Graph Theoretic Analysis Reveals Intranasal Oxytocin Induced Network Changes Over Frontal Regions

Effects of four-week intranasal oxytocin administration on large-scale brain networks in older adults

Oxytocin (OT) is a crucial modulator of social cognition and behavior. Previous work primarily examined effects of acute intranasal oxytocin administration (IN-OT) in younger males on isolated brain regions. Not well understood are (i) chronic IN-OT effects, (ii) in older adults, (iii) on large-scale brain networks, representative of OT's wider-ranging brain mechanisms. To address these research gaps, 60 generally healthy older adults (mean age = 70.12 years, range = 55-83) were randomly assigned to self-administer either IN-OT or placebo twice daily via nasal spray over four weeks. Chronic IN-OT reduced resting-state functional connectivity (rs-FC) of both the right insula and the left middle cingulate cortex with the salience network but enhanced rs-FC of the left medial prefrontal cortex with the default mode network as well as the left thalamus with the basal ganglia-thalamus network. No significant chronic IN-OT effects were observed for between-network rs-FC. However, chronic IN-OT increased selective rs-FC of the basal ganglia-thalamus network with the salience network and the default mode network, indicative of more specialized, efficient communication between these networks. Directly comparing chronic vs. acute IN-OT, reduced rs-FC of the right insula with the salience network and between the default mode network and the basal ganglia-thalamus network, and greater selective rs-FC of the salience network with the default mode network and the basal ganglia-thalamus network, were more pronounced after chronic than acute IN-OT. Our results delineate the modulatory role of IN-OT on large-scale brain networks among older adults.

Evolving brain network dynamics in early childhood: Insights from modular graph metrics

Modular dynamic graph theory metrics effectively capture the patterns of dynamic information interaction during human brain development. While existing research has employed modular algorithms to examine the overall impact of dynamic changes in community structure throughout development, there is a notable gap in understanding the cross-community dynamic changes within different functional networks during early childhood and their potential contributions to the efficiency of brain information transmission. This study seeks to address this gap by tracing the trajectories of cross-community structural changes within early childhood functional networks and modeling their contributions to information transmission efficiency. We analyzed 194 functional imaging scans from 83 children aged 2 to 8 years, who participated in passive viewing functional magnetic resonance imaging sessions. Utilizing sliding windows and modular algorithms, we evaluated three spatiotemporal metrics-temporal flexibility, spatiotemporal diversity, and within-community spatiotemporal diversity-and four centrality metrics: within-community degree centrality, eigenvector centrality, between-community degree centrality, and between-community eigenvector centrality. Mixed-effects linear models revealed significant age-related increases in the temporal flexibility of the default mode network (DMN), executive control network (ECN), and salience network (SN), indicating frequent adjustments in community structure within these networks during early childhood. Additionally, the spatiotemporal diversity of the SN also displayed significant age-related increases, highlighting its broad pattern of cross-community dynamic interactions. Conversely, within-community spatiotemporal diversity in the language network exhibited significant age-related decreases, reflecting the network's gradual functional specialization. Furthermore, our findings indicated significant age-related increases in between-community degree centrality across the DMN, ECN, SN, language network, and dorsal attention network, while between-community eigenvector centrality also increased significantly for the DMN, ECN, and SN. However, within-community eigenvector centrality remained stable across all functional networks during early childhood. These results suggest that while centrality of cross-community interactions in early childhood functional networks increases, centrality within communities remains stable. Finally, mediation analysis was conducted to explore the relationships between age, brain dynamic graph metrics, and both global and local efficiency based on community structure. The results indicated that the dynamic graph metrics of the SN primarily mediated the relationship between age and the decrease in global efficiency, while those of the DMN, language network, ECN, dorsal attention network, and SN primarily mediated the relationship between age and the increase in local efficiency. This pattern suggests a developmental trajectory in early childhood from global information integration to local information segregation, with the SN playing a pivotal role in this transformation. This study provides novel insights into the mechanisms by which early childhood brain functional development impacts information transmission efficiency through cross-community adjustments in functional networks.

The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis

The large number of different analytical choices used by researchers is partly responsible for the challenge of replication in neuroimaging studies. For an exhaustive robustness analysis, knowledge of the full space of analytical options is essential. We conducted a systematic literature review to identify the analytical decisions in functional neuroimaging data preprocessing and analysis in the emerging field of cognitive network neuroscience. We found 61 different steps, with 17 of them having debatable parameter choices. Scrubbing, global signal regression, and spatial smoothing are among the controversial steps. There is no standardized order in which different steps are applied, and the parameter settings within several steps vary widely across studies. By aggregating the pipelines across studies, we propose three taxonomic levels to categorize analytical choices: 1) inclusion or exclusion of specific steps, 2) parameter tuning within steps, and 3) distinct sequencing of steps. We have developed a decision support application with high educational value called METEOR to facilitate access to the data in order to design well-informed robustness (multiverse) analysis.

Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.

Neural mechanisms distinguishing two types of cooperative problem-solving approaches: An fNIRS hyperscanning study

Collaborative cooperation (CC) and division of labor cooperation (DLC) are two prevalent forms of cooperative problem-solving approaches in daily life. Despite extensive research on the neural mechanisms underlying cooperative problem-solving approaches, a notable gap exists between the neural processes that support CC and DLC. The present study utilized a functional near-infrared spectroscopy (fNIRS) hyperscanning technique along with a classic cooperative tangram puzzle task to investigate the neural mechanisms engaged by both friends and stranger dyads during CC versus DLC. The key findings of this study were as follows: (1) Dyads exhibited superior behavioral performance in the DLC task than in the CC task. The CC task bolstered intra-brain functional connectivity and inter-brain synchrony (IBS) in regions linked to the mirror neuron system (MNS), spatial perception (SP) and cognitive control. (2) Friend dyads showed stronger IBS in brain regions associated with the MNS than stranger dyads. (3) Perspective-taking predicted not only dyads' behavioral performance in the CC task but also their IBS in brain regions associated with SP during the DLC task. Taken together, these findings elucidate the divergent behavioral performance and neural connection patterns between the two cooperative problem-solving approaches. This study provides novel insights into the various neurocognitive processes underlying flexible coordination strategies in real-world cooperative contexts.

Attachment Reminders Trigger Widespread Synchrony across Multiple Brains

Infant stimuli elicit widespread neural and behavioral response in human adults, and such massive allocation of resources attests to the evolutionary significance of the primary attachment. Here, we examined whether attachment reminders also trigger cross-brain concordance and generate greater neural uniformity, as indicated by intersubject correlation. Human mothers were imaged twice in oxytocin/placebo administration design, and stimuli included four ecological videos of a standard unfamiliar mother and infant: two infant/mother alone (Alone) and two mother-infant dyadic contexts (Social). Theory-driven analysis measured cross-brain synchrony in preregistered nodes of the parental caregiving network (PCN), which integrates subcortical structures underpinning mammalian mothering with cortical areas implicated in simulation, mentalization, and emotion regulation, and data-driven analysis assessed brain-wide concordance using whole-brain parcellation. Results demonstrated widespread cross-brain synchrony in both the PCN and across the neuroaxis, from primary sensory/somatosensory areas, through insular-cingulate regions, to temporal and prefrontal cortices. The Social context yielded significantly more cross-brain concordance, with PCNs striatum, parahippocampal gyrus, superior temporal sulcus, ACC, and PFC displaying cross-brain synchrony only to mother-infant social cues. Moment-by-moment fluctuations in mother-infant social synchrony, ranging from episodes of low synchrony to tightly coordinated positive bouts, were tracked online by cross-brain concordance in the preregistered ACC. Findings indicate that social attachment stimuli, representing evolutionary-salient universal cues that require no verbal narrative, trigger substantial interbrain concordance and suggest that the mother-infant bond, an icon standing at the heart of human civilization, may function to glue brains into a unified experience and bind humans into social groups.SIGNIFICANCE STATEMENT Infant stimuli elicit widespread neural response in human adults, attesting to their evolutionary significance, but do they also trigger cross-brain concordance and induce neural uniformity among perceivers? We measured cross-brain synchrony to ecological mother-infant videos. We used theory-driven analysis, measuring cross-brain concordance in the parenting network, and data-driven analysis, assessing brain-wide concordance using whole-brain parcellation. Attachment cues triggered widespread cross-brain concordance in both the parenting network and across the neuroaxis. Moment-by-moment fluctuations in behavioral synchrony were tracked online by cross-brain variability in ACC. Attachment reminders bind humans' brains into a unitary experience and stimuli characterized by social synchrony enhance neural similarity among participants, describing one mechanism by which attachment bonds provide the neural template for the consolidation of social groups.

Effects of exogenous oxytocin and estradiol on resting-state functional connectivity in women and men

Possible interactions of the neuropeptide oxytocin and the sex hormone estradiol may contribute to previously observed sex-specific effects of oxytocin on resting-state functional connectivity (rsFC) of the amygdala and hippocampus. Therefore, we used a placebo-controlled, randomized, parallel-group functional magnetic resonance imaging study design and measured amygdala and hippocampus rsFC in healthy men (n = 116) and free-cycling women (n = 111), who received estradiol gel (2 mg) or placebo before the intranasal administration of oxytocin (24 IU) or placebo. Our results reveal significant interaction effects of sex and treatments on rsFC of the amygdala and hippocampus in a seed-to-voxel analysis. In men, both oxytocin and estradiol significantly decreased rsFC between the left amygdala and the right and left lingual gyrus, the right calcarine fissure, and the right superior parietal gyrus compared to placebo, while the combined treatment produced a significant increase in rsFC. In women, the single treatments significantly increased the rsFC between the right hippocampus and the left anterior cingulate gyrus, whereas the combined treatment had the opposite effect. Collectively, our study indicates that exogenous oxytocin and estradiol have different region-specific effects on rsFC in women and men and that the combined treatment may produce antagonistic effects.

The role of oxytocin in modulating self-other distinction in human brain: a pharmacological fMRI study

Self-other distinction is crucial for human interaction. Although with conflicting results, studies have found that oxytocin (OT) sharpens the self-other perceptual boundary. However, little is known about the effect of OT on self-other perception, especially its neural basis. Moreover, it is unclear whether OT influences self-other discrimination when the other is a child or an adult. This double-blind, placebo-controlled study investigated the effect of OT on self-face perception at the behavioral and neural levels. For the stimuli, we morphed participants' faces and child or adult strangers' faces, resulting in 4 conditions. After treatment with either OT or placebo, participants reported whether a stimulus resembled themselves while being scanned using functional magnetic resonance imaging (fMRI). Behavioral results showed that people judged adult-morphed faces better than child-morphed faces. Moreover, fMRI results showed that the OT group exhibited increased activity in visual areas and the inferior frontal gyrus for self-faces. This difference was more pronounced in the adult-face condition. In multivariate fMRI and region of interest analyses, better performance in the OT group indicated that OT increased self-other distinction, especially for adult faces and in the left hemisphere. Our study shows a significant effect of OT on self-referential processes, proving the potential effect of OT on a left hemisphere self-network.

Motor Imagery Decoding in the Presence of Distraction Using Graph Sequence Neural Networks

In this study, we propose a graph sequence neural network (GSNN) to accurately decode patterns of motor imagery from electroencephalograms (EEGs) in the presence of distractions. GSNN aims to build subgraphs by exploiting biological topologies among brain regions to capture local and global relationships across characteristic channels. Specifically, we model the similarity between pairwise EEG channels by the adjacency matrix of the graph sequence neural network. In addition, we propose a node domain attention selection network in which the connection and sparsity of the adjacency matrix can be adjusted dynamically according to the EEG signals acquired from different subjects. Extensive experiments on the public Berlin-distraction dataset show that in most experimental settings, our model performs considerably better than the state-of-the-art models. Moreover, comparative experiments indicate that our proposed node domain attention selection network plays a crucial role in improving the sensibility and adaptability of the GSNN model. The results show that the GSNN algorithm obtained superior classification accuracy (The average value of Recall, Precision, and F-score were 80.44%, 81.07% and 80.54%) compared to the state-of-the-art models. Finally, in the process of extracting the intermediate results, the relationships between important brain regions and channels were revealed to different influences in distraction themes.

Neural Functions of Hypothalamic Oxytocin and its Regulation

Oxytocin (OT), a nonapeptide, has a variety of functions. Despite extensive studies on OT over past decades, our understanding of its neural functions and their regulation remains incomplete. OT is mainly produced in OT neurons in the supraoptic nucleus (SON), paraventricular nucleus (PVN) and accessory nuclei between the SON and PVN. OT exerts neuromodulatory effects in the brain and spinal cord. While magnocellular OT neurons in the SON and PVN mainly innervate the pituitary and forebrain regions, and parvocellular OT neurons in the PVN innervate brainstem and spinal cord, the two sets of OT neurons have close interactions histologically and functionally. OT expression occurs at early life to promote mental and physical development, while its subsequent decrease in expression in later life stage accompanies aging and diseases. Adaptive changes in this OT system, however, take place under different conditions and upon the maturation of OT release machinery. OT can modulate social recognition and behaviors, learning and memory, emotion, reward, and other higher brain functions. OT also regulates eating and drinking, sleep and wakefulness, nociception and analgesia, sexual behavior, parturition, lactation and other instinctive behaviors. OT regulates the autonomic nervous system, and somatic and specialized senses. Notably, OT can have different modulatory effects on the same function under different conditions. Such divergence may derive from different neural connections, OT receptor gene dimorphism and methylation, and complex interactions with other hormones. In this review, brain functions of OT and their underlying neural mechanisms as well as the perspectives of their clinical usage are presented.

Kuramoto Model-Based Analysis Reveals Oxytocin Effects on Brain Network Dynamics

The oxytocin effects on large-scale brain networks such as Default Mode Network (DMN) and Frontoparietal Network (FPN) have been largely studied using fMRI data. However, these studies are mainly based on the statistical correlation or Bayesian causality inference, lacking interpretability at the physical and neuroscience level. Here, we propose a physics-based framework of the Kuramoto model to investigate oxytocin effects on the phase dynamic neural coupling in DMN and FPN. Testing on fMRI data of 59 participants administrated with either oxytocin or placebo, we demonstrate that oxytocin changes the topology of brain communities in DMN and FPN, leading to higher synchronization in the FPN and lower synchronization in the DMN, as well as a higher variance of the coupling strength within the DMN and more flexible coupling patterns at group level. These results together indicate that oxytocin may increase the ability to overcome the corresponding internal oscillation dispersion and support the flexibility in neural synchrony in various social contexts, providing new evidence for explaining the oxytocin modulated social behaviors. Our proposed Kuramoto model-based framework can be a potential tool in network neuroscience and offers physical and neural insights into phase dynamics of the brain.