Poster No:
1899
Submission Type:
Abstract Submission
Authors:
Mariya Chepisheva1, Xilin Shen2, Cheryl Lacadie2, Wenjing Luo2, Jenna Appleton3, Jagriti Arora2, Jitendra Bhawnani2, 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
Cheryl Lacadie
Radiology and Biomedical Imaging, Yale University
New Haven, CT
Wenjing Luo
Radiology and Biomedical Imaging, Yale University
New Haven, CT
Jagriti Arora
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 leading cause of disability worldwide. Yet, our understanding of the mechanisms of this condition is limited, especially in the acute setting. Here, we investigated the relationship between functional connectivity (FC) resting fMRI scans and common clinical assessments, like the Glasgow Coma Scale (GCS) and modified Rankin scale (mRS) to determine if FC can provide a broad representation of the brain's networks using these standard test scores.
Methods:
We analysed resting functional MRI and clinical data in 108 patients from 2018-2022 (46.4 ± 20.1yrs) across all TBI severities. Patients were scanned acutely (within 31days) or chronically (up to 2 yrs) and presented as a first or a repeat (i.e. not first) TBI case. Specifically, 58 patients (41.3 ± 18.6y) of this cohort were sub/acute first TBI patients and were used as a baseline cohort for the study. Anatomical and functional preprocessing was performed using standard procedures alongside regression of mean time courses in white matter, grey matter and CSF; global signal regression and low-pass Gaussian filtering. We used a 268-node functionally derived atlas to calculate the mean time course of each node and the correlation for each pair of nodes (edges). Fisher transformed Z-scores for 35,778 unique edges were derived. Lastly, we calculated the mean FC maps of 10 functionally derived resting state networks by averaging the correlation score between all the nodes that belonged to a specific network. In this study, we binarized the GCS (mild vs moderate-severe) and mRS (0-1 vs 2-6 and 0-2 vs 3-6) metrics.
Results:
Irrespective of GCS severity, sub/acute patients showed a significant Spearman's correlation between total GCS score and FC maps of the (1)Subcortical (P< 0.001), (2)Motor (P=0.005), (3)Salience (P=0.021) and (4)DMN (P= 0.011). When dividing sub/acute patients based on GCS severity though, only the Subcortical network showed a clear discrimination between mild and moderate-severe GCS at admission (P<0.001), indicating a hyperconnectivity for mild patients, and hypoconnectivity – for moderate-severe patients. In particular, both thalami contributed to this result (Right, P=0.002 and Left P<0.001). This held true also when investigating GCS subcomponents at admission (Eyes, Motor, Verbal, all P<0.001). Importantly, this thalamic FC pattern was observed predominantly in the absence of detectable thalamic damage. Next, we investigated if the importance of the Subcortical network remained irrespective of acuity of scanning (i.e. acute or chronic) or frequency of TBI (i.e. first or repeat). For sub/acute patients, GCS severity played the main role for the general FC level performance within the subcortical network, whereas for chronically scanned patients – acuity of scanning, alongside GCS severity contributed to these FC levels. Spearman correlation between mRS at 3months and the FC maps of the 10 RSN showed a significant association to (1)DMN (P=0.003) and (2)Motor (P=0.005). When divided as per mRS sub-groups, the mRS scores of sub/acute patients showed significant trends in correlations to the mean FC within the Motor, Cerebellum and Medial-Frontal networks, none of which passed multiple comparisons correction. Importantly, the correlation of mRS to the DMN showed a pattern of importance, weaker for the combined group of mRS = 0-1 subjects vs stronger for the mRS = 0-2 subjects. Nevertheless, the link between the DMN and mRS at 3months remained limited (r2= 0.18).
Conclusions:
We showed that FC within the subcortical network was significantly correlated with GCS at admission. Routine tests like the GCS and the mRS provide clinical metrics of TBI severity outcome, but their direct link and ability to represent overall brain network performance has been less clear. Acquiring resting state FC maps in this critical sub/acute stage can contribute to our better understanding of these relationships.
Learning and Memory:
Neural Plasticity and Recovery of Function
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Novel Imaging Acquisition Methods:
BOLD fMRI 1
Keywords:
DISORDERS
FUNCTIONAL MRI
MRI
Neurological
Sub-Cortical
Thalamus
Trauma
Other - GCS (Glasgow Coma Scale), mRS (Modified Rankin Scale), clinical assessments
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
Yes
Are you Internal Review Board (IRB) certified?
Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Yes, I have IRB or AUCC approval
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Functional MRI
Behavior
Other, Please specify
-
outcome evaluations: 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
Other, Please list
-
Yale BioImage Suite
Provide references using APA citation style.
(1) Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., & Constable, R. T. (2015). Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18(11), 1664–1671.
(2) Fischer, D., Threlkeld, Z. D., Bodien, Y. G., Kirsch, J. E., Huang, S. Y., Schaefer, P. W., Rapalino, O., Hochberg, L. R., Rosen, B. R., & Edlow, B. L. (2020). Intact Brain Network Function in an Unresponsive Patient with COVID-19. Annals of Neurology, 88(4), 851–854.
(3) Noble, S., Spann, M. N., Tokoglu, F., Shen, X., Constable, R. T., & Scheinost, D. (2017). Influences on the Test-Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility. Cerebral Cortex, 27(11), 5415–5429.
(4) Shen, X., Tokogly, F., Papademetris, X., & Constable, R. T. (2013). Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage, 0, 403–415.
(5) Woodrow, R. E., Menon, D. K., Stamatakis, E. A., Amrein, K., Andelic, N., Andreassen, L., Anke, A., Azouvi, P., Bellander, B., Benali, H., Buki, A., Caccioppola, A., Calappi, E., Carbonara, M., Citerio, G., Clusmann, H., Coburn, M., Coles, J., Correia, M., … Zoerle, T. (2024). Repeat traumatic brain injury exacerbates acute thalamic hyperconnectivity in humans. Brain Communications, 6(4), 1–8.
(6) Woodrow, R. E., Winzeck, S., Luppi, A. I., Kelleher-Unger, I. R., Spindler, L. R. B., Wilson, J. T. L., Newcombe, V. F. J., Coles, J. P., Amrein, K., Andelic, N., Andreassen, L., Anke, A., Azouvi, P., Bellander, B. M., Benali, H., Buki, A., Caccioppola, A., Calappi, E., Carbonara, M., … Stamatakis, E. A. (2023). Acute thalamic connectivity precedes chronic post-concussive symptoms in mild traumatic brain injury. Brain, 146(8), 3484–3499.
No