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Multivariate patterns of gray matter volume in thalamic nuclei are associated with positive schizotypy in healthy individuals

Published online by Cambridge University Press:  30 July 2019

Pasquale Di Carlo Open the ORCID record for Pasquale Di Carlo [Opens in a new window] ,
Giulio Pergola Open the ORCID record for Giulio Pergola [Opens in a new window] ,
Linda A. Antonucci Open the ORCID record for Linda A. Antonucci [Opens in a new window] ,
Aurora Bonvino Open the ORCID record for Aurora Bonvino [Opens in a new window] ,
Marina Mancini Open the ORCID record for Marina Mancini [Opens in a new window] ,
Tiziana Quarto ,
Antonio Rampino Open the ORCID record for Antonio Rampino [Opens in a new window] ,
Teresa Popolizio Open the ORCID record for Teresa Popolizio [Opens in a new window] ,
Alessandro Bertolino  and
Giuseppe Blasi Open the ORCID record for Giuseppe Blasi [Opens in a new window]
Pasquale Di Carlo
Affiliation:
Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs – University of Bari Aldo Moro, Bari, Italy Lieber Institute for Brain Development, Johns Hopkins Medical Campus – Baltimore, MD, USA
Giulio Pergola
Affiliation:
Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs – University of Bari Aldo Moro, Bari, Italy Lieber Institute for Brain Development, Johns Hopkins Medical Campus – Baltimore, MD, USA
Linda A. Antonucci
Affiliation:
Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs – University of Bari Aldo Moro, Bari, Italy Department of Psychiatry and Psychotherapy – Ludwig-Maximilians University, Munich, Germany
Aurora Bonvino
Affiliation:
Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs – University of Bari Aldo Moro, Bari, Italy IRCCS ‘Casa Sollievo della Sofferenza’, San Giovanni Rotondo, Italy
Marina Mancini
Affiliation:
Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs – University of Bari Aldo Moro, Bari, Italy
Tiziana Quarto
Affiliation:
Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs – University of Bari Aldo Moro, Bari, Italy
Antonio Rampino
Affiliation:
Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs – University of Bari Aldo Moro, Bari, Italy Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
Teresa Popolizio
Affiliation:
IRCCS ‘Casa Sollievo della Sofferenza’, San Giovanni Rotondo, Italy
Alessandro Bertolino
Affiliation:
Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs – University of Bari Aldo Moro, Bari, Italy Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
Giuseppe Blasi*
Affiliation:
Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs – University of Bari Aldo Moro, Bari, Italy Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
*
Author for correspondence: Giuseppe Blasi, E-mail: [email protected]
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Abstract

Background

Previous models suggest biological and behavioral continua among healthy individuals (HC), at-risk condition, and full-blown schizophrenia (SCZ). Part of these continua may be captured by schizotypy, which shares subclinical traits and biological phenotypes with SCZ, including thalamic structural abnormalities. In this regard, previous findings have suggested that multivariate volumetric patterns of individual thalamic nuclei discriminate HC from SCZ. These results were obtained using machine learning, which allows case–control classification at the single-subject level. However, machine learning accuracy is usually unsatisfactory possibly due to phenotype heterogeneity. Indeed, a source of misclassification may be related to thalamic structural characteristics of those HC with high schizotypy, which may resemble structural abnormalities of SCZ. We hypothesized that thalamic structural heterogeneity is related to schizotypy, such that high schizotypal burden would implicate misclassification of those HC whose thalamic patterns resemble SCZ abnormalities.

Methods

Following a previous report, we used Random Forests to predict diagnosis in a case–control sample (SCZ = 131, HC = 255) based on thalamic nuclei gray matter volumes estimates. Then, we investigated whether the likelihood to be classified as SCZ (π-SCZ) was associated with schizotypy in 174 HC, evaluated with the Schizotypal Personality Questionnaire.

Results

Prediction accuracy was 72.5%. Misclassified HC had higher positive schizotypy scores, which were correlated with π-SCZ. Results were specific to thalamic rather than whole-brain structural features.

Conclusions

These findings strengthen the relevance of thalamic structural abnormalities to SCZ and suggest that multivariate thalamic patterns are correlates of the continuum between schizotypy in HC and the full-blown disease.

Keywords

Machine learningrandom forestsmagnetic resonance imagingschizophreniaSchizotypal Personality Questionnaire (SPQ)thalamus
Type
Original Articles
Information
Psychological Medicine , Volume 50 , Issue 9 , July 2020 , pp. 1501 - 1509
Copyright
Copyright © Cambridge University Press 2019

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References

Abi-Dargham, A, Kegeles, LS, Zea-Ponce, Y, Mawlawi, O, Martinez, D, Mitropoulou, V, O'Flynn, K, Koenigsberg, HW, Van Heertum, R, Cooper, T, Laruelle, M and Siever, LJ (2004) Striatal amphetamine-induced dopamine release in patients with schizotypal personality disorder studied with single photon emission computed tomography and [123I]iodobenzamide. Biological Psychiatry 55, 1001–1006.CrossRefGoogle Scholar
Andreasen, NC, Arndt, S, Swayze, V, Cizadlo, T II, Flaum, M, O'Leary, D, Ehrhardt, JC and Yuh, WT (1994) Thalamic abnormalities in schizophrenia visualized through magnetic resonance image averaging. Science 266, 294–298.CrossRefGoogle ScholarPubMed
Anticevic, A, Cole, MW, Repovs, G, Murray, JD, Brumbaugh, MS, Winkler, AM, Savic, A, Krystal, JH, Pearlson, GD and Glahn, DC (2014) Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness. Cerebral Cortex 24, 3116–3130.CrossRefGoogle ScholarPubMed
Anticevic, A, Haut, K, Murray, JD, Repovs, G, Yang, GJ, Diehl, C, McEwen, SC, Bearden, CE, Addington, J, Goodyear, B, Cadenhead, KS, Mirzakhanian, H, Cornblatt, BA, Olvet, D, Mathalon, DH, McGlashan, TH, Perkins, DO, Belger, A, Seidman, LJ, Tsuang, MT, van Erp, TG, Walker, EF, Hamann, S, Woods, SW, Qiu, M and Cannon, TD (2015) Association of thalamic dysconnectivity and conversion to psychosis in youth and young adults at elevated clinical risk. JAMA Psychiatry 72, 882–891.CrossRefGoogle Scholar
Antonucci, LA, Taurisano, P, Fazio, L, Gelao, B, Romano, R, Quarto, T, Porcelli, A, Mancini, M, Di Giorgio, A, Caforio, G, Pergola, G, Popolizio, T, Bertolino, A and Blasi, G (2016) Association of familial risk for schizophrenia with thalamic and medial prefrontal functional connectivity during attentional control. Schizophrenia Research 173, 23–29.CrossRefGoogle ScholarPubMed
Barrantes-Vidal, N, Grant, P and Kwapil, TR (2015) The role of schizotypy in the study of the etiology of schizophrenia spectrum disorders. Schizophrenia Bulletin 41 (Suppl. 2), S408–S416.10.1093/schbul/sbu191CrossRefGoogle Scholar
Bolkan, SS, Stujenske, JM, Parnaudeau, S, Spellman, TJ, Rauffenbart, C, Abbas, AI, Harris, AZ, Gordon, JA and Kellendonk, C (2017) Thalamic projections sustain prefrontal activity during working memory maintenance. Nature Neuroscience 20, 987–996.CrossRefGoogle ScholarPubMed
Breiman, L (2001) Random forests. Machine Learning 45, 5–32.CrossRefGoogle Scholar
Brosey, E and Woodward, ND (2015) Schizotypy and clinical symptoms, cognitive function, and quality of life in individuals with a psychotic disorder. Schizophrenia Research 166, 92–97.CrossRefGoogle ScholarPubMed
Byne, W, Buchsbaum, MS, Kemether, E, Hazlett, EA, Shinwari, A, Mitropoulou, V and Siever, LJ (2001) Magnetic resonance imaging of the thalamic mediodorsal nucleus and pulvinar in schizophrenia and schizotypal personality disorder. Archives Of General Psychiatry 58, 133–140.10.1001/archpsyc.58.2.133CrossRefGoogle ScholarPubMed
Byne, W, Buchsbaum, MS, Mattiace, LA, Hazlett, EA, Kemether, E, Elhakem, SL, Purohit, DP, Haroutunian, V and Jones, L (2002) Postmortem assessment of thalamic nuclear volumes in subjects with schizophrenia. American Journal of Psychiatry 159, 59–65.CrossRefGoogle ScholarPubMed
Byne, W, Hazlett, EA, Buchsbaum, MS and Kemether, E (2009) The thalamus and schizophrenia: current status of research. Acta Neuropathologica 117, 347–368.CrossRefGoogle ScholarPubMed
Bzdok, D and Meyer-Lindenberg, A (2018) Machine learning for precision psychiatry: opportunities and challenges. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 3, 223–230.Google ScholarPubMed
Cicero, DC, Jonas, KG, Li, K, Perlman, G and Kotov, R (2019) Common taxonomy of traits and symptoms: linking schizophrenia symptoms, schizotypy, and normal personality. Schizophrenia Bulletin (published online ahead of print 9 February).10.1093/schbul/sbz005CrossRefGoogle ScholarPubMed
Claridge, G (1997) Theoretical background and issues. In Claridge, G (ed.), Schizotypy: Implications for Illness and Health. Oxford, UK: Oxford University Press, pp. 3–18.CrossRefGoogle Scholar
Cobia, DJ, Smith, MJ, Salinas, I, Ng, C, Gado, M, Csernansky, JG and Wang, L (2017) Progressive deterioration of thalamic nuclei relates to cortical network decline in schizophrenia. Schizophrenia Research 180, 21–27.CrossRefGoogle Scholar
Dorph-Petersen, KA and Lewis, DA (2017) Postmortem structural studies of the thalamus in schizophrenia. Schizophrenia Research 180, 28–35.10.1016/j.schres.2016.08.007CrossRefGoogle Scholar
Dwyer, DB, Cabral, C, Kambeitz-Ilankovic, L, Sanfelici, R, Kambeitz, J, Calhoun, V, Falkai, P, Pantelis, C, Meisenzahl, E and Koutsouleris, N (2018) Brain subtyping enhances the neuroanatomical discrimination of schizophrenia. Schizophrenia Bulletin 44, 1060–1069.CrossRefGoogle ScholarPubMed
Ericson, M, Tuvblad, C, Raine, A, Young-Wolff, K and Baker, LA (2011) Heritability and longitudinal stability of schizotypal traits during adolescence. Behaviour Genetics 41, 499–511.CrossRefGoogle ScholarPubMed
Ettinger, U, Meyhofer, I, Steffens, M, Wagner, M and Koutsouleris, N (2014) Genetics, cognition, and neurobiology of schizotypal personality: a review of the overlap with schizophrenia. Frontiers in Psychiatry 5, 18.CrossRefGoogle ScholarPubMed
Ettinger, U, Mohr, C, Gooding, DC, Cohen, AS, Rapp, A, Haenschel, C and Park, S (2015) Cognition and brain function in schizotypy: a selective review. Schizophrenia Bulletin 41 (Suppl. 2), S417–S426.10.1093/schbul/sbu190CrossRefGoogle ScholarPubMed
Fanous, AH, Neale, MC, Gardner, CO, Webb, BT, Straub, RE, O'Neill, FA, Walsh, D, Riley, BP and Kendler, KS (2007) Significant correlation in linkage signals from genome-wide scans of schizophrenia and schizotypy. Molecular Psychiatry 12, 958–965.CrossRefGoogle ScholarPubMed
Ferguson, BR and Gao, WJ (2014) Development of thalamocortical connections between the mediodorsal thalamus and the prefrontal cortex and its implication in cognition. Frontiers in Human Neuroscience 8, 1027.Google ScholarPubMed
Fonseca-Pedrero, E, Debbane, M, Ortuno-Sierra, J, Chan, RCK, Cicero, DC, Zhang, LC, Brenner, C, Barkus, E, Linscott, RJ, Kwapil, T, Barrantes-Vidal, N, Cohen, A, Raine, A, Compton, MT, Tone, EB, Suhr, J, Muniz, J, Fumero, A, Giakoumaki, S, Tsaousis, I, Preti, A, Chmielewski, M, Laloyaux, J, Mechri, A, Lahmar, MA, Wuthrich, V, Laroi, F, Badcock, JC and Jablensky, A (2018) The structure of schizotypal personality traits: a cross-national study. Psychological Medicine 48, 451–462.10.1017/S0033291717001829CrossRefGoogle ScholarPubMed
Fornito, A, Yucel, M, Patti, J, Wood, SJ and Pantelis, C (2009) Mapping grey matter reductions in schizophrenia: an anatomical likelihood estimation analysis of voxel-based morphometry studies. Schizophrenia Research 108, 104–113.CrossRefGoogle ScholarPubMed
Gilbert, CD and Sigman, M (2007) Brain states: top-down influences in sensory processing. Neuron 54, 677–696.10.1016/j.neuron.2007.05.019CrossRefGoogle ScholarPubMed
Glahn, DC, Laird, AR, Ellison-Wright, I, Thelen, SM, Robinson, JL, Lancaster, JL, Bullmore, E and Fox, PT (2008) Meta-analysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis. Biological Psychiatry 64, 774–781.CrossRefGoogle ScholarPubMed
Gooding, DC, Matts, CW and Rollmann, EA (2006) Sustained attention deficits in relation to psychometrically identified schizotypy: evaluating a potential endophenotypic marker. Schizophrenia Research 82, 27–37.CrossRefGoogle ScholarPubMed
Guloksuz, S and van Os, J (2018) The slow death of the concept of schizophrenia and the painful birth of the psychosis spectrum. Psychological Medicine 48, 229–244.10.1017/S0033291717001775CrossRefGoogle ScholarPubMed
Hollingshead, AA (1975) Four-factor index of Social status. Unpublished manuscript, New Haven, CT: Yale University.Google Scholar
Jakab, A, Blanc, R, Berenyi, EL and Szekely, G (2012) Generation of individualized thalamus target maps by using statistical shape models and thalamocortical tractography. American Journal of Neuroradiology 33, 2110–2116.CrossRefGoogle ScholarPubMed
Johns, LC and van Os, J (2001) The continuity of psychotic experiences in the general population. Clinical Psychology Review 21, 1125–1141.10.1016/S0272-7358(01)00103-9CrossRefGoogle ScholarPubMed
Jones, EG (2007) The Thalamus. Cambridge, UK; New York: Cambridge University Press, 1708.Google Scholar
Kambeitz, J, Kambeitz-Ilankovic, L, Leucht, S, Wood, S, Davatzikos, C, Malchow, B, Falkai, P and Koutsouleris, N (2015) Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies. Neuropsychopharmacology 40, 1742–1751.10.1038/npp.2015.22CrossRefGoogle ScholarPubMed
Kerns, JG and Becker, TM (2008) Communication disturbances, working memory, and emotion in people with elevated disorganized schizotypy. Schizophrenia Research 100, 172–180.CrossRefGoogle ScholarPubMed
Krauth, A, Blanc, R, Poveda, A, Jeanmonod, D, Morel, A and Szekely, G (2010) A mean three-dimensional atlas of the human thalamus: generation from multiple histological data. Neuroimage 49, 2053–2062.CrossRefGoogle ScholarPubMed
Kuhn, M (2008) Caret package. Journal of Statistical Software 28, 1–26.Google Scholar
Kuhn, S, Schubert, F and Gallinat, J (2012) Higher prefrontal cortical thickness in high schizotypal personality trait. Journal of Psychiatric Research 46, 960–965.10.1016/j.jpsychires.2012.04.007CrossRefGoogle ScholarPubMed
Lenzenweger, MF (2015) Thinking clearly about schizotypy: hewing to the schizophrenia liability core, considering interesting tangents, and avoiding conceptual quicksand. Schizophrenia Bulletin 41 (Suppl. 2), S483–S491.10.1093/schbul/sbu184CrossRefGoogle ScholarPubMed
Lynall, ME, Bassett, DS, Kerwin, R, McKenna, PJ, Kitzbichler, M, Muller, U and Bullmore, E (2010) Functional connectivity and brain networks in schizophrenia. Journal of Neuroscience 30, 9477–9487.10.1523/JNEUROSCI.0333-10.2010CrossRefGoogle Scholar
Mechelli, A, Price, CJ, Friston, KJ and Ashburner, J (2005) Voxel-based morphometry of the human brain: methods and applications. Current Medical Imaging Reviews 1, 105–113.CrossRefGoogle Scholar
Meehl, PE (1989) Schizotaxia revisited. Archives of General Psychiatry 46, 935–944.CrossRefGoogle ScholarPubMed
Nieuwenhuis, M, van Haren, NE, Hulshoff Pol, HE, Cahn, W, Kahn, RS and Schnack, HG (2012) Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples. Neuroimage 61, 606–612.10.1016/j.neuroimage.2012.03.079CrossRefGoogle ScholarPubMed
Nieuwenhuis, M, Schnack, HG, van Haren, NE, Lappin, J, Morgan, C, Reinders, AA, Gutierrez-Tordesillas, D, Roiz-Santianez, R, Schaufelberger, MS, Rosa, PG, Zanetti, MV, Busatto, GF, Crespo-Facorro, B, McGorry, PD, Velakoulis, D, Pantelis, C, Wood, SJ, Kahn, RS, Mourao-Miranda, J and Dazzan, P (2017) Multi-center MRI prediction models: predicting sex and illness course in first episode psychosis patients. Neuroimage 145, 246–253.CrossRefGoogle ScholarPubMed
Okada, N, Fukunaga, M, Yamashita, F, Koshiyama, D, Yamamori, H, Ohi, K, Yasuda, Y, Fujimoto, M, Watanabe, Y, Yahata, N, Nemoto, K, Hibar, DP, van Erp, TG, Fujino, H, Isobe, M, Isomura, S, Natsubori, T, Narita, H, Hashimoto, N, Miyata, J, Koike, S, Takahashi, T, Yamasue, H, Matsuo, K, Onitsuka, T, Iidaka, T, Kawasaki, Y, Yoshimura, R, Watanabe, Y, Suzuki, M, Turner, JA, Takeda, M, Thompson, PM, Ozaki, N, Kasai, K and Hashimoto, R (2016) Abnormal asymmetries in subcortical brain volume in schizophrenia. Molecular Psychiatry 21, 1460–1466.10.1038/mp.2015.209CrossRefGoogle Scholar
Oldfield, RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9, 97–113.CrossRefGoogle ScholarPubMed
Pergola, G, Selvaggi, P, Trizio, S, Bertolino, A and Blasi, G (2015) The role of the thalamus in schizophrenia from a neuroimaging perspective. Neuroscience and Biobehavioral Reviews 54, 57–75.CrossRefGoogle ScholarPubMed
Pergola, G, Trizio, S, Di Carlo, P, Taurisano, P, Mancini, M, Amoroso, N, Nettis, MA, Andriola, I, Caforio, G, Popolizio, T, Rampino, A, Di Giorgio, A, Bertolino, A and Blasi, G (2017) Grey matter volume patterns in thalamic nuclei are associated with familial risk for schizophrenia. Schizophrenia Research 180, 13–20.CrossRefGoogle ScholarPubMed
Peters, SK, Dunlop, K and Downar, J (2016) Cortico-striatal-thalamic loop circuits of the salience network: a central pathway in psychiatric disease and treatment. Frontiers in Systems Neuroscience 10, 104.CrossRefGoogle ScholarPubMed
Pratt, JA, Morris, B and Dawson, N (2018) Deconstructing schizophrenia: advances in preclinical models for biomarker identification. In: Pratt, J, Hall, J (eds) Biomarkers in Psychiatry. Current Topics in Behavioral Neurosciences, vol 40. Springer, Cham.Google Scholar
Pynn, LK and DeSouza, JF (2013) The function of efference copy signals: implications for symptoms of schizophrenia. Vision Research 76, 124–133.CrossRefGoogle ScholarPubMed
Raine, A (1991) The SPQ: a scale for the assessment of schizotypal personality based on DSM-III-R criteria. Schizophrenia Bulletin 17, 555–564.CrossRefGoogle ScholarPubMed
Rozycki, M, Satterthwaite, TD, Koutsouleris, N, Erus, G, Doshi, J, Wolf, DH, Fan, Y, Gur, RE, Gur, RC, Meisenzahl, EM, Zhuo, C, Ying, H, Yan, H, Yue, W, Zhang, D and Davatzikos, C (2017) Multisite machine learning analysis provides a robust structural imaging signature of schizophrenia detectable across diverse patient populations and within individuals. Schizophrenia Bulletin 44, 1035–1044.CrossRefGoogle Scholar
Salomon, R, Bleich-Cohen, M, Hahamy-Dubossarsky, A, Dinstien, I, Weizman, R, Poyurovsky, M, Kupchik, M, Kotler, M, Hendler, T and Malach, R (2011) Global functional connectivity deficits in schizophrenia depend on behavioral state. Journal of Neuroscience 31, 12972–12981.CrossRefGoogle ScholarPubMed
Salvador, R, Radua, J, Canales-Rodriguez, EJ, Solanes, A, Sarro, S, Goikolea, JM, Valiente, A, Monte, GC, Natividad, MDC, Guerrero-Pedraza, A, Moro, N, Fernandez-Corcuera, P, Amann, BL, Maristany, T, Vieta, E, McKenna, PJ and Pomarol-Clotet, E (2017) Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis. PLoS ONE 12, e0175683.CrossRefGoogle ScholarPubMed
Schmitt, LI, Wimmer, RD, Nakajima, M, Happ, M, Mofakham, S and Halassa, MM (2017) Thalamic amplification of cortical connectivity sustains attentional control. Nature 545, 219–223.CrossRefGoogle ScholarPubMed
Schwarz, E, Doan, NT, Pergola, G, Westlye, LT, Kaufmann, T, Wolfers, T, Brecheisen, R, Quarto, T, Ing, AJ, Di Carlo, P, Gurholt, TP, Harms, RL, Noirhomme, Q, Moberget, T, Agartz, I, Andreassen, OA, Bellani, M, Bertolino, A, Blasi, G, Brambilla, P, Buitelaar, JK, Cervenka, S, Flyckt, L, Frangou, S, Franke, B, Hall, J, Heslenfeld, DJ, Kirsch, P, McIntosh, AM, Nothen, MM, Papassotiropoulos, A, de Quervain, DJ, Rietschel, M, Schumann, G, Tost, H, Witt, SH, Zink, M and Meyer-Lindenberg, A and Imagemend Consortium (2019) Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder. Translational Psychiatry 9, 12.CrossRefGoogle ScholarPubMed
Sherman, SM (2016) Thalamus plays a central role in ongoing cortical functioning. Nature Neuroscience 19, 533–541.CrossRefGoogle Scholar
Takahashi, T, Suzuki, M, Zhou, SY, Nakamura, K, Tanino, R, Kawasaki, Y, Seal, ML, Seto, H and Kurachi, M (2008) Prevalence and length of the adhesio interthalamica in schizophrenia spectrum disorders. Psychiatry Research 164, 90–94.CrossRefGoogle ScholarPubMed
Taurisano, P, Romano, R, Mancini, M, Di Giorgio, A, Antonucci, LA, Fazio, L, Rampino, A, Quarto, T, Gelao, B, Porcelli, A, Papazacharias, A, Ursini, G, Caforio, G, Masellis, R, Niccoli-Asabella, A, Todarello, O, Popolizio, T, Rubini, G, Blasi, G and Bertolino, A (2014) Prefronto-striatal physiology is associated with schizotypy and is modulated by a functional variant of DRD2. Frontiers in Behavioral Neuroscience 8, 235.10.3389/fnbeh.2014.00235CrossRefGoogle ScholarPubMed
Tien, AY (1991) Distributions of hallucinations in the population. Social Psychiatry and Psychiatric Epidemiology 26, 287–292.CrossRefGoogle ScholarPubMed
Tzourio-Mazoyer, N, Landeau, B, Papathanassiou, D, Crivello, F, Etard, O, Delcroix, N, Mazoyer, B and Joliot, M (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289.CrossRefGoogle ScholarPubMed
van den Heuvel, MP, Mandl, RC, Stam, CJ, Kahn, RS and Hulshoff Pol, HE (2010) Aberrant frontal and temporal complex network structure in schizophrenia: a graph theoretical analysis. Journal of Neuroscience 30, 15915–15926.10.1523/JNEUROSCI.2874-10.2010CrossRefGoogle ScholarPubMed
van Erp, TG, Hibar, DP, Rasmussen, JM, Glahn, DC, Pearlson, GD, Andreassen, OA, Agartz, I, Westlye, LT, Haukvik, UK, Dale, AM, Melle, I, Hartberg, CB, Gruber, O, Kraemer, B, Zilles, D, Donohoe, G, Kelly, S, McDonald, C, Morris, DW, Cannon, DM, Corvin, A, Machielsen, MW, Koenders, L, de Haan, L, Veltman, DJ, Satterthwaite, TD, Wolf, DH, Gur, RC, Gur, RE, Potkin, SG, Mathalon, DH, Mueller, BA, Preda, A, Macciardi, F, Ehrlich, S, Walton, E, Hass, J, Calhoun, VD, Bockholt, HJ, Sponheim, SR, Shoemaker, JM, van Haren, NE, Hulshoff Pol, HE, Ophoff, RA, Kahn, RS, Roiz-Santianez, R, Crespo-Facorro, B, Wang, L, Alpert, KI, Jonsson, EG, Dimitrova, R, Bois, C, Whalley, HC, McIntosh, AM, Lawrie, SM, Hashimoto, R, Thompson, PM and Turner, JA (2016) Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Molecular Psychiatry 21, 547–553.CrossRefGoogle ScholarPubMed
van Os, J, Linscott, RJ, Myin-Germeys, I, Delespaul, P and Krabbendam, L (2009) A systematic review and meta-analysis of the psychosis continuum: evidence for a psychosis proneness-persistence-impairment model of psychotic disorder. Psychological Medicine 39, 179–195.CrossRefGoogle ScholarPubMed
Vollema, MG and Hoijtink, H (2000) The multidimensionality of self-report schizotypy in a psychiatric population: an analysis using multidimensional Rasch models. Schizophrenia Bulletin 26, 565–575.10.1093/oxfordjournals.schbul.a033478CrossRefGoogle Scholar
Vollema, MG and Postma, B (2002) Neurocognitive correlates of schizotypy in first degree relatives of schizophrenia patients. Schizophrenia Bulletin 28, 367–377.CrossRefGoogle ScholarPubMed
Vollema, MG, Sitskoorn, MM, Appels, MC and Kahn, RS (2002) Does the Schizotypal Personality Questionnaire reflect the biological-genetic vulnerability to schizophrenia? Schizophrenia Research 54, 39–45.10.1016/S0920-9964(01)00350-4CrossRefGoogle Scholar
Vukadinovic, Z (2014) NMDA receptor hypofunction and the thalamus in schizophrenia. Physiology and Behavior 131, 156–159.10.1016/j.physbeh.2014.04.038CrossRefGoogle Scholar
Walter, EE, Fernandez, F, Snelling, M and Barkus, E (2016) Genetic consideration of schizotypal traits: a review. Frontiers in Psychology 7, 1769.CrossRefGoogle ScholarPubMed
Woodward, TS, Leong, K, Sanford, N, Tipper, CM and Lavigne, KM (2016) Altered balance of functional brain networks in schizophrenia. Psychiatry Research 248, 94–104.10.1016/j.pscychresns.2016.01.003CrossRefGoogle Scholar
Zarogianni, E, Moorhead, TW and Lawrie, SM (2013) Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level. Neuroimage: Clinical 3, 279–289.CrossRefGoogle Scholar
Zarogianni, E, Storkey, AJ, Johnstone, EC, Owens, DG and Lawrie, SM (2017) Improved individualized prediction of schizophrenia in subjects at familial high risk, based on neuroanatomical data, schizotypal and neurocognitive features. Schizophrenia Research 181, 6–12.CrossRefGoogle ScholarPubMed
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