6 results
The interaction between early life complications and a polygenic risk score for schizophrenia is associated with brain activity during emotion processing in healthy participants
- Veronica Debora Toro, Linda A. Antonucci, Tiziana Quarto, Roberta Passiatore, Leonardo Fazio, Gianluca Ursini, Qiang Chen, Rita Masellis, Silvia Torretta, Leonardo Sportelli, Gianluca Christos Kikidis, Francesco Massari, Enrico D'Ambrosio, Antonio Rampino, Giulio Pergola, Daniel R. Weinberger, Alessandro Bertolino, Giuseppe Blasi
-
- Journal:
- Psychological Medicine , First View
- Published online by Cambridge University Press:
- 02 February 2024, pp. 1-10
-
- Article
- Export citation
-
Background
Previous evidence suggests that early life complications (ELCs) interact with polygenic risk for schizophrenia (SCZ) in increasing risk for the disease. However, no studies have investigated this interaction on neurobiological phenotypes. Among those, anomalous emotion-related brain activity has been reported in SCZ, even if evidence of its link with SCZ-related genetic risk is not solid. Indeed, it is possible this relationship is influenced by non-genetic risk factors. Thus, this study investigated the interaction between SCZ-related polygenic risk and ELCs on emotion-related brain activity.
Methods169 healthy participants (HP) in a discovery and 113 HP in a replication sample underwent functional magnetic resonance imaging (fMRI) during emotion processing, were categorized for history of ELCs and genome-wide genotyped. Polygenic risk scores (PRSs) were computed using SCZ-associated variants considering the most recent genome-wide association study. Furthermore, 75 patients with SCZ also underwent fMRI during emotion processing to verify consistency of their brain activity patterns with those associated with risk factors for SCZ in HP.
ResultsResults in the discovery and replication samples indicated no effect of PRSs, but an interaction between PRS and ELCs in left ventrolateral prefrontal cortex (VLPFC), where the greater the activity, the greater PRS only in presence of ELCs. Moreover, SCZ had greater VLPFC response than HP.
ConclusionsThese results suggest that emotion-related VLPFC response lies in the path from genetic and non-genetic risk factors to the clinical presentation of SCZ, and may implicate an updated concept of intermediate phenotype considering early non-genetic factors of risk for SCZ.
Prevalence of cognitive impairments and strengths in the early course of psychosis and depression
- Alexandra Stainton, Katharine Chisholm, Siân Lowri Griffiths, Lana Kambeitz-Ilankovic, Julian Wenzel, Carolina Bonivento, Paolo Brambilla, Mariam Iqbal, Theresa K. Lichtenstein, Marlene Rosen, Linda A. Antonucci, Eleonora Maggioni, Joseph Kambeitz, Stefan Borgwardt, Anita Riecher-Rössler, Christina Andreou, André Schmidt, Frauke Schultze-Lutter, Eva Meisenzahl, Stephan Ruhrmann, Raimo K. R. Salokangas, Christos Pantelis, Rebekka Lencer, Georg Romer, Alessandro Bertolino, Rachel Upthegrove, Nikolaos Koutsouleris, Kelly Allott, Stephen J. Wood, PRONIA Consortium
-
- Journal:
- Psychological Medicine / Volume 53 / Issue 13 / October 2023
- Published online by Cambridge University Press:
- 06 July 2023, pp. 5945-5957
-
- Article
-
- You have access Access
- Open access
- HTML
- Export citation
-
Background
Studies investigating cognitive impairments in psychosis and depression have typically compared the average performance of the clinical group against healthy controls (HC), and do not report on the actual prevalence of cognitive impairments or strengths within these clinical groups. This information is essential so that clinical services can provide adequate resources to supporting cognitive functioning. Thus, we investigated this prevalence in individuals in the early course of psychosis or depression.
MethodsA comprehensive cognitive test battery comprising 12 tests was completed by 1286 individuals aged 15–41 (mean age 25.07, s.d. 5.88) from the PRONIA study at baseline: HC (N = 454), clinical high risk for psychosis (CHR; N = 270), recent-onset depression (ROD; N = 267), and recent-onset psychosis (ROP; N = 295). Z-scores were calculated to estimate the prevalence of moderate or severe deficits or strengths (>2 s.d. or 1–2 s.d. below or above HC, respectively) for each cognitive test.
ResultsImpairment in at least two cognitive tests was as follows: ROP (88.3% moderately, 45.1% severely impaired), CHR (71.2% moderately, 22.4% severely impaired), ROD (61.6% moderately, 16.2% severely impaired). Across clinical groups, impairments were most prevalent in tests of working memory, processing speed, and verbal learning. Above average performance (>1 s.d.) in at least two tests was present for 40.5% ROD, 36.1% CHR, 16.1% ROP, and was >2 SDs in 1.8% ROD, 1.4% CHR, and 0% ROP.
ConclusionsThese findings suggest that interventions should be tailored to the individual, with working memory, processing speed, and verbal learning likely to be important transdiagnostic targets.
Clinical and psychological factors associated with resilience in patients with schizophrenia: data from the Italian network for research on psychoses using machine learning
- Linda A. Antonucci, Giulio Pergola, Antonio Rampino, Paola Rocca, Alessandro Rossi, Mario Amore, Eugenio Aguglia, Antonello Bellomo, Valeria Bianchini, Claudio Brasso, Paola Bucci, Bernardo Carpiniello, Liliana Dell'Osso, Fabio di Fabio, Massimo di Giannantonio, Andrea Fagiolini, Giulia Maria Giordano, Matteo Marcatilli, Carlo Marchesi, Paolo Meneguzzo, Palmiero Monteleone, Maurizio Pompili, Rodolfo Rossi, Alberto Siracusano, Antonio Vita, Patrizia Zeppegno, Silvana Galderisi, Alessandro Bertolino, Mario Maj, Italian Network for Research on Psychoses
-
- Journal:
- Psychological Medicine / Volume 53 / Issue 12 / September 2023
- Published online by Cambridge University Press:
- 11 October 2022, pp. 5717-5728
-
- Article
- Export citation
-
Background
Resilience is defined as the ability to modify thoughts to cope with stressful events. Patients with schizophrenia (SCZ) having higher resilience (HR) levels show less severe symptoms and better real-life functioning. However, the clinical factors contributing to determine resilience levels in patients remain unclear. Thus, based on psychological, historical, clinical and environmental variables, we built a supervised machine learning algorithm to classify patients with HR or lower resilience (LR).
MethodsSCZ from the Italian Network for Research on Psychoses (N = 598 in the Discovery sample, N = 298 in the Validation sample) underwent historical, clinical, psychological, environmental and resilience assessments. A Support Vector Machine algorithm (based on 85 variables extracted from the above-mentioned assessments) was built in the Discovery sample, and replicated in the Validation sample, to classify between HR and LR patients, within a nested, Leave-Site-Out Cross-Validation framework. We then investigated whether algorithm decision scores were associated with the cognitive and clinical characteristics of patients.
ResultsThe algorithm classified patients as HR or LR with a Balanced Accuracy of 74.5% (p < 0.0001) in the Discovery sample, and 80.2% in the Validation sample. Higher self-esteem, larger social network and use of adaptive coping strategies were the variables most frequently chosen by the algorithm to generate decisions. Correlations between algorithm decision scores, socio-cognitive abilities, and symptom severity were significant (pFDR < 0.05).
ConclusionsWe identified an accurate, meaningful and generalizable clinical-psychological signature associated with resilience in SCZ. This study delivers relevant information regarding psychological and clinical factors that non-pharmacological interventions could target in schizophrenia.
Using combined environmental–clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression
- Linda A. Antonucci, Nora Penzel, Rachele Sanfelici, Alessandro Pigoni, Lana Kambeitz-Ilankovic, Dominic Dwyer, Anne Ruef, Mark Sen Dong, Ömer Faruk Öztürk, Katharine Chisholm, Theresa Haidl, Marlene Rosen, Adele Ferro, Giulio Pergola, Ileana Andriola, Giuseppe Blasi, Stephan Ruhrmann, Frauke Schultze-Lutter, Peter Falkai, Joseph Kambeitz, Rebekka Lencer, Udo Dannlowski, Rachel Upthegrove, Raimo K. R. Salokangas, Christos Pantelis, Eva Meisenzahl, Stephen J. Wood, Paolo Brambilla, Stefan Borgwardt, Alessandro Bertolino, Nikolaos Koutsouleris, the PRONIA Consortium
-
- Journal:
- The British Journal of Psychiatry / Volume 220 / Issue 4 / April 2022
- Published online by Cambridge University Press:
- 14 February 2022, pp. 229-245
- Print publication:
- April 2022
-
- Article
-
- You have access Access
- HTML
- Export citation
-
Background
Clinical high-risk states for psychosis (CHR) are associated with functional impairments and depressive disorders. A previous PRONIA study predicted social functioning in CHR and recent-onset depression (ROD) based on structural magnetic resonance imaging (sMRI) and clinical data. However, the combination of these domains did not lead to accurate role functioning prediction, calling for the investigation of additional risk dimensions. Role functioning may be more strongly associated with environmental adverse events than social functioning.
AimsWe aimed to predict role functioning in CHR, ROD and transdiagnostically, by adding environmental adverse events-related variables to clinical and sMRI data domains within the PRONIA sample.
MethodBaseline clinical, environmental and sMRI data collected in 92 CHR and 95 ROD samples were trained to predict lower versus higher follow-up role functioning, using support vector classification and mixed k-fold/leave-site-out cross-validation. We built separate predictions for each domain, created multimodal predictions and validated them in independent cohorts (74 CHR, 66 ROD).
ResultsModels combining clinical and environmental data predicted role outcome in discovery and replication samples of CHR (balanced accuracies: 65.4% and 67.7%, respectively), ROD (balanced accuracies: 58.9% and 62.5%, respectively), and transdiagnostically (balanced accuracies: 62.4% and 68.2%, respectively). The most reliable environmental features for role outcome prediction were adult environmental adjustment, childhood trauma in CHR and childhood environmental adjustment in ROD.
ConclusionsFindings support the hypothesis that environmental variables inform role outcome prediction, highlight the existence of both transdiagnostic and syndrome-specific predictive environmental adverse events, and emphasise the importance of implementing real-world models by measuring multiple risk dimensions.
Multivariate patterns of gray matter volume in thalamic nuclei are associated with positive schizotypy in healthy individuals
- Pasquale Di Carlo, Giulio Pergola, Linda A. Antonucci, Aurora Bonvino, Marina Mancini, Tiziana Quarto, Antonio Rampino, Teresa Popolizio, Alessandro Bertolino, Giuseppe Blasi
-
- Journal:
- Psychological Medicine / Volume 50 / Issue 9 / July 2020
- Published online by Cambridge University Press:
- 30 July 2019, pp. 1501-1509
-
- Article
- Export citation
-
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.
MethodsFollowing 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.
ResultsPrediction 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.
ConclusionsThese 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.
Contributors
-
- By Isabella Aboderin, W. Andrew Achenbaum, Katherine R. Allen, Toni C. Antonucci, Sara Arber, Claudine Attias‐Donfut, Paul B. Baltes, Sandhi Maria Barreto, Vern L. Bengtson, Simon Biggs, Joanna Bornat, Julie B. Boron, Mike Boulton, Clive E. Bowman, Marjolein Broese van Groenou, Edna Brown, Robert N. Butler, Bill Bytheway, Neena L. Chappell, Neil Charness, Kaare Christensen, Peter G. Coleman, Ingrid Arnet Connidis, Neal E. Cutler, Sara J. Czaja, Svein Olav Daatland, Lia Susana Daichman, Adam Davey, Bleddyn Davies, Freya Dittmann‐Kohli, Glen H. Elder, Carroll L. Estes, Mike Featherstone, Amy Fiske, Alexandra Freund, Daphna Gans, Linda K. George, Roseann Giarrusso, Chris Gilleard, Jay Ginn, Edlira Gjonça, Elena L. Grigorenko, Jaber F. Gubrium, Sarah Harper, Jutta Heckhausen, Akiko Hashimoto, Jon Hendricks, Mike Hepworth, Charlotte Ikels, James S. Jackson, Yuri Jang, Bernard Jeune, Malcolm L. Johnson, Randi S. Jones, Alexandre Kalache, Robert L. Kane, Rosalie A. Kane, Ingrid Keller, Rose Anne Kenny, Thomas B. L. Kirkwood, Kees Knipscheer, Martin Kohli, Gisela Labouvie‐Vief, Kristina Larsson, Shu‐Chen Li, Charles F. Longino, Ariela Lowenstein, Erick McCarthy, Gerald E. McClearn, Brendan McCormack, Elizabeth MacKinlay, Alfons Marcoen, Michael Marmot, Tom Margrain, Victor W. Marshall, Elizabeth A. Maylor, Ruud ter Meulen, Harry R. Moody, Robert A. Neimeyer, Demi Patsios, Margaret J. Penning, Stephen A. Petrill, Chris Phillipson, Leonard W. Poon, Norella M. Putney, Jill Quadagno, Pat Rabbitt, Jennifer Reid Keene, Sandra G. Reynolds, Steven R. Sabat, Clive Seale, Merril Silverstein, Hannes B. Staehelin, Ursula M. Staudinger, Robert J. Sternberg, Debra Street, Philip Taylor, Fleur Thomése, Mats Thorslund, Jinzhou Tian, Theo van Tilburg, Fernando M. Torres‐Gil, Josy Ubachs‐Moust, Christina Victor, K. Warner Shaie, Anthony M. Warnes, James L. Werth, Sherry L. Willis, François‐Charles Wolff, Bob Woods
- Edited by Malcolm L. Johnson, University of Bristol
- Edited in association with Vern L. Bengtson, University of Southern California, Peter G. Coleman, University of Southampton, Thomas B. L. Kirkwood, University of Newcastle upon Tyne
-
- Book:
- The Cambridge Handbook of Age and Ageing
- Published online:
- 05 June 2016
- Print publication:
- 01 December 2005, pp xii-xvi
-
- Chapter
- Export citation