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Cattarinussi G, Di Camillo F, Grimaldi DA, Sambataro F. Diagnostic value of regional homogeneity and fractional amplitude of low-frequency fluctuations in the classification of schizophrenia and bipolar disorders. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01838-4. [PMID: 38914853 DOI: 10.1007/s00406-024-01838-4] [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: 02/12/2024] [Accepted: 06/03/2024] [Indexed: 06/26/2024]
Abstract
Schizophrenia (SCZ) and bipolar disorders (BD) show significant neurobiological and clinical overlap. In this study, we wanted to identify indexes of intrinsic brain activity that could differentiate these disorders. We compared the diagnostic value of the fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) estimated from resting-state functional magnetic resonance imaging in a support vector machine classification of 59 healthy controls (HC), 40 individuals with SCZ, and 43 individuals with BD type I. The best performance, measured by balanced accuracy (BAC) for binary classification relative to HC was achieved by a stacking model (87.4% and 90.6% for SCZ and BD, respectively), with ReHo performing better than fALFF, both in SCZ (86.2% vs. 79.4%) and BD (89.9% vs. 76.9%). BD were better differentiated from HC by fronto-temporal ReHo and striato-temporo-thalamic fALFF. SCZ were better classified from HC using fronto-temporal-cerebellar ReHo and insulo-tempo-parietal-cerebellar fALFF. In conclusion, we provided evidence of widespread aberrancies of spontaneous activity and local connectivity in SCZ and BD, demonstrating that ReHo features exhibited superior discriminatory power compared to fALFF and achieved higher classification accuracies. Our results support the complementarity of these measures in the classification of SCZ and BD and suggest the potential for multivariate integration to improve diagnostic precision.
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Affiliation(s)
- Giulia Cattarinussi
- Department of Neuroscience (DNS), Padova Neuroscience Center (PNC), University of Padova, Azienda Ospedaliera di Padova, Via Giustiniani, 2, Padua, I-35128, Italy
- Padova Neuroscience Center, University of Padova, Padua, Italy
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Fabio Di Camillo
- Department of Neuroscience (DNS), Padova Neuroscience Center (PNC), University of Padova, Azienda Ospedaliera di Padova, Via Giustiniani, 2, Padua, I-35128, Italy
| | - David Antonio Grimaldi
- Department of Neuroscience (DNS), Padova Neuroscience Center (PNC), University of Padova, Azienda Ospedaliera di Padova, Via Giustiniani, 2, Padua, I-35128, Italy
| | - Fabio Sambataro
- Department of Neuroscience (DNS), Padova Neuroscience Center (PNC), University of Padova, Azienda Ospedaliera di Padova, Via Giustiniani, 2, Padua, I-35128, Italy.
- Padova Neuroscience Center, University of Padova, Padua, Italy.
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2
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Xie Y, Li C, Guan M, Zhang T, Ma C, Wang Z, Ma Z, Wang H, Fang P. The efficacy of low frequency repetitive transcial magnetic stimulation for treating auditory verbal hallucinations in schizophrenia: Insights from functional gradient analyses. Heliyon 2024; 10:e30194. [PMID: 38707410 PMCID: PMC11066630 DOI: 10.1016/j.heliyon.2024.e30194] [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: 01/22/2024] [Revised: 04/20/2024] [Accepted: 04/22/2024] [Indexed: 05/07/2024] Open
Abstract
Background Auditory Verbal Hallucinations (AVH) constitute a prominent feature of schizophrenia. Although low-frequency repetitive transcranial magnetic stimulation (rTMS) has demonstrated therapeutic benefits in ameliorating AVH, the underlying mechanisms of its efficacy necessitate further elucidation. Objective This study investigated the cortical gradient characteristics and their associations with clinical responses in schizophrenia patients with AVH, mediated through 1 Hz rTMS targeting the left temporoparietal junction. Method Functional gradient metrics were employed to examine the hierarchy patterns of cortical organization, capturing whole-brain functional connectivity profiles in patients and controls. Results The 1 Hz rTMS treatment effectively ameliorated the positive symptoms in patients, specifically targeting AVH. Initial evaluations revealed expanded global gradient distribution patterns and specific principal gradient variations in certain brain regions in patients at baseline compared to a control cohort. Following treatment, these divergent global and local patterns showed signs of normalizing. Furthermore, there was observed a closer alignment in between-network dispersion among various networks after treatment, including the somatomotor, attention, and limbic networks, indicating a potential harmonization of brain functionality. Conclusion Low-frequency rTMS induces alternations in principal functional gradient patterns, may serve as imaging markers to elucidate the mechanisms underpinning the therapeutic efficacy of rTMS on AVH in schizophrenia.
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Affiliation(s)
- Yuanjun Xie
- Military Medical Psychology School, Fourth Military Medical University, Xi'an, China
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Chenxi Li
- Military Medical Psychology School, Fourth Military Medical University, Xi'an, China
| | - Muzhen Guan
- Department of Mental Health, Xi'an Medical College, Xi'an, China
| | - Tian Zhang
- Military Medical Psychology School, Fourth Military Medical University, Xi'an, China
| | - Chaozong Ma
- Military Medical Psychology School, Fourth Military Medical University, Xi'an, China
| | - Zhongheng Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Zhujing Ma
- Military Medical Psychology School, Fourth Military Medical University, Xi'an, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Peng Fang
- Military Medical Psychology School, Fourth Military Medical University, Xi'an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
- Military Medical Innovation Center, Fourth Military Medical University, Xi'an, China
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Song P, Li X, Yuan X, Pang L, Song X, Wang Y. Identifying frequency-dependent imaging genetic associations via hypergraph-structured multi-task sparse canonical correlation analysis. Comput Biol Med 2024; 171:108051. [PMID: 38335819 DOI: 10.1016/j.compbiomed.2024.108051] [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: 08/04/2023] [Revised: 01/03/2024] [Accepted: 01/26/2024] [Indexed: 02/12/2024]
Abstract
Identifying complex associations between genetic variations and imaging phenotypes is a challenging task in the research of brain imaging genetics. The previous study has proved that neuronal oscillations within distinct frequency bands are derived from frequency-dependent genetic modulation. Thus it is meaningful to explore frequency-dependent imaging genetic associations, which may give important insights into the pathogenesis of brain disorders. In this work, the hypergraph-structured multi-task sparse canonical correlation analysis (HS-MTSCCA) was developed to explore the associations between multi-frequency imaging phenotypes and single-nucleotide polymorphisms (SNPs). Specifically, we first created a hypergraph for the imaging phenotypes of each frequency and the SNPs, respectively. Then, a new hypergraph-structured constraint was proposed to learn high-order relationships among features in each hypergraph, which can introduce biologically meaningful information into the model. The frequency-shared and frequency-specific imaging phenotypes and SNPs could be identified using the multi-task learning framework. We also proposed a useful strategy to tackle this algorithm and then demonstrated its convergence. The proposed method was evaluated on four simulation datasets and a real schizophrenia dataset. The experimental results on synthetic data showed that HS-MTSCCA outperforms the other competing methods according to canonical correlation coefficients, canonical weights, and cosine similarity. And the results on real data showed that HS-MTSCCA could obtain superior canonical coefficients and canonical weights. Furthermore, the identified frequency-shared and frequency-specific biomarkers could provide more interesting and meaningful information, demonstrating that HS-MTSCCA is a powerful method for brain imaging genetics.
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Affiliation(s)
- Peilun Song
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Xue Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450001, Henan, China; Biological Psychiatry International Joint Laboratory of Henan/Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Xiuxia Yuan
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450001, Henan, China; Biological Psychiatry International Joint Laboratory of Henan/Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Lijuan Pang
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450001, Henan, China; Biological Psychiatry International Joint Laboratory of Henan/Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Xueqin Song
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450001, Henan, China; Biological Psychiatry International Joint Laboratory of Henan/Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Yaping Wang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, Henan, China.
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4
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Parsaei M, Taghavizanjani F, Cattarinussi G, Moghaddam HS, Di Camillo F, Akhondzadeh S, Sambataro F, Brambilla P, Delvecchio G. Classification of suicidality by training supervised machine learning models with brain MRI findings: A systematic review. J Affect Disord 2023; 340:766-791. [PMID: 37567348 DOI: 10.1016/j.jad.2023.08.034] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 07/03/2023] [Accepted: 08/04/2023] [Indexed: 08/13/2023]
Abstract
BACKGROUND Suicide is a global public health issue causing around 700,000 deaths worldwide each year. Therefore, identifying suicidal thoughts and behaviors in patients can help lower the suicide-related mortality rate. This review aimed to investigate the feasibility of suicidality identification by applying supervised Machine Learning (ML) methods to Magnetic Resonance Imaging (MRI) data. METHODS We conducted a systematic search on PubMed, Scopus, and Web of Science to identify studies examining suicidality by applying ML methods to MRI features. Also, the Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed for the quality assessment. RESULTS 23 studies met the inclusion criteria. Of these, 20 developed prediction models without external validation and 3 developed prediction models with external validation. The performance of ML models varied among the reviewed studies, with the highest reported values of accuracies and Area Under the Curve (AUC) ranging from 51.7 % to 100 % and 0.52 to 1, respectively. Over half of the studies that reported accuracy (12/21) or AUC (13/16) achieved values of ≥0.8. Our comparative analysis indicated that deep learning exhibited the highest predictive performance compared to other ML models. The most commonly identified discriminative imaging features were resting-state functional connectivity and grey matter volume within prefrontal-limbic structures. LIMITATIONS Small sample sizes, lack of external validation, heterogeneous study designs, and ML model development. CONCLUSIONS Most of the studies developed ML models capable of ML-based suicide identification, although ML models' predictive performance varied across the reviewed studies. Thus, further well-designed is necessary to uncover the true potential of different ML models in this field.
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Affiliation(s)
| | | | - Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padua Neuroscience Center, University of Padova, Padua, Italy
| | - Hossein Sanjari Moghaddam
- School of Medicine, Tehran University of Medical Science, Tehran, Iran; Psychiatry and Psychology Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Fabio Di Camillo
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
| | - Shahin Akhondzadeh
- Psychiatry and Psychology Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padua Neuroscience Center, University of Padova, Padua, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
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5
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Porter A, Fei S, Damme KSF, Nusslock R, Gratton C, Mittal VA. A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis. Mol Psychiatry 2023; 28:3278-3292. [PMID: 37563277 PMCID: PMC10618094 DOI: 10.1038/s41380-023-02195-9] [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: 10/03/2022] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND Psychotic disorders are characterized by structural and functional abnormalities in brain networks. Neuroimaging techniques map and characterize such abnormalities using unique features (e.g., structural integrity, coactivation). However, it is unclear if a specific method, or a combination of modalities, is particularly effective in identifying differences in brain networks of someone with a psychotic disorder. METHODS A systematic meta-analysis evaluated machine learning classification of schizophrenia spectrum disorders in comparison to healthy control participants using various neuroimaging modalities (i.e., T1-weighted imaging (T1), diffusion tensor imaging (DTI), resting state functional connectivity (rs-FC), or some combination (multimodal)). Criteria for manuscript inclusion included whole-brain analyses and cross-validation to provide a complete picture regarding the predictive ability of large-scale brain systems in psychosis. For this meta-analysis, we searched Ovid MEDLINE, PubMed, PsychInfo, Google Scholar, and Web of Science published between inception and March 13th 2023. Prediction results were averaged for studies using the same dataset, but parallel analyses were run that included studies with pooled sample across many datasets. We assessed bias through funnel plot asymmetry. A bivariate regression model determined whether differences in imaging modality, demographics, and preprocessing methods moderated classification. Separate models were run for studies with internal prediction (via cross-validation) and external prediction. RESULTS 93 studies were identified for quantitative review (30 T1, 9 DTI, 40 rs-FC, and 14 multimodal). As a whole, all modalities reliably differentiated those with schizophrenia spectrum disorders from controls (OR = 2.64 (95%CI = 2.33 to 2.95)). However, classification was relatively similar across modalities: no differences were seen across modalities in the classification of independent internal data, and a small advantage was seen for rs-FC studies relative to T1 studies in classification in external datasets. We found large amounts of heterogeneity across results resulting in significant signs of bias in funnel plots and Egger's tests. Results remained similar, however, when studies were restricted to those with less heterogeneity, with continued small advantages for rs-FC relative to structural measures. Notably, in all cases, no significant differences were seen between multimodal and unimodal approaches, with rs-FC and unimodal studies reporting largely overlapping classification performance. Differences in demographics and analysis or denoising were not associated with changes in classification scores. CONCLUSIONS The results of this study suggest that neuroimaging approaches have promise in the classification of psychosis. Interestingly, at present most modalities perform similarly in the classification of psychosis, with slight advantages for rs-FC relative to structural modalities in some specific cases. Notably, results differed substantially across studies, with suggestions of biased effect sizes, particularly highlighting the need for more studies using external prediction and large sample sizes. Adopting more rigorous and systematized standards will add significant value toward understanding and treating this critical population.
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Affiliation(s)
- Alexis Porter
- Department of Psychology, Northwestern University, Evanston, IL, USA.
| | - Sihan Fei
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Katherine S F Damme
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Institute for Innovations in Developmental Sciences, Northwestern University, Evanston and Chicago, IL, USA
| | - Robin Nusslock
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Caterina Gratton
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Institute for Innovations in Developmental Sciences, Northwestern University, Evanston and Chicago, IL, USA
- Department of Psychiatry, Northwestern University, Chicago, IL, USA
- Medical Social Sciences, Northwestern University, Chicago, IL, USA
- Institute for Policy Research, Northwestern University, Chicago, IL, USA
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [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: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Wolf RC, Werler F, Schmitgen MM, Wolf ND, Wittemann M, Reith W, Hirjak D. Functional correlates of neurological soft signs in heavy cannabis users. Addict Biol 2023; 28:e13270. [PMID: 36825488 DOI: 10.1111/adb.13270] [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: 08/10/2022] [Revised: 12/03/2022] [Accepted: 01/20/2023] [Indexed: 02/17/2023]
Abstract
Sensorimotor dysfunction has been previously reported in persons with cannabis dependence. Such individuals can exhibit increased levels of neurological soft signs (NSS), particularly involving motor coordination, sensorimotor integration and complex motor task performance. Abnormal NSS levels can also be detected in non-dependent individuals with heavy cannabis use (HCU), yet very little is known about the functional correlates underlying such deficits. Here, we used resting-state functional magnetic resonance imaging (MRI) to investigate associations between NSS and intrinsic neural activity (INA) in HCU (n = 21) and controls (n = 26). Compared with controls, individuals with HCU showed significantly higher NSS across all investigated subdomains. Three of these subdomains, that is, motor coordination, sensorimotor integration and complex motor task behaviour, were associated with specific use-dependent variables, particularly age of onset of cannabis use and current cannabis use. Between-group comparisons of INA revealed lower regional homogeneity (ReHo) in left precentral gyrus, left inferior occipital gyrus, right triangular pat of the inferior frontal gyrus and right precentral gyrus in HCU compared with controls. In addition, HCU showed also higher ReHo in right cerebellum and left postcentral gyrus compared with controls. Complex motor task behaviour in HCU was significantly related to INA in postcentral, inferior frontal and occipital cortices. Our findings indicate abnormal ReHo in HCU in regions associated with sensorimotor, executive control and visuomotor-integration processes. Importantly, we show associations between ReHo, cannabis-use behaviour and execution of complex motor tasks. Given convergent findings in manifest psychotic disorders, this study suggests an HCU endophenotype that may present with a cumulative risk for psychosis.
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Affiliation(s)
- Robert Christian Wolf
- Department of General Psychiatry at the Center for Psychosocial Medicine, Heidelberg University, Heidelberg, Germany
| | - Florian Werler
- Department of General Psychiatry at the Center for Psychosocial Medicine, Heidelberg University, Heidelberg, Germany
| | - Mike M Schmitgen
- Department of General Psychiatry at the Center for Psychosocial Medicine, Heidelberg University, Heidelberg, Germany
| | - Nadine D Wolf
- Department of General Psychiatry at the Center for Psychosocial Medicine, Heidelberg University, Heidelberg, Germany
| | - Miriam Wittemann
- Department of Psychiatry and Psychotherapy, Saarland University, Saarbrücken, Germany
| | - Wolfgang Reith
- Department of Neuroradiology, Saarland University, Saarbrücken, Germany
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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8
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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9
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Levman J, Jennings M, Rouse E, Berger D, Kabaria P, Nangaku M, Gondra I, Takahashi E. A morphological study of schizophrenia with magnetic resonance imaging, advanced analytics, and machine learning. Front Neurosci 2022; 16:926426. [PMID: 36046472 PMCID: PMC9420897 DOI: 10.3389/fnins.2022.926426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 07/05/2022] [Indexed: 11/16/2022] Open
Abstract
We have performed a morphological analysis of patients with schizophrenia and compared them with healthy controls. Our analysis includes the use of publicly available automated extraction tools to assess regional cortical thickness (inclusive of within region cortical thickness variability) from structural magnetic resonance imaging (MRI), to characterize group-wise abnormalities associated with schizophrenia based on a publicly available dataset. We have also performed a correlation analysis between the automatically extracted biomarkers and a variety of patient clinical variables available. Finally, we also present the results of a machine learning analysis. Results demonstrate regional cortical thickness abnormalities in schizophrenia. We observed a correlation (rho = 0.474) between patients’ depression and the average cortical thickness of the right medial orbitofrontal cortex. Our leading machine learning technology evaluated was the support vector machine with stepwise feature selection, yielding a sensitivity of 92% and a specificity of 74%, based on regional brain measurements, including from the insula, superior frontal, caudate, calcarine sulcus, gyrus rectus, and rostral middle frontal regions. These results imply that advanced analytic techniques combining MRI with automated biomarker extraction can be helpful in characterizing patients with schizophrenia.
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Affiliation(s)
- Jacob Levman
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
- Center for Clinical Research, Nova Scotia Health Authority - Research, Innovation and Discovery, Halifax, NS, Canada
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts Institute of Technology, Boston, MA, United States
- *Correspondence: Jacob Levman,
| | - Maxwell Jennings
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
- Department of Mathematics and Statistics, St. Francis Xavier University, Antigonish, NS, Canada
| | - Ethan Rouse
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
| | - Derek Berger
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
| | - Priya Kabaria
- Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Masahito Nangaku
- Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Iker Gondra
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
| | - Emi Takahashi
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts Institute of Technology, Boston, MA, United States
- Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
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Sadeghi D, Shoeibi A, Ghassemi N, Moridian P, Khadem A, Alizadehsani R, Teshnehlab M, Gorriz JM, Khozeimeh F, Zhang YD, Nahavandi S, Acharya UR. An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Comput Biol Med 2022; 146:105554. [DOI: 10.1016/j.compbiomed.2022.105554] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 12/21/2022]
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Algumaei AH, Algunaid RF, Rushdi MA, Yassine IA. Feature and decision-level fusion for schizophrenia detection based on resting-state fMRI data. PLoS One 2022; 17:e0265300. [PMID: 35609033 PMCID: PMC9129055 DOI: 10.1371/journal.pone.0265300] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Accepted: 02/28/2022] [Indexed: 12/01/2022] Open
Abstract
Mental disorders, especially schizophrenia, still pose a great challenge for diagnosis in early stages. Recently, computer-aided diagnosis techniques based on resting-state functional magnetic resonance imaging (Rs-fMRI) have been developed to tackle this challenge. In this work, we investigate different decision-level and feature-level fusion schemes for discriminating between schizophrenic and normal subjects. Four types of fMRI features are investigated, namely the regional homogeneity, voxel-mirrored homotopic connectivity, fractional amplitude of low-frequency fluctuations and amplitude of low-frequency fluctuations. Data denoising and preprocessing were first applied, followed by the feature extraction module. Four different feature selection algorithms were applied, and the best discriminative features were selected using the algorithm of feature selection via concave minimization (FSV). Support vector machine classifiers were trained and tested on the COBRE dataset formed of 70 schizophrenic subjects and 70 healthy subjects. The decision-level fusion method outperformed the single-feature-type approaches and achieved a 97.85% accuracy, a 98.33% sensitivity, a 96.83% specificity. Moreover, feature-fusion scheme resulted in a 98.57% accuracy, a 99.71% sensitivity, a 97.66% specificity, and an area under the ROC curve of 0.9984. In general, decision-level and feature-level fusion schemes boosted the performance of schizophrenia detectors based on fMRI features.
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Affiliation(s)
- Ali H. Algumaei
- Department of Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Giza, Egypt
| | - Rami F. Algunaid
- Department of Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Giza, Egypt
| | - Muhammad A. Rushdi
- Department of Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Giza, Egypt
| | - Inas A. Yassine
- Department of Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Giza, Egypt
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12
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Wu Y, Ren P, Chen R, Xu H, Xu J, Zeng L, Wu D, Jiang W, Tang N, Liu X. Detection of functional and structural brain alterations in female schizophrenia using elastic net logistic regression. Brain Imaging Behav 2022; 16:281-290. [PMID: 34313906 PMCID: PMC8825615 DOI: 10.1007/s11682-021-00501-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2021] [Indexed: 12/19/2022]
Abstract
Neuroimaging technique is a powerful tool to characterize the abnormality of brain networks in schizophrenia. However, the neurophysiological substrate of schizophrenia is still unclear. Here we investigated the patterns of brain functional and structural changes in female patients with schizophrenia using elastic net logistic regression analysis of resting-state functional magnetic resonance imaging data. Data from 52 participants (25 female schizophrenia patients and 27 healthy controls) were obtained. Using an elastic net penalty, the brain regions most relevant to schizophrenia pathology were defined in the models using the amplitude of low-frequency fluctuations (ALFF) and gray matter, respectively. The receiver operating characteristic analysis showed reliable classification accuracy with 85.7% in ALFF analysis, and 77.1% in gray matter analysis. Notably, our results showed eight common regions between the ALFF and gray matter analyses, including the Frontal-Inf-Orb-R, Rolandic-Oper-R, Olfactory-R, Angular-L, Precuneus-L, Precuenus-R, Heschl-L, and Temporal-Pole-Mid-R. In addition, the severity of symptoms was found positively associated with the ALFF within the Rolandic-Oper-R and Frontal-Inf-Orb-R. Our findings indicated that elastic net logistic regression could be a useful tool to identify the characteristics of schizophrenia -related brain deterioration, which provides novel insights into schizophrenia diagnosis and prediction.
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Affiliation(s)
- Ying Wu
- Key Lab of Statistical Modeling and Data Analysis of Yunnan, Yunnan University, Kunming, China
| | - Ping Ren
- Lab of Brain Health Assessment and Research, Shenzhen Mental Health Center, Shenzhen, China
- Department of Geriatric Psychiatry, Shenzhen Kangning Hospital, Shenzhen, China
| | - Rong Chen
- Female Ward of Acute Psychiatric Department, Shenzhen Kangning Hospital, Shenzhen, China
| | - Hong Xu
- Female Ward of Acute Psychiatric Department, Shenzhen Kangning Hospital, Shenzhen, China
| | - Jianxing Xu
- Neuropsychiatry Imaging Center, Shenzhen Mental Health Center, Shenzhen, China
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen, China
| | - Lin Zeng
- Neuropsychiatry Imaging Center, Shenzhen Mental Health Center, Shenzhen, China
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen, China
| | - Donghui Wu
- Department of Geriatric Psychiatry, Shenzhen Kangning Hospital, Shenzhen, China
- School of Mental Health, Jining Medical University, Jining, China
| | - Wentao Jiang
- Neuropsychiatry Imaging Center, Shenzhen Mental Health Center, Shenzhen, China
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen, China
| | - NianSheng Tang
- Key Lab of Statistical Modeling and Data Analysis of Yunnan, Yunnan University, Kunming, China.
| | - Xia Liu
- Neuropsychiatry Imaging Center, Shenzhen Mental Health Center, Shenzhen, China.
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen, China.
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13
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Zhong M, Wu Z, Jiang X, Shen B, Zhu J, Zhang L. Knowledge domain and emerging trends in visual hallucination research: A scientometric analysis. World J Psychiatry 2021; 11:491-506. [PMID: 34513610 PMCID: PMC8394690 DOI: 10.5498/wjp.v11.i8.491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 03/29/2021] [Accepted: 07/05/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Visual hallucination (VH) refers to a spontaneous visual perception without corresponding external stimuli and often occurs in ophthalmological and neuropsychiatric disorders. It is associated with poor quality of life, and increased patient hospitalization and nursing home admission. To date, a scientometric analysis of research on VH is lacking.
AIM To objectively summarize the features of VH research and gain insights into the emerging trends in research on VH.
METHODS CiteSpace V was used in this article. Publication outputs, document types, geographic distributions, co-authorship status, research hotspots, and co-citation status were analyzed. A total of 2176 original articles and 465 reviews were included in the database downloaded from the Web of Science Core Collection. We selected the top 50 most cited or occurring articles or items to create a visualized network with a 1-year interval. In the document co-citation analysis stage, we performed clustering analysis on co-cited references, and log likelihood tests were used to name the clusters.
RESULTS The results showed that most publications can be classified into neurology, sports, and ophthalmology studies. In addition, North America, Europe, Asia and Australia published the most documents. Some well-known authors have always had a leading role in this field; meanwhile, new authors keep emerging. A relatively stable cooperation has been formed among many authors. Furthermore, neuropsychiatric symptom and functional connectivity are the top hotspots. Research on VH in dementia with Lewy bodies and Parkinson’s disease (PD) have received much attention. Studies on VH in PD are likely to be the new emerging trends in the future, especially the mechanisms of VH.
CONCLUSION Research on VH has formed a complete system. More large-scale clinical and in-depth basic research are required to better understand the mechanisms underlying VH, which will contribute to our understanding of the pathophysiology and therapeutic options for VH.
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Affiliation(s)
- Min Zhong
- Department of Geriatric Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Zhuang Wu
- Department of Geriatric Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Xu Jiang
- Department of Geriatric Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Bo Shen
- Department of Geriatric Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Jun Zhu
- Department of Geriatric Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Li Zhang
- Department of Geriatric Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
- Institute of Neuropsychiatric Diseases, Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
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14
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Levman J, Jennings M, Kabaria P, Rouse E, Nangaku M, Berger D, Gondra I, Takahashi E, Tyrrell P. Hold-out validation for the assessment of stability and reliability of multivariable regression demonstrated with magnetic resonance imaging of patients with schizophrenia. Int J Dev Neurosci 2021; 81:655-662. [PMID: 34308560 DOI: 10.1002/jdn.10144] [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] [Received: 03/24/2021] [Revised: 07/16/2021] [Accepted: 07/21/2021] [Indexed: 11/11/2022] Open
Abstract
Neuroscience studies are very often tasked with identifying measurable differences between two groups of subjects, typically one group with a pathological condition and one group representing control subjects. It is often expected that the measurements acquired for comparing groups are also affected by a variety of additional patient characteristics such as sex, age, and comorbidities. Multivariable regression (MVR) is a statistical analysis technique commonly employed in neuroscience studies to "control for" or "adjust for" secondary effects (such as sex, age, and comorbidities) in order to ensure that the main study findings are focused on actual differences between the groups of interest associated with the condition under investigation. It is common practice in the neuroscience literature to utilize MVR to control for secondary effects; however, at present, it is not typically possible to assess whether the MVR adjustments correct for more error than they introduce. In common neuroscience practice, MVR models are not validated and no attempt to characterize deficiencies in the MVR model is made. In this article, we demonstrate how standard hold-out validation techniques (commonly used in machine learning analyses) that involve repeatedly randomly dividing datasets into training and testing samples can be adapted to the assessment of stability and reliability of MVR models with a publicly available neurological magnetic resonance imaging (MRI) dataset of patients with schizophrenia. Results demonstrate that MVR can introduce measurement error up to 30.06% and, on average across all considered measurements, introduce 9.84% error on this dataset. When hold-out validated MVR does not agree with the results of the standard use of MVR, the use of MVR in the given application is unstable. Thus, this paper helps evaluate the extent to which the simplistic use of MVR introduces study error in neuroscientific analyses with an analysis of patients with schizophrenia.
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Affiliation(s)
- Jacob Levman
- Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada.,Canada Research Chair in Bioinformatics, St. Francis Xavier University, Antigonish, Nova Scotia, Canada
| | - Maxwell Jennings
- Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada.,Department of Mathematics and Statistics, St. Francis Xavier University, Antigonish, Nova Scotia, Canada
| | - Priya Kabaria
- Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ethan Rouse
- Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada
| | - Masahito Nangaku
- Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Derek Berger
- Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada
| | - Iker Gondra
- Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada
| | - Emi Takahashi
- Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Pascal Tyrrell
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada.,Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
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15
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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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16
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Lai JW, Ang CKE, Acharya UR, Cheong KH. Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:6099. [PMID: 34198829 PMCID: PMC8201065 DOI: 10.3390/ijerph18116099] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 02/07/2023]
Abstract
Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.
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Affiliation(s)
- Joel Weijia Lai
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
| | - Candice Ke En Ang
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
- MOH Holdings Pte Ltd, 1 Maritime Square, Singapore 099253, Singapore
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore;
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Clementi 599491, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Kang Hao Cheong
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
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17
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Zhao Z, Li J, Niu Y, Wang C, Zhao J, Yuan Q, Ren Q, Xu Y, Yu Y. Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity. Front Neurosci 2021; 15:651439. [PMID: 34149345 PMCID: PMC8209471 DOI: 10.3389/fnins.2021.651439] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 04/19/2021] [Indexed: 11/13/2022] Open
Abstract
At present, lots of studies have tried to apply machine learning to different electroencephalography (EEG) measures for diagnosing schizophrenia (SZ) patients. However, most EEG measures previously used are either a univariate measure or a single type of brain connectivity, which may not fully capture the abnormal brain changes of SZ patients. In this paper, event-related potentials were collected from 45 SZ patients and 30 healthy controls (HCs) during a learning task, and then a combination of partial directed coherence (PDC) effective and phase lag index (PLI) functional connectivity were used as features to train a support vector machine classifier with leave-one-out cross-validation for classification of SZ from HCs. Our results indicated that an excellent classification performance (accuracy = 95.16%, specificity = 94.44%, and sensitivity = 96.15%) was obtained when the combination of functional and effective connectivity features was used, and the corresponding optimal feature number was 15, which included 12 PDC and three PLI connectivity features. The selected effective connectivity features were mainly located between the frontal/temporal/central and visual/parietal lobes, and the selected functional connectivity features were mainly located between the frontal/temporal and visual cortexes of the right hemisphere. In addition, most of the selected effective connectivity abnormally enhanced in SZ patients compared with HCs, whereas all the selected functional connectivity features decreased in SZ patients. The above results showed that our proposed method has great potential to become a tool for the auxiliary diagnosis of SZ.
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Affiliation(s)
- Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Xinxiang city, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang, China
| | - Jun Li
- School of International Education, Xinxiang Medical University, Xinxiang, China
| | - Yanxiang Niu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
| | - Chang Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Xinxiang city, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang, China
| | - Junqiang Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Xinxiang city, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang, China
| | - Qingli Yuan
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
| | - Qiongqiong Ren
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Xinxiang city, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
| | - Yongtao Xu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Xinxiang city, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang, China
| | - Yi Yu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Xinxiang city, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang, China
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18
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Masoudi B, Daneshvar S, Razavi SN. Multi-modal neuroimaging feature fusion via 3D Convolutional Neural Network architecture for schizophrenia diagnosis. INTELL DATA ANAL 2021. [DOI: 10.3233/ida-205113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Early and precise diagnosis of schizophrenia disorder (SZ) has an essential role in the quality of a patient’s life and future treatments. Structural and functional neuroimaging provides robust biomarkers for understanding the anatomical and functional changes associated with SZ. Each of the neuroimaging techniques shows only a different perspective on the functional or structural of the brain, while multi-modal fusion can reveal latent connections in the brain. In this paper, we propose an approach for the fusion of structural and functional brain data with a deep learning-based model to take advantage of data fusion and increase the accuracy of schizophrenia disorder diagnosis. The proposed method consists of an architecture of 3D convolutional neural networks (CNNs) that applied to magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI) extracted features. We use 3D MRI patches, fMRI spatial independent component analysis (ICA) map, and DTI fractional anisotropy (FA) as model inputs. Our method is validated on the COBRE dataset, and an average accuracy of 99.35% is obtained. The proposed method demonstrates promising classification performance and can be applied to real data.
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Affiliation(s)
- Babak Masoudi
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Sabalan Daneshvar
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
- Department of Electronic and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University, London, UK
| | - Seyed Naser Razavi
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
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EEG Source Network for the Diagnosis of Schizophrenia and the Identification of Subtypes Based on Symptom Severity-A Machine Learning Approach. J Clin Med 2020; 9:jcm9123934. [PMID: 33291657 PMCID: PMC7761931 DOI: 10.3390/jcm9123934] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 11/26/2020] [Accepted: 12/02/2020] [Indexed: 12/12/2022] Open
Abstract
A precise diagnosis and a comprehensive assessment of symptom severity are important clinical issues in patients with schizophrenia (SZ). We investigated whether electroencephalography (EEG) features obtained from EEG source network analyses could be effectively applied to classify the SZ subtypes based on symptom severity. Sixty-four electrode EEG signals were recorded from 119 patients with SZ (53 males and 66 females) and 119 normal controls (NC, 51 males and 68 females) during resting-state with closed eyes. Brain network features (global and local clustering coefficient and global path length) were calculated from EEG source activities. According to positive, negative, and cognitive/disorganization symptoms, the SZ patients were divided into two groups (high and low) by positive and negative syndrome scale (PANSS). To select features for classification, we used the sequential forward selection (SFS) method. The classification accuracy was evaluated using 10 by 10-fold cross-validation with the linear discriminant analysis (LDA) classifier. The best classification accuracy was 80.66% for estimating SZ patients from the NC group. The best classification accuracy between low and high groups in positive, negative, and cognitive/disorganization symptoms were 88.10%, 75.25%, and 77.78%, respectively. The selected features well-represented the pathological brain regions of SZ. Our study suggested that resting-state EEG network features could successfully classify between SZ patients and the NC, and between low and high SZ groups in positive, negative, and cognitive/disorganization symptoms.
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20
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Wolf RC, Rashidi M, Schmitgen MM, Fritze S, Sambataro F, Kubera KM, Hirjak D. Neurological Soft Signs Predict Auditory Verbal Hallucinations in Patients With Schizophrenia. Schizophr Bull 2020; 47:433-443. [PMID: 33097950 PMCID: PMC7965075 DOI: 10.1093/schbul/sbaa146] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Neurological soft signs (NSS) are well documented in individuals with schizophrenia (SZ), yet so far, the relationship between NSS and specific symptom expression is unclear. We studied 76 SZ patients using magnetic resonance imaging (MRI) to determine associations between NSS, positive symptoms, gray matter volume (GMV), and neural activity at rest. SZ patients were hypothesis-driven stratified according to the presence or absence of auditory verbal hallucinations (AVH; n = 34 without vs 42 with AVH) according to the Brief Psychiatric Rating Scale. Structural MRI data were analyzed using voxel-based morphometry, whereas intrinsic neural activity was investigated using regional homogeneity (ReHo) measures. Using ANCOVA, AVH patients showed significantly higher NSS in motor and integrative functions (IF) compared with non-hallucinating (nAVH) patients. Partial correlation revealed that NSS IF were positively associated with AVH symptom severity in AVH patients. Such associations were not confirmed for delusions. In region-of-interest ANCOVAs comprising the left middle and superior temporal gyri, right paracentral lobule, and right inferior parietal lobule (IPL) structure and function, significant differences between AVH and nAVH subgroups were not detected. In a binary logistic regression model, IF scores and right IPL ReHo were significant predictors of AVH. These data suggest significant interrelationships between sensorimotor integration abilities, brain structure and function, and AVH symptom expression.
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Affiliation(s)
- Robert C Wolf
- Department of General Psychiatry, Center for Psychosocial Medicine, Heidelberg University, Heidelberg, Germany,To whom correspondence should be addressed; Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, Voßstraße 4, 69115 Heidelberg, Germany; tel: +49-6221-564405, fax: +49-6221-564481, e-mail:
| | - Mahmoud Rashidi
- Department of General Psychiatry, Center for Psychosocial Medicine, Heidelberg University, Heidelberg, Germany,Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Mike M Schmitgen
- Department of General Psychiatry, Center for Psychosocial Medicine, Heidelberg University, Heidelberg, Germany
| | - Stefan Fritze
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy,Padua Neuroscience Center, University of Padova, Padua, Italy
| | - Katharina M Kubera
- Department of General Psychiatry, Center for Psychosocial Medicine, Heidelberg University, Heidelberg, Germany
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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21
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Porcaro C, Mayhew SD, Marino M, Mantini D, Bagshaw AP. Characterisation of Haemodynamic Activity in Resting State Networks by Fractal Analysis. Int J Neural Syst 2020; 30:2050061. [PMID: 33034533 DOI: 10.1142/s0129065720500616] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Intrinsic brain activity is organized into large-scale networks displaying specific structural-functional architecture, known as resting-state networks (RSNs). RSNs reflect complex neurophysiological processes and interactions, and have a central role in distinct sensory and cognitive functions, making it crucial to understand and quantify their anatomical and functional properties. Fractal dimension (FD) provides a parsimonious way of summarizing self-similarity over different spatial and temporal scales but despite its suitability for functional magnetic resonance imaging (fMRI) signal analysis its ability to characterize and investigate RSNs is poorly understood. We used FD in a large sample of healthy participants to differentiate fMRI RSNs and examine how the FD property of RSNs is linked with their functional roles. We identified two clusters of RSNs, one mainly consisting of sensory networks (C1, including auditory, sensorimotor and visual networks) and the other more related to higher cognitive (HCN) functions (C2, including dorsal default mode network and fronto-parietal networks). These clusters were defined in a completely data-driven manner using hierarchical clustering, suggesting that quantification of Blood Oxygen Level Dependent (BOLD) signal complexity with FD is able to characterize meaningful physiological and functional variability. Understanding the mechanisms underlying functional RSNs, and developing tools to study their signal properties, is essential for assessing specific brain alterations and FD could potentially be used for the early detection and treatment of neurological disorders.
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Affiliation(s)
- Camillo Porcaro
- Institute of Cognitive Sciences and Technologies (ISTC) - National Research Council (CNR) Rome, Italy.,Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK.,S. Anna Institute and Research in Advanced Neurorehabilitation (RAN), Crotone, Italy.,Department of Information Engineering - Università, Politecnica delle Marche, Ancona, Italy.,Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium
| | - Stephen D Mayhew
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Marco Marino
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Andrew P Bagshaw
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
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22
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Wang L, Li X, Zhu Y, Lin B, Bo Q, Li F, Wang C. Discriminative Analysis of Symptom Severity and Ultra-High Risk of Schizophrenia Using Intrinsic Functional Connectivity. Int J Neural Syst 2020; 30:2050047. [PMID: 32689843 DOI: 10.1142/s0129065720500471] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Past studies have consistently shown functional dysconnectivity of large-scale brain networks in schizophrenia. In this study, we aimed to further assess whether multivariate pattern analysis (MVPA) could yield a sensitive predictor of patient symptoms, as well as identify ultra-high risk (UHR) stage of schizophrenia from intrinsic functional connectivity of whole-brain networks. We first combined rank-based feature selection and support vector machine methods to distinguish between 43 schizophrenia patients and 52 healthy controls. The constructed classifier was then applied to examine functional connectivity profiles of 18 UHR individuals. The classifier indicated reliable relationship between MVPA measures and symptom severity, with higher classification accuracy in more severely affected schizophrenia patients. The UHR subjects had classification scores falling between those of healthy controls and patients, suggesting an intermediate level of functional brain abnormalities. Moreover, UHR individuals with schizophrenia-like connectivity profiles at baseline presented higher rate of conversion to full-blown illness in the follow-up visits. Spatial maps of discriminative brain regions implicated increases of functional connectivity in the default mode network, whereas decreases of functional connectivity in the cerebellum, thalamus and visual areas in schizophrenia. The findings may have potential utility in the early diagnosis and intervention of schizophrenia.
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Affiliation(s)
- Lubin Wang
- Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing 100850, P. R. China
| | - Xianbin Li
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, P. R. China
| | - Yuyang Zhu
- Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing 100850, P. R. China
| | - Bei Lin
- Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing 100850, P. R. China
| | - Qijing Bo
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, P. R. China
| | - Feng Li
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, P. R. China
| | - Chuanyue Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, P. R. China
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23
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Gong J, Wang J, Luo X, Chen G, Huang H, Huang R, Huang L, Wang Y. Abnormalities of intrinsic regional brain activity in first-episode and chronic schizophrenia: a meta-analysis of resting-state functional MRI. J Psychiatry Neurosci 2020; 45:55-68. [PMID: 31580042 PMCID: PMC6919918 DOI: 10.1503/jpn.180245] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Resting-state functional MRI (fMRI) studies have provided much evidence for abnormal intrinsic brain activity in schizophrenia, but results have been inconsistent. METHODS We conducted a meta-analysis of whole-brain, resting-state fMRI studies that explored differences in amplitude of low-frequency fluctuation (ALFF) between people with schizophrenia (including first episode and chronic) and healthy controls. RESULTS A systematic literature search identified 24 studies comparing a total of 1249 people with schizophrenia and 1179 healthy controls. Overall, patients with schizophrenia displayed decreased ALFF in the bilateral postcentral gyrus, bilateral precuneus, left inferior parietal gyri and right occipital lobe, and increased ALFF in the right putamen, right inferior frontal gyrus, left inferior temporal gyrus and right anterior cingulate cortex. In the subgroup analysis, patients with first-episode schizophrenia demonstrated decreased ALFF in the bilateral inferior parietal gyri, right precuneus and left medial prefrontal cortex, and increased ALFF in the bilateral putamen and bilateral occipital gyrus. Patients with chronic schizophrenia showed decreased ALFF in the bilateral postcentral gyrus, left precuneus and right occipital gyrus, and increased ALFF in the bilateral inferior frontal gyri, bilateral superior frontal gyrus, left amygdala, left inferior temporal gyrus, right anterior cingulate cortex and left insula. LIMITATIONS The small sample size of our subgroup analysis, predominantly Asian samples, processing steps and publication bias could have limited the accuracy of the results. CONCLUSION Our comprehensive meta-analysis suggests that findings of aberrant regional intrinsic brain activity during the initial stages of schizophrenia, and much more widespread damage with the progression of disease, may contribute to our understanding of the progressive pathophysiology of schizophrenia.
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Affiliation(s)
- Jiaying Gong
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou China (Gong, Luo, Chen, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong); the Department of Applied Psychology, Guangdong University of Foreign Studies, Guangzhou, China (Wang); the School of Psychology, Institute of Brain Research and Rehabilitation (IBRR), Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou China (Huang, Huang)
| | - Junjing Wang
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou China (Gong, Luo, Chen, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong); the Department of Applied Psychology, Guangdong University of Foreign Studies, Guangzhou, China (Wang); the School of Psychology, Institute of Brain Research and Rehabilitation (IBRR), Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou China (Huang, Huang)
| | - Xiaomei Luo
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou China (Gong, Luo, Chen, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong); the Department of Applied Psychology, Guangdong University of Foreign Studies, Guangzhou, China (Wang); the School of Psychology, Institute of Brain Research and Rehabilitation (IBRR), Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou China (Huang, Huang)
| | - Guanmao Chen
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou China (Gong, Luo, Chen, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong); the Department of Applied Psychology, Guangdong University of Foreign Studies, Guangzhou, China (Wang); the School of Psychology, Institute of Brain Research and Rehabilitation (IBRR), Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou China (Huang, Huang)
| | - Huiyuan Huang
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou China (Gong, Luo, Chen, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong); the Department of Applied Psychology, Guangdong University of Foreign Studies, Guangzhou, China (Wang); the School of Psychology, Institute of Brain Research and Rehabilitation (IBRR), Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou China (Huang, Huang)
| | - Ruiwang Huang
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou China (Gong, Luo, Chen, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong); the Department of Applied Psychology, Guangdong University of Foreign Studies, Guangzhou, China (Wang); the School of Psychology, Institute of Brain Research and Rehabilitation (IBRR), Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou China (Huang, Huang)
| | - Li Huang
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou China (Gong, Luo, Chen, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong); the Department of Applied Psychology, Guangdong University of Foreign Studies, Guangzhou, China (Wang); the School of Psychology, Institute of Brain Research and Rehabilitation (IBRR), Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou China (Huang, Huang)
| | - Ying Wang
- From the Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou China (Gong, Luo, Chen, Huang, Wang); the Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China (Gong); the Department of Applied Psychology, Guangdong University of Foreign Studies, Guangzhou, China (Wang); the School of Psychology, Institute of Brain Research and Rehabilitation (IBRR), Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou China (Huang, Huang)
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24
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Weber S, Johnsen E, Kroken RA, Løberg EM, Kandilarova S, Stoyanov D, Kompus K, Hugdahl K. Dynamic Functional Connectivity Patterns in Schizophrenia and the Relationship With Hallucinations. Front Psychiatry 2020; 11:227. [PMID: 32300313 PMCID: PMC7145395 DOI: 10.3389/fpsyt.2020.00227] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 03/10/2020] [Indexed: 11/13/2022] Open
Abstract
There is a wealth of evidence showing aberrant functional connectivity (FC) in schizophrenia but with considerable variability in findings across studies. Dynamic FC is an extension of traditional static FC, in that such analyses allow for explorations of temporal changes in connectivity. Thereby they also provide more detailed information on connectivity abnormalities in psychiatric disorders such as schizophrenia. The current study investigated dynamic FC in a sample of 80 schizophrenia patients and 80 matched healthy control subjects, replicating previous findings of aberrant dwell times in specific FC states, and further supporting a role for default mode network (DMN) dysfunction. Furthermore, relationships with hallucinations, a core symptom of schizophrenia, were explored. Two measures of hallucinations were used, one measure of current hallucination severity assessed on the day of scanning, and one trait-measure where hallucinations were assessed repeatedly over the course of 1 year. Current hallucination severity did not show a significant relationship with dynamic FC. However, the trait-measure of hallucination proneness over 1 year showed a significant relationship with dynamic FC. Patients with high hallucination proneness spent less time in connectivity states characterized by strong anti-correlation between the DMN and task-positive networks. The findings support theoretical models of hallucinations which have proposed an instability of the DMN and impaired cognitive control in patients with hallucinations. Furthermore, the results point to hallucination proneness as a potential marker for identifying distinct subgroups of schizophrenia patients.
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Affiliation(s)
- Sarah Weber
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.,Division of Psychiatry and NORMENT Centre of Excellence, Haukeland University Hospital, Bergen, Norway
| | - Erik Johnsen
- Division of Psychiatry and NORMENT Centre of Excellence, Haukeland University Hospital, Bergen, Norway.,Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Rune A Kroken
- Division of Psychiatry and NORMENT Centre of Excellence, Haukeland University Hospital, Bergen, Norway.,Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Else-Marie Løberg
- Division of Psychiatry and NORMENT Centre of Excellence, Haukeland University Hospital, Bergen, Norway.,Department of Addiction Medicine, Haukeland University Hospital, Bergen, Norway.,Department of Clinical Psychology, University of Bergen, Bergen, Norway
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, Medical University of Plovdiv, Plovdiv, Bulgaria.,Research Institute, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, Medical University of Plovdiv, Plovdiv, Bulgaria.,Research Institute, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Kristiina Kompus
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.,Division of Psychiatry and NORMENT Centre of Excellence, Haukeland University Hospital, Bergen, Norway.,Institute of Psychology, University of Tartu, Tartu, Estonia
| | - Kenneth Hugdahl
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.,Division of Psychiatry and NORMENT Centre of Excellence, Haukeland University Hospital, Bergen, Norway.,Department of Radiology, Haukeland University Hospital, Bergen, Norway
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25
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Ji L, Meda SA, Tamminga CA, Clementz BA, Keshavan MS, Sweeney JA, Gershon ES, Pearlson GD. Characterizing functional regional homogeneity (ReHo) as a B-SNIP psychosis biomarker using traditional and machine learning approaches. Schizophr Res 2020; 215:430-438. [PMID: 31439419 DOI: 10.1016/j.schres.2019.07.015] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 06/06/2019] [Accepted: 07/11/2019] [Indexed: 11/25/2022]
Abstract
BACKGROUND Recently, a biologically-driven psychosis classification (B-SNIP Biotypes) was derived using brain-based cognitive and electrophysiological markers. Here, we characterized a local functional-connectivity measure, regional homogeneity (ReHo), as a biomarker across Biotypes and conventional DSM diagnoses. METHODS Whole-brain ReHo measures of resting-state functional MRI were examined in psychosis patients and healthy controls organized by Biotype and by DSM-IV-TR diagnosis (n = 737). Group-level ANOVA and individual-level prediction models using support vector machines (SVM) were employed to evaluate the discriminative characteristics in comparisons of 1) DSM diagnostic groups, 2) Biotypes, to controls, and 3) within-proband subgroups with each other. RESULTS Probands grouped by Biotype versus controls showed a unique abnormality pattern: Biotype-1 displayed bidirectional ReHo differences in more widespread areas, with higher ReHo in para-hippocampus, fusiform, inferior temporal, cerebellum, thalamus and caudate, plus lower ReHo in the postcentral gyrus, middle temporal, cuneus, and middle occipital cortex; Biotype-2 and Biotype-3 showed lesser and unidirectional ReHo changes. Among diagnostic groups, only schizophrenia showed higher ReHo versus control values in the inferior/middle temporal area and fusiform gyrus. For within-patient comparisons, Biotype-1 showed characteristic ReHo when compared to Biotype-2 and Biotype-3. SVM results more accurately identified Biotypes than DSM diagnoses. CONCLUSION We characterized patterns of ReHo abnormalities across both Biotypes and DSM sub-groups. Both group-level statistical and machine-learning methods were more sensitive in capturing ReHo deficits in Biotypes than DSM. Overall ReHo is a robust psychosis biomarker.
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Affiliation(s)
- Lanxin Ji
- Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT, USA; Department of Psychiatry, Yale University, New Haven, CT, USA; Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Shashwath A Meda
- Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT, USA
| | - Carol A Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Brett A Clementz
- Department of Psychology, University of Georgia, Athens, GA, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Hospital, Harvard Medical School, Boston, MA, USA
| | - John A Sweeney
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Elliot S Gershon
- Department of Psychiatry, University of Chicago, Chicago, IL, USA
| | - Godfrey D Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT, USA; Department of Psychiatry, Yale University, New Haven, CT, USA; Department of Neuroscience, Yale University, New Haven, CT, USA.
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26
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Lei D, Pinaya WHL, Young J, van Amelsvoort T, Marcelis M, Donohoe G, Mothersill DO, Corvin A, Vieira S, Huang X, Lui S, Scarpazza C, Arango C, Bullmore E, Gong Q, McGuire P, Mechelli A. Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual. Hum Brain Mapp 2019; 41:1119-1135. [PMID: 31737978 PMCID: PMC7268084 DOI: 10.1002/hbm.24863] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 10/23/2019] [Accepted: 10/31/2019] [Indexed: 02/05/2023] Open
Abstract
Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain abnormalities. In the past few years, there has been growing interest in the application of machine learning techniques to neuroimaging data for the diagnostic and prognostic assessment of this disorder. However, the vast majority of studies published so far have used either structural or functional neuroimaging data, without accounting for the multimodal nature of the disorder. Structural MRI and resting‐state functional MRI data were acquired from a total of 295 patients with schizophrenia and 452 healthy controls at five research centers. We extracted features from the data including gray matter volume, white matter volume, amplitude of low‐frequency fluctuation, regional homogeneity and two connectome‐wide based metrics: structural covariance matrices and functional connectivity matrices. A support vector machine classifier was trained on each dataset separately to distinguish the subjects at individual level using each of the single feature as well as their combination, and 10‐fold cross‐validation was used to assess the performance of the model. Functional data allow higher accuracy of classification than structural data (mean 82.75% vs. 75.84%). Within each modality, the combination of images and matrices improves performance, resulting in mean accuracies of 81.63% for structural data and 87.59% for functional data. The use of all combined structural and functional measures allows the highest accuracy of classification (90.83%). We conclude that combining multimodal measures within a single model is a promising direction for developing biologically informed diagnostic tools in schizophrenia.
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Affiliation(s)
- Du Lei
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Walter H L Pinaya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Jonathan Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Therese van Amelsvoort
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Machteld Marcelis
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands.,Mental Health Care Institute Eindhoven (GGzE), Eindhoven, The Netherlands
| | - Gary Donohoe
- School of Psychology & Center for neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - David O Mothersill
- School of Psychology & Center for neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - Aiden Corvin
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK.,Department of General Psychology, University of Padua, Padua, Italy
| | - Celso Arango
- Child and Adolescent Department of Psychiatry, Hospital General Universitario Gregorio Marañon, School of Medicine, Universidad Complutense Madrid, IiSGM, CIBERSAM, Madrid, Spain
| | - Ed Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu, Sichuan, China.,Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
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27
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Han S, Wang Y, Liao W, Duan X, Guo J, Yu Y, Ye L, Li J, Chen X, Chen H. The distinguishing intrinsic brain circuitry in treatment-naïve first-episode schizophrenia: Ensemble learning classification. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.07.061] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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28
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Ruiz E, Ramírez J, Górriz JM, Casillas J. Alzheimer's Disease Computer-Aided Diagnosis: Histogram-Based Analysis of Regional MRI Volumes for Feature Selection and Classification. J Alzheimers Dis 2019; 65:819-842. [PMID: 29966190 DOI: 10.3233/jad-170514] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
This paper proposes a novel fully automatic computer-aided diagnosis (CAD) system for the early detection of Alzheimer's disease (AD) based on supervised machine learning methods. The novelty of the approach, which is based on histogram analysis, is twofold: 1) a feature extraction process that aims to detect differences in brain regions of interest (ROIs) relevant for the recognition of subjects with AD and 2) an original greedy algorithm that predicts the severity of the effects of AD on these regions. This algorithm takes account of the progressive nature of AD that affects the brain structure with different levels of severity, i.e., the loss of gray matter in AD is found first in memory-related areas of the brain such as the hippocampus. Moreover, the proposed feature extraction process generates a reduced set of attributes which allows the use of general-purpose classification machine learning algorithms. In particular, the proposed feature extraction approach assesses the ROI image separability between classes in order to identify the ones with greater discriminant power. These regions will have the highest influence in the classification decision at the final stage. Several experiments were carried out on segmented magnetic resonance images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in order to show the benefits of the overall method. The proposed CAD system achieved competitive classification results in a highly efficient and straightforward way.
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29
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Górriz JM, Ramírez J, Segovia F, Martínez FJ, Lai MC, Lombardo MV, Baron-Cohen S, Suckling J. A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms. Int J Neural Syst 2019; 29:1850058. [DOI: 10.1142/s0129065718500582] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autism remains enigmatic. One of the difficulties in investigating differences between the sexes in autism is the small sample sizes of available imaging datasets with mixed sex. Thus, the majority of the investigations have involved male samples, with females somewhat overlooked. This paper deploys machine learning on partial least squares feature extraction to reveal differences in regional brain structure between individuals with autism and typically developing participants. A four-class classification problem (sex and condition) is specified, with theoretical restrictions based on the evaluation of a novel upper bound in the resubstitution estimate. These conditions were imposed on the classifier complexity and feature space dimension to assure generalizable results from the training set to test samples. Accuracies above [Formula: see text] on gray and white matter tissues estimated from voxel-based morphometry (VBM) features are obtained in a sample of equal-sized high-functioning male and female adults with and without autism ([Formula: see text], [Formula: see text]/group). The proposed learning machine revealed how autism is modulated by biological sex using a low-dimensional feature space extracted from VBM. In addition, a spatial overlap analysis on reference maps partially corroborated predictions of the “extreme male brain” theory of autism, in sexual dimorphic areas.
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Affiliation(s)
- Juan M. Górriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain
| | - Javier Ramírez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain
| | - F. Segovia
- Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain
| | - Francisco J. Martínez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain
| | - Meng-Chuan Lai
- Centre for Addiction and Mental Health and The Hospital for Sick Children, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M6J 1H4, Canada
| | - Michael V. Lombardo
- Department of Psychology, University of Cyprus, 2109 Aglantzia, Nicosia, Cyprus
| | - Simon Baron-Cohen
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
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Lo Giudice P, Mammone N, Morabito FC, Pizzimenti RG, Ursino D, Virgili L. Leveraging network analysis to support experts in their analyses of subjects with MCI and AD. Med Biol Eng Comput 2019; 57:1961-1983. [PMID: 31301007 DOI: 10.1007/s11517-019-02004-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 06/09/2019] [Indexed: 11/25/2022]
Abstract
In this paper, we propose a network analysis-based approach to help experts in their analyses of subjects with mild cognitive impairment (hereafter, MCI) and Alzheimer's disease (hereafter, AD) and to investigate the evolution of these subjects over time. The inputs of our approach are the electroencephalograms (hereafter, EEGs) of the patients to analyze, performed at a certain time and, again, 3 months later. Given an EEG of a subject, our approach constructs a network with nodes that represent the electrodes and edges that denote connections between electrodes. Then, it applies several network-based techniques allowing the investigation of subjects with MCI and AD and the analysis of their evolution over time. (i) A connection coefficient, supporting experts to distinguish patients with MCI from patients with AD; (ii) A conversion coefficient, supporting experts to verify if a subject with MCI is converting to AD; (iii) Some network motifs, i.e., network patterns very frequent in one kind of patient and absent, or very rare, in the other. Patients with AD, just by the very nature of their condition, cannot be forced to stay motionless while undergoing examinations for a long time. EEG is a non-invasive examination that can be easily done on them. Since AD and MCI, if prodromal to AD, are associated with a loss of cortical connections, the adoption of network analysis appears suitable to investigate the effects of the progression of the disease on EEG. This paper confirms the suitability of this idea Graphical Abstract Ability of our proposed model to distinguish a control subject from a patient with MCI and a patient with AD. Blue edges represent strong connections among the corresponding brain areas; red edges denote middle connections, whereas green edges indicate weak connections. In the control subject (at the top), most connections are blue. In the patient with MCI (at the middle), most connections are red and green. In the patient with AD (at the bottom), most connections are either absent or green. .
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Affiliation(s)
- Paolo Lo Giudice
- DIIES, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy
| | - Nadia Mammone
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy
| | | | | | | | - Luca Virgili
- DII, Polytechnic University of Marche, Ancona, Italy
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Wang Y, Shao Y, Shi WQ, Jiang L, Wang XY, Zhu PW, Yuan Q, Gao G, Lv JL, Wang GX. The predictive potential of altered spontaneous brain activity patterns in diabetic retinopathy and nephropathy. EPMA J 2019; 10:249-259. [PMID: 31462942 DOI: 10.1007/s13167-019-00171-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 05/12/2019] [Indexed: 12/14/2022]
Abstract
Objective The amplitude of low-frequency fluctuation (ALFF) fMRI technique was used to study the changes of spontaneous brain activity in patients with diabetic retinopathy and nephropathy (DRN), and to explore the application of ALFF technique in the potential prediction and the targeted prevention of diabetic microangiopathy. Methods Nineteen patients with diabetic retinopathy and nephropathy and 19 healthy controls (HCs) were matched for age and gender. Spontaneous cerebral activity variations were investigated using the ALFF technique. The average ALFF values of the DRN patients and the HCs were classified utilizing receiver operating characteristic (ROC) curves. Results In contrast to the results in the HCs, the patients with DRN had significantly higher ALFF values in the cerebellum (bilaterally in the posterior and anterior lobes) and the left inferior temporal gyrus, but the ALFF values of the bilateral medial frontal gyrus, right superior temporal gyrus, right middle frontal gyrus, left middle/inferior frontal gyrus, bilateral precuneus, and left inferior parietal lobule were lower. ROC curve analysis of each brain region showed the accuracy of AUC was excellent. However, the mean ALFF values in the different regions did not correlate with clinical performance. The subjects showed abnormal neuronal synchronization in many areas of the brain, which is consistent with cognitive and visual functional deficits. Conclusion Abnormal spontaneous activity was detected in many areas of the brain, which may provide useful information for understanding the pathology of DRN. Abnormal ALFF values of these brain regions may be of predictive value in the development of early DRN and be a targeted intervention indicator for individualized treatment of diabetic microvascular diseases.
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Affiliation(s)
- Yu Wang
- 1Department of Nephrology, The First Affiliated Hospital of Nanchang University, No 17, YongWaiZheng Street, Nanchang, 330006 Jiangxi People's Republic of China
| | - Yi Shao
- 2Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006 Jiangxi China
| | - Wen-Qing Shi
- 2Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006 Jiangxi China
| | - Lei Jiang
- 1Department of Nephrology, The First Affiliated Hospital of Nanchang University, No 17, YongWaiZheng Street, Nanchang, 330006 Jiangxi People's Republic of China
| | - Xiao-Yu Wang
- 1Department of Nephrology, The First Affiliated Hospital of Nanchang University, No 17, YongWaiZheng Street, Nanchang, 330006 Jiangxi People's Republic of China
| | - Pei-Wen Zhu
- 2Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006 Jiangxi China
| | - Qing Yuan
- 2Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006 Jiangxi China
| | - Ge Gao
- 3Department of General Surgery, The First Affiliated Hospital of Nanchang University, No 17, YongWaiZheng Street, Nanchang, 330006 Jiangxi People's Republic of China
| | - Jin-Lei Lv
- 1Department of Nephrology, The First Affiliated Hospital of Nanchang University, No 17, YongWaiZheng Street, Nanchang, 330006 Jiangxi People's Republic of China
| | - Gong-Xian Wang
- 4Department of Urinary Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, 330006 Jiangxi China
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Cui LB, Cai M, Wang XR, Zhu YQ, Wang LX, Xi YB, Wang HN, Zhu X, Yin H. Prediction of early response to overall treatment for schizophrenia: A functional magnetic resonance imaging study. Brain Behav 2019; 9:e01211. [PMID: 30701701 PMCID: PMC6379641 DOI: 10.1002/brb3.1211] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 12/18/2018] [Accepted: 12/19/2018] [Indexed: 12/21/2022] Open
Abstract
INTRODUCTION Treatment response at an early stage of schizophrenia is of considerable value with regard to future management of the disorder; however, there are currently no biomarkers that can inform physicians about the likelihood of response. OBJECTS We aim to develop and validate regional brain activity derived from functional magnetic resonance imaging (fMRI) as a potential signature to predict early treatment response in schizophrenia. METHODS Amplitude of low-frequency fluctuation (ALFF) was measured at the start of the first/single episode resulting in hospitalization. Inpatients were included in a principal dataset (n = 79) and a replication dataset (n = 44). Two groups of healthy controls (n = 87; n = 106) were also recruited for each dataset. The clinical response was assessed at discharge from the hospital. The predictive capacity of normalized ALFF in patients by healthy controls, ALFFratio , was evaluated based on diagnostic tests and clinical correlates. RESULTS In the principal dataset, responders exhibited increased baseline ALFF in the left postcentral gyrus/inferior parietal lobule relative to non-responders. ALFFratio of responders before treatment was significantly higher than that of non-responders (p < 0.001). The area under the receiver operating characteristic curve was 0.746 for baseline ALFFratio to distinguish responders from non-responders, and the sensitivity, specificity, and accuracy were 72.7%, 68.6%, and 70.9%, respectively. Similar results were found in the independent replication dataset. CONCLUSIONS Baseline regional activity of the brain seems to be predictive of early response to treatment for schizophrenia. This study shows that psycho-neuroimaging holds promise for influencing the clinical treatment and management of schizophrenia.
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Affiliation(s)
- Long-Biao Cui
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.,School of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Min Cai
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Xing-Rui Wang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yuan-Qiang Zhu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Liu-Xian Wang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yi-Bin Xi
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Hua-Ning Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Xia Zhu
- School of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
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Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning. NPJ SCHIZOPHRENIA 2019; 5:2. [PMID: 30659193 PMCID: PMC6386753 DOI: 10.1038/s41537-018-0070-8] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 12/06/2018] [Indexed: 12/16/2022]
Abstract
In the literature, there are substantial machine learning attempts to classify schizophrenia based on alterations in resting-state (RS) brain patterns using functional magnetic resonance imaging (fMRI). Most earlier studies modelled patients undergoing treatment, entailing confounding with drug effects on brain activity, and making them less applicable to real-world diagnosis at the point of first medical contact. Further, most studies with classification accuracies >80% are based on small sample datasets, which may be insufficient to capture the heterogeneity of schizophrenia, limiting generalization to unseen cases. In this study, we used RS fMRI data collected from a cohort of antipsychotic drug treatment-naive patients meeting DSM IV criteria for schizophrenia (N = 81) as well as age- and sex-matched healthy controls (N = 93). We present an ensemble model -- EMPaSchiz (read as ‘Emphasis’; standing for ‘Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction’) that stacks predictions from several ‘single-source’ models, each based on features of regional activity and functional connectivity, over a range of different a priori parcellation schemes. EMPaSchiz yielded a classification accuracy of 87% (vs. chance accuracy of 53%), which out-performs earlier machine learning models built for diagnosing schizophrenia using RS fMRI measures modelled on large samples (N > 100). To our knowledge, EMPaSchiz is first to be reported that has been trained and validated exclusively on data from drug-naive patients diagnosed with schizophrenia. The method relies on a single modality of MRI acquisition and can be readily scaled-up without needing to rebuild parcellation maps from incoming training images.
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Dynamic Causal Modeling and machine learning for effective connectivity in Auditory Hallucination. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2016.08.157] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Sakai K, Yamada K. Machine learning studies on major brain diseases: 5-year trends of 2014–2018. Jpn J Radiol 2018; 37:34-72. [DOI: 10.1007/s11604-018-0794-4] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 11/14/2018] [Indexed: 12/17/2022]
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Plewnia C, Brendel B, Schwippel T, Martus P, Cordes J, Hasan A, Fallgatter AJ. Treatment of auditory hallucinations with bilateral theta burst stimulation (cTBS): protocol of a randomized, double-blind, placebo-controlled, multicenter trial. Eur Arch Psychiatry Clin Neurosci 2018; 268:663-673. [PMID: 29224040 DOI: 10.1007/s00406-017-0861-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Accepted: 12/02/2017] [Indexed: 12/20/2022]
Abstract
UNLABELLED Auditory verbal hallucinations (AH) are core symptoms of schizophrenia. They are often severely distressing and refractory to therapy. Their perception is associated with increased activity in temporoparietal areas of the brain. Repetitive transcranial magnetic stimulation (rTMS) can reduce focal brain hyperactivity and has been shown to ameliorate AH. However, controlled multicenter clinical trials are still missing, effect sizes are moderate, and the treatment with rTMS is time consuming. Continuous theta burst stimulation (cTBS) is a quicker and potentially more effective technique to reduce cortical hyperactivity. First case and pilot studies indicate effectiveness in the treatment of AH. In this randomized, sham-controlled, double-blind multicenter clinical trial, 86 patients with schizophrenia spectrum disorder will be randomized to either cTBS or sham to the left and right temporoparietal cortex during three consecutive weeks (15 sessions totally). In each session, both hemispheres will be stimulated sequentially. The order in the first session (left-right or right-left, respectively) will be determined by randomization and alternated in all following sessions. Primary outcome is the reduction of mean PSYRATS-AH score after cTBS as compared to sham treatment. Follow-up measurements will be performed 1, 3 and 6 months after the end of the treatment. Statistical analysis will be based on the intention-to-treat population including all randomized patients using an analysis of covariance. This multicenter-controlled clinical trial will be able to provide decisive evidence for the efficacy of cTBS in the treatment of AH. The results will be suitable to clarify the role of this innovative, pathophysiology-based therapeutic approach in treatment guidelines for AH. TRIAL REGISTRY ClinicalTrials.gov identifier: NCT02670291.
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Affiliation(s)
- Christian Plewnia
- Department of Psychiatry and Psychotherapy, Neurophysiology and Interventional Neuropsychiatry, University of Tübingen, Calwerstrasse 14, 72076, Tübingen, Germany.
| | - Bettina Brendel
- Department of Psychiatry and Psychotherapy, Neurophysiology and Interventional Neuropsychiatry, University of Tübingen, Calwerstrasse 14, 72076, Tübingen, Germany.,Institute of Clinical Epidemiology and Applied Biometry of the University of Tübingen, Silcherstr. 5, 72076, Tübingen, Germany
| | - Tobias Schwippel
- Department of Psychiatry and Psychotherapy, Neurophysiology and Interventional Neuropsychiatry, University of Tübingen, Calwerstrasse 14, 72076, Tübingen, Germany
| | - Peter Martus
- Institute of Clinical Epidemiology and Applied Biometry of the University of Tübingen, Silcherstr. 5, 72076, Tübingen, Germany
| | - Joachim Cordes
- LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine Universität Düsseldorf, 40629, Düsseldorf, Germany
| | - Alkomiet Hasan
- Department of Psychiatry and Psychotherapy, Klinikum der Universität München, Ludwig-Maximilians University Munich, Nußbaumstraße 7, 80336, Munich, Germany
| | - Andreas J Fallgatter
- Department of Psychiatry and Psychotherapy, Neurophysiology and Interventional Neuropsychiatry, University of Tübingen, Calwerstrasse 14, 72076, Tübingen, Germany
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Jiang Y, Duan M, Chen X, Zhang X, Gong J, Dong D, Li H, Yi Q, Wang S, Wang J, Luo C, Yao D. Aberrant Prefrontal-Thalamic-Cerebellar Circuit in Schizophrenia and Depression: Evidence From a Possible Causal Connectivity. Int J Neural Syst 2018; 29:1850032. [PMID: 30149746 DOI: 10.1142/s0129065718500326] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Neuroimaging studies have suggested the presence of abnormalities in the prefrontal-thalamic-cerebellar circuit in schizophrenia (SCH) and depression (DEP). However, the common and distinct structural and causal connectivity abnormalities in this circuit between the two disorders are still unclear. In the current study, structural and resting-state functional magnetic resonance imaging (fMRI) data were acquired from 20 patients with SCH, 20 depressive patients and 20 healthy controls (HC). Voxel-based morphometry analysis was first used to assess gray matter volume (GMV). Granger causality analysis, seeded at regions with altered GMVs, was subsequently conducted. To discover the differences between the groups, ANCOVA and post hoc tests were performed. Then, the relationships between the structural changes, causal connectivity and clinical variables were investigated. Finally, a leave-one-out resampling method was implemented to test the consistency. Statistical analyses showed the GMV and causal connectivity changes in the prefrontal-thalamic-cerebellar circuit. Compared with HC, both SCH and DEP exhibited decreased GMV in middle frontal gyrus (MFG), and a lower GMV in MFG and medial prefrontal cortex (MPFC) in SCH than DEP. Compared with HC, both patient groups showed increased causal flow from the right cerebellum to the MPFC (common causal connectivity abnormalities). And distinct causal connectivity abnormalities (increased causal connectivity from the left thalamus to the MPFC in SCH than HC and DEP, and increased causal connectivity from the right cerebellum to the left thalamus in DEP than HC and SCH). In addition, the structural deficits in the MPFC and its causal connectivity from the cerebellum were associated with the negative symptom severity in SCH. This study found common/distinct structural deficits and aberrant causal connectivity patterns in the prefrontal-thalamic-cerebellar circuit in SCH and DEP, which may provide a potential direction for understanding the convergent and divergent psychiatric pathological mechanisms between SCH and DEP. Furthermore, concomitant structural and causal connectivity deficits in the MPFC may jointly contribute to the negative symptoms of SCH.
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Affiliation(s)
- 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 P. R. China
| | - Mingjun Duan
- * 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 P. R. China.,† Department of psychiatry, Chengdu Mental Health Center, Chengdu, P. R. China
| | - Xi Chen
- * 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 P. R. China
| | - Xingxing Zhang
- * 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 P. R. China
| | - Jinnan Gong
- * 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 P. R. China
| | - Debo Dong
- * 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 P. R. China
| | - Hui Li
- * 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 P. R. China.,† Department of psychiatry, Chengdu Mental Health Center, Chengdu, P. R. China
| | - Qizhong Yi
- ‡ Psychological Medicine Center, The First Affiliated Hospital of Xinjiang, Medical University, Urumqi, P. R. China
| | - Shuya Wang
- § Biology Department, Emory University, Atlanta, GA, USA
| | - Jijun Wang
- ¶ Department of EEG Source Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P. R. 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 P. R. 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 P. R. China
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Zhu Q, Huang J, Xu X. Non-negative discriminative brain functional connectivity for identifying schizophrenia on resting-state fMRI. Biomed Eng Online 2018. [PMID: 29534759 PMCID: PMC5851331 DOI: 10.1186/s12938-018-0464-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Background Schizophrenia is a clinical syndrome, and its causes have not been well determined. The objective of this study was to investigate the alteration of brain functional connectivity between schizophrenia and healthy control, and present a practical solution for accurately identifying schizophrenia at single-subject level. Methods 24 schizophrenia patients and 21 matched healthy subjects were recruited to undergo the resting-state functional magnetic resonance imaging (rs-fMRI) scanning. First, we constructed the brain network by calculating the Pearson correlation coefficient between each pair of the brain regions. Then, this study proposed a novel non-negative discriminant functional connectivity selection method, i.e. non-negative elastic-net based method (N2EN), to extract the alteration of brain functional connectivity between schizophrenia and healthy control. It ranks the significance of the connectivity with a uniform criterion by introducing the non-negative constraint. Finally, kernel discriminant analysis (KDA) is exploited to classify the subjects with the selected discriminant brain connectivity features. Results The proposed method is applied into schizophrenia classification, and achieves the sensitivity, specificity and accuracy of 100, 90.48 and 95.56%, respectively. Our findings also indicate the alteration of functional network can be used as the biomarks for guiding the schizophrenia diagnosis. The regions of cuneus, superior frontal gyrus, medial, paracentral lobule, calcarine fissure, surrounding cortex, etc. are highly relevant to schizophrenia. Conclusions This study provides a method for accurately identifying schizophrenia, which outperforms several state-of-the-art methods, including conventional brain network classification, multi-threshold brain network based classification, frequent sub-graph based brain network classification and support vector machine. Our investigation suggested that the selected discriminant resting-state functional connectivities are meaningful features for classifying schizophrenia and healthy control.
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Affiliation(s)
- Qi Zhu
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China. .,Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, 210093, China.
| | - Jiashuang Huang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
| | - Xijia Xu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China
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Mammone N, Ieracitano C, Adeli H, Bramanti A, Morabito FC. Permutation Jaccard Distance-Based Hierarchical Clustering to Estimate EEG Network Density Modifications in MCI Subjects. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018; 29:5122-5135. [PMID: 29994428 DOI: 10.1109/tnnls.2018.2791644] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In this paper, a novel electroencephalographic (EEG)-based method is introduced for the quantification of brain-electrical connectivity changes over a longitudinal evaluation of mild cognitive impaired (MCI) subjects. In the proposed method, a dissimilarity matrix is constructed by estimating the coupling strength between every pair of EEG signals, Hierarchical clustering is then applied to group the related electrodes according to the dissimilarity estimated on pairs of EEG recordings. Subsequently, the connectivity density of the electrodes network is calculated. The technique was tested over two different coupling strength descriptors: wavelet coherence (WC) and permutation Jaccard distance (PJD), a novel metric of coupling strength between time series introduced in this paper. Twenty-five MCI patients were enrolled within a follow-up program that consisted of two successive evaluations, at time T0 and at time T1, three months later. At T1, four subjects were diagnosed to have converted to Alzheimer's Disease (AD). When applying the PJD-based method, the converted patients exhibited a significantly increased PJD (p < 0.05), i.e., a reduced overall coupling strength, specifically in delta and θ bands and in the overall range (0.5-32 Hz). In addition, in contrast to stable MCI patients, converted patients exhibited a network density reduction in every subband (delta, θ, alpha, and beta). When WC was used as coupling strength descriptor, the method resulted in a less sensitive and specific outcome. The proposed method, mixing nonlinear analysis to a machine learning approach, appears to provide an objective evaluation of the connectivity density modifications associated to the MCI-AD conversion, just processing noninvasive EEG signals.
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Mirzaei G, Adeli A, Adeli H. Imaging and machine learning techniques for diagnosis of Alzheimer's disease. Rev Neurosci 2018; 27:857-870. [PMID: 27518905 DOI: 10.1515/revneuro-2016-0029] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Accepted: 06/19/2016] [Indexed: 11/15/2022]
Abstract
Alzheimer's disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.
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Gao S, Lu S, Shi X, Ming Y, Xiao C, Sun J, Yao H, Xu X. Distinguishing Between Treatment-Resistant and Non-Treatment-Resistant Schizophrenia Using Regional Homogeneity. Front Psychiatry 2018; 9:282. [PMID: 30127752 PMCID: PMC6088138 DOI: 10.3389/fpsyt.2018.00282] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 06/11/2018] [Indexed: 12/16/2022] Open
Abstract
Background: Patients with treatment-resistant schizophrenia (TRS) and non-treatment-resistant schizophrenia (NTRS) respond to antipsychotic drugs differently. Previous studies demonstrated that patients with TRS or NTRS exhibited abnormal neural activity in different brain regions. Accordingly, in the present study, we tested the hypothesis that a regional homogeneity (ReHo) approach could be used to distinguish between patients with TRS and NTRS. Methods: A total of 17 patients with TRS, 17 patients with NTRS, and 29 healthy controls (HCs) matched in sex, age, and education levels were recruited to undergo resting-state functional magnetic resonance imaging (RS-fMRI). ReHo was used to process the data. ANCOVA followed by post-hoc t-tests, receiver operating characteristic curves (ROC), and correlation analyses were applied for the data analysis. Results: ANCOVA analysis revealed widespread differences in ReHo among the three groups in the occipital, frontal, temporal, and parietal lobes. ROC results indicated that the optimal sensitivity and specificity of the ReHo values in the left postcentral gyrus, left inferior frontal gyrus/triangular part, and right fusiform could differentiate TRS from NTRS, TRS from HCs, and NTRS from HCs were 94.12 and 82.35%, 100 and 86.21%, and 82.35 and 93.10%, respectively. No correlation was found between abnormal ReHo and clinical symptoms in patients with TRS or NTRS. Conclusions: TRS and NTRS shared most brain regions with abnormal neural activity. Abnormal ReHo values in certain brain regions might be applied to differentiate TRS from NTRS, TRS from HC, and NTRS from HC with high sensitivity and specificity.
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Affiliation(s)
- Shuzhan Gao
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.,Department of Psychiatry, Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing, China
| | - Shuiping Lu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.,Department of Psychiatry, Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing, China
| | - Xiaomeng Shi
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.,Department of Psychiatry, Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing, China
| | - Yidan Ming
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.,Department of Psychiatry, Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing, China
| | - Chaoyong Xiao
- Department of Radiology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Jing Sun
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.,Department of Psychiatry, Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing, China
| | - Hui Yao
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.,Department of Psychiatry, Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing, China
| | - Xijia Xu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.,Department of Psychiatry, Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing, China
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42
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Role of Spontaneous Brain Activity in Explicit and Implicit Aspects of Cognitive Flexibility under Socially Conflicting Situations: A Resting-state fMRI Study using Fractional Amplitude of Low-frequency Fluctuations. Neuroscience 2017; 367:60-71. [DOI: 10.1016/j.neuroscience.2017.10.025] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2017] [Revised: 10/17/2017] [Accepted: 10/19/2017] [Indexed: 12/29/2022]
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43
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López-Sanz D, Garcés P, Álvarez B, Delgado-Losada ML, López-Higes R, Maestú F. Network Disruption in the Preclinical Stages of Alzheimer’s Disease: From Subjective Cognitive Decline to Mild Cognitive Impairment. Int J Neural Syst 2017; 27:1750041. [DOI: 10.1142/s0129065717500411] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Introduction: Subjective Cognitive Decline (SCD) is a largely unknown state thought to represent a preclinical stage of Alzheimer’s Disease (AD) previous to mild cognitive impairment (MCI). However, the course of network disruption in these stages is scarcely characterized. Methods: We employed resting state magnetoencephalography in the source space to calculate network smallworldness, clustering, modularity and transitivity. Nodal measures (clustering and node degree) as well as modular partitions were compared between groups. Results: The MCI group exhibited decreased smallworldness, clustering and transitivity and increased modularity in theta and beta bands. SCD showed similar but smaller changes in clustering and transitivity, while exhibiting alterations in the alpha band in opposite direction to those showed by MCI for modularity and transitivity. At the node level, MCI disrupted both clustering and nodal degree while SCD showed minor changes in the latter. Additionally, we observed an increase in modular partition variability in both SCD and MCI in theta and beta bands. Conclusion: SCD elders exhibit a significant network disruption, showing intermediate values between HC and MCI groups in multiple parameters. These results highlight the relevance of cognitive concerns in the clinical setting and suggest that network disorganization in AD could start in the preclinical stages before the onset of cognitive symptoms.
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Affiliation(s)
- David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid 28223, Spain
- Department of Basic Psychology II, Complutense University of Madrid 28223, Spain
| | - Pilar Garcés
- Laboratory of Cognitive Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid 28223, Spain
| | - Blanca Álvarez
- Memory Decline Prevention Center Madrid Salud, Ayuntamiento de Madrid 28006, Spain
| | | | - Ramón López-Higes
- Department of Basic Psychology II, Complutense University of Madrid 28223, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid 28223, Spain
- Department of Basic Psychology II, Complutense University of Madrid 28223, Spain
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44
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Machine Learning Applications to Resting-State Functional MR Imaging Analysis. Neuroimaging Clin N Am 2017; 27:609-620. [DOI: 10.1016/j.nic.2017.06.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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45
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Zhang J, Liu M, Gao Y, Shen D. Alzheimer's Disease Diagnosis Using Landmark-Based Features From Longitudinal Structural MR Images. IEEE J Biomed Health Inform 2017; 21:1607-1616. [PMID: 28534798 DOI: 10.1109/jbhi.2017.2704614] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Structural magnetic resonance imaging (MRI) has been proven to be an effective tool for Alzheimer's disease (AD) diagnosis. While conventional MRI-based AD diagnosis typically uses images acquired at a single time point, a longitudinal study is more sensitive in detecting early pathological changes of AD, making it more favorable for accurate diagnosis. In general, there are two challenges faced in MRI-based diagnosis. First, extracting features from structural MR images requires time-consuming nonlinear registration and tissue segmentation, whereas the longitudinal study with involvement of more scans further exacerbates the computational costs. Moreover, the inconsistent longitudinal scans (i.e., different scanning time points and also the total number of scans) hinder extraction of unified feature representations in longitudinal studies. In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which does not require nonlinear registration or tissue segmentation in the application stage and is also robust to inconsistencies among longitudinal scans. Specifically, first, the discriminative landmarks are automatically discovered from the whole brain using training images, and then efficiently localized using a fast landmark detection method for testing images, without the involvement of any nonlinear registration and tissue segmentation; and second, high-level statistical spatial features and contextual longitudinal features are further extracted based on those detected landmarks, which can characterize spatial structural abnormalities and longitudinal landmark variations. Using these spatial and longitudinal features, a linear support vector machine is finally adopted to distinguish AD subjects or mild cognitive impairment (MCI) subjects from healthy controls (HCs). Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate the superior performance and efficiency of the proposed method, with classification accuracies of 88.30% for AD versus HC and 79.02% for MCI versus HC, respectively.
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46
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Graña M, Ozaeta L, Chyzhyk D. Resting State Effective Connectivity Allows Auditory Hallucination Discrimination. Int J Neural Syst 2017; 27:1750019. [DOI: 10.1142/s0129065717500198] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Hallucinations are elusive phenomena that have been associated with psychotic behavior, but that have a high prevalence in healthy population. Some generative mechanisms of Auditory Hallucinations (AH) have been proposed in the literature, but so far empirical evidence is scarce. The most widely accepted generative mechanism hypothesis nowadays consists in the faulty workings of a network of brain areas including the emotional control, the audio and language processing, and the inhibition and self-attribution of the signals in the auditive cortex. In this paper, we consider two methods to analyze resting state fMRI (rs-fMRI) data, in order to measure effective connections between the brain regions involved in the AH generation process. These measures are the Dynamic Causal Modeling (DCM) cross-covariance function (CCF) coefficients, and the partially directed coherence (PDC) coefficients derived from Granger Causality (GC) analysis. Effective connectivity measures are treated as input classifier features to assess their significance by means of cross-validation classification accuracy results in a wrapper feature selection approach. Experimental results using Support Vector Machine (SVM) classifiers on an rs-fMRI dataset of schizophrenia patients with and without a history of AH confirm that the main regions identified in the AH generative mechanism hypothesis have significant effective connection values, under both DCM and PDC evaluation.
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Affiliation(s)
- Manuel Graña
- Computational Intelligence Group, University of the Basque Country, UPV/EHU, Spain
- ACPySS, San Sebastian, Spain
| | - Leire Ozaeta
- Computational Intelligence Group, University of the Basque Country, UPV/EHU, Spain
| | - Darya Chyzhyk
- Computational Intelligence Group, University of the Basque Country, UPV/EHU, Spain
- CISE Department, University of Florida, Gainesville, USA
- ACPySS, San Sebastian, Spain
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Abstract
BACKGROUND Sensory-processing deficits appear crucial to the clinical expression of symptoms of schizophrenia. The visual cortex displays both dysconnectivity and aberrant spontaneous activity in patients with persistent symptoms and cognitive deficits. In this paper, we examine visual cortex in the context of the remerging notion of thalamic dysfunction in schizophrenia. We examined specific regional and longer-range abnormalities in sensory and thalamic circuits in schizophrenia, and whether these patterns are strong enough to discriminate symptomatic patients from controls. METHOD Using publicly available resting fMRI data of 71 controls and 62 schizophrenia patients, we derived conjunction maps of regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF) to inform further seed-based Granger causality analysis (GCA) to study effective connectivity patterns. ReHo, fALFF and GCA maps were entered into a multiple kernel learning classifier, to determine whether patterns of local and effective connectivity can differentiate controls from patients. RESULTS Visual cortex shows both ReHo and fALFF reductions in patients. Visuothalamic effective connectivity in patients was significantly reduced. Local connectivity (ReHo) patterns discriminated patients from controls with the highest level of accuracy of 80.32%. CONCLUSIONS Both the inflow and outflow of Granger causal information between visual cortex and thalamus is affected in schizophrenia; this occurs in conjunction with highly discriminatory but localized dysconnectivity and reduced neural activity within the visual cortex. This may explain the visual-processing deficits that are present despite symptomatic remission in schizophrenia.
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Affiliation(s)
- S J Iwabuchi
- Translational Neuroimaging for Mental Health,Division of Psychiatry and Applied Psychology,University of Nottingham,Nottingham,UK
| | - L Palaniyappan
- Departments of Psychiatry & Medical Biophysics,University of Western Ontario,London,Ontario,Canada
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48
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Niu Q, Yang L, Song X, Chu C, Liu H, Zhang L, Li Y, Zhang X, Cheng J, Li Y. Abnormal resting-state brain activities in patients with first-episode obsessive-compulsive disorder. Neuropsychiatr Dis Treat 2017; 13:507-513. [PMID: 28243104 PMCID: PMC5317330 DOI: 10.2147/ndt.s117510] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE This paper attempts to explore the brain activity of patients with obsessive-compulsive disorder (OCD) and its correlation with the disease at resting duration in patients with first-episode OCD, providing a forceful imaging basis for clinic diagnosis and pathogenesis of OCD. METHODS Twenty-six patients with first-episode OCD and 25 healthy controls (HC group; matched for age, sex, and education level) underwent functional magnetic resonance imaging (fMRI) scanning at resting state. Statistical parametric mapping 8, data processing assistant for resting-state fMRI analysis toolkit, and resting state fMRI data analysis toolkit packages were used to process the fMRI data on Matlab 2012a platform, and the difference of regional homogeneity (ReHo) values between the OCD group and HC group was detected with independent two-sample t-test. With age as a concomitant variable, the Pearson correlation analysis was adopted to study the correlation between the disease duration and ReHo value of whole brain. RESULTS Compared with HC group, the ReHo values in OCD group were decreased in brain regions, including left thalamus, right thalamus, right paracentral lobule, right postcentral gyrus, and the ReHo value was increased in the left angular gyrus region. There was a negative correlation between disease duration and ReHo value in the bilateral orbitofrontal cortex (OFC). CONCLUSION OCD is a multifactorial disease generally caused by abnormal activities of many brain regions at resting state. Worse brain activity of the OFC is related to the OCD duration, which provides a new insight to the pathogenesis of OCD.
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Affiliation(s)
- Qihui Niu
- Department of Psychiatry, The First Affiliated Hospital, Zhengzhou University, Zhengzhou
| | - Lei Yang
- Department of Psychiatry, The First Affiliated Hospital, Zhengzhou University, Zhengzhou
| | - Xueqin Song
- Department of Psychiatry, The First Affiliated Hospital, Zhengzhou University, Zhengzhou
| | - Congying Chu
- Brainnetome Certer, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Hao Liu
- Department of Psychiatry, The First Affiliated Hospital, Zhengzhou University, Zhengzhou
| | - Lifang Zhang
- Department of Psychiatry, The First Affiliated Hospital, Zhengzhou University, Zhengzhou
| | - Yan Li
- Department of Psychiatry, The First Affiliated Hospital, Zhengzhou University, Zhengzhou
| | - Xiang Zhang
- Department of Psychiatry, The First Affiliated Hospital, Zhengzhou University, Zhengzhou
| | - Jingliang Cheng
- Department of Psychiatry, The First Affiliated Hospital, Zhengzhou University, Zhengzhou
| | - Youhui Li
- Department of Psychiatry, The First Affiliated Hospital, Zhengzhou University, Zhengzhou
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Kang DZ, Chen FX, Chen FY, Liu Y, Wu G, Yu LH, Lin YX, Lin ZY. Altered regional homogeneity of prefrontal cortex in Parkinson’s disease with mild cognitive impairment. Chin Neurosurg J 2016. [DOI: 10.1186/s41016-016-0028-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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50
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Mirzaei G, Adeli H. Resting state functional magnetic resonance imaging processing techniques in stroke studies. Rev Neurosci 2016; 27:871-885. [DOI: 10.1515/revneuro-2016-0052] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 10/01/2016] [Indexed: 01/15/2023]
Abstract
AbstractIn recent years, there has been considerable research interest in the study of brain connectivity using the resting state functional magnetic resonance imaging (rsfMRI). Studies have explored the brain networks and connection between different brain regions. These studies have revealed interesting new findings about the brain mapping as well as important new insights in the overall organization of functional communication in the brain network. In this paper, after a general discussion of brain networks and connectivity imaging, the brain connectivity and resting state networks are described with a focus on rsfMRI imaging in stroke studies. Then, techniques for preprocessing of the rsfMRI for stroke patients are reviewed, followed by brain connectivity processing techniques. Recent research on brain connectivity using rsfMRI is reviewed with an emphasis on stroke studies. The authors hope this paper generates further interest in this emerging area of computational neuroscience with potential applications in rehabilitation of stroke patients.
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Affiliation(s)
- Golrokh Mirzaei
- 1Department of Computer Science and Engineering, The Ohio State University, Marion, OH 43302, United States of America
| | - Hojjat Adeli
- 2Department of Biomedical Engineering, Biomedical Informatics, Neurology, Neuroscience, Electrical and Computer Engineering, Civil and Environmental Engineering, The Ohio State University, Columbus, OH 43210, United States of America
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