1
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Lee KM, Satpute AB. More than labels: neural representations of emotion words are widely distributed across the brain. Soc Cogn Affect Neurosci 2024; 19:nsae043. [PMID: 38903026 PMCID: PMC11259136 DOI: 10.1093/scan/nsae043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 03/15/2024] [Accepted: 06/20/2024] [Indexed: 06/22/2024] Open
Abstract
Although emotion words such as "anger," "disgust," "happiness," or "pride" are often thought of as mere labels, increasing evidence points to language as being important for emotion perception and experience. Emotion words may be particularly important for facilitating access to the emotion concepts. Indeed, deficits in semantic processing or impaired access to emotion words interfere with emotion perception. Yet, it is unclear what these behavioral findings mean for affective neuroscience. Thus, we examined the brain areas that support processing of emotion words using representational similarity analysis of functional magnetic resonance imaging data (N = 25). In the task, participants saw 10 emotion words (e.g. "anger," "happiness") while in the scanner. Participants rated each word based on its valence on a continuous scale ranging from 0 (Pleasant/Good) to 1 (Unpleasant/Bad) scale to ensure they were processing the words. Our results revealed that a diverse range of brain areas including prefrontal, midline cortical, and sensorimotor regions contained information about emotion words. Notably, our results overlapped with many regions implicated in decoding emotion experience by prior studies. Our results raise questions about what processes are being supported by these regions during emotion experience.
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Affiliation(s)
- Kent M Lee
- Department of Psychology, Northeastern University, 125 Nightingale Hall, Boston, MA 02115, USA
| | - Ajay B Satpute
- Department of Psychology, Northeastern University, 125 Nightingale Hall, Boston, MA 02115, USA
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2
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Abdel-Ghaffar SA, Huth AG, Lescroart MD, Stansbury D, Gallant JL, Bishop SJ. Occipital-temporal cortical tuning to semantic and affective features of natural images predicts associated behavioral responses. Nat Commun 2024; 15:5531. [PMID: 38982092 PMCID: PMC11233618 DOI: 10.1038/s41467-024-49073-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 05/22/2024] [Indexed: 07/11/2024] Open
Abstract
In everyday life, people need to respond appropriately to many types of emotional stimuli. Here, we investigate whether human occipital-temporal cortex (OTC) shows co-representation of the semantic category and affective content of visual stimuli. We also explore whether OTC transformation of semantic and affective features extracts information of value for guiding behavior. Participants viewed 1620 emotional natural images while functional magnetic resonance imaging data were acquired. Using voxel-wise modeling we show widespread tuning to semantic and affective image features across OTC. The top three principal components underlying OTC voxel-wise responses to image features encoded stimulus animacy, stimulus arousal and interactions of animacy with stimulus valence and arousal. At low to moderate dimensionality, OTC tuning patterns predicted behavioral responses linked to each image better than regressors directly based on image features. This is consistent with OTC representing stimulus semantic category and affective content in a manner suited to guiding behavior.
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Affiliation(s)
- Samy A Abdel-Ghaffar
- Department of Psychology, UC Berkeley, Berkeley, CA, 94720, USA
- Google LLC, San Francisco, CA, USA
| | - Alexander G Huth
- Centre for Theoretical and Computational Neuroscience, UT Austin, Austin, TX, 78712, USA
| | - Mark D Lescroart
- Department of Psychology University of Nevada Reno, Reno, NV, 89557, USA
| | - Dustin Stansbury
- Program in Vision Sciences, UC Berkeley, Berkeley, CA, 94720, USA
| | - Jack L Gallant
- Department of Psychology, UC Berkeley, Berkeley, CA, 94720, USA
- Program in Vision Sciences, UC Berkeley, Berkeley, CA, 94720, USA
- Helen Wills Neuroscience Institute, UC Berkeley, Berkeley, CA, 94720, USA
| | - Sonia J Bishop
- Department of Psychology, UC Berkeley, Berkeley, CA, 94720, USA.
- Helen Wills Neuroscience Institute, UC Berkeley, Berkeley, CA, 94720, USA.
- School of Psychology, Trinity College Dublin, Dublin, Ireland.
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, D02 PX31, Ireland.
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3
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Dautan D, Monai A, Maltese F, Chang X, Molent C, Mauro D, Galbusera A, Vecchia D, Antonelli F, Benedetti A, Drago F, Leggio GM, Pagani M, Fellin T, Gozzi A, Schumann G, Managò F, Papaleo F. Cortico-cortical transfer of socially derived information gates emotion recognition. Nat Neurosci 2024; 27:1318-1332. [PMID: 38769153 DOI: 10.1038/s41593-024-01647-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 04/09/2024] [Indexed: 05/22/2024]
Abstract
Emotion recognition and the resulting responses are important for survival and social functioning. However, how socially derived information is processed for reliable emotion recognition is incompletely understood. Here, we reveal an evolutionarily conserved long-range inhibitory/excitatory brain network mediating these socio-cognitive processes. Anatomical tracing in mice revealed the existence of a subpopulation of somatostatin (SOM) GABAergic neurons projecting from the medial prefrontal cortex (mPFC) to the retrosplenial cortex (RSC). Through optogenetic manipulations and Ca2+ imaging fiber photometry in mice and functional imaging in humans, we demonstrate the specific participation of these long-range SOM projections from the mPFC to the RSC, and an excitatory feedback loop from the RSC to the mPFC, in emotion recognition. Notably, we show that mPFC-to-RSC SOM projections are dysfunctional in mouse models relevant to psychiatric vulnerability and can be targeted to rescue emotion recognition deficits in these mice. Our findings demonstrate a cortico-cortical circuit underlying emotion recognition.
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Affiliation(s)
- Daniel Dautan
- Genetics of Cognition Laboratory, Neuroscience area, Istituto Italiano di Tecnologia, Genova, Italy
- Bioclinicum, Karolinska Institute, Stockholm, Sweden
| | - Anna Monai
- Genetics of Cognition Laboratory, Neuroscience area, Istituto Italiano di Tecnologia, Genova, Italy
| | - Federica Maltese
- Genetics of Cognition Laboratory, Neuroscience area, Istituto Italiano di Tecnologia, Genova, Italy
| | - Xiao Chang
- Centre for Population Neuroscience and Stratified Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, P. R. China
| | - Cinzia Molent
- Genetics of Cognition Laboratory, Neuroscience area, Istituto Italiano di Tecnologia, Genova, Italy
| | - Daniele Mauro
- Genetics of Cognition Laboratory, Neuroscience area, Istituto Italiano di Tecnologia, Genova, Italy
| | - Alberto Galbusera
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Dania Vecchia
- Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
| | - Federica Antonelli
- Genetics of Cognition Laboratory, Neuroscience area, Istituto Italiano di Tecnologia, Genova, Italy
| | - Arianna Benedetti
- Genetics of Cognition Laboratory, Neuroscience area, Istituto Italiano di Tecnologia, Genova, Italy
| | - Filippo Drago
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
| | - Gian Marco Leggio
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
| | - Marco Pagani
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Tommaso Fellin
- Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, P. R. China
- Centre for Population Neuroscience and Stratified Medicine (PONS), Charite Mental Health, Department of Psychiatry and Psychotherapy, CCM, Charite Universitätsmedizin Berlin, Berlin, Germany
| | - Francesca Managò
- Genetics of Cognition Laboratory, Neuroscience area, Istituto Italiano di Tecnologia, Genova, Italy
| | - Francesco Papaleo
- Genetics of Cognition Laboratory, Neuroscience area, Istituto Italiano di Tecnologia, Genova, Italy.
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4
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Feldman MJ, Bliss-Moreau E, Lindquist KA. The neurobiology of interoception and affect. Trends Cogn Sci 2024; 28:643-661. [PMID: 38395706 PMCID: PMC11222051 DOI: 10.1016/j.tics.2024.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 01/25/2024] [Accepted: 01/26/2024] [Indexed: 02/25/2024]
Abstract
Scholars have argued for centuries that affective states involve interoception, or representations of the state of the body. Yet, we lack a mechanistic understanding of how signals from the body are transduced, transmitted, compressed, and integrated by the brains of humans to produce affective states. We suggest that to understand how the body contributes to affect, we first need to understand information flow through the nervous system's interoceptive pathways. We outline such a model and discuss how unique anatomical and physiological aspects of interoceptive pathways may give rise to the qualities of affective experiences in general and valence and arousal in particular. We conclude by considering implications and future directions for research on interoception, affect, emotions, and human mental experiences.
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Affiliation(s)
- M J Feldman
- Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - E Bliss-Moreau
- Department of Psychology, University of California Davis, Davis, CA, USA; California National Primate Research Center, University of California Davis, Davis, CA, USA
| | - K A Lindquist
- Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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5
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Gan X, Zhou F, Xu T, Liu X, Zhang R, Zheng Z, Yang X, Zhou X, Yu F, Li J, Cui R, Wang L, Yuan J, Yao D, Becker B. A neurofunctional signature of subjective disgust generalizes to oral distaste and socio-moral contexts. Nat Hum Behav 2024; 8:1383-1402. [PMID: 38641635 DOI: 10.1038/s41562-024-01868-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 03/19/2024] [Indexed: 04/21/2024]
Abstract
While disgust originates in the hard-wired mammalian distaste response, the conscious experience of disgust in humans strongly depends on subjective appraisal and may even extend to socio-moral contexts. Here, in a series of studies, we combined functional magnetic resonance imaging with machine-learning-based predictive modelling to establish a comprehensive neurobiological model of subjective disgust. The developed neurofunctional signature accurately predicted momentary self-reported subjective disgust across discovery (n = 78) and pre-registered validation (n = 30) cohorts and generalized across core disgust (n = 34 and n = 26), gustatory distaste (n = 30) and socio-moral (unfair offers; n = 43) contexts. Disgust experience was encoded in distributed cortical and subcortical systems, and exhibited distinct and shared neural representations with subjective fear or negative affect in interoceptive-emotional awareness and conscious appraisal systems, while the signatures most accurately predicted the respective target experience. We provide an accurate functional magnetic resonance imaging signature for disgust with a high potential to resolve ongoing evolutionary debates.
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Affiliation(s)
- Xianyang Gan
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Feng Zhou
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Ting Xu
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaobo Liu
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Ran Zhang
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zihao Zheng
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xi Yang
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Xinqi Zhou
- Sichuan Key Laboratory of Psychology and Behavior of Discipline Inspection and Supervision, Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Fangwen Yu
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jialin Li
- Max Planck School of Cognition, Leipzig, Germany
| | - Ruifang Cui
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lan Wang
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiajin Yuan
- Sichuan Key Laboratory of Psychology and Behavior of Discipline Inspection and Supervision, Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Dezhong Yao
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
- State Key Laboratory for Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China.
- Department of Psychology, The University of Hong Kong, Hong Kong, China.
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6
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Van Overwalle F, Ma Q, Haihambo N, Bylemans T, Catoira B, Firouzi M, Li M, Pu M, Heleven E, Baeken C, Baetens K, Deroost N. A Functional Atlas of the Cerebellum Based on NeuroSynth Task Coordinates. CEREBELLUM (LONDON, ENGLAND) 2024; 23:993-1012. [PMID: 37608227 PMCID: PMC11102394 DOI: 10.1007/s12311-023-01596-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/15/2023] [Indexed: 08/24/2023]
Abstract
Although the human cerebellum has a surface that is about 80% of that of the cerebral cortex and has about four times as many neurons, its functional organization is still very much uncharted. Despite recent attempts to provide resting-state and task-based parcellations of the cerebellum, these two approaches lead to large discrepancies. This article describes a comprehensive task-based functional parcellation of the human cerebellum based on a large-scale functional database, NeuroSynth, involving an unprecedented diversity of tasks, which were reliably associated with ontological key terms referring to psychological functions. Involving over 44,500 participants from this database, we present a parcellation that exhibits replicability with earlier resting-state parcellations across cerebellar and neocortical structures. The functional parcellation of the cerebellum confirms the major networks revealed in prior work, including sensorimotor, directed (dorsal) attention, divided (ventral) attention, executive control, mentalizing (default mode) networks, tiny patches of a limbic network, and also a unilateral language network (but not the visual network), and the association of these networks with underlying ontological key terms confirms their major functionality. The networks are revealed at locations that are roughly similar to prior resting-state cerebellar parcellations, although they are less symmetric and more fragmented across the two hemispheres. This functional parcellation of the human cerebellum and associated key terms can provide a useful guide in designing studies to test specific functional hypotheses and provide a reference for interpreting the results.
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Affiliation(s)
- Frank Van Overwalle
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.
| | - Qianying Ma
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Naem Haihambo
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Tom Bylemans
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Beatriz Catoira
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Mahyar Firouzi
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Meijia Li
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Min Pu
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Elien Heleven
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Chris Baeken
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
- Department of Psychiatry, Universitair Ziekenhuis Brussel, Brussels, Belgium
- Department of Psychiatry, Ghent Experimental Psychiatry Lab, Ghent University, Ghent, Belgium
| | - Kris Baetens
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Natacha Deroost
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
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7
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Poublan-Couzardot A, Talmi D. Pain perception as hierarchical Bayesian inference: A test case for the theory of constructed emotion. Ann N Y Acad Sci 2024; 1536:42-59. [PMID: 38837401 DOI: 10.1111/nyas.15141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
An intriguing perspective about human emotion, the theory of constructed emotion considers emotions as generative models according to the Bayesian brain hypothesis. This theory brings fresh insight to existing findings, but its complexity renders it challenging to test experimentally. We argue that laboratory studies of pain could support the theory because although some may not consider pain to be a genuine emotion, the theory must at minimum be able to explain pain perception and its dysfunction in pathology. We review emerging evidence that bear on this question. We cover behavioral and neural laboratory findings, computational models, placebo hyperalgesia, and chronic pain. We conclude that there is substantial evidence for a predictive processing account of painful experience, paving the way for a better understanding of neuronal and computational mechanisms of other emotions.
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Affiliation(s)
- Arnaud Poublan-Couzardot
- Université Claude Bernard Lyon 1, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL, Bron, France
| | - Deborah Talmi
- Department of Psychology, University of Cambridge, Cambridge, UK
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8
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Proverbio AM, Cesati F. Neural correlates of recalled sadness, joy, and fear states: a source reconstruction EEG study. Front Psychiatry 2024; 15:1357770. [PMID: 38638416 PMCID: PMC11024723 DOI: 10.3389/fpsyt.2024.1357770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/18/2024] [Indexed: 04/20/2024] Open
Abstract
Introduction The capacity to understand the others' emotional states, particularly if negative (e.g. sadness or fear), underpins the empathic and social brain. Patients who cannot express their emotional states experience social isolation and loneliness, exacerbating distress. We investigated the feasibility of detecting non-invasive scalp-recorded electrophysiological signals that correspond to recalled emotional states of sadness, fear, and joy for potential classification. Methods The neural activation patterns of 20 healthy and right-handed participants were studied using an electrophysiological technique. Analyses were focused on the N400 component of Event-related potentials (ERPs) recorded during silent recall of subjective emotional states; Standardized weighted Low-resolution Electro-magnetic Tomography (swLORETA) was employed for source reconstruction. The study classified individual patterns of brain activation linked to the recollection of three distinct emotional states into seven regions of interest (ROIs). Results Statistical analysis (ANOVA) of the individual magnitude values revealed the existence of a common emotional circuit, as well as distinct brain areas that were specifically active during recalled sad, happy and fearful states. In particular, the right temporal and left superior frontal areas were more active for sadness, the left limbic region for fear, and the right orbitofrontal cortex for happy affective states. Discussion In conclusion, this study successfully demonstrated the feasibility of detecting scalp-recorded electrophysiological signals corresponding to internal and subjective affective states. These findings contribute to our understanding of the emotional brain, and have potential applications for future BCI classification and identification of emotional states in LIS patients who may be unable to express their emotions, thus helping to alleviate social isolation and sense of loneliness.
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Affiliation(s)
- Alice Mado Proverbio
- Cognitive Electrophysiology Lab, Department of Psychology, University of Milano-Bicocca, Milan, Italy
- NEURO-MI Milan Center for Neuroscience, Milan, Italy
| | - Federico Cesati
- Cognitive Electrophysiology Lab, Department of Psychology, University of Milano-Bicocca, Milan, Italy
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9
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Petrosini L, Picerni E, Termine A, Fabrizio C, Laricchiuta D, Cutuli D. The Cerebellum as an Embodying Machine. Neuroscientist 2024; 30:229-246. [PMID: 36052895 DOI: 10.1177/10738584221120187] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Whereas emotion theorists often keep their distance from the embodied approach, theorists of embodiment tend to treat emotion as a mainly physiologic process. However, intimate links between emotions and the body suggest that emotions are privileged phenomena to attempt to reintegrate mind and body and that the body helps the mind in shaping emotional responses. To date, research has favored the cerebrum over other parts of the brain as a substrate of embodied emotions. However, given the widely demonstrated contribution of the cerebellum to emotional processing, research in affective neuroscience should consider embodiment theory as a useful approach for evaluating the cerebellar role in emotion and affect. The aim of this review is to insert the cerebellum among the structures needed to embody emotions, providing illustrative examples of cerebellar involvement in embodied emotions (as occurring in empathic abilities) and in impaired identification and expression of embodied emotions (as occurring in alexithymia).
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Affiliation(s)
| | | | | | | | | | - Debora Cutuli
- Santa Lucia Foundation IRCCS, Rome, Italy
- Department of Psychology, University Sapienza of Rome, Rome, Italy
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10
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McVeigh K, Kleckner IR, Quigley KS, Satpute AB. Fear-related psychophysiological patterns are situation and individual dependent: A Bayesian model comparison approach. Emotion 2024; 24:506-521. [PMID: 37603002 PMCID: PMC10882564 DOI: 10.1037/emo0001265] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
Is there a universal mapping of physiology to emotion, or do these mappings vary substantially by person or situation? Psychologists, philosophers, and neuroscientists have debated this question for decades. Most previous studies have focused on differentiating emotions on the basis of accompanying autonomic responses using analytical approaches that often assume within-category homogeneity. In the present study, we took an alternative approach to this question. We determined the extent to which the relationship between subjective experience and autonomic reactivity generalizes across, or depends upon, the individual and situation for instances of a single emotion category, specifically, fear. Electrodermal activity and cardiac activity-two autonomic measures that are often assumed to show robust relationships with instances of fear-were recorded while participants reported fear experience in response to dozens of fear-evoking videos related to three distinct situations: spiders, heights, and social encounters. We formally translated assumptions from diverse theoretical models into a common framework for model comparison analyses. Results exceedingly favored a model that assumed situation-dependency in the relationship between fear experience and autonomic reactivity, with subject variance also significant but constrained by situation. Models that assumed generalization across situations and/or individuals performed much worse by comparison. These results call into question the assumption of generalizability of autonomic-subjective mappings across instances of fear, as required in translational research from nonhuman animals to humans, and advance a situated approach to understanding the autonomic correlates of fear experience. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Affiliation(s)
- Kieran McVeigh
- Department of Psychology, Northeastern University, 360 Huntington Ave, 125 NI, Boston, MA 02115
| | | | - Karen S. Quigley
- Department of Psychology, Northeastern University, 360 Huntington Ave, 125 NI, Boston, MA 02115
| | - Ajay B. Satpute
- Department of Psychology, Northeastern University, 360 Huntington Ave, 125 NI, Boston, MA 02115
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11
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Martino M, Magioncalda P. A three-dimensional model of neural activity and phenomenal-behavioral patterns. Mol Psychiatry 2024; 29:639-652. [PMID: 38114633 DOI: 10.1038/s41380-023-02356-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 11/16/2023] [Accepted: 11/27/2023] [Indexed: 12/21/2023]
Abstract
How phenomenal experience and behavior are related to neural activity in physiology and psychopathology represents a fundamental question in neuroscience and psychiatry. The phenomenal-behavior patterns may be deconstructed into basic dimensions, i.e., psychomotricity, affectivity, and thought, which might have distinct neural correlates. This work provides a data overview on the relationship of these phenomenal-behavioral dimensions with brain activity across physiological and pathological conditions (including major depressive disorder, bipolar disorder, schizophrenia, attention-deficit/hyperactivity disorder, anxiety disorders, addictive disorders, Parkinson's disease, Tourette syndrome, Alzheimer's disease, and frontotemporal dementia). Accordingly, we propose a three-dimensional model of neural activity and phenomenal-behavioral patterns. In this model, neural activity is organized into distinct units in accordance with connectivity patterns and related input/output processing, manifesting in the different phenomenal-behavioral dimensions. (1) An external neural unit, which involves the sensorimotor circuit/brain's sensorimotor network and is connected with the external environment, processes external inputs/outputs, manifesting in the psychomotor dimension (processing of exteroception/somatomotor activity). External unit hyperactivity manifests in psychomotor excitation (hyperactivity/hyperkinesia/catatonia), while external unit hypoactivity manifests in psychomotor inhibition (retardation/hypokinesia/catatonia). (2) An internal neural unit, which involves the interoceptive-autonomic circuit/brain's salience network and is connected with the internal/body environment, processes internal inputs/outputs, manifesting in the affective dimension (processing of interoception/autonomic activity). Internal unit hyperactivity manifests in affective excitation (anxiety/dysphoria-euphoria/panic), while internal unit hypoactivity manifests in affective inhibition (anhedonia/apathy/depersonalization). (3) An associative neural unit, which involves the brain's associative areas/default-mode network and is connected with the external/internal units (but not with the environment), processes associative inputs/outputs, manifesting in the thought dimension (processing of ideas). Associative unit hyperactivity manifests in thought excitation (mind-wandering/repetitive thinking/psychosis), while associative unit hypoactivity manifests in thought inhibition (inattention/cognitive deficit/consciousness loss). Finally, these neural units interplay and dynamically combine into various neural states, resulting in the complex phenomenal experience and behavior across physiology and neuropsychiatric disorders.
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Affiliation(s)
- Matteo Martino
- Graduate Institute of Mind Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.
| | - Paola Magioncalda
- Graduate Institute of Mind Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
- Department of Radiology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan.
- Department of Medical Research, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan.
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12
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Aranberri Ruiz A. Transcutaneous Auricular Vagus Nerve Stimulation to Improve Emotional State. Biomedicines 2024; 12:407. [PMID: 38398009 PMCID: PMC10886536 DOI: 10.3390/biomedicines12020407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 01/19/2024] [Accepted: 01/30/2024] [Indexed: 02/25/2024] Open
Abstract
Emotional experiences are a part of our lives. The maladaptive functioning of an individual's emotional field can lead to emotional disturbances of various kinds, such as anxiety and depression. Currently, there is an increasing prevalence of emotional disorders that cause great human suffering and high socioeconomic costs. Emotional processing has a biological basis. The major neuroscientific theories of emotion are based on biological functioning, and all of them take into account the anatomy and function of the tenth cranial nerve: the vagus nerve. The vagus nerve connects the subdiaphragmatic and supradiaphragmatic areas and modulates emotional processing as the basis of interoceptive functioning. Auricular vagus nerve stimulation is a new and innovative neuromodulation technique based on the function of the vagus nerve. Several interventions have shown that this new neurostimulation technique is a very promising resource for treating emotional disorders. In this paper, we summarise three neuroscientific theories of emotion, explain what transcutaneous auricular nerve stimulation is, and present arguments for its use and continued research.
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Affiliation(s)
- Ainara Aranberri Ruiz
- Department of Basic Psychological Process and Development, University of the Basque Country, 20018 San Sebastian, Spain
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13
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Whitehead JC, Spiousas I, Armony JL. Individual differences in the evaluation of ambiguous visual and auditory threat-related expressions. Eur J Neurosci 2024; 59:370-393. [PMID: 38185821 DOI: 10.1111/ejn.16220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 10/29/2023] [Accepted: 11/22/2023] [Indexed: 01/09/2024]
Abstract
This study investigated the neural correlates of the judgement of auditory and visual ambiguous threat-related information, and the influence of state anxiety on this process. Healthy subjects were scanned using a fast, high-resolution functional magnetic resonance imaging (fMRI) multiband sequence while they performed a two-alternative forced-choice emotion judgement task on faces and vocal utterances conveying explicit anger or fear, as well as ambiguous ones. Critically, the latter was specific to each subject, obtained through a morphing procedure and selected prior to scanning following a perceptual decision-making task. Behavioural results confirmed a greater task-difficulty for subject-specific ambiguous stimuli and also revealed a judgement bias for visual fear, and, to a lesser extent, for auditory anger. Imaging results showed increased activity in regions of the salience and frontoparietal control networks (FPCNs) and deactivation in areas of the default mode network for ambiguous, relative to explicit, expressions. In contrast, the right amygdala (AMG) responded more strongly to explicit stimuli. Interestingly, its response to the same ambiguous stimulus depended on the subjective judgement of the expression. Finally, we found that behavioural and neural differences between ambiguous and explicit expressions decreased as a function of state anxiety scores. Taken together, our results show that behavioural and brain responses to emotional expressions are determined not only by emotional clarity but also modality and the subjects' subjective perception of the emotion expressed, and that some of these responses are modulated by state anxiety levels.
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Affiliation(s)
- Jocelyne C Whitehead
- Human Neuroscience, Douglas Mental Health University Institute, Verdun, Quebec, Canada
- BRAMS Laboratory, Centre for Research on Brain, Language and Music, Montreal, Quebec, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Ignacio Spiousas
- BRAMS Laboratory, Centre for Research on Brain, Language and Music, Montreal, Quebec, Canada
- Laboratorio Interdisciplinario del Tiempo y la Experiencia (LITERA), CONICET, Universidad de San Andrés, Victoria, Argentina
| | - Jorge L Armony
- Human Neuroscience, Douglas Mental Health University Institute, Verdun, Quebec, Canada
- BRAMS Laboratory, Centre for Research on Brain, Language and Music, Montreal, Quebec, Canada
- Laboratorio Interdisciplinario del Tiempo y la Experiencia (LITERA), CONICET, Universidad de San Andrés, Victoria, Argentina
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
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14
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Gamble RS, Henry JD, Decety J, Vanman EJ. The role of external factors in affect-sharing and their neural bases. Neurosci Biobehav Rev 2024; 157:105540. [PMID: 38211739 DOI: 10.1016/j.neubiorev.2024.105540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/13/2024]
Abstract
Affect-sharing, the ability to vicariously feel another person's emotions, is the primary component of empathy that is typically thought to rely on the observer's capacity to feel the emotions of others. However, external signals, such as the target's physical characteristics, have been demonstrated to influence affect-sharing in the neuroscientific literature that speaks to the underappreciated role of external factors in eliciting affect-sharing. We consider factors that influence affect-sharing, including physical cues, emotional cues, situational factors, and observer-target relationships, as well as the neural circuits involved in these processes. Our review reveals that, while neural network activation is primarily responsible for processing affect-sharing, external factors also co-activate a top-down cognitive processing network to modulate the conscious process of affect-sharing. From this knowledge, an integrative framework of external factor interactions with affect-sharing are explained in detail. Finally, we identify critical areas for future research in social and affective neuroscience, including research gaps and incorporation of ecologically valid paradigms.
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Affiliation(s)
- Roger S Gamble
- School of Psychology, The University of Queensland, St Lucia, 4072 Brisbane, QLD, Australia.
| | - Julie D Henry
- School of Psychology, The University of Queensland, St Lucia, 4072 Brisbane, QLD, Australia
| | - Jean Decety
- Department of Psychology, Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - Eric J Vanman
- School of Psychology, The University of Queensland, St Lucia, 4072 Brisbane, QLD, Australia
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15
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Potegal M. How it ends: A review of behavioral and psychological phenomena, physiological processes and neural circuits in the termination of aggression in other animals and anger in people. Behav Brain Res 2024; 456:114676. [PMID: 37739229 DOI: 10.1016/j.bbr.2023.114676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/26/2023] [Accepted: 09/17/2023] [Indexed: 09/24/2023]
Abstract
More is known about aggression initiation and persistence in other animals, and anger in people, than about their cessation. This review summarizes knowledge of relevant factors in aggression, mostly in vertebrates, and anger termination in people. The latency, probability and intensity of offensive aggression in mice is controlled by activity in a neuronal subpopulation in ventromedial hypothalamus [VMH]. This activity instantiates an aggressive state termed angriffsbereitschaft ["attack-readiness"]. Fighting in many species is broken into bouts with interbout breaks due to fatigue and/or signals from dorsal raphe to VMH. Eventually, losers decide durations and outcomes of fighting by transitioning to submission or flight. Factors reducing angriffsbereitschaft and triggering these defeat behaviors could include metabolic costs, e.g., lactate accumulation and glucose depletion detected by the hypothalamus, central fatigue perhaps sensed by the Salience Network [insula and anterior cingulate gyrus] and pain of injuries, the latter insufficiently blunted by opioid and non-opioid stress analgesia and transduced by anterior VMH neurons. Winners' angriffsbereitschaft continue for awhile, as indicated by post-victory attacks and, perhaps, triumph displays of some species, including humans. In longer term situations, sensory and/or response habituation of aggression may explain the "Dear enemy" tolerance of competitive neighbors. Prolonged satiation of predatory behavior could involve habenula-regulated reduction of dopaminergic reward in nucleus accumbens. Termination of human anger involves at least three processes, metaphorically termed decay, quenching and catharsis. Hypothesized neural mechanisms include anger diminution by negative feedback from accumbens to anterior cingulate and/or activity in the Salience Network that controls anger's "accumulation/offset" phase.
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Affiliation(s)
- M Potegal
- University of Minnesota, United States.
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16
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Codispoti M, De Cesarei A, Ferrari V. Alpha-band oscillations and emotion: A review of studies on picture perception. Psychophysiology 2023; 60:e14438. [PMID: 37724827 DOI: 10.1111/psyp.14438] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 08/04/2023] [Accepted: 08/24/2023] [Indexed: 09/21/2023]
Abstract
Although alpha-band activity has long been a focus of psychophysiological research, its modulation by emotional value during picture perception has only recently been studied systematically. Here, we review these studies and report that the most consistent alpha oscillatory pattern indexing emotional processing is an enhanced desynchronization (ERD) over posterior sensors when viewing emotional compared with neutral pictures. This enhanced alpha ERD is not specific to unpleasant picture content, as previously proposed for other measures of affective response, but has also been observed for pleasant stimuli. Evidence suggests that this effect is not confined to the alpha band but that it also involves a desynchronization of the lower beta frequencies (8-20 Hz). The emotional modulation of alpha ERD occurs even after massive stimulus repetition and when emotional cues serve as task-irrelevant distractors, consistent with the hypothesis that evaluative processes are mandatory in emotional picture processing. A similar enhanced ERD has been observed for other significant cues (e.g., conditioned aversive stimuli, or in anticipation of a potential threat), suggesting that it reflects cortical excitability associated with the engagement of the motivational systems.
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Affiliation(s)
| | | | - Vera Ferrari
- Department of Medicine and Surgery, University of Parma, Parma, Italy
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17
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Pitcher D, Sliwinska MW, Kaiser D. TMS disruption of the lateral prefrontal cortex increases neural activity in the default mode network when naming facial expressions. Soc Cogn Affect Neurosci 2023; 18:nsad072. [PMID: 38048419 PMCID: PMC10695328 DOI: 10.1093/scan/nsad072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 10/17/2023] [Accepted: 11/15/2023] [Indexed: 12/06/2023] Open
Abstract
Recognizing facial expressions is dependent on multiple brain networks specialized for different cognitive functions. In the current study, participants (N = 20) were scanned using functional magnetic resonance imaging (fMRI), while they performed a covert facial expression naming task. Immediately prior to scanning thetaburst transcranial magnetic stimulation (TMS) was delivered over the right lateral prefrontal cortex (PFC), or the vertex control site. A group whole-brain analysis revealed that TMS induced opposite effects in the neural responses across different brain networks. Stimulation of the right PFC (compared to stimulation of the vertex) decreased neural activity in the left lateral PFC but increased neural activity in three nodes of the default mode network (DMN): the right superior frontal gyrus, right angular gyrus and the bilateral middle cingulate gyrus. A region of interest analysis showed that TMS delivered over the right PFC reduced neural activity across all functionally localised face areas (including in the PFC) compared to TMS delivered over the vertex. These results suggest that visually recognizing facial expressions is dependent on the dynamic interaction of the face-processing network and the DMN. Our study also demonstrates the utility of combined TMS/fMRI studies for revealing the dynamic interactions between different functional brain networks.
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Affiliation(s)
- David Pitcher
- Department of Psychology, University of York, Heslington, York YO105DD, UK
| | | | - Daniel Kaiser
- Mathematical Institute, Department of Mathematics and Computer Science, Physics, Geography, Justus-Liebig-Universität Gießen, Gießen 35392, Germany
- Center for Mind, Brain and Behaviour, Philipps-Universität Marburg, and Justus-Liebig-Universität Gießen, Marburg 35032, Germany
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18
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Morgenroth E, Vilaclara L, Muszynski M, Gaviria J, Vuilleumier P, Van De Ville D. Probing neurodynamics of experienced emotions-a Hitchhiker's guide to film fMRI. Soc Cogn Affect Neurosci 2023; 18:nsad063. [PMID: 37930850 PMCID: PMC10656947 DOI: 10.1093/scan/nsad063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 08/04/2023] [Accepted: 11/01/2023] [Indexed: 11/08/2023] Open
Abstract
Film functional magnetic resonance imaging (fMRI) has gained tremendous popularity in many areas of neuroscience. However, affective neuroscience remains somewhat behind in embracing this approach, even though films lend themselves to study how brain function gives rise to complex, dynamic and multivariate emotions. Here, we discuss the unique capabilities of film fMRI for emotion research, while providing a general guide of conducting such research. We first give a brief overview of emotion theories as these inform important design choices. Next, we discuss films as experimental paradigms for emotion elicitation and address the process of annotating them. We then situate film fMRI in the context of other fMRI approaches, and present an overview of results from extant studies so far with regard to advantages of film fMRI. We also give an overview of state-of-the-art analysis techniques including methods that probe neurodynamics. Finally, we convey limitations of using film fMRI to study emotion. In sum, this review offers a practitioners' guide to the emerging field of film fMRI and underscores how it can advance affective neuroscience.
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Affiliation(s)
- Elenor Morgenroth
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva 1202, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva 1202, Switzerland
- Swiss Center for Affective Sciences, University of Geneva, Geneva 1202, Switzerland
| | - Laura Vilaclara
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva 1202, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva 1202, Switzerland
| | - Michal Muszynski
- Department of Basic Neurosciences, University of Geneva, Geneva 1202, Switzerland
| | - Julian Gaviria
- Swiss Center for Affective Sciences, University of Geneva, Geneva 1202, Switzerland
- Department of Basic Neurosciences, University of Geneva, Geneva 1202, Switzerland
- Department of Psychiatry, University of Geneva, Geneva 1202, Switzerland
| | - Patrik Vuilleumier
- Swiss Center for Affective Sciences, University of Geneva, Geneva 1202, Switzerland
- Department of Basic Neurosciences, University of Geneva, Geneva 1202, Switzerland
- CIBM Center for Biomedical Imaging, Geneva 1202, Switzerland
| | - Dimitri Van De Ville
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva 1202, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva 1202, Switzerland
- CIBM Center for Biomedical Imaging, Geneva 1202, Switzerland
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19
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Kiprijanovska I, Stankoski S, Broulidakis MJ, Archer J, Fatoorechi M, Gjoreski M, Nduka C, Gjoreski H. Towards smart glasses for facial expression recognition using OMG and machine learning. Sci Rep 2023; 13:16043. [PMID: 37749176 PMCID: PMC10520037 DOI: 10.1038/s41598-023-43135-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 09/20/2023] [Indexed: 09/27/2023] Open
Abstract
This study aimed to evaluate the use of novel optomyography (OMG) based smart glasses, OCOsense, for the monitoring and recognition of facial expressions. Experiments were conducted on data gathered from 27 young adult participants, who performed facial expressions varying in intensity, duration, and head movement. The facial expressions included smiling, frowning, raising the eyebrows, and squeezing the eyes. The statistical analysis demonstrated that: (i) OCO sensors based on the principles of OMG can capture distinct variations in cheek and brow movements with a high degree of accuracy and specificity; (ii) Head movement does not have a significant impact on how well these facial expressions are detected. The collected data were also used to train a machine learning model to recognise the four facial expressions and when the face enters a neutral state. We evaluated this model in conditions intended to simulate real-world use, including variations in expression intensity, head movement and glasses position relative to the face. The model demonstrated an overall accuracy of 93% (0.90 f1-score)-evaluated using a leave-one-subject-out cross-validation technique.
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Affiliation(s)
| | | | | | | | | | - Martin Gjoreski
- Faculty of Informatics, Università della Svizzera Italiana, 6900, Lugano, Switzerland
| | | | - Hristijan Gjoreski
- Emteq Ltd., Brighton, BN1 9SB, UK
- Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje, 1000, Skopje, North Macedonia
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20
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Du C, Fu K, Wen B, He H. Topographic representation of visually evoked emotional experiences in the human cerebral cortex. iScience 2023; 26:107571. [PMID: 37664621 PMCID: PMC10470388 DOI: 10.1016/j.isci.2023.107571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/03/2023] [Accepted: 08/07/2023] [Indexed: 09/05/2023] Open
Abstract
Affective neuroscience seeks to uncover the neural underpinnings of emotions that humans experience. However, it remains unclear whether an affective space underlies the discrete emotion categories in the human brain, and how it relates to the hypothesized affective dimensions. To address this question, we developed a voxel-wise encoding model to investigate the cortical organization of human emotions. Results revealed that the distributed emotion representations are constructed through a fundamental affective space. We further compared each dimension of this space to 14 hypothesized affective dimensions, and found that many affective dimensions are captured by the fundamental affective space. Our results suggest that emotional experiences are represented by broadly spatial overlapping cortical patterns and form smooth gradients across large areas of the cortex. This finding reveals the specific structure of the affective space and its relationship to hypothesized affective dimensions, while highlighting the distributed nature of emotional representations in the cortex.
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Affiliation(s)
- Changde Du
- Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Science, Beijing 100190, China
| | - Kaicheng Fu
- Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Science, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bincheng Wen
- Center for Excellence in Brain Science and Intelligence Technology, Key Laboratory of Primate Neurobiology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Huiguang He
- Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Science, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
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21
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Grogans SE, Bliss-Moreau E, Buss KA, Clark LA, Fox AS, Keltner D, Cowen AS, Kim JJ, Kragel PA, MacLeod C, Mobbs D, Naragon-Gainey K, Fullana MA, Shackman AJ. The nature and neurobiology of fear and anxiety: State of the science and opportunities for accelerating discovery. Neurosci Biobehav Rev 2023; 151:105237. [PMID: 37209932 PMCID: PMC10330657 DOI: 10.1016/j.neubiorev.2023.105237] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/11/2023] [Accepted: 05/13/2023] [Indexed: 05/22/2023]
Abstract
Fear and anxiety play a central role in mammalian life, and there is considerable interest in clarifying their nature, identifying their biological underpinnings, and determining their consequences for health and disease. Here we provide a roundtable discussion on the nature and biological bases of fear- and anxiety-related states, traits, and disorders. The discussants include scientists familiar with a wide variety of populations and a broad spectrum of techniques. The goal of the roundtable was to take stock of the state of the science and provide a roadmap to the next generation of fear and anxiety research. Much of the discussion centered on the key challenges facing the field, the most fruitful avenues for future research, and emerging opportunities for accelerating discovery, with implications for scientists, funders, and other stakeholders. Understanding fear and anxiety is a matter of practical importance. Anxiety disorders are a leading burden on public health and existing treatments are far from curative, underscoring the urgency of developing a deeper understanding of the factors governing threat-related emotions.
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Affiliation(s)
- Shannon E Grogans
- Department of Psychology, University of Maryland, College Park, MD 20742, USA
| | - Eliza Bliss-Moreau
- Department of Psychology, University of California, Davis, CA 95616, USA; California National Primate Research Center, University of California, Davis, CA 95616, USA
| | - Kristin A Buss
- Department of Psychology, The Pennsylvania State University, University Park, PA 16802 USA
| | - Lee Anna Clark
- Department of Psychology, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Andrew S Fox
- Department of Psychology, University of California, Davis, CA 95616, USA; California National Primate Research Center, University of California, Davis, CA 95616, USA
| | - Dacher Keltner
- Department of Psychology, University of California, Berkeley, Berkeley, CA 94720, USA
| | | | - Jeansok J Kim
- Department of Psychology, University of Washington, Seattle, WA 98195, USA
| | - Philip A Kragel
- Department of Psychology, Emory University, Atlanta, GA 30322, USA
| | - Colin MacLeod
- Centre for the Advancement of Research on Emotion, School of Psychological Science, The University of Western Australia, Perth, WA 6009, Australia
| | - Dean Mobbs
- Department of Humanities and Social Sciences, California Institute of Technology, Pasadena, California 91125, USA; Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA 91125, USA
| | - Kristin Naragon-Gainey
- School of Psychological Science, University of Western Australia, Perth, WA 6009, Australia
| | - Miquel A Fullana
- Adult Psychiatry and Psychology Department, Institute of Neurosciences, Hospital Clinic, Barcelona, Spain; Imaging of Mood, and Anxiety-Related Disorders Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer, CIBERSAM, University of Barcelona, Barcelona, Spain
| | - Alexander J Shackman
- Department of Psychology, University of Maryland, College Park, MD 20742, USA; Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD 20742, USA; Maryland Neuroimaging Center, University of Maryland, College Park, MD 20742, USA.
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22
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Zhao W, Liu Q, Zhang X, Song X, Zhang Z, Qing P, Liu X, Zhu S, Yang W, Kendrick KM. Differential responses in the mirror neuron system during imitation of individual emotional facial expressions and association with autistic traits. Neuroimage 2023; 277:120263. [PMID: 37399932 DOI: 10.1016/j.neuroimage.2023.120263] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/15/2023] [Accepted: 06/30/2023] [Indexed: 07/05/2023] Open
Abstract
The mirror neuron system (MNS), including the inferior frontal gyrus (IFG), inferior parietal lobule (IPL) and superior temporal sulcus (STS) plays an important role in action representation and imitation and may be dysfunctional in autism spectrum disorder (ASD). However, it's not clear how these three regions respond and interact during the imitation of different basic facial expressions and whether the pattern of responses is influenced by autistic traits. Thus, we conducted a natural facial expression (happiness, angry, sadness and fear) imitation task in 100 healthy male subjects where expression intensity was measured using facial emotion recognition software (FaceReader) and MNS responses were recorded using functional near-infrared spectroscopy (fNIRS). Autistic traits were measured using the Autism Spectrum Quotient questionnaire. Results showed that imitation of happy expressions produced the highest expression intensity but a small deactivation in MNS responses, suggesting a lower processing requirement compared to other expressions. A cosine similarity analysis indicated a distinct pattern of MNS responses during imitation of each facial expression with functional intra-hemispheric connectivity between the left IPL and left STS being significantly higher during happy compared to other expressions, while inter-hemispheric connectivity between the left and right IPL differed between imitation of fearful and sad expressions. Furthermore, functional connectivity changes during imitation of each different expression could reliably predict autistic trait scores. Overall, the results provide evidence for distinct patterns of functional connectivity changes between MNS regions during imitation of different emotions which are also associated with autistic traits.
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Affiliation(s)
- Weihua Zhao
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China; Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan, 523808, China
| | - Qi Liu
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Xiaolu Zhang
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Xinwei Song
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Zhao Zhang
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Peng Qing
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Xiaolong Liu
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, 610066, China
| | - Siyu Zhu
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Wenxu Yang
- Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Keith M Kendrick
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China.
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23
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Dang Q, Ma F, Yuan Q, Fu Y, Chen K, Zhang Z, Lu C, Guo T. Processing negative emotion in two languages of bilinguals: Accommodation and assimilation of the neural pathways based on a meta-analysis. Cereb Cortex 2023:7133665. [PMID: 37083264 DOI: 10.1093/cercor/bhad121] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 03/14/2023] [Accepted: 03/17/2023] [Indexed: 04/22/2023] Open
Abstract
Numerous functional magnetic resonance imaging (fMRI) studies have examined the neural mechanisms of negative emotional words, but scarce evidence is available for the interactions among related brain regions from the functional brain connectivity perspective. Moreover, few studies have addressed the neural networks for negative word processing in bilinguals. To fill this gap, the current study examined the brain networks for processing negative words in the first language (L1) and the second language (L2) with Chinese-English bilinguals. To identify objective indicators associated with negative word processing, we first conducted a coordinate-based meta-analysis on contrasts between negative and neutral words (including 32 contrasts from 1589 participants) using the activation likelihood estimation method. Results showed that the left medial prefrontal cortex (mPFC), the left inferior frontal gyrus (IFG), the left posterior cingulate cortex (PCC), the left amygdala, the left inferior temporal gyrus (ITG), and the left thalamus were involved in processing negative words. Next, these six clusters were used as regions of interest in effective connectivity analyses using extended unified structural equation modeling to pinpoint the brain networks for bilingual negative word processing. Brain network results revealed two pathways for negative word processing in L1: a dorsal pathway consisting of the left IFG, the left mPFC, and the left PCC, and a ventral pathway involving the left amygdala, the left ITG, and the left thalamus. We further investigated the similarity and difference between brain networks for negative word processing in L1 and L2. The findings revealed similarities in the dorsal pathway, as well as differences primarily in the ventral pathway, indicating both neural assimilation and accommodation across processing negative emotion in two languages of bilinguals.
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Affiliation(s)
- Qinpu Dang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Fengyang Ma
- School of Education, University of Cincinnati, Cincinnati, OH 45219, USA
| | - Qiming Yuan
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yongben Fu
- The Psychological Education and Counseling Center, Huazhong Agricultural University, Wuhan 430070, China
| | - Keyue Chen
- Division of Psychology and Language Sciences, University College London, London WC1E 6BT, UK
| | - Zhaoqi Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Chunming Lu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
| | - Taomei Guo
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
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24
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Barborica A, Mindruta I, López-Madrona VJ, Alario FX, Trébuchon A, Donos C, Oane I, Pistol C, Mihai F, Bénar CG. Studying memory processes at different levels with simultaneous depth and surface EEG recordings. Front Hum Neurosci 2023; 17:1154038. [PMID: 37082152 PMCID: PMC10110965 DOI: 10.3389/fnhum.2023.1154038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/06/2023] [Indexed: 04/07/2023] Open
Abstract
Investigating cognitive brain functions using non-invasive electrophysiology can be challenging due to the particularities of the task-related EEG activity, the depth of the activated brain areas, and the extent of the networks involved. Stereoelectroencephalographic (SEEG) investigations in patients with drug-resistant epilepsy offer an extraordinary opportunity to validate information derived from non-invasive recordings at macro-scales. The SEEG approach can provide brain activity with high spatial specificity during tasks that target specific cognitive processes (e.g., memory). Full validation is possible only when performing simultaneous scalp SEEG recordings, which allows recording signals in the exact same brain state. This is the approach we have taken in 12 subjects performing a visual memory task that requires the recognition of previously viewed objects. The intracranial signals on 965 contact pairs have been compared to 391 simultaneously recorded scalp signals at a regional and whole-brain level, using multivariate pattern analysis. The results show that the task conditions are best captured by intracranial sensors, despite the limited spatial coverage of SEEG electrodes, compared to the whole-brain non-invasive recordings. Applying beamformer source reconstruction or independent component analysis does not result in an improvement of the multivariate task decoding performance using surface sensor data. By analyzing a joint scalp and SEEG dataset, we investigated whether the two types of signals carry complementary information that might improve the machine-learning classifier performance. This joint analysis revealed that the results are driven by the modality exhibiting best individual performance, namely SEEG.
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Affiliation(s)
- Andrei Barborica
- Department of Physics, University of Bucharest, Bucharest, Romania
- *Correspondence: Andrei Barborica
| | - Ioana Mindruta
- Epilepsy Monitoring Unit, Department of Neurology, Emergency University Hospital Bucharest, Bucharest, Romania
- Department of Neurology, Medical Faculty, Carol Davila University of Medicine and Pharmacy Bucharest, Bucharest, Romania
| | | | | | - Agnès Trébuchon
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- APHM, Timone Hospital, Functional and Stereotactic Neurosurgery, Marseille, France
| | - Cristian Donos
- Department of Physics, University of Bucharest, Bucharest, Romania
| | - Irina Oane
- Epilepsy Monitoring Unit, Department of Neurology, Emergency University Hospital Bucharest, Bucharest, Romania
| | | | - Felicia Mihai
- Department of Physics, University of Bucharest, Bucharest, Romania
| | - Christian G. Bénar
- Aix Marseille University, INSERM, INS, Institute of Neuroscience System, Marseille, France
- Christian G. Bénar
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25
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Potegal M, Nordman JC. Non-angry aggressive arousal and angriffsberietschaft: A narrative review of the phenomenology and physiology of proactive/offensive aggression motivation and escalation in people and other animals. Neurosci Biobehav Rev 2023; 147:105110. [PMID: 36822384 DOI: 10.1016/j.neubiorev.2023.105110] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 02/14/2023] [Accepted: 02/18/2023] [Indexed: 02/23/2023]
Abstract
Human aggression typologies largely correspond with those for other animals. While there may be no non-human equivalent of angry reactive aggression, we propose that human proactive aggression is similar to offense in other animals' dominance contests for territory or social status. Like predation/hunting, but unlike defense, offense and proactive aggression are positively reinforcing, involving dopamine release in accumbens. The drive these motivational states provide must suffice to overcome fear associated with initiating risky fights. We term the neural activity motivating proactive aggression "non-angry aggressive arousal", but use "angriffsberietschaft" for offense motivation in other animals to acknowledge possible differences. Temporal variation in angriffsberietschaft partitions fights into bouts; engendering reduced anti-predator vigilance, redirected aggression and motivational over-ride. Increased aggressive arousal drives threat-to-attack transitions, as in verbal-to-physical escalation and beyond that, into hyper-aggression. Proactive aggression and offense involve related neural activity states. Cingulate, insular and prefrontal cortices energize/modulate aggression through a subcortical core containing subnuclei for each aggression type. These proposals will deepen understanding of aggression across taxa, guiding prevention/intervention for human violence.
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Affiliation(s)
| | - Jacob C Nordman
- Department of Physiology, Southern Illinois University School of Medicine, Carbondale, IL, USA.
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26
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Xu S, Zhang Z, Li L, Zhou Y, Lin D, Zhang M, Zhang L, Huang G, Liu X, Becker B, Liang Z. Functional connectivity profiles of the default mode and visual networks reflect temporal accumulative effects of sustained naturalistic emotional experience. Neuroimage 2023; 269:119941. [PMID: 36791897 DOI: 10.1016/j.neuroimage.2023.119941] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/30/2023] [Accepted: 02/11/2023] [Indexed: 02/15/2023] Open
Abstract
Determining and decoding emotional brain processes under ecologically valid conditions remains a key challenge in affective neuroscience. The current functional Magnetic Resonance Imaging (fMRI) based emotion decoding studies are mainly based on brief and isolated episodes of emotion induction, while sustained emotional experience in naturalistic environments that mirror daily life experiences are scarce. Here we used 12 different 10-minute movie clips as ecologically valid emotion-evoking procedures in n = 52 individuals to explore emotion-specific fMRI functional connectivity (FC) profiles on the whole-brain level at high spatial resolution (432 parcellations including cortical and subcortical structures). Employing machine-learning based decoding and cross validation procedures allowed to investigate FC profiles contributing to classification that can accurately distinguish sustained happiness and sadness and that generalize across subjects, movie clips, and parcellations. Both functional brain network-based and subnetwork-based emotion classification results suggested that emotion manifests as distributed representation of multiple networks, rather than a single functional network or subnetwork. Further, the results showed that the Visual Network (VN) and Default Mode Network (DMN) associated functional networks, especially VN-DMN, exhibited a strong contribution to emotion classification. To further estimate the temporal accumulative effect of naturalistic long-term movie-based video-evoking emotions, we divided the 10-min episode into three stages: early stimulation (1∼200 s), middle stimulation (201∼400 s), and late stimulation (401∼600 s) and examined the emotion classification performance at different stimulation stages. We found that the late stimulation contributes most to the classification (accuracy=85.32%, F1-score=85.62%) compared to early and middle stimulation stages, implying that continuous exposure to emotional stimulation can lead to more intense emotions and further enhance emotion-specific distinguishable representations. The present work demonstrated that sustained happiness and sadness under naturalistic conditions are presented in emotion-specific network profiles and these expressions may play different roles in the generation and modulation of emotions. These findings elucidated the importance of network level adaptations for sustained emotional experiences during naturalistic contexts and open new venues for imaging network level contributions under naturalistic conditions.
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Affiliation(s)
- Shuyue Xu
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Zhiguo Zhang
- Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China; Peng Cheng Laboratory, Shenzhen 518055, China; Marshall Laboratory of Biomedical Engineering, Shenzhen 518060, China
| | - Linling Li
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Yongjie Zhou
- Department of Psychiatric Rehabilitation, Shenzhen Kangning Hospital, Shenzhen, China
| | - Danyi Lin
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Min Zhang
- Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China
| | - Li Zhang
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Gan Huang
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Xiqin Liu
- Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, MOE Key Laboratory for Neuroinformation, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Benjamin Becker
- Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, MOE Key Laboratory for Neuroinformation, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Zhen Liang
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China; Marshall Laboratory of Biomedical Engineering, Shenzhen 518060, China.
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27
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Speer SPH, Keysers C, Barrios JC, Teurlings CJS, Smidts A, Boksem MAS, Wager TD, Gazzola V. A multivariate brain signature for reward. Neuroimage 2023; 271:119990. [PMID: 36878456 DOI: 10.1016/j.neuroimage.2023.119990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 02/20/2023] [Accepted: 02/25/2023] [Indexed: 03/07/2023] Open
Abstract
The processing of reinforcers and punishers is crucial to adapt to an ever changing environment and its dysregulation is prevalent in mental health and substance use disorders. While many human brain measures related to reward have been based on activity in individual brain regions, recent studies indicate that many affective and motivational processes are encoded in distributed systems that span multiple regions. Consequently, decoding these processes using individual regions yields small effect sizes and limited reliability, whereas predictive models based on distributed patterns yield larger effect sizes and excellent reliability. To create such a predictive model for the processes of rewards and losses, termed the Brain Reward Signature (BRS), we trained a model to predict the signed magnitude of monetary rewards on the Monetary Incentive Delay task (MID; N = 39) and achieved a highly significant decoding performance (92% for decoding rewards versus losses). We subsequently demonstrate the generalizability of our signature on another version of the MID in a different sample (92% decoding accuracy; N = 12) and on a gambling task from a large sample (73% decoding accuracy, N = 1084). We further provided preliminary data to characterize the specificity of the signature by illustrating that the signature map generates estimates that significantly differ between rewarding and negative feedback (92% decoding accuracy) but do not differ for conditions that differ in disgust rather than reward in a novel Disgust-Delay Task (N = 39). Finally, we show that passively viewing positive and negatively valenced facial expressions loads positively on our signature, in line with previous studies on morbid curiosity. We thus created a BRS that can accurately predict brain responses to rewards and losses in active decision making tasks, and that possibly relates to information seeking in passive observational tasks.
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Affiliation(s)
- Sebastian P H Speer
- Social Brain Lab, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Christian Keysers
- Social Brain Lab, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands; Brain and Cognition, Department of Psychology, University of Amsterdam, The Netherlands
| | | | - Cas J S Teurlings
- Social Brain Lab, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Ale Smidts
- Rotterdam School of Management, Erasmus University, 3062 PA Rotterdam, The Netherlands
| | - Maarten A S Boksem
- Rotterdam School of Management, Erasmus University, 3062 PA Rotterdam, The Netherlands
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
| | - Valeria Gazzola
- Social Brain Lab, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands.
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28
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Dan R, Weinstock M, Goelman G. Emotional states as distinct configurations of functional brain networks. Cereb Cortex 2022; 33:5727-5739. [PMID: 36453449 DOI: 10.1093/cercor/bhac455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 10/20/2022] [Accepted: 10/22/2022] [Indexed: 12/05/2022] Open
Abstract
Abstract
The conceptualization of emotional states as patterns of interactions between large-scale brain networks has recently gained support. Yet, few studies have directly examined the brain’s network structure during emotional experiences. Here, we investigated the brain’s functional network organization during experiences of sadness, amusement, and neutral states elicited by movies, in addition to a resting-state. We tested the effects of the experienced emotion on individual variability in the brain’s functional connectome. Next, for each state, we defined a community structure of the brain and quantified its segregation and integration. We found that sadness, relative to amusement, was associated with higher modular integration and increased connectivity of cognitive control networks: the salience and fronto-parietal networks. Moreover, in both the functional connectome and the emotional report, the similarity between individuals was dependent on the sex. Our results suggest that the experience of emotion is linked to a reconfiguration of whole-brain distributed, not emotion-specific, functional networks and that the brain’s topological structure carries information about the subjective emotional experience.
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Affiliation(s)
- Rotem Dan
- Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University of Jerusalem , Jerusalem, 9190401 , Israel
- Department of Neurology, Hadassah Hebrew University Medical Center , Jerusalem, 9112001 , Israel
| | - Marta Weinstock
- Institute of Drug Research, The Hebrew University of Jerusalem , Jerusalem, 9112001 , Israel
| | - Gadi Goelman
- Department of Neurology, Hadassah Hebrew University Medical Center , Jerusalem, 9112001 , Israel
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29
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Ge R, Zhang S, Zhao H. Do expressions of sadness, anxiety and fear have different impacts on attracting donations? Evidence from a Chinese online charitable crowdfunding platform. INFORMATION TECHNOLOGY & PEOPLE 2022. [DOI: 10.1108/itp-12-2021-0927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
PurposeExtant research shows mixed results on the impact of expressed negative emotions on donations in online charitable crowdfunding. This study solves the puzzle by examining how different types of negative emotions (i.e. sadness, anxiety and fear) expressed in crowdfunding project descriptions affect donations.Design/methodology/approachData on 15,653 projects across four categories (medical assistance, education assistance, disaster assistance and poverty assistance) from September 2013 to May 2019 come from a leading online crowdfunding platform in China. Text analysis and regression models serve to test the hypotheses.FindingsIn the medical assistance category, the expression of sadness has an inverted U-shaped effect on donations, while the expression of anxiety has a negative effect. An appropriate number of sadness words is helpful but should not exceed five times. In the education assistance and disaster assistance categories, the expression of sadness has a positive effect on donations, but disclosure of anxiety and fear has no influence on donations. Expressions of sadness, anxiety and fear have no impact on donations in the poverty assistance category.Research limitations/implicationsThis work has important implications for fundraisers on how to regulate the fundraisers' expressions of negative emotions in a project's description to attract donations. These insights are also relevant for online crowdfunding platforms.Originality/valueOnline crowdfunding research often studies negative emotions as a whole and does not differentiate project types. The current work contributes by empirically testing the impact of three types of negative emotions on donations across four major online crowdfunding categories.
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30
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Yanagisawa H, Wu X, Ueda K, Kato T. Free energy model of emotional valence in dual-process perceptions. Neural Netw 2022; 157:422-436. [DOI: 10.1016/j.neunet.2022.10.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 10/21/2022] [Accepted: 10/27/2022] [Indexed: 11/06/2022]
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31
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Herzog P, Kube T, Fassbinder E. How childhood maltreatment alters perception and cognition - the predictive processing account of borderline personality disorder. Psychol Med 2022; 52:2899-2916. [PMID: 35979924 PMCID: PMC9693729 DOI: 10.1017/s0033291722002458] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 06/24/2022] [Accepted: 07/18/2022] [Indexed: 01/05/2023]
Abstract
Borderline personality disorder (BPD) is a severe mental disorder, comprised of heterogeneous psychological and neurobiological pathologies. Here, we propose a predictive processing (PP) account of BPD to integrate these seemingly unrelated pathologies. In particular, we argue that the experience of childhood maltreatment, which is highly prevalent in BPD, leaves a developmental legacy with two facets: first, a coarse-grained, alexithymic model of self and others - leading to a rigidity and inflexibility concerning beliefs about self and others. Second, this developmental legacy leads to a loss of confidence or precision afforded beliefs about the consequences of social behavior. This results in an over reliance on sensory evidence and social feedback, with concomitant lability, impulsivity and hypersensitivity. In terms of PP, people with BPD show a distorted belief updating in response to new information with two opposing manifestations: rapid changes in beliefs and a lack of belief updating despite disconfirmatory evidence. This account of distorted information processing has the potential to explain both the instability (of affect, self-image, and interpersonal relationships) and the rigidity (of beliefs about self and others) which is typical of BPD. At the neurobiological level, we propose that enhanced levels of dopamine are associated with the increased integration of negative social feedback, and we also discuss the hypothesis of an impaired inhibitory control of the prefrontal cortex in the processing of negative social information. Our account may provide a new understanding not only of the clinical aspects of BPD, but also a unifying theory of the corresponding neurobiological pathologies. We conclude by outlining some directions for future research on the behavioral, neurobiological, and computational underpinnings of this model, and point to some clinical implications of it.
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Affiliation(s)
- Philipp Herzog
- Department of Psychiatry and Psychotherapy, University of Lübeck, Ratzeburger Allee 160, D-23562 Lübeck, Germany
- Department of Psychiatry and Psychotherapy, Christian-Albrechts-University of Kiel, Niemannsweg 147, D-24105 Kiel, Germany
- Department of Psychology, University of Koblenz-Landau, Ostbahnstr. 10, 76829 Landau, Germany
| | - Tobias Kube
- Department of Psychology, University of Koblenz-Landau, Ostbahnstr. 10, 76829 Landau, Germany
| | - Eva Fassbinder
- Department of Psychiatry and Psychotherapy, Christian-Albrechts-University of Kiel, Niemannsweg 147, D-24105 Kiel, Germany
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32
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Donos C, Blidarescu B, Pistol C, Oane I, Mindruta I, Barborica A. A comparison of uni- and multi-variate methods for identifying brain networks activated by cognitive tasks using intracranial EEG. Front Neurosci 2022; 16:946240. [PMID: 36225734 PMCID: PMC9549146 DOI: 10.3389/fnins.2022.946240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 09/08/2022] [Indexed: 11/20/2022] Open
Abstract
Cognitive tasks are commonly used to identify brain networks involved in the underlying cognitive process. However, inferring the brain networks from intracranial EEG data presents several challenges related to the sparse spatial sampling of the brain and the high variability of the EEG trace due to concurrent brain processes. In this manuscript, we use a well-known facial emotion recognition task to compare three different ways of analyzing the contrasts between task conditions: permutation cluster tests, machine learning (ML) classifiers, and a searchlight implementation of multivariate pattern analysis (MVPA) for intracranial sparse data recorded from 13 patients undergoing presurgical evaluation for drug-resistant epilepsy. Using all three methods, we aim at highlighting the brain structures with significant contrast between conditions. In the absence of ground truth, we use the scientific literature to validate our results. The comparison of the three methods’ results shows moderate agreement, measured by the Jaccard coefficient, between the permutation cluster tests and the machine learning [0.33 and 0.52 for the left (LH) and right (RH) hemispheres], and 0.44 and 0.37 for the LH and RH between the permutation cluster tests and MVPA. The agreement between ML and MVPA is higher: 0.65 for the LH and 0.62 for the RH. To put these results in context, we performed a brief review of the literature and we discuss how each brain structure’s involvement in the facial emotion recognition task.
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Affiliation(s)
- Cristian Donos
- Department of Physics, University of Bucharest, Bucharest, Romania
- *Correspondence: Cristian Donos,
| | | | | | - Irina Oane
- Department of Physics, University of Bucharest, Bucharest, Romania
- Epilepsy Monitoring Unit, Department of Neurology, Emergency University Hospital Bucharest, Bucharest, Romania
| | - Ioana Mindruta
- Department of Physics, University of Bucharest, Bucharest, Romania
| | - Andrei Barborica
- Department of Physics, University of Bucharest, Bucharest, Romania
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33
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Deane G. Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence. ARTIFICIAL LIFE 2022; 28:289-309. [PMID: 35881678 DOI: 10.1162/artl_a_00368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
What role do affective feelings (feelings/emotions/moods) play in adaptive behaviour? What are the implications of this for understanding and developing artificial general intelligence? Leading theoretical models of brain function are beginning to shed light on these questions. While artificial agents have excelled within narrowly circumscribed and specialised domains, domain-general intelligence has remained an elusive goal in artificial intelligence research. By contrast, humans and nonhuman animals are characterised by a capacity for flexible behaviour and general intelligence. In this article I argue that computational models of mental phenomena in predictive processing theories of the brain are starting to reveal the mechanisms underpinning domain-general intelligence in biological agents, and can inform the understanding and development of artificial general intelligence. I focus particularly on approaches to computational phenomenology in the active inference framework. Specifically, I argue that computational mechanisms of affective feelings in active inference-affective self-modelling-are revealing of how biological agents are able to achieve flexible behavioural repertoires and general intelligence. I argue that (i) affective self-modelling functions to "tune" organisms to the most tractable goals in the environmental context; and (ii) affective and agentic self-modelling is central to the capacity to perform mental actions in goal-directed imagination and creative cognition. I use this account as a basis to argue that general intelligence of the level and kind found in biological agents will likely require machines to be implemented with analogues of affective self-modelling.
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Affiliation(s)
- George Deane
- University of Edinburgh, School of Philosophy, Psychology, and Language Sciences.
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34
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Revers H, Van Deun K, Strijbosch W, Vroomen J, Bastiaansen M. Decoding the neural responses to experiencing disgust and sadness. Brain Res 2022; 1793:148034. [DOI: 10.1016/j.brainres.2022.148034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 06/20/2022] [Accepted: 07/26/2022] [Indexed: 11/02/2022]
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35
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Posterior-prefrontal and medial orbitofrontal regions play crucial roles in happiness and sadness recognition. Neuroimage Clin 2022; 35:103072. [PMID: 35689975 PMCID: PMC9192961 DOI: 10.1016/j.nicl.2022.103072] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 05/31/2022] [Accepted: 05/31/2022] [Indexed: 11/23/2022]
Abstract
Brain areas underlying trade-off relations between emotions were identified. Damage to the PPF area reduces accuracy of happiness recognition. Damage to the PPF increases accuracy of sadness recognition. A similar tendency was observed in orbitofrontal regions for sadness recognition. Only a deficit in sadness, but not happiness, persisted in the chronic phase.
The core brain regions responsible for basic human emotions are not yet fully understood. We investigated the key areas responsible for emotion recognition of facial expressions of happiness and sadness using data obtained from patients who underwent local brain resection. A total of 44 patients with right cerebral hemispheric brain tumors and 33 healthy volunteers were enrolled and subjected to a facial expression recognition test. Voxel-based lesion-symptom mapping was performed to investigate the relationship between the accuracy of emotion recognition and the resected regions. Consequently, trade-off relationships were discovered: the posterior-prefrontal region was related to a low score of happiness recognition and a high score of sadness recognition (disorder-of-happiness group), whereas the medial orbitofrontal region was related to a low score of sadness recognition and a high score of happiness recognition (disorder-of-sadness group). The emotion recognition score in both the happiness and sadness disorder groups was significantly lower than that in the control group (p = 0.0009 and p = 0.021, respectively). Interestingly, the deficit in happiness recognition was temporary, whereas the deficit in sadness recognition persisted during the chronic phase. Using graph theoretical analysis, we identified structural connectivity between the posterior-prefrontal and medial orbitofrontal regions. When either of these regions was damaged, the tract volume connecting them was significantly reduced (p = 0.013). These results indicate that the posterior-prefrontal and medial orbitofrontal regions may be crucial for maintaining a balance between happiness and sadness recognition in humans. Investigating the clinical impact of certain area resections using lesion studies combined with connectivity analysis is a useful neuroimaging method for understanding neural networks.
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36
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Jungilligens J, Paredes-Echeverri S, Popkirov S, Barrett LF, Perez DL. A new science of emotion: implications for functional neurological disorder. Brain 2022; 145:2648-2663. [PMID: 35653495 PMCID: PMC9905015 DOI: 10.1093/brain/awac204] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/28/2022] [Accepted: 05/20/2022] [Indexed: 01/11/2023] Open
Abstract
Functional neurological disorder reflects impairments in brain networks leading to distressing motor, sensory and/or cognitive symptoms that demonstrate positive clinical signs on examination incongruent with other conditions. A central issue in historical and contemporary formulations of functional neurological disorder has been the mechanistic and aetiological role of emotions. However, the debate has mostly omitted fundamental questions about the nature of emotions in the first place. In this perspective article, we first outline a set of relevant working principles of the brain (e.g. allostasis, predictive processing, interoception and affect), followed by a focused review of the theory of constructed emotion to introduce a new understanding of what emotions are. Building on this theoretical framework, we formulate how altered emotion category construction can be an integral component of the pathophysiology of functional neurological disorder and related functional somatic symptoms. In doing so, we address several themes for the functional neurological disorder field including: (i) how energy regulation and the process of emotion category construction relate to symptom generation, including revisiting alexithymia, 'panic attack without panic', dissociation, insecure attachment and the influential role of life experiences; (ii) re-interpret select neurobiological research findings in functional neurological disorder cohorts through the lens of the theory of constructed emotion to illustrate its potential mechanistic relevance; and (iii) discuss therapeutic implications. While we continue to support that functional neurological disorder is mechanistically and aetiologically heterogenous, consideration of how the theory of constructed emotion relates to the generation and maintenance of functional neurological and functional somatic symptoms offers an integrated viewpoint that cuts across neurology, psychiatry, psychology and cognitive-affective neuroscience.
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Affiliation(s)
- Johannes Jungilligens
- Correspondence to: Johannes Jungilligens University Hospital Knappschaftskrankenhaus Bochum Department of Neurology In der Schornau 23-25 44892 Bochum, Germany E-mail:
| | | | - Stoyan Popkirov
- Department of Neurology, University Hospital Knappschaftskrankenhaus Bochum, Ruhr University Bochum, Bochum, Germany
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37
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Common and stimulus-type-specific brain representations of negative affect. Nat Neurosci 2022; 25:760-770. [DOI: 10.1038/s41593-022-01082-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 04/25/2022] [Indexed: 01/16/2023]
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38
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Shaffer C, Westlin C, Quigley KS, Whitfield-Gabrieli S, Barrett LF. Allostasis, Action, and Affect in Depression: Insights from the Theory of Constructed Emotion. Annu Rev Clin Psychol 2022; 18:553-580. [PMID: 35534123 PMCID: PMC9247744 DOI: 10.1146/annurev-clinpsy-081219-115627] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The theory of constructed emotion is a systems neuroscience approach to understanding the nature of emotion. It is also a general theoretical framework to guide hypothesis generation for how actions and experiences are constructed as the brain continually anticipates metabolic needs and attempts to meet those needs before they arise (termed allostasis). In this review, we introduce this framework and hypothesize that allostatic dysregulation is a trans-disorder vulnerability for mental and physical illness. We then review published findings consistent with the hypothesis that several symptoms in major depressive disorder (MDD), such as fatigue, distress, context insensitivity, reward insensitivity, and motor retardation, are associated with persistent problems in energy regulation. Our approach transforms the current understanding of MDD as resulting from enhanced emotional reactivity combined with reduced cognitive control and, in doing so, offers novel hypotheses regarding the development, progression, treatment, and prevention of MDD.
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Affiliation(s)
- Clare Shaffer
- Department of Psychology, Northeastern University, Boston, Massachusetts, USA; ,
| | - Christiana Westlin
- Department of Psychology, Northeastern University, Boston, Massachusetts, USA; ,
| | - Karen S Quigley
- Department of Psychology, Northeastern University, Boston, Massachusetts, USA; ,
- VA Bedford Healthcare System, Bedford, Massachusetts, USA
| | - Susan Whitfield-Gabrieli
- Department of Psychology, Northeastern University, Boston, Massachusetts, USA; ,
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Lisa Feldman Barrett
- Department of Psychology, Northeastern University, Boston, Massachusetts, USA; ,
- Department of Psychiatry and the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
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39
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Doyle CM, Lane ST, Brooks JA, Wilkins RW, Gates KM, Lindquist KA. Unsupervised classification reveals consistency and degeneracy in neural network patterns of emotion. Soc Cogn Affect Neurosci 2022; 17:995-1006. [PMID: 35445241 PMCID: PMC9629478 DOI: 10.1093/scan/nsac028] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 02/24/2022] [Accepted: 04/19/2022] [Indexed: 01/12/2023] Open
Abstract
In the present study, we used an unsupervised classification algorithm to reveal both consistency and degeneracy in neural network connectivity during anger and anxiety. Degeneracy refers to the ability of different biological pathways to produce the same outcomes. Previous research is suggestive of degeneracy in emotion, but little research has explicitly examined whether degenerate functional connectivity patterns exist for emotion categories such as anger and anxiety. Twenty-four subjects underwent functional magnetic resonance imaging (fMRI) while listening to unpleasant music and self-generating experiences of anger and anxiety. A data-driven model building algorithm with unsupervised classification (subgrouping Group Iterative Multiple Model Estimation) identified patterns of connectivity among 11 intrinsic networks that were associated with anger vs anxiety. As predicted, degenerate functional connectivity patterns existed within these overarching consistent patterns. Degenerate patterns were not attributable to differences in emotional experience or other individual-level factors. These findings are consistent with the constructionist account that emotions emerge from flexible functional neuronal assemblies and that emotion categories such as anger and anxiety each describe populations of highly variable instances.
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Affiliation(s)
- Cameron M Doyle
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Stephanie T Lane
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Jeffrey A Brooks
- Department of Psychology, University of California, Berkeley, CA 84720, USA,Hume AI, New York, NY 10010, USA
| | - Robin W Wilkins
- Gateway University of North Carolina Greensboro MRI Center, Greensboro, NC 27412, USA
| | - Kathleen M Gates
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Kristen A Lindquist
- Correspondence should be addressed to Kristen A. Lindquist, Department of Psychology and Neuroscience, University of North Carolina, CB #3270, 230 E. Cameron Avenue, Chapel Hill, NC 27599, USA. E-mail:
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40
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Reisch LM, Wegrzyn M, Mielke M, Mehlmann A, Woermann FG, Kissler J, Bien CG. Effects of left and right medial temporal lobe resections on hemodynamic correlates of negative and neutral scene processing. Hum Brain Mapp 2022; 43:3293-3305. [PMID: 35384132 PMCID: PMC9189037 DOI: 10.1002/hbm.25852] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 03/07/2022] [Accepted: 03/21/2022] [Indexed: 01/04/2023] Open
Abstract
Enhanced visual cortex activation by negative compared to neutral stimuli is often attributed to modulating feedback from the amygdala, but evidence from lesion studies is scarce, particularly regarding differential effects of left and right amygdala lesions. Therefore, we compared visual cortex activation by negative and neutral complex scenes in an event‐related fMRI study between 40 patients with unilateral temporal lobe resection (TLR; 19 left [lTLR], 21 right [rTLR]), including the amygdala, and 20 healthy controls. We found preserved hemodynamic emotion modulation of visual cortex in rTLR patients and only subtle reductions in lTLR patients. In contrast, rTLR patients showed a significant decrease in visual cortex activation irrespective of picture content. In line with this, healthy controls showed small emotional modulation of the left amygdala only, while their right amygdala was activated equally by negative and neutral pictures. Correlations of activation in amygdala and visual cortex were observed for both negative and neutral pictures in the controls. In both patient groups, this relationship was attenuated ipsilateral to the TLR. Our results support the notion of reentrant mechanisms between amygdala and visual cortex and suggest laterality differences in their emotion‐specificity. While right medial temporal lobe structures including the amygdala seem to influence visual processing in general, the left medial temporal lobe appears to contribute specifically to emotion processing. Still, effects of left TLR on visual emotion processing were relatively subtle. Therefore, hemodynamic correlates of visual emotion processing are likely supported by a distributed cerebral network, challenging an amygdalocentric view of emotion processing.
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Affiliation(s)
- Lea Marie Reisch
- Department of Psychology, Bielefeld University, Bielefeld, Germany.,Department of Epileptology (Krankenhaus Mara), Bielefeld University, Bielefeld, Germany
| | - Martin Wegrzyn
- Department of Psychology, Bielefeld University, Bielefeld, Germany
| | - Malena Mielke
- Department of Psychology, Bielefeld University, Bielefeld, Germany
| | | | - Friedrich G Woermann
- Department of Epileptology (Krankenhaus Mara), Bielefeld University, Bielefeld, Germany
| | - Johanna Kissler
- Department of Psychology, Bielefeld University, Bielefeld, Germany
| | - Christian G Bien
- Department of Epileptology (Krankenhaus Mara), Bielefeld University, Bielefeld, Germany
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41
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Jabakhanji R, Vigotsky AD, Bielefeld J, Huang L, Baliki MN, Iannetti G, Apkarian AV. Limits of decoding mental states with fMRI. Cortex 2022; 149:101-122. [PMID: 35219121 PMCID: PMC9238276 DOI: 10.1016/j.cortex.2021.12.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 11/22/2021] [Accepted: 12/13/2021] [Indexed: 12/15/2022]
Abstract
A growing number of studies claim to decode mental states using multi-voxel decoders of brain activity. It has been proposed that the fixed, fine-grained, multi-voxel patterns in these decoders are necessary for discriminating between and identifying mental states. Here, we present evidence that the efficacy of these decoders might be overstated. Across various tasks, decoder patterns were spatially imprecise, as decoder performance was unaffected by spatial smoothing; 90% redundant, as selecting a random 10% of a decoder's constituent voxels recovered full decoder performance; and performed similarly to brain activity maps used as decoders. We distinguish decoder performance in discriminating between mental states from performance in identifying a given mental state, and show that even when discrimination performance is adequate, identification can be poor. Finally, we demonstrate that simple and intuitive similarity metrics explain 91% and 62% of discrimination performance within- and across-subjects, respectively. These findings indicate that currently used across-subject decoders of mental states are superfluous and inappropriate for decision-making.
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Affiliation(s)
- Rami Jabakhanji
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Andrew D Vigotsky
- Departments of Biomedical Engineering and Statistics, Northwestern University, Evanston, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Jannis Bielefeld
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Lejian Huang
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Marwan N Baliki
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, USA; Shirley Ryan AbilityLab, Chicago, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Giandomenico Iannetti
- Division of Biosciences, University College London, London, UK; Neuroscience and Behaviour Laboratory, Italian Institute of Technology, Rome, Italy
| | - A Vania Apkarian
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, USA; Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, USA; Department of Anesthesiology, Feinberg School of Medicine, Northwestern University, Chicago, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA.
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42
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Kim MJ, Knodt AR, Hariri AR. Meta-Analytic Activation Maps Can Help Identify Affective Processes Captured by Contrast-Based Task fMRI: The Case of Threat-Related Facial Expressions. Soc Cogn Affect Neurosci 2022; 17:777-787. [PMID: 35137241 PMCID: PMC9433847 DOI: 10.1093/scan/nsac010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 10/30/2021] [Accepted: 02/07/2022] [Indexed: 12/02/2022] Open
Abstract
Meta-analysis of functional magnetic resonance imaging (fMRI) data is an effective method for capturing the distributed patterns of brain activity supporting discrete cognitive and affective processes. One opportunity presented by the resulting meta-analysis maps (MAMs) is as a reference for better understanding the nature of individual contrast maps (ICMs) derived from specific task fMRI data. Here, we compared MAMs from 148 neuroimaging studies representing emotion categories of fear, anger, disgust, happiness and sadness with ICMs from fearful > neutral and angry > neutral faces from an independent dataset of task fMRI (n = 1263). Analyses revealed that both fear and anger ICMs exhibited the greatest pattern similarity to fear MAMs. As the number of voxels included for the computation of pattern similarity became more selective, the specificity of MAM–ICM correspondence decreased. Notably, amygdala activity long considered critical for processing threat-related facial expressions was neither sufficient nor necessary for detecting MAM–ICM pattern similarity effects. Our analyses suggest that both fearful and angry facial expressions are best captured by distributed patterns of brain activity, a putative neural correlate of threat. More generally, our analyses demonstrate how MAMs can be leveraged to better understand affective processes captured by ICMs in task fMRI data.
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Affiliation(s)
- M Justin Kim
- Correspondence should be addressed to M. Justin Kim, Department of Psychology, Sungkyunkwan University, Seoul 03063, South Korea. E-mail:
| | - Annchen R Knodt
- Laboratory of NeuroGenetics, Department of Psychology and Neuroscience, Duke University, Durham, NC 27708 USA
| | - Ahmad R Hariri
- Laboratory of NeuroGenetics, Department of Psychology and Neuroscience, Duke University, Durham, NC 27708 USA
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43
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Gan X, Zhou X, Li J, Jiao G, Jiang X, Biswal B, Yao S, Klugah-Brown B, Becker B. Common and distinct neurofunctional representations of core and social disgust in the brain: Coordinate-based and network meta-analyses. Neurosci Biobehav Rev 2022; 135:104553. [PMID: 35122784 DOI: 10.1016/j.neubiorev.2022.104553] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 01/02/2022] [Accepted: 01/30/2022] [Indexed: 01/19/2023]
Abstract
Disgust represents a multifaceted defensive-avoidance response. On the behavioral level, the response includes withdrawal and a disgust-specific facial expression. While both serve the avoidance of pathogens, the latter additionally transmits social-communicative information. Given that common and distinct brain representation of the primary defensive-avoidance response (core disgust) and encoding of the social-communicative signal (social disgust) remain debated, we employed neuroimaging meta-analyses to (1) determine brain systems generally engaged in disgust processing, and (2) segregate common and distinct brain systems for core and social disgust. Disgust processing, in general, engaged a bilateral network encompassing the insula, amygdala, occipital and prefrontal regions. Core disgust evoked stronger reactivity in left-lateralized threat detection and defensive response network including amygdala, occipital and frontal regions, while social disgust engaged a right-lateralized superior temporal-frontal network engaged in social cognition. Anterior insula, inferior frontal and fusiform regions were commonly engaged during core and social disgust, suggesting a shared neurofunctional basis. We demonstrate a common and distinct neural basis of primary disgust responses and encoding of associated social-communicative signals.
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Affiliation(s)
- Xianyang Gan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
| | - Xinqi Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
| | - Jialin Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China; Max Planck School of Cognition, Leipzig 04103, Germany
| | - Guojuan Jiao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
| | - Xi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China; Department of Biomedical Engineering, New Jersey Institute of Technology, NJ 7102, United States
| | - Shuxia Yao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
| | - Benjamin Klugah-Brown
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China.
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China.
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44
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Menétrey MQ, Mohammadi G, Leitão J, Vuilleumier P. Emotion Recognition in a Multi-Componential Framework: The Role of Physiology. FRONTIERS IN COMPUTER SCIENCE 2022. [DOI: 10.3389/fcomp.2022.773256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The Component Process Model is a well-established framework describing an emotion as a dynamic process with five highly interrelated components: cognitive appraisal, expression, motivation, physiology and feeling. Yet, few empirical studies have systematically investigated discrete emotions through this full multi-componential view. We therefore elicited various emotions during movie watching and measured their manifestations across these components. Our goal was to investigate the relationship between physiological measures and the theoretically defined components, as well as to determine whether discrete emotions could be predicted from the multicomponent response patterns. By deploying a data-driven computational approach based on multivariate pattern classification, our results suggest that physiological features are encoded within each component, supporting the hypothesis of a synchronized recruitment during an emotion episode. Overall, while emotion prediction was higher when classifiers were trained with all five components, a model without physiology features did not significantly reduce the performance. The findings therefore support a description of emotion as a multicomponent process, in which emotion recognition requires the integration of all the components. However, they also indicate that physiology per se is the least significant predictor for emotion classification among these five components.
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45
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van 't Hof SR, Van Oudenhove L, Janssen E, Klein S, Reddan MC, Kragel PA, Stark R, Wager TD. The Brain Activation-Based Sexual Image Classifier (BASIC): A Sensitive and Specific fMRI Activity Pattern for Sexual Image Processing. Cereb Cortex 2021; 32:3014-3030. [PMID: 34905775 PMCID: PMC9290618 DOI: 10.1093/cercor/bhab397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 11/19/2022] Open
Abstract
Previous studies suggest there is a complex relationship between sexual and general affective stimulus processing, which varies across individuals and situations. We examined whether sexual and general affective processing can be distinguished at the brain level. In addition, we explored to what degree possible distinctions are generalizable across individuals and different types of sexual stimuli, and whether they are limited to the engagement of lower-level processes, such as the detection of visual features. Data on sexual images, nonsexual positive and negative images, and neutral images from Wehrum et al. (2013) (N = 100) were reanalyzed using multivariate support vector machine models to create the brain activation-based sexual image classifier (BASIC) model. This model was tested for sensitivity, specificity, and generalizability in cross-validation (N = 100) and an independent test cohort (N = 18; Kragel et al. 2019). The BASIC model showed highly accurate performance (94–100%) in classifying sexual versus neutral or nonsexual affective images in both datasets with forced choice tests. Virtual lesions and tests of individual large-scale networks (e.g., visual or attention networks) show that individual networks are neither necessary nor sufficient to classify sexual versus nonsexual stimulus processing. Thus, responses to sexual images are distributed across brain systems.
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Affiliation(s)
- Sophie R van 't Hof
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA.,Department of Psychiatry, Amsterdam Medical Centre, 1105 AZ, The Netherlands
| | - Lukas Van Oudenhove
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA.,Laboratory for Brain-Gut Axis Studies, Translational Research Center for Gastrointestinal Disorders, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven 3000, Belgium
| | - Erick Janssen
- Institute for Family and Sexuality Studies, KU Leuven, Leuven 3000, Belgium
| | - Sanja Klein
- Bender Institute of Neuroimaging (BION), Justus Liebig University Giessen, Giessen 35390, Germany.,Department of Psychotherapy and Systems Neuroscience, Justus Liebig University Giessen, Giessen 35390, Germany
| | - Marianne C Reddan
- Institute of Cognitive Science, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA.,Stanford University, Palo Alto, CA, USA
| | - Philip A Kragel
- Institute of Cognitive Science, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA.,Emory University, Atlanta, GA, USA
| | - Rudolf Stark
- Bender Institute of Neuroimaging (BION), Justus Liebig University Giessen, Giessen 35390, Germany.,Department of Psychotherapy and Systems Neuroscience, Justus Liebig University Giessen, Giessen 35390, Germany
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA.,Institute of Cognitive Science, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA
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46
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Saarimäki H, Glerean E, Smirnov D, Mynttinen H, Jääskeläinen IP, Sams M, Nummenmaa L. Classification of emotion categories based on functional connectivity patterns of the human brain. Neuroimage 2021; 247:118800. [PMID: 34896586 PMCID: PMC8803541 DOI: 10.1016/j.neuroimage.2021.118800] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 12/05/2021] [Accepted: 12/08/2021] [Indexed: 12/01/2022] Open
Abstract
Neurophysiological and psychological models posit that emotions depend on connections across wide-spread corticolimbic circuits. While previous studies using pattern recognition on neuroimaging data have shown differences between various discrete emotions in brain activity patterns, less is known about the differences in functional connectivity. Thus, we employed multivariate pattern analysis on functional magnetic resonance imaging data (i) to develop a pipeline for applying pattern recognition in functional connectivity data, and (ii) to test whether connectivity patterns differ across emotion categories. Six emotions (anger, fear, disgust, happiness, sadness, and surprise) and a neutral state were induced in 16 participants using one-minute-long emotional narratives with natural prosody while brain activity was measured with functional magnetic resonance imaging (fMRI). We computed emotion-wise connectivity matrices both for whole-brain connections and for 10 previously defined functionally connected brain subnetworks and trained an across-participant classifier to categorize the emotional states based on whole-brain data and for each subnetwork separately. The whole-brain classifier performed above chance level with all emotions except sadness, suggesting that different emotions are characterized by differences in large-scale connectivity patterns. When focusing on the connectivity within the 10 subnetworks, classification was successful within the default mode system and for all emotions. We thus show preliminary evidence for consistently different sustained functional connectivity patterns for instances of emotion categories particularly within the default mode system.
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Affiliation(s)
- Heini Saarimäki
- Faculty of Social Sciences, Tampere University, FI-33014 Tampere University, Tampere, Finland; Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland.
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland; Advanced Magnetic Imaging (AMI) Centre, Aalto NeuroImaging, School of Science, Aalto University, Espoo, Finland; Turku PET Centre and Department of Psychology, University of Turku, Turku, Finland; Department of Computer Science, School of Science, Aalto University, Espoo, Finland; International Laboratory of Social Neurobiology, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Dmitry Smirnov
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
| | - Henri Mynttinen
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
| | - Iiro P Jääskeläinen
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland; International Laboratory of Social Neurobiology, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Mikko Sams
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland; Department of Computer Science, School of Science, Aalto University, Espoo, Finland
| | - Lauri Nummenmaa
- Turku PET Centre and Department of Psychology, University of Turku, Turku, Finland
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47
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Gao X, Yu H, Peng L, Gong X, Xiang Y, Jiang C, Zhou X. The mutuality of social emotions: How the victim's reactive attitude influences the transgressor's emotional responses. Neuroimage 2021; 244:118631. [PMID: 34601131 DOI: 10.1016/j.neuroimage.2021.118631] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 09/08/2021] [Accepted: 09/29/2021] [Indexed: 11/25/2022] Open
Abstract
Would a transgressor be guiltier or less after receiving the victim's forgiving or blaming attitude? Everyday intuitions and empirical evidence are mixed in this regard, leaving how interpersonal attitudes shape the transgressor's reactive social emotions an open question. We combined a social interactive game with multivariate pattern analysis of fMRI data to address this question. Participants played an interactive game in an fMRI scanner where their incorrect responses could cause either high or low pain stimulation to an anonymous co-player. Following incorrect responses, participants were presented with the co-player's (i.e., the victim's) attitude towards the harm (Blame, Forgive, or Neutral). Behaviorally, the victim's attitude and the severity of harm interactively modulated the transgressor's social emotions, with expectation violation serving as a mediator. While unexpected forgiveness following severe harm amplified the participants' guilt, unexpected blame following minor harm reduced the participants' guilt and increased their anger. This role of expectation violation was supported by multivariate pattern analysis of fMRI, revealing a shared neural representation in ventral striatum in the processing of victim's attitude-induced guilt and anger. Moreover, we identified a neural re-appraisal process of guilt in the transgressor, with the involvement of area related to self-conscious processing (i.e., perigenual anterior cingulate cortex) before knowing the victim's attitude transiting to the involvement of other-regarding related area (i.e., temporoparietal junction) after knowing the victim's attitude. These findings uncover the neurocognitive bases underlying the transgressor's social emotional responses, and highlight the importance of the mutuality of social emotions.
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Affiliation(s)
- Xiaoxue Gao
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China; School of Psychological and Cognitive Sciences, Peking University, Beijing 100871, China.
| | - Hongbo Yu
- Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, 93106-9660, USA.
| | - Lu Peng
- School of Psychological and Cognitive Sciences, Peking University, Beijing 100871, China
| | - Xiaoliang Gong
- Key Laboratory of Embedded System and Service Computing (Ministry of Education), Tongji University, Shanghai 201804, China
| | - Yang Xiang
- Key Laboratory of Embedded System and Service Computing (Ministry of Education), Tongji University, Shanghai 201804, China
| | - Changjun Jiang
- Key Laboratory of Embedded System and Service Computing (Ministry of Education), Tongji University, Shanghai 201804, China
| | - Xiaolin Zhou
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China; School of Psychological and Cognitive Sciences, Peking University, Beijing 100871, China; School of Business and Management, Shanghai International Studies University, Shanghai 200083, China; Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China.
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48
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Kamaleddin MA. Degeneracy in the nervous system: from neuronal excitability to neural coding. Bioessays 2021; 44:e2100148. [PMID: 34791666 DOI: 10.1002/bies.202100148] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 09/26/2021] [Accepted: 09/28/2021] [Indexed: 02/04/2023]
Abstract
Degeneracy is ubiquitous across biological systems where structurally different elements can yield a similar outcome. Degeneracy is of particular interest in neuroscience too. On the one hand, degeneracy confers robustness to the nervous system and facilitates evolvability: Different elements provide a backup plan for the system in response to any perturbation or disturbance. On the other, a difficulty in the treatment of some neurological disorders such as chronic pain is explained in light of different elements all of which contribute to the pathological behavior of the system. Under these circumstances, targeting a specific element is ineffective because other elements can compensate for this modulation. Understanding degeneracy in the physiological context explains its beneficial role in the robustness of neural circuits. Likewise, understanding degeneracy in the pathological context opens new avenues of discovery to find more effective therapies.
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Affiliation(s)
- Mohammad Amin Kamaleddin
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.,Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
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49
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Yanagisawa H. Free-Energy Model of Emotion Potential: Modeling Arousal Potential as Information Content Induced by Complexity and Novelty. Front Comput Neurosci 2021; 15:698252. [PMID: 34867249 PMCID: PMC8641242 DOI: 10.3389/fncom.2021.698252] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 10/26/2021] [Indexed: 11/13/2022] Open
Abstract
Appropriate levels of arousal potential induce hedonic responses (i.e., emotional valence). However, the relationship between arousal potential and its factors (e.g., novelty, complexity, and uncertainty) have not been formalized. This paper proposes a mathematical model that explains emotional arousal using minimized free energy to represent information content processed in the brain after sensory stimuli are perceived and recognized (i.e., sensory surprisal). This work mathematically demonstrates that sensory surprisal represents the summation of information from novelty and uncertainty, and that the uncertainty converges to perceived complexity with sufficient sampling from a stimulus source. Novelty, uncertainty, and complexity all act as collative properties that form arousal potential. Analysis using a Gaussian generative model shows that the free energy is formed as a quadratic function of prediction errors based on the difference between prior expectation and peak of likelihood. The model predicts two interaction effects on free energy: that between prediction error and prior uncertainty (i.e., prior variance) and that between prediction error and sensory variance. A discussion on the potential of free energy as a mathematical principle is presented to explain emotion initiators. The model provides a general mathematical framework for understanding and predicting the emotions caused by novelty, uncertainty, and complexity. The mathematical model of arousal can help predict acceptable novelty and complexity based on a target population under different uncertainty levels mitigated by prior knowledge and experience.
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Affiliation(s)
- Hideyoshi Yanagisawa
- Design Engineering Laboratory, Department of Mechanical Engineering, The University of Tokyo, Tokyo, Japan
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50
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Singh A, Westlin C, Eisenbarth H, Reynolds Losin EA, Andrews-Hanna JR, Wager TD, Satpute AB, Barrett LF, Brooks DH, Erdogmus D. Variation is the Norm: Brain State Dynamics Evoked By Emotional Video Clips. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6003-6007. [PMID: 34892486 PMCID: PMC8784974 DOI: 10.1109/embc46164.2021.9630852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
For the last several decades, emotion research has attempted to identify a "biomarker" or consistent pattern of brain activity to characterize a single category of emotion (e.g., fear) that will remain consistent across all instances of that category, regardless of individual and context. In this study, we investigated variation rather than consistency during emotional experiences while people watched video clips chosen to evoke instances of specific emotion categories. Specifically, we developed a sequential probabilistic approach to model the temporal dynamics in a participant's brain activity during video viewing. We characterized brain states during these clips as distinct state occupancy periods between state transitions in blood oxygen level dependent (BOLD) signal patterns. We found substantial variation in the state occupancy probability distributions across individuals watching the same video, supporting the hypothesis that when it comes to the brain correlates of emotional experience, variation may indeed be the norm.
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Affiliation(s)
- Ashutosh Singh
- Department of Electrical and Computer Engineering, College of Engineering, Northeastern University, Boston, MA, USA
| | - Christiana Westlin
- Department of Psychology, College of Science, Northeastern University, Boston, MA, USA
| | - Hedwig Eisenbarth
- School of Psychology, Victoria University of Wellington, Wellington, New Zealand
| | | | - Jessica R. Andrews-Hanna
- Department of Psychology, University of Arizona, Tucson, AZ, USA
- Cognitive Science, University of Arizona, Tucson, AZ, USA
| | - Tor D. Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Ajay B. Satpute
- Department of Psychology, College of Science, Northeastern University, Boston, MA, USA
| | - Lisa Feldman Barrett
- Department of Psychology, College of Science, Northeastern University, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Dana H. Brooks
- Department of Electrical and Computer Engineering, College of Engineering, Northeastern University, Boston, MA, USA
| | - Deniz Erdogmus
- Department of Electrical and Computer Engineering, College of Engineering, Northeastern University, Boston, MA, USA
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