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Deng LR, Harmata GIS, Barsotti EJ, Williams AJ, Christensen GE, Voss MW, Saleem A, Rivera-Dompenciel AM, Richards JG, Sathyaputri L, Mani M, Abdolmotalleby H, Fiedorowicz JG, Xu J, Shaffer JJ, Wemmie JA, Magnotta VA. Machine learning with multiple modalities of brain magnetic resonance imaging data to identify the presence of bipolar disorder. J Affect Disord 2024:S0165-0327(24)01510-6. [PMID: 39278469 DOI: 10.1016/j.jad.2024.09.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 09/03/2024] [Accepted: 09/08/2024] [Indexed: 09/18/2024]
Abstract
BACKGROUND Bipolar disorder (BD) is a chronic psychiatric mood disorder that is solely diagnosed based on clinical symptoms. These symptoms often overlap with other psychiatric disorders. Efforts to use machine learning (ML) to create predictive models for BD based on data from brain imaging are expanding but have often been limited using only a single modality and the exclusion of the cerebellum, which may be relevant in BD. METHODS In this study, we sought to improve ML classification of BD by combining information from structural, functional, and diffusion-weighted imaging. Participants (108 BD I, 78 control) with BD type I and matched controls were recruited into an imaging study. This dataset was randomly divided into training and testing sets. For each of the three modalities, a separate ML model was selected, trained, and then used to generate a prediction of the class of each test subject. Majority voting was used to combine results from the three models to make a final prediction of whether a subject had BD. An independent replication sample was used to evaluate the ability of the ML classification to generalize to data collected at other sites. RESULTS Combining the three machine learning models through majority voting resulted in an accuracy of 89.5 % for classification of the test subjects as being in the BD or control group. Bootstrapping resulted in a 95 % confidence interval of 78.9 %-97.4 % for test accuracy. Performance was reduced when only using 2 of the 3 modalities. Analysis of feature importance revealed that the cerebellum and nodes of the emotional control network were among the most important regions for classification. The machine learning model performed at chance on the independent replication sample. CONCLUSION BD I could be identified with high accuracy in our relatively small sample by combining structural, functional, and diffusion-weighted imaging data within a single site but not generalize well to an independent replication sample. Future studies using harmonized imaging protocols may facilitate generalization of ML models.
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Affiliation(s)
- Lubin R Deng
- Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Gail I S Harmata
- Department of Radiology, University of Iowa, Iowa City, IA, USA; Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | | | | | - Gary E Christensen
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
| | - Michelle W Voss
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, USA
| | - Arshaq Saleem
- Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | | | | | | | - Merry Mani
- Department of Radiology, University of Iowa, Iowa City, IA, USA
| | | | | | - Jia Xu
- Department of Radiology, University of Iowa, Iowa City, IA, USA
| | - Joseph J Shaffer
- Department of Biosciences, Kansas City University, Kansas City, MO, USA
| | - John A Wemmie
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA; Department of Veterans Affairs Medical Center, Iowa City, IA, USA
| | - Vincent A Magnotta
- Department of Radiology, University of Iowa, Iowa City, IA, USA; Department of Psychiatry, University of Iowa, Iowa City, IA, USA.
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Seitz-Holland J, Nägele FL, Kubicki M, Pasternak O, Cho KIK, Hough M, Mulert C, Shenton ME, Crow TJ, James ACD, Lyall AE. Shared and distinct white matter abnormalities in adolescent-onset schizophrenia and adolescent-onset psychotic bipolar disorder. Psychol Med 2023; 53:4707-4719. [PMID: 35796024 PMCID: PMC11119277 DOI: 10.1017/s003329172200160x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND While adolescent-onset schizophrenia (ADO-SCZ) and adolescent-onset bipolar disorder with psychosis (psychotic ADO-BPD) present a more severe clinical course than their adult forms, their pathophysiology is poorly understood. Here, we study potentially state- and trait-related white matter diffusion-weighted magnetic resonance imaging (dMRI) abnormalities along the adolescent-onset psychosis continuum to address this need. METHODS Forty-eight individuals with ADO-SCZ (20 female/28 male), 15 individuals with psychotic ADO-BPD (7 female/8 male), and 35 healthy controls (HCs, 18 female/17 male) underwent dMRI and clinical assessments. Maps of extracellular free-water (FW) and fractional anisotropy of cellular tissue (FAT) were compared between individuals with psychosis and HCs using tract-based spatial statistics and FSL's Randomise. FAT and FW values were extracted, averaged across all voxels that demonstrated group differences, and then utilized to test for the influence of age, medication, age of onset, duration of illness, symptom severity, and intelligence. RESULTS Individuals with adolescent-onset psychosis exhibited pronounced FW and FAT abnormalities compared to HCs. FAT reductions were spatially more widespread in ADO-SCZ. FW increases, however, were only present in psychotic ADO-BPD. In HCs, but not in individuals with adolescent-onset psychosis, FAT was positively related to age. CONCLUSIONS We observe evidence for cellular (FAT) and extracellular (FW) white matter abnormalities in adolescent-onset psychosis. Although cellular white matter abnormalities were more prominent in ADO-SCZ, such alterations may reflect a shared trait, i.e. neurodevelopmental pathology, present across the psychosis spectrum. Extracellular abnormalities were evident in psychotic ADO-BPD, potentially indicating a more dynamic, state-dependent brain reaction to psychosis.
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Affiliation(s)
- Johanna Seitz-Holland
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Felix L. Nägele
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Kang Ik K. Cho
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Morgan Hough
- SANE POWIC, University Department of Psychiatry, Warneford Hospital, Oxford, UK
- Highfield Unit, University Department of Psychiatry, Warneford Hospital, Oxford, UK
| | - Christoph Mulert
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany
- Centre for Psychiatry and Psychotherapy, Justus-Liebig-University, Giessen, Germany
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Timothy J. Crow
- SANE POWIC, University Department of Psychiatry, Warneford Hospital, Oxford, UK
| | - Anthony C. D. James
- SANE POWIC, University Department of Psychiatry, Warneford Hospital, Oxford, UK
- Highfield Unit, University Department of Psychiatry, Warneford Hospital, Oxford, UK
| | - Amanda E. Lyall
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Campos-Ugaz WA, Palacios Garay JP, Rivera-Lozada O, Alarcón Diaz MA, Fuster-Guillén D, Tejada Arana AA. An Overview of Bipolar Disorder Diagnosis Using Machine Learning Approaches: Clinical Opportunities and Challenges. IRANIAN JOURNAL OF PSYCHIATRY 2023; 18:237-247. [PMID: 37383968 PMCID: PMC10293694 DOI: 10.18502/ijps.v18i2.12372] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/13/2022] [Accepted: 11/14/2022] [Indexed: 08/15/2023]
Abstract
Objective: Automatic diagnosis of psychiatric disorders such as bipolar disorder (BD) through machine learning techniques has attracted substantial attention from psychiatric and artificial intelligence communities. These approaches mostly rely on various biomarkers extracted from electroencephalogram (EEG) or magnetic resonance imaging (MRI)/functional MRI (fMRI) data. In this paper, we provide an updated overview of existing machine learning-based methods for bipolar disorder (BD) diagnosis using MRI and EEG data. Method : This study is a short non-systematic review with the aim of describing the current situation in automatic diagnosis of BD using machine learning methods. Therefore, an appropriate literature search was conducted via relevant keywords for original EEG/MRI studies on distinguishing BD from other conditions, particularly from healthy peers, in PubMed, Web of Science, and Google Scholar databases. Results: We reviewed 26 studies, including 10 EEG studies and 16 MRI studies (including structural and functional MRI), that used traditional machine learning methods and deep learning algorithms to automatically detect BD. The reported accuracies for EEG studies is about 90%, while the reported accuracies for MRI studies remains below the minimum level for clinical relevance, i.e. about 80% of the classification outcome for traditional machine learning methods. However, deep learning techniques have generally achieved accuracies higher than 95%. Conclusion: Research utilizing machine learning applied to EEG signals and brain images has provided proof of concept for how this innovative technique can help psychiatrists distinguish BD patients from healthy people. However, the results have been somewhat contradictory and we must keep away from excessive optimistic interpretations of the findings. Much progress is still needed to reach the level of clinical practice in this field.
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Investigation of endophenotype potential of decreased fractional anisotropy in pediatric bipolar disorder patients and unrelated offspring of bipolar disorder patients. CNS Spectr 2022; 27:709-715. [PMID: 34044907 DOI: 10.1017/s1092852921000584] [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] [Indexed: 01/29/2023]
Abstract
BACKGROUND Bipolar disorder (BD) is a severe psychiatric disorder associated with structural and functional brain abnormalities, some of which have been found in unaffected relatives as well. In this study, we examined the potential role of decreased fractional anisotropy (FA) as a BD endophenotype, in adolescents at high risk for BD. METHODS We included 15 offspring of patients with BD, 16 pediatric BD patients, and 16 matched controls. Diffusion weighted scans were obtained on a 3T scanner using an echo-planar sequence. Scans were segmented using FreeSurfer. RESULTS Our results showed significantly decreased FA in six brain areas of offspring group; left superior temporal gyrus (LSTG; P < .0001), left transverse temporal gyrus (LTTG; P = .002), left banks of the superior temporal sulcus (LBSTS; P = .002), left anterior cingulum (LAC; P = .003), right temporal pole (RTP; P = .004) and left frontal pole (LFP; P = .017). On analysis, LSTG, LAC, and RTP demonstrated a potential to be an endophenotype when comparing all three groups. FA values in three regions, LBSTS, LTTG, and LFP were increased only in controls. CONCLUSION Our findings point at decreased FA as a possible endophenotype for BD, as they were found in children of patients with BD. Most of these areas were previously found to have morphological and functional changes in adult and pediatric BD, and are thought to play important roles in affected domains of functioning. Prospective follow up studies should be performed to detect reliability of decreased FA as an endophenotype and effects of treatment on FA.
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Luttenbacher I, Phillips A, Kazemi R, Hadipour AL, Sanghvi I, Martinez J, Adamson MM. Transdiagnostic role of glutamate and white matter damage in neuropsychiatric disorders: A Systematic Review. J Psychiatr Res 2022; 147:324-348. [PMID: 35151030 DOI: 10.1016/j.jpsychires.2021.12.042] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/08/2021] [Accepted: 12/19/2021] [Indexed: 12/09/2022]
Abstract
Neuropsychiatric disorders including generalized anxiety disorder (GAD), obsessive-compulsive disorder (OCD), major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ) have been considered distinct categories of diseases despite their overlapping characteristics and symptomatology. We aimed to provide an in-depth review elucidating the role of glutamate/Glx and white matter (WM) abnormalities in these disorders from a transdiagnostic perspective. The PubMed online database was searched for studies published between 2010 and 2021. After careful screening, 401 studies were included. The findings point to decreased levels of glutamate in the Anterior Cingulate Cortex in both SZ and BD, whereas Glx is elevated in the Hippocampus in SZ and MDD. With regard to WM abnormalities, the Corpus Callosum and superior Longitudinal Fascicle were the most consistently identified brain regions showing decreased fractional anisotropy (FA) across all the reviewed disorders, except GAD. Additionally, the Uncinate Fasciculus displayed decreased FA in all disorders, except OCD. Decreased FA was also found in the inferior Longitudinal Fasciculus, inferior Fronto-Occipital Fasciculus, Thalamic Radiation, and Corona Radiata in SZ, BD, and MDD. Decreased FA in the Fornix and Corticospinal Tract were found in BD and SZ patients. The Cingulum and Anterior Limb of Internal Capsule exhibited decreased FA in MDD and SZ patients. The results suggest a gradual increase in severity from GAD to SZ defined by the number of brain regions with WM abnormality which may be partially caused by abnormal glutamate levels. WM damage could thus be considered a potential marker of some of the main neuropsychiatric disorders.
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Affiliation(s)
- Ines Luttenbacher
- Department of Social & Behavioral Sciences, University of Amsterdam, Amsterdam, Netherlands; Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Angela Phillips
- Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Reza Kazemi
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
| | - Abed L Hadipour
- Department of Cognitive Sciences, University of Messina, Messina, Italy
| | - Isha Sanghvi
- Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Department of Neuroscience, University of Southern California, Los Angeles, CA, USA
| | - Julian Martinez
- Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Palo Alto University, Palo Alto, CA, USA
| | - Maheen M Adamson
- Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
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Salari N, Kazeminia M, Sagha H, Daneshkhah A, Ahmadi A, Mohammadi M. The performance of various machine learning methods for Parkinson’s disease recognition: a systematic review. CURRENT PSYCHOLOGY 2022. [DOI: 10.1007/s12144-022-02949-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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High-Precise Bipolar Disorder Detection by Using Radial Basis Functions Based Neural Network. ELECTRONICS 2022. [DOI: 10.3390/electronics11030343] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Presently, several million people suffer from major depressive and bipolar disorders. Thus, the modelling, characterization, classification, diagnosis, and analysis of such mental disorders bears great significance in medical research. Electroencephalogram records provide important information to improve clinical diagnosis and are very useful in the scientific community. In this work, electroencephalogram records and patient data from the Hospital Virgen de la Luz in Cuenca (Spain) were processed for a correct classification of bipolar disorders. This work implemented an innovative radial basis function-based neural network employing a fuzzy means algorithm. The results show that the proposed method is an effective approach for discrimination of two kinds of classes, i.e., bipolar disorder patients and healthy persons. The proposed algorithm achieved the best performance compared with other machine learning techniques such as Bayesian linear discriminant analysis, Gaussian naive Bayes, decision trees, K-nearest neighbour, or support vector machine, showing a very high accuracy close to 97%. Therefore, the neural network technique presented could be used as a new tool for the diagnosis of bipolar disorder, considering the possibility of integrating this method into medical software.
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10
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Neuroanatomic and Functional Neuroimaging Findings. Curr Top Behav Neurosci 2020; 48:173-196. [PMID: 33040316 DOI: 10.1007/7854_2020_174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
The search for brain morphology findings that could explain behavioral disorders has gone through a long path in the history of psychiatry. With the advance of brain imaging technology, studies have been able to identify brain morphology and neural circuits associated with the pathophysiology of mental illnesses, such as bipolar disorders (BD). Promising results have also shown the potential of neuroimaging findings in the identification of outcome predictors and response to treatment among patients with BD. In this chapter, we present brain imaging structural and functional findings associated with BD, as well as their hypothesized relationship with the pathophysiological aspects of that condition and their potential clinical applications.
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White Matter Microstructure in Pediatric Bipolar Disorder and Disruptive Mood Dysregulation Disorder. J Am Acad Child Adolesc Psychiatry 2020; 59:1135-1145. [PMID: 31330239 PMCID: PMC9686453 DOI: 10.1016/j.jaac.2019.05.035] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 05/18/2019] [Accepted: 07/16/2019] [Indexed: 01/23/2023]
Abstract
OBJECTIVE Disruptive mood dysregulation disorder (DMDD) codifies severe, chronic irritability. Youths with bipolar disorder (BD) also present with irritability, but with an episodic course. To date, it is not clear whether aberrant white matter microstructure-a well-replicated finding in BD-can be observed in DMDD and relates to symptoms of irritability. METHOD We acquired diffusion tensor imaging data from 118 participants (BD = 36, DMDD = 44, healthy volunteers (HV = 38). Images of fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) were processed with tract-based spatial statistics controlling for age and sex. The data were also used to train Gaussian process classifiers to predict diagnostic group. RESULTS In BD vs DMDD, FA in the corticospinal tract was reduced. In DMDD vs HV, reductions in FA and AD were confined to the anterior corpus callosum. In BD vs HV, widespread reductions in FA and increased RD were observed. FA in the anterior corpus callosum and corticospinal tract was negatively associated with irritability. The Gaussian process classifier could not discriminate between BD and DMDD, but achieved 68% accuracy in predicting DMDD vs HV and 75% accuracy in predicting BD vs HV. CONCLUSION Aberrant white matter microstructure was associated with both categorical diagnosis and the dimension of irritability. Alterations in DMDD were regionally discrete and related to reduced AD. In BD, we observed widespread increases in RD, supporting the hypothesis of altered myelination in BD. These findings will contribute to the pathophysiological understanding of DMDD and its differentiation from BD. CLINICAL TRIAL REGISTRATION INFORMATION Studies of Brain Function and Course of Illness in Pediatric Bipolar Disorder; https://clinicaltrials.gov/; NCT00025935; Child & Adolescent Bipolar Disorder Brain Imaging and Treatment Study; https://clinicaltrials.gov/; NCT00006177.
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Claude LA, Houenou J, Duchesnay E, Favre P. Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions. Bipolar Disord 2020; 22:334-355. [PMID: 32108409 DOI: 10.1111/bdi.12895] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
OBJECTIVES The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic and prognostic tools, as well as identifying biologically valid subtypes of BD, research has recently turned towards the use of machine learning (ML) techniques. We assessed both supervised ML and unsupervised ML studies in BD to evaluate their robustness, reproducibility and the potential need for improvement. METHOD We systematically searched for studies using ML algorithms based on MRI data of patients with BD until February 2019. RESULT We identified 47 studies, 45 using supervised ML techniques and 2 including unsupervised ML analyses. Among supervised studies, 43 focused on diagnostic classification. The reported accuracies for classification of BD ranged between (a) 57% and 100%, for BD vs healthy controls; (b) 49.5% and 93.1% for BD vs patients with major depressive disorder; and (c) 50% and 96.2% for BD vs patients with schizophrenia. Reported accuracies for discriminating subjects genetically at risk for BD (either from control or from patients with BD) ranged between 64.3% and 88.93%. CONCLUSIONS Although there are strong methodological limitations in previous studies and an important need for replication in large multicentric samples, the conclusions of our review bring hope of future computer-aided diagnosis of BD and pave the way for other applications, such as treatment response prediction. To reinforce the reliability of future results we provide methodological suggestions for good practice in conducting and reporting MRI-based ML studies in BD.
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Affiliation(s)
- Laurie-Anne Claude
- APHP, Mondor University Hospitals, DMU IMPACT Psychiatry and Addictology, UPEC, Créteil, France.,Neurospin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.,INSERM Unit U955, IMRB, Team 15, "Neurotranslational Psychiatry", Créteil, France.,FondaMental Foundation, Créteil, France
| | - Josselin Houenou
- APHP, Mondor University Hospitals, DMU IMPACT Psychiatry and Addictology, UPEC, Créteil, France.,Neurospin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.,INSERM Unit U955, IMRB, Team 15, "Neurotranslational Psychiatry", Créteil, France.,FondaMental Foundation, Créteil, France
| | | | - Pauline Favre
- Neurospin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.,INSERM Unit U955, IMRB, Team 15, "Neurotranslational Psychiatry", Créteil, France.,FondaMental Foundation, Créteil, France
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13
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Appaji A, Nagendra B, Chako DM, Padmanabha A, Jacob A, Hiremath CV, Varambally S, Kesavan M, Venkatasubramanian G, Rao SV, Webers CAB, Berendschot TTJM, Rao NP. Examination of retinal vascular trajectory in schizophrenia and bipolar disorder. Psychiatry Clin Neurosci 2019; 73:738-744. [PMID: 31400288 DOI: 10.1111/pcn.12921] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 07/24/2019] [Accepted: 08/06/2019] [Indexed: 12/11/2022]
Abstract
AIM Evidence suggests microvascular dysfunction (wider retinal venules and narrower arterioles) in schizophrenia (SCZ) and bipolar disorder (BD). The vascular development is synchronous with neuronal development in the retina and brain. The retinal vessel trajectory is related to retinal nerve fiber layer thinning and cerebrovascular abnormalities in SCZ and BD and has not yet been examined. Hence, in this study we examined the retinal vascular trajectory in SCZ and BD in comparison with healthy volunteers (HV). METHODS Retinal images were acquired from 100 HV, SCZ patients, and BD patients, respectively, with a non-mydriatic fundus camera. Images were quantified to obtain the retinal arterial and venous trajectories using a validated, semiautomated algorithm. Analysis of covariance and regression analyses were conducted to examine group differences. A supervised machine-learning ensemble of bagged-trees method was used for automated classification of trajectory values. RESULTS There was a significant difference among groups in both the retinal venous trajectory (HV: 0.17 ± 0.08; SCZ: 0.25 ± 0.17; BD: 0.27 ± 0.20; P < 0.001) and the arterial trajectory (HV: 0.34 ± 0.15; SCZ: 0.29 ± 0.10; BD: 0.29 ± 0.11; P = 0.003) even after adjusting for age and sex (P < 0.001). On post-hoc analysis, the SCZ and BD groups differed from the HV on retinal venous and arterial trajectories, but there was no difference between SCZ and BD patients. The machine learning showed an accuracy of 86% and 73% for classifying HV versus SCZ and BD, respectively. CONCLUSION Smaller trajectories of retinal arteries indicate wider and flatter curves in SCZ and BD. Considering the relation between retinal/cerebral vasculatures and retinal nerve fiber layer thinness, the retinal vascular trajectory is a potential marker for SCZ and BD. As a relatively affordable investigation, retinal fundus photography should be further explored in SCZ and BD as a potential screening measure.
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Affiliation(s)
- Abhishek Appaji
- Department of Medical Electronics, B. M. S. College of Engineering, Bangalore, India.,University Eye Clinic Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Bhargavi Nagendra
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Dona M Chako
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Ananth Padmanabha
- Department of Medical Electronics, B. M. S. College of Engineering, Bangalore, India
| | - Arpitha Jacob
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Chaitra V Hiremath
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Shivarama Varambally
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Muralidharan Kesavan
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | | | - Shyam V Rao
- Department of Medical Electronics, B. M. S. College of Engineering, Bangalore, India.,University Eye Clinic Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Carroll A B Webers
- University Eye Clinic Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Tos T J M Berendschot
- University Eye Clinic Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Naren P Rao
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
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14
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Belizario GO, Junior RGB, Salvini R, Lafer B, Dias RDS. Predominant polarity classification and associated clinical variables in bipolar disorder: A machine learning approach. J Affect Disord 2019; 245:279-282. [PMID: 30419527 DOI: 10.1016/j.jad.2018.11.051] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Revised: 10/09/2018] [Accepted: 11/03/2018] [Indexed: 10/27/2022]
Abstract
BACKGROUND Bipolar disorder (BD) is a severe psychiatric disorder characterized by periodic episodes of manic and depressive symptomatology. Predominant polarity (PP) appears to be an important specifier of BD. The present study employed machine learning (ML) algorithms to accurately determine a patient´s PP without the inclusion of number and polarity of past episodes, while exploring associations between PP and demographic and clinical variables. METHODS From a cohort of 148 BD patients, demographic and clinical variables were collected using a customized questionnaire and the SCID-CV. The algorithm employed was the Random-Forest method. The algorithm was programed to classify patients into either depressive or manic predominant polarities and to reveal which variables were associated to the specifier. RESULTS The algorithm attained an AUC ROC of 74.72% (95% CI = 72.29-77.15%) in classifying patients into either manic or depressive PP. The variables selected by the algorithm were: (1) age at first depressive episode; (2) number of hospitalizations; (3) BD Type II; (4) manic onset; (5) delusions; (6) psychotic features at onset; (7) tobacco addiction; (8) family history of BD; (9) hallucinations; and (10) comorbid anxiety disorders, (11) alcohol dependence, (12) eating disorders and (13) substance dependence. LIMITATIONS The study is limited due to the small sample size, the inclusion of only self-reported and clinician-observed clinical variables and its cross-sectional design. DISCUSSION The results suggest that the ML approach could be effective in determining a patient´s PP. Furthermore, although not previously reported, some variables, such as tobacco use and comorbid eating disorders, appear to be closely associated with PP.
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Affiliation(s)
- Gabriel Okawa Belizario
- Bipolar Disorder Research Program (PROMAN), Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, Rua Dr. Ovidio Pires de Campos, 785 - 3 andar / Ala norte / Ceapesq / Sala 4, 05403-010 São Paulo, Brazil.
| | | | - Rogerio Salvini
- Instituto de Informática, Universidade Federal de Goiás (INF/UFG), Brazil.
| | - Beny Lafer
- Bipolar Disorder Research Program (PROMAN), Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, Rua Dr. Ovidio Pires de Campos, 785 - 3 andar / Ala norte / Ceapesq / Sala 4, 05403-010 São Paulo, Brazil
| | - Rodrigo da Silva Dias
- Bipolar Disorder Research Program (PROMAN), Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, Rua Dr. Ovidio Pires de Campos, 785 - 3 andar / Ala norte / Ceapesq / Sala 4, 05403-010 São Paulo, Brazil
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15
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From the microscope to the magnet: Disconnection in schizophrenia and bipolar disorder. Neurosci Biobehav Rev 2019; 98:47-57. [PMID: 30629976 DOI: 10.1016/j.neubiorev.2019.01.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 12/22/2018] [Accepted: 01/06/2019] [Indexed: 12/15/2022]
Abstract
White matter (WM) abnormalities have implicated schizophrenia (SZ) and bipolar disorder (BD) as disconnection syndromes, yet the extent to which these abnormalities are shared versus distinct remains unclear. Diffusion tensor imaging (DTI) studies yield a putative measure of WM integrity while neuropathological studies provide more specific microstructural information. We therefore systematically reviewed all neuropathological (n = 12) and DTI (n = 11) studies directly comparing patients with SZ and BD. Most studies (18/23) reported no difference between patient groups. Changes in oligodendrocyte density, myelin staining and gene, protein and mRNA expression were found in SZ and/or BD patients as compared to healthy individuals, while DTI studies showed common alterations in thalamic radiations, uncinate fasciculus, corpus callosum, longitudinal fasciculus and corona radiata. Altogether, findings suggest shared disconnectivity in SZ and BD, which are likely related to their considerable overlap. Above all, neuroimaging findings corroborated neuropathological findings in the prefrontal cortex, demonstrating the utility of integrating multiple methodologies. Focusing on clinical dimensions over disease entities will advance our understanding of disconnectivity and help inform preventive medicine.
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16
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Bonifazi P, Erramuzpe A, Diez I, Gabilondo I, Boisgontier MP, Pauwels L, Stramaglia S, Swinnen SP, Cortes JM. Structure-function multi-scale connectomics reveals a major role of the fronto-striato-thalamic circuit in brain aging. Hum Brain Mapp 2018; 39:4663-4677. [PMID: 30004604 DOI: 10.1002/hbm.24312] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 06/27/2018] [Accepted: 06/28/2018] [Indexed: 12/15/2022] Open
Abstract
Physiological aging affects brain structure and function impacting morphology, connectivity, and performance. However, whether some brain connectivity metrics might reflect the age of an individual is still unclear. Here, we collected brain images from healthy participants (N = 155) ranging from 10 to 80 years to build functional (resting state) and structural (tractography) connectivity matrices, both data sets combined to obtain different connectivity features. We then calculated the brain connectome age-an age estimator resulting from a multi-scale methodology applied to the structure-function connectome, and compared it to the chronological age (ChA). Our results were twofold. First, we found that aging widely affects the connectivity of multiple structures, such as anterior cingulate and medial prefrontal cortices, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus, and temporal pole. Second, we found that the connectivity between basal ganglia and thalamus to frontal areas, also known as the fronto-striato-thalamic (FST) circuit, makes the major contribution to age estimation. In conclusion, our results highlight the key role played by the FST circuit in the process of healthy aging. Notably, the same methodology can be generally applied to identify the structural-functional connectivity patterns correlating to other biomarkers than ChA.
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Affiliation(s)
- Paolo Bonifazi
- Biocruces Health Research Institute, Barakaldo, Spain.,IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain
| | | | - Ibai Diez
- Biocruces Health Research Institute, Barakaldo, Spain
| | | | - Matthieu P Boisgontier
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Lisa Pauwels
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Universita di Bari, and INFN, Sezione di Bari, Italy
| | - Stephan P Swinnen
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium.,Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Jesus M Cortes
- Biocruces Health Research Institute, Barakaldo, Spain.,IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain.,Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
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17
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Rubin-Falcone H, Zanderigo F, Thapa-Chhetry B, Lan M, Miller JM, Sublette ME, Oquendo MA, Hellerstein DJ, McGrath PJ, Stewart JW, Mann JJ. Pattern recognition of magnetic resonance imaging-based gray matter volume measurements classifies bipolar disorder and major depressive disorder. J Affect Disord 2018; 227:498-505. [PMID: 29156364 PMCID: PMC5805651 DOI: 10.1016/j.jad.2017.11.043] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 11/06/2017] [Accepted: 11/11/2017] [Indexed: 01/10/2023]
Abstract
BACKGROUND Bipolar Disorder (BD) cannot be reliably distinguished from Major Depressive Disorder (MDD) until the first manic or hypomanic episode. Consequently, many patients with BD are treated with antidepressants without mood stabilizers, a strategy that is often ineffective and carries a risk of inducing a manic episode. We previously reported reduced cortical thickness in right precuneus, right caudal middle-frontal cortex and left inferior parietal cortex in BD compared with MDD. METHODS This study extends our previous work by performing individual level classification of BD or MDD in an expanded, currently unmedicated, cohort using gray matter volume (GMV) based on Magnetic Resonance Imaging and a Support Vector Machine. All patients were in a Major Depressive Episode and a leave-two-out analysis was performed. RESULTS Nineteen out of 26 BD subjects and 20 out of 26 MDD subjects were correctly identified, for a combined accuracy of 75%. The three brain regions contributing to the classification were higher GMV in bilateral supramarginal gyrus and occipital cortex indicating MDD, and higher GMV in right dorsolateral prefrontal cortex indicating BD. LIMITATIONS This analysis included scans performed with two different headcoils and scan sequences, which limited the interpretability of results in an independent cohort analysis. CONCLUSIONS Our results add to previously published data which suggest that regional gray matter volume should be investigated further as a clinical diagnostic tool to predict BD before the appearance of a manic or hypomanic episode.
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Affiliation(s)
- Harry Rubin-Falcone
- Department of Psychiatry, Columbia University, New York, NY, USA; Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, NY, USA.
| | - Francesca Zanderigo
- Department of Psychiatry, Columbia University, New York, NY, USA; Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, NY, USA
| | - Binod Thapa-Chhetry
- Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, NY, USA
| | - Martin Lan
- Department of Psychiatry, Columbia University, New York, NY, USA; Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, NY, USA
| | - Jeffrey M Miller
- Department of Psychiatry, Columbia University, New York, NY, USA; Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, NY, USA
| | - M Elizabeth Sublette
- Department of Psychiatry, Columbia University, New York, NY, USA; Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, NY, USA
| | - Maria A Oquendo
- Now at Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA
| | - David J Hellerstein
- Department of Psychiatry, Columbia University, New York, NY, USA; Depression Evaluation Service, Division of Clinical Therapeutics, New York State Psychiatric Institute, New York, NY, USA
| | - Patrick J McGrath
- Department of Psychiatry, Columbia University, New York, NY, USA; Depression Evaluation Service, Division of Clinical Therapeutics, New York State Psychiatric Institute, New York, NY, USA
| | - Johnathan W Stewart
- Department of Psychiatry, Columbia University, New York, NY, USA; Depression Evaluation Service, Division of Clinical Therapeutics, New York State Psychiatric Institute, New York, NY, USA
| | - J John Mann
- Department of Psychiatry, Columbia University, New York, NY, USA; Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, NY, USA; Department of Radiology, Columbia University, New York, NY, USA
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18
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Chen Y, Sha M, Zhao X, Ma J, Ni H, Gao W, Ming D. Automated detection of pathologic white matter alterations in Alzheimer's disease using combined diffusivity and kurtosis method. Psychiatry Res Neuroimaging 2017; 264:35-45. [PMID: 28448817 DOI: 10.1016/j.pscychresns.2017.04.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Revised: 04/01/2017] [Accepted: 04/12/2017] [Indexed: 10/19/2022]
Abstract
Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) are important diffusion MRI techniques for detecting microstructure abnormities in diseases such as Alzheimer's. The advantages of DKI over DTI have been reported generally; however, the indistinct relationship between diffusivity and kurtosis has not been clearly revealed in clinical settings. In this study, we hypothesize that the combination of diffusivity and kurtosis in DKI improves the capacity of DKI to detect Alzheimer's disease compared with diffusivity or kurtosis alone. Specifically, a support vector machine-based approach was applied to combine diffusivity and kurtosis and to compare different indices datasets. Strict assessments were conducted to ensure the reliability of all classifiers. Then, data from the optimized classifiers were used to detect abnormalities. With the combination, high accuracy performances of 96.23% were obtained in 53 subjects, including 27 Alzheimer's patients. More highly scored abnormal regions were selected by the combination than alone. The results revealed that more precise diffusivity and complementary kurtosis mainly contributed to the high performance of the combination in DKI. This study provides further understanding of DKI and the relationship between diffusivity and kurtosis in pathologic white matter alterations in Alzheimer's disease.
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Affiliation(s)
- Yuanyuan Chen
- School of Electronics and Information Engineering, Tianjin University, Tianjin, China.
| | - Miao Sha
- The Neural Engineering & Rehabilitation lab, Tianjin University, Tianjin, China.
| | - Xin Zhao
- The Neural Engineering & Rehabilitation lab, Tianjin University, Tianjin, China.
| | - Jianguo Ma
- School of Electronics and Information Engineering, Tianjin University, Tianjin, China.
| | - Hongyan Ni
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China.
| | - Wei Gao
- Department of Biomedical Sciences and Academic Imaging, Cedars-Sinai Medical Center, CA, USA.
| | - Dong Ming
- The Neural Engineering & Rehabilitation lab, Tianjin University, Tianjin, China.
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19
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Fleck DE, Ernest N, Adler CM, Cohen K, Eliassen JC, Norris M, Komoroski RA, Chu WJ, Welge JA, Blom TJ, DelBello MP, Strakowski SM. Prediction of lithium response in first-episode mania using the LITHium Intelligent Agent (LITHIA): Pilot data and proof-of-concept. Bipolar Disord 2017; 19:259-272. [PMID: 28574156 PMCID: PMC5517343 DOI: 10.1111/bdi.12507] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Revised: 04/06/2017] [Accepted: 04/16/2017] [Indexed: 01/27/2023]
Abstract
OBJECTIVES Individualized treatment for bipolar disorder based on neuroimaging treatment targets remains elusive. To address this shortcoming, we developed a linguistic machine learning system based on a cascading genetic fuzzy tree (GFT) design called the LITHium Intelligent Agent (LITHIA). Using multiple objectively defined functional magnetic resonance imaging (fMRI) and proton magnetic resonance spectroscopy (1 H-MRS) inputs, we tested whether LITHIA could accurately predict the lithium response in participants with first-episode bipolar mania. METHODS We identified 20 subjects with first-episode bipolar mania who received an adequate trial of lithium over 8 weeks and both fMRI and 1 H-MRS scans at baseline pre-treatment. We trained LITHIA using 18 1 H-MRS and 90 fMRI inputs over four training runs to classify treatment response and predict symptom reductions. Each training run contained a randomly selected 80% of the total sample and was followed by a 20% validation run. Over a different randomly selected distribution of the sample, we then compared LITHIA to eight common classification methods. RESULTS LITHIA demonstrated nearly perfect classification accuracy and was able to predict post-treatment symptom reductions at 8 weeks with at least 88% accuracy in training and 80% accuracy in validation. Moreover, LITHIA exceeded the predictive capacity of the eight comparator methods and showed little tendency towards overfitting. CONCLUSIONS The results provided proof-of-concept that a novel GFT is capable of providing control to a multidimensional bioinformatics problem-namely, prediction of the lithium response-in a pilot data set. Future work on this, and similar machine learning systems, could help assign psychiatric treatments more efficiently, thereby optimizing outcomes and limiting unnecessary treatment.
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Affiliation(s)
- David E. Fleck
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA,Center for Imaging Research, University of Cincinnati College of Medicine, Cincinnati, OH, USA,Address correspondence to Dr. Fleck, University of Cincinnati Department of Psychiatry and Behavioral Neuroscience, Cincinnati OH 45229-3019. ; Telephone: (513) 558-4940; Fax: (513) 558-3399
| | | | - Caleb M. Adler
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA,Center for Imaging Research, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Kelly Cohen
- Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati College of Engineering and Applied Science, Cincinnati, OH, USA
| | - James C. Eliassen
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA,Center for Imaging Research, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Matthew Norris
- Center for Imaging Research, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Richard A. Komoroski
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA,Center for Imaging Research, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Wen-Jang Chu
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA,Center for Imaging Research, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Jeffrey A. Welge
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Thomas J. Blom
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Melissa P. DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Stephen M. Strakowski
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA,Center for Imaging Research, University of Cincinnati College of Medicine, Cincinnati, OH, USA
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20
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Wu MJ, Passos IC, Bauer IE, Lavagnino L, Cao B, Zunta-Soares GB, Kapczinski F, Mwangi B, Soares JC. Individualized identification of euthymic bipolar disorder using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and machine learning. J Affect Disord 2016; 192:219-25. [PMID: 26748737 PMCID: PMC4727980 DOI: 10.1016/j.jad.2015.12.053] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2015] [Revised: 12/17/2015] [Accepted: 12/26/2015] [Indexed: 11/26/2022]
Abstract
BACKGROUND Previous studies have reported that patients with bipolar disorder (BD) present with cognitive impairments during mood episodes as well as euthymic phase. However, it is still unknown whether reported neurocognitive abnormalities can objectively identify individual BD patients from healthy controls (HC). METHODS A total of 21 euthymic BD patients and 21 demographically matched HC were included in the current study. Participants performed the computerized Cambridge Neurocognitive Test Automated Battery (CANTAB) to assess cognitive performance. The least absolute shrinkage selection operator (LASSO) machine learning algorithm was implemented to identify neurocognitive signatures to distinguish individual BD patients from HC. RESULTS The LASSO machine learning algorithm identified individual BD patients from HC with an accuracy of 71%, area under receiver operating characteristic curve of 0.7143 and significant at p=0.0053. The LASSO algorithm assigned individual subjects with a probability score (0-healthy, 1-patient). Patients with rapid cycling (RC) were assigned increased probability scores as compared to patients without RC. A multivariate pattern of neurocognitive abnormalities comprising of affective Go/No-go and the Cambridge gambling task was relevant in distinguishing individual patients from HC. LIMITATIONS Our study sample was small as we only considered euthymic BD patients and demographically matched HC. CONCLUSION Neurocognitive abnormalities can distinguish individual euthymic BD patients from HC with relatively high accuracy. In addition, patients with RC had more cognitive impairments compared to patients without RC. The predictive neurocognitive signature identified in the current study can potentially be used to provide individualized clinical inferences on BD patients.
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Affiliation(s)
- Mon-Ju Wu
- UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
| | - Ives Cavalcante Passos
- UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA,Bipolar Disorder Program and Laboratory of Molecular Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Isabelle E. Bauer
- UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
| | - Luca Lavagnino
- UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
| | - Bo Cao
- UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
| | - Giovana B. Zunta-Soares
- UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
| | - Flávio Kapczinski
- UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA,Bipolar Disorder Program and Laboratory of Molecular Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Benson Mwangi
- UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, TX, USA.
| | - Jair C. Soares
- UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
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