Individualized prediction of multi-domain intelligence quotient in bipolar disorder patients using resting-state functional connectivity
Background: Although accumulating studies have explored the neural underpinnings of intelligence quotient (IQ) in patients with bipolar disorder (BD), these studies utilized a classification/comparison scheme that emphasized differences between BD and healthy controls at a group level. The present s...
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Elsevier
2025-03-01
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| Series: | Brain Research Bulletin |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0361923025000504 |
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| author | Xiaoyu Li Wei Wei Linze Qian Xiaojing Li Mingli Li Ioannis Kakkos Qiang Wang Hua Yu Wanjun Guo Xiaohong Ma George K. Matsopoulos Liansheng Zhao Wei Deng Yu Sun Tao Li |
| author_facet | Xiaoyu Li Wei Wei Linze Qian Xiaojing Li Mingli Li Ioannis Kakkos Qiang Wang Hua Yu Wanjun Guo Xiaohong Ma George K. Matsopoulos Liansheng Zhao Wei Deng Yu Sun Tao Li |
| author_sort | Xiaoyu Li |
| collection | DOAJ |
| description | Background: Although accumulating studies have explored the neural underpinnings of intelligence quotient (IQ) in patients with bipolar disorder (BD), these studies utilized a classification/comparison scheme that emphasized differences between BD and healthy controls at a group level. The present study aimed to infer BD patients’ IQ scores at the individual level using a prediction model. Methods: We applied a cross-validated Connectome-based Predictive Modeling (CPM) framework using resting-state fMRI functional connectivity (FCs) to predict BD patients’ IQ scores, including verbal IQ (VIQ), performance IQ (PIQ), and full-scale IQ (FSIQ). For each IQ domain, we selected the FCs that contributed to the predictions and described their distribution across eight widely-recognized functional networks. Moreover, we further explored the overlapping patterns of the contributed FCs for different IQ domains. Results: The CPM achieved statistically significant prediction performance for three IQ domains in BD patients. Regarding the contributed FCs, we observed a widespread distribution of internetwork FCs across somatomotor, visual, dorsal attention, and ventral attention networks, demonstrating their correspondence with aberrant FCs correlated to cognition deficits in BD patients. A convergent pattern in terms of contributed FCs for different IQ domains was observed, as evidenced by the shared-FCs with a leftward hemispheric dominance. Conclusions: The present study preliminarily explored the feasibility of inferring individual IQ scores in BD patients using the FCs-based CPM framework. It is a step toward the development of applicable techniques for quantitative and objective cognitive assessment in BD patients and contributes novel insights into understanding the complex neural mechanisms underlying different IQ domains. |
| format | Article |
| id | doaj-art-b0fb66594c8a42fcbe921146f194d2e1 |
| institution | Kabale University |
| issn | 1873-2747 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Brain Research Bulletin |
| spelling | doaj-art-b0fb66594c8a42fcbe921146f194d2e12025-02-09T04:59:39ZengElsevierBrain Research Bulletin1873-27472025-03-01222111238Individualized prediction of multi-domain intelligence quotient in bipolar disorder patients using resting-state functional connectivityXiaoyu Li0Wei Wei1Linze Qian2Xiaojing Li3Mingli Li4Ioannis Kakkos5Qiang Wang6Hua Yu7Wanjun Guo8Xiaohong Ma9George K. Matsopoulos10Liansheng Zhao11Wei Deng12Yu Sun13Tao Li14Key Laboratory for Biomedical Engineering of the Ministry of Education of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, ChinaDepartment of Psychiatry, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou 310013, China; Nanhu Brain-computer Interface Institute, Hangzhou 311100, China; Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou 311121, China; NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, ChinaKey Laboratory for Biomedical Engineering of the Ministry of Education of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, ChinaDepartment of Psychiatry, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou 310013, China; Nanhu Brain-computer Interface Institute, Hangzhou 311100, China; Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou 311121, China; NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, ChinaMental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu 610041, ChinaSchool of Electrical and Computer Engineering, National Technical University of Athens, Athens 15790, GreeceMental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu 610041, ChinaDepartment of Psychiatry, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou 310013, China; Nanhu Brain-computer Interface Institute, Hangzhou 311100, China; Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou 311121, China; NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, ChinaDepartment of Psychiatry, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou 310013, China; Nanhu Brain-computer Interface Institute, Hangzhou 311100, China; Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou 311121, China; NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, ChinaMental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu 610041, ChinaSchool of Electrical and Computer Engineering, National Technical University of Athens, Athens 15790, GreeceMental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu 610041, ChinaDepartment of Psychiatry, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou 310013, China; Nanhu Brain-computer Interface Institute, Hangzhou 311100, China; Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou 311121, China; NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, ChinaKey Laboratory for Biomedical Engineering of the Ministry of Education of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China; Correspondence: Key Laboratory for Biomedical Engineering of MOE of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.Department of Psychiatry, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou 310013, China; Nanhu Brain-computer Interface Institute, Hangzhou 311100, China; Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou 311121, China; NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China; Corresponding author at: Department of Psychiatry, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou 310013, China.Background: Although accumulating studies have explored the neural underpinnings of intelligence quotient (IQ) in patients with bipolar disorder (BD), these studies utilized a classification/comparison scheme that emphasized differences between BD and healthy controls at a group level. The present study aimed to infer BD patients’ IQ scores at the individual level using a prediction model. Methods: We applied a cross-validated Connectome-based Predictive Modeling (CPM) framework using resting-state fMRI functional connectivity (FCs) to predict BD patients’ IQ scores, including verbal IQ (VIQ), performance IQ (PIQ), and full-scale IQ (FSIQ). For each IQ domain, we selected the FCs that contributed to the predictions and described their distribution across eight widely-recognized functional networks. Moreover, we further explored the overlapping patterns of the contributed FCs for different IQ domains. Results: The CPM achieved statistically significant prediction performance for three IQ domains in BD patients. Regarding the contributed FCs, we observed a widespread distribution of internetwork FCs across somatomotor, visual, dorsal attention, and ventral attention networks, demonstrating their correspondence with aberrant FCs correlated to cognition deficits in BD patients. A convergent pattern in terms of contributed FCs for different IQ domains was observed, as evidenced by the shared-FCs with a leftward hemispheric dominance. Conclusions: The present study preliminarily explored the feasibility of inferring individual IQ scores in BD patients using the FCs-based CPM framework. It is a step toward the development of applicable techniques for quantitative and objective cognitive assessment in BD patients and contributes novel insights into understanding the complex neural mechanisms underlying different IQ domains.http://www.sciencedirect.com/science/article/pii/S0361923025000504Bipolar disorderCPMfMRI functional connectivityIntelligence quotientMulti-domain |
| spellingShingle | Xiaoyu Li Wei Wei Linze Qian Xiaojing Li Mingli Li Ioannis Kakkos Qiang Wang Hua Yu Wanjun Guo Xiaohong Ma George K. Matsopoulos Liansheng Zhao Wei Deng Yu Sun Tao Li Individualized prediction of multi-domain intelligence quotient in bipolar disorder patients using resting-state functional connectivity Brain Research Bulletin Bipolar disorder CPM fMRI functional connectivity Intelligence quotient Multi-domain |
| title | Individualized prediction of multi-domain intelligence quotient in bipolar disorder patients using resting-state functional connectivity |
| title_full | Individualized prediction of multi-domain intelligence quotient in bipolar disorder patients using resting-state functional connectivity |
| title_fullStr | Individualized prediction of multi-domain intelligence quotient in bipolar disorder patients using resting-state functional connectivity |
| title_full_unstemmed | Individualized prediction of multi-domain intelligence quotient in bipolar disorder patients using resting-state functional connectivity |
| title_short | Individualized prediction of multi-domain intelligence quotient in bipolar disorder patients using resting-state functional connectivity |
| title_sort | individualized prediction of multi domain intelligence quotient in bipolar disorder patients using resting state functional connectivity |
| topic | Bipolar disorder CPM fMRI functional connectivity Intelligence quotient Multi-domain |
| url | http://www.sciencedirect.com/science/article/pii/S0361923025000504 |
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