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Yu T, Pei WZ, Xu CY, Deng CC, Zhang XL. Identification of male schizophrenia patients using brain morphology based on machine learning algorithms. World J Psychiatry 2024; 14:804-811. [PMID: 38984327 PMCID: PMC11230103 DOI: 10.5498/wjp.v14.i6.804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 05/01/2024] [Accepted: 05/21/2024] [Indexed: 06/19/2024] Open
Abstract
BACKGROUND Schizophrenia is a severe psychiatric disease, and its prevalence is higher. However, diagnosis of early-stage schizophrenia is still considered a challenging task. AIM To employ brain morphological features and machine learning method to differentiate male individuals with schizophrenia from healthy controls. METHODS The least absolute shrinkage and selection operator and t tests were applied to select important features from structural magnetic resonance images as input features for classification. Four commonly used machine learning algorithms, the general linear model, random forest (RF), k-nearest neighbors, and support vector machine algorithms, were used to develop the classification models. The performance of the classification models was evaluated according to the area under the receiver operating characteristic curve (AUC). RESULTS A total of 8 important features with significant differences between groups were considered as input features for the establishment of classification models based on the four machine learning algorithms. Compared to other machine learning algorithms, RF yielded better performance in the discrimination of male schizophrenic individuals from healthy controls, with an AUC of 0.886. CONCLUSION Our research suggests that brain morphological features can be used to improve the early diagnosis of schizophrenia in male patients.
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Affiliation(s)
- Tao Yu
- Department of Clinical Nutrition, Hefei Fourth People’s Hospital, Hefei 230032, Anhui Province, China
| | - Wen-Zhi Pei
- Department of Psychiatry, Hefei Fourth People’s Hospital, Hefei 230032, Anhui Province, China
| | - Chun-Yuan Xu
- Department of Clinical Nutrition, Hefei Fourth People’s Hospital, Hefei 230032, Anhui Province, China
| | - Chen-Chen Deng
- Department of Gynaecology, Anhui Maternal and Child Health Hospital, Hefei 230032, Anhui Province, China
| | - Xu-Lai Zhang
- Department of Psychiatry, Hefei Fourth People’s Hospital, Hefei 230032, Anhui Province, China
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2
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Chen Y, Zhao W, Yi S, Liu J. The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis. Front Neurosci 2023; 17:1174080. [PMID: 37811326 PMCID: PMC10559726 DOI: 10.3389/fnins.2023.1174080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 08/11/2023] [Indexed: 10/10/2023] Open
Abstract
Objective Machine learning (ML) has been widely used to detect and evaluate major depressive disorder (MDD) using neuroimaging data, i.e., resting-state functional magnetic resonance imaging (rs-fMRI). However, the diagnostic efficiency is unknown. The aim of the study is to conduct an updated meta-analysis to evaluate the diagnostic performance of ML based on rs-fMRI data for MDD. Methods English databases were searched for relevant studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the methodological quality of the included studies. A random-effects meta-analytic model was implemented to investigate the diagnostic efficiency, including sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Regression meta-analysis and subgroup analysis were performed to investigate the cause of heterogeneity. Results Thirty-one studies were included in this meta-analysis. The pooled sensitivity, specificity, DOR, and AUC with 95% confidence intervals were 0.80 (0.75, 0.83), 0.83 (0.74, 0.82), 14.00 (9, 22.00), and 0.86 (0.83, 0.89), respectively. Substantial heterogeneity was observed among the studies included. The meta-regression showed that the leave-one-out cross-validation (loocv) (sensitivity: p < 0.01, specificity: p < 0.001), graph theory (sensitivity: p < 0.05, specificity: p < 0.01), n > 100 (sensitivity: p < 0.001, specificity: p < 0.001), simens equipment (sensitivity: p < 0.01, specificity: p < 0.001), 3.0T field strength (Sensitivity: p < 0.001, specificity: p = 0.04), and Beck Depression Inventory (BDI) (sensitivity: p = 0.04, specificity: p = 0.06) might be the sources of heterogeneity. Furthermore, the subgroup analysis showed that the sample size (n > 100: sensitivity: 0.71, specificity: 0.72, n < 100: sensitivity: 0.81, specificity: 0.79), the different levels of disease evaluated by the Hamilton Depression Rating Scale (HDRS/HAMD) (mild vs. moderate vs. severe: sensitivity: 0.52 vs. 0.86 vs. 0.89, specificity: 0.62 vs. 0.78 vs. 0.82, respectively), the depression scales in patients with comparable levels of severity. (BDI vs. HDRS/HAMD: sensitivity: 0.86 vs. 0.87, specificity: 0.78 vs. 0.80, respectively), and the features (graph vs. functional connectivity: sensitivity: 0.84 vs. 0.86, specificity: 0.76 vs. 0.78, respectively) selected might be the causes of heterogeneity. Conclusion ML showed high accuracy for the automatic diagnosis of MDD. Future studies are warranted to promote the potential use of these classification algorithms in clinical settings.
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Affiliation(s)
- Yanjing Chen
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wei Zhao
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan, China
| | - Sijie Yi
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jun Liu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan, China
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3
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Koen JD, Lewis L, Rugg MD, Clementz BA, Keshavan MS, Pearlson GD, Sweeney JA, Tamminga CA, Ivleva EI. Supervised machine learning classification of psychosis biotypes based on brain structure: findings from the Bipolar-Schizophrenia network for intermediate phenotypes (B-SNIP). Sci Rep 2023; 13:12980. [PMID: 37563219 PMCID: PMC10415369 DOI: 10.1038/s41598-023-38101-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 07/03/2023] [Indexed: 08/12/2023] Open
Abstract
Traditional diagnostic formulations of psychotic disorders have low correspondence with underlying disease neurobiology. This has led to a growing interest in using brain-based biomarkers to capture biologically-informed psychosis constructs. Building upon our prior work on the B-SNIP Psychosis Biotypes, we aimed to examine whether structural MRI (an independent biomarker not used in the Biotype development) can effectively classify the Biotypes. Whole brain voxel-wise grey matter density (GMD) maps from T1-weighted images were used to train and test (using repeated randomized train/test splits) binary L2-penalized logistic regression models to discriminate psychosis cases (n = 557) from healthy controls (CON, n = 251). A total of six models were evaluated across two psychosis categorization schemes: (i) three Biotypes (B1, B2, B3) and (ii) three DSM diagnoses (schizophrenia (SZ), schizoaffective (SAD) and bipolar (BD) disorders). Above-chance classification accuracies were observed in all Biotype (B1 = 0.70, B2 = 0.65, and B3 = 0.56) and diagnosis (SZ = 0.64, SAD = 0.64, and BD = 0.59) models. However, the only model that showed evidence of specificity was B1, i.e., the model was able to discriminate B1 vs. CON and did not misclassify other psychosis cases (B2 or B3) as B1 at rates above nominal chance. The GMD-based classifier evidence for B1 showed a negative association with an estimate of premorbid general intellectual ability, regardless of group membership, i.e. psychosis or CON. Our findings indicate that, complimentary to clinical diagnoses, the B-SNIP Psychosis Biotypes may offer a promising approach to capture specific aspects of psychosis neurobiology.
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Affiliation(s)
- Joshua D Koen
- Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA.
- Department of Psychology, University of Notre Dame, Notre Dame, IN, 46556, USA.
| | - Leslie Lewis
- Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA
| | - Michael D Rugg
- Center for Vital Longevity, University of Texas at Dallas, Dallas, TX, USA
- UT Southwestern Medical Center, Dallas, TX, USA
- University of East Anglia, Norwich, UK
| | | | | | - Godfrey D Pearlson
- Institute of Living, Hartford Hospital, Hartford, CT, USA
- Yale School of Medicine, New Haven, CT, USA
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4
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Porter A, Fei S, Damme KSF, Nusslock R, Gratton C, Mittal VA. A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis. Mol Psychiatry 2023; 28:3278-3292. [PMID: 37563277 PMCID: PMC10618094 DOI: 10.1038/s41380-023-02195-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND Psychotic disorders are characterized by structural and functional abnormalities in brain networks. Neuroimaging techniques map and characterize such abnormalities using unique features (e.g., structural integrity, coactivation). However, it is unclear if a specific method, or a combination of modalities, is particularly effective in identifying differences in brain networks of someone with a psychotic disorder. METHODS A systematic meta-analysis evaluated machine learning classification of schizophrenia spectrum disorders in comparison to healthy control participants using various neuroimaging modalities (i.e., T1-weighted imaging (T1), diffusion tensor imaging (DTI), resting state functional connectivity (rs-FC), or some combination (multimodal)). Criteria for manuscript inclusion included whole-brain analyses and cross-validation to provide a complete picture regarding the predictive ability of large-scale brain systems in psychosis. For this meta-analysis, we searched Ovid MEDLINE, PubMed, PsychInfo, Google Scholar, and Web of Science published between inception and March 13th 2023. Prediction results were averaged for studies using the same dataset, but parallel analyses were run that included studies with pooled sample across many datasets. We assessed bias through funnel plot asymmetry. A bivariate regression model determined whether differences in imaging modality, demographics, and preprocessing methods moderated classification. Separate models were run for studies with internal prediction (via cross-validation) and external prediction. RESULTS 93 studies were identified for quantitative review (30 T1, 9 DTI, 40 rs-FC, and 14 multimodal). As a whole, all modalities reliably differentiated those with schizophrenia spectrum disorders from controls (OR = 2.64 (95%CI = 2.33 to 2.95)). However, classification was relatively similar across modalities: no differences were seen across modalities in the classification of independent internal data, and a small advantage was seen for rs-FC studies relative to T1 studies in classification in external datasets. We found large amounts of heterogeneity across results resulting in significant signs of bias in funnel plots and Egger's tests. Results remained similar, however, when studies were restricted to those with less heterogeneity, with continued small advantages for rs-FC relative to structural measures. Notably, in all cases, no significant differences were seen between multimodal and unimodal approaches, with rs-FC and unimodal studies reporting largely overlapping classification performance. Differences in demographics and analysis or denoising were not associated with changes in classification scores. CONCLUSIONS The results of this study suggest that neuroimaging approaches have promise in the classification of psychosis. Interestingly, at present most modalities perform similarly in the classification of psychosis, with slight advantages for rs-FC relative to structural modalities in some specific cases. Notably, results differed substantially across studies, with suggestions of biased effect sizes, particularly highlighting the need for more studies using external prediction and large sample sizes. Adopting more rigorous and systematized standards will add significant value toward understanding and treating this critical population.
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Affiliation(s)
- Alexis Porter
- Department of Psychology, Northwestern University, Evanston, IL, USA.
| | - Sihan Fei
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Katherine S F Damme
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Institute for Innovations in Developmental Sciences, Northwestern University, Evanston and Chicago, IL, USA
| | - Robin Nusslock
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Caterina Gratton
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Institute for Innovations in Developmental Sciences, Northwestern University, Evanston and Chicago, IL, USA
- Department of Psychiatry, Northwestern University, Chicago, IL, USA
- Medical Social Sciences, Northwestern University, Chicago, IL, USA
- Institute for Policy Research, Northwestern University, Chicago, IL, USA
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5
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Wang Y, Wang J, Su W, Hu H, Xia M, Zhang T, Xu L, Zhang X, Taylor H, Osipowicz K, Young IM, Lin YH, Nicholas P, Tanglay O, Sughrue ME, Tang Y, Doyen S. Symptom-circuit mappings of the schizophrenia connectome. Psychiatry Res 2023; 323:115122. [PMID: 36889161 DOI: 10.1016/j.psychres.2023.115122] [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: 08/26/2022] [Revised: 02/15/2023] [Accepted: 02/18/2023] [Indexed: 02/27/2023]
Abstract
OBJECTIVE This paper aims to model the anatomical circuits underlying schizophrenia symptoms, and to explore patterns of abnormal connectivity among brain networks affected by psychopathology. METHODS T1 magnetic resonance imaging (MRI), diffusion weighted imaging (DWI), and resting-state functional MRI (rsfMRI) were obtained from a total of 126 patients with schizophrenia who were recruited for the study. The images were processed using the Omniscient software (https://www.o8t. com). We further apply the use of the Hollow-tree Super (HoTS) method to gain insights into what brain regions had abnormal connectivity that might be linked to the symptoms of schizophrenia. RESULTS The Positive and Negative Symptom Scale is characterised into 6 factors. Each symptom is mapped with specific anatomical abnormalities and circuits. Comparison between factors reveals co-occurrence in parcels in Factor 1 and Factor 2. Multiple large-scale networks are involved in SCZ symptomatology, with functional connectivity within Default Mode Network (DMN) and Central Executive Network (CEN) regions most frequently associated with measures of psychopathology. CONCLUSION We present a summary of the relevant anatomy for regions of the cortical areas as part of a larger effort to understand its contribution in schizophrenia. This unique machine learning-type approach maps symptoms to specific brain regions and circuits by bridging the diagnostic subtypes and analysing the features of the connectome.
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Affiliation(s)
- Yingchan Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
| | - Wenjun Su
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Hao Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Mengqing Xia
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Xia Zhang
- Xijia Medical Technology Company Limited, Shenzhen 518000, China; International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi'an 710082, China
| | - Hugh Taylor
- Omniscient Neurotechnology, Sydney, Australia
| | | | | | - Yueh-Hsin Lin
- Department of Neurosurgery, Prince of Wales Private Hospital, Sydney, Australia
| | | | | | - Michael E Sughrue
- International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi'an 710082, China; Omniscient Neurotechnology, Sydney, Australia
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
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6
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Levman J, Jennings M, Rouse E, Berger D, Kabaria P, Nangaku M, Gondra I, Takahashi E. A morphological study of schizophrenia with magnetic resonance imaging, advanced analytics, and machine learning. Front Neurosci 2022; 16:926426. [PMID: 36046472 PMCID: PMC9420897 DOI: 10.3389/fnins.2022.926426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 07/05/2022] [Indexed: 11/16/2022] Open
Abstract
We have performed a morphological analysis of patients with schizophrenia and compared them with healthy controls. Our analysis includes the use of publicly available automated extraction tools to assess regional cortical thickness (inclusive of within region cortical thickness variability) from structural magnetic resonance imaging (MRI), to characterize group-wise abnormalities associated with schizophrenia based on a publicly available dataset. We have also performed a correlation analysis between the automatically extracted biomarkers and a variety of patient clinical variables available. Finally, we also present the results of a machine learning analysis. Results demonstrate regional cortical thickness abnormalities in schizophrenia. We observed a correlation (rho = 0.474) between patients’ depression and the average cortical thickness of the right medial orbitofrontal cortex. Our leading machine learning technology evaluated was the support vector machine with stepwise feature selection, yielding a sensitivity of 92% and a specificity of 74%, based on regional brain measurements, including from the insula, superior frontal, caudate, calcarine sulcus, gyrus rectus, and rostral middle frontal regions. These results imply that advanced analytic techniques combining MRI with automated biomarker extraction can be helpful in characterizing patients with schizophrenia.
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Affiliation(s)
- Jacob Levman
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
- Center for Clinical Research, Nova Scotia Health Authority - Research, Innovation and Discovery, Halifax, NS, Canada
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts Institute of Technology, Boston, MA, United States
- *Correspondence: Jacob Levman,
| | - Maxwell Jennings
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
- Department of Mathematics and Statistics, St. Francis Xavier University, Antigonish, NS, Canada
| | - Ethan Rouse
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
| | - Derek Berger
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
| | - Priya Kabaria
- Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Masahito Nangaku
- Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Iker Gondra
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
| | - Emi Takahashi
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts Institute of Technology, Boston, MA, United States
- Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
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7
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Sadeghi D, Shoeibi A, Ghassemi N, Moridian P, Khadem A, Alizadehsani R, Teshnehlab M, Gorriz JM, Khozeimeh F, Zhang YD, Nahavandi S, Acharya UR. An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Comput Biol Med 2022; 146:105554. [DOI: 10.1016/j.compbiomed.2022.105554] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 12/21/2022]
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8
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Lavigne KM, Kanagasabai K, Palaniyappan L. Ultra-high field neuroimaging in psychosis: A narrative review. Front Psychiatry 2022; 13:994372. [PMID: 36506432 PMCID: PMC9730890 DOI: 10.3389/fpsyt.2022.994372] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/08/2022] [Indexed: 11/27/2022] Open
Abstract
Schizophrenia and related psychoses are complex neuropsychiatric diseases representing dysconnectivity across multiple scales, through the micro (cellular), meso (brain network), manifest (behavioral), and social (interpersonal) levels. In vivo human neuroimaging, particularly at ultra-high field (UHF), offers unprecedented opportunity to examine multiscale dysconnectivity in psychosis. In this review, we provide an overview of the literature to date on UHF in psychosis, focusing on microscale findings from magnetic resonance spectroscopy (MRS), mesoscale studies on structural and functional magnetic resonance imaging (fMRI), and multiscale studies assessing multiple neuroimaging modalities and relating UHF findings to behavior. We highlight key insights and considerations from multiscale and longitudinal studies and provide recommendations for future research on UHF neuroimaging in psychosis.
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Affiliation(s)
- Katie M Lavigne
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.,Montreal Neurological Institute-Hospital, McGill University, Montreal, QC, Canada.,Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Kesavi Kanagasabai
- Robarts Research Institute, Western University, London, ON, Canada.,Department of Medical Biophysics, Western University, London, ON, Canada
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.,Department of Psychiatry, McGill University, Montreal, QC, Canada.,Robarts Research Institute, Western University, London, ON, Canada.,Department of Medical Biophysics, Western University, London, ON, Canada
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9
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Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer's Disease, and Schizophrenia. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:349-361. [PMID: 34862559 DOI: 10.1007/978-3-030-85292-4_39] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Applications of machine learning (ML) in translational medicine include therapeutic drug creation, diagnostic development, surgical planning, outcome prediction, and intraoperative assistance. Opportunities in the neurosciences are rich given advancement in our understanding of the brain, expanding indications for intervention, and diagnostic challenges often characterized by multiple clinical and environmental factors. We present a review of ML in neuro-oncology, epilepsy, Alzheimer's disease, and schizophrenia to highlight recent progression in these field, optimizing machine learning capabilities in their current forms. Supervised learning models appear to be the most commonly incorporated algorithm models for machine learning across the reviewed neuroscience disciplines with primary aim of diagnosis. Accuracy ranges are high from 63% to 99% across all algorithms investigated. Machine learning contributions to neurosurgery, neurology, psychiatry, and the clinical and basic science neurosciences may enhance current medical best practices while also broadening our understanding of dynamic neural networks and the brain.
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Starke G, De Clercq E, Borgwardt S, Elger BS. Computing schizophrenia: ethical challenges for machine learning in psychiatry. Psychol Med 2021; 51:2515-2521. [PMID: 32536358 DOI: 10.1017/s0033291720001683] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Recent advances in machine learning (ML) promise far-reaching improvements across medical care, not least within psychiatry. While to date no psychiatric application of ML constitutes standard clinical practice, it seems crucial to get ahead of these developments and address their ethical challenges early on. Following a short general introduction concerning ML in psychiatry, we do so by focusing on schizophrenia as a paradigmatic case. Based on recent research employing ML to further the diagnosis, treatment, and prediction of schizophrenia, we discuss three hypothetical case studies of ML applications with view to their ethical dimensions. Throughout this discussion, we follow the principlist framework by Tom Beauchamp and James Childress to analyse potential problems in detail. In particular, we structure our analysis around their principles of beneficence, non-maleficence, respect for autonomy, and justice. We conclude with a call for cautious optimism concerning the implementation of ML in psychiatry if close attention is paid to the particular intricacies of psychiatric disorders and its success evaluated based on tangible clinical benefit for patients.
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Affiliation(s)
- Georg Starke
- Institute for Biomedical Ethics, University of Basel, Basel, Switzerland
| | - Eva De Clercq
- Institute for Biomedical Ethics, University of Basel, Basel, Switzerland
| | - Stefan Borgwardt
- Department of Psychiatry, University of Basel, Basel, Switzerland
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Bernice Simone Elger
- Institute for Biomedical Ethics, University of Basel, Basel, Switzerland
- University Center of Legal Medicine, University of Geneva, Geneva, Switzerland
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11
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Amidfar M, Quevedo J, Z Réus G, Kim YK. Grey matter volume abnormalities in the first depressive episode of medication-naïve adult individuals: a systematic review of voxel based morphometric studies. Int J Psychiatry Clin Pract 2021; 25:407-420. [PMID: 33351672 DOI: 10.1080/13651501.2020.1861632] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND To identify the reliable and consistent grey matter volume (GMV) abnormalities associated with major depressive disorder (MDD), we excluded the influence of confounding clinical characteristics, comorbidities and brain degeneration on brain morphological abnormalities by inclusion of non-comorbid and non-geriatric drug-naïve MDD individuals experiencing first episode depressive. METHODS The PubMed, Scopus, Web of Science, Science Direct and Google scholar databases were searched for papers published in English up to April 2020. RESULTS A total of 21 voxel based morphometric (VBM) studies comparing 845 individuals in the first depressive episode and medication-naïve with 940 healthy control subjects were included. The results showed a grey matter volumes reductions in the orbitofrontal cortex (OFC), prefrontal cortex (PFC), frontal and temporal gyri, temporal pole, insular lobe, thalamus, basal ganglia, cerebellum, hippocampus, cingulate cortex, and amygdala. In addition, increased grey matter volumes in the postcentral gyrus, superior frontal gyrus, insula, basal ganglia, thalamus, amygdala, cuneus, and precuneus differentiated the first depressive episode in medication-naïve individuals from healthy subjects. CONCLUSION The present systematic review provided additional support for the involvement of grey matter structural abnormalities in limbic-cortical circuits as possibly specific structural abnormalities in the early stage of MDD.Key pointsDistinct brain regions in MDD patients might be associated with the early stages of illness, and thus it is critical to study the causal relationship between brain structures and the onset of the disease to improve the evaluation in clinic.Grey matter alterations in the fronto-limbic networks in the first episode, medication-naïve MDD might suggest that these abnormalities may play an important role in the neuropathophysiology of MDD at its onset.First episode, medically naïve depressive patients show grey matter volume alterations in brain regions mainly associated with emotion regulation including parietal-temporal regions, PFC, insular lobe, thalamus, basal ganglia, cerebellum and limbic structures that may be specific changes in early stage of MDD.Genotype-diagnosis interaction effects on brain morphology in the cortico-limbic-striatal circuits, including the PFC, amygdala, hippocampus and striatum that might be implicated in the dysfunctional regulation of emotion in first-episode MDD patients.Future longitudinal and prospective studies should be conducted to identify the core structural brain changes in people at-risk for MDD and explore the association of their brain volumes with symptom onset.
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Affiliation(s)
| | - João Quevedo
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA.,Center of Excellence on Mood Disorders, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA.,Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Translational Psychiatry Laboratory, Graduate Program in Health Sciences, University of Southern Santa Catarina (UNESC), Criciúma, SC, Brazil
| | - Gislaine Z Réus
- Translational Psychiatry Laboratory, Graduate Program in Health Sciences, University of Southern Santa Catarina (UNESC), Criciúma, SC, Brazil
| | - Yong-Ku Kim
- Departments of Psychiatry, College of Medicine, Korea University, Seoul, South Korea
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12
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Levman J, Jennings M, Kabaria P, Rouse E, Nangaku M, Berger D, Gondra I, Takahashi E, Tyrrell P. Hold-out validation for the assessment of stability and reliability of multivariable regression demonstrated with magnetic resonance imaging of patients with schizophrenia. Int J Dev Neurosci 2021; 81:655-662. [PMID: 34308560 DOI: 10.1002/jdn.10144] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/16/2021] [Accepted: 07/21/2021] [Indexed: 11/11/2022] Open
Abstract
Neuroscience studies are very often tasked with identifying measurable differences between two groups of subjects, typically one group with a pathological condition and one group representing control subjects. It is often expected that the measurements acquired for comparing groups are also affected by a variety of additional patient characteristics such as sex, age, and comorbidities. Multivariable regression (MVR) is a statistical analysis technique commonly employed in neuroscience studies to "control for" or "adjust for" secondary effects (such as sex, age, and comorbidities) in order to ensure that the main study findings are focused on actual differences between the groups of interest associated with the condition under investigation. It is common practice in the neuroscience literature to utilize MVR to control for secondary effects; however, at present, it is not typically possible to assess whether the MVR adjustments correct for more error than they introduce. In common neuroscience practice, MVR models are not validated and no attempt to characterize deficiencies in the MVR model is made. In this article, we demonstrate how standard hold-out validation techniques (commonly used in machine learning analyses) that involve repeatedly randomly dividing datasets into training and testing samples can be adapted to the assessment of stability and reliability of MVR models with a publicly available neurological magnetic resonance imaging (MRI) dataset of patients with schizophrenia. Results demonstrate that MVR can introduce measurement error up to 30.06% and, on average across all considered measurements, introduce 9.84% error on this dataset. When hold-out validated MVR does not agree with the results of the standard use of MVR, the use of MVR in the given application is unstable. Thus, this paper helps evaluate the extent to which the simplistic use of MVR introduces study error in neuroscientific analyses with an analysis of patients with schizophrenia.
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Affiliation(s)
- Jacob Levman
- Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada.,Canada Research Chair in Bioinformatics, St. Francis Xavier University, Antigonish, Nova Scotia, Canada
| | - Maxwell Jennings
- Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada.,Department of Mathematics and Statistics, St. Francis Xavier University, Antigonish, Nova Scotia, Canada
| | - Priya Kabaria
- Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ethan Rouse
- Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada
| | - Masahito Nangaku
- Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Derek Berger
- Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada
| | - Iker Gondra
- Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada
| | - Emi Takahashi
- Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Pascal Tyrrell
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada.,Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
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13
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Lai JW, Ang CKE, Acharya UR, Cheong KH. Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:6099. [PMID: 34198829 PMCID: PMC8201065 DOI: 10.3390/ijerph18116099] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 02/07/2023]
Abstract
Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.
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Affiliation(s)
- Joel Weijia Lai
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
| | - Candice Ke En Ang
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
- MOH Holdings Pte Ltd, 1 Maritime Square, Singapore 099253, Singapore
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore;
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Clementi 599491, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Kang Hao Cheong
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
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14
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Najafpour Z, Fatemi A, Goudarzi Z, Goudarzi R, Shayanfard K, Noorizadeh F. Cost-effectiveness of neuroimaging technologies in management of psychiatric and insomnia disorders: A meta-analysis and prospective cost analysis. J Neuroradiol 2021; 48:348-358. [PMID: 33383065 DOI: 10.1016/j.neurad.2020.12.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 12/12/2020] [Accepted: 12/15/2020] [Indexed: 01/10/2023]
Abstract
BACKGROUND The optimal diagnostic strategy for patients with psychiatric and insomnia disorders has not been established yet. PURPOSE The purpose of this study was to perform cost-effectiveness analysis of six neuroimaging technologies in diagnosis of patients with psychiatric and insomnia disorders. METHODS An economic evaluation study was conducted in three parts, including a systematic review for determining diagnostic accuracy, a descriptive cross-sectional study with Activity-Based Costing (ABC) technique for tracing resource consumption, and a cost-effectiveness analysis using a short-term decision-analytic model. RESULTS In the first phase, 93 diagnostic accuracy studies were included in the systematic review. The accumulated results (meta-analysis) showed that the highest diagnostic accuracy for psychiatric and insomnia disorders was attributed to PET (sensitivity of 90% and specificity of 80%) and MRI (sensitivity of 76% and specificity of 78%) respectively. In the second phase of the study, we calculated the cost of each technology. The results showed that MRI has the lowest cost. Based on the results in the model of cost-effectiveness sMRI ($ 50.08 per accurate diagnosis) and MRI ($ 58.54 per accurate diagnosis) were more cost-effective neuroimaging technologies. CONCLUSION In psychiatric disorders, no single strategy was characterized by both low cost and high accuracy. However, MRI and PET scan had lower cost and higher accuracy for psychiatric disorders, respectively. MRI was the least costly with the highest diagnostic accuracy in insomnia disorders. Based on our model, sMRI in psychiatric disorders and MRI in insomnia disorders were the most cost-effective technologies.
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Affiliation(s)
- Zhila Najafpour
- Department of Health Care Management, School of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
| | - Asieh Fatemi
- Dpartment of Pharmacoeconomics and Pharmaceutical Administration, Faculty of Pharmacy, Tehran University of Medical Sciences, Faculty of Paramedical sciences, Rafsanjan University of Medical Sciences, Iran.
| | - Zahra Goudarzi
- Department of Pharmacoeconomics and Pharmaceutical Administration, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran.
| | - Reza Goudarzi
- Health Services Management Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
| | | | - Farsad Noorizadeh
- Basir Eye Health Research Center, Exceptional Talents Development Center, Tehran University of Medical Sciences, Tehran, Iran.
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15
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White matter alteration in adults with prelingual deafness: A TBSS and SBM analysis of fractional anisotropy data. Brain Cogn 2020; 148:105676. [PMID: 33388552 DOI: 10.1016/j.bandc.2020.105676] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 11/06/2020] [Accepted: 12/21/2020] [Indexed: 11/23/2022]
Abstract
A loss of hearing in early life leads to diversifications of important white matter networks. Previous studies related to WM alterations in adult deaf individuals mainly involved univariate analysis of fractional anisotropy (FA) data and volumetric analysis, which yielded inconsistent results. To address this issue, we investigated the FA value alterations in 38 prelingual adult deaf individuals and compared the results with those obtained from the same number of adults with normal hearing by using univariate (tract-based spatial statistics) and multivariate (source-based morphometry) methods. The findings from tract-based spatial statistics suggested an increased FA value in regions such as the left cingulate gyrus, left inferior frontal occipital fasciculus, left inferior longitudinal fasciculus and superior corona radiata; however, the results indicated a decreased FA value in the left planum temporale of adult deaf individuals. While source-based morphometry analysis outlined higher FA values in regions such as bilateral lingual gyrus, bilateral cerebellum, bilateral putamen and bilateral caudate, a considerable decrease was observed in the bilateral superior temporal region of the deaf group. These alterations in multiple neural regions might be linked to the compensatory cross-modal reorganizations attributed to early hearing loss.
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16
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Yamamoto M, Bagarinao E, Kushima I, Takahashi T, Sasabayashi D, Inada T, Suzuki M, Iidaka T, Ozaki N. Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites. PLoS One 2020; 15:e0239615. [PMID: 33232334 PMCID: PMC7685428 DOI: 10.1371/journal.pone.0239615] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 09/10/2020] [Indexed: 12/17/2022] Open
Abstract
Structural brain alterations have been repeatedly reported in schizophrenia; however, the pathophysiology of its alterations remains unclear. Multivariate pattern recognition analysis such as support vector machines can classify patients and healthy controls by detecting subtle and spatially distributed patterns of structural alterations. We aimed to use a support vector machine to distinguish patients with schizophrenia from control participants on the basis of structural magnetic resonance imaging data and delineate the patterns of structural alterations that significantly contributed to the classification performance. We used independent datasets from different sites with different magnetic resonance imaging scanners, protocols and clinical characteristics of the patient group to achieve a more accurate estimate of the classification performance of support vector machines. We developed a support vector machine classifier using the dataset from one site (101 participants) and evaluated the performance of the trained support vector machine using a dataset from the other site (97 participants) and vice versa. We assessed the performance of the trained support vector machines in each support vector machine classifier. Both support vector machine classifiers attained a classification accuracy of >70% with two independent datasets indicating a consistently high performance of support vector machines even when used to classify data from different sites, scanners and different acquisition protocols. The regions contributing to the classification accuracy included the bilateral medial frontal cortex, superior temporal cortex, insula, occipital cortex, cerebellum, and thalamus, which have been reported to be related to the pathogenesis of schizophrenia. These results indicated that the support vector machine could detect subtle structural brain alterations and might aid our understanding of the pathophysiology of these changes in schizophrenia, which could be one of the diagnostic findings of schizophrenia.
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Affiliation(s)
- Maeri Yamamoto
- Department of Psychiatry, Nagoya University, Graduate School of Medicine, Nagoya, Aichi, Japan
| | | | - Itaru Kushima
- Department of Psychiatry, Nagoya University, Graduate School of Medicine, Nagoya, Aichi, Japan
- Medical Genomics Center, Nagoya University Hospital, Nagoya, Aichi, Japan
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Toyama, Japan
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Toyama, Japan
| | - Toshiya Inada
- Department of Psychiatry, Nagoya University, Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Michio Suzuki
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Toyama, Japan
| | - Tetsuya Iidaka
- Brain & Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
- * E-mail:
| | - Norio Ozaki
- Department of Psychiatry, Nagoya University, Graduate School of Medicine, Nagoya, Aichi, Japan
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17
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Singh K, Singh S, Malhotra J. Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients. Proc Inst Mech Eng H 2020; 235:167-184. [PMID: 33124526 DOI: 10.1177/0954411920966937] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Schizophrenia is a fatal mental disorder, which affects millions of people globally by the disturbance in their thinking, feeling and behaviour. In the age of the internet of things assisted with cloud computing and machine learning techniques, the computer-aided diagnosis of schizophrenia is essentially required to provide its patients with an opportunity to own a better quality of life. In this context, the present paper proposes a spectral features based convolutional neural network (CNN) model for accurate identification of schizophrenic patients using spectral analysis of multichannel EEG signals in real-time. This model processes acquired EEG signals with filtering, segmentation and conversion into frequency domain. Then, given frequency domain segments are divided into six distinct spectral bands like delta, theta-1, theta-2, alpha, beta and gamma. The spectral features including mean spectral amplitude, spectral power and Hjorth descriptors (Activity, Mobility and Complexity) are extracted from each band. These features are independently fed to the proposed spectral features-based CNN and long short-term memory network (LSTM) models for classification. This work also makes use of raw time-domain and frequency-domain EEG segments for classification using temporal CNN and spectral CNN models of same architectures respectively. The overall analysis of simulation results of all models exhibits that the proposed spectral features based CNN model is an efficient technique for accurate and prompt identification of schizophrenic patients among healthy individuals with average classification accuracies of 94.08% and 98.56% for two different datasets with optimally small classification time.
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Affiliation(s)
- Kuldeep Singh
- Department of Electronics Technology, Guru Nanak Dev University, Amritsar, Punjab, India
| | - Sukhjeet Singh
- Machinery Fault Diagnostics & Signal Processing Laboratory, Department of Mechanical Engineering, University Institute of Technology, Guru Nanak Dev University, Amritsar, Punjab, India
| | - Jyoteesh Malhotra
- Department of Electronics and Communication Engineering, Guru Nanak Dev University, Jalandhar, Punjab, India
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18
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Cecchetti AA, Bhardwaj N, Murughiyan U, Kothakapu G, Sundaram U. Fueling Clinical and Translational Research in Appalachia: Informatics Platform Approach. JMIR Med Inform 2020; 8:e17962. [PMID: 33052114 PMCID: PMC7593861 DOI: 10.2196/17962] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 07/24/2020] [Accepted: 07/27/2020] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND The Appalachian population is distinct, not just culturally and geographically but also in its health care needs, facing the most health care disparities in the United States. To meet these unique demands, Appalachian medical centers need an arsenal of analytics and data science tools with the foundation of a centralized data warehouse to transform health care data into actionable clinical interventions. However, this is an especially challenging task given the fragmented state of medical data within Appalachia and the need for integration of other types of data such as environmental, social, and economic with medical data. OBJECTIVE This paper aims to present the structure and process of the development of an integrated platform at a midlevel Appalachian academic medical center along with its initial uses. METHODS The Appalachian Informatics Platform was developed by the Appalachian Clinical and Translational Science Institute's Division of Clinical Informatics and consists of 4 major components: a centralized clinical data warehouse, modeling (statistical and machine learning), visualization, and model evaluation. Data from different clinical systems, billing systems, and state- or national-level data sets were integrated into a centralized data warehouse. The platform supports research efforts by enabling curation and analysis of data using the different components, as appropriate. RESULTS The Appalachian Informatics Platform is functional and has supported several research efforts since its implementation for a variety of purposes, such as increasing knowledge of the pathophysiology of diseases, risk identification, risk prediction, and health care resource utilization research and estimation of the economic impact of diseases. CONCLUSIONS The platform provides an inexpensive yet seamless way to translate clinical and translational research ideas into clinical applications for regions similar to Appalachia that have limited resources and a largely rural population.
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Affiliation(s)
- Alfred A Cecchetti
- Department of Clinical and Translational Science, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Niharika Bhardwaj
- Department of Clinical and Translational Science, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Usha Murughiyan
- Department of Clinical and Translational Science, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Gouthami Kothakapu
- Department of Clinical and Translational Science, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Uma Sundaram
- Department of Clinical and Translational Science, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, United States
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19
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Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp 2020; 41:3468-3535. [PMID: 32374075 PMCID: PMC7375108 DOI: 10.1002/hbm.25013] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/06/2020] [Accepted: 04/06/2020] [Indexed: 01/10/2023] Open
Abstract
Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.
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Affiliation(s)
- Barnaly Rashid
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - Vince Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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20
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Washington P, Leblanc E, Dunlap K, Penev Y, Kline A, Paskov K, Sun MW, Chrisman B, Stockham N, Varma M, Voss C, Haber N, Wall DP. Precision Telemedicine through Crowdsourced Machine Learning: Testing Variability of Crowd Workers for Video-Based Autism Feature Recognition. J Pers Med 2020; 10:E86. [PMID: 32823538 PMCID: PMC7564950 DOI: 10.3390/jpm10030086] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 08/09/2020] [Accepted: 08/10/2020] [Indexed: 02/06/2023] Open
Abstract
Mobilized telemedicine is becoming a key, and even necessary, facet of both precision health and precision medicine. In this study, we evaluate the capability and potential of a crowd of virtual workers-defined as vetted members of popular crowdsourcing platforms-to aid in the task of diagnosing autism. We evaluate workers when crowdsourcing the task of providing categorical ordinal behavioral ratings to unstructured public YouTube videos of children with autism and neurotypical controls. To evaluate emerging patterns that are consistent across independent crowds, we target workers from distinct geographic loci on two crowdsourcing platforms: an international group of workers on Amazon Mechanical Turk (MTurk) (N = 15) and Microworkers from Bangladesh (N = 56), Kenya (N = 23), and the Philippines (N = 25). We feed worker responses as input to a validated diagnostic machine learning classifier trained on clinician-filled electronic health records. We find that regardless of crowd platform or targeted country, workers vary in the average confidence of the correct diagnosis predicted by the classifier. The best worker responses produce a mean probability of the correct class above 80% and over one standard deviation above 50%, accuracy and variability on par with experts according to prior studies. There is a weak correlation between mean time spent on task and mean performance (r = 0.358, p = 0.005). These results demonstrate that while the crowd can produce accurate diagnoses, there are intrinsic differences in crowdworker ability to rate behavioral features. We propose a novel strategy for recruitment of crowdsourced workers to ensure high quality diagnostic evaluations of autism, and potentially many other pediatric behavioral health conditions. Our approach represents a viable step in the direction of crowd-based approaches for more scalable and affordable precision medicine.
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Affiliation(s)
- Peter Washington
- Department of Bioengineering, Stanford University, 443 Via Ortega, Stanford, CA 94305, USA; (P.W.); (B.C.)
| | - Emilie Leblanc
- Department of Pediatrics (Systems Medicine), Stanford University, 1265 Welch Rd., Stanford, CA 94305, USA; (E.L.); (K.D.); (Y.P.); (A.K.)
| | - Kaitlyn Dunlap
- Department of Pediatrics (Systems Medicine), Stanford University, 1265 Welch Rd., Stanford, CA 94305, USA; (E.L.); (K.D.); (Y.P.); (A.K.)
| | - Yordan Penev
- Department of Pediatrics (Systems Medicine), Stanford University, 1265 Welch Rd., Stanford, CA 94305, USA; (E.L.); (K.D.); (Y.P.); (A.K.)
| | - Aaron Kline
- Department of Pediatrics (Systems Medicine), Stanford University, 1265 Welch Rd., Stanford, CA 94305, USA; (E.L.); (K.D.); (Y.P.); (A.K.)
| | - Kelley Paskov
- Department of Biomedical Data Science, Stanford University, 1265 Welch Rd., Stanford, CA 94305, USA; (K.P.); (M.W.S.)
| | - Min Woo Sun
- Department of Biomedical Data Science, Stanford University, 1265 Welch Rd., Stanford, CA 94305, USA; (K.P.); (M.W.S.)
| | - Brianna Chrisman
- Department of Bioengineering, Stanford University, 443 Via Ortega, Stanford, CA 94305, USA; (P.W.); (B.C.)
| | - Nathaniel Stockham
- Department of Neuroscience, Stanford University, 213 Quarry Rd., Stanford, CA 94305, USA;
| | - Maya Varma
- Department of Computer Science, Stanford University, 353 Jane Stanford Way, Stanford, CA 94305, USA; (M.V.); (C.V.)
| | - Catalin Voss
- Department of Computer Science, Stanford University, 353 Jane Stanford Way, Stanford, CA 94305, USA; (M.V.); (C.V.)
| | - Nick Haber
- School of Education, Stanford University, 485 Lasuen Mall, Stanford, CA 94305, USA;
| | - Dennis P. Wall
- Department of Pediatrics (Systems Medicine), Stanford University, 1265 Welch Rd., Stanford, CA 94305, USA; (E.L.); (K.D.); (Y.P.); (A.K.)
- Department of Biomedical Data Science, Stanford University, 1265 Welch Rd., Stanford, CA 94305, USA; (K.P.); (M.W.S.)
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21
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A Systematic Characterization of Structural Brain Changes in Schizophrenia. Neurosci Bull 2020; 36:1107-1122. [PMID: 32495122 DOI: 10.1007/s12264-020-00520-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 02/13/2020] [Indexed: 01/10/2023] Open
Abstract
A systematic characterization of the similarities and differences among different methods for detecting structural brain abnormalities in schizophrenia, such as voxel-based morphometry (VBM), tensor-based morphometry (TBM), and projection-based thickness (PBT), is important for understanding the brain pathology in schizophrenia and for developing effective biomarkers for a diagnosis of schizophrenia. However, such studies are still lacking. Here, we performed VBM, TBM, and PBT analyses on T1-weighted brain MR images acquired from 116 patients with schizophrenia and 116 healthy controls. We found that, although all methods detected wide-spread structural changes, different methods captured different information - only 10.35% of the grey matter changes in cortex were detected by all three methods, and VBM only detected 11.36% of the white matter changes detected by TBM. Further, pattern classification between patients and controls revealed that combining different measures improved the classification accuracy (81.9%), indicating that fusion of different structural measures serves as a better neuroimaging marker for the objective diagnosis of schizophrenia.
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22
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Chang YW, Tsai SJ, Wu YF, Yang AC. Development of an Al-Based Web Diagnostic System for Phenotyping Psychiatric Disorders. Front Psychiatry 2020; 11:542394. [PMID: 33250789 PMCID: PMC7674487 DOI: 10.3389/fpsyt.2020.542394] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 09/14/2020] [Indexed: 12/14/2022] Open
Abstract
Background: Artificial intelligence (AI)-based medical diagnostic applications are on the rise. Our recent study has suggested an explainable deep neural network (EDNN) framework for identifying key structural deficits related to the pathology of schizophrenia. Here, we presented an AI-based web diagnostic system for schizophrenia under the EDNN framework with three-dimensional (3D) visualization of subjects' neuroimaging dataset. Methods: This AI-based web diagnostic system consisted of a web server and a neuroimaging diagnostic database. The web server deployed the EDNN algorithm under the Node.js environment. Feature selection and network model building were performed on the dataset obtained from two hundred schizophrenic patients and healthy controls in the Taiwan Aging and Mental Illness (TAMI) cohort. We included an independent cohort with 88 schizophrenic patients and 44 healthy controls recruited at Tri-Service General Hospital Beitou Branch for validation purposes. Results: Our AI-based web diagnostic system achieved 84.00% accuracy (89.47% sensitivity, 80.62% specificity) for gray matter (GM) and 90.22% accuracy (89.21% sensitivity, 91.23% specificity) for white matter (WM) on the TAMI cohort. For the Beitou cohort as an unseen test set, the model achieved 77.27 and 70.45% accuracy for GM and WM. Furthermore, it achieved 85.50 and 88.20% accuracy after model retraining to mitigate the effects of drift on the predictive capability. Moreover, our system visualized the identified voxels in brain atrophy in a 3D manner with patients' structural image, optimizing the evaluation process of the diagnostic results. Discussion: Together, our approach under the EDNN framework demonstrated the potential future direction of making a schizophrenia diagnosis based on structural brain imaging data. Our deep learning model is explainable, arguing for the accuracy of the key information related to the pathology of schizophrenia when using the AI-based web assessment platform. The rationale of this approach is in accordance with the Research Domain Criteria suggested by the National Institute of Mental Health.
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Affiliation(s)
- Yu-Wei Chang
- Institute of Brain Science and Digital Medicine Center, National Yang-Ming University, Taipei, Taiwan
| | - Shih-Jen Tsai
- Institute of Brain Science and Digital Medicine Center, National Yang-Ming University, Taipei, Taiwan.,Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Yung-Fu Wu
- Department of Psychiatry, Beitou Branch, Tri-service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Albert C Yang
- Institute of Brain Science and Digital Medicine Center, National Yang-Ming University, Taipei, Taiwan.,Brain Medicine Center, Tao-Yuan Psychiatric Center, Tao-Yuan, Taiwan
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23
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Predicting cognitive behavioral therapy outcome in the outpatient sector based on clinical routine data: A machine learning approach. Behav Res Ther 2020; 124:103530. [DOI: 10.1016/j.brat.2019.103530] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 10/14/2019] [Accepted: 12/09/2019] [Indexed: 12/21/2022]
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Ji JL, Diehl C, Schleifer C, Tamminga CA, Keshavan MS, Sweeney JA, Clementz BA, Hill SK, Pearlson G, Yang G, Creatura G, Krystal JH, Repovs G, Murray J, Winkler A, Anticevic A. Schizophrenia Exhibits Bi-directional Brain-Wide Alterations in Cortico-Striato-Cerebellar Circuits. Cereb Cortex 2019; 29:4463-4487. [PMID: 31157363 PMCID: PMC6917525 DOI: 10.1093/cercor/bhy306] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 10/17/2018] [Indexed: 01/05/2023] Open
Abstract
Distributed neural dysconnectivity is considered a hallmark feature of schizophrenia (SCZ), yet a tension exists between studies pinpointing focal disruptions versus those implicating brain-wide disturbances. The cerebellum and the striatum communicate reciprocally with the thalamus and cortex through monosynaptic and polysynaptic connections, forming cortico-striatal-thalamic-cerebellar (CSTC) functional pathways that may be sensitive to brain-wide dysconnectivity in SCZ. It remains unknown if the same pattern of alterations persists across CSTC systems, or if specific alterations exist along key functional elements of these networks. We characterized connectivity along major functional CSTC subdivisions using resting-state functional magnetic resonance imaging in 159 chronic patients and 162 matched controls. Associative CSTC subdivisions revealed consistent brain-wide bi-directional alterations in patients, marked by hyper-connectivity with sensory-motor cortices and hypo-connectivity with association cortex. Focusing on the cerebellar and striatal components, we validate the effects using data-driven k-means clustering of voxel-wise dysconnectivity and support vector machine classifiers. We replicate these results in an independent sample of 202 controls and 145 patients, additionally demonstrating that these neural effects relate to cognitive performance across subjects. Taken together, these results from complementary approaches implicate a consistent motif of brain-wide alterations in CSTC systems in SCZ, calling into question accounts of exclusively focal functional disturbances.
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Affiliation(s)
- Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
| | - Caroline Diehl
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
| | - Charles Schleifer
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
| | - Carol A Tamminga
- Department of Psychiatry and Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - John A Sweeney
- Department of Psychiatry, University of Cincinnati, Cincinnati, OH, USA
| | - Brett A Clementz
- Department of Psychology, BioImaging Research Center, University of Georgia, Athens, GA, USA
- Department of Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA, USA
| | - S Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA
| | - Godfrey Pearlson
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
| | - Genevieve Yang
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
| | - Gina Creatura
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
| | - John H Krystal
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
| | - Grega Repovs
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - John Murray
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
| | - Anderson Winkler
- Nuffield Department of Clinical Neurosciences, Oxford University, John Radcliffe Hospital, Oxford University, Headington, Oxford, UK
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA
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Tai AMY, Albuquerque A, Carmona NE, Subramanieapillai M, Cha DS, Sheko M, Lee Y, Mansur R, McIntyre RS. Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry. Artif Intell Med 2019; 99:101704. [PMID: 31606109 DOI: 10.1016/j.artmed.2019.101704] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 03/04/2019] [Accepted: 08/08/2019] [Indexed: 01/16/2023]
Abstract
INTRODUCTION Machine learning capability holds promise to inform disease models, the discovery and development of novel disease modifying therapeutics and prevention strategies in psychiatry. Herein, we provide an introduction on how machine learning/Artificial Intelligence (AI) may instantiate such capabilities, as well as provide rationale for its application to psychiatry in both research and clinical ecosystems. METHODS Databases PubMed and PsycINFO were searched from 1966 to June 2016 for keywords:Big Data, Machine Learning, Precision Medicine, Artificial Intelligence, Mental Health, Mental Disease, Psychiatry, Data Mining, RDoC, and Research Domain Criteria. Articles selected for review were those that were determined to be aligned with the objective of this particular paper. RESULTS Results indicate that AI is a viable option to build useful predictors of outcome while offering objective and comparable accuracy metrics, a unique opportunity, particularly in mental health research. The approach has also consistently brought notable insight into disease models through processing the vast amount of already available multi-domain, semi-structured medical data. The opportunity for AI in psychiatry, in addition to disease-model refinement, is in characterizing those at risk, and it is likely also relevant to personalizing and discovering therapeutics. CONCLUSIONS Machine learning currently provides an opportunity to parse disease models in complex, multi-factorial disease states (e.g. mental disorders) and could possibly inform treatment selection with existing therapies and provide bases for domain-based therapeutic discovery.
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Affiliation(s)
- Andy M Y Tai
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | - Alcides Albuquerque
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | - Nicole E Carmona
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | | | - Danielle S Cha
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | - Margarita Sheko
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | - Yena Lee
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | - Rodrigo Mansur
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | - Roger S McIntyre
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Department of Pharmacology, University of Toronto, Toronto, ON, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada.
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Giordano GM, Borgwardt S. Current goals of neuroimaging for mental disorders: a report by the WPA Section on Neuroimaging in Psychiatry. World Psychiatry 2019; 18:241-242. [PMID: 31059605 PMCID: PMC6502460 DOI: 10.1002/wps.20652] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
| | - Stefan Borgwardt
- Department of Psychiatry, University Hospital Basel, University of Basel, Basel, Switzerland
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Rahaman MA, Turner JA, Gupta CN, Rachakonda S, Chen J, Liu J, van Erp TGM, Potkin S, Ford J, Mathalon D, Lee HJ, Jiang W, Mueller BA, Andreassen O, Agartz I, Sponheim SR, Mayer AR, Stephen J, Jung RE, Canive J, Bustillo J, Calhoun VD. N-BiC: A Method for Multi-Component and Symptom Biclustering of Structural MRI Data: Application to Schizophrenia. IEEE Trans Biomed Eng 2019; 67:110-121. [PMID: 30946659 PMCID: PMC7906485 DOI: 10.1109/tbme.2019.2908815] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE We propose and develop a novel biclustering (N-BiC) approach for performing N-way biclustering of neuroimaging data. Our approach is applicable to an arbitrary number of features from both imaging and behavioral data (e.g., symptoms). We applied it to structural MRI data from patients with schizophrenia. METHODS It uses a source-based morphometry approach [i.e., independent component analysis of gray matter segmentation maps] to decompose the data into a set of spatial maps, each of which includes regions that covary among individuals. Then, the loading parameters for components of interest are entered to an exhaustive search, which incorporates a modified depth-first search technique to carry out the biclustering, with the goal of obtaining submatrices where the selected rows (individuals) show homogeneity in their expressions of selected columns (components) and vice versa. RESULTS Findings demonstrate that multiple biclusters have an evident association with distinct brain networks for the different types of symptoms in schizophrenia. The study identifies two components: inferior temporal gyrus (16) and brainstem (7), which are related to positive (distortion/excess of normal function) and negative (diminution/loss of normal function) symptoms in schizophrenia, respectively. CONCLUSION N-BiC is a data-driven method of biclustering MRI data that can exhaustively explore relationships/substructures from a dataset without any prior information with a higher degree of robustness than earlier biclustering applications. SIGNIFICANCE The use of such approaches is important to investigate the underlying biological substrates of mental illness by grouping patients into homogeneous subjects, as the schizophrenia diagnosis is known to be relatively nonspecific and heterogeneous.
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Han W, Sorg C, Zheng C, Yang Q, Zhang X, Ternblom A, Mawuli CB, Gao L, Luo C, Yao D, Li T, Liang S, Shao J. Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder. Neuroimage Clin 2019; 22:101725. [PMID: 30798168 PMCID: PMC6389685 DOI: 10.1016/j.nicl.2019.101725] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2018] [Revised: 01/30/2019] [Accepted: 02/18/2019] [Indexed: 02/05/2023]
Abstract
Brain imaging studies have revealed that functional and structural brain connectivity in the so-called triple network (i.e., default mode network (DMN), salience network (SN) and central executive network (CEN)) are consistently altered in schizophrenia. However, similar changes have also been found in patients with major depressive disorder, prompting the question of specific triple network signatures for the two disorders. In this study, we proposed Supervised Convex Nonnegative Matrix Factorization (SCNMF) to extract distributed multi-modal brain patterns. These patterns distinguish schizophrenia and major depressive disorder in a latent low-dimensional space of the triple brain network. Specifically, 21 patients of schizophrenia and 25 patients of major depressive disorder were assessed by T1-weighted, diffusion-weighted, and resting-state functional MRIs. Individual structural and functional connectivity networks, based on pre-defined regions of the triple network were constructed, respectively. Afterwards, SCNMF was employed to extract the discriminative patterns. Experiments indicate that SCNMF allows extracting the low-rank discriminative patterns between the two disorders, achieving a classification accuracy of 82.6% based on the extracted functional and structural abnormalities with support vector machine. Experimental results show the specific brain patterns for schizophrenia and major depressive disorder that are multi-modal, complex, and distributed in the triple network. Parts of the prefrontal cortex including superior frontal gyri showed variation between patients with schizophrenia and major depression due to structural properties. In terms of functional properties, the middle cingulate cortex, inferior parietal lobule, and cingulate cortex were the most discriminative regions.
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Affiliation(s)
- Wei Han
- Data Mining Lab, University of Electronic Science and Technology of China, 611731 Chengdu, China; School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Christian Sorg
- Department of Neuroradiology, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Department of Psychiatry, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; TUM-Neuroimaging Center of Klinikum rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany
| | - Changgang Zheng
- Data Mining Lab, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Qinli Yang
- Data Mining Lab, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Xiaosong Zhang
- Data Mining Lab, University of Electronic Science and Technology of China, 611731 Chengdu, China; School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Arvid Ternblom
- Data Mining Lab, University of Electronic Science and Technology of China, 611731 Chengdu, China; School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Cobbinah Bernard Mawuli
- Data Mining Lab, University of Electronic Science and Technology of China, 611731 Chengdu, China; School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Lianli Gao
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Cheng Luo
- Center for Information in BioMedicine, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Dezhong Yao
- Center for Information in BioMedicine, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Tao Li
- West China Mental Health Centre, Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Sugai Liang
- West China Mental Health Centre, Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Junming Shao
- Data Mining Lab, University of Electronic Science and Technology of China, 611731 Chengdu, China; School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731 Chengdu, China; Center for Information in BioMedicine, University of Electronic Science and Technology of China, 611731 Chengdu, China.
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de Filippis R, Carbone EA, Gaetano R, Bruni A, Pugliese V, Segura-Garcia C, De Fazio P. Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review. Neuropsychiatr Dis Treat 2019; 15:1605-1627. [PMID: 31354276 PMCID: PMC6590624 DOI: 10.2147/ndt.s202418] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 04/09/2019] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Diagnosis of schizophrenia (SCZ) is made exclusively clinically, since specific biomarkers that can predict the disease accurately remain unknown. Machine learning (ML) represents a promising approach that could support clinicians in the diagnosis of mental disorders. OBJECTIVES A systematic review, according to the PRISMA statement, was conducted to evaluate its accuracy to distinguish SCZ patients from healthy controls. METHODS We systematically searched PubMed, Embase, MEDLINE, PsychINFO and the Cochrane Library through December 2018 using generic terms for ML techniques and SCZ without language or time restriction. Thirty-five studies were included in this review: eight of them used structural neuroimaging, twenty-six used functional neuroimaging and one both, with a minimum accuracy >60% (most of them 75-90%). Sensitivity, Specificity and accuracy were extracted from each publication or obtained directly from authors. RESULTS Support vector machine, the most frequent technique, if associated with other ML techniques achieved accuracy close to 100%. The prefrontal and temporal cortices appeared to be the most useful brain regions for the diagnosis of SCZ. ML analysis can efficiently detect significantly altered brain connectivity in patients with SCZ (eg, default mode network, visual network, sensorimotor network, frontoparietal network and salience network). CONCLUSION The greater accuracy demonstrated by these predictive models and the new models resulting from the integration of multiple ML techniques will be increasingly decisive for early diagnosis and evaluation of the treatment response and to establish the prognosis of patients with SCZ. To achieve a real benefit for patients, the future challenge is to reach an accurate diagnosis not only through clinical evaluation but also with the aid of ML algorithms.
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Affiliation(s)
- Renato de Filippis
- Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
| | - Elvira Anna Carbone
- Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
| | - Raffaele Gaetano
- Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
| | - Antonella Bruni
- Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
| | - Valentina Pugliese
- Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
| | - Cristina Segura-Garcia
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
| | - Pasquale De Fazio
- Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
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30
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Zaninotto L, Qian J, Sun Y, Bassi G, Solmi M, Salcuni S. Gender, Personality Traits and Experience With Psychiatric Patients as Predictors of Stigma in Italian Psychology Students. Front Public Health 2018; 6:362. [PMID: 30619803 PMCID: PMC6305330 DOI: 10.3389/fpubh.2018.00362] [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] [Subscribe] [Scholar Register] [Received: 09/16/2018] [Accepted: 11/26/2018] [Indexed: 01/19/2023] Open
Abstract
A sample of undergraduate Psychology students (n = 1005), prevalently females (82.4%), mean age 20.5 (sd 2.5), was examined regarding their attitudes toward people suffering from mental illness. The survey instrument included a brief form for demographic variables, the Attribution Questionnaire-9 (AQ-9), the Ten Items Personality Inventory (TIPI), and two questions exploring attitudes toward open-door and restraint-free policies in Psychiatry. Higher levels of stigmatizing attitudes were found in males (Pity, Blame, Help, and Avoidance) and in those (76.5%) who had never had any experience with psychiatric patients (Danger, Fear, Blame, Segregation, Help, Avoidance and Coercion). A similar trend was also found in those who don't share the policy of no seclusion/restraint, while subjects who are favorable to open-door policies reported higher Coercion scores. No correlations were found between dimensions of stigma and personality traits. A machine learning approach was then used to explore the role of demographic, academic and personality variables as predictors of stigmatizing attitudes. Agreeableness and Extraversion emerged as the most relevant predictors for blaming attitudes, while Emotional Stability and Openness appeared to be the most effective contributors to Anger. Our results confirmed that a training experience in Psychiatry might successfully reduce stigma in Psychology students. Further research, with increased generalizability of samples and more reliable instruments, should address the role of personality traits and gender on attitudes toward people suffering from mental illness.
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Affiliation(s)
- Leonardo Zaninotto
- Department of Mental Health, Local Health Unit n. 6 (“Euganea”), Padova, Italy
| | - Jia Qian
- Department of Information Engineering, University of Padova, Padova, Italy
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Yao Sun
- Department of Developmental Psychology and Socialisation, University of Padova, Padova, Italy
| | - Giulia Bassi
- Department of Developmental Psychology and Socialisation, University of Padova, Padova, Italy
| | - Marco Solmi
- Department of Neurosciences, University of Padova, Padova, Italy
| | - Silvia Salcuni
- Department of Developmental Psychology and Socialisation, University of Padova, Padova, Italy
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Moghimi P, Lim KO, Netoff TI. Data Driven Classification Using fMRI Network Measures: Application to Schizophrenia. Front Neuroinform 2018; 12:71. [PMID: 30425631 PMCID: PMC6218612 DOI: 10.3389/fninf.2018.00071] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 09/24/2018] [Indexed: 11/13/2022] Open
Abstract
Using classification to identify biomarkers for various brain disorders has become a common practice among the functional MR imaging community. Typical classification pipeline includes taking the time series, extracting features from them, and using them to classify a set of patients and healthy controls. The most informative features are then presented as novel biomarkers. In this paper, we compared the results of single and double cross validation schemes on a cohort of 170 subjects with schizophrenia and healthy control subjects. We used graph theoretic measures as our features, comparing the use of functional and anatomical atlases to define nodes and the effect of prewhitening to remove autocorrelation trends. We found that double cross validation resulted in a 20% decrease in classification performance compared to single cross validation. The anatomical atlas resulted in higher classification results. Prewhitening resulted in a 10% boost in classification performance. Overall, a classification performance of 80% was obtained with a double-cross validation scheme using prewhitened time series and an anatomical brain atlas. However, reproducibility of classification within subjects across scans was surprisingly low and comparable to across subject classification rates, indicating that subject state during the short scan significantly influences the estimated features and classification performance.
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Affiliation(s)
- Pantea Moghimi
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Kelvin O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States
| | - Theoden I Netoff
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
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Kanchanatawan B, Sriswasdi S, Thika S, Sirivichayakul S, Carvalho AF, Geffard M, Kubera M, Maes M. Deficit schizophrenia is a discrete diagnostic category defined by neuro-immune and neurocognitive features: results of supervised machine learning. Metab Brain Dis 2018. [PMID: 29527624 DOI: 10.1007/s11011-018-0208-4] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Deficit schizophrenia is characterized by neurocognitive impairments and changes in the patterning of IgA/IgM responses to plasma tryptophan catabolites (TRYCATs). In the current study, supervised pattern recognition methods, including logistic regression analysis (LRA), Support Vector Machine (SVM), and Soft Independent Modeling of Class Analogy (SIMCA), were used to examine whether deficit schizophrenia is a discrete diagnostic class with respect to Consortium To Establish a Registry for Alzheimer's disease (CERAD) and Cambridge Neuropsychological Test Automated Battery (CANTAB) tests and IgA/IgM responses to noxious (NOX) and generally more protective (PRO) TRYCATs. We recruited patients with (n = 40) and without (n = 40) deficit schizophrenia and healthy volunteers (n = 40). The combined use of TRYCAT and CERAD features strongly segregates deficit from nondeficit schizophrenia and healthy controls. Three out of the top five most important features in LRA, SVM and SIMCA agreed, namely two different NOX/PRO TRYCAT ratios and false memory recall. SIMCA shows that deficit schizophrenia is significantly separated from nondeficit schizophrenia and controls with as top 6 features IgA responses to picolinic acid, IgM responses to 3-OH-kynurenine and kynurenic acid, and impairments in Word List Memory and Verbal Fluency Tests and Mini-Mental State Examination. Nevertheless, nondeficit schizophrenia was not significantly separated from controls. The results show that schizophrenia is not a unitary disease with mere continuous differences in severity of illness between apparent subtypes. Deficit schizophrenia is a qualitatively distinct class defined by neuroimmune (autoimmune responses to TRYCATs) and neurocognitive (episodic and semantic memory) features coupled or not with clinical (negative) symptoms.
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Affiliation(s)
- Buranee Kanchanatawan
- Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Sira Sriswasdi
- Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Supaksorn Thika
- Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | | | - André F Carvalho
- Department of Clinical Medicine and Translational Psychiatry Research Group, Faculty of Medicine, Federal University of Ceará, Fortaleza, CE, Brazil
| | - Michel Geffard
- Research Department, IDRPHT, Talence, France
- GEMAC, Saint Jean d'Illac, France
| | - Marta Kubera
- Department of Experimental Neuroendocrinology, Institute of Pharmacology, Polish Academy of Sciences, Krakow, Poland
| | - Michael Maes
- Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
- Department of Psychiatry, Medical University of Plovdiv, Plovdiv, Bulgaria.
- IMPACT Strategic Research Center, Barwon Health, Deakin University, Geelong, Vic, Australia.
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Das T, Borgwardt S, Hauke DJ, Harrisberger F, Lang UE, Riecher-Rössler A, Palaniyappan L, Schmidt A. Disorganized Gyrification Network Properties During the Transition to Psychosis. JAMA Psychiatry 2018; 75:613-622. [PMID: 29710118 PMCID: PMC6137528 DOI: 10.1001/jamapsychiatry.2018.0391] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
IMPORTANCE There is urgent need to improve the limited prognostic accuracy of clinical instruments to predict psychosis onset in individuals at clinical high risk (CHR) for psychosis. As yet, no reliable biological marker has been established to delineate CHR individuals who will develop psychosis from those who will not. OBJECTIVES To investigate abnormalities in a graph-based gyrification connectome in the early stages of psychosis and to test the accuracy of this systems-based approach to predict a transition to psychosis among CHR individuals. DESIGN, SETTING, AND PARTICIPANTS This investigation was a cross-sectional magnetic resonance imaging (MRI) study with follow-up assessment to determine the transition status of CHR individuals. Participants were recruited from a specialized clinic for the early detection of psychosis at the Department of Psychiatry (Universitäre Psychiatrische Kliniken [UPK]), University of Basel, Basel, Switzerland. Participants included individuals in the following 4 study groups: 44 healthy controls (HC group), 63 at-risk mental state (ARMS) individuals without later transition to psychosis (ARMS-NT group), 16 ARMS individuals with later transition to psychosis (ARMS-T group), and 38 antipsychotic-free patients with first-episode psychosis (FEP group). The study dates were November 2008 to November 2014. The dates of analysis were March to November 2017. MAIN OUTCOMES AND MEASURES Gyrification-based structural covariance networks (connectomes) were constructed to quantify global integration, segregation, and small-worldness. Group differences in network measures were assessed using functional data analysis across a range of network densities. The extremely randomized trees algorithm with repeated 5-fold cross-validation was used to delineate ARMS-T individuals from ARMS-NT individuals. Permutation tests were conducted to assess the significance of classification performance measures. RESULTS The 4 study groups comprised 161 participants with mean (SD) ages ranging from 24.0 (4.7) to 25.9 (5.7) years. Small-worldness was reduced in the ARMS-T and FEP groups and was associated with decreased integration and increased segregation in both groups (Hedges g range, 0.666-1.050). Using the connectome properties as features, a good classification performance was obtained (accuracy, 90.49%; balanced accuracy, 81.34%; positive predictive value, 84.47%; negative predictive value, 92.18%; sensitivity, 66.11%; specificity, 96.58%; and area under the curve, 88.30%). CONCLUSIONS AND RELEVANCE These findings suggest that there is poor integration in the coordinated development of cortical folding in patients who develop psychosis. These results further suggest that gyrification-based connectomes might be a promising means to generate systems-based measures from anatomical data to improve individual prediction of a transition to psychosis in CHR individuals.
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Affiliation(s)
- Tushar Das
- Robarts Research Institute, University of Western Ontario, London, Ontario, Canada,Department of Psychiatry, University of Western Ontario, London, Ontario, Canada,Lawson Health Research Institute, London, Ontario, Canada
| | - Stefan Borgwardt
- Department of Psychiatry (Universitäre Psychiatrische Kliniken [UPK]), University of Basel, Basel, Switzerland
| | - Daniel J. Hauke
- Department of Psychiatry (Universitäre Psychiatrische Kliniken [UPK]), University of Basel, Basel, Switzerland
| | - Fabienne Harrisberger
- Department of Psychiatry (Universitäre Psychiatrische Kliniken [UPK]), University of Basel, Basel, Switzerland
| | - Undine E. Lang
- Department of Psychiatry (Universitäre Psychiatrische Kliniken [UPK]), University of Basel, Basel, Switzerland
| | - Anita Riecher-Rössler
- Department of Psychiatry (Universitäre Psychiatrische Kliniken [UPK]), University of Basel, Basel, Switzerland
| | - Lena Palaniyappan
- Robarts Research Institute, University of Western Ontario, London, Ontario, Canada,Department of Psychiatry, University of Western Ontario, London, Ontario, Canada,Lawson Health Research Institute, London, Ontario, Canada,Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada
| | - André Schmidt
- Department of Psychiatry (Universitäre Psychiatrische Kliniken [UPK]), University of Basel, Basel, Switzerland
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Tokmic F, Hadzikadic M, Cook JR, Tcheremissine OV. Development of a Behavioral Health Stigma Measure and Application of Machine Learning for Classification. INNOVATIONS IN CLINICAL NEUROSCIENCE 2018; 15:34-42. [PMID: 30013818 PMCID: PMC6040723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Objective: Given the growing public health importance of measuring the change in mental health stigma over time, the goal of this study was to demonstrate the potential for using machine learning as a tool to analyze patterns of social stigma as a complement to traditional research methods. Methods: A total of 1,904 participants were recruited through Sona Systems, Ltd (Tallinn, Estonia), an experiment management system for online research, to complete a self-reported survey. The collected data were used to develop a new measure of mental (behavioral) health stigma. To build a classification predictive model of stigma, a decision tree was used as the data mining tool, wherein a set of classification rules was generated and tested for its ability to examine the prevalence of stigma. Results: A three-factor stigma model was supported and confirmed. Results indicate that the measure is content-valid and internally consistent. Performance evaluation of the machine learning-based classification algorithm revealed a sufficient inter-rater reliability with a predictive accuracy of 92.4 percent. Conclusion: This study illustrates the potential for applying machine learning to derive a data-driven understanding of the extent to which stigma is prevalent in society. It establishes a framework for the development of an index to track stigma over time and to assist healthcare decision-makers with improving the health of populations and the experience of care for patients.
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Affiliation(s)
- Farah Tokmic
- Drs. Tokmic and Hadzikadic are with the Department of Software & Information Systems at the University of North Carolina, in Charlotte, North Carolina
- Dr. Cook is with the Department of Psychology at the University of North Carolina, in Charlotte, North Carolina
- Dr. Tcheremissine is with the Department of Psychiatry and Behavioral Sciences Carolinas HealthCare System in Charlotte, North Carolina
| | - Mirsad Hadzikadic
- Drs. Tokmic and Hadzikadic are with the Department of Software & Information Systems at the University of North Carolina, in Charlotte, North Carolina
- Dr. Cook is with the Department of Psychology at the University of North Carolina, in Charlotte, North Carolina
- Dr. Tcheremissine is with the Department of Psychiatry and Behavioral Sciences Carolinas HealthCare System in Charlotte, North Carolina
| | - James R Cook
- Drs. Tokmic and Hadzikadic are with the Department of Software & Information Systems at the University of North Carolina, in Charlotte, North Carolina
- Dr. Cook is with the Department of Psychology at the University of North Carolina, in Charlotte, North Carolina
- Dr. Tcheremissine is with the Department of Psychiatry and Behavioral Sciences Carolinas HealthCare System in Charlotte, North Carolina
| | - Oleg V Tcheremissine
- Drs. Tokmic and Hadzikadic are with the Department of Software & Information Systems at the University of North Carolina, in Charlotte, North Carolina
- Dr. Cook is with the Department of Psychology at the University of North Carolina, in Charlotte, North Carolina
- Dr. Tcheremissine is with the Department of Psychiatry and Behavioral Sciences Carolinas HealthCare System in Charlotte, North Carolina
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Goldsmith DR, Crooks CL, Walker EF, Cotes RO. An Update on Promising Biomarkers in Schizophrenia. FOCUS: JOURNAL OF LIFE LONG LEARNING IN PSYCHIATRY 2018; 16:153-163. [PMID: 31975910 DOI: 10.1176/appi.focus.20170046] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Given the heterogeneity of symptoms in patients with schizophrenia and current treatment limitations, biomarkers may play an important role in diagnosis, subtype stratification, and the assessment of treatment response. Though many potential biomarkers have been studied, we have chosen to focus on some of the most promising and potentially clinically relevant biomarkers to review herein. These include markers of inflammation, neuroimaging biomarkers, brain-derived neurotrophic factor, genetic/epigenetic markers, and speech analysis. This will provide a broad overview of putative biomarkers that could become clinically relevant in the future, though none currently appear ready to assist the clinician in identifying cases of schizophrenia, subtypes of the disorder, treatment choice, or response. Nonetheless, some biomarkers, such as C-reactive protein (CRP), may be useful at identifying individuals who may be more highly inflamed, which could drive treatment choice. Though checking CRP is not a standard of practice, this is one example of how biomarkers may drive treatment decisions in the future, supporting precision medicine. Similarly, technological advances may one day allow clinicians to detect changes in speech patterns, which could represent a noninvasive, clinically useful tool in the future. We conclude the review by highlighting two important potential clinical uses for biomarkers in schizophrenia: the identification of individuals who may convert from clinical high risk and the stratification of patients via different biomarkers that may supersede clinical diagnosis. Given the enormous burden of illness of schizophrenia, the search for clinically relevant biomarkers is of great importance to improve the lives of patients with the disorder.
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Affiliation(s)
- David R Goldsmith
- Dr. Goldsmith, Dr. Crooks, and Dr. Cotes are with the Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia. Dr. Crooks is also with the Electronic Systems Laboratory, Georgia Tech Research Institute, Atlanta. Dr. Walker is with the Department of Psychology, Emory University
| | - Courtney L Crooks
- Dr. Goldsmith, Dr. Crooks, and Dr. Cotes are with the Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia. Dr. Crooks is also with the Electronic Systems Laboratory, Georgia Tech Research Institute, Atlanta. Dr. Walker is with the Department of Psychology, Emory University
| | - Elaine F Walker
- Dr. Goldsmith, Dr. Crooks, and Dr. Cotes are with the Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia. Dr. Crooks is also with the Electronic Systems Laboratory, Georgia Tech Research Institute, Atlanta. Dr. Walker is with the Department of Psychology, Emory University
| | - Robert O Cotes
- Dr. Goldsmith, Dr. Crooks, and Dr. Cotes are with the Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia. Dr. Crooks is also with the Electronic Systems Laboratory, Georgia Tech Research Institute, Atlanta. Dr. Walker is with the Department of Psychology, Emory University
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Latha M, Kavitha G. Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2018; 31:483-499. [PMID: 29397450 DOI: 10.1007/s10334-018-0674-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2017] [Revised: 01/05/2018] [Accepted: 01/09/2018] [Indexed: 01/01/2023]
Abstract
OBJECTIVE Schizophrenia (SZ) is a psychiatric disorder that especially affects individuals during their adolescence. There is a need to study the subanatomical regions of SZ brain on magnetic resonance images (MRI) based on morphometry. In this work, an attempt was made to analyze alterations in structure and texture patterns in images of the SZ brain using the level-set method and Laws texture features. MATERIALS AND METHODS T1-weighted MRI of the brain from Center of Biomedical Research Excellence (COBRE) database were considered for analysis. Segmentation was carried out using the level-set method. Geometrical and Laws texture features were extracted from the segmented brain stem, corpus callosum, cerebellum, and ventricle regions to analyze pattern changes in SZ. RESULTS The level-set method segmented multiple brain regions, with higher similarity and correlation values compared with an optimized method. The geometric features obtained from regions of the corpus callosum and ventricle showed significant variation (p < 0.00001) between normal and SZ brain. Laws texture feature identified a heterogeneous appearance in the brain stem, corpus callosum and ventricular regions, and features from the brain stem were correlated with Positive and Negative Syndrome Scale (PANSS) score (p < 0.005). CONCLUSION A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.
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Affiliation(s)
- Manohar Latha
- Department of Electronics Engineering, Madras Institute of Technology, Chromepet, Chennai, India.
| | - Ganesan Kavitha
- Department of Electronics Engineering, Madras Institute of Technology, Chromepet, Chennai, India
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Li M, Das T, Deng W, Wang Q, Li Y, Zhao L, Ma X, Wang Y, Yu H, Li X, Meng Y, Palaniyappan L, Li T. Clinical utility of a short resting-state MRI scan in differentiating bipolar from unipolar depression. Acta Psychiatr Scand 2017; 136:288-299. [PMID: 28504840 DOI: 10.1111/acps.12752] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/18/2017] [Indexed: 02/05/2023]
Abstract
OBJECTIVE Depression in bipolar disorder (BipD) requires a therapeutic approach that is from treating unipolar major depressive disorder (UniD), but to date, no reliable methods could separate these two disorders. The aim of this study was to establish the clinical validity and utility of a non-invasive functional MRI-based method to classify BipD from UniD. METHOD The degree of connectivity (degree centrality or DC) of every small unit (voxel) with every other unit of the brain was estimated in 22 patients with BipD and 22 age, gender, and depressive severity-matched patients with UniD and 22 healthy controls. Pattern classification analysis was carried out using a support-vector machine (SVM) approach. RESULTS Degree centrality pattern from 8-min resting fMRI discriminated BipD from UniD with an accuracy of 86% and diagnostic odds ratio of 9.6. DC was reduced in the left insula and increased in bilateral precuneus in BipD when compared to UniD. In this sample with a high degree of uncertainty (50% prior probability), positive predictive value of the DC test was 79%. CONCLUSION Degree centrality maps are potential candidate measures to separate bipolar depression from unipolar depression. Test performance reported here requires further pragmatic evaluation in regular clinical practice.
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Affiliation(s)
- M Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - T Das
- Robarts Research Institute & The Brain and Mind Institute, University of Western Ontario, London, ON, Canada.,Department of Psychiatry, University of Western Ontario, London, ON, Canada.,Lawson Health Research Institute, London, ON, Canada
| | - W Deng
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Q Wang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Y Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - L Zhao
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - X Ma
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Y Wang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - H Yu
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - X Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Y Meng
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - L Palaniyappan
- Robarts Research Institute & The Brain and Mind Institute, University of Western Ontario, London, ON, Canada.,Department of Psychiatry, University of Western Ontario, London, ON, Canada.,Lawson Health Research Institute, London, ON, Canada
| | - T Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
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Taylor JA, Matthews N, Michie PT, Rosa MJ, Garrido MI. Auditory prediction errors as individual biomarkers of schizophrenia. NEUROIMAGE-CLINICAL 2017; 15:264-273. [PMID: 28560151 PMCID: PMC5435594 DOI: 10.1016/j.nicl.2017.04.027] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2017] [Revised: 04/24/2017] [Accepted: 04/26/2017] [Indexed: 11/28/2022]
Abstract
Schizophrenia is a complex psychiatric disorder, typically diagnosed through symptomatic evidence collected through patient interview. We aim to develop an objective biologically-based computational tool which aids diagnosis and relies on accessible imaging technologies such as electroencephalography (EEG). To achieve this, we used machine learning techniques and a combination of paradigms designed to elicit prediction errors or Mismatch Negativity (MMN) responses. MMN, an EEG component elicited by unpredictable changes in sequences of auditory stimuli, has previously been shown to be reduced in people with schizophrenia and this is arguably one of the most reproducible neurophysiological markers of schizophrenia. EEG data were acquired from 21 patients with schizophrenia and 22 healthy controls whilst they listened to three auditory oddball paradigms comprising sequences of tones which deviated in 10% of trials from regularly occurring standard tones. Deviant tones shared the same properties as standard tones, except for one physical aspect: 1) duration - the deviant stimulus was twice the duration of the standard; 2) monaural gap - deviants had a silent interval omitted from the standard, or 3) inter-aural timing difference, which caused the deviant location to be perceived as 90° away from the standards. We used multivariate pattern analysis, a machine learning technique implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo) to classify images generated through statistical parametric mapping (SPM) of spatiotemporal EEG data, i.e. event-related potentials measured on the two-dimensional surface of the scalp over time. Using support vector machine (SVM) and Gaussian processes classifiers (GPC), we were able classify individual patients and controls with balanced accuracies of up to 80.48% (p-values = 0.0326, FDR corrected) and an ROC analysis yielding an AUC of 0.87. Crucially, a GP regression revealed that MMN predicted global assessment of functioning (GAF) scores (correlation = 0.73, R2 = 0.53, p = 0.0006). The diagnostic utility of multiple auditory oddball stimulus paradigms is assessed. Greatest classification accuracy was achieved using a monaural gap stimulus paradigm. The full post-stimulus epoch contains relevant discriminatory components.
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Affiliation(s)
- J A Taylor
- Queensland Brain Institute, The University of Queensland, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, Australia
| | - N Matthews
- School of Psychology, The University of Queensland, Australia
| | - P T Michie
- School of Psychology, University of Newcastle, Callaghan, New South Wales, Australia; Priority Centre for Brain and Mental Health Research, University of Newcastle, Newcastle, New South Wales, Australia
| | - M J Rosa
- Max-Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, UK; Department of Computer Science, University College London, UK
| | - M I Garrido
- Queensland Brain Institute, The University of Queensland, Australia; School of Mathematics and Physics, The University of Queensland, Australia; Centre for Advanced Imaging, The University of Queensland, Australia; ARC Centre for Integrative Brain Function, Australia.
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Dluhoš P, Schwarz D, Cahn W, van Haren N, Kahn R, Španiel F, Horáček J, Kašpárek T, Schnack H. Multi-center machine learning in imaging psychiatry: A meta-model approach. Neuroimage 2017; 155:10-24. [PMID: 28428048 DOI: 10.1016/j.neuroimage.2017.03.027] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 03/06/2017] [Accepted: 03/14/2017] [Indexed: 01/17/2023] Open
Abstract
One of the biggest problems in automated diagnosis of psychiatric disorders from medical images is the lack of sufficiently large samples for training. Sample size is especially important in the case of highly heterogeneous disorders such as schizophrenia, where machine learning models built on relatively low numbers of subjects may suffer from poor generalizability. Via multicenter studies and consortium initiatives researchers have tried to solve this problem by combining data sets from multiple sites. The necessary sharing of (raw) data is, however, often hindered by legal and ethical issues. Moreover, in the case of very large samples, the computational complexity might become too large. The solution to this problem could be distributed learning. In this paper we investigated the possibility to create a meta-model by combining support vector machines (SVM) classifiers trained on the local datasets, without the need for sharing medical images or any other personal data. Validation was done in a 4-center setup comprising of 480 first-episode schizophrenia patients and healthy controls in total. We built SVM models to separate patients from controls based on three different kinds of imaging features derived from structural MRI scans, and compared models built on the joint multicenter data to the meta-models. The results showed that the combined meta-model had high similarity to the model built on all data pooled together and comparable classification performance on all three imaging features. Both similarity and performance was superior to that of the local models. We conclude that combining models is thus a viable alternative that facilitates data sharing and creating bigger and more informative models.
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Affiliation(s)
- Petr Dluhoš
- Behavioural and Social Neuroscience Group, CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic; Department of Psychiatry, University Hospital Brno and Masaryk University, Brno, Czech Republic.
| | - Daniel Schwarz
- Institute of Biostatistics and Analyses, Masaryk University, Brno, Czech Republic
| | - Wiepke Cahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - Neeltje van Haren
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - René Kahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - Filip Španiel
- National Institute of Mental Health, Klecany, Czech Republic
| | - Jiří Horáček
- National Institute of Mental Health, Klecany, Czech Republic
| | - Tomáš Kašpárek
- Behavioural and Social Neuroscience Group, CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic; Department of Psychiatry, University Hospital Brno and Masaryk University, Brno, Czech Republic
| | - Hugo Schnack
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
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Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 2017; 145:137-165. [PMID: 27012503 PMCID: PMC5031516 DOI: 10.1016/j.neuroimage.2016.02.079] [Citation(s) in RCA: 521] [Impact Index Per Article: 74.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 02/03/2016] [Accepted: 02/25/2016] [Indexed: 01/18/2023] Open
Abstract
Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.
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Affiliation(s)
- Mohammad R Arbabshirani
- The Mind Research Network, Albuquerque, NM 87106, USA; Geisinger Health System, Danville, PA 17822, USA
| | - Sergey Plis
- The Mind Research Network, Albuquerque, NM 87106, USA
| | - Jing Sui
- The Mind Research Network, Albuquerque, NM 87106, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of ECE, University of New Mexico, Albuquerque, NM, USA
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Ghiassian S, Greiner R, Jin P, Brown MRG. Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Identify ADHD and Autism. PLoS One 2016; 11:e0166934. [PMID: 28030565 PMCID: PMC5193362 DOI: 10.1371/journal.pone.0166934] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Accepted: 11/07/2016] [Indexed: 11/24/2022] Open
Abstract
A clinical tool that can diagnose psychiatric illness using functional or structural magnetic resonance (MR) brain images has the potential to greatly assist physicians and improve treatment efficacy. Working toward the goal of automated diagnosis, we propose an approach for automated classification of ADHD and autism based on histogram of oriented gradients (HOG) features extracted from MR brain images, as well as personal characteristic data features. We describe a learning algorithm that can produce effective classifiers for ADHD and autism when run on two large public datasets. The algorithm is able to distinguish ADHD from control with hold-out accuracy of 69.6% (over baseline 55.0%) using personal characteristics and structural brain scan features when trained on the ADHD-200 dataset (769 participants in training set, 171 in test set). It is able to distinguish autism from control with hold-out accuracy of 65.0% (over baseline 51.6%) using functional images with personal characteristic data when trained on the Autism Brain Imaging Data Exchange (ABIDE) dataset (889 participants in training set, 222 in test set). These results outperform all previously presented methods on both datasets. To our knowledge, this is the first demonstration of a single automated learning process that can produce classifiers for distinguishing patients vs. controls from brain imaging data with above-chance accuracy on large datasets for two different psychiatric illnesses (ADHD and autism). Working toward clinical applications requires robustness against real-world conditions, including the substantial variability that often exists among data collected at different institutions. It is therefore important that our algorithm was successful with the large ADHD-200 and ABIDE datasets, which include data from hundreds of participants collected at multiple institutions. While the resulting classifiers are not yet clinically relevant, this work shows that there is a signal in the (f)MRI data that a learning algorithm is able to find. We anticipate this will lead to yet more accurate classifiers, over these and other psychiatric disorders, working toward the goal of a clinical tool for high accuracy differential diagnosis.
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Affiliation(s)
- Sina Ghiassian
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
- Alberta Machine Learning Institute (AMII), formerly Alberta Innovates Centre for Machine Learning (AICML), Edmonton, Alberta, Canada
| | - Russell Greiner
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
- Alberta Machine Learning Institute (AMII), formerly Alberta Innovates Centre for Machine Learning (AICML), Edmonton, Alberta, Canada
| | - Ping Jin
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
- Alberta Machine Learning Institute (AMII), formerly Alberta Innovates Centre for Machine Learning (AICML), Edmonton, Alberta, Canada
| | - Matthew R. G. Brown
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada
- Alberta Machine Learning Institute (AMII), formerly Alberta Innovates Centre for Machine Learning (AICML), Edmonton, Alberta, Canada
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42
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Discriminative Analysis of Migraine without Aura: Using Functional and Structural MRI with a Multi-Feature Classification Approach. PLoS One 2016; 11:e0163875. [PMID: 27690138 PMCID: PMC5045214 DOI: 10.1371/journal.pone.0163875] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Accepted: 09/15/2016] [Indexed: 02/05/2023] Open
Abstract
Magnetic resonance imaging (MRI) is by nature a multi-modality technique that provides complementary information about different aspects of diseases. So far no attempts have been reported to assess the potential of multi-modal MRI in discriminating individuals with and without migraine, so in this study, we proposed a classification approach to examine whether or not the integration of multiple MRI features could improve the classification performance between migraine patients without aura (MWoA) and healthy controls. Twenty-one MWoA patients and 28 healthy controls participated in this study. Resting-state functional MRI data was acquired to derive three functional measures: the amplitude of low-frequency fluctuations, regional homogeneity and regional functional correlation strength; and structural MRI data was obtained to measure the regional gray matter volume. For each measure, the values of 116 pre-defined regions of interest were extracted as classification features. Features were first selected and combined by a multi-kernel strategy; then a support vector machine classifier was trained to distinguish the subjects at individual level. The performance of the classifier was evaluated using a leave-one-out cross-validation method, and the final classification accuracy obtained was 83.67% (with a sensitivity of 92.86% and a specificity of 71.43%). The anterior cingulate cortex, prefrontal cortex, orbitofrontal cortex and the insula contributed the most discriminative features. In general, our proposed framework shows a promising classification capability for MWoA by integrating information from multiple MRI features.
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Zhang Q, Wu Q, Zhu H, He L, Huang H, Zhang J, Zhang W. Multimodal MRI-Based Classification of Trauma Survivors with and without Post-Traumatic Stress Disorder. Front Neurosci 2016; 10:292. [PMID: 27445664 PMCID: PMC4919361 DOI: 10.3389/fnins.2016.00292] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Accepted: 06/10/2016] [Indexed: 02/05/2023] Open
Abstract
Post-traumatic stress disorder (PTSD) is a debilitating psychiatric disorder. It can be difficult to discern the symptoms of PTSD and obtain an accurate diagnosis. Different magnetic resonance imaging (MRI) modalities focus on different aspects, which may provide complementary information for PTSD discrimination. However, none of the published studies assessed the diagnostic potential of multimodal MRI in identifying individuals with and without PTSD. In the current study, we investigated whether the complementary information conveyed by multimodal MRI scans could be combined to improve PTSD classification performance. Structural and resting-state functional MRI (rs-fMRI) scans were conducted on 17 PTSD patients, 20 trauma-exposed controls without PTSD (TEC) and 20 non-traumatized healthy controls (HC). Gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and regional homogeneity were extracted as classification features, and in order to integrate the information of structural and functional MRI data, the extracted features were combined by a multi-kernel combination strategy. Then a support vector machine (SVM) classifier was trained to distinguish the subjects at individual level. The performance of the classifier was evaluated using the leave-one-out cross-validation (LOOCV) method. In the pairwise comparison of PTSD, TEC, and HC groups, classification accuracies obtained by the proposed approach were 2.70, 2.50, and 2.71% higher than the best single feature way, with the accuracies of 89.19, 90.00, and 67.57% for PTSD vs. HC, TEC vs. HC, and PTSD vs. TEC respectively. The proposed approach could improve PTSD identification at individual level. Additionally, it provides preliminary support to develop the multimodal MRI method as a clinical diagnostic aid.
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Affiliation(s)
- Qiongmin Zhang
- Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University Chengdu, China
| | - Qizhu Wu
- Monash Medical Imaging, Monash University Clayton, VIC, Australia
| | - Hongru Zhu
- Mental Health Center, West China Hospital of Sichuan University Chengdu, China
| | - Ling He
- Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University Chengdu, China
| | - Hua Huang
- Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University Chengdu, China
| | - Junran Zhang
- Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University Chengdu, China
| | - Wei Zhang
- Mental Health Center, West China Hospital of Sichuan University Chengdu, China
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Schnack HG, Kahn RS. Detecting Neuroimaging Biomarkers for Psychiatric Disorders: Sample Size Matters. Front Psychiatry 2016; 7:50. [PMID: 27064972 PMCID: PMC4814515 DOI: 10.3389/fpsyt.2016.00050] [Citation(s) in RCA: 141] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 03/16/2016] [Indexed: 11/13/2022] Open
Abstract
In a recent review, it was suggested that much larger cohorts are needed to prove the diagnostic value of neuroimaging biomarkers in psychiatry. While within a sample, an increase of diagnostic accuracy of schizophrenia (SZ) with number of subjects (N) has been shown, the relationship between N and accuracy is completely different between studies. Using data from a recent meta-analysis of machine learning (ML) in imaging SZ, we found that while low-N studies can reach 90% and higher accuracy, above N/2 = 50 the maximum accuracy achieved steadily drops to below 70% for N/2 > 150. We investigate the role N plays in the wide variability in accuracy results in SZ studies (63-97%). We hypothesize that the underlying cause of the decrease in accuracy with increasing N is sample heterogeneity. While smaller studies more easily include a homogeneous group of subjects (strict inclusion criteria are easily met; subjects live close to study site), larger studies inevitably need to relax the criteria/recruit from large geographic areas. A SZ prediction model based on a heterogeneous group of patients with presumably a heterogeneous pattern of structural or functional brain changes will not be able to capture the whole variety of changes, thus being limited to patterns shared by most patients. In addition to heterogeneity (sample size), we investigate other factors influencing accuracy and introduce a ML effect size. We derive a simple model of how the different factors, such as sample heterogeneity and study setup determine this ML effect size, and explain the variation in prediction accuracies found from the literature, both in cross-validation and independent sample testing. From this, we argue that smaller-N studies may reach high prediction accuracy at the cost of lower generalizability to other samples. Higher-N studies, on the other hand, will have more generalization power, but at the cost of lower accuracy. In conclusion, when comparing results from different ML studies, the sample sizes should be taken into account. To assess the generalizability of the models, validation (by direct application) of the prediction models should be tested in independent samples. The prediction of more complex measures such as outcome, which are expected to have an underlying pattern of more subtle brain abnormalities (lower effect size), will require large samples.
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Affiliation(s)
- Hugo G Schnack
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht , Utrecht , Netherlands
| | - René S Kahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht , Utrecht , Netherlands
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Palaniyappan L, Mahmood J, Balain V, Mougin O, Gowland PA, Liddle PF. Structural correlates of formal thought disorder in schizophrenia: An ultra-high field multivariate morphometry study. Schizophr Res 2015; 168:305-12. [PMID: 26232240 PMCID: PMC4604249 DOI: 10.1016/j.schres.2015.07.022] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Revised: 06/28/2015] [Accepted: 07/13/2015] [Indexed: 01/15/2023]
Abstract
BACKGROUND Persistent formal thought disorder (FTD) is one of the most characteristic features of schizophrenia. Several neuroimaging studies report spatially distinct neuroanatomical changes in association with FTD. Given that most studies so far have employed a univariate localisation approach that obscures the study of covarying interregional relationships, the present study focussed on the multivariate systemic pattern of anatomical changes that contribute to FTD. METHODS Speech samples from nineteen medicated clinically stable schizophrenia patients and 20 healthy controls were evaluated for subtle formal thought disorder. Ultra high-field (7T) anatomical Magnetic Resonance Imaging scans were obtained from all subjects. Multivariate morphometric patterns were identified using an independent component approach (source based morphometry). Using multiple regression analysis, the morphometric patterns predicting positive and negative FTD scores were identified. RESULTS Morphometric variations in grey matter predicted a substantial portion of inter-individual variance in negative but not positive FTD. A pattern of concomitant striato-insular/precuneus reduction along with frontocingular grey matter increase had a significant association with negative FTD. CONCLUSIONS These results suggest that concomitant increase and decrease in grey matter occur in association with persistent negative thought disorder in clinically stable individuals with schizophrenia.
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Affiliation(s)
- Lena Palaniyappan
- Translational Neuroimaging for Mental Health, Division of Psychiatry & Applied Psychology, Institute of Mental Health, University of Nottingham, UK; Early Intervention in Psychosis, Nottinghamshire Healthcare NHS Trust, Nottingham, UK.
| | - Jenaid Mahmood
- Translational Neuroimaging for Mental Health, Division of Psychiatry & Applied Psychology, Institute of Mental Health, University of Nottingham, UK
| | - Vijender Balain
- Penticton Regional Hospital, Penticton, British Columbia, Canada
| | - Olivier Mougin
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics, University of Nottingham, UK
| | - Penny A. Gowland
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics, University of Nottingham, UK
| | - Peter F. Liddle
- Translational Neuroimaging for Mental Health, Division of Psychiatry & Applied Psychology, Institute of Mental Health, University of Nottingham, UK
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Palaniyappan L, Maayan N, Bergman H, Davenport C, Adams CE, Soares‐Weiser K. Voxel-based morphometry for separation of schizophrenia from other types of psychosis in first episode psychosis. Cochrane Database Syst Rev 2015; 2015:CD011021. [PMID: 26252640 PMCID: PMC7104330 DOI: 10.1002/14651858.cd011021.pub2] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
BACKGROUND Schizophrenia is a psychiatric disorder which involves distortions in thought and perception, blunted affect, and behavioural disturbances. The longer psychosis goes unnoticed and untreated, the more severe the repercussions for relapse and recovery. There is some evidence that early intervention services can help, and diagnostic techniques that could contribute to early intervention may offer clinical utility in these situations. The index test being evaluated in this review is the structural magnetic resonance imaging (MRI) analysis technique known as voxel-based morphometry (VBM) that estimates the distribution of grey matter tissue volume across several brain regions. This review is an exploratory examination of the diagnostic 'potential' of VBM for use as an additional tool in the clinical examination of patients with first episode psychosis to establish whether an individual will progress on to developing schizophrenia as opposed to other types of psychosis. OBJECTIVES To determine whether VBM applied to the brain can be used to differentiate schizophrenia from other types of psychosis in participants who have received a clinical diagnosis of first episode psychosis. SEARCH METHODS In December 2013, we updated a previous search (May 2012) of MEDLINE, EMBASE, and PsycInfo using OvidSP. SELECTION CRITERIA We included retrospective and prospective studies that consecutively or randomly selected adolescent and adult participants (< 45 years) with a first episode of psychosis; and that evaluated the diagnostic accuracy of VBM for differentiating schizophrenia from other psychoses compared with a clinical diagnosis made by a qualified mental health professional, with or without the use of standard operational criteria or symptom checklists. We excluded studies in children, and in adult participants with organic brain disorders or who were at high risk for schizophrenia, such as people with a genetic predisposition. DATA COLLECTION AND ANALYSIS Two review authors screened all references for inclusion. We assessed the quality of studies using the QUADAS-2 instrument. Due to a lack of data, we were not able to extract 2 x 2 data tables for each study nor undertake any meta-analysis. MAIN RESULTS We included four studies with a total of 275 participants with first episode psychosis. VBM was not used to diagnose schizophrenia in any of the studies, instead VBM was used to quantify the magnitude of differences in grey matter volume. Therefore, none of the included studies reported data that could be used in the analysis, and we summarised the findings narratively for each study. AUTHORS' CONCLUSIONS There is no evidence to currently support diagnosing schizophrenia (as opposed to other psychotic disorders) using the pattern of brain changes seen in VBM studies in patients with first episode psychosis. VBM has the potential to discriminate between diagnostic categories but the methods to do this reliably are currently in evolution. In addition, the lack of applicability of the use of VBM to clinical practice in the studies to date limits the usefulness of VBM as a diagnostic aid to differentiate schizophrenia from other types of psychotic presentations in people with first episode of psychosis.
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Affiliation(s)
- Lena Palaniyappan
- The University of NottinghamDivison of Psychiatry, Institute of Mental HealthRoom 09, C FloorInnovation Park, Triumph RoadNottinghamUKNG7 2TU
| | - Nicola Maayan
- Enhance Reviews LtdCentral Office, Cobweb BuildingsThe Lane, LyfordWantageUKOX12 0EE
| | - Hanna Bergman
- Enhance Reviews LtdCentral Office, Cobweb BuildingsThe Lane, LyfordWantageUKOX12 0EE
| | - Clare Davenport
- University of BirminghamPublic Health, Epidemiology and BiostatisticsBirminghamUKB15 2TT
| | - Clive E Adams
- The University of NottinghamCochrane Schizophrenia GroupInstitute of Mental HealthInnovation Park, Triumph Road,NottinghamUKNG7 2TU
| | - Karla Soares‐Weiser
- Enhance Reviews LtdCentral Office, Cobweb BuildingsThe Lane, LyfordWantageUKOX12 0EE
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Wolfers T, Buitelaar JK, Beckmann CF, Franke B, Marquand AF. From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neurosci Biobehav Rev 2015; 57:328-49. [PMID: 26254595 DOI: 10.1016/j.neubiorev.2015.08.001] [Citation(s) in RCA: 183] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Revised: 07/29/2015] [Accepted: 08/02/2015] [Indexed: 12/19/2022]
Abstract
Psychiatric disorders are increasingly being recognised as having a biological basis, but their diagnosis is made exclusively behaviourally. A promising approach for 'biomarker' discovery has been based on pattern recognition methods applied to neuroimaging data, which could yield clinical utility in future. In this review we survey the literature on pattern recognition for making diagnostic predictions in psychiatric disorders, and evaluate progress made in translating such findings towards clinical application. We evaluate studies on many criteria, including data modalities used, the types of features extracted and algorithm applied. We identify problems common to many studies, such as a relatively small sample size and a primary focus on estimating generalisability within a single study. Furthermore, we highlight challenges that are not widely acknowledged in the field including the importance of accommodating disease prevalence, the necessity of more extensive validation using large carefully acquired samples, the need for methodological innovations to improve accuracy and to discriminate between multiple disorders simultaneously. Finally, we identify specific clinical contexts in which pattern recognition can add value in the short to medium term.
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Affiliation(s)
- Thomas Wolfers
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands; Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, PO Box 9101, 6500 HB Nijmegen, The Netherlands.
| | - Jan K Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands; Karakter Child and Adolescent Psychiatry University Centre, Radboud University Medical Centre, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Christian F Beckmann
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, PO Box 9101, 6500 HB Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands; Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom
| | - Barbara Franke
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands; Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, PO Box 9101, 6500 HB Nijmegen, The Netherlands; Department of Neuroimaging, Institute of Psychiatry, King's College London, LondonUnited Kingdom
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Nucifora PGP. Overdiagnosis in the era of neuropsychiatric imaging. Acad Radiol 2015; 22:995-9. [PMID: 25784322 DOI: 10.1016/j.acra.2015.02.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2014] [Revised: 01/28/2015] [Accepted: 02/05/2015] [Indexed: 12/17/2022]
Abstract
New guidelines proposed by the National Institute of Mental Health are intended to transform the management of patients with psychiatric disorders. It is anticipated that neuroimaging and other biomarkers will play a more prominent role in diagnosis and prognosis, especially in the prodromal phase of illness. Earlier treatment of psychiatric disorders has the potential to improve outcomes significantly. However, diagnosis in the absence of symptoms can lead to overdiagnosis. Overdiagnosis is a problem in many fields of medicine but could pose additional problems in psychiatry because of the stigmatization that often accompanies a diagnosis of mental illness. This review discusses the magnetic resonance imaging methods that hold the most promise for evaluating neuropsychiatric disorders, the likelihood that they could lead to overdiagnosis, and opportunities to minimize the impact of overdiagnosis in psychiatric disorders.
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Affiliation(s)
- Paolo G P Nucifora
- Department of Radiology, Philadelphia VA Medical Center, 3900 Woodland Ave, Philadelphia, PA 19104; Department of Radiology, University of Pennsylvania, 3400 Spruce St, Philadelphia, Pennsylvania.
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Janousova E, Schwarz D, Kasparek T. Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition. Psychiatry Res 2015; 232:237-49. [PMID: 25912090 DOI: 10.1016/j.pscychresns.2015.03.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2013] [Revised: 09/30/2014] [Accepted: 03/11/2015] [Indexed: 12/27/2022]
Abstract
We investigated a combination of three classification algorithms, namely the modified maximum uncertainty linear discriminant analysis (mMLDA), the centroid method, and the average linkage, with three types of features extracted from three-dimensional T1-weighted magnetic resonance (MR) brain images, specifically MR intensities, grey matter densities, and local deformations for distinguishing 49 first episode schizophrenia male patients from 49 healthy male subjects. The feature sets were reduced using intersubject principal component analysis before classification. By combining the classifiers, we were able to obtain slightly improved results when compared with single classifiers. The best classification performance (81.6% accuracy, 75.5% sensitivity, and 87.8% specificity) was significantly better than classification by chance. We also showed that classifiers based on features calculated using more computation-intensive image preprocessing perform better; mMLDA with classification boundary calculated as weighted mean discriminative scores of the groups had improved sensitivity but similar accuracy compared to the original MLDA; reducing a number of eigenvectors during data reduction did not always lead to higher classification accuracy, since noise as well as the signal important for classification were removed. Our findings provide important information for schizophrenia research and may improve accuracy of computer-aided diagnostics of neuropsychiatric diseases.
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Affiliation(s)
- Eva Janousova
- Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Kamenice 3, Brno 62500, Czech Republic.
| | - Daniel Schwarz
- Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Kamenice 3, Brno 62500, Czech Republic
| | - Tomas Kasparek
- Behavioural and Social Neuroscience Group, CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic; Department of Psychiatry, University Hospital Brno and Masaryk University, Brno, Czech Republic
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50
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Koch SP, Hägele C, Haynes JD, Heinz A, Schlagenhauf F, Sterzer P. Diagnostic classification of schizophrenia patients on the basis of regional reward-related FMRI signal patterns. PLoS One 2015; 10:e0119089. [PMID: 25799236 PMCID: PMC4370557 DOI: 10.1371/journal.pone.0119089] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Accepted: 12/07/2014] [Indexed: 01/15/2023] Open
Abstract
Functional neuroimaging has provided evidence for altered function of mesolimbic circuits implicated in reward processing, first and foremost the ventral striatum, in patients with schizophrenia. While such findings based on significant group differences in brain activations can provide important insights into the pathomechanisms of mental disorders, the use of neuroimaging results from standard univariate statistical analysis for individual diagnosis has proven difficult. In this proof of concept study, we tested whether the predictive accuracy for the diagnostic classification of schizophrenia patients vs. healthy controls could be improved using multivariate pattern analysis (MVPA) of regional functional magnetic resonance imaging (fMRI) activation patterns for the anticipation of monetary reward. With a searchlight MVPA approach using support vector machine classification, we found that the diagnostic category could be predicted from local activation patterns in frontal, temporal, occipital and midbrain regions, with a maximal cluster peak classification accuracy of 93% for the right pallidum. Region-of-interest based MVPA for the ventral striatum achieved a maximal cluster peak accuracy of 88%, whereas the classification accuracy on the basis of standard univariate analysis reached only 75%. Moreover, using support vector regression we could additionally predict the severity of negative symptoms from ventral striatal activation patterns. These results show that MVPA can be used to substantially increase the accuracy of diagnostic classification on the basis of task-related fMRI signal patterns in a regionally specific way.
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Affiliation(s)
- Stefan P. Koch
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité–Universitätsmedizin Berlin, Germany
- * E-mail:
| | - Claudia Hägele
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité–Universitätsmedizin Berlin, Germany
| | - John-Dylan Haynes
- Bernstein Center for Computational Neuroscience Berlin, Charité–Universitätsmedizin Berlin, Germany
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité–Universitätsmedizin Berlin, Germany
| | - Florian Schlagenhauf
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité–Universitätsmedizin Berlin, Germany
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Philipp Sterzer
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité–Universitätsmedizin Berlin, Germany
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