Biology subjects

Dams-O'Connor, K.

Publications and source records attributed to Dams-O'Connor, K..

3 recordsLinked to original sources

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↗

bioRxiv2023

Bridging Big Data: Procedures for Combining Non-equivalent Cognitive Measures from the ENIGMA Consortium

Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. These efforts unveil new questions about integrating data arising from distinct sources and instruments. We focus on the most frequently assessed cognitive domain - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated global raw data from 53 studies totaling N = 10,505 individuals. A mega-analysis was conducted using empirical bayes harmonization to remove site effects, followed by linear models adjusting for common covariates. A continuous item response theory (IRT) model estimated each individuals latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance while preserving covariate effects, and our conversion tool is freely available online. This demonstrates that large-scale data sharing and harmonization initiatives can address reproducibility and integration challenges across the behavioral sciences. TeaserWe present a global effort to devise harmonization procedures necessary to meaningfully leverage big data.

neuroscience↗

bioRxiv2019

Optimizing the Accuracy of Cortical Volumetric Analysis in Traumatic Brain Injury

Cortical volumetric analysis is widely used to study the anatomic basis of neurological deficits in patients with traumatic brain injury (TBI). However, patients with TBI-related lesions are often excluded from analysis, because cortical lesions may compromise the accuracy of reconstructed surfaces upon which volumetric measurements are based. Here, we propose a novel FreeSurfer-based lesion correction method and illustrate its impact on cortical volume measures in patients with chronic moderate-to-severe TBI. We performed MRI in 87 patients at mean+/-SD 10.9+/-9.1 years post-injury using a T1-weighted multi-echo MPRAGE sequence at 1 mm resolution. Following surface reconstruction, we parcellated the cerebral cortex into seven functional networks using FreeSurfers standard pipeline. Next, we manually labeled vertices on the cortical surface where lesions caused inaccuracies and removed them from network-based cortical volumetric measures. After performing this lesion correction procedure, we measured the surface area of lesion overlap with each network and the percent volume of each network affected by lesions. We identified 120 lesions that caused inaccuracies in the cortical surface in 46 patients. In these 46 patients, the most commonly lesioned networks were the limbic and default mode networks (95.7% each), followed by the executive control (78.3%), and salience (71.7%) networks. The limbic network had the largest average surface area of lesion overlap (4.4+/-3.7%) and the largest percent volume affected by lesions (12.7+/-9.7%). The lesion correction method has the potential to improve the accuracy of cortical volumetric measurements and permit inclusion of patients with lesioned brains in quantitative analyses, providing new opportunities to elucidate network-based mechanisms of neurological deficits in patients with TBI.

neuroscience↗