Transcranial direct current stimulation (tDCS) has the potential to modulate cognitive training in healthy aging; however, results from various studies have been inconsistent. We hypothesized that inter-individual differences in baseline brain state may contribute to the varied results. We aimed to explore whether baseline resting-state dynamic functional connectivity (rs-dFC) and/or conventional resting-state static functional connectivity (rs-sFC) may be related to the magnitude of cognitive aftereffects of tDCS. To achieve this aim, we used data from our double-blind randomized sham-controlled cross-over tDCS trial in 25 healthy seniors in which bifrontal tDCS combined with cognitive training had induced significant behavioral aftereffects. We performed a backward regression analysis including rs-sFC/rs-dFC measures to explain the variability in the magnitude of tDCS-induced improvements in visual object-matching task (VOMT) accuracy. Rs-dFC analysis revealed four rs-dFC states. The occurrence rate of a rs-dFC state 4, characterized by a high correlation between the left fronto-parietal control network and the language network, was significantly associated with tDCS-induced VOMT accuracy changes. The rs-sFC measure was not significantly associated with the cognitive outcome. We show that flexibility of the brain state representing readiness for top-down control of object identification implicated in the studied task is linked to the tDCS-enhanced task accuracy.
OBJECTIVE: Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. APPROACH: The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component's time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. MAIN RESULTS: We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. SIGNIFICANCE: Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.
- MeSH
- Time Factors MeSH
- Adult MeSH
- Electroencephalography methods MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods MeSH
- Young Adult MeSH
- Brain diagnostic imaging physiology MeSH
- Nerve Net diagnostic imaging physiology MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
We spend much our lives interacting with others in various social contexts. Although we deal with this myriad of interpersonal exchanges with apparent ease, each one relies upon a broad array of sophisticated cognitive processes. Recent research suggests that the cognitive operations supporting interactive behaviour are themselves underpinned by several canonical functional brain networks (CFNs) that integrate dynamically with one another in response to changing situational demands. Dynamic integrations among these CFNs should therefore play a pivotal role in coordinating interpersonal behaviour. Further, different types of interaction should present different demands on cognitive systems, thereby eliciting distinct patterns of dynamism among these CFNs. To investigate this, the present study performed functional magnetic resonance imaging (fMRI) on 30 individuals while they interacted with one another cooperatively or competitively. By applying a novel combination of analytical techniques to these brain imaging data, we identify six states of dynamic functional connectivity characterised by distinct patterns of integration and segregation among specific CFNs that differ systematically between these opposing types of interaction. Moreover, applying these same states to fMRI data acquired from an independent sample engaged in the same kinds of interaction, we were able to classify interpersonal exchanges as cooperative or competitive. These results provide the first direct evidence for the systematic involvement of CFNs during social interactions, which should guide neurocognitive models of interactive behaviour and investigations into biomarkers for the interpersonal dysfunction characterizing many neurological and psychiatric disorders.
- MeSH
- Mental Disorders * MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods MeSH
- Brain Mapping MeSH
- Brain physiology MeSH
- Social Interaction * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
BACKGROUND: Cannabis, and specifically one of its active compounds delta-9-tetrahydrocannabinol in recreational doses, has a variety of effects on cognitive processes. Most studies employ resting state functional magnetic resonance imaging techniques to assess the stationary effects of cannabis and to-date one report addressed the impact of delta-9-tetrahydrocannabinol on the dynamics of whole-brain functional connectivity. METHODS: Using a repeated-measures, within-subjects design, 19 healthy occasional cannabis users (smoking cannabis ⩽2 per week) underwent resting state functional magnetic resonance imaging scans. Each subject underwent two scans: in the intoxicated condition, shortly after smoking a cannabis cigarette, and in the non-intoxicated condition, with the subject being free from cannabinoids for at least one week before. All sessions were randomized and performed in a four-week interval. Data were analysed employing a standard independent component analysis approach with subsequent tracking of the functional connectivity dynamics, which allowed six connectivity clusters (states) to be individuated. RESULTS: Using standard independent component analysis in resting state functional connectivity, a group effect was found in the precuneus connectivity. With a dynamic independent component analysis approach, we identified one transient connectivity state, characterized by high connectivity within and between auditory and somato-motor cortices and anti-correlation with subcortical structures and the cerebellum that was only found during the intoxicated condition. Behavioural measures of the subjective experiences of changed perceptions and tetrahydrocannabinol plasma levels during intoxication were associated with this state. CONCLUSIONS: With the help of the dynamic connectivity approach we could elucidate neural correlates of the transitory perceptual changes induced by delta-9-tetrahydrocannabinol in cannabis users, and possibly identify a biomarker of cannabis intoxication.
- MeSH
- Adult MeSH
- Hallucinogens pharmacology MeSH
- Marijuana Smoking psychology MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Young Adult MeSH
- Brain diagnostic imaging drug effects MeSH
- Dronabinol pharmacology MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Randomized Controlled Trial MeSH
- Research Support, N.I.H., Extramural MeSH
The relationship between network structure and dynamics is one of the most extensively investigated problems in the theory of complex systems of recent years. Understanding this relationship is of relevance to a range of disciplines-from neuroscience to geomorphology. A major strategy of investigating this relationship is the quantitative comparison of a representation of network architecture (structural connectivity, SC) with a (network) representation of the dynamics (functional connectivity, FC). Here, we show that one can distinguish two classes of functional connectivity-one based on simultaneous activity (co-activity) of nodes, the other based on sequential activity of nodes. We delineate these two classes in different categories of dynamical processes-excitations, regular and chaotic oscillators-and provide examples for SC/FC correlations of both classes in each of these models. We expand the theoretical view of the SC/FC relationships, with conceptual instances of the SC and the two classes of FC for various application scenarios in geomorphology, ecology, systems biology, neuroscience and socio-ecological systems. Seeing the organisation of dynamical processes in a network either as governed by co-activity or by sequential activity allows us to bring some order in the myriad of observations relating structure and function of complex networks.
- MeSH
- Ecology * MeSH
- Ecosystem * MeSH
- Brain MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
PURPOSE: To investigate the fundamental connectivity architecture of neural structures involved in the goal-directed processing of target events. METHODS: Twenty healthy volunteers underwent event-related functional magnetic resonance imaging (fMRI) while performing a standard oddball task. In the task, two types of visual stimuli - rare (target) and frequent - were randomly presented, and subjects were instructed to mentally count the target stimuli. Dynamic causal modeling (DCM), in combination with Bayes factors was used to compare competing neurophysiological models with different intrinsic connectivity structures and input regions within the network of brain regions underlying target stimulus processing. RESULTS: Conventional analysis of fMRI data revealed significantly greater activation in response to the target stimuli (in comparison to the frequent stimuli) in several brain regions, including the intraparietal sulci and supramarginal gyri, the anterior and posterior cingulate gyri, the inferior and middle frontal gyri, the superior temporal sulcus, the precuneus/cuneus, and the subcortical grey matter (caudate and thalamus). The most extensive cortical activations were found in the right intraparietal sulcus (IPS), the anterior cingulate cortex (ACC), and the right lateral prefrontal cortex (PFC). These three regions were entered into the DCM. A comparison on a group level revealed that the dynamic causal models in which the ACC and alternatively the IPS served as input regions were superior to a model in which the PFC was assumed to receive external inputs. No significant difference was observed between the fully connected models with ACC and IPS as input regions. Subsequent analysis of the intrinsic connectivity within two investigated models (IPS and ACC) disclosed significant parallel forward connections from the IPS to the frontal areas and from the ACC to the PFC and the IPS. CONCLUSION: Our findings indicate that during target stimulus processing there is a bidirectional frontoparietal information flow, very likely reflecting parallel activation of two distinct but partially overlapping attentional or attentional/event-encoding neural systems. Additionally, a simple hierarchy within the right frontal lobe is suggested with the ACC exerting influence over the PFC.
OBJECTIVES: Abnormal task-related activation and connectivity is present in schizophrenia. The aim of this study was the analysis of functional networks in schizophrenia patients in remission after the first episode. EXPERIMENTAL DESIGN: Twenty-nine male patients in remission after the first episode of schizophrenia and 22 healthy controls underwent examination by functional magnetic resonance during verbal fluency tasks (VFT). The functional connectivity of brain networks was analyzed using independent component analysis. RESULTS: The patients showed lower activation of the salience network during VFT. They also showed lower deactivation of the default mode network (DMN) during VFT processing. Spectral analysis of the component time courses showed decreased power in slow frequencies of signal fluctuations in the salience and DMNs and increased power in higher frequencies in the left frontoparietal cortex reflecting higher fluctuations of the network activity. Moreover, there was decreased similarity of component time courses in schizophrenia—the patients had smaller negative correlation between VFT activated and deactivated networks, and smaller positive correlations between DMN subcomponents. CONCLUSIONS: There is still an abnormal functional connectivity of several brain networks in remission after the first episode of schizophrenia. The effect of different treatment modalities on brain connectivity, together with temporal dynamics of this functional abnormality should be the objective of further studies to assess its potential as a marker of disease stabilization.
- MeSH
- Principal Component Analysis MeSH
- Adult MeSH
- Functional Laterality MeSH
- Oxygen blood MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Brain Mapping * MeSH
- Young Adult MeSH
- Brain blood supply pathology MeSH
- Neural Pathways pathology MeSH
- Image Processing, Computer-Assisted MeSH
- Psychiatric Status Rating Scales MeSH
- Schizophrenia pathology MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Modern imaging methods allow a non-invasive assessment of both structural and functional brain connectivity. This has lead to the identification of disease-related alterations affecting functional connectivity. The mechanism of how such alterations in functional connectivity arise in a structured network of interacting neural populations is as yet poorly understood. Here we use a modeling approach to explore the way in which this can arise and to highlight the important role that local population dynamics can have in shaping emergent spatial functional connectivity patterns. The local dynamics for a neural population is taken to be of the Wilson-Cowan type, whilst the structural connectivity patterns used, describing long-range anatomical connections, cover both realistic scenarios (from the CoComac database) and idealized ones that allow for more detailed theoretical study. We have calculated graph-theoretic measures of functional network topology from numerical simulations of model networks. The effect of the form of local dynamics on the observed network state is quantified by examining the correlation between structural and functional connectivity. We document a profound and systematic dependence of the simulated functional connectivity patterns on the parameters controlling the dynamics. Importantly, we show that a weakly coupled oscillator theory explaining these correlations and their variation across parameter space can be developed. This theoretical development provides a novel way to characterize the mechanisms for the breakdown of functional connectivity in diseases through changes in local dynamics.
- MeSH
- Humans MeSH
- Models, Neurological MeSH
- Brain physiology MeSH
- Nerve Net physiology MeSH
- Computational Biology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Nervové systémy mozku vykazují charakteristické vzorce časové korelace, jež jsou výsledkem funkční interakce složité strukturální sítě. Ve světle nových studií přibývají důkazy o tom, že tyto specifické vzorce nervové aktivace a funkční konektivity jsou neuronálním korelátem percepČních a kognitivních procesů. Článek přináší pohled na dynamické systémy mozku a jejich funkční a efektivní konektivitu a na metody, jež umožňují jejich deskripci.
Neural systems exhibit characteristic patterns of temporal correlations that emerge as the result of functional interactions within a structural network. There is mounting evidence that specific patterns of neuronal activation as well as patterns of functional connectivity are possible neural correlates of perceptual and cognitive processes. The article reviews the dynamic brain systems and their functional and effective connectivity as well as methods, which allow the description of these processes.
- MeSH
- Central Nervous System physiology MeSH
- Electroencephalography methods utilization MeSH
- Electromagnetic Fields MeSH
- Research Support as Topic MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods MeSH
- Magnetoencephalography methods utilization MeSH
- Brain anatomy & histology physiopathology pathology MeSH
- Systems Theory MeSH
- Tomography methods MeSH
- Check Tag
- Humans MeSH
To identify the neurocognitive mechanisms underpinning the social difficulties that characterize autism, we performed functional magnetic resonance imaging on pairs of autistic and non-autistic adults simultaneously whilst they interacted with one another on the iterated Ultimatum Game (iUG)-an interactive task that emulates the reciprocal characteristic of naturalistic interpersonal exchanges. Two age-matched sets of male-male dyads were investigated: 16 comprised an autistic Responder and a non-autistic Proposer, and 19 comprised non-autistic pairs of Responder and Proposer. Players' round-by-round behavior on the iUG was modeled as reciprocal choices, and dynamic functional connectivity (dFC) was measured to identify the neural mechanisms underpinning reciprocal behaviors. Behavioral expressions of reciprocity were significantly reduced in autistic compared with non-autistic Responders, yet no such differences were observed between the non-autistic Proposers in either set of dyads. Furthermore, we identified latent dFC states with temporal properties associated with reciprocity. Autistic interactants spent less time in brain states characterized by dynamic inter-network integration and segregation among the Default Mode Network and cognitive control networks, suggesting that their reduced expressions of social-emotional reciprocity reflect less efficient reconfigurations among brain networks supporting flexible cognition and behavior. These findings advance our mechanistic understanding of the social difficulties characterizing autism.
- MeSH
- Autistic Disorder * physiopathology psychology diagnostic imaging MeSH
- Adult MeSH
- Emotions * physiology MeSH
- Interpersonal Relations * MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Young Adult MeSH
- Brain * physiopathology diagnostic imaging MeSH
- Social Behavior * MeSH
- Social Interaction * MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Publication type
- Journal Article MeSH
