Poster No:
544
Submission Type:
Abstract Submission
Authors:
Celia Durkin1, Kendrick Kay1, Kathryn Cullen1
Institutions:
1University of Minnesota, Minneapolis, MN
First Author:
Co-Author(s):
Introduction:
The heterogeneity of depression has posed a challenge for mapping its underlying neural circuitry. A relatively new approach, focusing on inter-individual differences, leverages between-subject variability. However, the efficacy of this approach hinges on the type of data collected and its subsequent analysis. In scientific research, tasks that endure are typically those that are reliable, and that elicit consistent effects across subjects (low between-subject variability). These tasks are then repurposed to probe individual differences, which they were originally designed to minimize. We propose that imaging data collected while subjects perform guided, but non-prescriptive tasks, which allow subjects to vary from one another, will amplify meaningful inter-individual differences that map onto depression profiles. Here, we find converging results across two datasets.
Methods:
We analyzed neuroimaging data and questionnaire responses from adolescents with a history of depression collected across two studies–Imagination Central (n=14) and Creativity Camp (n=39). In the Imagination Central (IC) study, participants were enrolled in a class on imaginative thinking and were scanned once. In the Creativity Camp study, participants were scanned before and after a two-week arts intervention. In each study, participants completed multiple depression questionnaires, and were scanned during rest and while completing an image-viewing task.
During the image-viewing task, participants viewed images in 25-30 second blocks of time and were instructed to either mentally imagine something cued by the image (Imagine condition) or attend to the perceptual features of the image (Describe condition). We predicted that 1) the Imagine condition would elicit BOLD responses that vary across people more so than the Describe condition and 2) BOLD responses elicited by the Imagine condition would train models to better classify depression subtypes and treatment effects.
To test our first prediction, we used inter-subject correlation to compute inter-individual differences in brain signal patterns during each block, and compared the magnitude of inter-individual differences elicited between Imagine and Describe conditions. To test our second prediction, we trained separate classifiers on the Imagine and Describe conditions to predict depression severity (PHQ-9 or CDI) and treatment effects (change in CDI in response to creativity camp intervention).
Results:
Results from the IC study showed that viewing images in the Imagine condition (vs. Describe) elicits more between-subject variability in certain brain regions–the pTFC (t=2.05; p<.05), Precuneus (t=2.97, p<.01) and PCC (2.38, p<.01). In those same regions, classifiers trained on BOLD responses from the Imagine condition (vs. Describe) better predict depression severity (score on the PHQ-9). Applying similar classification techniques to pre-camp imaging data from the CC study showed similar results. In the pTFC, the PCC, and the PC, classifiers trained on BOLD response from the Imagine condition (vs. Describe) more accurately classified baseline CDI score. In the pTFC and PCC, classifiers trained on the Imagine condition predicted clinical response to treatment, and in the PC, classifiers trained on both Imagine and Describe conditions predicted treatment response.
Conclusions:
In conclusion, we found that both Imagine and Describe conditions elicit brain responses that signal meaningful inter-individual differences. However, in three brain regions, imagination drives more idiosyncratic brain responses, and these responses are related to current depression severity and predictive of treatment effects. Overall, these guided, but non-prescriptive tasks prove to be advantageous in driving idiosyncratic signals, underscoring their potential utility in unraveling the complexities of depression heterogeneity by amplifying individual differences that define subtypes or separate treatment responders from non-responders.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Higher Cognitive Functions:
Higher Cognitive Functions Other
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Keywords:
Affective Disorders
Cognition
FUNCTIONAL MRI
Machine Learning
Psychiatric Disorders
Other - individual differences
1|2Indicates the priority used for review
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Functional MRI
Behavior
Neuropsychological testing
Computational modeling
For human MRI, what field strength scanner do you use?
3.0T
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FSL
Free Surfer
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