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Lai PH, Hu RY, Huang X. Alterations in dynamic regional homogeneity within default mode network in patients with thyroid-associated ophthalmopathy. Neuroreport 2024; 35:702-711. [PMID: 38829952 DOI: 10.1097/wnr.0000000000002056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Thyroid-associated ophthalmopathy (TAO) is a significant autoimmune eye disease known for causing exophthalmos and substantial optic nerve damage. Prior investigations have solely focused on static functional MRI (fMRI) scans of the brain in TAO patients, neglecting the assessment of temporal variations in local brain activity. This study aimed to characterize alterations in dynamic regional homogeneity (dReHo) in TAO patients and differentiate between TAO patients and healthy controls using support vector machine (SVM) classification. Thirty-two patients with TAO and 32 healthy controls underwent resting-state fMRI scans. We calculated dReHo using sliding-window methods to evaluate changes in regional brain activity and compared these findings between the two groups. Subsequently, we employed SVM, a machine learning algorithm, to investigate the potential use of dReHo maps as diagnostic markers for TAO. Compared to healthy controls, individuals with active TAO demonstrated significantly higher dReHo values in the right angular gyrus, left precuneus, right inferior parietal as well as the left superior parietal gyrus. The SVM model demonstrated an accuracy ranging from 65.62 to 68.75% in distinguishing between TAO patients and healthy controls based on dReHo variability in these identified brain regions, with an area under the curve of 0.70 to 0.76. TAO patients showed increased dReHo in default mode network-related brain regions. The accuracy of classifying TAO patients and healthy controls based on dReHo was notably high. These results offer new insights for investigating the pathogenesis and clinical diagnostic classification of individuals with TAO.
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Affiliation(s)
- Ping-Hong Lai
- Department of Ophthalmology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Rui-Yang Hu
- School of Ophthalmology and Optometry, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Xin Huang
- Department of Ophthalmology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
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Du Y, Niu J, Xing Y, Li B, Calhoun VD. Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade. Schizophr Bull 2024:sbae110. [PMID: 38982882 DOI: 10.1093/schbul/sbae110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years. STUDY DESIGN The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade. STUDY RESULTS Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders. CONCLUSIONS We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ju Niu
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ying Xing
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Bang Li
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Vince D Calhoun
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, 30303, GA, USA
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Hu Y, Ma J, Chen B, Pang J, Liang W, Wu W. The Duration of Chronic Pain Can Affect Brain Functional Changes of the Pain Matrix in Patients with Chronic Back Pain: A Resting-State fMRI Study. J Pain Res 2024; 17:1941-1951. [PMID: 38828086 PMCID: PMC11141710 DOI: 10.2147/jpr.s457575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 05/13/2024] [Indexed: 06/05/2024] Open
Abstract
Purpose This study was conducted to explore the differences in functional changes in the pain matrix in patients with chronic back pain (CBP) at different stages and identify whether these brain changes were related to the pain duration. Patients and Methods In this study, 29 healthy individuals and 54 patients with CBP were recruited. According to the pain duration, 25 patients (3 to 12 months) were divided into the CBP-S group and 29 patients (≥ 24 months) were divided into the CBP-L group. All subjects completed clinical pain-related measurement and functional magnetic resonance imaging (fMRI) scans. Moreover, the amplitude of low-frequency fluctuation (ALFF), functional connectivity (FC), and correlation analysis were conducted in this study. Results Compared with healthy controls, patients in the CBP-L group showed significantly decreased ALFF in the left precuneus. In the FC analysis, patients in the CBP-S and CBP-L groups showed significantly decreased FC in several regions in the bilateral orbitofrontal cortices (OFC) and the left ventral posterior insula. Moreover, there were significant differences in the FC between the left hyper granular insula and the probabilistic area in OFC in pairwise group comparisons. The correlation analysis results demonstrated that pain duration was correlated with these functional brain changes, and the ANCOVA results revealed that pain intensity and pain interference scores did not affect the FC analysis results. Conclusion There are different changes in the pain neural matrix in patients with chronic pain at different stages. Furthermore, the pain duration is related to brain functional changes.
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Affiliation(s)
- Yingxuan Hu
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Junqin Ma
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Bingmei Chen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Jiahui Pang
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Wen Liang
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Wen Wu
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
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Zhong M, Liu Z, Wang F, Yang J, Chen E, Lee E, Wu G, Yang J. Effects of long-term antipsychotic medication on brain instability in first-episode schizophrenia patients: a resting-state fMRI study. Front Pharmacol 2024; 15:1387123. [PMID: 38846088 PMCID: PMC11153814 DOI: 10.3389/fphar.2024.1387123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 05/02/2024] [Indexed: 06/09/2024] Open
Abstract
Early initiation of antipsychotic treatment plays a crucial role in the management of first-episode schizophrenia (FES) patients, significantly improving their prognosis. However, limited attention has been given to the long-term effects of antipsychotic drug therapy on FES patients. In this research, we examined the changes in abnormal brain regions among FES patients undergoing long-term treatment using a dynamic perspective. A total of 98 participants were included in the data analysis, comprising 48 FES patients, 50 healthy controls, 22 patients completed a follow-up period of more than 6 months with qualified data. We processed resting-state fMRI data to calculate coefficient of variation of fractional amplitude of low-frequency fluctuations (CVfALFF), which reflects the brain regional activity stability. Data analysis was performed at baseline and after long-term treatment. We observed that compared with HCs, patients at baseline showed an elevated CVfALFF in the supramarginal gyrus (SMG), parahippocampal gyrus (PHG), caudate, orbital part of inferior frontal gyrus (IOG), insula, and inferior frontal gyrus (IFG). After long-term treatment, the instability in SMG, PHG, caudate, IOG, insula and inferior IFG have ameliorated. Additionally, there was a positive correlation between the decrease in dfALFF in the SMG and the reduction in the SANS total score following long-term treatment. In conclusion, FES patients exhibit unstable regional activity in widespread brain regions at baseline, which can be ameliorated with long-term treatment. Moreover, the extent of amelioration in SMG instability is associated with the amelioration of negative symptoms.
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Affiliation(s)
- Maoxing Zhong
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhening Liu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Feiwen Wang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Jun Yang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Eric Chen
- Department of Psychiatry, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Edwin Lee
- Department of Psychiatry, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Guowei Wu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Jie Yang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
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Xie A, Sun Y, Chen H, Li L, Liu P, Liao Y, Li Y. Altered dynamic functional connectivity of insular subdivisions among male cigarette smokers. Front Psychiatry 2024; 15:1353103. [PMID: 38827448 PMCID: PMC11140567 DOI: 10.3389/fpsyt.2024.1353103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 05/06/2024] [Indexed: 06/04/2024] Open
Abstract
Background Insular subdivisions show distinct patterns of resting state functional connectivity with specific brain regions, each with different functional significance in chronic cigarette smokers. This study aimed to explore the altered dynamic functional connectivity (dFC) of distinct insular subdivisions in smokers. Methods Resting-state BOLD data of 31 smokers with nicotine dependence and 27 age-matched non-smokers were collected. Three bilateral insular regions of interest (dorsal, ventral, and posterior) were set as seeds for analyses. Sliding windows method was used to acquire the dFC metrics of different insular seeds. Support vector machine based on abnormal insular dFC was applied to classify smokers from non-smokers. Results We found that smokers showed lower dFC variance between the left ventral anterior insula and both the right superior parietal cortex and the left inferior parietal cortex, as well as greater dFC variance the right ventral anterior insula with the right middle cingulum cortex relative to non-smokers. Moreover, compared to non-smokers, it is found that smokers demonstrated altered dFC variance of the right dorsal insula and the right middle temporal gyrus. Correlation analysis showed the higher dFC between the right dorsal insula and the right middle temporal gyrus was associated with longer years of smoking. The altered insular subdivision dFC can classify smokers from non-smokers with an accuracy of 89.66%, a sensitivity of 96.30% and a specify of 83.87%. Conclusions Our findings highlighted the abnormal patterns of fluctuating connectivity of insular subdivision circuits in smokers and suggested that these abnormalities may play a significant role in the mechanisms underlying nicotine addiction and could potentially serve as a neural biomarker for addiction treatment.
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Affiliation(s)
- An Xie
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Department of Radiology, The People’s Hospital of Hunan Province (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan, China
- Center for Mind & Brain Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Yunkai Sun
- Department of Psychiatry, Sir Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Haobo Chen
- Department of Radiology, The People’s Hospital of Hunan Province (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan, China
- Center for Mind & Brain Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Ling Li
- Department of Psychiatry, Sir Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Peng Liu
- Department of Radiology, The People’s Hospital of Hunan Province (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan, China
- Center for Mind & Brain Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Yanhui Liao
- Department of Radiology, The People’s Hospital of Hunan Province (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan, China
- Center for Mind & Brain Sciences, Hunan Normal University, Changsha, Hunan, China
- Department of Psychiatry, Sir Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yonggang Li
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
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Anhøj S, Ebdrup B, Nielsen MØ, Antonsen P, Glenthøj B, Rostrup E. Functional Connectivity Between Auditory and Medial Temporal Lobe Networks in Antipsychotic-Naïve Patients With First-Episode Schizophrenia Predicts the Effects of Dopamine Antagonism on Auditory Verbal Hallucinations. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:308-316. [PMID: 38298804 PMCID: PMC10829637 DOI: 10.1016/j.bpsgos.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 02/02/2024] Open
Abstract
Background Understanding how antipsychotic medication ameliorates auditory verbal hallucinations (AVHs) through modulation of brain circuitry is pivotal for understanding the pathophysiology of psychosis and for predicting treatment response. Methods This case-control study included examinations at baseline and at follow-up after 6 weeks. Initially, antipsychotic-naïve patients with first-episode schizophrenia who were experiencing AVHs were recruited together with healthy control participants. Antipsychotic treatment with the relatively selective D2 receptor antagonist amisulpride was administered as monotherapy. Functional connectivity measured by resting-state functional magnetic resonance imaging between networks of interest was used to study the effects of D2 blockade on brain circuitry and predict clinical treatment response. Hallucinations were rated with the Positive and Negative Syndrome Scale. Results Thirty-two patients experiencing AVHs and 34 healthy control participants were scanned at baseline. Twenty-two patients and 34 healthy control participants were rescanned at follow-up. Connectivity between the auditory network and the medial temporal lobe network was increased in patients at baseline (p = .002) and normalized within 6 weeks of D2 blockade (p = .018). At baseline, the connectivity between these networks was positively correlated with ratings of hallucinations (t = 2.67, p = .013). Moreover, baseline connectivity between the auditory network and the medial temporal lobe network predicted reduction in hallucinations (t = 2.34, p = .032). Conclusions Functional connectivity between the auditory network and the medial temporal lobe predicted response to initial antipsychotic treatment. These findings demonstrate that connectivity between networks involved in auditory processing, internal monitoring, and memory is associated with the clinical effect of dopamine antagonism.
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Affiliation(s)
- Simon Anhøj
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Center Glostrup, Glostrup, Denmark
| | - Bjørn Ebdrup
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Center Glostrup, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mette Ødegaard Nielsen
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Center Glostrup, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Patrick Antonsen
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Center Glostrup, Glostrup, Denmark
| | - Birte Glenthøj
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Center Glostrup, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Egill Rostrup
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Center Glostrup, Glostrup, Denmark
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Cattarinussi G, Di Giorgio A, Moretti F, Bondi E, Sambataro F. Dynamic functional connectivity in schizophrenia and bipolar disorder: A review of the evidence and associations with psychopathological features. Prog Neuropsychopharmacol Biol Psychiatry 2023; 127:110827. [PMID: 37473954 DOI: 10.1016/j.pnpbp.2023.110827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/05/2023] [Accepted: 07/10/2023] [Indexed: 07/22/2023]
Abstract
Alterations of functional network connectivity have been implicated in the pathophysiology of schizophrenia (SCZ) and bipolar disorder (BD). Recent studies also suggest that the temporal dynamics of functional connectivity (dFC) can be altered in these disorders. Here, we summarized the existing literature on dFC in SCZ and BD, and their association with psychopathological and cognitive features. We systematically searched PubMed, Web of Science, and Scopus for studies investigating dFC in SCZ and BD and identified 77 studies. Our findings support a general model of dysconnectivity of dFC in SCZ, whereas a heterogeneous picture arose in BD. Although dFC alterations are more severe and widespread in SCZ compared to BD, dysfunctions of a triple network system underlying goal-directed behavior and sensory-motor networks were present in both disorders. Furthermore, in SCZ, positive and negative symptoms were associated with abnormal dFC. Implications for understanding the pathophysiology of disorders, the role of neurotransmitters, and treatments on dFC are discussed. The lack of standards for dFC metrics, replication studies, and the use of small samples represent major limitations for the field.
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Affiliation(s)
- Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Italy; Padova Neuroscience Center, University of Padova, Italy
| | - Annabella Di Giorgio
- Department of Mental Health and Addictions, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Federica Moretti
- Department of Medicine and Surgery, University of Milan Bicocca, Milan, Italy
| | - Emi Bondi
- Department of Mental Health and Addictions, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Italy; Padova Neuroscience Center, University of Padova, Italy.
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8
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Ma Y, Wang Q, Cao L, Li L, Zhang C, Qiao L, Liu M. Multi-Scale Dynamic Graph Learning for Brain Disorder Detection With Functional MRI. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3501-3512. [PMID: 37643109 DOI: 10.1109/tnsre.2023.3309847] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the detection of brain disorders such as autism spectrum disorder based on various machine/deep learning techniques. Learning-based methods typically rely on functional connectivity networks (FCNs) derived from blood-oxygen-level-dependent time series of rs-fMRI data to capture interactions between brain regions-of-interest (ROIs). Graph neural networks have been recently used to extract fMRI features from graph-structured FCNs, but cannot effectively characterize spatiotemporal dynamics of FCNs, e.g., the functional connectivity of brain ROIs is dynamically changing in a short period of time. Also, many studies usually focus on single-scale topology of FCN, thereby ignoring the potential complementary topological information of FCN at different spatial resolutions. To this end, in this paper, we propose a multi-scale dynamic graph learning (MDGL) framework to capture multi-scale spatiotemporal dynamic representations of rs-fMRI data for automated brain disorder diagnosis. The MDGL framework consists of three major components: 1) multi-scale dynamic FCN construction using multiple brain atlases to model multi-scale topological information, 2) multi-scale dynamic graph representation learning to capture spatiotemporal information conveyed in fMRI data, and 3) multi-scale feature fusion and classification. Experimental results on two datasets show that MDGL outperforms several state-of-the-art methods.
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Sreeraj VS, Shivakumar V, Bhalerao GV, Kalmady SV, Narayanaswamy JC, Venkatasubramanian G. Resting-state functional connectivity correlates of antipsychotic treatment in unmedicated schizophrenia. Asian J Psychiatr 2023; 82:103459. [PMID: 36682158 DOI: 10.1016/j.ajp.2023.103459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 01/03/2023] [Accepted: 01/07/2023] [Indexed: 01/09/2023]
Abstract
BACKGROUND Antipsychotics may modulate the resting state functional connectivity(rsFC) to improve clinical symptoms in schizophrenia(Sz). Existing literature has potential confounders like past medication effects and evaluating preselected regions/networks. We aimed to evaluate connectivity pattern changes with antipsychotics in unmedicated Sz using Multivariate pattern analysis(MVPA), a data-driven technique for whole-brain connectome analysis. METHODS Forty-seven unmedicated patients with Sz(DSM-IV-TR) underwent clinical evaluation and neuroimaging at baseline and after 3-months of antipsychotic treatment. Resting-state functional MRI was analysed using group-MVPA to derive 5-components. The brain region with significant connectivity pattern changes with antipsychotics was identified, and post-hoc seed-to-voxel analysis was performed to identify connectivity changes and their association with symptom changes. RESULTS Connectome-MVPA analysis revealed the connectivity pattern of a cluster localised to left anterior cingulate and paracingulate gyri (ACC/PCG) (peak coordinates:x = -04,y = +30,z = +26;k = 12;cluster-pFWE=0.002) to differ significantly after antipsychotics. Specifically, its connections with clusters of precuneus/posterior cingulate cortex(PCC) and left inferior temporal gyrus(ITG) correlated with improvement in positive and negative symptoms scores, respectively. CONCLUSION ACC/PCG, a hub of the default mode network, seems to mediate the antipsychotic effects in unmedicated Sz. Evaluating causality models with data from randomised controlled design using the MVPA approach would further enhance our understanding of therapeutic connectomics in Sz.
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Affiliation(s)
- Vanteemar S Sreeraj
- InSTAR Clinic and Translational Psychiatry Lab, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India.
| | - Venkataram Shivakumar
- InSTAR Clinic and Translational Psychiatry Lab, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India; Department of Integrative Medicine, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | | | - Sunil V Kalmady
- Alberta Machine Intelligence Institute, Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada; Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada
| | | | - Ganesan Venkatasubramanian
- InSTAR Clinic and Translational Psychiatry Lab, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
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Wang Y, Sun Z, Zhou Z. Aberrant changes of dynamic global synchronization in patients with Parkinson's disease. Acta Radiol 2023; 64:784-791. [PMID: 35484787 DOI: 10.1177/02841851221094967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Patients with Parkinson's disease (PD) have been documented with disrupted dynamic profiles of functional connectivity. However, the complementary information that is relevant to the dynamic pattern of global synchronization in patients with PD requires further investigation. PURPOSE To reveal the aberrant dynamic profiles of global synchronization involved in PD with a focus on temporal variability, strength, and property. MATERIAL AND METHODS A total of 46 patients with PD and 50 matched healthy controls (HCs) were enrolled. Degree centrality (DC) was used as the metric of global synchronization. The intergroup differences in the dynamic DC (dDC) pattern were compared, followed by further analysis of their clinical relevance in PD. RESULTS Relative to HCs, the PD group showed decreased dDC variability in right inferior occipital gyrus, right insula, right middle occipital gyrus (MOG), and bilateral postcentral gyrus. The dDC variability in the MOG was significantly correlated with MoCA score. Two states (state I and state II) were suggested. Relative to HCs, the PD group demonstrated a shorter mean dwell time (MDT) in state I, a longer MDT in state II, and fewer transitions. For the PD group, dDC properties were significantly correlated with UPDRS-III scores. In state II, significantly decreased dynamic dDC strength in bilateral supplementary motor area was observed in the PD group, with a significant correlation with UPDRS-III scores. CONCLUSION These findings on PD imply that dynamic alterations of global synchronization are engaged in the dysfunction of movement and cognition, deepening the understanding of deteriorations that underlie PD with complementary evidence.
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Affiliation(s)
- Yong Wang
- Department of Radiology, 372209Taizhou People's Hospital, Fifth Affiliated Hospital of Nantong University, Taizhou, Jiangsu, PR China
| | - Zhongru Sun
- Department of Radiology, 372209Taizhou People's Hospital, Fifth Affiliated Hospital of Nantong University, Taizhou, Jiangsu, PR China
| | - Zhijun Zhou
- Department of Radiology, 372209Taizhou People's Hospital, Fifth Affiliated Hospital of Nantong University, Taizhou, Jiangsu, PR China
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Lu F, Chen Y, Cui Q, Guo Y, Pang Y, Luo W, Yu Y, Chen J, Gao J, Sheng W, Tang Q, Zeng Y, Jiang K, Gao Q, He Z, Chen H. Shared and distinct patterns of dynamic functional connectivity variability of thalamo-cortical circuit in bipolar depression and major depressive disorder. Cereb Cortex 2023:6987621. [PMID: 36642500 DOI: 10.1093/cercor/bhac534] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 01/17/2023] Open
Abstract
Evidence has indicated abnormalities of thalamo-cortical functional connectivity (FC) in bipolar disorder during a depressive episode (BDD) and major depressive disorder (MDD). However, the dynamic FC (dFC) within this system is poorly understood. We explored the thalamo-cortical dFC pattern by dividing thalamus into 16 subregions and combining with a sliding-window approach. Correlation analysis was performed between altered dFC variability and clinical data. Classification analysis with a linear support vector machine model was conducted. Compared with healthy controls (HCs), both patients revealed increased dFC variability between thalamus subregions with hippocampus (HIP), angular gyrus and caudate, and only BDD showed increased dFC variability of the thalamus with superior frontal gyrus (SFG), HIP, insula, middle cingulate gyrus, and postcentral gyrus. Compared with MDD and HCs, only BDD exhibited enhanced dFC variability of the thalamus with SFG and superior temporal gyrus. Furthermore, the number of depressive episodes in MDD was significantly positively associated with altered dFC variability. Finally, the disrupted dFC variability could distinguish BDD from MDD with 83.44% classification accuracy. BDD and MDD shared common disrupted dFC variability in the thalamo-limbic and striatal-thalamic circuitries, whereas BDD exhibited more extensive and broader aberrant dFC variability, which may facilitate distinguish between these 2 mood disorders.
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Affiliation(s)
- Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Yingmenkou Road, Jinniu District, 611731, PR China
| | - Yanchi Chen
- Glasgow College, University of Electronic Science and Technology of China, Chengdu, No. 2006, Xiyuan Ave, West Hi-Tech Zone, 611731, PR China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, No. 2006, Xiyuan Ave, West Hi-Tech Zone, 611731, PR China
| | - Yuanhong Guo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Yingmenkou Road, Jinniu District, 611731, PR China
| | - Yajing Pang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, No. 100 Science Avenue, High-tech Zone, 450001, PR China
| | - Wei Luo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Yingmenkou Road, Jinniu District, 611731, PR China
| | - Yue Yu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Yingmenkou Road, Jinniu District, 611731, PR China
| | - Jiajia Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Yingmenkou Road, Jinniu District, 611731, PR China
| | - Jingjing Gao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, No. 2006, Xiyuan Ave, West Hi-Tech Zone, 611731, China
| | - Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Yingmenkou Road, Jinniu District, 611731, PR China
| | - Qin Tang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Yingmenkou Road, Jinniu District, 611731, PR China
| | - Yuhong Zeng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Yingmenkou Road, Jinniu District, 611731, PR China
| | - Kexing Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Yingmenkou Road, Jinniu District, 611731, PR China
| | - Qing Gao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Yingmenkou Road, Jinniu District, 611731, PR China.,School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, No. 2006, Xiyuan Ave, West Hi-Tech Zone, 611731, PR China
| | - Zongling He
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Yingmenkou Road, Jinniu District, 611731, PR China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Yingmenkou Road, Jinniu District, 611731, PR China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, No. 2006, Xiyuan Ave, West Hi-Tech Zone, 611731, PR China
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12
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Deep rTMS of the insula and prefrontal cortex in smokers with schizophrenia: Proof-of-concept study. SCHIZOPHRENIA 2022; 8:6. [PMID: 35217662 PMCID: PMC8881463 DOI: 10.1038/s41537-022-00224-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 12/17/2021] [Indexed: 11/20/2022]
Abstract
Patients with schizophrenia have a high prevalence of cigarette smoking and respond poorly to conventional treatments, highlighting the need for new therapies. We conducted a mechanistic, proof-of-concept study using bilateral deep repetitive transcranial magnetic stimulation (dTMS) of insular and prefrontal cortices at high frequency, using the specialized H4 coil. Feasibility of dTMS was tested for disruption of tobacco self-administration, insula target engagement, and insula circuit modulation, all of which were a priori outcomes of interest. Twenty patients completed the study, consisting of weekday dTMS sessions (randomization to active dTMS or sham; double-blind; 10 patients per group), a laboratory tobacco self-administration paradigm (pre/post assessments), and multimodal imaging (three MRI total sessions). Results showed that participants assigned to active dTMS were slower to initiate smoking their first cigarette compared with sham, consistent with smoking disruption. The imaging analyses did not reveal significant Time × Group interactions, but effects were in the anticipated directions. In arterial spin labeling analyses testing for target engagement, an overall decrease in insula blood flow, measured during a post-treatment MRI versus baseline, was numerically more pronounced in the active dTMS group than sham. In fMRI analyses, resting-state connectivity between the insula and default mode network showed a numerically greater change from baseline in the active dTMS group than sham, consistent with a functional change to insula circuits. Exploratory analyses further suggested a therapeutic effect of dTMS on symptoms of psychosis. These initial observations pave the way for future confirmatory studies of dTMS in smoking patients with schizophrenia.
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13
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Wang Y, Kessel E, Lee S, Hong S, Raffanello E, Hulvershorn LA, Margolis A, Peterson BS, Posner J. Causal effects of psychostimulants on neural connectivity: a mechanistic, randomized clinical trial. J Child Psychol Psychiatry 2022; 63:1381-1391. [PMID: 35141898 PMCID: PMC9360200 DOI: 10.1111/jcpp.13585] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/04/2022] [Indexed: 12/20/2022]
Abstract
BACKGROUND Psychostimulants are frequently used to treat attention-deficit/hyperactivity disorder (ADHD), but side effects are common leading to many patients discontinuing treatment. Identifying neural mechanisms by which psychostimulants attenuate symptoms may guide the development of more refined and tolerable therapeutics. METHODS We conducted a 12-week, randomized, placebo-controlled trial (RCT) of a long-acting amphetamine, lisdexamfetamine (LDEX), in patients with ADHD, ages 6-25 years old. Of the 58 participants who participated in the RCT, 49 completed pre- and post-RCT magnetic resonance imaging scanning with adequate data quality. Healthy controls (HCs; n = 46) were included for comparison. Treatment effects on striatal and thalamic functional connectivity (FC) were identified using static (time-averaged) and dynamic (time-varying) measures and then correlated with symptom improvement. Analyses were repeated in independent samples from the Adolescent Brain Cognitive Development study (n = 103) and the ADHD-200 Consortium (n = 213). RESULTS In 49 participants (25 LDEX; 24 Placebo), LDEX increased static and decreased dynamic FC (DFC). However, only DFC was associated with the therapeutic effects of LDEX. Additionally, at baseline, DFC was elevated in unmedicated-ADHD participants relative to HCs. Independent samples yielded similar findings - ADHD was associated with increased DFC, and psychostimulants with reduced DFC. Static FC findings were inconsistent across samples. CONCLUSIONS Changes in dynamic, but not static, FC were associated with the therapeutic effects of psychostimulants. While prior research has focused on static FC, DFC may offer a more reliable target for new ADHD interventions aimed at stabilizing network dynamics, though this needs confirmation with subsequent investigations.
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Affiliation(s)
- Yun Wang
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA,New York State Psychiatric Institute, New York, NY, USA
| | - Ellen Kessel
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA,New York State Psychiatric Institute, New York, NY, USA
| | - Seonjoo Lee
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA,New York State Psychiatric Institute, New York, NY, USA
| | - Susie Hong
- New York State Psychiatric Institute, New York, NY, USA
| | | | | | - Amy Margolis
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA,New York State Psychiatric Institute, New York, NY, USA
| | - Bradley S. Peterson
- Department of Psychiatry, Keck School of Medicine, Los Angeles, CA, USA,Institute for the Developing Mind, Saban Research Institute, CHLA, Los Angeles, CA, USA
| | - Jonathan Posner
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA,New York State Psychiatric Institute, New York, NY, USA
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14
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Jiang F, Jin H, Gao Y, Xie X, Cummings J, Raj A, Nagarajan S. Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging. Neuroimage 2022; 254:119131. [PMID: 35337963 PMCID: PMC9942947 DOI: 10.1016/j.neuroimage.2022.119131] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 02/04/2022] [Accepted: 03/21/2022] [Indexed: 01/26/2023] Open
Abstract
Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occur over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods that are inherently non-adaptive, have various limitations such as high-dimensionality, an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multiply functional imaging modalities. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between a low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns the low-dimensional manifold of dynamic RSFC and detects dynamic state transitions in data. TVDN is applicable to multiple modalities of functional neuroimaging such as fMRI and MEG/EEG. The estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.
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Affiliation(s)
- Fei Jiang
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA.
| | - Huaqing Jin
- Department of Statistics and Actuarial Science, the University of Hong Kong, CN, Hong Kong
| | - Yijing Gao
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA
| | - Xihe Xie
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA
| | - Jennifer Cummings
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.
| | - Srikantan Nagarajan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.
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15
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Deng M, Liu Z, Shen Y, Cao H, Zhang M, Xi C, Zhang W, Tan W, Zhang J, Chen E, Lee E, Pu W. Treatment Effect of Long-Term Antipsychotics on Default-Mode Network Dysfunction in Drug-Naïve Patients With First-Episode Schizophrenia: A Longitudinal Study. Front Pharmacol 2022; 13:833518. [PMID: 35685640 PMCID: PMC9171718 DOI: 10.3389/fphar.2022.833518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 04/13/2022] [Indexed: 11/30/2022] Open
Abstract
Background: The maintenance of antipsychotic treatment is an efficient way to prevent the relapse of schizophrenia (SCZ). Previous studies have identified beneficial effects of antipsychotics on brain structural and functional abnormalities during mostly the acute phase in SCZ, but seldom is known about the effects of long-term antipsychotics on the brain. The present study focused on the long-term antipsychotic effect on the default mode network (DMN) dysfunction in SCZ. Methods: A longitudinal study of the functional connectivity (FC) of 11 DMN subdivisions was conducted in 86 drug-naive first-episode patients with SCZ at the baseline and after a long-term atypical antipsychotic treatment (more than 6 months) based on the resting-state functional magnetic resonance image. In total, 52 patients completed the follow-up of clinical and neuroimaging investigations. Results: At the baseline, relative to healthy controls, altered connectivities within the DMN and between the DMN and the external attention system (EAS) were observed in patients. After treatment, along with significant relief of symptoms, most FC alterations between the DMN and the EAS at the baseline were improved after treatment, although the rehabilitation of FC within the DMN was only observed at the link between the posterior cingulate cortex and precuneus. Greater reductions in negative and positive symptoms were both related to the changes of DMN-EAS FC in patients. Conclusion: Our findings provide evidence that maintenance antipsychotics on SCZ is beneficial for the improvement of DMN-EAS competitive imbalance, which may partly contribute to the efficient relapse prevention of this severe mental disorder.
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Affiliation(s)
- Mengjie Deng
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
- China National Clinical Research Center for Mental Health Disorders, Changsha, China
| | - Zhening Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
- China National Clinical Research Center for Mental Health Disorders, Changsha, China
| | - Yanyu Shen
- The First Clinical Medical College, Lanzhou University, Lanzhou, China
| | - Hengyi Cao
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Hempstead, NY, United States
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, United States
| | - Manqi Zhang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, United States
- School of Psychology, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
| | - Chang Xi
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
- China National Clinical Research Center for Mental Health Disorders, Changsha, China
| | - Wen Zhang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
- China National Clinical Research Center for Mental Health Disorders, Changsha, China
| | - Wenjian Tan
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
- China National Clinical Research Center for Mental Health Disorders, Changsha, China
| | - Jinqiang Zhang
- Department of Clinical Psychology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Eric Chen
- Department of Psychiatry, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Edwin Lee
- Department of Psychiatry, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Weidan Pu
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
- China National Clinical Research Center for Mental Health Disorders, Changsha, China
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, China
- *Correspondence: Weidan Pu,
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16
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Striatal functional connectivity in psychosis relapse: A hypothesis generating study. Schizophr Res 2022; 243:342-348. [PMID: 34183210 DOI: 10.1016/j.schres.2021.06.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 05/12/2021] [Accepted: 06/18/2021] [Indexed: 12/31/2022]
Abstract
Most individuals with psychotic disorders relapse over their course of illness, yet the neural processes that may lead to symptom worsening are poorly understood. Importantly, such processes could be potentially affected by antipsychotic adherence status upon relapse (i.e., relapse despite ongoing antipsychotic maintenance vs following antipsychotic discontinuation), reflecting distinct mechanisms. As a first foray into this question, we aim to compare the striatal connectivity index (SCI), a biomarker derived from striatal resting state functional connectivity predictive of treatment response, by adherence status upon relapse. In order to confirm adherence status upon relapse, we compared individuals treated with long-acting injectable antipsychotics upon relapse (i.e., breakthrough psychosis) (n = 23), with individuals who had decided to interrupt antipsychotic treatment and then relapsed (n = 27), as well as healthy controls (n = 26). We acquired for each individual >10 min of resting state fMRI, to generate functional connectivity maps. Region of interest (ROI) analyses were conducted to calculate SCI values for each participant. These values were entered as dependent variable in a linear regression adjusted for sex and age for which adherence status was the independent variable. Individuals in the breakthrough psychosis group had significantly lower SCI values than healthy controls (Cohen's d = 0.99, p < 0.001), and non-adherent individuals upon relapse (Cohen's d = 0.58, p = 0.032), whereas non-adherent individuals had also trend level lower SCI values than healthy controls (Cohen's d = 0.44, p = 0.09). These results suggest the hypothesis that striatal functional connectivity may be aberrant in psychosis relapse, and that this dysfunction may be greater among individuals who developed relapse despite ongoing antipsychotic treatment.
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17
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Fountoulakis KN, Stahl SM. The effect of first- and second-generation antipsychotics on brain morphology in schizophrenia: A systematic review of longitudinal magnetic resonance studies with a randomized allocation to treatment arms. J Psychopharmacol 2022; 36:428-438. [PMID: 35395911 DOI: 10.1177/02698811221087645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Schizophrenia manifests as loss of brain volume in specific areas in a progressive nature and an important question concerns whether long-term treatment with medications contributes to this. The aim of the current PRISMA systematic review was to search for prospective studies involving randomization to treatment. PROSPERO ID: CRD42020197874. The MEDLINE/PUBMED was searched and it returned 2638 articles; 3 were fulfilling the inclusion criteria. A fourth was published later; they included 359 subjects, of whom 86 were healthy controls, while the rest were first-episode patients, with 91 under olanzapine, 93 under haloperidol, 48 under risperidone, 5 under paliperidone, 6 under ziprasidone, and 30 under placebo. Probably one-third of patients were suffering from a psychotic disorder other than schizophrenia. The consideration of their results suggested that there is no significant difference between these medications concerning their effects on brain structure and also in comparison to healthy subjects. There does not seem to be any strong support to the opinion that medications that treat psychosis cause loss of brain volume in patients with schizophrenia. On the contrary, the data might imply the possible presence of a protective effect for D2, 5-HT2, and NE alpha-2 antagonists (previously called SGAs). However, the literature is limited and focused research in large study samples is essential to clarify the issue, since important numerical differences do exist. The possibility of the results and their heterogeneity to be artifacts secondary to a modification of magnetic resonance imaging (MRI) signal by antipsychotics should not be easily rejected until relevant data are available.
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Affiliation(s)
- Konstantinos N Fountoulakis
- 3rd Department of Psychiatry, Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Stephen M Stahl
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.,Department of Psychiatry, Cambridge University, Cambridge, UK
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18
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Zhao L, Xue SW, Sun YK, Lan Z, Zhang Z, Xue Y, Wang X, Jin Y. Altered dynamic functional connectivity of insular subregions could predict symptom severity of male patients with autism spectrum disorder. J Affect Disord 2022; 299:504-512. [PMID: 34953921 DOI: 10.1016/j.jad.2021.12.093] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 10/15/2021] [Accepted: 12/19/2021] [Indexed: 12/28/2022]
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties with social communication and restricted or repetitive patterns of behavior. This disorder was characterized by widespread abnormalities involving distributed brain networks. As one such key network node, the insular cortex has been regarded as a research focus of ASD neuropathology. The insula is a functionally complex brain structure. However, it is not fully clear if dynamic characteristics of resting-state functional magnetic resonance imaging (R-fMRI) signals in insular heterogeneous could be used to depict abnormalities in ASD. To address this question, we investigated dynamic functional connectivity (dFC) of 12 insular subregions. Data were obtained from 44 individuals with ASD and 65 typically developing age-matched controls (TDC). We assessed dFC by sliding-window method and quantified its temporal variability. Multivariable linear regression models were constructed to determine whether dFC support complementary information about symptom severity of individuals with ASD rather than static functional connectivity (sFC). The results showed that individuals with ASD exhibited dFC and sFC alterations in distinct insular subregions. Some brain regions showed only abnormal dFC but not sFC with insular subregions. These abnormal dFC could significantly predict the symptom severity of individuals with ASD. Our findings might advance our knowledge about the potential of insular heterogeneity and dynamic characteristics in understanding the neuropathology mechanism of ASD and in developing neuroimaging biomarkers for clinical applications.
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Affiliation(s)
- Lei Zhao
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China
| | - Shao-Wei Xue
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China.
| | - Yun-Kai Sun
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China
| | - Zhihui Lan
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China; Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
| | - Ziqi Zhang
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
| | - Yichen Xue
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China
| | - Xuan Wang
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
| | - Yuxin Jin
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
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19
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Zhao C, Huang WJ, Feng F, Zhou B, Yao HX, Guo YE, Wang P, Wang LN, Shu N, Zhang X. Abnormal characterization of dynamic functional connectivity in Alzheimer's disease. Neural Regen Res 2022; 17:2014-2021. [PMID: 35142691 PMCID: PMC8848607 DOI: 10.4103/1673-5374.332161] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Numerous studies have shown abnormal brain functional connectivity in individuals with Alzheimer's disease (AD) or amnestic mild cognitive impairment (aMCI). However, most studies examined traditional resting state functional connections, ignoring the instantaneous connection mode of the whole brain. In this case-control study, we used a new method called dynamic functional connectivity (DFC) to look for abnormalities in patients with AD and aMCI. We calculated dynamic functional connectivity strength from functional magnetic resonance imaging data for each participant, and then used a support vector machine to classify AD patients and normal controls. Finally, we highlighted brain regions and brain networks that made the largest contributions to the classification. We found differences in dynamic function connectivity strength in the left precuneus, default mode network, and dorsal attention network among normal controls, aMCI patients, and AD patients. These abnormalities are potential imaging markers for the early diagnosis of AD.
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Affiliation(s)
- Cui Zhao
- Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing; Department of Geriatrics, Affiliated Hospital of Chengde Medical University, Chengde, Hebei Province, China
| | - Wei-Jie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning; Center for Collaboration and Innovation in Brain and Learning Sciences; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Feng Feng
- Department of Neurology, First Medical Center, Chinese PLA General Hospital; Department of Neurology, PLA Rocket Force Characteristic Medical Center, Beijing, China
| | - Bo Zhou
- Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
| | - Hong-Xiang Yao
- Department of Radiology, Second Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yan-E Guo
- Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
| | - Lu-Ning Wang
- Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning; Center for Collaboration and Innovation in Brain and Learning Sciences; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Xi Zhang
- Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
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20
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Present and future antipsychotic drugs: a systematic review of the putative mechanisms of action for efficacy and a critical appraisal under a translational perspective. Pharmacol Res 2022; 176:106078. [PMID: 35026403 DOI: 10.1016/j.phrs.2022.106078] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 12/23/2021] [Accepted: 01/07/2022] [Indexed: 01/10/2023]
Abstract
Antipsychotics represent the mainstay of schizophrenia pharmacological therapy, and their role has been expanded in the last years to mood disorders treatment. Although introduced in 1952, many years of research were required before an accurate picture of how antipsychotics work began to emerge. Despite the well-recognized characterization of antipsychotics in typical and atypical based on their liability to induce motor adverse events, their main action at dopamine D2R to elicit the "anti-psychotic" effect, as well as the multimodal action at other classes of receptors, their effects on intracellular mechanisms starting with receptor occupancy is still not completely understood. Significant lines of evidence converge on the impact of these compounds on multiple molecular signaling pathways implicated in the regulation of early genes and growth factors, dendritic spine shape, brain inflammation, and immune response, tuning overall the function and architecture of the synapse. Here we present, based on PRISMA approach, a comprehensive and systematic review of the above mechanisms under a translational perspective to disentangle those intracellular actions and signaling that may underline clinically relevant effects and represent potential targets for further innovative strategies in antipsychotic therapy.
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21
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Yang W, Xu X, Wang C, Cheng Y, Li Y, Xu S, Li J. Alterations of dynamic functional connectivity between visual and executive-control networks in schizophrenia. Brain Imaging Behav 2022; 16:1294-1302. [PMID: 34997915 DOI: 10.1007/s11682-021-00592-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 10/20/2021] [Indexed: 01/28/2023]
Abstract
Schizophrenia is a chronic mental disorder characterized by continuous or relapsing episodes of psychosis. While previous studies have detected functional network connectivity alterations in patients with schizophrenia, and most have focused on static functional connectivity. However, brain activity is believed to change dynamically over time. Therefore, we computed dynamic functional network connectivity using the sliding window method in 38 patients with schizophrenia and 31 healthy controls. We found that patients with schizophrenia exhibited higher occurrences in the weakly and sparsely connected state (state 3) than healthy controls, positively correlated with negative symptoms. In addition, patients exhibited fewer occurrences in a strongly connected state (state 4) than healthy controls. Lastly, the dynamic functional network connectivity between the right executive-control network and the medial visual network was decreased in schizophrenia patients compared to healthy controls. Our results further prove that brain activity is dynamic, and that alterations of dynamic functional network connectivity features might be a fundamental neural mechanism in schizophrenia.
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Affiliation(s)
- Weiliang Yang
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Xuexin Xu
- Department of Radiology, MRI Center, Tianjin Children Hospital, Tianjin Medical University Affiliated Tianjin Children Hospital, Tianjin, China
| | - Chunxiang Wang
- Department of Radiology, MRI Center, Tianjin Children Hospital, Tianjin Medical University Affiliated Tianjin Children Hospital, Tianjin, China
| | - Yongying Cheng
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Yan Li
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Shuli Xu
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Jie Li
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China.
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22
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Li P, Zhao SW, Wu XS, Zhang YJ, Song L, Wu L, Liu XF, Fu YF, Wu D, Wu WJ, Zhang YH, Yin H, Cui LB, Guo F. The Association Between Lentiform Nucleus Function and Cognitive Impairments in Schizophrenia. Front Hum Neurosci 2021; 15:777043. [PMID: 34744673 PMCID: PMC8566813 DOI: 10.3389/fnhum.2021.777043] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 09/29/2021] [Indexed: 01/10/2023] Open
Abstract
Introduction: Cognitive decline is the core schizophrenia symptom, which is now well accepted. Holding a role in various aspects of cognition, lentiform nucleus (putamen and globus pallidus) dysfunction contributes to the psychopathology of this disease. However, the effects of lentiform nucleus function on cognitive impairments in schizophrenia are yet to be investigated. Objectives: We aim to detect the fractional amplitude of low-frequency fluctuation (fALFF) alterations in patients with schizophrenia, and examine how their behavior correlates in relation to the cognitive impairments of the patients. Methods: All participants underwent magnetic resonance imaging (MRI) and cognitive assessment (digit span and digit symbol coding tests). Screening of brain regions with significant changes in fALFF values was based on analysis of the whole brain. The data were analyzed between Jun 2020 and Mar 2021. There were no interventions beyond the routine therapy determined by their clinicians on the basis of standard clinical practice. Results: There were 136 patients (75 men and 61 women, 24.1 ± 7.4 years old) and 146 healthy controls (82 men and 64 women, 24.2 ± 5.2 years old) involved in the experiments seriatim. Patients with schizophrenia exhibited decreased raw scores in cognitive tests (p < 0.001) and increased fALFF in the bilateral lentiform nuclei (left: 67 voxels; x = −24, y = −6, z = 3; peak t-value = 6.90; right: 16 voxels; x = 18, y = 0, z = 3; peak t-value = 6.36). The fALFF values in the bilateral lentiform nuclei were positively correlated with digit span-backward test scores (left: r = 0.193, p = 0.027; right: r = 0.190, p = 0.030), and the right lentiform nucleus was positively correlated with digit symbol coding scores (r = 0.209, p = 0.016). Conclusion: This study demonstrates that cognitive impairments in schizophrenia are associated with lentiform nucleus function as revealed by MRI, involving working memory and processing speed.
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Affiliation(s)
- Ping Li
- Medical Imaging Department 1, Xi'an Mental Health Center, Xi'an, China
| | - Shu-Wan Zhao
- Department of Radiology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Xu-Sha Wu
- Department of Radiology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Ya-Juan Zhang
- Department of Clinical Psychology, School of Medical Psychology, The Fourth Military Medical University, Xi'an, China
| | - Lei Song
- Department of Clinical Psychology, School of Medical Psychology, The Fourth Military Medical University, Xi'an, China
| | - Lin Wu
- Department of Clinical Psychology, School of Medical Psychology, The Fourth Military Medical University, Xi'an, China
| | - Xiao-Fan Liu
- Department of Radiology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Yu-Fei Fu
- Department of Radiology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Di Wu
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Wen-Jun Wu
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Ya-Hong Zhang
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Hong Yin
- Department of Radiology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Long-Biao Cui
- Department of Clinical Psychology, School of Medical Psychology, The Fourth Military Medical University, Xi'an, China.,Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Fan Guo
- Department of Radiology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
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23
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Wang Y, Jiang Y, Collin G, Liu D, Su W, Xu L, Wei Y, Tang Y, Zhang T, Tang X, Hu Y, Zhang J, Cui H, Wang J, Yao D, Luo C, Wang J. The effects of antipsychotics on interactions of dynamic functional connectivity in the triple-network in first episode schizophrenia. Schizophr Res 2021; 236:29-37. [PMID: 34365083 DOI: 10.1016/j.schres.2021.07.038] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 05/08/2021] [Accepted: 07/28/2021] [Indexed: 11/15/2022]
Abstract
BACKGROUND Brain dynamics abnormalities in the triple-network, which involves the salience network (SN), the default mode network (DMN) and the central executive network (CEN), have been reported in schizophrenia. However, it remains to be clarified how antipsychotics affect dynamic functional connectivity (DFC) within the triple-network and whether differences in clinical outcomes are associated with varying levels of network model dysfunction. METHODS Resting-state functional magnetic resonance imaging scans were obtained from 64 first-episode schizophrenia patients (SZ) and 67 healthy controls (HC). All patients were scanned before and after 12-week antipsychotic treatment and the HC were scanned only at baseline. RESULTS At baseline, SZ participants showed significantly reduced dynamic functional interactions across the triple-network compared to HC. The SZ group displayed a pattern of reduction in resting-state DFC among the triple-network compared with HC. After medication, the mean dynamic network interaction index (dNII) value was improved. A significant quadratic relation was observed between longitudinal change of mean dNII and the reduction ratio of PANSS total score within the SZ group. The DFC within inter-network (between DMN and SN, and between DMN and CEN) and intra-network connections of DMN were significantly higher relative to baseline. Intra-SN DFC, intra-DMN DFC and DFC between SN and DMN were found to be predictive of clinical features at baseline. Intra-CEN DFC and DFC between DMN and CEN were predictive of treatment response. CONCLUSIONS Aberrant brain dynamics in the triple-network could be regulated with medication. DFC organization in the triple network was found to predict the clinical outcome.
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Affiliation(s)
- Yingchan Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China.
| | - Yuchao Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China.
| | - Guusje Collin
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Dengtang Liu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China.
| | - Wenjun Su
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China.
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China.
| | - Yanyan Wei
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China.
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China.
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China.
| | - Xiaochen Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China.
| | - Yegang Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China.
| | - Jianye Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China.
| | - Huiru Cui
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China.
| | - Jinhong Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China.
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China.
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China.
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China; CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai 200031, PR China; Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai 200030, PR China.
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24
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Wang X, Liao W, Han S, Li J, Wang Y, Zhang Y, Zhao J, Chen H. Frequency-specific altered global signal topography in drug-naïve first-episode patients with adolescent-onset schizophrenia. Brain Imaging Behav 2021; 15:1876-1885. [PMID: 33188473 DOI: 10.1007/s11682-020-00381-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Adolescent-onset schizophrenia (AOS) is a severe neuropsychiatric disease associated with frequency-specific abnormalities across distributed neural systems in a slow rhythm. Recently, functional magnetic resonance imaging (fMRI) studies have determined that the global signal. (GS) is an important source of the local neuronal activity in 0.01-0.1 Hz frequency band. However, it remains unknown whether the effects follow a specific spatially preferential pattern in different frequency bands in schizophrenia. To address this issue, resting-state fMRI data from 39 drug-naïve AOS patients and 31 healthy controls (HCs) were used to assess the changes in GS topography patterns in the slow-4 (0.027-0.073 Hz) and slow-5 bands (0.01-0.027 Hz). Results revealed that GS mainly affects the default mode network (DMN) in slow-4 and sensory regions in the slow-5 band respectively, and GS has a stronger driving effect in the slow-5 band. Moreover, significant frequency-by-group interaction was observed in the frontoparietal network. Compared with HCs, patients with AOS exhibited altered GS topography mainly located in the DMN. Our findings demonstrated that the influence of the GS on brain networks altered in a frequency-specific way in schizophrenia.
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Affiliation(s)
- Xiao Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Shaoqiang Han
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Yifeng Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Yan Zhang
- Key Laboratory for Mental Health of Hunan Province, Mental Health Institute, the Second Xiangya Hospital of Central South University, Changsha, China
| | - Jingping Zhao
- Mental Health Institute, the Second Xiangya Hospital of Central South University, 139, Middle Renmin Road, Changsha, 410011, Hunan, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China. .,Radiology department of the First Affiliated Hospital, the Third Military Medical University, Chongqing, 400038, China.
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25
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Fan YS, Li H, Guo J, Pang Y, Li L, Hu M, Li M, Wang C, Sheng W, Liu H, Gao Q, Chen X, Zong X, Chen H. Tracking positive and negative symptom improvement in first-episode schizophrenia treated with risperidone using individual-level functional connectivity. Brain Connect 2021; 12:454-464. [PMID: 34210149 DOI: 10.1089/brain.2021.0061] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND To improve the treatment outcomes of patients with schizophrenia, research efforts have focused on identifying brain-based markers of treatment response. Personal characteristics regarding disease-related behaviors likely stem from inter-individual variability in the organization of brain functional systems. This study aimed to track dimension-specific changes in psychotic symptoms following risperidone treatment using individual-level functional connectivity (FC). METHODS A reliable cortical parcellation approach that accounts for individual heterogeneity in cortical functional anatomy was used to localize functional regions in a longitudinal cohort, consisting of 42 drug-naive first-episodes schizophrenia (FES) patients at baseline and after 8 weeks of risperidone treatment. FC was calculated in individually specified brain regions and used to predict the baseline severity and improvement of positive and negative symptoms in FES. RESULTS Distinct sets of individual-specific FC were separately associated with the positive and negative symptom burden at baseline, which could be used to track the corresponding symptom resolution in FES patients following risperidone treatment. Between-network connections of the fronto-parietal network (FPN) contributed the most to predicting the positive symptom domain. A combination of between-network connections of the default mode network, FPN, and within-network connections of the FPN contributed markedly to the prediction model of negative symptom. CONCLUSION This novel study, which accounts for individual brain variation, take a step toward establishing individual-specific theranostic biomarkers in schizophrenia.
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Affiliation(s)
- Yun-Shuang Fan
- University of Electronic Science and Technology of China, 12599, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Chengdu, Sichuan, China;
| | - Haoru Li
- University of Electronic Science and Technology of China, 12599, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Chengdu, Sichuan, China;
| | - Jing Guo
- University of Electronic Science and Technology of China, 12599, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Chengdu, Sichuan, China;
| | - Yajing Pang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China;
| | - Liang Li
- University of Electronic Science and Technology of China, 12599, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Chengdu, Sichuan, China;
| | - Maolin Hu
- Department of Psychiatry, the Second Xiangya Hospital, Central South University, Changsha, PR China, Changsha, China;
| | - Meiling Li
- University of Electronic Science and Technology of China, 610054, China, School of Life Science & Technology,, Chengdu, Sichuan, China.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA, Charlestown, United States;
| | - Chong Wang
- University of Electronic Science and Technology of China, 12599, The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, Chengdu, China.,University of Electronic Science and Technology of China, 12599, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Chengdu, China;
| | - Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China, chengdu, China;
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA, Charlestown, MA, United States;
| | - Qing Gao
- University of Electronic Science and Technology of China, 12599, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, China, 610054;
| | - Xiaogang Chen
- Department of Psychiatry, the Second Xiangya Hospital, Central South University, Changsha, PR China, Changsha, China;
| | - Xiaofen Zong
- Department of Psychiatry, the Second Xiangya Hospital, Central South University, Changsha, PR China, Changsha, China;
| | - Huafu Chen
- University of Electronic Science and Technology of China,, School of Life Science and Technology, University of Electronic Science and Technology of China, Sichuan,Chengdu 610054, China, chengdu, China, 610054;
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26
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Dynamic functional connectivity and its anatomical substrate reveal treatment outcome in first-episode drug-naïve schizophrenia. Transl Psychiatry 2021; 11:282. [PMID: 33980821 PMCID: PMC8115129 DOI: 10.1038/s41398-021-01398-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 04/09/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Convergent evidence has suggested a significant effect of antipsychotic exposure on brain structure and function in patients with schizophrenia, yet the characteristics of favorable treatment outcome remains largely unknown. In this work, we aimed to examine how large-scale brain networks are modulated by antipsychotic treatment, and whether the longitudinal changes could track the improvements of psychopathologic scores. Thirty-four patients with first-episode drug-naïve schizophrenia and 28 matched healthy controls were recruited at baseline from Shanghai Mental Health Center. After 8 weeks of antipsychotic treatment, 24 patients were re-scanned. Through a systematical dynamic functional connectivity (dFC) analysis, we investigated the schizophrenia-related intrinsic alterations of dFC at baseline, followed by a longitudinal study to examine the influence of antipsychotic treatment on these abnormalities by comparing patients at baseline and follow-up. A structural connectivity (SC) association analysis was further carried out to investigate longitudinal anatomical changes that underpin the alterations of dFC. We found a significant symptomatic improvement-related increase in the occurrence of a dFC state characterized by stronger inter-network integration. Furthermore, symptom reduction was correlated with increased FC variability in a unique connectomic signature, particularly in the connections within the default mode network and between the auditory, cognitive control, and cerebellar network to other networks. Additionally, we observed that the SC between the superior frontal gyrus and medial prefrontal cortex was decreased after treatment, suggesting a relaxation of normal constraints on dFC. Taken together, these findings provide new evidence to extend the dysconnectivity hypothesis in schizophrenia from static to dynamic brain network. Moreover, our identified neuroimaging markers tied to the neurobiology of schizophrenia could be used as potential indicators in predicting the treatment outcome of antipsychotics.
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27
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Lu F, Zhao Y, He Z, Ma X, Yao X, Liu P, Wang X, Yang G, Zhou J. Altered dynamic regional homogeneity in patients with conduct disorder. Neuropsychologia 2021; 157:107865. [PMID: 33894243 DOI: 10.1016/j.neuropsychologia.2021.107865] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 03/23/2021] [Accepted: 04/18/2021] [Indexed: 10/21/2022]
Abstract
Conduct disorder (CD) is a psychiatric condition characterized by severe aggressive and antisocial behaviors. Prior neuroimaging work reported that CD is associated with abnormal resting-state local intrinsic brain activity (IBA). However, few studies detected the time-varying brain activity patterns in CD. In this study, eighteen adolescent patients with CD and 18 typically developing controls underwent resting-state functional magnetic resonance imaging scans. We then compared the dynamic characteristics of IBA by calculating the dynamic regional homogeneity (dReHo) through a sliding-window approach between the two groups, and the correlations between the dReHo variability and clinical symptoms in CD were further examined. Moreover, the statistical between-group differences in dReHo were selected as classification features to help distinguish CD patients from controls by adopting a linear support vector machine (SVM) classifier. CD patients showed increased dReHo variability in the left precuneus, right postcentral gyrus, right precentral gyrus, left middle cingulate gyrus, and left paracentral lobule compared to controls, and dReHo variability in the left precuneus was significantly positively associated with impulsiveness scores in CD patients. Importantly, SVM combined with the leave-one-out cross-validation method results demonstrated that 75% (p < 0.001) subjects were correctly classified with sensitivity of 61% and specificity of 89%. Our results provided the initial evidence that CD is characterized by abnormal dynamic IBA patterns, giving novel insights into the neuropathological mechanisms of CD. Further, our findings exhibited that the dReHo variability may distinguish CD patients from controls with high accuracy.
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Affiliation(s)
- Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yi Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Zongling He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xujing Ma
- Department of Medical Technology, Cangzhou Medical College, Cangzhou, 061001, PR China
| | - Xudong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Peiqu Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China; National Clinical Research Center on Mental Disorders, Changsha, 410011, Hunan, China
| | - Xiaoping Wang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China; National Clinical Research Center on Mental Disorders, Changsha, 410011, Hunan, China
| | - Guocheng Yang
- Department of Information Science and Technology, Chengdu University of Technology, China.
| | - Jiansong Zhou
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China; National Clinical Research Center on Mental Disorders, Changsha, 410011, Hunan, China.
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28
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Pan C, Ren J, Li L, Li Y, Xu J, Xue C, Hu G, Yu M, Chen Y, Zhang L, Zhang W, Hu X, Sun Y, Liu W, Chen J. Differential functional connectivity of insular subdivisions in de novo Parkinson's disease with mild cognitive impairment. Brain Imaging Behav 2021; 16:1-10. [PMID: 33770371 DOI: 10.1007/s11682-021-00471-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 03/08/2021] [Indexed: 02/01/2023]
Abstract
The insula, consisting of functionally diverse subdivisions, plays a significant role in Parkinson's disease (PD)-related cognitive disorders. However, the functional connectivity (FC) patterns of insular subdivisions in PD remain unclear. Our aim is to investigate the changes in FC patterns of insular subdivisions and their relationships with cognitive domains. Three groups of participants were recruited in this study, including PD patients with mild cognitive impairment (PD-MCI, n = 25), PD patients with normal cognition (PD-NC, n = 13), and healthy controls (HCs, n = 17). Resting-state functional magnetic resonance imaging (rs-fMRI) was used to investigate the FC in insular subdivisions of the three groups. Moreover, all participants underwent a neuropsychological battery to assess cognition so that the relationship between altered FC and cognitive performance could be elucidated. Compared with the PD-NC group, the PD-MCI group exhibited increased FC between the left dorsal anterior insular (dAI) and the right superior parietal gyrus (SPG), and altered FC was negatively correlated with memory and executive function. Compared with the HC group, the PD-MCI group showed significantly increased FC between the right dAI and the right median cingulate and paracingulate gyri (DCG), and altered FC was positively related to attention/working memory, visuospatial function, and language. Our findings highlighted the different abnormal FC patterns of insular subdivisions in PD patients with different cognitive abilities. Furthermore, dysfunction of the dAI may partly contribute to the decline in executive function and memory in early drug-naïve PD patients.
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Affiliation(s)
- Chenxi Pan
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, No.264, Guangzhou Road, Gulou District, Nanjing, Jiangsu, 210029, China
| | - Jingru Ren
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, No.264, Guangzhou Road, Gulou District, Nanjing, Jiangsu, 210029, China
| | - Lanting Li
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, No.264, Guangzhou Road, Gulou District, Nanjing, Jiangsu, 210029, China
| | - Yuqian Li
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, No.264, Guangzhou Road, Gulou District, Nanjing, Jiangsu, 210029, China
| | - Jianxia Xu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, No.264, Guangzhou Road, Gulou District, Nanjing, Jiangsu, 210029, China
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Guanjie Hu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Miao Yu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, No.264, Guangzhou Road, Gulou District, Nanjing, Jiangsu, 210029, China
| | - Yong Chen
- Department of Laboratory Medicine, The Affiliated Brain Hospital of Nanjing Medical University, 210029, Nanjing, Jiangsu, China
| | - Li Zhang
- Department of Geriatrics, The Affiliated Brain Hospital of Nanjing Medical University, 210029, Nanjing, Jiangsu, China
| | - Wenbing Zhang
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Xiao Hu
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Yu Sun
- School of Biology Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210029, China
| | - Weiguo Liu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, No.264, Guangzhou Road, Gulou District, Nanjing, Jiangsu, 210029, China.
| | - Jiu Chen
- Institute of Brain Functional Imaging, Nanjing Medical University, 210029, Nanjing, Jiangsu, China.
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, No.264, Guangzhou Road, Gulou District, Nanjing, Jiangsu, 210029, China.
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29
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Yang C, Tang J, Liu N, Yao L, Xu M, Sun H, Tao B, Gong Q, Cao H, Zhang W, Lui S. The Effects of Antipsychotic Treatment on the Brain of Patients With First-Episode Schizophrenia: A Selective Review of Longitudinal MRI Studies. Front Psychiatry 2021; 12:593703. [PMID: 34248691 PMCID: PMC8264251 DOI: 10.3389/fpsyt.2021.593703] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 05/28/2021] [Indexed: 02/05/2023] Open
Abstract
A large number of neuroimaging studies have detected brain abnormalities in first-episode schizophrenia both before and after treatment, but it remains unclear how these abnormalities reflect the effects of antipsychotic treatment on the brain. To summarize the findings in this regard and provide potential directions for future work, we reviewed longitudinal structural and functional imaging studies in patients with first-episode schizophrenia before and after antipsychotic treatment. A total of 36 neuroimaging studies was included, involving 21 structural imaging studies and 15 functional imaging studies. Both anatomical and functional brain changes in patients after treatment were consistently observed in the frontal and temporal lobes, basal ganglia, limbic system and several key components within the default mode network (DMN). Alterations in these regions were affected by factors such as antipsychotic type, course of treatment, and duration of untreated psychosis (DUP). Over all we showed that: (a) The striatum and DMN were core target regions of treatment in schizophrenia, and their changes were related to different antipsychotics; (b) The gray matter of frontal and temporal lobes tended to reduce after long-term treatment; and (c) Longer DUP was accompanied with faster hippocampal atrophy after initial treatment, which was also associated with poorer outcome. These findings are in accordance with previous notions but should be interpreted with caution. Future studies are needed to clarify the effects of different antipsychotics in multiple conditions and to identify imaging or other biomarkers that may predict antipsychotic treatment response. With such progress, it may help choose effective pharmacological interventional strategies for individuals experiencing recent-onset schizophrenia.
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Affiliation(s)
- Chengmin Yang
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Psychoradiology Research Unit, Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Tang
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Naici Liu
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Psychoradiology Research Unit, Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, China
| | - Li Yao
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Psychoradiology Research Unit, Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, China
| | - Mengyuan Xu
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Psychoradiology Research Unit, Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, China
| | - Hui Sun
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Psychoradiology Research Unit, Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, China
| | - Bo Tao
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Psychoradiology Research Unit, Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, China
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Psychoradiology Research Unit, Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, China
| | - Hengyi Cao
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, United States.,Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, United States
| | - Wenjing Zhang
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Psychoradiology Research Unit, Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, China
| | - Su Lui
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Psychoradiology Research Unit, Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, China
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30
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Fan Y, Li L, Peng Y, Li H, Guo J, Li M, Yang S, Yao M, Zhao J, Liu H, Liao W, Guo X, Han S, Cui Q, Duan X, Xu Y, Zhang Y, Chen H. Individual-specific functional connectome biomarkers predict schizophrenia positive symptoms during adolescent brain maturation. Hum Brain Mapp 2020; 42:1475-1484. [PMID: 33289223 PMCID: PMC7927287 DOI: 10.1002/hbm.25307] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 11/09/2020] [Accepted: 11/23/2020] [Indexed: 11/06/2022] Open
Abstract
Even with an overarching functional dysconnectivity model of adolescent-onset schizophrenia (AOS), there have been no functional connectome (FC) biomarkers identified for predicting patients' specific symptom domains. Adolescence is a period of dramatic brain maturation, with substantial interindividual variability in brain anatomy. However, existing group-level hypotheses of AOS lack precision in terms of neuroanatomical boundaries. This study aimed to identify individual-specific FC biomarkers associated with schizophrenic symptom manifestation during adolescent brain maturation. We used a reliable individual-level cortical parcellation approach to map functional brain regions in each subject, that were then used to identify FC biomarkers for predicting dimension-specific psychotic symptoms in 30 antipsychotic-naïve first-episode AOS patients (recruited sample of 39). Age-related changes in biomarker expression were compared between these patients and 31 healthy controls. Moreover, 29 antipsychotic-naïve first-episode AOS patients (analyzed sample of 25) were recruited from another center to test the generalizability of the prediction model. Individual-specific FC biomarkers could significantly and better predict AOS positive-dimension symptoms with a relatively stronger generalizability than at the group level. Specifically, positive symptom domains were estimated based on connections between the frontoparietal control network (FPN) and salience network and within FPN. Consistent with the neurodevelopmental hypothesis of schizophrenia, the FPN-SN connection exhibited aberrant age-associated alteration in AOS. The individual-level findings reveal reproducible FPN-based FC biomarkers associated with AOS positive symptom domains, and highlight the importance of accounting for individual variation in the study of adolescent-onset disorders.
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Affiliation(s)
- Yun‐Shuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Liang Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Yue Peng
- Department of PsychiatryThe Second Affiliated Hospital of Xinxiang Medical UniversityXinxiangChina
| | - Haoru Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Jing Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Meiling Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusettsUSA
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Meng Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Jingping Zhao
- Institute of Mental HealthThe Second Xiangya Hospital, Central South UniversityChangshaChina
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusettsUSA
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Xiaonan Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Shaoqiang Han
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Qian Cui
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Yong Xu
- Department of PsychiatryFirst Hospital/First Clinical Medical College of Shanxi Medical UniversityTaiyuanChina
| | - Yan Zhang
- Department of PsychiatryThe Second Affiliated Hospital of Xinxiang Medical UniversityXinxiangChina
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
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Yao Y, He H, Duan M, Li S, Li C, Chen X, Yao G, Chang X, Shu H, Wang H, Luo C. The Effects of Music Intervention on Pallidum-DMN Circuit of Schizophrenia. BIOMED RESEARCH INTERNATIONAL 2020; 2020:4107065. [PMID: 33015164 PMCID: PMC7525302 DOI: 10.1155/2020/4107065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2019] [Revised: 11/04/2019] [Accepted: 12/03/2019] [Indexed: 11/20/2022]
Abstract
Music intervention has been applied to improve symptoms of schizophrenic subjects as a complementary treatment in medicine. Although the psychiatric symptoms, especially for motivation and emotion, could be increased in schizophrenia, the underlying neural mechanisms remain poorly understood. We employed a longitudinal study to measure the alteration of striatum functional networks in schizophrenic subjects undergoing Mozart music listening using resting-state functional magnetic resonance imaging (fMRI). Forty-five schizophrenic inpatients were recruited and randomly assigned to two groups. Under the standard care with antipsychotic medication, one group received music intervention for 1 month and the other group is set as control. Both schizophrenic groups were compared to healthy subjects. Resting-state fMRI was acquired from schizophrenic subjects at baseline and after one-month music intervention and from healthy subjects at baseline. Striatum network was assessed through seed-based static and dynamic functional connectivity (FC) analyses. After music intervention, increased static FC was observed between pallidum and ventral hippocampus in schizophrenic subjects. Increased dynamic FCs were also found between pallidus and subregions of default mode network (DMN), including cerebellum crus and posterior cingulate cortex. Moreover, static pallidus-hippocampus FC increment was positively correlated with the improvement of negative symptoms in schizophrenic subjects. Together, these findings provided evidence that music intervention might have an effect on the FC of the striatum-DMN circuit and might be related to the remission of symptoms of schizophrenia.
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Affiliation(s)
- Yutong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shicai Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Cheng Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xi Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Gang Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xin Chang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Haifeng Shu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hongming Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
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32
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The Effects of Antipsychotics on the Synaptic Plasticity Gene Homer1a Depend on a Combination of Their Receptor Profile, Dose, Duration of Treatment, and Brain Regions Targeted. Int J Mol Sci 2020; 21:ijms21155555. [PMID: 32756473 PMCID: PMC7432375 DOI: 10.3390/ijms21155555] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 07/28/2020] [Accepted: 07/31/2020] [Indexed: 02/07/2023] Open
Abstract
Background: Antipsychotic agents modulate key molecules of the postsynaptic density (PSD), including the Homer1a gene, implicated in dendritic spine architecture. How the antipsychotic receptor profile, dose, and duration of administration may influence synaptic plasticity and the Homer1a pattern of expression is yet to be determined. Methods: In situ hybridization for Homer1a was performed on rat tissue sections from cortical and striatal regions of interest (ROI) after acute or chronic administration of three antipsychotics with divergent receptor profile: Haloperidol, asenapine, and olanzapine. Univariate and multivariate analyses of the effects of topography, treatment, dose, and duration of antipsychotic administration were performed. Results: All acute treatment regimens were found to induce a consistently higher expression of Homer1a compared to chronic ones. Haloperidol increased Homer1a expression compared to olanzapine in striatum at the acute time-point. A dose effect was also observed for acute administration of haloperidol. Conclusions: Biological effects of antipsychotics on Homer1a varied strongly depending on the combination of their receptor profile, dose, duration of administration, and throughout the different brain regions. These molecular data may have translational valence and may reflect behavioral sensitization/tolerance phenomena observed with prolonged antipsychotics.
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33
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Fu X, Liu F, Cui Z, Guo W. Editorial: Dynamic Functional Connectivity in Neuropsychiatric Disorders: Methods and Applications. Front Neurosci 2020; 14:332. [PMID: 32410935 PMCID: PMC7202571 DOI: 10.3389/fnins.2020.00332] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Accepted: 03/20/2020] [Indexed: 11/25/2022] Open
Affiliation(s)
- Xiaoya Fu
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center on Mental Disorders, Changsha, China
| | - Feng Liu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Zaixu Cui
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Wenbin Guo
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center on Mental Disorders, Changsha, China
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Yao G, Li J, Wang J, Liu S, Li X, Cao X, Chen H, Xu Y. Improved Resting-State Functional Dynamics in Post-stroke Depressive Patients After Shugan Jieyu Capsule Treatment. Front Neurosci 2020; 14:297. [PMID: 32372901 PMCID: PMC7177051 DOI: 10.3389/fnins.2020.00297] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 03/16/2020] [Indexed: 12/31/2022] Open
Abstract
Shugan Jieyu Capsule (SG), a Chinese herbal medicine mainly composed of Acanthopanax and Hypericum perforatum, has been used to ameliorate cognitive impairments and emotional problems induced by post-stroke depression (PSD), while the altered brain dynamics underlying the ameliorative effects of SG have remained unclear. Our study focused on investigating the potential neurobiological mechanisms of SG in improving the cognitive function of PSD patients via resting-state functional magnetic resonance imaging (fMRI). Fifteen PSD patients (mean ages: 64.13 ± 6.01 years) were instructed to take 0.72 g of SG twice a day for 8 weeks. PSD patients underwent fMRIs, the 24-item Hamilton Depression Scale (HAMD-24) and the Montreal Cognitive Assessment (MoCA) at baseline and the end of intervention, and these assessments were also performed on twenty-one healthy controls (HC) (mean ages: 60.67 ± 6.95 years). Additionally, the dynamic amplitude of low-frequency fluctuations (dALFF) and functional connectivity (dFC) were determined to reveal changes in dynamic functional patterns. We found that taking SG significantly reduced the depressive symptoms assessed by HAMD-24 and improved cognitive functions assessed by MoCA in PSD patients. Furthermore, at baseline, PSD patients showed decreased dALFF in the right precuneus and increased dFC between the right precuneus and left angular gyrus, compared with HC. After intervention, the dALFF and dFC variances of the abnormal patterns were reversed. Additionally, the dALFF variance in the right precuneus was positively correlated with MoCA scores in PSD patients after SG treatment. Collectively, our results indicate that SG may improve the cognitive function of PSD patients through alteration of brain dynamics. Our findings lay a foundation for the exploration of the neurobiological mechanisms of SG in ameliorating symptoms of PSD patients.
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Affiliation(s)
- Guanqun Yao
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Jing Li
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China.,Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Jiaojian Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Sha Liu
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China.,Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Xinrong Li
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Xiaohua Cao
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yong Xu
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China.,Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.,MDT Center for Cognitive Impairment and Sleep Disorders, First Hospital of Shanxi Medical University, Taiyuan, China
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35
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Blair Thies M, DeRosse P, Sarpal DK, Argyelan M, Fales CL, Gallego JA, Robinson DG, Lencz T, Homan P, Malhotra AK. Interaction of Cannabis Use Disorder and Striatal Connectivity in Antipsychotic Treatment Response. SCHIZOPHRENIA BULLETIN OPEN 2020; 1:sgaa014. [PMID: 32803161 PMCID: PMC7418867 DOI: 10.1093/schizbullopen/sgaa014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Antipsychotic (AP) medications are the mainstay for the treatment of schizophrenia spectrum disorders (SSD), but their efficacy is unpredictable and widely variable. Substantial efforts have been made to identify prognostic biomarkers that can be used to guide optimal prescription strategies for individual patients. Striatal regions involved in salience and reward processing are disrupted as a result of both SSD and cannabis use, and research demonstrates that striatal circuitry may be integral to response to AP drugs. In the present study, we used functional magnetic resonance imaging (fMRI) to investigate the relationship between a history of cannabis use disorder (CUD) and a striatal connectivity index (SCI), a previously developed neural biomarker for AP treatment response in SSD. Patients were part of a 12-week randomized, double-blind controlled treatment study of AP drugs. A sample of 48 first-episode SSD patients with no more than 2 weeks of lifetime exposure to AP medications, underwent a resting-state fMRI scan pretreatment. Treatment response was defined a priori as a binary (response/nonresponse) variable, and a SCI was calculated in each patient. We examined whether there was an interaction between lifetime CUD history and the SCI in relation to treatment response. We found that CUD history moderated the relationship between SCI and treatment response, such that it had little predictive value in SSD patients with a CUD history. In sum, our findings highlight that biomarker development can be critically impacted by patient behaviors that influence neurobiology, such as a history of CUD.
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Affiliation(s)
- Melanie Blair Thies
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY
| | - Pamela DeRosse
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY
| | - Deepak K Sarpal
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA
| | - Miklos Argyelan
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY
| | - Christina L Fales
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY
| | - Juan A Gallego
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY
- Graduate Center—City University of New York, New York, NY
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY
| | - Delbert G Robinson
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY
| | - Todd Lencz
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY
| | - Philipp Homan
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY
| | - Anil K Malhotra
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY
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