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Biotyping in psychosis: using multiple computational approaches with one data set

Abstract

Focusing on biomarker identification and using biomarkers individually or in clusters to define biological subgroups in psychiatry requires a re-orientation from behavioral phenomenology to quantifying brain features, requiring big data approaches for data integration. Much still needs to be accomplished, not only to refine but also to build support for the application and customization of such an analytical phenotypic approach. In this review, we present some of what Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has learned so far to guide future applications of multivariate phenotyping and their analyses to understanding psychosis. This paper describes several B-SNIP projects that use phenotype data and big data computations to generate novel outcomes and glimpse what phenotypes contribute to disease understanding and, with aspiration, to treatment. The source of the phenotypes varies from genetic data, structural neuroanatomic localization, immune markers, brain physiology, and cognition. We aim to see guiding principles emerge and areas of commonality revealed. And, we will need to demonstrate not only data stability but also the usefulness of biomarker information for subgroup identification enhancing target identification and treatment development.

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Fig. 1: Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) cartoon representing the path to psyhosis Biotypes.
Fig. 2: Average cortical grey matter reduction by Biotype (B-, B-2 and B-3) in the B-SNIP population.
Fig. 3: Big data routines directed to B-SNIP volumetric data.
Fig. 4: Gene enrichment in the default mode networks in psychosis.
Fig. 5: SVM classification results on regional homogeneity (ReHo) connectivity metrics.
Fig. 6: Effects of permutations on Minor Alle Frequency.
Fig. 7: These regions represent areas where high psychosis symptoms correlate significantly with low cortical thickness.
Fig. 8: PDS2 score comparisons across Biotypes.
Fig. 9: CRP and IL6 group comparisons.
Fig. 10: Choroid plexus group comparisons.
Fig. 11: Overall structural MRI (top plot) and cognition and neurophysiology (bottom plot) standard scores as a function of cognitive ability.
Fig. 12: MRI measures that deviate from the BANCC pattern.
Fig. 13: EEG measures that deviate from the BANCC pattern.

References

  1. 1.

    Tamminga CA, Ivleva EI, Keshavan MS, Pearlson GD, Clementz BA, Witte B, et al. Clinical phenotypes of psychosis in the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP). Am J Psychiatry. 2013;170:1263–74.

    Google Scholar 

  2. 2.

    Sullivan PF, Geschwind DH. Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders. Cell. 2019;177:162–83.

    CAS  Google Scholar 

  3. 3.

    Clementz BA, Sweeney JA, Hamm JP, Ivleva EI, Etheridge E, Pearlson GD, et al. Identification of distinct psychosis biotypes using brain-based biomarkers. Am J Psychiatry. 2016;173:373–84.

    Google Scholar 

  4. 4.

    Ivleva EI, Clementz BA, Dutcher AM, Arnold SJM, Jeon-Slaughter H, ASlan S, et al. Brain structure biomarkers in the psychosis biotypes: findings from the bipolarschizophrenia network for intermediate phenotypes. Biol Psychiatry. 2017;82:26–39.

    Google Scholar 

  5. 5.

    Pearlson GD Functional MRI in schizophrenia. In: Kubicki M & Shenton M, editors. Neuroimaging in schizophrenia. Springer Press; 2020.

  6. 6.

    Levman J, Takahashi E. Multivariate Analyses Applied to Healthy Neurodevelopment in Fetal, Neonatal, and Pediatric MRI. Front Neuroanat. 2016;9:163.

    Google Scholar 

  7. 7.

    Liu J, Calhoun VD. A review of multivariate analyses in imaging genetics. Front Neuroinform. 2014;8:29.

    Google Scholar 

  8. 8.

    Meda SA, Ruano G, Windemuth A, O'Neil K, Berwise C, Dunn SM, et al. Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia. Proc Natl Acad Sci USA. 2014;111:E2066–75.

    CAS  Google Scholar 

  9. 9.

    Wang Z, Meda SA, Keshavan MS, Tamminga CA, Sweeney CA, Clementz BA, et al. Large-scale fusion of gray matter and resting-state functional MRI reveals common and distinct biological markers across the psychosis spectrum in the B-SNIP Cohort. Front Psychiatry. 2015;6:174.

    Google Scholar 

  10. 10.

    Chen J, Calhoun VD, Lin D, et al. Shared genetic risk of schizophrenia and gray matter reduction in 6p22.1. Schizophr Bull. 2019;45:222–32.

    Google Scholar 

  11. 11.

    Plis SM, Amin MF, Chekroud A, Hjelm D, Damaraju E, Lee HJ, et al. Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia. NeuroImage. 2018;181:734–47.

    Google Scholar 

  12. 12.

    Abrol A, Fu Z, Salman M, Du Y, Sui J, Gao S, et al. Hype versus hope: deep learning encodes more predictive and robust brain imaging representations than standard machine learning. Preprint at https://www.biorxiv.org/content/10.1101/ 2020.04.14.041582v1.

  13. 13.

    Pearlson GD, Calhoun VD, Liu J. An introductory review of parallel independent component analysis (p-ICA) and a guide to applying p-ICA to genetic data and imaging phenotypes to identify disease-associated biological pathways and systems in common complex disorders. Front Genet. 2015;6:276.

    Google Scholar 

  14. 14.

    Tandon N, Nanda P, Padmanabhan JL, Matthew IT, Eack SM, Narayanan B, et al. Novel gene-brain structure relationships in psychotic disorder revealed using parallel independent component analyses. Schizophr Res. 2017;182:74–83.

    Google Scholar 

  15. 15.

    Ji L, Meda SA, Tamminga CA, Clementz BA, Keshavan MS, Sweeney JA, et al. Characterizing functional regional homogeneity (ReHo) as a B-SNIP psychosis biomarker using traditional and machine learning approaches. Schiophrenia Res. 2020;215:430–8.

    Google Scholar 

  16. 16.

    Du Y, Pearlson GD, Lin D, Sui j, Chen j, Salman M, et al. Identifying dynamic functional connectivity biomarkers using GIG-ICA: application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder. Hum Brain Mapp. 2017;38:2683–708.

    Google Scholar 

  17. 17.

    Rashid B, Arbabshirani MR, Damaraju E, Cetin MS, Miller R, Pearlson GD, et al. Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity. NeuroImage. 2016;134:645–57.

    Google Scholar 

  18. 18.

    Rahaman MA, Turner JA, Gupta CN, Rachakonda S, Chen J, Liu J, et al. N-BiC: a method for multi-component and symptom biclustering of structural MRI data: application to schizophrenia. IEEE Trans Biomed Eng. 2020;67:110–21.

    Google Scholar 

  19. 19.

    Du Y, Pearlson GD, Liu J, Sui J, Yu Q, He H, et al. A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders. NeuroImage. 2015;122:272–80.

    Google Scholar 

  20. 20.

    Sui J, Qi S, van Erp TGM, Bustillo J, Jiang R, Lin D, et al. Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion. Nat Commun. 2018;9:3028.

    Google Scholar 

  21. 21.

    Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010;167:748–51.

    Google Scholar 

  22. 22.

    Insel TR, Cuthbert BN. Endophenotypes: bridging genomic complexity and disorder heterogeneity. Biol Psychiatry. 2009;66:988–9.

    Google Scholar 

  23. 23.

    Gottesman II, Gould TD. The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry. 2003;160:636–45.

    Google Scholar 

  24. 24.

    Gottesman II, Shields J. Genetic theorizing and schizophrenia. Br J Psychiatry. 1973;122:15–30.

    CAS  Google Scholar 

  25. 25.

    Conneely KN, Boehnke M. So many correlated tests, so little time! Rapid adjustment of P values for multiple correlated tests. Am J Hum Genet. 2007;81:1158–68.

    CAS  Google Scholar 

  26. 26.

    Ioannidis JP. Non-replication and inconsistency in the genome-wide association setting. Hum Hered. 2007;64:203–13.

    CAS  Google Scholar 

  27. 27.

    Cheverud JM. A simple correction for multiple comparisons in interval mapping genome scans. Heredity. 2001;87:52–8.

    CAS  Google Scholar 

  28. 28.

    Fischl B. FreeSurfer. Neuroimage. 2012;62:774–81.

    Google Scholar 

  29. 29.

    Tamminga CA, Pearlson G, Keshavan M, Sweeney J, Clementz B, Thaker G. Bipolar and schizophrenia network for intermediate phenotypes: outcomes across the psychosis continuum. Schizophr Bull 2014;40 Suppl 2:S131–7.

    Google Scholar 

  30. 30.

    Asif H, Alliey-Rodriguez N, Keedy S, Tamminga CA, Sweeney JH, clementz BA, et al. GWAS significance thresholds for deep phenotyping studies can depend upon minor allele frequencies and sample size. Mol Psychiatry. 2020.

  31. 31.

    Gibbons RD, Hedeker DR. Full-information item bifactor analysis. Psychometrika. 1992;57:423–36.

    Google Scholar 

  32. 32.

    Tamminga CA, Stan AD, Wagner AD. The hippocampal formation in schizophrenia. Am J Psychiatry. 2010;167:1178–93.

    Google Scholar 

  33. 33.

    van Haren NE, Schnack HG, Cahn W, van den Heuvel MP, lepage C, Collins L, et al. Changes in cortical thickness during the course of illness in schizophrenia. Arch Gen Psychiatry. 2011;68:871–80.

    Google Scholar 

  34. 34.

    Jung WH, Kim JS, Jang JH, Choi JS, Jung MD, Park JY, et al. Cortical thickness reduction in individuals at ultra-high-risk for psychosis. Schizophr Bull. 2011;37:839–49.

    Google Scholar 

  35. 35.

    Buchy L, Ad-Dab’bagh Y, Malla A, Lepage C, Bodnar M, Joober R, et al. Cortical thickness is associated with poor insight in first-episode psychosis. J Psychiatr Res. 2011;45:781–7.

    Google Scholar 

  36. 36.

    Stan AD, Tamminga CA, Han K, Kim JB, Padmanabhan J, Tandon N, et al. Associating psychotic symptoms with altered brain anatomy in psychotic disorders using multidimensional item response theory models. Cereb Cortex. 2020;30:2939–47.

    Google Scholar 

  37. 37.

    Barta PE, Pearlson GD, Powers RE, Richards SS, Tune LE. Auditory hallucinations and smaller superior temporal gyral volume in schizophrenia. Am J Psychiatry. 1990;147:1457–62.

    CAS  Google Scholar 

  38. 38.

    Weinberger DR. Schizophrenia, the prefrontal cortex, and a mechanism of genetic susceptibility. Eur Psychiatry. 2002;Suppl 4:355s–362s.

    Google Scholar 

  39. 39.

    Collado-Torres L, Burke EE, Peterson A, Shin J, Straub RE, Rajpurohit A, et al. Regional heterogeneity in gene expression, regulation, and coherence in the frontal cortex and hippocampus across development and schizophrenia. Neuron. 2019;103:203–16.

    CAS  Google Scholar 

  40. 40.

    Samudra N, Ivleva EI, Hubbard NA, Rypma B, Sweeney JA, Clementz BA, et al. Alterations in hippocampal connectivity across the psychosis dimension. Psychiatry Res. 2015;233:148–57.

    Google Scholar 

  41. 41.

    Li W, Ghose S, Gleason K, Begovic A, Perez J, Bartko J, et al. Synaptic proteins in the hippocampus indicative of increased neuronal activity in CA3 in schizophrenia. Am J Psychiatry. 2015;172:373–82.

    Google Scholar 

  42. 42.

    Segev A, Yanagi M, Scott D, Southcott SA, lister JM, tan C, et al. Reduced GluN1 in mouse dentate gyrus is associated with CA3 hyperactivity and psychosis-like behaviors. Mol. Psychiatry. 2018.

  43. 43.

    Kirkpatrick B, Buchanan RWFAU, Breier AF, Carpenter WT Jr. Case identification and stability of the deficit syndrome of schizophrenia. Psychiatry Res. 1993;47:47–56.

    CAS  Google Scholar 

  44. 44.

    Goetz RR, Corcoran CF, Yale S, FAU - Stanford A, Stanford A, Kimhy D, et al. Validity of a ‘proxy’ for the deficit syndrome derived from the Positive And Negative Syndrome Scale (PANSS). Schizophr Res. 2007;93:169–77.

    Google Scholar 

  45. 45.

    Spalletta G, De RP, Piras F. Brain white matter microstructure in deficit and nondeficit subtypes of schizophrenia. Psychiatry Res. 2015;231:252–61.

    Google Scholar 

  46. 46.

    Heckers S, Goff D, Schacter DL, Savage CR, Fishmand AJ, Alpert NM, et al. Functional imaging of memory retrieval in deficit vs nondeficit schizophrenia. Arch Gen Psychiatry. 1999;56:1117–23.

    CAS  Google Scholar 

  47. 47.

    Cohen AS, Saperstein AM, FAU-Gold J, Gold JM, FAU - Kirkpatrick B, Kirkpatrick BF, et al. Neuropsychology of the deficit syndrome: new data and meta-analysis of findings to date. Schizophr Bull. 2007;33:1201–12.

    Google Scholar 

  48. 48.

    Hudgens-Haney ME, Clementz BA, Ivleva EI, Keshavan MS, Pearlson GD, Gershon ES, et al. Cognitive impairment and diminished neural responses constitute abiomarker signature of negative symptoms in psychosis. Schizophr Bull. 2020; sbaa001. https://doi.org/10.1093/schbul/sbaa001.

  49. 49.

    Fillman SG, Weickert TW, Lenroot RK, Catts SV, Bruggemann JM, Catts VS, et al. Elevated peripheral cytokines characterize a subgroup of people with schizophrenia displaying poor verbal fluency and reduced Broca’s area volume. Mol Psychiatry. 2016;21:1090–8.

    CAS  Google Scholar 

  50. 50.

    Goldsmith DR, Rapaport MH, Miller BJ. A meta-analysis of blood cytokine network alterations in psychiatric patients: comparisons between schizophrenia, bipolar disorder and depression. Mol Psychiatry. 2016;21:1696–709.

    CAS  Google Scholar 

  51. 51.

    Girgis RR, Ciarleglio A, Choo T, Haynes G, Bathon JM, Cremers S, et al. A randomized, double-blind, placebo-controlled clinical trial of tocilizumab, an interleukin-6 receptor antibody, for residual symptoms in schizophrenia. Neuropsychopharmacology. 2018;43:1317–23.

    CAS  Google Scholar 

  52. 52.

    Miller BJ, Dias JK, FAU - Lemos H, Lemos HP, FAU - Buckley P, Buckley PF. An open-label, pilot trial of adjunctive tocilizumab in schizophrenia. J Clin Psychiatry. 2016;77:275–6.

    Google Scholar 

  53. 53.

    McIntyre RS, Subramaniapillai M, Lee Y, Pan Z, Carmona NE, Shekotiktina M, et al. Efficacy of adjunctive infliximab vs placebo in the treatment of adults with bipolar I/II depression: a Randomized Clinical Trial. JAMA Psychiatry. 2019;76:783–90.

    Google Scholar 

  54. 54.

    Muller N, Myint AM, FAU -, Krause D, Krause DF, Weidinger E. Anti-inflammatory treatment in schizophrenia. J Clin Psychiatry. 2013;42:146–53.

    Google Scholar 

  55. 55.

    Raison CL, Miller AH. Malaise, melancholia and madness: the evolutionary legacy of an inflammatory bias. Brain Behav Immun. 2013;31:1–8.

    CAS  Google Scholar 

  56. 56.

    Carson MJ, Doose JM, FAU - Melchior B, Melchior BF, Schmid CD, FAU - Ploix C, et al. CNS immune privilege: hiding in plain sight. Immunol Rev. 2006;213:48–65.

    Google Scholar 

  57. 57.

    Kamintsky L, Cairns KA, Veksler R, Bowne C, Beyea SD, Friedman A, et al. Bloodbrain barrier imaging as a potential biomarker for bipolar disorder progression. NeuroImage Clin. 2020. https://doi.org/10.1016/j.nicl.2019.102049.

  58. 58.

    Busse S, Busse MF, Schiltz KF, Bielau H, Gos T, Brisch R, et al. Different distribution patterns of lymphocytes and microglia in the hippocampus of patients with residual versus paranoid schizophrenia: further evidence for disease courserelated immune alterations? Brain, Behav, Immun. 2012;26:1273–9.

    CAS  Google Scholar 

  59. 59.

    Bechter K, Reiber H, Herzog S, Fuchs D, Tumani H, Maxeiner HG. Cerebrospinal fluid analysis in affective and schizophrenic spectrum disorders: identification of subgroups with immune responses and blood-CSF barrier dysfunction. J Psychiatr Res. 2010;44:321–30.

    CAS  Google Scholar 

  60. 60.

    Kim S, Hwang Y, Lee D, Webster MJ. Transcriptome sequencing of the choroid plexus in schizophrenia. Transl Psychiatry. 2016;6:e964.

    CAS  Google Scholar 

  61. 61.

    Wang T, Wang BR, Zhao H-Z, Kuang F, Fan J, Duan X-L, et al. Lipopolysaccharide up-regulates IL-6R alpha expression in cultured leptomeningeal cells via activation of ERK1/2 pathway. Neurochem Res. 2008;33:1901–10.

    CAS  Google Scholar 

  62. 62.

    Tanaka T, Narazaki M, Masuda K, Kishimoto T. Regulation of IL-6 in immunity and diseases. Adv Exp Med Biol. 2016;941:79–88.

    CAS  Google Scholar 

  63. 63.

    Hill SK, Reilly JL, Keefe RS, Gold JM, Bishop JR, Gershon ES, et al. Neuropsychological Impairments in schizophrenia and psychotic bipolar disorder: findings from the Bipolar and Schizophrenia Network on Intermediate Phenotypes (BSNIP) Study. Am J Psychiatry. 2013;170:1275–84.

    Google Scholar 

  64. 64.

    Hamm JP, Ethridge LE, Boutros NN, Keshavan MS, Sweeney JA, Pearlson GD, et al. Diagnostic specificity and familiality of early versus late evoked potentials to auditory paired stimuli across the schizophrenia-bipolar psychosis spectrum. Psychophysiology. 2014;51:348–57.

    Google Scholar 

  65. 65.

    Reilly JL, Frankovich K, Hill S, Gershon ES, Keefe RSE, Keshavan MS, et al. Elevated antisaccade error rate as an intermediate phenotype for psychosis across diagnostic categories. Schizophr Bull. 2014;40:1011–21.

    Google Scholar 

  66. 66.

    Ethridge LE, Hamm JP, Pearlson GD, Tamminga CA, Sweeney JA, keshavan MS, et al. Event-related potential and time-frequency endophenotypes for schizophrenia and psychotic bipolar disorder. Biol Psychiatry. 2015;77:127–36.

    Google Scholar 

  67. 67.

    Keshavan MS, Kelly S, Hall MH. The core deficit of “classical” schizophrenia cuts across the psychosis spectrum. Can J Psychiatry. 2020;65:231–4.

    Google Scholar 

  68. 68.

    McTeague LM, Huemer J, Carreon DM, Jiang Y, Eickhoff SB, Etkin A. Identification of common neural circuit disruptions in cognitive control across psychiatric disorders. Am J Psychiatry. 2017;174:676–85.

    Google Scholar 

  69. 69.

    MacKenzie LE, Uher R, Pavlova B. Cognitive performance in first-degree relatives of individuals with vs without major depressive disorder: a meta-analysis. JAMA Psychiatry. 2019;76:297–305.

    Google Scholar 

  70. 70.

    Zhu Y, Womer FY, Leng H, Chang M, Yin Z, Wei Y, et al. The relationship between cognitive dysfunction and symptom dimensions across schizophrenia, bipolar disorder, and major depressive disorder. Front Psychiatry. 2019;10:253.

    Google Scholar 

  71. 71.

    Green MF, Kern RS, Braff DL, Mintz J. Neurocognitive deficits and functional outcome in schizophrenia: are we measuring the “right stuff”? Schizophr Bull. 2000;26:119–36.

    CAS  Google Scholar 

  72. 72.

    Green MF, Kern RS, Heaton RK. Longitudinal studies of cognition and functional outcome in schizophrenia: implications for MATRICS. Schizophr Res. 2004;72:41–51.

    Google Scholar 

  73. 73.

    McTeague LM, Goodkind MS, Etkin A. Transdiagnostic impairment of cognitive control in mental illness. J Psychiatr Res. 2016;83:37–46.

    Google Scholar 

  74. 74.

    Lerman-Sinkoff DB, Kandala S, Calhoun VD, Barch DM, Mamah DT. Transdiagnostic multimodal neuroimaging in psychosis: structural, resting-state, and task magnetic resonance imaging correlates of cognitive control. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4:870–80.

    Google Scholar 

  75. 75.

    Smucny J, Barch DM, Gold JM, Strauss ME, MacDonald AW, Boudewyn MA, et al. Cross-diagnostic analysis of cognitive control in mental illness: insights from the CNTRACS consortium. Schizophr Res. 2019;208:377–83.

    Google Scholar 

  76. 76.

    Tamminga CA, Pearlson GD, Stan AD, Gibbons RD, Padmanabhan J, Keshavan MS, et al. Strategies for advancing disease definition using biomarkers and genetics: the bipolar and schizophrenia network for intermediate phenotypes. Biol Psychiatry Cogn Neurosci Neuroimaging. 2017;2:20–7.

    Google Scholar 

  77. 77.

    Hedeker DR, Gibbons RD Longitudinal data analysis. New Jersey: John Wiley & Sons; 2006.

  78. 78.

    Blakey R, Ranlund S, Zartaloudi E, Chan W, Cal;afato S, Colizzi M, et al. Associations between psychosis endophenotypes across brain functional, structural, and cognitive domains. Psychol Med. 2018;48:1325–40.

    CAS  Google Scholar 

  79. 79.

    Ranlund S, Calafato S, Thygesen JH, Lin K, Cahn W, Crespo-Facorro B, et al. A polygenic risk score analysis of psychosis endophenotypes across brain functional, structural, and cognitive domains. Am J Med Genet B Neuropsychiatr Genet. 2018;177:21–34.

    Google Scholar 

  80. 80.

    Alliey-Rodriguez N, Grey TA, Shafee R, Asif H, Lutz O, Bolo N, et al. NRXN1 is associated with enlargement of the temporal horns of the lateral ventricles in psychosis. Transl Psychiatry. 2019;9:230.

    Google Scholar 

  81. 81.

    Schaaf CP, Boone PM, Sampath S, Williams C, Bader PI, Mueller JM, et al. Phenotypic spectrum and genotype-phenotype correlations of NRXN1 exon deletions. Eur J Hum Genet. 2012;20:1240–7.

    CAS  Google Scholar 

  82. 82.

    Toulopoulou T, Picchioni MF, Rijsdijk FF. Substantial genetic overlap between neurocognition and schizophrenia: genetic modeling in twin samples. Arch Gen Psychiatry. 2007;64:1348–55.

    Google Scholar 

  83. 83.

    Kotov R, Krueger RF, Watson D, Forbes MK, Eaton NR, Ruggero CJ, et al. The Hierarchical Taxonomy of Psychopathology (HiTOP): a dimensional alternative to traditional nosologies. J Abnorm Psychol. 2017;126:454–77.

    Google Scholar 

  84. 84.

    Clementz BA, Trotti RL, Pearlson GD, Keshavan MS, Gershon ES, Keedy SK, et al. Testing psychosis phenotypes from bipolar-schizophrenia network for intermediate phenotypes for clinical application: biotype characteristics and targets. 2013;170:1263–74.

  85. 85.

    Hill SK, Reilly JL, Keefe RS, Gold JM, Bishop JR, Gershon ES, et al. Neuropsychological impairments in schizophrenia and psychotic bipolar disorder: findings from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) Study. Am J Psychiatry. 2013;170:1275–84.

    Google Scholar 

  86. 86.

    Rodrigue AL, McDowell JE, Tandon N, Keshavan MS, Tamminga CA, Pearlson GD, et al. Multivariate relationships between cognition and brain anatomy across the psychosis spectrum short title: cognition and brain anatomy in psychosis. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3:992–1002.

    Google Scholar 

  87. 87.

    Keefe RSE, Kahn RS. Cognitive decline and disrupted cognitive trajectory in schizophrenia. JAMA Psychiatry. 2017;74:535–6.

    Google Scholar 

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CT takes overall responsibility for the paper and composed the Introduction and Discussion. Specific sections were composed by GP, SAM, VDC; ESG, NA-R, SK, HA; CT, RG, EII, MH-H; MK; JRB, PL; BAC, JM in order. All authors commented and edited the whole paper extensively.

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Correspondence to Carol A. Tamminga.

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Tamminga, C.A., Clementz, B.A., Pearlson, G. et al. Biotyping in psychosis: using multiple computational approaches with one data set. Neuropsychopharmacol. 46, 143–155 (2021). https://doi.org/10.1038/s41386-020-00849-8

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