1. C3NL
Imperial TBI
Magnetic resonance spectroscopy in chronic traumatic brain injury: a useful tool?
Karl Zimmerman, Gregory Scott, Ines Violante, Claire Feeney, David J Sharp
The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Department of Medicine, Imperial College London, Hammersmith Hospital Campus, London, UK
INTRODUCTION
Traumatic brain injury (TBI) patients may deteriorate clinically long after the initial insult.
A possible underlying cause chronic neuroinflammation, in the form of persistent microglial activation. Chronic neuroinflammation has previously been demonstrated in the thalamus, using positron emission tomography (PET)1.
Magnetic resonance spectroscopy (MRS) is an emerging non-invasive tool for evaluating TBI, and provides an assessment of metabolic changes after injury.
We assessed whether metabolite concentration changes associated with neuroinflammation are detectable in the thalamus of chronic TBI patients using MRS.
FIGURE 2 – Chronic TBI patients have higher concentrations of myo-inositol and lower NAA in the thalamus
a) Default mode network in normals
b) TBI integration > Controls
X = 10
Z = 30
Z = 36
left
ant
Significantly higher levels of myo-inositol (N=19, p=0.027) and lower concentrations of NAA (N=19, p=0.022) were found in chronic TBI patients compared to controls. These differences were not due to age, fraction of white matter or months since injury. Myo-inositol is a proposed glial marker, and hence higher levels in the thalamus may indicate increased glial proliferation as a response to injury which has persisted. Additionally, lower NAA concentrations are related to neuronal loss and/or mitochondrial dysfunction3, of which neuronal loss in the thalamus is unlikely to be caused by the initial lesion.
FIGURE 1 – Magnetic Resonance Spectroscopy data processing
Schematic of data processing. Post segmentation and partial volume correction, LCModel was used to create a fit of estimated concentrations to the raw data. As a quality control check, T1 images were reorientated and brain extracted from surrounding structure using FSL. The brain images were then registered to standard space and overlayed to analyse the accuracy of voxel placement. Spectra and voxels were then manually checked for errors.
Patients’ two visits individually represented as connected circles plotted against months since injury with blue points indicating a decrease, and green indicating an increase between the two scans. Myo-inositol (n= 14) and creatine (n=18) levels were observed to decrease (p=0.043, p=0.025 respectively) with no relation to months since injury, while no significant changes were observed in NAA, choline or glutamate levels.
a) ‘Executive’ network in normals
b) TBI integration > Controls
X = 6
Z = 48
X = 12
left
Cluster corr p < 0.05
ant
FIGURE 3 – Longitudinal changes in chronic TBI patients
METHODS
Participants: A total of 27 patients (4 females, mean age ± standard deviation (SD) 45 ± 12.2 years, range years). All had suffered from a single moderate to severe TBI based on the Mayo criteria. Image acquisition and controls: All TBI patients underwent 1-H MRS and T1 MRI scanning with a voxel of interest placed over the left thalamus. 11 age and gender matched healthy controls (3 females, mean age ± SD 43 ± 10.3 years, range years) underwent 1-H MRS and T1 MRI scans. 21 patients and 11 controls then completed a 6 month follow up 1-H MRS after their initial assessment. MRS processing and analysis: MRS data was obtained in a DICOM format and converted into a SIEMENS RDA format via a MATLAB (v2013b) script. The MRS data in the RDA format was analysed using LCModel2 from ppm. Previous studies have reported metabolite concentrations as a ratio using creatine as a reference, however more recently it has been observed that creatine concentrations may change post-injury2. Therefore we used water concentration as a reference, which requires partial volume correction to correct for differential concentrations of water in different tissues and accounts for the lack of metabolites in the csf. Segmentation of the tissue within the voxel of interest was completed using the T1 MRI image and the SPM8 toolbox (2015, FIL Methods Group) to calculate the fraction of white and grey matter for partial volume correction. The proportions were input into a formula to calculate a WCONC value: WCONC= (43300 fgm fwm fcsf) / (1-fcsf), where fgm, fwm and fcsf is the fraction of grey and white matter and cerebral spinal fluid in the voxel, with fgm + fwm + fcsf = 1. Initial checks on the output page generated from LCModel were done manually and rejection of spectra was performed in line with instruction from the LCModel manual. In total, 6 whole spectra were removed from analysis (5 patient, 1 control- 5 baseline spectra and 1 6-month follow up). The Cramer-Rao lower bounds ratio of the fit to the peak of interest by LCModel was used as a criterion to exclude poor-quality data (>20%) of individual metabolite levels from further analysis. In total, 23 patients and 10 control baseline visits were included and 20 6-month follow-ups.
CONCLUSIONS
The results demonstrate that the abnormal MRS markers that have previously been associated with injury and inflammation are present in the thalamus up to 12 years after TBI.
Myo-inositol and N-acetyl aspartate levels measured by MRS provide potential biomarkers for chronic inflammation after TBI.
While these results provide further evidence for chronic inflammation persisting in the thalamus, however further correlations of MRS with other markers of persistent injury and cognitive impairment are needed.
Longitudinal data inconclusive, and highlights inter-visit variability problem with MRS.
REFERENCES
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(2) Yeo, R. A., Gasparovic, C., Merideth, F., Ruhl, D., Doezema, D. & Mayer, A. R. (2011) A Longitudinal Proton Magnetic Resonance Spectroscopy Study of Mild Traumatic Brain Injury. Journal of Neurotrauma. 28 (1), 1-11.
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(4) Rae, C. (2014) A Guide to the Metabolic Pathways and Function of Metabolites Observed in Human Brain 1H Magnetic Resonance Spectra. Neurochemical Research. 39 (1), 1-36.