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King FK, Perry MS, Papadelis C, Cooper CM. Spatiotemporal responses to emotional conflict and its psychiatric correlates in adolescents with epilepsy using magnetoencephalography. Epilepsy Behav 2024; 157:109869. [PMID: 38851125 DOI: 10.1016/j.yebeh.2024.109869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 05/26/2024] [Accepted: 05/27/2024] [Indexed: 06/10/2024]
Abstract
People with epilepsy often suffer from comorbid psychiatric disorders, which negatively affects their quality of life. Emotion regulation is an important cognitive process that is impaired in individuals with psychiatric disorders, such as depression. Adults with epilepsy also show difficulties in emotion regulation, particularly during later-stage, higher-order cognitive processing. Yet, the spatiotemporal and frequency correlates of these functional brain deficits in epilepsy remain unknown, as do the nature of these deficits in adolescent epilepsy. Here, we aim to elucidate the spatiotemporal profile of emotional conflict processing in adolescents with epilepsy, relative to controls, using magnetoencephalography (MEG) and relate these findings to anxiety and depression symptom severity assessed with self-report scales. We hypothesized to see blunted brain activity during emotional conflict in adolescents with epilepsy, relative to controls, in the posterior parietal, prefrontal and cingulate cortices due to their role in explicit and implicit regulation around participant response (500-1000 ms). We analyzed MEG recordings from 53 adolescents (28 epilepsy [14focal,14generalized], 25 controls) during an emotional conflict task. We showed that while controls exhibited behavioral interference to emotional conflict, adolescents with epilepsy failed to exhibit this normative response time pattern. Adolescents with epilepsy showed blunted brain responses to emotional conflict in brain regions related to error evaluation and learning around the average response time (500-700 ms), and in regions involved in decision making during post-response monitoring (800-1000 ms). Interestingly, behavioral patterns and psychiatric symptom severity varied between epilepsy subgroups, wherein those with focal epilepsy showed preserved response time interference. Thus, brain responses were regressed with depression and anxiety levels for each epilepsy subgroup separately. Analyses revealed that under activation in error evaluation regions (500-600 ms) predicted anxiety and depression in focal epilepsy, while regions related to learning (600-700 ms) predicted anxiety in generalized epilepsy, suggesting differential mechanisms of dysfunction in these subgroups. Despite similar rates of anxiety and depression across the groups, adolescents with epilepsy still exhibited deficits in emotional conflict processing in brain and behavioral responses. This suggests that these deficits may exist independently from psychopathology and may stem from underlying dysfunctions that predispose these individuals to develop both disorders. Findings such as these may provide potential targets for future research and therapies.
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Affiliation(s)
- F Kathryn King
- Jane and John Justin Institute for Mind Health, Neurosciences Research Center, Cook Children's Health Care System, Fort Worth, TX, United States; Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health, Neurosciences Research Center, Cook Children's Health Care System, Fort Worth, TX, United States
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health, Neurosciences Research Center, Cook Children's Health Care System, Fort Worth, TX, United States; Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States; Department of Pediatrics, Texas Christian University School of Medicine, Fort Worth, TX, United States
| | - Crystal M Cooper
- Jane and John Justin Institute for Mind Health, Neurosciences Research Center, Cook Children's Health Care System, Fort Worth, TX, United States; Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States; Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, United States; Department of Psychology, University of Texas at Arlington, Arlington, TX, United States.
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Polo EM, Farabbi A, Mollura M, Mainardi L, Barbieri R. Understanding the role of emotion in decision making process: using machine learning to analyze physiological responses to visual, auditory, and combined stimulation. Front Hum Neurosci 2024; 17:1286621. [PMID: 38259333 PMCID: PMC10800655 DOI: 10.3389/fnhum.2023.1286621] [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: 08/31/2023] [Accepted: 12/11/2023] [Indexed: 01/24/2024] Open
Abstract
Emotions significantly shape decision-making, and targeted emotional elicitations represent an important factor in neuromarketing, where they impact advertising effectiveness by capturing potential customers' attention intricately associated with emotional triggers. Analyzing biometric parameters after stimulus exposure may help in understanding emotional states. This study investigates autonomic and central nervous system responses to emotional stimuli, including images, auditory cues, and their combination while recording physiological signals, namely the electrocardiogram, blood volume pulse, galvanic skin response, pupillometry, respiration, and the electroencephalogram. The primary goal of the proposed analysis is to compare emotional stimulation methods and to identify the most effective approach for distinct physiological patterns. A novel feature selection technique is applied to further optimize the separation of four emotional states. Basic machine learning approaches are used in order to discern emotions as elicited by different kinds of stimulation. Electroencephalographic signals, Galvanic skin response and cardio-respiratory coupling-derived features provided the most significant features in distinguishing the four emotional states. Further findings highlight how auditory stimuli play a crucial role in creating distinct physiological patterns that enhance classification within a four-class problem. When combining all three types of stimulation, a validation accuracy of 49% was achieved. The sound-only and the image-only phases resulted in 52% and 44% accuracy respectively, whereas the combined stimulation of images and sounds led to 51% accuracy. Isolated visual stimuli yield less distinct patterns, necessitating more signals for relatively inferior performance compared to other types of stimuli. This surprising significance arises from limited auditory exploration in emotional recognition literature, particularly contrasted with the pleathora of studies performed using visual stimulation. In marketing, auditory components might hold a more relevant potential to significantly influence consumer choices.
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Affiliation(s)
- Edoardo Maria Polo
- SpinLabs, Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
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Quettier T, Maffei A, Gambarota F, Ferrari PF, Sessa P. Testing EEG functional connectivity between sensorimotor and face processing visual regions in individuals with congenital facial palsy. Front Syst Neurosci 2023; 17:1123221. [PMID: 37215358 PMCID: PMC10196055 DOI: 10.3389/fnsys.2023.1123221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 04/17/2023] [Indexed: 05/24/2023] Open
Abstract
Moebius syndrome (MBS) is characterized by the congenital absence or underdevelopment of cranial nerves VII and VI, leading to facial palsy and impaired lateral eye movements. As a result, MBS individuals cannot produce facial expressions and did not develop motor programs for facial expressions. In the latest model of sensorimotor simulation, an iterative communication between somatosensory, motor/premotor cortices, and visual regions has been proposed, which should allow more efficient discriminations among subtle facial expressions. Accordingly, individuals with congenital facial motor disability, specifically with MBS, should exhibit atypical communication within this network. Here, we aimed to test this facet of the sensorimotor simulation models. We estimated the functional connectivity between the visual cortices for face processing and the sensorimotor cortices in healthy and MBS individuals. To this aim, we studied the strength of beta band functional connectivity between these two systems using high-density EEG, combined with a change detection task with facial expressions (and a control condition involving non-face stimuli). The results supported our hypothesis such that when discriminating subtle facial expressions, participants affected by congenital facial palsy (compared to healthy controls) showed reduced connectivity strength between sensorimotor regions and visual regions for face processing. This effect was absent for the condition with non-face stimuli. These findings support sensorimotor simulation models and the communication between sensorimotor and visual areas during subtle facial expression processing.
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Affiliation(s)
- Thomas Quettier
- Department of Developmental and Social Psychology, University of Padua, Padua, Italy
- Padova Neuroscience Center (PNC), University of Padua, Padua, Italy
| | - Antonio Maffei
- Department of Developmental and Social Psychology, University of Padua, Padua, Italy
- Padova Neuroscience Center (PNC), University of Padua, Padua, Italy
| | - Filippo Gambarota
- Department of Developmental and Social Psychology, University of Padua, Padua, Italy
- Padova Neuroscience Center (PNC), University of Padua, Padua, Italy
| | - Pier Francesco Ferrari
- Institut des Sciences Cognitives Marc Jeannerod, CNRS/Université Claude Bernard Lyon 1, Bron, France
| | - Paola Sessa
- Department of Developmental and Social Psychology, University of Padua, Padua, Italy
- Padova Neuroscience Center (PNC), University of Padua, Padua, Italy
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Dynamic Functional Connectivity of Emotion Processing in Beta Band with Naturalistic Emotion Stimuli. Brain Sci 2022; 12:brainsci12081106. [PMID: 36009166 PMCID: PMC9405988 DOI: 10.3390/brainsci12081106] [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: 08/02/2022] [Revised: 08/11/2022] [Accepted: 08/16/2022] [Indexed: 11/17/2022] Open
Abstract
While naturalistic stimuli, such as movies, better represent the complexity of the real world and are perhaps crucial to understanding the dynamics of emotion processing, there is limited research on emotions with naturalistic stimuli. There is a need to understand the temporal dynamics of emotion processing and their relationship to different dimensions of emotion experience. In addition, there is a need to understand the dynamics of functional connectivity underlying different emotional experiences that occur during or prior to such experiences. To address these questions, we recorded the EEG of participants and asked them to mark the temporal location of their emotional experience as they watched a video. We also obtained self-assessment ratings for emotional multimedia stimuli. We calculated dynamic functional the connectivity (DFC) patterns in all the frequency bands, including information about hubs in the network. The change in functional networks was quantified in terms of temporal variability, which was then used in regression analysis to evaluate whether temporal variability in DFC (tvDFC) could predict different dimensions of emotional experience. We observed that the connectivity patterns in the upper beta band could differentiate emotion categories better during or prior to the reported emotional experience. The temporal variability in functional connectivity dynamics is primarily related to emotional arousal followed by dominance. The hubs in the functional networks were found across the right frontal and bilateral parietal lobes, which have been reported to facilitate affect, interoception, action, and memory-related processing. Since our study was performed with naturalistic real-life resembling emotional videos, the study contributes significantly to understanding the dynamics of emotion processing. The results support constructivist theories of emotional experience and show that changes in dynamic functional connectivity can predict aspects of our emotional experience.
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Choi KM, Kim JY, Kim YW, Han JW, Im CH, Lee SH. Comparative analysis of default mode networks in major psychiatric disorders using resting-state EEG. Sci Rep 2021; 11:22007. [PMID: 34759276 PMCID: PMC8580995 DOI: 10.1038/s41598-021-00975-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 10/15/2021] [Indexed: 11/09/2022] Open
Abstract
Default mode network (DMN) is a set of functional brain structures coherently activated when individuals are in resting-state. In this study, we constructed multi-frequency band resting-state EEG-based DMN functional network models for major psychiatric disorders to easily compare their pathophysiological characteristics. Phase-locking values (PLVs) were evaluated to quantify functional connectivity; global and nodal clustering coefficients (CCs) were evaluated to quantify global and local connectivity patterns of DMN nodes, respectively. DMNs of patients with post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD), panic disorder, major depressive disorder (MDD), bipolar disorder, schizophrenia (SZ), mild cognitive impairment (MCI), and Alzheimer's disease (AD) were constructed relative to their demographically-matched healthy control groups. Overall DMN patterns were then visualized and compared with each other. In global CCs, SZ and AD showed hyper-clustering in the theta band; OCD, MCI, and AD showed hypo-clustering in the low-alpha band; OCD and MDD showed hypo-clustering and hyper-clustering in low-beta, and high-beta bands, respectively. In local CCs, disease-specific patterns were observed. In the PLVs, lowered theta-band functional connectivity between the left lingual gyrus and the left hippocampus was frequently observed. Our comprehensive comparisons suggest EEG-based DMN as a useful vehicle for understanding altered brain networks of major psychiatric disorders.
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Affiliation(s)
- Kang-Min Choi
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea.,School of Electronic Engineering, Hanyang University, Seoul, Republic of Korea
| | - Jeong-Youn Kim
- Center for Bionics, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
| | - Yong-Wook Kim
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea.,Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Jung-Won Han
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea.,School of Psychology, Sogang University, Seoul, Republic of Korea
| | - Chang-Hwan Im
- School of Electronic Engineering, Hanyang University, Seoul, Republic of Korea. .,Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea.
| | - Seung-Hwan Lee
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea. .,Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, Juhwa-ro 170, Ilsanseo-Gu, Goyang, 10370, Republic of Korea. .,Bwave Inc, Juhwa-ro, Goyang, 10380, Republic of Korea.
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Kheirkhah M, Baumbach P, Leistritz L, Witte OW, Walter M, Gilbert JR, Zarate Jr. CA, Klingner CM. The Right Hemisphere Is Responsible for the Greatest Differences in Human Brain Response to High-Arousing Emotional versus Neutral Stimuli: A MEG Study. Brain Sci 2021; 11:960. [PMID: 34439579 PMCID: PMC8412101 DOI: 10.3390/brainsci11080960] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/09/2021] [Accepted: 07/16/2021] [Indexed: 11/17/2022] Open
Abstract
Studies investigating human brain response to emotional stimuli-particularly high-arousing versus neutral stimuli-have obtained inconsistent results. The present study was the first to combine magnetoencephalography (MEG) with the bootstrapping method to examine the whole brain and identify the cortical regions involved in this differential response. Seventeen healthy participants (11 females, aged 19 to 33 years; mean age, 26.9 years) were presented with high-arousing emotional (pleasant and unpleasant) and neutral pictures, and their brain responses were measured using MEG. When random resampling bootstrapping was performed for each participant, the greatest differences between high-arousing emotional and neutral stimuli during M300 (270-320 ms) were found to occur in the right temporo-parietal region. This finding was observed in response to both pleasant and unpleasant stimuli. The results, which may be more robust than previous studies because of bootstrapping and examination of the whole brain, reinforce the essential role of the right hemisphere in emotion processing.
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Affiliation(s)
- Mina Kheirkhah
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20814, USA; (J.R.G.); (C.A.Z.)
- Biomagnetic Center, Jena University Hospital, 07747 Jena, Germany;
- Department of Psychiatry and Psychotherapy, Jena University Hospital, 07743 Jena, Germany;
| | - Philipp Baumbach
- Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, 07747 Jena, Germany;
| | - Lutz Leistritz
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, 07740 Jena, Germany;
| | - Otto W. Witte
- Hans Berger Department of Neurology, Jena University Hospital, 07747 Jena, Germany;
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, 07743 Jena, Germany;
| | - Jessica R. Gilbert
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20814, USA; (J.R.G.); (C.A.Z.)
| | - Carlos A. Zarate Jr.
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20814, USA; (J.R.G.); (C.A.Z.)
| | - Carsten M. Klingner
- Biomagnetic Center, Jena University Hospital, 07747 Jena, Germany;
- Hans Berger Department of Neurology, Jena University Hospital, 07747 Jena, Germany;
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Automated emotion classification in the early stages of cortical processing: An MEG study. Artif Intell Med 2021; 115:102063. [PMID: 34001320 DOI: 10.1016/j.artmed.2021.102063] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 12/16/2020] [Accepted: 03/29/2021] [Indexed: 11/20/2022]
Abstract
PURPOSE Here we aimed to automatically classify human emotion earlier than is typically attempted. There is increasing evidence that the human brain differentiates emotional categories within 100-300 ms after stimulus onset. Therefore, here we evaluate the possibility of automatically classifying human emotions within the first 300 ms after the stimulus and identify the time-interval of the highest classification performance. METHODS To address this issue, MEG signals of 17 healthy volunteers were recorded in response to three different picture stimuli (pleasant, unpleasant, and neutral pictures). Six Linear Discriminant Analysis (LDA) classifiers were used based on two binary comparisons (pleasant versus neutral and unpleasant versus neutral) and three different time-intervals (100-150 ms, 150-200 ms, and 200-300 ms post-stimulus). The selection of the feature subsets was performed by Genetic Algorithm and LDA. RESULTS We demonstrated significant classification performances in both comparisons. The best classification performance was achieved with a median AUC of 0.83 (95 %- CI [0.71; 0.87]) classifying brain responses evoked by unpleasant and neutral stimuli within 100-150 ms, which is at least 850 ms earlier than attempted by other studies. CONCLUSION Our results indicate that using the proposed algorithm, brain emotional responses can be significantly classified at very early stages of cortical processing (within 300 ms). Moreover, our results suggest that emotional processing in the human brain occurs within the first 100-150 ms.
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