Hostname: page-component-76fb5796d-qxdb6 Total loading time: 0 Render date: 2024-04-29T16:53:19.060Z Has data issue: false hasContentIssue false
  • English
  • Français

Neurocognitive skills and vulnerability for psychosis in depression and across the psychotic spectrum: findings from the PRONIA Consortium

Published online by Cambridge University Press:  17 October 2023

Carolina Bonivento Open the ORCID record for Carolina Bonivento [Opens in a new window] ,
Lana Kambeitz-Ilankovic Open the ORCID record for Lana Kambeitz-Ilankovic [Opens in a new window] ,
Eleonora Maggioni ,
Stefan Borgwardt ,
Rebekka Lencer ,
Eva Meisenzahl ,
Joseph Kambeitz Open the ORCID record for Joseph Kambeitz [Opens in a new window] ,
Stephan Ruhrmann ,
Raimo K. R. Salokangas  and
Alessandro Bertolino
...Show all authors
Carolina Bonivento
Affiliation:
Scientific Institute, IRCCS E. Medea, Pasian di Prato, Udine, Italy
Lana Kambeitz-Ilankovic
Affiliation:
Faculty of Medicine and University Hospital of Cologne, University of Cologne, Germany; and Faculty of Psychology and Educational Sciences, Department of Psychology, Ludwig-Maximilian University, Germany
Eleonora Maggioni
Affiliation:
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy
Stefan Borgwardt
Affiliation:
Translational Psychiatry Unit, Department of Psychiatry and Psychotherapy, University of Lübeck, Germany
Rebekka Lencer
Affiliation:
Institute for Translational Psychiatry, Münster University, Germany
Eva Meisenzahl
Affiliation:
Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Germany
Joseph Kambeitz
Affiliation:
Faculty of Medicine and University Hospital of Cologne, University of Cologne, Germany
Stephan Ruhrmann
Affiliation:
Faculty of Medicine and University Hospital of Cologne, University of Cologne, Germany
Raimo K. R. Salokangas
Affiliation:
Department of Psychiatry, University of Turku, Finland
Alessandro Bertolino
Affiliation:
Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Italy
Alexandra Stainton
Affiliation:
Orygen, Melbourne, Australia; and Centre for Youth Mental Health, University of Melbourne, Australia
Julian Wenzel
Affiliation:
Faculty of Medicine and University Hospital of Cologne, University of Cologne, Germany
Christos Pantelis
Affiliation:
Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Australia
Stephen J. Wood
Affiliation:
Orygen, Melbourne, Australia; School of Psychology, University of Birmingham, UK; and Centre for Youth Mental Health, University of Melbourne, Australia
Rachel Upthegrove
Affiliation:
School of Psychology, University of Birmingham, UK; Institute for Mental Health, University of Birmingham, UK; and Centre for Human Brain Health, University of Birmingham, UK
Nikolaos Koutsouleris
Affiliation:
Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Germany; Max Planck Institute for Psychiatry, Germany; and Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
Paolo Brambilla*
Affiliation:
Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Italy; and Department of Pathophysiology and Transplantation, University of Milan, Italy
*
Correspondence: Paolo Brambilla. Email: paolo.brambilla1@unimi.it
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Neurocognitive deficits are a core feature of psychosis and depression. Despite commonalities in cognitive alterations, it remains unclear if and how the cognitive deficits in patients at clinical high risk for psychosis (CHR) and those with recent-onset psychosis (ROP) are distinct from those seen in recent-onset depression (ROD).

Aims

This study was carried out within the European project ‘Personalized Prognostic Tools for Early Psychosis Management’, and aimed to characterise the cognitive profiles of patients with psychosis or depression.

Method

We examined cognitive profiles for patients with ROP (n = 105), patients with ROD (n = 123), patients at CHR (n = 116) and healthy controls (n = 372) across seven sites in five European countries. Confirmatory factor analysis identified four cognitive factors independent of gender, education and site: speed of processing, attention and working memory, verbal learning and spatial learning.

Results

Patients with ROP performed worse than healthy controls in all four domains (P < 0.001), whereas performance of patients with ROD was not affected (P > 0.05). Patients at CHR performed worse than healthy controls in speed of processing (P = 0.001) and spatial learning (P = 0.003), but better than patients with ROP across all cognitive domains (all P ≤ 0.01). CHR and ROD groups did not significantly differ in any cognitive domain. These findings were independent of comorbid depressive symptoms, substance consumption and illness duration.

Conclusions

These results show that neurocognitive abilities are affected in CHR and ROP, whereas ROD seems spared. Although our findings may support the notion that those at CHR have a specific vulnerability to psychosis, future studies investigating broader transdiagnostic risk cohorts in longitudinal designs are needed.

Keywords

Cognitive neuroscienceclinical high riskrecent-onset psychosisrecent-onset depressionrisk assessment
Type
Paper
Information
The British Journal of Psychiatry , Volume 223 , Issue 4 , October 2023 , pp. 485 - 492
Check for updates
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press on behalf of the Royal College of Psychiatrists

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

Joint first authors.

Joseph Kambeitz was originally incorrectly listed as having a second affiliation. This hasnow been corrected and a corrigendum published at https://doi.org/10.1192/bjp.2024.43.

References

Green, MF. Cognitive impairment and functional outcome in schizophrenia and bipolar disorder. J Clin Psychiatry 2006; 67(10): 3–8.10.4088/JCP.1006e12CrossRefGoogle ScholarPubMed
Sheffield, JM, Karcher, NR, Barch, DM. Cognitive deficits in psychotic disorders: a lifespan perspective. Neuropsychol Rev 2018; 28(4): 509–33.10.1007/s11065-018-9388-2CrossRefGoogle ScholarPubMed
Fusar-Poli, P, Deste, G, Smieskova, R, Barlati, S, Yung, AR, Howes, O, et al. Cognitive functioning in prodromal psychosis. JAMA Psychiatry 2012; 69(6): 562–71.Google ScholarPubMed
Fusar-Poli, P, Rocchetti, M, Sardella, A, Avila, A, Brandizzi, M, Caverzasi, E, et al. Disorder, not just state of risk: meta-analysis of functioning and quality of life in people at high risk of psychosis. Br J Psychiatry 2015; 207(3): 198–206.10.1192/bjp.bp.114.157115CrossRefGoogle Scholar
Cornblatt, B, Obuchowski, M, Roberts, S, Pollack, S, Erlenmeyer-Kimling, L. Cognitive and behavioral precursors of schizophrenia. Dev Psychopathol 1999; 11(3): 487–508.10.1017/S0954579499002175CrossRefGoogle ScholarPubMed
Rapoport, JL, Giedd, JN, Gogtay, N. Neurodevelopmental model of schizophrenia: update 2012. Mol Psychiatry 2012; 17(12): 1228–38.10.1038/mp.2012.23CrossRefGoogle ScholarPubMed
Johnstone, EC, Ebmeier, KP, Miller, P, Owens, DGC, Lawrie, SM. Predicting schizophrenia: findings from the Edinburgh high-risk study. Br J Psychiatry 2005; 186: 18–25.10.1192/bjp.186.1.18CrossRefGoogle ScholarPubMed
Niendam, TA, Bearden, CE, Johnson, JK, McKinley, M, Loewy, R, O'Brien, M, et al. Neurocognitive performance and functional disability in the psychosis prodrome. Schizophr Res 2006; 84: 100–11.10.1016/j.schres.2006.02.005CrossRefGoogle ScholarPubMed
Seidman, LJ, Giuliano, AJ, Meyer, EC, Addington, J, Cadenhead, KS, Cannon, TD, et al. Neuropsychology of the prodrome to psychosis in the NAPLS consortium: relationship to family history and conversion to psychosis. Arch Gen Psychiatry 2010; 67(6): 578–88.10.1001/archgenpsychiatry.2010.66CrossRefGoogle ScholarPubMed
Koutsouleris, N, Gaser, C, Patschurek-Kliche, K, Scheuerecker, J, Bottlender, R, Decker, P, et al. Multivariate patterns of brain-cognition associations relating to vulnerability and clinical outcome in the at-risk mental states for psychosis. Hum Brain Mapp 2012; 33(9): 2104–24.10.1002/hbm.21342CrossRefGoogle ScholarPubMed
Lin, A, Yung, AR, Nelson, B, Brewer, WJ, Riley, R, Simmons, M, et al. Neurocognitive predictors of transition to psychosis: medium-to long-term findings from a sample at ultra-high risk for psychosis. Psychol Med 2013; 43(11): 2349–60.10.1017/S0033291713000123CrossRefGoogle ScholarPubMed
Seidman, LJ, Shapiro, DI, Stone, WS, Woodberry, KA, Ronzio, A, Cornblatt, BA, et al. Association of neurocognition with transition to psychosis baseline functioning in the second phase of the North American prodrome longitudinal study. JAMA Psychiatry 2016; 02115: 1239–48.10.1001/jamapsychiatry.2016.2479CrossRefGoogle Scholar
Zanelli, J, Reichenberg, A, Morgan, K, Fearon, P, Kravariti, E, Dazzan, P, et al. Specific and generalized neuropsychological deficits: a comparison of patients with various first-episode psychosis presentations. Am J Psychiatry 2010; 167(1): 78–85.10.1176/appi.ajp.2009.09010118CrossRefGoogle ScholarPubMed
Mallawaarachchi, SR, Amminger, GP, Farhall, J, Bolt, LK, Nelson, B, Yuen, HP, et al. Cognitive functioning in ultra-high risk for psychosis individuals with and without depression: secondary analysis of findings from the NEURAPRO randomized clinical trial. Schizophr Res 2020; 218: 48–54.10.1016/j.schres.2020.03.008CrossRefGoogle ScholarPubMed
Squarcina, L, Kambeitz-Ilankovic, L, Bonivento, C, Prunas, C, Oldani, L, Wenzel, J, et al. Relationships between global functioning and neuropsychological predictors in subjects at high risk of psychosis or with a recent onset of depression. World J Biol Psychiatry 2022; 23(8): 573–81.10.1080/15622975.2021.2014955CrossRefGoogle ScholarPubMed
McGorry, PD, Hartmann, JA, Spooner, R, Nelson, B. Beyond the “at risk mental state” concept: transitioning to transdiagnostic psychiatry. World Psychiatry 2018; 17(2): 133–42.10.1002/wps.20514CrossRefGoogle ScholarPubMed
Salazar De Pablo, G, Soardo, L, Cabras, A, Pereira, J, Kaur, S, Besana, F, et al. Clinical outcomes in individuals at clinical high risk of psychosis who do not transition to psychosis: a meta-analysis. Epidemiol Psychiatr Sci 2022; 31: e9.10.1017/S2045796021000639CrossRefGoogle Scholar
Addington, J, Farris, MS, Liu, L, Cadenhead, KS, Cannon, TD, Cornblatt, BA, et al. Depression: an actionable outcome for those at clinical high-risk. Schizophr Res 2021; 227: 38–43.10.1016/j.schres.2020.10.001CrossRefGoogle ScholarPubMed
Koutsouleris, N, Kambeitz-Ilankovic, L, Ruhrmann, S, Rosen, M, Ruef, A, Dwyer, DB, et al. Prediction models of functional outcomes for individuals in the clinical high-risk state for psychosis or with recent-onset depression: a multimodal, multisite machine learning analysis. JAMA Psychiatry 2018; 75(11): 1156–72.10.1001/jamapsychiatry.2018.2165CrossRefGoogle ScholarPubMed
Green, MF, Nuechterlein, KH, Kern, RS, Baade, LE, Fenton, WS, Gold, JM, et al. Functional co-primary measures for clinical trials in schizophrenia: results from the MATRICS psychometric and standardization study. Am J Psychiatry 2008; 165(2): 221–8.10.1176/appi.ajp.2007.07010089CrossRefGoogle ScholarPubMed
Nuechterlein, KH, Green, MF, Kern, RS, Baade, LE, Barch, DM, Cohen, JD, et al. The MATRICS Consensus Cognitive Battery, part 1: test selection, reliability, and validity. Am J Psychiatry 2008; 165(2): 203–13.10.1176/appi.ajp.2007.07010042CrossRefGoogle ScholarPubMed
Nowicki S, Duke, M. Nonverbal receptivity: the diagnostic analysis of nonverbal accuracy (DANVA). In Interpersonal Sensitivity: Theory and Measurement (eds Hall, JA, Bernieri, FJ): 183–98. Lawrence Erlbaum Associates Publishers, 2001.Google Scholar
Rosseel, Y. Lavaan: an R package for structural equation modeling. J Stat Softw 2012; 48(2): 1–36.10.18637/jss.v048.i02CrossRefGoogle Scholar
Seabold, S, Perktold, J. Statsmodels: econometric and statistical modeling with Python. 9th Python in Science Conference (Austin, Texas, 28 Jun – 3 Jul 2010). SciPy. 2010.10.25080/Majora-92bf1922-011CrossRefGoogle Scholar
Beck, AT, Steer, R, Brown, GK. Manual for the Beck Depression Inventory-II. Psychological Corporation, 1996.Google Scholar
Allott, K, Fisher, CA, Amminger, GP, Goodall, J, Hetrick, S. Characterizing neurocognitive impairment in young people with major depression: state, trait, or scar? Brain Behav 2016; 6(10): 1–12.10.1002/brb3.527CrossRefGoogle ScholarPubMed
Anda, L, Brønnick, KK, Johannessen, JO, Joa, I, Kroken, RA, Johnsen, E, et al. Cognitive profile in ultra high risk for psychosis and schizophrenia: a comparison using coordinated norms. Front Psychiatry 2019; 10: 695.10.3389/fpsyt.2019.00695CrossRefGoogle ScholarPubMed
Cannon, TD, Yu, C, Addington, J, Bearden, CE, Cadenhead, KS, Cornblatt, BA, et al. An individualized risk calculator for research in prodromal psychosis. Am J Psychiatry 2016; 173(10): 980–8.10.1176/appi.ajp.2016.15070890CrossRefGoogle ScholarPubMed
Koutsouleris, N, Dwyer, DB, Degenhardt, F, Maj, C, Urquijo-Castro, MF, Sanfelici, R, et al. Multimodal machine learning workflows for prediction of psychosis in patients with clinical high-risk syndromes and recent-onset depression. JAMA Psychiatry 2021; 78(2): 195–209.10.1001/jamapsychiatry.2020.3604CrossRefGoogle ScholarPubMed
Van Os, J, Guloksuz, S. A critique of the “ultra-high risk” and “transition” paradigm. World Psychiatry 2017; 16: 200–6.10.1002/wps.20423CrossRefGoogle ScholarPubMed
Cambridge, OR, Knight, MJ, Mills, N, Baune, BT. The clinical relationship between cognitive impairment and psychosocial functioning in major depressive disorder: a systematic review. Psychiatry Res 2018; 269: 157–71.10.1016/j.psychres.2018.08.033CrossRefGoogle ScholarPubMed
Supplementary material: File

Bonivento et al. supplementary material

Bonivento et al. supplementary material 1

Download Bonivento et al. supplementary material(File)
File 138.3 KB
Supplementary material: PDF

Bonivento et al. supplementary material

Bonivento et al. supplementary material 2

Download Bonivento et al. supplementary material(PDF)
PDF 83.6 KB
Supplementary material: File

Bonivento et al. supplementary material

Bonivento et al. supplementary material 3

Download Bonivento et al. supplementary material(File)
File 13.4 KB
Supplementary material: PDF

Bonivento et al. supplementary material

Bonivento et al. supplementary material 4

Download Bonivento et al. supplementary material(PDF)
PDF 41.9 KB
Submit a response

eLetters

No eLetters have been published for this article.