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Kim WS, Shen J, Tsogt U, Odkhuu S, Cheraghi S, Rami FZ, Chung YC. Altered thalamic volumes and functional connectivity in the recovered patients with psychosis. Psychiatry Res 2024; 331:115688. [PMID: 38141265 DOI: 10.1016/j.psychres.2023.115688] [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: 07/26/2023] [Revised: 12/20/2023] [Accepted: 12/20/2023] [Indexed: 12/25/2023]
Abstract
BACKGROUND Investigating neural correlates in recovered patients with psychosis is important in terms of identifying biological markers associated with recovery status or predicting a possible future relapse. We sought to examine thalamic nuclei volumes and thalamus-centered functional connectivity (FC) in recovered patients with psychosis who discontinued their medication. METHODS Thirty patients with psychosis who satisfied the criteria for full recovery and 50 healthy controls (HC) matched for age, sex, and education underwent magnetic resonance imaging and clinical evaluation. The recovered patients were divided into the maintained and relapsed subjects according to their clinical status on the follow-ups. Thalamic nuclei volumes and thalamus-centered FC were measured between the recovered patients and HC. Correlations between the thalamic nuclei or altered FC, and clinical symptoms and cognitive functioning were explored. RESULTS Modest cognitive impairments and reduced thalamic nuclei volumes were evident in the recovered patients. Moreover, we found altered thalamo-cortical connectivity and its associations with negative symptoms and cognitive functioning in the recovered patients compared with HC. CONCLUSION These findings suggest that there are still cognitive impairments, and aberrant neuronal changes in the recovered patients. The implication of differential FC patterns between the maintained and the relapsed patients remain to be further explored.
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Affiliation(s)
- Woo-Sung Kim
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea; Research Institute of Clinical Medicine of Jeonbuk National, University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Jie Shen
- Medical School, Department of Psychiatry, Jeonbuk National University, Jeonju, Korea; Research Institute of Clinical Medicine of Jeonbuk National, University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Uyanga Tsogt
- Medical School, Department of Psychiatry, Jeonbuk National University, Jeonju, Korea; Research Institute of Clinical Medicine of Jeonbuk National, University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Soyolsaikhan Odkhuu
- Medical School, Department of Psychiatry, Jeonbuk National University, Jeonju, Korea; Research Institute of Clinical Medicine of Jeonbuk National, University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Sahar Cheraghi
- Medical School, Department of Psychiatry, Jeonbuk National University, Jeonju, Korea; Research Institute of Clinical Medicine of Jeonbuk National, University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Fatima Zahra Rami
- Medical School, Department of Psychiatry, Jeonbuk National University, Jeonju, Korea; Research Institute of Clinical Medicine of Jeonbuk National, University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Young-Chul Chung
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea; Medical School, Department of Psychiatry, Jeonbuk National University, Jeonju, Korea; Research Institute of Clinical Medicine of Jeonbuk National, University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea.
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Odkhuu S, Kim WS, Tsogt U, Shen J, Cheraghi S, Li L, Rami FZ, Le TH, Lee KH, Kang NI, Kim SW, Chung YC. Network biomarkers in recovered psychosis patients who discontinued antipsychotics. Mol Psychiatry 2023; 28:3717-3726. [PMID: 37773447 PMCID: PMC10730417 DOI: 10.1038/s41380-023-02279-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 09/08/2023] [Accepted: 09/20/2023] [Indexed: 10/01/2023]
Abstract
There are no studies investigating topological properties of resting-state fMRI (rs-fMRI) in patients who have recovered from psychosis and discontinued medication (hereafter, recovered patients [RP]). This study aimed to explore topological organization of the functional brain connectome in the RP using graph theory approach. We recruited 30 RP and 50 age and sex-matched healthy controls (HC). The RP were further divided into the subjects who were relapsed after discontinuation of antipsychotics (RP-R) and who maintained recovered state without relapse (RP-M). Using graph-based network analysis of rs-fMRI signals, global and local metrics and hub information were obtained. The robustness of the network was tested with random failure and targeted attack. As an ancillary analysis, Network-Based Statistic (NBS) was performed. Association of significant findings with psychopathology and cognitive functioning was also explored. The RP showed intact network properties in terms of global and local metrics. However, higher global functional connectivity strength and hyperconnectivity in the interconnected component were observed in the RP compared to HC. In the subgroup analysis, the RP-R were found to have lower global efficiency, longer characteristic path length and lower robustness whereas no such abnormalities were identified in the RP-M. Associations of the degree centrality of some hubs with cognitive functioning were identified in the RP-M. Even though network properties of the RP were intact, subgroup analysis revealed more altered topological organizations in the RP-R. The findings in the RP-R and RP-M may serve as network biomarkers for predicting relapse or maintained recovery after the discontinuation of antipsychotics.
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Affiliation(s)
- Soyolsaikhan Odkhuu
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Woo-Sung Kim
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea
| | - Uyanga Tsogt
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Jie Shen
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Sahar Cheraghi
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Ling Li
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Fatima Zahra Rami
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Thi-Hung Le
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Keon-Hak Lee
- Department of Psychiatry, Maeumsarang Hospital, Wanju, Korea
| | - Nam-In Kang
- Department of Psychiatry, Maeumsarang Hospital, Wanju, Korea
| | - Sung-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Young-Chul Chung
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea.
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea.
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea.
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Yang YS, Smucny J, Zhang H, Maddock RJ. Meta-analytic evidence of elevated choline, reduced N-acetylaspartate, and normal creatine in schizophrenia and their moderation by measurement quality, echo time, and medication status. Neuroimage Clin 2023; 39:103461. [PMID: 37406595 PMCID: PMC10509531 DOI: 10.1016/j.nicl.2023.103461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 07/07/2023]
Abstract
BACKGROUND Brain metabolite abnormalities measured with magnetic resonance spectroscopy (MRS) provide insight into pathological processes in schizophrenia. Prior meta-analyses have not yet answered important questions about the influence of clinical and technical factors on neurometabolite abnormalities and brain region differences. To address these gaps, we performed an updated meta-analysis of N-acetylaspartate (NAA), choline, and creatine levels in patients with schizophrenia and assessed the moderating effects of medication status, echo time, measurement quality, and other factors. METHODS We searched citations from three earlier meta-analyses and the PubMed database after the most recent meta-analysis to identify studies for screening. In total, 113 publications reporting 366 regional metabolite datasets met our inclusion criteria and reported findings in medial prefrontal cortex (MPFC), dorsolateral prefrontal cortex, frontal white matter, hippocampus, thalamus, and basal ganglia from a total of 4445 patient and 3944 control observations. RESULTS Patients with schizophrenia had reduced NAA in five of the six brain regions, with a statistically significant sparing of the basal ganglia. Patients had elevated choline in the basal ganglia and both prefrontal cortical regions. Patient creatine levels were normal in all six regions. In some regions, the NAA and choline differences were greater in studies enrolling predominantly medicated patients compared to studies enrolling predominantly unmedicated patients. Patient NAA levels were more reduced in hippocampus and frontal white matter in studies using longer echo times than those using shorter echo times. MPFC choline and NAA abnormalities were greater in studies reporting better metabolite measurement quality. CONCLUSIONS Choline is elevated in the basal ganglia and prefrontal cortical regions, suggesting regionally increased membrane turnover or glial activation in schizophrenia. The basal ganglia are significantly spared from the well-established widespread reduction of NAA in schizophrenia suggesting a regional difference in disease-associated factors affecting NAA. The echo time findings agree with prior reports and suggest microstructural changes cause faster NAA T2 relaxation in hippocampus and frontal white matter in schizophrenia. Separating the effects of medication status and illness chronicity on NAA and choline abnormalities will require further patient-level studies. Metabolite measurement quality was shown to be a critical factor in MRS studies of schizophrenia.
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Affiliation(s)
- Yvonne S Yang
- VISN22 Mental Illness Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, 11301 Wilshire Blvd, Los Angeles, CA 90073, USA; Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90095, USA.
| | - Jason Smucny
- Imaging Research Center, University of California, Davis, 4701 X Street, Sacramento, CA 95817, USA; Department of Psychiatry and Biobehavioral Sciences, University of California, Davis, 2230 Stockton Blvd, Sacramento, CA 95817, USA
| | - Huailin Zhang
- Department of Internal Medicine, Adventist Health White Memorial, 1720 E Cesar E Chavez Ave, Los Angeles, CA 90033, USA
| | - Richard J Maddock
- Imaging Research Center, University of California, Davis, 4701 X Street, Sacramento, CA 95817, USA; Department of Psychiatry and Biobehavioral Sciences, University of California, Davis, 2230 Stockton Blvd, Sacramento, CA 95817, USA.
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Messina A, Cuccì G, Crescimanno C, Signorelli MS. Clinical anatomy of the precuneus and pathogenesis of the schizophrenia. Anat Sci Int 2023:10.1007/s12565-023-00730-w. [PMID: 37340095 DOI: 10.1007/s12565-023-00730-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 05/12/2023] [Indexed: 06/22/2023]
Abstract
Recent evidence has shown that the precuneus plays a role in the pathogenesis of schizophrenia. The precuneus is a structure of the parietal lobe's medial and posterior cortex, representing a central hub involved in multimodal integration processes. Although neglected for several years, the precuneus is highly complex and crucial for multimodal integration. It has extensive connections with different cerebral areas and is an interface between external stimuli and internal representations. In human evolution, the precuneus has increased in size and complexity, allowing the development of higher cognitive functions, such as visual-spatial ability, mental imagery, episodic memory, and other tasks involved in emotional processing and mentalization. This paper reviews the functions of the precuneus and discusses them concerning the psychopathological aspects of schizophrenia. The different neuronal circuits, such as the default mode network (DMN), in which the precuneus is involved and its alterations in the structure (grey matter) and the disconnection of pathways (white matter) are described.
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Affiliation(s)
- Antonino Messina
- Department of Clinical and Experimental Medicine, Psychiatry Unit, University of Catania, Catania, Italy.
| | | | | | - Maria Salvina Signorelli
- Department of Clinical and Experimental Medicine, Psychiatry Unit, University of Catania, Catania, Italy
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Glutamatergic dysfunction in Schizophrenia. Transl Psychiatry 2022; 12:500. [PMID: 36463316 PMCID: PMC9719533 DOI: 10.1038/s41398-022-02253-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 11/05/2022] [Accepted: 11/09/2022] [Indexed: 12/04/2022] Open
Abstract
The NMDA-R hypofunction model of schizophrenia started with the clinical observation of the precipitation of psychotic symptoms in patients with schizophrenia exposed to PCP or ketamine. Healthy volunteers exposed to acute low doses of ketamine experienced mild psychosis but also negative and cognitive type symptoms reminiscent of the full clinical picture of schizophrenia. In rodents, acute systemic ketamine resulted in a paradoxical increase in extracellular frontal glutamate as well as of dopamine. Similar increase in prefrontal glutamate was documented with acute ketamine in healthy volunteers with 1H-MRS. Furthermore, sub-chronic low dose PCP lead to reductions in frontal dendritic tree density in rodents. In post-mortem ultrastructural studies in schizophrenia, a broad reduction in dendritic complexity and somal volume of pyramidal cells has been repeatedly described. This most likely accounts for the broad, subtle progressive cortical thinning described with MRI in- vivo. Additionally, prefrontal reductions in the obligatory GluN1 subunit of the NMDA-R has been repeatedly found in post-mortem tissue. The vast 1H-MRS literature in schizophrenia has documented trait-like small increases in glutamate concentrations in striatum very early in the illness, before antipsychotic treatment (the same structure where increased pre-synaptic release of dopamine has been reported with PET). The more recent genetic literature has reliably detected very small risk effects for common variants involving several glutamate-related genes. The pharmacological literature has followed two main tracks, directly informed by the NMDA-R hypo model: agonism at the glycine site (as mostly add-on studies targeting negative and cognitive symptoms); and pre-synaptic modulation of glutamatergic release (as single agents for acute psychosis). Unfortunately, both approaches have failed so far. There is little doubt that brain glutamatergic abnormalities are present in schizophrenia and that some of these are related to the etiology of the illness. The genetic literature directly supports a non- specific etiological role for glutamatergic dysfunction. Whether NMDA-R hypofunction as a specific mechanism accounts for any important component of the illness is still not evident. However, a glutamatergic model still has heuristic value to guide future research in schizophrenia. New tools to jointly examine brain glutamatergic, GABA-ergic and dopaminergic systems in-vivo, early in the illness, may lay the ground for a next generation of clinical trials that go beyond dopamine D2 blockade.
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Cengiz S, Arslan DB, Kicik A, Erdogdu E, Yildirim M, Hatay GH, Tufekcioglu Z, Uluğ AM, Bilgic B, Hanagasi H, Demiralp T, Gurvit H, Ozturk-Isik E. Identification of metabolic correlates of mild cognitive impairment in Parkinson's disease using magnetic resonance spectroscopic imaging and machine learning. MAGMA (NEW YORK, N.Y.) 2022; 35:997-1008. [PMID: 35867235 DOI: 10.1007/s10334-022-01030-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE To investigate metabolic changes of mild cognitive impairment in Parkinson's disease (PD-MCI) using proton magnetic resonance spectroscopic imaging (1H-MRSI). METHODS Sixteen healthy controls (HC), 26 cognitively normal Parkinson's disease (PD-CN) patients, and 34 PD-MCI patients were scanned in this prospective study. Neuropsychological tests were performed, and three-dimensional 1H-MRSI was obtained at 3 T. Metabolic parameters and neuropsychological test scores were compared between PD-MCI, PD-CN, and HC. The correlations between neuropsychological test scores and metabolic intensities were also assessed. Supervised machine learning algorithms were applied to classify HC, PD-CN, and PD-MCI groups based on metabolite levels. RESULTS PD-MCI had a lower corrected total N-acetylaspartate over total creatine ratio (tNAA/tCr) in the right precentral gyrus, corresponding to the sensorimotor network (p = 0.01), and a lower tNAA over myoinositol ratio (tNAA/mI) at a part of the default mode network, corresponding to the retrosplenial cortex (p = 0.04) than PD-CN. The HC and PD-MCI patients were classified with an accuracy of 86.4% (sensitivity = 72.7% and specificity = 81.8%) using bagged trees. CONCLUSION 1H-MRSI revealed metabolic changes in the default mode, ventral attention/salience, and sensorimotor networks of PD-MCI patients, which could be summarized mainly as 'posterior cortical metabolic changes' related with cognitive dysfunction.
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Affiliation(s)
- Sevim Cengiz
- Institute of Biomedical Engineering, Bogazici University, 34684, Istanbul, Turkey
| | - Dilek Betul Arslan
- Institute of Biomedical Engineering, Bogazici University, 34684, Istanbul, Turkey
| | - Ani Kicik
- Neuroimaging Unit, Hulusi Behcet Life Sciences Research Center, Istanbul University, Istanbul, Turkey
- Department of Physiology, Faculty of Medicine, Demiroglu Bilim University, Istanbul, Turkey
| | - Emel Erdogdu
- Neuroimaging Unit, Hulusi Behcet Life Sciences Research Center, Istanbul University, Istanbul, Turkey
- Department of Psychology, Faculty of Economics and Administrative Sciences, Isik University, Istanbul, Turkey
| | - Muhammed Yildirim
- Institute of Biomedical Engineering, Bogazici University, 34684, Istanbul, Turkey
| | - Gokce Hale Hatay
- Institute of Biomedical Engineering, Bogazici University, 34684, Istanbul, Turkey
| | - Zeynep Tufekcioglu
- Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
- Department of Neurology, Faculty of Medicine, Istanbul Aydin University, Istanbul, Turkey
| | - Aziz Müfit Uluğ
- Institute of Biomedical Engineering, Bogazici University, 34684, Istanbul, Turkey
- CorTechs Labs, San Diego, CA, USA
| | - Basar Bilgic
- Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Hasmet Hanagasi
- Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Tamer Demiralp
- Neuroimaging Unit, Hulusi Behcet Life Sciences Research Center, Istanbul University, Istanbul, Turkey
- Department of Physiology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Hakan Gurvit
- Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Esin Ozturk-Isik
- Institute of Biomedical Engineering, Bogazici University, 34684, Istanbul, Turkey.
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Kong M, Chen T, Gao S, Ni S, Ming Y, Chai X, Ling C, Xu X. Abnormal network homogeneity of default-mode network and its relationships with clinical symptoms in antipsychotic-naïve first-diagnosis schizophrenia. Front Neurosci 2022; 16:921547. [PMID: 35968384 PMCID: PMC9369006 DOI: 10.3389/fnins.2022.921547] [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: 04/16/2022] [Accepted: 07/06/2022] [Indexed: 11/17/2022] Open
Abstract
Schizophrenia is a severe mental disorder affecting around 0.5–1% of the global population. A few studies have shown the functional disconnection in the default-mode network (DMN) of schizophrenia patients. However, the findings remain discrepant. In the current study, we compared the intrinsic network organization of DMN of 57 first-diagnosis drug-naïve schizophrenia patients with 50 healthy controls (HCs) using a homogeneity network (NH) and explored the relationships of DMN with clinical characteristics of schizophrenia patients. Receiver operating characteristic (ROC) curves analysis and support vector machine (SVM) analysis were applied to calculate the accuracy of distinguishing schizophrenia patients from HCs. Our results showed that the NH values of patients were significantly higher in the left superior medial frontal gyrus (SMFG) and right cerebellum Crus I/Crus II and significantly lower in the right inferior temporal gyrus (ITG) and bilateral posterior cingulate cortex (PCC) compared to those of HCs. Additionally, negative correlations were shown between aberrant NH values in the right cerebellum Crus I/Crus II and general psychopathology scores, between NH values in the left SMFG and negative symptom scores, and between the NH values in the right ITG and speed of processing. Also, patients’ age and the NH values in the right cerebellum Crus I/Crus II and the right ITG were the predictors of performance in the social cognition test. ROC curves analysis and SVM analysis showed that a combination of NH values in the left SMFG, right ITG, and right cerebellum Crus I/Crus II could distinguish schizophrenia patients from HCs with high accuracy. The results emphasized the vital role of DMN in the neuropathological mechanisms underlying schizophrenia.
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Affiliation(s)
- Mingjun Kong
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Tian Chen
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Shuzhan Gao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Sulin Ni
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Yidan Ming
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Xintong Chai
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Chenxi Ling
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
| | - Xijia Xu
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Brain Hospital, Nanjing, China
- Department of Psychiatry, Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing, China
- *Correspondence: Xijia Xu,
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