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Cortical morphology development in patients with 22q11.2 deletion syndrome at ultra-high risk of psychosis

Published online by Cambridge University Press:  17 January 2018

Maria Carmela Padula*
Affiliation:
Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland
Marie Schaer
Affiliation:
Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland
Marco Armando
Affiliation:
Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland
Corrado Sandini
Affiliation:
Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland
Daniela Zöller
Affiliation:
Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland Medical Image Processing Laboratory, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Elisa Scariati
Affiliation:
Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland
Maude Schneider
Affiliation:
Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland
Stephan Eliez
Affiliation:
Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland Department of Genetic Medicine and Development, University of Geneva School of Medicine, Geneva, Switzerland
*
Author for correspondence: Maria Carmela Padula, E-mail: [email protected].

Abstract

Background

Patients with 22q11.2 deletion syndrome (22q11DS) present a high risk of developing psychosis. While clinical and cognitive predictors for the conversion towards a full-blown psychotic disorder are well defined and largely used in practice, neural biomarkers do not yet exist. However, a number of investigations indicated an association between abnormalities in cortical morphology and higher symptoms severities in patients with 22q11DS. Nevertheless, few studies included homogeneous groups of patients differing in their psychotic symptoms profile.

Methods

In this study, we included 22 patients meeting the criteria for an ultra-high-risk (UHR) psychotic state and 22 age-, gender- and IQ-matched non-UHR patients. Measures of cortical morphology, including cortical thickness, volume, surface area and gyrification, were compared between the two groups using mass-univariate and multivariate comparisons. Furthermore, the development of these measures was tested in the two groups using a mixed-model approach.

Results

Our results showed differences in cortical volume and surface area in UHR patients compared with non-UHR. In particular, we found a positive association between surface area and the rate of change of global functioning, suggesting that higher surface area is predictive of improved functioning with age. We also observed accelerated cortical thinning during adolescence in UHR patients with 22q11DS.

Conclusions

These results, although preliminary, suggest that alterations in cortical volume and surface area as well as altered development of cortical thickness may be associated to a greater probability to develop psychosis in 22q11DS.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2018 

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References

Addington, J, Cornblatt, BA, Cadenhead, KS, Cannon, TD, McGlashan, TH, Perkins, DO et al. (2011). At clinical high risk for psychosis: outcome for nonconverters. The American Journal of Psychiatry 168, 800805.Google Scholar
Allen, P, Chaddock, CA, Egerton, A, Howes, OD, Barker, G, Bonoldi, I et al. (2015). Functional outcome in people at high risk for psychosis predicted by thalamic glutamate levels and prefronto-striatal activation. Schizophrenia Bulletin 41, 429439.Google Scholar
Arbabshirani, MR, Plis, S, Sui, J and Calhoun, VD (2017). Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. NeuroImage 145(Pt B), 137165.Google Scholar
Armando, M, Schneider, M, Pontillo, M, Vicari, S, Debbané, M, Schultze-Lutter, F et al. (2017). No age effect in the prevalence and clinical significance of ultra-high risk symptoms and criteria for psychosis in 22q11 deletion syndrome: confirmation of the genetically driven risk for psychosis? PLoS ONE 12, e0174797.Google Scholar
Bakker, G, Caan, MWA, Vingerhoets, WAM, da Silva-Alves, F, de Koning, M, Boot, E et al. (2016). Cortical morphology differences in subjects at increased vulnerability for developing a psychotic disorder: a comparison between subjects with ultra-high risk and 22q11.2 deletion syndrome. PLoS ONE 11, e0159928.Google Scholar
Bostelmann, M, Schneider, M, Padula, MC, Maeder, J, Schaer, M, Scariati, E et al. (2016). Visual memory profile in 22q11.2 microdeletion syndrome: are there differences in performance and neurobiological substrates between tasks linked to ventral and dorsal visual brain structures? A cross-sectional and longitudinal study. Journal of Neurodevelopmental Disorders 8, 41.Google Scholar
Cannon, TD (2015). How schizophrenia develops: cognitive and brain mechanisms underlying onset of psychosis. Trends in Cognitive Sciences 19, 744756.Google Scholar
Cropley, VL, Lin, A, Nelson, B, Reniers, RLEP, Yung, AR, Bartholomeusz, CF et al. (2016). Baseline grey matter volume of non-transitioned ‘ultra high risk’ for psychosis individuals with and without attenuated psychotic symptoms at long-term follow-up. Schizophrenia Research 173, 152158.Google Scholar
Dale, AM, Fischl, B and Sereno, MI (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage 9, 179194.Google Scholar
Davatzikos, C (2004). Why voxel-based morphometric analysis should be used with great caution when characterizing group differences. NeuroImage 23, 1720.Google Scholar
Dazzan, P, Soulsby, B, Mechelli, A, Wood, SJ, Velakoulis, D, Phillips, LJ et al. (2012). Volumetric abnormalities predating the onset of schizophrenia and affective psychoses: an MRI study in subjects at ultrahigh risk of psychosis. Schizophrenia Bulletin 38, 10831091.Google Scholar
Debbané, M, Lazouret, M, Lagioia, A, Schneider, M, Van De Ville, D and Eliez, S (2012). Resting-state networks in adolescents with 22q11.2 deletion syndrome: associations with prodromal symptoms and executive functions. Schizophrenia Research 139, 3339.Google Scholar
Desikan, RS, Ségonne, F, Fischl, B, Quinn, BT, Dickerson, BC, Blacker, D et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31, 968980.Google Scholar
de Wit, S, Wierenga, LM, Oranje, B, Ziermans, TB, Schothorst, PF, van Engeland, H et al. (2016). Brain development in adolescents at ultra-high risk for psychosis: longitudinal changes related to resilience. NeuroImage: Clinical 12, 542549.Google Scholar
Dukart, J, Smieskova, R, Harrisberger, F, Lenz, C, Schmidt, A, Walter, A et al. (2017). Age-related brain structural alterations as an intermediate phenotype of psychosis. Journal of Psychiatry & Neuroscience: JPN 42, 307319.Google Scholar
First, M, Gibbon, M, Spitzer, R, Williams, J and Benjamin, L (1996). Structured Clinical Interview for the DSM-IV Axis I Disorders (SCID-I). Washington, DC: American Psychiatric Association.Google Scholar
Fischl, B and Dale, AM (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences of the USA 97, 1105011055.Google Scholar
Fusar-Poli, P, Borgwardt, S, Bechdolf, A, Addington, J, Riecher-Rössler, A, Schultze-Lutter, F et al. (2013). The psychosis high-risk state: a comprehensive state-of-the-art review. JAMA Psychiatry 70, 107120.Google Scholar
Fusar-Poli, P, Borgwardt, S, Crescini, A, Deste, G, Kempton, MJ, Lawrie, S et al. (2011). Neuroanatomy of vulnerability to psychosis: a voxel-based meta-analysis. Neuroscience and Biobehavioral Reviews 35, 11751185.Google Scholar
Gothelf, D, Hoeft, F, Ueno, T, Sugiura, L, Lee, AD, Thompson, P et al. (2011). Developmental changes in multivariate neuroanatomical patterns that predict risk for psychosis in 22q11.2 deletion syndrome. Journal of Psychiatric Research 45, 322331.Google Scholar
Gothelf, D, Schaer, M and Eliez, S (2008). Genes, brain development and psychiatric phenotypes in velo-cardio-facial syndrome. Developmental Disabilities Research Reviews 14, 5968.Google Scholar
Gothelf, D, Schneider, M, Green, T, Debbané, M, Frisch, A, Glaser, B et al. (2013). Risk factors and the evolution of psychosis in 22q11.2 deletion syndrome: a longitudinal 2-site study. Journal of the American Academy of Child & Adolescent Psychiatry 52, 11921203.e3.Google Scholar
Jalbrzikowski, M, Jonas, R, Senturk, D, Patel, A, Chow, C, Green, MF et al. (2013). Structural abnormalities in cortical volume, thickness, and surface area in 22q11.2 microdeletion syndrome: relationship with psychotic symptoms. NeuroImage: Clinical 3, 405415.Google Scholar
Kates, WR, Antshel, KM, Faraone, SV, Fremont, WP, Higgins, AM, Shprintzen, RJ et al. (2011). Neuroanatomic predictors to prodromal psychosis in velocardiofacial syndrome (22q11.2 deletion syndrome): a longitudinal study. Biological Psychiatry 69, 945952.Google Scholar
Kaufman, J, Birmaher, B, Brent, D, Rao, U, Flynn, C, Moreci, P et al. (1997). Schedule for affective disorders and schizophrenia for school-age children-present and lifetime version (K-SADS-PL): initial reliability and validity data. Journal of the American Academy of Child and Adolescent Psychiatry 36, 980988.Google Scholar
Klauser, P, Zhou, J, Lim, JKW, Poh, JS, Zheng, H, Tng, HY et al. (2015). Lack of evidence for regional brain volume or cortical thickness abnormalities in youths at clinical high risk for psychosis: findings from the longitudinal youth at risk study. Schizophrenia Bulletin 41, 12851293.Google Scholar
Kunwar, A, Ramanathan, S, Nelson, J, Antshel, KM, Fremont, W, Higgins, AM et al. (2012). Cortical gyrification in velo-cardio-facial (22q11.2 deletion) syndrome: a longitudinal study. Schizophrenia Research 137, 2025.Google Scholar
Maeder, J, Schneider, M, Bostelmann, M, Debbané, M, Glaser, B, Menghetti, S et al. (2016). Developmental trajectories of executive functions in 22q11.2 deletion syndrome. Journal of Neurodevelopmental Disorders 8, 10.Google Scholar
McGuffin, P, Owen, MJ and Farmer, AE (1995). Genetic basis of schizophrenia. Lancet (London, England) 346, 678682.Google Scholar
Mechelli, A, Riecher-Rössler, A, Meisenzahl, EM, Tognin, S, Wood, SJ, Borgwardt, SJ et al. (2011). Neuroanatomical abnormalities that predate the onset of psychosis: a multicenter study. Archives of General Psychiatry 68, 489495.Google Scholar
Midbari Kufert, Y, Nachmani, A, Nativ, E, Weizman, A and Gothelf, D (2016). Association between prematurity and the evolution of psychotic disorders in 22q11.2 deletion syndrome. Journal of Neural Transmission (Vienna, Austria 1996) 123, 14911497.Google Scholar
Mihailov, A, Padula, MC, Scariati, E, Schaer, M, Schneider, M and Eliez, S (2017). Morphological brain changes associated with negative symptoms in patients with 22q11.2 deletion syndrome. Schizophrenia Research 188, 5258.Google Scholar
Miller, TJ, McGlashan, TH, Rosen, JL, Somjee, L, Markovich, PJ, Stein, K et al. (2002). Prospective diagnosis of the initial prodrome for schizophrenia based on the structured interview for prodromal syndromes: preliminary evidence of interrater reliability and predictive validity. The American Journal of Psychiatry 159, 863865.Google Scholar
Modinos, G, Allen, P, Frascarelli, M, Tognin, S, Valmaggia, L, Xenaki, L et al. (2014). Are we really mapping psychosis risk? Neuroanatomical signature of affective disorders in subjects at ultra high risk. Psychological Medicine 44, 34913501.Google Scholar
Mutlu, AK, Schneider, M, Debbané, M, Badoud, D, Eliez, S and Schaer, M (2013). Sex differences in thickness, and folding developments throughout the cortex. NeuroImage 82, 200207.Google Scholar
Palaniyappan, L (2017). Progressive cortical reorganisation: a framework for investigating structural changes in schizophrenia. Neuroscience and Biobehavioral Reviews 79, 113.Google Scholar
Palaniyappan, L, Das, T and Dempster, K (2017). The neurobiology of transition to psychosis: clearing the cache. Journal of Psychiatry & Neuroscience: JPN 42, 294299.Google Scholar
Radoeva, PD, Bansal, R, Antshel, KM, Fremont, W, Peterson, BS and Kates, WR (2017). Longitudinal study of cerebral surface morphology in youth with 22q11.2 deletion syndrome, and association with positive symptoms of psychosis. Journal of Child Psychology and Psychiatry, and Allied Disciplines 58, 305314.Google Scholar
Ramanathan, S, Mattiaccio, LM, Coman, IL, Botti, J-AC, Fremont, W, Faraone, SV et al. (2017). Longitudinal trajectories of cortical thickness as a biomarker for psychosis in individuals with 22q11.2 deletion syndrome. Schizophrenia Research 188, 3541.Google Scholar
Raznahan, A, Shaw, P, Lalonde, F, Stockman, M, Wallace, GL, Greenstein, D et al. (2011). How does your cortex grow? Journal of Neuroscience 31, 71747177.Google Scholar
Reich, W (2000). Diagnostic interview for children and adolescents (DICA). Journal of the American Academy of Child and Adolescent Psychiatry 39, 5966.Google Scholar
Sandini, C, Scariati, E, Padula, MC, Schneider, M, Schaer, M, Ville, DVD et al. (2017). Cortical dysconnectivity measured by structural covariance is associated with the presence of psychotic symptoms in 22q11.2 deletion syndrome. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.Google Scholar
Sannino, S, Padula, MC, Managò, F, Schaer, M, Schneider, M, Armando, M et al. (2017). Adolescence is the starting point of sex-dichotomous COMT genetic effects. Translational Psychiatry 7, e1141.Google Scholar
Scariati, E, Schaer, M, Richiardi, J, Schneider, M, Debbané, M, Van De Ville, D et al. (2014). Identifying 22q11.2 deletion syndrome and psychosis using resting-state connectivity patterns. Brain Topography 27, 808821.Google Scholar
Schaer, M, Cuadra, MB, Tamarit, L, Lazeyras, F, Eliez, S and Thiran, JP (2008). A surface-based approach to quantify local cortical gyrification. IEEE Transactions on Medical Imaging 27, 161170.Google Scholar
Schaer, M, Debbané, M, Bach Cuadra, M, Ottet, M-C, Glaser, B, Thiran, J-P et al. (2009). Deviant trajectories of cortical maturation in 22q11.2 deletion syndrome (22q11DS): a cross-sectional and longitudinal study. Schizophrenia Research 115, 182190.Google Scholar
Schaer, M, Schmitt, JE, Glaser, B, Lazeyras, F, Delavelle, J and Eliez, S (2006). Abnormal patterns of cortical gyrification in velo-cardio-facial syndrome (deletion 22q11.2): an MRI study. Psychiatry Research 146, 111.Google Scholar
Schaufelberger, MS, Lappin, JM, Duran, FLS, Rosa, PGP, Uchida, RR, Santos, LC et al. (2011). Lack of progression of brain abnormalities in first-episode psychosis: a longitudinal magnetic resonance imaging study. Psychological Medicine 41, 16771689.Google Scholar
Schmitt, JE, Vandekar, S, Yi, J, Calkins, ME, Ruparel, K, Roalf, DR et al. (2015). Aberrant cortical morphometry in the 22q11.2 deletion syndrome. Biological Psychiatry 78, 135143.Google Scholar
Schneider, M, Armando, M, Pontillo, M, Vicari, S, Debbané, M, Schultze-Lutter, F et al. (2016). Ultra high risk status and transition to psychosis in 22q11.2 deletion syndrome. World psychiatry 15, 259265.Google Scholar
Schneider, M, Debbané, M, Bassett, AS, Chow, EW, Fung, WLA, van den Bree, MB et al. (2014a). Psychiatric disorders from childhood to adulthood in 22q11. 2 deletion syndrome: results from the International Consortium on Brain and Behavior in 22q11. 2 deletion syndrome. American Journal of Psychiatry 171, 627639.Google Scholar
Schneider, M, Schaer, M, Mutlu, AK, Menghetti, S, Glaser, B, Debbané, M et al. (2014b). Clinical and cognitive risk factors for psychotic symptoms in 22q11.2 deletion syndrome: a transversal and longitudinal approach. European Child & Adolescent Psychiatry 23, 425436.Google Scholar
Schultze-Lutter, F, Michel, C, Schmidt, SJ, Schimmelmann, BG, Maric, NP, Salokangas, RKR et al. (2015). EPA guidance on the early detection of clinical high risk states of psychoses. European Psychiatry 30, 405416.Google Scholar
Schwarz, G (1978). Estimating the dimension of a model. The Annals of Statistics 6, 461464.Google Scholar
Shaffer, D, Gould, MS, Brasic, J, Ambrosini, P, Fisher, P, Bird, H et al. (1983). A children's global assessment scale (CGAS). Archives of General Psychiatry 40, 12281231.Google Scholar
Srivastava, S, Buonocore, MH and Simon, TJ (2012). Atypical developmental trajectory of functionally significant cortical areas in children with chromosome 22q11.2 deletion syndrome. Human Brain Mapping 33, 213223.Google Scholar
Swillen, A and McDonald-McGinn, D (2015). Developmental trajectories in 22q11.2 deletion syndrome. American Journal of Medical Genetics Part C: Seminars in Medical Genetics 169, 172181.Google Scholar
Tang, KL, Antshel, KM, Fremont, WP and Kates, WR (2015). Behavioral and psychiatric phenotypes in 22q11.2 deletion syndrome. Journal of Developmental and Behavioral Pediatrics: JDBP 36, 639650.Google Scholar
Van, L, Butcher, NJ, Costain, G, Ogura, L, Chow, EWC and Bassett, AS (2016). Fetal growth and gestational factors as predictors of schizophrenia in 22q11.2 deletion syndrome. Genetics in Medicine 18, 350355.Google Scholar
Vorstman, JAS, Breetvelt, EJ, Duijff, SN, Eliez, S, Schneider, M, Jalbrzikowski, M et al. , International Consortium on Brain and Behavior in 22q11.2 Deletion Syndrome (2015). Cognitive decline preceding the onset of psychosis in patients with 22q11.2 deletion syndrome. JAMA Psychiatry 72, 377385.Google Scholar
Wechsler, D (1991). The Wechsler Intelligence Scale for Children, 3rd edn. San Antonio, TX: The Psychological Corporation.Google Scholar
Wechsler, D (1997). Wechsler Adult Intelligence Scale (WAIS-3), 3rd edn. San Antonio, TX: The Psychological Corporation.Google Scholar
Wechsler, D (2004). The Wechsler Intelligence Scale for Children, 4th edn. London: Pearson.Google Scholar
Wechsler, D (2008). Wechsler adult intelligence scale (WAIS-4), 4th edn. London: Pearson.Google Scholar
Ziermans, TB, Durston, S, Sprong, M, Nederveen, H, van Haren, NEM, Schnack, HG et al. (2009). No evidence for structural brain changes in young adolescents at ultra high risk for psychosis. Schizophrenia Research 112, 16.Google Scholar
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