Neural Mechanisms of Risk Tolerance and Reward Sensitivity in Subthreshold Depression
Poster No:
722
Submission Type:
Late-Breaking Abstract Submission
Authors:
Dipayan Roy1, Bhoomika Kar1
Institutions:
1Centre of Behavioural and Cognitive Sciences, Prayagraj, Uttar Pradesh
First Author:
Co-Author:
Introduction:
Individuals with major depressive disorder (MDD) show poor risk tolerance and impaired reward processing (Gotlib & Joormann, 2010). The subthreshold depression (StD) population is larger and more heterogeneous than MDD, mostly undiagnosed and untreated. Their cognitive vulnerabilities reflect later in life with the development of MDD in ways that are not fully understood. We aimed to investigate the neural underpinnings of decision-making under risk and reward sensitivity in StD and healthy control (HC) populations.
Methods:
33 healthy adult volunteers (20 StD, 13 HC, mean±SD age 21.03±2.52 years, 24 males and 9 females) undertook the balloon analogue risk task (BART) − a sequential risky decision paradigm − inside fMRI. Participants with a score of ≥10 (Wang & Gorenstein, 2013) on the Beck Depression Inventory-II scale were screened as StD. We employed a modified version of BART (programmed in E-Prime 3.0) with three conditions [NEU: neutral (no risk, no reward), RWR: reward with risk (traditional BART paradigm, where the participant is asked to either take reward corresponding to the current balloon or pump up the balloon to increase the reward value; an explosion of the balloon brings the reward of that trial to zero), RWOR: reward without risk (no risk of loss even with balloon explosion)], each having twenty trials or virtual balloons. FMRI data preprocessing and statistics were performed using FEAT (FMRI Expert Analysis Tool, v6.00), part of FSL (FMRIB's Software Library, www.fmrib.ox.ac.uk/fsl). We fitted the general linear model for each participant with their respective 'onset (EV) files' in each condition. The average contrast of parameter estimates was generated within and between conditions. The cluster-defining Z-threshold of 3.1 and familywise error rate of 0.05 (Eklund, Nichols & Knutsson, 2016) were used to identify which clusters of voxels were significant.
Results:
Behavioural results showed a significance between conditions (p<0.01), with higher adjusted mean pumps, total explosions, and total earnings for the RWOR condition. For each condition, BART measures were not significantly different between StD and HC (all p>0.30). Mean RT was different across the three conditions for all participants and HC (both p<0.01), but not for StD, implying that risky decision-making may be impaired in StD. Additionally, both groups differed significantly only in mean RT (mean±SD for StD: 10.36±5.02, HC: 7.01±1.94, p=0.029) for the RWR condition, which means the StD group took longer to make risky choices during BART. We observed from neuroimaging data that in RWR and RWOR conditions, neural resources used by StD and HC groups differ (Figures 1 & 2). The StD group showed significant clusters in precuneus, cingulate gyrus, middle temporal gyrus, superior frontal gyrus, and superior parietal lobule for RWR condition (risky decision-making), while only cerebellar clusters were observed in HC. In RWOR, significant clusters for StD included supramarginal and precentral gyri, whereas lateral occipital cortex, middle temporal, and cingulate gyri were significant for HC.
Conclusions:
We found that reward sensitivity dominated the performance on BART across both groups. However, fMRI results provide support towards differences in risk tolerance and reward processing in StD individuals compared to HCs with cerebellum coding for risk tolerance in HC (Quan et al., 2022) and parietal cortex for reward valuation (Panidi et al., 2024) during risky choices in StD. StD seems to be associated with risk avoidance, supported by the slower RTs on the RWR condition and the engagement of neural resources associated with reward processing (Onge & Floresco, 2010). Our study is limited by the participation of university students with a narrow age range. Further exploration is necessary to investigate the interaction between the observed neural correlates of risk tolerance and reward processing in StD.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Emotion, Motivation and Social Neuroscience:
Reward and Punishment
Higher Cognitive Functions:
Decision Making 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Keywords:
Experimental Design
FUNCTIONAL MRI
Psychiatric Disorders
Other - Balloon Analogue Risk Task (BART); Subthreshold Depression
1|2Indicates the priority used for review
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2. Gotlib, I. H., & Joormann, J. (2010). Cognition and depression: current status and future directions. Annual review of clinical psychology, 6, 285–312.
3. Panidi, K., Vorobiova, A. N., Feurra, M., & Klucharev, V. (2024). Posterior parietal cortex is causally involved in reward valuation but not in probability weighting during risky choice. Cerebral cortex (New York, N.Y.: 1991), 34(1), bhad446.
4. Quan, P., He, L., Mao, T., Fang, Z., Deng, Y., Pan, Y., Zhang, X., Zhao, K., Lei, H., Detre, J. A., Kable, J. W., & Rao, H. (2022). Cerebellum anatomy predicts individual risk-taking behavior and risk tolerance. NeuroImage, 254, 119148.
5. St Onge, J. R., & Floresco, S. B. (2010). Prefrontal cortical contribution to risk-based decision making. Cerebral cortex (New York, N.Y.: 1991), 20(8), 1816–1828.
6. Wang, Y. P., & Gorenstein, C. (2013). Psychometric properties of the Beck Depression Inventory-II: a comprehensive review. Revista brasileira de psiquiatria (Sao Paulo, Brazil: 1999), 35(4), 416–431.