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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 2024:10.1038/s41562-024-01868-x. [PMID: 38641635 DOI: 10.1038/s41562-024-01868-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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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2
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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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3
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Vaccaro AG, Wu H, Iyer R, Shakthivel S, Christie NC, Damasio A, Kaplan J. Neural patterns associated with mixed valence feelings differ in consistency and predictability throughout the brain. Cereb Cortex 2024; 34:bhae122. [PMID: 38566509 DOI: 10.1093/cercor/bhae122] [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: 01/05/2024] [Revised: 03/07/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024] Open
Abstract
Mixed feelings, the simultaneous presence of feelings with positive and negative valence, remain an understudied topic. They pose a specific set of challenges due to individual variation, and their investigation requires analtyic approaches focusing on individually self-reported states. We used functional magnetic resonance imaging (fMRI) to scan 27 subjects watching an animated short film chosen to induce bittersweet mixed feelings. The same subjects labeled when they had experienced positive, negative, and mixed feelings. Using hidden-Markov models, we found that various brain regions could predict the onsets of new feeling states as determined by self-report. The ability of the models to identify these transitions suggests that these states may exhibit unique and consistent neural signatures. We next used the subjects' self-reports to evaluate the spatiotemporal consistency of neural patterns for positive, negative, and mixed states. The insula had unique and consistent neural signatures for univalent states, but not for mixed valence states. The anterior cingulate and ventral medial prefrontal cortex had consistent neural signatures for both univalent and mixed states. This study is the first to demonstrate that subjectively reported changes in feelings induced by naturalistic stimuli can be predicted from fMRI and the first to show direct evidence for a neurally consistent representation of mixed feelings.
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Affiliation(s)
- Anthony G Vaccaro
- Department of Psychology, Brain and Creativity Institute, University of Southern California, 3620 McClintock Avenue, Los Angeles, CA 90089, United States
| | - Helen Wu
- Department of Psychology, Brain and Creativity Institute, University of Southern California, 3620 McClintock Avenue, Los Angeles, CA 90089, United States
| | - Rishab Iyer
- Department of Psychology, Brain and Creativity Institute, University of Southern California, 3620 McClintock Avenue, Los Angeles, CA 90089, United States
| | - Shruti Shakthivel
- Department of Psychology, Brain and Creativity Institute, University of Southern California, 3620 McClintock Avenue, Los Angeles, CA 90089, United States
| | - Nina C Christie
- Department of Psychology, Brain and Creativity Institute, University of Southern California, 3620 McClintock Avenue, Los Angeles, CA 90089, United States
| | - Antonio Damasio
- Department of Psychology, Brain and Creativity Institute, University of Southern California, 3620 McClintock Avenue, Los Angeles, CA 90089, United States
| | - Jonas Kaplan
- Department of Psychology, Brain and Creativity Institute, University of Southern California, 3620 McClintock Avenue, Los Angeles, CA 90089, United States
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Liu X, Jiao G, Zhou F, Kendrick KM, Yao D, Gong Q, Xiang S, Jia T, Zhang XY, Zhang J, Feng J, Becker B. A neural signature for the subjective experience of threat anticipation under uncertainty. Nat Commun 2024; 15:1544. [PMID: 38378947 PMCID: PMC10879105 DOI: 10.1038/s41467-024-45433-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 01/22/2024] [Indexed: 02/22/2024] Open
Abstract
Uncertainty about potential future threats and the associated anxious anticipation represents a key feature of anxiety. However, the neural systems that underlie the subjective experience of threat anticipation under uncertainty remain unclear. Combining an uncertainty-variation threat anticipation paradigm that allows precise modulation of the level of momentary anxious arousal during functional magnetic resonance imaging (fMRI) with multivariate predictive modeling, we train a brain model that accurately predicts subjective anxious arousal intensity during anticipation and test it across 9 samples (total n = 572, both gender). Using publicly available datasets, we demonstrate that the whole-brain signature specifically predicts anxious anticipation and is not sensitive in predicting pain, general anticipation or unspecific emotional and autonomic arousal. The signature is also functionally and spatially distinguishable from representations of subjective fear or negative affect. We develop a sensitive, generalizable, and specific neuroimaging marker for the subjective experience of uncertain threat anticipation that can facilitate model development.
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Affiliation(s)
- Xiqin Liu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- 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, Sichuan, China
| | - Guojuan Jiao
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Feng Zhou
- Faculty of Psychology, Southwest University, Chongqing, China
- MOE Key Laboratory of Cognition and Personality, Chongqing, China
| | - Keith M Kendrick
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Dezhong Yao
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
- The Centre for Population Neuroscience and Stratified Medicine (PONS), ISTBI, Fudan University, Shanghai, China
- SGDP Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Xiao-Yong Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Benjamin Becker
- State Key Laboratory of 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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5
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Zhao Y, Kong X, Zheng W, Ahmad S. Emotion generation method in online physical education teaching based on data mining of teacher-student interactions. PeerJ Comput Sci 2024; 10:e1814. [PMID: 38259880 PMCID: PMC10803077 DOI: 10.7717/peerj-cs.1814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024]
Abstract
Different from conventional educational paradigms, online education lacks the direct interplay between instructors and learners, particularly in the sphere of virtual physical education. Regrettably, extant research seldom directs its focus toward the intricacies of emotional arousal within the teacher-student course dynamic. The formulation of an emotion generation model exhibits constraints necessitating refinement tailored to distinct educational cohorts, disciplines, and instructional contexts. This study proffers an emotion generation model rooted in data mining of teacher-student course interactions to refine emotional discourse and enhance learning outcomes in the realm of online physical education. This model includes techniques for data preprocessing and augmentation, a multimodal dialogue text emotion recognition model, and a topic-expanding emotional dialogue generation model based on joint decoding. The encoder assimilates the input sentence into a fixed-length vector, culminating in the final state, wherein the vector produced by the context recurrent neural network is conjoined with the preceding word's vector and employed as the decoder's input. Leveraging the long-short-term memory neural network facilitates the modeling of emotional fluctuations across multiple rounds of dialogue, thus fulfilling the mandate of emotion prediction. The evaluation of the model against the DailyDialog dataset demonstrates its superiority over the conventional end-to-end model in terms of loss and confusion values. Achieving an accuracy rate of 84.4%, the model substantiates that embedding emotional cues within dialogues augments response generation. The proposed emotion generation model augments emotional discourse and learning efficacy within online physical education, offering fresh avenues for refining and advancing emotion generation models.
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Affiliation(s)
| | | | - Wei Zheng
- Langfang 16th Middle School, LangFang, China
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6
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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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7
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Reddan M, Ong D, Wager T, Mattek S, Kahhale I, Zaki J. Neural signatures of emotional inference and experience align during social consensus. RESEARCH SQUARE 2023:rs.3.rs-3487248. [PMID: 38014230 PMCID: PMC10680919 DOI: 10.21203/rs.3.rs-3487248/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Humans seamlessly transform dynamic social signals into inferences about the internal states of the people around them. To understand the neural processes that sustain this transformation, we collected fMRI data from participants (N = 100) while they rated the emotional intensity of people (targets) describing significant life events. Targets rated themselves on the same scale to indicate the intended "ground truth" emotional intensity of their videos. Next, we developed two multivariate models of observer brain activity- the first predicted the "ground truth" (r = 0.50, p < 0.0001) and the second predicted observer inferences (r = 0.53, p < 0.0001). When individuals make more accurate inferences, there is greater moment-by-moment concordance between these two models, suggesting that an observer's brain activity contains latent representations of other people's emotional states. Using naturalistic socioemotional stimuli and machine learning, we developed reliable brain signatures that predict what an observer thinks about a target, what the target thinks about themselves, and the correspondence between them. These signatures can be applied in clinical data to better our understanding of socioemotional dysfunction.
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8
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Kragel PA, Treadway MT, Admon R, Pizzagalli DA, Hahn EC. A mesocorticolimbic signature of pleasure in the human brain. Nat Hum Behav 2023; 7:1332-1343. [PMID: 37386105 DOI: 10.1038/s41562-023-01639-0] [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: 08/27/2022] [Accepted: 05/22/2023] [Indexed: 07/01/2023]
Abstract
Pleasure is a fundamental driver of human behaviour, yet its neural basis remains largely unknown. Rodent studies highlight opioidergic neural circuits connecting the nucleus accumbens, ventral pallidum, insula and orbitofrontal cortex as critical for the initiation and regulation of pleasure, and human neuroimaging studies exhibit some translational parity. However, whether activation in these regions conveys a generalizable representation of pleasure regulated by opioidergic mechanisms remains unclear. Here we use pattern recognition techniques to develop a human functional magnetic resonance imaging signature of mesocorticolimbic activity unique to states of pleasure. In independent validation tests, this signature is sensitive to pleasant tastes and affect evoked by humour. The signature is spatially co-extensive with mu-opioid receptor gene expression, and its response is attenuated by the opioid antagonist naloxone. These findings provide evidence for a basis of pleasure in humans that is distributed across brain systems.
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Affiliation(s)
- Philip A Kragel
- Department of Psychology, Emory University, Atlanta, GA, USA.
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA.
| | - Michael T Treadway
- Department of Psychology, Emory University, Atlanta, GA, USA
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Roee Admon
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Belmont, MA, USA
- School of Psychological Sciences, University of Haifa, Haifa, Israel
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Belmont, MA, USA
| | - Emma C Hahn
- Department of Psychology, Emory University, Atlanta, GA, USA
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9
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Camacho MC, Nielsen AN, Balser D, Furtado E, Steinberger DC, Fruchtman L, Culver JP, Sylvester CM, Barch DM. Large-scale encoding of emotion concepts becomes increasingly similar between individuals from childhood to adolescence. Nat Neurosci 2023; 26:1256-1266. [PMID: 37291338 DOI: 10.1038/s41593-023-01358-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 05/12/2023] [Indexed: 06/10/2023]
Abstract
Humans require a shared conceptualization of others' emotions for adaptive social functioning. A concept is a mental blueprint that gives our brains parameters for predicting what will happen next. Emotion concepts undergo refinement with development, but it is not known whether their neural representations change in parallel. Here, in a sample of 5-15-year-old children (n = 823), we show that the brain represents different emotion concepts distinctly throughout the cortex, cerebellum and caudate. Patterns of activation to each emotion changed little across development. Using a model-free approach, we show that activation patterns were more similar between older children than between younger children. Moreover, scenes that required inferring negative emotional states elicited higher default mode network activation similarity in older children than younger children. These results suggest that representations of emotion concepts are relatively stable by mid to late childhood and synchronize between individuals during adolescence.
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Affiliation(s)
- M Catalina Camacho
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA.
| | - Ashley N Nielsen
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Dori Balser
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Emily Furtado
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - David C Steinberger
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Leah Fruchtman
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Joseph P Culver
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Physics, Washington University in St. Louis, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Chad M Sylvester
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Deanna M Barch
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
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10
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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 2023:105237. [PMID: 37209932 DOI: 10.1016/j.neubiorev.2023.105237] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [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)
| | - Eliza Bliss-Moreau
- Department of Psychology; 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; 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, Georgia 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; Computation and Neural Systems Program, California Institute of Technology, Pasadena, California 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; Neuroscience and Cognitive Science Program; Maryland Neuroimaging Center, University of Maryland, College Park, MD 20742 USA.
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11
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Pasquini L, Noohi F, Veziris CR, Kosik EL, Holley SR, Lee A, Brown JA, Roy ARK, Chow TE, Allen I, Rosen HJ, Kramer JH, Miller BL, Saggar M, Seeley WW, Sturm VE. Dynamic autonomic nervous system states arise during emotions and manifest in basal physiology. Psychophysiology 2023; 60:e14218. [PMID: 36371680 PMCID: PMC10038867 DOI: 10.1111/psyp.14218] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 09/08/2022] [Accepted: 09/16/2022] [Indexed: 11/15/2022]
Abstract
The outflow of the autonomic nervous system (ANS) is continuous and dynamic, but its functional organization is not well understood. Whether ANS patterns accompany emotions, or arise in basal physiology, remain unsettled questions in the field. Here, we searched for brief ANS patterns amidst continuous, multichannel physiological recordings in 45 healthy older adults. Participants completed an emotional reactivity task in which they viewed video clips that elicited a target emotion (awe, sadness, amusement, disgust, or nurturant love); each video clip was preceded by a pre-trial baseline period and followed by a post-trial recovery period. Participants also sat quietly for a separate 2-min resting period to assess basal physiology. Using principal components analysis and unsupervised clustering algorithms to reduce the second-by-second physiological data during the emotional reactivity task, we uncovered five ANS states. Each ANS state was characterized by a unique constellation of patterned physiological changes that differentiated among the trials of the emotional reactivity task. These ANS states emerged and dissipated over time, with each instance lasting several seconds on average. ANS states with similar structures were also detectable in the resting period but were intermittent and of smaller magnitude. Our results offer new insights into the functional organization of the ANS. By assembling short-lived, patterned changes, the ANS is equipped to generate a wide range of physiological states that accompany emotions and that contribute to the architecture of basal physiology.
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Affiliation(s)
- Lorenzo Pasquini
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Fatemeh Noohi
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Christina R. Veziris
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Eena L. Kosik
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Sarah R. Holley
- San Francisco State University, San Francisco, CA, USA
- Department of Psychiatry & Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Alex Lee
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Jesse A. Brown
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Ashlin R. K. Roy
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Tiffany E. Chow
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Isabel Allen
- Department of Epidemiology & Biostatistics, University of California, San Francisco, CA, USA
| | - Howard J. Rosen
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Joel H. Kramer
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Bruce L. Miller
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Manish Saggar
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - William W. Seeley
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, CA, USA
| | - Virginia E. Sturm
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
- Global Brain Health Institute, Memory and Aging Center, University of California, San Francisco, CA, USA
- Department of Psychiatry & Behavioral Sciences, University of California, San Francisco, CA, USA
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12
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Nummenmaa L, Malèn T, Nazari-Farsani S, Seppälä K, Sun L, Santavirta S, Karlsson HK, Hudson M, Hirvonen J, Sams M, Scott S, Putkinen V. Decoding brain basis of laughter and crying in natural scenes. Neuroimage 2023; 273:120082. [PMID: 37030414 DOI: 10.1016/j.neuroimage.2023.120082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 03/08/2023] [Accepted: 03/31/2023] [Indexed: 04/08/2023] Open
Abstract
Laughter and crying are universal signals of prosociality and distress, respectively. Here we investigated the functional brain basis of perceiving laughter and crying using naturalistic functional magnetic resonance imaging (fMRI) approach. We measured haemodynamic brain activity evoked by laughter and crying in three experiments with 100 subjects in each. The subjects i) viewed a 20-minute medley of short video clips, and ii) 30 minutes of a full-length feature film, and iii) listened to 15 minutes of a radio play that all contained bursts of laughter and crying. Intensity of laughing and crying in the videos and radio play was annotated by independent observes, and the resulting time series were used to predict hemodynamic activity to laughter and crying episodes. Multivariate pattern analysis (MVPA) was used to test for regional selectivity in laughter and crying evoked activations. Laughter induced widespread activity in ventral visual cortex and superior and middle temporal and motor cortices. Crying activated thalamus, cingulate cortex along the anterior-posterior axis, insula and orbitofrontal cortex. Both laughter and crying could be decoded accurately (66-77% depending on the experiment) from the BOLD signal, and the voxels contributing most significantly to classification were in superior temporal cortex. These results suggest that perceiving laughter and crying engage distinct neural networks, whose activity suppresses each other to manage appropriate behavioral responses to others' bonding and distress signals.
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13
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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: 6] [Impact Index Per Article: 6.0] [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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14
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Fornari L, Ioumpa K, Nostro AD, Evans NJ, De Angelis L, Speer SPH, Paracampo R, Gallo S, Spezio M, Keysers C, Gazzola V. Neuro-computational mechanisms and individual biases in action-outcome learning under moral conflict. Nat Commun 2023; 14:1218. [PMID: 36878911 PMCID: PMC9988878 DOI: 10.1038/s41467-023-36807-3] [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: 11/25/2021] [Accepted: 02/17/2023] [Indexed: 03/08/2023] Open
Abstract
Learning to predict action outcomes in morally conflicting situations is essential for social decision-making but poorly understood. Here we tested which forms of Reinforcement Learning Theory capture how participants learn to choose between self-money and other-shocks, and how they adapt to changes in contingencies. We find choices were better described by a reinforcement learning model based on the current value of separately expected outcomes than by one based on the combined historical values of past outcomes. Participants track expected values of self-money and other-shocks separately, with the substantial individual difference in preference reflected in a valuation parameter balancing their relative weight. This valuation parameter also predicted choices in an independent costly helping task. The expectations of self-money and other-shocks were biased toward the favored outcome but fMRI revealed this bias to be reflected in the ventromedial prefrontal cortex while the pain-observation network represented pain prediction errors independently of individual preferences.
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Affiliation(s)
- Laura Fornari
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands
| | - Kalliopi Ioumpa
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands
| | - Alessandra D Nostro
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands
| | - Nathan J Evans
- School of Psychology, University of Queensland, Brisbane, QLD, Australia
| | - Lorenzo De Angelis
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands
| | - Sebastian P H Speer
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands
| | - Riccardo Paracampo
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands
| | - Selene Gallo
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands
| | - Michael Spezio
- Psychology, Neuroscience, & Data Science, Scripps College, 1030 Columbia Ave, CA 91711, Claremont, CA, USA
| | - Christian Keysers
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands.,Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 WT, Amsterdam, The Netherlands
| | - Valeria Gazzola
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands. .,Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 WT, Amsterdam, The Netherlands.
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15
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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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16
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Moulds DJ, Meyer J, McLean JF, Kempe V. Exploring effects of response biases in affect induction procedures. PLoS One 2023; 18:e0285706. [PMID: 37167316 PMCID: PMC10174507 DOI: 10.1371/journal.pone.0285706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 05/02/2023] [Indexed: 05/13/2023] Open
Abstract
This study examined whether self-reports or ratings of experienced affect, often used as manipulation checks on the efficacy of affect induction procedures (AIPs), reflect genuine changes in affective states rather than response biases arising from demand characteristics or social desirability effects. In a between-participants design, participants were exposed to positive, negative and neutral images with valence-congruent music or sound to induce happy, sad and neutral mood. Half of the participants had to actively appraise each image whereas the other half viewed images passively. We hypothesised that if ratings of affective valence are subject to response biases then they should reflect the target mood in the same way for active appraisal and passive exposure as participants encountered the same affective stimuli in both conditions. We also tested whether the AIP resulted in mood-congruent changes in facial expressions analysed by FaceReader to see whether behavioural indicators corroborate the self-reports. The results showed that while participants' ratings reflected the induced target valence, the difference between positive and negative AIP was significantly attenuated in the active appraisal condition, suggesting that self-reports of mood experienced after the AIP are not entirely a reflection of response biases. However, there were no effects of the AIP on FaceReader valence scores, in line with theories questioning the existence of cross-culturally and inter-individually universal behavioural indicators of affective states. Efficacy of AIPs is therefore best checked using self-reports.
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Affiliation(s)
- David J Moulds
- Division of Psychology, School of Applied Sciences, Abertay University, Dundee, United Kingdom
| | - Jona Meyer
- Division of Psychology, School of Applied Sciences, Abertay University, Dundee, United Kingdom
| | - Janet F McLean
- Division of Psychology, School of Applied Sciences, Abertay University, Dundee, United Kingdom
| | - Vera Kempe
- Division of Psychology, School of Applied Sciences, Abertay University, Dundee, United Kingdom
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17
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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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18
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Lee S, Parthasarathi T, Cooper N, Zauberman G, Lerman C, Kable JW. A neural signature of the vividness of prospective thought is modulated by temporal proximity during intertemporal decision making. Proc Natl Acad Sci U S A 2022; 119:e2214072119. [PMID: 36279433 PMCID: PMC9636959 DOI: 10.1073/pnas.2214072119] [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: 08/19/2022] [Accepted: 09/27/2022] [Indexed: 11/18/2022] Open
Abstract
Why do people discount future rewards? Multiple theories in psychology argue that one reason is that future events are imagined less vividly than immediate events, thereby diminishing their perceived value. Here we provide neuroscientific evidence for this proposal. First, we construct a neural signature of the vividness of prospective thought, using an fMRI dataset where the vividness of imagined future events is orthogonal to their valence by design. Then, we apply this neural signature in two additional fMRI datasets, each using a different delay-discounting task, to show that neural measures of vividness decline as rewards are delayed farther into the future.
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Affiliation(s)
- Sangil Lee
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104
| | | | - Nicole Cooper
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104
| | - Gal Zauberman
- Yale School of Management, Yale University, New Haven, CT 06511
| | - Caryn Lerman
- University of Southern California Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033
| | - Joseph W. Kable
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104
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19
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Barrett LF. Context reconsidered: Complex signal ensembles, relational meaning, and population thinking in psychological science. AMERICAN PSYCHOLOGIST 2022; 77:894-920. [PMID: 36409120 PMCID: PMC9683522 DOI: 10.1037/amp0001054] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
This article considers the status and study of "context" in psychological science through the lens of research on emotional expressions. The article begins by updating three well-trod methodological debates on the role of context in emotional expressions to reconsider several fundamental assumptions lurking within the field's dominant methodological tradition: namely, that certain expressive movements have biologically prepared, inherent emotional meanings that issue from singular, universal processes which are independent of but interact with contextual influences. The second part of this article considers the scientific opportunities that await if we set aside this traditional understanding of "context" as a moderator of signals with inherent psychological meaning and instead consider the possibility that psychological events emerge in ecosystems of signal ensembles, such that the psychological meaning of any individual signal is entirely relational. Such a fundamental shift has radical implications not only for the science of emotion but for psychological science more generally. It offers opportunities to improve the validity and trustworthiness of psychological science beyond what can be achieved with improvements to methodological rigor alone. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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20
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Grollero D, Petrolini V, Viola M, Morese R, Lettieri G, Cecchetti L. The structure underlying core affect and perceived affective qualities of human vocal bursts. Cogn Emot 2022; 37:1-17. [PMID: 36300588 DOI: 10.1080/02699931.2022.2139661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Vocal bursts are non-linguistic affectively-laden sounds with a crucial function in human communication, yet their affective structure is still debated. Studies showed that ratings of valence and arousal follow a V-shaped relationship in several kinds of stimuli: high arousal ratings are more likely to go on a par with very negative or very positive valence. Across two studies, we asked participants to listen to 1,008 vocal bursts and judge both how they felt when listening to the sound (i.e. core affect condition), and how the speaker felt when producing it (i.e. perception of affective quality condition). We show that a V-shaped fit outperforms a linear model in explaining the valence-arousal relationship across conditions and studies, even after equating the number of exemplars across emotion categories. Also, although subjective experience can be significantly predicted using affective quality ratings, core affect scores are significantly lower in arousal, less extreme in valence, more variable between individuals, and less reproducible between studies. Nonetheless, stimuli rated with opposite valence between conditions range from 11% (study 1) to 17% (study 2). Lastly, we demonstrate that ambiguity in valence (i.e. high between-participants variability) explains violations of the V-shape and relates to higher arousal.
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Affiliation(s)
- Demetrio Grollero
- Social and Affective Neuroscience (SANe) Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Valentina Petrolini
- Lindy Lab - Language in Neurodiversity, Department of Linguistics and Basque Studies, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain
| | - Marco Viola
- Department of Philosophy and Education, University of Turin, Turin, Italy
| | - Rosalba Morese
- Faculty of Communication, Culture and Society, Università della Svizzera Italiana, Lugano, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland
| | - Giada Lettieri
- Social and Affective Neuroscience (SANe) Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
- Crossmodal Perception and Plasticity Laboratory, IPSY, University of Louvain, Louvain-la-Neuve, Belgium
| | - Luca Cecchetti
- Social and Affective Neuroscience (SANe) Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
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21
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Tsoi L, Burns SM, Falk EB, Tamir DI. The promises and pitfalls of functional magnetic resonance imaging hyperscanning for social interaction research. SOCIAL AND PERSONALITY PSYCHOLOGY COMPASS 2022; 16:e12707. [PMID: 36407123 PMCID: PMC9667901 DOI: 10.1111/spc3.12707] [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: 06/03/2022] [Revised: 08/23/2022] [Accepted: 08/26/2022] [Indexed: 11/28/2022]
Abstract
Social neuroscience combines tools and perspectives from social psychology and neuroscience to understand how people interact with their social world. Here we discuss a relatively new method-hyperscanning-to study real-time, interactive social interactions using functional magnetic resonance imaging (fMRI). We highlight three contributions that fMRI hyperscanning makes to the study of the social mind: (1) Naturalism: it shifts the focus from tightly-controlled stimuli to more naturalistic social interactions; (2) Multi-person Dynamics: it shifts the focus from individuals as the unit of analysis to dyads and groups; and (3) Neural Resolution: fMRI hyperscanning captures high-resolution neural patterns and dynamics across the whole brain, unlike other neuroimaging hyperscanning methods (e.g., electroencephalogram, functional near-infrared spectroscopy). Finally, we describe the practical considerations and challenges that fMRI hyperscanning researchers must navigate. We hope researchers will harness this powerful new paradigm to address pressing questions in today's society.
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Affiliation(s)
- Lily Tsoi
- School of Psychology and CounselingCaldwell UniversityCaldwellNew JerseyUSA
| | - Shannon M. Burns
- Department of Psychological SciencePomona CollegeClaremontCaliforniaUSA,Department of NeurosciencePomona CollegeClaremontCaliforniaUSA
| | - Emily B. Falk
- Annenberg School for CommunicationUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA,Department of PsychologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA,Wharton Marketing DepartmentUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA,Operations, Information, and Decisions DepartmentUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Diana I. Tamir
- Department of PsychologyPrinceton UniversityPrincetonNew JerseyUSA,Princeton Neuroscience InstitutePrinceton UniversityPrincetonNew JerseyUSA
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22
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Klein S, Kruse O, Tapia León I, Van Oudenhove L, van 't Hof SR, Klucken T, Wager TD, Stark R. Cross-paradigm integration shows a common neural basis for aversive and appetitive conditioning. Neuroimage 2022; 263:119594. [PMID: 36041642 DOI: 10.1016/j.neuroimage.2022.119594] [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: 03/03/2022] [Revised: 07/22/2022] [Accepted: 08/25/2022] [Indexed: 10/31/2022] Open
Abstract
Sharing imaging data and comparing them across different psychological tasks is becoming increasingly possible as the open science movement advances. Such cross-paradigm integration has the potential to identify commonalities in findings that neighboring areas of study thought to be paradigm-specific. However, even the integration of research from closely related paradigms, such as aversive and appetitive classical conditioning is rare - even though qualitative comparisons already hint at how similar the 'fear network' and 'reward network' may be. We aimed to validate these theories by taking a multivariate approach to assess commonalities across paradigms empirically. Specifically, we quantified the similarity of an aversive conditioning pattern derived from meta-analysis to appetitive conditioning fMRI data. We tested pattern expression in three independent appetitive conditioning studies with 29, 76 and 38 participants each. During fMRI scanning, participants in each cohorts performed an appetitive conditioning task in which a CS+ was repeatedly rewarded with money and a CS- was never rewarded. The aversive pattern was highly similar to appetitive CS+ > CS- contrast maps across samples and variations of the appetitive conditioning paradigms. Moreover, the pattern distinguished the CS+ from the CS- with above-chance accuracy in every sample. These findings provide robust empirical evidence for an underlying neural system common to appetitive and aversive learning. We believe that this approach provides a way to empirically integrate the steadily growing body of fMRI findings across paradigms.
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Affiliation(s)
- Sanja Klein
- Department of Psychotherapy and Systems Neuroscience, Justus Liebig University, Giessen 35394, Germany; Bender Institute for Neuroimaging (BION), Justus Liebig University, Giessen 35394, Germany; Center of Mind, Brain and Behavior, Universities of Marburg and Giessen, Marburg 35032, Germany.
| | - Onno Kruse
- Department of Psychotherapy and Systems Neuroscience, Justus Liebig University, Giessen 35394, Germany; Bender Institute for Neuroimaging (BION), Justus Liebig University, Giessen 35394, Germany
| | - Isabell Tapia León
- Bender Institute for Neuroimaging (BION), Justus Liebig University, Giessen 35394, Germany; Clinical Psychology and Psychotherapy, University Siegen, Siegen 57076, Germany
| | - Lukas Van Oudenhove
- Department of Chronic Diseases and Metabolism (CHROMETA), Laboratory for Brain-Gut Axis Studies (LaBGAS), Translational Research Centre for Gastrointestinal Disorders TARGID, KU Leuven, Leuven, Belgium; Leuven Brain Institute, KU Leuven, Leuven, Belgium; Department of Psychological and Brain Sciences, Cognitive and Affective Neuroscience Lab, Dartmouth College, Hanover, NH, USA
| | - Sophie R van 't Hof
- Department of Psychiatry, Amsterdam University Medical Centers, Amsterdam 1105 AZ, The Netherlands
| | - Tim Klucken
- Clinical Psychology and Psychotherapy, University Siegen, Siegen 57076, Germany
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Cognitive and Affective Neuroscience Lab, Dartmouth College, Hanover, NH, USA
| | - Rudolf Stark
- Department of Psychotherapy and Systems Neuroscience, Justus Liebig University, Giessen 35394, Germany; Bender Institute for Neuroimaging (BION), Justus Liebig University, Giessen 35394, Germany; Center of Mind, Brain and Behavior, Universities of Marburg and Giessen, Marburg 35032, Germany
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23
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Majumdar G, Yazin F, Banerjee A, Roy D. Emotion dynamics as hierarchical Bayesian inference in time. Cereb Cortex 2022; 33:3750-3772. [PMID: 36030379 DOI: 10.1093/cercor/bhac305] [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: 03/13/2022] [Revised: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 11/13/2022] Open
Abstract
What fundamental property of our environment would be most valuable and optimal in characterizing the emotional dynamics we experience in daily life? Empirical work has shown that an accurate estimation of uncertainty is necessary for our optimal perception, learning, and decision-making. However, the role of this uncertainty in governing our affective dynamics remains unexplored. Using Bayesian encoding, decoding and computational modeling, on a large-scale neuroimaging and behavioral data on a passive movie-watching task, we showed that emotions naturally arise due to ongoing uncertainty estimations about future outcomes in a hierarchical neural architecture. Several prefrontal subregions hierarchically encoded a lower-dimensional signal that highly correlated with the evolving uncertainty. Crucially, the lateral orbitofrontal cortex (lOFC) tracked the temporal fluctuations of this uncertainty and was predictive of the participants' predisposition to anxiety. Furthermore, we observed a distinct functional double-dissociation within OFC with increased connectivity between medial OFC and DMN, while with that of lOFC and FPN in response to the evolving affect. Finally, we uncovered a temporally predictive code updating an individual's beliefs spontaneously with fluctuating outcome uncertainty in the lOFC. A biologically relevant and computationally crucial parameter in the theories of brain function, we propose uncertainty to be central to the definition of complex emotions.
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Affiliation(s)
- Gargi Majumdar
- Cognitive Brain Dynamics Lab, National Brain Research Centre, NH 8, Manesar, Gurgaon, Haryana 122052, India
| | - Fahd Yazin
- Cognitive Brain Dynamics Lab, National Brain Research Centre, NH 8, Manesar, Gurgaon, Haryana 122052, India
| | - Arpan Banerjee
- Cognitive Brain Dynamics Lab, National Brain Research Centre, NH 8, Manesar, Gurgaon, Haryana 122052, India
| | - Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, NH 8, Manesar, Gurgaon, Haryana 122052, India.,Centre for Brain Science and Applications, School of AIDE, IIT Jodhpur, NH 62, Surpura Bypass Rd, Karwar, Rajasthan 342030, India
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24
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Xu L, Chavez-Echeagaray ME, Berisha V. Unsupervised EEG channel selection based on nonnegative matrix factorization. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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25
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Lee S, Bradlow ET, Kable JW. Fast construction of interpretable whole-brain decoders. CELL REPORTS METHODS 2022; 2:100227. [PMID: 35784649 PMCID: PMC9243546 DOI: 10.1016/j.crmeth.2022.100227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 04/11/2022] [Accepted: 05/16/2022] [Indexed: 01/15/2023]
Abstract
Researchers often seek to decode mental states from brain activity measured with functional MRI. Rigorous decoding requires the use of formal neural prediction models, which are likely to be the most accurate if they use the whole brain. However, the computational burden and lack of interpretability of off-the-shelf statistical methods can make whole-brain decoding challenging. Here, we propose a method to build whole-brain neural decoders that are both interpretable and computationally efficient. We extend the partial least squares algorithm to build a regularized model with variable selection that offers a unique "fit once, tune later" approach: users need to fit the model only once and can choose the best tuning parameters post hoc. We show in real data that our method scales well with increasing data size and yields interpretable predictors. The algorithm is publicly available in multiple languages in the hope that interpretable whole-brain predictors can be implemented more widely in neuroimaging research.
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Affiliation(s)
- Sangil Lee
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Marketing Department, Wharton School, University of Pennsylvania, PA 19104, USA
- Social Science Matrix, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Eric T. Bradlow
- Marketing Department, Wharton School, University of Pennsylvania, PA 19104, USA
| | - Joseph W. Kable
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Marketing Department, Wharton School, University of Pennsylvania, PA 19104, USA
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26
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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: 45] [Impact Index Per Article: 22.5] [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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27
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Sun Y, Ma J, Huang M, Yi Y, Wang Y, Gu Y, Lin Y, Li LMW, Dai Z. Functional connectivity dynamics as a function of the fluctuation of tension during film watching. Brain Imaging Behav 2022; 16:1260-1274. [PMID: 34988779 DOI: 10.1007/s11682-021-00593-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/12/2021] [Indexed: 11/28/2022]
Abstract
To advance the understanding of the dynamic relationship between brain activities and emotional experiences, we examined the neural patterns of tension, a unique emotion that highly depends on how an event unfolds. Specifically, the present study explored the temporal relationship between functional connectivity patterns within and between different brain functional modules and the fluctuation in tension during film watching. Due to the highly contextualized and time-varying nature of tension, we expected that multiple neural networks would be involved in the dynamic tension experience. Using the neuroimaging data of 546 participants, we conducted a dynamic brain analysis to identify the intra- and inter-module functional connectivity patterns that are significantly correlated with the fluctuation of tension over time. The results showed that the inter-module connectivity of cingulo-opercular network, fronto-parietal network, and default mode network is involved in the dynamic experience of tension. These findings demonstrate a close relationship between brain functional connectivity patterns and emotional dynamics, which supports the importance of functional connectivity dynamics in understanding our cognitive and emotional processes.
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Affiliation(s)
- Yadi Sun
- Department of Psychology, Sun Yat-sen University, Guangzhou, 510006, China
| | - Junji Ma
- Department of Psychology, Sun Yat-sen University, Guangzhou, 510006, China
| | - Miner Huang
- Department of Psychology, Sun Yat-sen University, Guangzhou, 510006, China
| | - Yangyang Yi
- Department of Psychology, Sun Yat-sen University, Guangzhou, 510006, China
| | - Yiheng Wang
- Institute of Applied Psychology, Guangdong University of Finance, Guangzhou, 510006, China
| | - Yue Gu
- Department of Psychology, Sun Yat-sen University, Guangzhou, 510006, China
| | - Ying Lin
- Department of Psychology, Sun Yat-sen University, Guangzhou, 510006, China
| | - Liman Man Wai Li
- Department of Psychology and Centre for Psychosocial Health, The Education University of Hong Kong, Hong Kong SAR, China.
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou, 510006, China.
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28
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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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29
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Vaccaro AG, Heydari P, Christov-Moore L, Damasio A, Kaplan JT. Perspective-taking is associated with increased discriminability of affective states in the ventromedial prefrontal cortex. Soc Cogn Affect Neurosci 2022; 17:1082-1090. [PMID: 35579186 PMCID: PMC9714424 DOI: 10.1093/scan/nsac035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/05/2022] [Accepted: 05/16/2022] [Indexed: 01/12/2023] Open
Abstract
Recent work using multivariate-pattern analysis (MVPA) on functional magnetic resonance imaging (fMRI) data has found that distinct affective states produce correspondingly distinct patterns of neural activity in the cerebral cortex. However, it is unclear whether individual differences in the distinctiveness of neural patterns evoked by affective stimuli underlie empathic abilities such as perspective-taking (PT). Accordingly, we examined whether we could predict PT tendency from the classification of blood-oxygen-level-dependent (BOLD) fMRI activation patterns while participants (n = 57) imagined themselves in affectively charged scenarios. We used an MVPA searchlight analysis to map where in the brain activity patterns permitted the classification of four affective states: happiness, sadness, fear and disgust. Classification accuracy was significantly above chance levels in most of the prefrontal cortex and in the posterior medial cortices. Furthermore, participants' self-reported PT was positively associated with classification accuracy in the ventromedial prefrontal cortex and insula. This finding has implications for understanding affective processing in the prefrontal cortex and for interpreting the cognitive significance of classifiable affective brain states. Our multivariate approach suggests that PT ability may rely on the grain of internally simulated affective representations rather than simply the global strength.
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Affiliation(s)
- Anthony G Vaccaro
- Jon Brain and Creativity Institute, Department of Psychology, University of Southern California, Los Angeles, CA 90089-0001, USA
| | - Panthea Heydari
- Jon Brain and Creativity Institute, Department of Psychology, University of Southern California, Los Angeles, CA 90089-0001, USA
| | - Leonardo Christov-Moore
- Jon Brain and Creativity Institute, Department of Psychology, University of Southern California, Los Angeles, CA 90089-0001, USA
| | - Antonio Damasio
- Jon Brain and Creativity Institute, Department of Psychology, University of Southern California, Los Angeles, CA 90089-0001, USA
| | - Jonas T Kaplan
- Correspondence should be addressed to Jonas T. Kaplan, Brain and Creativity Institute, 3620A McClintock Ave, Los Angeles, CA 90089, USA. E-mail:
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30
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Kragel PA, Hariri AR, LaBar KS. The Temporal Dynamics of Spontaneous Emotional Brain States and Their Implications for Mental Health. J Cogn Neurosci 2022; 34:715-728. [PMID: 34705046 PMCID: PMC9026845 DOI: 10.1162/jocn_a_01787] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Temporal processes play an important role in elaborating and regulating emotional responding during routine mind wandering. However, it is unknown whether the human brain reliably transitions among multiple emotional states at rest and how psychopathology alters these affect dynamics. Here, we combined pattern classification and stochastic process modeling to investigate the chronometry of spontaneous brain activity indicative of six emotions (anger, contentment, fear, happiness, sadness, and surprise) and a neutral state. We modeled the dynamic emergence of these brain states during resting-state fMRI and validated the results across two population cohorts-the Duke Neurogenetics Study and the Nathan Kline Institute Rockland Sample. Our findings indicate that intrinsic emotional brain dynamics are effectively characterized as a discrete-time Markov process, with affective states organized around a neutral hub. The centrality of this network hub is disrupted in individuals with psychopathology, whose brain state transitions exhibit greater inertia and less frequent resetting from emotional to neutral states. These results yield novel insights into how the brain signals spontaneous emotions and how alterations in their temporal dynamics contribute to compromised mental health.
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31
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Grall C, Finn ES. Leveraging the Power of Media to Drive Cognition: A Media-Informed Approach to Naturalistic Neuroscience. Soc Cogn Affect Neurosci 2022; 17:598-608. [PMID: 35257180 PMCID: PMC9164202 DOI: 10.1093/scan/nsac019] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 02/01/2022] [Accepted: 03/07/2022] [Indexed: 11/18/2022] Open
Abstract
So-called ‘naturalistic’ stimuli have risen in popularity in cognitive, social and affective neuroscience over the last 15 years. However, a critical property of these stimuli is frequently overlooked: Media—like film, television, books and podcasts—are ‘fundamentally not natural’. They are deliberately crafted products meant to elicit particular human thought, emotion and behavior. Here, we argue for a more informed approach to adopting media stimuli in experimental paradigms. We discuss the pitfalls of combining stimuli that are designed for research with those that are designed for other purposes (e.g. entertainment) under the umbrella term of ‘naturalistic’ and present strategies to improve rigor in the stimulus selection process. We assert that experiencing media should be considered a task akin to any other experimental task(s) and explain how this shift in perspective will compel more nuanced and generalizable research using these stimuli. Throughout, we offer theoretical and practical knowledge from multidisciplinary media research to raise the standard for the treatment of media stimuli in neuroscience research.
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32
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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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33
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Jääskeläinen IP, Ahveninen J, Klucharev V, Shestakova AN, Levy J. Behavioral Experience-Sampling Methods in Neuroimaging Studies With Movie and Narrative Stimuli. Front Hum Neurosci 2022; 16:813684. [PMID: 35153706 PMCID: PMC8828971 DOI: 10.3389/fnhum.2022.813684] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 01/06/2022] [Indexed: 01/22/2023] Open
Abstract
Movies and narratives are increasingly utilized as stimuli in functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and electroencephalography (EEG) studies. Emotional reactions of subjects, what they pay attention to, what they memorize, and their cognitive interpretations are all examples of inner experiences that can differ between subjects during watching of movies and listening to narratives inside the scanner. Here, we review literature indicating that behavioral measures of inner experiences play an integral role in this new research paradigm via guiding neuroimaging analysis. We review behavioral methods that have been developed to sample inner experiences during watching of movies and listening to narratives. We also review approaches that allow for joint analyses of the behaviorally sampled inner experiences and neuroimaging data. We suggest that building neurophenomenological frameworks holds potential for solving the interrelationships between inner experiences and their neural underpinnings. Finally, we tentatively suggest that recent developments in machine learning approaches may pave way for inferring different classes of inner experiences directly from the neuroimaging data, thus potentially complementing the behavioral self-reports.
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Affiliation(s)
- Iiro P. Jääskeläinen
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- International Laboratory of Social Neurobiology, Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
- *Correspondence: Iiro P. Jääskeläinen,
| | - Jyrki Ahveninen
- Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
| | - Vasily Klucharev
- International Laboratory of Social Neurobiology, Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | - Anna N. Shestakova
- International Laboratory of Social Neurobiology, Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | - Jonathan Levy
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- Baruch Ivcher School of Psychology, Interdisciplinary Center Herzliya, Reichman University, Herzliya, Israel
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34
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Den Ouden L, Suo C, Albertella L, Greenwood LM, Lee RSC, Fontenelle LF, Parkes L, Tiego J, Chamberlain SR, Richardson K, Segrave R, Yücel M. Transdiagnostic phenotypes of compulsive behavior and associations with psychological, cognitive, and neurobiological affective processing. Transl Psychiatry 2022; 12:10. [PMID: 35013101 PMCID: PMC8748429 DOI: 10.1038/s41398-021-01773-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 12/02/2021] [Accepted: 12/16/2021] [Indexed: 01/10/2023] Open
Abstract
Compulsivity is a poorly understood transdiagnostic construct thought to underlie multiple disorders, including obsessive-compulsive disorder, addictions, and binge eating. Our current understanding of the causes of compulsive behavior remains primarily based on investigations into specific diagnostic categories or findings relying on one or two laboratory measures to explain complex phenotypic variance. This proof-of-concept study drew on a heterogeneous sample of community-based individuals (N = 45; 18-45 years; 25 female) exhibiting compulsive behavioral patterns in alcohol use, eating, cleaning, checking, or symmetry. Data-driven statistical modeling of multidimensional markers was utilized to identify homogeneous subtypes that were independent of traditional clinical phenomenology. Markers were based on well-defined measures of affective processing and included psychological assessment of compulsivity, behavioral avoidance, and stress, neurocognitive assessment of reward vs. punishment learning, and biological assessment of the cortisol awakening response. The neurobiological validity of the subtypes was assessed using functional magnetic resonance imaging. Statistical modeling identified three stable, distinct subtypes of compulsivity and affective processing, which we labeled "Compulsive Non-Avoidant", "Compulsive Reactive" and "Compulsive Stressed". They differed meaningfully on validation measures of mood, intolerance of uncertainty, and urgency. Most importantly, subtypes captured neurobiological variance on amygdala-based resting-state functional connectivity, suggesting they were valid representations of underlying neurobiology and highlighting the relevance of emotion-related brain networks in compulsive behavior. Although independent larger samples are needed to confirm the stability of subtypes, these data offer an integrated understanding of how different systems may interact in compulsive behavior and provide new considerations for guiding tailored intervention decisions.
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Affiliation(s)
- Lauren Den Ouden
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia.
| | - Chao Suo
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Lucy Albertella
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Lisa-Marie Greenwood
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
- Research School of Psychology, ANU College of Health and Medicine, The Australian National University, Canberra, Australia
| | - Rico S C Lee
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Leonardo F Fontenelle
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
- D'Or Institute for Research and Education and Anxiety, Obsessive, Compulsive Research Program, Institute of Psychiatry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Linden Parkes
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jeggan Tiego
- Neural Systems and Behaviour Lab, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Samuel R Chamberlain
- Department of Psychiatry, University of Southampton, Southampton, UK
- Southern Health NHS Foundation Trust, Southampton, UK
| | - Karyn Richardson
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Rebecca Segrave
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Murat Yücel
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
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35
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Preston AJ, Rew L. Connectedness, Self-Esteem, and Prosocial Behaviors Protect Adolescent Mental Health Following Social Isolation: A Systematic Review. Issues Ment Health Nurs 2022; 43:32-41. [PMID: 34346800 DOI: 10.1080/01612840.2021.1948642] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Societal trends and COVID-19 quarantines have increased the number of adolescents experiencing social isolation, placing them at heightened risk for mental health issues. The aim of this review is to explore protective factors that might mitigate psychological harm in the presence of social isolation. A systematic literature review was conducted using Fink's step-by-step process. Four library databases were searched, and results were reported using PRISMA. Of the 246 studies reviewed, 12 studies were retained following the quality assessment. The sample includes 14,064 participants from USA, Australia, and Europe, ranging from 10-19 years old. Social connectedness (ie., family connectedness, school connectedness, social support), self-esteem, and prosocial behaviors were the most common protective factors to social isolation. Additional factors such as self-efficacy, optimism, and ethnic identity are discussed. Implications for future research are recommended, including the need to explore spiritual, biological, and sociocultural factors influencing social connectedness and mental health in adolescents.
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Affiliation(s)
| | - Lynn Rew
- School of Nursing, University of Texas, Austin, Texas, USA
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36
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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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37
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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: 4] [Impact Index Per Article: 1.3] [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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38
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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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39
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Zhou F, Zhao W, Qi Z, Geng Y, Yao S, Kendrick KM, Wager TD, Becker B. A distributed fMRI-based signature for the subjective experience of fear. Nat Commun 2021; 12:6643. [PMID: 34789745 PMCID: PMC8599690 DOI: 10.1038/s41467-021-26977-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 10/25/2021] [Indexed: 11/24/2022] Open
Abstract
The specific neural systems underlying the subjective feeling of fear are debated in affective neuroscience. Here, we combine functional MRI with machine learning to identify and evaluate a sensitive and generalizable neural signature predictive of the momentary self-reported subjective fear experience across discovery (n = 67), validation (n = 20) and generalization (n = 31) cohorts. We systematically demonstrate that accurate fear prediction crucially requires distributed brain systems, with important contributions from cortical (e.g., prefrontal, midcingulate and insular cortices) and subcortical (e.g., thalamus, periaqueductal gray, basal forebrain and amygdala) regions. We further demonstrate that the neural representation of subjective fear is distinguishable from the representation of conditioned threat and general negative affect. Overall, our findings suggest that subjective fear, which exhibits distinct neural representation with some other aversive states, is encoded in distributed systems rather than isolated 'fear centers'.
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Affiliation(s)
- Feng Zhou
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Weihua Zhao
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Ziyu Qi
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yayuan Geng
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Shuxia Yao
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Keith M Kendrick
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
| | - Benjamin Becker
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
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40
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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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41
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Mitchell WJ, Tepfer LJ, Henninger NM, Perlman SB, Murty VP, Helion C. Developmental Differences in Affective Representation Between Prefrontal and Subcortical Structures. Soc Cogn Affect Neurosci 2021; 17:nsab093. [PMID: 34331538 PMCID: PMC8881632 DOI: 10.1093/scan/nsab093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 06/16/2021] [Accepted: 07/30/2021] [Indexed: 01/09/2023] Open
Abstract
Developmental studies have identified differences in prefrontal and subcortical affective structures between children and adults, which correspond with observed cognitive and behavioral maturations from relatively simplistic emotional experiences and expressions to more nuanced, complex ones. However, developmental changes in the neural representation of emotions have not yet been well explored. It stands to reason that adults and children may demonstrate observable differences in the representation of affect within key neurological structures implicated in affective cognition. Forty-five participants (25 children; 20 adults) passively viewed positive, negative, and neutral clips from popular films while undergoing functional magnetic resonance imaging (fMRI). Using representational similarity analysis (RSA) to measure variability in neural pattern similarity, we found developmental differences between children and adults in the amygdala, nucleus accumbens (NAcc), and ventromedial prefrontal cortex (vmPFC), such that children generated less pattern similarity within subcortical structures relative to the vmPFC; a phenomenon not replicated among their older counterparts. Furthermore, children generated valence-specific differences in representational patterns across regions; these valence-specific patterns were not found in adults. These results may suggest that affective representations grow increasingly dissimilar over development as individuals mature from visceral affective responses to more evaluative analyses.
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Affiliation(s)
- William J Mitchell
- Department of Psychology, Weiss Hall, Temple University, Philadelphia, PA 19122, USA
| | - Lindsey J Tepfer
- Department of Psychological and Brain Sciences, Moore Hall, Dartmouth College, Hanover, NH 03755, USA
| | - Nicole M Henninger
- Klein College of Media and Communication, Annenberg Hall, Temple University, Philadelphia, PA 19122, USA
| | - Susan B Perlman
- Department of Psychiatry, Washington University of St Louis, St Louis, MO 63110, USA
| | - Vishnu P Murty
- Department of Psychology, Weiss Hall, Temple University, Philadelphia, PA 19122, USA
| | - Chelsea Helion
- Department of Psychology, Weiss Hall, Temple University, Philadelphia, PA 19122, USA
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42
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Prajapati V, Guha R, Routray A. Multimodal prediction of trait emotional intelligence-Through affective changes measured using non-contact based physiological measures. PLoS One 2021; 16:e0254335. [PMID: 34242354 PMCID: PMC8270480 DOI: 10.1371/journal.pone.0254335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 06/25/2021] [Indexed: 11/19/2022] Open
Abstract
Inability to efficiently deal with emotionally laden situations, often leads to poor interpersonal interactions. This adversely affects the individual's psychological functioning. A higher trait emotional intelligence (EI) is not only associated with psychological wellbeing, educational attainment, and job-related success, but also with willingness to seek professional and non-professional help for personal-emotional problems, depression and suicidal ideation. Thus, it is important to identify low (EI) individuals who are more prone to mental health problems than their high EI counterparts, and give them the appropriate EI training, which will aid in preventing the onset of various mood related disorders. Since people may be unaware of their level of EI/emotional skills or may tend to fake responses in self-report questionnaires in high stake situations, a system that assesses EI using physiological measures can prove affective. We present a multimodal method for detecting the level of trait Emotional intelligence using non-contact based autonomic sensors. To our knowledge, this is the first work to predict emotional intelligence level from physiological/autonomic (cardiac and respiratory) response patterns to emotions. Trait EI of 50 users was measured using Schutte Self Report Emotional Intelligence Test (SSEIT) along with their cardiovascular and respiratory data, which was recorded using FMCW radar sensor both at baseline and while viewing affective movie clips. We first examine relationships between users' Trait EI scores and autonomic response and reactivity to the clips. Our analysis suggests a significant relationship between EI and autonomic response and reactivity. We finally attempt binary EI level detection using linear SVM. We also attempt to classify each sub factor of EI, namely-perception of emotion, managing own emotions, managing other's emotions, and utilization of emotions. The proposed method achieves an EI classification accuracy of 84%, while accuracies ranging from 58 to 76% is achieved for recognition of the sub factors. This is the first step towards identifying EI of an individual purely through physiological responses. Limitation and future directions are discussed.
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Affiliation(s)
- Vrinda Prajapati
- Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
| | - Rajlakshmi Guha
- Centre for Education Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
| | - Aurobinda Routray
- Department of Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
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43
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Volynets S, Smirnov D, Saarimäki H, Nummenmaa L. Statistical pattern recognition reveals shared neural signatures for displaying and recognizing specific facial expressions. Soc Cogn Affect Neurosci 2021; 15:803-813. [PMID: 33007782 PMCID: PMC7543934 DOI: 10.1093/scan/nsaa110] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 05/25/2020] [Accepted: 08/03/2020] [Indexed: 12/30/2022] Open
Abstract
Human neuroimaging and behavioural studies suggest that somatomotor ‘mirroring’ of seen facial expressions may support their recognition. Here we show that viewing specific facial expressions triggers the representation corresponding to that expression in the observer’s brain. Twelve healthy female volunteers underwent two separate fMRI sessions: one where they observed and another where they displayed three types of facial expressions (joy, anger and disgust). Pattern classifier based on Bayesian logistic regression was trained to classify facial expressions (i) within modality (trained and tested with data recorded while observing or displaying expressions) and (ii) between modalities (trained with data recorded while displaying expressions and tested with data recorded while observing the expressions). Cross-modal classification was performed in two ways: with and without functional realignment of the data across observing/displaying conditions. All expressions could be accurately classified within and also across modalities. Brain regions contributing most to cross-modal classification accuracy included primary motor and somatosensory cortices. Functional realignment led to only minor increases in cross-modal classification accuracy for most of the examined ROIs. Substantial improvement was observed in the occipito-ventral components of the core system for facial expression recognition. Altogether these results support the embodied emotion recognition model and show that expression-specific somatomotor neural signatures could support facial expression recognition.
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Affiliation(s)
- Sofia Volynets
- Correspondence should be addressed to Lauri Nummenmaa, Turku PET Centre c/o Turku University Hospital, Kiinamyllynkatu 4-6, 20520 Turku, Finland. E-mail:
| | - Dmitry Smirnov
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, FI-0076 Aalto, Finland
| | - Heini Saarimäki
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, FI-0076 Aalto, Finland
- Faculty of Social Sciences, Tampere University, FI-33014 Tampere, Finland
| | - Lauri Nummenmaa
- Turku PET Centre and Department of Psychology, University of Turku, FI-20520 Turku, Finland
- Turku University Hospital, University of Turku, FI-20520 Turku, Finland
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44
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Saarimäki H. Naturalistic Stimuli in Affective Neuroimaging: A Review. Front Hum Neurosci 2021; 15:675068. [PMID: 34220474 PMCID: PMC8245682 DOI: 10.3389/fnhum.2021.675068] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/17/2021] [Indexed: 11/13/2022] Open
Abstract
Naturalistic stimuli such as movies, music, and spoken and written stories elicit strong emotions and allow brain imaging of emotions in close-to-real-life conditions. Emotions are multi-component phenomena: relevant stimuli lead to automatic changes in multiple functional components including perception, physiology, behavior, and conscious experiences. Brain activity during naturalistic stimuli reflects all these changes, suggesting that parsing emotion-related processing during such complex stimulation is not a straightforward task. Here, I review affective neuroimaging studies that have employed naturalistic stimuli to study emotional processing, focusing especially on experienced emotions. I argue that to investigate emotions with naturalistic stimuli, we need to define and extract emotion features from both the stimulus and the observer.
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Affiliation(s)
- Heini Saarimäki
- Human Information Processing Laboratory, Faculty of Social Sciences, Tampere University, Tampere, Finland
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45
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At the Neural Intersection Between Language and Emotion. AFFECTIVE SCIENCE 2021; 2:207-220. [PMID: 36043170 DOI: 10.1007/s42761-021-00032-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 01/25/2021] [Indexed: 10/21/2022]
Abstract
What role does language play in emotion? Behavioral research shows that emotion words such as "anger" and "fear" alter emotion experience, but questions still remain about mechanism. Here, we review the neuroscience literature to examine whether neural processes associated with semantics are also involved in emotion. Our review suggests that brain regions involved in the semantic processing of words: (i) are engaged during experiences of emotion, (ii) coordinate with brain regions involved in affect to create emotions, (iii) hold representational content for emotion, and (iv) may be necessary for constructing emotional experience. We relate these findings with respect to four theoretical relationships between language and emotion, which we refer to as "non-interactive," "interactive," "constitutive," and "deterministic." We conclude that findings are most consistent with the interactive and constitutive views with initial evidence suggestive of a constitutive view, in particular. We close with several future directions that may help test hypotheses of the constitutive view.
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46
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Affective neural signatures do not distinguish women with emotion dysregulation from healthy controls: A mega-analysis across three task-based fMRI studies. NEUROIMAGE: REPORTS 2021. [DOI: 10.1016/j.ynirp.2021.100019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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47
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48
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Tang TB, Chong JS, Kiguchi M, Funane T, Lu CK. Detection of Emotional Sensitivity Using fNIRS Based Dynamic Functional Connectivity. IEEE Trans Neural Syst Rehabil Eng 2021; 29:894-904. [PMID: 33970862 DOI: 10.1109/tnsre.2021.3078460] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In this study, we proposed an analytical framework to identify dynamic task-based functional connectivity (FC) features as new biomarkers of emotional sensitivity in nursing students, by using a combination of unsupervised and supervised machine learning techniques. The dynamic FC was measured by functional Near-Infrared Spectroscopy (fNIRS), and computed using a sliding window correlation (SWC) analysis. A k -means clustering technique was applied to derive four recurring connectivity states. The states were characterized by both graph theory and semi-metric analysis. Occurrence probability and state transition were extracted as dynamic FC network features, and a Random Forest (RF) classifier was implemented to detect emotional sensitivity. The proposed method was trialled on 39 nursing students and 19 registered nurses during decision-making, where we assumed registered nurses have developed strategies to cope with emotional sensitivity. Emotional stimuli were selected from International Affective Digitized Sound System (IADS) database. Experiment results showed that registered nurses demonstrated single dominant connectivity state of task-relevance, while nursing students displayed in two states and had higher level of task-irrelevant state connectivity. The results also showed that students were more susceptive to emotional stimuli, and the derived dynamic FC features provided a stronger discriminating power than heart rate variability (accuracy of 81.65% vs 71.03%) as biomarkers of emotional sensitivity. This work forms the first study to demonstrate the stability of fNIRS based dynamic FC states as a biomarker. In conclusion, the results support that the state distribution of dynamic FC could help reveal the differentiating factors between the nursing students and registered nurses during decision making, and it is anticipated that the biomarkers might be used as indicators when developing professional training related to emotional sensitivity.
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49
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Yousefi Heris A. Emotions and two senses of simulation. PHILOSOPHICAL PSYCHOLOGY 2021. [DOI: 10.1080/09515089.2021.1914831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Ali Yousefi Heris
- Department of Philosophy, Shahid Beheshti University, Tehran, Iran
- School of Cognitive Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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50
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Putkinen V, Nazari-Farsani S, Seppälä K, Karjalainen T, Sun L, Karlsson HK, Hudson M, Heikkilä TT, Hirvonen J, Nummenmaa L. Decoding Music-Evoked Emotions in the Auditory and Motor Cortex. Cereb Cortex 2021; 31:2549-2560. [PMID: 33367590 DOI: 10.1093/cercor/bhaa373] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 10/16/2020] [Accepted: 11/06/2020] [Indexed: 11/14/2022] Open
Abstract
Music can induce strong subjective experience of emotions, but it is debated whether these responses engage the same neural circuits as emotions elicited by biologically significant events. We examined the functional neural basis of music-induced emotions in a large sample (n = 102) of subjects who listened to emotionally engaging (happy, sad, fearful, and tender) pieces of instrumental music while their hemodynamic brain activity was measured with functional magnetic resonance imaging (fMRI). Ratings of the four categorical emotions and liking were used to predict hemodynamic responses in general linear model (GLM) analysis of the fMRI data. Multivariate pattern analysis (MVPA) was used to reveal discrete neural signatures of the four categories of music-induced emotions. To map neural circuits governing non-musical emotions, the subjects were scanned while viewing short emotionally evocative film clips. The GLM revealed that most emotions were associated with activity in the auditory, somatosensory, and motor cortices, cingulate gyrus, insula, and precuneus. Fear and liking also engaged the amygdala. In contrast, the film clips strongly activated limbic and cortical regions implicated in emotional processing. MVPA revealed that activity in the auditory cortex and primary motor cortices reliably discriminated the emotion categories. Our results indicate that different music-induced basic emotions have distinct representations in regions supporting auditory processing, motor control, and interoception but do not strongly rely on limbic and medial prefrontal regions critical for emotions with survival value.
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Affiliation(s)
- Vesa Putkinen
- Turku PET Centre, and Turku University Hospital, University of Turku, 20520, Turku, Finland
| | - Sanaz Nazari-Farsani
- Turku PET Centre, and Turku University Hospital, University of Turku, 20520, Turku, Finland
| | - Kerttu Seppälä
- Turku PET Centre, and Turku University Hospital, University of Turku, 20520, Turku, Finland
| | - Tomi Karjalainen
- Turku PET Centre, and Turku University Hospital, University of Turku, 20520, Turku, Finland
| | - Lihua Sun
- Turku PET Centre, and Turku University Hospital, University of Turku, 20520, Turku, Finland
| | - Henry K Karlsson
- Turku PET Centre, and Turku University Hospital, University of Turku, 20520, Turku, Finland
| | - Matthew Hudson
- Turku PET Centre, and Turku University Hospital, University of Turku, 20520, Turku, Finland.,National College of Ireland, D01 K6W2, Dublin, Ireland
| | - Timo T Heikkilä
- Department of Psychology, University of Turku, FI-20014, Turku, Finland
| | - Jussi Hirvonen
- Turku PET Centre, and Turku University Hospital, University of Turku, 20520, Turku, Finland.,Department of Radiology, Turku University Hospital, 20520, Turku, Finland
| | - Lauri Nummenmaa
- Turku PET Centre, and Turku University Hospital, University of Turku, 20520, Turku, Finland.,Department of Psychology, University of Turku, FI-20014, Turku, Finland
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