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Arjmandi M, Fattahi M, Motevassel M, Rezaveisi H. Evaluating algorithms of decision tree, support vector machine and regression for anode side catalyst data in proton exchange membrane water electrolysis. Sci Rep 2023; 13:20309. [PMID: 37985795 PMCID: PMC10662483 DOI: 10.1038/s41598-023-47174-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/09/2023] [Indexed: 11/22/2023] Open
Abstract
Nowadays, due to the various type of problems stemmed from using chemical compounds and fossil fuels which have widely influence on whole environment including acid rain, polar ice melting and etc., number of researches have been leading on replacing the nonrenewable energy sources with renewable ones in order to produce clean fuels. Among these, hydrogen emerges as a quintessential clean fuel, garnering substantial attention for its potential to be synthesized from the electric power generated by renewable sources like nuclear and solar energies. This is achieved through the employment of a proton exchange membrane water electrolysis (PEMWE) system, widely recognized as one of the most proficient and economically viable technologies for effecting the separation of H2O into H+ and OH-. In this study, the important affecting parameters on the anode side of catalyst in PEMWE and analyzed them by machine-learning (ML) algorithms through developing a data science (DS) procedure were discussed. Various machine learning models were subjected to comparison, wherein the Decision Tree models, specifically those configured with maximum depths of 3 and 4, emerged as the optimal choices, attaining a perfect 100% accuracy across both Dataset 1 and Dataset 2. Moreover, notable enhancements in accuracy values were observed for the Support Vector Machine (SVM) model, registering increments from 0.79 to 0.82 for Dataset 1 and 2, respectively. In stark contrast, the remaining models experienced a decrement in their accuracy scores. This phenomenon underscores the pivotal role played by the data generation process in rendering the models more faithful to real-world scenarios.
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Affiliation(s)
- Mahdi Arjmandi
- Chemical Engineering Department, Abadan Faculty of Petroleum Engineering, Petroleum University of Technology, Abadan, Iran
| | - Moslem Fattahi
- Chemical Engineering Department, Abadan Faculty of Petroleum Engineering, Petroleum University of Technology, Abadan, Iran.
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada.
| | - Mohsen Motevassel
- Chemical Engineering Department, Abadan Faculty of Petroleum Engineering, Petroleum University of Technology, Abadan, Iran
| | - Hosna Rezaveisi
- Chemical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, Iran
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2
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Wilson JP, Kumbhare D, Ronkon C, Guthikonda B, Hoang S. Application of Machine Learning Strategies to Model the Effects of Sevoflurane on Somatosensory-Evoked Potentials during Spine Surgery. Diagnostics (Basel) 2023; 13:3389. [PMID: 37958285 PMCID: PMC10648293 DOI: 10.3390/diagnostics13213389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 11/01/2023] [Accepted: 11/04/2023] [Indexed: 11/15/2023] Open
Abstract
In this study, a small sample of patients' neuromonitoring data was analyzed using machine learning (ML) tools to provide proof of concept for quantifying complex signals. Intraoperative neurophysiological monitoring (IONM) is a valuable asset for monitoring the neurological status of a patient during spine surgery. Notably, this technology, when operated by neurophysiologists and surgeons familiar with proper alarm criteria, is capable of detecting neurological deficits. However, non-surgical factors, such as volatile anesthetics like sevoflurane, can negatively influence robust IONM signal generation. While sevoflurane has been shown to affect the latency and amplitude of somatosensory evoked potential (SSEP), a more complex and nuanced analysis of the SSEP waveform has not been performed. In this study, signal processing and machine learning techniques were used to more intricately characterize and predict SSEP waveform changes as a function of varying end-tidal sevoflurane concentration. With data from ten patients who underwent spinal procedures, features describing the SSEP waveforms were generated using principal component analysis (PCA), phase space curves (PSC), and time-frequency analysis (TFA). A minimum redundancy maximum relevance (MRMR) feature selection technique was then used to identify the most important SSEP features associated with changing sevoflurane concentrations. Once the features carrying the maximum amount of information about the majority of signal waveform variability were identified, ML models were used to predict future changes in SSEP waveforms. Linear regression, regression trees, support vector machines, and neural network ML models were then selected for testing. Using SSEP data from eight patients, the models were trained using a range of features selected during MRMR calculations. During the training phase of model development, the highest performing models were identified as support vector machines and regression trees. After identifying the highest performing models for each nerve group, we tested these models using the remaining two patients' data. We compared the models' performance metrics using the root mean square error values (RMSEs). The feasibility of the methodology described provides a general framework for the applications of machine learning strategies to further delineate the effects of surgical and non-surgical factors affecting IONM signals.
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Affiliation(s)
| | | | | | | | - Stanley Hoang
- Department of Neurosurgery, Louisiana State University Health Shreveport, Shreveport, LA 71103, USA; (J.P.W.J.); (D.K.); (C.R.); (B.G.)
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Starke G, D’Imperio A, Ienca M. Out of their minds? Externalist challenges for using AI in forensic psychiatry. Front Psychiatry 2023; 14:1209862. [PMID: 37692304 PMCID: PMC10483237 DOI: 10.3389/fpsyt.2023.1209862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 08/07/2023] [Indexed: 09/12/2023] Open
Abstract
Harnessing the power of machine learning (ML) and other Artificial Intelligence (AI) techniques promises substantial improvements across forensic psychiatry, supposedly offering more objective evaluations and predictions. However, AI-based predictions about future violent behaviour and criminal recidivism pose ethical challenges that require careful deliberation due to their social and legal significance. In this paper, we shed light on these challenges by considering externalist accounts of psychiatric disorders which stress that the presentation and development of psychiatric disorders is intricately entangled with their outward environment and social circumstances. We argue that any use of predictive AI in forensic psychiatry should not be limited to neurobiology alone but must also consider social and environmental factors. This thesis has practical implications for the design of predictive AI systems, especially regarding the collection and processing of training data, the selection of ML methods, and the determination of their explainability requirements.
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Affiliation(s)
- Georg Starke
- Faculty of Medicine, Institute for History and Ethics of Medicine, Technical University of Munich, Munich, Germany
- École Polytechnique Fédérale de Lausanne, College of Humanities, Lausanne, Switzerland
- Munich School of Philosophy, Munich, Germany
| | - Ambra D’Imperio
- Faculty of Medicine, Institute for History and Ethics of Medicine, Technical University of Munich, Munich, Germany
- Department of Psychiatry, Hôpitaux Universitaires de Genève, Geneva, Switzerland
- Service of Forensic Psychiatry CURML, Geneva University Hospitals, Geneva, Switzerland
| | - Marcello Ienca
- Faculty of Medicine, Institute for History and Ethics of Medicine, Technical University of Munich, Munich, Germany
- École Polytechnique Fédérale de Lausanne, College of Humanities, Lausanne, Switzerland
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4
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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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] [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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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Chen ZS, Kulkarni P(P, Galatzer-Levy IR, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. PATTERNS (NEW YORK, N.Y.) 2022; 3:100602. [PMID: 36419447 PMCID: PMC9676543 DOI: 10.1016/j.patter.2022.100602] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA
| | | | - Isaac R. Galatzer-Levy
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Meta Reality Lab, New York, NY, USA
| | - Benedetta Bigio
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Carla Nasca
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA
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Li Y, Yu Z, Zhou X, Wu P, Chen J. Aberrant interhemispheric functional reciprocities of the default mode network and motor network in subcortical ischemic stroke patients with motor impairment: A longitudinal study. Front Neurol 2022; 13:996621. [PMID: 36267883 PMCID: PMC9577250 DOI: 10.3389/fneur.2022.996621] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/15/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose The purpose of the present study was to explore the longitudinal changes in functional homotopy in the default mode network (DMN) and motor network and its relationships with clinical characteristics in patients with stroke. Methods Resting-state functional magnetic resonance imaging was performed in stroke patients with subcortical ischemic lesions and healthy controls. The voxel-mirrored homotopic connectivity (VMHC) method was used to examine the differences in functional homotopy in patients with stroke between the two time points. Support vector machine (SVM) and correlation analyses were also applied to investigate whether the detected significant changes in VMHC were the specific feature in patients with stroke. Results The patients with stroke had significantly lower VMHC in the DMN and motor-related regions than the controls, including in the precuneus, parahippocampus, precentral gyrus, supplementary motor area, and middle frontal gyrus. Longitudinal analysis revealed that the impaired VMHC of the superior precuneus showed a significant increase at the second time point, which was no longer significantly different from the controls. Between the two time points, the changes in VMHC in the superior precuneus were significantly correlated with the changes in clinical scores. SVM analysis revealed that the VMHC of the superior precuneus could be used to correctly identify the patients with stroke from the controls with a statistically significant accuracy of 81.25% (P ≤ 0.003). Conclusions Our findings indicated that the increased VMHC in the superior precuneus could be regarded as the neuroimaging manifestation of functional recovery. The significant correlation and the discriminative power in classification results might provide novel evidence to understand the neural mechanisms responsible for brain reorganization after stroke.
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Affiliation(s)
- Yongxin Li
- School of Traditional Chinese Medicine, Formula-Pattern Research Center, Jinan University, Guangzhou, China
- *Correspondence: Yongxin Li
| | - Zeyun Yu
- Acupuncture and Tuina School/Tird Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Xuan Zhou
- School of Traditional Chinese Medicine, Formula-Pattern Research Center, Jinan University, Guangzhou, China
| | - Ping Wu
- Acupuncture and Tuina School/Tird Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Ping Wu
| | - Jiaxu Chen
- School of Traditional Chinese Medicine, Formula-Pattern Research Center, Jinan University, Guangzhou, China
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9
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Khobo IL, Jankiewicz M, Holmes MJ, Little F, Cotton MF, Laughton B, van der Kouwe AJW, Moreau A, Nwosu E, Meintjes EM, Robertson FC. Multimodal magnetic resonance neuroimaging measures characteristic of early cART-treated pediatric HIV: A feature selection approach. Hum Brain Mapp 2022; 43:4128-4144. [PMID: 35575438 PMCID: PMC9374890 DOI: 10.1002/hbm.25907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 04/03/2022] [Accepted: 04/26/2022] [Indexed: 11/09/2022] Open
Abstract
Children with perinatally acquired HIV (CPHIV) have poor cognitive outcomes despite early combination antiretroviral therapy (cART). While CPHIV-related brain alterations can be investigated separately using proton magnetic resonance spectroscopy (1 H-MRS), structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and functional MRI (fMRI), a set of multimodal MRI measures characteristic of children on cART has not been previously identified. We used the embedded feature selection of a logistic elastic-net (EN) regularization to select neuroimaging measures that distinguish CPHIV from controls and measured their classification performance via the area under the receiver operating characteristic curve (AUC) using repeated cross validation. We also wished to establish whether combining MRI modalities improved the models. In single modality analysis, sMRI volumes performed best followed by DTI, whereas individual EN models on spectroscopic, gyrification, and cortical thickness measures showed no class discrimination capability. Adding DTI and 1 H-MRS in basal measures to sMRI volumes produced the highest classification performancevalidation accuracy = 85 % AUC = 0.80 . The best multimodal MRI set consisted of 22 DTI and sMRI volume features, which included reduced volumes of the bilateral globus pallidus and amygdala, as well as increased mean diffusivity (MD) and radial diffusivity (RD) in the right corticospinal tract in cART-treated CPHIV. Consistent with previous studies of CPHIV, select subcortical volumes obtained from sMRI provide reasonable discrimination between CPHIV and controls. This may give insight into neuroimaging measures that are relevant in understanding the effects of HIV on the brain, thereby providing a starting point for evaluating their link with cognitive performance in CPHIV.
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Affiliation(s)
- Isaac L. Khobo
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Marcin Jankiewicz
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- Cape Universities Body Imaging CenterUniversity of Cape TownCape TownSouth Africa
| | - Martha J. Holmes
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Francesca Little
- Department of Statistical SciencesUniversity of Cape TownCape TownSouth Africa
| | - Mark F. Cotton
- Department of Pediatrics & Child Health, Family Center for Research with Ubuntu, Tygerberg HospitalStellenbosch UniversityCape TownSouth Africa
| | - Barbara Laughton
- Department of Pediatrics & Child Health, Family Center for Research with Ubuntu, Tygerberg HospitalStellenbosch UniversityCape TownSouth Africa
| | - Andre J. W. van der Kouwe
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- A.A. Martinos Centre for Biomedical ImagingMassachusetts General HospitalBostonMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
| | | | - Emmanuel Nwosu
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
| | - Ernesta M. Meintjes
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- Cape Universities Body Imaging CenterUniversity of Cape TownCape TownSouth Africa
| | - Frances C. Robertson
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- Cape Universities Body Imaging CenterUniversity of Cape TownCape TownSouth Africa
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10
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Abstract
OBJECTIVE Neuropsychiatric disorders in brain tumor patients are commonly observed. It is difficult to anticipate these disorders in different types of brain tumors. The goal of the study was to see how well machine learning (ML)-based decision algorithms might predict neuropsychiatric problems in different types of brain tumors. METHODS 145 histopathologically-confirmed primary brain tumors of both gender aged 25-65 years of age, were included for neuropsychiatric assessments. The datasets of brain tumor patients were employed for building the models. Four different decision ML classification trees/models (J48, Random Forest, Random Tree & Hoeffding Tree) with supervised learning were trained, tested, and validated on class labeled data of brain tumor patients. The models were compared in order to determine the best accurate classifier in predicting neuropsychiatric problems in various brain tumors. Following categorical attributes as independent variables (predictors) were included from the data of brain tumor patients: age, gender, depression, dementia, and brain tumor types. With the machine learning decision tree/model techniques, a multi-target classification was performed with classes of neuropsychiatric diseases that were predicted from the selected attributes. RESULTS 86 percent of patients were depressed, and 55 percent were suffering from dementia. Anger was the most often reported neuropsychiatric condition in brain tumor patients (92.41%), followed by sleep disorders (83%), apathy (80%), and mood swings (76.55%). When compared to other tumor types, glioblastoma patients had a higher rate of depression (20%) and dementia (20.25%). The developed models Random Forest and Random Tree were found successful with an accuracy of up to 94% (10-folds) for the prediction of neuropsychiatric disorders in brain tumor patients. The multiclass target (neuropsychiatric ailments) accuracies were having good measures of precision (0.9-1.0), recall (0.9-1.0), F-measure (0.9-1.0), and ROC area (0.9-1.0) in decision models. CONCLUSION Random Forest Trees can be used to accurately predict neuropsychiatric illnesses. Based on the model output, the ML-decision trees will aid the physician in pre-diagnosing the mental issue and deciding on the best therapeutic approach to avoid subsequent neuropsychiatric issues in brain tumor patients.
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Affiliation(s)
- Saman Shahid
- Department of Sciences & Humanities, National University of Computer & Emerging Sciences (NUCES), Foundation for Advancement of Science and Technology (FAST), Lahore, Pakistan
| | - Sadaf Iftikhar
- Department of Neurology, King Edward Medical University (KEMU), Mayo Hospital, Lahore, Pakistan
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11
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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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12
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Chen YL, Tu PC, Huang TH, Bai YM, Su TP, Chen MH, Wu YT. Identifying subtypes of bipolar disorder based on clinical and neurobiological characteristics. Sci Rep 2021; 11:17082. [PMID: 34429498 PMCID: PMC8385023 DOI: 10.1038/s41598-021-96645-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 08/13/2021] [Indexed: 02/06/2023] Open
Abstract
The ability to classify patients with bipolar disorder (BD) is restricted by their heterogeneity, which limits the understanding of their neuropathology. Therefore, we aimed to investigate clinically discernible and neurobiologically distinguishable BD subtypes. T1-weighted and resting-state functional magnetic resonance images of 112 patients with BD were obtained, and patients were segregated according to diagnostic subtype (i.e., types I and II) and clinical patterns, including the number of episodes and hospitalizations and history of suicide and psychosis. For each clinical pattern, fewer and more occurrences subgroups and types I and II were classified through nested cross-validation for robust performance, with minimum redundancy and maximum relevance, in feature selection. To assess the proportion of variance in cognitive performance explained by the neurobiological markers, multiple linear regression between verbal memory and the selected features was conducted. Satisfactory performance (mean accuracy, 73.60%) in classifying patients with a high or low number of episodes was attained through functional connectivity, mostly from default-mode and motor networks. Moreover, these neurobiological markers explained 62% of the variance in verbal memory. The number of episodes is a potentially critical aspect of the neuropathology of BD. Neurobiological markers can help identify BD neuroprogression.
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Affiliation(s)
- Yen-Ling Chen
- Institute of Biophotonics, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Taipei, 112, Taiwan.,Brain Research Center, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Pei-Chi Tu
- Department of Medical Research and Education, Taipei Veterans General Hospital, Taipei, 112, Taiwan.,Department of Psychiatry, Taipei Veterans General Hospital, Taipei, 112, Taiwan.,Division of Psychiatry, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan.,Institute of Philosophy of Mind and Cognition, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Tzu-Hsuan Huang
- Institute of Biophotonics, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Taipei, 112, Taiwan.,Brain Research Center, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Ya-Mei Bai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, 112, Taiwan.,Division of Psychiatry, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Tung-Ping Su
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, 112, Taiwan.,Division of Psychiatry, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan.,Department of Psychiatry, Cheng-Hsin General Hospital, Taipei, 112, Taiwan
| | - Mu-Hong Chen
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, 112, Taiwan.,Division of Psychiatry, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Taipei, 112, Taiwan. .,Brain Research Center, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan.
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13
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Li W, Wang Q, Liu X, Yu Y. Simple action for depression detection: using kinect-recorded human kinematic skeletal data. BMC Psychiatry 2021; 21:205. [PMID: 33888072 PMCID: PMC8063381 DOI: 10.1186/s12888-021-03184-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 03/23/2021] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Depression, a common worldwide mental disorder, which brings huge challenges to family and social burden around the world is different from fluctuant emotion and psychological pressure in their daily life. Although body signs have been shown to present manifestations of depression in general, few researches focus on whole body kinematic cues with the help of machine learning methods to aid depression recognition. Using the Kinect V2 device to record participants' simple kinematic skeleton data of the participant's body joints, the presented spatial features and low-level features is directly extracted from the record original Kinect-3D coordinates. This research aimed to constructed machine learning model with the preprocessed data importing, which could be used for depression automatic classification. METHODS Considering some patients' conditions and current status and refer to psychiatrists' advices, simple and significant designed stimulus task will lead human skeleton data collection job. With original Kinect skeleton data extracting and preprocessing, the proposed experiment demonstrated four strong machine learning tools: Support Vector Machine, Logistic Regression, Random Forest and Gradient Boosting. Using the precision, recall, sensitivity, specificity, roc-curve, confusion matrix et.al, indicators were calculated as the measurement of methods, which were commonly used to evaluate classification methodologies. RESULTS Across screened 64 pairs with age and gender totally matching in depression and control group, and Gradient Boosting achieved the best performance with the prediction accuracy of 76.92%. Sorted by female (54.69%) and male for the gender-based depression recognition, we applied best performance classifier Gradient Boosting got prediction accuracy of 66.67% in the male group, and 71.73% in the female group. Utilizing the best model Gradient Boosting for age-based classification, prediction accuracy got 76.92% in the older group (age >40, 50% of total) and 53.85% accuracy in the younger group (age <= 40). CONCLUSION The depression and non-depression individuals can be well classified by computational models using Kinect captured skeletal data. The Gradient Boosting, an excellent machine learning tool, get the performance in the four methods we demonstrated. Meanwhile, in the gender-based depression classification also gets reasonable accuracy. In particular, the recognition results of the old group are significantly better than that of the young group. All these findings suggest that kinematic skeletal data based depression recognition can be applied as an effective tool for assisting in depression analysis.
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Affiliation(s)
- Wentao Li
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Qingxiang Wang
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China.
| | - Xin Liu
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yanhong Yu
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
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14
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Zhang Y, Wu W, Toll RT, Naparstek S, Maron-Katz A, Watts M, Gordon J, Jeong J, Astolfi L, Shpigel E, Longwell P, Sarhadi K, El-Said D, Li Y, Cooper C, Chin-Fatt C, Arns M, Goodkind MS, Trivedi MH, Marmar CR, Etkin A. Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography. Nat Biomed Eng 2021; 5:309-323. [PMID: 33077939 PMCID: PMC8053667 DOI: 10.1038/s41551-020-00614-8] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 08/24/2020] [Indexed: 12/21/2022]
Abstract
The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.
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Affiliation(s)
- Yu Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Wei Wu
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Russell T Toll
- Department of Psychiatry, Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Sharon Naparstek
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Adi Maron-Katz
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Mallissa Watts
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Joseph Gordon
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Jisoo Jeong
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Laura Astolfi
- Department of Computer, Control and Management Engineering "Antonio Ruberti", University of Rome Sapienza, Rome, Italy
- IRCCF Fondazione Santa Lucia, Rome, Italy
| | - Emmanuel Shpigel
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Parker Longwell
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Kamron Sarhadi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Dawlat El-Said
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Yuanqing Li
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
- Pazhou Lab, Guangzhou, China
| | - Crystal Cooper
- Department of Psychiatry, Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Cherise Chin-Fatt
- Department of Psychiatry, Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Martijn Arns
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands
- neuroCare Group, Munich, Germany
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Location AMC, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | | | - Madhukar H Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
- O'Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Charles R Marmar
- Steven and Alexandra Cohen Veterans Center for Post-traumatic Stress and Traumatic Brain Injury, New York University Langone School of Medicine, New York, NY, USA
- Center for Alcohol Use Disorder and PTSD, New York University Langone School of Medicine, New York, NY, USA
- Department of Psychiatry, New York University Langone School of Medicine, New York, NY, USA
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
- Alto Neuroscience, Inc., Los Altos, CA, USA.
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15
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Hirjak D, Reininghaus U, Braun U, Sack M, Tost H, Meyer-Lindenberg A. [Cross-sectoral therapeutic concepts and innovative technologies: new opportunities for the treatment of patients with mental disorders]. DER NERVENARZT 2021; 93:288-296. [PMID: 33674965 PMCID: PMC8897366 DOI: 10.1007/s00115-021-01086-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/01/2021] [Indexed: 01/04/2023]
Abstract
Mental disorders are widespread and a major public health problem. The risk of developing a mental disorder at some point in life is around 40%. Therefore, mental disorders are among the most common diseases. Despite the introduction of newer psychotropic drugs, disorder-specific psychotherapy and stimulation techniques, many of those affected still show insufficient symptom remission and a chronic course of the disorder. Conceptual and technological progress in recent years has enabled a new, more flexible and personalized form of mental health care. Both the traditional therapeutic concepts and newer decentralized, modularly structured, track units, together with innovative digital technologies, will offer individualized therapeutic options in order to alleviate symptoms and improve quality of life of patients with mental illnesses. The primary goal of closely combining inpatient care concepts with innovative technologies is to provide comprehensive therapy and aftercare concepts for all individual needs of patients with mental disorders. Last but not least, this also ensures that specialist psychiatric treatment is available regardless of location. In twenty-first century psychiatry, modern care structures must be effectively linked to the current dynamics of digital transformation. This narrative review is dedicated to the theoretical and practical aspects of a cross-sectoral treatment system combined with innovative digital technologies in the psychiatric-psychotherapeutic field. The authors aim to illuminate these therapy modalities using the example of the Central Institute of Mental Health in Mannheim.
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Affiliation(s)
- Dusan Hirjak
- Klinik für Psychiatrie und Psychotherapie, Zentralinstitut für Seelische Gesundheit, Medizinische Fakultät Mannheim, Universität Heidelberg, J5, 68159, Mannheim, Deutschland.
| | - Ulrich Reininghaus
- Abteilung Public Mental Health, Zentralinstitut für Seelische Gesundheit, Medizinische Fakultät Mannheim, Universität Heidelberg, Mannheim, Deutschland.,ESRC Centre for Society and Mental Health, King's College London, London, Großbritannien.,Centre for Epidemiology and Public Health, Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, Großbritannien
| | - Urs Braun
- Klinik für Psychiatrie und Psychotherapie, Zentralinstitut für Seelische Gesundheit, Medizinische Fakultät Mannheim, Universität Heidelberg, J5, 68159, Mannheim, Deutschland
| | - Markus Sack
- Abteilung Neuroimaging, Zentralinstitut für Seelische Gesundheit, Medizinische Fakultät Mannheim, Universität Heidelberg, Mannheim, Deutschland
| | - Heike Tost
- Klinik für Psychiatrie und Psychotherapie, Zentralinstitut für Seelische Gesundheit, Medizinische Fakultät Mannheim, Universität Heidelberg, J5, 68159, Mannheim, Deutschland
| | - Andreas Meyer-Lindenberg
- Klinik für Psychiatrie und Psychotherapie, Zentralinstitut für Seelische Gesundheit, Medizinische Fakultät Mannheim, Universität Heidelberg, J5, 68159, Mannheim, Deutschland
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16
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Neuroprogression as an Illness Trajectory in Bipolar Disorder: A Selective Review of the Current Literature. Brain Sci 2021; 11:brainsci11020276. [PMID: 33672401 PMCID: PMC7926350 DOI: 10.3390/brainsci11020276] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 02/01/2021] [Accepted: 02/15/2021] [Indexed: 01/29/2023] Open
Abstract
Bipolar disorder (BD) is a chronic and disabling psychiatric condition that is linked to significant disability and psychosocial impairment. Although current neuropsychological, molecular, and neuroimaging evidence support the existence of neuroprogression and its effects on the course and outcome of this condition, whether and to what extent neuroprogressive changes may impact the illness trajectory is still poorly understood. Thus, this selective review was aimed toward comprehensively and critically investigating the link between BD and neurodegeneration based on the currently available evidence. According to the most relevant findings of the present review, most of the existing neuropsychological, neuroimaging, and molecular evidence demonstrates the existence of neuroprogression, at least in a subgroup of BD patients. These studies mainly focused on the most relevant effects of neuroprogression on the course and outcome of BD. The main implications of this assumption are discussed in light of specific shortcomings/limitations, such as the inability to carry out a meta-analysis, the inclusion of studies with small sample sizes, retrospective study designs, and different longitudinal investigations at various time points.
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17
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Popovic D, Schiltz K, Falkai P, Koutsouleris N. Präzisionspsychiatrie und der Beitrag von Brain Imaging und anderen Biomarkern. FORTSCHRITTE DER NEUROLOGIE-PSYCHIATRIE 2020; 88:778-785. [PMID: 33307561 DOI: 10.1055/a-1300-2162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
'Precision Psychiatry' as the psychiatric variant of 'Precision Medicine' aims to provide high-level diagnosis and treatment based on robust biomarkers and tailored to the individual clinical, neurobiological, and genetic constitution of the patient. The specific peculiarity of psychiatry, in which disease entities are normatively defined based on clinical experience and are also significantly influenced by contemporary history, society and philosophy, has so far made the search for valid and reliable psychobiological connections difficult. Nevertheless, considerable progress has now been made in all areas of psychiatric research, made possible above all by the critical review and renewal of previous concepts of disease and psychopathology, the increased orientation towards neurobiology and genetics, and in particular the use of machine learning methods. Notably, modern machine learning methods make it possible to integrate high-dimensional and multimodal data sets and generate models which provide new psychobiological insights and offer the possibility of individualized, biomarker-driven single-subject prediction of diagnosis, therapy response and prognosis. The aim of the present review is therefore to introduce the concept of 'Precision Psychiatry' to the interested reader, to concisely present modern, machine learning methods required for this, and to clearly present the current state and future of biomarker-based 'precision psychiatry'.
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Affiliation(s)
- David Popovic
- Klinikum der Universität München, Klinik und Poliklinik für Psychiatrie und Psychotherapie.,International Max Planck Research School for Translational Psychiatry
| | - Kolja Schiltz
- Klinikum der Universität München, Klinik und Poliklinik für Psychiatrie und Psychotherapie
| | - Peter Falkai
- Klinikum der Universität München, Klinik und Poliklinik für Psychiatrie und Psychotherapie.,International Max Planck Research School for Translational Psychiatry
| | - Nikolaos Koutsouleris
- Klinikum der Universität München, Klinik und Poliklinik für Psychiatrie und Psychotherapie.,International Max Planck Research School for Translational Psychiatry
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18
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Wang C, Zhao H, Zhang H. Chinese College Students Have Higher Anxiety in New Semester of Online Learning During COVID-19: A Machine Learning Approach. Front Psychol 2020; 11:587413. [PMID: 33343461 PMCID: PMC7744590 DOI: 10.3389/fpsyg.2020.587413] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 11/16/2020] [Indexed: 12/13/2022] Open
Abstract
The COVID-19 pandemic has caused tremendous loss starting from early this year. This article aims to investigate the change of anxiety severity and prevalence among non-graduating undergraduate students in the new semester of online learning during COVID-19 in China and also to evaluate a machine learning model based on the XGBoost model. A total of 1172 non-graduating undergraduate students aged between 18 and 22 from 34 provincial-level administrative units and 260 cities in China were enrolled onto this study and asked to fill in a sociodemographic questionnaire and the Self-Rating Anxiety Scale (SAS) twice, respectively, during February 15 to 17, 2020, before the new semester started, and March 15 to 17, 2020, 1 month after the new semester based on online learning had started. SPSS 22.0 was used to conduct t-test and single factor analysis. XGBoost models were implemented to predict the anxiety level of students 1 month after the start of the new semester. There were 184 (15.7%, Mean = 58.45, SD = 7.81) and 221 (18.86%, Mean = 57.68, SD = 7.58) students who met the cut-off of 50 and were screened as positive for anxiety, respectively, in the two investigations. The mean SAS scores in the second test was significantly higher than those in the first test (P < 0.05). Significant differences were also found among all males, females, and students majoring in arts and sciences between the two studies (P < 0.05). The results also showed students from Hubei province, where most cases of COVID-19 were confirmed, had a higher percentage of participants meeting the cut-off of being anxious. This article applied machine learning to establish XGBoost models to successfully predict the anxiety level and changes of anxiety levels 4 weeks later based on the SAS scores of the students in the first test. It was concluded that, during COVID-19, Chinese non-graduating undergraduate students showed higher anxiety in the new semester based on online learning than before the new semester started. More students from Hubei province had a different level of anxiety than other provinces. Families, universities, and society as a whole should pay attention to the psychological health of non-graduating undergraduate students and take measures accordingly. It also confirmed that the XGBoost model had better prediction accuracy compared to the traditional multiple stepwise regression model on the anxiety status of university students.
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Affiliation(s)
- Chongying Wang
- Department of Social Psychology, Zhou Enlai School of Government, Nankai University, Tianjin, China
| | - Hong Zhao
- Department of General Computer, College of Computer Science, Nankai University, Tianjin, China
| | - Haoran Zhang
- Department of General Computer, College of Computer Science, Nankai University, Tianjin, China
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19
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Huang J, Li Y, Xie H, Yang S, Jiang C, Sun W, Li D, Liao Y, Ba X, Xiao L. Abnormal Intrinsic Brain Activity and Neuroimaging-Based fMRI Classification in Patients With Herpes Zoster and Postherpetic Neuralgia. Front Neurol 2020; 11:532110. [PMID: 33192967 PMCID: PMC7642867 DOI: 10.3389/fneur.2020.532110] [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] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 09/01/2020] [Indexed: 01/20/2023] Open
Abstract
Objective: Neuroimaging studies on neuropathic pain have discovered abnormalities in brain structure and function. However, the brain pattern changes from herpes zoster (HZ) to postherpetic neuralgia (PHN) remain unclear. The present study aimed to compare the brain activity between HZ and PHN patients and explore the potential neural mechanisms underlying cognitive impairment in neuropathic pain patients. Methods: Resting-state functional magnetic resonance imaging (MRI) was carried out among 28 right-handed HZ patients, 24 right-handed PHN patients, and 20 healthy controls (HC), using a 3T MRI system. The amplitude of low-frequency fluctuation (ALFF) was analyzed to detect the brain activity of the patients. Correlations between ALFF and clinical pain scales were assessed in two groups of patients. Differences in brain activity between groups were examined and used in a support vector machine (SVM) algorithm for the subjects' classification. Results: Spontaneous brain activity was reduced in both patient groups. Compared with HC, patients from both groups had decreased ALFF in the precuneus, posterior cingulate cortex, and middle temporal gyrus. Meanwhile, the neural activities of angular gyrus and middle frontal gyrus were lowered in HZ and PHN patients, respectively. Reduced ALFF in these regions was associated with clinical pain scales in PHN patients only. Using SVM algorithm, the decreased brain activity in these regions allowed for the classification of neuropathic pain patients (HZ and PHN) and HC. Moreover, HZ and PHN patients are also roughly classified by the same model. Conclusion: Our study indicated that mean ALFF values in these pain-related regions can be used as a functional MRI-based biomarker for the classification of subjects with different pain conditions. Altered brain activity might contribute to PHN-induced pain.
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Affiliation(s)
- Jiabin Huang
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Yongxin Li
- Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Huijun Xie
- Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Shaomin Yang
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Changyu Jiang
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Wuping Sun
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Disen Li
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Yuliang Liao
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Xiyuan Ba
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Lizu Xiao
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
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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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21
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Neuroanatomic and Functional Neuroimaging Findings. Curr Top Behav Neurosci 2020; 48:173-196. [PMID: 33040316 DOI: 10.1007/7854_2020_174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
The search for brain morphology findings that could explain behavioral disorders has gone through a long path in the history of psychiatry. With the advance of brain imaging technology, studies have been able to identify brain morphology and neural circuits associated with the pathophysiology of mental illnesses, such as bipolar disorders (BD). Promising results have also shown the potential of neuroimaging findings in the identification of outcome predictors and response to treatment among patients with BD. In this chapter, we present brain imaging structural and functional findings associated with BD, as well as their hypothesized relationship with the pathophysiological aspects of that condition and their potential clinical applications.
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Jiang T, Gradus JL, Rosellini AJ. Supervised Machine Learning: A Brief Primer. Behav Ther 2020; 51:675-687. [PMID: 32800297 PMCID: PMC7431677 DOI: 10.1016/j.beth.2020.05.002] [Citation(s) in RCA: 139] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 05/13/2020] [Accepted: 05/13/2020] [Indexed: 12/23/2022]
Abstract
Machine learning is increasingly used in mental health research and has the potential to advance our understanding of how to characterize, predict, and treat mental disorders and associated adverse health outcomes (e.g., suicidal behavior). Machine learning offers new tools to overcome challenges for which traditional statistical methods are not well-suited. This paper provides an overview of machine learning with a specific focus on supervised learning (i.e., methods that are designed to predict or classify an outcome of interest). Several common supervised learning methods are described, along with applied examples from the published literature. We also provide an overview of supervised learning model building, validation, and performance evaluation. Finally, challenges in creating robust and generalizable machine learning algorithms are discussed.
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Affiliation(s)
| | - Jaimie L Gradus
- Boston University School of Public Health; Boston University School of Medicine
| | - Anthony J Rosellini
- Center for Anxiety and Related Disorders, Boston University; Department of Psychological and Brain Sciences, Boston University.
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23
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Liu M, Meng Y, Wei W, Li T. [Relationship between circadian rhythm related brain dysfunction and bipolar disorder]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2020; 40:822-827. [PMID: 32895204 DOI: 10.12122/j.issn.1673-4254.2020.06.08] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
OBJECTIVE To investigate the changes of functional connectivity (FC) in the suprachiasmatic nucleus (SCN) of patients with bipolar disorder and perform a cluster analysis of patients with bipolar disorder based on FC. METHODS The study recruited 138 patients with bipolar disorder (BD) diagnosed according to the 4th edition of Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) and 150 healthy control subjects. All the participants underwent resting-state functional magnetic resonance brain scans. DPARSF software was used to generate the FC diagram of the SCN. Based on the FC data, principal components analysis (PCA) and k-means in scikit-learn 0.20.1 were used for cluster analysis of the patients with bipolar disorder. RESULTS Compared with the healthy controls, the patients showed enhanced functional connections between the SCN and the paraventricular nucleus and between the SCN and the dorsomedial hypothalamus nucleus. Based on these FC values, the optimal cluster of unsupervised k-means machine learning for bipolar disorder was 2, and the Silhouette coefficient was 0.49. CONCLUSIONS Patients with bipolar disorder have changes in the FC of the SCN, and the FC of the rhythm pathway can divide bipolar disorder into two subtypes, suggesting that biological rhythm is one of the potential biomarkers of bipolar disorder.
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Affiliation(s)
- Manli Liu
- Mental Health Center, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yajing Meng
- Mental Health Center, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Wei Wei
- Mental Health Center, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Tao Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu 610041, China
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24
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Claude LA, Houenou J, Duchesnay E, Favre P. Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions. Bipolar Disord 2020; 22:334-355. [PMID: 32108409 DOI: 10.1111/bdi.12895] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
OBJECTIVES The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic and prognostic tools, as well as identifying biologically valid subtypes of BD, research has recently turned towards the use of machine learning (ML) techniques. We assessed both supervised ML and unsupervised ML studies in BD to evaluate their robustness, reproducibility and the potential need for improvement. METHOD We systematically searched for studies using ML algorithms based on MRI data of patients with BD until February 2019. RESULT We identified 47 studies, 45 using supervised ML techniques and 2 including unsupervised ML analyses. Among supervised studies, 43 focused on diagnostic classification. The reported accuracies for classification of BD ranged between (a) 57% and 100%, for BD vs healthy controls; (b) 49.5% and 93.1% for BD vs patients with major depressive disorder; and (c) 50% and 96.2% for BD vs patients with schizophrenia. Reported accuracies for discriminating subjects genetically at risk for BD (either from control or from patients with BD) ranged between 64.3% and 88.93%. CONCLUSIONS Although there are strong methodological limitations in previous studies and an important need for replication in large multicentric samples, the conclusions of our review bring hope of future computer-aided diagnosis of BD and pave the way for other applications, such as treatment response prediction. To reinforce the reliability of future results we provide methodological suggestions for good practice in conducting and reporting MRI-based ML studies in BD.
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Affiliation(s)
- Laurie-Anne Claude
- APHP, Mondor University Hospitals, DMU IMPACT Psychiatry and Addictology, UPEC, Créteil, France.,Neurospin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.,INSERM Unit U955, IMRB, Team 15, "Neurotranslational Psychiatry", Créteil, France.,FondaMental Foundation, Créteil, France
| | - Josselin Houenou
- APHP, Mondor University Hospitals, DMU IMPACT Psychiatry and Addictology, UPEC, Créteil, France.,Neurospin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.,INSERM Unit U955, IMRB, Team 15, "Neurotranslational Psychiatry", Créteil, France.,FondaMental Foundation, Créteil, France
| | | | - Pauline Favre
- Neurospin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.,INSERM Unit U955, IMRB, Team 15, "Neurotranslational Psychiatry", Créteil, France.,FondaMental Foundation, Créteil, France
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25
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Levchenko A, Nurgaliev T, Kanapin A, Samsonova A, Gainetdinov RR. Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders. Heliyon 2020; 6:e03990. [PMID: 32462093 PMCID: PMC7240336 DOI: 10.1016/j.heliyon.2020.e03990] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 10/31/2019] [Accepted: 05/12/2020] [Indexed: 12/13/2022] Open
Abstract
A personalized medicine approach seems to be particularly applicable to psychiatry. Indeed, considering mental illness as deregulation, unique to each patient, of molecular pathways, governing the development and functioning of the brain, seems to be the most justified way to understand and treat disorders of this medical category. In order to extract correct information about the implicated molecular pathways, data can be drawn from sampling phenotypic and genetic biomarkers and then analyzed by a machine learning algorithm. This review describes current difficulties in the field of personalized psychiatry and gives several examples of possibly actionable biomarkers of psychotic and other psychiatric disorders, including several examples of genetic studies relevant to personalized psychiatry. Most of these biomarkers are not yet ready to be introduced in clinical practice. In a next step, a perspective on the path personalized psychiatry may take in the future is given, paying particular attention to machine learning algorithms that can be used with the goal of handling multidimensional datasets.
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Affiliation(s)
- Anastasia Levchenko
- Theodosius Dobzhansky Center for Genome Bioinformatics, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
| | - Timur Nurgaliev
- Institute of Translational Biomedicine, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
| | - Alexander Kanapin
- Theodosius Dobzhansky Center for Genome Bioinformatics, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
| | - Anastasia Samsonova
- Theodosius Dobzhansky Center for Genome Bioinformatics, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
| | - Raul R. Gainetdinov
- Institute of Translational Biomedicine, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
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26
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Kittel-Schneider S, Hahn T, Haenisch F, McNeill R, Reif A, Bahn S. Proteomic Profiling as a Diagnostic Biomarker for Discriminating Between Bipolar and Unipolar Depression. Front Psychiatry 2020; 11:189. [PMID: 32372978 PMCID: PMC7184109 DOI: 10.3389/fpsyt.2020.00189] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 02/26/2020] [Indexed: 12/21/2022] Open
Abstract
INTRODUCTION Affective disorders are a major global burden, with approximately 15% of people worldwide suffering from some form of affective disorder. In patients experiencing their first depressive episode, in most cases it cannot be distinguished whether this is due to bipolar disorder (BD) or major depressive disorder (MDD). Valid fluid biomarkers able to discriminate between the two disorders in a clinical setting are not yet available. MATERIAL AND METHODS Seventy depressed patients suffering from BD (bipolar I and II subtypes) and 42 patients with major MDD were recruited and blood samples were taken for proteomic analyses after 8 h fasting. Proteomic profiles were analyzed using the Multiplex Immunoassay platform from Myriad Rules Based Medicine (Myriad RBM; Austin, Texas, USA). Human DiscoveryMAPTM was used to measure the concentration of various proteins, peptides, and small molecules. A multivariate predictive model was consequently constructed to differentiate between BD and MDD. RESULTS Based on the various proteomic profiles, the algorithm could discriminate depressed BD patients from MDD patients with an accuracy of 67%. DISCUSSION The results of this preliminary study suggest that future discrimination between bipolar and unipolar depression in a single case could be possible, using predictive biomarker models based on blood proteomic profiling.
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Affiliation(s)
- Sarah Kittel-Schneider
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, University Hospital, University of Würzburg, Würzburg, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe-University of Frankfurt, Frankfurt, Germany
| | - Tim Hahn
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, University Hospital, University of Münster, Münster, Germany
| | - Frieder Haenisch
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
| | - Rhiannon McNeill
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, University Hospital, University of Würzburg, Würzburg, Germany
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe-University of Frankfurt, Frankfurt, Germany
| | - Sabine Bahn
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
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27
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Achalia R, Sinha A, Jacob A, Achalia G, Kaginalkar V, Venkatasubramanian G, Rao NP. A proof of concept machine learning analysis using multimodal neuroimaging and neurocognitive measures as predictive biomarker in bipolar disorder. Asian J Psychiatr 2020; 50:101984. [PMID: 32143176 DOI: 10.1016/j.ajp.2020.101984] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 02/18/2020] [Accepted: 02/24/2020] [Indexed: 01/10/2023]
Abstract
BACKGROUND Concomitant use of complementary, multimodal imaging measures and neurocognitive measures is reported to have higher accuracy as a biomarker in Alzheimer's dementia. However, such an approach has not been examined to differentiate healthy individuals from Bipolar disorder. In this study, we examined the utility of support vector machine (SVM) technique to differentiate bipolar disorder patients and healthy using structural, functional and diffusion tensor images of brain and neurocognitive measures. METHODS 30 patients with Bipolar disorder-I and 30 age, sex matched individuals participated in the study. Structural MRI, resting state functional MRI and diffusion tensor images were obtained using a 1.5 T scanner. All participants were administered neuropsychological tests to measure executive functions. SVM, a supervised machine learning technique was applied to differentiate patients and healthy individuals with k-fold cross validation over 10 trials. RESULTS The composite marker consisting of both neuroimaging and neuropsychological measures, had an accuracy of 87.60 %, sensitivity of 82.3 % and specificity of 92.7 %. The performance of composite marker was better compared to that of individual markers on classificatory. CONCLUSIONS We were able to achieve a high accuracy for machine learning technique in distinguishing BD from HV using a combination of multimodal neuroimaging and neurocognitive measures. Findings of this proof of concept study, if replicated in larger samples, could have potential clinical applications.
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Affiliation(s)
| | - Anannya Sinha
- National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Arpitha Jacob
- National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Garimaa Achalia
- Achalia Neuropsychiatry Hospital, Aurangabad, Maharashtra, India
| | | | | | - Naren P Rao
- National Institute of Mental Health and Neurosciences, Bangalore, India.
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28
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Silveira ÉDM, Passos IC, Scott J, Bristot G, Scotton E, Teixeira Mendes LS, Umpierre Knackfuss AC, Gerchmann L, Fijtman A, Trasel AR, Salum GA, Kauer-Sant'Anna M. Decoding rumination: A machine learning approach to a transdiagnostic sample of outpatients with anxiety, mood and psychotic disorders. J Psychiatr Res 2020; 121:207-213. [PMID: 31865210 DOI: 10.1016/j.jpsychires.2019.12.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 11/15/2019] [Accepted: 12/05/2019] [Indexed: 10/25/2022]
Abstract
OBJECTIVE To employ machine learning algorithms to examine patterns of rumination from RDoC perspective and to determine which variables predict high levels of maladaptive rumination across a transdiagnostic sample. METHOD Sample of 200 consecutive, consenting outpatient referrals with clinical diagnoses of schizophrenia, schizoaffective, bipolar, depression, anxiety disorders, obsessive compulsive and post-traumatic stress. Machine learning algorithms used a range of variables including sociodemographics, serum levels of immune markers (IL-6, IL-1β, IL-10, TNF-α and CCL11) and BDNF, psychiatric symptoms and disorders, history of suicide and hospitalizations, functionality, medication use and comorbidities. RESULTS The best model (with recursive feature elimination) included the following variables: socioeconomic status, illness severity, worry, generalized anxiety and depressive symptoms, and current diagnosis of panic disorder. Linear support vector machine learning differentiated individuals with high levels of rumination from those ones with low (AUC = 0.83, sensitivity = 75, specificity = 71). CONCLUSIONS Rumination is known to be associated with poor prognosis in mental health. This study suggests that rumination is a maladaptive coping style associated not only with worry, distress and illness severity, but also with socioeconomic status. Also, rumination demonstrated a specific association with panic disorder.
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Affiliation(s)
- Érico de Moura Silveira
- Laboratory of Molecular Psychiatry, Graduate Program in Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.
| | - Ives Cavalcante Passos
- Laboratory of Molecular Psychiatry, Graduate Program in Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Jan Scott
- Professor at the Academic Psychiatry, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Giovana Bristot
- Graduate Program in Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Ellen Scotton
- Laboratory of Molecular Psychiatry, Graduate Program in Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Lorenna Sena Teixeira Mendes
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Ana Claudia Umpierre Knackfuss
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Luciana Gerchmann
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Adam Fijtman
- Laboratory of Molecular Psychiatry, Graduate Program in Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Andrea Ruschel Trasel
- Laboratory of Molecular Psychiatry, Graduate Program in Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Giovanni Abrahão Salum
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Márcia Kauer-Sant'Anna
- Laboratory of Molecular Psychiatry, Graduate Program in Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
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Vieira S, Lopez Pinaya WH, Mechelli A. Introduction to machine learning. Mach Learn 2020. [DOI: 10.1016/b978-0-12-815739-8.00001-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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30
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A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science. UNSUPERVISED AND SEMI-SUPERVISED LEARNING 2020. [DOI: 10.1007/978-3-030-22475-2_1] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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A computational model of the Cambridge gambling task with applications to substance use disorders. Drug Alcohol Depend 2020; 206:107711. [PMID: 31735532 PMCID: PMC6980771 DOI: 10.1016/j.drugalcdep.2019.107711] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 10/07/2019] [Accepted: 10/08/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND Impulsivity is central to all forms of externalizing psychopathology, including problematic substance use. The Cambridge Gambling task (CGT) is a popular neurocognitive task used to assess impulsivity in both clinical and healthy populations. However, the traditional methods of analysis in the CGT do not fully capture the multiple cognitive mechanisms that give rise to impulsive behavior, which can lead to underpowered and difficult-to-interpret behavioral measures. OBJECTIVES The current study presents the cognitive modeling approach as an alternative to traditional methods and assesses predictive and convergent validity across and between approaches. METHODS We used hierarchical Bayesian modeling to fit a series of cognitive models to data from healthy controls (N = 124) and individuals with histories of substance use disorders (Heroin: N = 79; Amphetamine: N = 76; Polysubstance: N = 103; final total across groups N = 382). Using Bayesian model comparison, we identified the best fitting model, which was then used to identify differences in cognitive model parameters between groups. RESULTS The cognitive modeling approach revealed differences in quality of decision making and impulsivity between controls and individuals with substance use disorders that traditional methods alone did not detect. Crucially, convergent validity between traditional measures and cognitive model parameters was strong across all groups. CONCLUSION The cognitive modeling approach is a viable method of measuring the latent mechanisms that give rise to choice behavior in the CGT, which allows for stronger statistical inferences and a better understanding of impulsive and risk-seeking behavior.
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Laskoski PB, Serralta FB, Passos IC, Hauck S. Machine-learning approaches in psychotherapy: a promising tool for advancing the understanding of the psychotherapeutic process. BRAZILIAN JOURNAL OF PSYCHIATRY 2019; 41:568-569. [PMID: 31826096 PMCID: PMC6899368 DOI: 10.1590/1516-4446-2018-0295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 08/08/2019] [Indexed: 11/28/2022]
Affiliation(s)
- Pricilla B Laskoski
- Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
| | - Fernanda B Serralta
- Programa de Pós-Graduação em Psicologia, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo, RS, Brazil
| | - Ives C Passos
- Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.,Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Simone Hauck
- Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.,Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
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33
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Passos IC, Ballester PL, Barros RC, Librenza-Garcia D, Mwangi B, Birmaher B, Brietzke E, Hajek T, Lopez Jaramillo C, Mansur RB, Alda M, Haarman BCM, Isometsa E, Lam RW, McIntyre RS, Minuzzi L, Kessing LV, Yatham LN, Duffy A, Kapczinski F. Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force. Bipolar Disord 2019; 21:582-594. [PMID: 31465619 DOI: 10.1111/bdi.12828] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVES The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. METHOD A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. RESULTS The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding. CONCLUSION Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.
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Affiliation(s)
- Ives C Passos
- Laboratory of Molecular Psychiatry and Bipolar Disorder Program, Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Pedro L Ballester
- School of Technology, Pontifícia Universidade Católica do Rio Grande do Sul, Rio Grande do Sul, Brazil
| | - Rodrigo C Barros
- School of Technology, Pontifícia Universidade Católica do Rio Grande do Sul, Rio Grande do Sul, Brazil
| | - Diego Librenza-Garcia
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Benson Mwangi
- Department of Psychiatry and Behavioral Sciences, UT Center of Excellence on Mood Disorders, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Boris Birmaher
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Elisa Brietzke
- Department of Psychiatry, Queen's University School of Medicine, Kingston, ON, Canada
| | - Tomas Hajek
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada.,National Institute of Mental Health, Klecany, Czech Republic
| | - Carlos Lopez Jaramillo
- Research Group in Psychiatry, Department of Psychiatry, Faculty of Medicine, University of Antioquia, Medellín, Colombia.,Mood Disorders Program, Hospital Universitario San Vicente Fundación, Medellín, Colombia
| | - Rodrigo B Mansur
- Mood Disorders Psychopharmacology Unit (MDPU), University Health Network, University of Toronto, Toronto, ON, Canada
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Bartholomeus C M Haarman
- Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Erkki Isometsa
- Department of Psychiatry, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Roger S McIntyre
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Luciano Minuzzi
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Lars V Kessing
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Lakshmi N Yatham
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Anne Duffy
- Department of Psychiatry, Queen's University School of Medicine, Kingston, ON, Canada
| | - Flavio Kapczinski
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
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Li Y, Tan Z, Wang Y, Wang Y, Li D, Chen Q, Huang W. Detection of differentiated changes in gray matter in children with progressive hydrocephalus and chronic compensated hydrocephalus using voxel-based morphometry and machine learning. Anat Rec (Hoboken) 2019; 303:2235-2247. [PMID: 31654555 DOI: 10.1002/ar.24306] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 08/31/2019] [Accepted: 09/22/2019] [Indexed: 12/22/2022]
Abstract
Currently, no neuroimaging study has reported the detection of specific imaging biomarkers that distinguish the progressive hydrocephalus (PH) and chronic compensated hydrocephalus (CH). Our main focus is to evaluate the different structural changes in classifying the two types of hydrocephalus children. Twenty-two children with hydrocephalus (12 PHs and 10 CHs) and 30 age-matched healthy controls were enrolled and the T1-weighted imaging was collected in the study. A customized voxel-based morphometry (VBM) approach and support vector machine (SVM) were combined to investigate the structural changes and group classification. Comparing with the controls and CH, PH groups invariably showed a significant decrease of GM volume in the bilateral hippocampus/parahippocampus, insula, and motor-related areas. SVM applied to the GM volumes of bilateral hippocampus/parahippocampus, insula, and motor-related areas correctly identified hydrocephalus children from normal controls with a statistically significant accuracy of 88.46% (p ≤ .001). In addition, SVM applied to GM volumes of the same regions correctly identified PH from CH with a statistically significant accuracy of 77.27% (p ≤ .009). Using VBM analysis, we characterized and visualized the GM changes in children with hydrocephalus. Machine learning results further confirmed that a significant decrease of the bilateral hippocampus/parahippocampus, insula, and motor-related GM volume can serve as a specific neuroimaging index to distinguish the children with PH from the children with CH and controls at individual. The findings could help to aid the identification of individuals with PH in clinical practice.
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Affiliation(s)
- Yongxin Li
- Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Zhen Tan
- Health Management Center, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen, China
| | - Ya Wang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Yanfang Wang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Ding Li
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Qian Chen
- Department of Pediatric Neurosurgery, Shenzhen Children's Hospital, Shenzhen, China
| | - Wenhua Huang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
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Li X, Guo N, Li Q. Functional Neuroimaging in the New Era of Big Data. GENOMICS, PROTEOMICS & BIOINFORMATICS 2019; 17:393-401. [PMID: 31809864 PMCID: PMC6943787 DOI: 10.1016/j.gpb.2018.11.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 09/17/2018] [Accepted: 12/25/2018] [Indexed: 12/15/2022]
Abstract
The field of functional neuroimaging has substantially advanced as a big data science in the past decade, thanks to international collaborative projects and community efforts. Here we conducted a literature review on functional neuroimaging, with focus on three general challenges in big data tasks: data collection and sharing, data infrastructure construction, and data analysis methods. The review covers a wide range of literature types including perspectives, database descriptions, methodology developments, and technical details. We show how each of the challenges was proposed and addressed, and how these solutions formed the three core foundations for the functional neuroimaging as a big data science and helped to build the current data-rich and data-driven community. Furthermore, based on our review of recent literature on the upcoming challenges and opportunities toward future scientific discoveries, we envisioned that the functional neuroimaging community needs to advance from the current foundations to better data integration infrastructure, methodology development toward improved learning capability, and multi-discipline translational research framework for this new era of big data.
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Affiliation(s)
- Xiang Li
- Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Ning Guo
- Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Quanzheng Li
- Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
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36
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Bauer IE, Suchting R, Van Rheenen TE, Wu MJ, Mwangi B, Spiker D, Zunta-Soares GB, Soares JC. The use of component-wise gradient boosting to assess the possible role of cognitive measures as markers of vulnerability to pediatric bipolar disorder. Cogn Neuropsychiatry 2019; 24:93-107. [PMID: 30774035 PMCID: PMC6675623 DOI: 10.1080/13546805.2019.1580190] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 01/27/2019] [Indexed: 01/22/2023]
Abstract
BACKGROUND AND AIMS Cognitive impairments are primary hallmarks symptoms of bipolar disorder (BD). Whether these deficits are markers of vulnerability or symptoms of the disease is still unclear. This study used a component-wise gradient (CGB) machine learning algorithm to identify cognitive measures that could accurately differentiate pediatric BD, unaffected offspring of BD parents, and healthy controls. METHODS 59 healthy controls (HC; 11.19 ± 3.15 yo; 30 girls), 119 children and adolescents with BD (13.31 ± 3.02 yo, 52 girls) and 49 unaffected offspring of BD parents (UO; 9.36 ± 3.18 yo; 22 girls) completed the CANTAB cognitive battery. RESULTS CGB achieved accuracy of 73.2% and an AUROC of 0.785 in classifying individuals as either BD or non-BD on a dataset held out for validation for testing. The strongest cognitive predictors of BD were measures of processing speed and affective processing. Measures of cognition did not differentiate between UO and HC. CONCLUSIONS Alterations in processing speed and affective processing are markers of BD in pediatric populations. Longitudinal studies should determine whether UO with a cognitive profile similar to that of HC are at less or equal risk for mood disorders. Future studies should include relevant measures for BD such as verbal memory and genetic risk scores.
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Affiliation(s)
- Isabelle E. Bauer
- The University of Texas Health Science Center at Houston, Department of Psychiatry and Behavioral Sciences, Houston (Texas), USA
| | - Robert Suchting
- The University of Texas Health Science Center at Houston, Department of Psychiatry and Behavioral Sciences, Houston (Texas), USA
| | - Tamsyn E. Van Rheenen
- Melbourne Neuropsychiatry Centre, Level 3, Alan Gilbert Building, 161 Barry St, Carlton, VIC 3053, Australia
- Brain and Psychological Sciences Research Centre (BPsyC), Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Victoria, Australia
| | - Mon-Ju Wu
- The University of Texas Health Science Center at Houston, Department of Psychiatry and Behavioral Sciences, Houston (Texas), USA
| | - Benson Mwangi
- The University of Texas Health Science Center at Houston, Department of Psychiatry and Behavioral Sciences, Houston (Texas), USA
| | - Danielle Spiker
- The University of Texas Health Science Center at Houston, Department of Psychiatry and Behavioral Sciences, Houston (Texas), USA
| | - Giovana B. Zunta-Soares
- The University of Texas Health Science Center at Houston, Department of Psychiatry and Behavioral Sciences, Houston (Texas), USA
| | - Jair C. Soares
- The University of Texas Health Science Center at Houston, Department of Psychiatry and Behavioral Sciences, Houston (Texas), USA
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Chakrabarty T, Yatham LN. Objective and biological markers in bipolar spectrum presentations. Expert Rev Neurother 2019; 19:195-209. [PMID: 30761925 DOI: 10.1080/14737175.2019.1580145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
INTRODUCTION Subthreshold presentations of bipolarity (BSPs) pose a diagnostic conundrum, in terms of whether they should be conceptualized and treated similarly as traditionally defined bipolar disorders (BD). While it has been argued that BSPs are on a pathophysiologic continuum with traditionally defined BDs, there has been limited examination of biological and objective markers in these presentations to validate this assertion. Areas covered: The authors review studies examining genetic, neurobiological, cognitive and peripheral markers in BSPs, encompassing clinical and non-clinical populations with subthreshold hypo/manic symptoms. Results are placed in the context of previously identified markers in traditionally defined BDs. Expert commentary: There have been few studies of objective and biological markers in subthreshold presentations of BD, and results are mixed. While abnormalities in brain structure/functioning, peripheral inflammatory, and cognitive markers have been reported, it is unclear whether these findings are specific to BD or indicative of broad affective pathology. However, some studies suggest that increased sensitivity to reward and positive stimuli are shared between subthreshold and traditionally defined BDs, and may represent a point of departure from unipolar major depression. Further examination of such markers may improve understanding of subthreshold bipolar presentations, and provide guidance in terms of therapeutic strategies.
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Affiliation(s)
- Trisha Chakrabarty
- a Department of Psychiatry , University of British Columbia , Vancouver , BC , Canada
| | - Lakshmi N Yatham
- a Department of Psychiatry , University of British Columbia , Vancouver , BC , Canada
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From the microscope to the magnet: Disconnection in schizophrenia and bipolar disorder. Neurosci Biobehav Rev 2019; 98:47-57. [PMID: 30629976 DOI: 10.1016/j.neubiorev.2019.01.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 12/22/2018] [Accepted: 01/06/2019] [Indexed: 12/15/2022]
Abstract
White matter (WM) abnormalities have implicated schizophrenia (SZ) and bipolar disorder (BD) as disconnection syndromes, yet the extent to which these abnormalities are shared versus distinct remains unclear. Diffusion tensor imaging (DTI) studies yield a putative measure of WM integrity while neuropathological studies provide more specific microstructural information. We therefore systematically reviewed all neuropathological (n = 12) and DTI (n = 11) studies directly comparing patients with SZ and BD. Most studies (18/23) reported no difference between patient groups. Changes in oligodendrocyte density, myelin staining and gene, protein and mRNA expression were found in SZ and/or BD patients as compared to healthy individuals, while DTI studies showed common alterations in thalamic radiations, uncinate fasciculus, corpus callosum, longitudinal fasciculus and corona radiata. Altogether, findings suggest shared disconnectivity in SZ and BD, which are likely related to their considerable overlap. Above all, neuroimaging findings corroborated neuropathological findings in the prefrontal cortex, demonstrating the utility of integrating multiple methodologies. Focusing on clinical dimensions over disease entities will advance our understanding of disconnectivity and help inform preventive medicine.
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Wang J, Li Y, Wang Y, Huang W. Multimodal Data and Machine Learning for Detecting Specific Biomarkers in Pediatric Epilepsy Patients With Generalized Tonic-Clonic Seizures. Front Neurol 2018; 9:1038. [PMID: 30619025 PMCID: PMC6297879 DOI: 10.3389/fneur.2018.01038] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 11/19/2018] [Indexed: 01/16/2023] Open
Abstract
Previous neuroimaging studies of epilepsy with generalized tonic-clonic seizures (GTCS) focus mainly on adults. However, the neural mechanisms that underline this type of epilepsy remain unclear, especially for children. The aim of the present study was to detect the effect of epilepsy on brains of children with GTCS and to investigate whether the changes in the brain can be used to discriminate between epileptic children and healthy children at the level of the individual. To achieve this purpose, we measured gray matter (GM) volume and fractional amplitude of low-frequency fluctuation (fALFF) differences on multimodel magnetic resonance imaging in 14 children with GTCS and 30 age- and gender-matched healthy controls. The patients showed GM volume reduction and a fALFF increase in the thalamus, hippocampus, temporal and other deep nuclei. A significant decrease of fALFF was mainly found in the default mode network (DMN). In addition, epileptic duration was significantly negatively related to the GM volumes and significantly positively related to the fALFF value of right thalamus. A support vector machine (SVM) applied to the GM volume of the right thalamus correctly identified epileptic children with a statistically significant accuracy of 74.42% (P < 0.002). A SVM applied to the fALFF of the right thalamus correctly identified epileptic children with a statistically significant accuracy of 83.72% (P < 0.002). The consistent neuroimaging results indicated that the right thalamus plays an important role in reflecting the chronic damaging effect of GTCS epilepsy in children. The length of time of a child's epileptic history was correlated with greater GM volume reduction and a fALFF increase in the right thalamus. GM volumes and fALFF values in the right thalamus can identify children with GTCS from the healthy controls with high accuracy and at an individual subject level. These results are likely to be valuable in explaining the clinical problems and understanding the brain abnormalities underlying this disorder.
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Affiliation(s)
- Jianping Wang
- The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yongxin Li
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Ya Wang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenhua Huang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
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Teixeira AL, Colpo GD, Fries GR, Bauer IE, Selvaraj S. Biomarkers for bipolar disorder: current status and challenges ahead. Expert Rev Neurother 2018; 19:67-81. [PMID: 30451546 DOI: 10.1080/14737175.2019.1550361] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
INTRODUCTION Bipolar disorder (BD) is a chronic psychiatric disorder marked by clinical and pathophysiological heterogeneity. There is a high expectation that personalized approaches can improve the management of patients with BD. For that, identification and validation of potential biomarkers are fundamental. Areas covered: This manuscript will critically review the current status of different biomarkers for BD, including peripheral, genetic, neuroimaging, and neurophysiological candidates, discussing the challenges to move the field forward. Expert commentary: There are no lab or complementary tests currently recommended for the diagnosis or management of patients with BD. Panels composed by multiple biomarkers will probably contribute to stratifying patients according to their clinical stage, therapeutic response, and prognosis.
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Affiliation(s)
- Antonio L Teixeira
- a Department of Psychiatry & Behavioral Sciences , McGovern Medical School, UT Health , Houston , TX , USA.,b Laboratório Interdisciplinar de Investigação Médica, Faculdade de Medicina , Universidade Federal de Minas Gerais (UFMG) , Belo Horizonte , Brazil
| | - Gabriela D Colpo
- a Department of Psychiatry & Behavioral Sciences , McGovern Medical School, UT Health , Houston , TX , USA
| | - Gabriel R Fries
- a Department of Psychiatry & Behavioral Sciences , McGovern Medical School, UT Health , Houston , TX , USA
| | - Isabelle E Bauer
- a Department of Psychiatry & Behavioral Sciences , McGovern Medical School, UT Health , Houston , TX , USA
| | - Sudhakar Selvaraj
- a Department of Psychiatry & Behavioral Sciences , McGovern Medical School, UT Health , Houston , TX , USA
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Frank B, Hurley L, Scott TM, Olsen P, Dugan P, Barr WB. Machine learning as a new paradigm for characterizing localization and lateralization of neuropsychological test data in temporal lobe epilepsy. Epilepsy Behav 2018; 86:58-65. [PMID: 30082202 DOI: 10.1016/j.yebeh.2018.07.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 07/05/2018] [Accepted: 07/05/2018] [Indexed: 10/28/2022]
Abstract
In this study, we employed a kernel support vector machine to predict epilepsy localization and lateralization for patients with a diagnosis of epilepsy (n = 228). We assessed the accuracy to which indices of verbal memory, visual memory, verbal fluency, and naming would localize and lateralize seizure focus in comparison to standard electroencephalogram (EEG). Classification accuracy was defined as models that produced the least cross-validated error (CVϵ). In addition, we assessed whether the inclusion of norm-based standard scores, demographics, and emotional functioning data would reduce CVϵ. Finally, we obtained class probabilities (i.e., the probability of a particular classification for each case) and produced receiver operating characteristic (ROC) curves for the primary analyses. We obtained the least error assessing localization data with the Gaussian radial basis kernel function (RBF; support vectors = 157, CVϵ = 0.22). There was no overlap between the localization and lateralization models, such that the poorest localization model (the hyperbolic tangent kernel function; support vectors = 91, CVϵ = 0.36) outperformed the strongest lateralization model (RBF; support vectors = 201, CVϵ = 0.39). Contrary to our hypothesis, the addition of norm, demographics, and emotional functioning data did not improve the accuracy of the models. Receiver operating characteristic curves suggested clinical utility in classifying epilepsy lateralization and localization using neuropsychological indicators, albeit with better discrimination for localizing determinations. This study adds to the existing literature by employing an analytic technique with inherent advantages in generalizability when compared to traditional single-sample, not cross-validated models. In the future, class probabilities extracted from these and similar analyses could supplement neuropsychological practice by offering a quantitative guide to clinical judgements.
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Affiliation(s)
- Brandon Frank
- Department of Psychology, Fordham University, 441 East Fordham Road, Bronx, NY 10458, United States of America
| | - Landon Hurley
- Department of Psychology, Fordham University, 441 East Fordham Road, Bronx, NY 10458, United States of America
| | - Travis M Scott
- Department of Psychology, Fordham University, 441 East Fordham Road, Bronx, NY 10458, United States of America
| | - Pat Olsen
- Department of Psychology, Fordham University, 441 East Fordham Road, Bronx, NY 10458, United States of America
| | - Patricia Dugan
- Department of Neurology, NYU School of Medicine, New York, NY 10016, United States of America
| | - William B Barr
- Department of Neurology, NYU School of Medicine, New York, NY 10016, United States of America.
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Sun Z, Qiao Y, Lelieveldt BPF, Staring M. Integrating spatial-anatomical regularization and structure sparsity into SVM: Improving interpretation of Alzheimer's disease classification. Neuroimage 2018; 178:445-460. [DOI: 10.1016/j.neuroimage.2018.05.051] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Revised: 04/10/2018] [Accepted: 05/21/2018] [Indexed: 12/21/2022] Open
Affiliation(s)
- Zhuo Sun
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, 2300, RC, Leiden, The Netherlands
| | - Yuchuan Qiao
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, 2300, RC, Leiden, The Netherlands
| | - Boudewijn P F Lelieveldt
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, 2300, RC, Leiden, The Netherlands; Intelligent System Group, Faculty of EEMCS, Delft University of Technology, 2600, GA, Delft, The Netherlands
| | - Marius Staring
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, 2300, RC, Leiden, The Netherlands; Intelligent System Group, Faculty of EEMCS, Delft University of Technology, 2600, GA, Delft, The Netherlands.
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Scott J, Etain B, Bellivier F. Can an Integrated Science Approach to Precision Medicine Research Improve Lithium Treatment in Bipolar Disorders? Front Psychiatry 2018; 9:360. [PMID: 30186186 PMCID: PMC6110814 DOI: 10.3389/fpsyt.2018.00360] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 07/19/2018] [Indexed: 12/20/2022] Open
Abstract
Clinical practice guidelines identify lithium as a first line treatment for mood stabilization and reduction of suicidality in bipolar disorders (BD); however, most individuals show sub-optimal response. Identifying biomarkers for lithium response could enable personalization of treatment and refine criteria for stratification of BD cases into treatment-relevant subgroups. Existing systematic reviews identify potential biomarkers of lithium response, but none directly address the conceptual issues that need to be addressed to enhance translation of research into precision prescribing of lithium. For example, although clinical syndrome subtyping of BD has not led to customized individual treatments, we emphasize the importance of assessing clinical response phenotypes in biomarker research. Also, we highlight the need to give greater consideration to the quality of prospective longitudinal monitoring of illness activity and the differentiation of non-response from partial or non-adherence with medication. It is unlikely that there is a single biomarker for lithium response or tolerability, so this review argues that more research should be directed toward the exploration of biosignatures. Importantly, we emphasize that an integrative science approach may improve the likelihood of discovering the optimal combination of clinical factors and multimodal biomarkers (e.g., blood omics, neuroimaging, and actigraphy derived-markers). This strategy could uncover a valid lithium response phenotype and facilitate development of a composite prediction algorithm. Lastly, this narrative review discusses how these strategies could improve eligibility criteria for lithium treatment in BD, and highlights barriers to translation to clinical practice including the often-overlooked issue of the cost-effectiveness of introducing biomarker tests in psychiatry.
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Affiliation(s)
- Jan Scott
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
- Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Faculté de Médecine, Université Paris Diderot, Paris, France
| | - Bruno Etain
- Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Faculté de Médecine, Université Paris Diderot, Paris, France
- AP-HP, Groupe Hospitalier Saint-Louis-Lariboisière-Fernand Widal, Paris, France
- INSERM, Unité UMR-S 1144, Variabilité de Réponse aux Psychotropes, Université Paris Descartes-Paris Diderot, Paris, France
- AP-HP, Groupe Henri Mondor-Albert Chenevier, Pôle de Psychiatrie, Créteil, France
- INSERM, Unité 955, IMRB, Equipe de Psychiatrie Translationnelle, Créteil, France
| | - Frank Bellivier
- Faculté de Médecine, Université Paris Diderot, Paris, France
- AP-HP, Groupe Hospitalier Saint-Louis-Lariboisière-Fernand Widal, Paris, France
- INSERM, Unité UMR-S 1144, Variabilité de Réponse aux Psychotropes, Université Paris Descartes-Paris Diderot, Paris, France
- AP-HP, Groupe Henri Mondor-Albert Chenevier, Pôle de Psychiatrie, Créteil, France
- INSERM, Unité 955, IMRB, Equipe de Psychiatrie Translationnelle, Créteil, France
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Muneer A, Mazommil R. The Staging of Major Mood Disorders: Clinical and Neurobiological Correlates. Psychiatry Investig 2018; 15:747-758. [PMID: 30134644 PMCID: PMC6111216 DOI: 10.30773/pi.2018.05.26] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2018] [Revised: 04/12/2018] [Accepted: 05/26/2018] [Indexed: 01/27/2023] Open
Abstract
OBJECTIVE Staging of psychiatric disorders is gaining momentum and the purpose of this review is to examine whether major mood disorders can be defined according to stages. METHODS In April 2018 the PubMed electronic data base was scrutinized by a combination of various search terms like "major depressive disorder and staging," "bipolar disorder and neuroprogression," etc. To incorporate the latest findings the search was limited to the last 10 years. Both original and review articles were examined by reading the abstracts, and papers which were found to be particularly applicable were read in full and their reference lists were also consulted. RESULTS A significant increase occurred in the number of papers published on the topic of staging of mood disorders. Staging formats were found for both major mood disorders, with the caveat that many more articles were discovered for bipolar disorder. Current evidence points to allostatic load and neuroprogression as the basis for staging of mood disorders. CONCLUSION Principal affective illnesses may be characterized by distinct stages, for instance early, intermediate and late. These phases inform the management so that clinicians should incorporate the staging schema into everyday practice and implement treatment strategies according to the phase of the illness.
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Affiliation(s)
- Ather Muneer
- Islamic International Medical College, Riphah International University, Islamabad, Pakistan
| | - Rana Mazommil
- Department of Psychiatry, Government Khawaja Safdar Medical College, Sialkot, Pakistan
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Dwyer DB, Cabral C, Kambeitz-Ilankovic L, Sanfelici R, Kambeitz J, Calhoun V, Falkai P, Pantelis C, Meisenzahl E, Koutsouleris N. Brain Subtyping Enhances The Neuroanatomical Discrimination of Schizophrenia. Schizophr Bull 2018; 44. [PMID: 29529270 PMCID: PMC6101481 DOI: 10.1093/schbul/sby008] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Identifying distinctive subtypes of schizophrenia could ultimately enhance diagnostic and prognostic accuracy. We aimed to uncover neuroanatomical subtypes of chronic schizophrenia patients to test whether stratification can enhance computer-aided discrimination of patients from control subjects. Unsupervised, data-driven clustering of structural MRI (sMRI) data was used to identify 2 subtypes of schizophrenia patients drawn from a US-based open science repository (n = 71) and we quantified classification improvements compared to controls (n = 74) using supervised machine learning. We externally validated the unsupervised and supervised learning models in a heterogeneous German validation sample (n = 316), and characterized symptom, cognition, and longitudinal symptom change signatures. Stratification improved classification accuracies from 68.5% to 73% (subgroup 1) and 78.8% (subgroup 2), respectively. Increased accuracy was also found when models were externally validated, and an average gain of 9% was found in supplementary analyses. The first subgroup was associated with cortical and subcortical volume reductions coupled with substantially longer illness duration, whereas the second subgroup was mainly characterized by cortical reductions, reduced illness duration, and comparatively less negative symptoms. Individuals within each subgroup could be identified using just 10 clinical questions at an accuracy of 81.2%, and differential cognitive and symptom course signatures were suggested in multivariate analyses. Our findings suggest that sMRI-based subtyping enhances the neuroanatomical discrimination of schizophrenia by identifying generalizable brain patterns that align with a clinical staging model of the disorder. These findings could be used to improve illness stratification for biomarker-based computer-aided diagnoses.
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Affiliation(s)
- Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany,To whom correspondence should be addressed; Section for Neurodiagnostic Applications, Department of Psychiatry, Ludwig Maximilian University, Nussbaumstrasse 7, 80336, Munich, Bavaria, Germany; tel: +49 (0)89 4400 55757, fax: +49 (0)89 4400 55776, e-mail:
| | - Carlos Cabral
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | | | - Rachele Sanfelici
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Vince Calhoun
- Mind Research Network and Lovelace Biomedical and Environmental Research Network, Albuquerque, NM,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia,Centre for Neural Engineering, Department of Electrical and Electronic Engineering, University of Melbourne, Carlton South, VIC, Australia
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
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Mikolas P, Hlinka J, Skoch A, Pitra Z, Frodl T, Spaniel F, Hajek T. Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy. BMC Psychiatry 2018; 18:97. [PMID: 29636016 PMCID: PMC5891928 DOI: 10.1186/s12888-018-1678-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Accepted: 03/27/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Early diagnosis of schizophrenia could improve the outcome of the illness. Unlike classical between-group comparisons, machine learning can identify subtle disease patterns on a single subject level, which could help realize the potential of MRI in establishing a psychiatric diagnosis. Machine learning has previously been predominantly tested on gray-matter structural or functional MRI data. In this paper we used a machine learning classifier to differentiate patients with a first episode of schizophrenia-spectrum disorder (FES) from healthy controls using diffusion tensor imaging. METHODS We applied linear support-vector machine (SVM) and traditional tract based spatial statistics between group analyses to brain fractional anisotropy (FA) data from 77 FES and 77 age and sex matched healthy controls. We also evaluated the effects of medication and symptoms on the SVM classification. RESULTS The SVM distinguished between patients and controls with significant accuracy of 62.34% (p = 0.005). Participants with FES showed widespread FA reductions relative to controls in a large cluster (N = 56,647 voxels, corrected p = 0.002). The white matter regions, which contributed to the correct identification of participants with FES, overlapped with the regions, which showed lower FA in patients relative to controls. There was no association between the classification performance and medication or symptoms. CONCLUSIONS Our results provide a proof of concept that SVM might help differentiate FES patients early in the course of illness from healthy controls using white-matter fractional anisotropy. As there was no effect of medications or symptoms, the SVM classification seemed to be based on trait rather than state markers and appeared to capture the lower FA in FES participants relative to controls.
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Affiliation(s)
- Pavol Mikolas
- 0000 0001 1018 4307grid.5807.aDepartment of Psychiatry and Psychotherapy, Otto von Guericke University, Leipziger Str. 44, 39120 Magdeburg, Germany ,0000 0004 1937 116Xgrid.4491.83rd Faculty of Medicine, Charles University, Ruska 87, 100 00 Prague, Czech Republic ,grid.447902.cNational Institute of Mental Health, Topolova 748, 250 67 Klecany, Czech Republic
| | - Jaroslav Hlinka
- grid.447902.cNational Institute of Mental Health, Topolova 748, 250 67 Klecany, Czech Republic ,0000 0004 0369 3922grid.448092.3Institute of Computer Science of the Czech Academy of Sciences, Pod Vodarenskou vezi 271/2, 182 07 Prague, Czech Republic
| | - Antonin Skoch
- grid.447902.cNational Institute of Mental Health, Topolova 748, 250 67 Klecany, Czech Republic ,0000 0001 2299 1368grid.418930.7MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Videnska 1958/9, 140 21 Prague, Czech Republic
| | - Zbynek Pitra
- grid.447902.cNational Institute of Mental Health, Topolova 748, 250 67 Klecany, Czech Republic ,0000 0004 0369 3922grid.448092.3Institute of Computer Science of the Czech Academy of Sciences, Pod Vodarenskou vezi 271/2, 182 07 Prague, Czech Republic ,0000000121738213grid.6652.7Faculty of Nuclear Sciences and Physical Engineering Czech Technical University in Prague, Prague, Brehova 78/7, 110 00 Praha, Czech Republic
| | - Thomas Frodl
- 0000 0001 1018 4307grid.5807.aDepartment of Psychiatry and Psychotherapy, Otto von Guericke University, Leipziger Str. 44, 39120 Magdeburg, Germany
| | - Filip Spaniel
- 0000 0004 1937 116Xgrid.4491.83rd Faculty of Medicine, Charles University, Ruska 87, 100 00 Prague, Czech Republic ,grid.447902.cNational Institute of Mental Health, Topolova 748, 250 67 Klecany, Czech Republic
| | - Tomas Hajek
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic. .,Department of Psychiatry, Dalhousie University, QEII HSC, A.J.Lane Bldg., Room 3093, 5909 Veteran's Memorial Lane, Halifax, NS, B3H 2E2, Canada.
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Cabeen RP, Laidlaw DH, Ruggieri A, Dickstein DP. Preliminary mapping of the structural effects of age in pediatric bipolar disorder with multimodal MR imaging. Psychiatry Res 2018; 273:54-62. [PMID: 29361347 PMCID: PMC5815932 DOI: 10.1016/j.pscychresns.2017.12.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 12/31/2017] [Accepted: 12/31/2017] [Indexed: 10/18/2022]
Abstract
This study investigates multimodal structural MR imaging biomarkers of development trajectories in pediatric bipolar disorder. T1-weighted and diffusion-weighted MR imaging was conducted to investigate cross-sectional group differences with age between typically developing controls (N = 26) and youths diagnosed with bipolar disorder (N = 26). Region-based analysis was used to examine cortical thickness of gray matter and diffusion tensor parameters in superficial white matter, and tractography-based analysis was used to examine deep white matter fiber bundles. Patients and controls showed significantly different maturation trajectories across brain areas; however, the magnitude of differences varied by region. The rate of cortical thinning with age was greater in patients than controls in the left frontal pole. While controls showed increasing fractional anisotropy (FA) and axial diffusivity (AD) with age, patients showed an opposite trend of decreasing FA and AD with age in fronto-temporal-striatal regions located in both superficial and deep white matter. The findings support fronto-temporal-striatal alterations in the developmental trajectories of youths diagnosed with bipolar disorder, and further, show the value of multimodal computational techniques in the assessment of neuropsychiatric disorders. These preliminary results warrant further investigation into longitudinal changes and the effects of treatment in the brain areas identified in this study.
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Affiliation(s)
- Ryan P Cabeen
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
| | - David H Laidlaw
- Department of Computer Science, Brown University, Providence, RI, USA
| | - Amanda Ruggieri
- Pediatric Mood, Imaging & NeuroDevelopment Program, Bradley Hospital, Alpert Medical School of Brown University, Providence, RI, USA
| | - Daniel P Dickstein
- Pediatric Mood, Imaging & NeuroDevelopment Program, Bradley Hospital, Alpert Medical School of Brown University, Providence, RI, USA
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Bourla A, Mouchabac S, El Hage W, Ferreri F. e-PTSD: an overview on how new technologies can improve prediction and assessment of Posttraumatic Stress Disorder (PTSD). Eur J Psychotraumatol 2018; 9:1424448. [PMID: 29441154 PMCID: PMC5804808 DOI: 10.1080/20008198.2018.1424448] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 12/18/2017] [Indexed: 02/01/2023] Open
Abstract
Background: New technologies may profoundly change our way of understanding psychiatric disorders including posttraumatic stress disorder (PTSD). Imaging and biomarkers, along with technological and medical informatics developments, might provide an answer regarding at-risk patient's identification. Recent advances in the concept of 'digital phenotype', which refers to the capture of characteristics of a psychiatric disorder by computerized measurement tools, is one paradigmatic example. Objective: The impact of the new technologies on health professionals practice in PTSD care remains to be determined. The recent evolutions could disrupt the clinical practices and practitioners in their beliefs, ethics and representations, going as far as questioning their professional culture. In the present paper, we conducted an extensive search to highlight the articles which reflect the potential of these new technologies. Method: We conducted an overview by querying PubMed database with the terms [PTSD] [Posttraumatic stress disorder] AND [Computer] OR [Computerized] OR [Mobile] OR [Automatic] OR [Automated] OR [Machine learning] OR [Sensor] OR [Heart rate variability] OR [HRV] OR [actigraphy] OR [actimetry] OR [digital] OR [motion] OR [temperature] OR [virtual reality]. Results: We summarized the synthesized literature in two categories: prediction and assessment (including diagnostic, screening and monitoring). Two independent reviewers screened, extracted data and quality appraised the sources. Results were synthesized narratively. Conclusions: This overview shows that many studies are underway allowing researchers to start building a PTSD digital phenotype using passive data obtained by biometric sensors. Active data obtained from Ecological Momentary Assessment (EMA) could allow clinicians to assess PTSD patients. The place of connected objects, Artificial Intelligence and remote monitoring of patients with psychiatric pathology remains to be defined. These tools must be explained and adapted to the different profiles of physicians and patients. The involvement of patients, caregivers and health professionals is essential to the design and evaluation of these new tools.
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Affiliation(s)
- Alexis Bourla
- Department of Psychiatry, Sorbonne Université, AP-HP, Hôpital Saint-Antoine, Service de Psychiatrie, Paris, France
| | - Stephane Mouchabac
- Department of Psychiatry, Sorbonne Université, AP-HP, Hôpital Saint-Antoine, Service de Psychiatrie, Paris, France
| | - Wissam El Hage
- Clinique Psychiatrique Universitaire, CHRU de Tours, Université François-Rabelais de Tours, Tours, France
| | - Florian Ferreri
- Department of Psychiatry, Sorbonne Université, AP-HP, Hôpital Saint-Antoine, Service de Psychiatrie, Paris, France
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Rubin-Falcone H, Zanderigo F, Thapa-Chhetry B, Lan M, Miller JM, Sublette ME, Oquendo MA, Hellerstein DJ, McGrath PJ, Stewart JW, Mann JJ. Pattern recognition of magnetic resonance imaging-based gray matter volume measurements classifies bipolar disorder and major depressive disorder. J Affect Disord 2018; 227:498-505. [PMID: 29156364 PMCID: PMC5805651 DOI: 10.1016/j.jad.2017.11.043] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 11/06/2017] [Accepted: 11/11/2017] [Indexed: 01/10/2023]
Abstract
BACKGROUND Bipolar Disorder (BD) cannot be reliably distinguished from Major Depressive Disorder (MDD) until the first manic or hypomanic episode. Consequently, many patients with BD are treated with antidepressants without mood stabilizers, a strategy that is often ineffective and carries a risk of inducing a manic episode. We previously reported reduced cortical thickness in right precuneus, right caudal middle-frontal cortex and left inferior parietal cortex in BD compared with MDD. METHODS This study extends our previous work by performing individual level classification of BD or MDD in an expanded, currently unmedicated, cohort using gray matter volume (GMV) based on Magnetic Resonance Imaging and a Support Vector Machine. All patients were in a Major Depressive Episode and a leave-two-out analysis was performed. RESULTS Nineteen out of 26 BD subjects and 20 out of 26 MDD subjects were correctly identified, for a combined accuracy of 75%. The three brain regions contributing to the classification were higher GMV in bilateral supramarginal gyrus and occipital cortex indicating MDD, and higher GMV in right dorsolateral prefrontal cortex indicating BD. LIMITATIONS This analysis included scans performed with two different headcoils and scan sequences, which limited the interpretability of results in an independent cohort analysis. CONCLUSIONS Our results add to previously published data which suggest that regional gray matter volume should be investigated further as a clinical diagnostic tool to predict BD before the appearance of a manic or hypomanic episode.
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Affiliation(s)
- Harry Rubin-Falcone
- Department of Psychiatry, Columbia University, New York, NY, USA; Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, NY, USA.
| | - Francesca Zanderigo
- Department of Psychiatry, Columbia University, New York, NY, USA; Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, NY, USA
| | - Binod Thapa-Chhetry
- Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, NY, USA
| | - Martin Lan
- Department of Psychiatry, Columbia University, New York, NY, USA; Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, NY, USA
| | - Jeffrey M Miller
- Department of Psychiatry, Columbia University, New York, NY, USA; Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, NY, USA
| | - M Elizabeth Sublette
- Department of Psychiatry, Columbia University, New York, NY, USA; Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, NY, USA
| | - Maria A Oquendo
- Now at Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA
| | - David J Hellerstein
- Department of Psychiatry, Columbia University, New York, NY, USA; Depression Evaluation Service, Division of Clinical Therapeutics, New York State Psychiatric Institute, New York, NY, USA
| | - Patrick J McGrath
- Department of Psychiatry, Columbia University, New York, NY, USA; Depression Evaluation Service, Division of Clinical Therapeutics, New York State Psychiatric Institute, New York, NY, USA
| | - Johnathan W Stewart
- Department of Psychiatry, Columbia University, New York, NY, USA; Depression Evaluation Service, Division of Clinical Therapeutics, New York State Psychiatric Institute, New York, NY, USA
| | - J John Mann
- Department of Psychiatry, Columbia University, New York, NY, USA; Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, NY, USA; Department of Radiology, Columbia University, New York, NY, USA
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50
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Dwyer DB, Falkai P, Koutsouleris N. Machine Learning Approaches for Clinical Psychology and Psychiatry. Annu Rev Clin Psychol 2018; 14:91-118. [PMID: 29401044 DOI: 10.1146/annurev-clinpsy-032816-045037] [Citation(s) in RCA: 391] [Impact Index Per Article: 65.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide an accessible understanding of why this approach is important for future practice given its potential to augment decisions associated with the diagnosis, prognosis, and treatment of people suffering from mental illness using clinical and biological data. To this end, the limitations of current statistical paradigms in mental health research are critiqued, and an introduction is provided to critical machine learning methods used in clinical studies. A selective literature review is then presented aiming to reinforce the usefulness of machine learning methods and provide evidence of their potential. In the context of promising initial results, the current limitations of machine learning approaches are addressed, and considerations for future clinical translation are outlined.
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Affiliation(s)
- Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich 80638, Germany; , ,
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich 80638, Germany; , ,
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich 80638, Germany; , ,
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