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Belov V, Erwin-Grabner T, Aghajani M, Aleman A, Amod AR, Basgoze Z, Benedetti F, Besteher B, Bülow R, Ching CRK, Connolly CG, Cullen K, Davey CG, Dima D, Dols A, Evans JW, Fu CHY, Gonul AS, Gotlib IH, Grabe HJ, Groenewold N, Hamilton JP, Harrison BJ, Ho TC, Mwangi B, Jaworska N, Jahanshad N, Klimes-Dougan B, Koopowitz SM, Lancaster T, Li M, Linden DEJ, MacMaster FP, Mehler DMA, Melloni E, Mueller BA, Ojha A, Oudega ML, Penninx BWJH, Poletti S, Pomarol-Clotet E, Portella MJ, Pozzi E, Reneman L, Sacchet MD, Sämann PG, Schrantee A, Sim K, Soares JC, Stein DJ, Thomopoulos SI, Uyar-Demir A, van der Wee NJA, van der Werff SJA, Völzke H, Whittle S, Wittfeld K, Wright MJ, Wu MJ, Yang TT, Zarate C, Veltman DJ, Schmaal L, Thompson PM, Goya-Maldonado R. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures. Sci Rep 2024; 14:1084. [PMID: 38212349 PMCID: PMC10784593 DOI: 10.1038/s41598-023-47934-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 11/19/2023] [Indexed: 01/13/2024] Open
Abstract
Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.
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Affiliation(s)
- Vladimir Belov
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Georg-August University, Von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Tracy Erwin-Grabner
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Georg-August University, Von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Moji Aghajani
- Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Institute of Education and Child Studies, Section Forensic Family and Youth Care, Leiden University, Leiden, The Netherlands
| | - Andre Aleman
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Alyssa R Amod
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Zeynep Basgoze
- Department of Psychiatry and Behavioral Science, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Francesco Benedetti
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Bianca Besteher
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Robin Bülow
- Institute for Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Colm G Connolly
- Department of Biomedical Sciences, Florida State University, Tallahassee, FL, USA
| | - Kathryn Cullen
- Department of Psychiatry and Behavioral Science, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Christopher G Davey
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia
| | - Danai Dima
- Department of Psychology, School of Arts and Social Sciences, City, University of London, London, UK
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Annemiek Dols
- Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jennifer W Evans
- Experimental Therapeutics and Pathophysiology Branch, National Institute for Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Cynthia H Y Fu
- School of Psychology, University of East London, London, UK
- Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ali Saffet Gonul
- SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Nynke Groenewold
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - J Paul Hamilton
- Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
- Center for Medical Imaging and Visualization, Linköping University, Linköping, Sweden
| | - Ben J Harrison
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia
| | - Tiffany C Ho
- Department of Psychiatry and Behavioral Sciences, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Benson Mwangi
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Center Of Excellence On Mood Disorders, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Natalia Jaworska
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | | | | | - Thomas Lancaster
- Cardiff University Brain Research Imaging Center, Cardiff University, Cardiff, UK
- MRC Center for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Meng Li
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - David E J Linden
- Cardiff University Brain Research Imaging Center, Cardiff University, Cardiff, UK
- MRC Center for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Frank P MacMaster
- Departments of Psychiatry and Pediatrics, University of Calgary, Calgary, AB, Canada
| | - David M A Mehler
- Cardiff University Brain Research Imaging Center, Cardiff University, Cardiff, UK
- MRC Center for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
| | - Elisa Melloni
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Bryon A Mueller
- Department of Psychiatry and Behavioral Science, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Amar Ojha
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mardien L Oudega
- Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sara Poletti
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Catalonia, Spain
| | - Maria J Portella
- Sant Pau Mental Health Research Group, Institut de Recerca de L'Hospital de La Santa Creu I Sant Pau, Barcelona, Catalonia, Spain
| | - Elena Pozzi
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Liesbeth Reneman
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Matthew D Sacchet
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Anouk Schrantee
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Jair C Soares
- Center Of Excellence On Mood Disorders, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Dan J Stein
- SA MRC Research Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Aslihan Uyar-Demir
- SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey
| | - Nic J A van der Wee
- Leiden Institute for Brain and Cognition, Leiden University Medical Center, Leiden, The Netherlands
| | - Steven J A van der Werff
- Leiden Institute for Brain and Cognition, Leiden University Medical Center, Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Sarah Whittle
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/ Greifswald, Greifswald, Germany
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
| | - Mon-Ju Wu
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Center Of Excellence On Mood Disorders, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Tony T Yang
- Department of Psychiatry and Behavioral Sciences, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Carlos Zarate
- Section on the Neurobiology and Treatment of Mood Disorders, National Institute of Mental Health, Bethesda, MD, USA
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lianne Schmaal
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Roberto Goya-Maldonado
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Georg-August University, Von-Siebold-Str. 5, 37075, Göttingen, Germany.
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Mou J, Zheng T, Long Z, Mei L, Wang Y, Yuan Y, Guo X, Yang H, Gong Q, Qiu L. Sex differences of brain cortical structure in major depressive disorder. PSYCHORADIOLOGY 2023; 3:kkad014. [PMID: 38666130 PMCID: PMC10939343 DOI: 10.1093/psyrad/kkad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 08/16/2023] [Accepted: 09/01/2023] [Indexed: 04/28/2024]
Abstract
Background Major depressive disorder (MDD) has different clinical presentations in males and females. However, the neuroanatomical mechanisms underlying these sex differences are not fully understood. Objective The purpose of present study was to explore the sex differences in brain cortical thickness (CT) and surface area (SA) of MDD and the relationship between these differences and clinical manifestations in different gender. Methods High-resolution T1-weighted images were acquired from 61 patients with MDD and 61 healthy controls (36 females and 25 males, both). The sex differences in CT and SA were obtained using the FreeSurfer software and compared between every two groups by post hoc test. Spearman correlation analysis was also performed to explore the relationships between these regions and clinical characteristics. Results In male patients with MDD, the CT of the right precentral was thinner compared to female patients, although this did not survive Bonferroni correction. The SA of several regions, including right superior frontal, medial orbitofrontal gyrus, inferior frontal gyrus triangle, superior temporal, middle temporal, lateral occipital gyrus, and inferior parietal lobule in female patients with MDD was smaller than that in male patients (P < 0.01 after Bonferroni correction). In female patients, the SA of the right superior temporal (r = 0.438, P = 0.008), middle temporal (r = 0.340, P = 0.043), and lateral occipital gyrus (r = 0.372, P = 0.025) were positively correlated with illness duration. Conclusion The current study provides evidence of sex differences in CT and SA in patients with MDD, which may improve our understanding of the sex-specific neuroanatomical changes in the development of MDD.
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Affiliation(s)
- Jingping Mou
- Department of Radiology, the Second People's Hospital of Yibin, Yibin 644000, China
- Department of Radiology, the Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
- Department of Radiology, the First People's Hospital of Yibin, Yibin 644000, China
| | - Ting Zheng
- Department of Radiology, the Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
| | - Zhiliang Long
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Lan Mei
- Department of Radiology, the Second People's Hospital of Yibin, Yibin 644000, China
| | - Yuting Wang
- Department of Radiology, the Second People's Hospital of Yibin, Yibin 644000, China
- Department of Radiology, the Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
| | - Yizhi Yuan
- Department of Radiology, the Second People's Hospital of Yibin, Yibin 644000, China
| | - Xin Guo
- Department of Radiology, the Second People's Hospital of Yibin, Yibin 644000, China
- Department of Radiology, the Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
| | - Hongli Yang
- Department of Radiology, the Second People's Hospital of Yibin, Yibin 644000, China
- Department of Radiology, the Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu 610041, China
| | - Lihua Qiu
- Department of Radiology, the Second People's Hospital of Yibin, Yibin 644000, China
- Department of Radiology, the Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
- Research Center of Neuroimaging big data, the Second People's Hospital of Yibin,Yibin 644000, China
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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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Xu M, Zhang X, Li Y, Chen S, Zhang Y, Zhou Z, Lin S, Dong T, Hou G, Qiu Y. Identification of suicidality in patients with major depressive disorder via dynamic functional network connectivity signatures and machine learning. Transl Psychiatry 2022; 12:383. [PMID: 36097160 PMCID: PMC9467986 DOI: 10.1038/s41398-022-02147-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/24/2022] [Accepted: 09/01/2022] [Indexed: 11/09/2022] Open
Abstract
Major depressive disorder (MDD) is a severe brain disease associated with a significant risk of suicide. Identification of suicidality is sometimes life-saving for MDD patients. We aimed to explore the use of dynamic functional network connectivity (dFNC) for suicidality detection in MDD patients. A total of 173 MDD patients, including 48 without suicide risk (NS), 74 with suicide ideation (SI), and 51 having attempted suicide (SA), participated in the present study. Thirty-eight healthy controls were also recruited for comparison. A sliding window approach was used to derive the dFNC, and the K-means clustering method was used to cluster the windowed dFNC. A linear support vector machine was used for classification, and leave-one-out cross-validation was performed for validation. Other machine learning methods were also used for comparison. MDD patients had widespread hypoconnectivity in both the strongly connected states (states 2 and 5) and the weakly connected state (state 4), while the dysfunctional connectivity within the weakly connected state (state 4) was mainly driven by suicidal attempts. Furthermore, dFNC matrices, especially the weakly connected state, could be used to distinguish MDD from healthy controls (area under curve [AUC] = 82), and even to identify suicidality in MDD patients (AUC = 78 for NS vs. SI, AUC = 88 for NS vs. SA, and AUC = 74 for SA vs. SI), with vision-related and default-related inter-network connectivity serving as important features. Thus, the dFNC abnormalities observed in this study might further improve our understanding of the neural substrates of suicidality in MDD patients.
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Affiliation(s)
- Manxi Xu
- grid.410737.60000 0000 8653 1072Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan district, Guangzhou, People’s Republic of China ,grid.33199.310000 0004 0368 7223Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, 518000 People’s Republic of China
| | - Xiaojing Zhang
- grid.263488.30000 0001 0472 9649Guangdong Provincial Key Laboratory of Genome Stability and Disease Prevention and Regional Immunity and Diseases, Department of Pathology, Shenzhen University School of Medicine, Shenzhen, Guangdong, 518060 People’s Republic of China
| | - Yanqing Li
- grid.410737.60000 0000 8653 1072Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan district, Guangzhou, People’s Republic of China
| | - Shengli Chen
- grid.33199.310000 0004 0368 7223Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, 518000 People’s Republic of China
| | - Yingli Zhang
- grid.452897.50000 0004 6091 8446Department of Psychiatry, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, 518020 People’s Republic of China
| | - Zhifeng Zhou
- grid.452897.50000 0004 6091 8446Department of Radiology, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, 518020 People’s Republic of China
| | - Shiwei Lin
- grid.33199.310000 0004 0368 7223Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, 518000 People’s Republic of China
| | - Tianfa Dong
- grid.410737.60000 0000 8653 1072Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan district, Guangzhou, People’s Republic of China
| | - Gangqiang Hou
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, 518020, People's Republic of China.
| | - Yingwei Qiu
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, 518000, People's Republic of China.
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Personalized Diagnosis and Treatment for Neuroimaging in Depressive Disorders. J Pers Med 2022; 12:jpm12091403. [PMID: 36143188 PMCID: PMC9504356 DOI: 10.3390/jpm12091403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/26/2022] [Accepted: 08/26/2022] [Indexed: 01/10/2023] Open
Abstract
Depressive disorders are highly heterogeneous in nature. Previous studies have not been useful for the clinical diagnosis and prediction of outcomes of major depressive disorder (MDD) at the individual level, although they provide many meaningful insights. To make inferences beyond group-level analyses, machine learning (ML) techniques can be used for the diagnosis of subtypes of MDD and the prediction of treatment responses. We searched PubMed for relevant studies published until December 2021 that included depressive disorders and applied ML algorithms in neuroimaging fields for depressive disorders. We divided these studies into two sections, namely diagnosis and treatment outcomes, for the application of prediction using ML. Structural and functional magnetic resonance imaging studies using ML algorithms were included. Thirty studies were summarized for the prediction of an MDD diagnosis. In addition, 19 studies on the prediction of treatment outcomes for MDD were reviewed. We summarized and discussed the results of previous studies. For future research results to be useful in clinical practice, ML enabling individual inferences is important. At the same time, there are important challenges to be addressed in the future.
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Abstract
BACKGROUND In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis. METHODS This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis. RESULTS A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test. CONCLUSION The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.
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Affiliation(s)
- Sweta Bhadra
- Department of CS & IT, Cotton University, Guwahati, India
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Zhou Z, Wang K, Tang J, Wei D, Song L, Peng Y, Fu Y, Qiu J. Cortical thickness distinguishes between major depression and schizophrenia in adolescents. BMC Psychiatry 2021; 21:361. [PMID: 34284747 PMCID: PMC8293570 DOI: 10.1186/s12888-021-03373-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 07/08/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Early diagnosis of adolescent psychiatric disorder is crucial for early intervention. However, there is extensive comorbidity between affective and psychotic disorders, which increases the difficulty of precise diagnoses among adolescents. METHODS We obtained structural magnetic resonance imaging scans from 150 adolescents, including 67 and 47 patients with major depressive disorder (MDD) and schizophrenia (SCZ), as well as 34 healthy controls (HC) to explore whether psychiatric disorders could be identified using a machine learning technique. Specifically, we used the support vector machine and the leave-one-out cross-validation method to distinguish among adolescents with MDD and SCZ and healthy controls. RESULTS We found that cortical thickness was a classification feature of a) MDD and HC with 79.21% accuracy where the temporal pole had the highest weight; b) SCZ and HC with 69.88% accuracy where the left superior temporal sulcus had the highest weight. Notably, adolescents with MDD and SCZ could be classified with 62.93% accuracy where the right pars triangularis had the highest weight. CONCLUSIONS Our findings suggest that cortical thickness may be a critical biological feature in the diagnosis of adolescent psychiatric disorders. These findings might be helpful to establish an early prediction model for adolescents to better diagnose psychiatric disorders.
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Affiliation(s)
- Zheyi Zhou
- grid.419897.a0000 0004 0369 313XKey Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715 China ,grid.263906.8Faculty of Psychology, Southwest University, No.2 Tiansheng Road, Beibei District, Chongqing, 400715 China
| | - Kangcheng Wang
- grid.410585.d0000 0001 0495 1805Faculty of Psychology, Shandong Normal University, Jinan, 250014 Shandong China
| | - Jinxiang Tang
- grid.452206.7Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, No.1, Yixueyuan Road, Yuzhong District, Chongqing, 400016 China ,Sleep and Psychology Center, The Bishan Hospital of Chongqing, Chongqing, 402760 China
| | - Dongtao Wei
- grid.419897.a0000 0004 0369 313XKey Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715 China ,grid.263906.8Faculty of Psychology, Southwest University, No.2 Tiansheng Road, Beibei District, Chongqing, 400715 China
| | - Li Song
- grid.419897.a0000 0004 0369 313XKey Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715 China ,grid.263906.8Faculty of Psychology, Southwest University, No.2 Tiansheng Road, Beibei District, Chongqing, 400715 China
| | - Yadong Peng
- grid.452206.7Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, No.1, Yixueyuan Road, Yuzhong District, Chongqing, 400016 China ,Department of Psychology, Chongqing Health Center for Women and Children, Chongqing, 401147 China
| | - Yixiao Fu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, No.1, Yixueyuan Road, Yuzhong District, Chongqing, 400016, China.
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China. .,Faculty of Psychology, Southwest University, No.2 Tiansheng Road, Beibei District, Chongqing, 400715, China. .,Collaborative Innovation Center of Assessment Toward Basic Education Quality, Southwest University Branch, Beijing Normal University, Beijing, 100875, China.
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8
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Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies. Front Med 2021; 15:528-540. [PMID: 33511554 DOI: 10.1007/s11684-020-0798-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 04/25/2020] [Indexed: 02/05/2023]
Abstract
Major depressive disorder (MDD) causes great decrements in health and quality of life with increments in healthcare costs, but the causes and pathogenesis of depression remain largely unknown, which greatly prevent its early detection and effective treatment. With the advancement of neuroimaging approaches, numerous functional and structural alterations in the brain have been detected in MDD and more recently attempts have been made to apply these findings to clinical practice. In this review, we provide an updated summary of the progress in translational application of psychoradiological findings in MDD with a specified focus on potential clinical usage. The foreseeable clinical applications for different MRI modalities were introduced according to their role in disorder classification, subtyping, and prediction. While evidence of cerebral structural and functional changes associated with MDD classification and subtyping was heterogeneous and/or sparse, the ACC and hippocampus have been consistently suggested to be important biomarkers in predicting treatment selection and treatment response. These findings underlined the potential utility of brain biomarkers for clinical practice.
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9
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Stolicyn A, Harris MA, Shen X, Barbu MC, Adams MJ, Hawkins EL, de Nooij L, Yeung HW, Murray AD, Lawrie SM, Steele JD, McIntosh AM, Whalley HC. Automated classification of depression from structural brain measures across two independent community-based cohorts. Hum Brain Mapp 2020; 41:3922-3937. [PMID: 32558996 PMCID: PMC7469862 DOI: 10.1002/hbm.25095] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 05/16/2020] [Accepted: 05/25/2020] [Indexed: 12/30/2022] Open
Abstract
Major depressive disorder (MDD) has been the subject of many neuroimaging case-control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically-ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well-phenotyped community-based group of current MDD cases with clinical interview-based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, 'STRADL'). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types-SVM, penalised logistic regression or decision tree-either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population-based sample with self-reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses-remitted MDD in STRADL, and lifetime-experienced MDD in UK Biobank. The highest cross-validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self-reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime-experienced MDD (52.68-60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.
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Affiliation(s)
- Aleks Stolicyn
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Mathew A. Harris
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Xueyi Shen
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Miruna C. Barbu
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Mark J. Adams
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Emma L. Hawkins
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Laura de Nooij
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Hon Wah Yeung
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Alison D. Murray
- Aberdeen Biomedical Imaging CentreUniversity of AberdeenLilian Sutton Building, ForesterhillAberdeenUK
| | - Stephen M. Lawrie
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - J. Douglas Steele
- School of Medicine (Division of Imaging Science and Technology)University of DundeeDundeeUK
| | - Andrew M. McIntosh
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
| | - Heather C. Whalley
- Division of Psychiatry, University of EdinburghKennedy Tower, Royal Edinburgh Hospital, Morningside ParkEdinburghUK
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10
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Cortical and subcortical gray matter alterations in first-episode drug-naïve adolescents with major depressive disorder. Neuroreport 2020; 30:1172-1178. [PMID: 31568197 PMCID: PMC6855326 DOI: 10.1097/wnr.0000000000001336] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Major depressive disorder is a major mental disorder affecting adolescents. Cortical thickness provides a sensitive measure of age-associated changes. Previous studies using cortical thickness analysis reported inconsistent results on brain structural changes in adolescent major depressive disorder. The neuroanatomical substrates of major depressive disorder in adolescents are not fully understood. We aimed to compare the anatomical structures of the brain in first-onset drug-naïve adolescents with major depressive disorder to normal controls. Twenty-seven first-episode drug-naïve adolescents with major depressive disorder and an equal number of age-matched control subjects were scanned on a 3T MRI scanner. Comparisons between those two groups were performed using surface-based morphometry analysis for cortical thickness and volumetric analysis of subcortical gray matter. The correlations between morphometric indexes and clinical measures (Hamilton depression rating scale score or children’s depression inventory score) were also calculated. We found that the cortical area is thinner in major depressive disorder patients than in controls, specifically in the left occipital area (precuneus and cuneus, cluster-level family-wise corrected P < 0.05). The hippocampus volume was also smaller in major depressive disorder patients than in the control group. No significant correlations were found between morphometric indexes (average cortical thickness extracted from the left precuneus cluster and hippocampal volume) and clinical measures. The left occipital cortical regions may have a role in the pathophysiology of adolescent major depressive disorder, and the involvement of the hippocampus is important for pathogenic changes even in the early stages of major depressive disorder.
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11
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Huang X, Gong Q, Sweeney JA, Biswal BB. Progress in psychoradiology, the clinical application of psychiatric neuroimaging. Br J Radiol 2019; 92:20181000. [PMID: 31170803 PMCID: PMC6732936 DOI: 10.1259/bjr.20181000] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 05/09/2019] [Accepted: 05/21/2019] [Indexed: 02/05/2023] Open
Abstract
Psychoradiology is an emerging field that applies radiological imaging technologies to psychiatric conditions. In the past three decades, brain imaging techniques have rapidly advanced understanding of illness and treatment effects in psychiatry. Based on these advances, radiologists have become increasingly interested in applying these advances for differential diagnosis and individualized patient care selection for common psychiatric illnesses. This shift from research to clinical practice represents the beginning evolution of psychoradiology. In this review, we provide a summary of recent progress relevant to this field based on their clinical functions, namely the (1) classification and subtyping; (2) prediction and monitoring of treatment outcomes; and (3) treatment selection. In addition, we provide guidelines for the practice of psychoradiology in clinical settings and suggestions for future research to validate broader clinical applications. Given the high prevalence of psychiatric disorders and the importance of increased participation of radiologists in this field, a guide regarding advances in this field and a description of relevant clinical work flow patterns help radiologists contribute to this fast-evolving field.
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Affiliation(s)
| | | | - John A. Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, USA
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12
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Yang J, Zhang M, Ahn H, Zhang Q, Jin TB, Li I, Nemesure M, Joshi N, Jiang H, Miller JM, Ogden RT, Petkova E, Milak MS, Sublette ME, Sullivan GM, Trivedi MH, Weissman M, McGrath PJ, Fava M, Kurian BT, Pizzagalli DA, Cooper CM, McInnis M, Oquendo MA, Mann JJ, Parsey RV, DeLorenzo C. Development and evaluation of a multimodal marker of major depressive disorder. Hum Brain Mapp 2018; 39:4420-4439. [PMID: 30113112 DOI: 10.1002/hbm.24282] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 05/16/2018] [Accepted: 06/04/2018] [Indexed: 12/30/2022] Open
Abstract
This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniques-penalized logistic regression, random forest, and support vector machine (SVM)-were used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r = .36 correlation coefficient (p < .001, continuous). Using SVM, R2 values for prediction of any MDD factors were <10%. Binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. Though observed classification rates are too low for clinical utility, four image-based features contributed to accuracy across all models and analyses-two dMRI-based measures (average fractional anisotropy in the right cuneus and left insula) and two sMRI-based measures (asymmetry in the volume of the pars triangularis and the cerebellum) and may serve as a priori regions for future analyses. The poor accuracy of classification and predictive results found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification. Further, this study suggests a paradigm (e.g., multiple classifier evaluation with external validation) for future studies to avoid nongeneralizable results.
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Affiliation(s)
- Jie Yang
- Department of Family, Population and Preventive Medicine, Stony Brook University, New York, New York
| | - Mengru Zhang
- Department of Applied Mathematics and Statistics, Stony Brook University, New York, New York
| | - Hongshik Ahn
- Department of Applied Mathematics and Statistics, Stony Brook University, New York, New York
| | - Qing Zhang
- Department of Applied Mathematics and Statistics, Stony Brook University, New York, New York
| | - Tony B Jin
- Department of Psychiatry, Stony Brook University, New York, New York
| | - Ien Li
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey
| | - Matthew Nemesure
- Integrative Neuroscience Program, Binghamton University, Binghamton, New York
| | - Nandita Joshi
- Department of Electrical and Computer Engineering, Stony Brook University, New York, New York
| | - Haoran Jiang
- Department of Applied Mathematics and Statistics, Stony Brook University, New York, New York
| | - Jeffrey M Miller
- Department of Psychiatry, Columbia University, New York, New York
| | | | - Eva Petkova
- Department of Child & Adolescent Psychiatry, Department of Population Health, New York University, New York, New York
| | - Matthew S Milak
- Department of Psychiatry, Columbia University, New York, New York
| | | | - Gregory M Sullivan
- Chief Medical Officer, Clinical Research and Development program, Tonix Pharmaceuticals, Inc., New York, New York
| | - Madhukar H Trivedi
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Myrna Weissman
- Department of Psychiatry, Columbia University, New York, New York
| | | | - Maurizio Fava
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
| | - Benji T Kurian
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas
| | | | - Crystal M Cooper
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Melvin McInnis
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan
| | - Maria A Oquendo
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Joseph John Mann
- Department of Psychiatry, Columbia University, New York, New York
| | - Ramin V Parsey
- Department of Psychiatry, Stony Brook University, New York, New York
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13
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Guo H, Li Y, Xu Y, Jin Y, Xiang J, Chen J. Resting-State Brain Functional Hyper-Network Construction Based on Elastic Net and Group Lasso Methods. Front Neuroinform 2018; 12:25. [PMID: 29867426 PMCID: PMC5962886 DOI: 10.3389/fninf.2018.00025] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Accepted: 04/25/2018] [Indexed: 12/16/2022] Open
Abstract
Brain network analysis has been widely applied in neuroimaging studies. A hyper-network construction method was previously proposed to characterize the high-order relationships among multiple brain regions, where every edge is connected to more than two brain regions and can be represented by a hyper-graph. A brain functional hyper-network is constructed by a sparse linear regression model using resting-state functional magnetic resonance imaging (fMRI) time series, which in previous studies has been solved by the lasso method. Despite its successful application in many studies, the lasso method has some limitations, including an inability to explain the grouping effect. That is, using the lasso method may cause relevant brain regions be missed in selecting related regions. Ideally, a hyper-edge construction method should be able to select interacting brain regions as accurately as possible. To solve this problem, we took into account the grouping effect among brain regions and proposed two methods to improve the construction of the hyper-network: the elastic net and the group lasso. The three methods were applied to the construction of functional hyper-networks in depressed patients and normal controls. The results showed structural differences among the hyper-networks constructed by the three methods. The hyper-network structure obtained by the lasso was similar to that obtained by the elastic net method but very different from that obtained by the group lasso. The classification results indicated that the elastic net method achieved better classification results than the lasso method with the two proposed methods of hyper-network construction. The elastic net method can effectively solve the grouping effect and achieve better classification performance.
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Affiliation(s)
- Hao Guo
- Department of Software Engineering, College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.,National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, China
| | - Yao Li
- Department of Software Engineering, College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yong Xu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Yanyi Jin
- Department of Software Engineering, College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- Department of Software Engineering, College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Junjie Chen
- Department of Software Engineering, College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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14
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Rummel C, Slavova N, Seiler A, Abela E, Hauf M, Burren Y, Weisstanner C, Vulliemoz S, Seeck M, Schindler K, Wiest R. Personalized structural image analysis in patients with temporal lobe epilepsy. Sci Rep 2017; 7:10883. [PMID: 28883420 PMCID: PMC5589799 DOI: 10.1038/s41598-017-10707-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Accepted: 08/15/2017] [Indexed: 12/29/2022] Open
Abstract
Volumetric and morphometric studies have demonstrated structural abnormalities related to chronic epilepsies on a cohort- and population-based level. On a single-patient level, specific patterns of atrophy or cortical reorganization may be widespread and heterogeneous but represent potential targets for further personalized image analysis and surgical therapy. The goal of this study was to compare morphometric data analysis in 37 patients with temporal lobe epilepsies with expert-based image analysis, pre-informed by seizure semiology and ictal scalp EEG. Automated image analysis identified abnormalities exceeding expert-determined structural epileptogenic lesions in 86% of datasets. If EEG lateralization and expert MRI readings were congruent, automated analysis detected abnormalities consistent on a lobar and hemispheric level in 82% of datasets. However, in 25% of patients EEG lateralization and expert readings were inconsistent. Automated analysis localized to the site of resection in 60% of datasets in patients who underwent successful epilepsy surgery. Morphometric abnormalities beyond the mesiotemporal structures contributed to subtype characterisation. We conclude that subject-specific morphometric information is in agreement with expert image analysis and scalp EEG in the majority of cases. However, automated image analysis may provide non-invasive additional information in cases with equivocal radiological and neurophysiological findings.
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Affiliation(s)
- Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital Bern, University of Bern, Bern, Switzerland.
| | - Nedelina Slavova
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital Bern, University of Bern, Bern, Switzerland
| | - Andrea Seiler
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital Bern, University of Bern, Bern, Switzerland.,Sleep-Wake- Epilepsy-Center, Department of Neurology, Inselspital Bern, University of Bern, Bern, Switzerland
| | - Eugenio Abela
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital Bern, University of Bern, Bern, Switzerland.,Sleep-Wake- Epilepsy-Center, Department of Neurology, Inselspital Bern, University of Bern, Bern, Switzerland
| | - Martinus Hauf
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital Bern, University of Bern, Bern, Switzerland.,Epilepsy Clinic, Tschugg, Switzerland
| | - Yuliya Burren
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital Bern, University of Bern, Bern, Switzerland.,University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Christian Weisstanner
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital Bern, University of Bern, Bern, Switzerland
| | - Serge Vulliemoz
- Presurgical Epilepsy Evaluation Unit, Neurology Department, University Hospital of Geneva, Geneva, Switzerland
| | - Margitta Seeck
- Presurgical Epilepsy Evaluation Unit, Neurology Department, University Hospital of Geneva, Geneva, Switzerland
| | - Kaspar Schindler
- Sleep-Wake- Epilepsy-Center, Department of Neurology, Inselspital Bern, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital Bern, University of Bern, Bern, Switzerland
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15
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Lui S, Zhou XJ, Sweeney JA, Gong Q. Psychoradiology: The Frontier of Neuroimaging in Psychiatry. Radiology 2017; 281:357-372. [PMID: 27755933 DOI: 10.1148/radiol.2016152149] [Citation(s) in RCA: 172] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Unlike neurologic conditions, such as brain tumors, dementia, and stroke, the neural mechanisms for all psychiatric disorders remain unclear. A large body of research obtained with structural and functional magnetic resonance imaging, positron emission tomography/single photon emission computed tomography, and optical imaging has demonstrated regional and illness-specific brain changes at the onset of psychiatric disorders and in individuals at risk for such disorders. Many studies have shown that psychiatric medications induce specific measurable changes in brain anatomy and function that are related to clinical outcomes. As a result, a new field of radiology, termed psychoradiology, seems primed to play a major clinical role in guiding diagnostic and treatment planning decisions in patients with psychiatric disorders. This article will present the state of the art in this area, as well as perspectives regarding preparations in the field of radiology for its evolution. Furthermore, this article will (a) give an overview of the imaging and analysis methods for psychoradiology; (b) review the most robust and important radiologic findings and their potential clinical value from studies of major psychiatric disorders, such as depression and schizophrenia; and (c) describe the main challenges and future directions in this field. An ongoing and iterative process of developing biologically based nomenclatures with which to delineate psychiatric disorders and translational research to predict and track response to different therapeutic drugs is laying the foundation for a shift in diagnostic practice in psychiatry from a psychologic symptom-based approach to an imaging-based approach over the next generation. This shift will require considerable innovations for the acquisition, analysis, and interpretation of brain images, all of which will undoubtedly require the active involvement of radiologists. © RSNA, 2016 Online supplemental material is available for this article.
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Affiliation(s)
- Su Lui
- From the Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China (S.L., J.A.S., Q.G.); and Center for MR Research and Departments of Radiology, Neurosurgery and Bioengineering, University of Illinois at Chicago, Chicago, Ill (X.J.Z.)
| | - Xiaohong Joe Zhou
- From the Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China (S.L., J.A.S., Q.G.); and Center for MR Research and Departments of Radiology, Neurosurgery and Bioengineering, University of Illinois at Chicago, Chicago, Ill (X.J.Z.)
| | - John A Sweeney
- From the Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China (S.L., J.A.S., Q.G.); and Center for MR Research and Departments of Radiology, Neurosurgery and Bioengineering, University of Illinois at Chicago, Chicago, Ill (X.J.Z.)
| | - Qiyong Gong
- From the Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China (S.L., J.A.S., Q.G.); and Center for MR Research and Departments of Radiology, Neurosurgery and Bioengineering, University of Illinois at Chicago, Chicago, Ill (X.J.Z.)
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16
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Sankar A, Zhang T, Gaonkar B, Doshi J, Erus G, Costafreda SG, Marangell L, Davatzikos C, Fu CHY. Diagnostic potential of structural neuroimaging for depression from a multi-ethnic community sample. BJPsych Open 2016; 2:247-254. [PMID: 27703783 PMCID: PMC4995169 DOI: 10.1192/bjpo.bp.115.002493] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Revised: 05/04/2016] [Accepted: 05/16/2016] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND At present, we do not have any biological tests which can contribute towards a diagnosis of depression. Neuroimaging measures have shown some potential as biomarkers for diagnosis. However, participants have generally been from the same ethnic background while the applicability of a biomarker would require replication in individuals of diverse ethnicities. AIMS We sought to examine the diagnostic potential of the structural neuroanatomy of depression in a sample of a wide ethnic diversity. METHOD Structural magnetic resonance imaging (MRI) scans were obtained from 23 patients with major depressive disorder in an acute depressive episode (mean age: 39.8 years) and 20 matched healthy volunteers (mean age: 38.8 years). Participants were of Asian, African and Caucasian ethnicity recruited from the general community. RESULTS Structural neuroanatomy combining white and grey matter distinguished patients from controls at the highest accuracy of 81% with the most stable pattern being at around 70%. A widespread network encompassing frontal, parietal, occipital and cerebellar regions contributed towards diagnostic classification. CONCLUSIONS These findings provide an important step in the development of potential neuroimaging-based tools for diagnosis as they demonstrate that the identification of depression is feasible within a multi-ethnic group from the community. DECLARATION OF INTERESTS C.H.Y.F. has held recent research grants from Eli Lilly and Company and GlaxoSmithKline. L.M. is a former employee and stockholder of Eli Lilly and Company. COPYRIGHT AND USAGE © The Royal College of Psychiatrists 2016. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) licence.
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Affiliation(s)
- Anjali Sankar
- PhD, Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Tianhao Zhang
- PhD, Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Pennsylvania, USA
| | - Bilwaj Gaonkar
- MS, Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Pennsylvania, USA
| | - Jimit Doshi
- MS, Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Pennsylvania, USA
| | - Guray Erus
- PhD, Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Pennsylvania, USA
| | - Sergi G Costafreda
- MD, Division of Psychiatry, Faculty of Brain Sciences, University College London, London, UK
| | - Lauren Marangell
- MD, Department of Psychiatry, University of Texas Health Science Center, Houston, Texas, USA
| | - Christos Davatzikos
- PhD, Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Pennsylvania, USA
| | - Cynthia H Y Fu
- MD, School of Psychology, University of East London, London, UK
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17
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Na KS, Won E, Kang J, Chang HS, Yoon HK, Tae WS, Kim YK, Lee MS, Joe SH, Kim H, Ham BJ. Brain-derived neurotrophic factor promoter methylation and cortical thickness in recurrent major depressive disorder. Sci Rep 2016; 6:21089. [PMID: 26876488 PMCID: PMC4753411 DOI: 10.1038/srep21089] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 01/18/2016] [Indexed: 11/29/2022] Open
Abstract
Recent studies have reported that methylation of the brain-derived neurotrophic factor (BDNF) gene promoter is associated with major depressive disorder (MDD). This study aimed to investigate the association between cortical thickness and methylation of BDNF promoters as well as serum BDNF levels in MDD. The participants consisted of 65 patients with recurrent MDD and 65 age- and gender-matched healthy controls. Methylation of BDNF promoters and cortical thickness were compared between the groups. The right medial orbitofrontal, right lingual, right lateral occipital, left lateral orbitofrontal, left pars triangularis, and left lingual cortices were thinner in patients with MDD than in healthy controls. Among the MDD group, right pericalcarine, right medical orbitofrontal, right rostral middle frontal, right postcentral, right inferior temporal, right cuneus, right precuneus, left frontal pole, left superior frontal, left superior temporal, left rostral middle frontal and left lingual cortices had inverse correlations with methylation of BDNF promoters. Higher levels of BDNF promoter methylation may be closely associated with the reduced cortical thickness among patients with MDD. Serum BDNF levels were significantly lower in MDD, and showed an inverse relationship with BDNF methylation only in healthy controls. Particularly the prefrontal and occipital cortices seem to indicate key regions in which BDNF methylation has a significant effect on structure.
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Affiliation(s)
- Kyoung-Sae Na
- Department of Psychiatry, Gachon University Gil Medical Center, Incheon, Republic of Korea
| | - Eunsoo Won
- Department of Psychiatry, College of Medicine, Korea University, Seoul, Republic of Korea
| | - June Kang
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Hun Soo Chang
- Department of Medical Bioscience, Graduate school, Soonchunhyang University, Bucheon, Republic of Korea
| | - Ho-Kyoung Yoon
- Department of Psychiatry, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Woo Suk Tae
- Brain Convergence Research Center, Korea University Anam Hospital, Seoul, Republic of South Korea
| | - Yong-Ku Kim
- Department of Psychiatry, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Min-Soo Lee
- Department of Psychiatry, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Sook-Haeng Joe
- Department of Psychiatry, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Hyun Kim
- Department of Anatomy, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Byung-Joo Ham
- Department of Psychiatry, College of Medicine, Korea University, Seoul, Republic of Korea
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18
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Peng D, Shi F, Li G, Fralick D, Shen T, Qiu M, Liu J, Jiang K, Shen D, Fang Y. Surface vulnerability of cerebral cortex to major depressive disorder. PLoS One 2015; 10:e0120704. [PMID: 25793287 PMCID: PMC4368815 DOI: 10.1371/journal.pone.0120704] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2014] [Accepted: 01/25/2015] [Indexed: 12/30/2022] Open
Abstract
Major depressive disorder (MDD) is accompanied by atypical brain structure. This study first presents the alterations in the cortical surface of patients with MDD using multidimensional structural patterns that reflect different neurodevelopment. Sixteen first-episode, untreated patients with MDD and 16 matched healthy controls underwent a magnetic resonance imaging (MRI) scan. The cortical maps of thickness, surface area, and gyrification were examined using the surface-based morphometry (SBM) approach. Increase of cortical thickness was observed in the right posterior cingulate region and the parietal cortex involving the bilateral inferior, left superior parietal and right paracentral regions, while decreased thickness was noted in the parietal cortex including bilateral pars opercularis and left precentral region, as well as the left rostral-middle frontal regions in patients with MDD. Likewise, increased or decreased surface area was found in five sub-regions of the cingulate gyrus, parietal and frontal cortices (e.g., bilateral inferior parietal and superior frontal regions). In addition, MDD patients exhibited a significant hypergyrification in the right precentral and supramarginal region. This integrated structural assessment of cortical surface suggests that MDD patients have cortical alterations of the frontal, parietal and cingulate regions, indicating a vulnerability to MDD during earlier neurodevelopmental process.
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Affiliation(s)
- Daihui Peng
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Feng Shi
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Drew Fralick
- Suicide Research and Prevention Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Palo Alto University, Palo Alto, California, United States of America
| | - Ting Shen
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Meihui Qiu
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Liu
- The Fifth People’s Hospital of Shanghai, Fudan University, Shanghai, China
| | - Kaida Jiang
- Huashan Hospital, Fudan University, Shanghai, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Yiru Fang
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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19
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Abstract
Interindividual differences in the effects of reward on performance are prevalent and poorly understood, with some individuals being more dependent than others on the rewarding outcomes of their actions. The origin of this variability in reward dependence is unknown. Here, we tested the relationship between reward dependence and brain structure in healthy humans. Subjects trained on a visuomotor skill-acquisition task and received performance feedback in the presence or absence of reward. Reward dependence was defined as the statistical trial-by-trial relation between reward and subsequent performance. We report a significant relationship between reward dependence and the lateral prefrontal cortex, where regional gray-matter volume predicted reward dependence but not feedback alone. Multivoxel pattern analysis confirmed the anatomical specificity of this relationship. These results identified a likely anatomical marker for the prospective influence of reward on performance, which may be of relevance in neurorehabilitative settings.
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20
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Rosa MJ, Portugal L, Hahn T, Fallgatter AJ, Garrido MI, Shawe-Taylor J, Mourao-Miranda J. Sparse network-based models for patient classification using fMRI. Neuroimage 2015; 105:493-506. [PMID: 25463459 PMCID: PMC4275574 DOI: 10.1016/j.neuroimage.2014.11.021] [Citation(s) in RCA: 103] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2014] [Revised: 10/21/2014] [Accepted: 11/08/2014] [Indexed: 12/20/2022] Open
Abstract
Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces.
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Affiliation(s)
- Maria J Rosa
- Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, London, UK; Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, King's College London, London, UK.
| | - Liana Portugal
- Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, London, UK; LABNEC, Universidade Federal Fluminense, Rio de Janeiro, Brazil
| | - Tim Hahn
- Department of Cognitive Psychology II, Johann Wolfgang Goethe University Frankfurt am Main, Germany
| | - Andreas J Fallgatter
- University of Tuebingen, Department of Psychiatry and Psychotherapy, Tuebingen, Germany
| | - Marta I Garrido
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia; Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function, Australia
| | - John Shawe-Taylor
- Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, London, UK
| | - Janaina Mourao-Miranda
- Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, London, UK
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21
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Neuroanatomical classification in a population-based sample of psychotic major depression and bipolar I disorder with 1 year of diagnostic stability. BIOMED RESEARCH INTERNATIONAL 2014; 2014:706157. [PMID: 24575411 PMCID: PMC3915628 DOI: 10.1155/2014/706157] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2013] [Revised: 12/10/2013] [Accepted: 12/10/2013] [Indexed: 12/13/2022]
Abstract
The presence of psychotic features in the course of a depressive disorder is known to increase the risk for bipolarity, but the early identification of such cases remains challenging in clinical practice. In the present study, we evaluated the diagnostic performance of a neuroanatomical pattern classification method in the discrimination between psychotic major depressive disorder (MDD), bipolar I disorder (BD-I), and healthy controls (HC) using a homogenous sample of patients at an early course of their illness. Twenty-three cases of first-episode psychotic mania (BD-I) and 19 individuals with a first episode of psychotic MDD whose diagnosis remained stable during 1 year of followup underwent 1.5 T MRI at baseline. A previously validated multivariate classifier based on support vector machine (SVM) was employed and measures of diagnostic performance were obtained for the discrimination between each diagnostic group and subsamples of age- and gender-matched controls recruited in the same neighborhood of the patients. Based on T1-weighted images only, the SVM-classifier afforded poor discrimination in all 3 pairwise comparisons: BD-I versus HC; MDD versus HC; and BD-I versus MDD. Thus, at the population level and using structural MRI only, we failed to achieve good discrimination between BD-I, psychotic MDD, and HC in this proof of concept study.
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