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  Temporal discounting in major depression

Lekscha Sedlinska, T., Brückner, L., Hübner, M., Rentsch, C., Falke, D., Mühle, C., et al. (2020). Temporal discounting in major depression. Poster presented at Bernstein Conference 2020. doi:10.12751/nncn.bc2020.0115.

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Lekscha Sedlinska, T1, 2, Author              
Brückner, L, Author
Hübner, M, Author
Rentsch, C, Author
Falke, D, Author
Mühle, C, Author
Suc, J, Author
Weinland, C, Author
Kornhuber, J, Author
Lenz, B, Author
Dayan, P1, 2, Author              
Affiliations:
1Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3017468              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Depression is one of the largest contributors to the burden of disease worldwide and better understanding of the disorder is needed. One promising direction is the use of cognitive phenomena, such as temporal discounting, as intermediate phenotypes revealing its underlying nature and as markers of therapeutic success. We administered a temporal discounting task and the Beck’s Depression Inventory (BDI) to 170 in-ward patients from the Psychiatric University Hospital in Erlangen and to 176 healthy controls. All patients fulfilled the ICD-10 criteria for moderate to severe depression. Healthy controls had sub-threshold scores for depression and had no neuropsychiatric history. We first examined group differences in the mean responses and in the inconsistency of the choices. Then we used Bayesian information criterion (BIC) to compare 3 models of delay discounting all based on the hyperbolical discounting model Q(R,D,k)=R1+k∗D where R =reward, k=discount rate, and D=delay, comparing the subjective value of the immediate option Qi=Q(Ri,0,k) against the delayed option Qd=Q(Rd,D,k), and generating the probability of choosing the immediate option pi=1−pd, where softmax σ(ζ)=11+exp(−ζ). (1) preference-temperature model (k, β): pi=σ(β(Qi−Qd)) (2) preference-uncertainty model (µ,σ): draw:log(ks)fromN(µ,σ): if:Qi>Q(Rd,D,ks),pi=1 otherwise:pi=0 (3) trembling-hand model (k, β, lapse): λ=(356)σ(lapse)+1e−4 pi=(1−2λ)σ(β(Qi−Qd))+λ Model-agnostic analysis showed no group difference in the mean responses (t-test: t=1.686, p=0.092), but choosing the immediate reward was correlated with BDI (Pearson: r=0.248, p=5.382e-06). Patients were less consistent in their responses (t=-1.9963, p=0.046), but the inconsistency was not correlated with BDI (r=0.092, p=0.093). Model (1) yielded the best fit (BIC(1)=3.817, BIC(2)=3.893, BIC(3)=7.001). There was no significant group difference in the discount rate k (t=1.074, p=0.284) or inverse temperature β (t=-0.263, p=0.793) and there was no correlation of k or β with BDI (k: r= 0.0866, p=0.117; β: r=-0.071, p=0.197). We conclude that depressed patients were more inconsistent in their choices and depressiveness was correlated with choosing immediate over delayed rewards. One limitation of our study was that the delay discounting task was hypothetical and covered only a limited range of discount rates.

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 Dates: 2020-09
 Publication Status: Published online
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 Identifiers: DOI: 10.12751/nncn.bc2020.0115
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Title: Bernstein Conference 2020
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Start-/End Date: 2020-09-29 - 2020-10-01

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Title: Bernstein Conference 2020
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: P 96 Start / End Page: - Identifier: -