Poster No:
567
Submission Type:
Late-Breaking Abstract Submission
Authors:
Joanne Kenney1, Emily Dennis2,3,4, Robert Whelan5, Laura Rueda-Delgado6, Paul Thompson7,8, David Tate9,8, Elisabeth Wilde9,10
Institutions:
1Edinburgh Medical School, University of Edinburgh, Edinburgh, Scotland, UK, 2Department of Neurology, University of Utah School of Medicine, Utah, USA, 3George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA, 4Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey , CA, USA, 5School of Psychology & Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland, 6Cumulus Neuroscience, Dublin, Ireland, 7Department of Neurology, University of Southern California (USC),, Los Angeles, CA, USA, 8Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey , CA, 9Department of Neurology, University of Utah, Salt Lake City, UT, USA, 10Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey , CA. USA
First Author:
Joanne Kenney, PhD
Edinburgh Medical School, University of Edinburgh
Edinburgh, Scotland, UK
Co-Author(s):
Emily Dennis, PhD
Department of Neurology, University of Utah School of Medicine|George E. Wahlen Veterans Affairs Medical Center|Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC
Utah, USA|Salt Lake City, UT, USA|Marina del Rey , CA, USA
Robert Whelan
School of Psychology & Global Brain Health Institute, Trinity College Dublin
Dublin, Ireland
Paul Thompson
Department of Neurology, University of Southern California (USC),|Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC
Los Angeles, CA, USA|Marina del Rey , CA
David Tate
Department of Neurology, University of Utah|Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC
Salt Lake City, UT, USA|Marina del Rey , CA
Elisabeth Wilde
Department of Neurology, University of Utah|Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC
Salt Lake City, UT, USA|Marina del Rey , CA. USA
Introduction:
Machine Learning holds significant promise in advancing precision psychiatry. Post-psychiatric complications such as PTSD and depression are common after a Traumatic Brain Injury (TBI) (Mayer & Quinn, 2022, Ahmed et al., 2017). Yet, we still lack standard diagnostic criteria for post-TBI psychiatric complications, leaving many individuals undiagnosed and without appropriate healthcare. Through the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Brain Injury working group, we addressed this issue by applying state-of-the-art machine learning and imaging analysis techniques to identify specific and localized neural markers of psychiatric illness in TBI. The findings of this research can assist in developing sensitive, personalised biomarkers for early diagnosis of psychiatric disorders in TBI, guiding treatment strategies (Siqueira Pinto et al., 2023).
Methods:
Machine learning using logistic regression with Elastic Net regularization was applied to segmented 3D T1-weighted and diffusion MRI data of the brain to classify 1) individuals with TBI only (n= 547) vs healthy controls with no TBI (HC) (n=150) 2) TBI with psychiatric diagnosis vs HC (TBI/PTSD: n = 196, HC: n = 132; TBI/Depression: n = 194, HC: n = 150; TBI/PTSD & Dep: n = 144, HC: n = 123). Age, sex, and intracranial volume were included as covariates in all models. Participants consisted of n=73 females and n = 624 males (mean age: 47.2 ± 15.6 years). The dataset consisted of LIMBIC-CENC, ADNI-DoD and Duke University datasets. Data was harmonised across consortia using the ComBat algorithm and consisted mostly of deployment-related TBI. White matter features were segmented using the JHU White Matter atlas in a TBSS approach; grey matter cortical and subcortical features were segmented using FreeSurfer. The total number of grey and white matter features included in each model was 239.
Results:
Neuroimaging data classified individuals with TBI and depression vs HCs returning an area under the curve (AUC) of 0.66. The cingulum section adjoining the hippocampus (CGH) was a top discriminant feature - revealing reductions in mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD) in right and left CGH and increases in fractional anisotropy (FA) in the cingulum in the cingulated cortex (CGC) predominantly in the left hemisphere. The TBI/Depression/PTSD vs HC model returned an AUC of 0.64 again showing reductions in MD, AD, RD in CGH. The TBI/PTSD vs HC model returned an AUC of 0.58 with reductions in MD, AD and RD in tracts such as ALIC, CST and CGH. The TBI-only vs HC model returned an AUC of 0.59. There were increases in FA across a range of limbic and association tracts, including pathways involved in emotion regulation and cognitive processing, while reductions in MD, AD, and RD were observed in projection and cingulum-related tracts. All models performed significantly better than a null model.
Conclusions:
The results from four machine learning models identify distinct neuroimaging biomarkers associated with traumatic brain injury (TBI) and psychiatric comorbidities. In TBI and depression, disruptions in the microstructural organization of the CGH may contribute to both cognitive and emotional symptoms commonly seen in post-TBI depression. The cingulum is critical for emotional processing, mood regulation, and linking the hippocampus to other emotion-related areas. Its involvement in depression is well established. In this group, increased FA in the CGC may reflect compensatory structural changes in response to CGH damage. As one of the last white matter tracts to mature- reaching peak FA around 42 years old-the cingulum may be particularly vulnerable to environmental impacts (Dennis et al., 2023). Its disruption could serve as a neurobiological marker for post-TBI depression, aiding in early identification of at-risk patients and enabling targeted interventions to mitigate long-term psychological consequences.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Diffusion MRI Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
White Matter Anatomy, Fiber Pathways and Connectivity
Neuroanatomy Other
Novel Imaging Acquisition Methods:
Anatomical MRI
Diffusion MRI
Keywords:
Cortex
Data analysis
Machine Learning
MRI
Neurological
Psychiatric
Psychiatric Disorders
STRUCTURAL MRI
Tractography
White Matter
1|2Indicates the priority used for review
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
I accept
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information.
Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
I do not want to participate in the reproducibility challenge.
Please indicate below if your study was a "resting state" or "task-activation” study.
Other
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
Structural MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
FSL
Free Surfer
Provide references using APA citation style.
1. Ahmed, S., Venigalla, H., Mekala, H.M., Dar, S., Hassan, M., & Ayub, S. (2017). Traumatic brain injury and neuropsychiatric complications. Indian Journal of Psychological Medicine, 39(2), 114-121.
2. Dennis, E.L., Newsome, M.R., Lindsey, H.M., Adamson, M., Austin, T.A., Disner, S.G., Eapen, B.C., Esopenko, C., Franz, C.E., Geuze, E., Haswell, C., Hinds, S.R. 2nd, Hodges, C.B., Irimia, A., Kenney, K., Koerte, I.K., Kremen, W.S., Levin, H.S., Morey, R.A., Ollinger, J., Rowland, J.A., Scheibel, R.S., Shenton, M.E., Sullivan, D.R., Talbert, L.D., Thomopoulos, S.I., Troyanskaya, M., Walker, W.C., Wang, X., Ware, A.L., Werner, J.K., Williams, W., Thompson, P.M., Tate, D.F., & Wilde, E.A. (2023). Altered lateralization of the cingulum in deployment-related traumatic brain injury: An ENIGMA military-relevant brain injury study. Human Brain Mapping, 44(5), 1888-1900.
3. Mayer, A.R., & Quinn, D.K. (2022). Neuroimaging biomarkers of new-onset psychiatric disorders following traumatic brain injury. Biological Psychiatry, 91(5), 459-469.
4. Siqueira Pinto, M., Winzeck, S., Kornaropoulos, E.N., Richter, S., Paolella, R., Correia, M.M., Glocker, B., Williams, G., Vik, A., Posti, J.P., Håberg, A., Stenberg, J., Guns, P.J., den Dekker, A.J., Menon, D.K., Sijbers, J., Van Dyck, P., & Newcombe, V.F.J. (2023). Use of support vector machines approach via ComBat harmonized diffusion tensor imaging for the diagnosis and prognosis of mild traumatic brain injury: A CENTER-TBI study. Journal of Neurotrauma, 40(13-14), 1317-1338.
No