Project description:People often change their beliefs by succumbing to an opinion of others. Such changes are often referred to as effects of social influence. While some previous studies have focused on the reinforcement learning mechanisms of social influence or on its internalization, others have reported evidence of changes in sensory processing evoked by social influence of peer groups. In this study, we used magnetoencephalographic (MEG) source imaging to further investigate the long-term effects of agreement and disagreement with the peer group. The study was composed of two sessions. During the first session, participants rated the trustworthiness of faces and subsequently learned group rating of each face. In the first session, a neural marker of an immediate mismatch between individual and group opinions was found in the posterior cingulate cortex, an area involved in conflict-monitoring and reinforcement learning. To identify the neural correlates of the long-lasting effect of the group opinion, we analysed MEG activity while participants rated faces during the second session. We found MEG traces of past disagreement or agreement with the peers at the parietal cortices 230 ms after the face onset. The neural activity of the superior parietal lobule, intraparietal sulcus, and precuneus was significantly stronger when the participant's rating had previously differed from the ratings of the peers. The early MEG correlates of disagreement with the majority were followed by activity in the orbitofrontal cortex 320 ms after the face onset. Altogether, the results reveal the temporal dynamics of the neural mechanism of long-term effects of disagreement with the peer group: early signatures of modified face processing were followed by later markers of long-term social influence on the valuation process at the ventromedial prefrontal cortex.
Project description:Although the neural systems that underlie spoken language are well-known, how they adapt to evolving social cues during natural conversations remains an unanswered question. In this work we investigate the neural correlates of face-to-face conversations between two individuals using functional near infrared spectroscopy (fNIRS) and acoustical analyses of concurrent audio recordings. Nineteen pairs of healthy adults engaged in live discussions on two controversial topics where their opinions were either in agreement or disagreement. Participants were matched according to their a priori opinions on these topics as assessed by questionnaire. Acoustic measures of the recorded speech including the fundamental frequency range, median fundamental frequency, syllable rate, and acoustic energy were elevated during disagreement relative to agreement. Consistent with both the a priori opinion ratings and the acoustic findings, neural activity associated with long-range functional networks, rather than the canonical language areas, was also differentiated by the two conditions. Specifically, the frontoparietal system including bilateral dorsolateral prefrontal cortex, left supramarginal gyrus, angular gyrus, and superior temporal gyrus showed increased activity while talking during disagreement. In contrast, talking during agreement was characterized by increased activity in a social and attention network including right supramarginal gyrus, bilateral frontal eye-fields, and left frontopolar regions. Further, these social and visual attention networks were more synchronous across brains during agreement than disagreement. Rather than localized modulation of the canonical language system, these findings are most consistent with a model of distributed and adaptive language-related processes including cross-brain neural coupling that serves dynamic verbal exchanges.
Project description:Neuroelectric measures derived from human magnetoencephalographic (MEG) recordings hold promise as aides to diagnosis and treatment monitoring and targeting for chronic sequelae of traumatic brain injury (TBI). This study tests novel MEG-derived regional brain measures of tonic neuroelectric activation for long-term test-retest reliability and sensitivity to symptoms. Resting state MEG recordings were obtained from a normative cohort (CamCAN, baseline: n = 613; mean 16-month follow-up: n = 245) and a chronic symptomatic TBI cohort (TEAM-TBI, baseline: n = 62; mean 6-month follow-up: n = 40). The MEG-derived neuroelectric measures were corrected for the empty-room contribution using a random forest classifier. The mean 16-month correlation between baseline and 16-month follow-up CamCAN measures was 0.67; test-retest reliability was markedly improved in this study compared with previous work. The TEAM-TBI cohort was screened for depression, somatization, and anxiety with the Brief Symptom Inventory and for insomnia with the Insomnia Severity Index and was assessed via adjudication for six clinical syndromes: chronic pain, psychological health, and oculomotor, vestibular, cognitive, and sleep dysfunction. Linear classifiers constructed from the 136 regional measures from each TEAM-TBI cohort member distinguished those with and without each symptom, p < 0.0003 for each, i.e., the tonic regional neuroelectric measures of activation are sensitive to the presence/absence of these symptoms and clinical syndromes. The novel regional MEG-derived neuroelectric measures obtained and tested in this study demonstrate the necessary and sufficient properties to be clinically useful, i.e., good test-retest reliability, sensitivity to symptoms in each individual, and obtainable using automatic processing without human judgement or intervention.