scholarly journals A generalizable multivariate brain pattern for interpersonal guilt

2019 ◽  
Author(s):  
Hongbo Yu ◽  
Leonie Koban ◽  
Luke J. Chang ◽  
Ullrich Wagner ◽  
Anjali Krishnan ◽  
...  
Keyword(s):  
Brain Activity ◽  
Test Sample ◽  
Training Sample ◽  
Brain State ◽  
Emotional States ◽  
Social Emotional ◽  
Social Emotions ◽  
Interpersonal Guilt ◽  
Experienced Pain ◽  
Independent Test Sample

AbstractFeeling guilty when we have wronged another is a crucial aspect of prosociality, but its neurobiological bases are elusive. Although multivariate patterns of brain activity show promise for developing brain measures linked to specific emotions, it is less clear whether brain activity can be trained to detect more complex social emotional states such as guilt. Here, we identified a distributed Guilt-Related Brain Signature (GRBS) across two independent neuroimaging datasets that used interpersonal interactions to evoke guilt. This signature discriminated conditions associated with interpersonal guilt from closely matched control conditions in a cross-validated training sample (N = 24; Chinese population) and in an independent test sample (N = 19; Swiss population). However, it did not respond to observed or experienced pain, or recalled guilt. Moreover, the GRBS only exhibited weak spatial similarity with other brain signatures of social affective processes, further indicating the specificity of the brain state it represents. These findings provide a step towards developing biological markers of social emotions, which could serve as important tools to investigate guilt-related brain processes in both healthy and clinical populations.

scholarly journals A Generalizable Multivariate Brain Pattern for Interpersonal Guilt

2020 ◽  
Vol 30 (6) ◽  
pp. 3558-3572 ◽  
Author(s):  
Hongbo Yu ◽  
Leonie Koban ◽  
Luke J Chang ◽  
Ullrich Wagner ◽  
Anjali Krishnan ◽  
...  
Keyword(s):  
Brain Activity ◽  
Test Sample ◽  
Training Sample ◽  
Brain State ◽  
Emotional States ◽  
Social Emotional ◽  
Social Emotions ◽  
Interpersonal Guilt ◽  
Experienced Pain ◽  
Independent Test Sample

Abstract Feeling guilty when we have wronged another is a crucial aspect of prosociality, but its neurobiological bases are elusive. Although multivariate patterns of brain activity show promise for developing brain measures linked to specific emotions, it is less clear whether brain activity can be trained to detect more complex social emotional states such as guilt. Here, we identified a distributed guilt-related brain signature (GRBS) across two independent neuroimaging datasets that used interpersonal interactions to evoke guilt. This signature discriminated conditions associated with interpersonal guilt from closely matched control conditions in a cross-validated training sample (N = 24; Chinese population) and in an independent test sample (N = 19; Swiss population). However, it did not respond to observed or experienced pain, or recalled guilt. Moreover, the GRBS only exhibited weak spatial similarity with other brain signatures of social-affective processes, further indicating the specificity of the brain state it represents. These findings provide a step toward developing biological markers of social emotions, which could serve as important tools to investigate guilt-related brain processes in both healthy and clinical populations.

The Temporal Dynamics of Spontaneous Emotional Brain States and Their Implications for Mental Health

2021 ◽  
pp. 1-14
Author(s):  
Philip A. Kragel ◽  
Ahmad R. Hariri ◽  
Kevin S. LaBar
Keyword(s):  
Mental Health ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Resting State Fmri ◽  
State Transitions ◽  
Brain State ◽  
Emotional States ◽  
Affective States ◽  
Emotional Responding ◽  
Brain States

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.

Emotional Reactivity to Visual Content as Revealed by ERP Component Clustering

2015 ◽  
Vol 29 (4) ◽  
pp. 135-146 ◽  
Author(s):  
Miroslaw Wyczesany ◽  
Szczepan J. Grzybowski ◽  
Jan Kaiser
Keyword(s):  
Emotional Reactivity ◽  
Brain Activity ◽  
Affective State ◽  
Occipital Lobe ◽  
Stimulus Onset ◽  
Emotional States ◽  
Neural Basis ◽  
Temporoparietal Junction ◽  
The Right ◽  
The Impact

Abstract. In the study, the neural basis of emotional reactivity was investigated. Reactivity was operationalized as the impact of emotional pictures on the self-reported ongoing affective state. It was used to divide the subjects into high- and low-responders groups. Independent sources of brain activity were identified, localized with the DIPFIT method, and clustered across subjects to analyse the visual evoked potentials to affective pictures. Four of the identified clusters revealed effects of reactivity. The earliest two started about 120 ms from the stimulus onset and were located in the occipital lobe and the right temporoparietal junction. Another two with a latency of 200 ms were found in the orbitofrontal and the right dorsolateral cortices. Additionally, differences in pre-stimulus alpha level over the visual cortex were observed between the groups. The attentional modulation of perceptual processes is proposed as an early source of emotional reactivity, which forms an automatic mechanism of affective control. The role of top-down processes in affective appraisal and, finally, the experience of ongoing emotional states is also discussed.

scholarly journals An Analysis of the External Validity of EEG Spectral Power in an Uncontrolled Outdoor Environment during Default and Complex Neurocognitive States

2021 ◽  
Vol 11 (3) ◽  
pp. 330
Author(s):  
Dalton J. Edwards ◽  
Logan T. Trujillo
Keyword(s):  
External Validity ◽  
Spectral Power ◽  
Brain Activity ◽  
Outdoor Environment ◽  
Oscillatory Activity ◽  
Brain State ◽  
Beta Band ◽  
Eeg Spectral Power ◽  
Eeg Power ◽  
Human Participants

Traditionally, quantitative electroencephalography (QEEG) studies collect data within controlled laboratory environments that limit the external validity of scientific conclusions. To probe these validity limits, we used a mobile EEG system to record electrophysiological signals from human participants while they were located within a controlled laboratory environment and an uncontrolled outdoor environment exhibiting several moderate background influences. Participants performed two tasks during these recordings, one engaging brain activity related to several complex cognitive functions (number sense, attention, memory, executive function) and the other engaging two default brain states. We computed EEG spectral power over three frequency bands (theta: 4–7 Hz, alpha: 8–13 Hz, low beta: 14–20 Hz) where EEG oscillatory activity is known to correlate with the neurocognitive states engaged by these tasks. Null hypothesis significance testing yielded significant EEG power effects typical of the neurocognitive states engaged by each task, but only a beta-band power difference between the two background recording environments during the default brain state. Bayesian analysis showed that the remaining environment null effects were unlikely to reflect measurement insensitivities. This overall pattern of results supports the external validity of laboratory EEG power findings for complex and default neurocognitive states engaged within moderately uncontrolled environments.

scholarly journals A Detection algorithm based on Convolutional Neural Network

2018 ◽  
Author(s):  
Fei Rong ◽  
Li Shasha ◽  
Xu Qingzheng ◽  
Liu Kun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Training Model ◽  
Test Sample ◽  
Training Sample ◽  
Detection Algorithm ◽  
Small Scale ◽  
Detection Model ◽  
Sample Data ◽  
Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

scholarly journals Functional neuroimaging studies of the effects of psychotherapy

2014 ◽  
Vol 16 (1) ◽  
pp. 75-81 ◽  
Keyword(s):  
Functional Neuroimaging ◽  
Brain Activity ◽  
Behavioral Changes ◽  
Neural Mechanisms ◽  
Psychological Interventions ◽  
Emotional States ◽  
Major Depressive ◽  
Psychotherapeutic Interventions ◽  
Functional Brain Activity ◽  
Neuroimaging Techniques

It has been long established that psychological interventions can markedly alter patients' thinking patterns, beliefs, attitudes, emotional states, and behaviors. Little was known about the neural mechanisms mediating such alterations before the advent of functional neuroimaging techniques. Since the turn of the new millenium, several functional neuroimaging studies have been conducted to tackle this important issue. Some of these studies have explored the neural impact of various forms of psychotherapy in individuals with major depressive disorder. Other neuroimaging studies have investigated the effects of psychological interventions for anxiety disorders. I review these studies in the present article, and discuss the putative neural mechanisms of change in psychotherapy. The findings of these studies suggest that mental and behavioral changes occurring during psychotherapeutic interventions can lead to a normalization of functional brain activity at a global level.

scholarly journals Age-dependent electroencephalogram (EEG) patterns during sevoflurane general anesthesia in infants

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Laura Cornelissen ◽  
Seong-Eun Kim ◽  
Patrick L Purdon ◽  
Emery N Brown ◽  
Charles B Berde
Keyword(s):  
General Anesthesia ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Body Movement ◽  
Video Recording ◽  
Brain State ◽  
Alpha Power ◽  
State Monitoring ◽  
Spatio Temporal ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) approaches may provide important information about developmental changes in brain-state dynamics during general anesthesia. We used multi-electrode EEG, analyzed with multitaper spectral methods and video recording of body movement to characterize the spatio-temporal dynamics of brain activity in 36 infants 0–6 months old when awake, and during maintenance of and emergence from sevoflurane general anesthesia. During maintenance: (1) slow-delta oscillations were present in all ages; (2) theta and alpha oscillations emerged around 4 months; (3) unlike adults, all infants lacked frontal alpha predominance and coherence. Alpha power was greatest during maintenance, compared to awake and emergence in infants at 4–6 months. During emergence, theta and alpha power decreased with decreasing sevoflurane concentration in infants at 4–6 months. These EEG dynamic differences are likely due to developmental factors including regional differences in synaptogenesis, glucose metabolism, and myelination across the cortex. We demonstrate the need to apply age-adjusted analytic approaches to develop neurophysiologic-based strategies for pediatric anesthetic state monitoring.

Abstract TMP81: Artificial Intelligence Beats Conventional Regression Analysis in Predicting Short and Long Term Risk of Stroke in Patients Undergoing Percutaneous Coronary Interventions

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Pradyumna Agasthi ◽  
Chieh-Ju Chao ◽  
Han Lun Wu ◽  
Farouk Mookadam ◽  
Nithin Venepally ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Area Under The Curve ◽  
Coronary Intervention ◽  
Test Sample ◽  
Training Sample ◽  
Logistic Regression Models ◽  
Percutaneous Coronary ◽  
Short And Long Term

Introduction: Ischemic stroke (IS) causes substantial morbidity and mortality in patients undergoing percutaneous coronary intervention (PCI) with a 5 yr incidence ~ 3%. We sought the test the accuracy of Machine learning (ML) algorithms in predicting IS in patients undergoing PCI. Methods: Mayo Clinic CathPCI registry data were retrospectively analyzed from Jan 2003 - June 2018 including 21,872 patients who underwent PCI. The cohort was randomly divided into a training sample (75%, n=16404) and a unique test sample (25%, n=5468) prior to model generation. The risk prediction model was generated utilizing a random forest algorithm (RF model) on 188 unique variables to predict the risk of IS at 6-month, 1, 2, and 5-year post PCI. Conventional risk factors for stroke were used for logistic regression. The receiver operating characteristic (ROC) curve and area under the curve for the RF and logistic regression models were compared for the test cohort. Results: The mean age was 66.9 ± 12.4 years, and 71% were male. Patient demographics and outcomes are shown in Table 1 . The ROC area under the curve for the RF model was superior compared to the logistic regression model in predicting IS at 6 months, 1,2 and 5 yrs for the test cohort ( Figure 1 .) Conclusions: The RF model accurately predicts short and long term risk of IS and outperforms logistic regression analysis in patients undergoing PCI.

The risk score for in-hospital mortality in patients with ST-segment elevation myocardial infarction

Kardiologiia ◽  
2021 ◽  
Vol 61 (9) ◽  
pp. 11-19
Author(s):  
I. S. Bessonov ◽  
V. A. Kuznetsov ◽  
S. S. Sapozhnikov ◽  
E. A. Gorbatenko ◽  
A. A. Shadrin
Keyword(s):  
Diabetes Mellitus ◽  
Myocardial Infarction ◽  
Scale Score ◽  
Test Sample ◽  
Training Sample ◽  
Individual Risk ◽  
Hospital Death ◽  
St Segment Elevation ◽  
St Segment ◽  
History Of

Aim    To develop a scale (score system) for predicting the individual risk of in-hospital death in patients with ST segment elevation acute myocardial infarction (STEMI) with an account of results of percutaneous coronary intervention (PCI).Material and methods    The analysis used data of 1 649 sequential patients with STEMI included into the hospital registry of PCI from 2006 through 2017. To test the model predictability, the original sample was divided into two groups: a training group consisting of 1150 (70 %) patients and a test group consisting of 499 (30 %) patients. The training sample was used for computing an individual score. To this purpose, β-coefficients of each variable obtained at the last stage of the multivariate logistic regression model were subjected to linear transformation. The scale was verified using the test sample.Results    Seven independent predictors of in-hospital death were determined: age ≥65 years, acute heart failure (Killip class III-IV), total myocardial ischemia time ≥180 min, anterior localization of myocardial infarction, failure of PCI, SYNTAX scale score ≥16, glycemia on admission ≥7.78 mmol/l for patients without a history of diabetes mellitus and ≥14.35 mmol/l for patients with a history of diabetes mellitus. The contribution of each value to the risk of in-hospital death was ranked from 0 to 7. A threshold total score of 10 was determined; a score ≥10 corresponded to a high probability of in-hospital death (18.2 %). In the training sample, the sensitivity was 81 %, the specificity was 80.6 %, and the area under the curve (AUC) was 0.902. In the test sample, the sensitivity was 96.2 %, the specificity was 83.3 %, and the AUC was 0.924.Conclusion    The developed scale has a good predictive accuracy in identifying patients with acute STEMI who have a high risk of fatal outcome at the hospital stage.

Visualizing Emotional States: A Method Based on Human Brain Activity

2019 ◽  
pp. 248-258
Author(s):  
Yanfang Long ◽  
Wanzeng Kong ◽  
Xuanyu Jin ◽  
Jili Shang ◽  
Can Yang
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
Emotional States
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