1
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Misra J, Pessoa L. Brain dynamics and spatiotemporal trajectories during threat processing. bioRxiv 2024:2024.04.06.588389. [PMID: 38617278 PMCID: PMC11014591 DOI: 10.1101/2024.04.06.588389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
In the past decades, functional MRI research has investigated mental states and their brain bases in largely static fashion based on evoked responses during blocked and event-related designs. Despite some progress in naturalistic designs, our understanding of threat processing remains largely limited to those obtained with standard paradigms. In the present paper, we applied Switching Linear Dynamical Systems to uncover the dynamics of threat processing during a continuous threat-of-shock paradigm. Importantly, unlike studies in systems neuroscience that frequently assume that systems are decoupled from external inputs, we characterized both endogenous and exogenous contributions to dynamics. First, we demonstrated that the SLDS model learned the regularities of the experimental paradigm, such that states and state transitions estimated from fMRI time series data from 85 ROIs reflected both the proximity of the circles and their direction (approach vs. retreat). After establishing that the model captured key properties of threat-related processing, we characterized the dynamics of the states and their transitions. The results revealed that threat processing can profitably be viewed in terms of dynamic multivariate patterns whose trajectories are a combination of intrinsic and extrinsic factors that jointly determine how the brain temporally evolves during dynamic threat. We propose that viewing threat processing through the lens of dynamical systems offers important avenues to uncover properties of the dynamics of threat that are not unveiled with standard experimental designs and analyses.
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Affiliation(s)
- Joyneel Misra
- Departmentof Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America
| | - Luiz Pessoa
- Departmentof Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, Maryland, United States of America
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2
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Pessoa L. The Spiraling Cognitive-Emotional Brain: Combinatorial, Reciprocal, and Reentrant Macro-organization. J Cogn Neurosci 2024:1-15. [PMID: 38530327 DOI: 10.1162/jocn_a_02146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
This article proposes a framework for understanding the macro-scale organization of anatomical pathways in the mammalian brain. The architecture supports flexible behavioral decisions across a spectrum of spatio-temporal scales. The proposal emphasizes the combinatorial, reciprocal, and reentrant connectivity-called CRR neuroarchitecture-between cortical, BG, thalamic, amygdala, hypothalamic, and brainstem circuits. Thalamic nuclei, especially midline/intralaminar nuclei, are proposed to act as hubs routing the flow of signals between noncortical areas and pFC. The hypothalamus also participates in multiregion circuits via its connections with cortex and thalamus. At slower timescales, long-range behaviors integrate signals across levels of the neuroaxis. At fast timescales, parallel engagement of pathways allows urgent behaviors while retaining flexibility. Overall, the proposed architecture enables context-dependent, adaptive behaviors spanning proximate to distant spatio-temporal scales. The framework promotes an integrative perspective and a distributed, heterarchical view of brain function.
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3
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Pessoa L. Noncortical cognition: integration of information for close-proximity behavioral problem-solving. Curr Opin Behav Sci 2024; 55:101329. [PMID: 38655379 PMCID: PMC11034795 DOI: 10.1016/j.cobeha.2023.101329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Animals face behavioral problems that can be conceptualized in terms of a gradient of spatial and temporal proximity. I propose that solving close-proximity behavioral problems involves integrating disparate types of information in complex and flexible ways. In this framework, the midbrain periaqueductal gray (PAG) is understood as a key region involved in close-proximity motivated cognition. Anatomically, the PAG has access to signals across the neuroaxis via extensive connectivity with cortex, subcortex, and brainstem. However, the flow of signals is not unidirectional, as the PAG projects to the cortex directly, and further ascending signal flow is attained via the midline thalamus. Overall, the anatomical organization of the PAG allows is to be a critical hub engaged in cognition "here and now".
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Affiliation(s)
- Luiz Pessoa
- Department of Psychology, Department of Electrical and Computer Engineering, Maryland Neuroimaging Center, University of Maryland, College Park, MD 20742, USA
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4
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Vafaii H, Mandino F, Desrosiers-Grégoire G, O'Connor D, Markicevic M, Shen X, Ge X, Herman P, Hyder F, Papademetris X, Chakravarty M, Crair MC, Constable RT, Lake EMR, Pessoa L. Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization. Nat Commun 2024; 15:229. [PMID: 38172111 PMCID: PMC10764905 DOI: 10.1038/s41467-023-44363-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024] Open
Abstract
Large-scale functional networks have been characterized in both rodent and human brains, typically by analyzing fMRI-BOLD signals. However, the relationship between fMRI-BOLD and underlying neural activity is complex and incompletely understood, which poses challenges to interpreting network organization obtained using this technique. Additionally, most work has assumed a disjoint functional network organization (i.e., brain regions belong to one and only one network). Here, we employ wide-field Ca2+ imaging simultaneously with fMRI-BOLD in mice expressing GCaMP6f in excitatory neurons. We determine cortical networks discovered by each modality using a mixed-membership algorithm to test the hypothesis that functional networks exhibit overlapping organization. We find that there is considerable network overlap (both modalities) in addition to disjoint organization. Our results show that multiple BOLD networks are detected via Ca2+ signals, and networks determined by low-frequency Ca2+ signals are only modestly more similar to BOLD networks. In addition, the principal gradient of functional connectivity is nearly identical for BOLD and Ca2+ signals. Despite similarities, important differences are also detected across modalities, such as in measures of functional connectivity strength and diversity. In conclusion, Ca2+ imaging uncovers overlapping functional cortical organization in the mouse that reflects several, but not all, properties observed with fMRI-BOLD signals.
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Affiliation(s)
- Hadi Vafaii
- Department of Physics, University of Maryland, College Park, MD, 20742, USA.
| | - Francesca Mandino
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Gabriel Desrosiers-Grégoire
- Computional Brain Anatomy Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, QC, H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, H3A 0G4, Canada
| | - David O'Connor
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Marija Markicevic
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Xinxin Ge
- Department of Physiology, School of Medicine, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Peter Herman
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Fahmeed Hyder
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Xenophon Papademetris
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Section of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Mallar Chakravarty
- Computional Brain Anatomy Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, QC, H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, H3A 0G4, Canada
- Department of Psychiatry, McGill University, Montreal, QC, H3A 0G4, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, H3A 0G4, Canada
| | - Michael C Crair
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, 06510, USA
- Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT, 06510, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Evelyn M R Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA.
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA.
| | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, 20742, USA.
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, 20742, USA.
- Maryland Neuroimaging Center, University of Maryland, College Park, MD, 20742, USA.
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5
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Uddin LQ, Betzel RF, Cohen JR, Damoiseaux JS, De Brigard F, Eickhoff SB, Fornito A, Gratton C, Gordon EM, Laird AR, Larson-Prior L, McIntosh AR, Nickerson LD, Pessoa L, Pinho AL, Poldrack RA, Razi A, Sadaghiani S, Shine JM, Yendiki A, Yeo BTT, Spreng RN. Controversies and progress on standardization of large-scale brain network nomenclature. Netw Neurosci 2023; 7:864-905. [PMID: 37781138 PMCID: PMC10473266 DOI: 10.1162/netn_a_00323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 05/10/2023] [Indexed: 10/03/2023] Open
Abstract
Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.
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Affiliation(s)
- Lucina Q. Uddin
- Department of Psychiatry and Biobehavioral Sciences and Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Jessica R. Cohen
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Jessica S. Damoiseaux
- Institute of Gerontology and Department of Psychology, Wayne State University, Detroit, MI, USA
| | | | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Evan M. Gordon
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Linda Larson-Prior
- Deptartment of Psychiatry and Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - A. Randal McIntosh
- Institute for Neuroscience and Neurotechnology, Simon Fraser University, Vancouver, BC, Canada
| | | | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Ana Luísa Pinho
- Brain and Mind Institute, Western University, London, Ontario, Canada
| | | | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Sepideh Sadaghiani
- Department of Psychology, University of Illinois, Urbana Champaign, IL, USA
| | - James M. Shine
- Brain and Mind Center, University of Sydney, Sydney, Australia
| | - Anastasia Yendiki
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - R. Nathan Spreng
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
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6
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Vafaii H, Mandino F, Desrosiers-Grégoire G, O’Connor D, Shen X, Ge X, Herman P, Hyder F, Papademetris X, Chakravarty M, Crair MC, Constable RT, Lake EMR, Pessoa L. Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization. Res Sq 2023:rs.3.rs-2823802. [PMID: 37162818 PMCID: PMC10168440 DOI: 10.21203/rs.3.rs-2823802/v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Large-scale functional networks have been characterized in both rodent and human brains, typically by analyzing fMRI-BOLD signals. However, the relationship between fMRI-BOLD and underlying neural activity is complex and incompletely understood, which poses challenges to interpreting network organization obtained using this technique. Additionally, most work has assumed a disjoint functional network organization (i.e., brain regions belong to one and only one network). Here, we employed wide-field Ca2+ imaging simultaneously with fMRI-BOLD in mice expressing GCaMP6f in excitatory neurons. We determined cortical networks discovered by each modality using a mixed-membership algorithm to test the hypothesis that functional networks are overlapping rather than disjoint. Our results show that multiple BOLD networks are detected via Ca2+ signals; there is considerable network overlap (both modalities); networks determined by low-frequency Ca2+ signals are only modestly more similar to BOLD networks; and, despite similarities, important differences are detected across modalities (e.g., brain region "network diversity"). In conclusion, Ca2+ imaging uncovered overlapping functional cortical organization in the mouse that reflected several, but not all, properties observed with fMRI-BOLD signals.
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Affiliation(s)
- Hadi Vafaii
- Department of Physics, University of Maryland, College Park, MD, 20742, USA
| | - Francesca Mandino
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Gabriel Desrosiers-Grégoire
- Comp. Brain Anatomy Laboratory, Cerebral Imaging Center, Douglas Mental Health Univ. Institute, Montreal, QC, H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, H3A 0G4, Canada
| | - David O’Connor
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06511, USA
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Xinxin Ge
- Department of Physiology, School of Medicine, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Peter Herman
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Fahmeed Hyder
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Xenophon Papademetris
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Mallar Chakravarty
- Comp. Brain Anatomy Laboratory, Cerebral Imaging Center, Douglas Mental Health Univ. Institute, Montreal, QC, H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, H3A 0G4, Canada
- Department of Psychiatry, McGill University, Montreal, QC, H3A 0G4, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, H3A 0G4, Canada
| | - Michael C. Crair
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, 06510, USA
- Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT, 06510, USA
| | - R. Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06511, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Evelyn MR. Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, 20742, USA
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, 20742, USA
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7
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Murty DVPS, Song S, Surampudi SG, Pessoa L. Threat and Reward Imminence Processing in the Human Brain. J Neurosci 2023; 43:2973-2987. [PMID: 36927571 PMCID: PMC10124955 DOI: 10.1523/jneurosci.1778-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 03/03/2023] [Accepted: 03/12/2023] [Indexed: 03/18/2023] Open
Abstract
In the human brain, aversive and appetitive processing have been studied with controlled stimuli in rather static settings. In addition, the extent to which aversive-related and appetitive-related processing engage distinct or overlapping circuits remains poorly understood. Here, we sought to investigate the dynamics of aversive and appetitive processing while male and female participants engaged in comparable trials involving threat avoidance or reward seeking. A central goal was to characterize the temporal evolution of responses during periods of threat or reward imminence. For example, in the aversive domain, we predicted that the bed nucleus of the stria terminalis (BST), but not the amygdala, would exhibit anticipatory responses given the role of the former in anxious apprehension. We also predicted that the periaqueductal gray (PAG) would exhibit threat-proximity responses based on its involvement in proximal-threat processes, and that the ventral striatum would exhibit threat-imminence responses given its role in threat escape in rodents. Overall, we uncovered imminence-related temporally increasing ("ramping") responses in multiple brain regions, including the BST, PAG, and ventral striatum, subcortically, and dorsal anterior insula and anterior midcingulate, cortically. Whereas the ventral striatum generated anticipatory responses in the proximity of reward as expected, it also exhibited threat-related imminence responses. In fact, across multiple brain regions, we observed a main effect of arousal. In other words, we uncovered extensive temporally evolving, imminence-related processing in both the aversive and appetitive domain, suggesting that distributed brain circuits are dynamically engaged during the processing of biologically relevant information regardless of valence, findings further supported by network analysis.SIGNIFICANCE STATEMENT In the human brain, aversive and appetitive processing have been studied with controlled stimuli in rather static settings. Here, we sought to investigate the dynamics of aversive/appetitive processing while participants engaged in trials involving threat avoidance or reward seeking. A central goal was to characterize the temporal evolution of responses during periods of threat or reward imminence. We uncovered imminence-related temporally increasing ("ramping") responses in multiple brain regions, including the bed nucleus of the stria terminalis, periaqueductal gray, and ventral striatum, subcortically, and dorsal anterior insula and anterior midcingulate, cortically. Overall, we uncovered extensive temporally evolving, imminence-related processing in both the aversive and appetitive domain, suggesting that distributed brain circuits are dynamically engaged during the processing of biologically relevant information regardless of valence.
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Affiliation(s)
| | - Songtao Song
- Department of Psychology, University of Maryland, College Park, Maryland 20742
| | | | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, Maryland 20742
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8
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Pessoa L. How many brain regions are needed to elucidate the neural bases of fear and anxiety? Neurosci Biobehav Rev 2023; 146:105039. [PMID: 36634832 PMCID: PMC11019846 DOI: 10.1016/j.neubiorev.2023.105039] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 01/06/2023] [Accepted: 01/08/2023] [Indexed: 01/11/2023]
Abstract
We suggest that to understand complex behaviors associated with fear and anxiety, we need to understand brain processes at the collective, network level. But what should be the type and spatial scale of the targeted circuits/networks? Not only are multi-region interactions essential-including complex reciprocal interactions, loops, and other types of arrangement-but it is profitable to characterize circuits spanning the entire neuroaxis. In particular, it is productive to conceptualize the circuits contributing to fear/anxiety as embedded into large-scale connectional systems. We discuss circuits involving the basolateral amygdala that contribute to aversive conditioning and fear extinction. In addition, we highlight the importance of the extended amygdala (central nucleus of the amygdala and bed nucleus of the stria terminalis) cortical-subcortical loop, which allows large swaths of cortex and subcortex to influence fear and anxiety. In this manner, fear/anxiety can be understood not only based on traditional "descending" mechanisms involving the hypothalamus and brainstem, but in terms of a considerably broader reentrant organization.
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Affiliation(s)
- Luiz Pessoa
- Department of Psychology, Department of Electrical and Computer Engineering, Maryland Neuroimaging Center, University of Maryland, College Park, MD 20742, USA.
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9
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Abstract
The Entangled Brain (Pessoa, L., 2002. MIT Press) promotes the idea that we need to understand the brain as a complex, entangled system. Why does the complex systems perspective, one that entails emergent properties, matter for brain science? In fact, many neuroscientists consider these ideas a distraction. We discuss three principles of brain organization that inform the question of the interactional complexity of the brain: (1) massive combinatorial anatomical connectivity; (2) highly distributed functional coordination; and (3) networks/circuits as functional units. To motivate the challenges of mapping structure and function, we discuss neural circuits illustrating the high anatomical and functional interactional complexity typical in the brain. We discuss potential avenues for testing for network-level properties, including those relying on distributed computations across multiple regions. We discuss implications for brain science, including the need to characterize decentralized and heterarchical anatomical-functional organization. The view advocated has important implications for causation, too, because traditional accounts of causality provide poor candidates for explanation in interactionally complex systems like the brain given the distributed, mutual, and reciprocal nature of the interactions. Ultimately, to make progress understanding how the brain supports complex mental functions, we need to dissolve boundaries within the brain-those suggested to be associated with perception, cognition, action, emotion, motivation-as well as outside the brain, as we bring down the walls between biology, psychology, mathematics, computer science, philosophy, and so on.
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10
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Pessoa L. Disentangling Some Conceptual Knots. J Cogn Neurosci 2023; 35:391-395. [PMID: 36626350 PMCID: PMC11019943 DOI: 10.1162/jocn_a_01961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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11
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Murty DVPS, Song S, Surampudi SG, Pessoa L. Threat and reward imminence processing in the human brain. bioRxiv 2023:2023.01.20.524987. [PMID: 36711746 PMCID: PMC9882302 DOI: 10.1101/2023.01.20.524987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
In the human brain, aversive and appetitive processing have been studied with controlled stimuli in rather static settings. In addition, the extent to which aversive- and appetitive-related processing engage distinct or overlapping circuits remains poorly understood. Here, we sought to investigate the dynamics of aversive and appetitive processing while male and female participants engaged in comparable trials involving threat-avoidance or reward-seeking. A central goal was to characterize the temporal evolution of responses during periods of threat or reward imminence . For example, in the aversive domain, we predicted that the bed nucleus of the stria terminalis (BST), but not the amygdala, would exhibit anticipatory responses given the role of the former in anxious apprehension. We also predicted that the periaqueductal gray (PAG) would exhibit threat-proximity responses based on its involvement in proximal-threat processes, and that the ventral striatum would exhibit threat-imminence responses given its role in threat escape in rodents. Overall, we uncovered imminence-related temporally increasing ("ramping") responses in multiple brain regions, including the BST, PAG, and ventral striatum, subcortically, and dorsal anterior insula and anterior midcingulate, cortically. Whereas the ventral striatum generated anticipatory responses in the proximity of reward as expected, it also exhibited threat-related imminence responses. In fact, across multiple brain regions, we observed a main effect of arousal. In other words, we uncovered extensive temporally-evolving, imminence-related processing in both the aversive and appetitive domain, suggesting that distributed brain circuits are dynamically engaged during the processing of biologically relevant information irrespective of valence, findings further supported by network analysis. Significance Statement In the human brain, aversive and appetitive processing have been studied with controlled stimuli in rather static settings. Here, we sought to investigate the dynamics of aversive/appetitive processing while participants engaged in trials involving threat-avoidance or reward-seeking. A central goal was to characterize the temporal evolution of responses during periods of threat or reward imminence . We uncovered imminence-related temporally increasing ("ramping") responses in multiple brain regions, including the bed nucleus of the stria terminalis, periaqueductal gray, and ventral striatum, subcortically, and dorsal anterior insula and anterior midcingulate, cortically. Overall, we uncovered extensive temporally-evolving, imminence-related processing in both the aversive and appetitive domain, suggesting that distributed brain circuits are dynamically engaged during the processing of biologically relevant information irrespective of valence.
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12
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Barack DL, Miller EK, Moore CI, Packer AM, Pessoa L, Ross LN, Rust NC. A call for more clarity around causality in neuroscience. Trends Neurosci 2022; 45:654-655. [PMID: 35810023 PMCID: PMC9996677 DOI: 10.1016/j.tins.2022.06.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 06/10/2022] [Indexed: 11/29/2022]
Abstract
In neuroscience, the term 'causality' is used to refer to different concepts, leading to confusion. Here we illustrate some of those variations, and we suggest names for them. We then introduce four ways to enhance clarity around causality in neuroscience.
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Affiliation(s)
- David L Barack
- Departments of Neuroscience and Philosophy, University of Pennsylvania, Philadelphia, PA, USA.
| | - Earl K Miller
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Christopher I Moore
- Carney Institute for Brain Science, Department of Neuroscience, Brown University, Providence, RI, USA.
| | - Adam M Packer
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK.
| | - Luiz Pessoa
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, MD, USA.
| | - Lauren N Ross
- Department of Logic and Philosophy of Science, University of California, Irvine, CA, USA.
| | - Nicole C Rust
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA.
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13
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Abstract
Mental terms—such as perception, cognition, action, emotion, as well as attention, memory, decision-making—are epistemically sterile. We support our thesis based on extensive comparative neuroanatomy knowledge of the organization of the vertebrate brain. Evolutionary pressures have moulded the central nervous system to promote survival. Careful characterization of the vertebrate brain shows that its architecture supports an enormous amount of communication and integration of signals, especially in birds and mammals. The general architecture supports a degree of ‘computational flexibility’ that enables animals to cope successfully with complex and ever-changing environments. Here, we suggest that the vertebrate neuroarchitecture does not respect the boundaries of standard mental terms, and propose that neuroscience should aim to unravel the dynamic coupling between large-scale brain circuits and complex, naturalistic behaviours. This article is part of the theme issue ‘Systems neuroscience through the lens of evolutionary theory’.
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Affiliation(s)
- Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD 20742, USA
| | - Loreta Medina
- Department of Experimental Medicine, Institut de Recerca Biomèdica de Lleida Fundació Dr. Pifarré (IRBLleida), University of Lleida, 25198 Lleida, Spain
| | - Ester Desfilis
- Department of Experimental Medicine, Institut de Recerca Biomèdica de Lleida Fundació Dr. Pifarré (IRBLleida), University of Lleida, 25198 Lleida, Spain
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14
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Abstract
In the present fMRI study, we examined how anxious apprehension is processed in the human brain. A central goal of the study was to test the prediction that a subset of brain regions would exhibit sustained response profiles during threat periods, including the anterior insula, a region implicated in anxiety disorders. A second important goal was to evaluate the responses in the amygdala and the bed nucleus of the stria terminals, regions that have been suggested to be involved in more transient and sustained threat, respectively. A total of 109 participants performed an experiment in which they encountered "threat" or "safe" trials lasting approximately 16 sec. During the former, they experienced zero to three highly unpleasant electrical stimulations, whereas in the latter, they experienced zero to three benign electrical stimulations (not perceived as unpleasant). The timing of the stimulation during trials was randomized, and as some trials contained no stimulation, stimulation delivery was uncertain. We contrasted responses during threat and safe trials that did not contain electrical stimulation, but only the potential that unpleasant (threat) or benign (safe) stimulation could occur. We employed Bayesian multilevel analysis to contrast responses to threat and safe trials in 85 brain regions implicated in threat processing. Our results revealed that the effect of anxious apprehension is distributed across the brain and that the temporal evolution of the responses is quite varied, including more transient and more sustained profiles, as well as signal increases and decreases with threat.
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Affiliation(s)
| | | | | | | | - Kesong Hu
- Lake Superior State University, Sault Ste. Marie, MI
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15
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Mittal A, Aggarwal P, Pessoa L, Gupta A. Robust Brain State Decoding using Bidirectional Long Short Term Memory Networks in functional MRI. Proc Indian Conf Comput Vis Graphics Image Proc 2021; 2021:12. [PMID: 36350798 PMCID: PMC9639335 DOI: 10.1145/3490035.3490269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Decoding brain states of the underlying cognitive processes via learning discriminative feature representations has recently gained a lot of interest in brain imaging studies. Particularly, there has been an impetus to encode the dynamics of brain functioning by analyzing temporal information available in the fMRI data. Long-short term memory (LSTM), a class of machine learning model possessing a "memory" component, to retain previously seen temporal information, is increasingly being observed to perform well in various applications with dynamic temporal behavior, including brain state decoding. Because of the dynamics and inherent latency in fMRI BOLD responses, future temporal context is crucial. However, it is neither encoded nor captured by the conventional LSTM model. This paper performs robust brain state decoding via information encapsulation from both the past and future instances of fMRI data via bi-directional LSTM. This allows for explicitly modeling the dynamics of BOLD response without any delay adjustment. To this end, we utilize a bidirectional LSTM, wherein, the input sequence is fed in normal time-order for one LSTM network, and in the reverse time-order, for another. The two hidden activations of forward and reverse directions in bi-LSTM are collated to build the "memory" of the model and are used to robustly predict the brain states at every time instance. Working memory data from the Human Connectome Project (HCP) is utilized for validation and was observed to perform 18% better than it's unidirectional counterpart in terms of accuracy in predicting the brain states.
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Affiliation(s)
| | | | - Luiz Pessoa
- Laboratory of Cognition and Emotion, University of Maryland, USA
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16
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Misra J, Surampudi SG, Venkatesh M, Limbachia C, Jaja J, Pessoa L. Learning brain dynamics for decoding and predicting individual differences. PLoS Comput Biol 2021; 17:e1008943. [PMID: 34478442 PMCID: PMC8445454 DOI: 10.1371/journal.pcbi.1008943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 09/16/2021] [Accepted: 08/19/2021] [Indexed: 12/04/2022] Open
Abstract
Insights from functional Magnetic Resonance Imaging (fMRI), as well as recordings of large numbers of neurons, reveal that many cognitive, emotional, and motor functions depend on the multivariate interactions of brain signals. To decode brain dynamics, we propose an architecture based on recurrent neural networks to uncover distributed spatiotemporal signatures. We demonstrate the potential of the approach using human fMRI data during movie-watching data and a continuous experimental paradigm. The model was able to learn spatiotemporal patterns that supported 15-way movie-clip classification (∼90%) at the level of brain regions, and binary classification of experimental conditions (∼60%) at the level of voxels. The model was also able to learn individual differences in measures of fluid intelligence and verbal IQ at levels comparable to that of existing techniques. We propose a dimensionality reduction approach that uncovers low-dimensional trajectories and captures essential informational (i.e., classification related) properties of brain dynamics. Finally, saliency maps and lesion analysis were employed to characterize brain-region/voxel importance, and uncovered how dynamic but consistent changes in fMRI activation influenced decoding performance. When applied at the level of voxels, our framework implements a dynamic version of multivariate pattern analysis. Our approach provides a framework for visualizing, analyzing, and discovering dynamic spatially distributed brain representations during naturalistic conditions.
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Affiliation(s)
- Joyneel Misra
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America
| | - Srinivas Govinda Surampudi
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America
| | - Manasij Venkatesh
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America
| | - Chirag Limbachia
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, Maryland, United States of America
| | - Joseph Jaja
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America
| | - Luiz Pessoa
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, Maryland, United States of America
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17
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Abbas K, Liu M, Venkatesh M, Amico E, Kaplan AD, Ventresca M, Pessoa L, Harezlak J, Goñi J. Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints. Brain Connect 2021; 11:333-348. [PMID: 33470164 PMCID: PMC8215418 DOI: 10.1089/brain.2020.0881] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Functional connectomes (FCs) have been shown to provide a reproducible individual fingerprint, which has opened the possibility of personalized medicine for neuro/psychiatric disorders. Thus, developing accurate ways to compare FCs is essential to establish associations with behavior and/or cognition at the individual level. Methods: Canonically, FCs are compared using Pearson's correlation coefficient of the entire functional connectivity profiles. Recently, it has been proposed that the use of geodesic distance is a more accurate way of comparing FCs, one which reflects the underlying non-Euclidean geometry of the data. Computing geodesic distance requires FCs to be positive-definite and hence invertible matrices. As this requirement depends on the functional magnetic resonance imaging scanning length and the parcellation used, it is not always attainable and sometimes a regularization procedure is required. Results: In the present work, we show that regularization is not only an algebraic operation for making FCs invertible, but also that an optimal magnitude of regularization leads to systematically higher fingerprints. We also show evidence that optimal regularization is data set-dependent and varies as a function of condition, parcellation, scanning length, and the number of frames used to compute the FCs. Discussion: We demonstrate that a universally fixed regularization does not fully uncover the potential of geodesic distance on individual fingerprinting and indeed could severely diminish it. Thus, an optimal regularization must be estimated on each data set to uncover the most differentiable across-subject and reproducible within-subject geodesic distances between FCs. The resulting pairwise geodesic distances at the optimal regularization level constitute a very reliable quantification of differences between subjects.
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Affiliation(s)
- Kausar Abbas
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Mintao Liu
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Manasij Venkatesh
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, USA
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | | | - Mario Ventresca
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Luiz Pessoa
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, USA
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, Maryland, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University, Bloomington, Indiana, USA
| | - Joaquín Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
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18
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Charpentier CJ, Faulkner P, Pool ER, Ly V, Tollenaar MS, Kluen LM, Fransen A, Yamamori Y, Lally N, Mkrtchian A, Valton V, Huys QJM, Sarigiannidis I, Morrow KA, Krenz V, Kalbe F, Cremer A, Zerbes G, Kausche FM, Wanke N, Giarrizzo A, Pulcu E, Murphy S, Kaltenboeck A, Browning M, Paul LK, Cools R, Roelofs K, Pessoa L, Harmer CJ, Chase HW, Grillon C, Schwabe L, Roiser JP, Robinson OJ, O'Doherty JP. How Representative are Neuroimaging Samples? Large-Scale Evidence for Trait Anxiety Differences Between fMRI and Behaviour-Only Research Participants. Soc Cogn Affect Neurosci 2021; 16:1057-1070. [PMID: 33950220 PMCID: PMC8483285 DOI: 10.1093/scan/nsab057] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 03/13/2021] [Accepted: 05/04/2021] [Indexed: 12/15/2022] Open
Abstract
Over the past three decades, functional magnetic resonance imaging (fMRI) has become crucial to study how cognitive processes are implemented in the human brain. However, the question of whether participants recruited into fMRI studies differ from participants recruited into other study contexts has received little to no attention. This is particularly pertinent when effects fail to generalize across study contexts: for example, a behavioural effect discovered in a non-imaging context not replicating in a neuroimaging environment. Here, we tested the hypothesis, motivated by preliminary findings (N = 272), that fMRI participants differ from behaviour-only participants on one fundamental individual difference variable: trait anxiety. Analysing trait anxiety scores and possible confounding variables from healthy volunteers across multiple institutions (N = 3317), we found robust support for lower trait anxiety in fMRI study participants, consistent with a sampling or self-selection bias. The bias was larger in studies that relied on phone screening (compared with full in-person psychiatric screening), recruited at least partly from convenience samples (compared with community samples), and in pharmacology studies. Our findings highlight the need for surveying trait anxiety at recruitment and for appropriate screening procedures or sampling strategies to mitigate this bias.
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Affiliation(s)
- Caroline J Charpentier
- California Institute of Technology, Pasadena, CA, USA.,Institute of Cognitive Neuroscience, University College London, London, UK
| | | | - Eva R Pool
- University of Geneva, Geneva, Switzerland
| | - Verena Ly
- Department of Clinical Psychology, Leiden University; Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Marieke S Tollenaar
- Department of Clinical Psychology, Leiden University; Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Lisa M Kluen
- California Institute of Technology, Pasadena, CA, USA
| | - Aniek Fransen
- California Institute of Technology, Pasadena, CA, USA
| | - Yumeya Yamamori
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Níall Lally
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Anahit Mkrtchian
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Vincent Valton
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Quentin J M Huys
- Institute of Cognitive Neuroscience, University College London, London, UK
| | | | | | | | | | | | | | | | | | | | - Erdem Pulcu
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Susannah Murphy
- Department of Psychiatry, University of Oxford, Oxford, UK.,Oxford Health NHS Trust, Oxford, UK
| | - Alexander Kaltenboeck
- Department of Psychiatry, University of Oxford, Oxford, UK.,Department of Psychiatry and Psychotherapy, Clinical Division of Social Psychiatry, Medical University of Vienna, Austria
| | - Michael Browning
- Department of Psychiatry, University of Oxford, Oxford, UK.,Oxford Health NHS Trust, Oxford, UK
| | - Lynn K Paul
- California Institute of Technology, Pasadena, CA, USA
| | - Roshan Cools
- Donders Institute for Brain Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.,Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Karin Roelofs
- Donders Institute for Brain Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Luiz Pessoa
- University of Maryland, College Park, MD, USA
| | - Catherine J Harmer
- Department of Psychiatry, University of Oxford, Oxford, UK.,Oxford Health NHS Trust, Oxford, UK
| | - Henry W Chase
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | - Jonathan P Roiser
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Oliver J Robinson
- Institute of Cognitive Neuroscience, University College London, London, UK
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19
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Buffalo EA, Courtney SM, De Weerd P, Doyon J, Jiang Y, Karni A, Kastner S, Pessoa L, Rossi A. Lessons from Leslie: A Tribute to an Extraordinary Scientist and Mentor. Trends Neurosci 2021; 44:241-243. [PMID: 33642091 DOI: 10.1016/j.tins.2021.02.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 02/03/2021] [Indexed: 11/15/2022]
Affiliation(s)
- Elizabeth A Buffalo
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA; Washington Primate Research Center, University of Washington, Seattle, WA, USA
| | - Susan M Courtney
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA; Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA
| | - Peter De Weerd
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, The Netherlands
| | - Julien Doyon
- McConnell Brain Imaging Center, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Yang Jiang
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Avi Karni
- Sagol Department of Neurobiology, University of Haifa, Haifa, Israel; E.J. Safra Brain Research Centre, University of Haifa, Haifa, Israel
| | - Sabine Kastner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Psychology, Princeton University, Princeton, NJ, USA.
| | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Andrew Rossi
- Executive Functions Program, Division of Neuroscience and Basic Behavioral Science, National Institute of Mental Health (NIMH), Bethesda, MD, USA
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20
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Chen G, Padmala S, Chen Y, Taylor PA, Cox RW, Pessoa L. To pool or not to pool: Can we ignore cross-trial variability in FMRI? Neuroimage 2021; 225:117496. [PMID: 33181352 PMCID: PMC7861143 DOI: 10.1016/j.neuroimage.2020.117496] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 09/29/2020] [Accepted: 10/19/2020] [Indexed: 11/22/2022] Open
Abstract
In this work, we investigate the importance of explicitly accounting for cross-trial variability in neuroimaging data analysis. To attempt to obtain reliable estimates in a task-based experiment, each condition is usually repeated across many trials. The investigator may be interested in (a) condition-level effects, (b) trial-level effects, or (c) the association of trial-level effects with the corresponding behavior data. The typical strategy for condition-level modeling is to create one regressor per condition at the subject level with the underlying assumption that responses do not change across trials. In this methodology of complete pooling, all cross-trial variability is ignored and dismissed as random noise that is swept under the rug of model residuals. Unfortunately, this framework invalidates the generalizability from the confine of specific trials (e.g., particular faces) to the associated stimulus category ("face"), and may inflate the statistical evidence when the trial sample size is not large enough. Here we propose an adaptive and computationally tractable framework that meshes well with the current two-level pipeline and explicitly accounts for trial-by-trial variability. The trial-level effects are first estimated per subject through no pooling. To allow generalizing beyond the particular stimulus set employed, the cross-trial variability is modeled at the population level through partial pooling in a multilevel model, which permits accurate effect estimation and characterization. Alternatively, trial-level estimates can be used to investigate, for example, brain-behavior associations or correlations between brain regions. Furthermore, our approach allows appropriate accounting for serial correlation, handling outliers, adapting to data skew, and capturing nonlinear brain-behavior relationships. By applying a Bayesian multilevel model framework at the level of regions of interest to an experimental dataset, we show how multiple testing can be addressed and full results reported without arbitrary dichotomization. Our approach revealed important differences compared to the conventional method at the condition level, including how the latter can distort effect magnitude and precision. Notably, in some cases our approach led to increased statistical sensitivity. In summary, our proposed framework provides an effective strategy to capture trial-by-trial responses that should be of interest to a wide community of experimentalists.
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Affiliation(s)
- Gang Chen
- Scientific and Statistical Computing Core, NIMH, National Institutes of Health, USA.
| | - Srikanth Padmala
- Centre for Neuroscience, Indian Institute of Science, Bangalore, India
| | - Yi Chen
- German Center for Neurodegenerative Diseases, Magdeburg, Germany; IKND, Universität Magdeburg, Germany
| | - Paul A Taylor
- Scientific and Statistical Computing Core, NIMH, National Institutes of Health, USA
| | - Robert W Cox
- Scientific and Statistical Computing Core, NIMH, National Institutes of Health, USA
| | - Luiz Pessoa
- Department of Psychology, Department of Electrical and Computer Engineering, Maryland Neuroimaging Center, University of Maryland, College Park, USA
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21
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Limbachia C, Morrow K, Khibovska A, Meyer C, Padmala S, Pessoa L. Controllability over stressor decreases responses in key threat-related brain areas. Commun Biol 2021; 4:42. [PMID: 33402686 PMCID: PMC7785729 DOI: 10.1038/s42003-020-01537-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 11/27/2020] [Indexed: 12/20/2022] Open
Abstract
Controllability over stressors has major impacts on brain and behavior. In humans, however, the effect of controllability on responses to stressors is poorly understood. Using functional magnetic resonance imaging (fMRI), we investigated how controllability altered responses to a shock-plus-sound stressor with a between-group yoked design, where participants in controllable and uncontrollable groups experienced matched stressor exposure. Employing Bayesian multilevel analysis at the level of regions of interest and voxels in the insula, and standard voxelwise analysis, we found that controllability decreased stressor-related responses across threat-related regions, notably in the bed nucleus of the stria terminalis and anterior insula. Posterior cingulate cortex, posterior insula, and possibly medial frontal gyrus showed increased responses during control over stressor. Our findings support the idea that the aversiveness of stressors is reduced when controllable, leading to decreased responses across key regions involved in anxiety-related processing, even at the level of the extended amygdala.
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Affiliation(s)
- Chirag Limbachia
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Kelly Morrow
- Department of Psychology, University of Maryland, College Park, MD, USA
- Neuroscience and Cognitive Sciences program, University of Maryland, College Park, MD, USA
| | - Anastasiia Khibovska
- Department of Psychology, University of Maryland, College Park, MD, USA
- Department of Psychology, Stony Brook University, Stony Brook, NY, USA
| | - Christian Meyer
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA
| | | | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, USA.
- Neuroscience and Cognitive Sciences program, University of Maryland, College Park, MD, USA.
- Maryland Neuroimaging Center, University of Maryland, College Park, MD, USA.
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA.
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22
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Affiliation(s)
| | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, USA
| | - Tor D Wager
- Department of Psychological & Brain Sciences, Dartmouth College, USA
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23
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Lima Portugal LC, Alves RDCS, Junior OF, Sanchez TA, Mocaiber I, Volchan E, Smith Erthal F, David IA, Kim J, Oliveira L, Padmala S, Chen G, Pessoa L, Pereira MG. Interactions between emotion and action in the brain. Neuroimage 2020; 214:116728. [PMID: 32199954 PMCID: PMC7485650 DOI: 10.1016/j.neuroimage.2020.116728] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 03/04/2020] [Accepted: 03/07/2020] [Indexed: 11/16/2022] Open
Abstract
A growing literature supports the existence of interactions between emotion and action in the brain, and the central participation of the anterior midcingulate cortex (aMCC) in this regard. In the present functional magnetic resonance imaging study, we sought to investigate the role of self-relevance during such interactions by varying the context in which threating pictures were presented (with guns pointed towards or away from the observer). Participants performed a simple visual detection task following exposure to such stimuli. Except for voxelwise tests, we adopted a Bayesian analysis framework which evaluated evidence for the hypotheses of interest, given the data, in a continuous fashion. Behaviorally, our results demonstrated a valence by context interaction such that there was a tendency of speeding up responses to targets after viewing threat pictures directed towards the participant. In the brain, interaction patterns that paralleled those observed behaviorally were observed most notably in the middle temporal gyrus, supplementary motor area, precentral gyrus, and anterior insula. In these regions, activity was overall greater during threat conditions relative to neutral ones, and this effect was enhanced in the directed towards context. A valence by context interaction was observed in the aMCC too, where we also observed a correlation (across participants) of evoked responses and reaction time data. Taken together, our study revealed the context-sensitive engagement of motor-related areas during emotional perception, thus supporting the idea that emotion and action interact in important ways in the brain.
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Affiliation(s)
- Liana Catarina Lima Portugal
- Department of Physiology and Pharmacology, Laboratory of Neurophysiology of Behavior, Biomedical Institute, Federal Fluminense University, Niterói, RJ, Brazil
| | - Rita de Cássia Soares Alves
- Department of Physiology and Pharmacology, Laboratory of Neurophysiology of Behavior, Biomedical Institute, Federal Fluminense University, Niterói, RJ, Brazil
| | - Orlando Fernandes Junior
- Laboratory of Neuroimaging and Psychophysiology, Department of Radiology, Faculty of Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Tiago Arruda Sanchez
- Laboratory of Neuroimaging and Psychophysiology, Department of Radiology, Faculty of Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Izabela Mocaiber
- Laboratory of Cognitive Psychophysiology, Department of Natural Sciences, Institute of Humanities and Health, Federal Fluminense University, Rio das Ostras, RJ, Brazil
| | - Eliane Volchan
- Laboratory of Neurobiology II, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Fátima Smith Erthal
- Laboratory of Neurobiology II, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Isabel Antunes David
- Department of Physiology and Pharmacology, Laboratory of Neurophysiology of Behavior, Biomedical Institute, Federal Fluminense University, Niterói, RJ, Brazil
| | - Jongwan Kim
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Leticia Oliveira
- Department of Physiology and Pharmacology, Laboratory of Neurophysiology of Behavior, Biomedical Institute, Federal Fluminense University, Niterói, RJ, Brazil
| | | | - Gang Chen
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA
| | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, USA; Maryland Neuroimaging Center, University of Maryland, College Park, MD, USA
| | - Mirtes Garcia Pereira
- Department of Physiology and Pharmacology, Laboratory of Neurophysiology of Behavior, Biomedical Institute, Federal Fluminense University, Niterói, RJ, Brazil.
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24
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Venkatesh M, Jaja J, Pessoa L. Comparing functional connectivity matrices: A geometry-aware approach applied to participant identification. Neuroimage 2020; 207:116398. [PMID: 31783117 PMCID: PMC6995445 DOI: 10.1016/j.neuroimage.2019.116398] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 11/06/2019] [Accepted: 11/22/2019] [Indexed: 12/11/2022] Open
Abstract
Understanding the correlation structure associated with multiple brain measurements informs about potential "functional groupings" and network organization. The correlation structure can be conveniently captured in a matrix format that summarizes the relationships among a set of brain measurements involving two regions, for example. Such functional connectivity matrix is an important component of many types of investigation focusing on network-level properties of the brain, including clustering brain states, characterizing dynamic functional states, performing participant identification (so-called "fingerprinting") understanding how tasks reconfigure brain networks, and inter-subject correlation analysis. In these investigations, some notion of proximity or similarity of functional connectivity matrices is employed, such as their Euclidean distance or Pearson correlation (by correlating the matrix entries). Here, we propose the use of a geodesic distance metric that reflects the underlying non-Euclidean geometry of functional correlation matrices. The approach is evaluated in the context of participant identification (fingerprinting): given a participant's functional connectivity matrix based on resting-state or task data, how effectively can the participant be identified? Using geodesic distance, identification accuracy was over 95% on resting-state data, and exceeded the Pearson correlation approach by 20%. For whole-cortex regions, accuracy improved on a range of tasks by between 2% and as much as 20%. We also investigated identification using pairs of subnetworks (say, dorsal attention plus default mode), and particular combinations improved accuracy over whole-cortex participant identification by over 10%. The geodesic distance also outperformed Pearson correlation when the former employed a fourth of the data as the latter. Finally, we suggest that low-dimensional distance visualizations based on the geodesic approach help uncover the geometry of task functional connectivity in relation to that during resting-state. We propose that the use of the geodesic distance is an effective way to compare the correlation structure of the brain across a broad range of studies.
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Affiliation(s)
- Manasij Venkatesh
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA.
| | - Joseph Jaja
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA
| | - Luiz Pessoa
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, MD, USA.
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25
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Chen G, Taylor PA, Cox RW, Pessoa L. Fighting or embracing multiplicity in neuroimaging? neighborhood leverage versus global calibration. Neuroimage 2020; 206:116320. [PMID: 31698079 PMCID: PMC6980934 DOI: 10.1016/j.neuroimage.2019.116320] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/23/2019] [Accepted: 10/27/2019] [Indexed: 01/24/2023] Open
Abstract
Neuroimaging faces the daunting challenge of multiple testing - an instance of multiplicity - that is associated with two other issues to some extent: low inference efficiency and poor reproducibility. Typically, the same statistical model is applied to each spatial unit independently in the approach of massively univariate modeling. In dealing with multiplicity, the general strategy employed in the field is the same regardless of the specifics: trust the local "unbiased" effect estimates while adjusting the extent of statistical evidence at the global level. However, in this approach, modeling efficiency is compromised because each spatial unit (e.g., voxel, region, matrix element) is treated as an isolated and independent entity during massively univariate modeling. In addition, the required step of multiple testing "correction" by taking into consideration spatial relatedness, or neighborhood leverage, can only partly recoup statistical efficiency, resulting in potentially excessive penalization as well as arbitrariness due to thresholding procedures. Moreover, the assigned statistical evidence at the global level heavily relies on the data space (whole brain or a small volume). The present paper reviews how Stein's paradox (1956) motivates a Bayesian multilevel (BML) approach that, rather than fighting multiplicity, embraces it to our advantage through a global calibration process among spatial units. Global calibration is accomplished via a Gaussian distribution for the cross-region effects whose properties are not a priori specified, but a posteriori determined by the data at hand through the BML model. Our framework therefore incorporates multiplicity as integral to the modeling structure, not a separate correction step. By turning multiplicity into a strength, we aim to achieve five goals: 1) improve the model efficiency with a higher predictive accuracy, 2) control the errors of incorrect magnitude and incorrect sign, 3) validate each model relative to competing candidates, 4) reduce the reliance and sensitivity on the choice of data space, and 5) encourage full results reporting. Our modeling proposal reverberates with recent proposals to eliminate the dichotomization of statistical evidence ("significant" vs. "non-significant"), to improve the interpretability of study findings, as well as to promote reporting the full gamut of results (not only "significant" ones), thereby enhancing research transparency and reproducibility.
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Affiliation(s)
- Gang Chen
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA.
| | - Paul A Taylor
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA
| | - Robert W Cox
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA
| | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, USA; Department of Electrical and Computer Engineering, University of Maryland, College Park, USA; Maryland Neuroimaging Center, University of Maryland, College Park, USA
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26
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Pessoa L. Neural dynamics of emotion and cognition: From trajectories to underlying neural geometry. Neural Netw 2019; 120:158-166. [PMID: 31522827 PMCID: PMC6899176 DOI: 10.1016/j.neunet.2019.08.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 07/15/2019] [Accepted: 08/09/2019] [Indexed: 01/31/2023]
Abstract
How can we study, characterize, and understand the neural underpinnings of cognitive-emotional behaviors as inherently dynamic processes? In the past 50 years, Stephen Grossberg has developed a research program that embraces the themes of dynamics, decentralized computation, emergence, selection and competition, and autonomy. The present paper discusses how these principles can be heeded by experimental scientists to advance the understanding of the brain basis of behavior. It is suggested that a profitable way forward is to focus on investigating the dynamic multivariate structure of brain data. Accordingly, central research problems involve characterizing "neural trajectories" and the associated geometry of the underlying "neural space." Finally, it is argued that, at a time when the development of neurotechniques has reached a fever pitch, neuroscience needs to redirect its focus and invest comparable energy in the conceptual and theoretical dimensions of its research endeavor. Otherwise we run the risk of being able to measure "every atom" in the brain in a theoretical vacuum.
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Affiliation(s)
- Luiz Pessoa
- Department of Psychology, Department of Electrical and Computer Engineering, Maryland Neuroimaging Center, University of Maryland, College Park, USA.
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27
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Palenciano AF, González-García C, Arco JE, Pessoa L, Ruz M. Representational Organization of Novel Task Sets during Proactive Encoding. J Neurosci 2019; 39:8386-8397. [PMID: 31427394 PMCID: PMC6794921 DOI: 10.1523/jneurosci.0725-19.2019] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 07/19/2019] [Accepted: 08/13/2019] [Indexed: 11/21/2022] Open
Abstract
Recent multivariate analyses of brain data have boosted our understanding of the organizational principles that shape neural coding. However, most of this progress has focused on perceptual visual regions (Connolly et al., 2012), whereas far less is known about the organization of more abstract, action-oriented representations. In this study, we focused on humans' remarkable ability to turn novel instructions into actions. While previous research shows that instruction encoding is tightly linked to proactive activations in frontoparietal brain regions, little is known about the structure that orchestrates such anticipatory representation. We collected fMRI data while participants (both males and females) followed novel complex verbal rules that varied across control-related variables (integrating within/across stimuli dimensions, response complexity, target category) and reward expectations. Using representational similarity analysis (Kriegeskorte et al., 2008), we explored where in the brain these variables explained the organization of novel task encoding, and whether motivation modulated these representational spaces. Instruction representations in the lateral PFC were structured by the three control-related variables, whereas intraparietal sulcus encoded response complexity and the fusiform gyrus and precuneus organized its activity according to the relevant stimulus category. Reward exerted a general effect, increasing the representational similarity among different instructions, which was robustly correlated with behavioral improvements. Overall, our results highlight the flexibility of proactive task encoding, governed by distinct representational organizations in specific brain regions. They also stress the variability of motivation-control interactions, which appear to be highly dependent on task attributes, such as complexity or novelty.SIGNIFICANCE STATEMENT In comparison with other primates, humans display a remarkable success in novel task contexts thanks to our ability to transform instructions into effective actions. This skill is associated with proactive task-set reconfigurations in frontoparietal cortices. It remains yet unknown, however, how the brain encodes in anticipation the flexible, rich repertoire of novel tasks that we can achieve. Here we explored cognitive control and motivation-related variables that might orchestrate the representational space for novel instructions. Our results showed that different dimensions become relevant for task prospective encoding, depending on the brain region, and that the lateral PFC simultaneously organized task representations following different control-related variables. Motivation exerted a general modulation upon this process, diminishing rather than increasing distances among instruction representations.
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Affiliation(s)
- Ana F Palenciano
- Mind, Brain, and Behavior Research Center, University of Granada, 18011, Granada, Spain
| | | | - Juan E Arco
- Mind, Brain, and Behavior Research Center, University of Granada, 18011, Granada, Spain
| | - Luiz Pessoa
- Psychology Department, University of Maryland 20742
| | - María Ruz
- Mind, Brain, and Behavior Research Center, University of Granada, 18011, Granada, Spain,
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28
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Pessoa L, Medina L, Hof PR, Desfilis E. Neural architecture of the vertebrate brain: implications for the interaction between emotion and cognition. Neurosci Biobehav Rev 2019; 107:296-312. [PMID: 31541638 DOI: 10.1016/j.neubiorev.2019.09.021] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 09/06/2019] [Accepted: 09/13/2019] [Indexed: 11/15/2022]
Abstract
Cognition is considered a hallmark of the primate brain that requires a high degree of signal integration, such as achieved in the prefrontal cortex. Moreover, it is often assumed that cognitive capabilities imply "superior" computational mechanisms compared to those involved in emotion or motivation. In contrast to these ideas, we review data on the neural architecture across vertebrates that support the concept that association and integration are basic features of the vertebrate brain, which are needed to successfully adapt to a changing world. This property is not restricted to a few isolated brain centers, but rather resides in neuronal networks working collectively in a context-dependent manner. In different vertebrates, we identify shared large-scale connectional systems involving the midbrain, hypothalamus, thalamus, basal ganglia, and amygdala. The high degree of crosstalk and association between these systems at different levels supports the notion that cognition, emotion, and motivation cannot be separated - all of them involve a high degree of signal integration.
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Affiliation(s)
- Luiz Pessoa
- Department of Psychology, Department of Electrical and Computer Engineering, Maryland Neuroimaging Center, University of Maryland, College Park, MD 20742, USA.
| | - Loreta Medina
- Laboratory of Evolutionary and Developmental Neurobiology, Department of Experimental Medicine, University of Lleida, Lleida Institute for Biomedical Research Dr. Pifarré Foundation (IRBLleida), 25198 Lleida, Spain
| | - Patrick R Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ester Desfilis
- Laboratory of Evolutionary and Developmental Neurobiology, Department of Experimental Medicine, University of Lleida, Lleida Institute for Biomedical Research Dr. Pifarré Foundation (IRBLleida), 25198 Lleida, Spain
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29
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Chen G, Bürkner PC, Taylor PA, Li Z, Yin L, Glen DR, Kinnison J, Cox RW, Pessoa L. An integrative Bayesian approach to matrix-based analysis in neuroimaging. Hum Brain Mapp 2019; 40:4072-4090. [PMID: 31188535 DOI: 10.1002/hbm.24686] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 03/29/2019] [Accepted: 05/27/2019] [Indexed: 12/21/2022] Open
Abstract
Understanding the correlation structure associated with brain regions is a central goal in neuroscience, as it informs about interregional relationships and network organization. Correlation structure can be conveniently captured in a matrix that indicates the relationships among brain regions, which could involve electroencephalogram sensors, electrophysiology recordings, calcium imaging data, or functional magnetic resonance imaging (FMRI) data-We call this type of analysis matrix-based analysis, or MBA. Although different methods have been developed to summarize such matrices across subjects, including univariate general linear models (GLMs), the available modeling strategies tend to disregard the interrelationships among the regions, leading to "inefficient" statistical inference. Here, we develop a Bayesian multilevel (BML) modeling framework that simultaneously integrates the analyses of all regions, region pairs (RPs), and subjects. In this approach, the intricate relationships across regions as well as across RPs are quantitatively characterized. The adoption of the Bayesian framework allows us to achieve three goals: (a) dissolve the multiple testing issue typically associated with seeking evidence for the effect of each RP under the conventional univariate GLM; (b) make inferences on effects that would be treated as "random" under the conventional linear mixed-effects framework; and (c) estimate the effect of each brain region in a manner that indexes their relative "importance". We demonstrate the BML methodology with an FMRI dataset involving a cognitive-emotional task and compare it to the conventional GLM approach in terms of model efficiency, performance, and inferences. The associated program MBA is available as part of the AFNI suite for general use.
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Affiliation(s)
- Gang Chen
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, Maryland
| | | | - Paul A Taylor
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, Maryland
| | - Zhihao Li
- School of Psychology and Sociology, Shenzhen University, Shenzhen, China
| | - Lijun Yin
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Daniel R Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, Maryland
| | - Joshua Kinnison
- Department of Psychology, University of Maryland, College Park, Maryland
| | - Robert W Cox
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, Maryland
| | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, Maryland.,Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland.,Maryland Neuroimaging Center, University of Maryland, College Park, Maryland
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30
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Abstract
Emotion is fundamental to our being, and an essential aspect guiding behavior when rapid responding is required. This includes whether we approach or avoid a stimulus, and the accompanying physiological responses. A common tenet is that threat-related content drives stimulus processing and biases visual attention, so that rapid responding can be initiated. In this paper, it will be argued instead that prioritization of threatening stimuli should be encompassed within a motivational relevance framework. To more fully understand what is, or is not, prioritized for visual processing one must, however, additionally consider: (i) stimulus ambiguity and perceptual saliency; (ii) task demands, including both perceptual load and cognitive load; and (iii) endogenous/affective states of the individual. Combined with motivational relevance, this then leads to a multifactorial approach to understanding the drivers of prioritized visual processing. This accords with current recognition that the brain basis allowing for visual prioritization is also multifactorial, including transient, dynamic and overlapping networks. Taken together, the paper provides a reconceptualization of how "emotional" information prioritizes visual processing.
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Affiliation(s)
- Frances Anne Maratos
- Department of Psychology and Human Sciences Research Centre, University of Derby, Derby, United Kingdom.
| | - Luiz Pessoa
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, MD, United States
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31
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Abstract
During real-life situations, multiple factors interact dynamically to determine threat level. In the current fMRI study involving healthy adult human volunteers, we investigated interactions between proximity, direction (approach vs. retreat), and speed during a dynamic threat-of-shock paradigm. As a measure of threat-evoked physiological arousal, skin conductance responses were recorded during fMRI scanning. Some brain regions tracked individual threat-related factors, and others were also sensitive to combinations of these variables. In particular, signals in the anterior insula tracked the interaction between proximity and direction where approach versus retreat responses were stronger when threat was closer compared with farther. A parallel proximity-by-direction interaction was also observed in physiological skin conductance responses. In the right amygdala, we observed a proximity by direction interaction, but intriguingly in the opposite direction as the anterior insula; retreat versus approach responses were stronger when threat was closer compared with farther. In the right bed nucleus of the stria terminalis, we observed an effect of threat proximity, whereas in the right periaqueductal gray/midbrain we observed an effect of threat direction and a proximity by direction by speed interaction (the latter was detected in exploratory analyses but not in a voxelwise fashion). Together, our study refines our understanding of the brain mechanisms involved during aversive anticipation in the human brain. Importantly, it emphasizes that threat processing should be understood in a manner that is both context-sensitive and dynamic.
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Affiliation(s)
- Christian Meyer
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Srikanth Padmala
- Department of Psychology, University of Maryland, College Park, MD, USA
- Neuroscience and Cognitive Sciences program, University of Maryland, College Park, MD, USA
| | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, USA
- Neuroscience and Cognitive Sciences program, University of Maryland, College Park, MD, USA
- Maryland Neuroimaging Center, University of Maryland, College Park, MD, USA
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32
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Sours C, Kinnison J, Padmala S, Gullapalli RP, Pessoa L. Altered segregation between task-positive and task-negative regions in mild traumatic brain injury. Brain Imaging Behav 2019; 12:697-709. [PMID: 28456880 DOI: 10.1007/s11682-017-9724-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Changes in large-scale brain networks that accompany mild traumatic brain injury (mTBI) were investigated using functional magnetic resonance imaging (fMRI) during the N-back working memory task at two cognitive loads (1-back and 2-back). Thirty mTBI patients were examined during the chronic stage of injury and compared to 28 control participants. Demographics and behavioral performance were matched across groups. Due to the diffuse nature of injury, we hypothesized that there would be an imbalance in the communication between task-positive and Default Mode Network (DMN) regions in the context of effortful task execution. Specifically, a graph-theoretic measure of modularity was used to quantify the extent to which groups of brain regions tended to segregate into task-positive and DMN sub-networks. Relative to controls, mTBI patients showed reduced segregation between the DMN and task-positive networks, but increased functional connectivity within the DMN regions during the more cognitively demanding 2-back task. Together, our findings reveal that patients exhibit alterations in the communication between and within neural networks during a cognitively demanding task. These findings reveal altered processes that persist through the chronic stage of injury, highlighting the need for longitudinal research to map the neural recovery of mTBI patients.
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Affiliation(s)
- Chandler Sours
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 22 South Greene Street, Baltimore, MD, 21201, USA.
| | - Joshua Kinnison
- Department of Psychology, University of Maryland, College Park, MD, 20742, USA
| | - Srikanth Padmala
- Department of Psychology, University of Maryland, College Park, MD, 20742, USA
| | - Rao P Gullapalli
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 22 South Greene Street, Baltimore, MD, 21201, USA
| | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, 20742, USA
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33
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Venkatesh M, Jaja J, Pessoa L. Brain dynamics and temporal trajectories during task and naturalistic processing. Neuroimage 2019; 186:410-423. [PMID: 30453032 PMCID: PMC6545598 DOI: 10.1016/j.neuroimage.2018.11.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 11/09/2018] [Accepted: 11/12/2018] [Indexed: 10/27/2022] Open
Abstract
Human functional Magnetic Resonance Imaging (fMRI) data are acquired while participants engage in diverse perceptual, motor, cognitive, and emotional tasks. Although data are acquired temporally, they are most often treated in a quasi-static manner. Yet, a fuller understanding of the mechanisms that support mental functions necessitates the characterization of dynamic properties. Here, we describe an approach employing a class of recurrent neural networks called reservoir computing, and show the feasibility and potential of using it for the analysis of temporal properties of brain data. We show that reservoirs can be used effectively both for condition classification and for characterizing lower-dimensional "trajectories" of temporal data. Classification accuracy was approximately 90% for short clips of "social interactions" and around 70% for clips extracted from movie segments. Data representations with 12 or fewer dimensions (from an original space with over 300) attained classification accuracy within 5% of the full data. We hypothesize that such low-dimensional trajectories may provide "signatures" that can be associated with tasks and/or mental states. The approach was applied across participants (that is, training in one set of participants, and testing in a separate group), showing that representations generalized well to unseen participants. Taken together, we believe the present approach provides a promising framework to characterize dynamic fMRI information during both tasks and naturalistic conditions.
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Affiliation(s)
- Manasij Venkatesh
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA.
| | - Joseph Jaja
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA
| | - Luiz Pessoa
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, MD, USA
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34
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Michel M, Beck D, Block N, Blumenfeld H, Brown R, Carmel D, Carrasco M, Chirimuuta M, Chun M, Cleeremans A, Dehaene S, Fleming SM, Frith C, Haggard P, He BJ, Heyes C, Goodale MA, Irvine L, Kawato M, Kentridge R, King JR, Knight RT, Kouider S, Lamme V, Lamy D, Lau H, Laureys S, LeDoux J, Lin YT, Liu K, Macknik SL, Martinez-Conde S, Mashour GA, Melloni L, Miracchi L, Mylopoulos M, Naccache L, Owen AM, Passingham RE, Pessoa L, Peters MAK, Rahnev D, Ro T, Rosenthal D, Sasaki Y, Sergent C, Solovey G, Schiff ND, Seth A, Tallon-Baudry C, Tamietto M, Tong F, van Gaal S, Vlassova A, Watanabe T, Weisberg J, Yan K, Yoshida M. Opportunities and challenges for a maturing science of consciousness. Nat Hum Behav 2019; 3:104-107. [PMID: 30944453 PMCID: PMC6568255 DOI: 10.1038/s41562-019-0531-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Matthias Michel
- Department of Philosophy, Sorbonne Université, Paris, France.
| | - Diane Beck
- Department of Psychology and Beckman Institute, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Ned Block
- Department of Philosophy, New York University, New York, New York, USA
| | - Hal Blumenfeld
- Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Richard Brown
- Philosophy Program, LaGuardia Community College, The City University of New York, Long Island City, New York, USA
| | - David Carmel
- School of Psychology, Victoria University of Wellington, Wellington, New Zealand
| | - Marisa Carrasco
- Department of Psychology and Center for Neural Science, New York University, New York, New York, USA
| | - Mazviita Chirimuuta
- Department of History and Philosophy of Science, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Marvin Chun
- Department of Psychology, Yale University, New Haven, Connecticut, USA
| | - Axel Cleeremans
- Center for Cognition & Neurosciences, Université libre de Bruxelles, Bruxelles, Belgium
| | - Stanislas Dehaene
- Chair of Experimental Cognitive Psychology, College de France, Paris, France.,Cognitive Neuroimaging Unit, Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, Gif/Yvette, France
| | - Stephen M Fleming
- Wellcome Centre for Human Neuroimaging, University College London, London, UK.
| | - Chris Frith
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Patrick Haggard
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Biyu J He
- Neuroscience Institute, New York University Langone Medical Center, New York, New York, USA
| | - Cecilia Heyes
- All Souls College and Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Melvyn A Goodale
- The Brain and Mind Institute, The University of Western Ontario, London, ON, Canada
| | - Liz Irvine
- School of Philosophy, Cardiff University, Cardiff, UK
| | - Mitsuo Kawato
- Department of Decoded Neurofeedback, Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International, Kyoto, Japan
| | | | - Jean-Remi King
- Department of Psychology, New York University, New York, New York, United States.,Frankfurt Institute for Advanced Studies, Frankfurt, Germany
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California, Berkeley, USA
| | - Sid Kouider
- Brain and Consciousness group (ENS, EHESS, CNRS), Département d'Études Cognitives, École Normale Supérieure - PSL Research University, Paris, France
| | - Victor Lamme
- Amsterdam Brain and Cognition, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Dominique Lamy
- School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Hakwan Lau
- Department of Psychology and Brain Research Institute, UCLA, Los Angeles, USA. .,Department of Psychology, University of Hong Kong, Hong Kong, China. .,State Key Laboratory of Brain and Cognitive Sciences, HKU, Hong Kong, China.
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
| | - Joseph LeDoux
- Center for Neural Science, New York University, New York, New York, USA
| | - Ying-Tung Lin
- Institute of Philosophy of Mind and Cognition, National Yang-Ming University, Taipei, Taiwan
| | - Kayuet Liu
- Department of Sociology, UCLA, Los Angeles, California, USA
| | - Stephen L Macknik
- State University of New York, Downstate Medical Center, Brooklyn, New York, USA
| | | | - George A Mashour
- Center for Consciousness Science, Department of Anesthesiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Lucia Melloni
- Department of Neurology, NYU School of Medicine, New York, New York, USA
| | - Lisa Miracchi
- Department of Philosophy, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Myrto Mylopoulos
- Department of Philosophy and Institute of Cognitive Science, Carleton University, Ottawa, Ontario, Canada
| | | | - Adrian M Owen
- The Brain & Mind Institute, Western University, London, Ontario, Canada
| | | | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, Maryland, USA
| | - Megan A K Peters
- Department of Bioengineering, University of California, Riverside, California, USA
| | - Dobromir Rahnev
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Tony Ro
- Psychology and Biology, Graduate Center, City University of New York, New York, New York, USA
| | - David Rosenthal
- Philosophy and Cognitive Science, Graduate Center, City University of New York, New York, New York, USA
| | - Yuka Sasaki
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, Rhode Island, USA
| | - Claire Sergent
- Laboratoire Psychologie de la Perception, Université Paris Descartes, CNRS, Paris, France
| | - Guillermo Solovey
- Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Nicholas D Schiff
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Anil Seth
- Sackler Centre for Consciousness Science, University of Sussex, Brighton, UK
| | - Catherine Tallon-Baudry
- Cognitive Neuroscience Laboratory, INSERM, École Normale Supérieure - PSL Research University, Paris, France
| | - Marco Tamietto
- Department of Medical and Clinical Psychology, Tilburg University, Tilburg, The Netherlands.,Department of Psychology, University of Torino, Torino, Italy
| | - Frank Tong
- Psychology Department, Vanderbilt University, Nashville, Tennessee, USA
| | - Simon van Gaal
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Alexandra Vlassova
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Takeo Watanabe
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, Rhode Island, USA
| | - Josh Weisberg
- Department of Philosophy, University of Houston, Houston, Texas, USA
| | - Karen Yan
- Institute of Philosophy of Mind and Cognition, National Yang-Ming University, Taipei, Taiwan
| | - Masatoshi Yoshida
- Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Japan
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35
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Uher J, Trofimova I, Sulis W, Netter P, Pessoa L, Posner MI, Rothbart MK, Rusalov V, Peterson IT, Schmidt LA. Diversity in action: exchange of perspectives and reflections on taxonomies of individual differences. Philos Trans R Soc Lond B Biol Sci 2019; 373:rstb.2017.0172. [PMID: 29483355 DOI: 10.1098/rstb.2017.0172] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/22/2018] [Indexed: 12/31/2022] Open
Abstract
Throughout the last 2500 years, the classification of individual differences in healthy people and their extreme expressions in mental disorders has remained one of the most difficult challenges in science that affects our ability to explore individuals' functioning, underlying psychobiological processes and pathways of development. To facilitate analyses of the principles required for studying individual differences, this theme issue brought together prominent scholars from diverse backgrounds of which many bring unique combinations of cross-disciplinary experiences and perspectives that help establish connections and promote exchange across disciplines. This final paper presents brief commentaries of some of our authors and further scholars exchanging perspectives and reflecting on the contributions of this theme issue.This article is part of the theme issue 'Diverse perspectives on diversity: multi-disciplinary approaches to taxonomies of individual differences'.
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Affiliation(s)
- Jana Uher
- University of Greenwich, Old Royal Naval College, Park Row, London SE10 9LS, United Kingdom .,London School of Economics, Houghton Street, WC2A 2AE London, United Kingdom
| | - Irina Trofimova
- Department of Psychiatry and Behavioral Neurosciences, McMaster University, Canada
| | - William Sulis
- Department of Psychiatry and Behavioral Neurosciences, McMaster University, Canada
| | - Petra Netter
- Department of Psychology, University of Giessen, Germany
| | - Luiz Pessoa
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, Maryland, USA
| | | | | | - Vladimir Rusalov
- Institute of Psychology, Russian Academy of Sciences, Druzhinin Laboratory of Abilities, Moscow, Russia
| | - Isaac T Peterson
- Department of Psychological and Brain Sciences, University of Iowa, USA
| | - Louis A Schmidt
- Department of Psychology, Neuroscience & Behaviour, McMaster University, Canada
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Padmala S, Sambuco N, Pessoa L. Interactions between reward motivation and emotional processing. Progress in Brain Research 2019; 247:1-21. [DOI: 10.1016/bs.pbr.2019.03.023] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Abstract
The present paper addresses conceptual issues that are central to emotion research. What is emotion? What are its defining characteristics? The field struggles with questions like these almost constantly. I argue that definitions, and deciding what is the proper status of emotion, are not a requirement for scientific progress - in fact, they can hinder it. Therefore, "emotion" researchers should strive to develop a science of complex behaviours, and worry less about their exact nature. But for interesting behaviours, is most of the explaining that is needed present at the level of isolated systems (perception, cognition, etc.) or at the level of interactions between them? I suggest that the level of interactions is where most of the work is needed. Accordingly, I advocate that it is important to embrace integration, and not to strive to necessarily disentangle the multiple contributions underlying behaviours. More generally, it is argued that we need to revise models of causation adopted when reasoning about the mind and brain. Instead, a "complex systems" approach is required where the interactions between multiple components lead to system-level - emergent - properties that cannot be isolated or attributed to more elementary parts.
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Affiliation(s)
- Luiz Pessoa
- a Department of Psychology and Maryland Neuroimaging Center , University of Maryland , College Park , MD , USA
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Oliveira J, Duarte A, Santos C, Pessoa L, Filho CM, Lima J, Carvalho D, Xavier T, Figueiredo E, Giovanetti M, Almeida B, Goes J, Lima F, Alcantara L, Siqueira I. Prevalence of Zika, dengue and Chikungunya virus infection in pregnant women and surveillance of congenital Zika infection in Salvador, Brazil. Int J Infect Dis 2018. [DOI: 10.1016/j.ijid.2018.04.3826] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022] Open
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Abstract
In this response, I suggest that the focus of “emotion” researchers should be more on striving to develop a science of brain and behavior than on deciding what is the proper status of emotion. Because structure and function are closely intertwined in biological systems, advancing our understanding of complex behaviors will necessitate researching their brain substrates.
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Affiliation(s)
- Luiz Pessoa
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, USA
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Abstract
Although emotion is closely associated with motivation, and interacts with perception, cognition, and action, many conceptualizations still treat emotion as separate from these domains. Here, a comparative/evolutionary anatomy framework is presented to motivate the idea that long-range, distributed circuits involving the midbrain, thalamus, and forebrain are central to emotional processing. It is proposed that emotion can be understood in terms of large-scale network interactions spanning the neuroaxis that form "functionally integrated systems." At the broadest level, the argument is made that we need to move beyond a Newtonian view of causation to one involving complex systems where bidirectional influences and nonlinearities abound. Therefore, understanding interactions between subsystems and signal integration becomes central to unraveling the organization of the emotional brain.
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Affiliation(s)
- Luiz Pessoa
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, USA
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Padmala S, Sirbu M, Pessoa L. Potential reward reduces the adverse impact of negative distractor stimuli. Soc Cogn Affect Neurosci 2018; 12:1402-1413. [PMID: 28505380 PMCID: PMC5629819 DOI: 10.1093/scan/nsx067] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 04/23/2017] [Indexed: 11/23/2022] Open
Abstract
Knowledge about interactions between reward and negative processing is rudimentary. Here, we employed functional MRI to probe how potential reward signaled by advance cues alters aversive distractor processing during perception. Behaviorally, the influence of aversive stimuli on task performance was reduced during the reward compared to no-reward condition. In the brain, at the task phase, paralleling the observed behavioral pattern, we observed significant interactions in the anterior insula and dorsal anterior cingulate cortex, such that responses during the negative (vs neutral) condition were reduced during the reward compared to no-reward condition. Notably, negative distractor processing in the amygdala appeared to be independent of the reward manipulation. During the initial cue phase, we observed increased reward-related responses in the ventral striatum/accumbens, which were correlated with behavioral interference scores at the subsequent task phase, revealing that participants with increased reward-related responses exhibited a greater behavioral benefit of reward in reducing the adverse effect of negative images. Furthermore, during processing of reward (vs no-reward) cues, the ventral striatum exhibited stronger functional connectivity with fronto-parietal regions important for attentional control. Together, our findings contribute to the understanding of how potential reward influences attentional control and reduces negative distractor processing in the human brain.
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Affiliation(s)
- Srikanth Padmala
- Department of Psychology.,Neuroscience and Cognitive Sciences Program, University of Maryland, College Park, MD, USA
| | | | - Luiz Pessoa
- Department of Psychology.,Neuroscience and Cognitive Sciences Program, University of Maryland, College Park, MD, USA
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Abstract
Humans frequently make choices that involve risk for health and well-being. At the same time, information about others’ choices is omnipresent due to new forms of social media and information technology. However, while past research has shown that peers can exert a strong influence on such risky choices, understanding how information about risky decisions of others affects one’s own risky decisions is still lacking. We therefore developed a behavioral task to measure how information about peer choices affects risky decision-making and call it the social Balloon Analogue Risk Task (sBART). We tested this novel paradigm in a sample of 52 college young adults. Here we show that risky decisions were influenced in the direction of the perceived choices of others – riskier choices of others led to riskier behavior whereas safer choices of others led to less risky behavior. These findings indicate that information about peer choices is sufficient to shape one’s own risky behavior.
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Affiliation(s)
- Livia Tomova
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, USA.,Maryland Neuroimaging Center, University of Maryland, College Park, USA
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Liu C, JaJa J, Pessoa L. LEICA: Laplacian eigenmaps for group ICA decomposition of fMRI data. Neuroimage 2018; 169:363-373. [PMID: 29246846 DOI: 10.1016/j.neuroimage.2017.12.018] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Revised: 12/05/2017] [Accepted: 12/09/2017] [Indexed: 10/18/2022] Open
Abstract
Independent component analysis (ICA) is a data-driven method that has been increasingly used for analyzing functional Magnetic Resonance Imaging (fMRI) data. However, generalizing ICA to multi-subject studies is non-trivial due to the high-dimensionality of the data, the complexity of the underlying neuronal processes, the presence of various noise sources, and inter-subject variability. Current group ICA based approaches typically use several forms of the Principal Component Analysis (PCA) method to extend ICA for generating group inferences. However, linear dimensionality reduction techniques have serious limitations including the fact that the underlying BOLD signal is a complex function of several nonlinear processes. In this paper, we propose an effective non-linear ICA-based model for extracting group-level spatial maps from multi-subject fMRI datasets. We use a non-linear dimensionality reduction algorithm based on Laplacian eigenmaps to identify a manifold subspace common to the group, such that this mapping preserves the correlation among voxels' time series as much as possible. These eigenmaps are modeled as linear mixtures of a set of group-level spatial features, which are then extracted using ICA. The resulting algorithm is called LEICA (Laplacian Eigenmaps for group ICA decomposition). We introduce a number of methods to evaluate LEICA using 100-subject resting state and 100-subject working memory task fMRI datasets from the Human Connectome Project (HCP). The test results show that the extracted spatial maps from LEICA are meaningful functional networks similar to those produced by some of the best known methods. Importantly, relative to state-of-the-art methods, our algorithm compares favorably in terms of the functional cohesiveness of the spatial maps generated, as well as in terms of the reproducibility of the results.
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Affiliation(s)
- Chihuang Liu
- Institute for Advanced Computer Studies and Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA.
| | - Joseph JaJa
- Institute for Advanced Computer Studies and Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA
| | - Luiz Pessoa
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, MD 20742, USA
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Padmala S, Sambuco N, Codispoti M, Pessoa L. Attentional capture by simultaneous pleasant and unpleasant emotional distractors. ACTA ACUST UNITED AC 2018; 18:1189-1194. [PMID: 29494204 DOI: 10.1037/emo0000401] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Both high-arousal pleasant and unpleasant task-irrelevant stimuli capture attention and divert processing away from the main task leading to impaired behavioral performance in concurrent tasks. Most studies have separately investigated interference effects of unpleasant and pleasant stimuli on behavior. Thus, little is known about how pleasant and unpleasant task-irrelevant stimuli influence behavior simultaneously. In the present study, we investigated this question during a visual-letter search task. We tested two alternative hypotheses about the influence of simultaneous pleasant and unpleasant task-irrelevant stimuli on task performance. If behavior is purely determined by the intensity of the distractor stimuli (independent of valence), then we would expect the interference effect of simultaneous pleasant and unpleasant distractors to be similar to the influence of two pleasant or two unpleasant distractor stimuli. In contrast, because of opponent interactions between appetitive and aversive motivational systems, the interference effect of simultaneous pleasant and unpleasant stimuli might be weakened. We found that the interference effect of a compound pleasant-plus-unpleasant stimulus was greater than that of a neutral-plus-emotional stimulus and similar to that of two pleasant or two unpleasant stimuli. These results suggest that at the level of behavior, the influence of joint pleasant and unpleasant task-irrelevant stimuli during perception is mainly determined by the intensity of the stimuli, and independent of their valence. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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Affiliation(s)
| | | | | | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park
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Abstract
How do large-scale brain networks reorganize during the waxing and waning of anxious anticipation? Here, threat was dynamically modulated during human functional MRI as two circles slowly meandered on the screen; if they touched, an unpleasant shock was delivered. We employed intersubject correlation analysis, which allowed the investigation of network-level functional connectivity across brains, and sought to determine how network connectivity changed during periods of approach (circles moving closer) and periods of retreat (circles moving apart). Analysis of positive connection weights revealed that dynamic threat altered connectivity within and between the salience, executive, and task-negative networks. For example, dynamic functional connectivity increased within the salience network during approach and decreased during retreat. The opposite pattern was found for the functional connectivity between the salience and task-negative networks: decreases during approach and increases during approach. Functional connections between subcortical regions and the salience network also changed dynamically during approach and retreat periods. Subcortical regions exhibiting such changes included the putative periaqueductal gray, putative habenula, and putative bed nucleus of the stria terminalis. Additional analysis of negative functional connections revealed dynamic changes, too. For example, negative weights within the salience network decreased during approach and increased during retreat, opposite what was found for positive weights. Together, our findings unraveled dynamic features of functional connectivity of large-scale networks and subcortical regions across participants while threat levels varied continuously, and demonstrate the potential of characterizing emotional processing at the level of dynamic networks.
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Affiliation(s)
- Mahshid Najafi
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, College Park, MD, United States
| | - Joshua Kinnison
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, College Park, MD, United States
| | - Luiz Pessoa
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, College Park, MD, United States
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46
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Abstract
Emotional processing appears to be interlocked with perception, cognition, motivation, and action. These interactions are supported by the brain's large-scale non-modular anatomical and functional architectures. An important component of this organization involves characterizing the brain in terms of networks. Two aspects of brain networks are discussed: brain networks should be considered as inherently overlapping (not disjoint) and dynamic (not static). Recent work on multivariate pattern analysis shows that affective dimensions can be detected in the activity of distributed neural systems that span cortical and subcortical regions. More broadly, the paper considers how we should think of causation in complex systems like the brain, so as to inform the relationship between emotion and other mental aspects, such as cognition.
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Affiliation(s)
- Luiz Pessoa
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, Biology/Psychology Building, University of Maryland, College Park, MD 20742, USA
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Abstract
How do motivation and cognitive control interact in brain and behavior? The past decade has witnessed a steady growth in studies investigating both the behavioral and the brain basis of these interactions. In this paper, I describe such interactions in the context of the dual completion model, which proposes that motivational significance influences both perceptual and executive competition. Embracing a research agenda that attempts to understand cognition-motivation interactions highlights considerable challenges faced by investigators. For example, even the standard language utilized, with terms such as "perception," "attention," "cognition," and "motivation," encourages a modular-like conceptualization of the underlying processes and mechanisms. I propose that large-scale interactions involving both task-related and valuation-related networks help understand how motivation shapes executive function. I argue that, ultimately, the mind and brain sciences need to move beyond "boxes and arrows" and fully embrace the richness and complexity of the interactions between motivation and cognition. In the last 10 years, the study in humans of the interactions of motivation with perception and cognition has grown at a fast pace. The growth has included behavioral studies characterizing the processes involved, and neuroimaging studies investigating the regions and circuits underlying the behaviors in question. This literature acknowledges the fact that perception and cognition do not happen in a vacuum but are, instead, situated in contexts that feature value. Although this assertion is uncontroversial, the mind and brain sciences have studied perception and cognition for many decades by largely extricating value from them. Fortunately, this state of affairs has now changed and the field has a newfound vigor in attempting to understand the impact of motivation on these mental functions.
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Affiliation(s)
- Luiz Pessoa
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park
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Abstract
What is the place of emotion in intelligent robots? Researchers have advocated the inclusion of some emotion-related components in the information-processing architecture of autonomous agents. It is argued here that emotion needs to be merged with all aspects of the architecture: cognitive-emotional integration should be a key design principle.
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Affiliation(s)
- Luiz Pessoa
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, MD, USA.
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49
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Abstract
Emotion is often understood in terms of a circumscribed set of cortical and subcortical brain regions. I propose, instead, that emotion should be understood in terms of large-scale network interactions spanning the entire neuroaxis. I describe multiple anatomical and functional principles of brain organization that lead to the concept of 'functionally integrated systems', cortical-subcortical systems that anchor the organization of emotion in the brain. The proposal is illustrated by describing the cortex-amygdala integrated system and how it intersects with systems involving the ventral striatum/accumbens, septum, hippocampus, hypothalamus, and brainstem. The important role of the thalamus is also highlighted. Overall, the model clarifies why the impact of emotion is wide-ranging, and how emotion is interlocked with perception, cognition, motivation, and action.
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Affiliation(s)
- Luiz Pessoa
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, MD 20742, USA.
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50
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Abstract
Research on the emotional brain has often focused on a few structures thought to be central to this type of processing-hypothalamus, amygdala, insula, and so on. Conceptual thinking about emotion has viewed this mental faculty as linked to broader brain circuits, too, including early ideas by Papez and others. In this article, we discuss research that embraces a distributed view of emotion circuits and efforts to unravel the impact on emotional manipulations on the processing of several large-scale brain networks that are chiefly important for mental operations traditionally labeled with terms such as "perception," "action," and "cognition." Furthermore, we describe networks as dynamic processes and how emotion-laden stimuli strongly affect network structure. As networks are not static entities, their organization unfolds temporally, such that specific brain regions affiliate with them in a time-varying fashion. Thus, at a specific moment, brain regions participate more strongly in some networks than others. In this dynamic view of brain function, emotion has broad, distributed effects on processing in a manner that transcends traditional boundaries and inflexible labels, such as "emotion" and "cognition." What matters is the coordinated action that supports behaviors.
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Affiliation(s)
- Luiz Pessoa
- 1 Department of Psychology, University of Maryland, College Park, MD, USA
| | - Brenton McMenamin
- 1 Department of Psychology, University of Maryland, College Park, MD, USA
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