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A Connectome-wide Functional Signature of Transdiagnostic Risk for Mental Illness

      Abstract

      Background

      High rates of comorbidity, shared risk, and overlapping therapeutic mechanisms have led psychopathology research toward transdiagnostic dimensional investigations of clustered symptoms. One influential framework accounts for these transdiagnostic phenomena through a single general factor, sometimes referred to as the p factor, associated with risk for all common forms of mental illness.

      Methods

      We build on previous research identifying unique structural neural correlates of the p factor by conducting a data-driven analysis of connectome-wide intrinsic functional connectivity (n = 605).

      Results

      We demonstrate that higher p factor scores and associated risk for common mental illness maps onto hyperconnectivity between visual association cortex and both frontoparietal and default mode networks.

      Conclusions

      These results provide initial evidence that the transdiagnostic risk for common forms of mental illness is associated with patterns of inefficient connectome-wide intrinsic connectivity between visual association cortex and networks supporting executive control and self-referential processes, networks that are often impaired across categorical disorders.

      Keywords

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      References

        • Lahey B.B.
        • Applegate B.
        • Hakes J.K.
        • Zald D.H.
        • Hariri A.R.
        • Rathouz P.J.
        Is there a general factor of prevalent psychopathology during adulthood?.
        J Abnorm Psychol. 2012; 121: 971-977
        • Lahey B.B.
        • Krueger R.F.
        • Rathouz P.J.
        • Waldman I.D.
        • Zald D.H.
        A hierarchical causal taxonomy of psychopathology across the life span.
        Psychol Bull. 2017; 143: 142-186
        • Snyder H.R.
        • Young J.F.
        • Hankin B.L.
        Strong homotypic continuity in common psychopathology-, internalizing-, and externalizing-specific factors over time in adolescents.
        Clin Psychol Sci. 2017; 5: 98-110
        • Laceulle O.M.
        • Vollebergh W.A.M.
        • Ormel J.
        The structure of psychopathology in adolescence: Replication of a general psychopathology factor in the TRAILS study.
        Clin Psychol Sci. 2015; 3: 850-860
        • Neumann A.
        • Pappa I.
        • Lahey B.B.
        • Verhulst F.C.
        • Medina-Gomez C.
        • Jaddoe V.W.
        • et al.
        Single nucleotide polymorphism heritability of a general psychopathology factor in children.
        J Am Acad Child Adolesc Psychiatry. 2016; : 551038-551045.e4
        • Murray A.L.
        • Eisner M.
        • Ribeaud D.
        The development of the general factor of psychopathology “p factor” through childhood and adolescence.
        J Abnorm Child Psychol. 2016; 44: 1573-1586
        • Caspi A.
        • Houts R.M.
        • Belsky D.W.
        • Goldman-Mellor S.J.
        • Harrington H.
        • Israel S.
        • et al.
        The p factor: One general psychopathology factor in the structure of psychiatric disorders?.
        Clin Psychol Sci. 2014; 2: 119-137
        • Clark L.A.
        • Watson D.
        • Reynolds S.
        Diagnosis and classification of psychopathology: Challenges to the current system and future directions.
        Annu Rev Psychol. 1995; 46: 121-153
        • Lee S.H.
        • Ripke S.
        • Neale B.M.
        • Faraone S.V.
        • Purcell S.M.
        • et al.
        • Cross-Disorder Group of the Psychiatric Genomics Consortium
        Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs.
        Nat Genet. 2013; 45: 984-994
        • Zald D.H.
        • Lahey B.B.
        Implications of the hierarchical structure of psychopathology for psychiatric neuroimaging.
        Biol Psychiatry Cogn Neurosci Neuroimaging. 2017; 2: 310-317
        • Goodkind M.
        • Eickhoff S.B.
        • Oathes D.J.
        • Jiang Y.
        • Chang A.
        • Jones-Hagata L.B.
        • et al.
        Identification of a common neurobiological substrate for mental illness.
        JAMA Psychiatry. 2015; 72: 305
        • Romer A.L.
        • Knodt A.R.
        • Houts R.
        • Brigidi B.D.
        • Moffitt T.E.
        • Caspi A.
        • Hariri A.R.
        Structural alterations within cerebellar circuitry are associated with general liability for common mental disorders.
        Mol Psychiatry. 2018; 23: 1084-1090
        • Ito M.
        Control of mental activities by internal models in the cerebellum.
        Nat Rev Neurosci. 2008; 9: 304-313
        • E KH
        • Chen S.H.
        • Ho M.H.
        • Desmond J.E.
        A meta-analysis of cerebellar contributions to higher cognition from PET and fMRI studies.
        Hum Brain Mapp. 2014; 35: 593-615
        • Buckner R.L.
        • Krienen F.M.
        • Castellanos A.
        • Diaz J.C.
        • Yeo B.T.T.
        The organization of the human cerebellum estimated by intrinsic functional connectivity.
        J Neurophysiol. 2011; 106: 2322-2345
        • Greicius M.
        Resting-state functional connectivity in neuropsychiatric disorders.
        Curr Opin Neurol. 2008; 21: 424-430
        • Fox M.D.
        • Greicius M.
        Clinical applications of resting state functional connectivity.
        Front Syst Neurosci. 2010; 4: 19
        • Shehzad Z.
        • Kelly A.M.C.
        • Reiss P.T.
        • Gee D.G.
        • Gotimer K.
        • Uddin L.Q.
        • et al.
        The resting brain: Unconstrained yet reliable.
        Cereb Cortex. 2009; 19: 2209-2229
        • Glahn D.C.
        • Winkler A.M.
        • Kochunov P.
        • Almasy L.
        • Duggirala R.
        • Carless M.A.
        • et al.
        Genetic control over the resting brain.
        Proc Natl Acad Sci U S A. 2010; 107: 1223-1228
        • Ge T.
        • Holmes A.J.
        • Buckner R.L.
        • Smoller J.W.
        • Sabuncu M.R.
        Heritability analysis with repeat measurements and its application to resting-state functional connectivity.
        Proc Natl Acad Sci U S A. 2017; 114: 5521-5526
        • Cole M.W.
        • Reynolds J.R.
        • Power J.D.
        • Repovs G.
        • Anticevic A.
        • Braver T.S.
        Multi-task connectivity reveals flexible hubs for adaptive task control.
        Nat Neurosci. 2013; 16: 1348-1355
        • Cole M.W.
        • Repovš G.
        • Anticevic A.
        The frontoparietal control system.
        Neurosci. 2014; 20: 652-664
        • Broyd S.J.
        • Demanuele C.
        • Debener S.
        • Helps S.K.
        • James C.J.
        • Sonuga-Barke E.J.S.
        Default-mode brain dysfunction in mental disorders: A systematic review.
        Neurosci Biobehav Rev. 2009; 33: 279-296
        • Buckholtz J.W.
        • Meyer-Lindenberg A.
        Psychopathology and the human connectome: Toward a transdiagnostic model of risk for mental illness.
        Neuron. 2012; 74: 990-1004
        • Shehzad Z.
        • Kelly C.
        • Reiss P.T.
        • Cameron Craddock R.
        • Emerson J.W.
        • McMahon K.
        • et al.
        A multivariate distance-based analytic framework for connectome-wide association studies.
        Neuroimage. 2014; 93: 74-94
        • Zapala M.A.
        • Schork N.J.
        Statistical properties of multivariate distance matrix regression for high-dimensional data analysis.
        Front Genet. 2012; 3: 1-10
        • Thirion B.
        • Varoquaux G.
        • Dohmatob E.
        • Poline J.B.
        Which fMRI clustering gives good brain parcellations?.
        Front Neurosci. 2014; 8: 167
        • Beckmann C.F.
        Modelling with independent components.
        Neuroimage. 2012; 62: 891-901
        • Bullmore E.
        • Sporns O.
        Complex brain networks: graph theoretical analysis of structural and functional systems.
        Nat Publ Gr. 2009; 10: 186-198
        • Lecrubier Y.
        • Sheehan D.V.
        • Weiller E.
        • Amorim P.
        • Bonora I.
        • Sheehan K.H.
        • et al.
        The Mini International Neuropsychiatric Interview (MINI). A short diagnostic structured interview: Reliability and validity according to the CIDI.
        Eur Psychiatry. 1997; 12: 224-231
        • First M.B.
        • Spitzer R.L.
        • Gibbon M.
        • Williams J.B.W.
        Structured Clinical Interview for DSM-IV Axis I Disorders, Clinician Version (SCID-CV).
        American Pyschiatric Press, Inc, Washington, DC1997
        • Substance Abuse and Mental Health Services Administration
        Results from the 2015 National Survey on Drug Use and Health: Detailed tables.
        (Available at:) (Accessed April 13, 2018)
        • Avants B.B.
        • Epstein C.L.
        • Grossman M.
        • Gee J.C.
        Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain.
        Med Image Anal. 2008; 12: 26-41
        • Klein A.
        • Andersson J.
        • Ardekani B.A.
        • Ashburner J.
        • Avants B.
        • Chiang M.C.
        • et al.
        Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.
        Neuroimage. 2009; 46: 786-802
        • Cox R.W.
        AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages.
        Comput Biomed Res. 1996; 29: 162-173
        • Greve D.N.
        • Fischl B.
        Accurate and robust brain image alignment using boundary-based registration.
        Neuroimage. 2009; 48: 63-72
        • Jo H.J.
        • Gotts S.J.
        • Reynolds R.C.
        • Bandettini P.A.
        • Martin A.
        • Cox R.W.
        • Saad Z.S.
        Effective preprocessing procedures virtually eliminate distance-dependent motion artifacts in resting state FMRI.
        J Appl Math May 21;2013. 2013;
        • Satterthwaite T.D.
        • Elliott M.A.
        • Gerraty R.T.
        • Ruparel K.
        • Loughead J.
        • Calkins M.E.
        • et al.
        An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.
        Neuroimage. 2013; 64: 240-256
        • Behzadi Y.
        • Restom K.
        • Liau J.
        • Liu T.T.
        A component based noise correction method (CompCor) for BOLD and perfusion based fMRI.
        Neuroimage. 2007; 37: 90-101
        • Power J.D.
        • Mitra A.
        • Laumann T.O.
        • Snyder A.Z.
        • Schlaggar B.L.
        • Petersen S.E.
        Methods to detect, characterize, and remove motion artifact in resting state fMRI.
        Neuroimage. 2014; 84: 320-341
      1. Nichols TE (2017): Notes on creating a standardized version of DVARS [published online ahead of print Apr 5]. arXiv:1704.1469 [stat.AP].

        • Benjamini Y.
        • Hochberg Y.
        Controlling the false discovery rate: A practical and powerful approach to multiple testing.
        J R Stat Soc. 1995; 57: 289-300
        • Satterthwaite T.D.
        • Vandekar S.N.
        • Wolf D.H.
        • Bassett D.S.
        • Ruparel K.
        • Shehzad Z.
        • et al.
        Connectome-wide network analysis of youth with psychosis-spectrum symptoms.
        Mol Psychiatry. 2015; 20: 1508-1515
        • Satterthwaite T.D.
        • Cook P.A.
        • Bruce S.E.
        • Conway C.
        • Mikkelsen E.
        • Satchell E.
        • et al.
        Dimensional depression severity in women with major depression and post-traumatic stress disorder correlates with fronto-amygdalar hypoconnectivty.
        Mol Psychiatry. 2016; 21: 894-902
        • Calhoun V.D.
        • Adali T.
        • McGinty V.B.
        • Pekar J.J.
        • Watson T.D.
        • Pearlson G.D.
        fMRI activation in a visual-perception task: Network of areas detected using the general linear model and independent components analysis.
        Neuroimage. 2001; 14: 1080-1088
        • Margulies D.S.
        • Ghosh S.S.
        • Goulas A.
        • Falkiewicz M.
        • Huntenburg J.M.
        • Langs G.
        • et al.
        Situating the default-mode network along a principal gradient of macroscale cortical organization.
        Proc Natl Acad Sci U S A. 2016; 113: 12574-12579
        • Yeo B.T.T.
        • Krienen F.M.
        • Sepulcre J.
        • Sabuncu M.R.
        • Lashkari D.
        • Hollinshead M.
        • et al.
        The organization of the human cerebral cortex estimated by intrinsic functional connectivity.
        J Neurophysiol. 2011; 106: 1125-1165
        • Karten A.
        • Pantazatos S.P.
        • Khalil D.
        • Zhang X.
        • Hirsch J.
        Dynamic coupling between the lateral occipital-cortex, default-mode, and frontoparietal networks during bistable perception.
        Brain Connect. 2013; 3: 286-293
        • Chadick J.Z.
        • Gazzaley A.
        Differential coupling of visual cortex with default or frontal-parietal network based on goals.
        Nat Neurosci. 2011; 14: 830-832
        • Purves D.
        • Beau Lotto R.
        • Mark Williams S.
        • Nundy S.
        • Yang Z.
        Why we see things the way we do: Evidence for a wholly empirical strategy of vision.
        Philos Trans R Soc B Biol Sci. 2001; 356: 285-297
        • Mesulam M.M.
        From sensation to perception.
        Brain. 1998; 121: 1013-1052
        • van de Ven V.
        • Rotarska Jagiela A.
        • Oertel-Knöchel V.
        • Linden D.E.J.
        Reduced intrinsic visual cortical connectivity is associated with impaired perceptual closure in schizophrenia.
        Neuroimage Clin. 2017; 15: 45-52
        • Shaffer Jr., J.J.
        • Johnson C.P.
        • Fiedorowicz J.G.
        • Christensen G.E.
        • Wemmie J.A.
        • Magnotta V.A.
        Impaired sensory processing measured by functional MRI in bipolar disorder manic and depressed mood states.
        Brain Imaging Behav. 2018; 12: 837-847
        • Le T.M.
        • Borghi J.A.
        • Kujawa A.J.
        • Klein D.N.
        • Leung H.C.
        Alterations in visual cortical activation and connectivity with prefrontal cortex during working memory updating in major depressive disorder.
        Neuroimage Clin. 2017; 14: 43-53
        • Desseilles M.
        • Schwartz S.
        • Dang-Vu T.T.
        • Sterpenich V.
        • Ansseau M.
        • Maquet P.
        • Phillips C.
        Depression alters “top-down” visual attention: A dynamic causal modeling comparison between depressed and healthy subjects.
        Neuroimage. 2011; 54: 1662-1668
        • Sehatpour P.
        • Dias E.C.
        • Butler P.D.
        • Revheim N.
        • Guilfoyle D.N.
        • Foxe J.J.
        • Javitt D.C.
        Impaired visual object processing across an occipital-frontal-hippocampal brain network in schizophrenia: An integrated neuroimaging study.
        Arch Gen Psychiatry. 2010; 67: 772-782
        • Harvey P.D.
        • Strassnig M.
        Predicting the severity of everyday functional disability in people with schizophrenia: Cognitive deficits, functional capacity, symptoms, and health status.
        World Psychiatry. 2012; 11: 73-79
        • Austin M.P.
        • Mitchell P.
        • Goodwin G.M.
        Cognitive deficits in depression: Possible implications for functional neuropathology.
        Br J Psychiatry. 2001; 178: 200-206
        • Depp C.A.
        • Mausbach B.T.
        • Harmell A.L.
        • Savla G.N.
        • Bowie C.R.
        • Harvey P.D.
        • Patterson T.L.
        Meta-analysis of the association between cognitive abilities and everyday functioning in bipolar disorder.
        Bipolar Disord. 2012; 14: 217-226
        • Zeng L.L.
        • Shen H.
        • Liu L.
        • Wang L.
        • Li B.
        • Fang P.
        • et al.
        Identifying major depression using whole-brain functional connectivity: A multivariate pattern analysis.
        Brain. 2012; 135: 1498-1507
        • Zalesky A.
        • Fornito A.
        • Egan G.F.
        • Pantelis C.
        • Bullmore E.T.
        The relationship between regional and inter-regional functional connectivity deficits in schizophrenia.
        Hum Brain Mapp. 2012; 33: 2535-2549
        • Zanto T.P.
        • Gazzaley A.
        Fronto-parietal network: Flexible hub of cognitive control.
        Trends Cogn Sci. 2013; 17: 602-603
        • Dosenbach N.U.F.
        • Fair D.A.
        • Cohen A.L.
        • Schlaggar B.L.
        • Petersen S.E.
        A dual-networks architecture of top-down control.
        Trends Cogn Sci. 2008; 12: 99-105
        • Smallwood J.
        • Brown K.
        • Baird B.
        • Schooler J.W.
        Cooperation between the default mode network and the frontal-parietal network in the production of an internal train of thought.
        Brain Res. 2012; 1428: 60-70
        • Anticevic A.
        • Cole M.W.
        • Murray J.D.
        • Corlett P.R.
        • Wang X.J.
        • Krystal J.H.
        The role of default network deactivation in cognition and disease.
        Trends Cogn Sci. 2012; 16: 584-592
        • Whitfield-Gabrieli S.
        • Ford J.M.
        Default mode network activity and connectivity in psychopathology.
        Annu Rev Clin Psychol. 2012; 8: 49-76
        • Ptak R.
        The frontoparietal attention network of the human brain: Action, saliency, and a priority map of the environment.
        Neurosci. 2012; 18: 502-515
        • Raichle M.E.
        The brain’s default mode network.
        Annu Rev Neurosci. 2015; 38: 433-447
        • Diamond A.
        Executive functions.
        Annu Rev Psychol. 2013; 64: 135-168
        • Barch D.M.
        • Ceaser A.
        Cognition in schizophrenia: Core psychological and neural mechanisms.
        Trends Cogn Sci. 2012; 16: 27-34
        • Kaiser R.H.
        • Andrews-Hanna J.R.
        • Wager T.D.
        • Pizzagalli D.A.
        Large-scale network dysfunction in major depressive disorder: A meta-analysis of resting-state functional connectivity.
        JAMA Psychiatry. 2015; 72: 603-611
        • Anticevic A.
        • Cole M.W.
        • Repovs G.
        • Murray J.D.
        • Brumbaugh M.S.
        • Winkler A.M.
        • et al.
        Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness.
        Cereb Cortex. 2014; 24: 3116-3130
        • Buckner R.L.
        • Andrews-Hanna J.R.
        • Schacter D.L.
        The brain’s default network.
        Ann N Y Acad Sci. 2008; 1124: 1-38
        • Button K.S.
        • Ioannidis J.P.A.
        • Mokrysz C.
        • Nosek B.A.
        • Flint J.
        • Robinson E.S.J.
        • Munafò M.R.
        Power failure: Why small sample size undermines the reliability of neuroscience.
        Nat Rev Neurosci. 2013; 14: 365-376

      Linked Article

      • A Network Perspective on the Search for Common Transdiagnostic Brain Mechanisms
        Biological PsychiatryVol. 84Issue 6
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          The way we conceptualize and categorize psychiatric symptoms and mental disorders has evolved significantly over time—beginning with mental disorders being classified into distinct diagnostic entities purely based on phenomenological criteria, toward a multidimensional description of mental disorders along symptom clusters across diagnostic boundaries that are potentially closer aligned to underlying biological (patho)mechanisms (1). Despite its attractions from a biological/precision medicine point of view, the utility of a transdiagnostic, multidimensional description of mental disorders is challenged by longitudinal studies suggesting that a common single factor underlies the vulnerability to a broad spectrum of psychiatric illnesses ranging from depression to psychosis (2).
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