Aging-related Changes in Reward-based Decision-making Depend on Punishment Frequency: An FMRI Study

Overview

Journal Front Aging Neurosci

Specialty Geriatrics

Date 2023 Mar 23

PMID 36949775

Authors

Ping Ren

Guozhi Luo

Jiayin Huang

Meiling Tan

Donghui Wu

Han Rong

Affiliations

Soon will be listed here.

Abstract

Introduction: Aging is often accompanied by significant cognitive decline and altered decision making. Previous studies have found that older adults have difficulty in processing reward/risk information, leading to suboptimal decision strategy. However, it is still under investigated about the neural substrates of risky decision-making under ambiguity in aging.

Methods: Using the Iowa Gambling Task, the current study investigated inter-individual differences of risk-taking behaviors in healthy older adults with task-related functional magnetic resonance imaging.

Results: It was found that participants were able to improve their decisions in advantageous decks, but failed to avoid disadvantageous decks during task performance. The task-related activations within multiple brain regions were observed significantly different across the four decks, and showed negative correlations with age in disadvantageous decks but not in advantageous decks. Consistently, age-related whole brain analyses confirmed the negative age-effect on brain activations in disadvantageous decks, especially in high punishment frequency. In addition, the relationship between age and task performance in high punishment frequency was mediated by activation in the frontal subregions such as the middle frontal cortex and superior medial frontal cortex.

Discussion: Our findings shed light on the neural substrates of altered risk-taking behaviors in aging, suggesting a greater sensitivity to high punishment frequency in older adults.

References

Halfmann K, Hedgcock W, Bechara A, Denburg N . Functional neuroimaging of the Iowa Gambling Task in older adults. Neuropsychology. 2014; 28(6):870-80. DOI: 10.1037/neu0000120. View

Smallwood J, Bernhardt B, Leech R, Bzdok D, Jefferies E, Margulies D . The default mode network in cognition: a topographical perspective. Nat Rev Neurosci. 2021; 22(8):503-513. DOI: 10.1038/s41583-021-00474-4. View

Tymula A, Rosenberg Belmaker L, Ruderman L, Glimcher P, Levy I . Like cognitive function, decision making across the life span shows profound age-related changes. Proc Natl Acad Sci U S A. 2013; 110(42):17143-8. PMC: 3801020. DOI: 10.1073/pnas.1309909110. View

Ding Y, Pereira F, Hoehne A, Beaulieu M, Lepage M, Turecki G . Altered brain processing of decision-making in healthy first-degree biological relatives of suicide completers. Mol Psychiatry. 2016; 22(8):1149-1154. DOI: 10.1038/mp.2016.221. View

Sun T, Xie T, Wang J, Zhang L, Tian Y, Wang K . Decision-Making Under Ambiguity or Risk in Individuals With Alzheimer's Disease and Mild Cognitive Impairment. Front Psychiatry. 2020; 11:218. PMC: 7093589. DOI: 10.3389/fpsyt.2020.00218. View

Bechara A, Damasio A, Damasio H, Anderson S . Insensitivity to future consequences following damage to human prefrontal cortex. Cognition. 1994; 50(1-3):7-15. DOI: 10.1016/0010-0277(94)90018-3. View

Bhandari R, Kirilina E, Caan M, Suttrup J, De Sanctis T, De Angelis L . Does higher sampling rate (multiband + SENSE) improve group statistics - An example from social neuroscience block design at 3T. Neuroimage. 2020; 213:116731. PMC: 7181191. DOI: 10.1016/j.neuroimage.2020.116731. View

Siegel J, Power J, Dubis J, Vogel A, Church J, Schlaggar B . Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points. Hum Brain Mapp. 2013; 35(5):1981-96. PMC: 3895106. DOI: 10.1002/hbm.22307. View

Harding I, Yucel M, Harrison B, Pantelis C, Breakspear M . Effective connectivity within the frontoparietal control network differentiates cognitive control and working memory. Neuroimage. 2014; 106:144-53. DOI: 10.1016/j.neuroimage.2014.11.039. View

10.

Pertl M, Benke T, Zamarian L, Delazer M . Decision Making and Ratio Processing in Patients with Mild Cognitive Impairment. J Alzheimers Dis. 2015; 48(3):765-79. DOI: 10.3233/JAD-150291. View

11.

Rutledge R, Smittenaar P, Zeidman P, Brown H, Adams R, Lindenberger U . Risk Taking for Potential Reward Decreases across the Lifespan. Curr Biol. 2016; 26(12):1634-1639. PMC: 4920952. DOI: 10.1016/j.cub.2016.05.017. View

12.

Lawrence N, Jollant F, ODaly O, Zelaya F, Phillips M . Distinct roles of prefrontal cortical subregions in the Iowa Gambling Task. Cereb Cortex. 2008; 19(5):1134-43. DOI: 10.1093/cercor/bhn154. View

13.

Folstein M, Folstein S, McHugh P . "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975; 12(3):189-98. DOI: 10.1016/0022-3956(75)90026-6. View

14.

Chen K, Xu Y, Chu A, Ding D, Liang X, Nasreddine Z . Validation of the Chinese Version of Montreal Cognitive Assessment Basic for Screening Mild Cognitive Impairment. J Am Geriatr Soc. 2016; 64(12):e285-e290. DOI: 10.1111/jgs.14530. View

15.

Mata R, Josef A, Samanez-Larkin G, Hertwig R . Age differences in risky choice: a meta-analysis. Ann N Y Acad Sci. 2011; 1235:18-29. PMC: 3332530. DOI: 10.1111/j.1749-6632.2011.06200.x. View

16.

Ortiz-Rios M, Azevedo F, Kusmierek P, Balla D, Munk M, Keliris G . Widespread and Opponent fMRI Signals Represent Sound Location in Macaque Auditory Cortex. Neuron. 2017; 93(4):971-983.e4. PMC: 5757378. DOI: 10.1016/j.neuron.2017.01.013. View

17.

dAcremont M, Lu Z, Li X, Van der Linden M, Bechara A . Neural correlates of risk prediction error during reinforcement learning in humans. Neuroimage. 2009; 47(4):1929-39. DOI: 10.1016/j.neuroimage.2009.04.096. View

18.

An R, Liu G . Cognitive impairment and mortality among the oldest-old Chinese. Int J Geriatr Psychiatry. 2016; 31(12):1345-1353. DOI: 10.1002/gps.4442. View

19.

Yan C, Wang X, Zuo X, Zang Y . DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics. 2016; 14(3):339-51. DOI: 10.1007/s12021-016-9299-4. View

20.

Lin C, Chiu Y, Lee P, Hsieh J . Is deck B a disadvantageous deck in the Iowa Gambling Task?. Behav Brain Funct. 2007; 3:16. PMC: 1839101. DOI: 10.1186/1744-9081-3-16. View