Resting-state anticorrelated networks in Schizophrenia
•Altered resting state anticorrelated networks are present in schizophrenia.•Decreased anti-correlated connectivity is found in thalamus and basal ganglia.•Increased anticorrelation in medial temporal lobe regions and posterior cingulate gyri.•Support vector machines using AMA discriminated schizoph...
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Published in | Psychiatry research. Neuroimaging Vol. 284; pp. 1 - 8 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Netherlands
Elsevier B.V
28.02.2019
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Abstract | •Altered resting state anticorrelated networks are present in schizophrenia.•Decreased anti-correlated connectivity is found in thalamus and basal ganglia.•Increased anticorrelation in medial temporal lobe regions and posterior cingulate gyri.•Support vector machines using AMA discriminated schizophrenia with accuracy of 74%.
Converging evidences from different lines of research suggest abnormalities in functional brain connectivity in schizophrenia. While positively correlated brain networks have been well researched, anticorrelated functional connectivity remains under explored. Hence, in this study we examined (1) the resting-state anticorrelated networks in schizophrenia, and (2) the accuracy of support vector machines (SVMs) in differentiating healthy individuals from schizophrenia patients using these anticorrelated networks. The sample consisted of 56 patients with DSM-IV schizophrenia and 56 healthy controls. We computed functional connectivity matrices and used Anticorrelation after Mean of Antilog method (AMA) to select predominantly anticorrelated networks. The basal ganglia, thalamus, lingual gyrus, and cerebellar vermis showed significantly different, Type A (decreased anticorrelation) connections. The medial temporal lobe and posterior cingulate gyri showed significantly different, Type B (increased anticorrelation) connections. Use of SVM on AMA networks showed moderate accuracy in differentiating schizophrenia and healthy controls. Our results suggest that anticorrelated networks between the sub-cortical and cortical areas are abnormal in schizophrenia and this has potential to be a differential biomarker. These preliminary findings, if replicated in future studies with larger number of patients, and advanced machine learning techniques could have potential clinical applications. |
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AbstractList | Converging evidences from different lines of research suggest abnormalities in functional brain connectivity in schizophrenia. While positively correlated brain networks have been well researched, anticorrelated functional connectivity remains under explored. Hence, in this study we examined (1) the resting-state anticorrelated networks in schizophrenia, and (2) the accuracy of support vector machines (SVMs) in differentiating healthy individuals from schizophrenia patients using these anticorrelated networks. The sample consisted of 56 patients with DSM-IV schizophrenia and 56 healthy controls. We computed functional connectivity matrices and used Anticorrelation after Mean of Antilog method (AMA) to select predominantly anticorrelated networks. The basal ganglia, thalamus, lingual gyrus, and cerebellar vermis showed significantly different, Type A (decreased anticorrelation) connections. The medial temporal lobe and posterior cingulate gyri showed significantly different, Type B (increased anticorrelation) connections. Use of SVM on AMA networks showed moderate accuracy in differentiating schizophrenia and healthy controls. Our results suggest that anticorrelated networks between the sub-cortical and cortical areas are abnormal in schizophrenia and this has potential to be a differential biomarker. These preliminary findings, if replicated in future studies with larger number of patients, and advanced machine learning techniques could have potential clinical applications. •Altered resting state anticorrelated networks are present in schizophrenia.•Decreased anti-correlated connectivity is found in thalamus and basal ganglia.•Increased anticorrelation in medial temporal lobe regions and posterior cingulate gyri.•Support vector machines using AMA discriminated schizophrenia with accuracy of 74%. Converging evidences from different lines of research suggest abnormalities in functional brain connectivity in schizophrenia. While positively correlated brain networks have been well researched, anticorrelated functional connectivity remains under explored. Hence, in this study we examined (1) the resting-state anticorrelated networks in schizophrenia, and (2) the accuracy of support vector machines (SVMs) in differentiating healthy individuals from schizophrenia patients using these anticorrelated networks. The sample consisted of 56 patients with DSM-IV schizophrenia and 56 healthy controls. We computed functional connectivity matrices and used Anticorrelation after Mean of Antilog method (AMA) to select predominantly anticorrelated networks. The basal ganglia, thalamus, lingual gyrus, and cerebellar vermis showed significantly different, Type A (decreased anticorrelation) connections. The medial temporal lobe and posterior cingulate gyri showed significantly different, Type B (increased anticorrelation) connections. Use of SVM on AMA networks showed moderate accuracy in differentiating schizophrenia and healthy controls. Our results suggest that anticorrelated networks between the sub-cortical and cortical areas are abnormal in schizophrenia and this has potential to be a differential biomarker. These preliminary findings, if replicated in future studies with larger number of patients, and advanced machine learning techniques could have potential clinical applications. |
Author | Sharma, Abhinav Rao, Naren P. Ramkiran, Shukti |
Author_xml | – sequence: 1 givenname: Shukti orcidid: 0000-0001-8253-1873 surname: Ramkiran fullname: Ramkiran, Shukti – sequence: 2 givenname: Abhinav surname: Sharma fullname: Sharma, Abhinav – sequence: 3 givenname: Naren P. surname: Rao fullname: Rao, Naren P. email: docnaren@gmail.com |
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CitedBy_id | crossref_primary_10_1080_23279095_2021_1938051 crossref_primary_10_1007_s40846_022_00726_3 crossref_primary_10_1016_j_psychres_2022_114934 crossref_primary_10_1093_schbul_sbae069 crossref_primary_10_1016_j_nicl_2019_101998 crossref_primary_10_1007_s42979_023_01947_2 crossref_primary_10_1111_pcn_12921 crossref_primary_10_1016_j_bbi_2023_07_014 crossref_primary_10_1007_s11042_022_13809_9 crossref_primary_10_1016_j_schres_2023_09_018 crossref_primary_10_1016_j_compbiomed_2022_105554 crossref_primary_10_1007_s11920_022_01399_0 crossref_primary_10_1007_s11571_022_09897_w crossref_primary_10_3389_fpsyt_2020_00588 crossref_primary_10_3389_fpsyt_2022_949512 crossref_primary_10_1109_TNSRE_2023_3310340 crossref_primary_10_1038_s41398_024_02854_7 crossref_primary_10_1093_cercor_bhab465 crossref_primary_10_1016_j_bandc_2023_105976 |
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Keywords | Schizophrenia Resting state fMRI Anticorrelated network Thalamus Support vector machine Machine learning |
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Snippet | •Altered resting state anticorrelated networks are present in schizophrenia.•Decreased anti-correlated connectivity is found in thalamus and basal... Converging evidences from different lines of research suggest abnormalities in functional brain connectivity in schizophrenia. While positively correlated... |
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SubjectTerms | Adult Anticorrelated network Brain - physiopathology Brain Mapping - methods Case-Control Studies Female Gyrus Cinguli - physiopathology Humans Machine Learning Magnetic Resonance Imaging - methods Male Neural Pathways - physiopathology Rest - psychology Resting state fMRI Schizophrenia Schizophrenia - diagnosis Support Vector Machine Temporal Lobe - physiopathology Thalamus Thalamus - physiopathology |
Title | Resting-state anticorrelated networks in Schizophrenia |
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