1
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Verdonk C, Teed AR, White EJ, Ren X, Stewart JL, Paulus MP, Khalsa SS. Heartbeat-evoked neural response abnormalities in generalized anxiety disorder during peripheral adrenergic stimulation. Neuropsychopharmacology 2024; 49:1246-1254. [PMID: 38291167 PMCID: PMC11224228 DOI: 10.1038/s41386-024-01806-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 12/22/2023] [Accepted: 01/16/2024] [Indexed: 02/01/2024]
Abstract
Hyperarousal symptoms in generalized anxiety disorder (GAD) are often incongruent with the observed physiological state, suggesting that abnormal processing of interoceptive signals is a characteristic feature of the disorder. To examine the neural mechanisms underlying interoceptive dysfunction in GAD, we evaluated whether adrenergic modulation of cardiovascular signaling differentially affects the heartbeat-evoked potential (HEP), an electrophysiological marker of cardiac interoception, during concurrent electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) scanning. Intravenous infusions of the peripheral adrenergic agonist isoproterenol (0.5 and 2.0 micrograms, μg) were administered in a randomized, double-blinded and placebo-controlled fashion to dynamically perturb the cardiovascular system while recording the associated EEG-fMRI responses. During the 0.5 μg isoproterenol infusion, the GAD group (n = 24) exhibited significantly larger changes in HEP amplitude in an opposite direction than the healthy comparison (HC) group (n = 24). In addition, the GAD group showed significantly larger absolute HEP amplitudes than the HC group during saline infusions, when cardiovascular tone did not increase. No significant group differences in HEP amplitude were identified during the 2.0 μg isoproterenol infusion. Using analyzable blood oxygenation level-dependent fMRI data from participants with concurrent EEG-fMRI data (21 GAD and 21 HC), we found that the aforementioned HEP effects were uncorrelated with fMRI signals in the insula, ventromedial prefrontal cortex, dorsal anterior cingulate cortex, amygdala, and somatosensory cortex, brain regions implicated in cardiac signal processing in prior fMRI studies. These findings provide additional evidence of dysfunctional cardiac interoception in GAD and identify neural processes at the electrophysiological level that may be independent from blood oxygen level-dependent responses during peripheral adrenergic stimulation.
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Affiliation(s)
- Charles Verdonk
- Laureate Institute for Brain Research, Tulsa, OK, USA
- VIFASOM (EA 7330 Vigilance Fatigue, Sommeil et Santé Publique), Université Paris Cité, Paris, France
- French Armed Forces Biomedical Research Institute, Brétigny-sur-Orge, France
| | - Adam R Teed
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Evan J White
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Xi Ren
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Jennifer L Stewart
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA
| | - Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA
| | - Sahib S Khalsa
- Laureate Institute for Brain Research, Tulsa, OK, USA.
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA.
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2
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Ging-Jehli NR, Kuhn M, Blank JM, Chanthrakumar P, Steinberger DC, Yu Z, Herrington TM, Dillon DG, Pizzagalli DA, Frank MJ. Cognitive Signatures of Depressive and Anhedonic Symptoms and Affective States Using Computational Modeling and Neurocognitive Testing. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:726-736. [PMID: 38401881 PMCID: PMC11227402 DOI: 10.1016/j.bpsc.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 02/03/2024] [Accepted: 02/09/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND Deeper phenotyping may improve our understanding of depression. Because depression is heterogeneous, extracting cognitive signatures associated with severity of depressive symptoms, anhedonia, and affective states is a promising approach. METHODS Sequential sampling models decomposed behavior from an adaptive approach-avoidance conflict task into computational parameters quantifying latent cognitive signatures. Fifty unselected participants completed clinical scales and the approach-avoidance conflict task by either approaching or avoiding trials offering monetary rewards and electric shocks. RESULTS Decision dynamics were best captured by a sequential sampling model with linear collapsing boundaries varying by net offer values, and with drift rates varying by trial-specific reward and aversion, reflecting net evidence accumulation toward approach or avoidance. Unlike conventional behavioral measures, these computational parameters revealed distinct associations with self-reported symptoms. Specifically, passive avoidance tendencies, indexed by starting point biases, were associated with greater severity of depressive symptoms (R = 0.34, p = .019) and anhedonia (R = 0.49, p = .001). Depressive symptoms were also associated with slower encoding and response execution, indexed by nondecision time (R = 0.37, p = .011). Higher reward sensitivity for offers with negative net values, indexed by drift rates, was linked to more sadness (R = 0.29, p = .042) and lower positive affect (R = -0.33, p = .022). Conversely, higher aversion sensitivity was associated with more tension (R = 0.33, p = .025). Finally, less cautious response patterns, indexed by boundary separation, were linked to more negative affect (R = -0.40, p = .005). CONCLUSIONS We demonstrated the utility of multidimensional computational phenotyping, which could be applied to clinical samples to improve characterization and treatment selection.
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Affiliation(s)
- Nadja R Ging-Jehli
- Carney Institute for Brain Science, Department of Cognitive, Linguistic, & Psychological Sciences, Brown University, Providence, Rhode Island.
| | - Manuel Kuhn
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Jacob M Blank
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts
| | - Pranavan Chanthrakumar
- Carney Institute for Brain Science, Department of Cognitive, Linguistic, & Psychological Sciences, Brown University, Providence, Rhode Island; Warren Alpert Medical School of Brown University, Providence, Rhode Island
| | - David C Steinberger
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts
| | - Zeyang Yu
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Todd M Herrington
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Daniel G Dillon
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Diego A Pizzagalli
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Michael J Frank
- Carney Institute for Brain Science, Department of Cognitive, Linguistic, & Psychological Sciences, Brown University, Providence, Rhode Island
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3
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Klaassen FH, de Voogd LD, Hulsman AM, O'Reilly JX, Klumpers F, Figner B, Roelofs K. The neurocomputational link between defensive cardiac states and approach-avoidance arbitration under threat. Commun Biol 2024; 7:576. [PMID: 38755409 PMCID: PMC11099143 DOI: 10.1038/s42003-024-06267-6] [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: 09/20/2023] [Accepted: 04/30/2024] [Indexed: 05/18/2024] Open
Abstract
Avoidance, a hallmark of anxiety-related psychopathology, often comes at a cost; avoiding threat may forgo the possibility of a reward. Theories predict that optimal approach-avoidance arbitration depends on threat-induced psychophysiological states, like freezing-related bradycardia. Here we used model-based fMRI analyses to investigate whether and how bradycardia states are linked to the neurocomputational underpinnings of approach-avoidance arbitration under varying reward and threat magnitudes. We show that bradycardia states are associated with increased threat-induced avoidance and more pronounced reward-threat value comparison (i.e., a stronger tendency to approach vs. avoid when expected reward outweighs threat). An amygdala-striatal-prefrontal circuit supports approach-avoidance arbitration under threat, with specific involvement of the amygdala and dorsal anterior cingulate (dACC) in integrating reward-threat value and bradycardia states. These findings highlight the role of human freezing states in value-based decision making, relevant for optimal threat coping. They point to a specific role for amygdala/dACC in state-value integration under threat.
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Affiliation(s)
- Felix H Klaassen
- Radboud University, Donders Institute for Brain, Cognition, and Behaviour, Thomas van Aquinostraat 4, 6525 GD, Nijmegen, The Netherlands.
| | - Lycia D de Voogd
- Radboud University, Donders Institute for Brain, Cognition, and Behaviour, Thomas van Aquinostraat 4, 6525 GD, Nijmegen, The Netherlands
- Radboud University, Behavioural Science Institute (BSI), Thomas van Aquinostraat 4, 6525 GD, Nijmegen, The Netherlands
- Leiden University, Institute of Psychology and Leiden Institute for Brain and Cognition (LIBC), Rapenburg 70, 2311 EZ, Leiden, The Netherlands
| | - Anneloes M Hulsman
- Radboud University, Donders Institute for Brain, Cognition, and Behaviour, Thomas van Aquinostraat 4, 6525 GD, Nijmegen, The Netherlands
- Radboud University, Behavioural Science Institute (BSI), Thomas van Aquinostraat 4, 6525 GD, Nijmegen, The Netherlands
| | - Jill X O'Reilly
- Department of Experimental Psychology, University of Oxford, Woodstock Road, OX2 6GG, Oxford, UK
| | - Floris Klumpers
- Radboud University, Donders Institute for Brain, Cognition, and Behaviour, Thomas van Aquinostraat 4, 6525 GD, Nijmegen, The Netherlands
- Radboud University, Behavioural Science Institute (BSI), Thomas van Aquinostraat 4, 6525 GD, Nijmegen, The Netherlands
| | - Bernd Figner
- Radboud University, Donders Institute for Brain, Cognition, and Behaviour, Thomas van Aquinostraat 4, 6525 GD, Nijmegen, The Netherlands
- Radboud University, Behavioural Science Institute (BSI), Thomas van Aquinostraat 4, 6525 GD, Nijmegen, The Netherlands
| | - Karin Roelofs
- Radboud University, Donders Institute for Brain, Cognition, and Behaviour, Thomas van Aquinostraat 4, 6525 GD, Nijmegen, The Netherlands.
- Radboud University, Behavioural Science Institute (BSI), Thomas van Aquinostraat 4, 6525 GD, Nijmegen, The Netherlands.
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4
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Verdonk C, Teed AR, White EJ, Ren X, Stewart JL, Paulus MP, Khalsa SS. Heartbeat-evoked neural response abnormalities in generalized anxiety disorder during peripheral adrenergic stimulation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.09.23291166. [PMID: 37398268 PMCID: PMC10312828 DOI: 10.1101/2023.06.09.23291166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Hyperarousal symptoms in generalized anxiety disorder (GAD) are often incongruent with the observed physiological state, suggesting that abnormal processing of interoceptive signals is a characteristic feature of the disorder. To examine the neural mechanisms underlying interoceptive dysfunction in GAD, we evaluated whether adrenergic modulation of cardiovascular signaling differentially affects the heartbeat evoked potential (HEP), an electrophysiological marker of cardiac interoception, during concurrent electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) scanning. Intravenous infusions of the peripheral adrenergic agonist isoproterenol (0.5 and 2.0 micrograms, μg) were administered in a randomized, double-blinded and placebo-controlled fashion to dynamically perturb the cardiovascular system while recording the associated EEG-fMRI responses. During the 0.5 μg isoproterenol infusion, the GAD group (n=24) exhibited significantly larger changes in HEP amplitude in an opposite direction than the HC group (n=24). In addition, the GAD group showed significantly larger absolute HEP amplitudes than HC during saline infusions, when cardiovascular tone did not increase. No significant group differences in HEP amplitude were identified during the 2.0 μg isoproterenol infusion. Using analyzable blood oxygenation level dependent fMRI data from participants with concurrent EEG-fMRI data (21 GAD and 21 HC), we found that the aforementioned HEP effects were uncorrelated with fMRI signals in the insula, ventromedial prefrontal cortex, dorsal anterior cingulate cortex, amygdala, and somatosensory cortex, brain regions implicated in cardiac signal processing according to prior fMRI studies. These findings provide additional evidence of dysfunctional cardiac interoception in GAD and identify neural processes at the electrophysiological level that may be independent from blood oxygen level-dependent responses during peripheral adrenergic stimulation.
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Affiliation(s)
- Charles Verdonk
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States
- VIFASOM (EA 7330 Vigilance Fatigue, Sommeil et Santé Publique), Université Paris Cité, Paris, France
- French Armed Forces Biomedical Research Institute, Brétigny-sur-Orge, France
| | - Adam R. Teed
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States
| | - Evan J. White
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States
| | - Xi Ren
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States
| | - Jennifer L. Stewart
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States
- Oxley College of Health Sciences, University of Tulsa, Tulsa, Oklahoma, United States
| | - Martin P. Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States
- Oxley College of Health Sciences, University of Tulsa, Tulsa, Oklahoma, United States
| | - Sahib S. Khalsa
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States
- Oxley College of Health Sciences, University of Tulsa, Tulsa, Oklahoma, United States
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5
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Kim H, Hur JK, Kwon M, Kim S, Zoh Y, Ahn WY. Causal role of the dorsolateral prefrontal cortex in modulating the balance between Pavlovian and instrumental systems in the punishment domain. PLoS One 2023; 18:e0286632. [PMID: 37267307 PMCID: PMC10237433 DOI: 10.1371/journal.pone.0286632] [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: 09/02/2022] [Accepted: 05/19/2023] [Indexed: 06/04/2023] Open
Abstract
Previous literature suggests that a balance between Pavlovian and instrumental decision-making systems is critical for optimal decision-making. Pavlovian bias (i.e., approach toward reward-predictive stimuli and avoid punishment-predictive stimuli) often contrasts with the instrumental response. Although recent neuroimaging studies have identified brain regions that may be related to Pavlovian bias, including the dorsolateral prefrontal cortex (dlPFC), it is unclear whether a causal relationship exists. Therefore, we investigated whether upregulation of the dlPFC using transcranial current direct stimulation (tDCS) would reduce Pavlovian bias. In this double-blind study, participants were assigned to the anodal or the sham group; they received stimulation over the right dlPFC for 3 successive days. On the last day, participants performed a reinforcement learning task known as the orthogonalized go/no-go task; this was used to assess each participant's degree of Pavlovian bias in reward and punishment domains. We used computational modeling and hierarchical Bayesian analysis to estimate model parameters reflecting latent cognitive processes, including Pavlovian bias, go bias, and choice randomness. Several computational models were compared; the model with separate Pavlovian bias parameters for reward and punishment domains demonstrated the best model fit. When using a behavioral index of Pavlovian bias, the anodal group showed significantly lower Pavlovian bias in the punishment domain, but not in the reward domain, compared with the sham group. In addition, computational modeling showed that Pavlovian bias parameter in the punishment domain was lower in the anodal group than in the sham group, which is consistent with the behavioral findings. The anodal group also showed a lower go bias and choice randomness, compared with the sham group. These findings suggest that anodal tDCS may lead to behavioral suppression or change in Pavlovian bias in the punishment domain, which will help to improve comprehension of the causal neural mechanism.
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Affiliation(s)
- Hyeonjin Kim
- Department of Psychology, Seoul National University, Seoul, Korea
| | - Jihyun K. Hur
- Department of Psychology, Yale University, New Haven, Connecticut, United States of America
| | - Mina Kwon
- Department of Psychology, Seoul National University, Seoul, Korea
| | - Soyeon Kim
- Department of Psychology, Seoul National University, Seoul, Korea
| | - Yoonseo Zoh
- Department of Psychology, Princeton University, Princeton, New Jersey, United States of America
| | - Woo-Young Ahn
- Department of Psychology, Seoul National University, Seoul, Korea
- Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Korea
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6
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Liu Q, Zhong R, Ji X, Law S, Xiao F, Wei Y, Fang S, Kong X, Zhang X, Yao S, Wang X. Decision-making biases in suicide attempters with major depressive disorder: A computational modeling study using the balloon analog risk task (BART). Depress Anxiety 2022; 39:845-857. [PMID: 36329675 DOI: 10.1002/da.23291] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/30/2022] [Accepted: 10/22/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND In the last decade, suicidality has been increasingly theorized as a distinct phenomenon from major depressive disorder (MDD), with unique psychological and neural mechanisms, rather than being mostly a severe symptom of MDD. Although decision-making biases have been widely reported in suicide attempters with MDD, little is known regarding what components of these biases can be distinguished from depressiveness itself. METHODS Ninety-three patients with current MDD (40 with suicide attempts [SA group] and 53 without suicide attempts [NS group]) and 65 healthy controls (HCs) completed psychometric assessments and the balloon analog risk task (BART). To analyze and compare decision-making components among the three groups, we applied a five-parameter Bayesian computational modeling. RESULTS Psychological assessments showed that the SA group had greater suicidal ideation and psychological pain avoidance than the NS group. Computational modeling showed that both MDD groups had higher risk preference and lower ability to learn and adapt from within-task observations than HCs, without differences between the SA and NS patient groups. The SA group also had higher loss aversion than the NS and HC groups, which had similar loss aversion. CONCLUSIONS Our BART and computational modeling findings suggest that psychological pain avoidance and loss aversion may be important suicide risk factor that are distinguishable from depression illness itself.
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Affiliation(s)
- Qinyu Liu
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
| | - Runqing Zhong
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
| | - Xinlei Ji
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
| | - Samuel Law
- Department of Psychiatry, University of Toronto, Ontario, Toronto, Canada
| | - Fan Xiao
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
| | - Yiming Wei
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Shulin Fang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
| | - Xinyuan Kong
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
| | - Xiaocui Zhang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
| | - Shuqiao Yao
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
| | - Xiang Wang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
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7
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Using expectation violation models to improve the outcome of psychological treatments. Clin Psychol Rev 2022; 98:102212. [PMID: 36371900 DOI: 10.1016/j.cpr.2022.102212] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 06/14/2022] [Accepted: 10/11/2022] [Indexed: 01/27/2023]
Abstract
Expectations are a central maintaining mechanism in mental disorders and most psychological treatments aim to directly or indirectly modify clinically relevant expectations. Therefore, it is crucial to examine why patients with mental disorders maintain dysfunctional expectations, even in light of disconfirming evidence, and how expectation-violating situations should be created in treatment settings to optimize treatment outcome and reduce the risk of treatment failures. The different psychological subdisciplines offer various approaches for understanding the underlying mechanisms of expectation development, persistence, and change. Here, we convey recommendations on how to improve psychological treatments by considering these different perspectives. Based on our expectation violation model, we argue that the outcome of expectation violation depends on several characteristics: features of the expectation-violating situation; the dynamics between the magnitude of expectation violation and cognitive immunization processes; dealing with uncertainties during and after expectation change; controlled and automatic attention processes; and the costs of expectation changes. Personality factors further add to predict outcomes and may offer a basis for personalized treatment planning. We conclude with a list of recommendations derived from basic psychology that could contribute to improved treatment outcome and to reduced risks of treatment failures.
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8
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Moradi M, Ekhtiari H, Kuplicki R, McKinney B, Stewart JL, Victor TA, Paulus MP. Evaluating the resource allocation index as a potential fMRI-based biomarker for substance use disorder. Drug Alcohol Depend 2020; 216:108211. [PMID: 32805548 PMCID: PMC7609625 DOI: 10.1016/j.drugalcdep.2020.108211] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 07/24/2020] [Accepted: 07/27/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND There is a lack of neuroscience-based biomarkers for the diagnosis, treatment and monitoring of individuals with substance use disorders (SUD). The resource allocation index (RAI), a measure of the interrelationship between salience, executive control and default-mode brain networks (SN, ECN, and DMN), has been proposed as one such biomarker. However, the RAI has yet to be extensively tested in SUD samples. METHODS The present analysis compared RAI scores between individuals with stimulant and/or opioid use disorders (SUD; n = 139, abstinent 4-365 days) and healthy controls (HC; n = 56) who had completed resting-state functional magnetic resonance imaging (fMRI) scans within the context of the Tulsa 1000 cohort. First, we used independent component analysis (ICA) to identify the SN, ECN, and DMN and extract their time series data. Second, we used multiple permutations of automatically identified networks to compute RAI as reported in the fMRI literature. RESULTS First, the RAI as a metric depended substantially on the approach that was used to define the network components. Second, regardless of the selection of networks, after controlling for multiple testing there was no difference in RAI scores between SUD and HC. Third, the RAI was not associated with any substance use-related self-report measures. CONCLUSION Taken together, these findings do not provide evidence that RAI can be used as an fMRI-derived biomarker for the severity or diagnosis of individuals with SUD.
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Affiliation(s)
- Mahdi Moradi
- Laureate Institute for Brain Research, 6655 South Yale Ave., Tulsa, OK, 74136, United States; Department of Computer Science, J. Newton Rayzor Hall, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK, 74104, United States.
| | - Hamed Ekhtiari
- Laureate Institute for Brain Research, 6655 South Yale Ave., Tulsa, OK, 74136, United States.
| | - Rayus Kuplicki
- Laureate Institute for Brain Research, 6655 South Yale Ave., Tulsa, OK, 74136, United States.
| | - Brett McKinney
- Department of Computer Science, J. Newton Rayzor Hall, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK, 74104, United States; Department of Mathematics, Keplinger Hall 3085, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK, 74104, United States.
| | - Jennifer L Stewart
- Laureate Institute for Brain Research, 6655 South Yale Ave., Tulsa, OK, 74136, United States; Department of Community Medicine, Oxley Health Sciences, The University of Tulsa, 1215 S. Boulder Ave, Tulsa, OK, 74119, United States.
| | - Teresa A Victor
- Laureate Institute for Brain Research, 6655 South Yale Ave., Tulsa, OK, 74136, United States.
| | - Martin P Paulus
- Laureate Institute for Brain Research, 6655 South Yale Ave., Tulsa, OK, 74136, United States; Department of Community Medicine, Oxley Health Sciences, The University of Tulsa, 1215 S. Boulder Ave, Tulsa, OK, 74119, United States; Department of Psychiatry, University of California, San Diego, United States.
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9
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Howlett JR, Paulus MP. Where perception meets belief updating: Computational evidence for slower updating of visual expectations in anxious individuals. J Affect Disord 2020; 266:633-638. [PMID: 32056939 PMCID: PMC7140731 DOI: 10.1016/j.jad.2020.02.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 01/31/2020] [Accepted: 02/01/2020] [Indexed: 11/23/2022]
Abstract
BACKGROUND Surprising events are important sources of internal model updating which adjusts expectations for both decision-making and perceptual processing circuits. Anxious individuals display relatively intact updating of internal models used to make decisions, however how these individuals update their perceptual expectations remains poorly understood. Based on previous work, we hypothesized that anxious individuals experienced exaggerated surprise to predictable events, which imbues them with undue salience. METHODS To model the rate of updating of internal models for both decision-making and perceptual processing, we applied a hybrid Rescorla Wagner (RW)/Drift Diffusion Model (DDM) to a change point detection task in a transdiagnostic group of individuals with mood and anxiety disorders and examined the relationship between learning rates and anxiety and negative affect. RESULTS Model comparison provided evidence that decision-making and perceptual processing rely on separate internal models with different learning rates. Anxiety and older age were associated with slower updating of models used in perceptual processing, but not those used in decision-making. LIMITATIONS This was a cross-sectional study and lacked neural data to examine the role of specific brain circuits in updating of perceptual predictions. CONCLUSIONS Anxious individuals display slower updating of internal models used in perceptual processing, but not those used in decision-making. This deficit could contribute to exaggerated salience of harmless stimuli in anxiety. The results have implications for the assessment and treatment of basic processing dysfunctions in anxiety.
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Affiliation(s)
- Jonathon R Howlett
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Laureate Institute for Brain Research, Tulsa, OK, USA.
| | - Martin P Paulus
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Laureate Institute for Brain Research, Tulsa, OK, USA
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10
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Williams LM. Transforming Psychiatry Through Novel Neuroscience: Computational and Developmental Frameworks Guided by Research Domain Criteria. Biol Psychiatry 2020; 87:314-315. [PMID: 32040419 DOI: 10.1016/j.biopsych.2019.12.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 12/06/2019] [Indexed: 11/25/2022]
Affiliation(s)
- Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California.
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11
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Langenecker SA, Phillips ML. Innovations in Clinical Neuroscience: Tools, Techniques, and Transformative Frameworks. Biol Psychiatry 2020; 87:308-311. [PMID: 32040417 DOI: 10.1016/j.biopsych.2019.12.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 12/04/2019] [Indexed: 12/26/2022]
Affiliation(s)
- Scott A Langenecker
- Department of Psychiatry, Multifaceted Explorations of the Neurobiology of Depressive Disorders Laboratory, University of Utah, Salt Lake City, Utah.
| | - Mary L Phillips
- Department of Psychiatry, Mood Disorders Research Collaborative, Clinical and Translational Affective Neuroscience Program, University of Pittsburgh, Pittsburgh, Pennsylvania
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Bullmore E. Getting Below the Surface of Behavioral Symptoms in Psychiatry. Biol Psychiatry 2020; 87:316-317. [PMID: 32040420 DOI: 10.1016/j.biopsych.2019.12.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 12/08/2019] [Indexed: 12/15/2022]
Affiliation(s)
- Edward Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
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Keep your interoceptive streams under control: An active inference perspective on anorexia nervosa. COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2020; 20:427-440. [DOI: 10.3758/s13415-020-00777-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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