1
|
Nassan M. Proposal for a Mechanistic Disease Conceptualization in Clinical Neurosciences: The Neural Network Components (NNC) Model. Harv Rev Psychiatry 2024; 32:150-159. [PMID: 38990903 DOI: 10.1097/hrp.0000000000000399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
ABSTRACT Clinical neurosciences, and psychiatry specifically, have been challenged by the lack of a comprehensive and practical framework that explains the core mechanistic processes of variable psychiatric presentations. Current conceptualization and classification of psychiatric presentations are primarily centered on a non-biologically based clinical descriptive approach. Despite various attempts, advances in neuroscience research have not led to an improved conceptualization or mechanistic classification of psychiatric disorders. This perspective article proposes a new-work-in-progress-framework for conceptualizing psychiatric presentations based on neural network components (NNC). This framework could guide the development of mechanistic disease classification, improve understanding of underpinning pathology, and provide specific intervention targets. This model also has the potential to dissolve artificial barriers between the fields of psychiatry and neurology.
Collapse
Affiliation(s)
- Malik Nassan
- From Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Northwestern University, Chicago, IL; Department of Neurology and Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine (Dr. Nassan)
| |
Collapse
|
2
|
Harp NR, Wager TD, Kober H. Neuromarkers in addiction: definitions, development strategies, and recent advances. J Neural Transm (Vienna) 2024; 131:509-523. [PMID: 38630190 DOI: 10.1007/s00702-024-02766-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 03/12/2024] [Indexed: 04/28/2024]
Abstract
Substance use disorders (SUDs) are the most costly and prevalent psychiatric conditions. Recent calls emphasize a need for biomarkers-measurable, stable indicators of normal and abnormal processes and response to treatment or environmental agents-and, in particular, brain-based neuromarkers that will advance understanding of the neurobiological basis of SUDs and clinical practice. To develop neuromarkers, researchers must be grounded in evidence that a putative marker (i) is sensitive and specific to the psychological phenomenon of interest, (ii) constitutes a predictive model, and (iii) generalizes to novel observations (e.g., through internal cross-validation and external application to novel data). These neuromarkers may be used to index risk of developing SUDs (susceptibility), classify individuals with SUDs (diagnostic), assess risk for progression to more severe pathology (prognostic) or index current severity of pathology (monitoring), detect response to treatment (response), and predict individualized treatment outcomes (predictive). Here, we outline guidelines for developing and assessing neuromarkers, we then review recent advances toward neuromarkers in addiction neuroscience centering our discussion around neuromarkers of craving-a core feature of SUDs. In doing so, we specifically focus on the Neurobiological Craving Signature (NCS), which show great promise for meeting the demand of neuromarkers.
Collapse
Affiliation(s)
- Nicholas R Harp
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Tor D Wager
- Department of Psychological & Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Hedy Kober
- Department of Psychiatry, Yale University, New Haven, CT, USA.
| |
Collapse
|
3
|
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
|
4
|
Fahey MP, Yee DM, Leng X, Tarlow M, Shenhav A. Motivational context determines the impact of aversive outcomes on mental effort allocation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.27.564461. [PMID: 37961466 PMCID: PMC10634922 DOI: 10.1101/2023.10.27.564461] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
It is well known that people will exert effort on a task if sufficiently motivated, but how they distribute these efforts across different strategies (e.g., efficiency vs. caution) remains uncertain. Past work has shown that people invest effort differently for potential positive outcomes (rewards) versus potential negative outcomes (penalties). However, this research failed to account for differences in the context in which negative outcomes motivate someone - either as punishment or reinforcement. It is therefore unclear whether effort profiles differ as a function of outcome valence, motivational context, or both. Using computational modeling and our novel Multi-Incentive Control Task, we show that the influence of aversive outcomes on one's effort profile is entirely determined by their motivational context. Participants (N:91) favored increased caution in response to larger penalties for incorrect responses, and favored increased efficiency in response to larger reinforcement for correct responses, whether positively or negatively incentivized.
Collapse
Affiliation(s)
- Mahalia Prater Fahey
- Cognitive, Linguistic, and Psychological Sciences, Brown University Carney Institute for Brain Science, Brown University
| | - Debbie M Yee
- Cognitive, Linguistic, and Psychological Sciences, Brown University Carney Institute for Brain Science, Brown University
| | - Xiamin Leng
- Cognitive, Linguistic, and Psychological Sciences, Brown University Carney Institute for Brain Science, Brown University
| | - Maisy Tarlow
- Cognitive, Linguistic, and Psychological Sciences, Brown University Carney Institute for Brain Science, Brown University
| | - Amitai Shenhav
- Cognitive, Linguistic, and Psychological Sciences, Brown University Carney Institute for Brain Science, Brown University
| |
Collapse
|
5
|
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
|
6
|
Karvelis P, Paulus MP, Diaconescu AO. Individual differences in computational psychiatry: a review of current challenges. Neurosci Biobehav Rev 2023; 148:105137. [PMID: 36940888 DOI: 10.1016/j.neubiorev.2023.105137] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/04/2023] [Accepted: 03/14/2023] [Indexed: 03/23/2023]
Abstract
Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. We find that many computational measures suffer from poor psychometric properties, which poses a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We provide recommendations for how to address these problems and, crucially, embed them within a broader perspective on key developments that are needed for translating computational assays to clinical practice.
Collapse
Affiliation(s)
- Povilas Karvelis
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada.
| | - Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, OK, USA; Oxley College of Health Sciences, The University of Tulsa, Tulsa, OK, USA
| | - Andreea O Diaconescu
- Krembil Centre for Neuroinformatics, Centre 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
|
7
|
Self-judgment dissected: A computational modeling analysis of self-referential processing and its relationship to trait mindfulness facets and depression symptoms. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2023; 23:171-189. [PMID: 36168080 PMCID: PMC9931629 DOI: 10.3758/s13415-022-01033-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/29/2022] [Indexed: 11/08/2022]
Abstract
Cognitive theories of depression, and mindfulness theories of well-being, converge on the notion that self-judgment plays a critical role in mental health. However, these theories have rarely been tested via tasks and computational modeling analyses that can disentangle the information processes operative in self-judgments. We applied a drift-diffusion computational model to the self-referential encoding task (SRET) collected before and after an 8-week mindfulness intervention (n = 96). A drift-rate regression parameter representing positive-relative to negative-self-referential judgment strength positively related to mindful awareness and inversely related to depression, both at baseline and over time; however, this parameter did not significantly relate to the interaction between mindful awareness and nonjudgmentalness. At the level of individual depression symptoms, at baseline, a spectrum of symptoms (inversely) correlated with the drift-rate regression parameter, suggesting that many distinct depression symptoms relate to valenced self-judgment between subjects. By contrast, over the intervention, changes in only a smaller subset of anhedonia-related depression symptoms showed substantial relationships with this parameter. Both behavioral and model-derived measures showed modest split-half and test-retest correlations. Results support cognitive theories that implicate self-judgment in depression and mindfulness theories, which imply that mindful awareness should lead to more positive self-views.
Collapse
|
8
|
Kao CH, Feng GW, Hur JK, Jarvis H, Rutledge RB. Computational models of subjective feelings in psychiatry. Neurosci Biobehav Rev 2023; 145:105008. [PMID: 36549378 PMCID: PMC9990828 DOI: 10.1016/j.neubiorev.2022.105008] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 12/02/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
Research in computational psychiatry is dominated by models of behavior. Subjective experience during behavioral tasks is not well understood, even though it should be relevant to understanding the symptoms of psychiatric disorders. Here, we bridge this gap and review recent progress in computational models for subjective feelings. For example, happiness reflects not how well people are doing, but whether they are doing better than expected. This dependence on recent reward prediction errors is intact in major depression, although depressive symptoms lower happiness during tasks. Uncertainty predicts subjective feelings of stress in volatile environments. Social prediction errors influence feelings of self-worth more in individuals with low self-esteem despite a reduced willingness to change beliefs due to social feedback. Measuring affective state during behavioral tasks provides a tool for understanding psychiatric symptoms that can be dissociable from behavior. When smartphone tasks are collected longitudinally, subjective feelings provide a potential means to bridge the gap between lab-based behavioral tasks and real-life behavior, emotion, and psychiatric symptoms.
Collapse
Affiliation(s)
- Chang-Hao Kao
- Department of Psychology, Yale University, New Haven, CT, USA.
| | - Gloria W Feng
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Jihyun K Hur
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Huw Jarvis
- Department of Psychology, Yale University, New Haven, CT, USA; Turner Institute for Brain and Mental Health, Monash University, Clayton, Victoria, Australia; School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
| | - Robb B Rutledge
- Department of Psychology, Yale University, New Haven, CT, USA; Wellcome Centre for Human Neuroimaging, University College London, London, UK.
| |
Collapse
|