Poster No:
1150
Submission Type:
Abstract Submission
Authors:
AJ Simon1, Anja Samardzija1, Santino Iannone1, Saloni Mehta1, Fuyuze Tokoglu2, Jagriti Arora1, Dustin Scheinost2, Xilin Shen2, Todd Constable1
Institutions:
1Yale University, New Haven, CT, 2Yale School of Medicine, New Haven, CT
First Author:
Co-Author(s):
Introduction:
Identifying relationships between psychological processes and brain activity has garnered attention in psychiatric research in recent years (Insel et al., 2010). This initiative holds the potential to improve our understanding of the neurobiology underlying symptoms, which can help to develop diagnostics and better targeted treatments. Connectome-based predictive modeling (CPM) (Finn et al., 2015; Shen et al., 2017) has proven useful in linking functional brain networks linked to numerous cognitive and clinically relevant measures, such as sustained attention (Rosenberg et al., 2016), memory (Barron et al., 2021), craving (Garrison et al., 2023), and social functioning (Lake et al., 2019). However, a comprehensive characterization of which phenotypes CPM can and cannot predict has yet to be reported. In this study, we report the results of using CPM to model 63 different clinically relevant measures from 7 assessment scales and 36 cognitive performance measures from 6 testing batteries in a transdiagnostic population.
Methods:
Clinically scales, cognitive, and fMRI data were collected from a transdiagnostic sample at Yale University (n=317). The clinical scales collected were the Behavior Rating Inventory of Executive Function (BRIEF), Brief Symptom Inventory (BSI), Interpersonal Reactivity Index (IRI), Perceived Stress Scale (PSS), Adult Temperament Questionnaire (ATQ), Positive and Negative Affective Schedule (PANAS), and Pittsburgh Sleep Quality Index (PSQI). The cognitive tests were collected from items from the Boston Naming Test (BNT), Wide Range Achievement Test (WRAT), Wide Range Assessment of Memory and Learning (WRAML), Wechsler Adult Intelligence Scale (WAIS), Delis-Kaplan Executive Function System (DKEFS), and Wechsler Abbreviated Scale of Intelligence (WASI). A total of 8 functional scans were collected (2 resting state, 6 task). Scans were parcellated using the Shen 268 node atlas (Shen et al., 2013). Functional connectivity matrices were derived by computing the BOLD time series correlations between all nodes. The 8 matrices were averaged for each participant and input to a 10-fold cross validation model to predict each cognitive and clinically relevant behavioral measure. Significance was determined using permutation testing. All p-values were adjusted for multiple comparisons using an FDR correction.
Results:
CPM provide significant prediction models for 33 of 63 clinical scores (52.38%), while 17 of the 36 cognitive measures (72.22%) yielded significant predictive models. The median prediction strength of clinical measures was r = 0.172 (range: 0.133 – 0.249), while the median prediction strength of cognitive measures was r = 0.264 (range: 0.165 – 0.347). Although we were able to predict clinical variables from each of the assessment inventories (except for PSS), the best predictions were for measures related to sleep (PSQI). Conversely, measures related to social functioning and temperament were the hardest measures to build predictive models for (IRI and ATQ). CPM predicted numerous cognitive measures from each of the cognitive batteries. However, predictions on measures from the DKEFS were the least predictable, with only 10/18 significantly predicted. The DKEFS measures that did not yield good predictive models tended to be the easier tests to perform. Test-retest reliability and score distributions for clinical and cognitive measures were not related to prediction strength.
Conclusions:
Here, we determine which cognitive and clinical phenotypic measures have clear brain correlates using CPM. In general, cognitive measures yielded more consistent and accurate predictive models than the clinical test measures, potentially reflecting objective measures being easier to model than self-report. However, more than half of the clinical measures had clear brain correlates. These findings should help to guide transdiagnostic patient studies that focus on understanding the links between brain and behavior.
Modeling and Analysis Methods:
Classification and Predictive Modeling 1
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 2
Methods Development
Keywords:
Cognition
Computational Neuroscience
FUNCTIONAL MRI
Machine Learning
Modeling
Psychiatric
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
Task-activation
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
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Free Surfer
Other, Please list
-
Bioimage suite
Provide references using APA citation style.
1. Barron, D. S., Gao, S., Dadashkarimi, J., Greene, A. S., Spann, M. N., Noble, S., Lake, E. M. R., Krystal, J. H., Constable, R. T., & Scheinost, D. (2021). Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders. Cereb Cortex, 31(5), 2523-2533. https://doi.org/10.1093/cercor/bhaa371
2. 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. Nat Neurosci, 18(11), 1664-1671. https://doi.org/10.1038/nn.4135
3. Garrison, K. A., Sinha, R., Potenza, M. N., Gao, S., Liang, Q., Lacadie, C., & Scheinost, D. (2023). Transdiagnostic Connectome-Based Prediction of Craving. Am J Psychiatry, 180(6), 445-453. https://doi.org/10.1176/appi.ajp.21121207
4. Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., Sanislow, C., & Wang, P. (2010). Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry, 167(7), 748-751. https://doi.org/10.1176/appi.ajp.2010.09091379
5. Lake, E. M. R., Finn, E. S., Noble, S. M., Vanderwal, T., Shen, X., Rosenberg, M. D., Spann, M. N., Chun, M. M., Scheinost, D., & Constable, R. T. (2019). The Functional Brain Organization of an Individual Allows Prediction of Measures of Social Abilities Transdiagnostically in Autism and Attention-Deficit/Hyperactivity Disorder. Biol Psychiatry, 86(4), 315-326. https://doi.org/10.1016/j.biopsych.2019.02.019
6. Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., & Chun, M. M. (2016). A neuromarker of sustained attention from whole-brain functional connectivity. Nat Neurosci, 19(1), 165-171. https://doi.org/10.1038/nn.4179
7. Shen, X., Finn, E. S., Scheinost, D., Rosenberg, M. D., Chun, M. M., Papademetris, X., & Constable, R. T. (2017). Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nat Protoc, 12(3), 506-518. https://doi.org/10.1038/nprot.2016.178
8. Shen, X., Tokoglu, F., Papademetris, X., & Constable, R. T. (2013). Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage, 82, 403-415. https://doi.org/10.1016/j.neuroimage.2013.05.081
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