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Luther L, Jarvis SA, Spilka MJ, Strauss GP. Global reward processing deficits predict negative symptoms transdiagnostically and transphasically in a severe mental illness-spectrum sample. Eur Arch Psychiatry Clin Neurosci 2024; 274:1729-1740. [PMID: 38051397 DOI: 10.1007/s00406-023-01714-7] [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] [Received: 03/22/2023] [Accepted: 10/29/2023] [Indexed: 12/07/2023]
Abstract
Reward processing impairments are a key factor associated with negative symptoms in those with severe mental illnesses. However, past findings are inconsistent regarding which reward processing components are impaired and most strongly linked to negative symptoms. The current study examined the hypothesis that these mixed findings may be the result of multiple reward processing pathways (i.e., equifinality) to negative symptoms that cut across diagnostic boundaries and phases of illness. Participants included healthy controls (n = 100) who served as a reference sample and a severe mental illness-spectrum sample (n = 92) that included psychotic-like experiences, clinical high-risk for psychosis, bipolar disorder, and schizophrenia participants. All participants completed tasks measuring four RDoC Positive Valence System constructs: value representation, reinforcement learning, effort-cost computation, and hedonic reactivity. A k-means cluster analysis of the severe mental illness-spectrum samples identified three clusters with differential reward processing profiles that were characterized by: (1) global reward processing deficits (22.8%), (2) selective impairments in hedonic reactivity alone (40.2%), and (3) preserved reward processing (37%). Elevated negative symptoms were only observed in the global reward processing cluster. All clusters contained participants from each clinical group, and the distribution of these groups did not significantly differ among the clusters. Findings identified one pathway contributing to negative symptoms that was transdiagnostic and transphasic. Future work further characterizing divergent pathways to negative symptoms may help to improve symptom trajectories and personalized treatments.
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Affiliation(s)
- Lauren Luther
- Department of Psychology, University of Georgia, 125 Baldwin St., Athens, GA, 30602, USA.
| | - Sierra A Jarvis
- Department of Psychology, University of Georgia, 125 Baldwin St., Athens, GA, 30602, USA
| | - Michael J Spilka
- Department of Psychology, University of Georgia, 125 Baldwin St., Athens, GA, 30602, USA
| | - Gregory P Strauss
- Department of Psychology, University of Georgia, 125 Baldwin St., Athens, GA, 30602, USA.
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2
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Meyhoefer I, Sprenger A, Derad D, Grotegerd D, Leenings R, Leehr EJ, Breuer F, Surmann M, Rolfes K, Arolt V, Romer G, Lappe M, Rehder J, Koutsouleris N, Borgwardt S, Schultze-Lutter F, Meisenzahl E, Kircher TTJ, Keedy SS, Bishop JR, Ivleva EI, McDowell JE, Reilly JL, Hill SK, Pearlson GD, Tamminga CA, Keshavan MS, Gershon ES, Clementz BA, Sweeney JA, Hahn T, Dannlowski U, Lencer R. Evidence from comprehensive independent validation studies for smooth pursuit dysfunction as a sensorimotor biomarker for psychosis. Sci Rep 2024; 14:13859. [PMID: 38879556 PMCID: PMC11180169 DOI: 10.1038/s41598-024-64487-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 06/10/2024] [Indexed: 06/19/2024] Open
Abstract
Smooth pursuit eye movements are considered a well-established and quantifiable biomarker of sensorimotor function in psychosis research. Identifying psychotic syndromes on an individual level based on neurobiological markers is limited by heterogeneity and requires comprehensive external validation to avoid overestimation of prediction models. Here, we studied quantifiable sensorimotor measures derived from smooth pursuit eye movements in a large sample of psychosis probands (N = 674) and healthy controls (N = 305) using multivariate pattern analysis. Balanced accuracies of 64% for the prediction of psychosis status are in line with recent results from other large heterogenous psychiatric samples. They are confirmed by external validation in independent large samples including probands with (1) psychosis (N = 727) versus healthy controls (N = 292), (2) psychotic (N = 49) and non-psychotic bipolar disorder (N = 36), and (3) non-psychotic affective disorders (N = 119) and psychosis (N = 51) yielding accuracies of 65%, 66% and 58%, respectively, albeit slightly different psychosis syndromes. Our findings make a significant contribution to the identification of biologically defined profiles of heterogeneous psychosis syndromes on an individual level underlining the impact of sensorimotor dysfunction in psychosis.
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Affiliation(s)
- Inga Meyhoefer
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Duesseldorf/LVR, Duesseldorf, Germany
| | - Andreas Sprenger
- Department of Neurology, University of Luebeck, Luebeck, Germany
| | - David Derad
- Department of Neurology, University of Luebeck, Luebeck, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Fabian Breuer
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Marian Surmann
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Karen Rolfes
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Volker Arolt
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
| | - Georg Romer
- Department of Child Adolescence Psychiatry and Psychotherapy, University of Muenster, Muenster, Germany
| | - Markus Lappe
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
- Institute of Psychology, University of Muenster, Muenster, Germany
| | - Johanna Rehder
- Institute of Psychology, University of Muenster, Muenster, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University Munich, Munich, Germany
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Max-Planck-Institute of Psychiatry Munich, Munich, Germany
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany
- Department of Psychiatry, Psychiatric University Hospital, University of Basel, Basel, Switzerland
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Duesseldorf/LVR, Duesseldorf, Germany
- Department of Psychology, Faculty of Psychology, Airlangga University, Surabaya, Indonesia
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Duesseldorf/LVR, Duesseldorf, Germany
| | - Tilo T J Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Sarah S Keedy
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, USA
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology and Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, USA
| | - Elena I Ivleva
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jennifer E McDowell
- Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - James L Reilly
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Scot Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale School of Medicine, and Olin Research Center, Institute of Living/Hartford Hospital, Hartford, CT, USA
| | - Carol A Tamminga
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, USA
| | - Brett A Clementz
- Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, USA
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany.
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany.
- Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany.
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Fernández-Linsenbarth I, Mijancos-Martínez G, Bachiller A, Núñez P, Rodríguez-González V, Beño-Ruiz-de-la-Sierra RM, Roig-Herrero A, Arjona-Valladares A, Poza J, Mañanas MÁ, Molina V. Relation between task-related activity modulation and cortical inhibitory function in schizophrenia and healthy controls: a TMS-EEG study. Eur Arch Psychiatry Clin Neurosci 2024; 274:837-847. [PMID: 38243018 PMCID: PMC11127880 DOI: 10.1007/s00406-023-01745-0] [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] [Received: 05/11/2023] [Accepted: 12/11/2023] [Indexed: 01/21/2024]
Abstract
Schizophrenia has been associated with a reduced task-related modulation of cortical activity assessed through electroencephalography (EEG). However, to the best of our knowledge, no study so far has assessed the underpinnings of this decreased EEG modulation in schizophrenia. A possible substrate of these findings could be a decreased inhibitory function, a replicated finding in the field. In this pilot study, our aim was to explore the association between EEG modulation during a cognitive task and the inhibitory system function in vivo in a sample including healthy controls and patients with schizophrenia. We hypothesized that the replicated decreased task-related activity modulation during a cognitive task in schizophrenia would be related to a hypofunction of the inhibitory system. For this purpose, 27 healthy controls and 22 patients with schizophrenia (including 13 first episodes) performed a 3-condition auditory oddball task from which the spectral entropy modulation was calculated. In addition, cortical reactivity-as an index of the inhibitory function-was assessed by the administration of 75 monophasic transcranial magnetic stimulation single pulses over the left dorsolateral prefrontal cortex. Our results replicated the task-related cortical activity modulation deficit in schizophrenia patients. Moreover, schizophrenia patients showed higher cortical reactivity following transcranial magnetic stimulation single pulses over the left dorsolateral prefrontal cortex compared to healthy controls. Cortical reactivity was inversely associated with EEG modulation, supporting the idea that a hypofunction of the inhibitory system could hamper the task-related modulation of EEG activity.
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Affiliation(s)
- Inés Fernández-Linsenbarth
- Psychiatry Department, School of Medicine, University of Valladolid, Av. Ramón y Cajal, 7, 47005, Valladolid, Spain
| | - Gema Mijancos-Martínez
- Biomedical Engineering Research Centre (CREB), Department of Automatic Control (ESAII), Polytechnic University of Catalonia, Barcelona, Spain
- Institute of Research Sant Joan de Déu, Barcelona, Spain
| | - Alejandro Bachiller
- Biomedical Engineering Research Centre (CREB), Department of Automatic Control (ESAII), Polytechnic University of Catalonia, Barcelona, Spain
- Institute of Research Sant Joan de Déu, Barcelona, Spain
| | - Pablo Núñez
- Coma Science Group, CIGA-Consciousness, University of Liège, Liège, Belgium
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Biomaterials and Nanomedicine (BICER-BBN), CIBER of Bioengineering, Madrid, Spain
| | - Víctor Rodríguez-González
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Biomaterials and Nanomedicine (BICER-BBN), CIBER of Bioengineering, Madrid, Spain
| | | | - Alejandro Roig-Herrero
- Psychiatry Department, School of Medicine, University of Valladolid, Av. Ramón y Cajal, 7, 47005, Valladolid, Spain
- Imaging Processing Laboratory, University of Valladolid, Valladolid, Spain
| | - Antonio Arjona-Valladares
- Psychiatry Department, School of Medicine, University of Valladolid, Av. Ramón y Cajal, 7, 47005, Valladolid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Biomaterials and Nanomedicine (BICER-BBN), CIBER of Bioengineering, Madrid, Spain
- Instituto de Investigación en Matemáticas (IMUCA), University of Valladolid, Valladolid, Spain
| | - Miguel Ángel Mañanas
- Biomedical Engineering Research Centre (CREB), Department of Automatic Control (ESAII), Polytechnic University of Catalonia, Barcelona, Spain
- Institute of Research Sant Joan de Déu, Barcelona, Spain
- Biomaterials and Nanomedicine (BICER-BBN), CIBER of Bioengineering, Madrid, Spain
| | - Vicente Molina
- Psychiatry Department, School of Medicine, University of Valladolid, Av. Ramón y Cajal, 7, 47005, Valladolid, Spain.
- Psychiatry Service, Clinical Hospital of Valladolid, Valladolid, Spain.
- Neurosciences Institute of Castilla y Léon (INCYL), University of Salamanca, Salamanca, Spain.
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4
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Vescovo E, Cardellicchio P, Tomassini A, Fadiga L, D'Ausilio A. Excitatory/inhibitory motor balance reflects individual differences during joint action coordination. Eur J Neurosci 2024; 59:3403-3421. [PMID: 38666628 DOI: 10.1111/ejn.16365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 03/07/2024] [Accepted: 04/06/2024] [Indexed: 06/15/2024]
Abstract
Joint action (JA) is a continuous process of motor co-regulation based on the integration of contextual (top-down) and kinematic (bottom-up) cues from partners. The fine equilibrium between excitation and inhibition in sensorimotor circuits is, thus, central to such a dynamic process of action selection and execution. In a bimanual task adapted to become a unimanual JA task, the participant held a bottle (JA), while a confederate had to reach and unscrew either that bottle or another stabilized by a mechanical clamp (No_JA). Prior knowledge was manipulated in each trial such that the participant knew (K) or not (No_K) the target bottle in advance. Online transcranial magnetic stimulation (TMS) was administered at action-relevant landmarks to explore corticospinal excitability (CSE) and inhibition (cortical silent period [cSP]). CSE was modulated early on before the action started if prior information was available. In contrast, cSP modulation emerged later during the reaching action, regardless of prior information. These two indexes could thus reflect the concurrent elaboration of contextual priors (top-down) and the online sampling of partner's kinematic cues (bottom-up). Furthermore, participants selected either one of two possible behavioural strategies, preferring early or late force exertion on the bottle. One translates into a reduced risk of motor coordination failure and the other into reduced metabolic expenditure. Each strategy was characterised by a specific excitatory/inhibitory profile. In conclusion, the study of excitatory/inhibitory balance paves the way for the neurophysiological determination of individual differences in the combination of top-down and bottom-up processing during JA coordination.
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Affiliation(s)
- Enrico Vescovo
- Center for Translational Neurophysiology of Speech and Communication, Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy
| | - Pasquale Cardellicchio
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy
- Physical Medicine and Rehabilitation Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Alice Tomassini
- Center for Translational Neurophysiology of Speech and Communication, Istituto Italiano di Tecnologia, Ferrara, Italy
| | - Luciano Fadiga
- Center for Translational Neurophysiology of Speech and Communication, Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy
| | - Alessandro D'Ausilio
- Center for Translational Neurophysiology of Speech and Communication, Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy
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Andrés-Camazón P, Diaz-Caneja CM, Ballem R, Chen J, Calhoun VD, Iraji A. Neurobiology-based Cognitive Biotypes Using Multi-scale Intrinsic Connectivity Networks in Psychotic Disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.14.24307341. [PMID: 38798576 PMCID: PMC11118619 DOI: 10.1101/2024.05.14.24307341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Objective Understanding the neurobiology of cognitive dysfunction in psychotic disorders remains elusive, as does developing effective interventions. Limited knowledge about the biological heterogeneity of cognitive dysfunction hinders progress. This study aimed to identify subgroups of patients with psychosis with distinct patterns of functional brain alterations related to cognition (cognitive biotypes). Methods B-SNIP consortium data (2,270 participants including participants with psychotic disorders, relatives, and controls) was analyzed. Researchers used reference-informed independent component analysis and the NeuroMark 100k multi-scale intrinsic connectivity networks (ICN) template to obtain subject-specific ICNs and whole-brain functional network connectivity (FNC). FNC features associated with cognitive performance were identified through multivariate joint analysis. K-means clustering identified subgroups of patients based on these features in a discovery set. Subgroups were further evaluated in a replication set and in relatives. Results Two biotypes with different functional brain alteration patterns were identified. Biotype 1 exhibited brain-wide alterations, involving hypoconnectivity in cerebellar-subcortical and somatomotor-visual networks and worse cognitive performance. Biotype 2 exhibited hyperconnectivity in somatomotor-subcortical networks and hypoconnectivity in somatomotor-high cognitive processing networks, and better preserved cognitive performance. Demographic, clinical, cognitive, and FNC characteristics of biotypes were consistent in discovery and replication sets, and in relatives. 70.12% of relatives belonged to the same biotype as their affected family members. Conclusions These findings suggest two distinctive psychosis-related cognitive biotypes with differing functional brain patterns shared with their relatives. Patient stratification based on these biotypes instead of traditional diagnosis may help to optimize future research and clinical trials addressing cognitive dysfunction in psychotic disorders.
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Affiliation(s)
- Pablo Andrés-Camazón
- Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, ISCIII, School of Medicine, Universidad Complutense, Madrid, Spain
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State University, Georgia Institute of Technology, Emory University), Atlanta, Georgia, United States
| | - Covadonga Martínez Diaz-Caneja
- Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, ISCIII, School of Medicine, Universidad Complutense, Madrid, Spain
| | - Ram Ballem
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State University, Georgia Institute of Technology, Emory University), Atlanta, Georgia, United States
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State University, Georgia Institute of Technology, Emory University), Atlanta, Georgia, United States
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State University, Georgia Institute of Technology, Emory University), Atlanta, Georgia, United States
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State University, Georgia Institute of Technology, Emory University), Atlanta, Georgia, United States
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Jørgensen KN, Nerland S, Slapø NB, Norbom LB, Mørch-Johnsen L, Wortinger LA, Barth C, Andreou D, Maximov II, Geier OM, Andreassen OA, Jönsson EG, Agartz I. Assessing regional intracortical myelination in schizophrenia spectrum and bipolar disorders using the optimized T1w/T2w-ratio. Psychol Med 2024:1-11. [PMID: 38563302 DOI: 10.1017/s0033291724000503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
BACKGROUND Dysmyelination could be part of the pathophysiology of schizophrenia spectrum (SCZ) and bipolar disorders (BPD), yet few studies have examined myelination of the cerebral cortex. The ratio of T1- and T2-weighted magnetic resonance images (MRI) correlates with intracortical myelin. We investigated the T1w/T2w-ratio and its age trajectories in patients and healthy controls (CTR) and explored associations with antipsychotic medication use and psychotic symptoms. METHODS Patients with SCZ (n = 64; mean age = 30.4 years, s.d. = 9.8), BPD (n = 91; mean age 31.0 years, s.d. = 10.2), and CTR (n = 155; mean age = 31.9 years, s.d. = 9.1) who participated in the TOP study (NORMENT, University of Oslo, Norway) were clinically assessed and scanned using a General Electric 3 T MRI system. T1w/T2w-ratio images were computed using an optimized pipeline with intensity normalization and field inhomogeneity correction. Vertex-wise regression models were used to compare groups and examine group × age interactions. In regions showing significant differences, we explored associations with antipsychotic medication use and psychotic symptoms. RESULTS No main effect of diagnosis was found. However, age slopes of the T1w/T2w-ratio differed significantly between SCZ and CTR, predominantly in frontal and temporal lobe regions: Lower T1w/T2w-ratio values with higher age were found in CTR, but not in SCZ. Follow-up analyses revealed a more positive age slope in patients who were using antipsychotics and patients using higher chlorpromazine-equivalent doses. CONCLUSIONS While we found no evidence of reduced intracortical myelin in SCZ or BPD relative to CTR, different regional age trajectories in SCZ may suggest a promyelinating effect of antipsychotic medication.
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Affiliation(s)
- Kjetil Nordbø Jørgensen
- The Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatry, Telemark Hospital, Skien, Norway
| | - Stener Nerland
- The Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Nora Berz Slapø
- The Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Linn B Norbom
- The Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, PROMENTA Research Center, University of Oslo, Oslo, Norway
| | - Lynn Mørch-Johnsen
- The Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatry & Department of Clinical Research, Østfold Hospital, Grålum, Norway
| | - Laura Anne Wortinger
- The Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Claudia Barth
- The Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Dimitrios Andreou
- The Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden & Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Ivan I Maximov
- Department of Psychology, University of Oslo, Oslo, Norway
- The Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Oliver M Geier
- Department of Physics and Computational Radiology, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- The Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- The Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Erik G Jönsson
- The Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden & Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Ingrid Agartz
- The Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden & Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
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7
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Kammerer MK, Bott A, Strakeljahn F, Lincoln TM. Sleep spindle activity and psychotic experiences: Examining the mediating roles of attentional performance and perceptual distortions in a daytime nap study. Sleep Med 2024; 116:43-50. [PMID: 38422784 DOI: 10.1016/j.sleep.2024.02.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 02/10/2024] [Accepted: 02/11/2024] [Indexed: 03/02/2024]
Abstract
Decreased sleep spindle activity in individuals with psychotic disorders is well studied, but its contribution to psychotic symptom formation is not well understood. This study explored potential underlying mechanisms explaining the association between decreased sleep spindle activity and psychotic symptoms. To this end, we analysed the links between sleep spindle activity and psychotic experiences and probed for the mediating roles of attentional performance and perceptual distortions in a community sample of young adults (N = 70; 26.33 ± 4.84 years). Polysomnography was recorded during a 90-min daytime nap and duration, amplitude, and density from slow (10-13 Hz) and fast (13-16 Hz) spindles were extracted. Attentional performance was assessed via a test battery and with an antisaccadic eye movement task. Psychotic experiences (i.e., paranoid thoughts; hallucinatory experiences) and perceptual distortions (i.e., anomalous perceptions; sensory gating deficits) were assessed via self-report questionnaires. We conducted sequential mediation analyses with spindle activity as predictor, psychotic experiences as dependent variable, and attentional performance and perceptual distortions as mediators. We found reduced right central spindle amplitude to be associated with paranoid thoughts. Increased antisaccadic error rate was associated with anomalous perceptions and perceptual distortions were associated with psychotic experiences. We did not find significant mediation effects. The findings support the notion that reduced sleep spindle activity is involved in the formation of paranoid thoughts and that decreased antisaccadic performance is indicative of perceptual distortions as potential precursors for psychotic experiences. However, further research is needed to corroborate the proposed mediation hypothesis.
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Affiliation(s)
- Mathias K Kammerer
- Clinical Psychology and Psychotherapy, Institute of Psychology, Faculty of Psychology and Movement Sciences, Universität Hamburg, Germany.
| | - Antonia Bott
- Clinical Psychology and Psychotherapy, Institute of Psychology, Faculty of Psychology and Movement Sciences, Universität Hamburg, Germany
| | - Felix Strakeljahn
- Clinical Psychology and Psychotherapy, Institute of Psychology, Faculty of Psychology and Movement Sciences, Universität Hamburg, Germany
| | - Tania M Lincoln
- Clinical Psychology and Psychotherapy, Institute of Psychology, Faculty of Psychology and Movement Sciences, Universität Hamburg, Germany
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8
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Luo C, Pi X, Zhang Q, Hu N, Xiao Y, Sweeney JA, Bishop JR, Gong Q, Xie D, Lui S. A subtype of schizophrenia patients with altered methylation level of genes related to immune cell activity. Psychol Med 2024:1-9. [PMID: 38505948 DOI: 10.1017/s0033291724000667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
BACKGROUND Epigenetic changes are plausible molecular sources of clinical heterogeneity in schizophrenia. A subgroup of schizophrenia patients with elevated inflammatory or immune-dysregulation has been reported by previous studies. However, little is known about epigenetic changes in genes related to immune activation in never-treated first-episode patients with schizophrenia (FES) and its consistency with that in treated long-term ill (LTS) patients. METHODS In this study, epigenome-wide profiling with a DNA methylation array was applied using blood samples of both FES and LTS patients, as well as their corresponding healthy controls. Non-negative matrix factorization (NMF) and k -means clustering were performed to parse heterogeneity of schizophrenia, and the consistency of subtyping results from two cohorts. was tested. RESULTS This study identified a subtype of patients in FES participants (47.5%) that exhibited widespread methylation level alterations of genes enriched in immune cell activity and a significantly higher proportion of neutrophils. This clustering of FES patients was validated in LTS patients, with high correspondence in epigenetic and clinical features across two cohorts. CONCLUSIONS In summary, this study demonstrated a subtype of schizophrenia patients across both FES and LTS cohorts, defined by widespread alterations in methylation profile of genes related to immune function and distinguishing clinical features. This finding illustrates the promise of novel treatment strategies targeting immune dysregulation for a subpopulation of schizophrenia patients.
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Affiliation(s)
- Chunyan Luo
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
| | - Xuenan Pi
- Laboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Qi Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
| | - Na Hu
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
| | - Yuan Xiao
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
| | - John A Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati OH 45219, USA
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology and Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
| | - Dan Xie
- Laboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
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Ishida T, Yamada S, Yasuda K, Uenishi S, Tamaki A, Tabata M, Ikeda N, Takahashi S, Kimoto S. Aberrant brain dynamics of large-scale functional networks across schizophrenia and mood disorder. Neuroimage Clin 2024; 41:103574. [PMID: 38346380 PMCID: PMC10944194 DOI: 10.1016/j.nicl.2024.103574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/05/2024] [Accepted: 02/05/2024] [Indexed: 03/16/2024]
Abstract
INTRODUCTION The dynamics of large-scale networks, which are known as distributed sets of functionally synchronized brain regions and include the visual network (VIN), somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network (FPN), and default mode network (DMN), play important roles in emotional and cognitive processes in humans. Although disruptions in these large-scale networks are considered critical for the pathophysiological mechanisms of psychiatric disorders, their role in psychiatric disorders remains unknown. We aimed to elucidate the aberrant dynamics across large-scale networks in patients with schizophrenia (SZ) and mood disorders. METHODS We performed energy-landscape analysis to investigate the aberrant brain dynamics of seven large-scale networks across 50 healthy controls (HCs), 36 patients with SZ, and 42 patients with major depressive disorder (MDD) recruited at Wakayama Medical University. We identified major patterns of brain activity using energy-landscape analysis and estimated their duration, occurrence, and ease of transition. RESULTS We identified four major brain activity patterns that were characterized by the activation patterns of the DMN and VIN (state 1, DMN (-) VIN (-); state 2, DMN (+) VIN (+); state 3, DMN (-) VIN (+); and state 4, DMN (+) VIN (-)). The duration of state 1 and the occurrence of states 1 and 2 were shorter in the SZ group than in HCs and the MDD group, and the duration of state 3 was longer in the SZ group. The ease of transition between states 3 and 4 was larger in the SZ group than in the HCs and the MDD group. The ease of transition from state 3 to state 4 was negatively associated with verbal fluency in patients with SZ. The current study showed that the brain dynamics was more disrupted in SZ than in MDD. CONCLUSIONS Energy-landscape analysis revealed aberrant brain dynamics across large-scale networks between SZ and MDD and their associations with cognitive abilities in SZ, which cannot be captured by conventional functional connectivity analyses. These results provide new insights into the pathophysiological mechanisms underlying SZ and mood disorders.
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Affiliation(s)
- Takuya Ishida
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan.
| | - Shinichi Yamada
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan
| | - Kasumi Yasuda
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Neuropsychiatry, Hanwa Izumi Hospital, Osaka 594-1157, Japan
| | - Shinya Uenishi
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Psychiatry, Hidaka Hospital, Wakayama 644-0002, Japan
| | - Atsushi Tamaki
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Psychiatry, Wakayama Prefectural Mental Health Care Center, Wakayama 643-0811, Japan
| | - Michiyo Tabata
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Neuropsychiatry, Nokamikosei Hospital, Wakayama 640-1141, Japan
| | - Natsuko Ikeda
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan
| | - Shun Takahashi
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka 565-0871, Japan; Clinical Research and Education Center, Asakayama General Hospital, Osaka 590-0018, Japan; Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka 583-8555, Japan
| | - Sohei Kimoto
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan
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10
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Williams LM, Carpenter WT, Carretta C, Papanastasiou E, Vaidyanathan U. Precision psychiatry and Research Domain Criteria: Implications for clinical trials and future practice. CNS Spectr 2024; 29:26-39. [PMID: 37675453 DOI: 10.1017/s1092852923002420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Psychiatric disorders are associated with significant social and economic burdens, many of which are related to issues with current diagnosis and treatments. The coronavirus (COVID-19) pandemic is estimated to have increased the prevalence and burden of major depressive and anxiety disorders, indicating an urgent need to strengthen mental health systems globally. To date, current approaches adopted in drug discovery and development for psychiatric disorders have been relatively unsuccessful. Precision psychiatry aims to tailor healthcare more closely to the needs of individual patients and, when informed by neuroscience, can offer the opportunity to improve the accuracy of disease classification, treatment decisions, and prevention efforts. In this review, we highlight the growing global interest in precision psychiatry and the potential for the National Institute of Health-devised Research Domain Criteria (RDoC) to facilitate the implementation of transdiagnostic and improved treatment approaches. The need for current psychiatric nosology to evolve with recent scientific advancements and increase awareness in emerging investigators/clinicians of the value of this approach is essential. Finally, we examine current challenges and future opportunities of adopting the RDoC-associated translational and transdiagnostic approaches in clinical studies, acknowledging that the strength of RDoC is that they form a dynamic framework of guiding principles that is intended to evolve continuously with scientific developments into the future. A collaborative approach that recruits expertise from multiple disciplines, while also considering the patient perspective, is needed to pave the way for precision psychiatry that can improve the prognosis and quality of life of psychiatric patients.
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Affiliation(s)
- Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - William T Carpenter
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Evangelos Papanastasiou
- Boehringer Ingelheim Pharma GmbH & Co, Ingelheim am Rhein, Rhineland-Palatinate, Germany
- HMNC Holding GmbH, Wilhelm-Wagenfeld-Strasse 20, 80807Munich, Bavaria, Germany
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11
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Tandon R, Nasrallah H, Akbarian S, Carpenter WT, DeLisi LE, Gaebel W, Green MF, Gur RE, Heckers S, Kane JM, Malaspina D, Meyer-Lindenberg A, Murray R, Owen M, Smoller JW, Yassin W, Keshavan M. The schizophrenia syndrome, circa 2024: What we know and how that informs its nature. Schizophr Res 2024; 264:1-28. [PMID: 38086109 DOI: 10.1016/j.schres.2023.11.015] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/23/2023] [Accepted: 11/29/2023] [Indexed: 03/01/2024]
Abstract
With new data about different aspects of schizophrenia being continually generated, it becomes necessary to periodically revisit exactly what we know. Along with a need to review what we currently know about schizophrenia, there is an equal imperative to evaluate the construct itself. With these objectives, we undertook an iterative, multi-phase process involving fifty international experts in the field, with each step building on learnings from the prior one. This review assembles currently established findings about schizophrenia (construct, etiology, pathophysiology, clinical expression, treatment) and posits what they reveal about its nature. Schizophrenia is a heritable, complex, multi-dimensional syndrome with varying degrees of psychotic, negative, cognitive, mood, and motor manifestations. The illness exhibits a remitting and relapsing course, with varying degrees of recovery among affected individuals with most experiencing significant social and functional impairment. Genetic risk factors likely include thousands of common genetic variants that each have a small impact on an individual's risk and a plethora of rare gene variants that have a larger individual impact on risk. Their biological effects are concentrated in the brain and many of the same variants also increase the risk of other psychiatric disorders such as bipolar disorder, autism, and other neurodevelopmental conditions. Environmental risk factors include but are not limited to urban residence in childhood, migration, older paternal age at birth, cannabis use, childhood trauma, antenatal maternal infection, and perinatal hypoxia. Structural, functional, and neurochemical brain alterations implicate multiple regions and functional circuits. Dopamine D-2 receptor antagonists and partial agonists improve psychotic symptoms and reduce risk of relapse. Certain psychological and psychosocial interventions are beneficial. Early intervention can reduce treatment delay and improve outcomes. Schizophrenia is increasingly considered to be a heterogeneous syndrome and not a singular disease entity. There is no necessary or sufficient etiology, pathology, set of clinical features, or treatment that fully circumscribes this syndrome. A single, common pathophysiological pathway appears unlikely. The boundaries of schizophrenia remain fuzzy, suggesting the absence of a categorical fit and need to reconceptualize it as a broader, multi-dimensional and/or spectrum construct.
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Affiliation(s)
- Rajiv Tandon
- Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, MI 49008, United States of America.
| | - Henry Nasrallah
- Department of Psychiatry, University of Cincinnati College of Medicine Cincinnati, OH 45267, United States of America
| | - Schahram Akbarian
- Department of Psychiatry, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, United States of America
| | - William T Carpenter
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21201, United States of America
| | - Lynn E DeLisi
- Department of Psychiatry, Cambridge Health Alliance and Harvard Medical School, Cambridge, MA 02139, United States of America
| | - Wolfgang Gaebel
- Department of Psychiatry and Psychotherapy, LVR-Klinikum Dusseldorf, Heinrich-Heine University, Dusseldorf, Germany
| | - Michael F Green
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute of Neuroscience and Human Behavior, UCLA, Los Angeles, CA 90024, United States of America; Greater Los Angeles Veterans' Administration Healthcare System, United States of America
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States of America
| | - Stephan Heckers
- Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN 37232, United States of America
| | - John M Kane
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Glen Oaks, NY 11004, United States of America
| | - Dolores Malaspina
- Department of Psychiatry, Neuroscience, Genetics, and Genomics, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, United States of America
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannhein/Heidelberg University, Mannheim, Germany
| | - Robin Murray
- Institute of Psychiatry, Psychology, and Neuroscience, Kings College, London, UK
| | - Michael Owen
- Centre for Neuropsychiatric Genetics and Genomics, and Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Jordan W Smoller
- Center for Precision Psychiatry, Department of Psychiatry, Psychiatric and Neurodevelopmental Unit, Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States of America
| | - Walid Yassin
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, United States of America
| | - Matcheri Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, United States of America
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12
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Ji Y, Pearlson G, Bustillo J, Kochunov P, Turner JA, Jiang R, Shao W, Zhang X, Fu Z, Li K, Liu Z, Xu X, Zhang D, Qi S, Calhoun VD. Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering. Schizophr Res 2024; 264:130-139. [PMID: 38128344 DOI: 10.1016/j.schres.2023.12.013] [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] [Received: 08/29/2022] [Revised: 07/19/2023] [Accepted: 12/10/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Similarities among schizophrenia (SZ), schizoaffective disorder (SAD) and bipolar disorder (BP) including clinical phenotypes, brain alterations and risk genes, make it challenging to perform reliable separation among them. However, previous subtype identification that transcend traditional diagnostic boundaries were based on group-level neuroimaging features, ignoring individual-level inferences. METHODS 455 psychoses (178 SZs, 134 SADs and 143 BPs), their first-degree relatives (N = 453) and healthy controls (HCs, N = 220) were collected from Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP I) consortium. Individualized covariance structural differential networks (ICSDNs) were constructed for each patient and multi-site clustering was used to identify psychosis subtypes. Group differences between subtypes in clinical phenotypes and voxel-wise fractional amplitude of low frequency fluctuation (fALFF) were calculated, as well as between the corresponding relatives. RESULTS Two psychosis subtypes were identified with increased whole brain structural covariance, with decreased connectivity between amygdala-hippocampus and temporal-occipital cortex in subtype I (S-I) compared to subtype II (S-II), which was replicated under different clustering methods, number of edges and across datasets (B-SNIP II) and different brain atlases. S-I had higher emotional-related symptoms than S-II and showed significant fALFF decrease in temporal and occipital cortex, while S-II was more similar to HC. This pattern was consistently validated on relatives of S-I and S-II in both fALFF and clinical symptoms. CONCLUSIONS These findings reconcile categorical and dimensional perspectives of psychosis neurobiological heterogeneity, indicating that relatives of S-I might have greater predisposition in developing psychosis, while relatives of S-II are more likely to be healthy. This study contributes to the development of neuroimaging informed diagnostic classifications within psychosis spectrum.
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Affiliation(s)
- Yixin Ji
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China
| | - Godfrey Pearlson
- Departments of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, CT, USA; Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Juan Bustillo
- Departments of Neurosciences and Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Peter Kochunov
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
| | - Rongtao Jiang
- Departments of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Wei Shao
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China
| | - Xiao Zhang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Kaicheng Li
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zhaowen Liu
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Xijia Xu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China.
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China.
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; Department of Electrical and Computer Engineering, Georgia Tech University, Atlanta, GA, USA
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13
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Parker D, Trotti R, McDowell J, Keedy S, Keshavan M, Pearlson G, Gershon E, Ivleva E, Huang LY, Sauer K, Hill S, Sweeny J, Tamminga C, Clementz B. Differentiating Biomarker Features and Familial Characteristics of B-SNIP Psychosis Biotypes. RESEARCH SQUARE 2024:rs.3.rs-3702638. [PMID: 38260530 PMCID: PMC10802686 DOI: 10.21203/rs.3.rs-3702638/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Idiopathic psychosis shows considerable biological heterogeneity across cases. B-SNIP used psychosis-relevant biomarkers to identity psychosis Biotypes, which will aid etiological and targeted treatment investigations. Psychosis probands from the B-SNIP consortium (n = 1907), their first-degree biological relatives (n = 705), and healthy participants (n = 895) completed a biomarker battery composed of cognition, saccades, and auditory EEG measurements. ERP quantifications were substantially modified from previous iterations of this approach. Multivariate integration reduced multiple biomarker outcomes to 11 "bio-factors". Twenty-four different approaches indicated bio-factor data among probands were best distributed as three subgroups. Numerical taxonomy with k-means constructed psychosis Biotypes, and rand indices evaluated consistency of Biotype assignments. Psychosis subgroups, their non-psychotic first-degree relatives, and healthy individuals were compared across bio-factors. The three psychosis Biotypes differed significantly on all 11 bio-factors, especially prominent for general cognition, antisaccades, ERP magnitude, and intrinsic neural activity. Rand indices showed excellent consistency of clustering membership when samples included at least 1100 subjects. Canonical discriminant analysis described composite bio-factors that simplified group comparisons and captured neural dysregulation, neural vigor, and stimulus salience variates. Neural dysregulation captured Biotype-2, low neural vigor captured Biotype-1, and deviations of stimulus salience captured Biotype-3. First-degree relatives showed similar patterns as their Biotyped proband relatives on general cognition, antisaccades, ERP magnitudes, and intrinsic brain activity. Results extend previous efforts by the B-SNIP consortium to characterize biologically distinct psychosis Biotypes. They also show that at least 1100 observations are necessary to achieve consistent outcomes. First-degree relative data implicate specific bio-factor deviations to the subtype of their proband and may inform studies of genetic risk.
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14
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Paul SM, Potter WZ. Finding new and better treatments for psychiatric disorders. Neuropsychopharmacology 2024; 49:3-9. [PMID: 37582978 PMCID: PMC10700311 DOI: 10.1038/s41386-023-01690-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 07/07/2023] [Accepted: 07/24/2023] [Indexed: 08/17/2023]
Abstract
In contrast to most fields of medicine, progress to discover and develop new and improved psychiatric drugs has been slow and disappointing. The vast majority of currently prescribed drugs to treat schizophrenia, mood and anxiety disorders are arguably no more effective than the first generation of psychiatric drugs introduced well over 50 years ago. With only a few exceptions current psychiatric drugs work via the same fundamental mechanisms of action as first-generation agents. Here we describe the reasons for this slow progress and outline a number of areas of research that involve a greater reliance on experimental therapeutics utilizing recent advances in neuroscience to better understand disease biology. We exemplify the potential impact of these areas of research focus with several recent examples of novel agents that have emerged and which support our optimism that newer, more effective and better tolerated agents, are on the horizon. Together with existing drugs these newer agents and novel mechanisms could offer markedly improved functional outcomes for the millions of people still disabled by psychiatric disorders.
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Affiliation(s)
- Steven M Paul
- Karuna Therapeutics, Washington University School of Medicine, St. Louis, MO, USA.
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15
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Byrne JF, Mongan D, Murphy J, Healy C, Fӧcking M, Cannon M, Cotter DR. Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal. Transl Psychiatry 2023; 13:333. [PMID: 37898606 PMCID: PMC10613280 DOI: 10.1038/s41398-023-02623-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 09/15/2023] [Accepted: 10/06/2023] [Indexed: 10/30/2023] Open
Abstract
Accumulating evidence suggests individuals with psychotic disorder show abnormalities in metabolic and inflammatory processes. Recently, several studies have employed blood-based predictors in models predicting transition to psychotic disorder in risk-enriched populations. A systematic review of the performance and methodology of prognostic models using blood-based biomarkers in the prediction of psychotic disorder from risk-enriched populations is warranted. Databases (PubMed, EMBASE and PsycINFO) were searched for eligible texts from 1998 to 15/05/2023, which detailed model development or validation studies. The checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) was used to guide data extraction from eligible texts and the Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and applicability of the studies. A narrative synthesis of the included studies was performed. Seventeen eligible studies were identified: 16 eligible model development studies and one eligible model validation study. A wide range of biomarkers were assessed, including nucleic acids, proteins, metabolites, and lipids. The range of C-index (area under the curve) estimates reported for the models was 0.67-1.00. No studies assessed model calibration. According to PROBAST criteria, all studies were at high risk of bias in the analysis domain. While a wide range of potentially predictive biomarkers were identified in the included studies, most studies did not account for overfitting in model performance estimates, no studies assessed calibration, and all models were at high risk of bias according to PROBAST criteria. External validation of the models is needed to provide more accurate estimates of their performance. Future studies which follow the latest available methodological and reporting guidelines and adopt strategies to accommodate required sample sizes for model development or validation will clarify the value of including blood-based biomarkers in models predicting psychosis.
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Affiliation(s)
- Jonah F Byrne
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland.
- SFI FutureNeuro Research Centre, Royal College of Surgeons in Ireland, Dublin, Ireland.
| | - David Mongan
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
- Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom
| | - Jennifer Murphy
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Colm Healy
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Melanie Fӧcking
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Mary Cannon
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
- SFI FutureNeuro Research Centre, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - David R Cotter
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
- SFI FutureNeuro Research Centre, Royal College of Surgeons in Ireland, Dublin, Ireland
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16
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Clementz BA, Chattopadhyay I, Trotti RL, Parker DA, Gershon ES, Hill SK, Ivleva EI, Keedy SK, Keshavan MS, McDowell JE, Pearlson GD, Tamminga CA, Gibbons RD. Clinical characterization and differentiation of B-SNIP psychosis Biotypes: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT)-1. Schizophr Res 2023; 260:143-151. [PMID: 37657281 PMCID: PMC10712427 DOI: 10.1016/j.schres.2023.08.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 08/08/2023] [Accepted: 08/09/2023] [Indexed: 09/03/2023]
Abstract
Clinically defined psychosis diagnoses are neurobiologically heterogeneous. The B-SNIP consortium identified and validated more neurobiologically homogeneous psychosis Biotypes using an extensive battery of neurocognitive and psychophysiological laboratory measures. However, typically the first step in any diagnostic evaluation is the clinical interview. In this project, we evaluated if psychosis Biotypes have clinical characteristics that can support their differentiation in addition to obtaining laboratory testing. Clinical interview data from 1907 individuals with a psychosis Biotype were used to create a diagnostic algorithm. The features were 58 ratings from standard clinical scales. Extremely randomized tree algorithms were used to evaluate sensitivity, specificity, and overall classification success. Biotype classification accuracy peaked at 91 % with the use of 57 items on average. A reduced feature set of 28 items, though, also showed 81 % classification accuracy. Using this reduced item set, we found that only 10-11 items achieved a one-vs-all (Biotype-1 or not, Biotype-2 or not, Biotype-3 or not) area under the sensitivity-specificity curve of .78 to .81. The top clinical characteristics for differentiating psychosis Biotypes, in order of importance, were (i) difficulty in abstract thinking, (ii) multiple indicators of social functioning, (iii) conceptual disorganization, (iv) severity of hallucinations, (v) stereotyped thinking, (vi) suspiciousness, (vii) unusual thought content, (viii) lack of spontaneous speech, and (ix) severity of delusions. These features were remarkably different from those that differentiated DSM psychosis diagnoses. This low-burden adaptive algorithm achieved reasonable classification accuracy and will support Biotype-specific etiological and treatment investigations even in under-resourced clinical and research environments.
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Affiliation(s)
- Brett A Clementz
- Department of Psychology, BioImaging Research Center, University of Georgia, Athens, GA 30602, United States of America.
| | - Ishanu Chattopadhyay
- Department of Medicine, Section of Hospital Medicine, University of Chicago, Chicago, IL, United States of America
| | - Rebekah L Trotti
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States of America
| | - David A Parker
- Department of Human Genetics, Emory University School of Medicine, Atlanta VA Medical Center, Atlanta, GA, United States of America
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, United States of America
| | - S Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, United States of America
| | - Elena I Ivleva
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - Sarah K Keedy
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, United States of America
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States of America
| | - Jennifer E McDowell
- Department of Psychology, Owens Institute for Behavioral Research, University of Georgia, Athens, GA 30602, United States of America
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven, CT, United States of America; Olin NeuroPsychiatry Research Center, Institute of Living, Hartford, CT, United States of America
| | - Carol A Tamminga
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - Robert D Gibbons
- Center for Health Statistics, Departments of Medicine and Public Health Sciences, University of Chicago, Chicago, IL, United States of America
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17
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Duda M, Faghiri A, Belger A, Bustillo JR, Ford JM, Mathalon DH, Mueller BA, Pearlson GD, Potkin SG, Preda A, Sui J, Van Erp TGM, Calhoun VD. Alterations in grey matter structure linked to frequency-specific cortico-subcortical connectivity in schizophrenia via multimodal data fusion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.05.547840. [PMID: 37461731 PMCID: PMC10350020 DOI: 10.1101/2023.07.05.547840] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Schizophrenia (SZ) is a complex psychiatric disorder that is currently defined by symptomatic and behavioral, rather than biological, criteria. Neuroimaging is an appealing avenue for SZ biomarker development, as several neuroimaging-based studies comparing individuals with SZ to healthy controls (HC) have shown measurable group differences in brain structure, as well as functional brain alterations in both static and dynamic functional network connectivity (sFNC and dFNC, respectively). The recently proposed filter-banked connectivity (FBC) method extends the standard dFNC sliding-window approach to estimate FNC within an arbitrary number of distinct frequency bands. The initial implementation used a set of filters spanning the full connectivity spectral range, providing a unified approach to examine both sFNC and dFNC in a single analysis. Initial FBC results found that individuals with SZ spend more time in a less structured, more disconnected low-frequency (i.e., static) FNC state than HC, as well as preferential SZ occupancy in high-frequency connectivity states, suggesting a frequency-specific component underpinning the functional dysconnectivity observed in SZ. Building on these findings, we sought to link such frequency-specific patterns of FNC to covarying data-driven structural brain networks in the context of SZ. Specifically, we employ a multi-set canonical correlation analysis + joint independent components analysis (mCCA + jICA) data fusion framework to study the connection between grey matter volume (GMV) maps and FBC states across the full connectivity frequency spectrum. Our multimodal analysis identified two joint sources that captured co-varying patterns of frequency-specific functional connectivity and alterations in GMV with significant group differences in loading parameters between the SZ group and HC. The first joint source linked frequency-modulated connections between the subcortical and sensorimotor networks and GMV alterations in the frontal and temporal lobes, while the second joint source identified a relationship between low-frequency cerebellar-sensorimotor connectivity and structural changes in both the cerebellum and motor cortex. Together, these results show a strong connection between cortico-subcortical functional connectivity at both high and low frequencies and alterations in cortical GMV that may be relevant to the pathogenesis and pathophysiology of SZ.
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Affiliation(s)
- Marlena Duda
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Juan R Bustillo
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Judith M Ford
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
- Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
| | - Daniel H Mathalon
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
- Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
| | - Bryon A Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Theo G M Van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
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18
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Trotti RL, Parker DA, Sabatinelli D, Keshavan MS, Keedy SK, Gershon ES, Pearlson GD, Hill SK, Tamminga CA, McDowell JE, Clementz BA. Emotional scene processing in biotypes of psychosis. Psychiatry Res 2023; 324:115227. [PMID: 37121219 PMCID: PMC10175237 DOI: 10.1016/j.psychres.2023.115227] [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] [Received: 11/30/2022] [Revised: 04/14/2023] [Accepted: 04/23/2023] [Indexed: 05/02/2023]
Abstract
Social-emotional deficits in psychosis may be indexed by deviations in emotional scene processing, but event-related potential (ERP) studies indicate such deviations may not map cleanly to diagnostic categories. Neurobiologically defined psychosis subgroups offer an alternative that may better capture neurophysiological correlates of social-emotional deficits. The current study investigates emotional scene-elicited ERPs in Biotypes of psychosis in a large (N = 622), well-characterized sample. Electroencephalography was recorded in healthy persons (N = 129), Biotype-1 (N = 195), Biotype-2 (N = 131), and Biotype-3 (N = 167) psychosis cases. ERPs were measured from posterior and centroparietal scalp locations. Neural responses to emotional scenes were compared between healthy and psychosis groups. Multivariate group discrimination analyses resulted in two composite variates that differentiated groups. The first variate displayed large differences between low-cognition (Biotype-1, Biotype-2) and intact-cognition groups (Biotype-3, healthy persons). The second indicated a small-to-moderate distinction of Biotypes-2 and -3 from Biotype-1 and healthy persons. Two multivariate correlations were identified indicating associations between 1) self-reported emotional experience and generalized cognition and 2) socio-occupational functioning and late-stage emotional processing. Psychosis Biotypes displayed emotional processing deficits not apparent in DSM psychosis subgroups. Future translational research may benefit from exploring emotional scene processing in such neurobiologically-defined psychosis groups.
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Affiliation(s)
- R L Trotti
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA.
| | - D A Parker
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - D Sabatinelli
- Department of Psychology, University of Georgia, Athens, GA, USA
| | - M S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - S K Keedy
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - E S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - G D Pearlson
- Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - S K Hill
- Department of Psychology, Rosalind Franklin University, North Chicago, IL, USA
| | - C A Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - J E McDowell
- Department of Psychology, University of Georgia, Athens, GA, USA
| | - B A Clementz
- Department of Psychology, University of Georgia, Athens, GA, USA
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19
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Rokham H, Falakshahi H, Fu Z, Pearlson G, Calhoun VD. Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification. Hum Brain Mapp 2023; 44:3180-3195. [PMID: 36919656 PMCID: PMC10171526 DOI: 10.1002/hbm.26273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 02/20/2023] [Accepted: 02/27/2023] [Indexed: 03/16/2023] Open
Abstract
The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing efforts, neurological observations alongside clinical evaluations are considered to be potential solutions to address diagnostic problems. The Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) has published multiple papers attempting to reclassify psychotic illnesses based on biological rather than symptomatic measures. However, the effort to investigate the relationship between this new categorization approach and other neuroimaging techniques, including resting-state fMRI data, is still limited. This study focused on investigating the relationship between different psychotic disorders categorization methods and resting-state fMRI-based measures called dynamic functional network connectivity (dFNC) using state-of-the-art artificial intelligence (AI) approaches. We applied our method to 613 subjects, including individuals with psychosis and healthy controls, which were classified using both the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and the B-SNIP biomarker-based (Biotype) approach. Statistical group differences and cross-validated classifiers were performed within each framework to assess how different categories. Results highlight interesting differences in occupancy in both DSM-IV and Biotype categorizations compared to healthy individuals, which are distributed across specific transient connectivity states. Biotypes tended to show less distinctiveness in occupancy level and included fewer cellwise differences. Classification accuracy obtained by DSM-IV and Biotype categories were both well above chance. Results provided new insights and highlighted the benefits of both DSM-IV and biology-based categories while also emphasizing the importance of future work in this direction, including employing further data types.
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Affiliation(s)
- Hooman Rokham
- Department of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
- Tri‐institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, and Emory UniversityGeorgia State UniversityAtlantaGeorgiaUSA
| | - Haleh Falakshahi
- Department of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
- Tri‐institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, and Emory UniversityGeorgia State UniversityAtlantaGeorgiaUSA
| | - Zening Fu
- Tri‐institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, and Emory UniversityGeorgia State UniversityAtlantaGeorgiaUSA
| | - Godfrey Pearlson
- Department of PsychiatryYale UniversityNew HavenConnecticutUSA
- Department of NeuroscienceYale UniversityNew HavenConnecticutUSA
- Olin Neuropsychiatry Research CenterHartford HospitalHartfordConnecticutUSA
| | - Vince D. Calhoun
- Department of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
- Tri‐institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, and Emory UniversityGeorgia State UniversityAtlantaGeorgiaUSA
- Department of PsychiatryYale UniversityNew HavenConnecticutUSA
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
- Department of PsychologyGeorgia State UniversityAtlantaGeorgiaUSA
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20
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Dondé C, Kantrowitz JT, Medalia A, Saperstein AM, Balla A, Sehatpour P, Martinez A, O'Connell MN, Javitt DC. Early auditory processing dysfunction in schizophrenia: Mechanisms and implications. Neurosci Biobehav Rev 2023; 148:105098. [PMID: 36796472 PMCID: PMC10106448 DOI: 10.1016/j.neubiorev.2023.105098] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 02/08/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023]
Abstract
Schizophrenia is a major mental disorder that affects approximately 1% of the population worldwide. Cognitive deficits are a key feature of the disorder and a primary cause of long-term disability. Over the past decades, significant literature has accumulated demonstrating impairments in early auditory perceptual processes in schizophrenia. In this review, we first describe early auditory dysfunction in schizophrenia from both a behavioral and neurophysiological perspective and examine their interrelationship with both higher order cognitive constructs and social cognitive processes. Then, we provide insights into underlying pathological processes, especially in relationship to glutamatergic and N-methyl-D-aspartate receptor (NMDAR) dysfunction models. Finally, we discuss the utility of early auditory measures as both treatment targets for precision intervention and as translational biomarkers for etiological investigation. Altogether, this review points out the crucial role of early auditory deficits in the pathophysiology of schizophrenia, in addition to major implications for early intervention and auditory-targeted approaches.
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Affiliation(s)
- Clément Dondé
- Univ. Grenoble Alpes, F-38000 Grenoble, France; INSERM, U1216, F-38000 Grenoble, France; Psychiatry Department, CHU Grenoble Alpes, F-38000 Grenoble, France; Psychiatry Department, CH Alpes-Isère, F-38000 Saint-Egrève, France.
| | - Joshua T Kantrowitz
- Department of Psychiatry, Columbia University, 1051 Riverside Drive, New York, NY 10032, United States; Schizophrenia Research Center, Nathan Kline Institute, 140 Old Orangeburg Road, Orangeburg, NY 10962, United States
| | - Alice Medalia
- New York State Psychiatric Institute, Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons and New York Presbyterian, New York, NY 10032, United States
| | - Alice M Saperstein
- New York State Psychiatric Institute, Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons and New York Presbyterian, New York, NY 10032, United States
| | - Andrea Balla
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, United States
| | - Pejman Sehatpour
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, United States; Division of Experimental Therapeutics, College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Antigona Martinez
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, United States; Division of Experimental Therapeutics, College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Monica N O'Connell
- Translational Neuroscience Division, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, United States
| | - Daniel C Javitt
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, United States; Division of Experimental Therapeutics, College of Physicians and Surgeons, Columbia University, New York, NY, United States.
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21
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ElSayed MW, Novick AM, Cooper JJ, Ross DA. The Sweet Taste of Progress-The Evolving Role of Electroencephalography in Psychiatry. Biol Psychiatry 2023; 93:e21-e23. [PMID: 36948760 PMCID: PMC10590208 DOI: 10.1016/j.biopsych.2022.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 03/24/2023]
Affiliation(s)
- Mohamed W ElSayed
- Department of Psychiatry, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
| | - Andrew M Novick
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Joseph J Cooper
- Department of Psychiatry, University of Illinois Chicago, Chicago, Illinois
| | - David A Ross
- Department of Psychiatry, University of Alberta Faculty of Medicine and Dentistry, Edmonton, Alberta, Canada
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22
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Keshavan MS, Clementz BA. Precision medicine for psychosis: a revolution at the interface of psychiatry and neurology. Nat Rev Neurol 2023; 19:193-194. [PMID: 36879032 DOI: 10.1038/s41582-023-00788-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Affiliation(s)
- Matcheri S Keshavan
- Beth Israel Deaconess Medical Center, Boston, MA, USA. .,Harvard Medical School, Harvard University, Boston, MA, USA.
| | - Brett A Clementz
- Department of Psychology, University of Georgia, Athens, GA, USA.,Department of Neuroscience, University of Georgia, Athens, GA, USA
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23
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Tandon R. Computational psychiatry and the psychopathology of psychosis: Promising leads and blind alleys. Schizophr Res 2023; 254:143-145. [PMID: 36889180 DOI: 10.1016/j.schres.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 02/03/2023] [Accepted: 02/04/2023] [Indexed: 03/10/2023]
Affiliation(s)
- Rajiv Tandon
- Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, MI, United States of America.
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24
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Clementz BA. The Carpenter-Strauss Quest to Save Schizophrenia: How DSM Shifted the Construct From Its Historical Core. Biomark Neuropsychiatry 2023. [DOI: 10.1016/j.bionps.2023.100061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/18/2023] Open
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25
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Vinogradov S, Chafee MV, Lee E, Morishita H. Psychosis spectrum illnesses as disorders of prefrontal critical period plasticity. Neuropsychopharmacology 2023; 48:168-185. [PMID: 36180784 PMCID: PMC9700720 DOI: 10.1038/s41386-022-01451-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/17/2022] [Accepted: 08/21/2022] [Indexed: 01/05/2023]
Abstract
Emerging research on neuroplasticity processes in psychosis spectrum illnesses-from the synaptic to the macrocircuit levels-fill key gaps in our models of pathophysiology and open up important treatment considerations. In this selective narrative review, we focus on three themes, emphasizing alterations in spike-timing dependent and Hebbian plasticity that occur during adolescence, the critical period for prefrontal system development: (1) Experience-dependent dysplasticity in psychosis emerges from activity decorrelation within neuronal ensembles. (2) Plasticity processes operate bidirectionally: deleterious environmental and experiential inputs shape microcircuits. (3) Dysregulated plasticity processes interact across levels of scale and time and include compensatory mechanisms that have pathogenic importance. We present evidence that-given the centrality of progressive dysplastic changes, especially in prefrontal cortex-pharmacologic or neuromodulatory interventions will need to be supplemented by corrective learning experiences for the brain if we are to help people living with these illnesses to fully thrive.
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Affiliation(s)
- Sophia Vinogradov
- Department of Psychiatry & Behavioral Science, University of Minnesota Medical School, Minneapolis, MN, USA.
| | - Matthew V Chafee
- Department of Neuroscience, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Erik Lee
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Hirofumi Morishita
- Department of Psychiatry, Neuroscience, & Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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26
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Li Q, Yao L, You W, Liu J, Deng S, Li B, Luo L, Zhao Y, Wang Y, Wang Y, Zhang Q, Long F, Sweeney JA, Gu S, Li F, Gong Q. Controllability of Functional Brain Networks and Its Clinical Significance in First-Episode Schizophrenia. Schizophr Bull 2022; 49:659-668. [PMID: 36402458 PMCID: PMC10154712 DOI: 10.1093/schbul/sbac177] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
BACKGROUND AND HYPOTHESIS Disrupted control of brain state transitions may contribute to the diverse dysfunctions of cognition, emotion, and behavior that are fundamental to schizophrenia. Control theory provides the rationale for evaluating brain state transitions from a controllability perspective, which may help reveal the brain mechanism for clinical features such as cognitive control deficits associated with schizophrenia. We hypothesized that brain controllability would be altered in patients with schizophrenia, and that controllability of brain networks would be related to clinical symptomatology. STUDY DESIGN Controllability measurements of functional brain networks, including average controllability and modal controllability, were calculated and compared between 125 first-episode never-treated patients with schizophrenia and 133 healthy controls (HCs). Associations between controllability metrics and clinical symptoms were evaluated using sparse canonical correlation analysis. STUDY RESULTS Compared to HCs, patients showed significantly increased average controllability (PFDR = .023) and decreased modal controllability (PFDR = .023) in dorsal anterior cingulate cortex (dACC). General psychopathology symptoms and positive symptoms were positively correlated with average controllability in regions of default mode network and negatively associated with average controllability in regions of sensorimotor, dorsal attention, and frontoparietal networks. CONCLUSIONS Our findings suggest that altered controllability of functional activity in dACC may play a critical role in the pathophysiology of schizophrenia, consistent with the importance of this region in cognitive and brain state control operations. The demonstration of associations of functional controllability with psychosis symptoms suggests that the identified alterations in average controllability of brain function may contribute to the severity of acute psychotic illness in schizophrenia.
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Affiliation(s)
- Qian Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Li Yao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Wanfang You
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Jiang Liu
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Shikuang Deng
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Bin Li
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Lekai Luo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Youjin Zhao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Yuxia Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Yaxuan Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Qian Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Fenghua Long
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH 45219, USA
| | - Shi Gu
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
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27
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Rootes-Murdy K, Edmond JT, Jiang W, Rahaman MA, Chen J, Perrone-Bizzozero NI, Calhoun VD, van Erp TGM, Ehrlich S, Agartz I, Jönsson EG, Andreassen OA, Westlye LT, Wang L, Pearlson GD, Glahn DC, Hong E, Buchanan RW, Kochunov P, Voineskos A, Malhotra A, Tamminga CA, Liu J, Turner JA. Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach. Front Hum Neurosci 2022; 16:1001692. [PMID: 36438633 PMCID: PMC9684186 DOI: 10.3389/fnhum.2022.1001692] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 10/17/2022] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND Structural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotypes associated with SZ and BP may allow for a more precise categorization than the current diagnostic criteria. In this study, we sought to identify GM alterations that were unique to each disorder and whether those alterations were also related to unique symptom profiles. MATERIALS AND METHODS We analyzed the GM patterns and clinical symptom presentations using independent component analysis (ICA), hierarchical clustering, and n-way biclustering in a large (N ∼ 3,000), merged dataset of neuroimaging data from healthy volunteers (HV), and individuals with either SZ or BP. RESULTS Component A showed a SZ and BP < HV GM pattern in the bilateral insula and cingulate gyrus. Component B showed a SZ and BP < HV GM pattern in the cerebellum and vermis. There were no significant differences between diagnostic groups in these components. Component C showed a SZ < HV and BP GM pattern bilaterally in the temporal poles. Hierarchical clustering of the PANSS scores and the ICA components did not yield new subgroups. N-way biclustering identified three unique subgroups of individuals within the sample that mapped onto different combinations of ICA components and symptom profiles categorized by the PANSS but no distinct diagnostic group differences. CONCLUSION These multivariate results show that diagnostic boundaries are not clearly related to structural differences or distinct symptom profiles. Our findings add support that (1) BP tend to have less severe symptom profiles when compared to SZ on the PANSS without a clear distinction, and (2) all the gray matter alterations follow the pattern of SZ < BP < HV without a clear distinction between SZ and BP.
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Affiliation(s)
- Kelly Rootes-Murdy
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jesse T. Edmond
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Wenhao Jiang
- Department of Psychosomatics and Psychiatry, Medical School, Zhongda Hospital, Institute of Psychosomatics, Southeast University, Nanjing, China
| | - Md A. Rahaman
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | | | - Vince D. Calhoun
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Theo G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| | - Stefan Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Ingrid Agartz
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institute and Stockholm Health Care Services, Stockholm, Sweden
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Erik G. Jönsson
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institute and Stockholm Health Care Services, Stockholm, Sweden
| | - Ole A. Andreassen
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Lei Wang
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, United States
| | - Godfrey D. Pearlson
- Department of Psychiatry, Yale University, New Haven, CT, United States
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, United States
| | - David C. Glahn
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, United States
- Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Elliot Hong
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Robert W. Buchanan
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Aristotle Voineskos
- Department of Psychiatry, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
| | - Anil Malhotra
- Division of Psychiatry Research, Zucker Hillside Hospital, Queens, NY, United States
| | - Carol A. Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical School, Dallas, TX, United States
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jessica A. Turner
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, United States
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28
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Vinogradov S, Hamid AA, Redish AD. Etiopathogenic Models of Psychosis Spectrum Illnesses Must Resolve Four Key Features. Biol Psychiatry 2022; 92:514-522. [PMID: 35931575 PMCID: PMC9809152 DOI: 10.1016/j.biopsych.2022.06.024] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/22/2022] [Accepted: 06/22/2022] [Indexed: 01/07/2023]
Abstract
Etiopathogenic models for psychosis spectrum illnesses are converging on a number of key processes, such as the influence of specific genes on the synthesis of proteins important in synaptic functioning, alterations in how neurons respond to synaptic inputs and engage in synaptic pruning, and microcircuit dysfunction that leads to more global cortical information processing vulnerabilities. Disruptions in prefrontal operations then accumulate and propagate over time, interacting with environmental factors, developmental processes, and homeostatic mechanisms, eventually resulting in symptoms of psychosis and disability. However, there are 4 key features of psychosis spectrum illnesses that are of primary clinical relevance but have been difficult to assimilate into a single model and have thus far received little direct attention: 1) the bidirectionality of the causal influences for the emergence of psychosis, 2) the catastrophic clinical threshold seen in first episodes of psychosis and why it is irreversible in some individuals, 3) observed biotypes that are neurophysiologically distinct but clinically both convergent and divergent, and 4) a reconciliation of the role of striatal dopaminergic dysfunction with models of prefrontal cortical state instability. In this selective review, we briefly describe these 4 hallmark features and we argue that theoretically driven computational perspectives making use of both algorithmic and neurophysiologic models are needed to reduce this complexity and variability of psychosis spectrum illnesses in a principled manner.
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Affiliation(s)
- Sophia Vinogradov
- Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, Minnesota.
| | - Arif A Hamid
- Department of Neuroscience, University of Minnesota Medical School, Minneapolis, Minnesota
| | - A David Redish
- Department of Neuroscience, University of Minnesota Medical School, Minneapolis, Minnesota
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29
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Schizophrenia: A Narrative Review of Etiopathogenetic, Diagnostic and Treatment Aspects. J Clin Med 2022; 11:jcm11175040. [PMID: 36078967 PMCID: PMC9457502 DOI: 10.3390/jcm11175040] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/23/2022] [Accepted: 08/24/2022] [Indexed: 11/25/2022] Open
Abstract
Although schizophrenia is currently conceptualized as being characterized as a syndrome that includes a collection of signs and symptoms, there is strong evidence of heterogeneous and complex underpinned etiological, etiopathogenetic, and psychopathological mechanisms, which are still under investigation. Therefore, the present viewpoint review is aimed at providing some insights into the recently investigated schizophrenia research fields in order to discuss the potential future research directions in schizophrenia research. The traditional schizophrenia construct and diagnosis were progressively revised and revisited, based on the recently emerging neurobiological, genetic, and epidemiological research. Moreover, innovative diagnostic and therapeutic approaches are pointed to build a new construct, allowing the development of better clinical and treatment outcomes and characterization for schizophrenic individuals, considering a more patient-centered, personalized, and tailored-based dimensional approach. Further translational studies are needed in order to integrate neurobiological, genetic, and environmental studies into clinical practice and to help clinicians and researchers to understand how to redesign a new schizophrenia construct.
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30
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Morris SE, Sanislow CA, Pacheco J, Vaidyanathan U, Gordon JA, Cuthbert BN. Revisiting the seven pillars of RDoC. BMC Med 2022; 20:220. [PMID: 35768815 PMCID: PMC9245309 DOI: 10.1186/s12916-022-02414-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 05/23/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND In 2013, a few years after the launch of the National Institute of Mental Health's Research Domain Criteria (RDoC) initiative, Cuthbert and Insel published a paper titled "Toward the future of psychiatric diagnosis: the seven pillars of RDoC." The RDoC project is a translational research effort to encourage new ways of studying psychopathology through a focus on disruptions in normal functions (such as reward learning or attention) that are defined jointly by observable behavior and neurobiological measures. The paper outlined the principles of the RDoC research framework, including emphases on research that acquires data from multiple measurement classes to foster integrative analyses, adopts dimensional approaches, and employs novel methods for ascertaining participants and identifying valid subgroups. DISCUSSION To mark the first decade of the RDoC initiative, we revisit the seven pillars and highlight new research findings and updates to the framework that are related to each. This reappraisal emphasizes the flexible nature of the RDoC framework and its application in diverse areas of research, new findings related to the importance of developmental trajectories within and across neurobehavioral domains, and the value of computational approaches for clarifying complex multivariate relations among behavioral and neurobiological systems. CONCLUSION The seven pillars of RDoC have provided a foundation that has helped to guide a surge of new studies that have examined neurobehavioral domains related to mental disorders, in the service of informing future psychiatric nosology. Building on this footing, future areas of emphasis for the RDoC project will include studying central-peripheral interactions, developing novel approaches to phenotyping for genomic studies, and identifying new targets for clinical trial research to facilitate progress in precision psychiatry.
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Affiliation(s)
- Sarah E Morris
- National Institute of Mental Health, Neuroscience Center, 6001 Executive Blvd, Bethesda, MD, 20892, USA.
| | | | - Jenni Pacheco
- National Institute of Mental Health, Neuroscience Center, 6001 Executive Blvd, Bethesda, MD, 20892, USA
| | - Uma Vaidyanathan
- National Institute of Mental Health, Neuroscience Center, 6001 Executive Blvd, Bethesda, MD, 20892, USA.,Present affiliation: Boehringer Ingelheim, Ingelheim am Rhein, Germany
| | - Joshua A Gordon
- National Institute of Mental Health, Neuroscience Center, 6001 Executive Blvd, Bethesda, MD, 20892, USA
| | - Bruce N Cuthbert
- National Institute of Mental Health, Neuroscience Center, 6001 Executive Blvd, Bethesda, MD, 20892, USA
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31
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Real-time facial emotion recognition deficits across the psychosis spectrum: A B-SNIP Study. Schizophr Res 2022; 243:489-499. [PMID: 34887147 PMCID: PMC9236198 DOI: 10.1016/j.schres.2021.11.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 11/15/2021] [Accepted: 11/17/2021] [Indexed: 11/24/2022]
Abstract
Affective and non-affective psychotic disorders are associated with variable levels of impairment in affective processing, but this domain typically has been examined via presentation of static facial images. We compared performance on a dynamic facial expression identification task across six emotions (sad, fear, surprise, disgust, anger, happy) in individuals with psychotic disorders (bipolar with psychotic features [PBD] = 113, schizoaffective [SAD] = 163, schizophrenia [SZ] = 181) and healthy controls (HC; n = 236) derived from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP). These same individuals with psychotic disorders were also grouped by B-SNIP-derived Biotype (Biotype 1 [B1] = 115, Biotype 2 [B2] = 132, Biotype 3 [B3] = 158), derived from a cluster analysis applied to a large biomarker panel that did not include the current data. Irrespective of the depicted emotion, groups differed in accuracy of emotion identification (P < 0.0001). The SZ group demonstrated lower accuracy versus HC and PBD groups; the SAD group was less accurate than the HC group (Ps < 0.02). Similar overall group differences were evident in speed of identifying emotional expressions. Controlling for general cognitive ability did not eliminate most group differences on accuracy but eliminated almost all group differences on reaction time for emotion identification. Results from the Biotype groups indicated that B1 and B2 had more severe deficits in emotion recognition than HC and B3, meanwhile B3 did not show significant deficits. In sum, this characterization of facial emotion recognition deficits adds to our emerging understanding of social/emotional deficits across the psychosis spectrum.
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32
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Clementz BA. Psychosis and fever revisited. Schizophr Res 2022; 242:17-19. [PMID: 34903400 PMCID: PMC8923948 DOI: 10.1016/j.schres.2021.11.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/15/2021] [Accepted: 11/16/2021] [Indexed: 01/05/2023]
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33
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Tandon R, Keshavan M, Nasrallah H. Reinventing schizophrenia. Updating the construct. Schizophr Res 2022; 242:1-3. [PMID: 35241314 DOI: 10.1016/j.schres.2022.02.024] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 02/17/2022] [Indexed: 02/04/2023]
Affiliation(s)
- Rajiv Tandon
- Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, MI, United States of America.
| | - Matcheri Keshavan
- Department of Psychiatry, BIDMC, Harvard Medical School, Boston, MA, United States of America
| | - Henry Nasrallah
- Department of Psychiatry, University of Cincinnati Medical School, Cincinnati, OH, United States of America
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34
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Tandon R. Agreement on the contours of schizophrenia: The first order of business. Schizophr Res 2022; 242:135-137. [PMID: 35067457 DOI: 10.1016/j.schres.2022.01.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 01/02/2022] [Indexed: 11/25/2022]
Affiliation(s)
- Rajiv Tandon
- Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, MI, United States of America.
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35
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Tamminga CA, Pearlson G, Gershon E, Keedy S, Hudgens-Haney ME, Ivleva EI, Parker DA, McDowell JE, Clementz B. Using psychosis biotypes and the Framingham model for parsing psychosis biology. Schizophr Res 2022; 242:132-134. [PMID: 35123865 DOI: 10.1016/j.schres.2022.01.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 01/11/2022] [Indexed: 12/28/2022]
Abstract
The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has invested in the collection and use of multiple biomarkers in individuals with psychosis. We expect psychosis biology and its distinctive types to be reflected in the biomarkers, as they are the 'behaviors' of the brain. Like infectious diseases, we expect the etiologies of these biomarker-driven entities to be multiple and complex. Biomarkers have not yet been annotated with disease characteristics and need to be. As a model, we seek to adopt aspects of the Framingham Heart Study (FHS) to guide and organize these observations.
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Affiliation(s)
| | | | - Elliot Gershon
- University of Chicago, Chicago, IL, United States of America
| | - Sarah Keedy
- University of Chicago, Chicago, IL, United States of America
| | | | | | - David A Parker
- University of Georgia, Athens, GA, United States of America
| | | | - Brett Clementz
- University of Georgia, Athens, GA, United States of America
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36
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St Clair D, MacLennan G, Beedie SA, Nouzová E, Lemmon H, Rujescu D, Benson PJ, McIntosh A, Nath M. Eye Movement Patterns Can Distinguish Schizophrenia From the Major Affective Disorders and Healthy Control Subjects. SCHIZOPHRENIA BULLETIN OPEN 2022; 3:sgac032. [PMID: 35669867 PMCID: PMC9155263 DOI: 10.1093/schizbullopen/sgac032] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Background and hypothesis No objective tests are currently available to help diagnosis of major psychiatric disorders. This study evaluates the potential of eye movement behavior patterns to predict schizophrenia subjects compared to those with major affective disorders and control groups. Study design Eye movements were recorded from a training set of UK subjects with schizophrenia (SCZ; n = 120), bipolar affective disorder (BPAD; n = 141), major depressive disorder (MDD; n = 136), and healthy controls (CON; n = 142), and from a hold-out set of 133 individuals with proportional group sizes. A German cohort of SCZ (n = 60) and a Scottish cohort of CON subjects (n = 184) acted as a second semi-independent test set. All patients met DSMIV and ICD10 criteria for SCZ, BPAD, and MDD. Data from 98 eye movement features were extracted. We employed a gradient boosted (GB) decision tree multiclass classifier to develop a predictive model. We calculated the area under the curve (AUC) as the primary performance metric. Study results Estimates of AUC in one-versus-all comparisons were: SCZ (0.85), BPAD (0.78), MDD (0.76), and CON (0.85). Estimates on part-external validation were SCZ (0.89) and CON (0.65). In all cases, there was good specificity but only moderate sensitivity. The best individual discriminators included free viewing, fixation duration, and smooth pursuit tasks. The findings appear robust to potential confounders such as age, sex, medication, or mental state at the time of testing. Conclusions Eye movement patterns can discriminate schizophrenia from major mood disorders and control subjects with around 80% predictive accuracy.
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Affiliation(s)
- David St Clair
- Division of Applied Medicine, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
- Clinical Research Centre, Royal Cornhill Hospital, Aberdeen, UK
| | - Graeme MacLennan
- Centre for Healthcare Randomised Trials (CHaRT), University of Aberdeen, Aberdeen, UK
| | - Sara A Beedie
- Clinical Research Centre, Royal Cornhill Hospital, Aberdeen, UK
| | - Eva Nouzová
- Clinical Research Centre, Royal Cornhill Hospital, Aberdeen, UK
| | - Helen Lemmon
- Clinical Research Centre, Royal Cornhill Hospital, Aberdeen, UK
| | - Dan Rujescu
- Department of Psychiatry, Medical University of Vienna, Vienna, Austria
| | - Philip J Benson
- Department of Psychology, University of Aberdeen, Aberdeen, UK
| | - Andrew McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, Edinburgh, UK
| | - Mintu Nath
- Medical Statistics Team, Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
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