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1. Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.

Author

Zhu Y, Maikusa N, Radua J, Sämann PG, Fusar-Poli P, Agartz I, Andreassen OA, Bachman P, Baeza I, Chen X, Choi S, Corcoran CM, Ebdrup BH, Fortea A, Garani RR, Glenthøj BY, Glenthøj LB, Haas SS, Hamilton HK, Hayes RA, He Y, Heekeren K, Kasai K, Katagiri N, Kim M, Kristensen TD, Kwon JS, Lawrie SM, Lebedeva I, Lee J, Loewy RL, Mathalon DH, McGuire P, Mizrahi R, Mizuno M, Møller P, Nemoto T, Nordholm D, Omelchenko MA, Raghava JM, Røssberg JI, Rössler W, Salisbury DF, Sasabayashi D, Smigielski L, Sugranyes G, Takahashi T, Tamnes CK, Tang J, Theodoridou A, Tomyshev AS, Uhlhaas PJ, Værnes TG, van Amelsvoort TAMJ, Waltz JA, Westlye LT, Zhou JH, Thompson PM, Hernaus D, Jalbrzikowski M, and Koike S

Subjects
Humans, Male, Female, Adult, Young Adult, Adolescent, Prodromal Symptoms, Psychotic Disorders pathology, Psychotic Disorders diagnostic imaging, Magnetic Resonance Imaging methods, Brain pathology, Brain diagnostic imaging, Neuroimaging methods, Machine Learning
Abstract

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings., (© 2024. The Author(s).)

Published
2024
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2. Neuroanatomical heterogeneity and homogeneity in individuals at clinical high risk for psychosis

Author

Baldwin, Helen, Radua, Joaquim, Antoniades, Mathilde, Haas, Shalaila S, Frangou, Sophia, Agartz, Ingrid, Allen, Paul, Andreassen, Ole A, Atkinson, Kimberley, Bachman, Peter, Baeza, Inmaculada, Bartholomeusz, Cali F, Chee, Michael WL, Colibazzi, Tiziano, Cooper, Rebecca E, Corcoran, Cheryl M, Cropley, Vanessa L, Ebdrup, Bjørn H, Fortea, Adriana, Glenthøj, Louise Birkedal, Hamilton, Holly K, Haut, Kristen M, Hayes, Rebecca A, He, Ying, Heekeren, Karsten, Kaess, Michael, Kasai, Kiyoto, Katagiri, Naoyuki, Kim, Minah, Kindler, Jochen, Klaunig, Mallory J, Koike, Shinsuke, Koppel, Alex, Kristensen, Tina D, Bin Kwak, Yoo, Kwon, Jun Soo, Lawrie, Stephen M, Lebedeva, Irina, Lee, Jimmy, Lin, Ashleigh, Loewy, Rachel L, Mathalon, Daniel H, Michel, Chantal, Mizrahi, Romina, Møller, Paul, Nelson, Barnaby, Nemoto, Takahiro, Nordholm, Dorte, Omelchenko, Maria A, Pantelis, Christos, Raghava, Jayachandra M, Røssberg, Jan I, Rössler, Wulf, Salisbury, Dean F, Sasabayashi, Daiki, Schall, Ulrich, Smigielski, Lukasz, Sugranyes, Gisela, Suzuki, Michio, Takahashi, Tsutomu, Tamnes, Christian K, Tang, Jinsong, Theodoridou, Anastasia, Thomopoulos, Sophia I, Tomyshev, Alexander S, Uhlhaas, Peter J, Værnes, Tor G, van Amelsvoort, Therese AMJ, Van Erp, Theo GM, Waltz, James A, Westlye, Lars T, Wood, Stephen J, Zhou, Juan H, McGuire, Philip, Thompson, Paul M, Jalbrzikowski, Maria, Hernaus, Dennis, and Fusar-Poli, Paolo

Subjects
Serious Mental Illness, Prevention, Biomedical Imaging, Mental Health, Clinical Research, Neurosciences, Brain, Humans, Magnetic Resonance Imaging, Psychotic Disorders, ENIGMA Clinical High Risk for Psychosis Working Group, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

Individuals at Clinical High Risk for Psychosis (CHR-P) demonstrate heterogeneity in clinical profiles and outcome features. However, the extent of neuroanatomical heterogeneity in the CHR-P state is largely undetermined. We aimed to quantify the neuroanatomical heterogeneity in structural magnetic resonance imaging measures of cortical surface area (SA), cortical thickness (CT), subcortical volume (SV), and intracranial volume (ICV) in CHR-P individuals compared with healthy controls (HC), and in relation to subsequent transition to a first episode of psychosis. The ENIGMA CHR-P consortium applied a harmonised analysis to neuroimaging data across 29 international sites, including 1579 CHR-P individuals and 1243 HC, offering the largest pooled CHR-P neuroimaging dataset to date. Regional heterogeneity was indexed with the Variability Ratio (VR) and Coefficient of Variation (CV) ratio applied at the group level. Personalised estimates of heterogeneity of SA, CT and SV brain profiles were indexed with the novel Person-Based Similarity Index (PBSI), with two complementary applications. First, to assess the extent of within-diagnosis similarity or divergence of neuroanatomical profiles between individuals. Second, using a normative modelling approach, to assess the 'normativeness' of neuroanatomical profiles in individuals at CHR-P. CHR-P individuals demonstrated no greater regional heterogeneity after applying FDR corrections. However, PBSI scores indicated significantly greater neuroanatomical divergence in global SA, CT and SV profiles in CHR-P individuals compared with HC. Normative PBSI analysis identified 11 CHR-P individuals (0.70%) with marked deviation (>1.5 SD) in SA, 118 (7.47%) in CT and 161 (10.20%) in SV. Psychosis transition was not significantly associated with any measure of heterogeneity. Overall, our examination of neuroanatomical heterogeneity within the CHR-P state indicated greater divergence in neuroanatomical profiles at an individual level, irrespective of psychosis conversion. Further large-scale investigations are required of those who demonstrate marked deviation.

Published
2022

3. Clinical, cognitive and neuroanatomical associations of serum NMDAR autoantibodies in people at clinical high risk for psychosis

Author

Pollak, Thomas A, Kempton, Matthew J, Iyegbe, Conrad, Vincent, Angela, Irani, Sarosh R, Coutinho, Ester, Menassa, David A, Jacobson, Leslie, de Haan, Lieuwe, Ruhrmann, Stephan, Sachs, Gabriele, Riecher-Roessler, Anita, Krebs, Marie-Odile, Amminger, Paul, Glenthoj, Birte, Barrantes-Vidal, Neus, van Os, Jim, Rutten, Bart PF, Bressan, Rodrigo A, van der Gaag, Mark, Yolken, Robert, Hotopf, Matthew, Valmaggia, Lucia, Stone, James, David, Anthony S, McGuire, Philip, Calem, Maria, Tognin, Stefania, Modinos, Gemma, Velthorst, Eva, Kraan, Tamar C, van Dam, Daniella S, Burger, Nadine, Nelson, Barnaby, McGorry, Patrick, Pantelis, Christos, Politis, Athena, Goodall, Joanne, Borgwardt, Stefan, Ittig, Sarah, Studerus, Erich, Smieskova, Renata, Gadelha, Ary, Brietzke, Elisa, Asevedo, Graccielle, Asevedo, Elson, Zugman, Andre, Rosa, Araceli, Racioppi, Anna, Monsonet, Manel, Hinojosa-Marques, Lidia, Kwapil, Thomas R, Kazes, Mathilde, Daban, Claire, Bourgin, Julie, Gay, Olivier, Mam-Lam-Fook, Celia, Nordholm, Dorte, Randers, Lasse, Krakauer, Kristine, Glenthoj, Louise, Nordentoft, Merete, Gebhard, Dominika, Arnhold, Julia, Klosterkoetter, Joachim, Lasser, Iris, Winklbaur, Bernadette, Delespaul, Philippe A, Clinical Psychology, APH - Mental Health, RS: MHeNs - R2 - Mental Health, MUMC+: MA Psychiatrie (3), MUMC+: Hersen en Zenuw Centrum (3), Psychiatrie & Neuropsychologie, RS: MHeNs - R3 - Neuroscience, Adult Psychiatry, Amsterdam Neuroscience - Complex Trait Genetics, and Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep

Subjects
Psychosis, Biochemistry & Molecular Biology, NEURONAL AUTOANTIBODIES, Verbal learning, Prognostic markers, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, ASPARTATE RECEPTOR AUTOANTIBODIES, medicine, 1ST-EPISODE PSYCHOSIS, LIMBIC ENCEPHALITIS, RATING-SCALE, BRAIN, Molecular Biology, Autoimmune disease, Autoimmune encephalitis, Psychiatry, Science & Technology, HIPPOCAMPAL, business.industry, Limbic encephalitis, Case-control study, Autoantibody, Neurosciences, medicine.disease, 030227 psychiatry, Psychiatry and Mental health, INDIVIDUALS, nervous system, Immunology, ANTIBODIES, Schizophrenia, MENTAL STATE, Neurosciences & Neurology, business, Life Sciences & Biomedicine, 030217 neurology & neurosurgery, Neuroscience, Psychopathology
Abstract

Serum neuronal autoantibodies, such as those to the NMDA receptor (NMDAR), are detectable in a subgroup of patients with psychotic disorders. It is not known if they are present before the onset of psychosis or whether they are associated with particular clinical features or outcomes. In a case–control study, sera from 254 subjects at clinical high risk (CHR) for psychosis and 116 healthy volunteers were tested for antibodies against multiple neuronal antigens implicated in CNS autoimmune disorders, using fixed and live cell-based assays (CBAs). Within the CHR group, the relationship between NMDAR antibodies and symptoms, cognitive function and clinical outcomes over 24 month follow-up was examined. CHR subjects were not more frequently seropositive for neuronal autoantibodies than controls (8.3% vs. 5.2%; OR = 1.50; 95% CI: 0.58–3.90). The NMDAR was the most common target antigen and NMDAR IgGs were more sensitively detected with live versus fixed CBAs (p p p p p

Published
2021
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4. Association of Adverse Outcomes With Emotion Processing and Its Neural Substrate in Individuals at Clinical High Risk for Psychosis

Author

Modinos, Gemma, Kempton, Matthew J, Tognin, Stefania, Calem, Maria, Porffy, Lilla, Antoniades, Mathilde, Mason, Ava, Azis, Matilda, Allen, Paul, Nelson, Barnaby, McGorry, Patrick, Pantelis, Christos, Riecher-Rossler, Anita, Borgwardt, Stefan, Bressan, Rodrigo, Barrantes-Vidal, Neus, Krebs, Marie-Odile, Nordentoft, Merete, Glenthoj, Birte, Ruhrmann, Stephan, Sachs, Gabriele, Rutten, Bart, van Os, Jim, de Haan, Lieuwe, Velthorst, Eva, van der Gaag, Mark, Valmaggia, Lucia R, McGuire, Philip, Kraan, Tamar C, van Dam, Daniella S, Burger, Nadine, Amminger, G Paul, Politis, Athena, Goodall, Joanne, Rapp, Charlotte, Ittig, Sarah, Studerus, Erich, Smieskova, Renata, Gadelha, Ary, Brietzke, Elisa, Asevedo, Graccielle, Asevedo, Elson, Zugman, Andre, Dominguez-Martinez, Tecelli, Monsonet, Manel, Hinojosa, Lidia, Racioppi, Anna, Kwapil, Thomas R, Kazes, Mathilde, Daban, Claire, Bourgin, Julie, Gay, Olivier, Mam-Lam-Fook, Celia, Nordholm, Dorte, Randers, Lasse, Krakauer, Kristine, Glenthoj, Louise Birkedal, Gebhard, Dominika, Arnhold, Julia, Klosterkotter, Joachim, Lasser, Iris, Winklbaur, Bernadette, Delespaul, Philippe A, MUMC+: MA Psychiatrie (3), Psychiatrie & Neuropsychologie, RS: MHeNs - R3 - Neuroscience, RS: MHeNs - R2 - Mental Health, Adult Psychiatry, ANS - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Mental Health, King‘s College London, University of Roehampton, United Kingdom, University of Melbourne, University of Copenhagen = Københavns Universitet (KU), University of Basel (Unibas), Universidade Federal de São Paulo-Escola Paulista de Medicina [Brazil] (UNIFESP-EPM), Universitat Autònoma de Barcelona (UAB), Institut de psychiatrie et neurosciences de Paris (IPNP - U1266 Inserm), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), University of Cologne, Medizinische Universität Wien = Medical University of Vienna, Maastricht University [Maastricht], University Medical Center [Utrecht], Amsterdam UMC, Icahn School of Medicine at Mount Sinai [New York] (MSSM), Vrije Universiteit Amsterdam [Amsterdam] (VU), Parnassia Psychiatric Institute [The Hague], South London and Maudsley NHS Foundation Trust, EU-GEI High Risk Study Group, University of Copenhagen = Københavns Universitet (UCPH), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Amsterdam UMC - Amsterdam University Medical Center, Martinez Rico, Clara, and Clinical Psychology

Subjects
Male, Global Assessment of Functioning, Emotions, Psychological intervention, FEARFUL FACES, PREFRONTAL CORTEX, 0302 clinical medicine, SCHIZOPHRENIA, Young adult, Gray Matter, Emotional Intelligence, Psychiatry, [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, Socioemotional selectivity theory, Brain, Magnetic Resonance Imaging, NEUTRAL FACES, 3. Good health, Psychiatry and Mental health, SOCIAL COGNITION, Female, Life Sciences & Biomedicine, Facial Recognition, Clinical psychology, Psychosis, longitudinal, Bipolar disorder, Neuroimaging, functioning, 03 medical and health sciences, Young Adult, medicine, trajectories, Humans, GRAY-MATTER VOLUME, METAANALYSIS, Psychiatric Status Rating Scales, Science & Technology, business.industry, Case-control study, RECOGNITION, prediction, medicine.disease, 030227 psychiatry, Psychotic Disorders, ULTRA-HIGH RISK, Case-Control Studies, ONSET, business, Insula, 030217 neurology & neurosurgery, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract

Ajuts: Project is funded by grant agreement HEALTH-F2-2010-241909 (Project EU-GEI) from the European Community Seventh Framework Programme. Additional financial support was obtained from the Institut National de la Santé et de la Recherche Médicale (recurrent funding and fellowships) and by Fondation Pierre Deniker. The study received grant 08-MNP-007 from the French government Agence Nationale de la Recherche and grant AOM-07-118 (Influence of Cannabis Psychopathological Outcome in At-risk Mental State [ICAAR study]) from the French Health Ministry Programme Hospitalier de Recherche Clinique. The Sainte-Anne Hospital Center promoted the study. Dr Kempton was supported by a Medical Research Council Fellowship grant MR/J008915/1. Dr Pantelis was supported by Australia's National Health and Medical Research Council Senior Principal Research Fellowship (ID: 628386 & 1105825) and by grant R246-2016-3237 from the Lundbeck Foundation. Dr Barrantes-Vidal was supported by the Ministerio de Ciencia, Innovación e Universidades (PSI2017-87512-C2-1-R), and the Generalitat de Catalunya (2017SGR1612 and ICREA Academia Award). Dr Modinos was supported by a Sir Henry Dale Fellowship #202397/Z/16/Z, jointly funded by The Wellcome Trust and the Royal Society. This case-control study analyzes emotion recognition and neuroimaging data as well as clinical and functional outcomes for individuals at risk for transition to psychosis and those without psychiatric or neurological disorders. Is altered emotion recognition associated with adverse clinical and functional outcomes in people at clinical high risk for psychosis? In this case-control study of 213 individuals at clinical high risk for psychosis and 52 healthy participants, abnormalities in the recognition of negative emotion at baseline were associated with neuroanatomical alterations in the medial prefrontal cortex and hippocampus and with a low level of functioning at a 12-month follow-up. This study found that, in people with high risk for developing psychosis, functional outcomes are associated with the degree to which their emotion processing is altered. The development of adverse clinical outcomes in patients with psychosis has been associated with behavioral and neuroanatomical deficits related to emotion processing. However, the association between alterations in brain regions subserving emotion processing and clinical outcomes remains unclear. To examine the association between alterations in emotion processing and regional gray matter volumes in individuals at clinical high risk (CHR) for psychosis, and the association with subsequent clinical outcomes. This naturalistic case-control study with clinical follow-up at 12 months was conducted from July 1, 2010, to August 31, 2016, and collected data from 9 psychosis early detection centers (Amsterdam, Basel, Cologne, Copenhagen, London, Melbourne, Paris, The Hague, and Vienna). Participants (213 individuals at CHR and 52 healthy controls) were enrolled in the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) project. Data were analyzed from October 1, 2018, to April 24, 2019. Emotion recognition was assessed with the Degraded Facial Affect Recognition Task. Three-Tesla magnetic resonance imaging scans were acquired from all participants, and gray matter volume was measured in regions of interest (medial prefrontal cortex, amygdala, hippocampus, and insula). Clinical outcomes at 12 months were evaluated for transition to psychosis using the Comprehensive Assessment of At-Risk Mental States criteria, and the level of overall functioning was measured through the Global Assessment of Functioning [GAF] scale. A total of 213 individuals at CHR (105 women [49.3%]; mean [SD] age, 22.9 [4.7] years) and 52 healthy controls (25 women [48.1%]; mean [SD] age, 23.3 [4.0] years) were included in the study at baseline. At the follow-up within 2 years of baseline, 44 individuals at CHR (20.7%) had developed psychosis and 169 (79.3%) had not. Of the individuals at CHR reinterviewed with the GAF, 39 (30.0%) showed good overall functioning (GAF score, ≥65), whereas 91 (70.0%) had poor overall functioning (GAF score

Published
2019
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