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Calarco N, Oliver LD, Joseph M, Hawco C, Dickie EW, DeRosse P, Gold JM, Foussias G, Argyelan M, Malhotra AK, Buchanan RW, Voineskos AN. Multivariate Associations Among White Matter, Neurocognition, and Social Cognition Across Individuals With Schizophrenia Spectrum Disorders and Healthy Controls. Schizophr Bull 2023; 49:1518-1529. [PMID: 36869812 PMCID: PMC10686342 DOI: 10.1093/schbul/sbac216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
BACKGROUND AND HYPOTHESIS Neurocognitive and social cognitive abilities are important contributors to functional outcomes in schizophrenia spectrum disorders (SSDs). An unanswered question of considerable interest is whether neurocognitive and social cognitive deficits arise from overlapping or distinct white matter impairment(s). STUDY DESIGN We sought to fill this gap, by harnessing a large sample of individuals from the multi-center Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) dataset, unique in its collection of advanced diffusion imaging and an extensive battery of cognitive assessments. We applied canonical correlation analysis to estimates of white matter microstructure, and cognitive performance, across people with and without an SSD. STUDY RESULTS Our results established that white matter circuitry is dimensionally and strongly related to both neurocognition and social cognition, and that microstructure of the uncinate fasciculus and the rostral body of the corpus callosum may assume a "privileged role" subserving both. Further, we found that participant-wise estimates of white matter microstructure, weighted by cognitive performance, were largely consistent with participants' categorical diagnosis, and predictive of (cross-sectional) functional outcomes. CONCLUSIONS The demonstrated strength of the relationship between white matter circuitry and neurocognition and social cognition underscores the potential for using relationships among these variables to identify biomarkers of functioning, with potential prognostic and therapeutic implications.
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Affiliation(s)
- Navona Calarco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Lindsay D Oliver
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Michael Joseph
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Pamela DeRosse
- Division of Psychiatry Research, Division of Northwell Health, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - James M Gold
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - George Foussias
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Miklos Argyelan
- Division of Psychiatry Research, Division of Northwell Health, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Anil K Malhotra
- Division of Psychiatry Research, Division of Northwell Health, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Robert W Buchanan
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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Chauvière L. Early cognitive comorbidities before disease onset: A common symptom towards prevention of related brain diseases? Heliyon 2022; 8:e12259. [PMID: 36590531 PMCID: PMC9800323 DOI: 10.1016/j.heliyon.2022.e12259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 12/01/2022] [Accepted: 12/02/2022] [Indexed: 12/15/2022] Open
Abstract
Brain diseases are very heterogeneous; however they also display multiple common risk factors and comorbidities. With a paucity of disease-modifying therapies, prevention became a health priority. Towards prevention, one strategy is to focus on similar symptoms of brain diseases occurring before disease onset. Cognitive deficits are a promising candidate as they occur across brain diseases before disease onset. Based on recent research, this review highlights the similarity of brain diseases and discusses how early cognitive deficits can be exploited to tackle disease prevention. After briefly introducing common risk factors, I review common comorbidities across brain diseases, with a focus on cognitive deficits before disease onset, reporting both experimental and clinical findings. Next, I describe network abnormalities associated with early cognitive deficits and discuss how these abnormalities can be targeted to prevent disease onset. A scenario on brain disease etiology with the idea that early cognitive deficits may constitute a common symptom of brain diseases is proposed.
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Allen P, Hird EJ, Orlov N, Modinos G, Bossong M, Antoniades M, Sampson C, Azis M, Howes O, Stone J, Perez J, Broome M, Grace AA, McGuire P. Adverse clinical outcomes in people at clinical high-risk for psychosis related to altered interactions between hippocampal activity and glutamatergic function. Transl Psychiatry 2021; 11:579. [PMID: 34759289 PMCID: PMC8580992 DOI: 10.1038/s41398-021-01705-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 10/26/2021] [Indexed: 12/30/2022] Open
Abstract
Preclinical rodent models suggest that psychosis involves alterations in the activity and glutamatergic function in the hippocampus, driving dopamine activity through projections to the striatum. The extent to which this model applies to the onset of psychosis in clinical subjects is unclear. We assessed whether interactions between hippocampal glutamatergic function and activity/striatal connectivity are associated with adverse clinical outcomes in people at clinical high-risk (CHR) for psychosis. We measured functional Magnetic Resonance Imaging of hippocampal activation/connectivity, and 1H-Magnetic Resonance Spectroscopy of hippocampal glutamatergic metabolites in 75 CHR participants and 31 healthy volunteers. At follow-up, 12 CHR participants had transitioned to psychosis and 63 had not. Within the clinical high-risk cohort, at follow-up, 35 and 17 participants had a poor or a good functional outcome, respectively. The onset of psychosis (ppeakFWE = 0.003, t = 4.4, z = 4.19) and a poor functional outcome (ppeakFWE < 0.001, t = 5.52, z = 4.81 and ppeakFWE < 0.001, t = 5.25, z = 4.62) were associated with a negative correlation between the hippocampal activation and hippocampal Glx concentration at baseline. In addition, there was a negative association between hippocampal Glx concentration and hippocampo-striatal connectivity (ppeakFWE = 0.016, t = 3.73, z = 3.39, ppeakFWE = 0.014, t = 3.78, z = 3.42, ppeakFWE = 0.011, t = 4.45, z = 3.91, ppeakFWE = 0.003, t = 4.92, z = 4.23) in the total CHR sample, not seen in healthy volunteers. As predicted by preclinical models, adverse clinical outcomes in people at risk for psychosis are associated with altered interactions between hippocampal activity and glutamatergic function.
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Affiliation(s)
- Paul Allen
- Department of Psychology, University of Roehampton, London, UK
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Icahn School of Medicine, Mount Sinai Hospital, New York, NY, USA
| | - Emily J Hird
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- National Institute of Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust, London, UK.
| | - Natasza Orlov
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Liu Lab, Harvard Medical School, Athinoula Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Lab for Precision Brain Imaging, Department of Neuroscience, Precision Brain Imaging Lab, Medical University of South Carolina, Charleston, SC, USA
| | - Gemma Modinos
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Neuroimaging, Institute of Psychiatry, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Matthijs Bossong
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mathilde Antoniades
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Carly Sampson
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Matilda Azis
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Oliver Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- National Institute of Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust, London, UK
- Medical Research Council London Institute of Medical Sciences, Hammersmith Hospital, London, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - James Stone
- Department of Neuroimaging, Institute of Psychiatry, King's College London, London, UK
| | - Jesus Perez
- CAMEO Early Intervention in Psychosis Service, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Matthew Broome
- School of Psychology, University of Birmingham, Birmingham, UK
| | - Anthony A Grace
- Departments of Neuroscience, Psychiatry and Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- National Institute of Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust, London, UK
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Early magnetic resonance imaging biomarkers of schizophrenia spectrum disorders: Toward a fetal imaging perspective. Dev Psychopathol 2021; 33:899-913. [PMID: 32489161 DOI: 10.1017/s0954579420000218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
There is mounting evidence to implicate the intrauterine environment as the initial pathogenic stage for neuropsychiatric disease. Recent developments in magnetic resonance imaging technology are making a multimodal analysis of the fetal central nervous system a reality, allowing analysis of structural and functional parameters. Exposures to a range of pertinent risk factors whether preconception or in utero can now be indexed using imaging techniques within the fetus' physiological environment. This approach may determine the first "hit" required for diseases that do not become clinically manifest until adulthood, and which only have subtle clinical markers during childhood and adolescence. A robust characterization of a "multi-hit" hypothesis may necessitate a longitudinal birth cohort; within this investigative paradigm, the full range of genetic and environmental risk factors can be assessed for their impact on the early developing brain. This will lay the foundation for the identification of novel biomarkers and the ability to devise methods for early risk stratification and disease prevention. However, these early markers must be followed over time: first, to account for neural plasticity, and second, to assess the effects of postnatal exposures that continue to drive the individual toward disease. We explore these issues using the schizophrenia spectrum disorders as an illustrative paradigm. However, given the potential richness of fetal magnetic resonance imaging, and the likely overlap of biomarkers, these concepts may extend to a range of neuropsychiatric conditions.
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Scarpazza C, Miolla A, Zampieri I, Melis G, Sartori G, Ferracuti S, Pietrini P. Translational Application of a Neuro-Scientific Multi-Modal Approach Into Forensic Psychiatric Evaluation: Why and How? Front Psychiatry 2021; 12:597918. [PMID: 33613339 PMCID: PMC7892615 DOI: 10.3389/fpsyt.2021.597918] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 01/14/2021] [Indexed: 01/01/2023] Open
Abstract
A prominent body of literature indicates that insanity evaluations, which are intended to provide influential expert reports for judges to reach a decision "beyond any reasonable doubt," suffer from a low inter-rater reliability. This paper reviews the limitations of the classical approach to insanity evaluation and the criticisms to the introduction of neuro-scientific approach in court. Here, we explain why in our opinion these criticisms, that seriously hamper the translational implementation of neuroscience into the forensic setting, do not survive scientific scrutiny. Moreover, we discuss how the neuro-scientific multimodal approach may improve the inter-rater reliability in insanity evaluation. Critically, neuroscience does not aim to introduce a brain-based concept of insanity. Indeed, criteria for responsibility and insanity are and should remain clinical. Rather, following the falsificationist approach and the convergence of evidence principle, the neuro-scientific multimodal approach is being proposed as a way to improve reliability of insanity evaluation and to mitigate the influence of cognitive biases on the formulation of insanity opinions, with the final aim to reduce errors and controversies.
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Affiliation(s)
- Cristina Scarpazza
- Department of General Psychology, University of Padova, Padova, Italy
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Alessio Miolla
- Department of General Psychology, University of Padova, Padova, Italy
| | - Ilaria Zampieri
- Molecular Mind Laboratory, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Giulia Melis
- Department of General Psychology, University of Padova, Padova, Italy
| | - Giuseppe Sartori
- Department of General Psychology, University of Padova, Padova, Italy
| | - Stefano Ferracuti
- Department of Human Neurosciences, “Sapienza” University of Rome, Rome, Italy
| | - Pietro Pietrini
- Molecular Mind Laboratory, IMT School for Advanced Studies Lucca, Lucca, Italy
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Dickens AM, Sen P, Kempton MJ, Barrantes-Vidal N, Iyegbe C, Nordentoft M, Pollak T, Riecher-Rössler A, Ruhrmann S, Sachs G, Bressan R, Krebs MO, Amminger GP, de Haan L, van der Gaag M, Valmaggia L, Hyötyläinen T, Orešič M, McGuire P. Dysregulated Lipid Metabolism Precedes Onset of Psychosis. Biol Psychiatry 2021; 89:288-297. [PMID: 32928501 DOI: 10.1016/j.biopsych.2020.07.012] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 06/16/2020] [Accepted: 07/19/2020] [Indexed: 01/21/2023]
Abstract
BACKGROUND A key clinical challenge in the management of individuals at clinical high risk for psychosis (CHR) is that it is difficult to predict their future clinical outcomes. Here, we investigated if the levels of circulating molecular lipids are related to adverse clinical outcomes in this group. METHODS Serum lipidomic analysis was performed in 263 CHR individuals and 51 healthy control subjects, who were then clinically monitored for up to 5 years. Machine learning was used to identify lipid profiles that discriminated between CHR and control subjects, and between subgroups of CHR subjects with distinct clinical outcomes. RESULTS At baseline, compared with control subjects, CHR subjects (independent of outcome) had higher levels of triacylglycerols with a low acyl carbon number and a double bond count, as well as higher levels of lipids in general. CHR subjects who subsequently developed psychosis (n = 50) were distinguished from those that did not (n = 213) on the basis of lipid profile at baseline using a model with an area under the receiver operating curve of 0.81 (95% confidence interval = 0.69-0.93). CHR subjects who became psychotic had lower levels of ether phospholipids than CHR individuals who did not (p < .01). CONCLUSIONS Collectively, these data suggest that lipidomic abnormalities predate the onset of psychosis and that blood lipidomic measures may be useful in predicting which CHR individuals are most likely to develop psychosis.
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Affiliation(s)
- Alex M Dickens
- Turku Bioscience Center, University of Turku and Åbo Akademi University, Turku, Finland
| | - Partho Sen
- Turku Bioscience Center, University of Turku and Åbo Akademi University, Turku, Finland
| | - Matthew J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Neus Barrantes-Vidal
- Departament de Psicologia Clínica i de la Salut, Universitat Autònoma de Barcelona, Fundació Sanitària Sant Pere Claver, Spanish Mental Health Research Network, Barcelona, Spain
| | - Conrad Iyegbe
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Merete Nordentoft
- Mental Health Center Copenhagen and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Center Glostrup, Mental Health Services in the Capital Region of Copenhagen, University of Copenhagen, Glostrup, Denmark
| | - Thomas Pollak
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany
| | - Gabriele Sachs
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Rodrigo Bressan
- Lab Interdisciplinar Neurociências Clínicas, Departimento Psiquiatria, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Marie-Odile Krebs
- University of Paris, Groupe Hospitalier Universitaire Paris Sainte-Anne, Centre d'Évaluation Pour Jeunes Adultes et Adolescents, Institut National de la Santé et de la Recherche Médicale U1266, Institut de Psychiatrie, Centre National de la Recherche Scientifique 3557, Paris, France
| | - G Paul Amminger
- Centre for Youth Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Lieuwe de Haan
- Department of Psychiatry, Amsterdam University Medical Center, Faculty of Behavioural and Movement Sciences, Vrije Universiteit, Amsterdam, The Netherlands
| | - Mark van der Gaag
- Department of Clinical Psychology and EMGO+ Institute for Health and Care Research, Faculty of Behavioural and Movement Sciences, Vrije Universiteit, Amsterdam, The Netherlands; Department of Psychosis Research, Parnassia Psychiatric Institute, The Hague, The Netherlands
| | - Lucia Valmaggia
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | | | - Matej Orešič
- Turku Bioscience Center, University of Turku and Åbo Akademi University, Turku, Finland; School of Medical Sciences, Örebro University, Örebro, Sweden.
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
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Lower speech connectedness linked to incidence of psychosis in people at clinical high risk. Schizophr Res 2021; 228:493-501. [PMID: 32951966 DOI: 10.1016/j.schres.2020.09.002] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 05/29/2020] [Accepted: 09/07/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND Formal thought disorder is a cardinal feature of psychotic disorders, and is also evident in subtle forms before psychosis onset in individuals at clinical high-risk for psychosis (CHR-P). Assessing speech output or assessing expressive language with speech as the medium at this stage may be particularly useful in predicting later transition to psychosis. METHOD Speech samples were acquired through administration of the Thought and Language Index (TLI) in 24 CHR-P participants, 16 people with first-episode psychosis (FEP) and 13 healthy controls. The CHR-P individuals were then followed clinically for a mean of 7 years (s.d. = 1.5) to determine if they transitioned to psychosis. Non-semantic speech graph analysis was used to assess the connectedness of transcribed speech in all groups. RESULTS Speech was significantly more disconnected in the FEP group than in both healthy controls (p < .01) and the CHR-P group (p < .05). Results remained significant when IQ was included as a covariate. Significant correlations were found between speech connectedness measures and scores on the TLI, a manual assessment of formal thought disorder. In the CHR-P group, lower scores on two measures of speech connectedness were associated with subsequent transition to psychosis (8 transitions, 16 non-transitions; p < .05). CONCLUSION These findings support the utility and validity of speech graph analysis methods in characterizing speech connectedness in the early phases of psychosis. This approach has the potential to be developed into an automated, objective and time-efficient way of stratifying individuals at CHR-P according to level of psychosis risk.
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Clinical, cognitive and neuroanatomical associations of serum NMDAR autoantibodies in people at clinical high risk for psychosis. Mol Psychiatry 2021; 26:2590-2604. [PMID: 33077853 PMCID: PMC8440194 DOI: 10.1038/s41380-020-00899-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2019] [Revised: 07/28/2020] [Accepted: 09/21/2020] [Indexed: 12/28/2022]
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 < 0.001). Preliminary phenotypic analyses revealed that within the CHR sample, the NMDAR antibody seropositive subjects had higher levels of current depression, performed worse on the Rey Auditory Verbal Learning Task (p < 0.05), and had a markedly lower IQ (p < 0.01). NMDAR IgGs were not more frequent in subjects who later became psychotic than those who did not. NMDAR antibody serostatus and titre was associated with poorer levels of functioning at follow-up (p < 0.05) and the presence of a neuronal autoantibody was associated with larger amygdala volumes (p < 0.05). Altogether, these findings demonstrate that NMDAR autoantibodies are detectable in a subgroup of CHR subjects at equal rates to controls. In the CHR group, they are associated with affective psychopathology, impairments in verbal memory, and overall cognitive function: these findings are qualitatively and individually similar to core features of autoimmune encephalitis and/or animal models of NMDAR antibody-mediated CNS disease. Overall the current work supports further evaluation of NMDAR autoantibodies as a possible prognostic biomarker and aetiological factor in a subset of people already meeting CHR criteria.
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Wang L, Li X, Zhu Y, Lin B, Bo Q, Li F, Wang C. Discriminative Analysis of Symptom Severity and Ultra-High Risk of Schizophrenia Using Intrinsic Functional Connectivity. Int J Neural Syst 2020; 30:2050047. [PMID: 32689843 DOI: 10.1142/s0129065720500471] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Past studies have consistently shown functional dysconnectivity of large-scale brain networks in schizophrenia. In this study, we aimed to further assess whether multivariate pattern analysis (MVPA) could yield a sensitive predictor of patient symptoms, as well as identify ultra-high risk (UHR) stage of schizophrenia from intrinsic functional connectivity of whole-brain networks. We first combined rank-based feature selection and support vector machine methods to distinguish between 43 schizophrenia patients and 52 healthy controls. The constructed classifier was then applied to examine functional connectivity profiles of 18 UHR individuals. The classifier indicated reliable relationship between MVPA measures and symptom severity, with higher classification accuracy in more severely affected schizophrenia patients. The UHR subjects had classification scores falling between those of healthy controls and patients, suggesting an intermediate level of functional brain abnormalities. Moreover, UHR individuals with schizophrenia-like connectivity profiles at baseline presented higher rate of conversion to full-blown illness in the follow-up visits. Spatial maps of discriminative brain regions implicated increases of functional connectivity in the default mode network, whereas decreases of functional connectivity in the cerebellum, thalamus and visual areas in schizophrenia. The findings may have potential utility in the early diagnosis and intervention of schizophrenia.
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Affiliation(s)
- Lubin Wang
- Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing 100850, P. R. China
| | - Xianbin Li
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, P. R. China
| | - Yuyang Zhu
- Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing 100850, P. R. China
| | - Bei Lin
- Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing 100850, P. R. China
| | - Qijing Bo
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, P. R. China
| | - Feng Li
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, P. R. China
| | - Chuanyue Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, P. R. China
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Scarpazza C, Ha M, Baecker L, Garcia-Dias R, Pinaya WHL, Vieira S, Mechelli A. Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders. Transl Psychiatry 2020; 10:107. [PMID: 32313006 PMCID: PMC7170931 DOI: 10.1038/s41398-020-0798-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 03/25/2020] [Indexed: 12/14/2022] Open
Abstract
A pivotal aim of psychiatric and neurological research is to promote the translation of the findings into clinical practice to improve diagnostic and prognostic assessment of individual patients. Structural neuroimaging holds much promise, with neuroanatomical measures accounting for up to 40% of the variance in clinical outcome. Building on these findings, a number of imaging-based clinical tools have been developed to make diagnostic and prognostic inferences about individual patients from their structural Magnetic Resonance Imaging scans. This systematic review describes and compares the technical characteristics of the available tools, with the aim to assess their translational potential into real-world clinical settings. The results reveal that a total of eight tools. All of these were specifically developed for neurological disorders, and as such are not suitable for application to psychiatric disorders. Furthermore, most of the tools were trained and validated in a single dataset, which can result in poor generalizability, or using a small number of individuals, which can cause overoptimistic results. In addition, all of the tools rely on two strategies to detect brain abnormalities in single individuals, one based on univariate comparison, and the other based on multivariate machine-learning algorithms. We discuss current barriers to the adoption of these tools in clinical practice and propose a checklist of pivotal characteristics that should be included in an "ideal" neuroimaging-based clinical tool for brain disorders.
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Affiliation(s)
- C Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, UK.
- Department of General Psychology, University of Padova, Padova, Italy.
| | - M Ha
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, UK
| | - L Baecker
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, UK
| | - R Garcia-Dias
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, UK
| | - W H L Pinaya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, UK
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
| | - S Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, UK
| | - A Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, UK
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11
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Cole DM, Diaconescu AO, Pfeiffer UJ, Brodersen KH, Mathys CD, Julkowski D, Ruhrmann S, Schilbach L, Tittgemeyer M, Vogeley K, Stephan KE. Atypical processing of uncertainty in individuals at risk for psychosis. NEUROIMAGE-CLINICAL 2020; 26:102239. [PMID: 32182575 PMCID: PMC7076146 DOI: 10.1016/j.nicl.2020.102239] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 02/24/2020] [Accepted: 03/06/2020] [Indexed: 12/28/2022]
Abstract
Humans at psychosis clinical high risk (CHR) over-estimate environmental volatility. Low-level prediction error (PE) signals evoke increased frontal activity in CHR. Volatility-related PEs are associated with reduced frontal activity in CHR. Frontal cortical activation to low-level PEs reflects impaired clinical functioning. Atypical PE learning signal representations may promote delusion formation in CHR.
Current theories of psychosis highlight the role of abnormal learning signals, i.e., prediction errors (PEs) and uncertainty, in the formation of delusional beliefs. We employed computational analyses of behaviour and functional magnetic resonance imaging (fMRI) to examine whether such abnormalities are evident in clinical high risk (CHR) individuals. Non-medicated CHR individuals (n = 13) and control participants (n = 13) performed a probabilistic learning paradigm during fMRI data acquisition. We used a hierarchical Bayesian model to infer subject-specific computations from behaviour – with a focus on PEs and uncertainty (or its inverse, precision) at different levels, including environmental ‘volatility’ – and used these computational quantities for analyses of fMRI data. Computational modelling of CHR individuals’ behaviour indicated volatility estimates converged to significantly higher levels than in controls. Model-based fMRI demonstrated increased activity in prefrontal and insular regions of CHR individuals in response to precision-weighted low-level outcome PEs, while activations of prefrontal, orbitofrontal and anterior insula cortex by higher-level PEs (that serve to update volatility estimates) were reduced. Additionally, prefrontal cortical activity in response to outcome PEs in CHR was negatively associated with clinical measures of global functioning. Our results suggest a multi-faceted learning abnormality in CHR individuals under conditions of environmental uncertainty, comprising higher levels of volatility estimates combined with reduced cortical activation, and abnormally high activations in prefrontal and insular areas by precision-weighted outcome PEs. This atypical representation of high- and low-level learning signals might reflect a predisposition to delusion formation.
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Affiliation(s)
- David M Cole
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland; Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Psychiatric Hospital of the University of Zurich, Zurich, Switzerland.
| | - Andreea O Diaconescu
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland; Department of Psychiatry (UPK), University of Basel, Basel, Switzerland; Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), University of Toronto, Toronto, Canada
| | - Ulrich J Pfeiffer
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Kay H Brodersen
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland
| | - Christoph D Mathys
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland; Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy; Interacting Minds Centre, Aarhus University, Aarhus, Denmark
| | - Dominika Julkowski
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Leonhard Schilbach
- Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany; Graduate School for Systemic Neuroscience, Munich, Germany; International Max Planck Research School for Translational Psychiatry, Munich, Germany; Ludwig-Maximilians-Universität München, Munich, Germany; Kliniken der Heinrich-Heine-Universität/LVR-Klinik Düsseldorf, Düsseldorf, Germany
| | - Marc Tittgemeyer
- Max Planck Institute for Metabolism Research, Cologne, Germany; Cologne Cluster of Excellence in Cellular Stress and Aging associated Disease (CECAD), Germany
| | - Kai Vogeley
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany; Institute for Neuroscience and Medicine - Cognitive Neuroscience (INM3), Research Center Juelich, Juelich, Germany
| | - Klaas E Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany; Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
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12
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Schmidt A, Borgwardt S. Implementing MR Imaging into Clinical Routine Screening in Patients with Psychosis? Neuroimaging Clin N Am 2020; 30:65-72. [DOI: 10.1016/j.nic.2019.09.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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13
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Vieira S, Lopez Pinaya WH, Mechelli A. Introduction to machine learning. Mach Learn 2020. [DOI: 10.1016/b978-0-12-815739-8.00001-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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14
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Huang X, Gong Q, Sweeney JA, Biswal BB. Progress in psychoradiology, the clinical application of psychiatric neuroimaging. Br J Radiol 2019; 92:20181000. [PMID: 31170803 PMCID: PMC6732936 DOI: 10.1259/bjr.20181000] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 05/09/2019] [Accepted: 05/21/2019] [Indexed: 02/05/2023] Open
Abstract
Psychoradiology is an emerging field that applies radiological imaging technologies to psychiatric conditions. In the past three decades, brain imaging techniques have rapidly advanced understanding of illness and treatment effects in psychiatry. Based on these advances, radiologists have become increasingly interested in applying these advances for differential diagnosis and individualized patient care selection for common psychiatric illnesses. This shift from research to clinical practice represents the beginning evolution of psychoradiology. In this review, we provide a summary of recent progress relevant to this field based on their clinical functions, namely the (1) classification and subtyping; (2) prediction and monitoring of treatment outcomes; and (3) treatment selection. In addition, we provide guidelines for the practice of psychoradiology in clinical settings and suggestions for future research to validate broader clinical applications. Given the high prevalence of psychiatric disorders and the importance of increased participation of radiologists in this field, a guide regarding advances in this field and a description of relevant clinical work flow patterns help radiologists contribute to this fast-evolving field.
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Affiliation(s)
| | | | - John A. Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, USA
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15
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Abstract
Mental health and substance use disorders are the leading cause of long-term disability and a cause of significant mortality, worldwide. However, it is widely recognised that clinical practice in psychiatry has not fundamentally changed for over half a century. The Royal College of Psychiatrists is reviewing its trainee curriculum to identify neuroscience that relates to psychiatric practice. To date though, neuroscience has had very little impact on routine clinical practice. We discuss how a pragmatic approach to neuroscience can address this problem together with a route to implementation in National Health Service care. This has implications for altered funding priorities and training future psychiatrists. Five training recommendations for psychiatrists are identified.Declaration of interestJ.D.S. receives direct funding from MRC Program Grant MR/S010351/1 aimed at developing machine learning-based methods for routinely acquired NHS data and indirect funding from the Wellcome Trust STRADL study. M.P.P. receives payments for an UpToDate chapter on methamphetamine and is principal investigator on the following grants: NIGMS P20GM121312 and NIDA U01 DA041089 and receives support from the William K. Warren Foundation.
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Affiliation(s)
| | - Martin P Paulus
- Laureate Institute for Brain Research,University of Tulsa,USA
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16
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Pollak TA, Rogers JP, Nagele RG, Peakman M, Stone JM, David AS, McGuire P. Antibodies in the Diagnosis, Prognosis, and Prediction of Psychotic Disorders. Schizophr Bull 2019; 45:233-246. [PMID: 29474698 PMCID: PMC6293207 DOI: 10.1093/schbul/sby021] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Blood-based biomarker discovery for psychotic disorders has yet to impact upon routine clinical practice. In physical disorders antibodies have established roles as diagnostic, prognostic and predictive (theranostic) biomarkers, particularly in disorders thought to have a substantial autoimmune or infective aetiology. Two approaches to antibody biomarker identification are distinguished: a "top-down" approach, in which antibodies to specific antigens are sought based on the known function of the antigen and its putative role in the disorder, and emerging "bottom-up" or "omics" approaches that are agnostic as to the significance of any one antigen, using high-throughput arrays to identify distinctive components of the antibody repertoire. Here we review the evidence for antibodies (to self-antigens as well as infectious organism and dietary antigens) as biomarkers of diagnosis, prognosis, and treatment response in psychotic disorders. Neuronal autoantibodies have current, and increasing, clinical utility in the diagnosis of organic or atypical psychosis syndromes. Antibodies to selected infectious agents show some promise in predicting cognitive impairment and possibly other symptom domains (eg, suicidality) within psychotic disorders. Finally, infectious antibodies and neuronal and other autoantibodies have recently emerged as potential biomarkers of response to anti-infective therapies, immunotherapies, or other novel therapeutic strategies in psychotic disorders, and have a clear role in stratifying patients for future clinical trials. As in nonpsychiatric disorders, combining biomarkers and large-scale use of "bottom-up" approaches to biomarker identification are likely to maximize the eventual clinical utility of antibody biomarkers in psychotic disorders.
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Affiliation(s)
- Thomas A Pollak
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Joint first authors
| | - Jonathan P Rogers
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Joint first authors
| | - Robert G Nagele
- Biomarker Discovery Center, New Jersey Institute for Successful Aging, Rowan University School of Osteopathic Medicine, Stratford, NJ
| | - Mark Peakman
- Department of Immunobiology, Faculty of Life Sciences & Medicine, King’s College London, London, UK
| | - James M Stone
- Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Anthony S David
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
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17
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Chin R, You AX, Meng F, Zhou J, Sim K. Recognition of Schizophrenia with Regularized Support Vector Machine and Sequential Region of Interest Selection using Structural Magnetic Resonance Imaging. Sci Rep 2018; 8:13858. [PMID: 30218016 PMCID: PMC6138658 DOI: 10.1038/s41598-018-32290-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 09/05/2018] [Indexed: 12/17/2022] Open
Abstract
Structural brain abnormalities in schizophrenia have been well characterized with the application of univariate methods to magnetic resonance imaging (MRI) data. However, these traditional techniques lack sensitivity and predictive value at the individual level. Machine-learning approaches have emerged as potential diagnostic and prognostic tools. We used an anatomically and spatially regularized support vector machine (SVM) framework to categorize schizophrenia and healthy individuals based on whole-brain gray matter densities estimated using voxel-based morphometry from structural MRI scans. The regularized SVM model yielded recognition accuracy of 86.6% in the training set of 127 individuals and validation accuracy of 83.5% in an independent set of 85 individuals. A sequential region-of-interest (ROI) selection step was adopted for feature selection, improving recognition accuracy to 92.0% in the training set and 89.4% in the validation set. The combined model achieved 96.6% sensitivity and 74.1% specificity. Seven ROIs were identified as the optimal discriminatory subset: the occipital fusiform gyrus, middle frontal gyrus, pars opercularis of the inferior frontal gyrus, anterior superior temporal gyrus, superior frontal gyrus, left thalamus and left lateral ventricle. These findings demonstrate the utility of spatial and anatomical priors in SVM for neuroimaging analyses in conjunction with sequential ROI selection in the recognition of schizophrenia.
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Affiliation(s)
- Rowena Chin
- Research Division, Institute of Mental Health, Singapore, 10 Buangkok View, Singapore, 539747, Singapore
| | - Alex Xiaobin You
- Health Services & Outcomes Research, National Healthcare Group, 3 Fusionopolis Link, Singapore, 138543, Singapore
| | - Fanwen Meng
- Health Services & Outcomes Research, National Healthcare Group, 3 Fusionopolis Link, Singapore, 138543, Singapore
| | - Juan Zhou
- Neuroscience & Behavioral Disorders Program, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
| | - Kang Sim
- Research Division, Institute of Mental Health, Singapore, 10 Buangkok View, Singapore, 539747, Singapore.
- West Region, Institute of Mental Health/Woodbridge Hospital, Singapore, 10 Buangkok View, Singapore, 539747, Singapore.
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18
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Using clinical information to make individualized prognostic predictions in people at ultra high risk for psychosis. Schizophr Res 2017; 184:32-38. [PMID: 27923525 PMCID: PMC5477095 DOI: 10.1016/j.schres.2016.11.047] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2016] [Revised: 10/14/2016] [Accepted: 11/29/2016] [Indexed: 11/22/2022]
Abstract
Recent studies have reported an association between psychopathology and subsequent clinical and functional outcomes in people at ultra-high risk (UHR) for psychosis. This has led to the suggestion that psychopathological information could be used to make prognostic predictions in this population. However, because the current literature is based on inferences at group level, the translational value of the findings for everyday clinical practice is unclear. Here we examined whether psychopathological information could be used to make individualized predictions about clinical and functional outcomes in people at UHR. Participants included 416 people at UHR followed prospectively at the Personal Assessment and Crisis Evaluation (PACE) Clinic in Melbourne, Australia. The data were analysed using Support Vector Machine (SVM), a supervised machine learning technique that allows inferences at the individual level. SVM predicted transition to psychosis with a specificity of 60.6%, a sensitivity of 68.6% and an accuracy of 64.6% (p<0.001). In addition, SVM predicted functioning with a specificity of 62.5%, a sensitivity of 62.5% and an accuracy of 62.5% (p=0.008). Prediction of transition was driven by disorder of thought content, attenuated positive symptoms and functioning, whereas functioning was best predicted by attention disturbances, anhedonia-asociality and disorder of thought content. These results indicate that psychopathological information allows individualized prognostic predictions with statistically significant accuracy. However, this level of accuracy may not be sufficient for clinical translation in real-world clinical practice. Accuracy might be improved by combining psychopathological information with other types of data using a multivariate machine learning framework.
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19
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Schmidt A, Crossley NA, Harrisberger F, Smieskova R, Lenz C, Riecher-Rössler A, Lang UE, McGuire P, Fusar-Poli P, Borgwardt S. Structural Network Disorganization in Subjects at Clinical High Risk for Psychosis. Schizophr Bull 2017; 43:583-591. [PMID: 27481826 PMCID: PMC5464048 DOI: 10.1093/schbul/sbw110] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Previous network studies in chronic schizophrenia patients revealed impaired structural organization of the brain's rich-club members, a set of highly interconnected hub regions that play an important integrative role for global brain communication. Moreover, impaired rich-club connectivity has also been found in unaffected siblings of schizophrenia patients, suggesting that abnormal rich-club connectivity is related to familiar, possibly reflecting genetic, vulnerability for schizophrenia. However, no study has yet investigated whether structural rich-club organization is also impaired in individuals with a clinical risk syndrome for psychosis. Diffusion tensor imaging and probabilistic tractography was used to construct structural whole-brain networks in 24 healthy controls and 24 subjects with an at-risk mental state (ARMS). Graph theory was applied to quantify the structural rich-club organization and global network properties. ARMS subjects revealed a significantly altered structural rich-club organization compared with the control group. The disruption of rich-club organization was associated with the severity of negative psychotic symptoms and led to an elevated level of modularity in ARMS subjects. This study shows that abnormal structural rich-club organization is already evident in clinical high-risk subjects for psychosis and further demonstrates the impact of rich-club disorganization on global network communication. Together with previous evidence in chronic schizophrenia patients and unaffected siblings, our findings suggest that abnormal structural rich-club organization may reflect an endophenotypic marker of psychosis.
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Affiliation(s)
- André Schmidt
- Department of Psychosis Studies, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, PO63 De Crespigny Park, London SE5 8AF, UK
| | - Nicolas A. Crossley
- Department of Psychosis Studies, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, PO63 De Crespigny Park, London SE5 8AF, UK
| | | | - Renata Smieskova
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Claudia Lenz
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | | | - Undine E. Lang
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Philip McGuire
- Department of Psychosis Studies, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, PO63 De Crespigny Park, London SE5 8AF, UK
| | - Paolo Fusar-Poli
- Department of Psychosis Studies, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, PO63 De Crespigny Park, London SE5 8AF, UK
| | - Stefan Borgwardt
- Department of Psychosis Studies, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, PO63 De Crespigny Park, London SE5 8AF, UK;,Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
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20
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Dietsche B, Kircher T, Falkenberg I. Structural brain changes in schizophrenia at different stages of the illness: A selective review of longitudinal magnetic resonance imaging studies. Aust N Z J Psychiatry 2017; 51:500-508. [PMID: 28415873 DOI: 10.1177/0004867417699473] [Citation(s) in RCA: 124] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
OBJECTIVE Schizophrenia is a devastating mental disorder accompanied by aberrant structural brain connectivity. The question whether schizophrenia is a progressive brain disorder is yet to be resolved. Thus, it is not clear when these structural alterations occur and how they develop over time. METHODS In our selective review, we summarized recent findings from longitudinal magnetic resonance imaging studies investigating structural brain alterations and its impact on clinical outcome at different stages of the illness: (1) subjects at ultra-high risk of developing psychosis, (2) patients with a first episode psychosis, and (3) chronically ill patients. Moreover, we reviewed studies examining the longitudinal effects of medication on brain structure in patients with schizophrenia. RESULTS (1) Studies from pre-clinical stages to conversion showed a more pronounced cortical gray matter loss (i.e. superior temporal and inferior frontal regions) in those individuals who later made transition to psychosis. (2) Studies investigating patients with a first episode psychosis revealed a decline in multiple gray matter regions (i.e. frontal regions and thalamus) over time as well as progressive cortical thinning in the superior and inferior frontal cortex. (3) Studies focusing on patients with chronic schizophrenia showed that gray matter decreased to a greater extent (i.e. frontal and temporal areas, thalamus, and cingulate cortices)-especially in poor-outcome patients. Very few studies reported effects on white matter microstructure in the longitudinal course of the illness. CONCLUSION There is adequate evidence to suggest that schizophrenia is associated with progressive gray matter abnormalities particularly during the initial stages of illness. However, causal relationships between structural changes and illness course-especially in chronically ill patients-should be interpreted with caution. Findings might be confounded by longer periods of treatment and higher doses of antipsychotics or epiphenomena related to the illness.
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Affiliation(s)
- Bruno Dietsche
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
| | - Irina Falkenberg
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
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21
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McGuire P. The Role of the Corpus Callosum in Psychosis. Biol Psychiatry 2016; 80:579-80. [PMID: 27663066 DOI: 10.1016/j.biopsych.2016.08.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 08/22/2016] [Indexed: 01/22/2023]
Affiliation(s)
- Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
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22
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Wang C, Ji F, Hong Z, Poh JS, Krishnan R, Lee J, Rekhi G, Keefe RSE, Adcock RA, Wood SJ, Fornito A, Pasternak O, Chee MWL, Zhou J. Disrupted salience network functional connectivity and white-matter microstructure in persons at risk for psychosis: findings from the LYRIKS study. Psychol Med 2016; 46:2771-2783. [PMID: 27396386 PMCID: PMC5358474 DOI: 10.1017/s0033291716001410] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2016] [Revised: 05/12/2016] [Accepted: 05/16/2016] [Indexed: 11/30/2022]
Abstract
BACKGROUND Salience network (SN) dysconnectivity has been hypothesized to contribute to schizophrenia. Nevertheless, little is known about the functional and structural dysconnectivity of SN in subjects at risk for psychosis. We hypothesized that SN functional and structural connectivity would be disrupted in subjects with At-Risk Mental State (ARMS) and would be associated with symptom severity and disease progression. METHOD We examined 87 ARMS and 37 healthy participants using both resting-state functional magnetic resonance imaging and diffusion tensor imaging. Group differences in SN functional and structural connectivity were examined using a seed-based approach and tract-based spatial statistics. Subject-level functional connectivity measures and diffusion indices of disrupted regions were correlated with CAARMS scores and compared between ARMS with and without transition to psychosis. RESULTS ARMS subjects exhibited reduced functional connectivity between the left ventral anterior insula and other SN regions. Reduced fractional anisotropy (FA) and axial diffusivity were also found along white-matter tracts in close proximity to regions of disrupted functional connectivity, including frontal-striatal-thalamic circuits and the cingulum. FA measures extracted from these disrupted white-matter regions correlated with individual symptom severity in the ARMS group. Furthermore, functional connectivity between the bilateral insula and FA at the forceps minor were further reduced in subjects who transitioned to psychosis after 2 years. CONCLUSIONS Our findings support the insular dysconnectivity of the proximal SN hypothesis in the early stages of psychosis. Further developed, the combined structural and functional SN assays may inform the prognosis of persons at-risk for psychosis.
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Affiliation(s)
- C. Wang
- Center for Cognitive Neuroscience,
Neuroscience and Behavioral Disorder Program, Duke-NUS
Medical School, National University of Singapore,
Singapore
| | - F. Ji
- Center for Cognitive Neuroscience,
Neuroscience and Behavioral Disorder Program, Duke-NUS
Medical School, National University of Singapore,
Singapore
| | - Z. Hong
- Center for Cognitive Neuroscience,
Neuroscience and Behavioral Disorder Program, Duke-NUS
Medical School, National University of Singapore,
Singapore
| | - J. S. Poh
- Center for Cognitive Neuroscience,
Neuroscience and Behavioral Disorder Program, Duke-NUS
Medical School, National University of Singapore,
Singapore
| | - R. Krishnan
- Center for Cognitive Neuroscience,
Neuroscience and Behavioral Disorder Program, Duke-NUS
Medical School, National University of Singapore,
Singapore
| | - J. Lee
- Research Division,
Institute of Mental Health, Singapore
- Office of Clinical Sciences,
Duke-NUS Medical School, Singapore
| | - G. Rekhi
- Research Division,
Institute of Mental Health, Singapore
| | - R. S. E. Keefe
- Department of Psychiatry and Behavioral
Sciences, Duke University, Durham,
NC, USA
| | - R. A. Adcock
- Department of Psychiatry and Behavioral
Sciences, Duke University, Durham,
NC, USA
- Center for Cognitive Neuroscience,
Duke University, Durham, NC,
USA
| | - S. J. Wood
- School of Psychology,
University of Birmingham, Edgbaston,
UK
- Department of Psychiatry,
Melbourne Neuropsychiatry Centre, University of
Melbourne and Melbourne Health, Victoria,
Australia
| | - A. Fornito
- Monash Clinical and Imaging
Neuroscience, School of Psychology and Psychiatry & Monash
Biomedical Imaging, Monash University,
Australia
| | - O. Pasternak
- Departments of Psychiatry and Radiology,
Brigham and Women's Hospital, Harvard Medical
School, Boston, MA, USA
| | - M. W. L. Chee
- Center for Cognitive Neuroscience,
Neuroscience and Behavioral Disorder Program, Duke-NUS
Medical School, National University of Singapore,
Singapore
| | - J. Zhou
- Center for Cognitive Neuroscience,
Neuroscience and Behavioral Disorder Program, Duke-NUS
Medical School, National University of Singapore,
Singapore
- Clinical Imaging Research Centre, the Agency for
Science, Technology and Research and National University of
Singapore, Singapore
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23
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Zugman A, Sato JR, Jackowski AP. Crisis in neuroimaging: is neuroimaging failing 15 years after the decade of the brain? REVISTA BRASILEIRA DE PSIQUIATRIA (SAO PAULO, BRAZIL : 1999) 2016; 38:267-269. [PMID: 27828560 PMCID: PMC7111344 DOI: 10.1590/1516-4446-2016-2071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- André Zugman
- Laboratório Interdisciplinar de Neurociências Clínicas (LiNC), Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo, SP Brazil
| | - João R. Sato
- Centro de Matemática, Computação e Cognição, Universidade Federal do ABC (UFABC), Santo André, SP, Brazil
| | - Andrea P. Jackowski
- Laboratório Interdisciplinar de Neurociências Clínicas (LiNC), Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo, SP Brazil
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24
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Abstract
Despite a lack of recent progress in the treatment of schizophrenia, our understanding of its genetic and environmental causes has considerably improved, and their relationship to aberrant patterns of neurodevelopment has become clearer. This raises the possibility that 'disease-modifying' strategies could alter the course to - and of - this debilitating disorder, rather than simply alleviating symptoms. A promising window for course-altering intervention is around the time of the first episode of psychosis, especially in young people at risk of transition to schizophrenia. Indeed, studies performed in both individuals at risk of developing schizophrenia and rodent models for schizophrenia suggest that pre-diagnostic pharmacotherapy and psychosocial or cognitive-behavioural interventions can delay or moderate the emergence of psychosis. Of particular interest are 'hybrid' strategies that both relieve presenting symptoms and reduce the risk of transition to schizophrenia or another psychiatric disorder. This Review aims to provide a broad-based consideration of the challenges and opportunities inherent in efforts to alter the course of schizophrenia.
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25
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Cipriani A, Geddes J. Predicting treatment outcome in depression: so far, so good. Lancet Psychiatry 2016; 3:192-4. [PMID: 26803398 DOI: 10.1016/s2215-0366(15)00542-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Revised: 11/21/2015] [Accepted: 11/23/2015] [Indexed: 12/31/2022]
Affiliation(s)
- Andrea Cipriani
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK.
| | - John Geddes
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
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