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Hua JPY, Fryer SL, Stuart B, Loewy RL, Vinogradov S, Mathalon DH. Adjustment of Regional Cortical Thickness Measures for Global Cortical Thickness Obscures Deficits Across the Schizophrenia Spectrum: A Cautionary Note about Normative Modeling of Brain Imaging Data. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00159-9. [PMID: 38908749 DOI: 10.1016/j.bpsc.2024.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/07/2024] [Accepted: 06/12/2024] [Indexed: 06/24/2024]
Abstract
Recent neuroimaging studies and publicly-disseminated analytic tools advocate that regional morphometric analyses covary for global thickness. We empirically demonstrate that this statistical approach severely underestimates regional thickness dysmorphology in psychiatric disorders. Study 1 included 90 healthy controls, 51 clinical high-risk for psychosis, and 78 early illness schizophrenia participants. Study 2 included 56 healthy controls, 83 non-affective psychosis, and 30 affective psychosis participants. We examined global and regional thickness correlations, global thickness group differences, and regional thickness group differences with/without global thickness covariation. Global and regional thickness were strongly correlated across groups. Global thickness was lower in schizophrenia-spectrum groups versus other groups. Regional thickness deficits in schizophrenia-spectrum groups were attenuated/eliminated with global thickness covariation. Depriving regional thickness of its shared variance with global thickness removes disease-related effects. This statistical method results in erroneous conclusions that regional thickness is normal in disorders like schizophrenia or clinical high-risk syndrome.
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Affiliation(s)
- Jessica P Y Hua
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA 94121; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA 94143
| | - Susanna L Fryer
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA 94121; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA 94143
| | - Barbara Stuart
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA 94143
| | - Rachel L Loewy
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA 94143
| | - Sophia Vinogradov
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN
| | - Daniel H Mathalon
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA 94121; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA 94143.
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2
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Vano LJ, Veronese M, McCutcheon RA, Howes OD. Neuromelanin-Sensitive MRI: A Biomarker for Treatment-Resistant Schizophrenia? Am J Psychiatry 2024; 181:468-470. [PMID: 38822586 DOI: 10.1176/appi.ajp.20240278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/03/2024]
Affiliation(s)
- Luke J Vano
- Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Vano, McCutcheon, Howes); Psychiatric Imaging Group, MRC Laboratory of Medical Sciences, Hammersmith Hospital, London (Vano, Howes); Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London (Vano, Howes); South London and Maudsley NHS Foundation Trust, London (Vano, Howes); Department of Neuroimaging, King's College London (Veronese); Department of Information Engineering, University of Padua, Padua, Italy (Veronese); Department of Psychiatry, University of Oxford, Oxford, U.K. (McCutcheon); Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, U.K. (McCutcheon)
| | - Mattia Veronese
- Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Vano, McCutcheon, Howes); Psychiatric Imaging Group, MRC Laboratory of Medical Sciences, Hammersmith Hospital, London (Vano, Howes); Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London (Vano, Howes); South London and Maudsley NHS Foundation Trust, London (Vano, Howes); Department of Neuroimaging, King's College London (Veronese); Department of Information Engineering, University of Padua, Padua, Italy (Veronese); Department of Psychiatry, University of Oxford, Oxford, U.K. (McCutcheon); Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, U.K. (McCutcheon)
| | - Robert A McCutcheon
- Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Vano, McCutcheon, Howes); Psychiatric Imaging Group, MRC Laboratory of Medical Sciences, Hammersmith Hospital, London (Vano, Howes); Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London (Vano, Howes); South London and Maudsley NHS Foundation Trust, London (Vano, Howes); Department of Neuroimaging, King's College London (Veronese); Department of Information Engineering, University of Padua, Padua, Italy (Veronese); Department of Psychiatry, University of Oxford, Oxford, U.K. (McCutcheon); Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, U.K. (McCutcheon)
| | - Oliver D Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Vano, McCutcheon, Howes); Psychiatric Imaging Group, MRC Laboratory of Medical Sciences, Hammersmith Hospital, London (Vano, Howes); Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London (Vano, Howes); South London and Maudsley NHS Foundation Trust, London (Vano, Howes); Department of Neuroimaging, King's College London (Veronese); Department of Information Engineering, University of Padua, Padua, Italy (Veronese); Department of Psychiatry, University of Oxford, Oxford, U.K. (McCutcheon); Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, U.K. (McCutcheon)
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3
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Hu Y, Shi T, Xu Z, Zhang M, Yang J, Liu Z, Wan Q, Liu Y. Heart failure potentially affects the cortical structure of the brain. Aging (Albany NY) 2024; 16:7357-7386. [PMID: 38656892 PMCID: PMC11087114 DOI: 10.18632/aging.205762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 03/25/2024] [Indexed: 04/26/2024]
Abstract
BACKGROUND Heart failure (HF) has been reported to affect cerebral cortex structure, but the underlying cause has not been determined. This study used Mendelian randomization (MR) to reveal the causal relationship between HF and structural changes in the cerebral cortex. METHODS HF was defined as the exposure variable, and cerebral cortex structure was defined as the outcome variable. Inverse-variance weighted (IVW), MR-Egger regression and weighted median (WME) were performed for MR analysis; MR-PRESSO and Egger's intercept was used to test horizontal pleiotropy; and "leave-one-out" was used for sensitivity analysis. RESULTS Fifty-two single nucleotide polymorphisms (SNPs) were defined as instrumental variables (IVs), and there was no horizontal pleiotropy in the IVs. According to the IVW analysis, the OR and 95% CI of cerebral cortex thickness were 0.9932 (0.9868-1.00) (P=0.0402), and the MR-Egger intercept was -15.6× 10-5 (P = 0.7974) and the Global test pval was 0.078. The P-value of the cerebral cortex surface was 0.2205, and the MR-Egger intercept was -34.69052 (P= 0.6984) and the Global Test pval was 0.045. HF had a causal effect on the surface area of the caudal middle frontal lobule (P=0.009), insula lobule (P=0.01), precuneus lobule (P=0.049) and superior parietal lobule (P=0.044). CONCLUSIONS HF was potentially associated with changes in cortical thickness and in the surface area of the caudal middle frontal lobule, insula lobule, precuneus lobule and superior parietal lobule.
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Affiliation(s)
- Yinqin Hu
- Department of Cardiovascular Disease, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Tianyun Shi
- Department of Cardiovascular Disease, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zhaohui Xu
- Department of Cardiovascular Disease, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Meng Zhang
- Department of Cardiovascular Disease, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiahui Yang
- Department of Cardiovascular Disease, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zhirui Liu
- Department of Cardiovascular Disease, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Qiqi Wan
- Department of Cardiovascular Disease, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yongming Liu
- Department of Cardiovascular Disease, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Anhui Provincial Hospital of Integrated Medicine, Anhui Hospital of Shuguang Hospital Affiliated to Shanghai University of TCM, Hefei 230011, Anhui, China
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4
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Ching CRK, Kang MJY, Thompson PM. Large-Scale Neuroimaging of Mental Illness. Curr Top Behav Neurosci 2024. [PMID: 38554248 DOI: 10.1007/7854_2024_462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2024]
Abstract
Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.
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Affiliation(s)
- Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Melody J Y Kang
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
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Cooper R, Hayes RA, Corcoran M, Sheth KN, Arnold TC, Stein JM, Glahn DC, Jalbrzikowski M. Bridging the gap: improving correspondence between low-field and high-field magnetic resonance images in young people. Front Neurol 2024; 15:1339223. [PMID: 38585353 PMCID: PMC10995930 DOI: 10.3389/fneur.2024.1339223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 01/19/2024] [Indexed: 04/09/2024] Open
Abstract
Background Portable low-field-strength magnetic resonance imaging (MRI) systems represent a promising alternative to traditional high-field-strength systems with the potential to make MR technology available at scale in low-resource settings. However, lower image quality and resolution may limit the research and clinical potential of these devices. We tested two super-resolution methods to enhance image quality in a low-field MR system and compared their correspondence with images acquired from a high-field system in a sample of young people. Methods T1- and T2-weighted structural MR images were obtained from a low-field (64mT) Hyperfine and high-field (3T) Siemens system in N = 70 individuals (mean age = 20.39 years, range 9-26 years). We tested two super-resolution approaches to improve image correspondence between images acquired at high- and low-field: (1) processing via a convolutional neural network ('SynthSR'), and (2) multi-orientation image averaging. We extracted brain region volumes, cortical thickness, and cortical surface area estimates. We used Pearson correlations to test the correspondence between these measures, and Steiger Z tests to compare the difference in correspondence between standard imaging and super-resolution approaches. Results Single pairs of T1- and T2-weighted images acquired at low field showed high correspondence to high-field-strength images for estimates of total intracranial volume, surface area cortical volume, subcortical volume, and total brain volume (r range = 0.60-0.88). Correspondence was lower for cerebral white matter volume (r = 0.32, p = 0.007, q = 0.009) and non-significant for mean cortical thickness (r = -0.05, p = 0.664, q = 0.664). Processing images with SynthSR yielded significant improvements in correspondence for total brain volume, white matter volume, total surface area, subcortical volume, cortical volume, and total intracranial volume (r range = 0.85-0.97), with the exception of global mean cortical thickness (r = 0.14). An alternative multi-orientation image averaging approach improved correspondence for cerebral white matter and total brain volume. Processing with SynthSR also significantly improved correspondence across widespread regions for estimates of cortical volume, surface area and subcortical volume, as well as within isolated prefrontal and temporal regions for estimates of cortical thickness. Conclusion Applying super-resolution approaches to low-field imaging improves regional brain volume and surface area accuracy in young people. Finer-scale brain measurements, such as cortical thickness, remain challenging with the limited resolution of low-field systems.
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Affiliation(s)
- Rebecca Cooper
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Rebecca A. Hayes
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
| | - Mary Corcoran
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
| | - Kevin N. Sheth
- Center for Brain and Mind Health, Yale School of Medicine, New Haven, CT, United States
| | - Thomas Campbell Arnold
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
| | - Joel M. Stein
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - David C. Glahn
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, United States
| | - Maria Jalbrzikowski
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
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Van Rheenen TE, Cotton SM, Dandash O, Cooper RE, Ringin E, Daglas-Georgiou R, Allott K, Chye Y, Suo C, Macneil C, Hasty M, Hallam K, McGorry P, Fornito A, Yücel M, Pantelis C, Berk M. Increased cortical surface area but not altered cortical thickness or gyrification in bipolar disorder following stabilisation from a first episode of mania. Prog Neuropsychopharmacol Biol Psychiatry 2023; 122:110687. [PMID: 36427550 DOI: 10.1016/j.pnpbp.2022.110687] [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: 06/23/2022] [Revised: 11/08/2022] [Accepted: 11/16/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Despite reports of altered brain morphology in established bipolar disorder (BD), there is limited understanding of when these morphological abnormalities emerge. Assessment of patients during the early course of illness can help to address this gap, but few studies have examined surface-based brain morphology in patients at this illness stage. METHODS We completed a secondary analysis of baseline data from a randomised control trial of BD individuals stabilised after their first episode of mania (FEM). The magnetic resonance imaging scans of n = 35 FEM patients and n = 29 age-matched healthy controls were analysed. Group differences in cortical thickness, surface area and gyrification were assessed at each vertex of the cortical surface using general linear models. Significant results were identified at p < 0.05 using cluster-wise correction. RESULTS The FEM group did not differ from healthy controls with regards to cortical thickness or gyrification. However, there were two clusters of increased surface area in the left hemisphere of FEM patients, with peak coordinates falling within the lateral occipital cortex and pars triangularis. CONCLUSIONS Cortical thickness and gyrification appear to be intact in the aftermath of a first manic episode, whilst cortical surface area in the inferior/middle prefrontal and occipitoparietal cortex is increased compared to age-matched controls. It is possible that increased surface area in the FEM group is the outcome of abnormalities in a premorbidly occurring process. In contrast, the findings raise the hypothesis that cortical thickness reductions seen in past studies of individuals with more established BD may be more attributable to post-onset factors.
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Affiliation(s)
- Tamsyn E Van Rheenen
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Melbourne, Australia; Centre for Mental Health, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Melbourne, Australia.
| | - Sue M Cotton
- Orygen, Parkville, VIC, Australia; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Orwa Dandash
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Melbourne, Australia; Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Australia
| | - Rebecca E Cooper
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Elysha Ringin
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Rothanthi Daglas-Georgiou
- Orygen, Parkville, VIC, Australia; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Kelly Allott
- Orygen, Parkville, VIC, Australia; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Yann Chye
- Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Australia
| | - Chao Suo
- Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Australia
| | - Craig Macneil
- Orygen Youth Health Clinical Program, Parkville, VIC, Australia
| | - Melissa Hasty
- Orygen Youth Health Clinical Program, Parkville, VIC, Australia
| | - Karen Hallam
- The Institute for Mental and Physical Health and Clinical Translation, Deakin University, Geelong, Australia
| | - Patrick McGorry
- Orygen, Parkville, VIC, Australia; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Australia
| | - Murat Yücel
- Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Melbourne, Australia; Florey Institute of Neuroscience and Mental Health, Clayton, VIC, Australia
| | - Michael Berk
- Orygen, Parkville, VIC, Australia; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; The Institute for Mental and Physical Health and Clinical Translation, Deakin University, Geelong, Australia; Barwon Health, PO Box 281, Geelong, Victoria, 3220, Australia
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7
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Chen J, Patil KR, Yeo BTT, Eickhoff SB. Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research. Biol Psychiatry 2023; 93:18-28. [PMID: 36307328 DOI: 10.1016/j.biopsych.2022.07.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/06/2022] [Accepted: 07/28/2022] [Indexed: 11/18/2022]
Abstract
Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.
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Affiliation(s)
- 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; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences & Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
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8
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Rootes-Murdy K, Edmond JT, Jiang W, Rahaman MA, Chen J, Perrone-Bizzozero NI, Calhoun VD, van Erp TGM, Ehrlich S, Agartz I, Jönsson EG, Andreassen OA, Westlye LT, Wang L, Pearlson GD, Glahn DC, Hong E, Buchanan RW, Kochunov P, Voineskos A, Malhotra A, Tamminga CA, Liu J, Turner JA. Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach. Front Hum Neurosci 2022; 16:1001692. [PMID: 36438633 PMCID: PMC9684186 DOI: 10.3389/fnhum.2022.1001692] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 10/17/2022] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND Structural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotypes associated with SZ and BP may allow for a more precise categorization than the current diagnostic criteria. In this study, we sought to identify GM alterations that were unique to each disorder and whether those alterations were also related to unique symptom profiles. MATERIALS AND METHODS We analyzed the GM patterns and clinical symptom presentations using independent component analysis (ICA), hierarchical clustering, and n-way biclustering in a large (N ∼ 3,000), merged dataset of neuroimaging data from healthy volunteers (HV), and individuals with either SZ or BP. RESULTS Component A showed a SZ and BP < HV GM pattern in the bilateral insula and cingulate gyrus. Component B showed a SZ and BP < HV GM pattern in the cerebellum and vermis. There were no significant differences between diagnostic groups in these components. Component C showed a SZ < HV and BP GM pattern bilaterally in the temporal poles. Hierarchical clustering of the PANSS scores and the ICA components did not yield new subgroups. N-way biclustering identified three unique subgroups of individuals within the sample that mapped onto different combinations of ICA components and symptom profiles categorized by the PANSS but no distinct diagnostic group differences. CONCLUSION These multivariate results show that diagnostic boundaries are not clearly related to structural differences or distinct symptom profiles. Our findings add support that (1) BP tend to have less severe symptom profiles when compared to SZ on the PANSS without a clear distinction, and (2) all the gray matter alterations follow the pattern of SZ < BP < HV without a clear distinction between SZ and BP.
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Affiliation(s)
- Kelly Rootes-Murdy
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jesse T. Edmond
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Wenhao Jiang
- Department of Psychosomatics and Psychiatry, Medical School, Zhongda Hospital, Institute of Psychosomatics, Southeast University, Nanjing, China
| | - Md A. Rahaman
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | | | - Vince D. Calhoun
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Theo G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| | - Stefan Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Ingrid Agartz
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institute and Stockholm Health Care Services, Stockholm, Sweden
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Erik G. Jönsson
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institute and Stockholm Health Care Services, Stockholm, Sweden
| | - Ole A. Andreassen
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Lei Wang
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, United States
| | - Godfrey D. Pearlson
- Department of Psychiatry, Yale University, New Haven, CT, United States
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, United States
| | - David C. Glahn
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, United States
- Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Elliot Hong
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Robert W. Buchanan
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Aristotle Voineskos
- Department of Psychiatry, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
| | - Anil Malhotra
- Division of Psychiatry Research, Zucker Hillside Hospital, Queens, NY, United States
| | - Carol A. Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical School, Dallas, TX, United States
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jessica A. Turner
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, United States
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Zhu Z, Zhao Y, Wen K, Li Q, Pan N, Fu S, Li F, Radua J, Vieta E, Kemp GJ, Biswa BB, Gong Q. Cortical thickness abnormalities in patients with bipolar disorder: A systematic review and meta-analysis. J Affect Disord 2022; 300:209-218. [PMID: 34971699 DOI: 10.1016/j.jad.2021.12.080] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 10/10/2021] [Accepted: 12/19/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND An increasing number of neuroimaging studies report alterations of cortical thickness (CT) related to the neuropathology of bipolar disorder (BD). We provide here a whole-brain vertex-wise meta-analysis, which may help improve the spatial precision of these identifications. METHODS A comprehensive meta-analysis was performed to investigate the differences in CT between patients with BD and healthy controls (HCs) by using a newly developed mask for CT analysis in seed-based d mapping (SDM) meta-analytic software. We used meta-regression to explore the effects of demographics and clinical characteristics on CT. This meta-review was conducted in accordance with PRISMA guideline. RESULTS We identified 21 studies meeting criteria for the systematic review, of which 11 were eligible for meta-analysis. The meta-analysis comprising 649 BD patients and 818 HCs showed significant cortical thinning in the left insula extending to left Rolandic operculum and Heschl gyrus, the orbital part of left inferior frontal gyrus (IFG), the medial part of left superior frontal gyrus (SFG) as well as bilateral anterior cingulate cortex (ACC) in BD. In meta-regression analyses, mean patient age was negatively correlated with reduced CT in the left insula. LIMITATIONS All enrolled studies were cross-sectional; we could not explore the potential effects of medication and mood states due to the limited data. CONCLUSIONS Our results suggest that BD patients have significantly thinner frontoinsular cortex than HCs, and the results may be helpful in revealing specific neuroimaging biomarkers of BD patients.
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Affiliation(s)
- Ziyu Zhu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Youjin Zhao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, Sichuan, China
| | - Keren Wen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qian Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Shiqin Fu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, Sichuan, China
| | - Joaquim Radua
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, Sichuan, China; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Mental Health Research Networking Center (CIBERSAM), Barcelona, Spain; Department of Clinical Neuroscience, Centre for Psychiatric Research and Education, Karolinska Institutet, Stockholm, Sweden; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, Northern Ireland United Kingdom
| | - Eduard Vieta
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Mental Health Research Networking Center (CIBERSAM), Barcelona, Spain; Barcelona Bipolar Disorders and Depressive Unit, Hospital Clinic, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Bharat B Biswa
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
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10
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Ching CRK, Hibar DP, Gurholt TP, Nunes A, Thomopoulos SI, Abé C, Agartz I, Brouwer RM, Cannon DM, de Zwarte SMC, Eyler LT, Favre P, Hajek T, Haukvik UK, Houenou J, Landén M, Lett TA, McDonald C, Nabulsi L, Patel Y, Pauling ME, Paus T, Radua J, Soeiro‐de‐Souza MG, Tronchin G, van Haren NEM, Vieta E, Walter H, Zeng L, Alda M, Almeida J, Alnæs D, Alonso‐Lana S, Altimus C, Bauer M, Baune BT, Bearden CE, Bellani M, Benedetti F, Berk M, Bilderbeck AC, Blumberg HP, Bøen E, Bollettini I, del Mar Bonnin C, Brambilla P, Canales‐Rodríguez EJ, Caseras X, Dandash O, Dannlowski U, Delvecchio G, Díaz‐Zuluaga AM, Dima D, Duchesnay É, Elvsåshagen T, Fears SC, Frangou S, Fullerton JM, Glahn DC, Goikolea JM, Green MJ, Grotegerd D, Gruber O, Haarman BCM, Henry C, Howells FM, Ives‐Deliperi V, Jansen A, Kircher TTJ, Knöchel C, Kramer B, Lafer B, López‐Jaramillo C, Machado‐Vieira R, MacIntosh BJ, Melloni EMT, Mitchell PB, Nenadic I, Nery F, Nugent AC, Oertel V, Ophoff RA, Ota M, Overs BJ, Pham DL, Phillips ML, Pineda‐Zapata JA, Poletti S, Polosan M, Pomarol‐Clotet E, Pouchon A, Quidé Y, Rive MM, Roberts G, Ruhe HG, Salvador R, Sarró S, Satterthwaite TD, Schene AH, Sim K, Soares JC, Stäblein M, Stein DJ, Tamnes CK, Thomaidis GV, Upegui CV, Veltman DJ, Wessa M, Westlye LT, Whalley HC, Wolf DH, Wu M, Yatham LN, Zarate CA, Thompson PM, Andreassen OA. What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group. Hum Brain Mapp 2022; 43:56-82. [PMID: 32725849 PMCID: PMC8675426 DOI: 10.1002/hbm.25098] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 05/31/2020] [Accepted: 06/04/2020] [Indexed: 12/17/2022] Open
Abstract
MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.
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Affiliation(s)
- Christopher R. K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | | | - Tiril P. Gurholt
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of OsloOsloNorway
- Division of Mental Health and Addicition, Oslo University HospitalOsloNorway
| | - Abraham Nunes
- Department of PsychiatryDalhousie UniversityHalifaxNova ScotiaCanada
- Faculty of Computer ScienceDalhousie UniversityHalifaxNova ScotiaCanada
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Christoph Abé
- Faculty of Computer ScienceDalhousie UniversityHalifaxNova ScotiaCanada
- Department of Clinical NeuroscienceKarolinska InstitutetStockholmSweden
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- Center for Psychiatric Research, Department of Clinical NeuroscienceKarolinska InstitutetStockholmSweden
| | - Rachel M. Brouwer
- Department of Psychiatry, University Medical Center Utrecht Brain Center, University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Dara M. Cannon
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health SciencesNational University of Ireland GalwayGalwayIreland
| | - Sonja M. C. de Zwarte
- Department of Psychiatry, University Medical Center Utrecht Brain Center, University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Lisa T. Eyler
- Department of PsychiatryUniversity of CaliforniaLa JollaCaliforniaUSA
- Desert‐Pacific MIRECCVA San Diego HealthcareSan DiegoCaliforniaUSA
| | - Pauline Favre
- INSERM U955, team 15 “Translational Neuro‐Psychiatry”CréteilFrance
- Neurospin, CEA Paris‐Saclay, team UNIACTGif‐sur‐YvetteFrance
| | - Tomas Hajek
- Division of Mental Health and Addicition, Oslo University HospitalOsloNorway
- National Institute of Mental HealthKlecanyCzech Republic
| | - Unn K. Haukvik
- Division of Mental Health and Addicition, Oslo University HospitalOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT)Oslo University HospitalOsloNorway
| | - Josselin Houenou
- INSERM U955, team 15 “Translational Neuro‐Psychiatry”CréteilFrance
- Neurospin, CEA Paris‐Saclay, team UNIACTGif‐sur‐YvetteFrance
- APHPMondor University Hospitals, DMU IMPACTCréteilFrance
| | - Mikael Landén
- Department of Neuroscience and PhysiologyUniversity of GothenburgGothenburgSweden
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
| | - Tristram A. Lett
- Department for Psychiatry and PsychotherapyCharité Universitätsmedizin BerlinBerlinGermany
- Department of Neurology with Experimental NeurologyCharité Universitätsmedizin BerlinBerlinGermany
| | - Colm McDonald
- Department of Psychiatry, University Medical Center Utrecht Brain Center, University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Leila Nabulsi
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of Psychiatry, University Medical Center Utrecht Brain Center, University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Yash Patel
- Bloorview Research InstituteHolland Bloorview Kids Rehabilitation HospitalTorontoOntarioCanada
| | - Melissa E. Pauling
- Desert‐Pacific MIRECCVA San Diego HealthcareSan DiegoCaliforniaUSA
- INSERM U955, team 15 “Translational Neuro‐Psychiatry”CréteilFrance
| | - Tomas Paus
- Bloorview Research InstituteHolland Bloorview Kids Rehabilitation HospitalTorontoOntarioCanada
- Departments of Psychology and PsychiatryUniversity of TorontoTorontoOntarioCanada
| | - Joaquim Radua
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM)BarcelonaSpain
- Early Psychosis: Interventions and Clinical‐detection (EPIC) lab, Department of Psychosis StudiesInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
- Stockholm Health Care ServicesStockholm County CouncilStockholmSweden
| | - Marcio G. Soeiro‐de‐Souza
- Mood Disorders Unit (GRUDA), Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de São PauloSão PauloSPBrazil
| | - Giulia Tronchin
- Department of Psychiatry, University Medical Center Utrecht Brain Center, University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Neeltje E. M. van Haren
- Department of Child and Adolescent Psychiatry/PsychologyErasmus Medical CenterRotterdamThe Netherlands
| | - Eduard Vieta
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM)BarcelonaSpain
- Barcelona Bipolar Disorders and Depressive Unit, Hospital Clinic, Institute of NeurosciencesUniversity of BarcelonaBarcelonaSpain
| | - Henrik Walter
- Department for Psychiatry and PsychotherapyCharité Universitätsmedizin BerlinBerlinGermany
| | - Ling‐Li Zeng
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina
| | - Martin Alda
- Division of Mental Health and Addicition, Oslo University HospitalOsloNorway
| | - Jorge Almeida
- Dell Medical SchoolThe University of Texas at AustinAustinTexasUSA
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of OsloOsloNorway
| | - Silvia Alonso‐Lana
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain
- CIBERSAMMadridSpain
| | - Cara Altimus
- Milken Institute Center for Strategic PhilanthropyWashingtonDistrict of ColumbiaUSA
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, Medical FacultyTechnische Universität DresdenDresdenGermany
| | - Bernhard T. Baune
- Department of PsychiatryUniversity of MünsterMünsterGermany
- Department of PsychiatryThe University of MelbourneMelbourneVictoriaAustralia
- The Florey Institute of Neuroscience and Mental HealthThe University of MelbourneMelbourneVictoriaAustralia
| | - Carrie E. Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human BehaviorUniversity of CaliforniaLos AngelesCaliforniaUSA
- Department of PsychologyUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Marcella Bellani
- Section of Psychiatry, Department of Neurosciences, Biomedicine and Movement SciencesUniversity of VeronaVeronaItaly
| | - Francesco Benedetti
- Vita‐Salute San Raffaele UniversityMilanItaly
- Division of Neuroscience, Psychiatry and Psychobiology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Michael Berk
- Department of Pathophysiology and TransplantationUniversity of MilanMilanItaly
- IMPACT Institute – The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon HealthDeakin UniversityGeelongVictoriaAustralia
| | - Amy C. Bilderbeck
- The National Centre of Excellence in Youth Mental Health, Centre for Youth Mental Health, Florey Institute for Neuroscience and Mental Health and the Department of Psychiatry, The University of MelbourneOrygenMelbourneVictoriaAustralia
- P1vital LtdWallingfordUK
| | | | - Erlend Bøen
- Mood Disorders Research ProgramYale School of MedicineNew HavenConnecticutUSA
| | - Irene Bollettini
- Division of Neuroscience, Psychiatry and Psychobiology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Caterina del Mar Bonnin
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM)BarcelonaSpain
- Barcelona Bipolar Disorders and Depressive Unit, Hospital Clinic, Institute of NeurosciencesUniversity of BarcelonaBarcelonaSpain
| | - Paolo Brambilla
- Psychosomatic and CL PsychiatryOslo University HospitalOsloNorway
- Department of Neurosciences and Mental HealthFondazione IRCCS Ca' Granda Ospedale Maggiore PoliclinicoMilanItaly
| | - Erick J. Canales‐Rodríguez
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain
- CIBERSAMMadridSpain
- Department of RadiologyCentre Hospitalier Universitaire Vaudois (CHUV)LausanneSwitzerland
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Xavier Caseras
- MRC Centre for Neuropsychiatric Genetics and GenomicsCardiff UniversityCardiffUK
| | - Orwa Dandash
- Melbourne Neuropsychiatry Centre, Department of PsychiatryUniversity of Melbourne and Melbourne HealthMelbourneVictoriaAustralia
- Brain, Mind and Society Research Hub, Turner Institute for Brain and Mental Health, School of Psychological SciencesMonash UniversityClaytonVictoriaAustralia
| | - Udo Dannlowski
- Department of PsychiatryUniversity of MünsterMünsterGermany
| | | | - Ana M. Díaz‐Zuluaga
- Research Group in Psychiatry GIPSI, Department of PsychiatryFaculty of Medicine, Universidad de AntioquiaMedellínColombia
| | - Danai Dima
- Department of Psychology, School of Social Sciences and ArtsCity, University of LondonLondonUK
- Department of Neuroimaging, Institute of Psychiatry, Psychology & NeuroscienceKing's College LondonLondonUK
| | | | - Torbjørn Elvsåshagen
- Norwegian Centre for Mental Disorders Research (NORMENT)Oslo University HospitalOsloNorway
- Department of NeurologyOslo University HospitalOsloNorway
- Institute of Clinical MedicineUniversity of OsloOsloNorway
| | - Scott C. Fears
- Center for Neurobehavioral GeneticsLos AngelesCaliforniaUSA
- Greater Los Angeles Veterans AdministrationLos AngelesCaliforniaUSA
| | - Sophia Frangou
- Centre for Brain HealthUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Janice M. Fullerton
- Neuroscience Research AustraliaRandwickNew South WalesAustralia
- School of Medical SciencesUniversity of New South WalesSydneyNew South WalesAustralia
| | - David C. Glahn
- Department of PsychiatryBoston Children's Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Jose M. Goikolea
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM)BarcelonaSpain
- Barcelona Bipolar Disorders and Depressive Unit, Hospital Clinic, Institute of NeurosciencesUniversity of BarcelonaBarcelonaSpain
| | - Melissa J. Green
- Neuroscience Research AustraliaRandwickNew South WalesAustralia
- School of PsychiatryUniversity of New South WalesSydneyNew South WalesAustralia
| | | | - Oliver Gruber
- Department of General PsychiatryHeidelberg UniversityHeidelbergGermany
| | - Bartholomeus C. M. Haarman
- Department of Psychiatry, University Medical Center GroningenUniversity of GroningenGroningenThe Netherlands
| | - Chantal Henry
- Department of PsychiatryService Hospitalo‐Universitaire, GHU Paris Psychiatrie & NeurosciencesParisFrance
- Université de ParisParisFrance
| | - Fleur M. Howells
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
| | | | - Andreas Jansen
- Core‐Facility Brainimaging, Faculty of MedicineUniversity of MarburgMarburgGermany
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | - Tilo T. J. Kircher
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | - Christian Knöchel
- Department of Psychiatry, Psychosomatic Medicine and PsychotherapyGoethe University FrankfurtFrankfurtGermany
| | - Bernd Kramer
- Department of General PsychiatryHeidelberg UniversityHeidelbergGermany
| | - Beny Lafer
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de PsiquiatriaHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São PauloSão PauloSPBrazil
| | - Carlos López‐Jaramillo
- Research Group in Psychiatry GIPSI, Department of PsychiatryFaculty of Medicine, Universidad de AntioquiaMedellínColombia
- Mood Disorders ProgramHospital Universitario Trastorno del ÁnimoMedellínColombia
| | - Rodrigo Machado‐Vieira
- Experimental Therapeutics and Molecular Pathophysiology Program, Department of PsychiatryUTHealth, University of TexasHoustonTexasUSA
| | - Bradley J. MacIntosh
- Hurvitz Brain SciencesSunnybrook Research InstituteTorontoOntarioCanada
- Department of Medical BiophysicsUniversity of TorontoTorontoOntarioCanada
| | - Elisa M. T. Melloni
- Vita‐Salute San Raffaele UniversityMilanItaly
- Division of Neuroscience, Psychiatry and Psychobiology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Philip B. Mitchell
- School of PsychiatryUniversity of New South WalesSydneyNew South WalesAustralia
| | - Igor Nenadic
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | - Fabiano Nery
- University of CincinnatiCincinnatiOhioUSA
- Universidade de São PauloSão PauloSPBrazil
| | | | - Viola Oertel
- Department of Psychiatry, Psychosomatic Medicine and PsychotherapyGoethe University FrankfurtFrankfurtGermany
| | - Roel A. Ophoff
- UCLA Center for Neurobehavioral GeneticsLos AngelesCaliforniaUSA
- Department of PsychiatryErasmus Medical Center, Erasmus UniversityRotterdamThe Netherlands
| | - Miho Ota
- Department of Mental Disorder ResearchNational Institute of Neuroscience, National Center of Neurology and PsychiatryTokyoJapan
| | | | - Daniel L. Pham
- Milken Institute Center for Strategic PhilanthropyWashingtonDistrict of ColumbiaUSA
| | - Mary L. Phillips
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | | | - Sara Poletti
- Vita‐Salute San Raffaele UniversityMilanItaly
- Division of Neuroscience, Psychiatry and Psychobiology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Mircea Polosan
- University of Grenoble AlpesCHU Grenoble AlpesGrenobleFrance
- INSERM U1216 ‐ Grenoble Institut des NeurosciencesLa TroncheFrance
| | - Edith Pomarol‐Clotet
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain
- CIBERSAMMadridSpain
| | - Arnaud Pouchon
- University of Grenoble AlpesCHU Grenoble AlpesGrenobleFrance
| | - Yann Quidé
- Neuroscience Research AustraliaRandwickNew South WalesAustralia
- School of PsychiatryUniversity of New South WalesSydneyNew South WalesAustralia
| | - Maria M. Rive
- Department of PsychiatryAmsterdam UMC, location AMCAmsterdamThe Netherlands
| | - Gloria Roberts
- School of PsychiatryUniversity of New South WalesSydneyNew South WalesAustralia
| | - Henricus G. Ruhe
- Department of PsychiatryRadboud University Medical CenterNijmegenThe Netherlands
- Donders Institute for Brain, Cognition and BehaviorRadboud UniversityNijmegenThe Netherlands
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain
- CIBERSAMMadridSpain
| | - Salvador Sarró
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain
- CIBERSAMMadridSpain
| | - Theodore D. Satterthwaite
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Aart H. Schene
- Department of PsychiatryRadboud University Medical CenterNijmegenThe Netherlands
| | - Kang Sim
- West Region, Institute of Mental HealthSingaporeSingapore
- Yong Loo Lin School of MedicineNational University of SingaporeSingaporeSingapore
| | - Jair C. Soares
- Center of Excellent on Mood DisordersUTHealth HoustonHoustonTexasUSA
- Department of Psychiatry and Behavioral SciencesUTHealth HoustonHoustonTexasUSA
| | - Michael Stäblein
- Department of Psychiatry, Psychosomatic Medicine and PsychotherapyGoethe University FrankfurtFrankfurtGermany
| | - Dan J. Stein
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
- SAMRC Unit on Risk & Resilience in Mental DisordersUniversity of Cape TownCape TownSouth Africa
| | - Christian K. Tamnes
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- PROMENTA Research Center, Department of PsychologyUniversity of OsloOsloNorway
| | - Georgios V. Thomaidis
- Papanikolaou General HospitalThessalonikiGreece
- Laboratory of Mechanics and MaterialsSchool of Engineering, Aristotle UniversityThessalonikiGreece
| | - Cristian Vargas Upegui
- Research Group in Psychiatry GIPSI, Department of PsychiatryFaculty of Medicine, Universidad de AntioquiaMedellínColombia
| | - Dick J. Veltman
- Department of PsychiatryAmsterdam UMCAmsterdamThe Netherlands
| | - Michèle Wessa
- Department of Neuropsychology and Clinical PsychologyJohannes Gutenberg‐University MainzMainzGermany
| | - Lars T. Westlye
- Department of PsychologyUniversity of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and AddictionOslo University HospitalOsloNorway
| | | | - Daniel H. Wolf
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Mon‐Ju Wu
- Department of Psychiatry and Behavioral SciencesUTHealth HoustonHoustonTexasUSA
| | - Lakshmi N. Yatham
- Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Carlos A. Zarate
- Chief Experimental Therapeutics & Pathophysiology BranchBethesdaMarylandUSA
- Intramural Research ProgramNational Institute of Mental HealthBethesdaMarylandUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of OsloOsloNorway
- Division of Mental Health and Addicition, Oslo University HospitalOsloNorway
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Cortical surface abnormalities are different depending on the stage of schizophrenia: A cross-sectional vertexwise mega-analysis of thickness, area and gyrification. Schizophr Res 2021; 236:104-114. [PMID: 34481405 DOI: 10.1016/j.schres.2021.08.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 05/28/2021] [Accepted: 08/09/2021] [Indexed: 11/23/2022]
Abstract
BACKGROUND Brain magnetic resonance imaging studies have not investigated the cortical surface comprehensively in schizophrenia subjects by assessing thickness, surface area and gyrification separately during the first-episode of psychosis (FEP) or chronic schizophrenia (ChSch). METHODS We investigated cortical surface abnormalities in 137 FEP patients and 240 ChSch subjects compared to 297 Healthy Controls (HC) contributed by five cohorts. Maps showing results of vertexwise between-group comparisons of cortical thickness, area, and gyrification were produced using T1-weighted datasets processed using FreeSurfer 5.3, followed by validated quality control protocols. RESULTS FEP subjects showed large clusters of increased area and gyrification relative to HC in prefrontal and insuli cortices (Cohen's d: 0.049 to 0.28). These between-group differences occurred partially beyond the effect of sample. ChSch subjects displayed reduced cortical thickness relative to HC in smaller fronto-temporal foci (d: -0.73 to -0.35), but not beyond the effect of sample. Differences between FEP and HC subjects were associated with male gender, younger age, and earlier illness onset, while differences between ChSch and HC were associated with treatment-resistance and first-generation antipsychotic (FGA) intake independently of sample effect. CONCLUSIONS Separate assessments of FEP and ChSch revealed abnormalities that differed in regional distribution, phenotypes affected and effect size. In FEP, associations of greater cortical area and gyrification abnormalities with earlier age of onset suggest an origin on anomalous neurodevelopment, while thickness reductions in ChSch are at least partially explained by treatment-resistance and FGA intake. Associations of between-group differences with clinical variables retained statistical significance beyond the effect of sample.
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Rokicki J, Wolfers T, Nordhøy W, Tesli N, Quintana DS, Alnaes D, Richard G, de Lange AMG, Lund MJ, Norbom L, Agartz I, Melle I, Naerland T, Selbaek G, Persson K, Nordvik JE, Schwarz E, Andreassen OA, Kaufmann T, Westlye LT. Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders. Hum Brain Mapp 2020; 42:1714-1726. [PMID: 33340180 PMCID: PMC7978139 DOI: 10.1002/hbm.25323] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/20/2020] [Accepted: 12/02/2020] [Indexed: 12/13/2022] Open
Abstract
The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub‐cortical volumes, cortical and subcortical T1/T2‐weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age‐matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two‐group case–control classifications revealed highest accuracy for AD using global T1‐weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF‐based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain‐based mapping of overlapping and distinct pathophysiology in common disorders.
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Affiliation(s)
- Jaroslav Rokicki
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway
| | - Thomas Wolfers
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway
| | - Wibeke Nordhøy
- Department of Diagnostic Physics, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Natalia Tesli
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Daniel S Quintana
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Dag Alnaes
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Genevieve Richard
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ann-Marie G de Lange
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,Department of Psychiatry, University of Oxford, Oxford, UK
| | - Martina J Lund
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Linn Norbom
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway.,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.,Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, and Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Ingrid Melle
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Terje Naerland
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Geir Selbaek
- Norwegian National Advisory Unit On Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway.,Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Karin Persson
- Norwegian National Advisory Unit On Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
| | | | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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13
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Singh K, Singh S, Malhotra J. Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients. Proc Inst Mech Eng H 2020; 235:167-184. [PMID: 33124526 DOI: 10.1177/0954411920966937] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Schizophrenia is a fatal mental disorder, which affects millions of people globally by the disturbance in their thinking, feeling and behaviour. In the age of the internet of things assisted with cloud computing and machine learning techniques, the computer-aided diagnosis of schizophrenia is essentially required to provide its patients with an opportunity to own a better quality of life. In this context, the present paper proposes a spectral features based convolutional neural network (CNN) model for accurate identification of schizophrenic patients using spectral analysis of multichannel EEG signals in real-time. This model processes acquired EEG signals with filtering, segmentation and conversion into frequency domain. Then, given frequency domain segments are divided into six distinct spectral bands like delta, theta-1, theta-2, alpha, beta and gamma. The spectral features including mean spectral amplitude, spectral power and Hjorth descriptors (Activity, Mobility and Complexity) are extracted from each band. These features are independently fed to the proposed spectral features-based CNN and long short-term memory network (LSTM) models for classification. This work also makes use of raw time-domain and frequency-domain EEG segments for classification using temporal CNN and spectral CNN models of same architectures respectively. The overall analysis of simulation results of all models exhibits that the proposed spectral features based CNN model is an efficient technique for accurate and prompt identification of schizophrenic patients among healthy individuals with average classification accuracies of 94.08% and 98.56% for two different datasets with optimally small classification time.
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Affiliation(s)
- Kuldeep Singh
- Department of Electronics Technology, Guru Nanak Dev University, Amritsar, Punjab, India
| | - Sukhjeet Singh
- Machinery Fault Diagnostics & Signal Processing Laboratory, Department of Mechanical Engineering, University Institute of Technology, Guru Nanak Dev University, Amritsar, Punjab, India
| | - Jyoteesh Malhotra
- Department of Electronics and Communication Engineering, Guru Nanak Dev University, Jalandhar, Punjab, India
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14
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Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:6405930. [PMID: 32300361 PMCID: PMC7142389 DOI: 10.1155/2020/6405930] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Revised: 01/07/2020] [Accepted: 01/16/2020] [Indexed: 12/11/2022]
Abstract
Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. This study proposed an ML framework based on coarse-to-fine feature selection. The proposed framework used two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant features with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision models with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the group level instead of at the individual level and cannot be widely applied. The method proposed in this study extends the diagnosis to the individual level and has a higher recognition rate than previous methods. The experimental results of this study demonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching over 85%. The identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed framework can be extended to diagnose other diseases.
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15
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Makowski C, Lewis JD, Lepage C, Malla AK, Joober R, Lepage M, Evans AC. Structural Associations of Cortical Contrast and Thickness in First Episode Psychosis. Cereb Cortex 2019; 29:5009-5021. [PMID: 30844050 PMCID: PMC6918925 DOI: 10.1093/cercor/bhz040] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 01/22/2019] [Indexed: 01/22/2023] Open
Abstract
There is growing evidence that psychosis is characterized by brain network abnormalities. Analyzing morphological abnormalities with T1-weighted structural MRI may be limited in discovering the extent of deviations in cortical associations. We assess whether structural associations of either cortical white-gray contrast (WGC) or cortical thickness (CT) allow for a better understanding of brain structural relationships in first episode of psychosis (FEP) patients. Principal component and structural covariance analyses were applied to WGC and CT derived from T1-weighted MRI for 116 patients and 88 controls, to explore sets of brain regions that showed group differences, and associations with symptom severity and cognitive ability in patients. We focused on 2 principal components: one encompassed primary somatomotor regions, which showed trend-like group differences in WGC, and the second included heteromodal cortices. Patients' component scores were related to general psychopathology for WGC, but not CT. Structural covariance analyses with WGC revealed group differences in pairwise correlations across widespread brain regions, mirroring areas derived from PCA. More group differences were uncovered with WGC compared with CT. WGC holds potential as a proxy measure of myelin from commonly acquired T1-weighted MRI and may be sensitive in detecting systems-level aberrations in early psychosis, and relationships with clinical/cognitive profiles.
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Affiliation(s)
- Carolina Makowski
- McGill Centre for Integrative Neuroscience, McGill University, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
- Department of Psychiatry, McGill University, Verdun, Canada
| | - John D Lewis
- McGill Centre for Integrative Neuroscience, McGill University, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
| | - Claude Lepage
- McGill Centre for Integrative Neuroscience, McGill University, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
| | - Ashok K Malla
- Department of Psychiatry, McGill University, Verdun, Canada
- Prevention and Early Intervention Program for Psychosis, Douglas Mental Health University Institute, Verdun, Canada
| | - Ridha Joober
- Department of Psychiatry, McGill University, Verdun, Canada
- Prevention and Early Intervention Program for Psychosis, Douglas Mental Health University Institute, Verdun, Canada
| | - Martin Lepage
- Department of Psychiatry, McGill University, Verdun, Canada
- Prevention and Early Intervention Program for Psychosis, Douglas Mental Health University Institute, Verdun, Canada
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, McGill University, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
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16
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Di Biase MA, Cropley VL, Cocchi L, Fornito A, Calamante F, Ganella EP, Pantelis C, Zalesky A. Linking Cortical and Connectional Pathology in Schizophrenia. Schizophr Bull 2019; 45:911-923. [PMID: 30215783 PMCID: PMC6581130 DOI: 10.1093/schbul/sby121] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Schizophrenia is associated with cortical thickness (CT) deficits and breakdown in white matter microstructure. Whether these pathological processes are related remains unclear. We used multimodal neuroimaging to investigate the relationship between regional cortical thinning and breakdown in adjacent infracortical white matter as a function of age and illness duration. Structural magnetic resonance and diffusion images were acquired in 218 schizophrenia patients and 167 age-matched healthy controls to map CT and fractional anisotropy in regionally adjacent infracortical white matter at various cortical depths. We found a robust and reproducible relationship between thickness and anisotropy deficits, which were inversely correlated across cortical regions (r = -.5, P < .0001): the most anisotropic infracortical white matter was found adjacent to regions with extensive cortical thinning. This pattern was evident in early (20 y: r = -.3, P = .005) and middle life (30 y: r = -.4, P = .004, 40 y: r = -.3, P = .04), but not beyond 50 years (P > .05). Frontal pathology contributed most to this pattern, with cortical thinning in patients compared to controls at all ages (P < .05); in contrast to initially elevated frontal white matter anisotropy in patients at 30 years, followed by rapid white matter decline with age (rate of annual decline; patients: 0.0012, controls 0.0006, P < .001). Our findings point to pathological dependencies between gray and white matter in a large sample of schizophrenia patients. We argue that elevated frontal anisotropy reflects regionally-specific, compensatory responses to cortical thinning, which are eventually overwhelmed with increasing illness duration.
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Affiliation(s)
- Maria Angelique Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Australia,Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA,Department of Psychiatry, The University of Melbourne, Parkville, Australia,To whom correspondence should be addressed; Psychiatry Neuroimaging Laboratory, Brigham & Women’s Hospital, Harvard Medical School, 1249 Boylston Street, 3rd Floor, Boston, MA 02215, US; tel: 617-525-6105, fax: 617-525-6170, e-mail:
| | - Vanessa L Cropley
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Australia,Department of Psychiatry, The University of Melbourne, Parkville, Australia
| | - Luca Cocchi
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Alexander Fornito
- Brain and Mental Health Research Hub, Monash University, Clayton, Australia
| | - Fernando Calamante
- Sydney Imaging and School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, Australia
| | - Eleni P Ganella
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Australia,Department of Psychiatry, The University of Melbourne, Parkville, Australia,Cooperative Research Centre for Mental Health, Carlton, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Australia,Department of Psychiatry, The University of Melbourne, Parkville, Australia,Cooperative Research Centre for Mental Health, Carlton, Australia,North Western Mental Health, Melbourne Health, Parkville, Australia,Centre for Neural Engineering, Department of Electrical and Electronic Engineering, University of Melbourne, Carlton South, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Australia,Department of Psychiatry, The University of Melbourne, Parkville, Australia,Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, Australia
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17
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Makowski C, Lepage M, Evans AC. Head motion: the dirty little secret of neuroimaging in psychiatry. J Psychiatry Neurosci 2019; 44:62-68. [PMID: 30565907 PMCID: PMC6306289 DOI: 10.1503/jpn.180022] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Psychiatry is at a crossroads when choosing final samples for analysis of neuroimaging data. Many patient populations exhibit significantly increased motion in the scanner compared with healthy controls, suggesting that more patients would need to be excluded to obtain a clean sample. However, this need is often overshadowed by the extensive amount of time and effort required to recruit these valuable and uncommon samples. This commentary sheds light on the impact of motion on imaging studies, drawing examples from psychiatric patient samples to better understand how head motion can confound interpretation of clinically oriented questions. We discuss the impact of even subtle motion artifacts on the interpretation of results as well as how different levels of stringency in quality control can affect findings within nearly identical samples. We also summarize recent initiatives toward harmonization of quality-control procedures as well as tools to prospectively and retrospectively correct for motion artifacts.
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Affiliation(s)
- Carolina Makowski
- From the McGill Centre for Integrative Neuroscience, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Que., Canada (Makowski, Evans); and the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Verdun, Que., Canada (Makowski, Lepage)
| | - Martin Lepage
- From the McGill Centre for Integrative Neuroscience, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Que., Canada (Makowski, Evans); and the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Verdun, Que., Canada (Makowski, Lepage)
| | - Alan C. Evans
- From the McGill Centre for Integrative Neuroscience, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Que., Canada (Makowski, Evans); and the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Verdun, Que., Canada (Makowski, Lepage)
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18
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Zhang YM, Chen MN, Yi XP, Li L, Gao JM, Zhang JL, Yuan XR, Zhang N, Liu LZ, Cai PQ, Chen BT, Zee C, Liao WH, Zhang YC. Cortical Surface Area Rather Than Cortical Thickness Potentially Differentiates Radiation Encephalopathy at Early Stage in Patients With Nasopharyngeal Carcinoma. Front Neurosci 2018; 12:599. [PMID: 30210281 PMCID: PMC6120047 DOI: 10.3389/fnins.2018.00599] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 08/08/2018] [Indexed: 12/13/2022] Open
Abstract
Radiation encephalopathy (RE) is one of the most severe complications in nasopharyngeal carcinoma (NPC) patients after radiotherapy (RT). However, the morphological alteration of early RE is insufficiently investigated. We aimed to investigate the cortical thickness and surface area alterations in NPC patients with or without RE in the follow-up. A total of 168 NPC patients each underwent a single scan and analysis at various times either Pre-RT (n = 56) or Post-RT (n = 112). We further divided the Post-RT NPC patients into three groups based on the time of the analysis following RT (Post-RTwithin 6 months and Post-RT7-12 months) or whether RE signs were detected in the analysis (Post-RTRE proved in follow-up). We confined the vertex-wise analyses of the cortical thickness and surface area to the bilateral temporal lobes. Interestingly, we revealed a gradual increase in the cortical surface area of the temporal lobe with increasing time after RT within the Post-RTRE proved in follow-up group, consistent with the between-group findings, which showed a significant increase in cortical surface area in the Post-RTRE proved in follow-up group relative to the Pre-RT group and the Post-RTwithin 6 months group. By contrast, such a trend was not observed in the cortical thickness findings. We concluded that the cortical surface area, rather than cortical thickness, may serve as a potential biomarker for early diagnosis of RE.
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Affiliation(s)
- You-Ming Zhang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Ming-Na Chen
- Department of Ultrasonic Imaging, Xiangya Hospital, Central South University, Changsha, China
| | - Xiao-Ping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Li Li
- Department of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jian-Ming Gao
- Department of Radiation Oncology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jin-Lei Zhang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xin-Ru Yuan
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Na Zhang
- School of Mathematical Sciences, University of Jinan, Jinan, China
| | - Li-Zhi Liu
- Department of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Pei-Qiang Cai
- Department of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Bihong T Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Chishing Zee
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Wei-Hua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Yuan-Chao Zhang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
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19
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Takahashi T, Suzuki M. Brain morphologic changes in early stages of psychosis: Implications for clinical application and early intervention. Psychiatry Clin Neurosci 2018; 72:556-571. [PMID: 29717522 DOI: 10.1111/pcn.12670] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/23/2018] [Indexed: 12/20/2022]
Abstract
To date, a large number of magnetic resonance imaging (MRI) studies have been conducted in schizophrenia, which generally demonstrate gray matter reduction, predominantly in the frontal and temporo-limbic regions, as well as gross brain abnormalities (e.g., a deviated sulcogyral pattern). Although the causes as well as timing and course of these findings remain elusive, these morphologic changes (especially gross brain abnormalities and medial temporal lobe atrophy) are likely present at illness onset, possibly reflecting early neurodevelopmental abnormalities. In addition, longitudinal MRI studies suggest that patients with schizophrenia and related psychoses also have progressive gray matter reduction during the transition period from prodrome to overt psychosis, as well as initial periods after psychosis onset, while such changes may become almost stable in the chronic stage. These active brain changes during the early phases seem to be relevant to the development of clinical symptoms in a region-specific manner (e.g., superior temporal gyrus atrophy and positive psychotic symptoms), but may be at least partly ameliorated by antipsychotic medication. Recently, increasing evidence from MRI findings in individuals at risk for developing psychosis has suggested that those who subsequently develop psychosis have baseline brain changes, which could be at least partly predictive of later transition into psychosis. In this article, we selectively review previous MRI findings during the course of psychosis and also refer to the possible clinical applicability of these neuroimaging research findings, especially in the diagnosis of schizophrenia and early intervention for psychosis.
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Affiliation(s)
- Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
| | - Michio Suzuki
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
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