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      Effects on Multimodal Connectivity Patterns in Female Schizophrenia During 8 Weeks of Antipsychotic Treatment

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          Abstract

          Background and Hypothesis

          Respective abnormal structural connectivity (SC) and functional connectivity (FC) have been reported in individuals with schizophrenia. However, transmodal associations between SC and FC following antipsychotic treatment, especially in female schizophrenia, remain unclear. We hypothesized that increased SC-FC coupling may be found in female schizophrenia, and could be normalized after antipsychotic treatment.

          Study Design

          Sixty-four female drug-naïve patients with first-diagnosed schizophrenia treated with antipsychotic drugs for 8 weeks, and 55 female healthy controls (HCs) were enrolled. Magnetic resonance imaging (MRI) data were collected from HCs at baseline and from patients at baseline and after treatment. SC and FC were analyzed by network-based statistics, calculating nonzero SC-FC coupling of the whole brain and altered connectivity following treatment. Finally, an Elastic-net logistic regression analysis was employed to establish a predictive model for evaluating the clinical efficacy treatment.

          Study Results

          At baseline, female schizophrenia patients exhibited abnormal SC in cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, and limbic-cerebellar connectivity compared to HCs, while FC showed no abnormalities. Following treatment, cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, temporal-cerebellar, and limbic-cerebellar connectivity were altered in both SC and FC. Additionally, SC-FC coupling of altered connectivity was higher in patients at baseline than in HC, trending toward normalization after treatment. Furthermore, identified FC or/and SC predicted changes in psychopathological symptoms and cognitive impairment among female schizophrenia following treatment.

          Conclusions

          SC-FC coupling may be a potential predictive biomarker of treatment response. Cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, temporal-cerebellar, and limbic-cerebellar could represent major targets for antipsychotic drugs in female schizophrenia.

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          Most cited references71

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          The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

          Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.
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            The Positive and Negative Syndrome Scale (PANSS) for Schizophrenia

            The variable results of positive-negative research with schizophrenics underscore the importance of well-characterized, standardized measurement techniques. We report on the development and initial standardization of the Positive and Negative Syndrome Scale (PANSS) for typological and dimensional assessment. Based on two established psychiatric rating systems, the 30-item PANSS was conceived as an operationalized, drug-sensitive instrument that provides balanced representation of positive and negative symptoms and gauges their relationship to one another and to global psychopathology. It thus constitutes four scales measuring positive and negative syndromes, their differential, and general severity of illness. Study of 101 schizophrenics found the four scales to be normally distributed and supported their reliability and stability. Positive and negative scores were inversely correlated once their common association with general psychopathology was extracted, suggesting that they represent mutually exclusive constructs. Review of five studies involving the PANSS provided evidence of its criterion-related validity with antecedent, genealogical, and concurrent measures, its predictive validity, its drug sensitivity, and its utility for both typological and dimensional assessment.
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              DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging.

              Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.

                Author and article information

                Contributors
                Journal
                Schizophr Bull
                Schizophr Bull
                schbul
                Schizophrenia Bulletin
                Oxford University Press (US )
                0586-7614
                1745-1701
                May 2025
                27 December 2024
                27 December 2024
                : 51
                : 3
                : 829-840
                Affiliations
                Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University , Nanjing, 210029, China
                Department of Psychiatry, Nanjing Brain Hospital, Medical School, Nanjing University , Nanjing, 210029, China
                Department of Psychiatry, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University , Hangzhou, 310016, China
                Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University , Nanjing, 210029, China
                Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University , Nanjing, 210029, China
                Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University , Nanjing, 210029, China
                Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University , Nanjing, 210029, China
                Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University , Nanjing, 210029, China
                Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University , Nanjing, 210029, China
                Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University , Nanjing, 210029, China
                Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University , Nanjing, 210029, China
                Department of Psychiatry, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University , Hangzhou, 310016, China
                College of Computer Science and Technology and the Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics , Nanjing, 211106, China
                College of Computer Science and Technology and the Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics , Nanjing, 211106, China
                College of Computer Science and Technology and the Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics , Nanjing, 211106, China
                Department of Psychiatry, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University , Hangzhou, 310016, China
                Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University , Nanjing, 210029, China
                Department of Psychiatry, Nanjing Brain Hospital, Medical School, Nanjing University , Nanjing, 210029, China
                Author notes
                To whom correspondence should be addressed: Xijia Xu, Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China ( xuxijia@ 123456c-nbh.com )
                Correspondence: Jinsong Tang, Department of Psychiatry, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, China ( tangjinsong@ 123456zju.edu.cn )
                Correspondence: Shile Qi, College of Computer Science and Technology and the Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China ( shile.qi@ 123456nuaa.edu.cn )

                S. Gao and Y. Sun contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-3796-1377
                https://orcid.org/0000-0002-0598-7188
                Article
                sbae176
                10.1093/schbul/sbae176
                12061653
                39729483
                d8890587-0123-43e9-ba9d-082e887c5427
                © The Author(s) 2024. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence ( https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.

                History
                Page count
                Pages: 12
                Funding
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 82172061
                Award ID: 81771444
                Award ID: 82171495
                Funded by: Key Research and Development Plan in Jiangsu;
                Award ID: BE2022677
                Funded by: 16th Batch of Six Talent Peak Projects in Jiangsu;
                Award ID: WSN-166
                Funded by: Nanjing Health Technology Development Project;
                Award ID: YKK23138
                Funded by: Training and Management of Young Talents in Nanjing Brain Hospital;
                Award ID: 23-25-289
                Funded by: National Key R&D Program of China, DOI 10.13039/501100012166;
                Award ID: 2016YFC1306900
                Categories
                Regular Articles
                AcademicSubjects/MED00810

                Neurology
                female schizophrenia,antipsychotic drug,structural/functional connectivity,network,multimodal

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