Poster No:
1921
Submission Type:
Abstract Submission
Authors:
Mariya Chepisheva1, Xilin Shen2, Jenna Appleton3, Sacit Omay3, Amit Mahajan2, Emily Gilmore1, Brian Edlow4, Todd Constable2, Jennifer Kim1
Institutions:
1Neurocritical Care and Emergency Neurology, Yale University, New Haven, CT, 2Radiology and Biomedical Imaging, Yale University, New Haven, CT, 3Neurosurgery, Yale-New Haven Hospital, New Haven, CT, 4Neurology, Massachusetts General Hospital, Boston, MA
First Author:
Mariya Chepisheva
Neurocritical Care and Emergency Neurology, Yale University
New Haven, CT
Co-Author(s):
Xilin Shen
Radiology and Biomedical Imaging, Yale University
New Haven, CT
Sacit Omay
Neurosurgery, Yale-New Haven Hospital
New Haven, CT
Amit Mahajan
Radiology and Biomedical Imaging, Yale University
New Haven, CT
Emily Gilmore
Neurocritical Care and Emergency Neurology, Yale University
New Haven, CT
Brian Edlow
Neurology, Massachusetts General Hospital
Boston, MA
Todd Constable
Radiology and Biomedical Imaging, Yale University
New Haven, CT
Jennifer Kim
Neurocritical Care and Emergency Neurology, Yale University
New Haven, CT
Introduction:
Traumatic brain injury (TBI) is a dynamic pathophysiologic process that does not obligatorily terminate at the point of the initial brain insult. Atrophy after TBI is not limited to the grey matter alone but is more often and at greater rates observed in the white matter (WM). WM damage leads to axonal lesions that will consequently damage short and long-distance WM connections, all of which crucial for the brain's overall communication. Here, we investigated the WM correlates of two routine clinical assessments, i.e. the Glasgow Coma Scale (GCS) at admission and modified Rankin Scale (mRS) at 3 months to see if these metrics can be reflected in the WM abnormalities present in sub/acute and/or chronic TBI patients.
Methods:
We analysed diffusion tensor imaging (DTI) data in 46 first-time TBI patients (2018-2022) (46.1yrs ± 18.6), scanned either sub/acutely (n = 27) or chronically (n = 19). DTI analysis was performed using the standard FSL (FMRIB) pipeline, including eddy current corrections, extraction of the regions of interest, rotation of vector files, skull stripping and fitting a diffusion tensor at each voxel. Next, we performed tract based spatial statistics (TBSS) and non-TBSS to calculate the mean skeleton mask and whole brain fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). Further, based on a priori hypothesis from one of our resting state functional MRI studies suggesting the importance of the thalamus for the discrimination of TBI GCS severity patients, we prepared binarized masks for a selection of thalamic WM tracts and both thalami to calculate the mean values for each tract/region. Then, we dichotomized GCS (mild vs moderate-severe) and mRS (0-1 vs 2-6 or 0-2 vs 3-6) scores. Lastly, we explored the difference among TBI patients based on the days passed between the TBI and the scan - (1) sub/acutely - < 7days or < 31days, or (2) chronically – up to 1.5yrs.
Results:
Whole brain Kruskal-Wallis test showed a main effect of GCS severity and scan's acuity for FA/MD/AD/RD. A subsequent Dunn's post-hoc revealed notable difference for: (1) "sub/acute mild - chronic mild patients" – FA (P = 0.028), MD (P = 0.019), RD (P = 0.01), and (2) "sub/acute moderate-severe patients – chronic mild patients" - FA (P = 0.018), MD/AD/RD (P < 0.001). Our next step, an analysis based on days between TBI and scan, showed a statistically notable Kruskal-Wallis test for all DTI metrics (i.e. for FA, P = 0.006 and for MD/AD/RD, P < 0.001). Importantly, this latter analysis pointed that the sub/acute moderate-severe TBI patients (scanned within 7days) contributed to the highest FA and corresponding lowest MD/AD/RD levels. Next, mask-based DTI analysis indicated that WM in both thalami (R/L) and the anterior thalamic radiation WM tracts (R/L) followed the whole brain value trends, described above regarding (1) GCS severity from acute to chronic time point and (2) acuity of scanning based on days. Lastly, division only within the sub/acute patients based on mRS at 3months indicated no statistical importance between less (0-1 or 0-2) vs more symptomatic patients (2-6 or 3-6, respectively) for the DTI parameters investigated.
Conclusions:
Acuity of scans can highly affect DTI metrics which may be due to more immediate complications such as inflammation/swelling of the damaged tissue. We observed a gradual decrease in whole brain FA metrics (and corresponding increase in MD/AD/RD) from sub/acute moderate-severe GCS to chronic mild GCS patients. In addition, based on days between TBI and scan – similar trends in DTI metrics were noted from scans performed < 7days, through < 31days and up to 1.5years. Currently, mRS analysis on a whole brain value level did not statistically discriminate between less and more symptomatic TBI patients. However, a larger acute cohort and/or a voxelwise analysis may elucidate more of the mRS white matter correlates.
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 2
Novel Imaging Acquisition Methods:
Diffusion MRI 1
Keywords:
DISORDERS
MRI
Neurological
STRUCTURAL MRI
Tractography
Trauma
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - clinical assessments, GCS (Glasgow Coma Scale), mRS (Modified Rankin Scale)
1|2Indicates the priority used for review
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Was this research conducted in the United States?
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Are you Internal Review Board (IRB) certified?
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Please indicate which methods were used in your research:
Structural MRI
Diffusion MRI
Behavior
Other, Please specify
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clinical assessments, GCS (Glasgow Coma Scale), mRS (Modified Rankin Scale)
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
SPM
FSL
Provide references using APA citation style.
(1) Mac Donald, C. L., Dikranian, K., Bayly, P., Holtzman, D., & Brody, D. (2007). Diffusion tensor imaging reliably detects experimental traumatic axonal injury and indicates approximate time of injury. Journal of Neuroscience, 27(44), 11869–11876.
(2) Sharp, D. J., Scott, G., & Leech, R. (2014). Network dysfunction after traumatic brain injury. Nature Reviews Neurology, 10(3), 156–166.
(3) Kinnunen, K. M., Greenwood, R., Powell, J. H., Leech, R., Hawkins, P. C., Bonnelle, V., Patel, M. C., Counsell, S. J., & Sharp, D. J. (2011). White matter damage and cognitive impairment after traumatic brain injury. Brain, 134(2), 449–463.
(4) Soares, . M., Mar ues, P., Alves, V., & Sousa, N. (2013). A hitchhiker’s guide to diffusion tensor imaging. Frontiers in Neuroscience, 7(7 MAR), 1–14.
(5) Edlow, B. L., Copen, W. A., Izzy, S., Bakhadirov, K., van der Kouwe, A., Glenn, M. B., Greenberg, S. M., Greer, D. M., & Wu, O. (2016). Diffusion tensor imaging in acute-to-subacute traumatic brain injury: A longitudinal analysis. BMC Neurology, 16(1), 1–11.
No