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Powers A, Angelos P, Bond A, Farina E, Fredericks C, Gandhi J, Greenwald M, Hernandez-Busot G, Hosein G, Kelley M, Mourgues C, Palmer W, Rodriguez-Sanchez J, Seabury R, Toribio S, Vin R, Weleff J, Benrimoh D. A computational account of the development and evolution of psychotic symptoms. ArXiv 2024:arXiv:2404.10954v1. [PMID: 38699166 PMCID: PMC11065053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
The mechanisms of psychotic symptoms like hallucinations and delusions are often investigated in fully-formed illness, well after symptoms emerge. These investigations have yielded key insights, but are not well-positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We will make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We will argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing an adaptive relative over-reliance on prior beliefs. This over-reliance on priors predisposes to hallucinations and covaries with hallucination severity. An over-reliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We will identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptomatology as a point of equilibrium among competing biological forces.
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Affiliation(s)
- Albert Powers
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Philip Angelos
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Alexandria Bond
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Emily Farina
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Carolyn Fredericks
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Jay Gandhi
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Maximillian Greenwald
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | | | - Gabriel Hosein
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Megan Kelley
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Catalina Mourgues
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - William Palmer
- Yale University Department of Psychology, New Haven, CT USA
| | | | - Rashina Seabury
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Silmilly Toribio
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Raina Vin
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Jeremy Weleff
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - David Benrimoh
- Department of Psychiatry, McGill University, Montreal, Canada
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Benrimoh D, Fisher VL, Seabury R, Sibarium E, Mourgues C, Chen D, Powers A. Evidence for Reduced Sensory Precision and Increased Reliance on Priors in Hallucination-Prone Individuals in a General Population Sample. Schizophr Bull 2024; 50:349-362. [PMID: 37830405 PMCID: PMC10919780 DOI: 10.1093/schbul/sbad136] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
BACKGROUND There is increasing evidence that people with hallucinations overweight perceptual beliefs relative to incoming sensory evidence. Past work demonstrating prior overweighting has used simple, nonlinguistic stimuli. However, auditory hallucinations in psychosis are often complex and linguistic. There may be an interaction between the type of auditory information being processed and its perceived quality in engendering hallucinations. STUDY DESIGN We administered a linguistic version of the conditioned hallucinations (CH) task to an online sample of 88 general population participants. Metrics related to hallucination-proneness, hallucination severity, stimulus thresholds, and stimulus detection rates were collected. Data were used to fit parameters of a Hierarchical Gaussian Filter (HGF) model of perceptual inference to determine how latent perceptual states influenced task behavior. STUDY RESULTS Replicating past results, higher CH rates were observed both in those with recent hallucinatory experiences as well as participants with high hallucination-proneness; CH rates were positively correlated with increased prior weighting; and increased prior weighting was related to hallucination severity. Unlike past results, participants with recent hallucinatory experiences as well as those with higher hallucination-proneness had higher stimulus thresholds, lower sensitivity to stimuli presented at the highest threshold, and had lower response confidence, consistent with lower precision of sensory evidence. CONCLUSIONS We replicate the finding that increased CH rates and recent hallucinations correlate with increased prior weighting using a linguistic version of the CH task. Results support a role for reduced sensory precision in the interplay between prior weighting and hallucination-proneness.
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Affiliation(s)
- David Benrimoh
- Department of Psychiatry, McGill University School of Medicine, Montreal, Canada
| | - Victoria L Fisher
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Rashina Seabury
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Ely Sibarium
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Catalina Mourgues
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Doris Chen
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Albert Powers
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
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3
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Benrimoh D, Kleinerman A, Furukawa TA, Iii CFR, Lenze EJ, Karp J, Mulsant B, Armstrong C, Mehltretter J, Fratila R, Perlman K, Israel S, Popescu C, Golden G, Qassim S, Anacleto A, Tanguay-Sela M, Kapelner A, Rosenfeld A, Turecki G. Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies. Am J Geriatr Psychiatry 2024; 32:280-292. [PMID: 37839909 DOI: 10.1016/j.jagp.2023.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 10/17/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is a heterogeneous condition; multiple underlying neurobiological and behavioral substrates are associated with treatment response variability. Understanding the sources of this variability and predicting outcomes has been elusive. Machine learning (ML) shows promise in predicting treatment response in MDD, but its application is limited by challenges to the clinical interpretability of ML models, and clinicians often lack confidence in model results. In order to improve the interpretability of ML models in clinical practice, our goal was to demonstrate the derivation of treatment-relevant patient profiles comprised of clinical and demographic information using a novel ML approach. METHODS We analyzed data from six clinical trials of pharmacological treatment for depression (total n = 5438) using the Differential Prototypes Neural Network (DPNN), a ML model that derives patient prototypes which can be used to derive treatment-relevant patient clusters while learning to generate probabilities for differential treatment response. A model classifying remission and outputting individual remission probabilities for five first-line monotherapies and three combination treatments was trained using clinical and demographic data. Prototypes were evaluated for interpretability by assessing differences in feature distributions (e.g. age, sex, symptom severity) and treatment-specific outcomes. RESULTS A 3-prototype model achieved an area under the receiver operating curve of 0.66 and an expected absolute improvement in remission rate for those receiving the best predicted treatment of 6.5% (relative improvement of 15.6%) compared to the population remission rate. We identified three treatment-relevant patient clusters. Cluster A patients tended to be younger, to have increased levels of fatigue, and more severe symptoms. Cluster B patients tended to be older, female, have less severe symptoms, and the highest remission rates. Cluster C patients had more severe symptoms, lower remission rates, more psychomotor agitation, more intense suicidal ideation, and more somatic genital symptoms. CONCLUSION It is possible to produce novel treatment-relevant patient profiles using ML models; doing so may improve interpretability of ML models and the quality of precision medicine treatments for MDD.
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Affiliation(s)
- David Benrimoh
- Department of Psychiatry (DB, KP, GT), McGill University, Montreal, Canada; Department of Psychiatry (DB), Stanford University, Stanford, CA; Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada.
| | | | - Toshi A Furukawa
- Department of Health Promotion and Human Behavior (TAF), Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan
| | - Charles F Reynolds Iii
- Department of Psychiatry (CFR), University of Pittsburgh School of Medicine, Pittsburgh, PA; Department of Psychiatry (CFR), Tufts University School of Medicine, Medford, MA
| | - Eric J Lenze
- Department of Psychiatry (EJL), Washington University School of Medicine, St. Louis, MS
| | - Jordan Karp
- Department of Psychiatry (JK), University of Arizona, Tucson, AZ
| | - Benoit Mulsant
- Department of Psychiatry (BM), University of Toronto, Toronto, ON, Canada
| | - Caitrin Armstrong
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Joseph Mehltretter
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Robert Fratila
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Kelly Perlman
- Department of Psychiatry (DB, KP, GT), McGill University, Montreal, Canada; Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Sonia Israel
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Christina Popescu
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Grace Golden
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Sabrina Qassim
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Alexandra Anacleto
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Myriam Tanguay-Sela
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Adam Kapelner
- Department of Mathematics (AK), Queens College, CUNY, New York, NY
| | | | - Gustavo Turecki
- Department of Psychiatry (DB, KP, GT), McGill University, Montreal, Canada
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Benrimoh D, Dlugunovych V, Wright AC, Phalen P, Funaro MC, Ferrara M, Powers AR, Woods SW, Guloksuz S, Yung AR, Srihari V, Shah J. Correction: On the proportion of patients who experience a prodrome prior to psychosis onset: a systematic review and meta-analysis. Mol Psychiatry 2024:10.1038/s41380-024-02481-0. [PMID: 38351175 DOI: 10.1038/s41380-024-02481-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Affiliation(s)
- David Benrimoh
- PEPP-Montréal, Department of Psychiatry and Douglas Research Center, McGill University, Montreal, QC, Canada.
- Department of Psychiatry, Stanford University, Stanford, CA, USA.
| | | | - Abigail C Wright
- Center of Excellence for Psychosocial and Systemic Research, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Peter Phalen
- Division of Psychiatric Services Research, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Melissa C Funaro
- Harvey Cushing/John Hay Whitney Medical Library, Yale University, New Haven, CT, USA
| | - Maria Ferrara
- Institute of Psychiatry, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy
- Specialized Treatment Early in Psychosis Program (STEP), Yale School of Medicine, New Haven, CT, USA
| | - Albert R Powers
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Scott W Woods
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Sinan Guloksuz
- Specialized Treatment Early in Psychosis Program (STEP), Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry and Neuropsychology Maastricht University Medical Center, Maastricht, Netherlands
| | - Alison R Yung
- Institute of Mental and Physical Health and Clinical Translation (IMPACT), School of Medicine, Deakin University, Melbourne, Australia
| | - Vinod Srihari
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Jai Shah
- PEPP-Montréal, Department of Psychiatry and Douglas Research Center, McGill University, Montreal, QC, Canada
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Benrimoh D, Dlugunovych V, Wright AC, Phalen P, Funaro MC, Ferrara M, Powers AR, Woods SW, Guloksuz S, Yung AR, Srihari V, Shah J. On the proportion of patients who experience a prodrome prior to psychosis onset: A systematic review and meta-analysis. Mol Psychiatry 2024:10.1038/s41380-024-02415-w. [PMID: 38302562 DOI: 10.1038/s41380-024-02415-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 12/20/2023] [Accepted: 01/04/2024] [Indexed: 02/03/2024]
Abstract
BACKGROUND Preventing or delaying the onset of psychosis requires identification of those at risk for developing psychosis. For predictive purposes, the prodrome - a constellation of symptoms which may occur before the onset of psychosis - has been increasingly recognized as having utility. However, it is unclear what proportion of patients experience a prodrome or how this varies based on the multiple definitions used. METHODS We conducted a systematic review and meta-analysis of studies of patients with psychosis with the objective of determining the proportion of patients who experienced a prodrome prior to psychosis onset. Inclusion criteria included a consistent prodrome definition and reporting the proportion of patients who experienced a prodrome. We excluded studies of only patients with a prodrome or solely substance-induced psychosis, qualitative studies without prevalence data, conference abstracts, and case reports/case series. We searched Ovid MEDLINE, Embase (Ovid), APA PsycInfo (Ovid), Web of Science Core Collection (Clarivate), Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, APA PsycBooks (Ovid), ProQuest Dissertation & Thesis, on March 3, 2021. Studies were assessed for quality using the Critical Appraisal Checklist for Prevalence Studies. Narrative synthesis and proportion meta-analysis were used to estimate prodrome prevalence. I2 and predictive interval were used to assess heterogeneity. Subgroup analyses were used to probe sources of heterogeneity. (PROSPERO ID: CRD42021239797). RESULTS Seventy-one articles were included, representing 13,774 patients. Studies varied significantly in terms of methodology and prodrome definition used. The random effects proportion meta-analysis estimate for prodrome prevalence was 78.3% (95% CI = 72.8-83.2); heterogeneity was high (I2 97.98% [95% CI = 97.71-98.22]); and the prediction interval was wide (95% PI = 0.411-0.936). There were no meaningful differences in prevalence between grouped prodrome definitions, and subgroup analyses failed to reveal a consistent source of heterogeneity. CONCLUSIONS This is the first meta-analysis on the prevalence of a prodrome prior to the onset of first episode psychosis. The majority of patients (78.3%) were found to have experienced a prodrome prior to psychosis onset. However, findings are highly heterogenous across study and no definitive source of heterogeneity was found despite extensive subgroup analyses. As most studies were retrospective in nature, recall bias likely affects these results. While the large majority of patients with psychosis experience a prodrome in some form, it is unclear if the remainder of patients experience no prodrome, or if ascertainment methods employed in the studies were not sensitive to their experiences. Given widespread investment in indicated prevention of psychosis through prospective identification and intervention during the prodrome, a resolution of this question as well as a consensus definition of the prodrome is much needed in order to effectively direct and organize services, and may be accomplished through novel, densely sampled and phenotyped prospective cohort studies that aim for representative sampling across multiple settings.
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Affiliation(s)
- David Benrimoh
- PEPP-Montréal, Department of Psychiatry and Douglas Research Center, McGill University, Montreal, QC, Canada.
- Department of Psychiatry, Stanford University, Stanford, CA, USA.
| | | | - Abigail C Wright
- Center of Excellence for Psychosocial and Systemic Research, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Peter Phalen
- Division of Psychiatric Services Research, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Melissa C Funaro
- Harvey Cushing/John Hay Whitney Medical Library, Yale University, New Haven, CT, USA
| | - Maria Ferrara
- Institute of Psychiatry, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy
- Specialized Treatment Early in Psychosis Program (STEP), Yale School of Medicine, New Haven, CT, USA
| | - Albert R Powers
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Scott W Woods
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Sinan Guloksuz
- Specialized Treatment Early in Psychosis Program (STEP), Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry and Neuropsychology Maastricht University Medical Center, Maastricht, Netherlands
| | - Alison R Yung
- Institute of Mental and Physical Health and Clinical Translation (IMPACT), School of Medicine, Deakin University, Melbourne, Australia
| | - Vinod Srihari
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Jai Shah
- PEPP-Montréal, Department of Psychiatry and Douglas Research Center, McGill University, Montreal, QC, Canada
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Popescu C, Golden G, Benrimoh D, Tanguay-Sela M, Slowey D, Lundrigan E, Williams J, Desormeau B, Kardani D, Perez T, Rollins C, Israel S, Perlman K, Armstrong C, Baxter J, Whitmore K, Fradette MJ, Felcarek-Hope K, Soufi G, Fratila R, Mehltretter J, Looper K, Steiner W, Rej S, Karp JF, Heller K, Parikh SV, McGuire-Snieckus R, Ferrari M, Margolese H, Turecki G. Correction: Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study. JMIR Form Res 2024; 8:e56570. [PMID: 38266244 PMCID: PMC10851111 DOI: 10.2196/56570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 01/19/2024] [Indexed: 01/26/2024] Open
Abstract
[This corrects the article DOI: 10.2196/31862.].
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Kelly Perlman
- Aifred Health Inc.Montreal, QCCanada
- McGill UniversityMontreal, QCCanada
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Manuela Ferrari
- Douglas Mental Health University InstituteMcGill UniversityMontreal, QCCanada
| | | | - Gustavo Turecki
- Douglas Mental Health University InstituteMcGill UniversityMontreal, QCCanada
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Palaniyappan L, Benrimoh D, Voppel A, Rocca R. Studying Psychosis Using Natural Language Generation: A Review of Emerging Opportunities. Biol Psychiatry Cogn Neurosci Neuroimaging 2023; 8:994-1004. [PMID: 38441079 DOI: 10.1016/j.bpsc.2023.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 04/16/2023] [Accepted: 04/19/2023] [Indexed: 03/07/2024]
Abstract
Disrupted language in psychotic disorders, such as schizophrenia, can manifest as false contents and formal deviations, often described as thought disorder. These features play a critical role in the social dysfunction associated with psychosis, but we continue to lack insights regarding how and why these symptoms develop. Natural language generation (NLG) is a field of computer science that focuses on generating human-like language for various applications. The theory that psychosis is related to the evolution of language in humans suggests that NLG systems that are sufficiently evolved to generate human-like language may also exhibit psychosis-like features. In this conceptual review, we propose using NLG systems that are at various stages of development as in silico tools to study linguistic features of psychosis. We argue that a program of in silico experimental research on the network architecture, function, learning rules, and training of NLG systems can help us understand better why thought disorder occurs in patients. This will allow us to gain a better understanding of the relationship between language and psychosis and potentially pave the way for new therapeutic approaches to address this vexing challenge.
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Affiliation(s)
- Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada.
| | - David Benrimoh
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, Stanford University, Palo Alto, California
| | - Alban Voppel
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, University of Groningen, Groningen, the Netherlands
| | - Roberta Rocca
- Interacting Minds Centre, Department of Culture, Cognition and Computation, Aarhus University, Aarhus, Denmark
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Benrimoh D, Fisher V, Mourgues C, Sheldon AD, Smith R, Powers AR. Barriers and solutions to the adoption of translational tools for computational psychiatry. Mol Psychiatry 2023; 28:2189-2196. [PMID: 37280282 PMCID: PMC10611570 DOI: 10.1038/s41380-023-02114-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 04/25/2023] [Accepted: 05/05/2023] [Indexed: 06/08/2023]
Abstract
Computational psychiatry is a field aimed at developing formal models of information processing in the human brain, and how alterations in this processing can lead to clinical phenomena. There has been significant progress in the development of tasks and how to model them, presenting an opportunity to incorporate computational psychiatry methodologies into large- scale research projects or into clinical practice. In this viewpoint, we explore some of the barriers to incorporation of computational psychiatry tasks and models into wider mainstream research directions. These barriers include the time required for participants to complete tasks, test-retest reliability, limited ecological validity, as well as practical concerns, such as lack of computational expertise and the expense and large sample sizes traditionally required to validate tasks and models. We then discuss solutions, such as the redesigning of tasks with a view toward feasibility, and the integration of tasks into more ecologically valid and standardized game platforms that can be more easily disseminated. Finally, we provide an example of how one task, the conditioned hallucinations task, might be translated into such a game. It is our hope that interest in the creation of more accessible and feasible computational tasks will help computational methods make more positive impacts on research as well as, eventually, clinical practice.
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Affiliation(s)
- David Benrimoh
- McGill University School of Medicine, Montreal, QC, Canada
| | - Victoria Fisher
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Catalina Mourgues
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Andrew D Sheldon
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Ryan Smith
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Albert R Powers
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA.
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9
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Fleming LM, Lemonde AC, Benrimoh D, Gold JM, Taylor JR, Malla A, Joober R, Iyer SN, Lepage M, Shah J, Corlett PR. Using dimensionality-reduction techniques to understand the organization of psychotic symptoms in persistent psychotic illness and first episode psychosis. Sci Rep 2023; 13:4841. [PMID: 36964175 PMCID: PMC10039017 DOI: 10.1038/s41598-023-31909-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 03/17/2023] [Indexed: 03/26/2023] Open
Abstract
Psychotic disorders are highly heterogeneous. Understanding relationships between symptoms will be relevant to their underlying pathophysiology. We apply dimensionality-reduction methods across two unique samples to characterize the patterns of symptom organization. We analyzed publicly-available data from 153 participants diagnosed with schizophrenia or schizoaffective disorder (fBIRN Data Repository and the Consortium for Neuropsychiatric Phenomics), as well as 636 first-episode psychosis (FEP) participants from the Prevention and Early Intervention Program for Psychosis (PEPP-Montreal). In all participants, the Scale for the Assessment of Positive Symptoms (SAPS) and Scale for the Assessment of Negative Symptoms (SANS) were collected. Multidimensional scaling (MDS) combined with cluster analysis was applied to SAPS and SANS scores across these two groups of participants. MDS revealed relationships between items of SAPS and SANS. Our application of cluster analysis to these results identified: 1 cluster of disorganization symptoms, 2 clusters of hallucinations/delusions, and 2 SANS clusters (asocial and apathy, speech and affect). Those reality distortion items which were furthest from auditory hallucinations had very weak to no relationship with hallucination severity. Despite being at an earlier stage of illness, symptoms in FEP presentations were similarly organized. While hallucinations and delusions commonly co-occur, we found that their specific themes and content sometimes travel together and sometimes do not. This has important implications, not only for treatment, but also for research-particularly efforts to understand the neurocomputational and pathophysiological mechanism underlying delusions and hallucinations.
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Affiliation(s)
- Leah M Fleming
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Department, Yale University, New Haven, CT, USA
| | | | - David Benrimoh
- Department of Psychiatry, McGill University Montreal, Qubec, Canada
| | - James M Gold
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jane R Taylor
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
- Department of Neuroscience, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Ashok Malla
- Department of Psychiatry, McGill University Montreal, Qubec, Canada
- The Prevention and Early Intervention Program for Psychosis (PEPP-Montreal), Douglas Mental Health University Institute, Qubec, Canada
| | - Ridha Joober
- Department of Psychiatry, McGill University Montreal, Qubec, Canada
- The Prevention and Early Intervention Program for Psychosis (PEPP-Montreal), Douglas Mental Health University Institute, Qubec, Canada
| | - Srividya N Iyer
- Department of Psychiatry, McGill University Montreal, Qubec, Canada
- The Prevention and Early Intervention Program for Psychosis (PEPP-Montreal), Douglas Mental Health University Institute, Qubec, Canada
| | - Martin Lepage
- Department of Psychiatry, McGill University Montreal, Qubec, Canada
- The Prevention and Early Intervention Program for Psychosis (PEPP-Montreal), Douglas Mental Health University Institute, Qubec, Canada
| | - Jai Shah
- Department of Psychiatry, McGill University Montreal, Qubec, Canada
- The Prevention and Early Intervention Program for Psychosis (PEPP-Montreal), Douglas Mental Health University Institute, Qubec, Canada
| | - Philip R Corlett
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
- Department of Psychology, Yale University, New Haven, CT, USA.
- Wu Tsai Institute, Yale University, New Haven, CT, USA.
- Connecticut Mental Health Center, 34 Park St, New Haven, CT, 06519, USA.
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10
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Kafadar E, Fisher VL, Quagan B, Hammer A, Jaeger H, Mourgues C, Thomas R, Chen L, Imtiaz A, Sibarium E, Negreira AM, Sarisik E, Polisetty V, Benrimoh D, Sheldon AD, Lim C, Mathys C, Powers AR. Conditioned Hallucinations and Prior Overweighting Are State-Sensitive Markers of Hallucination Susceptibility. Biol Psychiatry 2022; 92:772-780. [PMID: 35843743 PMCID: PMC10575690 DOI: 10.1016/j.biopsych.2022.05.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 04/08/2022] [Accepted: 05/02/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND Recent advances in computational psychiatry have identified latent cognitive and perceptual states that predispose to psychotic symptoms. Behavioral data fit to Bayesian models have demonstrated an overreliance on priors (i.e., prior overweighting) during perception in select samples of individuals with hallucinations, corresponding to increased precision of prior expectations over incoming sensory evidence. However, the clinical utility of this observation depends on the extent to which it reflects static symptom risk or current symptom state. METHODS To determine whether task performance and estimated prior weighting relate to specific elements of symptom expression, a large, heterogeneous, and deeply phenotyped sample of hallucinators (n = 249) and nonhallucinators (n = 209) performed the conditioned hallucination (CH) task. RESULTS We found that CH rates predicted stable measures of hallucination status (i.e., peak frequency). However, CH rates were more sensitive to hallucination state (i.e., recent frequency), significantly correlating with recent hallucination severity and driven by heightened reliance on past experiences (priors). To further test the sensitivity of CH rate and prior weighting to symptom severity, a subset of participants with hallucinations (n = 40) performed a repeated-measures version of the CH task. Changes in both CH frequency and prior weighting varied with changes in auditory hallucination frequency on follow-up. CONCLUSIONS These results indicate that CH rate and prior overweighting are state markers of hallucination status, potentially useful in tracking disease development and treatment response.
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Affiliation(s)
- Eren Kafadar
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | - Victoria L Fisher
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | - Brittany Quagan
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | - Allison Hammer
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | - Hale Jaeger
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | - Catalina Mourgues
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | - Rigi Thomas
- School of Naturopathic Medicine, Southwest College of Naturopathic Medicine and Health Sciences, Tempe, Arizona
| | - Linda Chen
- Faculty of Science, University of British Columbia, Vancouver, British Columbia, Canada
| | - Ayyub Imtiaz
- Department of Psychiatry, St Elizabeth's Hospital, Washington, DC
| | - Ely Sibarium
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | | | - Elif Sarisik
- Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey; Max Planck Institute for Psychiatry, Munich, Germany
| | - Vasishta Polisetty
- Department of Psychiatry, All India Institute of Medical Sciences, New Delhi, India
| | - David Benrimoh
- McGill University School of Medicine, Montreal, Quebec, Canada
| | - Andrew D Sheldon
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | - Chris Lim
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | - Christoph Mathys
- Interacting Minds Centre, Aarhus University, Aarhus C, Denmark; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zürich and ETH Zürich, Zurich, Switzerland; Neuroscience Area, Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy
| | - Albert R Powers
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut.
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11
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Tanguay-Sela M, Rollins C, Perez T, Qiang V, Golden G, Tunteng JF, Perlman K, Simard J, Benrimoh D, Margolese HC. A systematic meta-review of patient-level predictors of psychological therapy outcome in major depressive disorder. J Affect Disord 2022; 317:307-318. [PMID: 36029877 DOI: 10.1016/j.jad.2022.08.041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 08/16/2022] [Accepted: 08/19/2022] [Indexed: 10/31/2022]
Abstract
BACKGROUND Psychological therapies are effective for treating major depressive disorder, but current clinical guidelines do not provide guidance on the personalization of treatment choice. Established predictors of psychotherapy treatment response could help inform machine learning models aimed at predicting individual patient responses to different therapy options. Here we sought to comprehensively identify known predictors. METHODS EMBASE, Medline, PubMed, PsycINFO were searched for systematic reviews with or without meta-analysis published until June 2020 to identify individual patient-level predictors of response to psychological treatments. 3113 abstracts were identified and 300 articles assessed. We qualitatively synthesized our findings by predictor category (sociodemographic; symptom profile; social support; personality features; affective, cognitive, and behavioural; comorbidities; neuroimaging; genetics) and treatment type. We used the AMSTAR 2 to evaluate the quality of included reviews. RESULTS Following screening and full-text assessment, 27 systematic reviews including 12 meta-analyses were eligible for inclusion. 74 predictors emerged for various psychological treatments, primarily cognitive behavioural therapy, interpersonal therapy, and mindfulness-based cognitive therapy. LIMITATIONS A paucity of studies examining predictors of psychological treatment outcome, as well as methodological heterogeneities and publication biases limit the strength of the identified predictors. CONCLUSIONS The synthesized predictors could be used to supplement clinical decision-making in selecting psychological therapies based on individual patient characteristics. These predictors could also be used as a priori input features for machine learning models aimed at predicting a given patient's likelihood of response to different treatment options for depression, and may contribute toward the development of patient-specific treatment recommendations in clinical guidelines.
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Affiliation(s)
| | | | | | | | | | | | | | - Jade Simard
- Université du Québec à Montréal, Montreal, Quebec, Canada
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12
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Benrimoh D, Chheda FD, Margolese HC. The Best Predictor of the Future-the Metaverse, Mental Health, and Lessons Learned From Current Technologies. JMIR Ment Health 2022; 9:e40410. [PMID: 36306155 PMCID: PMC9652728 DOI: 10.2196/40410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 09/13/2022] [Accepted: 10/04/2022] [Indexed: 11/13/2022] Open
Abstract
The metaverse-a virtual world accessed via virtual reality technology-has been heralded as the next key digital experience. It is meant to provide the next evolution of human interaction after social media and telework. However, in the context of the growing awareness of the risks to mental health posed by current social media technologies, there is a great deal of uncertainty as to the potential effects of this new technology on mental health. This uncertainty is compounded by a lack of clarity regarding what form the metaverse will ultimately take and how widespread its application will be. Despite this, given the nascent state of the metaverse, there is an opportunity to plan the research and regulatory approaches needed to understand it and promote its positive effects while protecting vulnerable groups. In this viewpoint, we examine the following three current technologies whose functions comprise a portion of what the metaverse seeks to accomplish: teleworking, virtual reality, and social media. We attempted to understand in what ways the metaverse may have similar benefits and pitfalls to these technologies but also how it may fundamentally differ from them. These differences suggest potential research questions to be addressed in future work. We found that current technologies have enabled tools such as virtual reality-assisted therapy, avatar therapy, and teletherapy, which have had positive effects on mental health care, and that the metaverse may provide meaningful improvements to these tools. However, given its similarities to social media and its expansion upon the social media experience, the metaverse raises some of the same concerns that we have with social media, such as the possible exacerbation of certain mental health problems. These concerns led us to consider questions such as how the users will be protected and what regulatory mechanisms will be put in place to ensure user safety. Although clear answers to these questions are challenging in this early phase of metaverse research, in this viewpoint, we use the context provided by comparator technologies to provide recommendations to maximize the potential benefits and limit the putative harms of the metaverse. We hope that this paper encourages discussions among researchers and policy makers.
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Affiliation(s)
- David Benrimoh
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Forum D Chheda
- McGill University Healthcare Center, Montreal, QC, Canada
| | - Howard C Margolese
- Department of Psychiatry, McGill University, Montreal, QC, Canada.,McGill University Healthcare Center, Montreal, QC, Canada
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13
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Shen J, Golchi S, Moodie EEM, Benrimoh D. Bayesian group sequential designs for cluster‐randomized trials. Stat (Int Stat Inst) 2022. [DOI: 10.1002/sta4.487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Junwei Shen
- Department of Epidemiology, Biostatistics and Occupational Health McGill University Quebec Canada
| | - Shirin Golchi
- Department of Epidemiology, Biostatistics and Occupational Health McGill University Quebec Canada
| | - Erica E. M. Moodie
- Department of Epidemiology, Biostatistics and Occupational Health McGill University Quebec Canada
| | - David Benrimoh
- Aifred Health Quebec Canada
- Department of Psychiatry McGill University Quebec Canada
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14
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Kleinerman A, Rosenfeld A, Benrimoh D, Fratila R, Armstrong C, Mehltretter J, Shneider E, Yaniv-Rosenfeld A, Karp J, Reynolds CF, Turecki G, Kapelner A. Treatment selection using prototyping in latent-space with application to depression treatment. PLoS One 2021; 16:e0258400. [PMID: 34767577 PMCID: PMC8589171 DOI: 10.1371/journal.pone.0258400] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 09/26/2021] [Indexed: 12/28/2022] Open
Abstract
Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of patients; or a sub-grouping paradigm which ignores personal differences within the identified groups. While both paradigms have shown promising results, each of them suffers from important limitations. In this article, we propose a novel deep learning-based treatment selection approach that is shown to strike a balance between the two paradigms using latent-space prototyping. Our approach is specifically tailored for domains in which effective prototypes and sub-groups of patients are assumed to exist, but groupings relevant to the training objective are not observable in the non-latent space. In an extensive evaluation, using both synthetic and Major Depressive Disorder (MDD) real-world clinical data describing 4754 MDD patients from clinical trials for depression treatment, we show that our approach favorably compares with state-of-the-art approaches. Specifically, the model produced an 8% absolute and 23% relative improvement over random treatment allocation. This is potentially clinically significant, given the large number of patients with MDD. Therefore, the model can bring about a much desired leap forward in the way depression is treated today.
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Affiliation(s)
| | | | - David Benrimoh
- McGill University, Montreal, Canada
- Aifred Health, Montreal, Canada
| | | | | | | | | | - Amit Yaniv-Rosenfeld
- Shalvata Mental Health Center, Hod Hasharon, Israel
- Tel-Aviv University, Tel-Aviv, Israel
| | - Jordan Karp
- University of Arizona, Tucson, Arizona, United States of America
| | - Charles F. Reynolds
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | | | - Adam Kapelner
- Queens College, New York City, NY, United States of America
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15
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Popescu C, Golden G, Benrimoh D, Tanguay-Sela M, Slowey D, Lundrigan E, Williams J, Desormeau B, Kardani D, Perez T, Rollins C, Israel S, Perlman K, Armstrong C, Baxter J, Whitmore K, Fradette MJ, Felcarek-Hope K, Soufi G, Fratila R, Mehltretter J, Looper K, Steiner W, Rej S, Karp JF, Heller K, Parikh SV, McGuire-Snieckus R, Ferrari M, Margolese H, Turecki G. Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study. JMIR Form Res 2021; 5:e31862. [PMID: 34694234 PMCID: PMC8576598 DOI: 10.2196/31862] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/23/2021] [Accepted: 08/23/2021] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence-powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows. OBJECTIVE This study aims to examine the feasibility of an artificial intelligence-powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network-based individualized treatment remission prediction. METHODS Owing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews. RESULTS Data were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F2,24=0.805; mean squared error 58.08; P=.46). In total, 92% (12/13) of patients and 71% (5/7) of physicians felt that the tool was easy to use; 62% (8/13) of patients and 71% (5/7) of physicians rated that they trusted the CDSS. Of the 13 patients, 6 (46%) felt that the patient-clinician relationship significantly or somewhat improved, whereas 7 (54%) felt that it did not change. CONCLUSIONS Our findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies. TRIAL REGISTRATION ClinicalTrials.gov NCT04061642; http://clinicaltrials.gov/ct2/show/NCT04061642.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Kelly Perlman
- Aifred Health Inc., Montreal, QC, Canada
- McGill University, Montreal, QC, Canada
| | | | | | | | | | | | | | | | | | | | | | - Soham Rej
- McGill University, Montreal, QC, Canada
| | | | | | | | | | - Manuela Ferrari
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | | | - Gustavo Turecki
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
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16
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Pfotenhauer SM, Frahm N, Winickoff D, Benrimoh D, Illes J, Marchant G. Mobilizing the private sector for responsible innovation in neurotechnology. Nat Biotechnol 2021; 39:661-664. [PMID: 34099907 DOI: 10.1038/s41587-021-00947-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Sebastian M Pfotenhauer
- Munich Center for Technology in Society (MCTS) and TUM School of Management, Technical University of Munich, Munich, Germany.
| | - Nina Frahm
- Munich Center for Technology in Society (MCTS) and TUM School of Management, Technical University of Munich, Munich, Germany
| | - David Winickoff
- Working Party for Bio-, Nano- and Converging Technologies, Organisation For Economic Co-operation and Development (OECD), Paris, France
| | - David Benrimoh
- Department of Psychiatry, McGill University, Québec, Montréal, Canada.,Aifred Health, Montreal, Quebec, Canada
| | - Judy Illes
- Neuroethics Canada, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Gary Marchant
- Sandra Day O'Connor College of Law, Global Institute for Sustainability and Innovation, Arizona State University, Phoenix, AZ, USA
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17
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Benrimoh D, Israel S, Fratila R, Armstrong C, Perlman K, Rosenfeld A, Kapelner A. Editorial: ML and AI Safety, Effectiveness and Explainability in Healthcare. Front Big Data 2021; 4:727856. [PMID: 34322667 PMCID: PMC8312342 DOI: 10.3389/fdata.2021.727856] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 06/30/2021] [Indexed: 11/24/2022] Open
Affiliation(s)
- David Benrimoh
- Department of Psychiatry, McGill University, Montreal, QC, Canada.,Aifred Health, Inc., Montreal, QC, Canada
| | | | | | | | | | - Ariel Rosenfeld
- Department of Information Science, Bar-Ilan University, Ramat Gan, Israel
| | - Adam Kapelner
- Department of Mathematics, Queens College (CUNY), New York City, NY, United States
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18
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Cyr S, Guo DX, Marcil MJ, Dupont P, Jobidon L, Benrimoh D, Guertin MC, Brouillette J. Posttraumatic stress disorder prevalence in medical populations: A systematic review and meta-analysis. Gen Hosp Psychiatry 2021; 69:81-93. [PMID: 33582645 DOI: 10.1016/j.genhosppsych.2021.01.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 01/19/2021] [Accepted: 01/19/2021] [Indexed: 12/16/2022]
Abstract
OBJECTIVE PTSD is increasingly recognized following medical traumas although is highly heterogeneous. It is difficult to judge which medical contexts have the most traumatic potential and where to concentrate further research and clinical attention for prevention, early detection and treatment. The objective of this study was to compare PTSD prevalence in different medical populations. METHODS A systematic review of the literature on PTSD following medical traumas was conducted as well as a meta-analysis with final pooled result and 95% confidence intervals presented. A meta-regression was used to investigate the impact of potential effect modifiers (PTSD severity, age, sex, timeline) on study effect size between prevalence studies. RESULTS From 3278 abstracts, the authors extracted 292 studies reporting prevalence. Using clinician-administered reports, the highest 24 month or longer PTSD prevalence was found for intraoperative awareness (18.5% [95% CI=5.1%-36.6%]) and the lowest was found for epilepsy (4.5% [95% CI=0.2%-12.6%]). In the overall effect of the meta-regression, only medical events or procedures emerged as significant (p = 0.006) CONCLUSION: This review provides clinicians with greater awareness of medical contexts most associated with PTSD, which may assist them in the decision to engage in more frequent, earlier screening and referral to mental health services.
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Affiliation(s)
- Samuel Cyr
- Research Center, Montreal Heart Institute, Montreal, Quebec, Canada; Faculty of Pharmacy, Université de Montréal, Montreal, Quebec, Canada
| | - De Xuan Guo
- Research Center, Montreal Heart Institute, Montreal, Quebec, Canada
| | - Marie-Joëlle Marcil
- Research Center, Montreal Heart Institute, Montreal, Quebec, Canada; Department of Psychiatry and Addiction, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
| | - Patrice Dupont
- Health Sciences Library, Université de Montréal, Montreal, Quebec, Canada
| | - Laurence Jobidon
- Department of Psychiatry and Addiction, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
| | - David Benrimoh
- Department of Psychiatry, McGill University, Montreal, Canada
| | - Marie-Claude Guertin
- Montreal Health Innovations Coordinating Center, Montreal, Montreal, Quebec, Canada
| | - Judith Brouillette
- Research Center, Montreal Heart Institute, Montreal, Quebec, Canada; Department of Psychiatry and Addiction, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada.
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19
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Benrimoh D, Tanguay-Sela M, Perlman K, Israel S, Mehltretter J, Armstrong C, Fratila R, Parikh SV, Karp JF, Heller K, Vahia IV, Blumberger DM, Karama S, Vigod SN, Myhr G, Martins R, Rollins C, Popescu C, Lundrigan E, Snook E, Wakid M, Williams J, Soufi G, Perez T, Tunteng JF, Rosenfeld K, Miresco M, Turecki G, Gomez Cardona L, Linnaranta O, Margolese HC. Using a simulation centre to evaluate preliminary acceptability and impact of an artificial intelligence-powered clinical decision support system for depression treatment on the physician-patient interaction. BJPsych Open 2021; 7:e22. [PMID: 33403948 PMCID: PMC8058891 DOI: 10.1192/bjo.2020.127] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction. AIMS Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician-patient interaction. METHOD Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback. RESULTS All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician-patient interaction. CONCLUSIONS The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician-patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.
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Affiliation(s)
- David Benrimoh
- Department of Psychiatry, McGill University, Canada; Aifred Heath Inc., Montreal, Canada; and Faculty of Medicine, McGill University, Canada
| | - Myriam Tanguay-Sela
- Montreal Neurological Institute, McGill University, Canada; and Aifred Health Inc., Montreal, Canada
| | - Kelly Perlman
- Douglas Mental Health University Institute, Montreal, Canada; and Aifred Health Inc., Montreal, Canada
| | | | - Joseph Mehltretter
- Department of Computer Science, University of Southern California, Los Angeles, USA; and Aifred Health Inc., Montreal, Canada
| | - Caitrin Armstrong
- School of Computer Science, McGill University, Canada; and Aifred Health Inc., Montreal, Canada
| | | | | | - Jordan F Karp
- Department of Psychiatry, University of Pittsburgh, USA
| | | | - Ipsit V Vahia
- Department of Psychiatry, McLean Hospital/Harvard University, USA
| | | | | | | | - Gail Myhr
- Department of Psychiatry, McGill University, Canada
| | - Ruben Martins
- Douglas Mental Health University Institute, Montreal, Canada; and Department of Psychiatry, McGill University, Canada
| | - Colleen Rollins
- Department of Psychiatry, University of Cambridge, UK; and Aifred Health Inc., Montreal, Canada
| | - Christina Popescu
- Douglas Mental Health University Institute, Montreal, Canada; and Aifred Health Inc., Montreal, Canada
| | - Eryn Lundrigan
- Department of Anatomy and Cell Biology, McGill University, Canada
| | - Emily Snook
- Faculty of Medicine, University of Toronto, Canada
| | - Marina Wakid
- Douglas Mental Health University Institute, Montreal, Canada
| | | | | | - Tamara Perez
- Department of Experimental Medicine, McGill University, Canada
| | | | | | - Marc Miresco
- Department of Psychiatry, McGill University, Canada
| | - Gustavo Turecki
- Douglas Mental Health University Institute, Montreal, Canada; and Department of Psychiatry, McGill University, Canada
| | - Liliana Gomez Cardona
- Douglas Mental Health University Institute, Montreal, Canada; and Department of Psychiatry, McGill University, Canada
| | - Outi Linnaranta
- Douglas Mental Health University Institute, Montreal, Canada; and Department of Psychiatry, McGill University, Canada
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20
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Benrimoh D, Sheldon A, Sibarium E, Powers AR. Computational Mechanism for the Effect of Psychosis Community Treatment: A Conceptual Review From Neurobiology to Social Interaction. Front Psychiatry 2021; 12:685390. [PMID: 34385938 PMCID: PMC8353084 DOI: 10.3389/fpsyt.2021.685390] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 06/18/2021] [Indexed: 11/13/2022] Open
Abstract
The computational underpinnings of positive psychotic symptoms have recently received significant attention. Candidate mechanisms include some combination of maladaptive priors and reduced updating of these priors during perception. A potential benefit of models with such mechanisms is their ability to link multiple levels of explanation, from the neurobiological to the social, allowing us to provide an information processing-based account of how specific alterations in self-self and self-environment interactions result in the experience of positive symptoms. This is key to improving how we understand the experience of psychosis. Moreover, it points us toward more comprehensive avenues for therapeutic research by providing a putative mechanism that could allow for the generation of new treatments from first principles. In order to demonstrate this, our conceptual paper will discuss the application of the insights from previous computational models to an important and complex set of evidence-based clinical interventions with strong social elements, such as coordinated specialty care clinics (CSC) in early psychosis and assertive community treatment (ACT). These interventions may include but also go beyond psychopharmacology, providing, we argue, structure and predictability for patients experiencing psychosis. We develop the argument that this structure and predictability directly counteract the relatively low precision afforded to sensory information in psychosis, while also providing the patient more access to external cognitive resources in the form of providers and the structure of the programs themselves. We discuss how computational models explain the resulting reduction in symptoms, as well as the predictions these models make about potential responses of patients to modifications or to different variations of these interventions. We also link, via the framework of computational models, the patient's experiences and response to interventions to putative neurobiology.
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Affiliation(s)
- David Benrimoh
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Andrew Sheldon
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Ely Sibarium
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Albert R Powers
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
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Desai S, Tanguay-Sela M, Benrimoh D, Fratila R, Brown E, Perlman K, John A, DelPozo-Banos M, Low N, Israel S, Palladini L, Turecki G. Identification of Suicidal Ideation in the Canadian Community Health Survey-Mental Health Component Using Deep Learning. Front Artif Intell 2021; 4:561528. [PMID: 34250463 PMCID: PMC8264793 DOI: 10.3389/frai.2021.561528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 05/25/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: Suicidal ideation (SI) is prevalent in the general population, and is a risk factor for suicide. Predicting which patients are likely to have SI remains challenging. Deep Learning (DL) may be a useful tool in this context, as it can be used to find patterns in complex, heterogeneous, and incomplete datasets. An automated screening system for SI could help prompt clinicians to be more attentive to patients at risk for suicide. Methods: Using the Canadian Community Health Survey-Mental Health Component, we trained a DL model based on 23,859 survey responses to classify patients with and without SI. Models were created to classify both lifetime SI and SI over the last 12 months. From 582 possible parameters we produced 96- and 21-feature versions of the models. Models were trained using an undersampling procedure that balanced the training set between SI and non-SI; validation was done on held-out data. Results: For lifetime SI, the 96 feature model had an Area under the receiver operating curve (AUC) of 0.79 and the 21 feature model had an AUC of 0.77. For SI in the last 12 months the 96 feature model had an AUC of 0.71 and the 21 feature model had an AUC of 0.68. In addition, sensitivity analyses demonstrated feature relationships in line with existing literature. Discussion: Although further study is required to ensure clinical relevance and sample generalizability, this study is an initial proof of concept for the use of DL to improve identification of SI. Sensitivity analyses can help improve the interpretability of DL models. This kind of model would help start conversations with patients which could lead to improved care and a reduction in suicidal behavior.
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Affiliation(s)
- Sneha Desai
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Aifred Health Inc., Montreal, QC, Canada
| | - Myriam Tanguay-Sela
- Aifred Health Inc., Montreal, QC, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - David Benrimoh
- Aifred Health Inc., Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Faculty of Medicine, McGill University, Montreal, QC, Canada
- *Correspondence: David Benrimoh,
| | | | - Eleanor Brown
- Aifred Health Inc., Montreal, QC, Canada
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, United States
| | - Kelly Perlman
- Aifred Health Inc., Montreal, QC, Canada
- Douglas Mental Health University Institute, Montrea, QC, Canada
| | - Ann John
- Swansea University, Swansea, United Kingdom
| | | | - Nancy Low
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | | | - Lisa Palladini
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Gustavo Turecki
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Mental Health University Institute, Montrea, QC, Canada
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22
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Chen AG, Benrimoh D, Parr T, Friston KJ. A Bayesian Account of Generalist and Specialist Formation Under the Active Inference Framework. Front Artif Intell 2020; 3:69. [PMID: 33733186 PMCID: PMC7861269 DOI: 10.3389/frai.2020.00069] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 07/28/2020] [Indexed: 01/12/2023] Open
Abstract
This paper offers a formal account of policy learning, or habitual behavioral optimization, under the framework of Active Inference. In this setting, habit formation becomes an autodidactic, experience-dependent process, based upon what the agent sees itself doing. We focus on the effect of environmental volatility on habit formation by simulating artificial agents operating in a partially observable Markov decision process. Specifically, we used a "two-step" maze paradigm, in which the agent has to decide whether to go left or right to secure a reward. We observe that in volatile environments with numerous reward locations, the agents learn to adopt a generalist strategy, never forming a strong habitual behavior for any preferred maze direction. Conversely, in conservative or static environments, agents adopt a specialist strategy; forming strong preferences for policies that result in approach to a small number of previously-observed reward locations. The pros and cons of the two strategies are tested and discussed. In general, specialization offers greater benefits, but only when contingencies are conserved over time. We consider the implications of this formal (Active Inference) account of policy learning for understanding the relationship between specialization and habit formation.
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Affiliation(s)
- Anthony G. Chen
- Department of Physiology, McGill University, Montreal, QC, Canada
| | - David Benrimoh
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- The Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | - Thomas Parr
- The Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | - Karl J. Friston
- The Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
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23
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Mehltretter J, Rollins C, Benrimoh D, Fratila R, Perlman K, Israel S, Miresco M, Wakid M, Turecki G. Analysis of Features Selected by a Deep Learning Model for Differential Treatment Selection in Depression. Front Artif Intell 2020; 2:31. [PMID: 33733120 PMCID: PMC7861264 DOI: 10.3389/frai.2019.00031] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 12/06/2019] [Indexed: 12/13/2022] Open
Abstract
Background: Deep learning has utility in predicting differential antidepressant treatment response among patients with major depressive disorder, yet there remains a paucity of research describing how to interpret deep learning models in a clinically or etiologically meaningful way. In this paper, we describe methods for analyzing deep learning models of clinical and demographic psychiatric data, using our recent work on a deep learning model of STAR*D and CO-MED remission prediction. Methods: Our deep learning analysis with STAR*D and CO-MED yielded four models that predicted response to the four treatments used across the two datasets. Here, we use classical statistics and simple data representations to improve interpretability of the features output by our deep learning model and provide finer grained understanding of their clinical and etiological significance. Specifically, we use representations derived from our model to yield features predicting both treatment non-response and differential treatment response to four standard antidepressants, and use linear regression and t-tests to address questions about the contribution of trauma, education, and somatic symptoms to our models. Results: Traditional statistics were able to probe the input features of our deep learning models, reproducing results from previous research, while providing novel insights into depression causes and treatments. We found that specific features were predictive of treatment response, and were able to break these down by treatment and non-response categories; that specific trauma indices were differentially predictive of baseline depression severity; that somatic symptoms were significantly different between males and females, and that education and low income proved important psycho-social stressors associated with depression. Conclusion: Traditional statistics can augment interpretation of deep learning models. Such interpretation can lend us new hypotheses about depression and contribute to building causal models of etiology and prognosis. We discuss dataset-specific effects and ideal clinical samples for machine learning analysis aimed at improving tools to assist in optimizing treatment.
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Affiliation(s)
- Joseph Mehltretter
- Department of Computer Science, University of Southern California, Los Angeles, CA, United States
| | - Colleen Rollins
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - David Benrimoh
- Department of Psychiatry, McGill University, Montreal, QC, Canada.,Faculty of Medicine, McGill University, Montreal, QC, Canada.,Douglas Mental Health University Institute, Montreal, QC, Canada.,Aifred Health, Montreal, QC, Canada
| | | | - Kelly Perlman
- Douglas Mental Health University Institute, Montreal, QC, Canada.,Aifred Health, Montreal, QC, Canada
| | - Sonia Israel
- Douglas Mental Health University Institute, Montreal, QC, Canada.,Aifred Health, Montreal, QC, Canada
| | - Marc Miresco
- Aifred Health, Montreal, QC, Canada.,Department of Psychiatry, Jewish General Hospital, Montreal, QC, Canada
| | - Marina Wakid
- Douglas Mental Health University Institute, Montreal, QC, Canada
| | - Gustavo Turecki
- Department of Psychiatry, McGill University, Montreal, QC, Canada.,Douglas Mental Health University Institute, Montreal, QC, Canada
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24
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Perreault A, Benrimoh D, Fielding A. Euthanasia requests in a Canadian psychiatric outpatient clinic: A case series part 2 of the McGill University euthanasia in psychiatry case series. Int J Law Psychiatry 2019; 66:101464. [PMID: 31706386 DOI: 10.1016/j.ijlp.2019.101464] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 06/27/2019] [Accepted: 07/02/2019] [Indexed: 06/10/2023]
Abstract
The Canadian province of Quebec enacted in 2014 a legislation that permitted medical assistance in dying (MAID) under specific conditions and the rest of Canada followed suit in June 2016. In this article, which is the second in a set of case series of requests for MAID in Canadian psychiatry, we present the cases of two patients who made a request for MAID to their treating psychiatrist in an outpatient clinic. While one is advanced in age and suffering from intense physical and psychic pain with little if any psychiatric comorbidity, the other is a young and medically healthy woman who nonetheless suffers from extensive psychiatric comorbidity. This article discusses both cases in light of recent scientific literature and case law that is slowly emerging in Canada, focusing on the concepts of the end of life and its legal definition as well as psychic suffering and its management in those wishing to receive physician-assisted dying. In our conclusion, we stress the need to clarify the definition of treatment resistance, the necessity to determine each physician's role when many are involved, as well as the importance of treating psychic pain holistically, which can sometimes require going beyond standard psychiatric care.
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Affiliation(s)
- Antoine Perreault
- McGill University, Department of Psychiatry, 1033 avenue des Pins Ouest, Montreal, QC H3A 1A1, Canada.
| | - David Benrimoh
- McGill University, Department of Psychiatry, 1033 avenue des Pins Ouest, Montreal, QC H3A 1A1, Canada
| | - Allan Fielding
- Allan Memorial Institute, 1025 avenue des Pins Ouest, Montreal, QC H3A1A1, Canada
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25
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Abstract
Hallucinations, including auditory verbal hallucinations (AVH), occur in both the healthy population and in psychotic conditions such as schizophrenia (often developing after a prodromal period). In addition, hallucinations can be in-context (they can be consistent with the environment, such as when one hallucinates the end of a sentence that has been repeated many times), or out-of-context (such as the bizarre hallucinations associated with schizophrenia). In previous work, we introduced a model of hallucinations as false (positive) inferences based on a (Markov decision process) formulation of active inference. In this work, we extend this model to include content–to disclose the computational mechanisms behind in- and out-of-context hallucinations. In active inference, sensory information is used to disambiguate alternative hypotheses about the causes of sensations. Sensory information is balanced against prior beliefs, and when this balance is tipped in the favor of prior beliefs, hallucinations can occur. We show that in-context hallucinations arise when (simulated) subjects cannot use sensory information to correct prior beliefs about hearing a voice, but beliefs about content (i.e. the sequential order of a sentence) remain accurate. When hallucinating subjects also have inaccurate beliefs about state transitions, out-of-context hallucinations occur; i.e. their hallucinated speech content is disordered. Note that out-of-context hallucinations in this setting does not refer to inference about context, but rather to false perceptual inference that emerges when the confidence in–or precision of–sensory evidence is reduced. Furthermore, subjects with inaccurate beliefs about state transitions but an intact ability to use sensory information do not hallucinate and are reminiscent of prodromal patients. This work demonstrates the different computational mechanisms that may underlie the spectrum of hallucinatory experience–from the healthy population to psychotic states.
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Affiliation(s)
- David Benrimoh
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, England, United Kingdom
- McGill University, Department of Psychiatry, Montreal, Canada
- * E-mail:
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, England, United Kingdom
| | - Rick A. Adams
- Division of Psychiatry, University College London, London, England, United Kingdom
- Institute of Cognitive Neuroscience, University College London, London, England, United Kingdom
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, England, United Kingdom
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26
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Vincent P, Parr T, Benrimoh D, Friston KJ. With an eye on uncertainty: Modelling pupillary responses to environmental volatility. PLoS Comput Biol 2019; 15:e1007126. [PMID: 31276488 PMCID: PMC6636765 DOI: 10.1371/journal.pcbi.1007126] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Revised: 07/17/2019] [Accepted: 05/23/2019] [Indexed: 01/04/2023] Open
Abstract
Living creatures must accurately infer the nature of their environments. They do this despite being confronted by stochastic and context sensitive contingencies—and so must constantly update their beliefs regarding their uncertainty about what might come next. In this work, we examine how we deal with uncertainty that evolves over time. This prospective uncertainty (or imprecision) is referred to as volatility and has previously been linked to noradrenergic signals that originate in the locus coeruleus. Using pupillary dilatation as a measure of central noradrenergic signalling, we tested the hypothesis that changes in pupil diameter reflect inferences humans make about environmental volatility. To do so, we collected pupillometry data from participants presented with a stream of numbers. We generated these numbers from a process with varying degrees of volatility. By measuring pupillary dilatation in response to these stimuli—and simulating the inferences made by an ideal Bayesian observer of the same stimuli—we demonstrate that humans update their beliefs about environmental contingencies in a Bayes optimal way. We show this by comparing general linear (convolution) models that formalised competing hypotheses about the causes of pupillary changes. We found greater evidence for models that included Bayes optimal estimates of volatility than those without. We additionally explore the interaction between different causes of pupil dilation and suggest a quantitative approach to characterising a person’s prior beliefs about volatility. Humans are constantly confronted with surprising events. To navigate such a world, we must understand the chances of an unexpected event occurring at any given point in time. We do this by creating a model of the world around us, in which we allow for these unexpected events to occur by holding beliefs about how volatile our environment is. In this work we explore the way in which we update our beliefs, demonstrating that this updating relies on the number of unexpected events in relation to the expected number. We do this by examining the pupil diameter, since—in controlled environments—changes in pupil diameter reflect our response to unexpected observations. Finally, we show that our methodology is appropriate for assessing the individual participant’s prior expectations about the amount of uncertainty in their environment.
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Affiliation(s)
- Peter Vincent
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
- * E-mail:
| | - Thomas Parr
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | - David Benrimoh
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | - Karl J Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
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27
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Benrimoh D, Pomerleau VJ, Demoustier A, Poulin S, Maltais JR, Brouillette J, Ducharme S. Why We Still Use "Organic Causes": Results From a Survey of Psychiatrists and Residents. J Neuropsychiatry Clin Neurosci 2019; 31:57-64. [PMID: 30305004 DOI: 10.1176/appi.neuropsych.18050099] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The diagnostic category of "organic disorders" was officially removed from the psychiatric nosology in DSM-IV, published in 1994. Despite this change, physicians continue to use the term "organic causes" to refer to medical and neurological causes of psychiatric symptoms, and it remains part of the ICD-10 classification. In the context of increasing integration of psychiatric disorders within a medical and neuroscientific framework, the reasons behind the ongoing use of this term (reminiscent of mind-body dualism) have to be clarified. The authors conducted a survey of 391 Canadian psychiatrists and psychiatric residents to understand attitudes and beliefs related to this terminology and then applied qualitative and quantitative analyses. Results showed that the terminology is used by the majority (55.9%) of psychiatrists and residents for two main reasons: out of a habit that begins in residency training and because of the belief that other specialties do not fully understand alternative terminology. The authors found that some psychiatrists are concerned that their patients will not receive adequate investigation unless it is made clear through use of the "organic cause" term that other medical causes of psychiatric symptoms are suspected. Use of the "organic cause" term was predicted by being of younger age, performing emergency department calls, and finding alternative terminology difficult to use. These findings highlight the importance of reflecting on and discussing the effect of this terminology used in psychiatry.
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Affiliation(s)
- David Benrimoh
- From the Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, Quebec, Canada (DB, VJP, SD); the Department of Psychiatry and Addiction, Faculty of Medicine, Université de Montréal (JB); the Research Centre, Montreal Heart Institute and Université de Montréal (JB); the Department of Psychiatry, Université Laval, Quebec (SP); the Department of Nursing, University of Sherbrooke, Longueuil, Quebec (AD); the Department of Psychiatry, Université de Sherbrooke, Sherbrooke, Quebec (J-RM); and the McConnell Brain Imaging Centre, Montreal Neurological Institute (SD)
| | - Vincent Jetté Pomerleau
- From the Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, Quebec, Canada (DB, VJP, SD); the Department of Psychiatry and Addiction, Faculty of Medicine, Université de Montréal (JB); the Research Centre, Montreal Heart Institute and Université de Montréal (JB); the Department of Psychiatry, Université Laval, Quebec (SP); the Department of Nursing, University of Sherbrooke, Longueuil, Quebec (AD); the Department of Psychiatry, Université de Sherbrooke, Sherbrooke, Quebec (J-RM); and the McConnell Brain Imaging Centre, Montreal Neurological Institute (SD)
| | - Arnaud Demoustier
- From the Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, Quebec, Canada (DB, VJP, SD); the Department of Psychiatry and Addiction, Faculty of Medicine, Université de Montréal (JB); the Research Centre, Montreal Heart Institute and Université de Montréal (JB); the Department of Psychiatry, Université Laval, Quebec (SP); the Department of Nursing, University of Sherbrooke, Longueuil, Quebec (AD); the Department of Psychiatry, Université de Sherbrooke, Sherbrooke, Quebec (J-RM); and the McConnell Brain Imaging Centre, Montreal Neurological Institute (SD)
| | - Stéphane Poulin
- From the Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, Quebec, Canada (DB, VJP, SD); the Department of Psychiatry and Addiction, Faculty of Medicine, Université de Montréal (JB); the Research Centre, Montreal Heart Institute and Université de Montréal (JB); the Department of Psychiatry, Université Laval, Quebec (SP); the Department of Nursing, University of Sherbrooke, Longueuil, Quebec (AD); the Department of Psychiatry, Université de Sherbrooke, Sherbrooke, Quebec (J-RM); and the McConnell Brain Imaging Centre, Montreal Neurological Institute (SD)
| | - Jean-Robert Maltais
- From the Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, Quebec, Canada (DB, VJP, SD); the Department of Psychiatry and Addiction, Faculty of Medicine, Université de Montréal (JB); the Research Centre, Montreal Heart Institute and Université de Montréal (JB); the Department of Psychiatry, Université Laval, Quebec (SP); the Department of Nursing, University of Sherbrooke, Longueuil, Quebec (AD); the Department of Psychiatry, Université de Sherbrooke, Sherbrooke, Quebec (J-RM); and the McConnell Brain Imaging Centre, Montreal Neurological Institute (SD)
| | - Judith Brouillette
- From the Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, Quebec, Canada (DB, VJP, SD); the Department of Psychiatry and Addiction, Faculty of Medicine, Université de Montréal (JB); the Research Centre, Montreal Heart Institute and Université de Montréal (JB); the Department of Psychiatry, Université Laval, Quebec (SP); the Department of Nursing, University of Sherbrooke, Longueuil, Quebec (AD); the Department of Psychiatry, Université de Sherbrooke, Sherbrooke, Quebec (J-RM); and the McConnell Brain Imaging Centre, Montreal Neurological Institute (SD)
| | - Simon Ducharme
- From the Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, Quebec, Canada (DB, VJP, SD); the Department of Psychiatry and Addiction, Faculty of Medicine, Université de Montréal (JB); the Research Centre, Montreal Heart Institute and Université de Montréal (JB); the Department of Psychiatry, Université Laval, Quebec (SP); the Department of Nursing, University of Sherbrooke, Longueuil, Quebec (AD); the Department of Psychiatry, Université de Sherbrooke, Sherbrooke, Quebec (J-RM); and the McConnell Brain Imaging Centre, Montreal Neurological Institute (SD)
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Humpston CS, Adams RA, Benrimoh D, Broome MR, Corlett PR, Gerrans P, Horga G, Parr T, Pienkos E, Powers AR, Raballo A, Rosen C, Linden DEJ. From Computation to the First-Person: Auditory-Verbal Hallucinations and Delusions of Thought Interference in Schizophrenia-Spectrum Psychoses. Schizophr Bull 2019; 45:S56-S66. [PMID: 30715542 PMCID: PMC6357975 DOI: 10.1093/schbul/sby073] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Schizophrenia-spectrum psychoses are highly complex and heterogeneous disorders that necessitate multiple lines of scientific inquiry and levels of explanation. In recent years, both computational and phenomenological approaches to the understanding of mental illness have received much interest, and significant progress has been made in both fields. However, there has been relatively little progress bridging investigations in these seemingly disparate fields. In this conceptual review and collaborative project from the 4th Meeting of the International Consortium on Hallucination Research, we aim to facilitate the beginning of such dialogue between fields and put forward the argument that computational psychiatry and phenomenology can in fact inform each other, rather than being viewed as isolated or even incompatible approaches. We begin with an overview of phenomenological observations on the interrelationships between auditory-verbal hallucinations (AVH) and delusional thoughts in general, before moving on to review several theoretical frameworks and empirical findings in the computational modeling of AVH. We then relate the computational models to the phenomenological accounts, with a special focus on AVH and delusions that involve the senses of agency and ownership of thought (delusions of thought interference). Finally, we offer some tentative directions for future research, emphasizing the importance of a mutual understanding between separate lines of inquiry.
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Affiliation(s)
- Clara S Humpston
- Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom,School of Psychology, Cardiff University, Cardiff, United Kingdom,To whom correspondence should be addressed;Department of Psychological Medicine (PO72), Institute of Psychiatry, Psychology & Neuroscience, King’s College London, Denmark Hill, LondonSE5 8AF, United Kingdom; tel: +44 (0) 20 7848 0088, fax: +44 (0) 20 7848 0298, e-mail:
| | - Rick A Adams
- Division of Psychiatry, University College London, London, United Kingdom
| | - David Benrimoh
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom,Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Matthew R Broome
- Institute for Mental Health, School of Psychology, College of Life and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom,Department of Psychiatry,Medical Sciences Division, University of Oxford, Oxford, United Kingdom,Faculty of Philosophy, Humanities Division, University of Oxford, Oxford, United Kingdom
| | | | - Philip Gerrans
- Department of Philosophy, The University of Adelaide, Adelaide, South Australia, Australia
| | | | - Thomas Parr
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
| | - Elizabeth Pienkos
- Graduate Institute of ProfessionalPsychology, University of Hartford, West Hartford, CT
| | | | - Andrea Raballo
- Department of Psychology, Faculty of Social and Educational Sciences, Norwegian University of Science and Technology, Trondheim, Norway,Department of Medicine, Division of Psychiatry, Clinical Psychology and Rehabilitation, University of Perugia, Perugia, Italy
| | - Cherise Rosen
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL
| | - David E J Linden
- School of Psychology, Cardiff University, Cardiff, United Kingdom,Division of Psychological Medicine and Clinical Neuroscience, MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom
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29
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Perlman K, Benrimoh D, Israel S, Rollins C, Brown E, Tunteng JF, You R, You E, Tanguay-Sela M, Snook E, Miresco M, Berlim MT. A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder. J Affect Disord 2019; 243:503-515. [PMID: 30286415 DOI: 10.1016/j.jad.2018.09.067] [Citation(s) in RCA: 97] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 07/29/2018] [Accepted: 09/16/2018] [Indexed: 12/16/2022]
Abstract
INTRODUCTION The heterogeneity of symptoms and complex etiology of depression pose a significant challenge to the personalization of treatment. Meanwhile, the current application of generic treatment approaches to patients with vastly differing biological and clinical profiles is far from optimal. Here, we conduct a meta-review to identify predictors of response to antidepressant therapy in order to select robust input features for machine learning models of treatment response. These machine learning models will allow us to learn associations between patient features and treatment response which have predictive value at the individual patient level; this learning can be optimized by selecting high-quality input features for the model. While current research is difficult to directly apply to the clinic, machine learning models built using knowledge gleaned from current research may become useful clinical tools. METHODS The EMBASE and MEDLINE/PubMed online databases were searched from January 1996 to August 2017, using a combination of MeSH terms and keywords to identify relevant literature reviews. We identified a total of 1909 articles, wherein 199 articles met our inclusion criteria. RESULTS An array of genetic, immune, endocrine, neuroimaging, sociodemographic, and symptom-based predictors of treatment response were extracted, varying widely in clinical utility. LIMITATIONS Due to heterogeneous sample sizes, effect sizes, publication biases, and methodological disparities across reviews, we could not accurately assess the strength and directionality of every predictor. CONCLUSION Notwithstanding our cautious interpretation of the results, we have identified a multitude of predictors that can be used to formulate a priori hypotheses regarding the input features for a computational model. We highlight the importance of large-scale research initiatives and clinically accessible biomarkers, as well as the need for replication studies of current findings. In addition, we provide recommendations for future improvement and standardization of research efforts in this field.
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Affiliation(s)
- Kelly Perlman
- Montreal Neurological Institute, McGill University, 3801 Rue Université, Montréal, QC H3A 2B4, Canada.
| | - David Benrimoh
- Department of Psychiatry, McGill University, Montreal, Canada; Faculty of Medicine, McGill University, Montreal, Canada
| | - Sonia Israel
- Department of Psychiatry, McGill University, Montreal, Canada; Douglas Mental Health University Institute, Montreal, Canada
| | - Colleen Rollins
- Department of Psychiatry, University of Cambridge, Cambridge, England, UK
| | - Eleanor Brown
- Montreal Neurological Institute, McGill University, 3801 Rue Université, Montréal, QC H3A 2B4, Canada
| | - Jingla-Fri Tunteng
- Montreal Children's Hospital, McGill University Health Center, Montreal, Canada
| | - Raymond You
- School of Physical and Occupational Therapy, McGill University, Montreal, Canada
| | - Eunice You
- Faculty of Medicine, McGill University, Montreal, Canada
| | - Myriam Tanguay-Sela
- Montreal Neurological Institute, McGill University, 3801 Rue Université, Montréal, QC H3A 2B4, Canada
| | - Emily Snook
- Douglas Mental Health University Institute, Montreal, Canada
| | - Marc Miresco
- Department of Psychiatry, Jewish General Hospital, Montreal, Canada
| | - Marcelo T Berlim
- Department of Psychiatry, McGill University, Montreal, Canada; Douglas Mental Health University Institute, Montreal, Canada
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Long V, Guertin MC, Dyrda K, Benrimoh D, Brouillette J. Descriptive Study of Anxiety and Posttraumatic Stress Disorders in Cardiovascular Disease Patients: From Referral to Cardiopsychiatric Diagnoses. Psychother Psychosom 2019; 87:370-371. [PMID: 30078016 DOI: 10.1159/000491581] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 06/26/2018] [Indexed: 11/19/2022]
Affiliation(s)
- Valérie Long
- Research Centre, Montreal Heart Institute and Université de Montréal, Montreal, Québec, Canada
| | | | - Katia Dyrda
- Research Centre, Montreal Heart Institute and Université de Montréal, Montreal, Québec, Canada
| | - David Benrimoh
- Department of Psychiatry, McGill University, Montreal, Québec, Canada
| | - Judith Brouillette
- Research Centre, Montreal Heart Institute and Université de Montréal, Montreal, Québec, .,Department of Psychiatry and Addiction, Université de Montréal, Montreal, Québec,
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Benrimoh D, Parr T, Vincent P, Adams RA, Friston K. Active Inference and Auditory Hallucinations. Comput Psychiatr 2018; 2:183-204. [PMID: 30627670 PMCID: PMC6317754 DOI: 10.1162/cpsy_a_00022] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 10/03/2018] [Indexed: 01/27/2023]
Abstract
Auditory verbal hallucinations (AVH) are often distressing symptoms of several neuropsychiatric conditions, including schizophrenia. Using a Markov decision process formulation of active inference, we develop a novel model of AVH as false (positive) inference. Active inference treats perception as a process of hypothesis testing, in which sensory data are used to disambiguate between alternative hypotheses about the world. Crucially, this depends upon a delicate balance between prior beliefs about unobserved (hidden) variables and the sensations they cause. A false inference that a voice is present, even in the absence of auditory sensations, suggests that prior beliefs dominate perceptual inference. Here we consider the computational mechanisms that could cause this imbalance in perception. Through simulation, we show that the content of (and confidence in) prior beliefs depends on beliefs about policies (here sequences of listening and talking) and on beliefs about the reliability of sensory data. We demonstrate several ways in which hallucinatory percepts could occur when an agent expects to hear a voice in the presence of imprecise sensory data. This model expresses, in formal terms, alternative computational mechanisms that underwrite AVH and, speculatively, can be mapped onto neurobiological changes associated with schizophrenia. The interaction of action and perception is important in modeling AVH, given that speech is a fundamentally enactive and interactive process-and that hallucinators often actively engage with their voices.
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Affiliation(s)
| | - Thomas Parr
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Peter Vincent
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Rick A. Adams
- Division of Psychiatry, University College London, London, UK,Institute of Cognitive Neuroscience, University College London, London, UK
| | - Karl Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
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Abstract
Auditory verbal hallucinations (AVH) are often distressing symptoms of several neuropsychiatric conditions, including schizophrenia. Using a Markov decision process formulation of active inference, we develop a novel model of AVH as false (positive) inference. Active inference treats perception as a process of hypothesis testing, in which sensory data are used to disambiguate between alternative hypotheses about the world. Crucially, this depends upon a delicate balance between prior beliefs about unobserved (hidden) variables and the sensations they cause. A false inference that a voice is present, even in the absence of auditory sensations, suggests that prior beliefs dominate perceptual inference. Here we consider the computational mechanisms that could cause this imbalance in perception. Through simulation, we show that the content of (and confidence in) prior beliefs depends on beliefs about policies (here sequences of listening and talking) and on beliefs about the reliability of sensory data. We demonstrate several ways in which hallucinatory percepts could occur when an agent expects to hear a voice in the presence of imprecise sensory data. This model expresses, in formal terms, alternative computational mechanisms that underwrite AVH and, speculatively, can be mapped onto neurobiological changes associated with schizophrenia. The interaction of action and perception is important in modeling AVH, given that speech is a fundamentally enactive and interactive process-and that hallucinators often actively engage with their voices.
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Affiliation(s)
- David Benrimoh
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Thomas Parr
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Peter Vincent
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Rick A Adams
- Division of Psychiatry, University College London, London, UK
| | - Karl Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
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Fletcher A, Chen BY, Benrimoh D, Shemie S, Lubarsky S. Lessons learned from a student-driven initiative to design and implement an Organ and Tissue Donation course across Canadian medical schools. Perspect Med Educ 2018; 7:332-336. [PMID: 30276671 PMCID: PMC6191399 DOI: 10.1007/s40037-018-0454-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
The competencies required of the well-trained physician are constantly evolving, and medical education must adapt accordingly. In response, a growing number of influential medical education licensing and accreditation bodies have proposed frameworks that outline society's expectations of physician competencies. In Canada, undergraduate and graduate curricula have undergone major changes to meet the specifications of the CanMEDS framework, and similar efforts are underway internationally. Nonetheless, ensuring the values enshrined within such frameworks become integral to a physician's identity remains challenging. We believe that student-led curricular initiatives represent a novel way of approaching this shifting medical education landscape.In this article, we reflect on lessons we learned as medical students spearheading an initiative to change how organ and tissue donation is taught in Canadian medical schools. Citing relevant medical education literature where applicable, we include a detailed description of our approach as a roadmap for students contemplating their own curricular innovations. By outlining the factors influencing this project's implementation, as well as the benefits and limitations of student participation in curriculum reform, we offer educators a fresh perspective on optimizing the student role in this important process. Ultimately, the authors argue that not only can student participation render curricular content more accessible to learners, but that the responsibilities students take on in this role naturally lead to the development of CanMEDs-based competencies such as advocacy, scholarship, and inter-professionalism.
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Affiliation(s)
| | - Bing Yu Chen
- Department of Neurology, McMaster University, Hamilton, ON, Canada
| | - David Benrimoh
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Sam Shemie
- Pediatric Intensive Care Unit, McGill University Health Center, Montreal, QC, Canada
| | - Stuart Lubarsky
- Neurology Unit, Montreal General Hospital, Montreal, QC, Canada
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Benrimoh D, Fratila R, Israel S, Perlman K. Deep Learning: A New Horizon for Personalized Treatment of Depression? Mcgill J Med 2018. [DOI: 10.26443/mjm.v16i1.99] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Globally, depression affects 300 million people and is projected be the leading cause of disability by 2030. While different patients are known to benefit from different therapies, there is no principled way for clinicians to predict individual patient responses or side effect profiles. A form of machine learning based on artificial neural networks, deep learning, might be useful for generating a predictive model that could aid in clinical decision making. Such a model’s primary outcomes would be to help clinicians select the most effective treatment plans and mitigate adverse side effects, allowing doctors to provide greater personalized care to a larger number of patients. In this commentary, we discuss the need for personalization of depression treatment and how a deep learning model might be used to construct a clinical decision aid.
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Benrimoh D, Fratila R, Israel S, Perlman K, Mirchi N, Desai S, Rosenfeld A, Knappe S, Behrmann J, Rollins C, You RP, Aifred Health Team T. Aifred Health, a Deep Learning Powered Clinical Decision Support System for Mental Health. The NIPS '17 Competition: Building Intelligent Systems 2018. [DOI: 10.1007/978-3-319-94042-7_13] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Benrimoh D, Perreault A, Van Den Eynde F. Euthanasia requests in a Canadian psychiatric emergency room: A case series: Part 1 of the McGill University euthanasia in psychiatry case series. Int J Law Psychiatry 2017; 55:37-44. [PMID: 29157510 DOI: 10.1016/j.ijlp.2017.10.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Revised: 08/25/2017] [Accepted: 10/04/2017] [Indexed: 06/07/2023]
Abstract
Euthanasia was decriminalized in Quebec in December 2015, and Canada-wide in June 2016. Both the Provincial and Federal legislation have limited the right to medical assistance in dying (MAID) to end-of-life cases; which makes MAID inaccessible to most patients solely suffering from psychiatric illness. While some end-stage anorexia nervosa or elderly patients may meet the end-of-life criterion because of their medical comorbidities or their age (Kelly et al., 2003), repeated suicide attempts or psychotic disorganization would not qualify since they would not be seen as elements of an illness leading to a foreseeable "natural death" (Canada, 2016). This is in contradiction to other jurisdictions, such as Belgium and the Netherlands as well as the eligibility criteria stated in the Supreme Court of Canada's decision in Carter v. Canada (Supreme Court of Canada, 2015). Here we analyze three cases of patients who presented to a psychiatric emergency department and requested MAID for psychiatric reasons. While none of the patients were eligible for MAID under Canadian law, we find that their demographics match closely that of patients granted MAID for psychiatric reasons in jurisdictions where that practice is allowed. Based on these cases, we comment on potentially negative consequences that may come from decriminalizing MAID for psychiatric reasons (such as an increased assessment burden on ED staff) and potentially positive consequences (such as encouraging suffering patients who had not consulted to seek care). While it is by no means our intention to take a political or moral stand on this important issue, or to conclusively weigh the negatives and positives of allowing MAID for psychiatric reasons, we do stress the importance of an active voice for psychiatry in this ongoing public debate.
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Affiliation(s)
- David Benrimoh
- McGill University, Department of Psychiatry, 1033 avenue des Pins Ouest, Montreal, QC H3A 1A1, Canada.
| | - Antoine Perreault
- McGill University, Department of Psychiatry, 1033 avenue des Pins Ouest, Montreal, QC H3A 1A1, Canada
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Benrimoh D. Public Consultation: From Reflection to Action Quebec Health Politics and the Student Response. Mcgill J Med 2017. [DOI: 10.26443/mjm.v15i1.52] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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Benrimoh D, Warsi N, Hodgson E, Demko N, Chen BY, Habte R, Dandurand-Bolduc C, Silverberg S, Xia S, Chu L, Ruel-Laliberté J, Harris J, Pon K, Singh M, Agarwal A, Kim L, Whalen-Browne M, Ali E, Sahar N, Wellmeier L, Smith L, Arora N, Houde R, Devlin G, Zigby JA, Andermann A, Rousseau C, Ruiz-Casares M. An Advocacy and Leadership Curriculum to Train Socially Responsible Medical Learners. MedEdPublish 2016. [DOI: 10.15694/mep.2016.000062] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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