1
|
Thunberg C, Wiker T, Bundt C, Huster RJ. On the (un)reliability of common behavioral and electrophysiological measures from the stop signal task: Measures of inhibition lack stability over time. Cortex 2024; 175:81-105. [PMID: 38508968 DOI: 10.1016/j.cortex.2024.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 10/31/2023] [Accepted: 02/12/2024] [Indexed: 03/22/2024]
Abstract
Response inhibition, the intentional stopping of planned or initiated actions, is often considered a key facet of control, impulsivity, and self-regulation. The stop signal task is argued to be the purest inhibition task we have, and it is thus central to much work investigating the role of inhibition in areas like development and psychopathology. Most of this work quantifies stopping behavior by calculating the stop signal reaction time as a measure of individual stopping latency. Individual difference studies aiming to investigate why and how stopping latencies differ between people often do this under the assumption that the stop signal reaction time indexes a stable, dispositional trait. However, empirical support for this assumption is lacking, as common measures of inhibition and control tend to show low test-retest reliability and thus appear unstable over time. The reasons for this could be methodological, where low stability is driven by measurement noise, or substantive, where low stability is driven by a larger influence of state-like and situational factors. To investigate this, we characterized the split-half and test-retest reliability of a range of common behavioral and electrophysiological measures derived from the stop signal task. Across three independent studies, different measurement modalities, and a systematic review of the literature, we found a pattern of low temporal stability for inhibition measures and higher stability for measures of manifest behavior and non-inhibitory processing. This pattern could not be explained by measurement noise and low internal consistency. Consequently, response inhibition appears to have mostly state-like and situational determinants, and there is little support for the validity of conceptualizing common inhibition measures as reflecting stable traits.
Collapse
Affiliation(s)
- Christina Thunberg
- Multimodal Imaging and Cognitive Control Lab, Department of Psychology, University of Oslo, Oslo, Norway; Cognitive and Translational Neuroscience Cluster, Department of Psychology, University of Oslo, Oslo, Norway.
| | - Thea Wiker
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Research Center for Developmental Processes and Gradients in Mental Health, Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Carsten Bundt
- Multimodal Imaging and Cognitive Control Lab, Department of Psychology, University of Oslo, Oslo, Norway; Cognitive and Translational Neuroscience Cluster, Department of Psychology, University of Oslo, Oslo, Norway
| | - René J Huster
- Multimodal Imaging and Cognitive Control Lab, Department of Psychology, University of Oslo, Oslo, Norway; Cognitive and Translational Neuroscience Cluster, Department of Psychology, University of Oslo, Oslo, Norway
| |
Collapse
|
2
|
Diaconescu AO, Karvelis P, Hauke DJ. Rethinking interpersonal judgments: dopamine antagonists impact attributional dynamics. Trends Cogn Sci 2024:S1364-6613(24)00134-7. [PMID: 38797602 DOI: 10.1016/j.tics.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Accepted: 05/16/2024] [Indexed: 05/29/2024]
Abstract
Barnby et al. investigated the effects of haloperidol, a D2/D3 dopamine antagonist, on social attributions. Using computational modeling, they demonstrate that haloperidol increases belief flexibility, reducing paranoia-like interpretations by enhancing sensitivity to social context and reducing self-relevant perspective taking, offering a mechanistic explanation for its therapeutic potential in schizophrenia.
Collapse
Affiliation(s)
- Andreea O Diaconescu
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Institute of Medical Science, University of Toronto, Toronto, Canada.
| | - Povilas Karvelis
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada
| | - Daniel J Hauke
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| |
Collapse
|
3
|
Schurr R, Reznik D, Hillman H, Bhui R, Gershman SJ. Dynamic computational phenotyping of human cognition. Nat Hum Behav 2024; 8:917-931. [PMID: 38332340 DOI: 10.1038/s41562-024-01814-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 12/21/2023] [Indexed: 02/10/2024]
Abstract
Computational phenotyping has emerged as a powerful tool for characterizing individual variability across a variety of cognitive domains. An individual's computational phenotype is defined as a set of mechanistically interpretable parameters obtained from fitting computational models to behavioural data. However, the interpretation of these parameters hinges critically on their psychometric properties, which are rarely studied. To identify the sources governing the temporal variability of the computational phenotype, we carried out a 12-week longitudinal study using a battery of seven tasks that measure aspects of human learning, memory, perception and decision making. To examine the influence of state effects, each week, participants provided reports tracking their mood, habits and daily activities. We developed a dynamic computational phenotyping framework, which allowed us to tease apart the time-varying effects of practice and internal states such as affective valence and arousal. Our results show that many phenotype dimensions covary with practice and affective factors, indicating that what appears to be unreliability may reflect previously unmeasured structure. These results support a fundamentally dynamic understanding of cognitive variability within an individual.
Collapse
Affiliation(s)
- Roey Schurr
- Department of Psychology, Center for Brain Sciences, Harvard University, Cambridge, MA, USA.
| | - Daniel Reznik
- Department of Psychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Hanna Hillman
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Rahul Bhui
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Samuel J Gershman
- Department of Psychology, Center for Brain Sciences, Harvard University, Cambridge, MA, USA
- Center for Brains, Minds, and Machines, Massachusetts Institute of Technology, Cambridge, MA, USA
| |
Collapse
|
4
|
Wiker T, Pedersen ML, Ferschmann L, Beck D, Norbom LB, Dahl A, von Soest T, Agartz I, Andreassen OA, Moberget T, Westlye LT, Huster RJ, Tamnes CK. Assessing the Longitudinal Associations Between Decision-Making Processes and Attention Problems in Early Adolescence. Res Child Adolesc Psychopathol 2024; 52:803-817. [PMID: 38103132 PMCID: PMC11063004 DOI: 10.1007/s10802-023-01148-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/25/2023] [Indexed: 12/17/2023]
Abstract
Cognitive functions and psychopathology develop in parallel in childhood and adolescence, but the temporal dynamics of their associations are poorly understood. The present study sought to elucidate the intertwined development of decision-making processes and attention problems using longitudinal data from late childhood (9-10 years) to mid-adolescence (11-13 years) from the Adolescent Brain Cognitive Development (ABCD) Study (n = 8918). We utilised hierarchical drift-diffusion modelling of behavioural data from the stop-signal task, parent-reported attention problems from the Child Behavior Checklist (CBCL), and multigroup univariate and bivariate latent change score models. The results showed faster drift rate was associated with lower levels of inattention at baseline, as well as a greater reduction of inattention over time. Moreover, baseline drift rate negatively predicted change in attention problems in females, and baseline attention problems negatively predicted change in drift rate. Neither response caution (decision threshold) nor encoding- and responding processes (non-decision time) were significantly associated with attention problems. There were no significant sex differences in the associations between decision-making processes and attention problems. The study supports previous findings of reduced evidence accumulation in attention problems and additionally shows that development of this aspect of decision-making plays a role in developmental changes in attention problems in youth.
Collapse
Affiliation(s)
- Thea Wiker
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway.
- Division of Mental health and Substance Abuse, Diakonhjemmet Hospital, PoBox 23 Vinderen, Oslo, 0319, Norway.
| | - Mads L Pedersen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Lia Ferschmann
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Dani Beck
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Division of Mental health and Substance Abuse, Diakonhjemmet Hospital, PoBox 23 Vinderen, Oslo, 0319, Norway
| | - Linn B Norbom
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Andreas Dahl
- Division of Mental Health and Addiction, Institute of Clinical Medicine, NORMENT, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Tilmann von Soest
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Ingrid Agartz
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental health and Substance Abuse, Diakonhjemmet Hospital, PoBox 23 Vinderen, Oslo, 0319, Norway
- K.G. Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm Region, Sweden
| | - Ole A Andreassen
- K.G. Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Institute of Clinical Medicine, NORMENT, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Torgeir Moberget
- Division of Mental Health and Addiction, Institute of Clinical Medicine, NORMENT, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- K.G. Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Institute of Clinical Medicine, NORMENT, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Rene J Huster
- Department of Psychology, University of Oslo, Oslo, Norway
- Multimodal Imaging and Cognitive Control Lab, Department of Psychology, University of Oslo, Oslo, Norway
- Cognitive and Translational Neuroscience Cluster, Department of Psychology, University of Oslo, Oslo, Norway
| | - Christian K Tamnes
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Division of Mental health and Substance Abuse, Diakonhjemmet Hospital, PoBox 23 Vinderen, Oslo, 0319, Norway
| |
Collapse
|
5
|
Marzuki AA, Lim TV. Bridging minds and policies: supporting early career researchers in translating computational psychiatry research. Neuropsychopharmacology 2024; 49:903-904. [PMID: 38418567 DOI: 10.1038/s41386-024-01834-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 02/12/2024] [Accepted: 02/15/2024] [Indexed: 03/01/2024]
Affiliation(s)
- Aleya A Marzuki
- Department of Psychology, Sunway University, Petaling Jaya, Selangor, Malaysia.
- Department of Psychiatry and Psychotherapy, Medical School and University Hospital, Eberhard Karls University of Tübingen, Tübingen, Germany.
- German Center for Mental Health (DZPG), Tübingen, Germany.
| | - Tsen Vei Lim
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
| |
Collapse
|
6
|
Colas JT, O’Doherty JP, Grafton ST. Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts. PLoS Comput Biol 2024; 20:e1011950. [PMID: 38552190 PMCID: PMC10980507 DOI: 10.1371/journal.pcbi.1011950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 02/26/2024] [Indexed: 04/01/2024] Open
Abstract
Active reinforcement learning enables dynamic prediction and control, where one should not only maximize rewards but also minimize costs such as of inference, decisions, actions, and time. For an embodied agent such as a human, decisions are also shaped by physical aspects of actions. Beyond the effects of reward outcomes on learning processes, to what extent can modeling of behavior in a reinforcement-learning task be complicated by other sources of variance in sequential action choices? What of the effects of action bias (for actions per se) and action hysteresis determined by the history of actions chosen previously? The present study addressed these questions with incremental assembly of models for the sequential choice data from a task with hierarchical structure for additional complexity in learning. With systematic comparison and falsification of computational models, human choices were tested for signatures of parallel modules representing not only an enhanced form of generalized reinforcement learning but also action bias and hysteresis. We found evidence for substantial differences in bias and hysteresis across participants-even comparable in magnitude to the individual differences in learning. Individuals who did not learn well revealed the greatest biases, but those who did learn accurately were also significantly biased. The direction of hysteresis varied among individuals as repetition or, more commonly, alternation biases persisting from multiple previous actions. Considering that these actions were button presses with trivial motor demands, the idiosyncratic forces biasing sequences of action choices were robust enough to suggest ubiquity across individuals and across tasks requiring various actions. In light of how bias and hysteresis function as a heuristic for efficient control that adapts to uncertainty or low motivation by minimizing the cost of effort, these phenomena broaden the consilient theory of a mixture of experts to encompass a mixture of expert and nonexpert controllers of behavior.
Collapse
Affiliation(s)
- Jaron T. Colas
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, California, United States of America
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California, United States of America
- Computation and Neural Systems Program, California Institute of Technology, Pasadena, California, United States of America
| | - John P. O’Doherty
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California, United States of America
- Computation and Neural Systems Program, California Institute of Technology, Pasadena, California, United States of America
| | - Scott T. Grafton
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, California, United States of America
| |
Collapse
|
7
|
Howlett JR, Paulus MP. Out of control: computational dynamic control dysfunction in stress- and anxiety-related disorders. DISCOVER MENTAL HEALTH 2024; 4:5. [PMID: 38236488 PMCID: PMC10796870 DOI: 10.1007/s44192-023-00058-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 12/28/2023] [Indexed: 01/19/2024]
Abstract
Control theory, which has played a central role in technological progress over the last 150 years, has also yielded critical insights into biology and neuroscience. Recently, there has been a surging interest in integrating control theory with computational psychiatry. Here, we review the state of the field of using control theory approaches in computational psychiatry and show that recent research has mapped a neural control circuit consisting of frontal cortex, parietal cortex, and the cerebellum. This basic feedback control circuit is modulated by estimates of reward and cost via the basal ganglia as well as by arousal states coordinated by the insula, dorsal anterior cingulate cortex, amygdala, and locus coeruleus. One major approach within the broader field of control theory, known as proportion-integral-derivative (PID) control, has shown promise as a model of human behavior which enables precise and reliable estimates of underlying control parameters at the individual level. These control parameters correlate with self-reported fear and with both structural and functional variation in affect-related brain regions. This suggests that dysfunctional engagement of stress and arousal systems may suboptimally modulate parameters of domain-general goal-directed control algorithms, impairing performance in complex tasks involving movement, cognition, and affect. Future directions include clarifying the causal role of control deficits in stress- and anxiety-related disorders and developing clinically useful tools based on insights from control theory.
Collapse
Affiliation(s)
- Jonathon R Howlett
- VA San Diego Healthcare System, 3350 La Jolla Village Dr, San Diego, CA, 92161, USA.
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
| | | |
Collapse
|
8
|
Goodwin I, Hester R, Garrido MI. Temporal stability of Bayesian belief updating in perceptual decision-making. Behav Res Methods 2023:10.3758/s13428-023-02306-y. [PMID: 38129733 DOI: 10.3758/s13428-023-02306-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/24/2023] [Indexed: 12/23/2023]
Abstract
Bayesian inference suggests that perception is inferred from a weighted integration of prior contextual beliefs with current sensory evidence (likelihood) about the world around us. The perceived precision or uncertainty associated with prior and likelihood information is used to guide perceptual decision-making, such that more weight is placed on the source of information with greater precision. This provides a framework for understanding a spectrum of clinical transdiagnostic symptoms associated with aberrant perception, as well as individual differences in the general population. While behavioral paradigms are commonly used to characterize individual differences in perception as a stable characteristic, measurement reliability in these behavioral tasks is rarely assessed. To remedy this gap, we empirically evaluate the reliability of a perceptual decision-making task that quantifies individual differences in Bayesian belief updating in terms of the relative precision weighting afforded to prior and likelihood information (i.e., sensory weight). We analyzed data from participants (n = 37) who performed this task twice. We found that the precision afforded to prior and likelihood information showed high internal consistency and good test-retest reliability (ICC = 0.73, 95% CI [0.53, 0.85]) when averaged across participants, as well as at the individual level using hierarchical modeling. Our results provide support for the assumption that Bayesian belief updating operates as a stable characteristic in perceptual decision-making. We discuss the utility and applicability of reliable perceptual decision-making paradigms as a measure of individual differences in the general population, as well as a diagnostic tool in psychiatric research.
Collapse
Affiliation(s)
- Isabella Goodwin
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville Campus, Melbourne, Victoria, 3010, Australia.
| | - Robert Hester
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville Campus, Melbourne, Victoria, 3010, Australia
| | - Marta I Garrido
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville Campus, Melbourne, Victoria, 3010, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
| |
Collapse
|
9
|
Wilkinson CS, Luján MÁ, Hales C, Costa KM, Fiore VG, Knackstedt LA, Kober H. Listening to the Data: Computational Approaches to Addiction and Learning. J Neurosci 2023; 43:7547-7553. [PMID: 37940590 PMCID: PMC10634572 DOI: 10.1523/jneurosci.1415-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 11/10/2023] Open
Abstract
Computational approaches hold great promise for identifying novel treatment targets and creating translational therapeutics for substance use disorders. From circuitries underlying decision-making to computationally derived neural markers of drug-cue reactivity, this review is a summary of the approaches to data presented at our 2023 Society for Neuroscience Mini-Symposium. Here, we highlight data- and hypothesis-driven computational approaches that recently afforded advancements in addiction and learning neuroscience. First, we discuss the value of hypothesis-driven algorithmic modeling approaches, which integrate behavioral, neural, and cognitive outputs to refine hypothesis testing. Then, we review the advantages of data-driven dimensionality reduction and machine learning methods for uncovering novel predictor variables and elucidating relationships in high-dimensional data. Overall, this review highlights recent breakthroughs in cognitive mapping, model-based analysis of behavior/risky decision-making, patterns of drug taking, relapse, and neuromarker discovery, and showcases the benefits of novel modeling techniques, across both preclinical and clinical data.
Collapse
Affiliation(s)
| | - Miguel Á Luján
- Department of Neurobiology, University of Maryland, School of Medicine, Baltimore, Maryland 21201
| | - Claire Hales
- Department of Psychology, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Kauê M Costa
- National Institute on Drug Abuse Intramural Research Program, Baltimore, Maryland 21224
| | - Vincenzo G Fiore
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, New York 10029
| | - Lori A Knackstedt
- Department of Psychology, University of Florida, Gainesville, Florida 32611
| | - Hedy Kober
- Departments of Psychiatry, Psychology, and Neuroscience, Yale University, New Haven, Connecticut 06511
| |
Collapse
|
10
|
Kardan O, Sereeyothin C, Schertz KE, Angstadt M, Weigard AS, Berman MG, Heitzeg MM, Rosenberg MD. Neighborhood air pollution is negatively associated with neurocognitive maturation in early adolescence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.28.538763. [PMID: 37205398 PMCID: PMC10187199 DOI: 10.1101/2023.04.28.538763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
The ability to maintain focus and process task-relevant information continues developing during adolescence, but the specific physical environmental factors that influence this development remain poorly characterized. One candidate factor is air pollution. Evidence suggests that small particulate matter and NO2 concentrations in the air may negatively impact cognitive development in childhood. We assessed the relationship between neighborhood air pollution and the changes in performance on the n-back task, a test of attention and working memory, in the Adolescent Brain Cognitive Development (ABCD) Study's baseline (ages 9-10) and two-year-follow-up releases (Y2, ages 11-12; n = 5,256). In the behavioral domain, multiple linear regression showed that developmental change in n-back task performance was negatively associated with neighborhood air pollution (β = -.044, t = -3.11, p = .002), adjusted for covariates capturing baseline cognitive performance of the child, their parental income and education, family conflicts, and their neighborhood's population density, crime rate, perceived safety, and Area Deprivation Index (ADI). The strength of the adjusted association for air pollution was similar to parental income, family conflict, and neighborhood ADI. In the neuroimaging domain, we evaluated a previously published youth cognitive composite Connectome-based Predictive Model (ccCPM), and again found that decreased developmental change in the strength of the ccCPM from pre- to early adolescence was associated with neighborhood air pollution (β = -.110, t = -2.69, p = .007), adjusted for the covariates mentioned above and head motion. Finally, we found that the developmental change in ccCPM strength was predictive of the developmental change in n-back performance (r = .157, p < .001), and there was an indirect-only mediation where the effect of air pollution on change in n-back performance was mediated by the change in the ccCPM strength (βindirect effect = -.013, p = .029). In conclusion, neighborhood air pollution is associated with lags in the maturation of youth cognitive performance and decreased strengthening of the brain networks supporting cognitive abilities over time.
Collapse
Affiliation(s)
- Omid Kardan
- University of Chicago, Department of Psychology, Chicago, IL
- University of Michigan, Department of Psychology, Ann Arbor, MI
- University of Michigan, Department of Psychiatry, Ann Arbor, MI
| | | | - Kathryn E Schertz
- University of Chicago, Department of Psychology, Chicago, IL
- University of Michigan, Department of Psychology, Ann Arbor, MI
| | - Mike Angstadt
- University of Michigan, Department of Psychiatry, Ann Arbor, MI
| | | | - Marc G Berman
- University of Chicago, Department of Psychology, Chicago, IL
- University of Chicago, Neuroscience Institute, Chicago, IL
| | - Mary M Heitzeg
- University of Michigan, Department of Psychology, Ann Arbor, MI
- University of Michigan, Department of Psychiatry, Ann Arbor, MI
| | - Monica D Rosenberg
- University of Chicago, Department of Psychology, Chicago, IL
- University of Chicago, Neuroscience Institute, Chicago, IL
| |
Collapse
|
11
|
Manchia M, Murri MB. The role of pharmacogenomics in precision psychiatry. Pharmacogenomics 2023; 24:523-527. [PMID: 37458685 DOI: 10.2217/pgs-2023-0112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2023] Open
Abstract
The field of psychiatry is facing an important paradigm shift in the provision of clinical care and mental health service organization toward personalization and integration of multimodal data science. This approach, termed precision psychiatry, aims at identifying subgroups of patients more prone to the development of a certain phenotype, such as symptoms or severe mental disorders (risk detection), and/or to guide treatment selection. Pharmacogenomics and computational psychiatry are two fundamental tools of precision psychiatry, which have seen increasing levels of integration in clinical settings. Here we present a brief overview of these two applications of precision psychiatry in clinical settings.
Collapse
Affiliation(s)
- Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, 09127, Italy
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, 09127,Italy
- Department of Pharmacology, Dalhousie University, Halifax, NS, B3H 4R2, Canada
| | - Martino Belvederi Murri
- Institute of Psychiatry, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, 44121, Italy
| |
Collapse
|
12
|
Charlton CE, Karvelis P, McIntyre RS, Diaconescu AO. Suicide prevention and ketamine: insights from computational modeling. Front Psychiatry 2023; 14:1214018. [PMID: 37457775 PMCID: PMC10342546 DOI: 10.3389/fpsyt.2023.1214018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Suicide is a pressing public health issue, with over 700,000 individuals dying each year. Ketamine has emerged as a promising treatment for suicidal thoughts and behaviors (STBs), yet the complex mechanisms underlying ketamine's anti-suicidal effect are not fully understood. Computational psychiatry provides a promising framework for exploring the dynamic interactions underlying suicidality and ketamine's therapeutic action, offering insight into potential biomarkers, treatment targets, and the underlying mechanisms of both. This paper provides an overview of current computational theories of suicidality and ketamine's mechanism of action, and discusses various computational modeling approaches that attempt to explain ketamine's anti-suicidal effect. More specifically, the therapeutic potential of ketamine is explored in the context of the mismatch negativity and the predictive coding framework, by considering neurocircuits involved in learning and decision-making, and investigating altered connectivity strengths and receptor densities targeted by ketamine. Theory-driven computational models offer a promising approach to integrate existing knowledge of suicidality and ketamine, and for the extraction of model-derived mechanistic parameters that can be used to identify patient subgroups and personalized treatment approaches. Future computational studies on ketamine's mechanism of action should optimize task design and modeling approaches to ensure parameter reliability, and external factors such as set and setting, as well as psychedelic-assisted therapy should be evaluated for their additional therapeutic value.
Collapse
Affiliation(s)
- Colleen E. Charlton
- Krembil Center for Neuroinformatics, Center for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Povilas Karvelis
- Krembil Center for Neuroinformatics, Center for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Roger S. McIntyre
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | - Andreea O. Diaconescu
- Krembil Center for Neuroinformatics, Center for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
| |
Collapse
|