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Ding C, Li A, Xie S, Tian X, Li K, Fan L, Yan H, Chen J, Chen Y, Wang H, Guo H, Yang Y, Lv L, Wang H, Zhang H, Lu L, Zhang D, Zhang Z, Wang M, Jiang T, Liu B. Mapping Brain Synergy Dysfunction in Schizophrenia: Understanding Individual Differences and Underlying Molecular Mechanisms. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400929. [PMID: 38900070 PMCID: PMC11348140 DOI: 10.1002/advs.202400929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 05/22/2024] [Indexed: 06/21/2024]
Abstract
To elucidate the brain-wide information interactions that vary and contribute to individual differences in schizophrenia (SCZ), an information-resolved method is employed to construct individual synergistic and redundant interaction matrices based on regional pairwise BOLD time-series from 538 SCZ and 540 normal controls (NC). This analysis reveals a stable pattern of regionally-specific synergy dysfunction in SCZ. Furthermore, a hierarchical Bayesian model is applied to deconstruct the patterns of whole-brain synergy dysfunction into three latent factors that explain symptom heterogeneity in SCZ. Factor 1 exhibits a significant positive correlation with Positive and Negative Syndrome Scale (PANSS) positive scores, while factor 3 demonstrates significant negative correlations with PANSS negative and general scores. By integrating the neuroimaging data with normative gene expression information, this study identifies that each of these three factors corresponded to a subset of the SCZ risk gene set. Finally, by combining data from NeuroSynth and open molecular imaging sources, along with a spatially heterogeneous mean-field model, this study delineates three SCZ synergy factors corresponding to distinct symptom profiles and implicating unique cognitive, neurodynamic, and neurobiological mechanisms.
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Affiliation(s)
- Chaoyue Ding
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049China
- Brainnetome CenterInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Ang Li
- State Key Laboratory of Brain and Cognitive ScienceInstitute of BiophysicsChinese Academy of SciencesBeijing100101China
| | - Sangma Xie
- School of AutomationHangzhou Dianzi UniversityHangzhou310018China
| | - Xiaohan Tian
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100875China
| | - Kunchi Li
- Brainnetome CenterInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Lingzhong Fan
- Brainnetome CenterInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Hao Yan
- Institute of Mental HealthPeking University Sixth HospitalBeijing100191China
| | - Jun Chen
- Department of RadiologyRenmin Hospital of Wuhan UniversityWuhan430060China
| | - Yunchun Chen
- Department of PsychiatryXijing HospitalThe Fourth Military Medical UniversityXi'an710032China
| | - Huaning Wang
- Department of PsychiatryXijing HospitalThe Fourth Military Medical UniversityXi'an710032China
| | - Hua Guo
- Zhumadian Psychiatric HospitalZhumadian463000China
| | - Yongfeng Yang
- Department of PsychiatryHenan Mental HospitalThe Second Affiliated Hospital of Xinxiang Medical UniversityXinxiang453002China
| | - Luxian Lv
- Department of PsychiatryHenan Mental HospitalThe Second Affiliated Hospital of Xinxiang Medical UniversityXinxiang453002China
| | - Huiling Wang
- Department of PsychiatryRenmin Hospital of Wuhan UniversityWuhan430060China
| | - Hongxing Zhang
- Department of PsychiatryHenan Mental HospitalThe Second Affiliated Hospital of Xinxiang Medical UniversityXinxiang453002China
| | - Lin Lu
- Institute of Mental HealthPeking University Sixth HospitalBeijing100191China
| | - Dai Zhang
- Institute of Mental HealthPeking University Sixth HospitalBeijing100191China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100875China
| | - Meng Wang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100875China
| | - Tianzi Jiang
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049China
- Brainnetome CenterInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100875China
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2
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Du Y, Niu J, Xing Y, Li B, Calhoun VD. Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade. Schizophr Bull 2024:sbae110. [PMID: 38982882 DOI: 10.1093/schbul/sbae110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years. STUDY DESIGN The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade. STUDY RESULTS Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders. CONCLUSIONS We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ju Niu
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ying Xing
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Bang Li
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Vince D Calhoun
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, 30303, GA, USA
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3
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Wang G, Jiang N, Ma Y, Chen D, Wu J, Li G, Liang D, Yan T. Connectional-style-guided contextual representation learning for brain disease diagnosis. Neural Netw 2024; 175:106296. [PMID: 38653077 DOI: 10.1016/j.neunet.2024.106296] [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: 08/09/2023] [Revised: 01/26/2024] [Accepted: 04/06/2024] [Indexed: 04/25/2024]
Abstract
Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous DL-based approaches focused on local shapes and textures in brain sMRI that may be significant only within a particular domain. The learned representations are likely to contain spurious information and have poor generalization ability in other diseases and datasets. To facilitate capturing meaningful and robust features, it is necessary to first comprehensively understand the intrinsic pattern of the brain that is not restricted within a single data/task domain. Considering that the brain is a complex connectome of interlinked neurons, the connectional properties in the brain have strong biological significance, which is shared across multiple domains and covers most pathological information. In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis. Specifically, it has a vision transformer (ViT) encoder and leverages mask reconstruction as the proxy task and Gram matrices to guide the representation of connectional information. It facilitates the capture of global context and the aggregation of features with biological plausibility. The results indicate that CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases and outperforms state-of-the-art models. Furthermore, we demonstrate that CS-CRL captures more brain-network-like properties, and better aggregates features, is easier to optimize, and is more robust to noise, which explains its superiority in theory.
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Affiliation(s)
- Gongshu Wang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Ning Jiang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Yunxiao Ma
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Duanduan Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Jinglong Wu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Guoqi Li
- Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Dong Liang
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Tianyi Yan
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
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4
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Pfarr JK, Meller T, Brosch K, Stein F, Thomas-Odenthal F, Evermann U, Wroblewski A, Ringwald KG, Hahn T, Meinert S, Winter A, Thiel K, Flinkenflügel K, Jansen A, Krug A, Dannlowski U, Kircher T, Gaser C, Nenadić I. Data-driven multivariate identification of gyrification patterns in a transdiagnostic patient cohort: A cluster analysis approach. Neuroimage 2023; 281:120349. [PMID: 37683808 DOI: 10.1016/j.neuroimage.2023.120349] [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: 04/17/2023] [Revised: 07/14/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND Multivariate data-driven statistical approaches offer the opportunity to study multi-dimensional interdependences between a large set of biological parameters, such as high-dimensional brain imaging data. For gyrification, a putative marker of early neurodevelopment, direct comparisons of patterns among multiple psychiatric disorders and investigations of potential heterogeneity of gyrification within one disorder and a transdiagnostic characterization of neuroanatomical features are lacking. METHODS In this study we used a data-driven, multivariate statistical approach to analyze cortical gyrification in a large cohort of N = 1028 patients with major psychiatric disorders (Major depressive disorder: n = 783, bipolar disorder: n = 129, schizoaffective disorder: n = 44, schizophrenia: n = 72) to identify cluster patterns of gyrification beyond diagnostic categories. RESULTS Cluster analysis applied on gyrification data of 68 brain regions (DK-40 atlas) identified three clusters showing difference in overall (global) gyrification and minor regional variation (regions). Newly, data-driven subgroups are further discriminative in cognition and transdiagnostic disease risk factors. CONCLUSIONS Results indicate that gyrification is associated with transdiagnostic risk factors rather than diagnostic categories and further imply a more global role of gyrification related to mental health than a disorder specific one. Our findings support previous studies highlighting the importance of association cortices involved in psychopathology. Explorative, data-driven approaches like ours can help to elucidate if the brain imaging data on hand and its a priori applied grouping actually has the potential to find meaningful effects or if previous hypotheses about the phenotype as well as its grouping have to be revisited.
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Affiliation(s)
- Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Department of Psychology, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany.
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Ulrika Evermann
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Kai G Ringwald
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Germany; Institute for Translational Neuroscience, University of Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Germany
| | | | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany; Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Germany
| | - Axel Krug
- Department of Psychiatry und Psychotherapy, University Hospital Bonn, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Christian Gaser
- Department of Neurology, Jena University Hospital, Germany; Department of Psychiatry and Psychotherapy, Jena University Hospital, Germany; German Center for Mental Health (DZPG), Site Jena-Magdeburg-Halle, Germany; Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
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5
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Koen JD, Lewis L, Rugg MD, Clementz BA, Keshavan MS, Pearlson GD, Sweeney JA, Tamminga CA, Ivleva EI. Supervised machine learning classification of psychosis biotypes based on brain structure: findings from the Bipolar-Schizophrenia network for intermediate phenotypes (B-SNIP). Sci Rep 2023; 13:12980. [PMID: 37563219 PMCID: PMC10415369 DOI: 10.1038/s41598-023-38101-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 07/03/2023] [Indexed: 08/12/2023] Open
Abstract
Traditional diagnostic formulations of psychotic disorders have low correspondence with underlying disease neurobiology. This has led to a growing interest in using brain-based biomarkers to capture biologically-informed psychosis constructs. Building upon our prior work on the B-SNIP Psychosis Biotypes, we aimed to examine whether structural MRI (an independent biomarker not used in the Biotype development) can effectively classify the Biotypes. Whole brain voxel-wise grey matter density (GMD) maps from T1-weighted images were used to train and test (using repeated randomized train/test splits) binary L2-penalized logistic regression models to discriminate psychosis cases (n = 557) from healthy controls (CON, n = 251). A total of six models were evaluated across two psychosis categorization schemes: (i) three Biotypes (B1, B2, B3) and (ii) three DSM diagnoses (schizophrenia (SZ), schizoaffective (SAD) and bipolar (BD) disorders). Above-chance classification accuracies were observed in all Biotype (B1 = 0.70, B2 = 0.65, and B3 = 0.56) and diagnosis (SZ = 0.64, SAD = 0.64, and BD = 0.59) models. However, the only model that showed evidence of specificity was B1, i.e., the model was able to discriminate B1 vs. CON and did not misclassify other psychosis cases (B2 or B3) as B1 at rates above nominal chance. The GMD-based classifier evidence for B1 showed a negative association with an estimate of premorbid general intellectual ability, regardless of group membership, i.e. psychosis or CON. Our findings indicate that, complimentary to clinical diagnoses, the B-SNIP Psychosis Biotypes may offer a promising approach to capture specific aspects of psychosis neurobiology.
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Affiliation(s)
- Joshua D Koen
- Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA.
- Department of Psychology, University of Notre Dame, Notre Dame, IN, 46556, USA.
| | - Leslie Lewis
- Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA
| | - Michael D Rugg
- Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA
- UT Southwestern Medical Center, Dallas, TX, USA
- University of East Anglia, Norwich, UK
| | | | | | - Godfrey D Pearlson
- Institute of Living, Hartford Hospital, Hartford, CT, USA
- Yale School of Medicine, New Haven, CT, USA
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6
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Giangreco B, Dwir D, Klauser P, Jenni R, Golay P, Cleusix M, Baumann PS, Cuénod M, Conus P, Toni N, Do KQ. Characterization of early psychosis patients carrying a genetic vulnerability to redox dysregulation: a computational analysis of mechanism-based gene expression profile in fibroblasts. Mol Psychiatry 2023; 28:1983-1994. [PMID: 37002404 PMCID: PMC10575782 DOI: 10.1038/s41380-023-02034-x] [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: 08/03/2021] [Revised: 02/21/2023] [Accepted: 03/10/2023] [Indexed: 06/19/2023]
Abstract
In view of its heterogeneity, schizophrenia needs new diagnostic tools based on mechanistic biomarkers that would allow early detection. Complex interaction between genetic and environmental risk factors may lead to NMDAR hypofunction, inflammation and redox dysregulation, all converging on oxidative stress. Using computational analysis, the expression of 76 genes linked to these systems, known to be abnormally regulated in schizophrenia, was studied in skin-fibroblasts from early psychosis patients and age-matched controls (N = 30), under additional pro-oxidant challenge to mimic environmental stress. To evaluate the contribution of a genetic risk related to redox dysregulation, we investigated the GAG trinucleotide polymorphism in the key glutathione (GSH) synthesizing enzyme, glutamate-cysteine-ligase-catalytic-subunit (gclc) gene, known to be associated with the disease. Patients and controls showed different gene expression profiles that were modulated by GAG-gclc genotypes in combination with oxidative challenge. In GAG-gclc low-risk genotype patients, a global gene expression dysregulation was observed, especially in the antioxidant system, potentially induced by other risks. Both controls and patients with GAG-gclc high-risk genotype (gclcGAG-HR) showed similar gene expression profiles. However, under oxidative challenge, a boosting of other antioxidant defense, including the master regulator Nrf2 and TRX systems was observed only in gclcGAG-HR controls, suggesting a protective compensation against the genetic GSH dysregulation. Moreover, RAGE (redox/inflammation interaction) and AGMAT (arginine pathway) were increased in the gclcGAG-HR patients, suggesting some additional risk factors interacting with this genotype. Finally, the use of a machine-learning approach allowed discriminating patients and controls with an accuracy up to 100%, paving the way towards early detection of schizophrenia.
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Affiliation(s)
- Basilio Giangreco
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Daniella Dwir
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Paul Klauser
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- Service of Child and Adolescent Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Raoul Jenni
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Philippe Golay
- Service of Community Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Martine Cleusix
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Philipp S Baumann
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Michel Cuénod
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Philippe Conus
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Nicolas Toni
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Kim Q Do
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.
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7
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Kim J, Song J, Kambari Y, Plitman E, Shah P, Iwata Y, Caravaggio F, Brown EE, Nakajima S, Chakravarty MM, De Luca V, Remington G, Graff-Guerrero A, Gerretsen P. Cortical thinning in relation to impaired insight into illness in patients with treatment resistant schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:27. [PMID: 37120642 PMCID: PMC10148890 DOI: 10.1038/s41537-023-00347-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 03/12/2023] [Indexed: 05/01/2023]
Abstract
Impaired insight into illness is a common element of schizophrenia that contributes to treatment nonadherence and negative clinical outcomes. Previous studies suggest that impaired insight may arise from brain abnormalities. However, interpretations of these findings are limited due to small sample sizes and inclusion of patients with a narrow range of illness severity and insight deficits. In a large sample of patients with schizophrenia, the majority of which were designated as treatment-resistant, we investigated the associations between impaired insight and cortical thickness and subcortical volumes. A total of 94 adult participants with a schizophrenia spectrum disorder were included. Fifty-six patients (60%) had treatment-resistant schizophrenia. The core domains of insight were assessed with the VAGUS insight into psychosis scale. We obtained 3T MRI T1-weighted images, which were analysed using CIVET and MAGeT-Brain. Whole-brain vertex-wise analyses revealed impaired insight, as measured by VAGUS average scores, was related to cortical thinning in left frontotemporoparietal regions. The same analysis in treatment-resistant patients showed thinning in the same regions, even after controlling for age, sex, illness severity, and chlorpromazine antipsychotic dose equivalents. No association was found in non-treatment-resistant patients. Region-of-interest analyses revealed impaired general illness awareness was associated with cortical thinning in the left supramarginal gyrus when controlling for covariates. Reduced right and left thalamic volumes were associated with VAGUS symptom attribution and awareness of negative consequences subscale scores, respectively, but not after correction for multiple testing. Our results suggest impaired insight into illness is related to cortical thinning in left frontotemporoparietal regions in patients with schizophrenia, particularly those with treatment resistance where insight deficits may be more chronic.
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Affiliation(s)
- Julia Kim
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Jianmeng Song
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Yasaman Kambari
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Eric Plitman
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Parita Shah
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Yusuke Iwata
- University of Yamanashi, Faculty of Medicine, Department of Neuropsychiatry, Yamanashi, Japan
| | - Fernando Caravaggio
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Eric E Brown
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, CAMH, Toronto, ON, Canada
- Geriatric Mental Health Division, CAMH, Toronto, ON, Canada
| | - Shinichiro Nakajima
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Vincenzo De Luca
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Gary Remington
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, CAMH, Toronto, ON, Canada
- Schizophrenia Division, CAMH, Toronto, ON, Canada
| | - Ariel Graff-Guerrero
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Geriatric Mental Health Division, CAMH, Toronto, ON, Canada
- Schizophrenia Division, CAMH, Toronto, ON, Canada
| | - Philip Gerretsen
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada.
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Geriatric Mental Health Division, CAMH, Toronto, ON, Canada.
- Schizophrenia Division, CAMH, Toronto, ON, Canada.
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8
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Dhamala E, Yeo BTT, Holmes AJ. One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry. Biol Psychiatry 2023; 93:717-728. [PMID: 36577634 DOI: 10.1016/j.biopsych.2022.09.024] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 09/07/2022] [Accepted: 09/23/2022] [Indexed: 12/30/2022]
Abstract
Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way across individuals, and no two patients with a shared diagnosis exhibit identical symptom profiles. Over the last several decades, group-level analyses of in vivo neuroimaging data have led to fundamental advances in our understanding of the neurobiology of psychiatric illnesses. More recently, access to computational resources and large, publicly available datasets alongside the rise of predictive modeling and precision medicine approaches have facilitated the study of psychiatric illnesses at an individual level. Data-driven machine learning analyses can be applied to identify disease-relevant biological subtypes, predict individual symptom profiles, and recommend personalized therapeutic interventions. However, when developing these predictive models, methodological choices must be carefully considered to ensure accurate, robust, and interpretable results. Choices pertaining to algorithms, neuroimaging modalities and states, data transformation, phenotypes, parcellations, sample sizes, and populations we are specifically studying can influence model performance. Here, we review applications of neuroimaging-based machine learning models to study psychiatric illnesses and discuss the effects of different methodological choices on model performance. An understanding of these effects is crucial for the proper implementation of predictive models in psychiatry and will facilitate more accurate diagnoses, prognoses, and therapeutics.
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Affiliation(s)
- Elvisha Dhamala
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut.
| | - B T Thomas Yeo
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut.
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9
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Tepper Á, Vásquez Núñez J, Ramirez-Mahaluf JP, Aguirre JM, Barbagelata D, Maldonado E, Díaz Dellarossa C, Nachar R, González-Valderrama A, Undurraga J, Goñi J, Crossley N. Intra and inter-individual variability in functional connectomes of patients with First Episode of Psychosis. Neuroimage Clin 2023; 38:103391. [PMID: 37003128 PMCID: PMC10102560 DOI: 10.1016/j.nicl.2023.103391] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 03/24/2023] [Accepted: 03/25/2023] [Indexed: 03/31/2023]
Abstract
Patients with Schizophrenia may show different clinical presentations, not only regarding inter-individual comparisons but also in one specific subject over time. In fMRI studies, functional connectomes have been shown to carry valuable individual level information, which can be associated with cognitive and behavioral variables. Moreover, functional connectomes have been used to identify subjects within a group, as if they were fingerprints. For the particular case of Schizophrenia, it has been shown that there is reduced connectome stability as well as higher inter-individual variability. Here, we studied inter and intra-individual heterogeneity by exploring functional connectomes' variability and related it with clinical variables (PANSS Total scores and antipsychotic's doses). Our sample consisted of 30 patients with First Episode of Psychosis and 32 Healthy Controls, with a test-retest approach of two resting-state fMRI scanning sessions. In our patients' group, we found increased deviation from healthy functional connectomes and increased intragroup inter-subject variability, which was positively correlated to symptoms' levels in six subnetworks (visual, somatomotor, dorsal attention, ventral attention, frontoparietal and DMN). Moreover, changes in symptom severity were positively related to changes in deviation from healthy functional connectomes. Regarding intra-subject variability, we were unable to replicate previous findings of reduced connectome stability (i.e., increased intra-subject variability), but we found a trend suggesting that result. Our findings highlight the relevance of variability characterization in Schizophrenia, and they can be related to evidence of Schizophrenia patients having a noisy functional connectome.
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Affiliation(s)
- Ángeles Tepper
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile; Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, Santiago, Chile
| | | | | | - Juan Manuel Aguirre
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Daniella Barbagelata
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Elisa Maldonado
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | | | - Ruben Nachar
- Psychiatric Institute "Dr. José Horwitz Barak", Santiago. Chile
| | - Alfonso González-Valderrama
- Psychiatric Institute "Dr. José Horwitz Barak", Santiago. Chile; School of Medicine, Universidad Finis Terrae, Santiago, Chile
| | - Juan Undurraga
- Psychiatric Institute "Dr. José Horwitz Barak", Santiago. Chile; Department of Neurology and Psychiatry, Faculty of Medicine, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
| | - Joaquín Goñi
- CONNplexity Lab. Purdue University. West Lafayette, Indiana USA
| | - Nicolás Crossley
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile; Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, Santiago, Chile; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
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10
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Taylor VA, Roy A, Brewer JA. Cluster-based psychological phenotyping and differences in anxiety treatment outcomes. Sci Rep 2023; 13:3055. [PMID: 36810609 PMCID: PMC9944281 DOI: 10.1038/s41598-023-28660-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 01/23/2023] [Indexed: 02/24/2023] Open
Abstract
The identification of markers of mental health illness treatment response and susceptibility using personalized medicine has been elusive. In the context of psychological treatment for anxiety, we conducted two studies to identify psychological phenotypes with distinct characteristics related to: psychological intervention modalities (mindfulness training/awareness), mechanism of action (worry), and clinical outcome (generalized anxiety disorder scale scores). We also examined whether phenotype membership interacted with treatment response (Study 1) and mental health illness diagnosis (Studies 1-2). Interoceptive awareness, emotional reactivity, worry, and anxiety were assessed at baseline in treatment-seeking individuals (Study 1, n = 63) and from the general population (Study 2, n = 14,010). In Study 1, participants were randomly assigned to an app-delivered mindfulness program for anxiety for two months or treatment as usual. Changes in anxiety were assessed 1 and 2 months post-treatment initiation. In studies 1-2, three phenotypes were identified: 'severely anxious with body/emotional awareness' (cluster 1), 'body/emotionally unaware' (cluster 2), and 'non-reactive and aware' (cluster 3). Study 1's results revealed a significant treatment response relative to controls (ps < 0.001) for clusters 1 and 3, but not for cluster 2. Chi-square analyses revealed that phenotypes exhibited significantly different proportions of participants with mental health diagnoses (studies 1-2). These results suggest that psychological phenotyping can bring the application of personalized medicine into clinical settings.Registry name and URL: Developing a novel digital therapeutic for the treatment of generalized anxiety disorder https://clinicaltrials.gov/ct2/show/NCT03683472?term=judson+brewer&draw=1&rank=1 .Trial registration: Registered at clinicaltrials.gov (NCT03683472) on 25/09/2018.
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Affiliation(s)
- Veronique A. Taylor
- grid.40263.330000 0004 1936 9094Mindfulness Center, Brown University School of Public Health, 121 South Main Street, Providence, RI 02903 USA
| | - Alexandra Roy
- grid.40263.330000 0004 1936 9094Mindfulness Center, Brown University School of Public Health, 121 South Main Street, Providence, RI 02903 USA
| | - Judson A. Brewer
- grid.40263.330000 0004 1936 9094Mindfulness Center, Brown University School of Public Health, 121 South Main Street, Providence, RI 02903 USA ,grid.40263.330000 0004 1936 9094Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI USA
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11
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Montazeri M, Montazeri M, Bahaadinbeigy K, Montazeri M, Afraz A. Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review. Health Sci Rep 2023; 6:e962. [PMID: 36589632 PMCID: PMC9795991 DOI: 10.1002/hsr2.962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 11/10/2022] [Accepted: 11/15/2022] [Indexed: 12/29/2022] Open
Abstract
Background and Aim Schizophrenia and bipolar disorder (BD) are critical and high-risk inherited mental disorders with debilitating symptoms. Worldwide, 3% of the population suffers from these disorders. The mortality rate of these patients is higher compared to other people. Current procedures cannot effectively diagnose these disorders because it takes an average of 10 years from the onset of the first symptoms to the definitive diagnosis of the disease. Machine learning (ML) techniques are used to meet this need. This study aimed to summarize information on the use of ML techniques for predicting schizophrenia and BD to help early and timely diagnosis of the disease. Methods A systematic literature search included articles published until January 19, 2020 in 3 databases. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. PRISMA guidelines were followed to conduct the study, and the Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess included papers. Results In this review, 1243 papers were retrieved through database searches, of which 15 papers were included based on full-text assessment. ML techniques were used to predict schizophrenia and BDs. The main algorithms applied were support vector machine (SVM) (10 studies), random forests (RF) (5 studies), and gradient boosting (GB) (3 studies). Input and output characteristics were very diverse and have been kept to enable future research. RFs algorithms demonstrated significantly higher accuracy and sensitivity than SVM and GB. GB demonstrated significantly higher specificity than SVM and RF. We found no significant difference between RF and SVM in terms of specificity. Conclusion ML can precisely predict results and assist in making clinical decisions-concerning schizophrenia and BD. RF often performed better than other algorithms in supervised learning tasks. This study identified gaps in the literature and opportunities for future psychological ML research.
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Affiliation(s)
- Mahdieh Montazeri
- Department of Health Information Sciences, Faculty of Management and Medical Information SciencesKerman University of Medical SciencesKermanIran
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Mitra Montazeri
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Kambiz Bahaadinbeigy
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Mohadeseh Montazeri
- Department of Computer, Faculty of FatimahKerman Branch Technical and Vocational UniversityKermanIran
| | - Ali Afraz
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
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12
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Bambini V, Frau F, Bischetti L, Cuoco F, Bechi M, Buonocore M, Agostoni G, Ferri I, Sapienza J, Martini F, Spangaro M, Bigai G, Cocchi F, Cavallaro R, Bosia M. Deconstructing heterogeneity in schizophrenia through language: a semi-automated linguistic analysis and data-driven clustering approach. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:102. [PMID: 36446789 PMCID: PMC9708845 DOI: 10.1038/s41537-022-00306-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
Previous works highlighted the relevance of automated language analysis for predicting diagnosis in schizophrenia, but a deeper language-based data-driven investigation of the clinical heterogeneity through the illness course has been generally neglected. Here we used a semiautomated multidimensional linguistic analysis innovatively combined with a machine-driven clustering technique to characterize the speech of 67 individuals with schizophrenia. Clusters were then compared for psychopathological, cognitive, and functional characteristics. We identified two subgroups with distinctive linguistic profiles: one with higher fluency, lower lexical variety but greater use of psychological lexicon; the other with reduced fluency, greater lexical variety but reduced psychological lexicon. The former cluster was associated with lower symptoms and better quality of life, pointing to the existence of specific language profiles, which also show clinically meaningful differences. These findings highlight the importance of considering language disturbances in schizophrenia as multifaceted and approaching them in automated and data-driven ways.
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Affiliation(s)
- Valentina Bambini
- Department of Humanities and Life Sciences, University School for Advanced Studies IUSS, Pavia, Italy.
| | - Federico Frau
- Department of Humanities and Life Sciences, University School for Advanced Studies IUSS, Pavia, Italy
| | - Luca Bischetti
- Department of Humanities and Life Sciences, University School for Advanced Studies IUSS, Pavia, Italy
| | - Federica Cuoco
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Margherita Bechi
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Mariachiara Buonocore
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giulia Agostoni
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Ilaria Ferri
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Jacopo Sapienza
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Francesca Martini
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marco Spangaro
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giorgia Bigai
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Federica Cocchi
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Roberto Cavallaro
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Marta Bosia
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
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13
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Gallucci J, Tan T, Schifani C, Dickie EW, Voineskos AN, Hawco C. Greater individual variability in functional brain activity during working memory performance in Schizophrenia Spectrum Disorders (SSD). Schizophr Res 2022; 248:21-31. [PMID: 35908378 DOI: 10.1016/j.schres.2022.07.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 06/14/2022] [Accepted: 07/17/2022] [Indexed: 10/16/2022]
Abstract
Heterogeneity has been a persistent challenge in understanding Schizophrenia Spectrum Disorders (SSD). Traditional case-control comparisons often show variable results, and may not map well onto individuals. To better understand heterogeneity and group differences in SSD compared to typically developing controls (TDC), we examined variability in functional brain activity during a working memory (WM) task with known deficits in SSD. Neuroimaging and behavioural data were extracted from two datasets collectively providing 34 TDC and 56 individuals with SSD (n = 90). Functional activity in response to an N-Back WM task (3-Back vs 1-Back) was examined between and within groups. Individual variability was calculated via the correlational distance of fMRI activity maps between participants; mean correlational distance from one participant to all others was defined as a 'variability score'. Greater individual variability in functional activity was found in SSD compared to TDC (p = 0.00090). At the group level, a case-control comparison suggested SSD had reduced activity in task positive and task negative networks. However, when SSD were divided into high and low variability subgroups, the low variability groups showed no differences relative to TDC while the high variability group showed little activity at the group level. Our results imply prior case-control differences may be driven by a subgroup of SSD who do not show specific impairments but instead show more 'idiosyncratic' activity patterns. In SSD but not TDC, variability was also related to cognitive performance and age. This novel approach focusing on individual variability has important implications for understanding the neurobiology of SSD.
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Affiliation(s)
- Julia Gallucci
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Thomas Tan
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Christin Schifani
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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14
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Shi W, Fan L, Wang H, Liu B, Li W, Li J, Cheng L, Chu C, Song M, Sui J, Luo N, Cui Y, Dong Z, Lu Y, Ma Y, Ma L, Li K, Chen J, Chen Y, Guo H, Li P, Lu L, Lv L, Wan P, Wang H, Wang H, Yan H, Yan J, Yang Y, Zhang H, Zhang D, Jiang T. Two subtypes of schizophrenia identified by an individual-level atypical pattern of tensor-based morphometric measurement. Cereb Cortex 2022; 33:3683-3700. [PMID: 36005854 DOI: 10.1093/cercor/bhac301] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 07/11/2022] [Accepted: 07/12/2022] [Indexed: 11/12/2022] Open
Abstract
Difficulties in parsing the multiaspect heterogeneity of schizophrenia (SCZ) based on current nosology highlight the need to subtype SCZ using objective biomarkers. Here, utilizing a large-scale multisite SCZ dataset, we identified and validated 2 neuroanatomical subtypes with individual-level abnormal patterns of the tensor-based morphometric measurement. Remarkably, compared with subtype 1, which showed moderate deficits of some subcortical nuclei and an enlarged striatum and cerebellum, subtype 2, which showed cerebellar atrophy and more severe subcortical nuclei atrophy, had a higher subscale score of negative symptoms, which is considered to be a core aspect of SCZ and is associated with functional outcome. Moreover, with the neuroimaging-clinic association analysis, we explored the detailed relationship between the heterogeneity of clinical symptoms and the heterogeneous abnormal neuroanatomical patterns with respect to the 2 subtypes. And the neuroimaging-transcription association analysis highlighted several potential heterogeneous biological factors that may underlie the subtypes. Our work provided an effective framework for investigating the heterogeneity of SCZ from multilevel aspects and may provide new insights for precision psychiatry.
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Affiliation(s)
- Weiyang Shi
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Chinese Institute for Brain Research, Beijing 102206, China
| | - Wen Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Luqi Cheng
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
| | - Congying Chu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Ming Song
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yue Cui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhenwei Dong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuheng Lu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yawei Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kaixin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jun Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yunchun Chen
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an 710032, China
| | - Hua Guo
- Zhumadian Psychiatric Hospital, Zhumadian 463000, China
| | - Peng Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China.,Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Ping Wan
- Zhumadian Psychiatric Hospital, Zhumadian 463000, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an 710032, China
| | - Huiling Wang
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China.,Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Hao Yan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China
| | - Jun Yan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China
| | - Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China.,Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Hongxing Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China.,Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China.,Department of Psychology, Xinxiang Medical University, Xinxiang 453002, China
| | - Dai Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China.,Center for Life Sciences/PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100191, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China.,Innovation Academy for Artificial Intelligence, Chinese Academy of Sciences, Beijing 100190, China
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15
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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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16
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Allesøe RL, Nudel R, Thompson WK, Wang Y, Nordentoft M, Børglum AD, Hougaard DM, Werge T, Rasmussen S, Benros ME. Deep learning-based integration of genetics with registry data for stratification of schizophrenia and depression. SCIENCE ADVANCES 2022; 8:eabi7293. [PMID: 35767618 PMCID: PMC9242585 DOI: 10.1126/sciadv.abi7293] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
Currently, psychiatric diagnoses are, in contrast to most other medical fields, based on subjective symptoms and observable signs and call for new and improved diagnostics to provide the most optimal care. On the basis of a deep learning approach, we performed unsupervised patient stratification of 19,636 patients with depression [major depressive disorder (MDD)] and/or schizophrenia (SCZ) and 22,467 population controls from the iPSYCH2012 case cohort. We integrated data of disorder severity, history of mental disorders and disease comorbidities, genetics, and medical birth data. From this, we stratified the individuals in six and seven unique clusters for MDD and SCZ, respectively. When censoring data until diagnosis, we could predict MDD clusters with areas under the curve (AUCs) of 0.54 to 0.80 and SCZ clusters with AUCs of 0.71 to 0.86. Overall cases and controls could be predicted with an AUC of 0.81, illustrating the utility of data-driven subgrouping in psychiatry.
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Affiliation(s)
- Rosa Lundbye Allesøe
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ron Nudel
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
| | - Wesley K. Thompson
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Institute of Biological Psychiatry, Mental Health Centre Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, San Diego, CA, USA
| | - Yunpeng Wang
- Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Forskningsveien 3A, 0317 Oslo, Norway
| | - Merete Nordentoft
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anders D. Børglum
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Biomedicine, Aarhus University and Centre for Integrative Sequencing, iSEQ, Aarhus, Denmark
- Aarhus Genome Center, Aarhus, Denmark
| | - David M. Hougaard
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Thomas Werge
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Institute of Biological Psychiatry, Mental Health Centre Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael Eriksen Benros
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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17
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Wang J, Ke P, Zang J, Wu F, Wu K. Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study. Front Neurosci 2022; 15:785595. [PMID: 35087373 PMCID: PMC8787107 DOI: 10.3389/fnins.2021.785595] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 12/01/2021] [Indexed: 12/12/2022] Open
Abstract
Interest in the application of machine learning (ML) techniques to multimodal magnetic resonance imaging (MRI) data for the diagnosis of schizophrenia (SZ) at the individual level is growing. However, a few studies have applied the features of structural and functional brain networks derived from multimodal MRI data to the discriminative analysis of SZ patients at different clinical stages. In this study, 205 normal controls (NCs), 61 first-episode drug-naive SZ (FESZ) patients, and 79 chronic SZ (CSZ) patients were recruited. We acquired their structural MRI, diffusion tensor imaging, and resting-state functional MRI data and constructed brain networks for each participant, including the gray matter network (GMN), white matter network (WMN), and functional brain network (FBN). We then calculated 3 nodal properties for each brain network, including degree centrality, nodal efficiency, and betweenness centrality. Two classifications (SZ vs. NC and FESZ vs. CSZ) were performed using five ML algorithms. We found that the SVM classifier with the input features of the combination of nodal properties of both the GMN and FBN achieved the best performance to discriminate SZ patients from NCs [accuracy, 81.2%; area under the receiver operating characteristic curve (AUC), 85.2%; p < 0.05]. Moreover, the SVM classifier with the input features of the combination of the nodal properties of both the GMN and WMN achieved the best performance to discriminate FESZ from CSZ patients (accuracy, 86.2%; AUC, 92.3%; p < 0.05). Furthermore, the brain areas in the subcortical/cerebellum network and the frontoparietal network showed significant importance in both classifications. Together, our findings provide new insights to understand the neuropathology of SZ and further highlight the potential advantages of multimodal network properties for identifying SZ patients at different clinical stages.
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Affiliation(s)
- Jing Wang
- School of Biomedical Engineering, Guangzhou Xinhua University, Guangzhou, China
| | - Pengfei Ke
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Jinyu Zang
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Fengchun Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- *Correspondence: Fengchun Wu,
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
- Key Laboratory of Biomedical Engineering of Guangdong Province, South China University of Technology, Guangzhou, China
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
- Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- Kai Wu,
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18
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Chumakov E, Dorofeikova M, Tsyrenova K, Petrova N. A Cross-Sectional Study on Associations Between BDNF, CRP, IL-6 and Clinical Symptoms, Cognitive and Personal Performance in Patients With Paranoid Schizophrenia. Front Psychiatry 2022; 13:943869. [PMID: 35873262 PMCID: PMC9298757 DOI: 10.3389/fpsyt.2022.943869] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 06/13/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Cognitive impairment is among the core dimensions in schizophrenia and is a significant predictor of everyday functioning in people with schizophrenia. Given the enormous burden of schizophrenia, the search for its clinically relevant biomarkers is essential. Researchers have been trying to elucidate factors of cognitive impairment as well as personal performance, but the search is still ongoing. The aim of the study was to search for associations between BDNF, CRP, IL-6 and clinical symptoms, cognitive and personal performance in patients with paranoid schizophrenia. METHODS A total of 86 patients (53.5% women, mean age 31.1 ± 6.5) with paranoid schizophrenia (F20.0; ICD-10) in remission were examined. Clinical and neuropsychological examination included the Positive and Negative Syndrome Scale, Personal and Social Performance Scale, Calgary Depression Scale for Schizophrenia and the Brief Assessment of Cognitive Function in Schizophrenia. IL-6, BDNF, CRP levels were determined in the patients' blood serum. RESULTS Cognitive impairment was revealed in 79.1% of patients and was more profound in patients with higher number of hospitalizations (p = 0.006). The average BDNF levels were 13.38 ± 15.84 ng/ml, CRP concentration was 2.09 ± 2.54 mg/l, and IL-6 levels were 12.14 ± 5.88 pg/ml. There were no differences in biomarker levels or BACS results in patients that had different antipsychotic therapy or differed in the presence of anticholinergic therapy. CRP levels were higher in patients with longer disease duration, lower age of onset, more impaired personal social performance and processing speed. IL-6 was higher in individuals with lower working memory scores. PANSS negative subscale score negatively correlated and PSP score positively correlated with most cognitive domains. A linear regression established that the first episode vs. multiple episodes of schizophrenia could statistically significantly predict personal and social performance and cognition, including speech fluency and planning, as well as CRP levels. CONCLUSIONS This study continues the search for biomarkers of schizophrenia and cognitive impairment in schizophrenia to improve the reliability of diagnosing the disorder and find new treatment approaches. The role of the number of psychoses experienced (first episode vs. multiple episodes of schizophrenia) in cognition, personal and social performance and inflammation is shown.
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Affiliation(s)
- Egor Chumakov
- Department of Psychiatry and Addiction, Saint-Petersburg State University, Saint Petersburg, Russia.,Saint Petersburg Psychiatric Hospital No̱ 1 Named After P. P. Kashchenko, Saint Petersburg, Russia
| | - Mariia Dorofeikova
- Laboratory of Neurophysiology and Pathology of Behavior, Sechenov Institute of Evolutionary Physiology and Biochemistry, Saint Petersburg, Russia.,Center for Molecular Neurobiology, Institute of Developmental Neurophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Kristina Tsyrenova
- Department of Psychiatry and Addiction, Saint-Petersburg State University, Saint Petersburg, Russia
| | - Nataliia Petrova
- Department of Psychiatry and Addiction, Saint-Petersburg State University, Saint Petersburg, Russia
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19
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Solanes A, Radua J. Advances in Using MRI to Estimate the Risk of Future Outcomes in Mental Health - Are We Getting There? Front Psychiatry 2022; 13:fpsyt-13-826111. [PMID: 35492715 PMCID: PMC9039205 DOI: 10.3389/fpsyt.2022.826111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/16/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Aleix Solanes
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Department of Psychiatry and Forensic Medicine, School of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - Joaquim Radua
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Early Psychosis: Interventions and Clinical-detection Lab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Department of Clinical Neuroscience, Stockholm Health Care Services, Stockholm County Council, Karolinska Institutet, Stockholm, Sweden
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20
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Soldatos RF, Cearns M, Nielsen MØ, Kollias C, Xenaki LA, Stefanatou P, Ralli I, Dimitrakopoulos S, Hatzimanolis A, Kosteletos I, Vlachos II, Selakovic M, Foteli S, Nianiakas N, Mantonakis L, Triantafyllou TF, Ntigridaki A, Ermiliou V, Voulgaraki M, Psarra E, Sørensen ME, Bojesen KB, Tangmose K, Sigvard AM, Ambrosen KS, Meritt T, Syeda W, Glenthøj BY, Koutsouleris N, Pantelis C, Ebdrup BH, Stefanis N. Prediction of Early Symptom Remission in Two Independent Samples of First-Episode Psychosis Patients Using Machine Learning. Schizophr Bull 2021; 48:122-133. [PMID: 34535800 PMCID: PMC8781312 DOI: 10.1093/schbul/sbab107] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
BACKGROUND Validated clinical prediction models of short-term remission in psychosis are lacking. Our aim was to develop a clinical prediction model aimed at predicting 4-6-week remission following a first episode of psychosis. METHOD Baseline clinical data from the Athens First Episode Research Study was used to develop a Support Vector Machine prediction model of 4-week symptom remission in first-episode psychosis patients using repeated nested cross-validation. This model was further tested to predict 6-week remission in a sample of two independent, consecutive Danish first-episode cohorts. RESULTS Of the 179 participants in Athens, 120 were male with an average age of 25.8 years and average duration of untreated psychosis of 32.8 weeks. 62.9% were antipsychotic-naïve. Fifty-seven percent attained remission after 4 weeks. In the Danish cohort, 31% attained remission. Eleven clinical scale items were selected in the Athens 4-week remission cohort. These included the Duration of Untreated Psychosis, Personal and Social Performance Scale, Global Assessment of Functioning and eight items from the Positive and Negative Syndrome Scale. This model significantly predicted 4-week remission status (area under the receiver operator characteristic curve (ROC-AUC) = 71.45, P < .0001). It also predicted 6-week remission status in the Danish cohort (ROC-AUC = 67.74, P < .0001), demonstrating reliability. CONCLUSIONS Using items from common and validated clinical scales, our model significantly predicted early remission in patients with first-episode psychosis. Although replicated in an independent cohort, forward testing between machine learning models and clinicians' assessment should be undertaken to evaluate the possible utility as a routine clinical tool.
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Affiliation(s)
- Rigas F Soldatos
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece,Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia,World Federation of Societies of Biological Psychiatry, First Episode Psychosis Task Force, Barsbüttel, Germany,To whom correspondence should be addressed; First Department of Psychiatry, Eginition Hospital, 72-74 Vasilissis Sofias Avenue, Athens 11527, Greece, tel: +302107289400, e-mail:
| | - Micah Cearns
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia,Discipline of Psychiatry, School of Medicine, University of Adelaide, Australia
| | - Mette Ø Nielsen
- Center for Neuropsychiatric Schizophrenia Research (CNSR) & Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Costas Kollias
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Lida-Alkisti Xenaki
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Pentagiotissa Stefanatou
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Irene Ralli
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Stefanos Dimitrakopoulos
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Alex Hatzimanolis
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Ioannis Kosteletos
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Ilias I Vlachos
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Mirjana Selakovic
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Stefania Foteli
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Nikolaos Nianiakas
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Leonidas Mantonakis
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Theoni F Triantafyllou
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Aggeliki Ntigridaki
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Vanessa Ermiliou
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Marina Voulgaraki
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Evaggelia Psarra
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece
| | - Mikkel E Sørensen
- Center for Neuropsychiatric Schizophrenia Research (CNSR) & Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark
| | - Kirsten B Bojesen
- Center for Neuropsychiatric Schizophrenia Research (CNSR) & Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark
| | - Karen Tangmose
- Center for Neuropsychiatric Schizophrenia Research (CNSR) & Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Anne M Sigvard
- Center for Neuropsychiatric Schizophrenia Research (CNSR) & Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Karen S Ambrosen
- Center for Neuropsychiatric Schizophrenia Research (CNSR) & Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark
| | - Toni Meritt
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
| | - Warda Syeda
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
| | - Birte Y Glenthøj
- Center for Neuropsychiatric Schizophrenia Research (CNSR) & Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Nikolaos Koutsouleris
- World Federation of Societies of Biological Psychiatry, First Episode Psychosis Task Force, Barsbüttel, Germany,Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia,World Federation of Societies of Biological Psychiatry, First Episode Psychosis Task Force, Barsbüttel, Germany
| | - Bjørn H Ebdrup
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia,Center for Neuropsychiatric Schizophrenia Research (CNSR) & Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Nikos Stefanis
- First Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, Greece,World Federation of Societies of Biological Psychiatry, First Episode Psychosis Task Force, Barsbüttel, Germany,University Mental Health, Neurosciences and Precision Medicine Research Institute, 2 Soranou Efesiou, 11527 Athens, Greece
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21
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Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41:1427-1473. [PMID: 33295676 PMCID: PMC8043990 DOI: 10.1002/med.21764] [Citation(s) in RCA: 102] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 01/11/2023]
Abstract
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
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Affiliation(s)
- Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Avner Schlessinger
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Daniel Wacker
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - H. Ümit Kaniskan
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jian Jin
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ming‐Ming Zhou
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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22
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Cheng Y, Zhang G, Zhang X, Li Y, Li J, Zhou J, Huang L, Xie S, Shen W. Identification of minimal hepatic encephalopathy based on dynamic functional connectivity. Brain Imaging Behav 2021; 15:2637-2645. [PMID: 33755921 DOI: 10.1007/s11682-021-00468-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/02/2021] [Indexed: 12/26/2022]
Abstract
To investigate whether dynamic functional connectivity (DFC) metrics can better identify minimal hepatic encephalopathy (MHE) patients from cirrhotic patients without any hepatic encephalopathy (noHE) and healthy controls (HCs). Resting-state functional MRI data were acquired from 62 patients with cirrhosis (MHE, n = 30; noHE, n = 32) and 41 HCs. We used the sliding time window approach and functional connectivity analysis to extract the time-varying properties of brain connectivity. Three DFC characteristics (i.e., strength, stability, and variability) were calculated. For comparison, we also calculated the static functional connectivity (SFC). A linear support vector machine was used to differentiate MHE patients from noHE and HCs using DFC and SFC metrics as classification features. The leave-one-out cross-validation method was used to estimate the classification performance. The strength of DFC (DFC-Dstrength) achieved the best accuracy (MHE vs. noHE, 72.5%; MHE vs. HCs, 84%; and noHE vs. HCs, 88%) compared to the other dynamic features. Compared to static features, the classification accuracies of the DFC-Dstrength feature were improved by 10.5%, 8%, and 14% for MHE vs. noHE, MHE vs. HC, and noHE vs. HCs, respectively. Based on the DFC-Dstrength, seven nodes were identified as the most discriminant features to classify MHE from noHE, including left inferior parietal lobule, left supramarginal gyrus, left calcarine, left superior frontal gyrus, left cerebellum, right postcentral gyrus, and right insula. In summary, DFC characteristics have a higher classification accuracy in identifying MHE from cirrhosis patients. Our findings suggest the usefulness of DFC in capturing neural processes and identifying disease-related biomarkers important for MHE identification.
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Affiliation(s)
- Yue Cheng
- Department of Radiology, Tianjin First Center Hospital, Tianjin, 300192, China
| | - Gaoyan Zhang
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, 300072, China.
| | - Xiaodong Zhang
- Department of Radiology, Tianjin First Center Hospital, Tianjin, 300192, China
| | - Yuexuan Li
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, 300072, China
| | - Jingli Li
- Department of Radiology, Tianjin First Center Hospital, Tianjin, 300192, China
| | - Jiamin Zhou
- Department of Radiology, Tianjin First Center Hospital, Tianjin, 300192, China
| | - Lixiang Huang
- Department of Radiology, Tianjin First Center Hospital, Tianjin, 300192, China
| | - Shuangshuang Xie
- Department of Radiology, Tianjin First Center Hospital, Tianjin, 300192, China
| | - Wen Shen
- Department of Radiology, Tianjin First Center Hospital, Tianjin, 300192, China
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24
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Eitel F, Schulz MA, Seiler M, Walter H, Ritter K. Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research. Exp Neurol 2021; 339:113608. [PMID: 33513353 DOI: 10.1016/j.expneurol.2021.113608] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 01/07/2021] [Accepted: 01/09/2021] [Indexed: 12/13/2022]
Abstract
By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as modelling domain-specific priors. After reviewing recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the development of neuroimaging biomarkers, we discuss current challenges. This includes for example the difficulty of training models on small, heterogeneous and biased data sets, the lack of validity of clinical labels, algorithmic bias, and the influence of confounding variables.
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Affiliation(s)
- Fabian Eitel
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Marc-André Schulz
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Moritz Seiler
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Henrik Walter
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Kerstin Ritter
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany.
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25
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Segato A, Marzullo A, Calimeri F, De Momi E. Artificial intelligence for brain diseases: A systematic review. APL Bioeng 2020; 4:041503. [PMID: 33094213 PMCID: PMC7556883 DOI: 10.1063/5.0011697] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 09/09/2020] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.
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Affiliation(s)
- Alice Segato
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
| | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
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Lutz O, Lizano P, Mothi SS, Zeng V, Hegde RR, Hoang DT, Henson P, Brady R, Tamminga CA, Pearlson G, Clementz BA, Sweeney JA, Keshavan MS. Do neurobiological differences exist between paranoid and non-paranoid schizophrenia? Findings from the bipolar schizophrenia network on intermediate phenotypes study. Schizophr Res 2020; 223:96-104. [PMID: 32507376 DOI: 10.1016/j.schres.2020.02.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 10/23/2019] [Accepted: 02/26/2020] [Indexed: 02/06/2023]
Abstract
Subtypes of schizophrenia, constructed using clinical phenomenology to resolve illness heterogeneity, have faced criticism due to overlapping symptomatology and longitudinal instability; they were therefore dropped from the Diagnostic Statistical Manual-5. Cognitive and imaging findings comparing paranoid (P-SZ) and non-paranoid (disorganized, residual and undifferentiated; NP-SZ) schizophrenia have been limited due to small sample sizes. We assessed P-SZ and NP-SZ using symptomatology, cognition and brain structure and predicted that there would be few neurobiological differences. P-SZ (n = 237), NP-SZ (n = 127) and controls (n = 430) were included from a multi-site study. In a subset of this sample, structural imaging measures (P-SZ, n = 133; NP-SZ, n = 67; controls, n = 310) were calculated using Freesurfer 6.0. Group contrasts were run using analysis of covariance, controlling for age, sex, race and site, p-values were corrected using False Discovery Rate (FDR) and were repeated excluding the residual subtype. Compared to NP-SZ (with and without the residual subtype), P-SZ displayed fewer negative symptoms, faster speed of processing, larger bilateral hippocampus, right amygdala and their subfield volumes. Additionally, NP-SZ (with residual subtype) displayed fewer depressive symptoms and higher left transverse temporal cortical thickness (CT) but NP-SZ without residual subtype showed lower GAF scores and worse digit sequencing compared to P-SZ. No differences in positive symptoms and functioning (global or social) were detected. Subtle but significant differences were seen in cognition, symptoms, CT and subcortical volumes between P-SZ and NP-SZ. While the magnitude of these differences is not large enough to justify them as distinct categories, the paranoid- nonparanoid distinction in schizophrenia merits further investigation.
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Affiliation(s)
- Olivia Lutz
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, United States of America
| | - Paulo Lizano
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, United States of America; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America
| | - Suraj Sarvode Mothi
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, United States of America
| | - Victor Zeng
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, United States of America
| | - Rachal R Hegde
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, United States of America
| | - Dung T Hoang
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, United States of America
| | - Philip Henson
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, United States of America
| | - Roscoe Brady
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, United States of America; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America
| | - Carol A Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Godfrey Pearlson
- Department of Psychiatry, Yale University, New Haven, CT, United States of America
| | - Brett A Clementz
- Department of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, United States of America
| | - John A Sweeney
- Department of Psychiatry, University of Cincinnati, Cincinnati, OH, United States of America
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, United States of America; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America.
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A systematic review and narrative synthesis of data-driven studies in schizophrenia symptoms and cognitive deficits. Transl Psychiatry 2020; 10:244. [PMID: 32694510 PMCID: PMC7374614 DOI: 10.1038/s41398-020-00919-x] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 06/24/2020] [Accepted: 07/03/2020] [Indexed: 12/30/2022] Open
Abstract
To tackle the phenotypic heterogeneity of schizophrenia, data-driven methods are often applied to identify subtypes of its symptoms and cognitive deficits. However, a systematic review on this topic is lacking. The objective of this review was to summarize the evidence obtained from longitudinal and cross-sectional data-driven studies in positive and negative symptoms and cognitive deficits in patients with schizophrenia spectrum disorders, their unaffected siblings and healthy controls or individuals from general population. Additionally, we aimed to highlight methodological gaps across studies and point out future directions to optimize the translatability of evidence from data-driven studies. A systematic review was performed through searching PsycINFO, PubMed, PsycTESTS, PsycARTICLES, SCOPUS, EMBASE and Web of Science electronic databases. Both longitudinal and cross-sectional studies published from 2008 to 2019, which reported at least two statistically derived clusters or trajectories were included. Two reviewers independently screened and extracted the data. In this review, 53 studies (19 longitudinal and 34 cross-sectional) that conducted among 17,822 patients, 8729 unaffected siblings and 5520 controls or general population were included. Most longitudinal studies found four trajectories that characterized by stability, progressive deterioration, relapsing and progressive amelioration of symptoms and cognitive function. Cross-sectional studies commonly identified three clusters with low, intermediate (mixed) and high psychotic symptoms and cognitive profiles. Moreover, identified subgroups were predicted by numerous genetic, sociodemographic and clinical factors. Our findings indicate that schizophrenia symptoms and cognitive deficits are heterogeneous, although methodological limitations across studies are observed. Identified clusters and trajectories along with their predictors may be used to base the implementation of personalized treatment and develop a risk prediction model for high-risk individuals with prodromal symptoms.
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Manchia M, Pisanu C, Squassina A, Carpiniello B. Challenges and Future Prospects of Precision Medicine in Psychiatry. PHARMACOGENOMICS & PERSONALIZED MEDICINE 2020; 13:127-140. [PMID: 32425581 PMCID: PMC7186890 DOI: 10.2147/pgpm.s198225] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 04/14/2020] [Indexed: 12/21/2022]
Abstract
Precision medicine is increasingly recognized as a promising approach to improve disease treatment, taking into consideration the individual clinical and biological characteristics shared by specific subgroups of patients. In specific fields such as oncology and hematology, precision medicine has already started to be implemented in the clinical setting and molecular testing is routinely used to select treatments with higher efficacy and reduced adverse effects. The application of precision medicine in psychiatry is still in its early phases. However, there are already examples of predictive models based on clinical data or combinations of clinical, neuroimaging and biological data. While the power of single clinical predictors would remain inadequate if analyzed only with traditional statistical approaches, these predictors are now increasingly used to impute machine learning models that can have adequate accuracy even in the presence of relatively small sample size. These models have started to be applied to disentangle relevant clinical questions that could lead to a more effective management of psychiatric disorders, such as prediction of response to the mood stabilizer lithium, resistance to antidepressants in major depressive disorder or stratification of the risk and outcome prediction in schizophrenia. In this narrative review, we summarized the most important findings in precision medicine in psychiatry based on studies that constructed machine learning models using clinical, neuroimaging and/or biological data. Limitations and barriers to the implementation of precision psychiatry in the clinical setting, as well as possible solutions and future perspectives, will be presented.
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Affiliation(s)
- Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy.,Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy.,Department of Pharmacology, Dalhousie University, Halifax, NS, Canada
| | - Claudia Pisanu
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | - Alessio Squassina
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy.,Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Bernardo Carpiniello
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy.,Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy
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29
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Chakravarty MM. Guest editorial: Special issue on machine learning in schizophrenia. Schizophr Res 2019; 214:1-2. [PMID: 31711732 DOI: 10.1016/j.schres.2019.10.044] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 10/20/2019] [Indexed: 11/16/2022]
Affiliation(s)
- M Mallar Chakravarty
- Computational Brain Anatomy (CoBrA Laboratory), Douglas Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada.
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