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Yang T, Ou Y, Li H, Liu F, Li P, Xie G, Zhao J, Cui X, Guo W. Neural substrates of predicting anhedonia symptoms in major depressive disorder via connectome-based modeling. CNS Neurosci Ther 2024; 30:e14871. [PMID: 39037006 PMCID: PMC11261463 DOI: 10.1111/cns.14871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 06/23/2024] [Accepted: 07/09/2024] [Indexed: 07/23/2024] Open
Abstract
MAIN PROBLEM Anhedonia is a critical diagnostic symptom of major depressive disorder (MDD), being associated with poor prognosis. Understanding the neural mechanisms underlying anhedonia is of great significance for individuals with MDD, and it encourages the search for objective indicators that can reliably identify anhedonia. METHODS A predictive model used connectome-based predictive modeling (CPM) for anhedonia symptoms was developed by utilizing pre-treatment functional connectivity (FC) data from 59 patients with MDD. Node-based FC analysis was employed to compare differences in FC patterns between melancholic and non-melancholic MDD patients. The support vector machines (SVM) method was then applied for classifying these two subtypes of MDD patients. RESULTS CPM could successfully predict anhedonia symptoms in MDD patients (positive network: r = 0.4719, p < 0.0020, mean squared error = 23.5125, 5000 iterations). Compared to non-melancholic MDD patients, melancholic MDD patients showed decreased FC between the left cingulate gyrus and the right parahippocampus gyrus (p_bonferroni = 0.0303). This distinct FC pattern effectively discriminated between melancholic and non-melancholic MDD patients, achieving a sensitivity of 93.54%, specificity of 67.86%, and an overall accuracy of 81.36% using the SVM method. CONCLUSIONS This study successfully established a network model for predicting anhedonia symptoms in MDD based on FC, as well as a classification model to differentiate between melancholic and non-melancholic MDD patients. These findings provide guidance for clinical treatment.
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Affiliation(s)
- Tingyu Yang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaChina
- Department of Child HealthcareHunan Children's HospitalChangshaChina
| | - Yangpan Ou
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Huabing Li
- Department of RadiologyThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Feng Liu
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
| | - Ping Li
- Department of PsychiatryQiqihar Medical UniversityQiqiharChina
| | - Guangrong Xie
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Jingping Zhao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Xilong Cui
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Wenbin Guo
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaChina
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Geng H, Xu P, Aleman A, Qin S, Luo YJ. Dynamic Organization of Large-scale Functional Brain Networks Supports Interactions Between Emotion and Executive Control. Neurosci Bull 2024; 40:981-991. [PMID: 38261252 PMCID: PMC11250766 DOI: 10.1007/s12264-023-01168-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 10/05/2023] [Indexed: 01/24/2024] Open
Abstract
Emotion and executive control are often conceptualized as two distinct modes of human brain functioning. Little, however, is known about how the dynamic organization of large-scale functional brain networks that support flexible emotion processing and executive control, especially their interactions. The amygdala and prefrontal systems have long been thought to play crucial roles in these processes. Recent advances in human neuroimaging studies have begun to delineate functional organization principles among the large-scale brain networks underlying emotion, executive control, and their interactions. Here, we propose a dynamic brain network model to account for interactive competition between emotion and executive control by reviewing recent resting-state and task-related neuroimaging studies using network-based approaches. In this model, dynamic interactions among the executive control network, the salience network, the default mode network, and sensorimotor networks enable dynamic processes of emotion and support flexible executive control of multiple processes; neural oscillations across multiple frequency bands and the locus coeruleus-norepinephrine pathway serve as communicational mechanisms underlying dynamic synergy among large-scale functional brain networks. This model has important implications for understanding how the dynamic organization of complex brain systems and networks empowers flexible cognitive and affective functions.
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Affiliation(s)
- Haiyang Geng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Tianqiao and Chrissy, Chen Institute for Translational Research, Shanghai, 200040, China
| | - Pengfei Xu
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, 518107, China
| | - Andre Aleman
- University of Groningen, Department of Biomedical Sciences of Cells and Systems, Section Cognitive Neuroscience, University Medical Center Groningen, Groningen, The Netherlands
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
| | - Yue-Jia Luo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Institute for Neuropsychological Rehabilitation, University of Health and Rehabilitation Sciences, Qingdao, 266113, China.
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, 518060, China.
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Liu C, Li L, Zhu D, Lin S, Ren L, Zhen W, Tan W, Wang L, Tian L, Wang Q, Mao P, Pan W, Li B, Ma X. Individualized prediction of cognitive test scores from functional brain connectome in patients with first-episode late-life depression. J Affect Disord 2024; 352:32-42. [PMID: 38360359 DOI: 10.1016/j.jad.2024.02.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/17/2024] [Accepted: 02/08/2024] [Indexed: 02/17/2024]
Abstract
BACKGROUND In the realm of cognitive screening, the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are widely utilized for detecting cognitive deficits in patients with late-life depression (LLD), However, the interindividual variability in neuroimaging biomarkers contributing to individual-specific symptom severity remains poorly understood. In this study, we used a connectome-based predictive model (CPM) approach on resting-state functional magnetic resonance imaging data from patients with LLD to establish individualized prediction models for the MoCA and the MMSE scores. METHODS We recruited 135 individuals diagnosed with first-episode LLD for this research. Participants underwent the MMSE and MoCA tests, along with resting-state functional magnetic resonance imaging scans. Functional connectivity matrices derived from these scans were utilized in CPM models to predict MMSE or MoCA scores. Predictive precision was assessed by correlating predicted and observed scores, with the significance of prediction performance evaluated through a permutation test. RESULTS The negative model of the CPM procedure demonstrated a significant capacity to predict MoCA scores (r = -0.309, p = 0.002). Similarly, the CPM procedure could predict MMSE scores (r = -0.236, p = 0.016). The predictive models for cognitive test scores in LLD primarily involved the visual network, somatomotor network, dorsal attention network, and ventral attention network. CONCLUSIONS Brain functional connectivity emerges as a promising predictor of personalized cognitive test scores in LLD, suggesting that functional connectomes are potential neurobiological markers for cognitive performance in patients with LLD.
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Affiliation(s)
- Chaomeng Liu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Li Li
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Dandi Zhu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Shuo Lin
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Li Ren
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Wenfeng Zhen
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Weihao Tan
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Lina Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Lu Tian
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Qian Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Peixian Mao
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Weigang Pan
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Bing Li
- Hebei Provincial Mental Health Center, Baoding, China; Hebei Key Laboratory of Major Mental and Behavioral Disorders, Baoding, China; The Sixth Clinical Medical College of Hebei University, Baoding, China.
| | - Xin Ma
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
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Liu Y, Cui M, Gao X, Yang H, Chen H, Guan B, Ma X. Structural connectome combining DTI features predicts postoperative language decline and its recovery in glioma patients. Eur Radiol 2024; 34:2759-2771. [PMID: 37736802 DOI: 10.1007/s00330-023-10212-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 07/25/2023] [Accepted: 07/27/2023] [Indexed: 09/23/2023]
Abstract
OBJECTIVES A decline in language function is a common complication after glioma surgery, affecting patients' quality of life and survival. This study predicts the postoperative decline in language function and whether it can be recovered based on the preoperative white matter structural network. MATERIALS AND METHODS Eighty-one right-handed patients with glioma involving the left hemisphere were retrospectively included. Their language function was assessed using the Western Aphasia Battery before and 1 week and 3 months after surgery. Structural connectome combining DTI features was selected to predict postoperative language decline and recovery. Nested cross-validation was used to optimize the models, evaluate the prediction performance of the models, and identify the most predictive features. RESULTS Five, seven, and seven features were finally selected as the predictive features in each model and used to establish predictive models for postoperative language decline (1 week after surgery), long-term language decline (3 months after surgery), and language recovery, respectively. The overall accuracy of the three models in nested cross-validation and overall area under the receiver operating characteristic curve were 0.840, 0.790, and 0.867, and 0.841, 0.778, and 0.901, respectively. CONCLUSION We used machine learning algorithms to establish models to predict whether the language function of glioma patients will decline after surgery and whether postoperative language deficit can recover, which may help improve the development of treatment strategies. The difference in features in the non-language decline or the language recovery group may reflect the structural basis for the protection and compensation of language function in gliomas. CLINICAL RELEVANCE STATEMENT Models can predict the postoperative language decline and whether it can recover in glioma patients, possibly improving the development of treatment strategies. The difference in selected features may reflect the structural basis for the protection and compensation of language function. KEY POINTS • Structural connectome combining diffusion tensor imaging features predicted glioma patients' language decline after surgery. • Structural connectome combining diffusion tensor imaging features predicted language recovery of glioma patients with postoperative language disorder. • Diffusion tensor imaging and connectome features related to language function changes imply plastic brain regions and connections.
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Affiliation(s)
- Yukun Liu
- Department of Neurosurgery, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Meng Cui
- Department of Emergency Medicine, the Sixth Medical Centre, Chinese PLA General Hospital, Beijing, 100048, China
| | - Xin Gao
- Department of Neurosurgery, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
- Medical School of Chinese PLA, Beijing, China
| | - Hui Yang
- Department of Neurosurgery, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
- Medical School of Chinese PLA, Beijing, China
| | - Hewen Chen
- Department of Neurosurgery, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
- Medical School of Chinese PLA, Beijing, China
| | - Bing Guan
- Health Economics Department, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China.
| | - Xiaodong Ma
- Department of Neurosurgery, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China.
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Mao L, Wu Y, Hong X, Li P, Yuan X, Hu M. The influence of childhood maltreatment on trait depression in patients with major depressive disorder: A moderated mediation model of rumination and mindful attention awareness. J Affect Disord 2023; 331:130-138. [PMID: 36963511 DOI: 10.1016/j.jad.2023.03.052] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/06/2023] [Accepted: 03/18/2023] [Indexed: 03/26/2023]
Abstract
Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders. Individuals who were exposed to childhood maltreatment might be an especially vulnerable group and were more likely to meet the diagnostic criteria for depression than those who were not. Trait depression refers to a personality trait predisposition to depression, expressed as the frequency of symptoms rather than a transient depressive mood state. Clarifying the relationship between childhood maltreatment and trait depression in patients with MDD has therefore become an important field of research. Childhood Trauma Questionnaire-Short Form (CTQ-SF), Ruminative Responses Scale (RRS), State-Trait Depression Scale (ST-DEP), and Mindful Attention Awareness Scale (MAAS) were used as research instruments. SPSS 23.0 statistical software was used for statistical analysis and examined the moderated mediation models. A total of 288 patients with MDD were included in this study. After standardization of the variables, the model revealed childhood maltreatment was positively associated with trait depression (β = 0.215, p < 0.001) and that rumination partially mediated the effect between childhood trauma and trait depression. Mindfulness moderated the association between rumination and trait depression in depressed patients (β = 0.171, p < 0.001). Simple slope tests showed that rumination significantly predicted trait depression in patients with high levels of mindfulness (bsimple = 0.460, p < 0.001, 95%CI = [0.339, 0.581]), while this predictive effect was not significant in patients with low levels (bsimple = 0.119, p = 0.097, 95%CI = [-0.022, 0.261]). After adding mediating variables, we found that the negative impact of childhood maltreatment on trait depression was both directly and indirectly through the patients' own ruminative levels. However, mindfulness performed a critical moderating role in the overall mediating model, aggravating the negative impact of childhood maltreatment on trait depression. There are several limitations in this study: the history of childhood maltreatment was reviewed and reported; the MAAS was a single-dimensional questionnaire that fails to measure the content of other mindfulness factors; cross-sectional data could not be used to infer the causal relationship between variables.
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Affiliation(s)
- Lingyun Mao
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yunhong Wu
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xin Hong
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Pan Li
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xin Yuan
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Maorong Hu
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China.
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