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Gibson K, Cernasov P, Styner M, Walsh EC, Kinard JL, Kelley L, Bizzell J, Phillips R, Pfister C, Scott M, Freeman L, Pisoni A, Nagy GA, Oliver JA, Smoski MJ, Dichter GS. The effects of psychotherapy for anhedonia on subcortical brain volumes measured with ultra-high field MRI. J Affect Disord 2024; 361:128-138. [PMID: 38815760 PMCID: PMC11259027 DOI: 10.1016/j.jad.2024.05.140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 05/11/2024] [Accepted: 05/27/2024] [Indexed: 06/01/2024]
Abstract
BACKGROUND Anhedonia is a transdiagnostic symptom often resistant to treatment. The identification of biomarkers sensitive to anhedonia treatment will aid in the evaluation of novel anhedonia interventions. METHODS This is an exploratory analysis of changes in subcortical brain volumes accompanying psychotherapy in a transdiagnostic anhedonic sample using ultra-high field (7-Tesla) MRI. Outpatients with clinically impairing anhedonia (n = 116) received Behavioral Activation Treatment for Anhedonia, a novel psychotherapy, or Mindfulness-Based Cognitive Therapy (ClinicalTrials.gov Identifiers NCT02874534 and NCT04036136). Subcortical brain volumes were estimated via the MultisegPipeline, and regions of interest were the amygdala, caudate nucleus, hippocampus, pallidum, putamen, and thalamus. Bivariate mixed effects models estimated pre-treatment relations between anhedonia severity and subcortical brain volumes, change over time in subcortical brain volumes, and associations between changes in subcortical brain volumes and changes in anhedonia symptoms. RESULTS As reported previously (Cernasov et al., 2023), both forms of psychotherapy resulted in equivalent and significant reductions in anhedonia symptoms. Pre-treatment anhedonia severity and subcortical brain volumes were not related. No changes in subcortical brain volumes were observed over the course of treatment. Additionally, no relations were observed between changes in subcortical brain volumes and changes in anhedonia severity over the course of treatment. LIMITATIONS This trial included a modest sample size and did not have a waitlist-control condition or a non-anhedonic comparison group. CONCLUSIONS In this exploratory analysis, psychotherapy for anhedonia was not accompanied by changes in subcortical brain volumes, suggesting that subcortical brain volumes may not be a candidate biomarker sensitive to response to psychotherapy.
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Affiliation(s)
- Kathryn Gibson
- Department of Psychiatry, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA.
| | - Paul Cernasov
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - Martin Styner
- Department of Psychiatry, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - Erin C Walsh
- Department of Psychiatry, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - Jessica L Kinard
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27510, USA
| | - Lisalynn Kelley
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27705, USA
| | - Joshua Bizzell
- Department of Psychiatry, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27510, USA
| | - Rachel Phillips
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - Courtney Pfister
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - McRae Scott
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - Louise Freeman
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - Angela Pisoni
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27505, USA
| | - Gabriela A Nagy
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27705, USA
| | - Jason A Oliver
- Department of Family and Preventative Medicine, University of Oklahoma, Oklahoma City, OK 73117, USA
| | - Moria J Smoski
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27505, USA
| | - Gabriel S Dichter
- Department of Psychiatry, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA; Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27510, USA
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Jiang Z, Sullivan PF, Li T, Zhao B, Wang X, Luo T, Huang S, Guan PY, Chen J, Yang Y, Stein JL, Li Y, Liu D, Sun L, Zhu H. The pivotal role of the X-chromosome in the genetic architecture of the human brain. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.08.30.23294848. [PMID: 37693466 PMCID: PMC10491353 DOI: 10.1101/2023.08.30.23294848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Genes on the X-chromosome are extensively expressed in the human brain. However, little is known for the X-chromosome's impact on the brain anatomy, microstructure, and functional network. We examined 1,045 complex brain imaging traits from 38,529 participants in the UK Biobank. We unveiled potential autosome-X-chromosome interactions, while proposing an atlas outlining dosage compensation (DC) for brain imaging traits. Through extensive association studies, we identified 72 genome-wide significant trait-locus pairs (including 29 new associations) that share genetic architectures with brain-related disorders, notably schizophrenia. Furthermore, we discovered unique sex-specific associations and assessed variations in genetic effects between sexes. Our research offers critical insights into the X-chromosome's role in the human brain, underscoring its contribution to the differences observed in brain structure and functionality between sexes.
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Shibukawa S, Kan H, Honda S, Wada M, Tarumi R, Tsugawa S, Tobari Y, Maikusa N, Mimura M, Uchida H, Nakamura Y, Nakajima S, Noda Y, Koike S. Alterations in subcortical magnetic susceptibility and disease-specific relationship with brain volume in major depressive disorder and schizophrenia. Transl Psychiatry 2024; 14:164. [PMID: 38531856 DOI: 10.1038/s41398-024-02862-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 03/05/2024] [Accepted: 03/07/2024] [Indexed: 03/28/2024] Open
Abstract
Quantitative susceptibility mapping is a magnetic resonance imaging technique that measures brain tissues' magnetic susceptibility, including iron deposition and myelination. This study examines the relationship between subcortical volume and magnetic susceptibility and determines specific differences in these measures among patients with major depressive disorder (MDD), patients with schizophrenia, and healthy controls (HCs). This was a cross-sectional study. Sex- and age- matched patients with MDD (n = 49), patients with schizophrenia (n = 24), and HCs (n = 50) were included. Magnetic resonance imaging was conducted using quantitative susceptibility mapping and T1-weighted imaging to measure subcortical susceptibility and volume. The acquired brain measurements were compared among groups using analyses of variance and post hoc comparisons. Finally, a general linear model examined the susceptibility-volume relationship. Significant group-level differences were found in the magnetic susceptibility of the nucleus accumbens and amygdala (p = 0.045). Post-hoc analyses indicated that the magnetic susceptibility of the nucleus accumbens and amygdala for the MDD group was significantly higher than that for the HC group (p = 0.0054, p = 0.0065, respectively). However, no significant differences in subcortical volume were found between the groups. The general linear model indicated a significant interaction between group and volume for the nucleus accumbens in MDD group but not schizophrenia or HC groups. This study showed susceptibility alterations in the nucleus accumbens and amygdala in MDD patients. A significant relationship was observed between subcortical susceptibility and volume in the MDD group's nucleus accumbens, which indicated abnormalities in myelination and the dopaminergic system related to iron deposition.
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Affiliation(s)
- Shuhei Shibukawa
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan
- Faculty of Health Science, Department of Radiological Technology, Juntendo University, Tokyo, Japan
- Department of Radiology, Tokyo Medical University, Tokyo, Japan
| | - Hirohito Kan
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan.
| | - Shiori Honda
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Masataka Wada
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Ryosuke Tarumi
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Sakiko Tsugawa
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Yui Tobari
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan
| | - Masaru Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Hiroyuki Uchida
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Yuko Nakamura
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan
- University of Tokyo Institute for Diversity and Adaptation of Human Mind, The University of Tokyo, Tokyo, Japan
| | - Shinichiro Nakajima
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Yoshihiro Noda
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Shinsuke Koike
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan.
- University of Tokyo Institute for Diversity and Adaptation of Human Mind, The University of Tokyo, Tokyo, Japan.
- The International Research Center for Neurointelligence, University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan.
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Vike NL, Bari S, Kim BW, Katsaggelos AK, Blood AJ, Breiter HC. Characterizing major depressive disorder and substance use disorder using heatmaps and variable interactions: The utility of operant behavior and brain structure relationships. PLoS One 2024; 19:e0299528. [PMID: 38466739 PMCID: PMC10927130 DOI: 10.1371/journal.pone.0299528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 02/13/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Rates of depression and addiction have risen drastically over the past decade, but the lack of integrative techniques remains a barrier to accurate diagnoses of these mental illnesses. Changes in reward/aversion behavior and corresponding brain structures have been identified in those with major depressive disorder (MDD) and cocaine-dependence polysubstance abuse disorder (CD). Assessment of statistical interactions between computational behavior and brain structure may quantitatively segregate MDD and CD. METHODS Here, 111 participants [40 controls (CTRL), 25 MDD, 46 CD] underwent structural brain MRI and completed an operant keypress task to produce computational judgment metrics. Three analyses were performed: (1) linear regression to evaluate groupwise (CTRL v. MDD v. CD) differences in structure-behavior associations, (2) qualitative and quantitative heatmap assessment of structure-behavior association patterns, and (3) the k-nearest neighbor machine learning approach using brain structure and keypress variable inputs to discriminate groups. RESULTS This study yielded three primary findings. First, CTRL, MDD, and CD participants had distinct structure-behavior linear relationships, with only 7.8% of associations overlapping between any two groups. Second, the three groups had statistically distinct slopes and qualitatively distinct association patterns. Third, a machine learning approach could discriminate between CTRL and CD, but not MDD participants. CONCLUSIONS These findings demonstrate that variable interactions between computational behavior and brain structure, and the patterns of these interactions, segregate MDD and CD. This work raises the hypothesis that analysis of interactions between operant tasks and structural neuroimaging might aide in the objective classification of MDD, CD and other mental health conditions.
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Affiliation(s)
- Nicole L. Vike
- Department of Computer Science, University of Cincinnati, Cincinnati, Ohio, United States of America
| | - Sumra Bari
- Department of Computer Science, University of Cincinnati, Cincinnati, Ohio, United States of America
| | - Byoung Woo Kim
- Department of Computer Science, University of Cincinnati, Cincinnati, Ohio, United States of America
| | - Aggelos K. Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, Illinois, United States of America
- Department of Computer Science, Northwestern University, Evanston, Illinois, United States of America
- Department of Radiology, Northwestern University, Chicago, Illinois, United States of America
| | - Anne J. Blood
- Department of Psychiatry, Mood and Motor Control Laboratory (MAML), Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Psychiatry, Laboratory of Neuroimaging and Genetics, Massachusetts General Hospital and Harvard School of Medicine, Boston, Massachusetts, United States of America
| | - Hans C. Breiter
- Department of Computer Science, University of Cincinnati, Cincinnati, Ohio, United States of America
- Department of Psychiatry, Mood and Motor Control Laboratory (MAML), Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Psychiatry, Laboratory of Neuroimaging and Genetics, Massachusetts General Hospital and Harvard School of Medicine, Boston, Massachusetts, United States of America
- Department of Biomedical Engineering, University of Cincinnati, Cincinnati, Ohio, United States of America
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Bae EB, Han KM. A structural equation modeling approach using behavioral and neuroimaging markers in major depressive disorder. J Psychiatr Res 2024; 171:246-255. [PMID: 38325105 DOI: 10.1016/j.jpsychires.2024.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 12/16/2023] [Accepted: 02/01/2024] [Indexed: 02/09/2024]
Abstract
Major depressive disorder (MDD) has consistently proven to be a multifactorial and highly comorbid disease. Despite recent depression-related research demonstrating causalities between MDD-related factors and a small number of variables, including brain structural changes, a high-statistical power analysis of the various factors is yet to be conducted. We retrospectively analyzed data from 155 participants (84 healthy controls and 71 patients with MDD). We used magnetic resonance imaging and diffusion tensor imaging data, scales assessing childhood trauma, depression severity, cognitive dysfunction, impulsivity, and suicidal ideation. To simultaneously evaluate the causalities between multivariable, we implemented two types of MDD-specified structural equation models (SEM), the behavioral and neurobehavioral models. Behavioral SEM showed significant results in the MDD group: Comparative Fit Index [CFI] = 1.000, Root Mean Square Error of Approximation [RMSEA]) = 0.000), with a strong correlation in the scales for childhood trauma, depression severity, suicidal ideation, impulsivity, and cognitive dysfunction. Based on behavioral SEM, we established neurobehavioral models showing the best-fit in MDD, especially including the right cingulate cortex, central to the posterior corpus callosum, right putamen, pallidum, whole brainstem, and ventral diencephalon, including the thalamus (CFI >0.96, RMSEA <0.05). Our MDD-specific model revealed that the limbic-associated regions are strongly connected with childhood trauma rather than depression severity, and that they independently affect suicidal ideation and cognitive dysfunction. Furthermore, cognitive dysfunction could affect impulsivity.
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Affiliation(s)
- Eun Bit Bae
- Research Institute for Medical Bigdata Science, Korea University, Seoul, Republic of Korea; Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Kyu-Man Han
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
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Zhang J, Wu X, Si Y, Liu Y, Wang X, Geng Y, Chang Q, Jiang X, Zhang H. Abnormal caudate nucleus activity in patients with depressive disorder: Meta-analysis of task-based functional magnetic resonance imaging studies with behavioral domain. Psychiatry Res Neuroimaging 2024; 338:111769. [PMID: 38141592 DOI: 10.1016/j.pscychresns.2023.111769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 11/17/2023] [Accepted: 11/26/2023] [Indexed: 12/25/2023]
Abstract
During task-based functional magnetic resonance imaging (t-fMRI) patients with depressive disorder (DD) have shown abnormal caudate nucleus activation. There have been no meta-analyses that are conducted on the caudate nucleus using Activation Likelihood Estimation (ALE) in patients with DD, and the relationships between abnormal caudate activity and different behavior domains in patients with DD remain unclear. There were 24 previously published t-fMRI studies included in the study with the caudate nucleus as the region of interest. Meta-analyses were performed using the method of ALE. Included five ALE meta-analyses: (1) the hypoactivated caudate nucleus relative to healthy controls (HCs); (2) the hyper-activated caudate nucleus; (3) the abnormal activation in the caudate nucleus in the emotion domain; (4) the abnormal activation in cognition domain; (5) the abnormal activation in the affective cognition domain. Results revealed that the hypo-/hyper-activity in the caudate subregions is mainly located in the caudate body and head, while the relationships between abnormal caudate subregions and different behavior domains are complex. The hypoactivation of the caudate body and head plays a key role in the emotions which indicates there is a positive relationship between the decreased caudate activity and depressed emotional behaviors in patients with DD.
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Affiliation(s)
- Jiajia Zhang
- Department of Psychology, Xinxiang Medical University, Henan 453003, PR China; Xinxiang Key Laboratory of Psychopathology and Cognitive Neuroscience, Xinxiang, 453003, PR China; Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China
| | - Xin Wu
- Department of Psychology, Xinxiang Medical University, Henan 453003, PR China; Xinxiang Key Laboratory of Psychopathology and Cognitive Neuroscience, Xinxiang, 453003, PR China
| | - Yajing Si
- Department of Psychology, Xinxiang Medical University, Henan 453003, PR China; Xinxiang Key Laboratory of Psychopathology and Cognitive Neuroscience, Xinxiang, 453003, PR China
| | - Yahui Liu
- Department of Psychology, Xinxiang Medical University, Henan 453003, PR China; Xinxiang Key Laboratory of Psychopathology and Cognitive Neuroscience, Xinxiang, 453003, PR China
| | - Xueke Wang
- Department of Psychology, Xinxiang Medical University, Henan 453003, PR China; Xinxiang Key Laboratory of Psychopathology and Cognitive Neuroscience, Xinxiang, 453003, PR China; Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China
| | - Yibo Geng
- Department of Radiology, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, PR China
| | - Qiaohua Chang
- Department of Nursing, Xinxiang Medical University, Henan 453003, PR China
| | - Xiaoxiao Jiang
- Department of Nursing, Xinxiang Medical University, Henan 453003, PR China
| | - Hongxing Zhang
- Department of Psychology, Xinxiang Medical University, Henan 453003, PR China; Xinxiang Key Laboratory of Psychopathology and Cognitive Neuroscience, Xinxiang, 453003, PR China; Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China.
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7
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Fu CHY, Antoniades M, Erus G, Garcia JA, Fan Y, Arnone D, Arnott SR, Chen T, Choi KS, Fatt CC, Frey BN, Frokjaer VG, Ganz M, Godlewska BR, Hassel S, Ho K, McIntosh AM, Qin K, Rotzinger S, Sacchet MD, Savitz J, Shou H, Singh A, Stolicyn A, Strigo I, Strother SC, Tosun D, Victor TA, Wei D, Wise T, Zahn R, Anderson IM, Craighead WE, Deakin JFW, Dunlop BW, Elliott R, Gong Q, Gotlib IH, Harmer CJ, Kennedy SH, Knudsen GM, Mayberg HS, Paulus MP, Qiu J, Trivedi MH, Whalley HC, Yan CG, Young AH, Davatzikos C. Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo. NATURE. MENTAL HEALTH 2024; 2:164-176. [PMID: 38948238 PMCID: PMC11211072 DOI: 10.1038/s44220-023-00187-w] [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: 11/18/2022] [Accepted: 11/17/2023] [Indexed: 07/02/2024]
Abstract
Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples (N = 1,384) of medication-free individuals with first-episode and recurrent MDD (N = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls (N = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals (N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter (N = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter (N = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) (β = -18.3, 95% CI (-34.3 to -2.3), P = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response.
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Affiliation(s)
- Cynthia H. Y. Fu
- School of Psychology, University of East London, London, UK
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | - Mathilde Antoniades
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Jose A. Garcia
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Danilo Arnone
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | | | - Taolin Chen
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ki Sueng Choi
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Cherise Chin Fatt
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX USA
| | - Benicio N. Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario Canada
- Mood Disorders Treatment and Research Centre and Women’s Health Concerns Clinic, St Joseph’s Healthcare Hamilton, Hamilton, Ontario Canada
| | - Vibe G. Frokjaer
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Beata R. Godlewska
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Stefanie Hassel
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta Canada
| | - Keith Ho
- Department of Psychiatry, University Health Network, Toronto, Ontario Canada
| | - Andrew M. McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Kun Qin
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Susan Rotzinger
- Department of Psychiatry, University Health Network, Toronto, Ontario Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Ontario Canada
| | - Matthew D. Sacchet
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | | | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Aleks Stolicyn
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Irina Strigo
- Department of Psychiatry, University of California San Francisco, San Francisco, USA
| | - Stephen C. Strother
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario Canada
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA USA
| | | | - Dongtao Wei
- School of Psychology, Southwest University, Chongqing, China
| | - Toby Wise
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Roland Zahn
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | - Ian M. Anderson
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - W. Edward Craighead
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA USA
- Department of Psychology, Emory University, Atlanta, GA USA
| | - J. F. William Deakin
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Boadie W. Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA USA
| | - Rebecca Elliott
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ian H. Gotlib
- Department of Psychology, Stanford University, Stanford, CA USA
| | | | - Sidney H. Kennedy
- Department of Psychiatry, University Health Network, Toronto, Ontario Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Ontario Canada
| | - Gitte M. Knudsen
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Helen S. Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | | | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing, China
| | - Madhukar H. Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX USA
| | - Heather C. Whalley
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Chao-Gan Yan
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Allan H. Young
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
- South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, London, UK
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
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Wang J, Li T, Sun Q, Guo Y, Yu J, Yao Z, Hou N, Hu B. Automatic Diagnosis of Major Depressive Disorder Using a High- and Low-Frequency Feature Fusion Framework. Brain Sci 2023; 13:1590. [PMID: 38002549 PMCID: PMC10670347 DOI: 10.3390/brainsci13111590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 11/10/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023] Open
Abstract
Major Depressive Disorder (MDD) is a common mental illness resulting in immune disorders and even thoughts of suicidal behavior. Neuroimaging techniques serve as a quantitative tool for the assessment of MDD diagnosis. In the domain of computer-aided magnetic resonance imaging diagnosis, current research predominantly focuses on isolated local or global information, often neglecting the synergistic integration of multiple data sources, thus potentially overlooking valuable details. To address this issue, we proposed a diagnostic model for MDD that integrates high-frequency and low-frequency information using data from diffusion tensor imaging (DTI), structural magnetic resonance imaging (sMRI), and functional magnetic resonance imaging (fMRI). First, we designed a meta-low-frequency encoder (MLFE) and a meta-high-frequency encoder (MHFE) to extract the low-frequency and high-frequency feature information from DTI and sMRI, respectively. Then, we utilized a multilayer perceptron (MLP) to extract features from fMRI data. Following the feature cross-fusion, we designed the ensemble learning threshold voting method to determine the ultimate diagnosis for MDD. The model achieved accuracy, precision, specificity, F1-score, MCC, and AUC values of 0.724, 0.750, 0.882, 0.600, 0.421, and 0.667, respectively. This approach provides new research ideas for the diagnosis of MDD.
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Affiliation(s)
- Junyu Wang
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; (J.W.); (T.L.); (Q.S.); (J.Y.)
- Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China;
| | - Tongtong Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; (J.W.); (T.L.); (Q.S.); (J.Y.)
- Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China;
| | - Qi Sun
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; (J.W.); (T.L.); (Q.S.); (J.Y.)
- Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China;
| | - Yuhui Guo
- Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China;
- School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China
| | - Jiandong Yu
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; (J.W.); (T.L.); (Q.S.); (J.Y.)
- Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China;
| | - Zhijun Yao
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; (J.W.); (T.L.); (Q.S.); (J.Y.)
- Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China;
| | - Ning Hou
- Medical Department, The Third People’s Hospital of Tianshui, Tianshui 741000, China
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; (J.W.); (T.L.); (Q.S.); (J.Y.)
- Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China;
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
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9
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Zheng G, Zheng W, Zhang Y, Wang J, Chen M, Wang Y, Cai T, Yao Z, Hu B. An attention-based multi-modal MRI fusion model for major depressive disorder diagnosis. J Neural Eng 2023; 20:066005. [PMID: 37844568 DOI: 10.1088/1741-2552/ad038c] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 10/16/2023] [Indexed: 10/18/2023]
Abstract
Objective.Major depressive disorder (MDD) is one of the biggest threats to human mental health. MDD is characterized by aberrant changes in both structure and function of the brain. Although recent studies have developed some deep learning models based on multi-modal magnetic resonance imaging (MRI) for MDD diagnosis, the latent associations between deep features derived from different modalities were largely unexplored by previous studies, which we hypothesized may have potential benefits in improving the diagnostic accuracy of MDD.Approach.In this study, we proposed a novel deep learning model that fused both structural MRI (sMRI) and resting-state MRI (rs-fMRI) data to enhance the diagnosis of MDD by capturing the interactions between deep features extracted from different modalities. Specifically, we first employed a brain function encoder (BFE) and a brain structure encoder (BSE) to extract the deep features from fMRI and sMRI, respectively. Then, we designed a function and structure co-attention fusion (FSCF) module that captured inter-modal interactions and adaptively fused multi-modal deep features for MDD diagnosis.Main results.This model was evaluated on a large cohort and achieved a high classification accuracy of 75.2% for MDD diagnosis. Moreover, the attention distribution of the FSCF module assigned higher attention weights to structural features than functional features for diagnosing MDD.Significance.The high classification accuracy highlights the effectiveness and potential clinical of the proposed model.
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Affiliation(s)
- Guowei Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, People's Republic of China
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Yu Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Junyu Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Miao Chen
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Yin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Tianhong Cai
- Institute of Computer Software and Theory, School of information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
- School of Medical Technology, Beijing Institute of Technology, Beijing, People's Republic of China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China
- Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, People's Republic of China
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10
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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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11
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Thiel K, Meinert S, Winter A, Lemke H, Waltemate L, Breuer F, Gruber M, Leenings R, Wüste L, Rüb K, Pfarr JK, Stein F, Brosch K, Meller T, Ringwald KG, Nenadić I, Krug A, Repple J, Opel N, Koch K, Leehr EJ, Bauer J, Grotegerd D, Hahn T, Kircher T, Dannlowski U. Reduced fractional anisotropy in bipolar disorder v. major depressive disorder independent of current symptoms. Psychol Med 2023; 53:4592-4602. [PMID: 35833369 PMCID: PMC10388324 DOI: 10.1017/s0033291722001490] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 04/28/2022] [Accepted: 05/05/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND Patients with bipolar disorder (BD) show reduced fractional anisotropy (FA) compared to patients with major depressive disorder (MDD). Little is known about whether these differences are mood state-independent or influenced by acute symptom severity. Therefore, the aim of this study was (1) to replicate abnormalities in white matter microstructure in BD v. MDD and (2) to investigate whether these vary across depressed, euthymic, and manic mood. METHODS In this cross-sectional diffusion tensor imaging study, n = 136 patients with BD were compared to age- and sex-matched MDD patients and healthy controls (HC) (n = 136 each). Differences in FA were investigated using tract-based spatial statistics. Using interaction models, the influence of acute symptom severity and mood state on the differences between patient groups were tested. RESULTS Analyses revealed a main effect of diagnosis on FA across all three groups (ptfce-FWE = 0.003). BD patients showed reduced FA compared to both MDD (ptfce-FWE = 0.005) and HC (ptfce-FWE < 0.001) in large bilateral clusters. These consisted of several white matter tracts previously described in the literature, including commissural, association, and projection tracts. There were no significant interaction effects between diagnosis and symptom severity or mood state (all ptfce-FWE > 0.704). CONCLUSIONS Results indicated that the difference between BD and MDD was independent of depressive and manic symptom severity and mood state. Disruptions in white matter microstructure in BD might be a trait effect of the disorder. The potential of FA values to be used as a biomarker to differentiate BD from MDD should be further addressed in future studies using longitudinal designs.
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Affiliation(s)
- Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute of Translational Neuroscience, University of Münster, Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Hannah Lemke
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Lena Waltemate
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Fabian Breuer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Lucia Wüste
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Kathrin Rüb
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | | | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Kai Gustav Ringwald
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Koch
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Elisabeth J. Leehr
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jochen Bauer
- Department of Clinical Radiology, University of Muenster, Muenster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
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12
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Levenstein JM, Driver C, Boyes A, Parker M, Shan Z, Lagopoulos J, Hermens DF. Sex differences in brain volumes and psychological distress: The first hundred brains cohort of the longitudinal adolescent brain study. NEUROIMAGE: REPORTS 2023. [DOI: 10.1016/j.ynirp.2023.100167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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13
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Gao Y, Guo X, Zhong Y, Liu X, Tian S, Deng J, Lin X, Bao Y, Lu L, Wang G. Decreased dorsal attention network homogeneity as a potential neuroimaging biomarker for major depressive disorder. J Affect Disord 2023; 332:136-142. [PMID: 36990286 DOI: 10.1016/j.jad.2023.03.080] [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: 06/15/2022] [Revised: 03/14/2023] [Accepted: 03/24/2023] [Indexed: 03/31/2023]
Abstract
BACKGROUND Gaining insight into abnormal functional brain network homogeneity (NH) has the potential to aid efforts to target or otherwise study major depressive disorder (MDD). The NH of the dorsal attention network (DAN) in first-episode treatment-naive MDD patients, however, has yet to be studied. As such, the present study was developed to explore the NH of the DAN in order to determine the ability of this parameter to differentiate between MDD patients and healthy control (HC) individuals. METHODS This study included 73 patients with first-episode treatment-naive MDD and 73 age-, gender-, and educational level-matched healthy controls. All participants completed the attentional network test (ANT), Hamilton Rating Scale for Depression (HRSD), and resting-state functional magnetic resonance imaging (rs-fMRI) analyses. A group independent component analysis (ICA) was used to identify the DAN and to compute the NH of the DAN in patients with MDD. Spearman's rank correlation analyses were used to explore relationships between significant NH abnormalities in MDD patients, clinical parameters, and executive control reaction time. RESULTS Relative to HCs, patients exhibited reduced NH in the left supramarginal gyrus (SMG). Support vector machine (SVM) analyses and receiver operating characteristic curves indicated that the NH of the left SMG could be used to differentiate between HCs and MDD patients with respective accuracy, specificity, sensitivity, and AUC values of 92.47 %, 91.78 %, 93.15 %, and 65.39 %. A significant positive correlation was observed between the left SMG NH values and HRSD scores among MDD patients. CONCLUSIONS These results suggest that NH changes in the DAN may offer value as a neuroimaging biomarker capable of differentiating between MDD patients and healthy individuals.
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Affiliation(s)
- Yujun Gao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430000, China
| | - Xin Guo
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430000, China
| | - Yi Zhong
- Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Xiaoxin Liu
- Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Shanshan Tian
- Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Jiahui Deng
- Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Xiao Lin
- Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Yanpin Bao
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing 100191, China.
| | - Lin Lu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430000, China; Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China; National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China.
| | - Gaohua Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430000, China.
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14
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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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15
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Fang Y, Wang M, Potter GG, Liu M. Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification. Med Image Anal 2023; 84:102707. [PMID: 36512941 PMCID: PMC9850278 DOI: 10.1016/j.media.2022.102707] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 11/21/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022]
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) data have been widely used for automated diagnosis of brain disorders such as major depressive disorder (MDD) to assist in timely intervention. Multi-site fMRI data have been increasingly employed to augment sample size and improve statistical power for investigating MDD. However, previous studies usually suffer from significant inter-site heterogeneity caused for instance by differences in scanners and/or scanning protocols. To address this issue, we develop a novel discrepancy-based unsupervised cross-domain fMRI adaptation framework (called UFA-Net) for automated MDD identification. The proposed UFA-Net is designed to model spatio-temporal fMRI patterns of labeled source and unlabeled target samples via an attention-guided graph convolution module, and also leverage a maximum mean discrepancy constrained module for unsupervised cross-site feature alignment between two domains. To the best of our knowledge, this is one of the first attempts to explore unsupervised rs-fMRI adaptation for cross-site MDD identification. Extensive evaluation on 681 subjects from two imaging sites shows that the proposed method outperforms several state-of-the-art methods. Our method helps localize disease-associated functional connectivity abnormalities and is therefore well interpretable and can facilitate fMRI-based analysis of MDD in clinical practice.
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Affiliation(s)
- Yuqi Fang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Mingliang Wang
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Guy G Potter
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, United States.
| | - Mingxia Liu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
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Fu CHY, Erus G, Fan Y, Antoniades M, Arnone D, Arnott SR, Chen T, Choi KS, Fatt CC, Frey BN, Frokjaer VG, Ganz M, Garcia J, Godlewska BR, Hassel S, Ho K, McIntosh AM, Qin K, Rotzinger S, Sacchet MD, Savitz J, Shou H, Singh A, Stolicyn A, Strigo I, Strother SC, Tosun D, Victor TA, Wei D, Wise T, Woodham RD, Zahn R, Anderson IM, Deakin JFW, Dunlop BW, Elliott R, Gong Q, Gotlib IH, Harmer CJ, Kennedy SH, Knudsen GM, Mayberg HS, Paulus MP, Qiu J, Trivedi MH, Whalley HC, Yan CG, Young AH, Davatzikos C. AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale. BMC Psychiatry 2023; 23:59. [PMID: 36690972 PMCID: PMC9869598 DOI: 10.1186/s12888-022-04509-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 12/29/2022] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.
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Affiliation(s)
- Cynthia H Y Fu
- Department of Psychological Sciences, University of East London, London, UK.
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Mathilde Antoniades
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Danilo Arnone
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Department of Psychiatry and Behavioral Science, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | | | - Taolin Chen
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ki Sueng Choi
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Cherise Chin Fatt
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, USA
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada
- Mood Disorders Treatment and Research Centre and Women's Health Concerns Clinic, St Joseph's Healthcare Hamilton, Hamilton, Canada
| | - Vibe G Frokjaer
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Jose Garcia
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Beata R Godlewska
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Stefanie Hassel
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Keith Ho
- Department of Psychiatry, University Health Network, Toronto, Canada
| | - Andrew M McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Kun Qin
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Susan Rotzinger
- Department of Psychiatry, University Health Network, Toronto, Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Canada
| | - Matthew D Sacchet
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | | | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Aleks Stolicyn
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Irina Strigo
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Centre, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | | | - Dongtao Wei
- School of Psychology, Southwest University, Chongqing, China
| | - Toby Wise
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Rachel D Woodham
- Department of Psychological Sciences, University of East London, London, UK
| | - Roland Zahn
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Ian M Anderson
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - J F William Deakin
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Boadie W Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, USA
| | - Rebecca Elliott
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, USA
| | | | - Sidney H Kennedy
- Department of Psychiatry, University Health Network, Toronto, Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Canada
- Unity Health Toronto, Toronto, Canada
| | - Gitte M Knudsen
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Helen S Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, USA
| | | | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing, China
| | - Madhukar H Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, USA
| | - Heather C Whalley
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
| | - Allan H Young
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, London, UK
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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Du H, Xia J, Fan J, Gao F, Wang X, Han Y, Tan C, Zhu X. Spontaneous neural activity in the right fusiform gyrus and putamen is associated with consummatory anhedonia in obsessive compulsive disorder. Brain Imaging Behav 2022; 16:1708-1720. [DOI: 10.1007/s11682-021-00619-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/09/2021] [Indexed: 11/28/2022]
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A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan. Neural Comput Appl 2022; 35:11497-11516. [PMID: 35039718 PMCID: PMC8754538 DOI: 10.1007/s00521-021-06710-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 10/27/2021] [Indexed: 01/10/2023]
Abstract
This study investigated the application of a recurrent neural network for optimising pharmacological treatment for depression. A clinical dataset of 458 participants from specialist and community psychiatric services in Australia, New Zealand and Japan were extracted from an existing custom-built, web-based tool called Psynary . This data, which included baseline and self-completed reviews, was used to train and refine a novel algorithm which was a fully connected network feature extractor and long short-term memory algorithm was firstly trained in isolation and then integrated and annealed using slow learning rates due to the low dimensionality of the data. The accuracy of predicting depression remission before processing patient review data was 49.8%. After processing only 2 reviews, the accuracy was 76.5%. When considering a change in medication, the precision of changing medications was 97.4% and the recall was 71.4% . The medications with predicted best results were antipsychotics (88%) and selective serotonin reuptake inhibitors (87.9%). This is the first study that has created an all-in-one algorithm for optimising treatments for all subtypes of depression. Reducing treatment optimisation time for patients suffering with depression may lead to earlier remission and hence reduce the high levels of disability associated with the condition. Furthermore, in a setting where mental health conditions are increasing strain on mental health services, the utilisation of web-based tools for remote monitoring and machine/deep learning algorithms may assist clinicians in both specialist and primary care in extending specialist mental healthcare to a larger patient community.
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Liu Y, Chen K, Luo Y, Wu J, Xiang Q, Peng L, Zhang J, Zhao W, Li M, Zhou X. Distinguish bipolar and major depressive disorder in adolescents based on multimodal neuroimaging: Results from the Adolescent Brain Cognitive Development study ®. Digit Health 2022; 8:20552076221123705. [PMID: 36090673 PMCID: PMC9452797 DOI: 10.1177/20552076221123705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 08/16/2022] [Indexed: 01/10/2023] Open
Abstract
Background Major depressive disorder and bipolar disorder in adolescents are prevalent and are associated with cognitive impairment, executive dysfunction, and increased mortality. Early intervention in the initial stages of major depressive disorder and bipolar disorder can significantly improve personal health. Methods We collected 309 samples from the Adolescent Brain Cognitive Development study, including 116 adolescents with bipolar disorder, 64 adolescents with major depressive disorder, and 129 healthy adolescents, and employed a support vector machine to develop classification models for identification. We developed a multimodal model, which combined functional connectivity of resting-state functional magnetic resonance imaging and four anatomical measures of structural magnetic resonance imaging (cortical thickness, area, volume, and sulcal depth). We measured the performances of both multimodal and single modality classifiers. Results The multimodal classifiers showed outstanding performance compared with all five single modalities, and they are 100% for major depressive disorder versus healthy controls, 100% for bipolar disorder versus healthy control, 98.5% (95% CI: 95.4–100%) for major depressive disorder versus bipolar disorder, 100% for major depressive disorder versus depressed bipolar disorder and the leave-one-site-out analysis results are 77.4%, 63.3%, 79.4%, and 81.7%, separately. Conclusions The study shows that multimodal classifiers show high classification performances. Moreover, cuneus may be a potential biomarker to differentiate major depressive disorder, bipolar disorder, and healthy adolescents. Overall, this study can form multimodal diagnostic prediction workflows for clinically feasible to make more precise diagnose at the early stage and potentially reduce loss of personal pain and public society.
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Affiliation(s)
- Yujun Liu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Kai Chen
- School of Public Health, University of Texas Health Science Center at Houston, Houston, USA
| | - Yangyang Luo
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Jiqiu Wu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Qu Xiang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Li Peng
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Jian Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Weiling Zhao
- Center for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA
| | - Mingliang Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA
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20
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Chu Y, Wu J, Wang D, Huang J, Li W, Zhang S, Ren H. Altered voxel-mirrored homotopic connectivity in right temporal lobe epilepsy as measured using resting-state fMRI and support vector machine analyses. Front Psychiatry 2022; 13:958294. [PMID: 35958657 PMCID: PMC9360423 DOI: 10.3389/fpsyt.2022.958294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 06/28/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Prior reports revealed abnormalities in voxel-mirrored homotopic connectivity (VMHC) when analyzing neuroimaging data from patients with various psychiatric conditions, including temporal lobe epilepsy (TLE). Whether these VHMC changes can be leveraged to aid in the diagnosis of right TLE (rTLE), however, remains to be established. This study was thus developed to examine abnormal VMHC findings associated with rTLE to determine whether these changes can be used to guide rTLE diagnosis. METHODS The resultant imaging data of resting-state functional MRI (rs-fMRI) analyses of 59 patients with rTLE and 60 normal control individuals were analyzed using VMHC and support vector machine (SVM) approaches. RESULTS Relative to normal controls, patients with rTLE were found to exhibit decreased VMHC values in the bilateral superior and the middle temporal pole (STP and MTP), the bilateral middle and inferior temporal gyri (MTG and ITG), and the bilateral orbital portion of the inferior frontal gyrus (OrbIFG). These patients further exhibited increases in VMHC values in the bilateral precentral gyrus (PreCG), the postcentral gyrus (PoCG), and the supplemental motor area (SMA). The ROC curve of MTG VMHC values showed a great diagnostic efficacy in the diagnosis of rTLE with AUCs, sensitivity, specificity, and optimum cutoff values of 0.819, 0.831, 0.717, and 0.465. These findings highlight the value of the right middle temporal gyrus (rMTG) when differentiating between rTLE and control individuals, with a corresponding SVM analysis yielding respective accuracy, sensitivity, and specificity values of 70.59% (84/119), 78.33% (47/60), and 69.49% (41/59). CONCLUSION In summary, patients with rTLE exhibit various forms of abnormal functional connectivity, and SVM analyses support the potential value of abnormal VMHC values as a neuroimaging biomarker that can aid in the diagnosis of this condition.
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Affiliation(s)
- Yongqiang Chu
- Department of Imaging Center, Tianyou Hospital, Affiliated to Wuhan University of Science and Technology, Wuhan, China.,Key Laboratory of Occupational Hazards and Identification, Wuhan University of Science and Technology, Wuhan, China
| | - Jun Wu
- Department of Neurosurgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Du Wang
- Department of Imaging Center, Tianyou Hospital, Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Junli Huang
- Department of Imaging Center, Tianyou Hospital, Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Wei Li
- Department of Otolaryngology-Head and Neck Surgery, Wuhan Asia General Hospital, Wuhan, China
| | - Sheng Zhang
- Department of Psychiatry, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongwei Ren
- Department of Imaging Center, Tianyou Hospital, Affiliated to Wuhan University of Science and Technology, Wuhan, China
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Shi Y, Zhang L, He C, Yin Y, Song R, Chen S, Fan D, Zhou D, Yuan Y, Xie C, Zhang Z. Sleep disturbance-related neuroimaging features as potential biomarkers for the diagnosis of major depressive disorder: A multicenter study based on machine learning. J Affect Disord 2021; 295:148-155. [PMID: 34461370 DOI: 10.1016/j.jad.2021.08.027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/05/2021] [Accepted: 08/18/2021] [Indexed: 01/21/2023]
Abstract
BACKGROUND Objective biomarkers are crucial for overcoming the clinical dilemma in major depressive disorder (MDD), and the individualized diagnosis is essential to facilitate the precise medicine for MDD. METHODS Sleep disturbance-related magnetic resonance imaging (MRI) features was identified in the internal dataset (92 MDD patients) using the relevance vector regression algorithm, which was further verified in 460 MDD patients of an independent, multicenter dataset. Subsequently, using these MRI features, the eXtreme Gradient Boosting classification model was constructed in the current multicenter dataset (460 MDD patients and 470 normal controls). Meanwhile, the association between classification outputs and the severity of depressive symptoms was also investigated. RESULTS In MDD patients, the combination of gray matter density and fractional amplitude of low-frequency fluctuation can accurately predict individual sleep disturbance score that was calculated by the sum of item 4 score, item 5 score, and item 6 score of the 17-Item Hamilton Rating Scale for Depression (HAMD-17) (R2 = 0.158 in the internal dataset; R2 = 0.110 in multicenter dataset). Furthermore, the classification model based on these MRI features distinguished MDD patients from normal controls with 86.3% accuracy (area under the curve = 0.937). Importantly, the classification outputs significantly correlated with HAMD-17 scores in MDD patients. LIMITATION Lacking some specialized tools to assess the personal sleep quality, e.g. Pittsburgh Sleep Quality Index. CONCLUSION Neuroimaging features can reflect accurately individual sleep disturbance manifestation and serve as potential diagnostic biomarkers of MDD.
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Affiliation(s)
- Yachen Shi
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu 210009, China
| | - Linhai Zhang
- School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing, Jiangsu 211189, China
| | - Cancan He
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu 210009, China
| | - Yingying Yin
- Department of Psychosomatics and Psychiatry, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Ruize Song
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu 210009, China
| | - Suzhen Chen
- Department of Psychosomatics and Psychiatry, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Dandan Fan
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu 210009, China
| | - Deyu Zhou
- School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing, Jiangsu 211189, China
| | - Yonggui Yuan
- Department of Psychosomatics and Psychiatry, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China.
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu 210009, China; The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu 210009, China.
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu 210009, China; The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu 210009, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China; Research Center for Brain Health, Pazhou Lab, Guangzhou, Guangdong 510330, China.
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22
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Xiong G, Dong D, Cheng C, Jiang Y, Sun X, He J, Li C, Gao Y, Zhong X, Zhao H, Wang X, Yao S. Potential structural trait markers of depression in the form of alterations in the structures of subcortical nuclei and structural covariance network properties. NEUROIMAGE-CLINICAL 2021; 32:102871. [PMID: 34749291 PMCID: PMC8578037 DOI: 10.1016/j.nicl.2021.102871] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 10/20/2021] [Accepted: 10/29/2021] [Indexed: 11/18/2022]
Abstract
It has been proposed recently that major depressive disorder (MDD) could represent an adaptation to conserve energy after the perceived loss of an investment in a vital source, such as group identity, personal assets, or relationships. Energy conserving behaviors associated with MDD may form a persistent marker in brain regions and networks involved in cognition and emotion regulation. In this study, we examined whether subcortical regions and volume-based structural covariance networks (SCNs) have state-independent alterations (trait markers). First-episode drug-naïve currently depressed (cMDD) patients (N = 131), remitted MDD (RD) patients (N = 67), and healthy controls (HCs, N = 235) underwent structural magnetic resonance imaging (MRI). Subcortical gray matter volumes (GMVs) were calculated in FreeSurfer software, and group differences in GMVs and SCN were analyzed. Compared to HCs, major findings were decreased GMVs of left pallidum and pulvinar anterior of thalamus in the cMDD and RD groups, indicative of a trait marker. Relative to HCs, subcortical SCNs of both cMDD and RD patients were found to have reduced small-world-ness and path length, which together may represent a trait-like topological feature of depression. In sum, the left pallidum, left pulvinar anterior of thalamus volumetric alterations may represent trait marker and reduced small-world-ness, path length may represent trait-like topological feature of MDD.
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Affiliation(s)
- Ge Xiong
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Daifeng Dong
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Chang Cheng
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Yali Jiang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China; School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Xiaoqiang Sun
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Jiayue He
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Chuting Li
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China; China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan 410011, China
| | - Yidian Gao
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Xue Zhong
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Haofei Zhao
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Xiang Wang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China; China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan 410011, China
| | - Shuqiao Yao
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China; China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan 410011, China.
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23
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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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24
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Singh MK, Singh KK. A Review of Publicly Available Automatic Brain Segmentation Methodologies, Machine Learning Models, Recent Advancements, and Their Comparison. Ann Neurosci 2021; 28:82-93. [PMID: 34733059 PMCID: PMC8558983 DOI: 10.1177/0972753121990175] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 01/04/2021] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND The noninvasive study of the structure and functions of the brain using neuroimaging techniques is increasingly being used for its clinical and research perspective. The morphological and volumetric changes in several regions and structures of brains are associated with the prognosis of neurological disorders such as Alzheimer's disease, epilepsy, schizophrenia, etc. and the early identification of such changes can have huge clinical significance. The accurate segmentation of three-dimensional brain magnetic resonance images into tissue types (i.e., grey matter, white matter, cerebrospinal fluid) and brain structures, thus, has huge importance as they can act as early biomarkers. The manual segmentation though considered the "gold standard" is time-consuming, subjective, and not suitable for bigger neuroimaging studies. Several automatic segmentation tools and algorithms have been developed over the years; the machine learning models particularly those using deep convolutional neural network (CNN) architecture are increasingly being applied to improve the accuracy of automatic methods. PURPOSE The purpose of the study is to understand the current and emerging state of automatic segmentation tools, their comparison, machine learning models, their reliability, and shortcomings with an intent to focus on the development of improved methods and algorithms. METHODS The study focuses on the review of publicly available neuroimaging tools, their comparison, and emerging machine learning models particularly those based on CNN architecture developed and published during the last five years. CONCLUSION Several software tools developed by various research groups and made publicly available for automatic segmentation of the brain show variability in their results in several comparison studies and have not attained the level of reliability required for clinical studies. The machine learning models particularly three dimensional fully convolutional network models can provide a robust and efficient alternative with relation to publicly available tools but perform poorly on unseen datasets. The challenges related to training, computation cost, reproducibility, and validation across distinct scanning modalities for machine learning models need to be addressed.
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Affiliation(s)
| | - Krishna Kumar Singh
- Symbiosis Centre for Information
Technology, Hinjawadi, Pune, Maharashtra, India
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25
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Faria AV, Zhao Y, Ye C, Hsu J, Yang K, Cifuentes E, Wang L, Mori S, Miller M, Caffo B, Sawa A. Multimodal MRI assessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup. Hum Brain Mapp 2020; 42:1034-1053. [PMID: 33377594 PMCID: PMC7856640 DOI: 10.1002/hbm.25276] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 09/29/2020] [Accepted: 10/18/2020] [Indexed: 02/06/2023] Open
Abstract
Multi‐institutional brain imaging studies have emerged to resolve conflicting results among individual studies. However, adjusting multiple variables at the technical and cohort levels is challenging. Therefore, it is important to explore approaches that provide meaningful results from relatively small samples at institutional levels. We studied 87 first episode psychosis (FEP) patients and 62 healthy subjects by combining supervised integrated factor analysis (SIFA) with a novel pipeline for automated structure‐based analysis, an efficient and comprehensive method for dimensional data reduction that our group recently established. We integrated multiple MRI features (volume, DTI indices, resting state fMRI—rsfMRI) in the whole brain of each participant in an unbiased manner. The automated structure‐based analysis showed widespread DTI abnormalities in FEP and rs‐fMRI differences between FEP and healthy subjects mostly centered in thalamus. The combination of multiple modalities with SIFA was more efficient than the use of single modalities to stratify a subgroup of FEP (individuals with schizophrenia or schizoaffective disorder) that had more robust deficits from the overall FEP group. The information from multiple MRI modalities and analytical methods highlighted the thalamus as significantly abnormal in FEP. This study serves as a proof‐of‐concept for the potential of this methodology to reveal disease underpins and to stratify populations into more homogeneous sub‐groups.
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Affiliation(s)
- Andreia V Faria
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Yi Zhao
- Department of Biostatistics, Indiana University, School of Medicine, Indianapolis, Indiana, USA
| | - Chenfei Ye
- Department of Electronics and Information, Harbin Institute of Technology Shenzhen Graduate School, Guangdong, China
| | - Johnny Hsu
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kun Yang
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Elizabeth Cifuentes
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences and Radiology, Northwestern University, Evanston, Illinois, USA
| | - Susumu Mori
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Michael Miller
- Department of Biomedical Engineering, The Whiting School of Engineering, Baltimore, Maryland, USA
| | - Brian Caffo
- Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Akira Sawa
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, The Whiting School of Engineering, Baltimore, Maryland, USA.,Department of Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Mental Health, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
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26
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Yousef HA, ElSerogy YMBE, Abdelal SM, Abdel-Rahman SR. Voxel-based morphometry in patients with mood disorder bipolar I mania in comparison to normal controls. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2020. [DOI: 10.1186/s43055-019-0119-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Neuroimaging is used to study brain structural alterations in neuropsychiatric diseases including bipolar disorder (BD). Voxel-based morphometry (VBM) quantifies structural changes detected in magnetic resonance imaging (MRI). The aim of this study was to identify brain structural changes in patients with mood disorder bipolar I mania, compared to healthy controls; and detect any correlations between volumetric findings and different clinical aspects of the disease. VBM was used to identify structural changes in 24 patients with bipolar I mania compared to 16 controls. Young Mania Rating Scale (YMRS) was used to evaluate clinical severity of BD. t test was used to compare differences in volumetric data and Spearman’s rank correlation coefficient was used to detect potential correlations between volumetric data and clinical parameters of BD.
Results
Compared to controls, BD patients had significantly larger right globus pallidus and right lateral ventricle. There was significant correlation between volumetric data of different brain structures and clinical criteria of BD including age of onset, illness duration, YMRS, number of manic attacks, and duration of the last attack.
Conclusions
VBM could address specific structural findings in bipolar I mania that may contribute to pathophysiology of the disease and show significant correlation with different clinical aspects of the disease.
Trial registration
This clinical trial was registered at ClinicalTrials.gov under registration number NCT03181698, registered 11 June 2017.
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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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28
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Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp 2020; 41:3468-3535. [PMID: 32374075 PMCID: PMC7375108 DOI: 10.1002/hbm.25013] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/06/2020] [Accepted: 04/06/2020] [Indexed: 01/10/2023] Open
Abstract
Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.
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Affiliation(s)
- Barnaly Rashid
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - Vince Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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29
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Abstract
Support vector machines (SVMs) are being used increasingly in affective science as a data-driven classification method and feature reduction technique. Whereas traditional statistical methods typically compare group averages on selected variables, SVMs use a predictive algorithm to learn multivariate patterns that optimally discriminate between groups. In this review, we provide a framework for understanding the methods of SVM-based analyses and summarize the findings of seminal studies that use SVMs for classification or data reduction in the behavioral and neural study of emotion and affective disorders. We conclude by discussing promising directions and potential applications of SVMs in future research in affective science.
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Affiliation(s)
| | - Matthew D. Sacchet
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Harvard Medical School, USA
| | - Ian H. Gotlib
- Department of Psychology, Stanford Neurosciences Institute, Stanford University, USA
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30
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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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31
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Vai B, Parenti L, Bollettini I, Cara C, Verga C, Melloni E, Mazza E, Poletti S, Colombo C, Benedetti F. Predicting differential diagnosis between bipolar and unipolar depression with multiple kernel learning on multimodal structural neuroimaging. Eur Neuropsychopharmacol 2020; 34:28-38. [PMID: 32238313 DOI: 10.1016/j.euroneuro.2020.03.008] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 01/24/2020] [Accepted: 03/06/2020] [Indexed: 01/10/2023]
Abstract
One of the greatest challenges in providing early effective treatment in mood disorders is the early differential diagnosis between major depression (MDD) and bipolar disorder (BD). A remarkable need exists to identify reliable biomarkers for these disorders. We integrate structural neuroimaging techniques (i.e. Tract-based Spatial Statistics, TBSS, and Voxel-based morphometry) in a multiple kernel learning procedure in order to define a predictive function of BD against MDD diagnosis in a sample of 148 patients. We achieved a balanced accuracy of 73.65% with a sensitivity for BD of 74.32% and specificity for MDD of 72.97%. Mass-univariates analyses showed reduced grey matter volume in right hippocampus, amygdala, parahippocampal, fusiform gyrus, insula, rolandic and frontal operculum and cerebellum, in BD compared to MDD. Volumes in these regions and in anterior cingulate cortex were also reduced in BD compared to healthy controls (n = 74). TBSS analyses revealed widespread significant effects of diagnosis on fractional anisotropy, axial, radial, and mean diffusivity in several white matter tracts, suggesting disruption of white matter microstructure in depressed patients compared to healthy controls, with worse pattern for MDD. To best of our knowledge, this is the first study combining grey matter and diffusion tensor imaging in predicting BD and MDD diagnosis. Our results prompt brain quantitative biomarkers and multiple kernel learning as promising tool for personalized treatment in mood disorders.
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Affiliation(s)
- Benedetta Vai
- Division of Neuroscience, Psychiatry and Clinical Psychobiology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy; University Vita-Salute San Raffaele, Milano, Italy; Fondazione Centro San Raffaele, Milano, Italy.
| | - Lorenzo Parenti
- Division of Neuroscience, Psychiatry and Clinical Psychobiology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Irene Bollettini
- Division of Neuroscience, Psychiatry and Clinical Psychobiology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Cristina Cara
- Division of Neuroscience, Psychiatry and Clinical Psychobiology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Chiara Verga
- Division of Neuroscience, Psychiatry and Clinical Psychobiology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Elisa Melloni
- Division of Neuroscience, Psychiatry and Clinical Psychobiology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Elena Mazza
- Division of Neuroscience, Psychiatry and Clinical Psychobiology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy; University Vita-Salute San Raffaele, Milano, Italy
| | - Sara Poletti
- Division of Neuroscience, Psychiatry and Clinical Psychobiology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy; University Vita-Salute San Raffaele, Milano, Italy
| | - Cristina Colombo
- Division of Neuroscience, Psychiatry and Clinical Psychobiology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy; University Vita-Salute San Raffaele, Milano, Italy
| | - Francesco Benedetti
- Division of Neuroscience, Psychiatry and Clinical Psychobiology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy; University Vita-Salute San Raffaele, Milano, Italy
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32
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Achalia R, Sinha A, Jacob A, Achalia G, Kaginalkar V, Venkatasubramanian G, Rao NP. A proof of concept machine learning analysis using multimodal neuroimaging and neurocognitive measures as predictive biomarker in bipolar disorder. Asian J Psychiatr 2020; 50:101984. [PMID: 32143176 DOI: 10.1016/j.ajp.2020.101984] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 02/18/2020] [Accepted: 02/24/2020] [Indexed: 01/10/2023]
Abstract
BACKGROUND Concomitant use of complementary, multimodal imaging measures and neurocognitive measures is reported to have higher accuracy as a biomarker in Alzheimer's dementia. However, such an approach has not been examined to differentiate healthy individuals from Bipolar disorder. In this study, we examined the utility of support vector machine (SVM) technique to differentiate bipolar disorder patients and healthy using structural, functional and diffusion tensor images of brain and neurocognitive measures. METHODS 30 patients with Bipolar disorder-I and 30 age, sex matched individuals participated in the study. Structural MRI, resting state functional MRI and diffusion tensor images were obtained using a 1.5 T scanner. All participants were administered neuropsychological tests to measure executive functions. SVM, a supervised machine learning technique was applied to differentiate patients and healthy individuals with k-fold cross validation over 10 trials. RESULTS The composite marker consisting of both neuroimaging and neuropsychological measures, had an accuracy of 87.60 %, sensitivity of 82.3 % and specificity of 92.7 %. The performance of composite marker was better compared to that of individual markers on classificatory. CONCLUSIONS We were able to achieve a high accuracy for machine learning technique in distinguishing BD from HV using a combination of multimodal neuroimaging and neurocognitive measures. Findings of this proof of concept study, if replicated in larger samples, could have potential clinical applications.
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Affiliation(s)
| | - Anannya Sinha
- National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Arpitha Jacob
- National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Garimaa Achalia
- Achalia Neuropsychiatry Hospital, Aurangabad, Maharashtra, India
| | | | | | - Naren P Rao
- National Institute of Mental Health and Neurosciences, Bangalore, India.
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33
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Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group. Mol Psychiatry 2020; 25:2130-2143. [PMID: 30171211 PMCID: PMC7473838 DOI: 10.1038/s41380-018-0228-9] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Revised: 06/11/2018] [Accepted: 07/24/2018] [Indexed: 01/10/2023]
Abstract
Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.
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Linking personality types to depressive symptoms: A prospective typology based on neuroticism, extraversion and conscientiousness. Neuropsychologia 2019; 136:107289. [PMID: 31794711 DOI: 10.1016/j.neuropsychologia.2019.107289] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 10/28/2019] [Accepted: 11/29/2019] [Indexed: 12/22/2022]
Abstract
From a traditional variable-centered perspective, the personality traits specifically linked to depressive symptoms are neuroticism, extraversion, and conscientiousness (NEC). Few studies have considered the interactive effects of personality traits within individuals from the taxonomic perspective. We explored novel NEC personality subtypes in general and subthreshold depressive subjects by using graph-based classification algorithms and multiple external validators. Personality and depressive symptoms were assessed in 1055 healthy subjects (150 with subthreshold depression) using the NEO-PI-R and BDI at baseline. NEC personality data were used to identify subtypes in healthy and subthreshold depressive samples, and external validators, including current and longitudinal depressive symptoms and seven subcortical gray matter volumes, were examined. Four novel NEC personality types in the general sample showed superior current and longitudinal behavioral validation of depressive symptoms as well as some discrimination in biological indicators (putamen, nucleus accumbens, and amygdala). Four profiles defined in subthreshold depression selectively exhibited meaningful differences in longitudinal depressive symptoms. In both samples, some types adhere to the principles previously described NEC three-way interaction. The resulting typology, especially the four types in the general population, linked depressive symptoms provided a superior description of within-person organization of neuroticism, extraversion, and conscientiousness.
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Xiong G, Dong D, Cheng C, Jiang Y, Sun X, He J, Li C, Gao Y, Zhong X, Zhao H, Wang X, Yao S. State-independent and -dependent structural alterations in limbic-cortical regions in patients with current and remitted depression. J Affect Disord 2019; 258:1-10. [PMID: 31382099 DOI: 10.1016/j.jad.2019.07.065] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2019] [Revised: 07/24/2019] [Accepted: 07/29/2019] [Indexed: 12/21/2022]
Abstract
BACKGROUND The high recurrence of major depressive disorder (MDD) may derive from underlying state-independent structural alterations. METHODS First-episode drug-naïve currently depressed (cMDD) patients (N = 97), remitted depressed (RD) patients (N = 72), and healthy controls (HCs, N = 100) underwent structural magnetic resonance imaging (MRI). Group differences in cortical thickness (CT), surface area (SA), and local gyrification index (lGI) were analyzed in FreeSurfer. RESULTS Both groups of depressed patients had significantly decreased CT, relative to HCs, in the left precentral gyrus and significantly increased lGI values in the left superior frontal gyrus (SFG) indicative of state-independent alterations. Relative to HCs, the cMDD group had decreased CT of the SFG, caudal middle frontal gyrus (MFG), posterior cingulate cortex (PCC), and lateral occipital regions as well as increased SA or lGI of the superior temporal gyrus, precuneus, and pericalcarine, whereas the RD group had increased SA or lGI of the SFG, caudal MFG, and supramarginal gyrus; these alterations appeared to be state-dependent. SA or lGI values of the fusiform gyrus, inferior temporal gyrus, and superior parietal lobule differed between the cMDD and RD groups, consistent with state-dependent alterations. Beck depression inventory scores correlated with CT or lGI values of the caudal MFG, lateral occipital cortex in depressed patients. LIMITATIONS The structural features of several subcortical limbic regions were not analyzed. CONCLUSIONS Left precentral gyrus CT and left SFG gyrification alterations may represent state-independent alterations in MDD.
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Affiliation(s)
- Ge Xiong
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Daifeng Dong
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Chang Cheng
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Yali Jiang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Xiaoqiang Sun
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Jiayue He
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Chuting Li
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China; China National Clinical Research Center on Mental disorders (Xiangya), Changsha, Hunan 410011, China
| | - Yidian Gao
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Xue Zhong
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Haofei Zhao
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Xiang Wang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China; China National Clinical Research Center on Mental disorders (Xiangya), Changsha, Hunan 410011, China
| | - Shuqiao Yao
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China; China National Clinical Research Center on Mental disorders (Xiangya), Changsha, Hunan 410011, China.
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Pagnozzi AM, Fripp J, Rose SE. Quantifying deep grey matter atrophy using automated segmentation approaches: A systematic review of structural MRI studies. Neuroimage 2019; 201:116018. [PMID: 31319182 DOI: 10.1016/j.neuroimage.2019.116018] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 07/01/2019] [Accepted: 07/12/2019] [Indexed: 12/13/2022] Open
Abstract
The deep grey matter (DGM) nuclei of the brain play a crucial role in learning, behaviour, cognition, movement and memory. Although automated segmentation strategies can provide insight into the impact of multiple neurological conditions affecting these structures, such as Multiple Sclerosis (MS), Huntington's disease (HD), Alzheimer's disease (AD), Parkinson's disease (PD) and Cerebral Palsy (CP), there are a number of technical challenges limiting an accurate automated segmentation of the DGM. Namely, the insufficient contrast of T1 sequences to completely identify the boundaries of these structures, as well as the presence of iso-intense white matter lesions or extensive tissue loss caused by brain injury. Therefore in this systematic review, 269 eligible studies were analysed and compared to determine the optimal approaches for addressing these technical challenges. The automated approaches used among the reviewed studies fall into three broad categories, atlas-based approaches focusing on the accurate alignment of atlas priors, algorithmic approaches which utilise intensity information to a greater extent, and learning-based approaches that require an annotated training set. Studies that utilise freely available software packages such as FIRST, FreeSurfer and LesionTOADS were also eligible, and their performance compared. Overall, deep learning approaches achieved the best overall performance, however these strategies are currently hampered by the lack of large-scale annotated data. Improving model generalisability to new datasets could be achieved in future studies with data augmentation and transfer learning. Multi-atlas approaches provided the second-best performance overall, and may be utilised to construct a "silver standard" annotated training set for deep learning. To address the technical challenges, providing robustness to injury can be improved by using multiple channels, highly elastic diffeomorphic transformations such as LDDMM, and by following atlas-based approaches with an intensity driven refinement of the segmentation, which has been done with the Expectation Maximisation (EM) and level sets methods. Accounting for potential lesions should be achieved with a separate lesion segmentation approach, as in LesionTOADS. Finally, to address the issue of limited contrast, R2*, T2* and QSM sequences could be used to better highlight the DGM due to its higher iron content. Future studies could look to additionally acquire these sequences by retaining the phase information from standard structural scans, or alternatively acquiring these sequences for only a training set, allowing models to learn the "improved" segmentation from T1-sequences alone.
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Affiliation(s)
- Alex M Pagnozzi
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia.
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
| | - Stephen E Rose
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
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Dvorak J, Hilke M, Trettin M, Wenzler S, Hagen M, Ghirmai N, Müller M, Kraft D, Reif A, Oertel V. Aberrant brain network topology in fronto-limbic circuitry differentiates euthymic bipolar disorder from recurrent major depressive disorder. Brain Behav 2019; 9:e01257. [PMID: 31066228 PMCID: PMC6576154 DOI: 10.1002/brb3.1257] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 01/19/2019] [Accepted: 02/10/2019] [Indexed: 12/22/2022] Open
Abstract
INTRODUCTION Previous studies have established graph theoretical analysis of functional network connectivity (FNC) as a potential tool to detect neurobiological underpinnings of psychiatric disorders. Despite the promising outcomes in studies that examined FNC aberrancies in bipolar disorder (BD) and major depressive disorder (MDD), there is still a lack of research comparing both mood disorders, especially in a nondepressed state. In this study, we used graph theoretical network analysis to compare brain network properties of euthymic BD, euthymic MDD and healthy controls (HC) to evaluate whether these groups showed distinct features in FNC. METHODS We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 BD patients, 15 patients with recurrent MDD as well as 30 age- and gender-matched HC. Graph theoretical analyses were then applied to investigate functional brain networks on a global and regional network level. RESULTS Global network analysis revealed a significantly higher mean global clustering coefficient in BD compared to HC. We further detected frontal, temporal and subcortical nodes in emotion regulation areas such as the limbic system and associated regions exhibiting significant differences in network integration and segregation in BD compared to MDD patients and HC. Participants with MDD and HC only differed in frontal and insular network centrality. CONCLUSION In conclusion, our findings indicate that a significantly altered brain network topology in the limbic system might be a trait marker specific to BD. Brain network analysis in these regions may therefore be used to differentiate euthymic BD not only from HC but also from patients with MDD.
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Affiliation(s)
- Jannis Dvorak
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, Frankfurt, Germany
| | - Marietheres Hilke
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, Frankfurt, Germany.,Brain Imaging Center (BIC), Goethe University Frankfurt, Frankfurt, Germany
| | - Marco Trettin
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, Frankfurt, Germany
| | - Sofia Wenzler
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, Frankfurt, Germany
| | - Marleen Hagen
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, Frankfurt, Germany
| | - Naddy Ghirmai
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, Frankfurt, Germany
| | - Maximilian Müller
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, Frankfurt, Germany.,Brain Imaging Center (BIC), Goethe University Frankfurt, Frankfurt, Germany
| | - Dominik Kraft
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, Frankfurt, Germany.,Brain Imaging Center (BIC), Goethe University Frankfurt, Frankfurt, Germany
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, Frankfurt, Germany
| | - Viola Oertel
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, Frankfurt, Germany
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Bourgouin PA, Rahayel S, Gaubert M, Postuma RB, Montplaisir J, Carrier J, Monchi O, Pelletier A, Gagnon JF. Gray matter substrates of depressive and anxiety symptoms in idiopathic REM sleep behavior disorder. Parkinsonism Relat Disord 2019; 62:163-170. [DOI: 10.1016/j.parkreldis.2018.12.020] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 11/21/2018] [Accepted: 12/16/2018] [Indexed: 12/14/2022]
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He Z, Sheng W, Lu F, Long Z, Han S, Pang Y, Chen Y, Luo W, Yu Y, Nan X, Cui Q, Chen H. Altered resting-state cerebral blood flow and functional connectivity of striatum in bipolar disorder and major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2019; 90:177-185. [PMID: 30500413 DOI: 10.1016/j.pnpbp.2018.11.009] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 08/26/2018] [Accepted: 11/15/2018] [Indexed: 01/21/2023]
Abstract
BACKGROUND Clinically distinguishing bipolar disorder (BD) from major depressive disorder (MDD) during depressive states is difficult. Neuroimaging findings suggested that patients with BD and those with MDD differed with respect to the gray matter volumes of their subcortical structures, especially in their striatum. However, whether these disorders have different effects on functionally striatal neuronal activity and connectivity is unclear. METHODS Arterial spin labeling and resting-state functional MRI was performed on 25 currently depressive patients with BD, 25 depressive patients with MDD, and 34 healthy controls (HCs). The functional properties of striatal neuronal activity (cerebral blood flow, CBF) and its functional connectivity (FC) were analyzed, and the results from the three groups were compared. The result of the multiple comparisons was corrected on the basis of the Gaussian Random Field theory. RESULTS The patients with BD and those with MDD both had higher CBF values than the HCs in the right caudate and right putamen. The hyper-metabolism of right striatum in BD patients was associated with increased average duration per depressive episode. The two disorders showed commonly increased FC between the striatum and dorsolateral prefrontal cortex, whereas the altered FC of the striatum with precuneus/cuneus was observed only in patients with BD. CONCLUSIONS Patients with BD and those with MDD had a common deficit in their prefrontal-limbic-striatal circuits. The altered striato-precuneus FC can be considered as a marker for the differentiation of patients with BD from those with MDD.
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Affiliation(s)
- Zongling He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in BioMedicine, Key laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Sheng
- Center for Information in BioMedicine, Key laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in BioMedicine, Key laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhiliang Long
- Center for Information in BioMedicine, Key laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shaoqiang Han
- Center for Information in BioMedicine, Key laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yajing Pang
- Center for Information in BioMedicine, Key laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yuyan Chen
- Center for Information in BioMedicine, Key laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yue Yu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoyu Nan
- Center for Information in BioMedicine, Key laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qian Cui
- School of Public Administration, University of Electronic Science and Technology of China, Chengdu, China.
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in BioMedicine, Key laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
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Ancelin ML, Carrière I, Artero S, Maller J, Meslin C, Ritchie K, Ryan J, Chaudieu I. Lifetime major depression and grey-matter volume. J Psychiatry Neurosci 2019; 44:45-53. [PMID: 30565905 PMCID: PMC6306287 DOI: 10.1503/jpn.180026] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND There is evidence of structural brain alterations in major depressive disorder (MDD), but little is known about how these alterations might be affected by age at onset or genetic vulnerability. This study examines whether lifetime episodes of MDD are associated with specific alterations in grey-matter volume, and whether those alterations vary according to sex or serotonin transporter-linked promoter region (5-HTTLPR) genotype (LL, SL or SS). METHODS We used structural MRI to acquire anatomic scans from 610 community-dwelling participants. We derived quantitative regional estimates of grey-matter volume in 16 subregions using FreeSurfer software. We diagnosed MDD according to DSM-IV criteria. We adjusted analyses for age, sex, total brain volume, education level, head injury and comorbidities. RESULTS Lifetime MDD was associated with a smaller insula, thalamus, ventral diencephalon, pallidum and nucleus accumbens and with a larger pericalcarine region in both men and women. These associations remained after adjustment for false discovery rate. Lifetime MDD was also associated with a smaller caudate nucleus and amygdala in men and with a larger rostral anterior cingulate cortex in women. Late-onset first episodes of MDD (after age 50 years) were associated with a larger rostral anterior cingulate cortex and lingual and pericalcarine regions; early-onset MDD was associated with a smaller ventral diencephalon and nucleus accumbens. Some associations differed according to 5-HTTLPR genotype: the thalamus was smaller in participants with MDD and the LL genotype; pericalcarine and lingual volumes were higher in those with the SL genotype. LIMITATIONS This study was limited by its cross-sectional design. CONCLUSION Major depressive disorder was associated with persistent volume reductions in the deep nuclei and insula and with enlargements in visual cortex subregions; alterations varied according to age of onset and genotype.
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Affiliation(s)
- Marie-Laure Ancelin
- From INSERM, Univ Montpellier, Neuropsychiatry: Epidemiological and Clinical Research, Montpellier, France (Ancelin, Carrière, Artero, Ritchie, Ryan, Chaudieu); Monash Alfred Psychiatry Research Centre, Central Clinical School, Monash University and Alfred Hospital, Australia (Maller); Centre for Mental Health Research, Australian National University, Canberra, Australia (Maller, Meslin); General Electric Healthcare, Australia (Maller); Center for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom (Ritchie); and Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia (Ryan)
| | - Isabelle Carrière
- From INSERM, Univ Montpellier, Neuropsychiatry: Epidemiological and Clinical Research, Montpellier, France (Ancelin, Carrière, Artero, Ritchie, Ryan, Chaudieu); Monash Alfred Psychiatry Research Centre, Central Clinical School, Monash University and Alfred Hospital, Australia (Maller); Centre for Mental Health Research, Australian National University, Canberra, Australia (Maller, Meslin); General Electric Healthcare, Australia (Maller); Center for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom (Ritchie); and Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia (Ryan)
| | - Sylvaine Artero
- From INSERM, Univ Montpellier, Neuropsychiatry: Epidemiological and Clinical Research, Montpellier, France (Ancelin, Carrière, Artero, Ritchie, Ryan, Chaudieu); Monash Alfred Psychiatry Research Centre, Central Clinical School, Monash University and Alfred Hospital, Australia (Maller); Centre for Mental Health Research, Australian National University, Canberra, Australia (Maller, Meslin); General Electric Healthcare, Australia (Maller); Center for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom (Ritchie); and Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia (Ryan)
| | - Jerome Maller
- From INSERM, Univ Montpellier, Neuropsychiatry: Epidemiological and Clinical Research, Montpellier, France (Ancelin, Carrière, Artero, Ritchie, Ryan, Chaudieu); Monash Alfred Psychiatry Research Centre, Central Clinical School, Monash University and Alfred Hospital, Australia (Maller); Centre for Mental Health Research, Australian National University, Canberra, Australia (Maller, Meslin); General Electric Healthcare, Australia (Maller); Center for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom (Ritchie); and Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia (Ryan)
| | - Chantal Meslin
- From INSERM, Univ Montpellier, Neuropsychiatry: Epidemiological and Clinical Research, Montpellier, France (Ancelin, Carrière, Artero, Ritchie, Ryan, Chaudieu); Monash Alfred Psychiatry Research Centre, Central Clinical School, Monash University and Alfred Hospital, Australia (Maller); Centre for Mental Health Research, Australian National University, Canberra, Australia (Maller, Meslin); General Electric Healthcare, Australia (Maller); Center for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom (Ritchie); and Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia (Ryan)
| | - Karen Ritchie
- From INSERM, Univ Montpellier, Neuropsychiatry: Epidemiological and Clinical Research, Montpellier, France (Ancelin, Carrière, Artero, Ritchie, Ryan, Chaudieu); Monash Alfred Psychiatry Research Centre, Central Clinical School, Monash University and Alfred Hospital, Australia (Maller); Centre for Mental Health Research, Australian National University, Canberra, Australia (Maller, Meslin); General Electric Healthcare, Australia (Maller); Center for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom (Ritchie); and Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia (Ryan)
| | - Joanne Ryan
- From INSERM, Univ Montpellier, Neuropsychiatry: Epidemiological and Clinical Research, Montpellier, France (Ancelin, Carrière, Artero, Ritchie, Ryan, Chaudieu); Monash Alfred Psychiatry Research Centre, Central Clinical School, Monash University and Alfred Hospital, Australia (Maller); Centre for Mental Health Research, Australian National University, Canberra, Australia (Maller, Meslin); General Electric Healthcare, Australia (Maller); Center for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom (Ritchie); and Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia (Ryan)
| | - Isabelle Chaudieu
- From INSERM, Univ Montpellier, Neuropsychiatry: Epidemiological and Clinical Research, Montpellier, France (Ancelin, Carrière, Artero, Ritchie, Ryan, Chaudieu); Monash Alfred Psychiatry Research Centre, Central Clinical School, Monash University and Alfred Hospital, Australia (Maller); Centre for Mental Health Research, Australian National University, Canberra, Australia (Maller, Meslin); General Electric Healthcare, Australia (Maller); Center for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom (Ritchie); and Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia (Ryan)
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Shi J, Geng J, Yan R, Liu X, Chen Y, Zhu R, Wang X, Shao J, Bi K, Xiao M, Yao Z, Lu Q. Differentiation of Transformed Bipolar Disorder From Unipolar Depression by Resting-State Functional Connectivity Within Reward Circuit. Front Psychol 2018; 9:2586. [PMID: 30622492 PMCID: PMC6308204 DOI: 10.3389/fpsyg.2018.02586] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 12/03/2018] [Indexed: 01/23/2023] Open
Abstract
Previous studies have found that neural functional abnormalities detected by functional magnetic resonance imaging (fMRI) in brain regions implicated in reward processing during reward tasks show promise to distinguish bipolar from unipolar depression (UD), but little is known regarding resting-state functional connectivity (rsFC) within the reward circuit. In this study, we investigated neurobiomarkers for early recognition of bipolar disorder (BD) by retrospectively comparing rsFC within the reward circuit between UD and depressed BD. Sixty-six depressed patients were enrolled, none of whom had ever experienced any manic/hypomanic episodes before baseline. Simultaneously, 40 matched healthy controls (HC) were also recruited. Neuroimaging data of each participant were obtained from resting-state fMRI scans. Some patients began to manifest bipolar disorder (tBD) during the follow-up period. All patients were retrospectively divided into two groups (33 tBD and 33 UD) according to the presence or absence of mania/hypomania in the follow-up. rsFC between key regions of the reward circuit was calculated and compared among groups. Results showed decreased rsFC between the left ventral tegmental area (VTA) and left ventral striatum (VS) in the tBD group compared with the UD group, which showed good accuracy in predicting diagnosis (tBD vs. UD) according to receiver operating characteristic (ROC) analysis. No significant different rsFC was found within the reward circuit between any patient group and HC. Our preliminary findings indicated that bipolar disorder, in early depressive stages before onset of mania/hypomania attacks, already differs from UD in the reward circuit of VTA-VS functional synchronicity at the resting state.
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Affiliation(s)
- Jiabo Shi
- Department of Psychiatry, The Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Jiting Geng
- Department of Psychiatry, The Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Rui Yan
- Department of Psychiatry, The Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Xiaoxue Liu
- Department of Psychiatry, The Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Yu Chen
- Department of Psychiatry, The Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Rongxin Zhu
- Department of Psychiatry, The Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Xinyi Wang
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
- Key Laboratory of Child Development and Learning Science, Southeast University, Nanjing, China
| | - Junneng Shao
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
- Key Laboratory of Child Development and Learning Science, Southeast University, Nanjing, China
| | - Kun Bi
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
- Key Laboratory of Child Development and Learning Science, Southeast University, Nanjing, China
| | - Ming Xiao
- Nanjing Medical University, Nanjing, China
| | - Zhijian Yao
- Department of Psychiatry, The Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Qing Lu
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
- Key Laboratory of Child Development and Learning Science, Southeast University, Nanjing, China
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Gao S, Calhoun VD, Sui J. Machine learning in major depression: From classification to treatment outcome prediction. CNS Neurosci Ther 2018; 24:1037-1052. [PMID: 30136381 DOI: 10.1111/cns.13048] [Citation(s) in RCA: 160] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 07/19/2018] [Accepted: 07/21/2018] [Indexed: 01/10/2023] Open
Abstract
AIMS Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders. DISCUSSIONS In this study, we review popular machine-learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression. CONCLUSIONS We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care.
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Affiliation(s)
- Shuang Gao
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,CAS Centre for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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Akudjedu TN, Nabulsi L, Makelyte M, Scanlon C, Hehir S, Casey H, Ambati S, Kenney J, O’Donoghue S, McDermott E, Kilmartin L, Dockery P, McDonald C, Hallahan B, Cannon DM. A comparative study of segmentation techniques for the quantification of brain subcortical volume. Brain Imaging Behav 2018; 12:1678-1695. [DOI: 10.1007/s11682-018-9835-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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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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Sachs-Ericsson NJ, Hajcak G, Sheffler JL, Stanley IH, Selby EA, Potter GG, Steffens DC. Putamen Volume Differences Among Older Adults: Depression Status, Melancholia, and Age. J Geriatr Psychiatry Neurol 2018; 31:39-49. [PMID: 29251178 DOI: 10.1177/0891988717747049] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Individuals with major depressive disorder (MDD) may exhibit smaller striatal volumes reflecting deficits in the reward circuit. Deficits may change with age and be more pronounced among the melancholic subtype. Limited research has investigated striatal volume differences in older adults and by depression subtypes. METHOD We used baseline data from the Neurocognitive Outcomes of Depression in the Elderly study. We examined volumetric differences in the putamen and caudate nucleus among older adults (60 years and older), comparing healthy control participants (n = 134) to depressed participants (n = 226), and comparing nonmelancholic depressed participants (n = 93) to melancholic depressed participants (n = 133). Group-by-age interactions were examined. RESULTS There were no significant group differences for the caudate nucleus. For the left putamen, investigation of the significant group-by-age interaction revealed that volume size was greater for the healthy controls compared to the depressed participants but only at younger ages (60-65 years); group differences diminished with increasing age. Examining volume by depression subtype revealed that the melancholic depressed participants had a smaller left putamen compared to the nonmelancholic depressed participants. Anhedonia symptoms were related to both smaller left and right putamen. CONCLUSION Structural abnormalities in reward regions may underlie the anhedonic phenotype. Volume loss associated with MDD may attenuate in older age.
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Affiliation(s)
| | - Greg Hajcak
- 1 Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Julia L Sheffler
- 1 Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Ian H Stanley
- 1 Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Edward A Selby
- 2 Department of Psychology, Rutgers University, Piscataway, NJ, USA
| | - Guy G Potter
- 3 Department of Psychiatry, Duke University Medical Center, Durham, NC, USA
| | - David C Steffens
- 4 Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
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46
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Kambeitz J, Cabral C, Sacchet MD, Gotlib IH, Zahn R, Serpa MH, Walter M, Falkai P, Koutsouleris N. Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies. Biol Psychiatry 2017; 82:330-338. [PMID: 28110823 DOI: 10.1016/j.biopsych.2016.10.028] [Citation(s) in RCA: 89] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Revised: 09/27/2016] [Accepted: 10/20/2016] [Indexed: 12/24/2022]
Abstract
BACKGROUND Multiple studies have examined functional and structural brain alteration in patients diagnosed with major depressive disorder (MDD). The introduction of multivariate statistical methods allows investigators to utilize data concerning these brain alterations to generate diagnostic models that accurately differentiate patients with MDD from healthy control subjects (HCs). However, there is substantial heterogeneity in the reported results, the methodological approaches, and the clinical characteristics of participants in these studies. METHODS We conducted a meta-analysis of all studies using neuroimaging (volumetric measures derived from T1-weighted images, task-based functional magnetic resonance imaging [MRI], resting-state MRI, or diffusion tensor imaging) in combination with multivariate statistical methods to differentiate patients diagnosed with MDD from HCs. RESULTS Thirty-three (k = 33) samples including 912 patients with MDD and 894 HCs were included in the meta-analysis. Across all studies, patients with MDD were separated from HCs with 77% sensitivity and 78% specificity. Classification based on resting-state MRI (85% sensitivity, 83% specificity) and on diffusion tensor imaging data (88% sensitivity, 92% specificity) outperformed classifications based on structural MRI (70% sensitivity, 71% specificity) and task-based functional MRI (74% sensitivity, 77% specificity). CONCLUSIONS Our results demonstrate the high representational capacity of multivariate statistical methods to identify neuroimaging-based biomarkers of depression. Future studies are needed to elucidate whether multivariate neuroimaging analysis has the potential to generate clinically useful tools for the differential diagnosis of affective disorders and the prediction of both treatment response and functional outcome.
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Affiliation(s)
- Joseph Kambeitz
- Department of Psychiatry, Ludwig-Maximilians University Munich, Munich.
| | - Carlos Cabral
- Department of Psychiatry, Ludwig-Maximilians University Munich, Munich
| | - Matthew D Sacchet
- Neurosciences Program and Department of Psychology, Stanford University, Stanford, California
| | - Ian H Gotlib
- Neurosciences Program and Department of Psychology, Stanford University, Stanford, California
| | - Roland Zahn
- Institute of Psychiatry, King's College London, London, United Kingdom
| | - Mauricio H Serpa
- Laboratory of Psychiatric Neuroimaging, Institute and Department of Psychiatry, Sao Paulo, Brazil; Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of Sao Paulo, Sao Paulo, Brazil
| | - Martin Walter
- Clinical Affective Neuroimaging Laboratory, Department of Behavioural Neurology, Leibniz Institute for Neurobiology, Magdeburg; Department of Psychiatry and Psychotherapy, Eberhard Karls University, Tubingen, Germany
| | - Peter Falkai
- Department of Psychiatry, Ludwig-Maximilians University Munich, Munich
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The impact of machine learning techniques in the study of bipolar disorder: A systematic review. Neurosci Biobehav Rev 2017; 80:538-554. [PMID: 28728937 DOI: 10.1016/j.neubiorev.2017.07.004] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Revised: 06/15/2017] [Accepted: 07/08/2017] [Indexed: 01/10/2023]
Abstract
Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation.
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Shen X, Reus LM, Cox SR, Adams MJ, Liewald DC, Bastin ME, Smith DJ, Deary IJ, Whalley HC, McIntosh AM. Subcortical volume and white matter integrity abnormalities in major depressive disorder: findings from UK Biobank imaging data. Sci Rep 2017; 7:5547. [PMID: 28717197 PMCID: PMC5514104 DOI: 10.1038/s41598-017-05507-6] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Accepted: 05/30/2017] [Indexed: 01/18/2023] Open
Abstract
Previous reports of altered grey and white matter structure in Major Depressive Disorder (MDD) have been inconsistent. Recent meta-analyses have, however, reported reduced hippocampal grey matter volume in MDD and reduced white matter integrity in several brain regions. The use of different diagnostic criteria, scanners and imaging sequences may, however, obscure further anatomical differences. In this study, we tested for differences in subcortical grey matter volume (n = 1157) and white matter integrity (n = 1089) between depressed individuals and controls in the subset of 8590 UK Biobank Imaging study participants who had undergone depression assessments. Whilst we found no significant differences in subcortical volumes, significant reductions were found in depressed individuals versus controls in global white matter integrity, as measured by fractional anisotropy (FA) (β = −0.182, p = 0.005). We also found reductions in FA in association/commissural fibres (β = −0.184, pcorrected = 0.010) and thalamic radiations (β = −0.159, pcorrected = 0.020). Tract-specific FA reductions were also found in the left superior longitudinal fasciculus (β = −0.194, pcorrected = 0.025), superior thalamic radiation (β = −0.224, pcorrected = 0.009) and forceps major (β = −0.193, pcorrected = 0.025) in depression (all betas standardised). Our findings provide further evidence for disrupted white matter integrity in MDD.
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Affiliation(s)
- Xueyi Shen
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom.
| | - Lianne M Reus
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - David C Liewald
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Daniel J Smith
- Institute of Health and Wellbeing, Mental Health and Wellbeing Research Group, University of Glasgow, Glasgow, United Kingdom
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Heather C Whalley
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
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Zhao L, Wang Y, Jia Y, Zhong S, Sun Y, Zhou Z, Zhang Z, Huang L. Microstructural Abnormalities of Basal Ganglia and Thalamus in Bipolar and Unipolar Disorders: A Diffusion Kurtosis and Perfusion Imaging Study. Psychiatry Investig 2017; 14:471-482. [PMID: 28845175 PMCID: PMC5561406 DOI: 10.4306/pi.2017.14.4.471] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2016] [Revised: 06/23/2016] [Accepted: 07/25/2016] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVE Bipolar disorder (BD) is often misdiagnosed as unipolar depression (UD), leading to mistreatment and poor clinical outcomes. However, little is known about the similarities and differences in subcorticalgray matter regions between BD and UD. METHODS Thirty-five BD patients, 30 UD patients and 40 healthy controls underwent diffusional kurtosis imaging (DKI) and three dimensional arterial spin labeling (3D ASL). The parameters including mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (Da), radial diffusivity (Dr) and cerebral blood flow (CBF) were measured by using regions-of-interest analysis in the caudate, putamen and thalamus of the subcortical gray matter regions. RESULTS UD exhibited differences from controls for DKI measures and CBF in the left putamen and caudate. BD showed differences from controls for DKI measures in the left caudate. Additionally, BD showed lower Ka in right putamen, higher MD in right caudate compared with UD. Receiver operating characteristic analysis revealed the Kr of left caudate had the highest predictive power for distinguishing UD from controls. CONCLUSION The two disorders may have overlaps in microstructural abnormality in basal ganglia. The change of caudate may serve as a potential biomarker for UD.
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Affiliation(s)
- Lianping Zhao
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Department of Radiology, Gansu Provincial Hospital, Gansu, China
| | - Ying Wang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Clinical Experimental Center, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yanbin Jia
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Shuming Zhong
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yao Sun
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Zhifeng Zhou
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
| | | | - Li Huang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
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50
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Comparison of accuracy between FSL's FIRST and Freesurfer for caudate nucleus and putamen segmentation. Sci Rep 2017; 7:2418. [PMID: 28546533 PMCID: PMC5445091 DOI: 10.1038/s41598-017-02584-5] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Accepted: 04/12/2017] [Indexed: 11/08/2022] Open
Abstract
Although several methods have been developed to automatically delineate subcortical gray matter structures from MR images, the accuracy of these algorithms has not been comprehensively examined. Most of earlier studies focused primarily on the hippocampus. Here, we assessed the accuracy of two widely used non-commercial programs (FSL-FIRST and Freesurfer) for segmenting the caudate and putamen. T1-weighted 1 mm3 isotropic resolution MR images were acquired for thirty healthy subjects (15 females). Caudate nucleus and putamen were segmented manually by two independent observers and automatically by FIRST and Freesurfer (v4.5 and v5.3). Utilizing manual labels as reference standard the following measures were studied: Dice coefficient (D), percentage volume difference (PVD), absolute volume difference as well as intraclass correlation coefficient (ICC) for consistency and absolute agreement. For putamen segmentation, FIRST achieved higher D, lower PVD and higher ICC for absolute agreement with manual tracing than either version of Freesurfer. Freesurfer overestimated the putamen, while FIRST was not statistically different from manual tracing. The ICC for consistency with manual tracing was similar between the two methods. For caudate segmentation, FIRST and Freesurfer performed more similarly. In conclusion, Freesurfer and FIRST are not equivalent when comparing to manual tracing. FIRST was superior for putaminal segmentation.
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