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... Moreover, the highfrequency range of local field potentials was correlated with fMRI BOLD activity [35]. Finally, findings in epilepsy also drove the interest in frequencies above [70][71][72][73][74][75][76][77][78][79][80] Hz. Since interictal high-frequency oscillations (80-500 Hz) are considered to be strongly bound to the seizure onset zone, they were investigated as electrophysiological biomarkers (more information in [36]). ...
... However, several studies also focused on how brain regions interact and how these interactions depend on experimental conditions and behavioral measures. In 1991, the notion of functional synchronization was formally extended to the study of functional and effective connectivity [78]. Over time, the focus shifted from the highly localizationist approaches of early neuroimaging research to a focus on brain networks [5]. ...
... An illustrative example is comparing a model with and without a particular connection to infer the presence of EC [121]. Aertsen & Preissl [78] proposed that: ...
Thesis
We picture the brain as a complex network of structurally connected regions that are functionally coupled. Brain functions arise from the coordinated activity of distant cortical regions. Connectivity is used to represent the cooperation of segregated and functionally specialized brain regions. Whether it is the analysis of anatomical links, statistical dependencies, or causal interactions, connectivity reveals fundamental aspects of brain (dys)function. However, estimating and applying connectivity still faces many challenges; therefore, this work is devoted to tackling them. The first challenge stems from the detrimental effect of systematic noise (such as head movements) on connectivity estimates. We proposed an index that depicts connectivity quality and can reflect various artifacts, processing errors, and brain pathology, allowing extensive use in data quality screening and methodological investigations. Furthermore, connectivity alterations play an invaluable role in understanding brain dysfunction. Investigating the mechanisms of epilepsy, we show that connectivity can track gradual changes of seizure susceptibility and identify driving factors of seizure generation. Identifying critical times of connectivity changes could help in successful seizure prediction. Finally, how the brain adapts to task demands on fast timescales is not well understood. We present a combination of intracranial EEG and state-of-art measures to investigate network dynamics during recognition memory. Understanding how the brain dynamically faces rapid changes in cognitive demands is vital to our comprehension of the neural basis of cognition. In conclusion, the modest goal of this thesis is to at least partially answer some of the many challenges that current neuroscience is facing.
... The influence exerted by one neuronal system over another is known as effective connectivity [8]. It was proposed that effective connectivity should be understood as the time dependent, simplest possible circuit diagram that would replicate the observed timing relationships between the recorded neurons [9]. Effective connectivity is said to be dependent on two parameters: activity and model of interactions or coupling. ...
... For example, if g(x,y,θ)=x, u=0 and the rate of change of hidden states is zero, then, (1) and (2) lead to the following: (8) If the covariances are expressed in terms of coupling parameters, then structural equation models can be compared based on covariances using likelihood methods. (9) µ represents the coupling and covariance parameter estimates and can be estimated using standard techniques. Structural modeling is generally thought of as regression analysis since unknown coupling parameters are effectively explained using covariances. ...
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The availability of non-invasive techniques like fMRI for measuring brain activity has implied a considerable increase in neuro-scientific research since a few decades. Researchers have developed a wide variety of signal processing techniques and algorithms to process these signals. Since brain is considered to be a complex network, researchers found it interesting to study the connectivity in the brain network using these signal processing techniques in order to infer about the network behaviour of the brain. The current review paper discusses about the different methods used to determine connectivity. The various models used to determine effective connectivity have been compared with and without any experimental conditions. The practical issues and assumptions of these models have been briefly discussed. It is observed that functional connectivity can be studied efficiently than effective connectivity using Granger causality. Also, Granger causality model becomes similar to dynamic causal model after each mathematical refinement. Owing to the larger neuronal and hemodynamic time constants, it has been observed that the assumptions about slow random fluctuations in structural equation model are not suitable for fMRI time series analysis but instead could be used for analysing non-time series data. It is observed that structural equation modelling could be used for Gaussian network analysis assuming relaxed acyclic constraints. Studying the relationship between functional and effective connectivity could produce more interesting results in future. Therefore, a clear picture of various methods in the study of connectivity in the brain discussed in this paper provides useful information for connectivity studies. From this review, we feel that, an analysis of connectivity differences in brain and the underlying neuro-physiological activity under various cognitive tasks could be made for behavioural studies.
... The effective connectivity (EC) describes 'the influence one neural system exerts over another' (Friston, 1994), or as Aertsen and Preissl (1991) put it, EC is 'the simplest possible circuit diagram that would replicate the observed timing relations' between observed responses and therefore describes directed connectivity." Both the functional and effective connectivity are dynamic and potentially brainstate-dependent measures. ...
... As temporal precedence is not enforced, this is an under-determined problem with a potentially infinite number of solutions, so we additionally constrain the network to have the smallest number of non-zero connections using an L 1 minimisation on the entries of the connectivity matrix." Thus, the introduced approach corresponds to the definition of Aertsen and Preissl (1991) stating that effective connectivity is 'the simplest possible circuit diagram that would replicate the observed timing relations'. ...
Thesis
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The brain is a huge network with outstanding computational capabilities. Understanding the brain network and its principle of operation is of high interest. Therefore, brain activity can be measured to retrieve information about the brains functioning using functional magnetic resonance imaging, which is a great non-invasive tool to measure the neuronal activity. However, with this method the neuronal activity is measured only indirectly as the desired information is distorted by a specific low-pass filter, which differs in its form in different brain regions. In this thesis a methodology is presented to estimate this low pass filter for every brain region. Thereby, a high variability of this filter function throughout the brain is found. With the knowledge about the filter the data can be corrected for relative timing offsets between brain regions to yield a good estimate of the underlying neuronal activity. Using the corrected data, further a novel methodology is presented to estimate the sparse directed brain connectivity, which is applied to resting-state fMRI data of healthy subjects, where it proves superior to conventional undirected connectivity analysis.
... Effective connectivity can be interpreted as the indirect or direct influence of one neural system on another at either a synaptic level or a cortical level [48]. According to [49], the EC should be recognized as the time-dependent and simplest possible circuit diagram that replicates the timing relationships between the recorded neurons. There are several brain connectivity estimators, including non-linear estimators, linear estimators, bivariate estimators, and multivariate connectivity estimators. ...
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Motor imagery (MI)-based brain-computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain's neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms-an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain-computer interfaces.
... Effective connectivity can be understood as the simplest experimental time-dependent circuit that replicates the timing relationships between the recorded sources (Aertsen and Preißl, 1991). It studies the influence that one neural system exerts over another (Friston, 1994). ...
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Experts apply their experience to the proper development of their routine activities. Their acquired expertise or professionalization is expected to help in the development of those recurring tasks. Media professionals spend their daily work watching narrative contents on screens, so learning how they manage visual perception of those contents could be of interest in an increasingly audiovisual society. Media works require not only the understanding of the storytelling, but also the decoding of the formal rules and presentations. We recorded electroencephalographic (EEG) signals from 36 participants (18 media professionals and 18 non-media professionals) while they were watching audiovisual contents, and compared their eyeblink rate and their brain activity and connectivity. We found that media professionals decreased their blink rate after the cuts, suggesting that they can better manage the loss of visual information that blinks entail by sparing them when new visual information is being presented. Cuts triggered similar activation of basic brain processing in the visual cortex of the two groups, but different processing in medial and frontal cortical areas, where media professionals showed a lower activity. Effective brain connectivity occurred in a more organized way in media professionals–possibly due to a better communication between cortical areas that are coordinated for decoding new visual content after cuts.
... The functional and effective connectivity quantifies statistical dependencies between temporal characteristics of neuronal signals (Aertsen and Preissl, 1991;Friston, 2011). Such measures, while faithfully reflecting a functional connection, by definition include the synaptic efficacies of their connections as well as the properties of individual cells and their membranes. ...
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The function of the cerebral cortex essentially depends on the ability to form functional assemblies across different cortical areas serving different functions. Here we investigated how developmental hearing experience affects functional and effective interareal connectivity in the auditory cortex in an animal model with years-long and complete auditory deprivation (deafness) from birth, the congenitally deaf cat (CDC). Using intracortical multielectrode arrays, neuronal activity of adult hearing controls and CDCs was registered in the primary auditory cortex and the secondary posterior auditory field (PAF). Ongoing activity as well as responses to acoustic stimulation (in adult hearing controls) and electric stimulation applied via cochlear implants (in adult hearing controls and CDCs) were analyzed. As functional connectivity measures pairwise phase consistency and Granger causality were used. While the number of coupled sites was nearly identical between controls and CDCs, a reduced coupling strength between the primary and the higher order field was found in CDCs under auditory stimulation. Such stimulus-related decoupling was particularly pronounced in the alpha band and in top–down direction. Ongoing connectivity did not show such a decoupling. These findings suggest that developmental experience is essential for functional interareal interactions during sensory processing. The outcomes demonstrate that corticocortical couplings, particularly top-down connectivity, are compromised following congenital sensory deprivation.
Thesis
The neural function of the brain is characterized by activated brain regions and the connectivities among them. It is still unknown, how a static structural connectivity network affects the occurrence of task-dependent dynamic functional connectivity or why two structurally connected brain regions, are not functionally connected and vice-versa. Studies have shown, the underlying cause for many neurodegenerative diseases is the functional disruptions in neural connections. So understanding the relationship between structural and functional connectivity is important for understanding the impairment characteristics in the brain networks which is in essence depends upon the nature of signal flow through the structural connections in the brain. The purpose of this work is to characterize the signal propagation characteristics through structural connectivity and its influence on functional connectivity of the brain by applying a circuit theory based modelling approach. Modelling structural connections using circuit theory will allow the analysis of signal propagation in both time and frequency domains. So far the studies on the correlation between structural and functional connectivity were done from the time domain perspective of signal propagation. However, the very definition of functional connectivity indicates that the underlying structural connectivity networks has filter like properties and holds the frequency-phase characteristics. In this work, we explore this phenomenon following a step-by-step approach: (1) we develop an automated tool for extracting structural connectivity network from structural MRI image by considering a more general (compared to standard cortical mapping) non-anatomical equal-area parcellation process of the Regions of Interest (ROI) of the brain and extracting the geometrical properties of the white matter tracts between the ROIs, (2) developing circuit-based model for characterising signal propagation through a single myelinated axon fibre and representing it as a simplified transfer function encompassing its time and frequency properties, (3) extending this model for coupled axon fibres and characterising the time and frequency properties of the signal propagation through them under the influence of ephaphtic coupling between them and finally;(4) applying iv the models developed in (2) and (3) for creating an automated tool that is capable to characterising signal propagation through a bundle of axons - the typical scenario of a white matter tract. Our work results in an end-to-end tool taking inputs as the structural and diffusional MRI data and outputting the phase and frequency characteristics of the signal through the axon bundle with a defined geometrical property - the underlying phenomenon for deriving the relationship between structural and functional brain connectivity.
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Homeostatic centers in the mammalian brainstem are critical in responding to thermal challenges. These centers play a prominent role in human thermoregulation, but humans also respond to thermal challenges through behavior modification. Behavioral modifications are presumably sub served by interactions between the brainstem and interoceptive, cognitive and affective elements in human brain networks. Prior evidence suggests that interoceptive regions such as the insula, and cognitive/affective regions such as the orbitofrontal cortex and anterior cingulate cortex are crucial. Here we used dynamic causal modeling (DCM) to discover likely generative network architectures and estimate changes in the effective connectivity between nodes in a hierarchically organized thermoregulatory network (homeostatic–interoceptive–cognitive/affective). fMRI data were acquired while participants (N = 20) were subjected to a controlled whole body thermal challenge that alternatingly evoked sympathetic and parasympathetic responses. Using a competitive modeling framework (ten competing modeling architectures), we demonstrated that sympathetic responses (evoked by whole-body cooling) resulted in more complex network interactions along two ascending pathways: (i) homeostatic interoceptive and (ii) homeostatic cognitive/affective. Analyses of estimated connectivity coefficients demonstrated that sympathetic responses evoked greater network connectivity in key pathways compared to parasympathetic responses. These results reveal putative mechanisms by which human thermoregulatory networks evince a high degree of contextual sensitivity to thermoregulatory challenges. The patterns of the discovered interactions also reveal how information propagation from homeostatic regions to both interoceptive and cognitive/affective regions sub serves the behavioral repertoire that is an important aspect of thermoregulatory defense in humans.
Chapter
We are not aware of the vast majority of the cuts when watching media content. However, they affect our perception. This research analyzes the effects of cuts in synchronization (phase locking value, PLV) and Granger causality in 36 subjects, using electroencephalography (EEG) techniques. The PLV was studied as a phase synchronization index for the cut in theta, alpha, beta, and low gamma bands, before (from −500 to 0 ms) and after (from 0 to 500 ms) the cut. We found differences for the theta band in frontal, central, and occipital areas. We also evaluated the PLV depending on the style of edition in which cuts are inserted: The style of edition did not affect brain synchrony. Analyzing Granger causality differences for the 500 ms before the cut and 500 ms after the cut, we found Granger causality before the cut higher than after it. The style of edition seems not to affect causality either. This study proposes a new way to approach the study of media perception.
Thesis
The aim of this thesis was to find a systematic relationship between neuronal synchrony and firing rates, that would enable us to make inferences about one given knowledge of the other. Functional neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), are sensitive to changes in overall population synaptic activity, that can be interpreted in terms of rate coding for a particular stimulus or task. Characterising the relationship between synchrony and firing rates would facilitate inferences about fast neuronal interactions on the basis of macroscopic measures such as those obtained by fMRI. In this thesis, we used computer simulations of neuronal networks and fMRI in humans to investigate the relationship between mean synaptic activity and fast synchronous neuronal interactions. We found that the extent to which different neurons engage in fast dynamic interactions is largely dependent on the neuronal population firing rates and vice versa, i.e. as one metric changes (either activity or synchrony), so does the other. Additionally, as a result of the strong coupling between overall activity and neuronal synchrony, there is also a robust relationship between background activity and stimulus-evoked activity: Increased background activity increases the gain of the neurons, by decreasing effective membrane time constants, and enhancing stimulus-evoked population activity through the selection of fast synchronous dynamics. In concluding this thesis, we tested and confirmed, with fMRI in humans, that this mechanism may account for attentional modulation, i.e. the change in baseline neuronal firing rates associated with attention, in cell assemblies selectively responding to an attended sensory attribute, enhances responses elicited by presentation of that attribute.
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