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Dyadic social interaction is a complex coordination task involving a large number of interconnected variables. Previous research has shown that metastability -- persistence for an extended, but impermanent, period of time in a non-stable state of a system -- can be a useful lens for understanding what makes an interaction successful. However, this framework has thus far only been applied to para-conversational signals like heart rate and prosody -- not to the semantic content of a conversation. Here, we present pink noise analysis of semantic trajectories as a metric for conversational success and apply this technique to a large open conversation dataset. Our results demonstrate that pink noise in a conversation predicts a host of variables representing participants' perception of conversation quality. These results have implications for optimizing a whole host of difficult dyadic conversations -- like those between political partisans -- and human-computer interactions, with applications for improving large language models' adaptability.

Convergent processing of the world may be a factor that contributes to social connectedness. We use neuroimaging and network analysis to investigate the association between the social-network position (as measured by in-degree centrality) of first-year university students and their neural similarity while watching naturalistic audio-visual stimuli (specifically, videos). There were 119 students in the social-network study; 63 of them participated in the neuroimaging study. We show that more central individuals had similar neural responses to their peers and to each other in brain regions that are associated with high-level interpretations and social cognition (e.g., in the default mode network), whereas less-central individuals exhibited more variable responses. Self-reported enjoyment of and interest in stimuli followed a similar pattern, but accounting for these data did not change our main results. These findings show that neural processing of external stimuli is similar in highly-central individuals but is idiosyncratic in less-central individuals.

Irritability cuts across many pediatric disorders and is a common presenting complaint in child psychiatry; however, its neural mechanisms remain unclear. One core pathophysiological deficit of irritability is aberrant responses to frustrative nonreward. Here, we conducted a preliminary fMRI study to examine the ability of functional connectivity during frustrative nonreward to predict irritability in a transdiagnostic sample. This study included 69 youths (mean age = 14.55 years) with varying levels of irritability across diagnostic groups: disruptive mood dysregulation disorder (n = 20), attention-deficit/hyperactivity disorder (n = 14), anxiety disorder (n = 12), and controls (n = 23). During fMRI, participants completed a frustrating cognitive flexibility task. Frustration was evoked by manipulating task difficulty such that, on trials requiring cognitive flexibility, "frustration" blocks had a 50% error rate and some rigged feedback, while "nonfrustration" blocks had a 10% error rate. Frustration and nonfrustration blocks were randomly interspersed. Child and parent reports of the affective reactivity index were used as dimensional measures of irritability. Connectome-based predictive modeling, a machine learning approach, with tenfold cross-validation was conducted to identify networks predicting irritability. Connectivity during frustration (but not nonfrustration) blocks predicted child-reported irritability (ρ = 0.24, root mean square error = 2.02, p = 0.03, permutation testing, 1000 iterations, one-tailed). Results were adjusted for age, sex, medications, motion, ADHD, and anxiety symptoms. The predictive networks of irritability were primarily within motor-sensory networks; among motor-sensory, subcortical, and salience networks; and between these networks and frontoparietal and medial frontal networks. This study provides preliminary evidence that individual differences in irritability may be associated with functional connectivity during frustration, a phenotype-relevant state.

Different people listening to the same story may converge upon a largely shared interpretation while still developing idiosyncratic experiences atop that shared foundation. What linguistic properties support this individualized experience of natural language? Here, we investigate how the "concrete--abstract" axis--the extent to which a word is grounded in sensory experience--relates to within- and across-subject variability in the neural representations of language. Leveraging a dataset of human participants of both sexes who each listened to four auditory stories while undergoing functional magnetic resonance imaging, we demonstrate that neural representations of "concreteness" are both reliable across stories and relatively unique to individuals, while neural representations of "abstractness" are variable both within individuals and across the population. Using natural language processing tools, we show that concrete words exhibit similar neural representations despite spanning larger distances within a high-dimensional semantic space, which potentially reflects an underlying representational signature of sensory experience--namely, imageability-- shared by concrete words but absent from abstract words. Our findings situate the concrete--abstract axis as a core dimension that supports both shared and individualized representations of natural language. [ABSTRACT FROM AUTHOR]

Recent years have witnessed an increasing number of multisite MRI functional connectivity (fcMRI) studies. While multisite studies provide an efficient way to accelerate data collection and increase sample sizes, especially for rare clinical populations, any effects of site or MRI scanner could ultimately limit power and weaken results. Little data exists on the stability of functional connectivity measurements across sites and sessions. In this study, we assess the influence of site and session on resting state functional connectivity measurements in a healthy cohort of traveling subjects (8 subjects scanned twice at each of 8 sites) scanned as part of the North American Prodrome Longitudinal Study (NAPLS). Reliability was investigated in three types of connectivity analyses: (1) seed-based connectivity with posterior cingulate cortex (PCC), right motor cortex (RMC), and left thalamus (LT) as seeds; (2) the intrinsic connectivity distribution (ICD), a voxel-wise connectivity measure; and (3) matrix connectivity, a whole-brain, atlas-based approach to assessing connectivity between nodes. Contributions to variability in connectivity due to subject, site, and day-of-scan were quantified and used to assess between-session (test-retest) reliability in accordance with Generalizability Theory. Overall, no major site, scanner manufacturer, or day-of-scan effects were found for the univariate connectivity analyses; instead, subject effects dominated relative to the other measured factors. However, summaries of voxel-wise connectivity were found to be sensitive to site and scanner manufacturer effects. For all connectivity measures, although subject variance was three times the site variance, the residual represented 60-80% of the variance, indicating that connectivity differed greatly from scan to scan independent of any of the measured factors (i.e., subject, site, and day-of-scan). Thus, for a single 5min scan, reliability across connectivity measures was poor (ICC=0.07-0.17), but increased with increasing scan duration (ICC=0.21-0.36 at 25min). The limited effects of site and scanner manufacturer support the use of multisite studies, such as NAPLS, as a viable means of collecting data on rare populations and increasing power in univariate functional connectivity studies. However, the results indicate that aggregation of fcMRI data across longer scan durations is necessary to increase the reliability of connectivity estimates at the single-subject level.

The contents and dynamics of spontaneous thought are important factors for personality traits and mental health. However, assessing spontaneous thoughts is challenging due to their unconstrained nature, and directing participants' attention to report their thoughts may fundamentally alter them. Here, we aimed to decode two key content dimensions of spontaneous thought-self-relevance and valence-directly from brain activity. To train functional MRI-based predictive models, we used individually generated personal stories as stimuli in a story-reading task to mimic narrative-like spontaneous thoughts (n = 49). We then tested these models on multiple test datasets (total n = 199). The default mode, ventral attention, and frontoparietal networks played key roles in the predictions, with the anterior insula and midcingulate cortex contributing to self-relevance prediction and the left temporoparietal junction and dorsomedial prefrontal cortex contributing to valence prediction. Overall, this study presents brain models of internal thoughts and emotions, highlighting the potential for the brain decoding of spontaneous thought. [ABSTRACT FROM AUTHOR]

Focusing on the upside of negative events often promotes resilience. Yet, the underlying mechanisms that allow some people to spontaneously see the good in the bad remain unclear. The broaden-and-build theory of positive emotion has long suggested that positive affect, including positivity in the face of negative events, is linked to idiosyncratic thought patterns (i.e., atypical cognitive responses). Yet, evidence in support of this view has been limited, in part, due to difficulty in measuring idiosyncratic cognitive processes as they unfold. To overcome this barrier, we applied Inter-Subject Representational Similarity Analysis to test whether and how idiosyncratic neural responding supports positive reactions to negative experience. We found that idiosyncratic functional connectivity patterns in the brain's default network while resting after a negative experience predicts more positive descriptions of the event. This effect persisted when controlling for connectivity 1) before and during the negative experience, 2) before, during, and after a neutral experience, and 3) between other relevant brain regions (i.e., the limbic system). The relationship between idiosyncratic default network responding and positive affect was largely driven by functional connectivity patterns between the ventromedial prefrontal cortex and the rest of the default network and occurred relatively quickly during rest. We identified post-encoding rest as a key moment and the default network as a key brain system in which idiosyncratic responses correspond with seeing the good in the bad. [ABSTRACT FROM AUTHOR]