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Hayes JF, Ben Abdesslem F, Eloranta S, Osborn DPJ, Boman M. Predicting maintenance lithium response for bipolar disorder from electronic health records-a retrospective study. PeerJ 2024; 12:e17841. [PMID: 39421428 PMCID: PMC11485101 DOI: 10.7717/peerj.17841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 07/10/2024] [Indexed: 10/19/2024] Open
Abstract
Background Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and demographic features may predict response to lithium. However, attempts to personalise treatment choice have been limited. Method We aimed to determine if machine learning methods applied to electronic health records could predict differential response to lithium or olanzapine. From electronic United Kingdom primary care records, we extracted a cohort of individuals prescribed either lithium (19,106 individuals) or olanzapine (12,412) monotherapy. Machine learning models were used to predict successful monotherapy maintenance treatment, using 113 clinical and demographic variables, 8,017 (41.96%) lithium responders and 3,831 (30.87%) olanzapine responders. Results We found a quantitative structural difference in that lithium maintenance responders were weakly predictable in our holdout sample, consisting of the 5% of patients with the most recent exposure. Age at first diagnosis, age at first treatment and the time between these were the most important variables in all models. Discussion Even if we failed to predict successful monotherapy olanzapine treatment, and so to definitively separate lithium vs. olanzapine responders, the characterization of the two groups may be used for classification by proxy. This can, in turn, be useful for establishing maintenance therapy. The further exploration of machine learning methods on EHR data for drug treatment selection could in the future play a role for clinical decision support. Signals in the data encourage further experiments with larger datasets to definitively separate lithium vs. olanzapine responders.
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Affiliation(s)
- Joseph F. Hayes
- Department of Psychiatry, University College London, University of London, London, United Kingdom
- Camden and Islington NHS foundation Trust, London, United Kingdom
| | - Fehmi Ben Abdesslem
- Department of Psychiatry, University College London, University of London, London, United Kingdom
- Research Institutes of Sweden (RISE), Stockholm, Sweden
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Sandra Eloranta
- Division of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | - David P. J. Osborn
- Department of Psychiatry, University College London, University of London, London, United Kingdom
- Camden and Islington NHS foundation Trust, London, United Kingdom
| | - Magnus Boman
- Department of Psychiatry, University College London, University of London, London, United Kingdom
- Division of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- MedTechLabs, BioClinicum, Karolinska University Hospital, Stockholm, Sweden
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2
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Anbarasi J, Kumari R, Ganesh M, Agrawal R. Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights. BMC Neurol 2024; 24:364. [PMID: 39342171 PMCID: PMC11438080 DOI: 10.1186/s12883-024-03864-0] [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: 05/03/2024] [Accepted: 09/16/2024] [Indexed: 10/01/2024] Open
Abstract
Connectomics is a neuroscience paradigm focused on noninvasively mapping highly intricate and organized networks of neurons. The advent of neuroimaging has led to extensive mapping of the brain functional and structural connectome on a macroscale level through modalities such as functional and diffusion MRI. In parallel, the healthcare field has witnessed a surge in the application of machine learning and artificial intelligence for diagnostics, especially in imaging. While reviews covering machine learn ing and macroscale connectomics exist for specific disorders, none provide an overview that captures their evolving role, especially through the lens of clinical application and translation. The applications include understanding disorders, classification, identifying neuroimaging biomarkers, assessing severity, predicting outcomes and intervention response, identifying potential targets for brain stimulation, and evaluating the effects of stimulation intervention on the brain and connectome mapping in patients before neurosurgery. The covered studies span neurodegenerative, neurodevelopmental, neuropsychiatric, and neurological disorders. Along with applications, the review provides a brief of common ML methods to set context. Conjointly, limitations in ML studies within connectomics and strategies to mitigate them have been covered.
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Affiliation(s)
- Janova Anbarasi
- BrainSightAI, No. 677, 1st Floor, 27th Main, 13th Cross, HSR Layout, Sector 1, Adugodi, Bengaluru, Karnataka, 560102, India
| | - Radha Kumari
- BrainSightAI, No. 677, 1st Floor, 27th Main, 13th Cross, HSR Layout, Sector 1, Adugodi, Bengaluru, Karnataka, 560102, India
| | - Malvika Ganesh
- BrainSightAI, No. 677, 1st Floor, 27th Main, 13th Cross, HSR Layout, Sector 1, Adugodi, Bengaluru, Karnataka, 560102, India
| | - Rimjhim Agrawal
- BrainSightAI, No. 677, 1st Floor, 27th Main, 13th Cross, HSR Layout, Sector 1, Adugodi, Bengaluru, Karnataka, 560102, India.
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3
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Colic L, Sankar A, Goldman DA, Kim JA, Blumberg HP. Towards a neurodevelopmental model of bipolar disorder: a critical review of trait- and state-related functional neuroimaging in adolescents and young adults. Mol Psychiatry 2024:10.1038/s41380-024-02758-4. [PMID: 39333385 DOI: 10.1038/s41380-024-02758-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 09/12/2024] [Accepted: 09/18/2024] [Indexed: 09/29/2024]
Abstract
Neurodevelopmental mechanisms are increasingly implicated in bipolar disorder (BD), highlighting the importance of their study in young persons. Neuroimaging studies have demonstrated a central role for frontotemporal corticolimbic brain systems that subserve processing and regulation of emotions, and processing of reward in adults with BD. As adolescence and young adulthood (AYA) is a time when fully syndromal BD often emerges, and when these brain systems undergo dynamic maturational changes, the AYA epoch is implicated as a critical period in the neurodevelopment of BD. Functional magnetic resonance imaging (fMRI) studies can be especially informative in identifying the functional neuroanatomy in adolescents and young adults with BD (BDAYA) and at high risk for BD (HR-BDAYA) that is related to acute mood states and trait vulnerability to the disorder. The identification of early emerging brain differences, trait- and state-based, can contribute to the elucidation of the developmental neuropathophysiology of BD, and to the generation of treatment and prevention targets. In this critical review, fMRI studies of BDAYA and HR-BDAYA are discussed, and a preliminary neurodevelopmental model is presented based on a convergence of literature that suggests early emerging dysfunction in subcortical (e.g., amygdalar, striatal, thalamic) and caudal and ventral cortical regions, especially ventral prefrontal cortex (vPFC) and insula, and connections among them, persisting as trait-related features. More rostral and dorsal cortical alterations, and bilaterality progress later, with lateralization, and direction of functional imaging findings differing by mood state. Altered functioning of these brain regions, and regions they are strongly connected to, are implicated in the range of symptoms seen in BD, such as the insula in interoception, precentral gyrus in motor changes, and prefrontal cortex in cognition. Current limitations, and outlook on the future use of neuroimaging evidence to inform interventions and prevent the onset of mood episodes in BDAYA, are outlined.
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Affiliation(s)
- Lejla Colic
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- German Center for Mental Health, partner site Halle-Jena-Magdeburg, Jena, Germany
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Anjali Sankar
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Neurobiology Research Unit, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Danielle A Goldman
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
| | - Jihoon A Kim
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Columbia University, New York, NY, USA
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Hilary P Blumberg
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
- Child Study Center, Yale School of Medicine, New Haven, CT, USA.
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4
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Deng LR, Harmata GIS, Barsotti EJ, Williams AJ, Christensen GE, Voss MW, Saleem A, Rivera-Dompenciel AM, Richards JG, Sathyaputri L, Mani M, Abdolmotalleby H, Fiedorowicz JG, Xu J, Shaffer JJ, Wemmie JA, Magnotta VA. Machine learning with multiple modalities of brain magnetic resonance imaging data to identify the presence of bipolar disorder. J Affect Disord 2024; 368:448-460. [PMID: 39278469 DOI: 10.1016/j.jad.2024.09.025] [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] [Received: 01/13/2024] [Revised: 09/03/2024] [Accepted: 09/08/2024] [Indexed: 09/18/2024]
Abstract
BACKGROUND Bipolar disorder (BD) is a chronic psychiatric mood disorder that is solely diagnosed based on clinical symptoms. These symptoms often overlap with other psychiatric disorders. Efforts to use machine learning (ML) to create predictive models for BD based on data from brain imaging are expanding but have often been limited using only a single modality and the exclusion of the cerebellum, which may be relevant in BD. METHODS In this study, we sought to improve ML classification of BD by combining information from structural, functional, and diffusion-weighted imaging. Participants (108 BD I, 78 control) with BD type I and matched controls were recruited into an imaging study. This dataset was randomly divided into training and testing sets. For each of the three modalities, a separate ML model was selected, trained, and then used to generate a prediction of the class of each test subject. Majority voting was used to combine results from the three models to make a final prediction of whether a subject had BD. An independent replication sample was used to evaluate the ability of the ML classification to generalize to data collected at other sites. RESULTS Combining the three machine learning models through majority voting resulted in an accuracy of 89.5 % for classification of the test subjects as being in the BD or control group. Bootstrapping resulted in a 95 % confidence interval of 78.9 %-97.4 % for test accuracy. Performance was reduced when only using 2 of the 3 modalities. Analysis of feature importance revealed that the cerebellum and nodes of the emotional control network were among the most important regions for classification. The machine learning model performed at chance on the independent replication sample. CONCLUSION BD I could be identified with high accuracy in our relatively small sample by combining structural, functional, and diffusion-weighted imaging data within a single site but not generalize well to an independent replication sample. Future studies using harmonized imaging protocols may facilitate generalization of ML models.
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Affiliation(s)
- Lubin R Deng
- Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Gail I S Harmata
- Department of Radiology, University of Iowa, Iowa City, IA, USA; Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | | | | | - Gary E Christensen
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
| | - Michelle W Voss
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, USA
| | - Arshaq Saleem
- Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | | | | | | | - Merry Mani
- Department of Radiology, University of Iowa, Iowa City, IA, USA
| | | | | | - Jia Xu
- Department of Radiology, University of Iowa, Iowa City, IA, USA
| | - Joseph J Shaffer
- Department of Biosciences, Kansas City University, Kansas City, MO, USA
| | - John A Wemmie
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA; Department of Veterans Affairs Medical Center, Iowa City, IA, USA
| | - Vincent A Magnotta
- Department of Radiology, University of Iowa, Iowa City, IA, USA; Department of Psychiatry, University of Iowa, Iowa City, IA, USA.
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Meyhoefer I, Sprenger A, Derad D, Grotegerd D, Leenings R, Leehr EJ, Breuer F, Surmann M, Rolfes K, Arolt V, Romer G, Lappe M, Rehder J, Koutsouleris N, Borgwardt S, Schultze-Lutter F, Meisenzahl E, Kircher TTJ, Keedy SS, Bishop JR, Ivleva EI, McDowell JE, Reilly JL, Hill SK, Pearlson GD, Tamminga CA, Keshavan MS, Gershon ES, Clementz BA, Sweeney JA, Hahn T, Dannlowski U, Lencer R. Evidence from comprehensive independent validation studies for smooth pursuit dysfunction as a sensorimotor biomarker for psychosis. Sci Rep 2024; 14:13859. [PMID: 38879556 PMCID: PMC11180169 DOI: 10.1038/s41598-024-64487-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 06/10/2024] [Indexed: 06/19/2024] Open
Abstract
Smooth pursuit eye movements are considered a well-established and quantifiable biomarker of sensorimotor function in psychosis research. Identifying psychotic syndromes on an individual level based on neurobiological markers is limited by heterogeneity and requires comprehensive external validation to avoid overestimation of prediction models. Here, we studied quantifiable sensorimotor measures derived from smooth pursuit eye movements in a large sample of psychosis probands (N = 674) and healthy controls (N = 305) using multivariate pattern analysis. Balanced accuracies of 64% for the prediction of psychosis status are in line with recent results from other large heterogenous psychiatric samples. They are confirmed by external validation in independent large samples including probands with (1) psychosis (N = 727) versus healthy controls (N = 292), (2) psychotic (N = 49) and non-psychotic bipolar disorder (N = 36), and (3) non-psychotic affective disorders (N = 119) and psychosis (N = 51) yielding accuracies of 65%, 66% and 58%, respectively, albeit slightly different psychosis syndromes. Our findings make a significant contribution to the identification of biologically defined profiles of heterogeneous psychosis syndromes on an individual level underlining the impact of sensorimotor dysfunction in psychosis.
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Affiliation(s)
- Inga Meyhoefer
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Duesseldorf/LVR, Duesseldorf, Germany
| | - Andreas Sprenger
- Department of Neurology, University of Luebeck, Luebeck, Germany
| | - David Derad
- Department of Neurology, University of Luebeck, Luebeck, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Fabian Breuer
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Marian Surmann
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Karen Rolfes
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Volker Arolt
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
| | - Georg Romer
- Department of Child Adolescence Psychiatry and Psychotherapy, University of Muenster, Muenster, Germany
| | - Markus Lappe
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
- Institute of Psychology, University of Muenster, Muenster, Germany
| | - Johanna Rehder
- Institute of Psychology, University of Muenster, Muenster, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University Munich, Munich, Germany
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Max-Planck-Institute of Psychiatry Munich, Munich, Germany
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany
- Department of Psychiatry, Psychiatric University Hospital, University of Basel, Basel, Switzerland
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Duesseldorf/LVR, Duesseldorf, Germany
- Department of Psychology, Faculty of Psychology, Airlangga University, Surabaya, Indonesia
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Duesseldorf/LVR, Duesseldorf, Germany
| | - Tilo T J Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Sarah S Keedy
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, USA
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology and Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, USA
| | - Elena I Ivleva
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jennifer E McDowell
- Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - James L Reilly
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Scot Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale School of Medicine, and Olin Research Center, Institute of Living/Hartford Hospital, Hartford, CT, USA
| | - Carol A Tamminga
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, USA
| | - Brett A Clementz
- Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, USA
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany.
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany.
- Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany.
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6
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Jornkokgoud K, Baggio T, Bakiaj R, Wongupparaj P, Job R, Grecucci A. Narcissus reflected: Grey and white matter features joint contribution to the default mode network in predicting narcissistic personality traits. Eur J Neurosci 2024; 59:3273-3291. [PMID: 38649337 DOI: 10.1111/ejn.16345] [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: 12/13/2023] [Revised: 03/11/2024] [Accepted: 03/24/2024] [Indexed: 04/25/2024]
Abstract
Despite the clinical significance of narcissistic personality, its neural bases have not been clarified yet, primarily because of methodological limitations of the previous studies, such as the low sample size, the use of univariate techniques and the focus on only one brain modality. In this study, we employed for the first time a combination of unsupervised and supervised machine learning methods, to identify the joint contributions of grey matter (GM) and white matter (WM) to narcissistic personality traits (NPT). After preprocessing, the brain scans of 135 participants were decomposed into eight independent networks of covarying GM and WM via parallel ICA. Subsequently, stepwise regression and Random Forest were used to predict NPT. We hypothesized that a fronto-temporo parietal network, mainly related to the default mode network, may be involved in NPT and associated WM regions. Results demonstrated a distributed network that included GM alterations in fronto-temporal regions, the insula and the cingulate cortex, along with WM alterations in cerebellar and thalamic regions. To assess the specificity of our findings, we also examined whether the brain network predicting narcissism could also predict other personality traits (i.e., histrionic, paranoid and avoidant personalities). Notably, this network did not predict such personality traits. Additionally, a supervised machine learning model (Random Forest) was used to extract a predictive model for generalization to new cases. Results confirmed that the same network could predict new cases. These findings hold promise for advancing our understanding of personality traits and potentially uncovering brain biomarkers associated with narcissism.
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Affiliation(s)
- Khanitin Jornkokgoud
- Department of Research and Applied Psychology, Faculty of Education, Burapha University, Chonburi, Thailand
- Department of Psychology and Cognitive Science (DiPSCo), University of Trento, Rovereto, Italy
| | - Teresa Baggio
- Department of Psychology and Cognitive Science (DiPSCo), University of Trento, Rovereto, Italy
| | - Richard Bakiaj
- Department of Psychology and Cognitive Science (DiPSCo), University of Trento, Rovereto, Italy
| | - Peera Wongupparaj
- Department of Psychology, Faculty of Humanities and Social Sciences, Burapha University, Chonburi, Thailand
| | - Remo Job
- Department of Psychology and Cognitive Science (DiPSCo), University of Trento, Rovereto, Italy
| | - Alessandro Grecucci
- Department of Psychology and Cognitive Science (DiPSCo), University of Trento, Rovereto, Italy
- Centre for Medical Sciences (CISMed), University of Trento, Trento, Italy
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7
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Chen M, Xia X, Kang Z, Li Z, Dai J, Wu J, Chen C, Qiu Y, Liu T, Liu Y, Zhang Z, Shen Q, Tao S, Deng Z, Lin Y, Wei Q. Distinguishing schizophrenia and bipolar disorder through a Multiclass Classification model based on multimodal neuroimaging data. J Psychiatr Res 2024; 172:119-128. [PMID: 38377667 DOI: 10.1016/j.jpsychires.2024.02.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 02/05/2024] [Accepted: 02/07/2024] [Indexed: 02/22/2024]
Abstract
This study aimed to identify neural biomarkers for schizophrenia (SZ) and bipolar disorder (BP) by analyzing multimodal neuroimaging. Utilizing data from structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (rs-fMRI), multiclass classification models were created for SZ, BP, and healthy controls (HC). A total of 113 participants (BP: 31, SZ: 39, and HC: 43) were recruited under strict enrollment control, from which 272, 200, and 1875 features were extracted from sMRI, DTI, and rs-fMRI data, respectively. A support vector machine (SVM) with recursive feature elimination (RFE) was employed to build the models using a one-against-one approach and leave-one-out cross-validation, achieving a classification accuracy of 70.8%. The most discriminative features were primarily from rs-fMRI, along with significant findings in sMRI and DTI. Key biomarkers identified included the increased thickness of the left cuneus cortex and decreased regional functional connectivity strength (rFCS) in the left supramarginal gyrus as shared indicators for BP and SZ. Additionally, decreased fractional anisotropy in the left superior fronto-occipital fasciculus was suggested as specific to BP, while decreased rFCS in the left inferior parietal area might serve as a specific biomarker for SZ. These findings underscore the potential of multimodal neuroimaging in distinguishing between BP and SZ and contribute to the understanding of their neural underpinnings.
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Affiliation(s)
- Ming Chen
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Guangdong Mental Health Institute, Guangdong ProvincialPeople's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xiaowei Xia
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhuang Kang
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhinan Li
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiamin Dai
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Junyan Wu
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Cai Chen
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yong Qiu
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Psychiatry, Mindfront Caring Medical, Guangzhou, China
| | - Tong Liu
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Shaanxi, China
| | - Yanxi Liu
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ziyi Zhang
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Medical Division, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Qingni Shen
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Sichu Tao
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zixin Deng
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ying Lin
- Department of Psychology, Sun Yat-sen University, Guangzhou, China.
| | - Qinling Wei
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
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8
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Mikolas P, Marxen M, Riedel P, Bröckel K, Martini J, Huth F, Berndt C, Vogelbacher C, Jansen A, Kircher T, Falkenberg I, Lambert M, Kraft V, Leicht G, Mulert C, Fallgatter AJ, Ethofer T, Rau A, Leopold K, Bechdolf A, Reif A, Matura S, Bermpohl F, Fiebig J, Stamm T, Correll CU, Juckel G, Flasbeck V, Ritter P, Bauer M, Pfennig A. Prediction of estimated risk for bipolar disorder using machine learning and structural MRI features. Psychol Med 2024; 54:278-288. [PMID: 37212052 DOI: 10.1017/s0033291723001319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
BACKGROUND Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features. METHODS Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar). RESULTS For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11-0.361) and a balanced accuracy of 63.1% (95% CI 55.9-70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI -0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6-67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance. CONCLUSIONS Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.
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Affiliation(s)
- Pavol Mikolas
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Michael Marxen
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Philipp Riedel
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Kyra Bröckel
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Julia Martini
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Fabian Huth
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Christina Berndt
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Christoph Vogelbacher
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
- Department of Psychiatry, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Germany
| | - Andreas Jansen
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
- Department of Psychiatry, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Germany
| | - Tilo Kircher
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
- Department of Psychiatry, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Germany
| | - Irina Falkenberg
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
- Department of Psychiatry, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Germany
| | - Martin Lambert
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Vivien Kraft
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Gregor Leicht
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christoph Mulert
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Centre for Psychiatry, Justus-Liebig University Giessen, Giessen, Germany
| | - Andreas J Fallgatter
- Department of Psychiatry, Tuebingen Center for Mental Health, University of Tuebingen, Tuebingen, Germany
| | - Thomas Ethofer
- Department of Psychiatry, Tuebingen Center for Mental Health, University of Tuebingen, Tuebingen, Germany
| | - Anne Rau
- Department of Psychiatry, Tuebingen Center for Mental Health, University of Tuebingen, Tuebingen, Germany
| | - Karolina Leopold
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, Vivantes Hospital Am Urban and Vivantes Hospital Im Friedrichshain, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Bechdolf
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, Vivantes Hospital Am Urban and Vivantes Hospital Im Friedrichshain, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt - Goethe University, Frankfurt am Main, Germany
| | - Silke Matura
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt - Goethe University, Frankfurt am Main, Germany
| | - Felix Bermpohl
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charité University Medicine, Berlin, Germany
| | - Jana Fiebig
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charité University Medicine, Berlin, Germany
| | - Thomas Stamm
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charité University Medicine, Berlin, Germany
- Department of Clinical Psychiatry and Psychotherapy, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany
| | - Christoph U Correll
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Psychiatry, Northwell Health, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Georg Juckel
- Department of Psychiatry, Psychotherapy and Preventive Medicine, LWL University Hospital, Ruhr-University, Bochum, Germany
| | - Vera Flasbeck
- Department of Psychiatry, Psychotherapy and Preventive Medicine, LWL University Hospital, Ruhr-University, Bochum, Germany
| | - Philipp Ritter
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
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9
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Zhu T, Kou R, Hu Y, Yuan M, Yuan C, Luo L, Zhang W. Dissecting clinical and biological heterogeneity in clinical states of bipolar disorder: a 10-year retrospective study from China. Front Psychiatry 2023; 14:1128862. [PMID: 38179244 PMCID: PMC10764613 DOI: 10.3389/fpsyt.2023.1128862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 12/01/2023] [Indexed: 01/06/2024] Open
Abstract
Objectives To dissect clinical and biological heterogeneity in clinical states of bipolar disorder (BD), and investigate if neuropsychological symptomatology, comorbidity, vital signs, and blood laboratory indicators are predictors of distinct BD states. Methods A retrospective BD cohort was established with data extracted from a Chinese hospital's electronic medical records (EMR) between 2009 and 2018. Subjects were inpatients with a main discharge diagnosis of BD and were assessed for clinical state at hospitalization. We categorized all subjects into manic state, depressive state, and mixed state. Four machine learning classifiers were utilized to classify the subjects. A Shapley additive explanations (SHAP) algorithm was applied to the classifiers to aid in quantifying and visualizing the contributions of each feature that drive patient-specific classifications. Results A sample of 3,085 records was included (38.54% as manic, 56.69% as depressive, and 4.77% as mixed state). Mixed state showed more severe suicidal ideation and psychomotor abnormalities, while depressive state showed more common anxiety, sleep, and somatic-related symptoms and more comorbid conditions. Higher levels of body temperature, pulse, and systolic and diastolic blood pressures were present during manic episodes. Xgboost achieved the best AUC of 88.54% in manic/depressive states classification; Logistic regression and Random forest achieved the best AUCs of 75.5 and 75% in manic/mixed states and depressive/mixed states classifications, respectively. Myocardial enzymes and the non-enzymatic antioxidant uric acid and bilirubin contributed significantly to distinguish BD clinical states. Conclusion The observed novel biological associations with BD clinical states confirm that biological heterogeneity contributes to clinical heterogeneity of BD.
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Affiliation(s)
- Ting Zhu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Ran Kou
- Business School, Sichuan University, Chengdu, China
| | - Yao Hu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Minlan Yuan
- Mental Health Center of West China Hospital, Sichuan University, Chengdu, China
| | - Cui Yuan
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Li Luo
- Business School, Sichuan University, Chengdu, China
| | - Wei Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- Mental Health Center of West China Hospital, Sichuan University, Chengdu, China
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10
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Lei D, Qin K, Li W, Pinaya WHL, Tallman MJ, Patino LR, Strawn JR, Fleck D, Klein CC, Lui S, Gong Q, Adler CM, Mechelli A, Sweeney JA, DelBello MP. Brain morphometric features predict medication response in youth with bipolar disorder: a prospective randomized clinical trial. Psychol Med 2023; 53:4083-4093. [PMID: 35392995 PMCID: PMC10317810 DOI: 10.1017/s0033291722000757] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 01/17/2022] [Accepted: 02/27/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND Identification of treatment-specific predictors of drug therapies for bipolar disorder (BD) is important because only about half of individuals respond to any specific medication. However, medication response in pediatric BD is variable and not well predicted by clinical characteristics. METHODS A total of 121 youth with early course BD (acute manic/mixed episode) were prospectively recruited and randomized to 6 weeks of double-blind treatment with quetiapine (n = 71) or lithium (n = 50). Participants completed structural magnetic resonance imaging (MRI) at baseline before treatment and 1 week after treatment initiation, and brain morphometric features were extracted for each individual based on MRI scans. Positive antimanic treatment response at week 6 was defined as an over 50% reduction of Young Mania Rating Scale scores from baseline. Two-stage deep learning prediction model was established to distinguish responders and non-responders based on different feature sets. RESULTS Pre-treatment morphometry and morphometric changes occurring during the first week can both independently predict treatment outcome of quetiapine and lithium with balanced accuracy over 75% (all p < 0.05). Combining brain morphometry at baseline and week 1 allows prediction with the highest balanced accuracy (quetiapine: 83.2% and lithium: 83.5%). Predictions in the quetiapine and lithium group were found to be driven by different morphometric patterns. CONCLUSIONS These findings demonstrate that pre-treatment morphometric measures and acute brain morphometric changes can serve as medication response predictors in pediatric BD. Brain morphometric features may provide promising biomarkers for developing biologically-informed treatment outcome prediction and patient stratification tools for BD treatment development.
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Affiliation(s)
- Du Lei
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati 45219, OH, USA
| | - Kun Qin
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati 45219, OH, USA
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Wenbin Li
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati 45219, OH, USA
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Walter H. L. Pinaya
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, Westminster Bridge Road, London, UK
| | - Maxwell J. Tallman
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati 45219, OH, USA
| | - L. Rodrigo Patino
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati 45219, OH, USA
| | - Jeffrey R. Strawn
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati 45219, OH, USA
| | - David Fleck
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati 45219, OH, USA
| | - Christina C. Klein
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati 45219, OH, USA
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Caleb M. Adler
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati 45219, OH, USA
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK
| | - John A. Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati 45219, OH, USA
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Melissa P. DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati 45219, OH, USA
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11
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Huth F, Tozzi L, Marxen M, Riedel P, Bröckel K, Martini J, Berndt C, Sauer C, Vogelbacher C, Jansen A, Kircher T, Falkenberg I, Thomas-Odenthal F, Lambert M, Kraft V, Leicht G, Mulert C, Fallgatter AJ, Ethofer T, Rau A, Leopold K, Bechdolf A, Reif A, Matura S, Biere S, Bermpohl F, Fiebig J, Stamm T, Correll CU, Juckel G, Flasbeck V, Ritter P, Bauer M, Pfennig A, Mikolas P. Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes. Brain Sci 2023; 13:870. [PMID: 37371350 DOI: 10.3390/brainsci13060870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 05/23/2023] [Accepted: 05/25/2023] [Indexed: 06/29/2023] Open
Abstract
The pathophysiology of bipolar disorder (BD) remains mostly unclear. Yet, a valid biomarker is necessary to improve upon the early detection of this serious disorder. Patients with manifest BD display reduced volumes of the hippocampal subfields and amygdala nuclei. In this pre-registered analysis, we used structural MRI (n = 271, 7 sites) to compare volumes of hippocampus, amygdala and their subfields/nuclei between help-seeking subjects divided into risk groups for BD as estimated by BPSS-P, BARS and EPIbipolar. We performed between-group comparisons using linear mixed effects models for all three risk assessment tools. Additionally, we aimed to differentiate the risk groups using a linear support vector machine. We found no significant volume differences between the risk groups for all limbic structures during the main analysis. However, the SVM could still classify subjects at risk according to BPSS-P criteria with a balanced accuracy of 66.90% (95% CI 59.2-74.6) for 10-fold cross-validation and 61.9% (95% CI 52.0-71.9) for leave-one-site-out. Structural alterations of the hippocampus and amygdala may not be as pronounced in young people at risk; nonetheless, machine learning can predict the estimated risk for BD above chance. This suggests that neural changes may not merely be a consequence of BD and may have prognostic clinical value.
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Affiliation(s)
- Fabian Huth
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Leonardo Tozzi
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michael Marxen
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Philipp Riedel
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Kyra Bröckel
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Julia Martini
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Christina Berndt
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Cathrin Sauer
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Christoph Vogelbacher
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, 35037 Marburg, Germany
- Translational Clinical Psychology, Philipps-University Marburg, 35037 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and University Giessen, 35039 Marburg, Germany
| | - Andreas Jansen
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, 35037 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and University Giessen, 35039 Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Marburg, 35037 Marburg, Germany
| | - Tilo Kircher
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and University Giessen, 35039 Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Marburg, 35037 Marburg, Germany
| | - Irina Falkenberg
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and University Giessen, 35039 Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Marburg, 35037 Marburg, Germany
| | - Florian Thomas-Odenthal
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and University Giessen, 35039 Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Marburg, 35037 Marburg, Germany
| | - Martin Lambert
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Vivien Kraft
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Gregor Leicht
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Christoph Mulert
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and University Giessen, 35039 Marburg, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
- Centre for Psychiatry, Justus-Liebig University Giessen, 35390 Gießen, Germany
| | - Andreas J Fallgatter
- Department of Psychiatry, Tuebingen Center for Mental Health, University of Tuebingen, 72074 Tuebingen, Germany
| | - Thomas Ethofer
- Department of Psychiatry, Tuebingen Center for Mental Health, University of Tuebingen, 72074 Tuebingen, Germany
| | - Anne Rau
- Department of Psychiatry, Tuebingen Center for Mental Health, University of Tuebingen, 72074 Tuebingen, Germany
| | - Karolina Leopold
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, Vivantes Hospital Am Urban and Vivantes Hospital Im Friedrichshain, Charité-Universitätsmedizin, 10117 Berlin, Germany
| | - Andreas Bechdolf
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, Vivantes Hospital Am Urban and Vivantes Hospital Im Friedrichshain, Charité-Universitätsmedizin, 10117 Berlin, Germany
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, University Hospital, 60323 Frankfurt, Germany
| | - Silke Matura
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, University Hospital, 60323 Frankfurt, Germany
| | - Silvia Biere
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, University Hospital, 60323 Frankfurt, Germany
| | - Felix Bermpohl
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charité University Medicine, 10117 Berlin, Germany
| | - Jana Fiebig
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charité University Medicine, 10117 Berlin, Germany
| | - Thomas Stamm
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charité University Medicine, 10117 Berlin, Germany
- Department of Clinical Psychiatry and Psychotherapy, Brandenburg Medical School Theodor Fontane, 16816 Neuruppin, Germany
| | - Christoph U Correll
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, 10117 Berlin, Germany
- Department of Psychiatry, Northwell Health, The Zucker Hillside Hospital, Glen Oaks, New York, NY 11004, USA
- Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
| | - Georg Juckel
- Department of Psychiatry, Psychotherapy and Preventive Medicine, LWL University Hospital, Ruhr-University, 44791 Bochum, Germany
| | - Vera Flasbeck
- Department of Psychiatry, Psychotherapy and Preventive Medicine, LWL University Hospital, Ruhr-University, 44791 Bochum, Germany
| | - Philipp Ritter
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
| | - Pavol Mikolas
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, 01062 Dresden, Germany
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12
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Kondo F, Whitehead JC, Corbalán F, Beaulieu S, Armony JL. Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data. Int J Bipolar Disord 2023; 11:12. [PMID: 36964848 PMCID: PMC10039967 DOI: 10.1186/s40345-023-00292-w] [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] [Received: 12/08/2022] [Accepted: 03/13/2023] [Indexed: 03/26/2023] Open
Abstract
BACKGROUND Bipolar disorder type-I (BD-I) patients are known to show emotion regulation abnormalities. In a previous fMRI study using an explicit emotion regulation paradigm, we compared responses from 19 BD-I patients and 17 matched healthy controls (HC). A standard general linear model-based univariate analysis revealed that BD patients showed increased activations in inferior frontal gyrus when instructed to decrease their emotional response as elicited by neutral images. We implemented multivariate pattern recognition analyses on the same data to examine if we could classify conditions within-group as well as HC versus BD. METHODS We reanalyzed explicit emotion regulation data using a multivariate pattern recognition approach, as implemented in PRONTO software. The original experimental paradigm consisted of a full 2 × 2 factorial design, with valence (Negative/Neutral) and instruction (Look/Decrease) as within subject factors. RESULTS The multivariate models were able to accurately classify different task conditions when HC and BD were analyzed separately (63.24%-75.00%, p = 0.001-0.012). In addition, the models were able to correctly classify HC versus BD with significant accuracy in conditions where subjects were instructed to downregulate their felt emotion (59.60%-60.84%, p = 0.014-0.018). The results for HC versus BD classification demonstrated contributions from the salience network, several occipital and frontal regions, inferior parietal lobes, as well as other cortical regions, to achieve above-chance classifications. CONCLUSIONS Our multivariate analysis successfully reproduced some of the main results obtained in the previous univariate analysis, confirming that these findings are not dependent on the analysis approach. In particular, both types of analyses suggest that there is a significant difference of neural patterns between conditions within each subject group. The multivariate approach also revealed that reappraisal conditions provide the most informative activity for differentiating HC versus BD, irrespective of emotional valence (negative or neutral). The current results illustrate the importance of investigating the cognitive control of emotion in BD. We also propose a set of candidate regions for further study of emotional control in BD.
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Affiliation(s)
- Fumika Kondo
- Douglas Mental Health University Institute, Verdun, QC, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Jocelyne C Whitehead
- Douglas Mental Health University Institute, Verdun, QC, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | | | - Serge Beaulieu
- Douglas Mental Health University Institute, Verdun, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Jorge L Armony
- Douglas Mental Health University Institute, Verdun, QC, Canada.
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada.
- Department of Psychiatry, McGill University, Montreal, QC, Canada.
- Department of Psychology, Université de Montréal, Montreal, QC, Canada.
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13
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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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14
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Abstract
Bipolar disorder (BD) is a severe mental illness associated with alterations in brain organization. Neuroimaging studies have generated a large body of knowledge regarding brain morphological and functional abnormalities in BD. Current advances in the field have focussed on the need for more precise neuroimaging biomarkers. Here we present a selective overview of precision neuroimaging biomarkers for BD, focussing on personalized metrics and novel neuroimaging methods aiming to provide mechanistic insights into the brain alterations associated with BD. The evidence presented covers (a) machine learning techniques applied to neuroimaging data to differentiate patients with BD from healthy individuals or other clinical groups; (b) the 'brain-age-gap-estimation (brainAGE), which is an individualized measure of brain health; (c) diffusional kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI) and Positron Emission Tomography (PET) techniques that open new opportunities to measure microstructural changes in neurite/synaptic integrity and function.
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Affiliation(s)
- Delfina Janiri
- Department of Psychiatry, Fondazione Policlinico Universitario 'Agostino Gemelli' IRCCS, Roma, Italy.,Department of Psychiatry and Neurology, Sapienza University of Rome, Roma, Italy
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine, Mount Sinai, NY, USA.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver British Columbia, Canada
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15
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Hu X, Yu C, Dong T, Yang Z, Fang Y, Jiang Z. Biomarkers and detection methods of bipolar disorder. Biosens Bioelectron 2022; 220:114842. [DOI: 10.1016/j.bios.2022.114842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 09/16/2022] [Accepted: 10/19/2022] [Indexed: 12/01/2022]
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16
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Dou R, Gao W, Meng Q, Zhang X, Cao W, Kuang L, Niu J, Guo Y, Cui D, Jiao Q, Qiu J, Su L, Lu G. Machine learning algorithm performance evaluation in structural magnetic resonance imaging-based classification of pediatric bipolar disorders type I patients. Front Comput Neurosci 2022; 16:915477. [PMID: 36082304 PMCID: PMC9445985 DOI: 10.3389/fncom.2022.915477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/21/2022] [Indexed: 11/15/2022] Open
Abstract
The diagnosis based on clinical assessment of pediatric bipolar disorder (PBD) may sometimes lead to misdiagnosis in clinical practice. For the past several years, machine learning (ML) methods were introduced for the classification of bipolar disorder (BD), which were helpful in the diagnosis of BD. In this study, brain cortical thickness and subcortical volume of 33 PBD-I patients and 19 age-sex matched healthy controls (HCs) were extracted from the magnetic resonance imaging (MRI) data and set as features for classification. The dimensionality reduced feature subset, which was filtered by Lasso or f_classif, was sent to the six classifiers (logistic regression (LR), support vector machine (SVM), random forest classifier, naïve Bayes, k-nearest neighbor, and AdaBoost algorithm), and the classifiers were trained and tested. Among all the classifiers, the top two classifiers with the highest accuracy were LR (84.19%) and SVM (82.80%). Feature selection was performed in the six algorithms to obtain the most important variables including the right middle temporal gyrus and bilateral pallidum, which is consistent with structural and functional anomalous changes in these brain regions in PBD patients. These findings take the computer-aided diagnosis of BD a step forward.
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Affiliation(s)
- Ruhai Dou
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China
| | - Weijia Gao
- Department of Child Psychology, The Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qingmin Meng
- Department of Interventional Radiology, Taian Central Hospital, Taian, China
| | - Xiaotong Zhang
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China
| | - Weifang Cao
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China
| | - Liangfeng Kuang
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China
| | - Jinpeng Niu
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China
| | - Yongxin Guo
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China
| | - Dong Cui
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Qing Jiao
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China
- *Correspondence: Qing Jiao,
| | - Jianfeng Qiu
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China
| | - Linyan Su
- Key Laboratory of Psychiatry and Mental Health of Hunan Province, Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, China
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, China
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17
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Siegel-Ramsay JE, Bertocci MA, Wu B, Phillips ML, Strakowski SM, Almeida JRC. Distinguishing between depression in bipolar disorder and unipolar depression using magnetic resonance imaging: a systematic review. Bipolar Disord 2022; 24:474-498. [PMID: 35060259 DOI: 10.1111/bdi.13176] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
OBJECTIVES Magnetic resonance imaging (MRI) studies comparing bipolar and unipolar depression characterize pathophysiological differences between these conditions. However, it is difficult to interpret the current literature due to differences in MRI modalities, analysis methods, and study designs. METHODS We conducted a systematic review of publications using MRI to compare individuals with bipolar and unipolar depression. We grouped studies according to MRI modality and task design. Within the discussion, we critically evaluated and summarized the functional MRI research and then further complemented these findings by reviewing the structural MRI literature. RESULTS We identified 88 MRI publications comparing participants with bipolar depression and unipolar depressive disorder. Compared to individuals with unipolar depression, participants with bipolar disorder exhibited heightened function, increased within network connectivity, and reduced grey matter volume in salience and central executive network brain regions. Group differences in default mode network function were less consistent but more closely associated with depressive symptoms in participants with unipolar depression but distractibility in bipolar depression. CONCLUSIONS When comparing mood disorder groups, the neuroimaging evidence suggests that individuals with bipolar disorder are more influenced by emotional and sensory processing when responding to their environment. In contrast, depressive symptoms and neurofunctional response to emotional stimuli were more closely associated with reduced central executive function and less adaptive cognitive control of emotionally oriented brain regions in unipolar depression. Researchers now need to replicate and refine network-level trends in these heterogeneous mood disorders and further characterize MRI markers associated with early disease onset, progression, and recovery.
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Affiliation(s)
- Jennifer E Siegel-Ramsay
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas, Austin, Texas, USA
| | - Michele A Bertocci
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Bryan Wu
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas, Austin, Texas, USA
| | - Mary L Phillips
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Stephen M Strakowski
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas, Austin, Texas, USA
| | - Jorge R C Almeida
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas, Austin, Texas, USA
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18
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Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: a systematic review and meta-analysis. Neurosci Biobehav Rev 2022; 135:104552. [PMID: 35120970 DOI: 10.1016/j.neubiorev.2022.104552] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/11/2022] [Accepted: 01/30/2022] [Indexed: 01/10/2023]
Abstract
Applying machine learning (ML) to objective markers may overcome prognosis uncertainty due to the subjective nature of the diagnosis of bipolar disorder (BD). This PRISMA-compliant meta-analysis provides new systematic evidence of the BD classification accuracy reached by different markers and ML algorithms. We focused on neuroimaging, electrophysiological techniques, peripheral biomarkers, genetic data, neuropsychological or clinical measures, and multimodal approaches. PubMed, Embase and Scopus were searched through 3rd December 2020. Meta-analyses were performed using random-effect models. Overall, 81 studies were included in this systematic review and 65 in the meta-analysis (11,336 participants, 3,903 BD). The overall pooled classification accuracy was 0.77 (95%CI[0.75;0.80]). Despite subgroup analyses for diagnostic comparison group, psychiatric disorders, marker, ML algorithm, and validation procedure were not significant, linear discriminant analysis significantly outperformed support vector machine for peripheral biomarkers (p=0.03). Sample size was inversely related to accuracy. Evidence of publication bias was detected. Ultimately, although ML reached a high accuracy in differentiating BD from other psychiatric disorders, best practices in methodology are needed for the advancement of future studies.
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19
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Liu Y, Chen K, Luo Y, Wu J, Xiang Q, Peng L, Zhang J, Zhao W, Li M, Zhou X. Distinguish bipolar and major depressive disorder in adolescents based on multimodal neuroimaging: Results from the Adolescent Brain Cognitive Development study ®. Digit Health 2022; 8:20552076221123705. [PMID: 36090673 PMCID: PMC9452797 DOI: 10.1177/20552076221123705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 08/16/2022] [Indexed: 01/10/2023] Open
Abstract
Background Major depressive disorder and bipolar disorder in adolescents are prevalent and are associated with cognitive impairment, executive dysfunction, and increased mortality. Early intervention in the initial stages of major depressive disorder and bipolar disorder can significantly improve personal health. Methods We collected 309 samples from the Adolescent Brain Cognitive Development study, including 116 adolescents with bipolar disorder, 64 adolescents with major depressive disorder, and 129 healthy adolescents, and employed a support vector machine to develop classification models for identification. We developed a multimodal model, which combined functional connectivity of resting-state functional magnetic resonance imaging and four anatomical measures of structural magnetic resonance imaging (cortical thickness, area, volume, and sulcal depth). We measured the performances of both multimodal and single modality classifiers. Results The multimodal classifiers showed outstanding performance compared with all five single modalities, and they are 100% for major depressive disorder versus healthy controls, 100% for bipolar disorder versus healthy control, 98.5% (95% CI: 95.4–100%) for major depressive disorder versus bipolar disorder, 100% for major depressive disorder versus depressed bipolar disorder and the leave-one-site-out analysis results are 77.4%, 63.3%, 79.4%, and 81.7%, separately. Conclusions The study shows that multimodal classifiers show high classification performances. Moreover, cuneus may be a potential biomarker to differentiate major depressive disorder, bipolar disorder, and healthy adolescents. Overall, this study can form multimodal diagnostic prediction workflows for clinically feasible to make more precise diagnose at the early stage and potentially reduce loss of personal pain and public society.
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Affiliation(s)
- Yujun Liu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Kai Chen
- School of Public Health, University of Texas Health Science Center at Houston, Houston, USA
| | - Yangyang Luo
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Jiqiu Wu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Qu Xiang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Li Peng
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Jian Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Weiling Zhao
- Center for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA
| | - Mingliang Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA
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20
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Screening for bipolar disorder in a tertiary mental health centre using EarlyDetect: A machine learning-based pilot study. JOURNAL OF AFFECTIVE DISORDERS REPORTS 2021. [DOI: 10.1016/j.jadr.2021.100215] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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21
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Automatic Diagnosis of Bipolar Disorder Using Optical Coherence Tomography Data and Artificial Intelligence. J Pers Med 2021; 11:jpm11080803. [PMID: 34442447 PMCID: PMC8402059 DOI: 10.3390/jpm11080803] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 08/16/2021] [Indexed: 01/01/2023] Open
Abstract
Background: The aim of this study is to explore an objective approach that aids the diagnosis of bipolar disorder (BD), based on optical coherence tomography (OCT) data which are analyzed using artificial intelligence. Methods: Structural analyses of nine layers of the retina were analyzed in 17 type I BD patients and 42 controls, according to the areas defined by the Early Treatment Diabetic Retinopathy Study (ETDRS) chart. The most discriminating variables made up the feature vector of several automatic classifiers: Gaussian Naive Bayes, K-nearest neighbors and support vector machines. Results: BD patients presented retinal thinning affecting most layers, compared to controls. The retinal thickness of the parafoveolar area showed a high capacity to discriminate BD subjects from healthy individuals, specifically for the ganglion cell (area under the curve (AUC) = 0.82) and internal plexiform (AUC = 0.83) layers. The best classifier showed an accuracy of 0.95 for classifying BD versus controls, using as variables of the feature vector the IPL (inner nasal region) and the INL (outer nasal and inner inferior regions) thickness. Conclusions: Our patients with BD present structural alterations in the retina, and artificial intelligence seem to be a useful tool in BD diagnosis, but larger studies are needed to confirm our findings.
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22
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Yang T, Frangou S, Lam RW, Huang J, Su Y, Zhao G, Mao R, Zhu N, Zhou R, Lin X, Xia W, Wang X, Wang Y, Peng D, Wang Z, Yatham LN, Chen J, Fang Y. Probing the clinical and brain structural boundaries of bipolar and major depressive disorder. Transl Psychiatry 2021; 11:48. [PMID: 33446647 PMCID: PMC7809029 DOI: 10.1038/s41398-020-01169-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 12/07/2020] [Accepted: 12/11/2020] [Indexed: 12/12/2022] Open
Abstract
Bipolar disorder (BD) and major depressive disorder (MDD) have both common and distinct clinical features, that pose both conceptual challenges in terms of their diagnostic boundaries and practical difficulties in optimizing treatment. Multivariate machine learning techniques offer new avenues for exploring these boundaries based on clinical neuroanatomical features. Brain structural data were obtained at 3 T from a sample of 90 patients with BD, 189 patients with MDD, and 162 healthy individuals. We applied sparse partial least squares discriminant analysis (s-PLS-DA) to identify clinical and brain structural features that may discriminate between the two clinical groups, and heterogeneity through discriminative analysis (HYDRA) to detect patient subgroups with reference to healthy individuals. Two clinical dimensions differentiated BD from MDD (area under the curve: 0.76, P < 0.001); one dimension emphasized disease severity as well as irritability, agitation, anxiety and flight of ideas and the other emphasized mostly elevated mood. Brain structural features could not distinguish between the two disorders. HYDRA classified patients in two clusters that differed in global and regional cortical thickness, the distribution proportion of BD and MDD and positive family history of psychiatric disorders. Clinical features remain the most reliable discriminant attributed of BD and MDD depression. The brain structural findings suggests that biological partitions of patients with mood disorders are likely to lead to the identification of subgroups, that transcend current diagnostic divisions into BD and MDD and are more likely to be aligned with underlying genetic variation. These results set the foundation for future studies to enhance our understanding of brain-behavior relationships in mood disorders.
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Affiliation(s)
- Tao Yang
- grid.16821.3c0000 0004 0368 8293Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China ,grid.17091.3e0000 0001 2288 9830Department of Psychiatry, University of British Columbia, Vancouver, Canada
| | - Sophia Frangou
- grid.17091.3e0000 0001 2288 9830Department of Psychiatry, University of British Columbia, Vancouver, Canada ,grid.59734.3c0000 0001 0670 2351Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Raymond W. Lam
- grid.17091.3e0000 0001 2288 9830Department of Psychiatry, University of British Columbia, Vancouver, Canada
| | - Jia Huang
- grid.16821.3c0000 0004 0368 8293Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yousong Su
- grid.16821.3c0000 0004 0368 8293Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guoqing Zhao
- grid.460018.b0000 0004 1769 9639Department of Psychology, Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Ruizhi Mao
- grid.16821.3c0000 0004 0368 8293Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Na Zhu
- Shanghai Pudong New District Mental Health Center, Shanghai, China
| | - Rubai Zhou
- grid.16821.3c0000 0004 0368 8293Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiao Lin
- grid.16821.3c0000 0004 0368 8293Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weiping Xia
- grid.16821.3c0000 0004 0368 8293Department of Medical Psychology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xing Wang
- grid.16821.3c0000 0004 0368 8293Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yun Wang
- grid.16821.3c0000 0004 0368 8293Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Daihui Peng
- grid.16821.3c0000 0004 0368 8293Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zuowei Wang
- Division of Mood Disorders, Shanghai Hongkou District Mental Health Center, Shanghai, China
| | - Lakshmi N. Yatham
- grid.17091.3e0000 0001 2288 9830Department of Psychiatry, University of British Columbia, Vancouver, Canada
| | - Jun Chen
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Yiru Fang
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China. .,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China. .,Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China.
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23
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Chen YL, Tu PC, Huang TH, Bai YM, Su TP, Chen MH, Wu YT. Using Minimal-Redundant and Maximal-Relevant Whole-Brain Functional Connectivity to Classify Bipolar Disorder. Front Neurosci 2020; 14:563368. [PMID: 33192250 PMCID: PMC7641629 DOI: 10.3389/fnins.2020.563368] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 09/23/2020] [Indexed: 12/04/2022] Open
Abstract
Background A number of mental illness is often re-diagnosed to be bipolar disorder (BD). Furthermore, the prefronto-limbic-striatal regions seem to be associated with the main dysconnectivity of BD. Functional connectivity is potentially an appropriate objective neurobiological marker that can assist with BD diagnosis. Methods Health controls (HC; n = 173) and patients with BD who had been diagnosed by experienced physicians (n = 192) were separated into 10-folds, namely, a ninefold training set and a onefold testing set. The classification involved feature selection of the training set using minimum redundancy/maximum relevance. Support vector machine was used for training. The classification was repeated 10 times until each fold had been used as the testing set. Results The mean accuracy of the 10 testing sets was 76.25%, and the area under the curve was 0.840. The selected functional within-network/between-network connectivity was mainly in the subcortical/cerebellar regions and the frontoparietal network. Furthermore, similarity within the BD patients, calculated by the cosine distance between two functional connectivity matrices, was smaller than between groups before feature selection and greater than between groups after the feature selection. Limitations The major limitations were that all the BD patients were receiving medication and that no independent dataset was included. Conclusion Our approach effectively separates a relatively large group of BD patients from HCs. This was done by selecting functional connectivity, which was more similar within BD patients, and also seems to be related to the neuropathological factors associated with BD.
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Affiliation(s)
- Yen-Ling Chen
- Institute of Biophotonics, National Yang-Ming University, Taipei, Taiwan
| | - Pei-Chi Tu
- Department of Medical Research and Education, Taipei Veterans General Hospital, Taipei, Taiwan.,Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Psychiatry, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan.,Institute of Philosophy of Mind and Cognition, National Yang-Ming University, Taipei, Taiwan
| | - Tzu-Hsuan Huang
- Institute of Biophotonics, National Yang-Ming University, Taipei, Taiwan
| | - Ya-Mei Bai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Psychiatry, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Tung-Ping Su
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Psychiatry, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan.,Department of Psychiatry, Cheng-Hsin General Hospital, Taipei, Taiwan
| | - Mu-Hong Chen
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Psychiatry, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang-Ming University, Taipei, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei, Taiwan
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24
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Sonkurt HO, Altınöz AE, Çimen E, Köşger F, Öztürk G. The role of cognitive functions in the diagnosis of bipolar disorder: A machine learning model. Int J Med Inform 2020; 145:104311. [PMID: 33202371 DOI: 10.1016/j.ijmedinf.2020.104311] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 10/05/2020] [Accepted: 10/20/2020] [Indexed: 11/25/2022]
Abstract
BACKGROUND Considering the clinical heterogeneity of the bipolar disorder, difficulties are encountered in making the correct diagnosis. Although a number of common findings have been found in studies on the neurocognitive profile of bipolar disorder, the search for a neurocognitive endophenotype has failed. The aim of this study is to separate bipolar disorder patients from healthy controls with higher accuracy by using a broader neurocognitive evaluation and a novel machine-learning algorithm. METHODS Individuals who presented to the Bipolar Outpatient Clinic of the Medical Faculty of Eskişehir Osmangazi University and met the inclusion criteria of the research are included in the study. Six neurocognitive tests from the CANTAB test battery were used for neurocognitive evaluation, Polyhedral Conic Functions algorithm was used to classify the participants. RESULTS Bipolar disorder patients differentiated from healthy controls with an accuracy of 78 %. DISCUSSION Our study presents a prediction algorithm that separates bipolar disorder from healthy controls with high accuracy by using CANTAB neurocognitive battery.
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Affiliation(s)
| | - Ali Ercan Altınöz
- Department of Psychiatry, Faculty of Medicine, Eskişehir Osmangazi University, Eskişehir, Turkey.
| | - Emre Çimen
- Computational Intelligence and Optimization Laboratory, Department of Industrial Engineering, Eskisehir Technical University, Eskisehir, Turkey
| | - Ferdi Köşger
- Department of Psychiatry, Faculty of Medicine, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Gürkan Öztürk
- Computational Intelligence and Optimization Laboratory, Department of Industrial Engineering, Eskisehir Technical University, Eskisehir, Turkey
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25
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Zhao M, Feng Z. Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study. Neuropsychiatr Dis Treat 2020; 16:2743-2752. [PMID: 33209029 PMCID: PMC7669500 DOI: 10.2147/ndt.s275620] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 10/19/2020] [Indexed: 11/23/2022] Open
Abstract
PURPOSE Traditional questionnaires assessing the severity of depression are limited and might not be appropriate for military personnel. We intend to explore the diagnostic ability of three machine learning methods for evaluating the depression status of Chinese recruits, using the Chinese version of Beck Depression Inventory-II (BDI-II) as the standard. PATIENTS AND METHODS Our diagnostic study was carried out in Luoyang City (Henan Province, China; 10/16/2018-12/10/2018) with a sample of 1000 Chinese male recruits selected using cluster convenient sampling. All participants completed the BDI and 3 questionnaires including the data of demographics, military careers and 18 factors. The participants were randomly selected as the training set and the testing at 2:1. The machine learning methods tested for assessing the presence or absence of depression status were neural network (NN), support vector machine (SVM), and decision tree (DT). RESULTS A total of 1000 participants completed the questionnaires, with 223 reporting depression status and 777 not. The highest sensitivity was observed for DT (94.1%), followed by SVM (93.4%) and NN (93.1%). The highest specificity was observed for NN (60.0%), followed by SVM (58.8%) and DT (43.3%). The area under the curve (AUC) of the SVM was the largest (0.862) compared with NN (0.860) and DT (0.734). The regression prediction error and error volatility of the SVM were the smallest. CONCLUSION The SVM has the smallest prediction error and error volatility, as well as the largest AUC compared with NN and DT for assessing the presence or absence of depression status in Chinese recruits.
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Affiliation(s)
- Mengxue Zhao
- Department of Military Psychology, Faculty of Medical Psychology, Army Medical University, Chongqing, People's Republic of China
| | - Zhengzhi Feng
- Faculty of Medical Psychology, Army Medical University, Chongqing, People's Republic of China
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Zhu R, Tian S, Wang H, Jiang H, Wang X, Shao J, Wang Q, Yan R, Tao S, Liu H, Yao Z, Lu Q. Discriminating Suicide Attempters and Predicting Suicide Risk Using Altered Frontolimbic Resting-State Functional Connectivity in Patients With Bipolar II Disorder. Front Psychiatry 2020; 11:597770. [PMID: 33324262 PMCID: PMC7725800 DOI: 10.3389/fpsyt.2020.597770] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 11/04/2020] [Indexed: 01/04/2023] Open
Abstract
Bipolar II disorder (BD-II) major depression episode is highly associated with suicidality, and objective neural biomarkers could be key elements to assist in early prevention and intervention. This study aimed to integrate altered brain functionality in the frontolimbic system and machine learning techniques to classify suicidal BD-II patients and predict suicidality risk at the individual level. A cohort of 169 participants were enrolled, including 43 BD-II depression patients with at least one suicide attempt during a current depressive episode (SA), 62 BD-II depression patients without a history of attempted suicide (NSA), and 64 demographically matched healthy controls (HCs). We compared resting-state functional connectivity (rsFC) in the frontolimbic system among the three groups and explored the correlation between abnormal rsFCs and the level of suicide risk (assessed using the Nurses' Global Assessment of Suicide Risk, NGASR) in SA patients. Then, we applied support vector machines (SVMs) to classify SA vs. NSA in BD-II patients and predicted the risk of suicidality. SA patients showed significantly decreased frontolimbic rsFCs compared to NSA patients. The left amygdala-right middle frontal gyrus (orbital part) rsFC was negatively correlated with NGASR in the SA group, but not the severity of depressive or anxiety symptoms. Using frontolimbic rsFCs as features, the SVMs obtained an overall 84% classification accuracy in distinguishing SA and NSA. A significant correlation was observed between the SVMs-predicted NGASR and clinical assessed NGASR (r = 0.51, p = 0.001). Our results demonstrated that decreased rsFCs in the frontolimbic system might be critical objective features of suicidality in BD-II patients, and could be useful for objective prediction of suicidality risk in individuals.
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Affiliation(s)
- Rongxin Zhu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shui Tian
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Southeast University, Nanjing, China
| | - Huan Wang
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Southeast University, Nanjing, China
| | - Haiteng Jiang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xinyi Wang
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Southeast University, Nanjing, China
| | - Junneng Shao
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Southeast University, Nanjing, China
| | - Qiang Wang
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Rui Yan
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shiwan Tao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Haiyan Liu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Zhijian Yao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Qing Lu
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Southeast University, Nanjing, China
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