Behavioral modeling and neuroimaging of impaired risky decision making in patients with chronic musculoskeletal pain

Xinglin Zeng; Wen Tang; Fei Gao; Ziyue Tang; Zhou Zhang; Juan Zhang; Meng Du; Zhiyi Chen; Xueli Chen; Zhen Yuan

doi:10.1117/1.NPh.10.2.020901

Behavioral modeling and neuroimaging of impaired risky decision making in patients with chronic musculoskeletal pain

Neurophotonics. 2023 Apr;10(2):020901. doi: 10.1117/1.NPh.10.2.020901. Epub 2023 May 18.

Authors

Xinglin Zeng^{1

2}, Wen Tang³, Fei Gao^{1

4}, Ziyue Tang³, Zhou Zhang³, Juan Zhang¹, Meng Du⁵, Zhiyi Chen⁵, Xueli Chen⁶, Zhen Yuan^{1

2}

Affiliations

¹ University of Macau, Centre for Cognitive and Brain Sciences, Macau, China.
² University of Macau, Faculty of Health Sciences, Macau, China.
³ The First Affiliated Hospital, Sun Yat-Sen University, Department of Rehabilitation Medicine, Guangzhou, China.
⁴ Fudan University, Institute of Modern Languages and Linguistics, Shanghai, China.
⁵ University of South China, Institute of Medical Imaging, Hengyang Medical School, Hengyang, China.
⁶ Xidian University, School of Life Science and Technology, Xi'an, China.

Abstract

Significance: Performance during risky decision making is one of the essential cognitive functions that is impaired in several psychiatric disorders including addiction. However, the cognitive mechanism and neural correlates underlying risky decision making in chronic pain patients are unclear. To our knowledge, this study is among the first to construct computational models to detect the underlying cognitive process of chronic pain patients during risky decision making.

Aim: This study aimed at inspecting the significantly abnormal risky decision-making patterns of chronic pain patients and its neuro-cognitive correlates.

Approach: In this case-control study, 19 chronic pain patients and 32 healthy controls (HCs) were included to measure the risky decision making in a balloon analogue risk task (BART). Optical neuroimaging using functional near-infrared spectroscopy, together with computational modeling, was carried out to systematically characterize the specific impairments based on BART.

Results: Computational modeling findings on behavioral performance demonstrated that the chronic pain patient group exhibited significant deficits in learning during BART ( $p < 0.001$ ), tending to make decisions more randomly without deliberation ( $p < 0.01$ ). In addition, significant brain deactivation alternation in the prefrontal cortex (PFC) during the task was detected for the patient group compared with that from the control group ( $p < 0.005$ ).

Conclusions: Long-term aberrant pain responses significantly disrupted the PFC function and behavioral performance in chronic pain patients. The joint behavioral modeling and neuroimaging techniques open a new avenue for fully understanding the cognitive impairment and brain dysfunction of risky decision making associated with chronic pain.

Keywords: chronic musculoskeletal pain; computational modeling; functional near-infrared spectroscopy; prefrontal cortex; risky decision-making.