Network Connectivity Patterns Research Articles

Unified thalamic model generates multiple distinct oscillations with state-dependent entrainment by stimulation.

The thalamus plays a critical role in the genesis of thalamocortical oscillations, yet the underlying mechanisms remain elusive. To understand whether the isolated thalamus can generate multiple distinct oscillations, we developed a biophysical thalamic model to test the hypothesis that generation of and transition between distinct thalamic oscillations can be explained as a function of neuromodulation by acetylcholine (ACh) and norepinephrine (NE) and afferent synaptic excitation. Indeed, the model exhibited four distinct thalamic rhythms (delta, sleep spindle, alpha and gamma oscillations) that span the physiological states corresponding to different arousal levels from deep sleep to focused attention. Our simulation results indicate that generation of these distinct thalamic oscillations is a result of both intrinsic oscillatory cellular properties and specific network connectivity patterns. We then systematically varied the ACh/NE and input levels to generate a complete map of the different oscillatory states and their transitions. Lastly, we applied periodic stimulation to the thalamic network and found that entrainment of thalamic oscillations is highly state-dependent. Our results support the hypothesis that ACh/NE modulation and afferent excitation define thalamic oscillatory states and their response to brain stimulation. Our model proposes a broader and more central role of the thalamus in the genesis of multiple distinct thalamo-cortical rhythms than previously assumed.

Direct modulation of aberrant brain network connectivity through real-time NeuroFeedback.

The existence of abnormal connectivity patterns between resting state networks in neuropsychiatric disorders, including Autism Spectrum Disorder (ASD), has been well established. Traditional treatment methods in ASD are limited, and do not address the aberrant network structure. Using real-time fMRI neurofeedback, we directly trained three brain nodes in participants with ASD, in which the aberrant connectivity has been shown to correlate with symptom severity. Desired network connectivity patterns were reinforced in real-time, without participants' awareness of the training taking place. This training regimen produced large, significant long-term changes in correlations at the network level, and whole brain analysis revealed that the greatest changes were focused on the areas being trained. These changes were not found in the control group. Moreover, changes in ASD resting state connectivity following the training were correlated to changes in behavior, suggesting that neurofeedback can be used to directly alter complex, clinically relevant network connectivity patterns.

A tug of war: antagonistic effective connectivity patterns over the motor cortex and the severity of motor symptoms in Gilles de la Tourette syndrome.

We tested the hypothesis that Gilles de la Tourette syndrome (GTS) is characterized by perturbed connectivity within cortico-subcortical motor networks. To this end, we performed a dynamic causal modelling (DCM) analysis of fMRI data collected during a finger opposition task in 24 normal controls and 24 GTS patients. The DCM analysis allowed us to assess whether any GTS-specific patterns of brain activity were related to intrinsic and/or to task-dependent connectivity. While no abnormalities were found for task-dependent connectivity, intrinsic connectivity was abnormally increased in the premotor network, with stronger connections from the supplementary motor area (SMA), from the dorsolateral premotor cortex and from the putamen to the right superior frontal gyrus, an area where GTS showed over-activation in a previous univariate analysis. We also found a positive correlation between the connectivity strength from the right basal ganglia to the right primary motor cortex (M1) and disease severity measured by the Yale Global Tic Severity Scale (YGTSS). This pattern was mirrored by a negative correlation between the connection strength from the right SMA to the right area M1 and the YGTSS score. These two reverse correlation effects showed a specific relationship with individual disease severity: the greater the imbalance between subcortical and premotor connectivity towards area M1, the higher the YGTSS score. These results reveal the existence of perturbed intrinsic connectivity patterns in the motor networks of GTS patients with two competing forces operating in a tug of war-like mechanism: aberrant subcortical afferents to M1, compensated for by inputs from the premotor cortex.

Audience Effects in Territorial Defense of Male Cichlid Fish Are Associated with Differential Patterns of Activation of the Brain Social Decision-Making Network.

Animals communicate by exchanging signals frequently in the proximity of other conspecifics that may detect and intercept signals not directed to them. There is evidence that the presence of these bystanders modulates the signaling behavior of interacting individuals, a phenomenon that has been named audience effect. Research on the audience effect has predominantly focused on its function rather than on its proximate mechanisms. Here, we have investigated the physiological and neuromolecular correlates of the audience effect in a cichlid fish (Mozambique tilapia, Oreochromis mossambicus). A male was exposed to a territorial intrusion in the presence or absence of a female audience. Results showed that the presence of the female audience increased territorial defense, but elicited a lower androgen and cortisol response to the territorial intrusion. Furthermore, analysis of the expression of immediate early genes, used as markers of neuronal activity, in brain areas belonging to the social decision-making network (SDMN) revealed different patterns of network activity and connectivity across the different social contexts (i.e., audience × intrusion). Overall, these results suggest that socially driven plasticity in the expression of territorial behavior is accommodated in the central nervous system by rapid changes in functional connectivity between nodes of relevant networks (SDMN) rather than by localized changes of activity in specific brain nuclei.

Functional Network Connectivity Patterns between Idiopathic Generalized Epilepsy with Myoclonic and Absence Seizures.

The extensive cerebral cortex and subcortical structures are considered as the major regions related to the generalized epileptiform discharges in idiopathic generalized epilepsy. However, various clinical syndromes and electroencephalogram (EEG) signs exist across generalized seizures, such as the loss of consciousness during absence seizures (AS) and the jerk of limbs during myoclonic seizures (MS). It is presumed that various functional systems affected by discharges lead to the difference in syndromes of these seizures. Twenty epileptic patients with MS, 21 patients with AS, and 21 healthy controls were recruited in this study. The functional network connectivity was analyzed based on the resting-state functional magnetic resonance imaging scans. The statistical analysis was performed in three groups to assess the difference in the functional brain networks in two types of generalized seizures. Twelve resting-state networks were identified in three groups. Both patient groups showed common abnormalities, including decreased functional connectivity in salience network (SN), cerebellum network, and primary perceptional networks and decreased connection between SN and visual network, compared with healthy controls. Interestingly, the frontal part of high-level cognitive resting-state networks showed increased functional connectivity (FC) in patients with MS, but decreased FC in patients with AS. Moreover, patients with MS showed decreased negative connections between high-level cognitive networks and primary system. The common alteration in both patient groups, including SN, might reflect a similar mechanism associated with the loss of consciousness during generalized seizures. This study provided the evidence of brain network in generalized epilepsy to understand the difference between MS and AS.

Altered Brain Network Dynamics in Schizophrenia: A Cognitive Electroencephalography Study

BackgroundAlterations in the dynamic coordination of widespread brain networks are proposed to underlie cognitive symptoms of schizophrenia. However, there is limited understanding of the temporal evolution of these networks and how they relate to cognitive impairment. The current study was designed to explore dynamic patterns of network connectivity underlying cognitive features of schizophrenia. MethodsIn total, 21 inpatients with schizophrenia and 28 healthy control participants completed a cognitive task while electroencephalography data were simultaneously acquired. For each participant, Pearson cross-correlation was applied to electroencephalography data to construct correlation matrices that represent the static network (averaged over 1200 ms) and dynamic network (1200 ms divided into four windows of 300 ms) in response to cognitive stimuli. Global and regional network measures were extracted for comparison between groups. ResultsDynamic network analysis identified increased global efficiency; decreased clustering (globally and locally); reduced strength (weighted connectivity) around the frontal, parietal, and sensory-motor areas; and increased strength around the occipital lobes (a peripheral hub) in patients with schizophrenia. Regional network measures also correlated with clinical features of schizophrenia. Network differences were prominent 900 ms following the cognitive stimuli before returning to levels comparable to those of healthy control participants. ConclusionsPatients with schizophrenia exhibited altered dynamic patterns of network connectivity across both global and regional measures. These network differences were time sensitive and may reflect abnormalities in the flexibility of the network that underlies aspects of cognitive function. Further research into network dynamics is critical to better understanding cognitive features of schizophrenia and identification of network biomarkers to improve diagnosis and treatment models.

Relation between patterns of intrinsic network connectivity, cognitive functioning, and symptom presentation in trauma-exposed patients with major depressive disorder.

ObjectiveThe present study investigated resting fMRI connectivity within the default mode (DMN), salience (SN), and central executive (CEN) networks in relation to neurocognitive performance and symptom severity in trauma‐exposed patients with major depressive disorder (MDD).MethodGroup independent component analysis was conducted among patients with MDD (n = 21), examining DMN, SN, and CEN connectivity in relation to neurocognitive performance and symptom severity. Activation in these networks was also compared between the patient group and healthy controls (n = 20).ResultsAmong the patient group, higher levels of performance on measures of verbal memory and executive functioning were related to increased connectivity within the DMN (i.e., inferior parietal lobe; precuneus). Greater depression severity was related to reduced connectivity between the SN and a node of the DMN (i.e., posterior cingulate cortex) and higher depersonalization symptoms were related to enhanced connectivity between the SN and a node of the DMN (i.e., middle temporal gyrus). Higher symptoms of depersonalization were also associated with reduced integration of the DMN with the medial frontal gyrus. Relative to controls, patients with MDD showed greater connectivity of the ventromedial prefrontal cortex within the DMN.ConclusionIntrinsic connectivity network patterns are related to cognitive performance and symptom presentation among trauma‐exposed patients with MDD.

Cognitive Efficiency in Alzheimer's Disease is Associated with Increased Occipital Connectivity.

There are cognitive domains which remain fully functional in a proportion of Alzheimer's disease (AD) patients. It is unknown, however, what distinctive mechanisms sustain such efficient processing. The concept of "cognitive efficiency" was investigated in these patients by operationalizing it as a function of the level of performance shown on the Letter Fluency test, on which, very often, patients in the early stages of AD show unimpaired performance. Forty-five individuals at the prodromal/early stage of AD (diagnosis supported by subsequent clinical follow-ups) and 45 healthy controls completed a battery of neuropsychological tests and an MRI protocol which included resting state acquisitions. The Letter Fluency test was the only task on which no between-group difference in performance was found. Participants were divided into "low-performing" and "high-performing" according to the global median. Dual-regression methods were implemented to compute six patterns of network connectivity. The diagnosis-by-level of performance interaction was inferred on each pattern to determine the network distinctiveness of efficient performance in AD. Significant interactions were found in the anterior default mode network, and in both left and right executive control networks. For all three circuits, high-performing patients showed increased connectivity within the ventral and dorsal part of BA19, as confirmed by post hoc t tests. Peristriate remapping is suggested to play a compensatory role. Since the occipital lobe is the neurophysiological source of long-range cortical connectivity, it is speculated that the physiological mechanisms of functional connectivity might sustain occipital functional remapping in early AD, particularly for those functions which are sustained by areas not excessively affected by the prodromal disease.

Global patterns of network connectivity in schizophrenia: A TMS-EEG network study

Global patterns of network connectivity in schizophrenia: A TMS-EEG network study

Resting-State Network Topology Differentiates Task Signals across the Adult Life Span.

Brain network connectivity differs across individuals. For example, older adults exhibit less segregated resting-state subnetworks relative to younger adults (Chan et al., 2014). It has been hypothesized that individual differences in network connectivity impact the recruitment of brain areas during task execution. While recent studies have described the spatial overlap between resting-state functional correlation (RSFC) subnetworks and task-evoked activity, it is unclear whether individual variations in the connectivity pattern of a brain area (topology) relates to its activity during task execution. We report data from 238 cognitively normal participants (humans), sampled across the adult life span (20–89 years), to reveal that RSFC-based network organization systematically relates to the recruitment of brain areas across two functionally distinct tasks (visual and semantic). The functional activity of brain areas (network nodes) were characterized according to their patterns of RSFC: nodes with relatively greater connections to nodes in their own functional system (“non-connector” nodes) exhibited greater activity than nodes with relatively greater connections to nodes in other systems (“connector” nodes). This “activation selectivity” was specific to those brain systems that were central to each of the tasks. Increasing age was accompanied by less differentiated network topology and a corresponding reduction in activation selectivity (or differentiation) across relevant network nodes. The results provide evidence that connectional topology of brain areas quantified at rest relates to the functional activity of those areas during task. Based on these findings, we propose a novel network-based theory for previous reports of the “dedifferentiation” in brain activity observed in aging.SIGNIFICANCE STATEMENT Similar to other real-world networks, the organization of brain networks impacts their function. As brain network connectivity patterns differ across individuals, we hypothesized that individual differences in network connectivity would relate to differences in brain activity. Using functional MRI in a group of individuals sampled across the adult life span (20–89 years), we measured correlations at rest and related the functional connectivity patterns to measurements of functional activity during two independent tasks. Brain activity varied in relation to connectivity patterns revealed by large-scale network analysis. This relationship tracked the differences in connectivity patterns accompanied by older age, providing important evidence for a link between the topology of areal connectivity measured at rest and the functional recruitment of these areas during task performance.

Network Connectivity of the Right STS in Three Social Perception Localizers.

The posterior STS (pSTS) is an important brain region for perceptual analysis of social cognitive cues. This study seeks to characterize the pattern of network connectivity emerging from the pSTS in three core social perception localizers: biological motion perception, gaze recognition, and the interpretation of moving geometric shapes as animate. We identified brain regions associated with all three of these localizers and computed the functional connectivity pattern between them and the pSTS using a partial correlations metric that characterizes network connectivity. We find a core pattern of cortical connectivity that supports the hypothesis that the pSTS serves as a hub of the social brain network. The right pSTS was the most highly connected of the brain regions measured, with many long-range connections to pFC. Unlike other highly connected regions, connectivity to the pSTS was distinctly lateralized. We conclude that the functional importance of right pSTS is revealed when considering its role in the large-scale network of brain regions involved in various aspects of social cognition.

Dendritic and Axonal Propagation Delays Determine Emergent Structures of Neuronal Networks with Plastic Synapses

Spike-timing-dependent plasticity (STDP) modifies synaptic strengths based on the relative timing of pre- and postsynaptic spikes. The temporal order of spikes turned out to be crucial. We here take into account how propagation delays, composed of dendritic and axonal delay times, may affect the temporal order of spikes. In a minimal setting, characterized by neglecting dendritic and axonal propagation delays, STDP eliminates bidirectional connections between two coupled neurons and turns them into unidirectional connections. In this paper, however, we show that depending on the dendritic and axonal propagation delays, the temporal order of spikes at the synapses can be different from those in the cell bodies and, consequently, qualitatively different connectivity patterns emerge. In particular, we show that for a system of two coupled oscillatory neurons, bidirectional synapses can be preserved and potentiated. Intriguingly, this finding also translates to large networks of type-II phase oscillators and, hence, crucially impacts on the overall hierarchical connectivity patterns of oscillatory neuronal networks.

Intelligent algorithms for improving communication patterns in thematic P2P search

The Internet is a cooperative and decentralized network built out of millions of participants that store and share large amounts of information with other users. Peer-to-peer systems go hand-in-hand with this huge decentralized network, where each individual node can serve content as well as request it. In this scenario, the analysis, development and testing of distributed search algorithms is a key research avenue. In particular, thematic search algorithms should lead to and benefit from the emergence of semantic communities that are the result of the interaction among participants. As a result, intelligent algorithms for neighbor selection should give rise to a logical network topology reflecting efficient communication patterns. This paper presents a series of algorithms which are specifically aimed at reducing the propagation of queries in the network, by applying a novel approach for learning peers’ interests. These algorithms were constructed in an incremental way so that each new algorithm presents some improvements over the previous ones. Several simulations were completed to analyze the connectivity and query propagation patterns of the emergent logical networks. The results indicate that the algorithms with better behavior are those that induce greater collaboration among peers.

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach.

Combined source-imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a noninvasive fashion. Source-imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source-imaging algorithms to both find the network nodes [regions of interest (ROI)] and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses, and apply Granger analysis on the extracted series to study brain networks under realistic conditions. Source-imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from interictal and ictal signals recorded by EEG and/or Magnetoencephalography (MEG). Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ∼20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection. Our study indicates that combined source-imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.

Investigating Link Inference in Partially Observable Networks: Friendship Ties and Interaction

While privacy preserving mechanisms, such as hiding one’s friends list, may be available to withhold personal information on online social networking sites, it is not obvious whether to which degree a user’s social behavior renders such an attempt futile. In this paper, we study the impact of additional interaction information on the inference of links between nodes in partially covert networks. This investigation is based on the assumption that interaction might be a proxy for connectivity patterns in online social networks. For this purpose, we use data collected from 586 Facebook profiles consisting of friendship ties (conceptualized as the network) and comments on wall posts (serving as interaction information) by a total of 64 000 users. The link-inference problem is formulated as a binary classification problem using a comprehensive set of features and multiple supervised learning algorithms. Our results suggest that interactions reiterate the information contained in friendship ties sufficiently well to serve as a proxy when the majority of a network is unobserved.

Cross-Frequency rs-fMRI Network Connectivity Patterns Manifest Differently for Schizophrenia Patients and Healthy Controls.

Patterns of resting state fMRI functional network connectivity in schizophrenia patients have been shown to differ markedly from that of healthy controls. While some studies have explored connectivity within fixed frequency bands, the question of network phase synchrony across disparate frequency bands, or cross-frequency connectivity, has remained surprisingly underexplored. Computational modeling at the neuronal scale however has long acknowledged the existence of coupled fast and slow subsystems. Here we present preliminary evidence that cross-frequency coupling exists at the network level, that it patterns in meaningful ways over functional domains, and that this patterning differs between the healthy population and individuals with diagnosed schizophrenia.

Intrinsic network connectivity and own body perception in gender dysphoria.

Gender dysphoria (GD) is characterized by incongruence between one's identity and gender assigned at birth. The biological mechanisms of GD are unclear. We investigated brain network connectivity patterns involved in own body perception in the context of self in GD. Twenty-seven female-to-male (FtM) individuals with GD, 27 male controls, and 27 female controls underwent resting state fMRI. We compared functional connections within intrinsic connectivity networks involved in self-referential processes and own body perception -default mode network (DMN) and salience network - and visual networks, using independent components analyses. Behavioral correlates of network connectivity were also tested using self-perception ratings while viewing own body images morphed to their sex assigned at birth, and to the sex of their gender identity. FtM exhibited decreased connectivity of anterior and posterior cingulate and precuneus within the DMN compared with controls. In FtM, higher "self" ratings for bodies morphed towards the sex of their gender identity were associated with greater connectivity of the anterior cingulate within the DMN, during long viewing times. In controls, higher ratings for bodies morphed towards their gender assigned at birth were associated with right insula connectivity within the salience network, during short viewing times. Within visual networks FtM showed weaker connectivity in occipital and temporal regions. Results suggest disconnectivity within networks involved in own body perception in the context of self in GD. Moreover, perception of bodies in relation to self may be reflective rather than reflexive, as a function of mesial prefrontal processes. These may represent neurobiological correlates to the subjective disconnection between perception of body and self-identification.

Integrated Information and State Differentiation.

Integrated information (Φ) is a measure of the cause-effect power of a physical system. This paper investigates the relationship between Φ as defined in Integrated Information Theory and state differentiation (), the number of, and difference between potential system states. Here we provide theoretical justification of the relationship between Φ and , then validate the results using a simulation study. First, we show that a physical system in a state with high Φ necessarily has many elements and specifies many causal relationships. Furthermore, if the average value of integrated information across all states is high, the system must also have high differentiation. Next, we explore the use of as a proxy for Φ using artificial networks, evolved to have integrated structures. The results show a positive linear relationship between Φ and for multiple network sizes and connectivity patterns. Finally we investigate the differentiation evoked by sensory inputs and show that, under certain conditions, it is possible to estimate integrated information without a direct perturbation of its internal elements. In concluding, we discuss the need for further validation on larger networks and explore the potential applications of this work to the empirical study of consciousness, especially concerning the practical estimation of Φ from neuroimaging data.

Characterization of the Statistical Signatures of Micro-Movements Underlying Natural Gait Patterns in Children with Phelan McDermid Syndrome: Towards Precision-Phenotyping of Behavior in ASD

Background: There is a critical need for precision phenotyping across neurodevelopmental disorders, especially in individuals who receive a clinical diagnosis of autism spectrum disorder (ASD). Phelan-McDermid deletion syndrome (PMS) is one such example, as it has a high penetrance of ASD. At present, no biometric characterization of the behavioral phenotype within PMS exists.Methods: We introduce a data-type and statistical framework that permits the personalized profiling of naturalistic behaviors. Walking patterns were assessed in 30 participants (16 PMS, 3 idiopathic-ASD and 11 age- and sex-matched controls). Each individual's micro-movement signatures were recorded at 240 Hz. We empirically estimated the parameters of the continuous Gamma family of probability distributions and calculated their ranges. These estimated stochastic signatures were then mapped on the Gamma plane to obtain several statistical indexes for each child. To help visualize complex patterns across the cohort, we introduce new tools that enable the assessment of connectivity and modularity indexes across the peripheral network of rotational joints.Results: Typical walking signatures are absent in all children with PMS as well as in the children with idiopathic-ASD (iASD). Underlying these patterns are atypical leg rotational acceleration signatures that render participants with PMS unstable with rotations that are much faster than controls. The median values of the estimated Gamma parameters serve as a cutoff to automatically separate children with PMS 5–7 years old from adolescents with PMS 12–16 years old, the former displaying more randomness and larger noise. The fluctuations in the arm's motions during the walking also have atypical statistics that separate males from females in PMS and show higher rates of noise accumulation in idiopathic ASD (iASD) children. Despite high heterogeneity, all iASD children have excess noise, a narrow range of probability-distribution shapes across the body joints and a distinct joint network connectivity pattern. Both PMS and iASD have systemic issues with noise in micro-motions across the body with specific signatures for each child that, as a cohort, selectively deviates from controls.Conclusions: We provide a new methodology for precision behavioral phenotyping with the potential to use micro-movement output noise as a natural classifier of neurodevelopmental disorders of known etiology. This approach may help us better understand idiopathic neurodevelopmental disorders and personalize the assessments of natural movements in these populations.

Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients.

Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. We present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject’s trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (Nsz = 151) and healthy controls (Nhc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls.