Imaging and Genetic Biomarkers Predicting Transition to Psychosis

  • Stuart A. Hunter
  • Stephen M. Lawrie
Part of the Current Topics in Behavioral Neurosciences book series (CTBN, volume 40)


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.


Biomarkers Genetics High risk Imaging Psychosis Schizophrenia Transition 


  1. 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 188:21–32Google Scholar
  2. American Psychiatric Association (1994) Diagnostic and statistical manual of mental disorders. American Psychiatric Association, Washington, DCGoogle Scholar
  3. Andreasen NC (1989) Scale for the assessment of negative symptoms (SANS). Br J Psychiatry:49–58Google Scholar
  4. Ashburner J, Friston KJ (2000) Voxel-based morphometry – the methods. NeuroImage 11:805–821PubMedPubMedCentralGoogle Scholar
  5. 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 93:1–12Google Scholar
  6. 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 183:195–201Google Scholar
  7. 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 28:9239–9248PubMedPubMedCentralGoogle Scholar
  8. 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.
  9. 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 29:144–154Google Scholar
  10. Boksa P (2013) A way forward for research on biomarkers for psychiatric disorders. J Psychiatry Neurosci 38:75–55PubMedPubMedCentralGoogle Scholar
  11. 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 106:108–114Google Scholar
  12. Borgwardt S, McGuire P, Fusar-Poli P (2011) Gray matters! – mapping the transition to psychosis. Schizophr Res 133:63–67Google Scholar
  13. 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 39:1105–1114Google Scholar
  14. 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 140:239–250Google Scholar
  15. Brugger S, Howes O (2017) Heterogeneity and homogeneity of regional brain structure in schizophrenia: a meta-analysis. JAMA Psychiatry. PubMedPubMedCentralGoogle Scholar
  16. Buck CW, Carscallen HB, Hobbs GE (1955) The relation between oral and rectal temperatures in schizophrenic subjects. Psychiatry Q 29:28–32Google Scholar
  17. Burges C (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121–167Google Scholar
  18. 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 65:28–37PubMedPubMedCentralGoogle Scholar
  19. 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 77:147–157Google Scholar
  20. Cannon T, Yu C, Addington J et al (2016) An individualized risk calculator for research in prodromal psychosis. Am J Psychiatry 173:980–988PubMedPubMedCentralGoogle Scholar
  21. 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 97:12–17Google Scholar
  22. Chen C, Suckling J, Lennox B, Ooi C, Bullmore E (2011) A quantitative meta-analysis of fMRI studies in bipolar disorder. Bipolar Disord 13:1–15PubMedPubMedCentralGoogle Scholar
  23. Cobia DJ, Smith MJ, Wang L, Csernansky JG (2012) Longitudinal progression of frontal and temporal lobe changes in schizophrenia. Schizophr Res 139:1–6PubMedPubMedCentralGoogle Scholar
  24. Corcoran C, Malaspina D, Hercher L (2005) Prodromal interventions for schizophrenia vulnerability: the risks of being “at risk”. Schizophr Res 73:173–184PubMedPubMedCentralGoogle Scholar
  25. DATA D (1997) Structured clinical interview for DSM-IV axis I disorders. American Psychiatric Press, Washington DCGoogle Scholar
  26. 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 21:758–767PubMedPubMedCentralGoogle Scholar
  27. 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 125:69–76Google Scholar
  28. Dudbridge F (2013) Power and predictive accuracy of polygenic risk scores. PLoS Genet 9:e1003348PubMedPubMedCentralGoogle Scholar
  29. 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 32:1873–1878Google Scholar
  30. 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 54:1096–1102PubMedPubMedCentralGoogle Scholar
  31. Falkai P, Honer W, Kamer T et al (2007) Disturbed frontal gyrification within families affected with schizophrenia. J Psychiatr Res 41:805–813Google Scholar
  32. 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 39:1731–1743Google Scholar
  33. 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 58:713–723Google Scholar
  34. 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 63:656–662Google Scholar
  35. 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 34:418–432PubMedPubMedCentralGoogle Scholar
  36. 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 123:45–52Google Scholar
  37. 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 35:1175–1185Google Scholar
  38. 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 69:220–229Google Scholar
  39. 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 70:107–120PubMedPubMedCentralGoogle Scholar
  40. 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 28:315–326Google Scholar
  41. 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 73:113–120Google Scholar
  42. Fusar-Poli P, Cappucciati M, Bonoldi I et al (2016b) Prognosis of brief psychotic episodes: a meta-analysis. JAMA Psychiatry 73:211–220Google Scholar
  43. Fusar-Poli P, McGorry P, Kane J (2017a) Improving outcomes of first-episode psychosis: an overview. World Psychiatry 16:251–265PubMedPubMedCentralGoogle Scholar
  44. 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. PubMedPubMedCentralGoogle Scholar
  45. 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 133:76–85Google Scholar
  46. 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 74:135–147Google Scholar
  47. 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 64:774–781PubMedPubMedCentralGoogle Scholar
  48. Gottesman II (1991) Schizophrenia genesis: the origins of madness. W H Freeman, New York, NYGoogle Scholar
  49. 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 51:93–102Google Scholar
  50. Gur R, McGrath C, Chan R et al (2002) An fMRI study of facial emotion processing in patients with schizophrenia. Am J Psychiatry 159:1992–1999Google Scholar
  51. 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 164:813–819Google Scholar
  52. 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 39:1129–1138Google Scholar
  53. 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 32:166–178Google Scholar
  54. Hartz S, Horton A, Oehlert M et al (2017) Association between substance use disorder and polygenic liability to schizophrenia. Biol Psychiatry 82:709–715PubMedPubMedCentralGoogle Scholar
  55. 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 167:585–592Google Scholar
  56. 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 30:15915–15926Google Scholar
  57. 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 460:748–752PubMedPubMedCentralGoogle Scholar
  58. Jaffe A, Babuin L, Apple F (2006) Biomarkers in acute cardiac disease: the present and the future. J Am Coll Cardiol 48:1–11Google Scholar
  59. 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 8:395–400Google Scholar
  60. 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 78:105–113PubMedPubMedCentralGoogle Scholar
  61. Job D, Whalley H, Johnstone E, Lawrie S (2005) Grey matter changes over time in high risk subjects developing schizophrenia. NeuroImage 25:1023–1030Google Scholar
  62. 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 4:29PubMedPubMedCentralGoogle Scholar
  63. Johnstone EC, Crow TJ, Frith CD, Husband J, Kreel L (1976) Cerebral ventricular size and cognitive impairment in chronic schizophrenia. Lancet 2:924–926Google Scholar
  64. 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 46:1–15Google Scholar
  65. Johnstone E, Lawrie S, Cosway R (2002) What does the Edinburgh high-risk study tell us about schizophrenia? Am J Med Genet 114:906–912Google Scholar
  66. Johnstone E, Ebmeier K, Miller P, Owens D, Lawrie S (2005) Predicting schizophrenia: findings from the Edinburgh high-risk study. Br J Psychiatry 186:18–25Google Scholar
  67. 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. Google Scholar
  68. 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 9:321–333PubMedPubMedCentralGoogle Scholar
  69. 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 81:956–961Google Scholar
  70. 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 50:527–540Google Scholar
  71. Klöppel S, Abdulkadir A, Jack C, Koutsouleris N, Mourão-Miranda J, Vemuri P (2012) Diagnostic neuroimaging across diseases. NeuroImage 61:457–463Google Scholar
  72. Klosterkötter J, Hellmich M, Steinmeyer EM, Schultze-Lutter F (2001) Diagnosing schizophrenia in the initial prodromal phase. Arch Gen Psychiatry 58:158–164Google Scholar
  73. 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 66:700–712PubMedPubMedCentralGoogle Scholar
  74. 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 38:1200–1215Google Scholar
  75. 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 38:1234–1246Google Scholar
  76. 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 41:471–482PubMedPubMedCentralGoogle Scholar
  77. Kraguljac N, Srivastava A, Lahti A (2013) Memory deficits in schizophrenia: a selective review of functional magnetic resonance imaging (fMRI) studies. Behav Sci (Basel) 3:330–347Google Scholar
  78. 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 43:1240–1250PubMedPubMedCentralGoogle Scholar
  79. Lawrie S (2017) Parsing Heterogeneity. JAMA Psychiatry. Google Scholar
  80. 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 172:110–120Google Scholar
  81. 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 353:30–33Google Scholar
  82. 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 181:138–143Google Scholar
  83. 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. Google Scholar
  84. 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 100:1043–1049PubMedPubMedCentralGoogle Scholar
  85. Lee H, DeCandia T, Ripke S et al (2012) Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nat Genet 44:247–250PubMedPubMedCentralGoogle Scholar
  86. Ludwig J, Weinstein J (2005) Biomarkers in cancer staging, prognosis and treatment selection. Nat Rev Cancer 5:845–856Google Scholar
  87. 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 39:797–806Google Scholar
  88. 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 96:283–294PubMedPubMedCentralGoogle Scholar
  89. 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 18:497–511Google Scholar
  90. 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 31:1850–1858Google Scholar
  91. Martin P, Albers M (1995) Cerebellum and schizophrenia: a selective review. Schizophr Bull 21:241–250Google Scholar
  92. Mayeux R (2004) Biomarkers: potential uses and limitations. NeuroRx 1:182–188PubMedPubMedCentralGoogle Scholar
  93. 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 163:790–799Google Scholar
  94. 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 59:921–928Google Scholar
  95. 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 119:192–198Google Scholar
  96. Mechelli A, Riecher-Rössler A, Meisenzahl E et al (2011) Neuroanatomical abnormalities that predate the onset of psychosis: a multicenter study. Arch Gen Psychiatry 68:489–495Google Scholar
  97. 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 66:811–822PubMedPubMedCentralGoogle Scholar
  98. Mühleisen T, Leber M, Schulze T et al (2014) Genome-wide association study reveals two new risk loci for bipolar disorder. Nat Commun.
  99. 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 25:1–11Google Scholar
  100. 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 55:433–440Google Scholar
  101. Noble W (2006) What is a support vector machine? Nat Biotechnol 24:1565–1567Google Scholar
  102. 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 161:150–155Google Scholar
  103. 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 55:61–67Google Scholar
  104. 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 70:88–96Google Scholar
  105. Organization W (1992) The ICD-10 classification of mental and behavioural disorders: clinical descriptions and diagnostic guidelines. World Health Organization, GenevaGoogle Scholar
  106. 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 36:1140–1152Google Scholar
  107. Overall JE, Gorham DR (1962) The brief psychiatric rating scale. Psychol Rep. Google Scholar
  108. Palaniyappan L (2012) Does the salience network play a cardinal role in psychosis? An emerging hypothesis of insular dysfunction. J Psychiatry Neurosci 37:17–27PubMedPubMedCentralGoogle Scholar
  109. Palaniyappan L, Mallikarjun P, Joseph V, White T, Liddle P (2011) Folding of the prefrontal cortex in schizophrenia: regional differences in gyrification. Biol Psychiatry 69:974–979Google Scholar
  110. Palaniyappan L, Marques T, Taylor H et al (2013) Cortical folding defects as markers of poor treatment response in first-episode psychosis. JAMA Psychiatry 70:1031–1040Google Scholar
  111. 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 361:281–288Google Scholar
  112. 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 41:419–428Google Scholar
  113. 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 43:2547–2562PubMedPubMedCentralGoogle Scholar
  114. 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:e1001492PubMedPubMedCentralGoogle Scholar
  115. Phillips M (2012) Neuroimaging in psychiatry: bringing neuroscience into clinical practice. Br J Psychiatry J Ment Sci 201:1–3Google Scholar
  116. Phillips M, Vieta E (2007) Identifying functional neuroimaging biomarkers of bipolar disorder: toward DSM-V. Schizophr Bull 33:893–904PubMedPubMedCentralGoogle Scholar
  117. 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 58:145–158Google Scholar
  118. Prata D, Mechelli A, Kapur S (2014) Clinically meaningful biomarkers for psychosis: a systematic and quantitative review. Neurosci Biobehav Rev 45:134–141Google Scholar
  119. Pue AF, Hoare R, Adamson JD (1969) The “pink spot” and schizophrenia. Can Psychiatr Assoc J 14:397–401Google Scholar
  120. 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 81:559–575PubMedPubMedCentralGoogle Scholar
  121. 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 115:114–125Google Scholar
  122. 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 76:207–216Google Scholar
  123. 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 66:1023–1030Google Scholar
  124. Ripke S, Neale B, Corvin A et al (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511:421–427PubMedPubMedCentralGoogle Scholar
  125. 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 67:241–251Google Scholar
  126. 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 160:1606–1613Google Scholar
  127. Sandyk R, Kay S, Merriam A (2009) Atrophy of the cerebellar vermis: relevance to the symptoms of schizophrenia. Int J Neurosci 57:205–212Google Scholar
  128. 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 46:941–954Google Scholar
  129. 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 141:189–196PubMedPubMedCentralGoogle Scholar
  130. 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 7:345–360PubMedPubMedCentralGoogle Scholar
  131. 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 34–57Google Scholar
  132. 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 327:604–612Google Scholar
  133. 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:e0123524PubMedPubMedCentralGoogle Scholar
  134. 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 34:1207–1222Google Scholar
  135. 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 33:2281–2294Google Scholar
  136. 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 20:467–481PubMedPubMedCentralGoogle Scholar
  137. 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 88:548–565PubMedPubMedCentralGoogle Scholar
  138. 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–S18Google Scholar
  139. 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 322:789–794Google Scholar
  140. Sullivan P, Kendler K, Neale M (2003) Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch Gen Psychiatry 60:1187–1192Google Scholar
  141. 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 57:947–950Google Scholar
  142. Sumner P, Bell I, Rossell S (2017) A systematic review of the structural neuroimaging correlates of thought disorder. Neurosci Biobehav Rev. Google Scholar
  143. 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 108:85–92PubMedPubMedCentralGoogle Scholar
  144. 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 66:366–376Google Scholar
  145. Taylor TR, Evangelou N, Porter H, Lenthall R (2012) Primary care direct access MRI for the investigation of chronic headache. Clin Radiol 67:24–27Google Scholar
  146. 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 173:83–87Google Scholar
  147. 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 126:51–57Google Scholar
  148. Thompson A, Marwaha S, Broome MR (2016) At-risk mental state for psychosis: identification and current treatment approaches. BJPscyh Advances 22:186–193Google Scholar
  149. 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 168:1–8Google Scholar
  150. 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 52:1061–1070Google Scholar
  151. 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 207:130–134Google Scholar
  152. 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 160:687–697Google Scholar
  153. 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 81:470–477Google Scholar
  154. 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 56:133–141Google Scholar
  155. 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 63:139–149Google Scholar
  156. Venkatasubramanian G, Keshavan MS (2016) Biomarkers in psychiatry – a critique. Ann Neurosci 23:3–5PubMedPubMedCentralGoogle Scholar
  157. Weinberger D, Radulescu E (2016) Finding the elusive psychiatric “lesion” with 21st-century neuroanatomy: a note of caution. Am J Psychiatry 173:27–33Google Scholar
  158. 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. Google Scholar
  159. 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 60:454–462Google Scholar
  160. 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 39:1189–1199Google Scholar
  161. 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 52:33–35PubMedPubMedCentralGoogle Scholar
  162. 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 106:1279–1284PubMedPubMedCentralGoogle Scholar
  163. 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. Google Scholar
  164. 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, CambridgeGoogle Scholar
  165. 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 157:16–25Google Scholar
  166. 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 4:192PubMedPubMedCentralGoogle Scholar
  167. 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:e931PubMedPubMedCentralGoogle Scholar
  168. Yung AR, McGorry PD (1996) The prodromal phase of first-episode psychosis: past and current conceptualizations. Schizophr Bull. Google Scholar
  169. Yung AR, Nelson B (2013) The ultra-high risk concept – a review. Can J Psychiatry 58:5–12Google Scholar
  170. 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 172:14–20Google Scholar
  171. 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 67:131–142Google Scholar
  172. 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 22:283–303Google Scholar
  173. 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 3:279–289PubMedPubMedCentralGoogle Scholar
  174. 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 181:6–12Google Scholar
  175. 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.

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Division of Psychiatry, Royal Edinburgh HospitalUniversity of EdinburghEdinburghUK

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