1
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Cloos L, Mestdagh M, Vanpaemel W, Ceulemans E, Kuppens P. Measuring Continuous Affect in Daily Life With Intensity Profile Drawings. Assessment 2024:10731911241266286. [PMID: 39138593 DOI: 10.1177/10731911241266286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
We examined continuous affect drawings as innovative measure of affective experiences over time. Intensive longitudinal data often rely on discrete assessments, containing "blind spots" between measurements. With continuous affect drawings participants visually depict their affect fluctuations between assessments. In an experience sampling study, participants (N = 115) rated their momentary positive and negative affect 6 times daily. From the second daily rating on, they additionally drew their positive and negative affect changes and reported affective events between assessments. They received one measurement burst between assessments daily. The strength of the approach is a substantial amount of informational gain (average 7%) over linearly interpolated points between assessments. The additional information was subsequently categorized into positive and negative affect peaks and valleys, each occurring once a day per person on average. The probability of detecting peaks and valleys increased with reported events. The drawings correlated positively with momentary affect scores from the burst. Yet, the drawing predicted the bursts less well suggesting that the momentary ratings may yield different information than the drawings. Although the timing of retrospective drawings is less precise than individual momentary assessments, this method provides a comprehensive understanding of affective experiences between assessments, offering a unique perspective on affect dynamics.
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2
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Robinson MD, Irvin RL, Fereidouni H, Klein RJ. Feelings as a currency of care: A role for agreeableness in emotional reactivity. J Pers 2024. [PMID: 38780315 DOI: 10.1111/jopy.12951] [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: 11/10/2023] [Revised: 03/28/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024]
Abstract
OBJECTIVE AND BACKGROUND The personality trait of agreeableness is linked to a number of core tendencies (e.g., empathy, warmth) that operate in a feeling-based manner. Following considerations of this type, it is proposed that the motivations and characteristics of agreeable individuals, relative to disagreeable individuals, should render them more receptive to emotional events and more responsive to them for this reason. METHOD Potential links between agreeableness and emotional reactivity were assessed in two studies involving four samples (total N = 517) in which participants continuously rated their feeling states in response to a variety of affective images. RESULTS Agreeableness did not predict the speed with which emotional reactions began, but agreeable individuals exhibited higher-magnitude peak intensities, regardless of whether stimuli were appetitive (pleasant) or aversive (unpleasant) in nature. CONCLUSIONS The findings provide novel insights into the personality trait of agreeableness, emotional reactivity phenomena, and the dynamic processes that link agreeableness to emotion.
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Affiliation(s)
- Michael D Robinson
- Department of Psychology, North Dakota State University, Fargo, North Dakota, USA
| | - Roberta L Irvin
- Department of Psychology, North Dakota State University, Fargo, North Dakota, USA
| | - Hamidreza Fereidouni
- Department of Psychology, North Dakota State University, Fargo, North Dakota, USA
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3
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Liu P, Bo K, Ding M, Fang R. Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.16.537079. [PMID: 37163104 PMCID: PMC10168209 DOI: 10.1101/2023.04.16.537079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual system versus they arise through reentry from frontal emotion processing structures such as the amygdala. We examined this problem by combining convolutional neural network (CNN) models of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that (1) in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and (2) lesioning these neurons by setting their output to 0 or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the intrinsic ability to represent the affective significance of visual input and suggest that CNNs offer a fruitful platform for testing neuroscientific theories.
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Affiliation(s)
- Peng Liu
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Ke Bo
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Mingzhou Ding
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA
| | - Ruogu Fang
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
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4
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Liu P, Bo K, Ding M, Fang R. Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects. PLoS Comput Biol 2024; 20:e1011943. [PMID: 38547053 PMCID: PMC10977720 DOI: 10.1371/journal.pcbi.1011943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 02/24/2024] [Indexed: 04/02/2024] Open
Abstract
Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual system versus they arise through reentry from frontal emotion processing structures such as the amygdala. We examined this problem by combining convolutional neural network (CNN) models of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and lesioning these neurons by setting their output to zero or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the intrinsic ability to represent the affective significance of visual input and suggest that CNNs offer a fruitful platform for testing neuroscientific theories.
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Affiliation(s)
- Peng Liu
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Ke Bo
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Mingzhou Ding
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Ruogu Fang
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, Florida, United States of America
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5
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Xu S, Zhang Z, Li L, Zhou Y, Lin D, Zhang M, Zhang L, Huang G, Liu X, Becker B, Liang Z. Functional connectivity profiles of the default mode and visual networks reflect temporal accumulative effects of sustained naturalistic emotional experience. Neuroimage 2023; 269:119941. [PMID: 36791897 DOI: 10.1016/j.neuroimage.2023.119941] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/30/2023] [Accepted: 02/11/2023] [Indexed: 02/15/2023] Open
Abstract
Determining and decoding emotional brain processes under ecologically valid conditions remains a key challenge in affective neuroscience. The current functional Magnetic Resonance Imaging (fMRI) based emotion decoding studies are mainly based on brief and isolated episodes of emotion induction, while sustained emotional experience in naturalistic environments that mirror daily life experiences are scarce. Here we used 12 different 10-minute movie clips as ecologically valid emotion-evoking procedures in n = 52 individuals to explore emotion-specific fMRI functional connectivity (FC) profiles on the whole-brain level at high spatial resolution (432 parcellations including cortical and subcortical structures). Employing machine-learning based decoding and cross validation procedures allowed to investigate FC profiles contributing to classification that can accurately distinguish sustained happiness and sadness and that generalize across subjects, movie clips, and parcellations. Both functional brain network-based and subnetwork-based emotion classification results suggested that emotion manifests as distributed representation of multiple networks, rather than a single functional network or subnetwork. Further, the results showed that the Visual Network (VN) and Default Mode Network (DMN) associated functional networks, especially VN-DMN, exhibited a strong contribution to emotion classification. To further estimate the temporal accumulative effect of naturalistic long-term movie-based video-evoking emotions, we divided the 10-min episode into three stages: early stimulation (1∼200 s), middle stimulation (201∼400 s), and late stimulation (401∼600 s) and examined the emotion classification performance at different stimulation stages. We found that the late stimulation contributes most to the classification (accuracy=85.32%, F1-score=85.62%) compared to early and middle stimulation stages, implying that continuous exposure to emotional stimulation can lead to more intense emotions and further enhance emotion-specific distinguishable representations. The present work demonstrated that sustained happiness and sadness under naturalistic conditions are presented in emotion-specific network profiles and these expressions may play different roles in the generation and modulation of emotions. These findings elucidated the importance of network level adaptations for sustained emotional experiences during naturalistic contexts and open new venues for imaging network level contributions under naturalistic conditions.
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Affiliation(s)
- Shuyue Xu
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Zhiguo Zhang
- Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China; Peng Cheng Laboratory, Shenzhen 518055, China; Marshall Laboratory of Biomedical Engineering, Shenzhen 518060, China
| | - Linling Li
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Yongjie Zhou
- Department of Psychiatric Rehabilitation, Shenzhen Kangning Hospital, Shenzhen, China
| | - Danyi Lin
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Min Zhang
- Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China
| | - Li Zhang
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Gan Huang
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China
| | - Xiqin Liu
- Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, MOE Key Laboratory for Neuroinformation, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Benjamin Becker
- Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, MOE Key Laboratory for Neuroinformation, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Zhen Liang
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China; Marshall Laboratory of Biomedical Engineering, Shenzhen 518060, China.
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6
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Bottemanne H, Barberousse A, Fossati P. [Multidimensional and computational theory of mood]. Encephale 2022; 48:682-699. [PMID: 35987716 DOI: 10.1016/j.encep.2022.02.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 01/31/2022] [Accepted: 02/04/2022] [Indexed: 12/22/2022]
Abstract
What is mood? Despite its crucial place in psychiatric nosography and cognitive science, it is still difficult to delimit its conceptual ground. The distinction between emotion and mood is ambiguous: mood is often presented as an affective state that is more prolonged and less intense than emotion, or as an affective polarity distinguishing high and low mood swinging around a baseline. However, these definitions do not match the clinical reality of mood disorders such as unipolar depression and bipolar disorder, and do not allow us to understand the effect of mood on behaviour, perception and cognition. In this paper, we propose a multidimensional and computational theory of mood inspired by contemporary hypotheses in theoretical neuroscience and philosophy of emotion. After suggesting an operational distinction between emotion and mood, we show how a succession of emotions can cumulatively generate congruent mood over time, making mood an emerging state from emotion. We then present how mood determines mental and behavioral states when interacting with the environment, constituting a dispositional state of emotion, perception, belief, and action. Using this theoretical framework, we propose a computational representation of the emerging and dispositional dimensions of mood by formalizing mood as a layer of third-order Bayesian beliefs encoding the precision of emotion, and regulated by prediction errors associated with interoceptive predictions. Finally, we show how this theoretical framework sheds light on the processes involved in mood disorders, the emergence of mood congruent beliefs, or the mechanisms of antidepressant treatments in clinical psychiatry.
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Affiliation(s)
- Hugo Bottemanne
- Paris Brain Institute - Institut du Cerveau (ICM), Sorbonne University/CNRS/Inserm, Paris, France; Department of philosophy, Sciences Normes Démocratie research unit, Sorbonne university/CNRS, Paris, France; Department of psychiatry, DMU Neuroscience, Pitié-Salpêtrière hospital, Sorbonne university/Assistance publique-Hôpitaux de Paris (AP-HP), Paris, France.
| | - Anouk Barberousse
- Department of philosophy, Sciences Normes Démocratie research unit, Sorbonne university/CNRS, Paris, France
| | - Philippe Fossati
- Paris Brain Institute - Institut du Cerveau (ICM), Sorbonne University/CNRS/Inserm, Paris, France; Department of psychiatry, DMU Neuroscience, Pitié-Salpêtrière hospital, Sorbonne university/Assistance publique-Hôpitaux de Paris (AP-HP), Paris, France
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7
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Lettieri G, Handjaras G, Setti F, Cappello EM, Bruno V, Diano M, Leo A, Ricciardi E, Pietrini P, Cecchetti L. Default and control network connectivity dynamics track the stream of affect at multiple timescales. Soc Cogn Affect Neurosci 2022; 17:461-469. [PMID: 34673987 PMCID: PMC9071410 DOI: 10.1093/scan/nsab112] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 09/24/2021] [Accepted: 10/21/2021] [Indexed: 11/14/2022] Open
Abstract
In everyday life, the stream of affect results from the interaction between past experiences, expectations and the unfolding of events. How the brain represents the relationship between time and affect has been hardly explored, as it requires modeling the complexity of everyday life in the laboratory setting. Movies condense into hours a multitude of emotional responses, synchronized across subjects and characterized by temporal dynamics alike real-world experiences. Here, we use time-varying intersubject brain synchronization and real-time behavioral reports to test whether connectivity dynamics track changes in affect during movie watching. The results show that polarity and intensity of experiences relate to the connectivity of the default mode and control networks and converge in the right temporoparietal cortex. We validate these results in two experiments including four independent samples, two movies and alternative analysis workflows. Finally, we reveal chronotopic connectivity maps within the temporoparietal and prefrontal cortex, where adjacent areas preferentially encode affect at specific timescales.
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Affiliation(s)
- Giada Lettieri
- Social and Affective Neuroscience Group, IMT School for Advanced Studies Lucca, Lucca 55100, Italy
| | - Giacomo Handjaras
- Social and Affective Neuroscience Group, IMT School for Advanced Studies Lucca, Lucca 55100, Italy
| | - Francesca Setti
- Molecular Mind Laboratory, IMT School for Advanced Studies Lucca, Lucca 55100, Italy
| | - Elisa Morgana Cappello
- Social and Affective Neuroscience Group, IMT School for Advanced Studies Lucca, Lucca 55100, Italy
| | - Valentina Bruno
- MANIBUS Lab, Department of Psychology, University of Turin, Turin 10124, Italy
| | - Matteo Diano
- Department of Psychology, University of Turin, Turin 10124, Italy
| | - Andrea Leo
- Department of Translational Research and Advanced Technologies in Medicine and Surgery, University of Pisa, Pisa 56126, Italy
| | - Emiliano Ricciardi
- Molecular Mind Laboratory, IMT School for Advanced Studies Lucca, Lucca 55100, Italy
| | - Pietro Pietrini
- Molecular Mind Laboratory, IMT School for Advanced Studies Lucca, Lucca 55100, Italy
| | - Luca Cecchetti
- Social and Affective Neuroscience Group, IMT School for Advanced Studies Lucca, Lucca 55100, Italy
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8
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Gao Z, Cui X, Wan W, Zheng W, Gu Z. Long-range correlation analysis of high frequency prefrontal electroencephalogram oscillations for dynamic emotion recognition. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103291] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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9
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Bottemanne H, Chevance A, Joly L. [Psychiatry without mind?]. Encephale 2021; 47:605-612. [PMID: 34579938 DOI: 10.1016/j.encep.2021.05.006] [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: 02/08/2021] [Revised: 05/08/2021] [Accepted: 05/21/2021] [Indexed: 10/20/2022]
Abstract
Philosophy of Mind is currently one of the most prolific fields of research in philosophy and has witnessed a progressive hybridization with cognitive science. It focuses on fundamental questions to neuroscience and psychiatry, such as the nature of mental states and cognitive processes, or the relationships between mental states and the world. Anticipating the accumulation of experimental data from neuroscience, it provides a framework for the generation of theories in cognitive science. Philosophy of mind has thus laid the foundations of the conceptual space within which cognitive sciences have spread: a large part of contemporary theories in cognitive science result from a hybridization of conceptions forged by philosophers of mind and data produced by neuroscientists. Yet contemporary psychiatry is still reluctant to feed on the philosophy of mind, other than through the fragments that emerge from neuroscience. In this paper, we describe the evolution of contemporary philosophy of mind, and we detail its contributions around three central themes for psychiatry: naturalization of mind, mental causality, and subjectivity of mental states. We show how philosophy of mind provide the conceptual framework to link different levels of explanation in psychiatry: from biological to functional, from neurophysiology to cognition, from matter to mind.
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Affiliation(s)
- H Bottemanne
- Paris Brain Institute-Institut du Cerveau (ICM), UMR 7225/UMRS 1127, Sorbonne University/CNRS/Inserm, Paris, France; Sorbonne University, Department of Philosophy, SND Research Unit, UMR 8011, CNRS, Paris, France; Sorbonne University, Department of Psychiatry, Pitié-Salpêtrière Hospital, DMU Neuroscience, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France.
| | - A Chevance
- Centre of Research in Epidemiology and Statistics Sorbonne Paris Cité, Institute for Health and Medical Research, and French National Institute of Research for Agriculture, University of Paris, Paris, France
| | - L Joly
- Sorbonne University, Department of Psychiatry, Saint Antoine Hospital, DMU Neuroscience, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France
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10
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Hao Y, Yao L, Evans GW. Neural Responses During Emotion Transitions and Emotion Regulation. Front Psychol 2021; 12:666284. [PMID: 34484029 PMCID: PMC8416461 DOI: 10.3389/fpsyg.2021.666284] [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: 02/10/2021] [Accepted: 06/28/2021] [Indexed: 11/13/2022] Open
Abstract
Why are some people more susceptible to interference from previous emotional stimuli? Neural mechanisms underlying emotion regulation are typically studied with one-off positive or negative stimuli. Less is known about how they operate during dynamic emotional experiences, which more closely resemble how emotions occur in real life. Therefore, we investigated the interaction among temporal context, stimulus content, and regulatory strategy. Image sequences included either neutral to negative emotion or negative to neutral emotion. Participants were instructed to either passively watch the emotional stimuli or apply cognitive reappraisal during the image sequences presentation. Participants also reported their habitual use of cognitive reappraisal in their daily lives on a standard scale. We measured functional connectivity (FC) with electroencephalography (EEG) source localization. A three-way interaction suggested that, in addition to momentary emotional content and regulatory effort, the temporal context of stimuli impacts the FC between the ventromedial prefrontal cortex (vmPFC) and the ventral anterior cingulate cortex (ACC) in both alpha and beta frequency bands. In the reappraisal condition-but not the passive watch conditions-, individual differences in habitual reappraisal were manifested in the FC of vmPFC-ACC in alpha band. Emotion transitions may be more demanding because prefrontal-posterior FC in the beta band decreased during emotion transitions regardless of emotional content or regulation efforts. Flexible emotion regulation enables the recruiting of neural activities in response to the content of dynamic, ever-changing experiences encountered in daily life. Studying brain responses to dynamic emotional stimuli may shed light on individual differences in adaptation and psychological health. It also provides a more ecologically valid assessment of emotion regulation.
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Affiliation(s)
- Yu Hao
- Department of Design and Environmental Anlaysis, Cornell University, Ithaca, NY, United States
| | - Lin Yao
- Frontiers Science Center for Brain & Brain-Machine Integration, Zhejiang University, Hangzhou, China
- College of Computer Science, Zhejiang University, Hangzhou, China
| | - Gary W. Evans
- Department of Design and Environmental Anlaysis, Cornell University, Ithaca, NY, United States
- Department of Human Development, Cornell University, Ithaca, NY, United States
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11
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Liao HP, Pan XF, Yin XQ, Liu YF, Li JY, Wang JL. Decreased COVID-related adaptive behavior and increased negative affect: A multivariate latent growth curve model. J Health Psychol 2021; 27:2115-2128. [PMID: 34142597 DOI: 10.1177/13591053211021651] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Data from a longitudinal questionnaire investigation of three time waves were used to investigate affective and behavioral changes and their covariant relationship among Chinese general population during the COVID-19 pandemic from March to May 2020. 145 participants aging from 15 to 63 completed three waves of survey. Latent growth curve analyses found that negative affect gradually increased as the pandemic continued. A faster increase in negative affect was related to a greater decrease in adaptive behavior and faster increase in non-adaptive behavior. A higher initial level of negative affect was related to a slower increase in non-adaptive behavior.
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12
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Kim Y, Choi A. EEG-Based Emotion Classification Using Long Short-Term Memory Network with Attention Mechanism. SENSORS 2020; 20:s20236727. [PMID: 33255539 PMCID: PMC7727805 DOI: 10.3390/s20236727] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/16/2020] [Accepted: 11/18/2020] [Indexed: 11/16/2022]
Abstract
Recently, studies that analyze emotions based on physiological signals, such as electroencephalogram (EEG), by applying a deep learning algorithm have been actively conducted. However, the study of sequence modeling considering the change of emotional signals over time has not been fully investigated. To consider long-term interaction of emotion, in this study, we propose a long short-term memory network to consider changes in emotion over time and apply an attention mechanism to assign weights to the emotional states appearing at specific moments based on the peak–end rule in psychology. We used 32-channel EEG data from the DEAP database. Two-level (low and high) and three-level (low, middle, and high) classification experiments were performed on the valence and arousal emotion models. The results show accuracies of 90.1% and 87.9% using the two-level classification for the valence and arousal models with four-fold cross validation, respectively. In the case of the three-level classification, these values were obtained as 83.5% and 82.6%, respectively. Additional experiments were conducted using a network combining a convolutional neural network (CNN) submodule with the proposed model. The obtained results showed accuracies of 90.1% and 88.3% in the case of the two-level classification and 86.9% and 84.1% in the case of the three-level classification for the valence and arousal models with four-fold cross validation, respectively. In 10-fold cross validation, there were 91.8% for valence and 91.6% for arousal accuracy, respectively.
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13
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The neural bases of cognitive emotion regulation: The roles of strategy and intensity. COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2020; 20:387-407. [DOI: 10.3758/s13415-020-00775-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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14
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Pasquini L, Toller G, Staffaroni A, Brown JA, Deng J, Lee A, Kurcyus K, Shdo SM, Allen I, Sturm VE, Cobigo Y, Borghesani V, Battistella G, Gorno-Tempini ML, Rankin KP, Kramer J, Rosen HH, Miller BL, Seeley WW. State and trait characteristics of anterior insula time-varying functional connectivity. Neuroimage 2020; 208:116425. [PMID: 31805382 PMCID: PMC7225015 DOI: 10.1016/j.neuroimage.2019.116425] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 11/28/2019] [Accepted: 11/30/2019] [Indexed: 12/12/2022] Open
Abstract
The human anterior insula (aINS) is a topographically organized brain region, in which ventral portions contribute to socio-emotional function through limbic and autonomic connections, whereas the dorsal aINS contributes to cognitive processes through frontal and parietal connections. Open questions remain, however, regarding how aINS connectivity varies over time. We implemented a novel approach combining seed-to-whole-brain sliding-window functional connectivity MRI and k-means clustering to assess time-varying functional connectivity of aINS subregions. We studied three independent large samples of healthy participants and longitudinal datasets to assess inter- and intra-subject stability, and related aINS time-varying functional connectivity profiles to dispositional empathy. We identified four robust aINS time-varying functional connectivity modes that displayed both "state" and "trait" characteristics: while modes featuring connectivity to sensory regions were modulated by eye closure, modes featuring connectivity to higher cognitive and emotional processing regions were stable over time and related to empathy measures.
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Affiliation(s)
- Lorenzo Pasquini
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Gianina Toller
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Adam Staffaroni
- 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
| | - Jersey Deng
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Alex Lee
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Katarzyna Kurcyus
- Department of Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany; Department of Nuclear Medicine, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany
| | | | - Isabel Allen
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Virginia E Sturm
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Yann Cobigo
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Valentina Borghesani
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Giovanni Battistella
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | | | - Katherine P Rankin
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Joel Kramer
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Howard H Rosen
- 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
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA; Memory and Aging Center, Department of Pathology, University of California, San Francisco, CA, USA.
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15
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Yoshino Y, Dwivedi Y. Non-Coding RNAs in Psychiatric Disorders and Suicidal Behavior. Front Psychiatry 2020; 11:543893. [PMID: 33101077 PMCID: PMC7522197 DOI: 10.3389/fpsyt.2020.543893] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 08/14/2020] [Indexed: 12/18/2022] Open
Abstract
It is well known that only a small proportion of the human genome code for proteins; the rest belong to the family of RNAs that do not code for protein and are known as non-coding RNAs (ncRNAs). ncRNAs are further divided into two subclasses based on size: 1) long non-coding RNAs (lncRNAs; >200 nucleotides) and 2) small RNAs (<200 nucleotides). Small RNAs contain various family members that include microRNAs (miRNAs), small interfering RNAs (siRNAs), piwi-interacting RNAs (piRNAs), small nucleolar RNAs (snoRNAs), and small nuclear RNAs (snRNAs). The roles of ncRNAs, especially lncRNAs and miRNAs, are well documented in brain development, homeostasis, stress responses, and neural plasticity. It has also been reported that ncRNAs can influence the development of psychiatric disorders including schizophrenia, major depressive disorder, and bipolar disorder. More recently, their roles are being investigated in suicidal behavior. In this article, we have comprehensively reviewed the findings of lncRNA and miRNA expression changes and their functions in various psychiatric disorders including suicidal behavior. We primarily focused on studies that have been done in postmortem human brain. In addition, we have briefly reviewed the role of other small RNAs (e.g. piwiRNA, siRNA, snRNA, and snoRNAs) and their expression changes in psychiatric illnesses.
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Affiliation(s)
- Yuta Yoshino
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Yogesh Dwivedi
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, United States
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16
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Zhu X, Wang K, Cao A, Zhang Y, Qiu J. Personality traits and negative affect mediate the relationship between cortical thickness of superior frontal cortex and aggressive behavior. Neurosci Lett 2019; 718:134728. [PMID: 31899310 DOI: 10.1016/j.neulet.2019.134728] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 11/27/2019] [Accepted: 12/28/2019] [Indexed: 02/03/2023]
Abstract
Aggression reflects the psychological and physical behavior that perpetrator intends to harm victim. Initiation of aggression is influenced by the distal factors (e.g. personality) and proximate causes (e.g. affect) of perpetrator. However, few studies explored the brain structural basis of relationship between these traits and aggressive behavior. In this study, we first explored the association between cortical thickness and aggression in a large young adult sample from the Human Connectome Project. Results found aggressive behavior assessed by the Adult Self-Report was positively correlated with cortical thickness in left superior frontal gyrus (SFG), which was implicated in emotion regulation and executive function. Then, mediation analyses with distal and proximate factors separately showcased that the association between the left SFG thickness and aggressive behavior was partially mediated by negative affect (anger and sadness), and fully mediated by personality traits (agreeableness and neuroticism). Taken together, these experimental findings established dorsal prefrontal cortex as the key region in generating aggressive behavior, and gave a neutral explanation for why individuals with high negative affect and neuroticism exhibit more aggression. This study implicated the possible targeted brain region and behavioral intervention for such at-risk individuals initiating violence.
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Affiliation(s)
- Xingxing Zhu
- School of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Southwest University, Chongqing, 400715, China
| | - Kangcheng Wang
- School of Psychology, Southwest University, Chongqing, 400715, China; School of Psychology, Shandong Normal University, Jinan, 250358, China
| | - Aihua Cao
- Department of Pediatrics, Qilu Hospital of Shandong University, Brain Science Research Institute of Shandong University, Jinan, 250012, China
| | - Yong Zhang
- School of Foreign Languages, Southwest University of Political Science and Law, Chongqing, 401120, China
| | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Southwest University, Chongqing, 400715, China.
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17
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Résibois M, Rotgé JY, Delaveau P, Kuppens P, Van Mechelen I, Fossati P, Verduyn P. The impact of self-distancing on emotion explosiveness and accumulation: An fMRI study. PLoS One 2018; 13:e0206889. [PMID: 30399153 PMCID: PMC6219793 DOI: 10.1371/journal.pone.0206889] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Accepted: 10/22/2018] [Indexed: 11/30/2022] Open
Abstract
Emotions unfold over time with episodes differing in explosiveness (i.e., profiles having a steep vs. a gentle start) and accumulation (i.e., profiles increasing over time vs. going back to baseline). In the present fMRI study, we wanted to replicate and extend previous findings on the psychological and neural mechanisms underlying emotion explosiveness and accumulation. Specifically, we aimed to: (a) replicate the finding that different neural mechanisms are associated with emotion explosiveness and accumulation, (b) replicate the finding that adopting a self-distanced (vs. self-immersed) perspective decreases emotion explosiveness and accumulation at the level of self-report, and (c) examine whether adopting a self-distanced (vs. self-immersed) perspective similarly modulates activity in the brain regions associated with emotion explosiveness and accumulation. Participants in an fMRI scanner were asked to adopt a self-immersed or self-distanced perspective while reading and thinking about negative social feedback, and to report on felt changes in negative affect during that period using an emotion intensity profile tracking approach. We replicated previous findings showing that emotion explosiveness and accumulation were related to activity in regions involved in self-referential processing (such as the medial prefrontal cortex) and sustained visceral arousal (such as the posterior insula), respectively. The finding that adopting a self-distanced (vs. self-immersed) perspective lowers emotion explosiveness and accumulation was also replicated at a self-report level. However, perspective taking did not impact activity in the neural correlates of emotion explosiveness and accumulation.
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Affiliation(s)
- Maxime Résibois
- Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
- * E-mail:
| | - Jean-Yves Rotgé
- Inserm U 1127, CNRS UMR 7225, Sorbonne Université, UPMC Univ Paris 06, UMR S 1127, Institut du Cerveau et de la Moelle, ICM-A-IHU, Social and Affective Neuroscience (SAN) Laboratory & Prisme Platform, Paris, France
- AP-HP, Department of Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Pauline Delaveau
- Inserm U 1127, CNRS UMR 7225, Sorbonne Université, UPMC Univ Paris 06, UMR S 1127, Institut du Cerveau et de la Moelle, ICM-A-IHU, Social and Affective Neuroscience (SAN) Laboratory & Prisme Platform, Paris, France
| | - Peter Kuppens
- Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
| | - Iven Van Mechelen
- Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
| | - Philippe Fossati
- Inserm U 1127, CNRS UMR 7225, Sorbonne Université, UPMC Univ Paris 06, UMR S 1127, Institut du Cerveau et de la Moelle, ICM-A-IHU, Social and Affective Neuroscience (SAN) Laboratory & Prisme Platform, Paris, France
- AP-HP, Department of Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
- Centre de NeuroImagerie de Recherche – CENIR, Institut du cerveau et de la moelle (ICM), Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS – Hôpital Pitié-Salpêtrière, Paris, France
| | - Philippe Verduyn
- Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
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18
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Zhu X, Wang K, Chen L, Cao A, Chen Q, Li J, Qiu J. Together Means More Happiness: Relationship Status Moderates the Association between Brain Structure and Life Satisfaction. Neuroscience 2018; 384:406-416. [DOI: 10.1016/j.neuroscience.2018.05.018] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 05/11/2018] [Accepted: 05/14/2018] [Indexed: 11/27/2022]
Affiliation(s)
- Xingxing Zhu
- School of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Southwest University, Chongqing 400715, China
| | - Kangcheng Wang
- School of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Southwest University, Chongqing 400715, China
| | - Li Chen
- The Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
| | - Aihua Cao
- Department of Pediatrics, Qilu Hospital of Shandong University, Brain Science Research Institute of Shandong University, Jinan 250012, China
| | - Qunlin Chen
- School of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Southwest University, Chongqing 400715, China
| | - Junchao Li
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Southwest University, Chongqing 400715, China.
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19
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Sander D, Grandjean D, Scherer KR. An Appraisal-Driven Componential Approach to the Emotional Brain. EMOTION REVIEW 2018. [DOI: 10.1177/1754073918765653] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This article suggests that methodological and conceptual advancements in affective sciences militate in favor of adopting an appraisal-driven componential approach to further investigate the emotional brain. Here we propose to operationalize this approach by distinguishing five functional networks of the emotional brain: (a) the elicitation network, (b) the expression network, (c) the autonomic reaction network, (d) the action tendency network, and (e) the feeling network, and discuss these networks in the context of the affective neuroscience literature. We also propose that further investigating the “appraising brain” is the royal road to better understand the elicitation network, and may be key to revealing the neural causal mechanisms underlying the emotion process as a whole.
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Affiliation(s)
- David Sander
- Swiss Center for Affective Sciences, University of Geneva, Switzerland
- Department of Psychology, FPSE, University of Geneva, Switzerland
| | - Didier Grandjean
- Swiss Center for Affective Sciences, University of Geneva, Switzerland
- Department of Psychology, FPSE, University of Geneva, Switzerland
| | - Klaus R. Scherer
- Swiss Center for Affective Sciences, University of Geneva, Switzerland
- Department of Psychology, FPSE, University of Geneva, Switzerland
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20
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Résibois M, Kuppens P, Van Mechelen I, Fossati P, Verduyn P. Depression severity moderates the relation between self-distancing and features of emotion unfolding. PERSONALITY AND INDIVIDUAL DIFFERENCES 2018. [DOI: 10.1016/j.paid.2017.11.018] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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