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Budzinska A, Teysseire F, Flad E, Dupont P, Wölnerhanssen B, Meyer-Gerspach AC, Van Oudenhove L, Weltens N. Neural responses to oral administration of erythritol vs. sucrose and sucralose explain differences in subjective liking ratings. Appetite 2024; 200:107422. [PMID: 38788930 DOI: 10.1016/j.appet.2024.107422] [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: 12/11/2023] [Revised: 05/06/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024]
Abstract
INTRODUCTION High sugar intake is associated with many chronic diseases. However, non-caloric sweeteners (NCSs) might fail to successfully replace sucrose due to the mismatch between their rewarding sweet taste and lack of caloric content. The natural NCS erythritol has been proposed as a sugar substitute due to its satiating properties despite being non-caloric. We aimed to compare brain responses to erythritol vs. sucrose and the artificial NCS sucralose in a priori taste, homeostatic, and reward brain regions of interest (ROIs). METHODS We performed a within-subject, single-blind, counterbalanced fMRI study in 30 healthy men (mean ± SEM age:24.3 ± 0.8 years, BMI:22.3 ± 0.3 kg/m2). Before scanning, we individually matched the concentrations of both NCSs to the perceived sweetness intensity of a 10% sucrose solution. During scanning, participants received 1 mL sips of the individually titrated equisweet solutions of sucrose, erythritol, and sucralose, as well as water. After each sip, they rated subjective sweetness liking. RESULTS Liking ratings were significantly higher for sucrose and sucralose vs. erythritol (both pHolm = 0.0037); water ratings were neutral. General Linear Model (GLM) analyses of brain blood oxygen level-depended (BOLD) responses at qFDR<0.05 showed no differences between any of the sweeteners in a priori ROIs, but distinct differences were found between the individual sweeteners and water. These results were confirmed by Bayesian GLM and machine learning-based models. However, several brain response patterns mediating the differences in liking ratings between the sweeteners were found in whole-brain multivariate mediation analyses. Both subjective and neural responses showed large inter-subject variability. CONCLUSION We found lower liking ratings in response to oral administration of erythritol vs. sucrose and sucralose, but no differences in neural responses between any of the sweeteners in a priori ROIs. However, differences in liking ratings between erythritol vs. sucrose or sucralose are mediated by multiple whole-brain response patterns.
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Affiliation(s)
- Aleksandra Budzinska
- Laboratory for Brain-Gut Axis Studies (LaBGAS), Translational Research in Gastrointestinal Disorders (TARGID), Department of Chronic Diseases and Metabolism (CHROMETA), KU Leuven, Leuven, Belgium; Leuven Brain Institute, KU Leuven, Leuven, Belgium.
| | - Fabienne Teysseire
- St. Clara Research Ltd at St. Claraspital, Basel, Switzerland; University of Basel, Faculty of Medicine, Basel, Switzerland
| | - Emilie Flad
- St. Clara Research Ltd at St. Claraspital, Basel, Switzerland; University of Basel, Faculty of Medicine, Basel, Switzerland
| | - Patrick Dupont
- Leuven Brain Institute, KU Leuven, Leuven, Belgium; Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Bettina Wölnerhanssen
- St. Clara Research Ltd at St. Claraspital, Basel, Switzerland; University of Basel, Faculty of Medicine, Basel, Switzerland
| | - Anne Christin Meyer-Gerspach
- St. Clara Research Ltd at St. Claraspital, Basel, Switzerland; University of Basel, Faculty of Medicine, Basel, Switzerland
| | - Lukas Van Oudenhove
- Laboratory for Brain-Gut Axis Studies (LaBGAS), Translational Research in Gastrointestinal Disorders (TARGID), Department of Chronic Diseases and Metabolism (CHROMETA), KU Leuven, Leuven, Belgium; Leuven Brain Institute, KU Leuven, Leuven, Belgium; Cognitive and Affective Neuroscience Lab (CANlab), Department of Psychological and Brain Sciences, Dartmouth College, USA
| | - Nathalie Weltens
- Laboratory for Brain-Gut Axis Studies (LaBGAS), Translational Research in Gastrointestinal Disorders (TARGID), Department of Chronic Diseases and Metabolism (CHROMETA), KU Leuven, Leuven, Belgium; Leuven Brain Institute, KU Leuven, Leuven, Belgium
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Dietrich A, Schiemer R, Kurmann J, Zhang S, Hubbuch J. Raman-based PAT for VLP precipitation: systematic data diversification and preprocessing pipeline identification. Front Bioeng Biotechnol 2024; 12:1399938. [PMID: 38882637 PMCID: PMC11177211 DOI: 10.3389/fbioe.2024.1399938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/13/2024] [Indexed: 06/18/2024] Open
Abstract
Virus-like particles (VLPs) are a promising class of biopharmaceuticals for vaccines and targeted delivery. Starting from clarified lysate, VLPs are typically captured by selective precipitation. While VLP precipitation is induced by step-wise or continuous precipitant addition, current monitoring approaches do not support the direct product quantification, and analytical methods usually require various, time-consuming processing and sample preparation steps. Here, the application of Raman spectroscopy combined with chemometric methods may allow the simultaneous quantification of the precipitated VLPs and precipitant owing to its demonstrated advantages in analyzing crude, complex mixtures. In this study, we present a Raman spectroscopy-based Process Analytical Technology (PAT) tool developed on batch and fed-batch precipitation experiments of Hepatitis B core Antigen VLPs. We conducted small-scale precipitation experiments providing a diversified data set with varying precipitation dynamics and backgrounds induced by initial dilution or spiking of clarified Escherichia coli-derived lysates. For the Raman spectroscopy data, various preprocessing operations were systematically combined allowing the identification of a preprocessing pipeline, which proved to effectively eliminate initial lysate composition variations as well as most interferences attributed to precipitates and the precipitant present in solution. The calibrated partial least squares models seamlessly predicted the precipitant concentration with R 2 of 0.98 and 0.97 in batch and fed-batch experiments, respectively, and captured the observed precipitation trends with R 2 of 0.74 and 0.64. Although the resolution of fine differences between experiments was limited due to the observed non-linear relationship between spectral data and the VLP concentration, this study provides a foundation for employing Raman spectroscopy as a PAT sensor for monitoring VLP precipitation processes with the potential to extend its applicability to other phase-behavior dependent processes or molecules.
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Affiliation(s)
- Annabelle Dietrich
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Robin Schiemer
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Jasper Kurmann
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Shiqi Zhang
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Jürgen Hubbuch
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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Soutschek A, Burke CJ, Kang P, Wieland N, Netzer N, Tobler PN. Neural Reward Representations Enable Utilitarian Welfare Maximization. J Neurosci 2024; 44:e2376232024. [PMID: 38621996 PMCID: PMC11112638 DOI: 10.1523/jneurosci.2376-23.2024] [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: 12/19/2023] [Revised: 03/04/2024] [Accepted: 03/24/2024] [Indexed: 04/17/2024] Open
Abstract
From deciding which meal to prepare for our guests to trading off the proenvironmental effects of climate protection measures against their economic costs, we often must consider the consequences of our actions for the well-being of others (welfare). Vexingly, the tastes and views of others can vary widely. To maximize welfare according to the utilitarian philosophical tradition, decision-makers facing conflicting preferences of others should choose the option that maximizes the sum of the subjective value (utility) of the entire group. This notion requires comparing the intensities of preferences across individuals. However, it remains unclear whether such comparisons are possible at all and (if they are possible) how they might be implemented in the brain. Here, we show that female and male participants can both learn the preferences of others by observing their choices and represent these preferences on a common scale to make utilitarian welfare decisions. On the neural level, multivariate support vector regressions revealed that a distributed activity pattern in the ventromedial prefrontal cortex (VMPFC), a brain region previously associated with reward processing, represented the preference strength of others. Strikingly, also the utilitarian welfare of others was represented in the VMPFC and relied on the same neural code as the estimated preferences of others. Together, our findings reveal that humans can behave as if they maximized utilitarian welfare using a specific utility representation and that the brain enables such choices by repurposing neural machinery processing the reward others receive.
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Affiliation(s)
- Alexander Soutschek
- Department of Psychology, Ludwig Maximilian University Munich, Munich 80802, Germany
| | | | - Pyungwon Kang
- Department of Economics, University of Zurich, Zurich 8006, Switzerland
| | - Nuri Wieland
- Catholic University of Applied Sciences North Rhine-Westphalia, Cologne 50668, Germany
| | - Nick Netzer
- Department of Economics, University of Zurich, Zurich 8006, Switzerland
| | - Philippe N Tobler
- Department of Economics, University of Zurich, Zurich 8006, Switzerland
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Kopytin G, Ivanova M, Herrojo Ruiz M, Shestakova A. Evaluating the Influence of Musical and Monetary Rewards on Decision Making through Computational Modelling. Behav Sci (Basel) 2024; 14:124. [PMID: 38392477 PMCID: PMC10886002 DOI: 10.3390/bs14020124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/25/2024] [Accepted: 02/02/2024] [Indexed: 02/24/2024] Open
Abstract
A central question in behavioural neuroscience is how different rewards modulate learning. While the role of monetary rewards is well-studied in decision-making research, the influence of abstract rewards like music remains poorly understood. This study investigated the dissociable effects of these two reward types on decision making. Forty participants completed two decision-making tasks, each characterised by probabilistic associations between stimuli and rewards, with probabilities changing over time to reflect environmental volatility. In each task, choices were reinforced either by monetary outcomes (win/lose) or by the endings of musical melodies (consonant/dissonant). We applied the Hierarchical Gaussian Filter, a validated hierarchical Bayesian framework, to model learning under these two conditions. Bayesian statistics provided evidence for similar learning patterns across both reward types, suggesting individuals' similar adaptability. However, within the musical task, individual preferences for consonance over dissonance explained some aspects of learning. Specifically, correlation analyses indicated that participants more tolerant of dissonance behaved more stochastically in their belief-to-response mappings and were less likely to choose the response associated with the current prediction for a consonant ending, driven by higher volatility estimates. By contrast, participants averse to dissonance showed increased tonic volatility, leading to larger updates in reward tendency beliefs.
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Affiliation(s)
- Grigory Kopytin
- Institute for Cognitive Neuroscience, HSE University, 101000 Moscow, Russia
| | - Marina Ivanova
- Institute for Cognitive Neuroscience, HSE University, 101000 Moscow, Russia
| | - Maria Herrojo Ruiz
- Department of Psychology, Goldsmiths University of London, London SE14 6NW, UK
| | - Anna Shestakova
- Institute for Cognitive Neuroscience, HSE University, 101000 Moscow, Russia
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Konova AB, Ceceli AO, Horga G, Moeller SJ, Alia-Klein N, Goldstein RZ. Reduced neural encoding of utility prediction errors in cocaine addiction. Neuron 2023; 111:4058-4070.e6. [PMID: 37883973 PMCID: PMC10880133 DOI: 10.1016/j.neuron.2023.09.015] [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: 10/14/2022] [Revised: 07/18/2023] [Accepted: 09/13/2023] [Indexed: 10/28/2023]
Abstract
Influential accounts of addiction posit alterations in adaptive behavior driven by deficient dopaminergic prediction errors (PEs), signaling the discrepancy between actual and expected reward. Dopamine neurons encode these error signals in subjective terms, calibrated by individual risk preferences, as "utility" PEs. It remains unclear, however, whether people with drug addiction have PE deficits or their computational source. Here, using an analogous task to prior single-unit studies with known expectancies, we show that fMRI-measured PEs similarly reflect utility PEs. Relative to control participants, people with chronic cocaine addiction demonstrate reduced utility PEs in the dopaminoceptive ventral striatum, with similar trends in orbitofrontal cortex. Dissecting this PE signal into its subcomponent terms attributed these reductions to weaker striatal responses to received reward/utility, whereas suppression of activity with reward expectation was unchanged. These findings support that addiction may fundamentally disrupt PE signaling and reveal an underappreciated role for perceived reward value in this mechanism.
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Affiliation(s)
- Anna B Konova
- Department of Psychiatry, University Behavioral Health Care & the Brain Health Institute, Rutgers University-New Brunswick, Piscataway, NJ 08855, USA.
| | - Ahmet O Ceceli
- Departments of Psychiatry & Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, New York, NY 10024, USA
| | - Scott J Moeller
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY 11794, USA
| | - Nelly Alia-Klein
- Departments of Psychiatry & Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Rita Z Goldstein
- Departments of Psychiatry & Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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Li S, Seger CA, Zhang J, Liu M, Dong W, Liu W, Chen Q. Alpha oscillations encode Bayesian belief updating underlying attentional allocation in dynamic environments. Neuroimage 2023; 284:120464. [PMID: 37984781 DOI: 10.1016/j.neuroimage.2023.120464] [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: 08/14/2023] [Revised: 11/13/2023] [Accepted: 11/17/2023] [Indexed: 11/22/2023] Open
Abstract
In a dynamic environment, expectations of the future constantly change based on updated evidence and affect the dynamic allocation of attention. To further investigate the neural mechanisms underlying attentional expectancies, we employed a modified Central Cue Posner Paradigm in which the probability of cues being valid (that is, accurately indicated the upcoming target location) was manipulated. Attentional deployment to the cued location (α), which was governed by precision of predictions on previous trials, was estimated using a hierarchical Bayesian model and was included as a regressor in the analyses of electrophysiological (EEG) data. Our results revealed that before the target appeared, alpha oscillations (8∼13 Hz) for high-predictability cues (88 % valid) were significantly predicted by precision-dependent attention (α). This relationship was not observed under low-predictability conditions (69 % and 50 % valid cues). After the target appeared, precision-dependent attention (α) correlated with alpha band oscillations only in the valid cue condition and not in the invalid condition. Further analysis under conditions of significant attentional modulation by precision suggested a separate effect of cue orientation. These results provide new insights on how trial-by-trial Bayesian belief updating relates to alpha band encoding of environmentally-sensitive allocation of visual spatial attention.
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Affiliation(s)
- Siying Li
- School of Psychology, Shenzhen University, No. 3688, Nanhai Avenue, Shenzhen 518060, China
| | - Carol A Seger
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China; Department of Psychology, Colorado State University, Fort Collins, United States
| | - Jianfeng Zhang
- School of Psychology, Shenzhen University, No. 3688, Nanhai Avenue, Shenzhen 518060, China
| | - Meng Liu
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Wenshan Dong
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Wanting Liu
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Qi Chen
- School of Psychology, Shenzhen University, No. 3688, Nanhai Avenue, Shenzhen 518060, China.
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Ting CC, Salem-Garcia N, Palminteri S, Engelmann JB, Lebreton M. Neural and computational underpinnings of biased confidence in human reinforcement learning. Nat Commun 2023; 14:6896. [PMID: 37898640 PMCID: PMC10613217 DOI: 10.1038/s41467-023-42589-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 10/16/2023] [Indexed: 10/30/2023] Open
Abstract
While navigating a fundamentally uncertain world, humans and animals constantly evaluate the probability of their decisions, actions or statements being correct. When explicitly elicited, these confidence estimates typically correlates positively with neural activity in a ventromedial-prefrontal (VMPFC) network and negatively in a dorsolateral and dorsomedial prefrontal network. Here, combining fMRI with a reinforcement-learning paradigm, we leverage the fact that humans are more confident in their choices when seeking gains than avoiding losses to reveal a functional dissociation: whereas the dorsal prefrontal network correlates negatively with a condition-specific confidence signal, the VMPFC network positively encodes task-wide confidence signal incorporating the valence-induced bias. Challenging dominant neuro-computational models, we found that decision-related VMPFC activity better correlates with confidence than with option-values inferred from reinforcement-learning models. Altogether, these results identify the VMPFC as a key node in the neuro-computational architecture that builds global feeling-of-confidence signals from latent decision variables and contextual biases during reinforcement-learning.
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Affiliation(s)
- Chih-Chung Ting
- General Psychology, Universität Hamburg, Von-Melle-Park 11, 20146, Hamburg, Germany.
- CREED, Amsterdam School of Economics (ASE), Universiteit van Amsterdam, Roetersstraat 11, 1018 WB, Amsterdam, the Netherlands.
| | - Nahuel Salem-Garcia
- Swiss Center for Affective Science, Faculty of Psychology and Educational Sciences, University of Geneva, Chem. des Mines 9, 1202, Genève, Switzerland
| | - Stefano Palminteri
- Département d'Études Cognitives, École Normale Supérieure, PSL Research University, 29 rue d'Ulm, 75230, Paris cedex 05, France
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale, 29 rue d'Ulm 75230, Paris cedex 05, France
| | - Jan B Engelmann
- CREED, Amsterdam School of Economics (ASE), Universiteit van Amsterdam, Roetersstraat 11, 1018 WB, Amsterdam, the Netherlands.
- The Tinbergen Institute, Gustav Mahlerplein 117, 1082 MS, Amsterdam, the Netherlands.
| | - Maël Lebreton
- Swiss Center for Affective Science, Faculty of Psychology and Educational Sciences, University of Geneva, Chem. des Mines 9, 1202, Genève, Switzerland.
- Economics of Human Behavior group, Paris-Jourdan Sciences Économiques UMR8545, Paris School of Economics, 48 Boulevard Jourdan, 75014, Paris, France.
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Liu H, Ma T, Liu C, Liu S. Causal Responsibility Division of Chronological Continuous Treatment Based on Change-Point Detection. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1164. [PMID: 37628194 PMCID: PMC10453889 DOI: 10.3390/e25081164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 07/18/2023] [Accepted: 07/31/2023] [Indexed: 08/27/2023]
Abstract
This paper introduces a novel approach, called causal relation quantification, based on change-point detection to address the issue of harmonic responsibility division in power systems. The proposed method focuses on determining the causal effect of chronological continuous treatment, enabling the identification of crucial treatment intervals. Within each interval, three propensity-score-based algorithms are executed to assess their respective causal effects. By integrating the results from each interval, the overall causal effect of a chronological continuous treatment variable can be calculated. This calculated overall causal effect represents the causal responsibility of each harmonic customer. The effectiveness of the proposed method is evaluated through a simulation study and demonstrated in an empirical harmonic application. The results of the simulation study indicate that our method provides accurate and robust estimates, while the calculated results in the harmonic application align closely with the real-world scenario as verified by on-site investigations.
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Affiliation(s)
- Hang Liu
- School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China; (H.L.); (T.M.)
| | - Tiefeng Ma
- School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China; (H.L.); (T.M.)
| | - Conan Liu
- Business School, University of New South Wales, Sydney, NSW 2052, Australia;
| | - Shuangzhe Liu
- Faculty of Science and Technology, University of Canberra, Bruce, ACT 2617, Australia
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Yusif Rodriguez N, McKim TH, Basu D, Ahuja A, Desrochers TM. Monkey Dorsolateral Prefrontal Cortex Represents Abstract Visual Sequences during a No-Report Task. J Neurosci 2023; 43:2741-2755. [PMID: 36868856 PMCID: PMC10089245 DOI: 10.1523/jneurosci.2058-22.2023] [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: 11/01/2022] [Revised: 02/22/2023] [Accepted: 02/24/2023] [Indexed: 03/05/2023] Open
Abstract
Monitoring sequential information is an essential component of our daily lives. Many of these sequences are abstract, in that they do not depend on the individual stimuli, but do depend on an ordered set of rules (e.g., chop then stir when cooking). Despite the ubiquity and utility of abstract sequential monitoring, little is known about its neural mechanisms. Human rostrolateral prefrontal cortex (RLPFC) exhibits specific increases in neural activity (i.e., "ramping") during abstract sequences. Monkey dorsolateral prefrontal cortex (DLPFC) has been shown to represent sequential information in motor (not abstract) sequence tasks, and contains a subregion, area 46, with homologous functional connectivity to human RLPFC. To test the prediction that area 46 may represent abstract sequence information, and do so with parallel dynamics to those found in humans, we conducted functional magnetic resonance imaging (fMRI) in three male monkeys. When monkeys performed no-report abstract sequence viewing, we found that left and right area 46 responded to abstract sequential changes. Interestingly, responses to rule and number changes overlapped in right area 46 and left area 46 exhibited responses to abstract sequence rules with changes in ramping activation, similar to that observed in humans. Together, these results indicate that monkey DLPFC monitors abstract visual sequential information, potentially with a preference for different dynamics in the two hemispheres. More generally, these results show that abstract sequences are represented in functionally homologous regions across monkeys and humans.SIGNIFICANCE STATEMENT Daily, we complete sequences that are "abstract" because they depend on an ordered set of rules (e.g., chop then stir when cooking) rather than the identity of individual items. Little is known about how the brain tracks, or monitors, this abstract sequential information. Based on previous human work showing abstract sequence related dynamics in an analogous area, we tested whether monkey dorsolateral prefrontal cortex (DLPFC), specifically area 46, represents abstract sequential information using awake monkey functional magnetic resonance imaging (fMRI). We found that area 46 responded to abstract sequence changes, with a preference for more general responses on the right and dynamics similar to humans on the left. These results suggest that abstract sequences are represented in functionally homologous regions across monkeys and humans.
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Affiliation(s)
- Nadira Yusif Rodriguez
- Department of Neuroscience, Brown University, Providence, RI 02912
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI 02912
| | - Theresa H McKim
- Department of Neuroscience, Brown University, Providence, RI 02912
| | - Debaleena Basu
- Department of Neuroscience, Brown University, Providence, RI 02912
| | - Aarit Ahuja
- Department of Neuroscience, Brown University, Providence, RI 02912
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI 02912
| | - Theresa M Desrochers
- Department of Neuroscience, Brown University, Providence, RI 02912
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI 02912
- Department of Psychiatry and Human Behavior, Brown University, Providence, RI 02912
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Hein TP, Gong Z, Ivanova M, Fedele T, Nikulin V, Herrojo Ruiz M. Anterior cingulate and medial prefrontal cortex oscillations underlie learning alterations in trait anxiety in humans. Commun Biol 2023; 6:271. [PMID: 36922553 PMCID: PMC10017780 DOI: 10.1038/s42003-023-04628-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 02/27/2023] [Indexed: 03/18/2023] Open
Abstract
Anxiety has been linked to altered belief formation and uncertainty estimation, impacting learning. Identifying the neural processes underlying these changes is important for understanding brain pathology. Here, we show that oscillatory activity in the medial prefrontal, anterior cingulate and orbitofrontal cortex (mPFC, ACC, OFC) explains anxiety-related learning alterations. In a magnetoencephalography experiment, two groups of human participants pre-screened with high and low trait anxiety (HTA, LTA: 39) performed a probabilistic reward-based learning task. HTA undermined learning through an overestimation of volatility, leading to faster belief updating, more stochastic decisions and pronounced lose-shift tendencies. On a neural level, we observed increased gamma activity in the ACC, dmPFC, and OFC during encoding of precision-weighted prediction errors in HTA, accompanied by suppressed ACC alpha/beta activity. Our findings support the association between altered learning and belief updating in anxiety and changes in gamma and alpha/beta activity in the ACC, dmPFC, and OFC.
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Affiliation(s)
- Thomas P Hein
- Goldsmiths, University of London, Psychology Department, Whitehead Building New Cross, London, SE14 6NW, UK
| | - Zheng Gong
- Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Marina Ivanova
- Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Tommaso Fedele
- Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Vadim Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Maria Herrojo Ruiz
- Goldsmiths, University of London, Psychology Department, Whitehead Building New Cross, London, SE14 6NW, UK.
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Tecilla M, Großbach M, Gentile G, Holland P, Sporn S, Antonini A, Herrojo Ruiz M. Modulation of Motor Vigor by Expectation of Reward Probability Trial-by-Trial Is Preserved in Healthy Ageing and Parkinson's Disease Patients. J Neurosci 2023; 43:1757-1777. [PMID: 36732072 PMCID: PMC10010462 DOI: 10.1523/jneurosci.1583-22.2022] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 12/13/2022] [Accepted: 12/31/2022] [Indexed: 02/04/2023] Open
Abstract
Motor improvements, such as faster movement times or increased velocity, have been associated with reward magnitude in deterministic contexts. Yet whether individual inferences on reward probability influence motor vigor dynamically remains undetermined. We investigated how dynamically inferring volatile action-reward contingencies modulated motor performance trial-by-trial. We conducted three studies that coupled a reversal learning paradigm with a motor sequence task and used a validated hierarchical Bayesian model to fit trial-by-trial data. In Study 1, we tested healthy younger [HYA; 37 (24 females)] and older adults [HOA; 37 (17 females)], and medicated Parkinson's disease (PD) patients [20 (7 females)]. We showed that stronger predictions about the tendency of the action-reward contingency led to faster performance tempo, commensurate with movement time, on a trial-by-trial basis without robustly modulating reaction time (RT). Using Bayesian linear mixed models, we demonstrated a similar invigoration effect on performance tempo in HYA, HOA, and PD, despite HOA and PD being slower than HYA. In Study 2 [HYA, 39 (29 females)], we additionally showed that retrospective subjective inference about credit assignment did not contribute to differences in motor vigor effects. Last, Study 3 [HYA, 33 (27 females)] revealed that explicit beliefs about the reward tendency (confidence ratings) modulated performance tempo trial-by-trial. Our study is the first to reveal that the dynamic updating of beliefs about volatile action-reward contingencies positively biases motor performance through faster tempo. We also provide robust evidence for a preserved sensitivity of motor vigor to inferences about the action-reward mapping in aging and medicated PD.SIGNIFICANCE STATEMENT Navigating a world rich in uncertainty relies on updating beliefs about the probability that our actions lead to reward. Here, we investigated how inferring the action-reward contingencies in a volatile environment modulated motor vigor trial-by-trial in healthy younger and older adults, and in Parkinson's disease (PD) patients on medication. We found an association between trial-by-trial predictions about the tendency of the action-reward contingency and performance tempo, with stronger expectations speeding the movement. We additionally provided evidence for a similar sensitivity of performance tempo to the strength of these predictions in all groups. Thus, dynamic beliefs about the changing relationship between actions and their outcome enhanced motor vigor. This positive bias was not compromised by age or Parkinson's disease.
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Affiliation(s)
- Margherita Tecilla
- Department of Psychology, Goldsmiths, University of London, London SE146NW, United Kingdom
| | - Michael Großbach
- Institute of Music Physiology and Musicians' Medicine, Hannover University of Music Drama and Media, Hannover 30175, Germany
| | - Giovanni Gentile
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua 35131, Italy
| | - Peter Holland
- Department of Psychology, Goldsmiths, University of London, London SE146NW, United Kingdom
| | - Sebastian Sporn
- Department of Clinical and Movement Neuroscience, Queen Square Institute of Neurology, University College London, London WC1N3BG, United Kingdom
| | - Angelo Antonini
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua 35131, Italy
| | - Maria Herrojo Ruiz
- Department of Psychology, Goldsmiths, University of London, London SE146NW, United Kingdom
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12
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Marxen M, Graff JE, Riedel P, Smolka MN. Observing cognitive processes in time through functional MRI model comparison. Hum Brain Mapp 2023; 44:1359-1370. [PMID: 36288248 PMCID: PMC9921218 DOI: 10.1002/hbm.26114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 08/25/2022] [Accepted: 09/12/2022] [Indexed: 11/06/2022] Open
Abstract
The temporal specificity of functional magnetic resonance imaging (fMRI) is limited by a sluggish and locally variable hemodynamic response trailing the neural activity by seconds. Here, we demonstrate for an attention capture paradigm that it is, never the less, possible to extract information about the relative timing of regional brain activity during cognitive processes on the scale of 100 ms by comparing alternative signal models representing early versus late activation. We demonstrate that model selection is not driven by confounding regional differences in hemodynamic delay. We show, including replication, that the activity in the dorsal anterior insula is an early signal predictive of behavioral performance, while amygdala and ventral anterior insula signals are not. This specific finding provides new insights into how the brain assigns salience to stimuli and emphasizes the role of the dorsal anterior insula in this context. The general analytic approach, named "Cognitive Timing through Model Comparison" (CTMC), offers an exciting and novel method to identify functional brain subunits and their causal interactions.
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Affiliation(s)
- Michael Marxen
- Department of Psychiatry, Technische Universität Dresden, Dresden, Germany
| | - Johanna E Graff
- Department of Psychiatry, Technische Universität Dresden, Dresden, Germany
| | - Philipp Riedel
- Department of Psychiatry, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry, Technische Universität Dresden, Dresden, Germany
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13
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Kepinska O, Caballero J, Oliver M, Marks RA, Haft SL, Zekelman L, Kovelman I, Uchikoshi Y, Hoeft F. Language combinations of multilinguals are reflected in their first-language knowledge and processing. Sci Rep 2023; 13:1947. [PMID: 36732569 PMCID: PMC9895446 DOI: 10.1038/s41598-023-27952-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 01/10/2023] [Indexed: 02/04/2023] Open
Abstract
Consequences of multilingualism vary from offering cognitive benefits to poor educational and cognitive outcomes. One aspect of multilingualism that has not been systematically examined is the typology of multilinguals' languages: Do differences and similarities between languages multilinguals are exposed to contribute to the development of their cognition and brain? We investigated n = 162 5-6-year-olds with various language backgrounds on a monolingual-to-quintilingual continuum. Our results show that typological linguistic diversity can be related to expressive vocabulary knowledge in the dominant language. On neural level, it relates to brain activation patterns in (among others) the PGa area in the bilateral IPL, a brain region previously associated with multilingual experience, but never with language typology. We propose an ecologically valid way of describing the continuum of multilingual language experience and provide evidence for both the cognition and the brain of multilingual kindergartners to be related to the typological linguistic diversity of their environment.
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Affiliation(s)
- Olga Kepinska
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94143, USA.
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, 06269, USA.
- Brain and Language Lab, Cognitive Science Hub, University of Vienna, 1090, Vienna, Austria.
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, 1010, Vienna, Austria.
| | - Jocelyn Caballero
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Myriam Oliver
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94143, USA
- Faculdad de Ciencias de la Salud, Universidad Europea de Valencia, 46010, Valencia, Spain
| | - Rebecca A Marks
- Department of Psychology, University of Michigan, Ann Arbor, MI, 48109, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Stephanie L Haft
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94143, USA
- Department of Psychology, University of California Berkeley, Berkeley, CA, 94704, USA
| | - Leo Zekelman
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94143, USA
- Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA, USA
| | - Ioulia Kovelman
- Department of Psychology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Yuuko Uchikoshi
- School of Education, University of California, Davis, Davis, CA, 95616, USA
| | - Fumiko Hoeft
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94143, USA
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, 06269, USA
- Brain Imaging Research Center, University of Connecticut, Storrs, CT, 06269, USA
- Departments of Mathematics, Neuroscience, Psychiatry, Educational Psychology, Pediatrics, Computer Science and Engineering, University of Connecticut, Storrs, CT, 06269, USA
- Haskins Laboratories, New Haven, CT, 06511, USA
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14
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Liu M, Dong W, Wu Y, Verbeke P, Verguts T, Chen Q. Modulating hierarchical learning by high-definition transcranial alternating current stimulation at theta frequency. Cereb Cortex 2022; 33:4421-4431. [PMID: 36089836 DOI: 10.1093/cercor/bhac352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/09/2022] [Accepted: 08/11/2022] [Indexed: 11/12/2022] Open
Abstract
Considerable evidence highlights the dorsolateral prefrontal cortex (DLPFC) as a key region for hierarchical (i.e. multilevel) learning. In a previous electroencephalography (EEG) study, we found that the low-level prediction errors were encoded by frontal theta oscillations (4-7 Hz), centered on right DLPFC (rDLPFC). However, the causal relationship between frontal theta oscillations and hierarchical learning remains poorly understood. To investigate this question, in the current study, participants received theta (6 Hz) and sham high-definition transcranial alternating current stimulation (HD-tACS) over the rDLPFC while performing the probabilistic reversal learning task. Behaviorally, theta tACS induced a significant reduction in accuracy for the stable environment, but not for the volatile environment, relative to the sham condition. Computationally, we implemented a combination of a hierarchical Bayesian learning and a decision model. Theta tACS induced a significant increase in low-level (i.e. probability-level) learning rate and uncertainty of low-level estimation relative to sham condition. Instead, the temperature parameter of the decision model, which represents (inverse) decision noise, was not significantly altered due to theta stimulation. These results indicate that theta frequency may modulate the (low-level) learning rate. Furthermore, environmental features (e.g. its stability) may determine whether learning is optimized as a result.
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Affiliation(s)
- Meng Liu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Wenshan Dong
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Yiling Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Pieter Verbeke
- Department of Experimental Psychology, Ghent University, B-9000 Ghent, Belgium
| | - Tom Verguts
- Department of Experimental Psychology, Ghent University, B-9000 Ghent, Belgium
| | - Qi Chen
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
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15
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Liakoni V, Lehmann MP, Modirshanechi A, Brea J, Lutti A, Gerstner W, Preuschoff K. Brain signals of a Surprise-Actor-Critic model: Evidence for multiple learning modules in human decision making. Neuroimage 2021; 246:118780. [PMID: 34875383 DOI: 10.1016/j.neuroimage.2021.118780] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 08/03/2021] [Accepted: 12/04/2021] [Indexed: 11/25/2022] Open
Abstract
Learning how to reach a reward over long series of actions is a remarkable capability of humans, and potentially guided by multiple parallel learning modules. Current brain imaging of learning modules is limited by (i) simple experimental paradigms, (ii) entanglement of brain signals of different learning modules, and (iii) a limited number of computational models considered as candidates for explaining behavior. Here, we address these three limitations and (i) introduce a complex sequential decision making task with surprising events that allows us to (ii) dissociate correlates of reward prediction errors from those of surprise in functional magnetic resonance imaging (fMRI); and (iii) we test behavior against a large repertoire of model-free, model-based, and hybrid reinforcement learning algorithms, including a novel surprise-modulated actor-critic algorithm. Surprise, derived from an approximate Bayesian approach for learning the world-model, is extracted in our algorithm from a state prediction error. Surprise is then used to modulate the learning rate of a model-free actor, which itself learns via the reward prediction error from model-free value estimation by the critic. We find that action choices are well explained by pure model-free policy gradient, but reaction times and neural data are not. We identify signatures of both model-free and surprise-based learning signals in blood oxygen level dependent (BOLD) responses, supporting the existence of multiple parallel learning modules in the brain. Our results extend previous fMRI findings to a multi-step setting and emphasize the role of policy gradient and surprise signalling in human learning.
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Affiliation(s)
- Vasiliki Liakoni
- École Polytechnique Fédérale de Lausanne (EPFL), School of Computer and Communication Sciences and School of Life Sciences, Lausanne, Switzerland.
| | - Marco P Lehmann
- École Polytechnique Fédérale de Lausanne (EPFL), School of Computer and Communication Sciences and School of Life Sciences, Lausanne, Switzerland
| | - Alireza Modirshanechi
- École Polytechnique Fédérale de Lausanne (EPFL), School of Computer and Communication Sciences and School of Life Sciences, Lausanne, Switzerland
| | - Johanni Brea
- École Polytechnique Fédérale de Lausanne (EPFL), School of Computer and Communication Sciences and School of Life Sciences, Lausanne, Switzerland
| | - Antoine Lutti
- Laboratoire de recherche en neuroimagerie (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Wulfram Gerstner
- École Polytechnique Fédérale de Lausanne (EPFL), School of Computer and Communication Sciences and School of Life Sciences, Lausanne, Switzerland
| | - Kerstin Preuschoff
- Geneva Finance Research Institute & Interfaculty Center for Affective Sciences, University of Geneva, Geneva, Switzerland
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16
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Ritchie JB, Lee Masson H, Bracci S, Op de Beeck HP. The unreliable influence of multivariate noise normalization on the reliability of neural dissimilarity. Neuroimage 2021; 245:118686. [PMID: 34728244 DOI: 10.1016/j.neuroimage.2021.118686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 10/21/2021] [Accepted: 10/26/2021] [Indexed: 10/19/2022] Open
Abstract
Representational similarity analysis (RSA) is a key element in the multivariate pattern analysis toolkit. The central construct of the method is the representational dissimilarity matrix (RDM), which can be generated for datasets from different modalities (neuroimaging, behavior, and computational models) and directly correlated in order to evaluate their second-order similarity. Given the inherent noisiness of neuroimaging signals it is important to evaluate the reliability of neuroimaging RDMs in order to determine whether these comparisons are meaningful. Recently, multivariate noise normalization (NNM) has been proposed as a widely applicable method for boosting signal estimates for RSA, regardless of choice of dissimilarity metrics, based on evidence that the analysis improves the within-subject reliability of RDMs (Guggenmos et al. 2018; Walther et al. 2016). We revisited this issue with three fMRI datasets and evaluated the impact of NNM on within- and between-subject reliability and RSA effect sizes using multiple dissimilarity metrics. We also assessed its impact across regions of interest from the same dataset, its interaction with spatial smoothing, and compared it to GLMdenoise, which has also been proposed as a method that improves signal estimates for RSA (Charest et al. 2018). We found that across these tests the impact of NNM was highly variable, as also seems to be the case for other analysis choices. Overall, we suggest being conservative before adding steps and complexities to the (pre)processing pipeline for RSA.
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Affiliation(s)
- J Brendan Ritchie
- Department of Brain and Cognition, Leuven Brain Institute, KU Leuven, 3000 Leuven, Flemish Brabant, Belgium.
| | - Haemy Lee Masson
- Department of Cognitive Science, Johns Hopkins University, Baltimore, USA
| | - Stefania Bracci
- Centre for Mind/Brain Sciences, University of Trento, Rovereto, Italy
| | - Hans P Op de Beeck
- Department of Brain and Cognition, Leuven Brain Institute, KU Leuven, 3000 Leuven, Flemish Brabant, Belgium
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17
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Na S, Chung D, Hula A, Perl O, Jung J, Heflin M, Blackmore S, Fiore VG, Dayan P, Gu X. Humans use forward thinking to exploit social controllability. eLife 2021; 10:64983. [PMID: 34711304 PMCID: PMC8555988 DOI: 10.7554/elife.64983] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 09/30/2021] [Indexed: 12/27/2022] Open
Abstract
The controllability of our social environment has a profound impact on our behavior and mental health. Nevertheless, neurocomputational mechanisms underlying social controllability remain elusive. Here, 48 participants performed a task where their current choices either did (Controllable), or did not (Uncontrollable), influence partners’ future proposals. Computational modeling revealed that people engaged a mental model of forward thinking (FT; i.e., calculating the downstream effects of current actions) to estimate social controllability in both Controllable and Uncontrollable conditions. A large-scale online replication study (n=1342) supported this finding. Using functional magnetic resonance imaging (n=48), we further demonstrated that the ventromedial prefrontal cortex (vmPFC) computed the projected total values of current actions during forward planning, supporting the neural realization of the forward-thinking model. These findings demonstrate that humans use vmPFC-dependent FT to estimate and exploit social controllability, expanding the role of this neurocomputational mechanism beyond spatial and cognitive contexts.
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Affiliation(s)
- Soojung Na
- The Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.,Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Dongil Chung
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Andreas Hula
- Austrian Institute of Technology, Seibersdorf, Austria
| | - Ofer Perl
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Jennifer Jung
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, United States
| | - Matthew Heflin
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Sylvia Blackmore
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, United States.,Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Vincenzo G Fiore
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Peter Dayan
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,University of Tübingen, Tübingen, Germany
| | - Xiaosi Gu
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, United States
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18
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Herrojo Ruiz M, Maudrich T, Kalloch B, Sammler D, Kenville R, Villringer A, Sehm B, Nikulin VV. Modulation of neural activity in frontopolar cortex drives reward-based motor learning. Sci Rep 2021; 11:20303. [PMID: 34645848 PMCID: PMC8514446 DOI: 10.1038/s41598-021-98571-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 08/26/2021] [Indexed: 12/03/2022] Open
Abstract
The frontopolar cortex (FPC) contributes to tracking the reward of alternative choices during decision making, as well as their reliability. Whether this FPC function extends to reward gradients associated with continuous movements during motor learning remains unknown. We used anodal transcranial direct current stimulation (tDCS) over the right FPC to investigate its role in reward-based motor learning. Nineteen healthy human participants practiced novel sequences of finger movements on a digital piano with corresponding auditory feedback. Their aim was to use trialwise reward feedback to discover a hidden performance goal along a continuous dimension: timing. We additionally modulated the contralateral motor cortex (left M1) activity, and included a control sham stimulation. Right FPC-tDCS led to faster learning compared to lM1-tDCS and sham through regulation of motor variability. Bayesian computational modelling revealed that in all stimulation protocols, an increase in the trialwise expectation of reward was followed by greater exploitation, as shown previously. Yet, this association was weaker in lM1-tDCS suggesting a less efficient learning strategy. The effects of frontopolar stimulation were dissociated from those induced by lM1-tDCS and sham, as motor exploration was more sensitive to inferred changes in the reward tendency (volatility). The findings suggest that rFPC-tDCS increases the sensitivity of motor exploration to updates in reward volatility, accelerating reward-based motor learning.
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Affiliation(s)
- M Herrojo Ruiz
- Psychology Department, Goldsmiths University of London, London, UK. .,Center for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russian Federation. .,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - T Maudrich
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - B Kalloch
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - D Sammler
- Research Group Neurocognition of Music and Language, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - R Kenville
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - A Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - B Sehm
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Department of Neurology, University Hospital Halle (Saale), Halle, Germany
| | - V V Nikulin
- Center for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russian Federation. .,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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19
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Liu M, Dong W, Qin S, Verguts T, Chen Q. Electrophysiological Signatures of Hierarchical Learning. Cereb Cortex 2021; 32:626-639. [PMID: 34339505 DOI: 10.1093/cercor/bhab245] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 06/26/2021] [Accepted: 06/27/2021] [Indexed: 11/13/2022] Open
Abstract
Human perception and learning is thought to rely on a hierarchical generative model that is continuously updated via precision-weighted prediction errors (pwPEs). However, the neural basis of such cognitive process and how it unfolds during decision-making remain poorly understood. To investigate this question, we combined a hierarchical Bayesian model (i.e., Hierarchical Gaussian Filter [HGF]) with electroencephalography (EEG), while participants performed a probabilistic reversal learning task in alternatingly stable and volatile environments. Behaviorally, the HGF fitted significantly better than two control, nonhierarchical, models. Neurally, low-level and high-level pwPEs were independently encoded by the P300 component. Low-level pwPEs were reflected in the theta (4-8 Hz) frequency band, but high-level pwPEs were not. Furthermore, the expressions of high-level pwPEs were stronger for participants with better HGF fit. These results indicate that the brain employs hierarchical learning and encodes both low- and high-level learning signals separately and adaptively.
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Affiliation(s)
- Meng Liu
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, 510631 Guangzhou, China.,School of Psychology, South China Normal University, 510631 Guangzhou, China.,Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China.,Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Wenshan Dong
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, 510631 Guangzhou, China.,School of Psychology, South China Normal University, 510631 Guangzhou, China.,Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China.,Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, 100875 Beijing, China
| | - Tom Verguts
- Department of Experimental Psychology, Ghent University, B-9000 Ghent, Belgium
| | - Qi Chen
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, 510631 Guangzhou, China.,School of Psychology, South China Normal University, 510631 Guangzhou, China.,Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China.,Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
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20
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Darányi V, Hermann P, Homolya I, Vidnyánszky Z, Nagy Z. An empirical investigation of the benefit of increasing the temporal resolution of task-evoked fMRI data with multi-band imaging. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2021; 34:667-676. [PMID: 33763764 PMCID: PMC8421273 DOI: 10.1007/s10334-021-00918-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 02/26/2021] [Accepted: 03/03/2021] [Indexed: 11/24/2022]
Abstract
Objective There is a tendency for reducing TR in MRI experiments with multi-band imaging. We empirically investigate its benefit for the group-level statistical outcome in task-evoked fMRI. Methods Three visual fMRI data sets were collected from 17 healthy adult participants. Multi-band acquisition helped vary the TR (2000/1000/410 ms, respectively). Because these data sets capture different temporal aspects of the haemodynamic response (HRF), we tested several HRF models. We computed a composite descriptive statistic, H, from β’s of each first-level model fit and carried it to the group-level analysis. The number of activated voxels and the t value of the group-level analysis as well as a goodness-of-fit measure were used as surrogate markers of data quality for comparison. Results Increasing the temporal sampling rate did not provide a universal improvement in the group-level statistical outcome. Rather, both the voxel-wise and ROI-averaged group-level results varied widely with anatomical location, choice of HRF and the setting of the TR. Correspondingly, the goodness-of-fit of HRFs became worse with increasing the sampling frequency. Conclusion Rather than universally increasing the temporal sampling rate in cognitive fMRI experiments, these results advocate the performance of a pilot study for the specific ROIs of interest to identify the appropriate temporal sampling rate for the acquisition and the correspondingly suitable HRF for the analysis of the data. Supplementary Information The online version contains supplementary material available at 10.1007/s10334-021-00918-z.
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Affiliation(s)
- Virág Darányi
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary
| | - Petra Hermann
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary
| | - István Homolya
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary
| | - Zoltan Nagy
- Laboratory for Social and Neural Systems Research, University of Zürich, Rämistrasse 100, P.O. Box 149, Zürich, Switzerland.
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21
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Soch J, Richter A, Schütze H, Kizilirmak JM, Assmann A, Knopf L, Raschick M, Schult A, Maass A, Ziegler G, Richardson-Klavehn A, Düzel E, Schott BH. Bayesian model selection favors parametric over categorical fMRI subsequent memory models in young and older adults. Neuroimage 2021; 230:117820. [PMID: 33524573 DOI: 10.1016/j.neuroimage.2021.117820] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 01/25/2021] [Indexed: 01/10/2023] Open
Abstract
Subsequent memory paradigms allow to identify neural correlates of successful encoding by separating brain responses as a function of memory performance during later retrieval. In functional magnetic resonance imaging (fMRI), the paradigm typically elicits activations of medial temporal lobe, prefrontal and parietal cortical structures in young, healthy participants. This categorical approach is, however, limited by insufficient memory performance in older and particularly memory-impaired individuals. A parametric modulation of encoding-related activations with memory confidence could overcome this limitation. Here, we applied cross-validated Bayesian model selection (cvBMS) for first-level fMRI models to a visual subsequent memory paradigm in young (18-35 years) and older (51-80 years) adults. Nested cvBMS revealed that parametric models, especially with non-linear transformations of memory confidence ratings, outperformed categorical models in explaining the fMRI signal variance during encoding. We thereby provide a framework for improving the modeling of encoding-related activations and for applying subsequent memory paradigms to memory-impaired individuals.
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Affiliation(s)
- Joram Soch
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany; Bernstein Center for Computational Neuroscience (BCCN), Berlin, Germany.
| | - Anni Richter
- Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany
| | - Hartmut Schütze
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | | | - Anne Assmann
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | - Lea Knopf
- Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany; Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | - Matthias Raschick
- Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany; Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | - Annika Schult
- Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany; Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Gabriel Ziegler
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | | | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Otto von Guericke University, Medical Faculty, Magdeburg, Germany; Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany
| | - Björn H Schott
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany; Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany; Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany; Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany.
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22
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Hein TP, de Fockert J, Ruiz MH. State anxiety biases estimates of uncertainty and impairs reward learning in volatile environments. Neuroimage 2020; 224:117424. [PMID: 33035670 DOI: 10.1016/j.neuroimage.2020.117424] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 08/27/2020] [Accepted: 09/29/2020] [Indexed: 01/01/2023] Open
Abstract
Clinical and subclinical (trait) anxiety impairs decision making and interferes with learning. Less understood are the effects of temporary anxious states on learning and decision making in healthy populations, and whether these can serve as a model for clinical anxiety. Here we test whether anxious states in healthy individuals elicit a pattern of aberrant behavioural, neural, and physiological responses comparable with those found in anxiety disorders-particularly when processing uncertainty in unstable environments. In our study, both a state anxious and a control group learned probabilistic stimulus-outcome mappings in a volatile task environment while we recorded their electrophysiological (EEG) signals. By using a hierarchical Bayesian model of inference and learning, we assessed the effect of state anxiety on Bayesian belief updating with a focus on uncertainty estimates. State anxiety was associated with an underestimation of environmental uncertainty, and informational uncertainty about the reward tendency. Anxious individuals' beliefs about reward contingencies were more precise (had smaller uncertainty) and thus more resistant to updating, ultimately leading to impaired reward-based learning. State anxiety was also associated with greater uncertainty about volatility. We interpret this pattern as evidence that state anxious individuals are less tolerant to informational uncertainty about the contingencies governing their environment and more willing to be uncertain about the level of stability of the world itself. Further, we tracked the neural representation of belief update signals in the trial-by-trial EEG amplitudes. In control participants, lower-level precision-weighted prediction errors (pwPEs) about reward tendencies were represented in the ERP signals across central and parietal electrodes peaking at 496 ms, overlapping with the late P300 in classical ERP analysis. The state anxiety group did not exhibit a significant representation of low-level pwPEs, and there were no significant differences between the groups. Smaller variance in low-level pwPE about reward tendencies in state anxiety could partially account for the null results. Expanding previous computational work on trait anxiety, our findings establish that temporary anxious states in healthy individuals impair reward-based learning in volatile environments, primarily through changes in uncertainty estimates, which play a central role in current Bayesian accounts of perceptual inference and learning.
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Affiliation(s)
- Thomas P Hein
- Goldsmiths, University of London, Psychology Department, Whitehead Building, New Cross, London, SE146NW, United Kingdom
| | - Jan de Fockert
- Goldsmiths, University of London, Psychology Department, Whitehead Building, New Cross, London, SE146NW, United Kingdom
| | - Maria Herrojo Ruiz
- Goldsmiths, University of London, Psychology Department, Whitehead Building, New Cross, London, SE146NW, United Kingdom; Center for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russian Federation.
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23
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Charpentier CJ, Iigaya K, O'Doherty JP. A Neuro-computational Account of Arbitration between Choice Imitation and Goal Emulation during Human Observational Learning. Neuron 2020; 106:687-699.e7. [PMID: 32187528 PMCID: PMC7244377 DOI: 10.1016/j.neuron.2020.02.028] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 01/18/2020] [Accepted: 02/25/2020] [Indexed: 12/28/2022]
Abstract
When individuals learn from observing the behavior of others, they deploy at least two distinct strategies. Choice imitation involves repeating other agents' previous actions, whereas emulation proceeds from inferring their goals and intentions. Despite the prevalence of observational learning in humans and other social animals, a fundamental question remains unaddressed: how does the brain decide which strategy to use in a given situation? In two fMRI studies (the second a pre-registered replication of the first), we identify a neuro-computational mechanism underlying arbitration between choice imitation and goal emulation. Computational modeling, combined with a behavioral task that dissociated the two strategies, revealed that control over behavior was adaptively and dynamically weighted toward the most reliable strategy. Emulation reliability, the model's arbitration signal, was represented in the ventrolateral prefrontal cortex, temporoparietal junction, and rostral cingulate cortex. Our replicated findings illuminate the computations by which the brain decides to imitate or emulate others.
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Affiliation(s)
- Caroline J Charpentier
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA.
| | - Kiyohito Iigaya
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
| | - John P O'Doherty
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
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24
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Sporn S, Hein T, Herrojo Ruiz M. Alterations in the amplitude and burst rate of beta oscillations impair reward-dependent motor learning in anxiety. eLife 2020; 9:e50654. [PMID: 32423530 PMCID: PMC7237220 DOI: 10.7554/elife.50654] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 04/08/2020] [Indexed: 01/08/2023] Open
Abstract
Anxiety results in sub-optimal motor learning, but the precise mechanisms through which this effect occurs remain unknown. Using a motor sequence learning paradigm with separate phases for initial exploration and reward-based learning, we show that anxiety states in humans impair learning by attenuating the update of reward estimates. Further, when such estimates are perceived as unstable over time (volatility), anxiety constrains adaptive behavioral changes. Neurally, anxiety during initial exploration increased the amplitude and the rate of long bursts of sensorimotor and prefrontal beta oscillations (13-30 Hz). These changes extended to the subsequent learning phase, where phasic increases in beta power and burst rate following reward feedback were linked to smaller updates in reward estimates, with a higher anxiety-related increase explaining the attenuated belief updating. These data suggest that state anxiety alters the dynamics of beta oscillations during reward processing, thereby impairing proper updating of motor predictions when learning in unstable environments.
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Affiliation(s)
- Sebastian Sporn
- School of Psychology, University of BirminghamBirminghamUnited Kingdom
- Department of Psychology, Goldsmiths University of LondonLondonUnited Kingdom
| | - Thomas Hein
- Department of Psychology, Goldsmiths University of LondonLondonUnited Kingdom
| | - Maria Herrojo Ruiz
- Department of Psychology, Goldsmiths University of LondonLondonUnited Kingdom
- Center for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of EconomicsMoscowRussian Federation
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25
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Chen X, Chen J, Cheng G, Gong T. Topics and trends in artificial intelligence assisted human brain research. PLoS One 2020; 15:e0231192. [PMID: 32251489 PMCID: PMC7135272 DOI: 10.1371/journal.pone.0231192] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 03/18/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in application scope of AI-assisted human brain research. A comprehensive understanding of this field is necessary to assess research efficacy, (re)allocate research resources, and conduct collaborations. This paper combines the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale, unstructured text of AI-assisted human brain research publications in the past decade. Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics, promising research orientations, and diverse topical distributions in influential countries/regions and research institutes. These findings help better understand scientific and technological AI-assisted human brain research, provide insightful guidance for resource (re)allocation, and promote effective international collaborations.
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Affiliation(s)
- Xieling Chen
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China
| | - Juan Chen
- Center for the Study of Applied Psychology, Guangdong Key Laboratory of Mental Health and Cognitive Science and the School of Psychology, South China Normal University, Guangzhou, China
| | - Gary Cheng
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China
- * E-mail: (GC); (TG)
| | - Tao Gong
- Center for Linguistics and Applied Linguistics, Guangdong University of Foreign Studies, Guangzhou, China
- Educational Testing Service, Princeton, NJ, United States of America
- * E-mail: (GC); (TG)
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26
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Soch J, Allefeld C, Haynes JD. Inverse transformed encoding models – a solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding. Neuroimage 2020; 209:116449. [DOI: 10.1016/j.neuroimage.2019.116449] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 12/03/2019] [Accepted: 12/06/2019] [Indexed: 11/24/2022] Open
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27
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Seghier ML, Fahim MA, Habak C. Educational fMRI: From the Lab to the Classroom. Front Psychol 2019; 10:2769. [PMID: 31866920 PMCID: PMC6909003 DOI: 10.3389/fpsyg.2019.02769] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 11/25/2019] [Indexed: 12/23/2022] Open
Abstract
Functional MRI (fMRI) findings hold many potential applications for education, and yet, the translation of fMRI findings to education has not flowed. Here, we address the types of fMRI that could better support applications of neuroscience to the classroom. This 'educational fMRI' comprises eight main challenges: (1) collecting artifact-free fMRI data in school-aged participants and in vulnerable young populations, (2) investigating heterogenous cohorts with wide variability in learning abilities and disabilities, (3) studying the brain under natural and ecological conditions, given that many practical topics of interest for education can be addressed only in ecological contexts, (4) depicting complex age-dependent associations of brain and behaviour with multi-modal imaging, (5) assessing changes in brain function related to developmental trajectories and instructional intervention with longitudinal designs, (6) providing system-level mechanistic explanations of brain function, so that useful individualized predictions about learning can be generated, (7) reporting negative findings, so that resources are not wasted on developing ineffective interventions, and (8) sharing data and creating large-scale longitudinal data repositories to ensure transparency and reproducibility of fMRI findings for education. These issues are of paramount importance to the development of optimal fMRI practices for educational applications.
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Affiliation(s)
- Mohamed L Seghier
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
| | - Mohamed A Fahim
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
| | - Claudine Habak
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
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28
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Kim M, Maguire EA. Can we study 3D grid codes non-invasively in the human brain? Methodological considerations and fMRI findings. Neuroimage 2019; 186:667-678. [PMID: 30481593 PMCID: PMC6347569 DOI: 10.1016/j.neuroimage.2018.11.041] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 11/21/2018] [Accepted: 11/23/2018] [Indexed: 11/21/2022] Open
Abstract
Recent human functional magnetic resonance imaging (fMRI) and animal electrophysiology studies suggest that grid cells in entorhinal cortex are an efficient neural mechanism for encoding knowledge about the world, not only for spatial location but also for more abstract cognitive information. The world, be it physical or abstract, is often high-dimensional, but grid cells have been mainly studied on a simple two-dimensional (2D) plane. Recent theoretical studies have proposed how grid cells encode three-dimensional (3D) physical space, but it is unknown whether grid codes can be examined non-invasively in humans. Here, we investigated whether it was feasible to test different 3D grid models using fMRI based on the direction-modulated property of grid signals. In doing so, we developed interactive software to help researchers visualize 3D grid fields and predict grid activity in 3D as a function of movement directions. We found that a direction-modulated grid analysis was sensitive to one type of 3D grid model - a face-centred cubic (FCC) lattice model. As a proof of concept, we searched for 3D grid-like signals in human entorhinal cortex using a novel 3D virtual reality paradigm and a new fMRI analysis method. We found that signals in the left entorhinal cortex were explained by the FCC model. This is preliminary evidence for 3D grid codes in the human brain, notwithstanding the inherent methodological limitations of fMRI. We believe that our findings and software serve as a useful initial stepping-stone for studying grid cells in realistic 3D worlds and also, potentially, for interrogating abstract high-dimensional cognitive processes.
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Affiliation(s)
- Misun Kim
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK
| | - Eleanor A Maguire
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK.
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29
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Hinault T, Larcher K, Zazubovits N, Gotman J, Dagher A. Spatio-temporal patterns of cognitive control revealed with simultaneous electroencephalography and functional magnetic resonance imaging. Hum Brain Mapp 2018; 40:80-97. [PMID: 30259592 DOI: 10.1002/hbm.24356] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Revised: 08/01/2018] [Accepted: 08/02/2018] [Indexed: 02/02/2023] Open
Abstract
Optimal performance depends in part on the ability to inhibit the automatic processing of irrelevant information and also on the adjusting the level of control from one trial to the next. In this study, we investigated the spatio-temporal neural correlates of cognitive control using simultaneous functional magnetic resonance imaging and electroencephalography, while 22 participants (10 women) performed a numerical Stroop task. We investigated the spatial and temporal dynamic of the conflict adaptation effects (i.e., reduced interference on items that follow an incongruent stimulus compared to after a congruent stimulus). Joint independent component analysis linked the N200 component to activation of anterior cingulate cortex (ACC) and the conflict slow potential to widespread activations within the fronto-parietal executive control network. Connectivity analyses with psychophysiological interactions and dynamic causal modeling demonstrated coordinated engagement of the cognitive control network after the processing of an incongruent item, and this was correlated with better behavioral performance. Our results combined high spatial and temporal resolution to propose the following network of conflict adaptation effect and specify the time course of activation within this model: first, the anterior insula and inferior frontal gyrus are activated when incongruence is detected. These regions then signal the need for higher control to the ACC, which in turn activates the fronto-parietal executive control network to improve the performance on the next trial.
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Affiliation(s)
- Thomas Hinault
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Kevin Larcher
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Natalja Zazubovits
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Jean Gotman
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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