Toward Multimodal Modeling of Emotional Expressiveness.
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ABSTRACT:
SUBMITTER: Lin V
PROVIDER: S-EPMC8106384 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Predictions which invoke evolutionary mechanisms are hard to test. Agent-based modeling in artificial life offers a way to simulate behaviors and interactions in specific physical or social environments over many generations. The outcomes have implications for understanding adaptive value of behaviors in context. Pain-related behavior in animals is communicated to other animals that might protect or help, or might exploit or predate. An agent-based model simulated the effects of displaying or not displaying pain (expresser/nonexpresser strategies) when injured and of helping, ignoring, or exploiting another in pain (altruistic/nonaltruistic/selfish strategies). Agents modeled in MATLAB interacted at random while foraging (gaining energy); random injury interrupted foraging for a fixed time unless help from an altruistic agent, who paid an energy cost, speeded recovery. Environmental and social conditions also varied, and each model ran for 10,000 iterations. Findings were meaningful in that, in general, contingencies that evident from experimental work with a variety of mammals, over a few interactions, were replicated in the agent-based model after selection pressure over many generations. More energy-demanding expression of pain reduced its frequency in successive generations, and increasing injury frequency resulted in fewer expressers and altruists. Allowing exploitation of injured agents decreased expression of pain to near zero, but altruists remained. Decreasing costs or increasing benefits of helping hardly changed its frequency, whereas increasing interaction rate between injured agents and helpers diminished the benefits to both. Agent-based modeling allows simulation of complex behaviors and environmental pressures over evolutionary time.
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Project description:Parents are perhaps the most direct and profound influences on children's development of emotional competence. For example, how and what emotions parents express in the family has implications for children's ability to understand and regulate their emotions. What is less well understood is what potential environmental or contextual factors impact parents' emotional expressiveness, particularly in high-risk samples prone to atypical emotional expressiveness (e.g., deficits in the production and recognition of emotional expressions). The present longitudinal study examined the association between life changes and parents' expression of positive and negative emotions, as well as, how these associations changed over time in a sample of maltreating mothers. Eighty-eight mothers with a substantiated history of physical abuse completed measures of emotional expressiveness and life changes experienced over the past 6 months when their children were in preschool, kindergarten, and first grade. Results indicated that life changes decreased over time, while parental emotional expressiveness remained stable. Moreover, life changes were associated across time with the expression of negative emotions, but were unrelated to expressions of positive emotions. Findings have important implications for understanding emotional expressiveness in high-risk samples.
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Project description:The glutamatergic modulator ketamine rapidly reduces depressive symptoms in individuals with treatment-resistant major depressive disorder (TRD) and bipolar disorder. While its underlying mechanism of antidepressant action is not fully understood, modulating glutamatergically-mediated connectivity appears to be a critical component moderating antidepressant response. This double-blind, crossover, placebo-controlled study analyzed data from 19 drug-free individuals with TRD and 15 healthy volunteers who received a single intravenous infusion of ketamine hydrochloride (0.5 mg/kg) as well as an intravenous infusion of saline placebo. Magnetoencephalographic recordings were collected prior to the first infusion and 6-9 h after both drug and placebo infusions. During scanning, participants completed an attentional dot probe task that included emotional faces. Antidepressant response was measured across time points using the Montgomery-Asberg Depression Rating Scale (MADRS). Dynamic causal modeling (DCM) was used to measure changes in parameter estimates of connectivity via a biophysical model that included realistic local neuronal architecture and receptor channel signaling, modeling connectivity between the early visual cortex, fusiform cortex, amygdala, and inferior frontal gyrus. Clinically, ketamine administration significantly reduced depressive symptoms in TRD participants. Within the model, ketamine administration led to faster gamma aminobutyric acid (GABA) and N-methyl-D-aspartate (NMDA) transmission in the early visual cortex, faster NMDA transmission in the fusiform cortex, and slower NMDA transmission in the amygdala. Ketamine administration also led to direct and indirect changes in local inhibition in the early visual cortex and inferior frontal gyrus and to indirect increases in cortical excitability within the amygdala. Finally, reductions in depressive symptoms in TRD participants post-ketamine were associated with faster α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) transmission and increases in gain control of spiny stellate cells in the early visual cortex. These findings provide additional support for the GABA and NMDA inhibition and disinhibition hypotheses of depression and support the role of AMPA throughput in ketamine's antidepressant effects. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT00088699?term=NCT00088699&draw=2&rank=1, identifier NCT00088699.
| S-EPMC8249755 | biostudies-literature
Project description:Introduction: Given the direct association with malignant ventricular arrhythmias, cardiotoxicity is a major concern in drug design. In the past decades, computational models based on the quantitative structure-activity relationship have been proposed to screen out cardiotoxic compounds and have shown promising results. The combination of molecular fingerprint and the machine learning model shows stable performance for a wide spectrum of problems; however, not long after the advent of the graph neural network (GNN) deep learning model and its variant (e.g., graph transformer), it has become the principal way of quantitative structure-activity relationship-based modeling for its high flexibility in feature extraction and decision rule generation. Despite all these progresses, the expressiveness (the ability of a program to identify non-isomorphic graph structures) of the GNN model is bounded by the WL isomorphism test, and a suitable thresholding scheme that relates directly to the sensitivity and credibility of a model is still an open question. Methods: In this research, we further improved the expressiveness of the GNN model by introducing the substructure-aware bias by the graph subgraph transformer network model. Moreover, to propose the most appropriate thresholding scheme, a comprehensive comparison of the thresholding schemes was conducted. Results: Based on these improvements, the best model attains performance with 90.4% precision, 90.4% recall, and 90.5% F1-score with a dual-threshold scheme (active: <1μM; non-active: >30μM). The improved pipeline (graph subgraph transformer network model and thresholding scheme) also shows its advantages in terms of the activity cliff problem and model interpretability.
| S-EPMC10203956 | biostudies-literature
Project description:BackgroundLoneliness is a growing public health problem that has been exacerbated in vulnerable groups during the COVID-19 pandemic. Social support interventions have been shown to reduce loneliness, including when delivered through technology. Digital humans are a new type of computer agent that show promise as supportive peers in health care. For digital humans to be effective and engaging support persons, it is important that they develop closeness with people. Closeness can be increased by emotional expressiveness, particularly in female relationships. However, it is unknown whether emotional expressiveness improves relationships with digital humans and affects physiological responses.ObjectiveThe aim of this study is to investigate whether emotional expression by a digital human can affect psychological and physiological outcomes and whether the effects are moderated by the user's gender.MethodsA community sample of 198 adults (101 women, 95 men, and 2 gender-diverse individuals) was block-randomized by gender to complete a 15-minute self-disclosure conversation with a female digital human in 1 of 6 conditions. In these conditions, the digital human varied in modality richness and emotional expression on the face and in the voice (emotional, neutral, or no face; emotional or neutral voice). Perceived loneliness, closeness, social support, caring perceptions, and stress were measured after each interaction. Heart rate, skin temperature, and electrodermal activity were assessed during each interaction. 3-way factorial analyses of variance with post hoc tests were conducted.ResultsEmotional expression in the voice was associated with greater perceptions of caring and physiological arousal during the interaction, and unexpectedly, with lower feelings of support. User gender moderated the effect of emotional expressiveness on several outcomes. For women, an emotional voice was associated with increased closeness, social support, and caring perceptions, whereas for men, a neutral voice increased these outcomes. For women, interacting with a neutral face was associated with lower loneliness and subjective stress compared with no face. Interacting with no face (ie, a voice-only black screen) resulted in lower loneliness and subjective stress for men, compared with a neutral or emotional face. No significant results were found for heart rate or skin temperature. However, average electrodermal activity was significantly higher for men while interacting with an emotional voice.ConclusionsEmotional expressiveness in a female digital human has different effects on loneliness, social, and physiological outcomes for men and women. The results inform the design of digital human support persons and have theoretical implications. Further research is needed to evaluate how more pronounced emotional facial expressions in a digital human might affect the results.Trial registrationAustralia New Zealand Clinical Trials Registry (ANZCTR) ACTRN12621000865819; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=381816&isReview.
| S-EPMC8663575 | biostudies-literature
Project description:Mobile microrobots have a promising future in various applications. These include targeted drug delivery, local measurement, biopsy or microassembly. Studying mobile microrobots inside microfluidics is an essential step towards such applications. But in this environment that was not designed for the robot, integration process and propulsion robustness still pose technological challenges. In this paper, we present a helical microrobot with three different motions, designed to achieve these goals. These motions are rolling, spintop motion and swimming. Through these multiple motions, microrobots are able to selectively integrate a chip through a microfluidic channel. This enables them to perform propulsion characterizations, 3D (Three Dimensional) maneuverability, particle cargo transport manipulation and exit from the chip. The microrobot selective integration inside microfluidics could lead to various in-vitro biologic or in-vivo biomedical applications.
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Project description:ObjectiveImproving mechanisms to detect adverse drug reactions (ADRs) is key to strengthening post-marketing drug safety surveillance. Signal detection is presently unimodal, relying on a single information source. Multimodal signal detection is based on jointly analyzing multiple information sources. Building on, and expanding the work done in prior studies, the aim of the article is to further research on multimodal signal detection, explore its potential benefits, and propose methods for its construction and evaluation.Material and methodsFour data sources are investigated; FDA's adverse event reporting system, insurance claims, the MEDLINE citation database, and the logs of major Web search engines. Published methods are used to generate and combine signals from each data source. Two distinct reference benchmarks corresponding to well-established and recently labeled ADRs respectively are used to evaluate the performance of multimodal signal detection in terms of area under the ROC curve (AUC) and lead-time-to-detection, with the latter relative to labeling revision dates.ResultsLimited to our reference benchmarks, multimodal signal detection provides AUC improvements ranging from 0.04 to 0.09 based on a widely used evaluation benchmark, and a comparative added lead-time of 7-22 months relative to labeling revision dates from a time-indexed benchmark.ConclusionsThe results support the notion that utilizing and jointly analyzing multiple data sources may lead to improved signal detection. Given certain data and benchmark limitations, the early stage of development, and the complexity of ADRs, it is currently not possible to make definitive statements about the ultimate utility of the concept. Continued development of multimodal signal detection requires a deeper understanding the data sources used, additional benchmarks, and further research on methods to generate and synthesize signals.
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Project description:Regular alcohol consumption in unfamiliar social settings has been linked to problematic drinking. A large body of indirect evidence has accumulated to suggest that alcohol's rewarding emotional effects-both negative-mood relieving and positive-mood enhancing-will be magnified when alcohol is consumed within unfamiliar versus familiar social contexts. But empirical research has never directly examined links between contextual familiarity and alcohol reward. In the current study, we mobilized novel ambulatory technology to examine the effect of social familiarity on alcohol reward in everyday drinking contexts while also examining how alcohol reward observed in these field contexts corresponds to reward observed in the laboratory. Heavy social drinking participants (N = 48, 50% male) engaged in an intensive week of ambulatory assessment. Participants wore transdermal alcohol sensors while they reported on their mood and took photographs of their social contexts in response to random prompts. Participants also attended 2 laboratory beverage-administration sessions, during which their emotional responses were assessed and transdermal sensors were calibrated to estimate breathalyzer readings (eBrACs). Results indicated a significant interaction between social familiarity and alcohol episode in everyday drinking settings, with alcohol enhancing mood to a greater extent in relatively unfamiliar versus familiar social contexts. Findings also indicated that drinking in relatively unfamiliar social settings was associated with higher eBrACs. Finally, results indicated a correspondence between some mood effects of alcohol experienced inside and outside the laboratory. This study presents a novel methodology for examining alcohol reward and indicates social familiarity as a promising direction for research seeking to explain problematic drinking. (PsycINFO Database Record
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Project description:BACKGROUND:The amygdala is a central node in the brain network that processes aversive emotions and is extensively innervated by dorsal raphe nucleus (DRN) serotonin (5-hydroxytryptamine [5-HT]) neurons. Alterations in DRN 5-HT1A receptor availability cause phenotypes characterized by fearful behavior in preclinical models. However, it is unknown whether 5-HT1A receptor availability is linked specifically to the processing of aversive emotions in humans or whether it modulates connectivity in brain networks involved in emotion processing. To answer this question, we investigated the relationship between DRN 5-HT1A receptor availability and amygdala reactivity to aversive emotion and functional connectivity within the amygdala-cortical network. METHODS:We studied 15 healthy human participants who underwent positron emission tomography scanning with [(11)C]CUMI-101, a 5-HT1A partial agonist radioligand, and functional magnetic resonance imaging of brain responses during an incidental emotion processing task including happy, fearful, and neutral faces. Regional estimates of 5-HT1A receptor binding potential (nondisplaceable) were obtained by calculating total volumes of distribution for presynaptic DRN and amygdala. Connectivity between the amygdala and corticolimbic areas was assessed using psychophysiologic interaction analysis with the amygdala as the seed region. RESULTS:Analysis of the fear versus neutral contrast revealed a significant negative correlation between amygdala response and DRN binding potential (nondisplaceable) (r = -.87, p < .001). Availability of DRN 5-HT1A receptors positively correlated with amygdala connectivity with middle frontal gyrus, anterior cingulate cortex, bilateral precuneus, and left supramarginal gyrus for fearful (relative to neutral) faces. CONCLUSIONS:Our data show that DRN 5-HT1A receptor availability is linked specifically to the processing of aversive emotions in the amygdala and the modulation of amygdala-cortical connectivity.
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Project description:This study aimed to reveal the influence of emotional valence and sensory modality on neural activity in response to multimodal emotional stimuli using scalp EEG. In this study, 20 healthy participants completed the emotional multimodal stimulation experiment for three stimulus modalities (audio, visual, and audio-visual), all of which are from the same video source with two emotional components (pleasure or unpleasure), and EEG data were collected using six experimental conditions and one resting state. We analyzed power spectral density (PSD) and event-related potential (ERP) components in response to multimodal emotional stimuli, for spectral and temporal analysis. PSD results showed that the single modality (audio only/visual only) emotional stimulation PSD differed from multi-modality (audio-visual) in a wide brain and band range due to the changes in modality and not from the changes in emotional degree. The most pronounced N200-to-P300 potential shifts occurred in monomodal rather than multimodal emotional stimulations. This study suggests that emotional saliency and sensory processing efficiency perform a significant role in shaping neural activity during multimodal emotional stimulation, with the sensory modality being more influential in PSD. These findings contribute to our understanding of the neural mechanisms involved in multimodal emotional stimulation.
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