Reproducibility of functional brain alterations in major depressive disorder: Evidence from a multisite resting-state functional MRI study with 1,434 individuals

ABSTRACT Resting‐state functional MRI (R‐fMRI) studies have demonstrated widespread alterations in brain function in patients with major depressive disorder (MDD). However, a clear and consistent conclusion regarding a repeatable pattern of MDD‐relevant alterations is still limited due to the scarcity of large‐sample, multisite datasets. Here, we address this issue by including a large R‐fMRI dataset with 1434 participants (709 patients with MDD and 725 healthy controls) from five centers in China. Individual functional activity maps that represent very local to long‐range connections are computed using the amplitude of low‐frequency fluctuations, regional homogeneity and distance‐related functional connectivity strength. The reproducibility analyses involve different statistical strategies, global signal regression, across‐center consistency, clinical variables, and sample size. We observed significant hypoactivity in the orbitofrontal, sensorimotor, and visual cortices and hyperactivity in the frontoparietal cortices in MDD patients compared to the controls. These alterations are not affected by different statistical analysis strategies, global signal regression and medication status and are generally reproducible across centers. However, these between‐group differences are partially influenced by the episode status and the age of disease onset in patients, and the brain‐clinical variable relationship exhibits poor cross‐center reproducibility. Bootstrap analyses reveal that at least 400 subjects in each group are required to replicate significant alterations (an extent threshold of P<.05 and a height threshold of P<.001) at 50% reproducibility. Together, these results highlight reproducible patterns of functional alterations in MDD and relevant influencing factors, which provides crucial guidance for future neuroimaging studies of this disorder. HIGHLIGHTSSignificant hypoactivity in primary regions and orbitofrontal regions in MDD.Significant hyperactivity in frontoparietal cortices in MDD.Functional alterations are not affected by statistical method and medication statue.Correlations between brain and clinics have a poor across‐center reproducibility.At least 400 subjects in each group are needed to replicate significant alterations.

[1]  Keith A. Johnson,et al.  Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease , 2009, The Journal of Neuroscience.

[2]  Franco Benazzi,et al.  Classifying mood disorders by age-at-onset instead of polarity , 2009, Progress in Neuro-Psychopharmacology and Biological Psychiatry.

[3]  Y. Zang,et al.  Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI , 2007, Brain and Development.

[4]  André Luxen,et al.  Frontal and parietal metabolic disturbances in unipolar depression , 1994, Biological Psychiatry.

[5]  Kevin Murphy,et al.  Resting-state fMRI confounds and cleanup , 2013, NeuroImage.

[6]  O. Sporns,et al.  Rich-Club Organization of the Human Connectome , 2011, The Journal of Neuroscience.

[7]  L. Williams,et al.  Abnormal Structural Networks Characterize Major Depressive Disorder: A Connectome Analysis , 2014, Biological Psychiatry.

[8]  Yingli Lu,et al.  Regional homogeneity approach to fMRI data analysis , 2004, NeuroImage.

[9]  Yong He,et al.  Disrupted resting-state functional connectivity in minimally treated chronic schizophrenia , 2014, Schizophrenia Research.

[10]  Dorothee P. Auer,et al.  Localized connectivity in depression: A meta-analysis of resting state functional imaging studies , 2015, Neuroscience & Biobehavioral Reviews.

[11]  B. Mensour,et al.  Neural circuitry underlying voluntary suppression of sadness , 2003, Biological Psychiatry.

[12]  Q. Gong,et al.  Depression, Neuroimaging and Connectomics: A Selective Overview , 2015, Biological Psychiatry.

[13]  Xiang Wang,et al.  Evidence of a Dissociation Pattern in Resting-State Default Mode Network Connectivity in First-Episode, Treatment-Naive Major Depression Patients , 2012, Biological Psychiatry.

[14]  Yong He,et al.  Identifying and Mapping Connectivity Patterns of Brain Network Hubs in Alzheimer's Disease. , 2015, Cerebral cortex.

[15]  Dost Öngür,et al.  Anticorrelations in resting state networks without global signal regression , 2012, NeuroImage.

[16]  Michael W. Cole,et al.  Altered global brain signal in schizophrenia , 2014, Proceedings of the National Academy of Sciences.

[17]  M. Kringelbach.  The human orbitofrontal cortex: linking reward to hedonic experience , 2005, Nature Reviews Neuroscience.

[18]  Qiyong Gong,et al.  Resting-state functional connectivity in treatment-resistant depression. , 2011, The American journal of psychiatry.

[19]  Qiyong Gong,et al.  Psychoradiology: The Frontier of Neuroimaging in Psychiatry. , 2016, Radiology.

[20]  Yong He,et al.  Intrinsic Brain Hub Connectivity Underlies Individual Differences in Spatial Working Memory , 2016, Cerebral cortex.

[21]  Hans Knutsson,et al.  Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates , 2016, Proceedings of the National Academy of Sciences.

[22]  K. Phan,et al.  Neural substrates for voluntary suppression of negative affect: A functional magnetic resonance imaging study , 2005, Biological Psychiatry.

[23]  S. Alborzian,et al.  Brain metabolic changes in major depressive disorder from pre- to post-treatment with paroxetine , 1999, Psychiatry Research: Neuroimaging.

[24]  Bernadette A. Thomas,et al.  Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010 , 2012, The Lancet.

[25]  R Kawashima,et al.  Hypoperfusion in the limbic system and prefrontal cortex in depression: SPECT with anatomic standardization technique. , 1996, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[26]  Alex Martin,et al.  Fractionation of social brain circuits in autism spectrum disorders. , 2012, Brain : a journal of neurology.

[27]  Simon B. Eickhoff,et al.  An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data , 2013, NeuroImage.

[28]  R. DeRubeis,et al.  Cognitive therapy versus medication for depression: treatment outcomes and neural mechanisms , 2008, Nature Reviews Neuroscience.

[29]  M. Weissman,et al.  Statistical harmonization corrects site effects in functional connectivity measurements from multi‐site fMRI data , 2018, Human brain mapping.

[30]  M. Phillips,et al.  Subcortical and ventral prefrontal cortical neural responses to facial expressions distinguish patients with bipolar disorder and major depression , 2004, Biological Psychiatry.

[31]  Yong He,et al.  Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. , 2007, Brain & development.

[32]  Yong He,et al.  Functional connectomics from a “big data” perspective , 2017, NeuroImage.

[33]  T. Robbins,et al.  Inhibition and the right inferior frontal cortex , 2004, Trends in Cognitive Sciences.

[34]  Krzysztof J. Gorgolewski,et al.  Making big data open: data sharing in neuroimaging , 2014, Nature Neuroscience.

[35]  Yong He,et al.  Effects of Different Correlation Metrics and Preprocessing Factors on Small-World Brain Functional Networks: A Resting-State Functional MRI Study , 2012, PloS one.

[36]  J. Hemelrijk,et al.  Some remarks on the combination of independent tests , 1953 .

[37]  Stephan Felber,et al.  Gender differences in regional cerebral activity during the perception of emotion: A functional MRI study , 2006, NeuroImage.

[38]  Kevin Murphy,et al.  The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.

[39]  E. Rolls,et al.  Medial reward and lateral non-reward orbitofrontal cortex circuits change in opposite directions in depression. , 2016, Brain : a journal of neurology.

[40]  R. Forthofer,et al.  Rank Correlation Methods , 1981 .

[41]  G. Glover,et al.  Resting-State Functional Connectivity in Major Depression: Abnormally Increased Contributions from Subgenual Cingulate Cortex and Thalamus , 2007, Biological Psychiatry.

[42]  Dardo Tomasi,et al.  High-Resolution Functional Connectivity Density: Hub Locations, Sensitivity, Specificity, Reproducibility, and Reliability. , 2016, Cerebral cortex.

[43]  Edmund T. Rolls,et al.  A non-reward attractor theory of depression , 2016, Neuroscience & Biobehavioral Reviews.

[44]  A. Caspi,et al.  Influence of Life Stress on Depression: Moderation by a Polymorphism in the 5-HTT Gene , 2003, Science.

[45]  E. Bullmore,et al.  Meta-Analysis of Gray Matter Anomalies in Schizophrenia: Application of Anatomic Likelihood Estimation and Network Analysis , 2008, Biological Psychiatry.

[46]  Rainer Goebel,et al.  Neurofeedback: A promising tool for the self-regulation of emotion networks , 2010, NeuroImage.

[47]  Yong He,et al.  Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain , 2013, Proceedings of the National Academy of Sciences.

[48]  Thomas T. Liu,et al.  The global signal in fMRI: Nuisance or Information? , 2017, NeuroImage.

[49]  Jonathan D. Cohen,et al.  An fMRI Investigation of Emotional Engagement in Moral Judgment , 2001, Science.

[50]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[51]  M. Mintun,et al.  Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus , 2010, Proceedings of the National Academy of Sciences.

[52]  C. Schwarzbauer,et al.  Electroconvulsive therapy reduces frontal cortical connectivity in severe depressive disorder , 2012, Proceedings of the National Academy of Sciences.

[53]  Mario Dzemidzic,et al.  Resting state corticolimbic connectivity abnormalities in unmedicated bipolar disorder and unipolar depression , 2009, Psychiatry Research: Neuroimaging.

[54]  David Eidelberg,et al.  Serotonin Modulation of Cerebral Glucose Metabolism in Depressed Older Adults , 2009, Biological Psychiatry.

[55]  Lachlan T. Strike,et al.  Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group , 2015, Molecular Psychiatry.

[56]  Brian A. Nosek,et al.  Power failure: why small sample size undermines the reliability of neuroscience , 2013, Nature Reviews Neuroscience.

[57]  Kevin Murphy,et al.  Towards a consensus regarding global signal regression for resting state functional connectivity MRI , 2017, NeuroImage.

[58]  I. Melle,et al.  Mapping the Heterogeneous Phenotype of Schizophrenia and Bipolar Disorder Using Normative Models , 2018, JAMA psychiatry.

[59]  G. Pearlson,et al.  Altered Global Signal Topography in Schizophrenia , 2016, Cerebral cortex.

[60]  M. Fox,et al.  The global signal and observed anticorrelated resting state brain networks. , 2009, Journal of neurophysiology.

[61]  M E Phelps,et al.  Regional brain metabolic changes in patients with major depression treated with either paroxetine or interpersonal therapy: preliminary findings. , 2001, Archives of general psychiatry.

[62]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[63]  Yufeng Zang,et al.  Functional brain hubs and their test–retest reliability: A multiband resting-state functional MRI study , 2013, NeuroImage.

[64]  Simone Kühn,et al.  Resting-state brain activity in schizophrenia and major depression: a quantitative meta-analysis. , 2013, Schizophrenia bulletin.

[65]  J. P. Hamilton,et al.  Anomalous Gray Matter Structural Networks in Major Depressive Disorder , 2013, Biological Psychiatry.

[66]  J. Andrews-Hanna,et al.  Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity. , 2015, JAMA psychiatry.

[67]  Yong He,et al.  BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics , 2013, PloS one.

[68]  Lachlan T. Strike,et al.  Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group , 2016, Molecular Psychiatry.

[69]  A. Cavanna,et al.  The precuneus: a review of its functional anatomy and behavioural correlates. , 2006, Brain : a journal of neurology.

[70]  E. Rolls,et al.  The functional neuroanatomy of the human orbitofrontal cortex: evidence from neuroimaging and neuropsychology , 2004, Progress in Neurobiology.

[71]  I. Rezek,et al.  Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies , 2016, Biological Psychiatry.

[72]  R. Adolphs,et al.  A Role for Somatosensory Cortices in the Visual Recognition of Emotion as Revealed by Three-Dimensional Lesion Mapping , 2000, The Journal of Neuroscience.

[73]  L. Tan,et al.  Biological abnormality of impaired reading is constrained by culture , 2004, Nature.

[74]  M. First,et al.  Structured Clinical Interview for DSM-IV Axis I Disorders , 1997 .

[75]  Yong He,et al.  GRETNA: a graph theoretical network analysis toolbox for imaging connectomics , 2015, Front. Hum. Neurosci..

[76]  Andrew T. Drysdale,et al.  Resting-state connectivity biomarkers define neurophysiological subtypes of depression , 2016, Nature Medicine.

[77]  Janet B W Williams,et al.  A structured interview guide for the Hamilton Depression Rating Scale. , 1988, Archives of general psychiatry.

[78]  Alan C. Evans,et al.  Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. , 2009, Cerebral cortex.

[79]  Eric Vermetten,et al.  Reduced volume of orbitofrontal cortex in major depression , 2002, Biological Psychiatry.

[80]  E. Bullmore,et al.  A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs , 2006, The Journal of Neuroscience.

[81]  Yong He,et al.  Overlapping and segregated resting‐state functional connectivity in patients with major depressive disorder with and without childhood neglect , 2014, Human brain mapping.

[82]  Charles D. Smith,et al.  Dissociation of Automatic and Strategic Lexical-Semantics: Functional Magnetic Resonance Imaging Evidence for Differing Roles of Multiple Frontotemporal Regions , 2006, The Journal of Neuroscience.

[83]  Yong He,et al.  Shared and Distinct Functional Architectures of Brain Networks Across Psychiatric Disorders , 2019, Schizophrenia bulletin.

[84]  Xiaoqi Huang,et al.  Disrupted Brain Connectivity Networks in Drug-Naive, First-Episode Major Depressive Disorder , 2011, Biological Psychiatry.

[85]  Norito Kawakami,et al.  Lifetime prevalence and age-of-onset distributions of mental disorders in the World Health Organization's World Mental Health Survey Initiative. , 2007, World psychiatry : official journal of the World Psychiatric Association.

[86]  Yong He,et al.  Topological analyses of functional connectomics: A crucial role of global signal removal, brain parcellation, and null models , 2018, Human brain mapping.

[87]  Yufeng Zang,et al.  Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space , 2013, NeuroImage.

[88]  C. Ruff,et al.  Right Supramarginal Gyrus Is Crucial to Overcome Emotional Egocentricity Bias in Social Judgments , 2013, The Journal of Neuroscience.

[89]  Li Wang,et al.  The effects of antidepressant treatment on resting‐state functional brain networks in patients with major depressive disorder , 2015, Human brain mapping.

[90]  J. Rapoport,et al.  The anatomical distance of functional connections predicts brain network topology in health and schizophrenia. , 2013, Cerebral cortex.

[91]  Junming Shao,et al.  Aberrant topology of striatum's connectivity is associated with the number of episodes in depression. , 2014, Brain : a journal of neurology.

[92]  M. Kendall,et al.  Rank Correlation Methods , 1949 .

[93]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[94]  M. Schölvinck,et al.  Neural basis of global resting-state fMRI activity , 2010, Proceedings of the National Academy of Sciences.

[95]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .