High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns

Effects of a dissociative drug on fronto-limbic resting-state functional connectivity in individuals with posttraumatic stress disorder: a randomized controlled pilot study

RATIONALE

A subanesthetic dose of ketamine, a non-competitive N-methyl-D-aspartate glutamate receptor (NMDAR) antagonist, elicits dissociation in individuals with posttraumatic stress disorder (PTSD), who also often suffer from chronic dissociative symptoms in daily life. These debilitating symptoms have not only been linked to worse PTSD trajectories, but also to increased resting-state functional connectivity (RSFC) between medial prefrontal cortex (mPFC) and amygdala, supporting the conceptualization of dissociation as emotion overmodulation. Yet, as studies were observational, causal evidence is lacking.

OBJECTIVES

The present randomized controlled pilot study examines the effect of ketamine, a dissociative drug, on RSFC between mPFC subregions and amygdala in individuals with PTSD.

METHODS

Twenty-six individuals with PTSD received either ketamine (0.5mg/kg; n = 12) or the control drug midazolam (0.045mg/kg; n = 14) during functional magnetic resonance imaging (fMRI). RSFC between amygdala and mPFC subregions, i.e., ventromedial PFC (vmPFC), dorsomedial PFC (dmPFC) and anterior-medial PFC (amPFC), was assessed at baseline and during intravenous drug infusion.

RESULTS

Contrary to pre-registered predictions, ketamine did not promote a greater increase in RSFC between amygdala and mPFC subregions from baseline to infusion compared to midazolam. Instead, ketamine elicited a stronger transient decrease in vmPFC-amygdala RSFC compared to midazolam.

CONCLUSIONS

A dissociative drug did not increase fronto-limbic RSFC in individuals with PTSD. These preliminary experimental findings contrast with prior correlative findings and call for further exploration and, potentially, a more differentiated view on the neurobiological underpinning of dissociative phenomena in PTSD.

Living on the edge: network neuroscience beyond nodes

Network neuroscience has emphasized the connectional properties of neural elements - cells, populations, and regions. This has come at the expense of the anatomical and functional connections that link these elements to one another. A new perspective - namely one that emphasizes 'edges' - may prove fruitful in addressing outstanding questions in network neuroscience. We highlight one recently proposed 'edge-centric' method and review its current applications, merits, and limitations. We also seek to establish conceptual and mathematical links between this method and previously proposed approaches in the network science and neuroimaging literature. We conclude by presenting several avenues for future work to extend and refine existing edge-centric analysis.

Anxiety Shapes Amygdala-Prefrontal Dynamics During Movie Watching

Background

A well-characterized amygdala-dorsomedial prefrontal circuit is thought to be crucial for threat vigilance during anxiety. However, engagement of this circuitry within relatively naturalistic paradigms remains unresolved.

Methods

Using an open functional magnetic resonance imaging dataset (Cambridge Centre for Ageing Neuroscience; n = 630), we sought to investigate whether anxiety correlates with dynamic connectivity between the amygdala and dorsomedial prefrontal cortex during movie watching.

Results

Using an intersubject representational similarity approach, we saw no effect of anxiety when comparing pairwise similarities of dynamic connectivity across the entire movie. However, preregistered analyses demonstrated a relationship between anxiety, amygdala-prefrontal dynamics, and anxiogenic features of the movie (canonical suspense ratings). Our results indicated that amygdala-prefrontal circuitry was modulated by suspense in low-anxiety individuals but was less sensitive to suspense in high-anxiety individuals. We suggest that this could also be related to slowed habituation or amplified anticipation. Moreover, a measure of threat-relevant attentional bias (accuracy/reaction time to fearful faces) demonstrated an association with connectivity and suspense.

Conclusions

Overall, this study demonstrated the presence of anxiety-relevant differences in connectivity during movie watching, varying with anxiogenic features of the movie. Mechanistically, exactly how and when these differences arise remains an opportunity for future research.

Neural signatures of second language proficiency in narrative processing

Making sense of speech in a second language relies on multiple abilities. Differences in brain activity related to proficiency in language tasks have often been attributed to processing demands. However, during naturalistic narrative comprehension, listeners at different proficiency levels may form different representations of the same speech. We hypothesized that the intersubject synchronization of these representations could be used to measure second-language proficiency. Using a searchlight-shared response model, we found highly proficient participants showed synchronization in regions similar to those of native speakers, including in the default mode network and the lateral prefrontal cortex. In contrast, participants with low proficiency showed more synchronization in auditory cortex and word-level semantic processing areas in the temporal lobe. Moderate proficiency showed the greatest neural diversity, suggesting lower consistency in the source of this partial proficiency. Based on these synchronization differences, we were able to classify the proficiency level or predict behavioral performance on an independent English test in held-out participants, suggesting the identified neural systems represented proficiency-sensitive information that was generalizable to other individuals. These findings suggest higher second-language proficiency leads to more native-like neural processing of naturalistic language, including in systems beyond the cognitive control network or the core language network.

Don't You Forget About Me: The Importance of Studying the Brain Basis of Real-world Interpersonal Memory

Despite the fact that humans are a highly social species, we know relatively little about how people remember the rich interpersonal information filling their social lives. This gap is surprising: One function of memory has been suggested to be relationship maintenance [Neisser, U. Time present and time past. Practical Aspects of Memory: Current Research and Issues, 2, 545-560, 1988]. A major barrier to understanding the brain basis of interpersonal memory is that traditional brain imaging methods are not ideally suited to study memory for the nuanced interpersonal experiences comprising our social lives. Yet, recent and rapidly developing advances in the analysis of brain responses to naturalistic social information can help researchers surpass this methodological barrier. This perspective piece articulates the importance of studying the brain basis of real-world social memories and suggests new directions in interpersonal memory research. This includes investigating the brain mechanisms that represent the content and structure of real-world interpersonal memories as well as how they are altered in mental health conditions associated with social memory biases.

Leveraging edge-centric networks complements existing network-level inference for functional connectomes

The human connectome is modular with distinct brain regions clustering together to form large-scale communities, or networks. This concept has recently been leveraged in novel inferencing procedures by averaging the edge-level statistics within networks to induce more powerful inferencing at the network level. However, these networks are constructed based on the similarity between pairs of nodes. Emerging work has described novel edge-centric networks, which instead use the similarity between pairs of edges to construct networks. In this work, we use these edge-centric networks in a network-level inferencing procedure and compare this novel method to traditional inferential procedures and the network-level procedure using node-centric networks. We use data from the Human Connectome Project, the Healthy Brain Network, and the Philadelphia Neurodevelopmental Cohort and use a resampling technique with various sample sizes (n=40, 80, 120) to probe the power and specificity of each method. Across datasets and sample sizes, using the edge-centric networks outperforms using node-centric networks for inference as well as edge-level FDR correction and NBS. Additionally, the edge-centric networks were found to be more consistent in clustering effect sizes of similar values as compared to node-centric networks, although node-centric networks often had a lower average within-network effect size variability. Together, these findings suggest that using edge-centric networks for network-level inference can procure relatively powerful results while remaining similarly accurate to the underlying edge-level effects across networks, complementing previous inferential methods.

NeuMapper: A scalable computational framework for multiscale exploration of the brain's dynamical organization

For better translational outcomes, researchers and clinicians alike demand novel tools to distill complex neuroimaging data into simple yet behaviorally relevant representations at the single-participant level. Recently, the Mapper approach from topological data analysis (TDA) has been successfully applied on noninvasive human neuroimaging data to characterize the entire dynamical landscape of whole-brain configurations at the individual level without requiring any spatiotemporal averaging at the outset. Despite promising results, initial applications of Mapper to neuroimaging data were constrained by (1) the need for dimensionality reduction and (2) lack of a biologically grounded heuristic for efficiently exploring the vast parameter space. Here, we present a novel computational framework for Mapper-designed specifically for neuroimaging data-that removes limitations and reduces computational costs associated with dimensionality reduction and parameter exploration. We also introduce new meta-analytic approaches to better anchor Mapper-generated representations to neuroanatomy and behavior. Our new NeuMapper framework was developed and validated using multiple fMRI datasets where participants engaged in continuous multitask experiments that mimic "ongoing" cognition. Looking forward, we hope our framework will help researchers push the boundaries of psychiatric neuroimaging toward generating insights at the single-participant level across consortium-size datasets.

A Neuroscientific and Cognitive Literary Approach to the Treatment of Time in Calderón's Autos sacramentales

Time processing is a fundamental subject in cognitive sciences and neuroscience. Current research is deepening how our brains process time, revealing its essential role in human functionality and survival. In his autos sacramentales, Early Modern Spanish playwright Pedro Calderón de la Barca portrays the relationships between human inner workings and the Christian concept of time. These plays portray the experience of the present, the perception of the flow of time, the measure of time raging from seconds to eternity, and the mental travel necessary to inhabit the past and future with the help of memory and imagination. Calderón explores how the dramatic form can portray all these temporal phenomena and how that portrait of time can constrain the dramatic structure. The different parts of the brain in charge of executive decisions, projections, memories, computation, and calibration are the basis that leads these characters to make the choices that will take them to the future they have cast for themselves. This paper analyzes how the processes that Calderón ascribed to the soul of his characters in the 17th century relate to ongoing cognitive and neuroscientific findings.

Edges in brain networks: Contributions to models of structure and function

Network models describe the brain as sets of nodes and edges that represent its distributed organization. So far, most discoveries in network neuroscience have prioritized insights that highlight distinct groupings and specialized functional contributions of network nodes. Importantly, these functional contributions are determined and expressed by the web of their interrelationships, formed by network edges. Here, we underscore the important contributions made by brain network edges for understanding distributed brain organization. Different types of edges represent different types of relationships, including connectivity and similarity among nodes. Adopting a specific definition of edges can fundamentally alter how we analyze and interpret a brain network. Furthermore, edges can associate into collectives and higher order arrangements, describe time series, and form edge communities that provide insights into brain network topology complementary to the traditional node-centric perspective. Focusing on the edges, and the higher order or dynamic information they can provide, discloses previously underappreciated aspects of structural and functional network organization.