the NVIDIA FLARE (NVFlare) framework. NeuroFLAME couples a client-outbound-only communication framework with a graphical user interface specifically tailored for neuroscientists. Using certificate-based trust and containerized execution, the platform ensures reproducible analyses with baseline privacy guarantees through data localization and mTLS encryption; advanced protections such as differential privacy and homomorphic encryption are available as optional configurations. By ensuring that clients only need outbound communication and that raw data never leaves each site, NeuroFLAME is designed to support institutional data governance requirements and regulatory frameworks such as HIPAA and GDPR. We demonstrate the platform's utility through three federated analysis workflows, each implemented as a NeuroFLAME computation: multi-site federated closed form voxel-wise regression, decentralized constrained joint independent component analysis (dcjICA) and federated label-based dimensional prediction. Empirical validation of both workflows shows that NeuroFLAME achieves high consistency with established centralized approaches, effectively bridging the gap between experimental federated learning prototypes and production-ready collaborative tools for privacy-preserving, large-scale federated neuroimaging analysis.

neuroscience↗

bioRxiv2023

Accelerated Aging after Traumatic Brain Injury: an ENIGMA Multi-Cohort Mega-Analysis

ObjectiveThe long-term consequences of traumatic brain injury (TBI) on brain structure remain uncertain. In light of current evidence that even a single significant brain injury event increases the risk of dementia, brain-age estimation could provide a novel and efficient indexing of the long-term consequences of TBI. Brain-age procedures use predictive modeling to calculate brain-age scores for an individual using MRI data. Complicated mild, moderate and severe TBI (cmsTBI) is associated with a higher predicted (brain) age difference (PAD), but the progression of PAD over time remains unclear. Here we sought to examine whether PAD increases as a function of time since injury (TSI). MethodsAs part of the ENIGMA Adult Moderate and Severe (AMS)-TBI working group, we examine the largest TBI sample to date (n=343), along with controls, for a total sample size of 540, to reproduce and extend prior findings in the study of TBI brain age. T1w-MRI data were aggregated across 7 cohorts and brain age was established using a similar brain age algorithm to prior work in TBI. ResultsFindings show that PAD widens with longer TSI, and there was evidence for differences between sexes in PAD, with men showing more advanced brain age. We did not find evidence supporting a link between PAD and cognitive performance. InterpretationThis work provides evidence that changes in brain structure after cmsTBI are dynamic, with an initial period of change, followed by relative stability, eventually leading to further changes in the decades after a single cmsTBI.

neuroscience↗

bioRxiv2018

Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world

Over the past two decades, resting-state functional connectivity (RSFC) methods have provided new insights into the network organization of the human brain. Studies of brain disorders such as Alzheimers disease or depression have adapted tools from graph theory to characterize differences between healthy and patient populations. Here, we conducted a review of clinical network neuroscience, summarizing methodological details from 106 RSFC studies. Although this approach is prevalent and promising, our review identified four challenges. First, the composition of networks varied remarkably in terms of region parcellation and edge definition, which are fundamental to graph analyses. Second, many studies equated the number of connections across graphs, but this is conceptually problematic in clinical populations and may induce spurious group differences. Third, few graph metrics were reported in common, precluding meta-analyses. Fourth, some studies tested hypotheses at one level of the graph without a clear neurobiological rationale or considering how findings at one level (e.g., global topology) are contextualized by another (e.g., modular structure). Based on these themes, we conducted network simulations to demonstrate the impact of specific methodological decisions on case-control comparisons. Finally, we offer suggestions for promoting convergence across clinical studies in order to facilitate progress in this important field.

neuroscience↗