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Kotov R, Carpenter WT, Cicero DC, Correll CU, Martin EA, Young JW, Zald DH, Jonas KG. Psychosis superspectrum II: neurobiology, treatment, and implications. Mol Psychiatry 2024:10.1038/s41380-024-02410-1. [PMID: 38351173 DOI: 10.1038/s41380-024-02410-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 12/24/2023] [Accepted: 01/04/2024] [Indexed: 02/16/2024]
Abstract
Alternatives to traditional categorical diagnoses have been proposed to improve the validity and utility of psychiatric nosology. This paper continues the companion review of an alternative model, the psychosis superspectrum of the Hierarchical Taxonomy of Psychopathology (HiTOP). The superspectrum model aims to describe psychosis-related psychopathology according to data on distributions and associations among signs and symptoms. The superspectrum includes psychoticism and detachment spectra as well as narrow subdimensions within them. Auxiliary domains of cognitive deficit and functional impairment complete the psychopathology profile. The current paper reviews evidence on this model from neurobiology, treatment response, clinical utility, and measure development. Neurobiology research suggests that psychopathology included in the superspectrum shows similar patterns of neural alterations. Treatment response often mirrors the hierarchy of the superspectrum with some treatments being efficacious for psychoticism, others for detachment, and others for a specific subdimension. Compared to traditional diagnostic systems, the quantitative nosology shows an approximately 2-fold increase in reliability, explanatory power, and prognostic accuracy. Clinicians consistently report that the quantitative nosology has more utility than traditional diagnoses, but studies of patients with frank psychosis are currently lacking. Validated measures are available to implement the superspectrum model in practice. The dimensional conceptualization of psychosis-related psychopathology has implications for research, clinical practice, and public health programs. For example, it encourages use of the cohort study design (rather than case-control), transdiagnostic treatment strategies, and selective prevention based on subclinical symptoms. These approaches are already used in the field, and the superspectrum provides further impetus and guidance for their implementation. Existing knowledge on this model is substantial, but significant gaps remain. We identify outstanding questions and propose testable hypotheses to guide further research. Overall, we predict that the more informative, reliable, and valid characterization of psychopathology offered by the superspectrum model will facilitate progress in research and clinical care.
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Affiliation(s)
- Roman Kotov
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, NY, USA.
| | | | - David C Cicero
- Department of Psychology, University of North Texas, Denton, TX, USA
| | - Christoph U Correll
- Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Child and Adolescent Psychiatry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Elizabeth A Martin
- Department of Psychological Science, University of California, Irvine, Irvine, CA, USA
| | - Jared W Young
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Research Service, VA San Diego Healthcare System, San Diego, CA, USA
| | - David H Zald
- Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Katherine G Jonas
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, NY, USA
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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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Ekin M, Koçoğlu K, Eraslan Boz H, Akkoyun M, Tüfekci IY, Cesim E, Yalınçetin B, Özbek SU, Bora E, Akdal G. Antisaccade and memory-guided saccade in individuals at ultra-high-risk for bipolar disorder. J Affect Disord 2023; 339:965-972. [PMID: 37499914 DOI: 10.1016/j.jad.2023.07.109] [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: 02/22/2023] [Revised: 07/21/2023] [Accepted: 07/23/2023] [Indexed: 07/29/2023]
Abstract
BACKGROUND Ultra-high-risk for bipolar disorder (UHR-BD) is an important paradigm to investigate the potential early-stage biomarkers of bipolar disorder, including eye-tracking abnormalities and cognitive functions. Antisaccade (AS) described as looking in the opposite direction of the target, and memory-guided saccade (MGS), identified as maintaining fixation, and remembering the location of the target, were used in this study. The aim of this study was to evaluate the differences in saccadic eye movements between UHR-BD and healthy controls (HCs) via AS-MGS. METHODS The study included 28 UHR-BD and 29 HCs. Participants were selected using a structured clinical interview for prodromal symptoms of BD. AS-MGS were measured with parameters like uncorrected errors, anticipatory saccades, and latency. Eye movements were recorded with the EyeLink 1000-Plus eye-tracker. RESULTS In the AS, the number of correct saccades was significantly decreased in UHR-BD (p = 0.020). Anticipatory (p = 0.009) and express saccades (p = 0.040) were increased in UHR-BD. In the MGS paradigm, the correct saccades were reduced in UHR-BD (p = 0.031). In addition, anticipatory (p = 0.004) and express saccades (p = 0.012) were significantly increased in cue-screen in UHR-BD. CONCLUSIONS To our knowledge, this is the first study to evaluate cognitive functions with eye movements in individuals at UHR-BD. The current findings showed that eye movement functions, particularly in saccadic parameters related to inhibition and spatial perception, may be affected in the UHR-BD group. Therefore, assessment of oculomotor functions may provide observation of clinical and cognitive functions in the early-stage of bipolar disorder. However, further research is needed because the potential effects of medication may affect saccadic results.
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Affiliation(s)
- Merve Ekin
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylül University, İzmir, Türkiye; Institute of Psychology, SWPS University, Warsaw, Poland.
| | - Koray Koçoğlu
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylül University, İzmir, Türkiye
| | - Hatice Eraslan Boz
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylül University, İzmir, Türkiye
| | - Müge Akkoyun
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylül University, İzmir, Türkiye
| | - Işıl Yağmur Tüfekci
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylül University, İzmir, Türkiye
| | - Ezgi Cesim
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylül University, İzmir, Türkiye
| | - Berna Yalınçetin
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylül University, İzmir, Türkiye
| | - Simge Uzman Özbek
- Department of Psychiatry, Faculty of Medicine, Dokuz Eylül University, Izmir, Türkiye
| | - Emre Bora
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylül University, İzmir, Türkiye; Department of Psychiatry, Faculty of Medicine, Dokuz Eylül University, Izmir, Türkiye; Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Australia
| | - Gülden Akdal
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylül University, İzmir, Türkiye; Department of Neurology, Faculty of Medicine, Dokuz Eylül University, Izmir, Türkiye
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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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Calancie OG, Parr AC, Brien DC, Huang J, Pitigoi IC, Coe BC, Booij L, Khalid-Khan S, Munoz DP. Motor synchronization and impulsivity in pediatric borderline personality disorder with and without attention-deficit hyperactivity disorder: an eye-tracking study of saccade, blink and pupil behavior. Front Neurosci 2023; 17:1179765. [PMID: 37425020 PMCID: PMC10323365 DOI: 10.3389/fnins.2023.1179765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 05/30/2023] [Indexed: 07/11/2023] Open
Abstract
Shifting motor actions from reflexively reacting to an environmental stimulus to predicting it allows for smooth synchronization of behavior with the outside world. This shift relies on the identification of patterns within the stimulus - knowing when a stimulus is predictable and when it is not - and launching motor actions accordingly. Failure to identify predictable stimuli results in movement delays whereas failure to recognize unpredictable stimuli results in early movements with incomplete information that can result in errors. Here we used a metronome task, combined with video-based eye-tracking, to quantify temporal predictive learning and performance to regularly paced visual targets at 5 different interstimulus intervals (ISIs). We compared these results to the random task where the timing of the target was randomized at each target step. We completed these tasks in female pediatric psychiatry patients (age range: 11-18 years) with borderline personality disorder (BPD) symptoms, with (n = 22) and without (n = 23) a comorbid attention-deficit hyperactivity disorder (ADHD) diagnosis, against controls (n = 35). Compared to controls, BPD and ADHD/BPD cohorts showed no differences in their predictive saccade performance to metronome targets, however, when targets were random ADHD/BPD participants made significantly more anticipatory saccades (i.e., guesses of target arrival). The ADHD/BPD group also significantly increased their blink rate and pupil size when initiating movements to predictable versus unpredictable targets, likely a reflection of increased neural effort for motor synchronization. BPD and ADHD/BPD groups showed increased sympathetic tone evidenced by larger pupil sizes than controls. Together, these results support normal temporal motor prediction in BPD with and without ADHD, reduced response inhibition in BPD with comorbid ADHD, and increased pupil sizes in BPD patients. Further these results emphasize the importance of controlling for comorbid ADHD when querying BPD pathology.
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Affiliation(s)
- Olivia G. Calancie
- Queen’s Eye Movement Lab, Centre for Neuroscience Studies, Queen’s University, Kingston, ON, Canada
| | - Ashley C. Parr
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - Don C. Brien
- Queen’s Eye Movement Lab, Centre for Neuroscience Studies, Queen’s University, Kingston, ON, Canada
| | - Jeff Huang
- Queen’s Eye Movement Lab, Centre for Neuroscience Studies, Queen’s University, Kingston, ON, Canada
| | - Isabell C. Pitigoi
- Queen’s Eye Movement Lab, Centre for Neuroscience Studies, Queen’s University, Kingston, ON, Canada
| | - Brian C. Coe
- Queen’s Eye Movement Lab, Centre for Neuroscience Studies, Queen’s University, Kingston, ON, Canada
| | - Linda Booij
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Research Centre and Eating Disorders Continuum, Douglas Mental Health University Institute, Montreal, QC, Canada
| | - Sarosh Khalid-Khan
- Queen’s Eye Movement Lab, Centre for Neuroscience Studies, Queen’s University, Kingston, ON, Canada
- Divison of Child and Youth Psychiatry, Department of Psychiatry, School of Medicine, Queen’s University, Kingston, ON, Canada
| | - Douglas P. Munoz
- Queen’s Eye Movement Lab, Centre for Neuroscience Studies, Queen’s University, Kingston, ON, Canada
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Clementz BA, Parker DA, Trotti RL, McDowell JE, Keedy SK, Keshavan MS, Pearlson GD, Gershon ES, Ivleva EI, Huang LY, Hill SK, Sweeney JA, Thomas O, Hudgens-Haney M, Gibbons RD, Tamminga CA. Psychosis Biotypes: Replication and Validation from the B-SNIP Consortium. Schizophr Bull 2022; 48:56-68. [PMID: 34409449 PMCID: PMC8781330 DOI: 10.1093/schbul/sbab090] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Current clinical phenomenological diagnosis in psychiatry neither captures biologically homologous disease entities nor allows for individualized treatment prescriptions based on neurobiology. In this report, we studied two large samples of cases with schizophrenia, schizoaffective, and bipolar I disorder with psychosis, presentations with clinical features of hallucinations, delusions, thought disorder, affective, or negative symptoms. A biomarker approach to subtyping psychosis cases (called psychosis Biotypes) captured neurobiological homology that was missed by conventional clinical diagnoses. Two samples (called "B-SNIP1" with 711 psychosis and 274 healthy persons, and the "replication sample" with 717 psychosis and 198 healthy persons) showed that 44 individual biomarkers, drawn from general cognition (BACS), motor inhibitory (stop signal), saccadic system (pro- and anti-saccades), and auditory EEG/ERP (paired-stimuli and oddball) tasks of psychosis-relevant brain functions were replicable (r's from .96-.99) and temporally stable (r's from .76-.95). Using numerical taxonomy (k-means clustering) with nine groups of integrated biomarker characteristics (called bio-factors) yielded three Biotypes that were virtually identical between the two samples and showed highly similar case assignments to subgroups based on cross-validations (88.5%-89%). Biotypes-1 and -2 shared poor cognition. Biotype-1 was further characterized by low neural response magnitudes, while Biotype-2 was further characterized by overactive neural responses and poor sensory motor inhibition. Biotype-3 was nearly normal on all bio-factors. Construct validation of Biotype EEG/ERP neurophysiology using measures of intrinsic neural activity and auditory steady state stimulation highlighted the robustness of these outcomes. Psychosis Biotypes may yield meaningful neurobiological targets for treatments and etiological investigations.
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Affiliation(s)
- Brett A Clementz
- Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA, USA
| | - David A Parker
- Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA, USA
| | - Rebekah L Trotti
- Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA, USA
| | - Jennifer E McDowell
- Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA, USA
| | - Sarah K Keedy
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
- Institute of Living, Hartford Healthcare Corp, Hartford, CT, USA
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - Elena I Ivleva
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
| | - Ling-Yu Huang
- Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA, USA
| | - S Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA
| | - John A Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Olivia Thomas
- Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA, USA
| | | | - Robert D Gibbons
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - Carol A Tamminga
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
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