Skip to main content

Imaging and Genetic Biomarkers Predicting Transition to Psychosis

  • Chapter
  • First Online:
Biomarkers in Psychiatry

Part of the book series: Current Topics in Behavioral Neurosciences ((CTBN,volume 40))

Abstract

The search for diagnostic and prognostic biomarkers in schizophrenia care and treatment is the focus of many within the research community. Longitudinal cohorts of patients presenting at elevated genetic and clinical risk have provided a wealth of data that has informed our understanding of the development of schizophrenia and related psychotic disorders.

Imaging follow-up of high-risk cohorts has demonstrated changes in cerebral grey matter of those that eventually transition to schizophrenia that predate the onset of symptoms and evolve over the course of illness. Longitudinal follow-up studies demonstrate that observed grey matter changes can be employed to differentiate those who will transition to schizophrenia from those who will not prior to the onset of the disorder.

In recent years our understanding of the genetic makeup of schizophrenia has advanced significantly. The development of modern analysis techniques offers researchers the ability to objectively quantify genetic risk; these have been successfully applied within a high-risk paradigm to assist in differentiating between high-risk individuals who will subsequently become unwell and those who will not.

This chapter will discuss the application of imaging and genetic biomarkers within high-risk groups to predict future transition to schizophrenia and related psychotic disorders. We aim to provide an overview of current approaches focussing on grey matter changes that are predictive of future transition to illness, the developing field of genetic risk scores and other methods being developed to aid clinicians in diagnosis and prognosis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Alústiza I, Radua J, Pla M, Martin R, Ortuño F (2017) Meta-analysis of functional magnetic resonance imaging studies of timing and cognitive control in schizophrenia and bipolar disorder: evidence of a primary time deficit. Schizophr Res 188:21–32

    Article  PubMed  Google Scholar 

  • American Psychiatric Association (1994) Diagnostic and statistical manual of mental disorders. American Psychiatric Association, Washington, DC

    Google Scholar 

  • Andreasen NC (1989) Scale for the assessment of negative symptoms (SANS). Br J Psychiatry:49–58

    Article  Google Scholar 

  • Ashburner J, Friston KJ (2000) Voxel-based morphometry – the methods. NeuroImage 11:805–821

    Article  CAS  PubMed  Google Scholar 

  • Baiano M, David A, Versace A, Churchill R, Balestrieri M, Brambilla P (2007) Anterior cingulate volumes in schizophrenia: a systematic review and a meta-analysis of MRI studies. Schizophr Res 93:1–12

    Article  CAS  PubMed  Google Scholar 

  • Baig B, Whalley H, Hall J, McIntosh A, Job D, Cunningham-Owens D, Johnstone E, Lawrie S (2010) Functional magnetic resonance imaging of BDNF val66met polymorphism in unmedicated subjects at high genetic risk of schizophrenia performing a verbal memory task. Psychiatry Res 183:195–201

    Article  CAS  PubMed  Google Scholar 

  • Bassett D, Bullmore E, Verchinski B, Mattay V, Weinberger D, Meyer-Lindenberg A (2008) Hierarchical organization of human cortical networks in health and schizophrenia. J Neurosci 28:9239–9248

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bergman H, Khodabakhsh A, Maayan N, Kirkham A, Adams C, Soares-Weiser K (2014) Operational criteria checklist for psychotic illness and affective illness (OPCRIT+) for diagnosing schizophrenia in people with psychotic symptoms. Cochrane Libr. https://doi.org/10.1002/14651858.CD011104

  • Bois C, Whalley HC, McIntosh AM, Lawrie SM (2015) Structural magnetic resonance imaging markers of susceptibility and transition to schizophrenia: a review of familial and clinical high risk population studies. J Psychopharmacol 29:144–154

    Article  CAS  PubMed  Google Scholar 

  • Boksa P (2013) A way forward for research on biomarkers for psychiatric disorders. J Psychiatry Neurosci 38:75–55

    Article  PubMed  PubMed Central  Google Scholar 

  • Borgwardt SJ, McGuire PK, Aston J, Gschwandtner U, Pflüger MO, Stieglitz RD, Radue EW, Riecher-Rössler A (2008) Reductions in frontal, temporal and parietal volume associated with the onset of psychosis. Schizophr Res 106:108–114

    Article  PubMed  Google Scholar 

  • Borgwardt S, McGuire P, Fusar-Poli P (2011) Gray matters! – mapping the transition to psychosis. Schizophr Res 133:63–67

    Article  PubMed  Google Scholar 

  • Borgwardt S, Koutsouleris N, Aston J, Studerus E, Smieskova R, Riecher-Rössler A, Meisenzahl E (2013) Distinguishing prodromal from first-episode psychosis using neuroanatomical single-subject pattern recognition. Schizophr Bull 39:1105–1114

    Article  PubMed  Google Scholar 

  • Bottmer C, Bachmann S, Pantel J, Essig M, Amann M, Schad L, Magnotta V, Schröder J (2005) Reduced cerebellar volume and neurological soft signs in first-episode schizophrenia. Psychiatry Res Neuroimaging 140:239–250

    Article  Google Scholar 

  • Brugger S, Howes O (2017) Heterogeneity and homogeneity of regional brain structure in schizophrenia: a meta-analysis. JAMA Psychiatry. https://doi.org/10.1001/jamapsychiatry.2017.2663

    Article  PubMed  PubMed Central  Google Scholar 

  • Buck CW, Carscallen HB, Hobbs GE (1955) The relation between oral and rectal temperatures in schizophrenic subjects. Psychiatry Q 29:28–32

    Article  CAS  Google Scholar 

  • Burges C (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121–167

    Article  Google Scholar 

  • Cannon T, Cadenhead K, Cornblatt B et al (2008) Prediction of psychosis in youth at high clinical risk: a multisite longitudinal study in North America. Arch Gen Psychiatry 65:28–37

    Article  PubMed  PubMed Central  Google Scholar 

  • Cannon TD, Chung Y, He G, Sun D, Jacobson A, Van Erp TG, McEwen S, Addington J, Bearden CE, Cadenhead K, Cornblatt B (2015) Progressive reduction in cortical thickness as psychosis develops: a multisite longitudinal neuroimaging study of youth at elevated clinical risk. Biol Psychiatry 77:147–157

    Article  PubMed  Google Scholar 

  • Cannon T, Yu C, Addington J et al (2016) An individualized risk calculator for research in prodromal psychosis. Am J Psychiatry 173:980–988

    Article  PubMed  PubMed Central  Google Scholar 

  • Cardno AG, Gottesman II (2000) Twin studies of schizophrenia: from bow-and-arrow concordances to star wars Mx and functional genomics. Am J Med Genet 97:12–17

    Article  CAS  PubMed  Google Scholar 

  • Chen C, Suckling J, Lennox B, Ooi C, Bullmore E (2011) A quantitative meta-analysis of fMRI studies in bipolar disorder. Bipolar Disord 13:1–15

    Article  CAS  PubMed  Google Scholar 

  • Cobia DJ, Smith MJ, Wang L, Csernansky JG (2012) Longitudinal progression of frontal and temporal lobe changes in schizophrenia. Schizophr Res 139:1–6

    Article  PubMed  PubMed Central  Google Scholar 

  • Corcoran C, Malaspina D, Hercher L (2005) Prodromal interventions for schizophrenia vulnerability: the risks of being “at risk”. Schizophr Res 73:173–184

    Article  PubMed  PubMed Central  Google Scholar 

  • DATA D (1997) Structured clinical interview for DSM-IV axis I disorders. American Psychiatric Press, Washington DC

    Google Scholar 

  • Davies G, Marioni RE, Liewald DC et al (2016) Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N=112 151). Mol Psychiatry 21:758–767

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dragt S, Nieman D, Veltman D, Becker H, van de Fliert R, de Haan L, Linszen D (2011) Environmental factors and social adjustment as predictors of a first psychosis in subjects at ultra high risk. Schizophr Res 125:69–76

    Article  PubMed  Google Scholar 

  • Dudbridge F (2013) Power and predictive accuracy of polygenic risk scores. PLoS Genet 9:e1003348

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Eack S, Prasad K, Montrose D, Goradia D, Dworakowski D, Miewald J, Keshavan M (2008) An integrated psychobiological predictive model of emergent psychopathology among young relatives at risk for schizophrenia. Prog Neuro-Psychopharmacol Biol Psychiatry 32:1873–1878

    Article  Google Scholar 

  • Erlenmeyer-Kimling L, Adamo UH, Rock D, Roberts SA, Bassett AS, Squires-Wheeler E, Cornblatt BA, Endicott J, Pape S, Gottesman II (1997) The New York high-risk project: prevalence and comorbidity of axis I disorders in offspring of schizophrenic parents at 25-year follow-up. Arch Gen Psychiatry 54:1096–1102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Falkai P, Honer W, Kamer T et al (2007) Disturbed frontal gyrification within families affected with schizophrenia. J Psychiatr Res 41:805–813

    Article  PubMed  Google Scholar 

  • Fan Y, Batmanghelich N, Clark C, Davatzikos C, Initiative A (2008) Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. NeuroImage 39:1731–1743

    Article  PubMed  Google Scholar 

  • Farrow T, Whitford T, Williams L, Gomes L, Harris A (2005) Diagnosis-related regional gray matter loss over two years in first episode schizophrenia and bipolar disorder. Biol Psychiatry 58:713–723

    Article  PubMed  Google Scholar 

  • Fu C, Mourao-Miranda J, Costafreda S, Khanna A, Marquand A, Williams S, Brammer M (2008) Pattern classification of sad facial processing: toward the development of neurobiological markers in depression. Biol Psychiatry 63:656–662

    Article  PubMed  Google Scholar 

  • Fusar-Poli P, Placentino A, Carletti F et al (2009) Functional atlas of emotional faces processing: a voxel-based meta-analysis of 105 functional magnetic resonance imaging studies. J Psychiatry Neurosci 34:418–432

    PubMed  PubMed Central  Google Scholar 

  • Fusar-Poli P, Broome MR, Matthiasson P, Woolley JB, Johns LC, Tabraham P, Bramon E, Valmaggia L, Williams SC, McGuire P (2010) Spatial working memory in individuals at high risk for psychosis: longitudinal fMRI study. Schizophr Res 123:45–52

    Article  CAS  PubMed  Google Scholar 

  • Fusar-Poli P, Borgwardt S, Crescini A, Deste G, Kempton MJ, Lawrie S, Mc Guire P, Sacchetti E (2011) Neuroanatomy of vulnerability to psychosis: a voxel-based meta-analysis. Neurosci Biobehav Rev 35:1175–1185

    Article  CAS  PubMed  Google Scholar 

  • Fusar-Poli P, Bonoldi I, Yung A, Borgwardt S, Kempton M, Valmaggia L, Barale F, Caverzasi E, McGuire P (2012) Predicting psychosis: meta-analysis of transition outcomes in individuals at high clinical risk. Arch Gen Psychiatry 69:220–229

    Article  PubMed  Google Scholar 

  • Fusar-Poli P, Borgwardt S, Bechdolf A, Addington J, Riecher-Rössler A, Schultze-Lutter F, Keshavan M, Wood S, Ruhrmann S, Seidman LJ, Valmaggia L, Cannon T, Velthorst E, De Haan L, Cornblatt B, Bonoldi I, Birchwood M, McGlashan T, Carpenter W, McGorry P, Klosterkötter J, McGuire P, Yung A (2013a) The psychosis high-risk state: a comprehensive state-of-the-art review. JAMA Psychiatry 70:107–120

    Article  PubMed  PubMed Central  Google Scholar 

  • Fusar-Poli P, Byrne M, Badger S, Valmaggia LR, McGuire PK (2013b) Outreach and support in South London (OASIS), 2001–2011: ten years of early diagnosis and treatment for young individuals at high clinical risk for psychosis. Eur Psychiatry 28:315–326

    Article  CAS  PubMed  Google Scholar 

  • Fusar-Poli P, Cappucciati M, Borgwardt S et al (2016a) Heterogeneity of psychosis risk within individuals at clinical high risk: a meta-analytical stratification. JAMA Psychiatry 73:113–120

    Article  PubMed  Google Scholar 

  • Fusar-Poli P, Cappucciati M, Bonoldi I et al (2016b) Prognosis of brief psychotic episodes: a meta-analysis. JAMA Psychiatry 73:211–220

    Article  PubMed  Google Scholar 

  • Fusar-Poli P, McGorry P, Kane J (2017a) Improving outcomes of first-episode psychosis: an overview. World Psychiatry 16:251–265

    Article  PubMed  PubMed Central  Google Scholar 

  • Fusar-Poli P, Rutigliano G, Stahl D, Davies C, Bonoldi I, Reilly T, McGuire P (2017b) Development and validation of a clinically based risk calculator for the transdiagnostic prediction of psychosis. JAMA Psychiatry. https://doi.org/10.1001/jamapsychiatry.2017.0284

    Article  PubMed  PubMed Central  Google Scholar 

  • Fusar‐Poli P, Diaz‐Caneja CM, Patel R, Valmaggia L, Byrne M, Garety P, Shetty H, Broadbent M, Stewart R, McGuire P (2016) Services for people at high risk improve outcomes in patients with first episode psychosis. Acta Psychiatr Scand 133:76–85

    Article  PubMed  Google Scholar 

  • Giuliani N, Calhoun V, Pearlson G, Francis A, Buchanan R (2005) Voxel-based morphometry versus region of interest: a comparison of two methods for analyzing gray matter differences in schizophrenia. Schizophr Res 74:135–147

    Article  PubMed  Google Scholar 

  • Glahn D, Laird A, Ellison-Wright I, Thelen S, Robinson J, Lancaster J, Bullmore E, Fox P (2008) Meta-analysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis. Biol Psychiatry 64:774–781

    Article  PubMed  PubMed Central  Google Scholar 

  • Gottesman II (1991) Schizophrenia genesis: the origins of madness. W H Freeman, New York, NY

    Google Scholar 

  • Gottesman II, Erlenmeyer-Kimling L (2001) Family and twin strategies as a head start in defining prodromes and endophenotypes for hypothetical early-interventions in schizophrenia. Schizophr Res 51:93–102

    Article  CAS  PubMed  Google Scholar 

  • Gur R, McGrath C, Chan R et al (2002) An fMRI study of facial emotion processing in patients with schizophrenia. Am J Psychiatry 159:1992–1999

    Article  PubMed  Google Scholar 

  • Gur RE, Nimgaonkar VL, Almasy L, Calkins ME, Ragland JD, Pogue-Geile MF, Kanes S, Blangero J, Gur RC (2007) Neurocognitive endophenotypes in a multiplex multigenerational family study of schizophrenia. Am J Psychiatry 164:813–819

    Article  PubMed  Google Scholar 

  • Haijma S, Haren N, Cahn W, Koolschijn C, Pol H, Kahn R (2013) Brain volumes in schizophrenia: a meta-analysis in over 18 000 subjects. Schizophr Bull 39:1129–1138

    Article  PubMed  Google Scholar 

  • Haroun N, Dunn L, Haroun A, Cadenhead K (2006) Risk and protection in prodromal schizophrenia: ethical implications for clinical practice and future research. Schizophr Bull 32:166–178

    Article  PubMed  Google Scholar 

  • Hartz S, Horton A, Oehlert M et al (2017) Association between substance use disorder and polygenic liability to schizophrenia. Biol Psychiatry 82:709–715

    Article  PubMed  PubMed Central  Google Scholar 

  • Heath R, Franklin D, Shraberg D (1979) Gross pathology of the cerebellum in patients diagnosed and treated as functional psychiatric disorders. J Nerv Ment Dis 167:585–592

    Article  CAS  PubMed  Google Scholar 

  • Heuvel M, Mandl R, Stam C, Kahn R, Pol H (2010) Aberrant frontal and temporal complex network structure in schizophrenia: a graph theoretical analysis. J Neurosci 30:15915–15926

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • International Schizophrenia Consortium, Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, Sullivan PF, Sklar P (2009) Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460:748–752

    Article  PubMed Central  CAS  Google Scholar 

  • Jaffe A, Babuin L, Apple F (2006) Biomarkers in acute cardiac disease: the present and the future. J Am Coll Cardiol 48:1–11

    Article  CAS  PubMed  Google Scholar 

  • Janssens C, Aulchenko Y, Elefante S, Borsboom G, Steyerberg E, van Duijn C (2006) Predictive testing for complex diseases using multiple genes: fact or fiction? Genet Med 8:395–400

    Article  PubMed  Google Scholar 

  • Jha M, Minhajuddin A, Gadad B, Greer T, Grannemann B, Soyombo A, Mayes T, Rush J, Trivedi M (2017) Can C-reactive protein inform antidepressant medication selection in depressed outpatients? Findings from the CO-MED trial. Psychoneuroendocrinology 78:105–113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Job D, Whalley H, Johnstone E, Lawrie S (2005) Grey matter changes over time in high risk subjects developing schizophrenia. NeuroImage 25:1023–1030

    Article  PubMed  Google Scholar 

  • Job D, Whalley H, McIntosh A, Owens D, Johnstone E, Lawrie S (2006) Grey matter changes can improve the prediction of schizophrenia in subjects at high risk. BMC Med 4:29

    Article  PubMed  PubMed Central  Google Scholar 

  • Johnstone EC, Crow TJ, Frith CD, Husband J, Kreel L (1976) Cerebral ventricular size and cognitive impairment in chronic schizophrenia. Lancet 2:924–926

    Article  CAS  PubMed  Google Scholar 

  • Johnstone EC, Abukmeil SS, Byrne M, Clafferty R, Grant E, Hodges A, Lawrie SM, Owens DG (2000) Edinburgh high risk study – findings after four years: demographic, attainment and psychopathological issues. Schizophr Res 46:1–15

    Article  CAS  PubMed  Google Scholar 

  • Johnstone E, Lawrie S, Cosway R (2002) What does the Edinburgh high-risk study tell us about schizophrenia? Am J Med Genet 114:906–912

    Article  PubMed  Google Scholar 

  • Johnstone E, Ebmeier K, Miller P, Owens D, Lawrie S (2005) Predicting schizophrenia: findings from the Edinburgh high-risk study. Br J Psychiatry 186:18–25

    Article  PubMed  Google Scholar 

  • Jørgensen A, Teasdale TW, Parnas J, Schulsinger F, Schulsinger H, Mednick SA (1987) The Copenhagen high-risk project. The diagnosis of maternal schizophrenia and its relation to offspring diagnosis. Br J Psychiatry. https://doi.org/10.1192/bjp.151.6.753

    Article  Google Scholar 

  • Karageorgiou E, Schulz CS, Gollub RL, Andreasen NC, Ho B-C, Lauriello J, Calhoun VD, Bockholt JH, Sponheim SR, Georgopoulos AP (2011) Neuropsychological testing and structural magnetic resonance imaging as diagnostic biomarkers early in the course of schizophrenia and related psychoses. Neuroinformatics 9:321–333

    Article  PubMed  PubMed Central  Google Scholar 

  • Kattan M, Yu C, Stephenson A, Sartor O, Tombal B (2013) Clinicians versus nomogram: predicting future technetium-99m bone scan positivity in patients with rising prostate-specific antigen after radical prostatectomy for prostate cancer. Urology 81:956–961

    Article  PubMed  Google Scholar 

  • Kendler K, McGuire M, Gruenberg A, O’hare A, Spellman M, Walsh D (1993) The Roscommon family study: I. Methods, diagnosis of probands, and risk of schizophrenia in relatives. Arch Gen Psychiatry 50:527–540

    Article  CAS  PubMed  Google Scholar 

  • Klöppel S, Abdulkadir A, Jack C, Koutsouleris N, Mourão-Miranda J, Vemuri P (2012) Diagnostic neuroimaging across diseases. NeuroImage 61:457–463

    Article  PubMed  Google Scholar 

  • Klosterkötter J, Hellmich M, Steinmeyer EM, Schultze-Lutter F (2001) Diagnosing schizophrenia in the initial prodromal phase. Arch Gen Psychiatry 58:158–164

    Article  PubMed  Google Scholar 

  • Koutsouleris N, Meisenzahl E, Davatzikos C et al (2009) Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Arch Gen Psychiatry 66:700–712

    Article  PubMed  PubMed Central  Google Scholar 

  • Koutsouleris N, Davatzikos C, Bottlender R, Patschurek-Kliche K, Scheuerecker J, Decker P, Gaser C, Möller H-J, Meisenzahl E (2012a) Early recognition and disease prediction in the at-risk mental states for psychosis using neurocognitive pattern classification. Schizophr Bull 38:1200–1215

    Article  PubMed  Google Scholar 

  • Koutsouleris N, Borgwardt S, Meisenzahl E, Bottlender R, Möller H-J, Riecher-Rössler A (2012b) Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy study. Schizophr Bull 38:1234–1246

    Article  PubMed  Google Scholar 

  • Koutsouleris N, Riecher-Rössler A, Meisenzahl E, Smieskova R, Studerus E, Kambeitz-Ilankovic L, Saldern S, Cabral C, Reiser M, Falkai P (2014) Detecting the psychosis prodrome across high-risk populations using neuroanatomical biomarkers. Schizophr Bull 41:471–482

    Article  PubMed  PubMed Central  Google Scholar 

  • Kraguljac N, Srivastava A, Lahti A (2013) Memory deficits in schizophrenia: a selective review of functional magnetic resonance imaging (fMRI) studies. Behav Sci (Basel) 3:330–347

    Article  Google Scholar 

  • Kronbichler L, Tschernegg M, Martin A, Schurz M, Kronbichler M (2017) Abnormal brain activation during theory of mind tasks in schizophrenia: a meta-analysis. Schizophr Bull 43:1240–1250

    Article  PubMed  PubMed Central  Google Scholar 

  • Lawrie S (2017) Parsing Heterogeneity. JAMA Psychiatry. https://doi.org/10.1001/jamapsychiatry.2017.2953

    Article  PubMed  Google Scholar 

  • Lawrie SM, Abukmeil SS (1998) Brain abnormality in schizophrenia. A systematic and quantitative review of volumetric magnetic resonance imaging studies. Br J Psychiatry J Ment Sci 172:110–120

    Article  CAS  Google Scholar 

  • Lawrie SM, Whalley H, Kestelman JN, Abukmeil SS, Byrne M, Hodges A, Rimmington JE, Best JJ, Owens DG, Johnstone EC (1999) Magnetic resonance imaging of brain in people at high risk of developing schizophrenia. Lancet 353:30–33

    Article  CAS  PubMed  Google Scholar 

  • Lawrie S, Whalley H, Abukmeil S, Kestelman J, Miller P, Best J, Owens D, Johnstone E (2002) Temporal lobe volume changes in people at high risk of schizophrenia with psychotic symptoms. Br J Psychiatry J Ment Sci 181:138–143

    Article  Google Scholar 

  • Lawrie SM, McIntosh AM, Hall J, Owens DG, Johnstone EC (2008) Brain structure and function changes during the development of schizophrenia: the evidence from studies of subjects at increased genetic risk. Schizophr Bull. https://doi.org/10.1093/schbul/sbm158

    Article  Google Scholar 

  • Lee TH, Marcantonio ER, Mangione CM et al (1999) Derivation and prospective validation of a simple index for prediction of cardiac risk of major noncardiac surgery. Circulation 100:1043–1049

    Article  CAS  PubMed  Google Scholar 

  • Lee H, DeCandia T, Ripke S et al (2012) Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nat Genet 44:247–250

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ludwig J, Weinstein J (2005) Biomarkers in cancer staging, prognosis and treatment selection. Nat Rev Cancer 5:845–856

    Article  CAS  PubMed  Google Scholar 

  • Lungu O, Barakat M, Laventure S, Debas K, Proulx S, Luck D, Stip E (2013) The incidence and nature of cerebellar findings in schizophrenia: a quantitative review of fMRI literature. Schizophr Bull 39:797–806

    Article  PubMed  Google Scholar 

  • Maier R, Moser G, Chen G-B et al (2015) Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder. Am J Hum Genet 96:283–294

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium, Ripke S, Wray NR et al (2012) A mega-analysis of genome-wide association studies for major depressive disorder. Molecular psychiatry 18:497–511

    Article  CAS  Google Scholar 

  • Marjoram D, Job DE, Whalley HC, Gountouna VE, McIntosh AM, Simonotto E, Cunningham-Owens D, Johnstone EC, Lawrie S (2006) A visual joke fMRI investigation into theory of mind and enhanced risk of schizophrenia. NeuroImage 31:1850–1858

    Article  PubMed  Google Scholar 

  • Martin P, Albers M (1995) Cerebellum and schizophrenia: a selective review. Schizophr Bull 21:241–250

    Article  CAS  PubMed  Google Scholar 

  • Mayeux R (2004) Biomarkers: potential uses and limitations. NeuroRx 1:182–188

    Article  PubMed  PubMed Central  Google Scholar 

  • McGlashan T, Zipursky R, Perkins D et al (2006) Randomized, double-blind trial of olanzapine versus placebo in patients prodromally symptomatic for psychosis. Am J Psychiatry 163:790–799

    Article  PubMed  Google Scholar 

  • McGorry PD, Yung AR, Phillips LJ, Yuen HP, Francey S, Cosgrave EM, Germano D, Bravin J, McDonald T, Blair A, Adlard S, Jackson H (2002) Randomized controlled trial of interventions designed to reduce the risk of progression to first-episode psychosis in a clinical sample with subthreshold symptoms. Arch Gen Psychiatry 59:921–928

    Article  PubMed  Google Scholar 

  • McIntosh AM, Moorhead TW, McKirdy J, Hall J, Sussmann JE, Stanfield AC, Harris JM, Johnstone EC, Lawrie SM (2009) Prefrontal gyral folding and its cognitive correlates in bipolar disorder and schizophrenia. Acta Psychiatr Scand 119:192–198

    Article  CAS  PubMed  Google Scholar 

  • Mechelli A, Riecher-Rössler A, Meisenzahl E et al (2011) Neuroanatomical abnormalities that predate the onset of psychosis: a multicenter study. Arch Gen Psychiatry 68:489–495

    Article  PubMed  Google Scholar 

  • Minzenberg M, Laird A, Thelen S, Carter C, Glahn D (2009) Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia. Arch Gen Psychiatry 66:811–822

    Article  PubMed  PubMed Central  Google Scholar 

  • Mühleisen T, Leber M, Schulze T et al (2014) Genome-wide association study reveals two new risk loci for bipolar disorder. Nat Commun. https://doi.org/10.1038/ncomms4339

  • Neilson E, Bois C, Clarke TK, Hall L, Johnstone EC, Owens DGC, Whalley HC, McIntosh AM, Lawrie SM (2017) Polygenic risk of schizophrenia transition and cortical gyrification: a high-risk study. Psychol Med 25:1–11

    Google Scholar 

  • Nelson MD, Saykin AJ, Flashman LA, Riordan HJ (1998) Hippocampal volume reduction in schizophrenia as assessed by magnetic resonance imaging: a meta-analytic study. Arch Gen Psychiatry 55:433–440

    Article  CAS  PubMed  Google Scholar 

  • Noble W (2006) What is a support vector machine? Nat Biotechnol 24:1565–1567

    Article  CAS  PubMed  Google Scholar 

  • O’Donoghue B, Nelson B, Yuen H, Lane A, Wood S, Thompson A, Lin A, McGorry P, Yung A (2015) Social environmental risk factors for transition to psychosis in an ultra-high risk population. Schizophr Res 161:150–155

    Article  PubMed  Google Scholar 

  • Okugawa G, Sedvall G, Nordström M, Andreasen N, Pierson R, Magnotta V, Agartz I (2002) Selective reduction of the posterior superior vermis in men with chronic schizophrenia. Schizophr Res 55:61–67

    Article  PubMed  Google Scholar 

  • Olabi B, Ellison-Wright I, McIntosh A, Wood S, Bullmore E, Lawrie S (2011) Are there progressive brain changes in schizophrenia? A meta-analysis of structural magnetic resonance imaging studies. Biol Psychiatry 70:88–96

    Article  PubMed  Google Scholar 

  • Organization W (1992) The ICD-10 classification of mental and behavioural disorders: clinical descriptions and diagnostic guidelines. World Health Organization, Geneva

    Google Scholar 

  • Orrù G, Pettersson-Yeo W, Marquand A, Sartori G, Mechelli A (2012) Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev 36:1140–1152

    Article  PubMed  Google Scholar 

  • Overall JE, Gorham DR (1962) The brief psychiatric rating scale. Psychol Rep. https://doi.org/10.2466/pr0.1962.10.3.799

    Article  Google Scholar 

  • Palaniyappan L (2012) Does the salience network play a cardinal role in psychosis? An emerging hypothesis of insular dysfunction. J Psychiatry Neurosci 37:17–27

    Article  PubMed  PubMed Central  Google Scholar 

  • Palaniyappan L, Mallikarjun P, Joseph V, White T, Liddle P (2011) Folding of the prefrontal cortex in schizophrenia: regional differences in gyrification. Biol Psychiatry 69:974–979

    Article  PubMed  Google Scholar 

  • Palaniyappan L, Marques T, Taylor H et al (2013) Cortical folding defects as markers of poor treatment response in first-episode psychosis. JAMA Psychiatry 70:1031–1040

    Article  PubMed  Google Scholar 

  • Pantelis C, Velakoulis D, McGorry P et al (2003) Neuroanatomical abnormalities before and after onset of psychosis: a cross-sectional and longitudinal MRI comparison. Lancet 361:281–288

    Article  PubMed  Google Scholar 

  • Perkins D, Jeffries C, Addington J et al (2015) Towards a psychosis risk blood diagnostic for persons experiencing high-risk symptoms: preliminary results from the NAPLS project. Schizophr Bull 41:419–428

    Article  PubMed  Google Scholar 

  • Pettersson-Yeo W, Benetti S, Marquand AF, Dell’acqua F, Williams SC, Allen P, Prata D, McGuire P, Mechelli A (2013) Using genetic, cognitive and multi-modal neuroimaging data to identify ultra-high-risk and first-episode psychosis at the individual level. Psychol Med 43:2547–2562

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pfeiffer R, Park Y, Kreimer A et al (2013) Risk prediction for breast, endometrial, and ovarian cancer in white women aged 50 y or older: derivation and validation from population-based cohort studies. PLoS Med 10:e1001492

    Article  PubMed  PubMed Central  Google Scholar 

  • Phillips M (2012) Neuroimaging in psychiatry: bringing neuroscience into clinical practice. Br J Psychiatry J Ment Sci 201:1–3

    Article  Google Scholar 

  • Phillips M, Vieta E (2007) Identifying functional neuroimaging biomarkers of bipolar disorder: toward DSM-V. Schizophr Bull 33:893–904

    Article  PubMed  PubMed Central  Google Scholar 

  • Phillips L, Velakoulis D, Pantelis C, Wood S, Yuen H, Yung A, Desmond P, Brewer W, McGorry P (2002) Non-reduction in hippocampal volume is associated with higher risk of psychosis. Schizophr Res 58:145–158

    Article  PubMed  Google Scholar 

  • Prata D, Mechelli A, Kapur S (2014) Clinically meaningful biomarkers for psychosis: a systematic and quantitative review. Neurosci Biobehav Rev 45:134–141

    Article  CAS  PubMed  Google Scholar 

  • Pue AF, Hoare R, Adamson JD (1969) The “pink spot” and schizophrenia. Can Psychiatr Assoc J 14:397–401

    Article  CAS  PubMed  Google Scholar 

  • Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Riecher-Rössler A, Gschwandtner U, Aston J, Borgwardt S, Drewe M, Fuhr P, Pflüger M, Radü W, Schindler C, Stieglitz RD (2007) The Basel early detection of psychosis (FEPSY) study – design and preliminary results. Acta Psychiatr Scand 115:114–125

    Article  PubMed  Google Scholar 

  • Riecher-Rössler A, Aston J, Ventura J, Merlo M, Borgwardt S, Gschwandtner U, Stieglitz RD (2008) The Basel screening instrument for psychosis (BSIP): development, structure, reliability and validity. Fortschr Neurol Psychiatr 76:207–216

    Article  PubMed  Google Scholar 

  • Riecher-Rössler A, Pflueger MO, Aston J, Borgwardt SJ, Brewer WJ, Gschwandtner U, Stieglitz RD (2009) Efficacy of using cognitive status in predicting psychosis: a 7-year follow-up. Biol Psychiatry 66:1023–1030

    Article  PubMed  Google Scholar 

  • Ripke S, Neale B, Corvin A et al (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511:421–427

    Article  CAS  PubMed Central  Google Scholar 

  • Ruhrmann S, Schultze-Lutter F, Salokangas R et al (2010) Prediction of psychosis in adolescents and young adults at high risk: results from the prospective European prediction of psychosis study. Arch Gen Psychiatry 67:241–251

    Article  PubMed  Google Scholar 

  • Sallet P, Elkis H, Alves T, Oliveira J, Sassi E, de Castro C, Busatto G, Gattaz W (2003) Reduced cortical folding in schizophrenia: an MRI morphometric study. Am J Psychiatr 160:1606–1613

    Article  PubMed  Google Scholar 

  • Sandyk R, Kay S, Merriam A (2009) Atrophy of the cerebellar vermis: relevance to the symptoms of schizophrenia. Int J Neurosci 57:205–212

    Article  Google Scholar 

  • Seidman LJ, Faraone SV, Goldstein JM et al (1999) Thalamic and amygdala-hippocampal volume reductions in first-degree relatives of patients with schizophrenia: an MRI-based morphometric analysis. Biol Psychiatry 46:941–954

    Article  CAS  PubMed  Google Scholar 

  • Shah J, Eack S, Montrose D, Tandon N, Miewald J, Prasad K, Keshavan M (2012) Multivariate prediction of emerging psychosis in adolescents at high risk for schizophrenia. Schizophr Res 141:189–196

    Article  PubMed  PubMed Central  Google Scholar 

  • Shah J, Tandon N, Keshavan M (2013) Psychosis prediction and clinical utility in familial high-risk studies: selective review, synthesis, and implications for early detection and intervention. Early Interv Psychiatry 7:345–360

    Article  PubMed  PubMed Central  Google Scholar 

  • Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, Hergueta T, Baker R, Dunbar GC (1998) The mini-international neuropsychiatric interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry 59 suppl 20:22–33, quiz 34–57

    CAS  PubMed  Google Scholar 

  • Shenton ME, Kikinis R, Jolesz FA, Pollak SD, LeMay M, Wible CG, Hokama H, Martin J, Metcalf D, Coleman M et al (1992) Abnormalities of the left temporal lobe and thought disorder in schizophrenia. A quantitative magnetic resonance imaging study. N Engl J Med 327:604–612

    Article  CAS  PubMed  Google Scholar 

  • Shimizu Y, Yoshimoto J, Toki S, Takamura M, Yoshimura S, Okamoto Y, Yamawaki S, Doya K (2015) Toward probabilistic diagnosis and understanding of depression based on functional MRI data analysis with logistic group LASSO. PLoS One 10:e0123524

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Smieskova R, Fusar-Poli P, Allen P, Bendfeldt K, Stieglitz RD, Drewe J, Radue EW, McGuire PK, Riecher-Rössler A, Borgwardt SJ (2010) Neuroimaging predictors of transition to psychosis – a systematic review and meta-analysis. Neurosci Biobehav Rev 34:1207–1222

    Article  CAS  PubMed  Google Scholar 

  • Smieskova R, Allen P, Simon A et al (2012) Different duration of at-risk mental state associated with neurofunctional abnormalities. A multimodal imaging study. Hum Brain Mapp 33:2281–2294

    Article  PubMed  Google Scholar 

  • Smieskova R, Marmy J, Schmidt A, Bendfeldt K, Riecher-Rӧssler A, Walter M, Lang UE, Borgwardt S (2013) Do subjects at clinical high risk for psychosis differ from those with a genetic high risk? – a systematic review of structural and functional brain abnormalities. Curr Med Chem 20:467–481

    CAS  PubMed  PubMed Central  Google Scholar 

  • So H-C, Kwan J, Cherny S, Sham P (2011) Risk prediction of complex diseases from family history and known susceptibility loci, with applications for cancer screening. Am J Hum Genet 88:548–565

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sokolowska I, Wetie A, Wormwood K, Thome J, Darie C, Woods A (2015) The potential of biomarkers in psychiatry: focus on proteomics. J Neural Transm (Vienna) 122 suppl 1:S9–S18

    Article  CAS  Google Scholar 

  • Suddath RL, Christison GW, Torrey EF, Casanova MF, Weinberger DR (1990) Anatomical abnormalities in the brains of monozygotic twins discordant for schizophrenia. N Engl J Med 322:789–794

    Article  CAS  PubMed  Google Scholar 

  • Sullivan P, Kendler K, Neale M (2003) Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch Gen Psychiatry 60:1187–1192

    Article  PubMed  Google Scholar 

  • Sumich A, Chitnis X, Fannon D, O’Ceallaigh S, Doku V, Faldrowicz A, Sharma T (2005) Unreality symptoms and volumetric measures of Heschl’s gyrus and planum temporal in first-episode psychosis. Biol Psychiatry 57:947–950

    Article  PubMed  Google Scholar 

  • Sumner P, Bell I, Rossell S (2017) A systematic review of the structural neuroimaging correlates of thought disorder. Neurosci Biobehav Rev. https://doi.org/10.1016/j.neubiorev.2017.08.017

    Article  Google Scholar 

  • Sun D, Phillips L, Velakoulis D, Yung A, McGorry PD, Wood SJ, van Erp TG, Thompson PM, Toga AW, Cannon TD, Pantelis C (2009) Progressive brain structural changes mapped as psychosis develops in “at risk” individuals. Schizophr Res 108:85–92

    Article  PubMed  PubMed Central  Google Scholar 

  • Takahashi T, Wood SJ, Yung AR, Soulsby B, McGorry PD, Suzuki M, Kawasaki Y, Phillips LJ, Velakoulis D, Pantelis C (2009) Progressive gray matter reduction of the superior temporal gyrus during transition to psychosis. Arch Gen Psychiatry 66:366–376

    Article  PubMed  Google Scholar 

  • Taylor TR, Evangelou N, Porter H, Lenthall R (2012) Primary care direct access MRI for the investigation of chronic headache. Clin Radiol 67:24–27

    Article  CAS  PubMed  Google Scholar 

  • Thomann P, Roebel M, Santos V, Bachmann S, Essig M, Schröder J (2009) Cerebellar substructures and neurological soft signs in first-episode schizophrenia. Psychiatry Res 173:83–87

    Article  PubMed  Google Scholar 

  • Thompson A, Nelson B, Yung A (2011) Predictive validity of clinical variables in the “at risk” for psychosis population: international comparison with results from the North American Prodrome Longitudinal Study. Schizophr Res 126:51–57

    Article  PubMed  Google Scholar 

  • Thompson A, Marwaha S, Broome MR (2016) At-risk mental state for psychosis: identification and current treatment approaches. BJPscyh Advances 22:186–193

    Article  Google Scholar 

  • Tijms B, Sprooten E, Job D, Johnstone E, Owens D, Willshaw D, Seriès P, Lawrie S (2015) Grey matter networks in people at increased familial risk for schizophrenia. Schizophr Res 168:1–8

    Article  PubMed  Google Scholar 

  • Turetsky B, Cowell P, Gur R, Grossman R, Shtasel D, Gur R (1995) Frontal and temporal lobe brain volumes in schizophrenia: relationship to symptoms and clinical subtype. Arch Gen Psychiatry 52:1061–1070

    Article  CAS  PubMed  Google Scholar 

  • Valmaggia LR, Byrne M, Day F, Broome MR, Johns L, Howes O, Power P, Badger S, Fusar-Poli P, McGuire PK (2015) Duration of untreated psychosis and need for admission in patients who engage with mental health services in the prodromal phase. Br J Psychiatry J Ment Sci 207:130–134

    Article  Google Scholar 

  • Van Horn JD, McManus IC (1992) Ventricular enlargement in schizophrenia. A meta-analysis of studies of the ventricle: brain ratio (VBR). Br J Psychiatry J Ment Sci 160:687–697

    Article  Google Scholar 

  • Vassos E, Di Forti M, Coleman J, Iyegbe C, Prata D, Euesden J, O’Reilly P, Curtis C, Kolliakou A, Patel H, Newhouse S, Traylor M, Ajnakina O, Mondelli V, Marques TR, Gardner-Sood P, Aitchison KJ, Powell J, Atakan Z, Greenwood KE, Smith S, Ismail K, Pariante C, Gaughran F, Dazzan P, Markus HS, David AS, Lewis CM, Murray RM, Breen G (2017) An examination of polygenic score risk prediction in individuals with first-episode psychosis. Biol Psychiatry 81:470–477

    Article  PubMed  Google Scholar 

  • Velakoulis D, Pantelis C, McGorry PD et al (1999) Hippocampal volume in first-episode psychoses and chronic schizophrenia: a high-resolution magnetic resonance imaging study. Arch Gen Psychiatry 56:133–141

    Article  CAS  PubMed  Google Scholar 

  • Velakoulis D, Wood S, Wong M et al (2006) Hippocampal and amygdala volumes according to psychosis stage and diagnosis: a magnetic resonance imaging study of chronic schizophrenia, first-episode psychosis, and ultra-high-risk individuals. Arch Gen Psychiatry 63:139–149

    Article  PubMed  Google Scholar 

  • Venkatasubramanian G, Keshavan MS (2016) Biomarkers in psychiatry – a critique. Ann Neurosci 23:3–5

    Article  PubMed  PubMed Central  Google Scholar 

  • Weinberger D, Radulescu E (2016) Finding the elusive psychiatric “lesion” with 21st-century neuroanatomy: a note of caution. Am J Psychiatry 173:27–33

    Article  PubMed  Google Scholar 

  • Whalley HC, Simonotto E, Flett S, Marshall I, Ebmeier KP, Owens DG, Goddard NH, Johnstone EC, Lawrie SM (2004) fMRI correlates of state and trait effects in subjects at genetically enhanced risk of schizophrenia. Brain. https://doi.org/10.1093/brain/awh070

    Article  Google Scholar 

  • Whalley H, Simonotto E, Moorhead W, McIntosh A, Marshall I, Ebmeier K, Owens D, Goddard N, Johnstone E, Lawrie S (2006) Functional imaging as a predictor of schizophrenia. Biol Psychiatry 60:454–462

    Article  PubMed  Google Scholar 

  • Whalley HC, Gountouna VE, Hall J, McIntosh AM, Simonotto E, Job DE, Owens DG, Johnstone EC, Lawrie SM (2008) fMRI changes over time and reproducibility in unmedicated subjects at high genetic risk of schizophrenia. Psychol Med 39:1189–1199

    Article  PubMed  Google Scholar 

  • White P, Halliday-Pegg J, Collie D (2002) Open access neuroimaging for general practitioners – diagnostic yield and influence on patient management. Br J Gen Pract 52:33–35

    PubMed  PubMed Central  Google Scholar 

  • Whitfield-Gabrieli S, Thermenos H, Milanovic S et al (2009) Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc Natl Acad Sci U S A 106:1279–1284

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Whyte MC, Whalley HC, Simonotto E, Flett S, Shillcock R, Marshall I, Goddard NH, Johnstone EC, Lawrie SM (2006) Event-related fMRI of word classification and successful word recognition in subjects at genetically enhanced risk of schizophrenia. Psychol Med. https://doi.org/10.1017/S0033291706008178

    Article  PubMed  Google Scholar 

  • Wing J, Cooper J, Sartorius N (2012) Measurement and classification of psychiatric symptoms: an instruction manual for the PSE and CATEGO program. Cambridge University Press, Cambridge

    Google Scholar 

  • Wright IC, Rabe-Hesketh S, Woodruff PW, David AS, Murray RM, Bullmore ET (2000) Meta-analysis of regional brain volumes in schizophrenia. Am J Psychiatry 157:16–25

    Article  CAS  PubMed  Google Scholar 

  • Yang H, Liu J, Sui J, Pearlson G, Calhoun V (2010) A hybrid machine learning method for fusing fMRI and genetic data: combining both improves classification of schizophrenia. Front Hum Neurosci 4:192

    Article  PubMed  PubMed Central  Google Scholar 

  • Yu JS, Xue AY, Redei EE, Bagheri N (2016) A support vector machine model provides an accurate transcript-level-based diagnostic for major depressive disorder. Transl Psychiatry 6:e931

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yung AR, McGorry PD (1996) The prodromal phase of first-episode psychosis: past and current conceptualizations. Schizophr Bull. https://doi.org/10.1093/schbul/22.2.353

    Article  CAS  PubMed  Google Scholar 

  • Yung AR, Nelson B (2013) The ultra-high risk concept – a review. Can J Psychiatry 58:5–12

    Article  PubMed  Google Scholar 

  • Yung AR, Phillips LJ, McGorry PD, McFarlane CA, Francey S, Harrigan S, Patton GC, Jackson HJ (1998) Prediction of psychosis. A step towards indicated prevention of schizophrenia. Br J Psychiatry Suppl 172:14–20

    Article  CAS  PubMed  Google Scholar 

  • Yung A, Phillips L, Yuen H, McGorry P (2004a) Risk factors for psychosis in an ultra high-risk group: psychopathology and clinical features. Schizophr Res 67:131–142

    Article  PubMed  Google Scholar 

  • Yung AR, McGorry PD, McFarlane CA, Jackson HJ, Patton GC, Rakkar A (2004b) Monitoring and care of young people at incipient risk of psychosis. Schizophr Bull 22:283–303

    Article  Google Scholar 

  • Zarogianni E, Moorhead TW, Lawrie SM (2013) Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level. Neuroimage Clin 3:279–289

    Article  PubMed  PubMed Central  Google Scholar 

  • Zarogianni E, Storkey A, Johnstone E, Owens D, Lawrie S (2017a) Improved individualized prediction of schizophrenia in subjects at familial high risk, based on neuroanatomical data, schizotypal and neurocognitive features. Schizophr Res 181:6–12

    Article  PubMed  Google Scholar 

  • Zarogianni E, Storkey AJ, Borgwardt S, Smieskova R, Studerus E, Riecher-Rössler A, Lawrie SM (2017b) Individualized prediction of psychosis in subjects with an at-risk mental state. Schizophr Res. https://doi.org/10.1016/j.schres.2017.08.061

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stuart A. Hunter .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Hunter, S.A., Lawrie, S.M. (2018). Imaging and Genetic Biomarkers Predicting Transition to Psychosis. In: Pratt, J., Hall, J. (eds) Biomarkers in Psychiatry. Current Topics in Behavioral Neurosciences, vol 40. Springer, Cham. https://doi.org/10.1007/7854_2018_46

Download citation

Publish with us

Policies and ethics