Fusing EEG and fMRI based on a bottom-up model: inferring activation and effective connectivity in neural masses

The elucidation of the complex machinery used by the human brain to segregate and integrate information while performing high cognitive functions is a subject of imminent future consequences. The most significant contributions to date in this field, known as cognitive neuroscience, have been achieved by using innovative neuroimaging techniques, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), which measure variations in both the time and the space of some interpretable physical magnitudes. Extraordinary maps of cerebral activation involving function-restricted brain areas, as well as graphs of the functional connectivity between them, have been obtained from EEG and fMRI data by solving some spatio-temporal inverse problems, which constitutes a top-down approach. However, in many cases, a natural bridge between these maps/graphs and the causal physiological processes is lacking, leading to some misunderstandings in their interpretation. Recent advances in the comprehension of the underlying physiological mechanisms associated with different cerebral scales have provided researchers with an excellent scenario to develop sophisticated biophysical models that permit an integration of these neuroimage modalities, which must share a common aetiology. This paper proposes a bottom-up approach, involving physiological parameters in a specific mesoscopic dynamic equations system. Further observation equations encapsulating the relationship between the mesostates and the EEG/fMRI data are obtained on the basis of the physical foundations of these techniques. A methodology for the estimation of parameters from fused EEG/fMRI data is also presented. In this context, the concepts of activation and effective connectivity are carefully revised. This new approach permits us to examine and discuss some future prospects for the integration of multimodal neuroimages.     

Mapping human brain networks with cortico-cortical evoked potentials

The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex.     

The chronoarchitecture of the cerebral cortex

We review here a new approach to mapping the human cerebral cortex into distinct subdivisions. Unlike cytoarchitecture or traditional functional imaging, it does not rely on specific anatomical markers or functional hypotheses. Instead, we propose that the unique activity time course (ATC) of each cortical subdivision, elicited during natural conditions, acts as a temporal fingerprint that can be used to segregate cortical subdivisions, map their spatial extent, and reveal their functional and potentially anatomical connectivity. We argue that since the modular organisation of the brain and its connectivity evolved and developed in natural conditions, these are optimal for revealing its organisation. We review the concepts, methodology and first results of this approach, relying on data obtained with functional magnetic resonance imaging (fMRI) when volunteers viewed traditional stimuli or a James Bond movie. Independent component analysis (ICA) was used to identify voxels belonging to distinct functional subdivisions, based on their differential spatio-temporal fingerprints. Many more regions could be segregated during natural viewing, demonstrating that the complexity of natural stimuli leads to more differential responses in more functional modules. We demonstrate that, in a single experiment, a multitude of distinct regions can be identified across the whole brain, even within the visual cortex, including areas V1, V4 and V5. This differentiation is based entirely on the differential ATCs of different areas during natural viewing. Distinct areas can therefore be identified without any a priori hypothesis about their function or spatial location. The areas we identified corresponded anatomically across subjects, and their ATCs showed highly area-specific inter-subject correlations. Furthermore, natural conditions led to a significant de-correlation of interregional ATCs compared to rest, indicating an increase in regional specificity during natural conditions. In contrast, the correlation between ATCs of distant regions of known substantial anatomical connections increased and reflected their known anatomical connectivity pattern. We demonstrate this using the example of the language network involving Broca's and Wernicke's area and homologous areas in the two hemispheres. In conclusion, this new approach to brain mapping may not only serve to identify novel functional subdivisions, but to reveal their connectivity as well.     

Estimating causal interaction between prefrontal cortex and striatum by transfer entropy

Transfer entropy (TE) is an information-theoretic measure for the investigation of causal interaction between two systems without a requirement of pre-specific interaction model (such as: linear or nonlinear). We introduced an efficient algorithm to calculate TE values between two systems based on observed time signals. By this method, we demonstrated that the TE correctly estimated the coupling strength and the direction of information transmission of two nonlinearly coupled systems. We also calculated TE values of real local field potentials (LFPs) recorded simultaneously in the lateral prefrontal cortex (LPFC) and the striatum of the behavioral monkey, and observed that the TE value from the LPFC to the striatum was stronger than that from the striatum to the LPFC, consistent with anatomical structure between the two areas. Moreover, the TE value dynamically varied dependent on behavior stages of the monkey. These results from simulated and real LFPs data suggested that the TE was able to effectively estimate functional connectivity between different brain regions and characterized their dynamical properties.     

Frontal-to-parietal top-down causal streams along the dorsal attention network exclusively mediate voluntary orienting of attention

Previous effective connectivity analyses of functional magnetic resonance imaging (fMRI) have revealed dynamic causal streams along the dorsal attention network (DAN) during voluntary attentional control in the human brain. During resting state, however, fMRI has shown that the DAN is also intrinsically configured by functional connectivity, even in the absence of explicit task demands, and that may conflict with effective connectivity studies. To resolve this contradiction, we performed an effective connectivity analysis based on partial Granger causality (pGC) on event-related fMRI data during Posner's cueing paradigm while optimizing experimental and imaging parameters for pGC analysis. Analysis by pGC can factor out exogenous or latent influences due to unmeasured variables. Typical regions along the DAN with greater activation during orienting than withholding of attention were selected as regions of interest (ROIs). pGC analysis on fMRI data from the ROIs showed that frontal-to-parietal top-down causal streams along the DAN appeared during (voluntary) orienting, but not during other, less-attentive and/or resting-like conditions. These results demonstrate that these causal streams along the DAN exclusively mediate voluntary covert orienting. These findings suggest that neural representations of attention in frontal regions are at the top of the hierarchy of the DAN for embodying voluntary attentional control.     

Global relationship between anatomical connectivity and activity propagation in the cerebral cortex

Anatomical connectivity is a prerequisite for cooperative interactions between cortical areas, but it has yet to be demonstrated that association fibre networks determine the macroscopical flow of activity in the cerebral cortex. To test this notion, we constructed a large-scale model of cortical areas whose interconnections were based on published anatomical data from tracing studies. Using this model we simulated the propagation of activity in response to activation of individual cortical areas and compared the resulting topographic activation patterns to electrophysiological observations on the global spread of epileptic activity following intracortical stimulation. Here we show that a neural network with connectivity derived from experimental data reproduces cortical propagation of activity significantly better than networks with different types of neighbourhood-based connectivity or random connections. Our results indicate that association fibres and their relative connection strengths are useful predictors of global topographic activation patterns in the cerebral cortex. This global structure-function relationship may open a door to explicit interpretation of cortical activation data in terms of underlying anatomical connectivity.     

脑网络：从脑结构到脑功能

人脑是自然界中最复杂的系统之一,不同的功能区域相互作用、互相协调,共同构成一个网络来发挥其功能。人脑是一个复杂的网络,具有高效的“小世界”拓扑属性。本文从脑结构到脑功能方面介绍了从不同模态影像学数据构造脑网络的主要进展,并探讨不同的脑疾病患者脑网络拓扑结构是否发生了异常,以及这些异常特征能否用来进行疾病分类,最后对本领域未来的研究做了简单的展望。     

Hierarchical processing of auditory objects in humans

This work examines the computational architecture used by the brain during the analysis of the spectral envelope of sounds, an important acoustic feature for defining auditory objects. Dynamic causal modelling and Bayesian model selection were used to evaluate a family of 16 network models explaining functional magnetic resonance imaging responses in the right temporal lobe during spectral envelope analysis. The models encode different hypotheses about the effective connectivity between Heschl's Gyrus (HG), containing the primary auditory cortex, planum temporale (PT), and superior temporal sulcus (STS), and the modulation of that coupling during spectral envelope analysis. In particular, we aimed to determine whether information processing during spectral envelope analysis takes place in a serial or parallel fashion. The analysis provides strong support for a serial architecture with connections from HG to PT and from PT to STS and an increase of the HG to PT connection during spectral envelope analysis. The work supports a computational model of auditory object processing, based on the abstraction of spectro-temporal “templates” in the PT before further analysis of the abstracted form in anterior temporal lobe areas.     

Role of structural inhomogeneities in resting-state brain dynamics

Brain imaging methods allow a non-invasive assessment of both structural and functional connectivity. However, the mechanism of how functional connectivity arises 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 functional correlations arise from underlying structural connections taking into account inhomogeneities in the interactions between the brain regions of interest. The local dynamics of a neural population is assumed to be of phase-oscillator type. The considered structural connectivity patterns describe long-range anatomical connections between interacting neural elements. We find a dependence of the simulated functional connectivity patterns on the parameters governing the dynamics. We calculate graph-theoretic measures of the functional network topology obtained from numerical simulations. The effect of structural inhomogeneities in the coupling term on the observed network state is quantified by examining the relation between simulated and empirical functional connectivity. Importantly, we show that simulated and empirical functional connectivity agree for a narrow range of coupling strengths. We conclude that identification of functional connectivity during rest requires an analysis of the network dynamics.     

Condition-dependent functional connectivity: syntax networks in bilinguals

This paper introduces a method to study the variation of brain functional connectivity networks with respect to experimental conditions in fMRI data. It is related to the psychophysiological interaction technique introduced by Friston et al. and extends to networks of correlation modulation (CM networks). Extended networks containing several dozens of nodes are determined in which the links correspond to consistent correlation modulation across subjects. In addition, we assess inter-subject variability and determine networks in which the condition-dependent functional interactions can be explained by a subject-dependent variable. We applied the technique to data from a study on syntactical production in bilinguals and analysed functional interactions differentially across tasks (word reading or sentence production) and across languages. We find an extended network of consistent functional interaction modulation across tasks, whereas the network comparing languages shows fewer links. Interestingly, there is evidence for a specific network in which the differences in functional interaction across subjects can be explained by differences in the subjects' syntactical proficiency. Specifically, we find that regions, including ones that have previously been shown to be involved in syntax and in language production, such as the left inferior frontal gyrus, putamen, insula, precentral gyrus, as well as the supplementary motor area, are more functionally linked during sentence production in the second, compared with the first, language in syntactically more proficient bilinguals than in syntactically less proficient ones. Our approach extends conventional activation analyses to the notion of networks, emphasizing functional interactions between regions independently of whether or not they are activated. On the one hand, it gives rise to testable hypotheses and allows an interpretation of the results in terms of the previous literature, and on the other hand, it provides a basis for studying the structure of functional interactions as a whole, and hence represents a further step towards the notion of large-scale networks in functional imaging.     

Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals

A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural connectivity from network activity monitored through calcium imaging. We focus in this study on the inference of excitatory synaptic links. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the functional network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (bursting or non-bursting). Thus by conditioning with respect to the global mean activity, we improve the performance of our method. This allows us to focus the analysis to specific dynamical regimes of the network in which the inferred functional connectivity is shaped by monosynaptic excitatory connections, rather than by collective synchrony. Our method can discriminate between actual causal influences between neurons and spurious non-causal correlations due to light scattering artifacts, which inherently affect the quality of fluorescence imaging. Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good estimation of the excitatory network clustering coefficient, allowing for discrimination between weakly and strongly clustered topologies. Finally, we demonstrate the applicability of our method to analyses of real recordings of in vitro disinhibited cortical cultures where we suggest that excitatory connections are characterized by an elevated level of clustering compared to a random graph (although not extreme) and can be markedly non-local.     

An Adaptive Complex Network Model for Brain Functional Networks

Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution.     

Relating Structure and Function in the Human Brain: Relative Contributions of Anatomy,Stationary Dynamics,and Non-stationarities

Investigating the relationship between brain structure and function is a central endeavor for neuroscience research. Yet, the mechanisms shaping this relationship largely remain to be elucidated and are highly debated. In particular, the existence and relative contributions of anatomical constraints and dynamical physiological mechanisms of different types remain to be established. We addressed this issue by systematically comparing functional connectivity (FC) from resting-state functional magnetic resonance imaging data with simulations from increasingly complex computational models, and by manipulating anatomical connectivity obtained from fiber tractography based on diffusion-weighted imaging. We hypothesized that FC reflects the interplay of at least three types of components: (i) a backbone of anatomical connectivity, (ii) a stationary dynamical regime directly driven by the underlying anatomy, and (iii) other stationary and non-stationary dynamics not directly related to the anatomy. We showed that anatomical connectivity alone accounts for up to 15% of FC variance; that there is a stationary regime accounting for up to an additional 20% of variance and that this regime can be associated to a stationary FC; that a simple stationary model of FC better explains FC than more complex models; and that there is a large remaining variance (around 65%), which must contain the non-stationarities of FC evidenced in the literature. We also show that homotopic connections across cerebral hemispheres, which are typically improperly estimated, play a strong role in shaping all aspects of FC, notably indirect connections and the topographic organization of brain networks.     

Natural-constructive approach to modeling the cognitive process

We present a “natural-constructive approach” to modeling the cognitive process, which is based on the dynamic theory of information, the technique of nonlinear differential equations, and the concept of a “dynamic formal neuron.” The version of cognitive architecture that was designed within the natural-constructive approach is presented. One important constructive feature of this architecture consists in splitting up the entire system into two similar hemi-systems (by analogy with the right and left cerebral hemispheres). One of these is responsible for the generation of information and learning, with other one being responsible for the reception and processing of well-known information. This functional specialization is provided by the presence of noise (a random factor) in the generation hemi-system; in the reception hemi-system, all the processes should proceed successively rather than stochastically. The interpretation of the concepts of intuition, logic, consciousness, and sub-consciousness is discussed. The architecture that is designed within the natural-constructive approach is compared with other theoretical approaches (graph theory and the “cognitom” concept), and with anatomical data. The concept of an experiment is proposed that could verify or disprove the main inferences of the natural-constructive approach.     

Individual differences in white-matter microstructure reflect variation in functional connectivity during choice

The relation between brain structure and function is of fundamental importance in neuroscience. Comparisons between behavioral and brain-imaging measures suggest that variation in brain structure correlates with the presence of specific skills. Behavioral measures, however, reflect the integrated function of multiple brain regions. Rather than behavior, a physiological index of function could be a more sensitive and informative measure with which to compare structural measures. Here, we test for a relationship between a physiological measure of functional connectivity between two brain areas during a simple decision-making task and a measure of structural connectivity. Paired-pulse transcranial magnetic stimulation indexed functional connectivity between two regions important for action choices: the premotor and motor cortex. Fractional anisotropy (FA), a marker of microstructural integrity, indexed structural connectivity. Individual differences in functional connectivity during action selection show highly specific correlations with FA in localized regions of white-matter interconnecting regions, including the premotor and motor cortex. Probabilistic tractography, a technique for identifying fiber pathways from diffusion-weighted imaging (DWI), was used to reconstruct the anatomical networks linking the component brain regions involved in making decisions. These findings demonstrate a relationship between individual differences in functional and structural connectivity within human brain networks central to action choice.     

Altered Anatomical Network in Early Blindness Revealed by Diffusion Tensor Tractography

The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. Diffusion MRI studies have revealed the efficient small-world properties and modular structure of the anatomical network in normal subjects. However, no previous study has used diffusion MRI to reveal changes in the brain anatomical network in early blindness. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 17 early blind subjects and 17 age- and gender-matched sighted controls. We established the existence of structural connections between any pair of the 90 cortical and sub-cortical regions using deterministic tractography. Compared with controls, early blind subjects showed a decreased degree of connectivity, a reduced global efficiency, and an increased characteristic path length in their brain anatomical network, especially in the visual cortex. Moreover, we revealed some regions with motor or somatosensory function have increased connections with other brain regions in the early blind, which suggested experience-dependent compensatory plasticity. This study is the first to show alterations in the topological properties of the anatomical network in early blindness. From the results, we suggest that analyzing the brain''s anatomical network obtained using diffusion MRI data provides new insights into the understanding of the brain''s re-organization in the specific population with early visual deprivation.     

Are Structural Changes Induced by Lithium in the HIV Brain Accompanied by Changes in Functional Connectivity?

Lithium therapy has been shown to affect imaging measures of brain function and microstructure in human immunodeficiency virus (HIV)-infected subjects with cognitive impairment. The aim of this proof-of-concept study was to explore whether changes in brain microstructure also entail changes in functional connectivity. Functional MRI data of seven cognitively impaired HIV infected individuals enrolled in an open-label lithium study were included in the connectivity analysis. Seven regions of interest (ROI) were defined based on previously observed lithium induced microstructural changes measured by Diffusion Tensor Imaging. Generalized partial directed coherence (gPDC), based on time-variant multivariate autoregressive models, was used to quantify the degree of connectivity between the selected ROIs. Statistical analyses using a linear mixed model showed significant differences in the average node strength between pre and post lithium therapy conditions. Specifically, we found that lithium treatment in this population induced changes suggestive of increased strength in functional connectivity. Therefore, by exploiting the information about the strength of functional interactions provided by gPDC we can quantify the connectivity changes observed in relation to a given intervention. Furthermore, in conditions where the intervention is associated with clinical changes, we suggest that this methodology could enable an interpretation of such changes in the context of disease or treatment induced modulations in functional networks.     

Therapeutic Subthalamic Nucleus Deep Brain Stimulation Reverses Cortico-Thalamic Coupling during Voluntary Movements in Parkinson's Disease

Deep brain stimulation of the subthalamic nucleus (STN DBS) has become an accepted treatment for patients experiencing the motor complications of Parkinson''s disease (PD). While its successes are becoming increasingly apparent, the mechanisms underlying its action remain unclear. Multiple studies using radiotracer-based imaging have investigated DBS-induced regional changes in neural activity. However, little is known about the effect of DBS on connectivity within neural networks; in other words, whether DBS impacts upon functional integration of specialized regions of cortex. In this work, we report the first findings of fMRI in 10 subjects with PD and fully implanted DBS hardware receiving efficacious stimulation. Despite the technical demands associated with the safe acquisition of fMRI data from patients with implanted hardware, robust activation changes were identified in the insula cortex and thalamus in response to therapeutic STN DBS. We then quantified the neuromodulatory effects of DBS and compared sixteen dynamic causal models of effective connectivity between the two identified nodes. Using Bayesian model comparison, we found unequivocal evidence for the modulation of extrinsic (between region), i.e. cortico-thalamic and thalamo-cortical connections. Using Bayesian model parameter averaging we found that during voluntary movements, DBS reversed the effective connectivity between regions of the cortex and thalamus. This casts the therapeutic effects of DBS in a fundamentally new light, emphasising a role in changing distributed cortico-subcortical interactions. We conclude that STN DBS does impact upon the effective connectivity between the cortex and thalamus by changing their sensitivities to extrinsic afferents. Furthermore, we confirm that fMRI is both feasible and is tolerated well by these patients provided strict safety measures are adhered to.     

Reduction in inter-hemispheric connectivity in disorders of consciousness

Clinical diagnosis of disorders of consciousness (DOC) caused by brain injury poses great challenges since patients are often behaviorally unresponsive. A promising new approach towards objective DOC diagnosis may be offered by the analysis of ultra-slow (<0.1 Hz) spontaneous brain activity fluctuations measured with functional magnetic resonance imaging (fMRI) during the resting-state. Previous work has shown reduced functional connectivity within the "default network", a subset of regions known to be deactivated during engaging tasks, which correlated with the degree of consciousness impairment. However, it remains unclear whether the breakdown of connectivity is restricted to the "default network", and to what degree changes in functional connectivity can be observed at the single subject level. Here, we analyzed resting-state inter-hemispheric connectivity in three homotopic regions of interest, which could reliably be identified based on distinct anatomical landmarks, and were part of the "Extrinsic" (externally oriented, task positive) network (pre- and postcentral gyrus, and intraparietal sulcus). Resting-state fMRI data were acquired for a group of 11 healthy subjects and 8 DOC patients. At the group level, our results indicate decreased inter-hemispheric functional connectivity in subjects with impaired awareness as compared to subjects with intact awareness. Individual connectivity scores significantly correlated with the degree of consciousness. Furthermore, a single-case statistic indicated a significant deviation from the healthy sample in 5/8 patients. Importantly, of the three patients whose connectivity indices were comparable to the healthy sample, one was diagnosed as locked-in. Taken together, our results further highlight the clinical potential of resting-state connectivity analysis and might guide the way towards a connectivity measure complementing existing DOC diagnosis.