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Wang X, Chang Z, Wang R. Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia. Commun Biol 2023; 6:279. [PMID: 36932140 PMCID: PMC10023794 DOI: 10.1038/s42003-023-04637-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 02/28/2023] [Indexed: 03/19/2023] Open
Abstract
Schizophrenia is a severe psychotic disorder characterized by positive and negative symptoms, but their neural bases remain poorly understood. Here, we utilized a nested-spectral partition (NSP) approach to detect hierarchical modules in resting-state brain functional networks in schizophrenia patients and healthy controls, and we studied dynamic transitions of segregation and integration as well as their relationships with clinical symptoms. Schizophrenia brains showed a more stable integrating process and a more variable segregating process, thus maintaining higher segregation, especially in the limbic system. Hallucinations were associated with higher integration in attention systems, and avolition was related to a more variable segregating process in default-mode network (DMN) and control systems. In a machine-learning model, NSP-based features outperformed graph measures at predicting positive and negative symptoms. Multivariate analysis confirmed that positive and negative symptoms had opposite effects on dynamic segregation and integration of brain networks. Gene ontology analysis revealed that the effect of negative symptoms was related to autistic, aggressive and violent behavior; the effect of positive symptoms was associated with hyperammonemia and acidosis; and the interaction effect was correlated with abnormal motor function. Our findings could contribute to the development of more accurate diagnostic criteria for positive and negative symptoms in schizophrenia.
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Affiliation(s)
- Xinrui Wang
- College of Science, Xi'an University of Science and Technology, Xi'an, Shaanxi, China
| | - Zhao Chang
- College of Science, Xi'an University of Science and Technology, Xi'an, Shaanxi, China
| | - Rong Wang
- College of Science, Xi'an University of Science and Technology, Xi'an, Shaanxi, China.
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2
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Neuroimaging biomarkers for detecting schizophrenia: A resting-state functional MRI-based radiomics analysis. Heliyon 2022; 8:e12276. [PMID: 36582679 PMCID: PMC9793282 DOI: 10.1016/j.heliyon.2022.e12276] [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: 02/18/2022] [Revised: 05/19/2022] [Accepted: 12/02/2022] [Indexed: 12/14/2022] Open
Abstract
Schizophrenia (SZ) is a common psychiatric disorder that is difficult to accurately diagnose in clinical practice. Quantifiable biomarkers are urgently required to explore the potential physiological mechanism of SZ and improve its diagnostic accuracy. Thus, this study aimed to identify biomarkers that classify SZ patients and healthy control subjects and investigate the potential neural mechanisms of SZ using degree centrality (DC)- and voxel-mirrored homotopic connectivity (VMHC)-based radiomics. Radiomics features were extracted from DC and VMHC metrics generated via resting-state functional magnetic resonance imaging, and significant features were selected and dimensionality was reduced using t-tests and least absolute shrinkage and selection operator. Subsequently, we built our model using a support vector machine classifier. We observed that our method obtained great classification performance (area under the curve, 0.808; accuracy, 74.02%), and it could be generalized to different brain atlases. The regions that we identified as discriminative features mainly included bilateral dorsal caudate and front-parietal, somatomotor, limbic, and default mode networks. Our findings showed that the radiomics-based machine learning method could facilitate us to understand the potential pathological mechanism of SZ more comprehensively and contribute to the accurate diagnosis of patients with SZ.
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Pais-Roldán P, Yun SD, Shah NJ. Pre-processing of Sub-millimeter GE-BOLD fMRI Data for Laminar Applications. FRONTIERS IN NEUROIMAGING 2022; 1:869454. [PMID: 37555171 PMCID: PMC10406219 DOI: 10.3389/fnimg.2022.869454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 03/31/2022] [Indexed: 08/10/2023]
Abstract
Over the past 30 years, brain function has primarily been evaluated non-invasively using functional magnetic resonance imaging (fMRI) with gradient-echo (GE) sequences to measure blood-oxygen-level-dependent (BOLD) signals. Despite the multiple advantages of GE sequences, e.g., higher signal-to-noise ratio, faster acquisitions, etc., their relatively inferior spatial localization compromises the routine use of GE-BOLD in laminar applications. Here, in an attempt to rescue the benefits of GE sequences, we evaluated the effect of existing pre-processing methods on the spatial localization of signals obtained with EPIK, a GE sequence that affords voxel volumes of 0.25 mm3 with near whole-brain coverage. The methods assessed here apply to both task and resting-state fMRI data assuming the availability of reconstructed magnitude and phase images.
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Affiliation(s)
- Patricia Pais-Roldán
- Institute of Neuroscience and Medicine 4, Medical Imaging Physics, Forschungszentrum Jülich, Jülich, Germany
| | - Seong Dae Yun
- Institute of Neuroscience and Medicine 4, Medical Imaging Physics, Forschungszentrum Jülich, Jülich, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine 4, Medical Imaging Physics, Forschungszentrum Jülich, Jülich, Germany
- Institute of Neuroscience and Medicine 11, Molecular Neuroscience and Neuroimaging, Jülich Aachen Research Alliance, Forschungszentrum Jülich, Jülich, Germany
- Jlich Aachen Research Alliance, Brain - Translational Medicine, Aachen, Germany
- Department of Neurology, Rheinisch-Westfälische Technische Hochschule Aachen University, Aachen, Germany
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Shi D, Li Y, Zhang H, Yao X, Wang S, Wang G, Ren K. Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging. DISEASE MARKERS 2021; 2021:9963824. [PMID: 34211615 PMCID: PMC8208855 DOI: 10.1155/2021/9963824] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/03/2021] [Indexed: 01/10/2023]
Abstract
Schizophrenia (SZ) is a severe psychiatric illness, and it affects around 1% of the general population; however, its reliable diagnosis is challenging. Functional MRI (fMRI) and structural MRI (sMRI) are useful techniques for investigating the functional and structural abnormalities of the human brain, and a growing number of studies have reported that multimodal brain data can improve diagnostic accuracy. Machine learning (ML) is widely used in the diagnosis of neuroscience and neuropsychiatry diseases, and it can obtain high accuracy. However, the conventional ML which concatenated the features into a longer feature vector could not be sufficiently effective to combine different features from different modalities. There are considerable controversies over the use of global signal regression (GSR), and few studies have explored the role of GSR in ML in diagnosing neurological diseases. The current study utilized fMRI and sMRI data to implement a new method named multimodal imaging and multilevel characterization with multiclassifier (M3) to classify SZs and healthy controls (HCs) and investigate the influence of GSR in SZ classification. We found that when we used Brainnetome 246 atlas and without performed GSR, our method obtained a classification accuracy of 83.49%, with a sensitivity of 68.69%, a specificity of 93.75%, and an AUC of 0.8491, respectively. We also got great classification performances with different processing methods (with/without GSR and different brain parcellation schemes). We found that the accuracy and specificity of the models without GSR were higher than that of the models with GSR. Our findings indicate that the M3 method is an effective tool to distinguish SZs from HCs, and it can identify discriminative regions to detect SZ to explore the neural mechanisms underlying SZ. The global signal may contain important neuronal information; it can improve the accuracy and specificity of SZ detection.
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Affiliation(s)
- Dafa Shi
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Yanfei Li
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Haoran Zhang
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Xiang Yao
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Siyuan Wang
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Guangsong Wang
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Ke Ren
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
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Zhang M, Palaniyappan L, Deng M, Zhang W, Pan Y, Fan Z, Tan W, Wu G, Liu Z, Pu W. Abnormal Thalamocortical Circuit in Adolescents With Early-Onset Schizophrenia. J Am Acad Child Adolesc Psychiatry 2021; 60:479-489. [PMID: 32791099 DOI: 10.1016/j.jaac.2020.07.903] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 06/18/2020] [Accepted: 08/04/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Thalamic circuit imbalance characterized by increased sensorimotor-thalamic connectivity and decreased prefrontal-thalamic connectivity has been consistently observed in adult-onset schizophrenia (AOS), although it is unclear whether this pattern is also a feature of early-onset schizophrenia (EOS). If this is the case, thalamic circuit imbalance can be considered as a core mechanistic defect in schizophrenia, unconfounded by the age of onset. METHOD A total of 116 adolescents with EOS (63 drug-naive EOS) and 55 matched healthy controls (HC) were recruited and underwent resting-state functional magnetic resonance imaging scans. To define the specific location of the thalamic subregions in thalamocortical circuit, 16 atlas-based thalamic subdivisions were used in functional connectivity analysis. RESULTS The EOS group showed increased sensorimotor-thalamic connectivity and decreased prefrontal-cerebello-thalamic connectivity, consistent with AOS. Sensorimotor-thalamic hyperconnectivity was more prominent than prefrontal-thalamic hypoconnectivity, which was circumscribed to the medial prefrontal cortex (mPFC), in EOS. Of note, the EOS group specifically exhibited strengthened thalamic connectivity with the salience network (SN). In addition, the EOS showed a more prominent disruption of the lateral thalamic nuclear connectivity. CONCLUSION Thalamic dysconnectivity observed in the EOS extends the observations from adult patients. Sensorimotor-thalamic hyperconnectivity is critical for the expression of schizophrenia phenotype irrespective of the age of onset, raising the possibility of aberrant but accelerated functional network maturation in EOS. The specific thalamocortical dysconnectivity involving the SN and mPFC may underlie the distinctive features of multi-modal hallucinations and heightened emotional valence of psychosis seen in EOS.
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Affiliation(s)
- Manqi Zhang
- Central South University, Changsha, Hunan, China; the Medical Psychological Institute of Central South University, Changsha, Hunan, China; and the National Clinical Research Center for Mental Disorders, Changsha, Hunan, China
| | - Lena Palaniyappan
- Robarts Research Institute, University of Western Ontario, London, Ontario, Canada, and the Lawson Health Research Institute, London, Ontario, Canada
| | - Mengjie Deng
- The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wen Zhang
- The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yunzhi Pan
- The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zebin Fan
- The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wenjian Tan
- The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Guowei Wu
- The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhening Liu
- The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Weidan Pu
- Central South University, Changsha, Hunan, China; the Medical Psychological Institute of Central South University, Changsha, Hunan, China; and the National Clinical Research Center for Mental Disorders, Changsha, Hunan, China; National University of Defense Technology, Changsha, Hunan, China.
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Dey A, Dempster K, MacKinley M, Jeon P, Das T, Khan A, Gati J, Palaniyappan L. Conceptual disorganization and redistribution of resting-state cortical hubs in untreated first-episode psychosis: A 7T study. NPJ SCHIZOPHRENIA 2021; 7:4. [PMID: 33500416 PMCID: PMC7838254 DOI: 10.1038/s41537-020-00130-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 11/12/2020] [Indexed: 01/30/2023]
Abstract
Network-level dysconnectivity has been studied in positive and negative symptoms of schizophrenia. Conceptual disorganization (CD) is a symptom subtype that predicts impaired real-world functioning in psychosis. Systematic reviews have reported aberrant connectivity in formal thought disorder, a construct related to CD. However, no studies have investigated whole-brain functional correlates of CD in psychosis. We sought to investigate brain regions explaining the severity of CD in patients with first-episode psychosis (FEPs) compared with healthy controls (HCs). We computed whole-brain binarized degree centrality maps of 31 FEPs, 25 HCs, and characterized the patterns of network connectivity in the 2 groups. In FEPs, we related these findings to the severity of CD. We also studied the effect of positive and negative symptoms on altered network connectivity. Compared to HCs, reduced centrality of a right superior temporal gyrus (rSTG) cluster was observed in the FEPs. In patients exhibiting high CD, increased centrality of a medial superior parietal (mSPL) cluster was observed, compared to patients exhibiting low CD. This cluster was strongly correlated with CD scores but not with other symptom scores. Our observations are congruent with previous findings of reduced but not increased centrality. We observed increased centrality of mSPL suggesting that cortical reorganization occurs to provide alternate routes for information transfer. These findings provide insight into the underlying neural processes mediating the presentation of symptoms in untreated FEP. Longitudinal tracking of the symptom course will be useful to assess the mechanisms underlying these compensatory changes.
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Affiliation(s)
- Avyarthana Dey
- grid.39381.300000 0004 1936 8884Robarts Research Institute, London, ON Canada ,grid.39381.300000 0004 1936 8884Department of Psychiatry, University of Western Ontario, London, ON Canada
| | - Kara Dempster
- grid.39381.300000 0004 1936 8884Robarts Research Institute, London, ON Canada ,grid.39381.300000 0004 1936 8884Department of Psychiatry, University of Western Ontario, London, ON Canada ,grid.415847.b0000 0001 0556 2414Lawson Health Research Institute, London, ON Canada ,grid.55602.340000 0004 1936 8200Present Address: Department of Psychiatry, Dalhousie University, Halifax, NS Canada
| | - Michael MacKinley
- grid.39381.300000 0004 1936 8884Robarts Research Institute, London, ON Canada ,grid.39381.300000 0004 1936 8884Department of Psychiatry, University of Western Ontario, London, ON Canada ,grid.415847.b0000 0001 0556 2414Lawson Health Research Institute, London, ON Canada
| | - Peter Jeon
- grid.415847.b0000 0001 0556 2414Lawson Health Research Institute, London, ON Canada ,grid.39381.300000 0004 1936 8884Department of Medical Biophysics, University of Western Ontario, London, ON Canada
| | - Tushar Das
- grid.39381.300000 0004 1936 8884Robarts Research Institute, London, ON Canada ,grid.39381.300000 0004 1936 8884Department of Psychiatry, University of Western Ontario, London, ON Canada
| | - Ali Khan
- grid.39381.300000 0004 1936 8884Robarts Research Institute, London, ON Canada ,grid.39381.300000 0004 1936 8884Department of Medical Biophysics, University of Western Ontario, London, ON Canada ,grid.39381.300000 0004 1936 8884The Brain and Mind Institute, University of Western Ontario, London, ON Canada
| | - Joe Gati
- grid.39381.300000 0004 1936 8884Robarts Research Institute, London, ON Canada ,grid.39381.300000 0004 1936 8884Department of Medical Biophysics, University of Western Ontario, London, ON Canada
| | - Lena Palaniyappan
- grid.39381.300000 0004 1936 8884Robarts Research Institute, London, ON Canada ,grid.39381.300000 0004 1936 8884Department of Psychiatry, University of Western Ontario, London, ON Canada ,grid.415847.b0000 0001 0556 2414Lawson Health Research Institute, London, ON Canada ,grid.39381.300000 0004 1936 8884Department of Medical Biophysics, University of Western Ontario, London, ON Canada ,grid.39381.300000 0004 1936 8884The Brain and Mind Institute, University of Western Ontario, London, ON Canada
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Luo Q, Pan B, Gu H, Simmonite M, Francis S, Liddle PF, Palaniyappan L. Effective connectivity of the right anterior insula in schizophrenia: The salience network and task-negative to task-positive transition. Neuroimage Clin 2020; 28:102377. [PMID: 32805679 PMCID: PMC7451428 DOI: 10.1016/j.nicl.2020.102377] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 07/20/2020] [Accepted: 08/05/2020] [Indexed: 12/30/2022]
Abstract
Triple network dysfunction theory of schizophrenia postulates that the interaction between the default-mode and the fronto-parietal executive network is disrupted by aberrant salience signals from the right anterior insula (rAI). To date, it is not clear how the proposed resting-state disruption translates to task-processing inefficiency in subjects with schizophrenia. Using a contiguous resting and 2-back task performance fMRI paradigm, we quantified the change in effective connectivity that accompanies rest-to-task state transition in 29 clinically stable patients with schizophrenia and 31 matched healthy controls. We found an aberrant task-evoked increase in the influence of the rAI to both executive (Cohen's d = 1.35, p = 2.8 × 10-6) and default-mode (Cohen's d = 1.22, p = 1.5 × 10-5) network regions occur in patients when compared to controls. In addition, the effective connectivity from middle occipital gyrus (dorsal visual cortex) to insula is also increased in patients as compared with healthy controls. Aberrant insula to executive network influence is pronounced in patients with more severe negative symptom burden. These findings suggest that control signals from rAI are abnormally elevated and directed towards both task-positive and task-negative brain regions, when task-related demands arise in schizophrenia. This aberrant, undiscriminating surge in salience signalling may disrupt contextually appropriate allocation of resources in the neuronal workspace in patients with schizophrenia.
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Affiliation(s)
- Qiang Luo
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science and Human Phenome Institute, Fudan University, Shanghai 200433, China
| | - Baobao Pan
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, China
| | - Huaguang Gu
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, China
| | - Molly Simmonite
- Translational Neuroimaging for Mental Health, Division of Psychiatry and Applied Psychology, University of Nottingham, Nottingham, UK
| | - Susan Francis
- Sir Peter Mansfield Imaging Centre (SPMIC), School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Peter F Liddle
- Translational Neuroimaging for Mental Health, Division of Psychiatry and Applied Psychology, University of Nottingham, Nottingham, UK
| | - Lena Palaniyappan
- Robarts Research Institute & The Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada; Department of Psychiatry, University of Western Ontario, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada.
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