Network Connectivity Patterns Research Articles

Cathodal tDCS impacts the fear network activity and connectivity patterns during a therapy-like extinction session

Background Transcranial direct current stimulation (tDCS) has been studied as an add-on enhancer of extinction-based treatments for anxiety disorders. Previous studies have showed that although anodal tDCS enhances fear extinction it also leads to decreased stimuli discrimination and consequent generalization of the fear response. Here we aim to understand the impact of cathodal tDCS in the neural activity and connectivity patterns between the tDCS target site (the right dorsolateral prefrontal cortex; rDLPFC) and the extant fear network (the bilateral amygdala, bilateral hippocampus, bilateral insula and the ventro medial prefrontal cortex [vmPFC]). Additionally we aim to see if cathodal tDCS leads to adverse effects similar to the ones observed with anodal stimulation. Methods We used a three-day fear conditioning procedure (day 1–fear acquisition; day 2–20-min 1 mA cathodal tDCS (only tDCS group)+ fear extinction; day 3–fear re-instatement and fear re-extinction). Thirty-four female participants where randomly assigned to either the tDCS group (n = 16) or the control group (n = 18). In day 1 fear acquisition was confirmed by self-reported contingency ratings. In day 2 we observed the between group differences in neural activity and connectivity patterns during extinction. In day 3 we observed the self-reported measures of stimuli contingency and reaction times during an approach-avoidance task to tackle the unconscious, automatic and involuntary avoidance responses. Results Functional magnetic resonance imagining (fMRI) group-level maps showed that cathodal tDCS over rDLPFC effectively reduces the right hippocampus activity during the processing of the conditioned stimuli during extinction. Furthermore, group-level psychophysiological interaction analysis (PPI) showed that cathodal tDCS effectively interferes with the fear response neural network. In particular, during the processing of the conditioned stimuli in late extinction phase, tDCS inverts the positive coupling between the vmPFC and a broad range of prefrontal structures, in particular the right and left DLPFC that participate in the regulation of negative emotions. Finally, day 3 measures showed decreased contingency ratings for the neutral cue and increased stimuli discrimination in the tDCS group, suggesting that cathodal tDCS does not lead to increased generalization. Conclusion Our promising results show that 1 mA cathodal tDCS effectively interferes with a large-scale cortico-subcortical fear network during extinction, and confirms that tDCS is a potential add-on strategy to enhance extinction-based treatments. Future studies should observe the adequate parameters of stimulation for the simultaneous enhancement of extinction learning and increased stimuli discrimination.

Perturbing low dimensional activity manifolds in spiking neuronal networks.

Several recent studies have shown that neural activity in vivo tends to be constrained to a low-dimensional manifold. Such activity does not arise in simulated neural networks with homogeneous connectivity and it has been suggested that it is indicative of some other connectivity pattern in neuronal networks. In particular, this connectivity pattern appears to be constraining learning so that only neural activity patterns falling within the intrinsic manifold can be learned and elicited. Here, we use three different models of spiking neural networks (echo-state networks, the Neural Engineering Framework and Efficient Coding) to demonstrate how the intrinsic manifold can be made a direct consequence of the circuit connectivity. Using this relationship between the circuit connectivity and the intrinsic manifold, we show that learning of patterns outside the intrinsic manifold corresponds to much larger changes in synaptic weights than learning of patterns within the intrinsic manifold. Assuming larger changes to synaptic weights requires extensive learning, this observation provides an explanation of why learning is easier when it does not require the neural activity to leave its intrinsic manifold.

Extinction-induced community reorganization in bipartite networks

We study how the community structure of bipartite mutualistic networks changes in a dynamic context. First, we consider a real mutualistic network and introduce extinction events according to several scenarios. We model extinctions as node or interaction removals. For node removal, we consider random, directed and sequential extinctions; for interaction removal, we consider random extinctions. The bipartite network reorganizes showing an increase of the effective modularity and a fast decrease of the persistence of the species in the original communities with increasing number of extinction events. Second, we compare extinctions in a real mutualistic network with the growth of a bipartite network model. The modularity reaches a stationary value and nodes remain in the same community after joining the network. Our results show that perturbations and disruptive events affect the connectivity pattern of mutualistic networks at the mesoscale level. The increase of the effective modularity observed in some scenarios could provide some protection to the remaining ecosystem.

Aberrant interactions of cortical networks in chronic migraine: A resting-state fMRI study.

We investigated resting-state (RS)-fMRI using independent component analysis (ICA) to determine the functional connectivity (FC) between networks in chronic migraine (CM) patients and their correlation with clinical features. Twenty CM patients without preventive therapy or acute medication overuse underwent 3T MRI scans and were compared to a group of 20 healthy controls (HC). We used MRI to collect RS data in 3 selected networks, identified using group ICA: the default mode network (DMN), the executive control network (ECN), and the dorsal attention system (DAS). Compared to HC, CM patients had significantly reduced functional connectivity between the DMN and the ECN. Moreover, in patients, the DAS showed significantly stronger FC with the DMN and weaker FC with the ECN. The higher the severity of headache, the increased the strength of DAS connectivity, and the lower the strength of ECN connectivity. These results provide evidence for large-scale reorganization of functional cortical networks in chronic migraine. They suggest that the severity of headache is associated with opposite connectivity patterns in frontal executive and dorsal attentional networks.

O16. Distinct Neural Networks Predict Opioid Versus Cocaine Abstinence in Polysubstance Users

Opioid use disorder (OUD) is a major public health crisis; while effective treatments are available, it has proven difficult to predict treatment outcomes. Using a novel machine learning approach, connectome-based predictive modelling (CPM), we previously identified patterns of neural network connectivity that predict abstinence from cocaine during treatment and replicated these findings in an independent sample. Understanding neural mechanisms of treatment response in OUD may facilitate the development of personalized and/or novel treatment approaches.

Structure of auditory nerve synapses in normal and in hearing impaired

The neurons of the cochlear nuclei in the brainstem are the first central processors of auditory information, and they provide inputs to all the major brainstem and midbrain auditory nuclei. The synaptic connectivity pattern of neural networks in each part of the cochlear nuclear complex is an essential determinant of their role in information processing. The synapse of the auditory nerve on principal neurons in the cochlear nucleus is biophysically and anatomically specialized for the fast conduction of excitatory currents into the auditory pathway. A growing interest in the field is to determine how auditory nerve synapses adapt to fluctuations in the sound environment. In this paper, we will discuss novel findings related to the structural and molecular responses of auditory nerve synapses and enwrapping astrocytes in the normal hearing and in the hearing impaired.Support or Funding InformationNIDCD/NIH: R01DC013048; R56DC013048This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Dynamic network coding of working-memory domains and working-memory processes

The classic mapping of distinct aspects of working memory (WM) to mutually exclusive brain areas is at odds with the distributed processing mechanisms proposed by contemporary network science theory. Here, we use machine-learning to determine how aspects of WM are dynamically coded in the human brain. Using cross-validation across independent fMRI studies, we demonstrate that stimulus domains (spatial, number and fractal) and WM processes (encode, maintain, probe) are classifiable with high accuracy from the patterns of network activity and connectivity that they evoke. This is the case even when focusing on ‘multiple demands’ brain regions, which are active across all WM conditions. Contrary to early neuropsychological perspectives, these aspects of WM do not map exclusively to brain areas or processing streams; however, the mappings from that literature form salient features within the corresponding multivariate connectivity patterns. Furthermore, connectivity patterns provide the most precise basis for classification and become fine-tuned as maintenance load increases. These results accord with a network-coding mechanism, where the same brain regions support diverse WM demands by adopting different connectivity states.

Cortical and thalamic connectivity of temporal visual cortical areas 20a and 20b of the domestic ferret (Mustela putorius furo).

The present study describes the ipsilateral and contralateral corticocortical and corticothalamic connectivity of the temporal visual areas 20a and 20b in the ferret using standard anatomical tract-tracing methods. The two temporal visual areas are strongly interconnected, but area 20a is primarily connected to the occipital visual areas, whereas area 20b maintains more widespread connections with the occipital, parietal and suprasylvian visual areas and the secondary auditory cortex. The callosal connectivity, although homotopic, consists mainly of very weak anterograde labeling which was more widespread in area 20a than area 20b. Although areas 20a and 20b are well connected with the visual dorsal thalamus, the injection into area 20a resulted in more anterograde label, whereas more retrograde label was observed in the visual thalamus following the injection into area 20b. Most interestingly, comparisons to previous connectional studies of cat areas 20a and 20b reveal a common pattern of connectivity of the temporal visual cortex in carnivores, where the posterior parietal cortex and the central temporal region (PMLS) provide network points required for dorsal and ventral stream interaction enroute to integration in the prefrontal cortex. This pattern of network connectivity is not dissimilar to that observed in primates, which highlights the ferret as a useful animal model to understand visual sensory integration between the dorsal and ventral streams. The data generated will also contribute to a connectomics database, to facilitate cross species analysis of brain connectomes and wiring principles of the brain.

Diverse functional connectivity patterns of resting-state brain networks associated with good and poor hand outcomes following stroke

Motor stroke has been characterized by disruptions in multiple large-scale functional brain networks. However, it remains unclear whether stroke patients with good hand outcomes show different connectivity profiles within and between networks from those with poor hand outcomes. In this cross-sectional study, we recruited 52 chronic subcortical stroke patients [illness duration (mean±SD): 16±16.2 months] and 52 healthy controls from the local hospital and community from June 2010 to August 2016. We first performed independent component analysis (ICA) on resting-state fMRI data to extract fifteen resting-state networks. Then, we compared the functional connectivity within and between networks across 52 healthy controls, 26 patients with a partially paralyzed hand (PPH), and 26 patients with a completely paralyzed hand (CPH). Compared to the patients with a PPH, the patients with a CPH showed increased connectivity in the contralesional sensorimotor cortex within the contralesional sensorimotor network; the increased connectivity was negatively correlated with the performance of the paretic hand. Moreover, the patients with a CPH, compared to those with a PPH, showed decreased strengths of connectivity between the ipsilesional sensorimotor network and both the dorsal sensorimotor network and ventral visual network; the decreased strengths of connectivity were positively correlated with the performance of the paretic hand. Collectively, our findings suggest that stroke patients with different hand outcomes show distinct functional reorganization patterns in large-scale brain networks. These findings shed light on the network-level neuromechanisms that help explain why stroke survivors in the chronic stage show different hand outcomes.

Default Mode Network Connectivity in Alzheimer's Disease

Alzheimer's Disease (AD) is a neurodegenerative condition characterized by functional and structural changes in the brain that are increasingly better visualized with the advances in new brain imaging techniques. Connectivity changes under the resting state condition especially in the internal connectivity network, named as the default mode network (DMN), are observed in AD. This paper aimed to investigate and discuss the findings on DMN connectivity. The studies carried out by functional magnetic resonance imaging (fMRI), using the two most widely applied techniques, the seed-based method and independent component analysis (ICA), have been investigated. Studies generally indicate a progressive impairment in DMN connectivity during the course of AD. It has been also stated that DMN subsystems show differential connectivity patterns in the preclinical and prodromal stages of AD. There is also evidence suggesting that impairment in DMN connectivity could be associated with different connectivity patterns in other networks. Furthermore, findings point towards a relationship between DMN and AD-related neuropathology and genetic risk factors. It may be proposed that AD is a generalized disconnection syndrome that causes functional impairments in resting state networks, particularly in DMN. In addition to this, AD-related functional connectivity changes observed in preclinical cases and risk carriers might be a potential bio-marker for AD.

Contagion Processes on Time‐Varying Networks with Homophily‐Driven Group Interactions

The complicated interaction patterns among heterogeneous individuals have a profound impact on the contagion process in the networks. In recent years, there has been increasing evidence for the emergence of many‐body interactions between two or more nodes in a wide range of biological and social networks. To encode these multinode interactions explicitly, the simplicial complex is now a popular alternative to simple networks. Meanwhile, the time‐varying network has been acknowledged as a key ingredient of the contagion process. In this paper, we consider the connectivity pattern of networks affected by the homophily effect associated with individual attributes and investigate the impact of homophily‐driven group interactions on the contagion process in temporal networks. The simplicial complex modeling framework is adopted to capture stochastic interactions between passively selected nodes in the paradigm of activity‐driven networks. We study the evolution of infection and the epidemic threshold of the contagion process by both analytical and numerical methods. Our results on statistical topological properties of instantaneous network may shed light on accurately characterizing the evolution curve of infection. Furthermore, we show the impact of the homophily‐driven interaction pattern on the epidemic threshold, which generalizes the existing results on both the paradigmatic activity‐driven network and the simplicial activity‐driven network.

Long term power frequency electromagnetic fields exposure influences the causal network connection pattern of local field potentials during working memory

The possible influence of electromagnetic field (EMF) on the function of neural systems has been widely concerned. In this article, we intend to investigate the effects of long term power frequency EMF exposure on brain cognitive functions and it's mechanism. The Sprague-Dawley (SD) rats were randomly divided into 3 groups: the rats in EMF Ⅰ group were placed in the 2 mT power frequency EMF for 24 days. The rats in EMF Ⅱ group were placed in the 2 mT power frequency EMF for 48 days. The rats in control group were not exposed to the EMF. Then, the 16 channel local field potentials (LFPs) were recorded from rats' prefrontal cortex (PFC) in each group during the working memory (WM) tasks. The causal networks of LFPs were also established by applying the directed transfer function (DTF). Based on that, the differences of behavior and the LFPs network connection patterns between different groups were compared in order to investigate the influence of long term power frequency EMF exposure on working memory. The results showed the rats in the EMF Ⅱ group needed more training to reach the task correction criterion (over 80%). Moreover, the causal network connection strength and the global efficiency of the rats in EMF Ⅰ and EMF Ⅱ groups were significantly lower than the corresponding values of the control group. Meanwhile, significant differences of causal density values were found between EMF Ⅱ group and the other two groups. These results indicate that long term exposure to 2 mT power frequency EMF will reduce the connection strength and the information transfer efficiency of the LFPs causal network in the PFC, as well as the behavior performance of the rats. These results may explain the effect of EMF exposure on working memory from the view of neural network connectivity and provide a support for further studies on the mechanism of the effect of EMF on cognition.

Distinct Functional Network Connectivity for Abstract and Concrete Mental Imagery.

In several behavioral psycholinguistic studies, it has been shown that concrete words are processed more efficiently. They can be remembered faster, recognized better, and can be learned easier than abstract words. This fact is called concreteness effect. There are fMRI studies which compared the neural representations of concrete and abstract concepts in terms of activated regions. In the present study, a comparison has been made between the condition-specific connectivity of functional networks (obtained by group ICA) during imagery of abstract and concrete words. The obtained results revealed that the functional network connectivity between three pairs of networks during concrete imagery is significantly different from that of abstract imagery (FDR correction at the significance level of 0.05). These results suggest that abstract and concrete concepts have different representations in terms of functional network connectivity pattern. Remarkably, in all of these network pairs, the connectivity during concrete imagery is significantly higher than that of abstract imagery. These more coherent networks include both linguistic and visual regions with a higher engagement of the right hemisphere, so the results are in line with dual coding theory. Additionally, these three pairs of networks include the contrasting regions which have shown stronger activation either in concrete or abstract word processing in former studies. The findings imply that the brain is more integrated and synchronized at the time of concrete imagery and it may explain the reason of faster concrete words processing. In order to validate the results, we used functional network connectivity distributions (FNCD). Wilcoxon rank-sum test was used to check if the abstract and concrete FNCDs extracted from whole subjects are the same. The result revealed that the corresponding distributions are different which indicates two different patterns of connectivity for abstract and concrete word processing. Also, the mean of FNCD is significantly higher at the time of concrete imagery than that of abstract imagery. Furthermore, FNCDs at the single-subject level are significantly more left-skewed or equally, include more strong connectivity for concrete imagery.

The neural basis underlying procrastination: a large-scale study of brain networks

Procrastination is a type of irrational behavior during which an individual voluntarily delays the execution of a task despite perceiving such behavior as harmful. Procrastination adversely affects people’s emotions, academic performances, and social achievements; it reduces their subjective well-being, and worse, their physical and mental health. The brain systems that regulate procrastination are still largely undetermined; previous studies have only explored the relationships between procrastination and regional brain activities and cerebral functional connections. Because procrastination involves complex psychological motivations that are dependent on elements such as task value representation, cognitive control, and the approach-avoidance conflict, some limitations to exploring the neural basis of procrastination using univariate and regional brain activities may exist. Therefore, this study used graph-theoretic analysis to investigate the connectivity patterns of large-scale brain networks underlying procrastination. We first defined the 264 regions of interest (ROIs) introduced by Power et al. (2011) as nodes of the network, and then reconstructed the ten intrinsic large-scale brain networks using graph-theoretic analysis. The results showed that the constructed brain network had better network topology attributes, such as a higher local and global efficiency. The strength of functional connectivity within each of these ten brain networks and that between them was then calculated using Pearson’s correlation. Finally, the association between procrastination and the functional network connectivity of brain networks was examined. We found that procrastination was negatively associated with the functional connectivity within the cingulo-opercular network (CON), whereas it was positively associated with the functional connectivity within the subcortical network (SCN). We also found a positive correlation between procrastination and the functional connectivity between the salience network (SAN) and the SCN. These results suggest that the functional network connectivity within the CON and the SCN, as well as that between the SAN and the SCN may be the neural basis underlying procrastination, which highlights the critical role of cognitive control and impulsive value representation in procrastination.

Large-scale brain functional network topology disruptions underlie symptom heterogeneity in children with attention-deficit/hyperactivity disorder.

Accumulating evidence suggests brain network dysfunction in attention-deficit/hyperactivity disorder (ADHD). Whether large-scale brain network connectivity patterns reflect clinical heterogeneity in ADHD remains to be fully understood. This study aimed to characterize the differential within- and between-network functional connectivity (FC) changes in children with ADHD combined (ADHD-C) or inattentive (ADHD-I) subtypes and their associations with ADHD symptoms. We studied the task-free functional magnetic resonance imaging (fMRI) data of 58 boys with ADHD and 28 demographically matched healthy controls. We measured within- and between-network connectivity of both low-level (sensorimotor) and high-level (cognitive) large-scale intrinsic connectivity networks and network modularity. We found that children with ADHD-C but not those with ADHD-I exhibited hyper-connectivity within the anterior default mode network (DMN) compared with controls. Additionally, children with ADHD-C had higher inter-network FC between the left executive control (ECN) and the salience (SN) networks, between subcortical and visual networks, and between the DMN and left auditory networks than controls, while children with ADHD-I did not show differences compared with controls. Similarly, children with ADHD-C but not ADHD-I showed lower network modularity compared with controls. Importantly, these observed abnormal inter-network connectivity and network modularity metrics were associated with Child Behavioral Checklist (CBCL) attention-deficit/hyperactivity problems and internalizing problems in children with ADHD. This study revealed relatively greater loss of brain functional network segregation in childhood ADHD combined subtype compared to the inattentive subtype, suggesting differential large-scale functional brain network topology phenotype underlying childhood ADHD heterogeneity.

Stimulus-evoked activity in clustered networks of stochastic rate-based neurons

Understanding the effect of network connectivity patterns on the relation between the spontaneous and the stimulus-evoked network activity has become one of the outstanding issues in neuroscience. We address this problem by considering a clustered network of stochastic rate-based neurons influenced by external and intrinsic noise. The bifurcation analysis of an effective model of network dynamics, comprised of coupled mean-field models representing each of the clusters, is used to gain insight into the structure of metastable states characterizing the spontaneous and the induced dynamics. We show that the induced dynamics strongly depends on whether the excitation is aimed at a certain cluster or the same fraction of randomly selected units, whereby the targeted stimulation reduces macroscopic variability by biasing the network toward a particular collective state. The immediate effect of clustering on the induced dynamics is established by comparing the excitation rates of a clustered and a homogeneous random network.

Intersubject similarity of personality is associated with intersubject similarity of brain connectivity patterns

Personality is a central high-level psychological concept that defines individual human beings and has been associated with a variety of real-world outcomes (e.g., mental health and academic performance). Using 2 h, high resolution, functional magnetic resonance imaging (fMRI) resting state data of 984 (primary dataset N = 801, hold-out dataset N = 183) participants from the Human Connectome Project (HCP), we investigated the relationship between personality (five-factor model, FFM) and intrinsic whole-brain functional connectome. We found a pattern of functional brain connectivity (“global personality network”) related to personality traits. Consistent with the heritability of personality traits, the connectivity strength of this global personality network is also heritable (more similar between monozygotic twin pairs compared to the dizygotic twin pairs). Validated by both the repeated family-based 10-fold cross-validation and hold-out dataset, our intersubject network similarity analysis allowed us to identify participants' pairs with similar personality profiles. Across all the identified pairs of participants, we found a positive correlation between the network similarity and personality similarity, supporting our “similar brain, similar personality” hypothesis. Furthermore, the global personality network can be used to predict the individual subject's responses in the personality questionnaire on an item level. In sum, based on individual brain connectivity pattern, we could predict different facets of personality, and this prediction is not based on localized regions, but rather relies on the individual connectivity pattern in large-scale brain networks.

Reflections on the differential organization of mirror neuron systems for hand and mouth and their role in the evolution of communication in primates

It is now generally accepted that the motor system is not purely dedicated to the control of behavior, but also has cognitive functions. Mirror neurons have provided a new perspective on how sensory information regarding others' actions and gestures is coupled with the internal cortical motor representation of them. This coupling allows an individual to enrich his interpretation of the social world through the activation of his own motor representations. Such mechanisms have been highly preserved in evolution as they are present in humans, apes and monkeys. Recent neuroanatomical data showed that there are two different connectivity patterns in mirror neuron networks in the macaque: one is concerned with sensorimotor transformation in relation to reaching and hand grasping within the traditional parietal-premotor circuits; the second one is linked to the mouth/face motor control and the new data show that it is connected with limbic structures. The mouth mirror sector seems to be wired not only for ingestive behaviors but also for orofacial communicative gestures and vocalizations. Notably, the hand and mouth mirror networks partially overlap, suggesting the importance of hand-mouth synergies not only for sensorimotor transformation, but also for communicative purposes in order to better convey and control social signals.

The importance of hubs in large-scale networks.

Network neuroscience has begun to generate fundamental insights into the structures and dynamics that lie beneath human cognition. Targeting the question what creates differences between humans, a study finds that individual differences in connectivity patterns in brain networks underlie individual differences in task performance.

Information Transmitting and Cognition with a Spiking Neural Network Model**Supported by the National Natural Science Foundation of China under Grant Nos 11672107, 11402294, 11602224 and 11502062.

Information encoding plays a crucial role in neuroscience. One of the fundamental questions in cognitive neuroscience is how the brain encodes external stimuli in the sensory cortex. We use a network model based on the Hodgkin–Huxley type to study the information transmitting including its storage and recall. The model is inspired by psychological and neurobiological evidence on sequential memories. The model contains excitatory and inhibitory neurons with all-to-all connections whose architecture has two layers. A lower layer represents consecutive events during the information encoding process, and the upper layer is used to tag sequences of events represented in the lower layer. The spike-timing-dependent plasticity learning rule is used for sequential storage of excitatory connections between the modules. Computer simulations demonstrate that the synchronization status of multiple neurons is dependent on the network connectivity patterns, and also this model has good performance for different sequences of storage and recall.