Predicting Outcomes in mTBI using Fractional Anisotropy and Free Fatty Acids
Poster No:
138
Submission Type:
Abstract Submission
Authors:
Ruby Gilroy1, Amanda Jefferson1, Melissa Papini1, Jacinta Thorne1, Caerwen Ellery1, Andre Avila1, CREST Investigators1, Melinda Fitzgerald1, Sarah Hellewell1
Institutions:
1Curtin University, Perth, Western Australia
First Author:
Co-Author(s):
Introduction:
White matter (WM) tracts in the brain are susceptible to subtle alterations resulting from mild traumatic brain injury (mTBI) (Hill, 2016; Hellewell 2021), although these are not readily detectable using conventional clinical MRI or fluid biomarkers. There is a need to develop effective clinical tools, as up to 50% of people who sustain mTBI are burdened with persistent post-concussion symptoms (PPCS) (Theadom, 2016) which significantly impact quality of life (Voormolen, 2019). Currently, the aetiology of PPCS remains unknown, however these subtle WM changes have been associated with poorer outcomes (Newcombe, 2011), and may be evidence of secondary degeneration perpetuating post-injury. WM is enriched with lipids, of which the building blocks are free fatty acids (FFA) (Baumann 2001). Given lipids can cross the blood-brain barrier to the periphery, these bioactive molecules may be promising biomarker candidates and adjuncts to more sophisticated research MRI techniques to assess WM. This study aimed to determine whether there are significant differences in WM tract alteration and plasma FFA concentrations in participants with mTBI compared to healthy control participants. Further, we determined whether WM tract and plasma FFA alterations could be used to predict PPCS symptoms six months after injury.
Methods:
The Concussion REcovery STudy (CREST) recruited participants with mTBI and healthy controls from the Western Australian community. 48 participants (23 mTBI, 25 control; 40% female) were included with a mean age of 35 years. Plasma samples collected within 7 days of injury were analysed for free fatty acid (FFA) concentrations using a colorimetric assay. Diffusion MRI scans were acquired within 9 days, and fractional anisotropy (FA) values for mTBI participants were compared to controls using tract-based spatial statistics for voxel-based analysis. The MRI analysis workflow was conducted using Neurodesk (Renton, 2024) (1) using the FMRIB Software Library (FSL; v6.0.7.4) (Jenkinson, 2012) and MRtrix3 (v3.0.4) (Tournier, 2019). The Post-Concussion Symptom Scale-22 Item version (PCSS) and Quality of Life After Brain Injury (QOLIBRI) scores were collected at the time of assessment and at 6 months post-injury via telephone assessment, and were included in analysis with FFA concentrations and WM tract metrics. Statistical analysis for comparison and correlation was conducted in GraphPad Prism (v10.3.1). Finally, regression analyses paired with ANOVA were performed to determine whether any of these variables can predict the onset of PPCS, quality of life, or symptom burden post-injury.
Results:
Significant clusters of reduced WM FA were found in the corpus callosum, corticospinal tract, optic radiations and internal capsule in participants with mTBI compared to controls (Figure 1). There were no clusters of increased FA at this time. Participants with mTBI had significantly elevated FFA concentrations compared to controls, with mean concentrations of 0.035 ± 0.014 and 0.027 ± 0.0064 nmol/µL respectively (p<0.001). Multiple linear regression models were used to determine whether FA or FFA could predict PPCS and reduced quality of life at 6 months post-injury. We found that early WM FA values were significant predictors of both symptom severity (p<0.05) and quality of life (p<0.01) at 6 months, whilst FFA
could significantly predict total symptom scores at 6 months (p<0.05).
could significantly predict total symptom scores at 6 months (p<0.05).
Conclusions:
Taken together, our results suggest that plasma FFA and WM FA may be complementary to other techniques measuring mTBI pathology. The ability of these markers to predict long-term symptom severity and quality of life suggests that lipids may be useful adjuncts for early prognosis. Further research is warranted by our results, and if validated in a larger sample size, these biomarkers could produce highly impactful clinical translations if capable of predicting long-term outcomes.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Keywords:
ADULTS
Blood
MRI
Trauma
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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Provide references using APA citation style.
Hellewell SC. Characteristic patterns of white matter tract injury in sport-related concussion: An image based meta-analysis. Neuroimage Clinical. 2020;26:102253.
Hill CS. Traumatic Axonal Injury: Mechanisms and Translational Opportunities. Trends in Neuroscience. 2016 May;39(5):311–24.
Jenkinson, M. (2012). FSL. NeuroImage, 62(2), 782–790.
Newcombe V. Mapping Traumatic Axonal Injury Using Diffusion Tensor Imaging: Correlations with Functional Outcome. PLOS ONE. 2011 May 4;6(5):e19214.
Renton, A.I. Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging. Nature Methods 21, 804–808 (2024).
Theadom A. Persistent problems 1 year after mild traumatic brain injury: a longitudinal population study in New Zealand. British Journal of General Practice. 2016 Jan;66(642):e16–23.
Tournier JD. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neuroimage. 2019 Nov 15;202:116137.
Voormolen DC. The association between post-concussion symptoms and health-related quality of life in patients with mild traumatic brain injury. Injury. 2019 May 1;50(5):1068–74.