Eye movements as predictor of cognitive improvement after cognitive remediation therapy in patients with schizophrenia

Jiahui Zhu; Jinhao Li; Li Zhou; Lingzi Xu; Chengcheng Pu; Bingjie Huang; Qi Zhou; Yunhan Lin; Yajing Tang; Liu Yang; Chuan Shi

doi:10.3389/fpsyt.2024.1395198

Eye movements as predictor of cognitive improvement after cognitive remediation therapy in patients with schizophrenia

Front Psychiatry. 2024 Apr 16:15:1395198. doi: 10.3389/fpsyt.2024.1395198. eCollection 2024.

Authors

Jiahui Zhu^{1

2

3}, Jinhao Li⁴, Li Zhou⁵, Lingzi Xu⁶, Chengcheng Pu^{1

2

3}, Bingjie Huang^{1

2

3}, Qi Zhou⁷, Yunhan Lin^{1

2

3}, Yajing Tang^{1

2

3}, Liu Yang^{1

2

3}, Chuan Shi^{1

2

3}

Affiliations

¹ Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China.
² National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China.
³ NHC Key Laboratory of Mental Health, Peking University, Beijing, China.
⁴ Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, China.
⁵ Faculty of Education, East China Normal University, Shanghai, China.
⁶ Research and Development Department, Infinite Brain Technologies, Beijing, China.
⁷ The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.

Abstract

Aim: Baseline cognitive functions of patients predicted the efficacy of cognitive remediation therapy (CRT), but results are mixed. Eye movement is a more objective and advanced assessment of cognitive functions than neuropsychological testing. We aimed to investigate the applicability of eye movements in predicting cognitive improvement after patients with schizophrenia were treated with CRT.

Methods: We recruited 79 patients with schizophrenia to complete 8 weeks of CRT and assessed their cognitive improvement outcomes. Eye movements were assessed by prosaccades, antisaccades, and free-viewing tasks at baseline, and neuropsychological tests in four cognitive domains were assessed before and after treatment to calculate treatment outcomes. Predictors of demographic information, clinical characteristics, and eye movement measures at baseline on cognitive improvement outcomes were analyzed using logistic regression analysis. We further compared the predictive performance between eye movement measurements and neuropsychological test regarding the effect of CRT on cognitive improvement, and explored factors that could be affect the treatment outcomes in different cognitive domains.

Results: As operationally defined, 33 patients showed improved in cognition (improved group) and 46 patients did not (non-improved group) after CRT. Patients with schizophrenia being employed, lower directional error rate in antisaccade task, and lower the gap effect (i.e., the difference in saccadic latency between the gap condition and overlap condition) in prosaccade task at baseline predicted cognitive improvement in CRT. However, performance in the free-viewing task not associated with cognitive improvement in patients in CRT. Our results show that eye-movement prediction model predicted the effect of CRT on cognitive improvement in patients with schizophrenia better than neuropsychological prediction model in CRT. In addition, baseline eye-movements, cognitive reserve, antipsychotic medication dose, anticholinergic cognitive burden change, and number of training sessions were associated with improvements in four cognitive domains.

Conclusion: Eye movements as a non-invasiveness, objective, and sensitive method of evaluating cognitive function, and combined saccadic measurements in pro- and anti-saccades tasks could be more beneficial than free-viewing task in predicting the effect of CRT on cognitive improvement in patients with schizophrenia.

Keywords: antisaccades; cognitive improvement; cognitive intervention; free-viewing; prosaccades; schizophrenia.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by a grant from the National Natural Science Foundation of China (No. 82071509), and AI+ Health Collaborative Innovation Cultivation Program from Beijing Municipal Science & Technology Commission, Administrative Commission of Zhongguancun Science Park (No. Z221100003522025).