Large-scale neural models and dynamic causal modelling

Dopamine Modulates Effective Connectivity in Frontal Cortex

There is increasing evidence that the left lateral frontal cortex is hierarchically organized such that higher-order regions have an asymmetric top-down influence over lower order regions. However, questions remain about the underlying neuroarchitecture of this hierarchical control organization. Within the frontal cortex, dopamine plays an important role in cognitive control functions, and we hypothesized that dopamine may preferentially influence top-down connections within the lateral frontal hierarchy. Using a randomized, double-blind, within-subject design, we analyzed resting-state fMRI data of 66 healthy young participants who were scanned once each after administration of bromocriptine (a dopamine agonist with preferential affinity for D2 receptor), tolcapone (an inhibitor of catechol-O-methyltransferase), and placebo, to determine whether dopaminergic stimulation modulated effective functional connectivity between hierarchically organized frontal regions in the left hemisphere. We found that dopaminergic drugs modulated connections from the caudal middle frontal gyrus and the inferior frontal sulcus to both rostral and caudal frontal areas. In dorsal frontal regions, effectivity connectivity strength was increased, whereas in ventral frontal regions, effective connectivity strength was decreased. These findings suggest that connections within frontal cortex are differentially modulated by dopamine, which may bias the influence that frontal regions exert over each other.

Identifying oscillatory brain networks with hidden Gaussian graphical spectral models of MEEG

Identifying the functional networks underpinning indirectly observed processes poses an inverse problem for neurosciences or other fields. A solution of such inverse problems estimates as a first step the activity emerging within functional networks from EEG or MEG data. These EEG or MEG estimates are a direct reflection of functional brain network activity with a temporal resolution that no other in vivo neuroimage may provide. A second step estimating functional connectivity from such activity pseudodata unveil the oscillatory brain networks that strongly correlate with all cognition and behavior. Simulations of such MEG or EEG inverse problem also reveal estimation errors of the functional connectivity determined by any of the state-of-the-art inverse solutions. We disclose a significant cause of estimation errors originating from misspecification of the functional network model incorporated into either inverse solution steps. We introduce the Bayesian identification of a Hidden Gaussian Graphical Spectral (HIGGS) model specifying such oscillatory brain networks model. In human EEG alpha rhythm simulations, the estimation errors measured as ROC performance do not surpass 2% in our HIGGS inverse solution and reach 20% in state-of-the-art methods. Macaque simultaneous EEG/ECoG recordings provide experimental confirmation for our results with 1/3 times larger congruence according to Riemannian distances than state-of-the-art methods.

Task-Dependent Functional and Effective Connectivity during Conceptual Processing

Conceptual knowledge is central to cognition. Previous neuroimaging research indicates that conceptual processing involves both modality-specific perceptual-motor areas and multimodal convergence zones. For example, our previous functional magnetic resonance imaging (fMRI) study revealed that both modality-specific and multimodal regions respond to sound and action features of concepts in a task-dependent fashion (Kuhnke P, Kiefer M, Hartwigsen G. 2020b. Task-dependent recruitment of modality-specific and multimodal regions during conceptual processing. Cereb Cortex. 30:3938–3959.). However, it remains unknown whether and how modality-specific and multimodal areas interact during conceptual tasks. Here, we asked 1) whether multimodal and modality-specific areas are functionally coupled during conceptual processing, 2) whether their coupling depends on the task, 3) whether information flows top-down, bottom-up or both, and 4) whether their coupling is behaviorally relevant. We combined psychophysiological interaction analyses with dynamic causal modeling on the fMRI data of our previous study. We found that functional coupling between multimodal and modality-specific areas strongly depended on the task, involved both top-down and bottom-up information flow, and predicted conceptually guided behavior. Notably, we also found coupling between different modality-specific areas and between different multimodal areas. These results suggest that functional coupling in the conceptual system is extensive, reciprocal, task-dependent, and behaviorally relevant. We propose a new model of the conceptual system that incorporates task-dependent functional interactions between modality-specific and multimodal areas.

A Functional Data Method for Causal Dynamic Network Modeling of Task-Related fMRI

Functional MRI (fMRI) is a popular approach to investigate brain connections and activations when human subjects perform tasks. Because fMRI measures the indirect and convoluted signals of brain activities at a lower temporal resolution, complex differential equation modeling methods (e.g., Dynamic Causal Modeling) are usually employed to infer the neuronal processes and to fit the resulting fMRI signals. However, this modeling strategy is computationally expensive and remains to be mostly a confirmatory or hypothesis-driven approach. One major statistical challenge here is to infer, in a data-driven fashion, the underlying differential equation models from fMRI data. In this paper, we propose a causal dynamic network (CDN) method to estimate brain activations and connections simultaneously. Our method links the observed fMRI data with the latent neuronal states modeled by an ordinary differential equation (ODE) model. Using the basis function expansion approach in functional data analysis, we develop an optimization-based criterion that combines data-fitting errors and ODE fitting errors. We also develop and implement a block coordinate-descent algorithm to compute the ODE parameters efficiently. We illustrate the numerical advantages of our approach using data from realistic simulations and two task-related fMRI experiments. Compared with various effective connectivity methods, our method achieves higher estimation accuracy while improving the computational speed by from tens to thousands of times. Though our method is developed for task-related fMRI, we also demonstrate the potential applicability of our method (with a simple modification) to resting-state fMRI, by analyzing both simulated and real data from medium-sized networks.

Optimizing Data for Modeling Neuronal Responses

In this technical note, we address an unresolved challenge in neuroimaging statistics: how to determine which of several datasets is the best for inferring neuronal responses. Comparisons of this kind are important for experimenters when choosing an imaging protocol-and for developers of new acquisition methods. However, the hypothesis that one dataset is better than another cannot be tested using conventional statistics (based on likelihood ratios), as these require the data to be the same under each hypothesis. Here we present Bayesian data comparison (BDC), a principled framework for evaluating the quality of functional imaging data, in terms of the precision with which neuronal connectivity parameters can be estimated and competing models can be disambiguated. For each of several candidate datasets, neuronal responses are modeled using Bayesian (probabilistic) forward models, such as General Linear Models (GLMs) or Dynamic Casual Models (DCMs). Next, the parameters from subject-specific models are summarized at the group level using a Bayesian GLM. A series of measures, which we introduce here, are then used to evaluate each dataset in terms of the precision of (group-level) parameter estimates and the ability of the data to distinguish similar models. To exemplify the approach, we compared four datasets that were acquired in a study evaluating multiband fMRI acquisition schemes, and we used simulations to establish the face validity of the comparison measures. To enable people to reproduce these analyses using their own data and experimental paradigms, we provide general-purpose Matlab code via the SPM software.

Simulating laminar neuroimaging data for a visual delayed match-to-sample task

Invasive electrophysiological and neuroanatomical studies in nonhuman mammalian experimental preparations have helped elucidate the lamina (layer) dependence of neural computations and interregional connections. Noninvasive functional neuroimaging can, in principle, resolve cortical laminae (layers), and thus provide insight into human neural computations and interregional connections. However human neuroimaging data are noisy and difficult to interpret; biologically realistic simulations can aid experimental interpretation by relating the neuroimaging data to simulated neural activity. We illustrate the potential of laminar neuroimaging by upgrading an existing large-scale, multiregion neural model that simulates a visual delayed match-to-sample (DMS) task. The new laminar-based neural unit incorporates spiny stellate, pyramidal, and inhibitory neural populations which are divided among supragranular, granular, and infragranular laminae (layers). We simulated neural activity which is translated into local field potential-like data used to simulate conventional and laminar fMRI activity. We implemented the laminar connectivity schemes proposed by Felleman and Van Essen (Cerebral Cortex, 1991) for interregional connections. The hemodynamic model that we employ is a modified version of one due to Heinzle et al. (Neuroimage, 2016) that incorporates the effects of draining veins. We show that the laminar version of the model replicates the findings of the existing model. The laminar model shows the finer structure in fMRI activity and functional connectivity. Laminar differences in the magnitude of neural activities are a prominent finding; these are also visible in the simulated fMRI. We illustrate differences between task and control conditions in the fMRI signal, and demonstrate differences in interregional laminar functional connectivity that reflect the underlying connectivity scheme. These results indicate that multi-layer computational models can aid in interpreting layer-specific fMRI, and suggest that increased use of laminar fMRI could provide unique and fundamental insights to human neuroscience.

Causal evidence for lateral prefrontal cortex dynamics supporting cognitive control

The lateral prefrontal cortex (LPFC) is essential for higher-level cognition, but the nature of its interactions in supporting cognitive control remains elusive. Previously (Nee and D'Esposito, 2016), dynamic causal modeling (DCM) indicated that mid LPFC integrates abstract, rostral and concrete, caudal influences to inform context-appropriate action. Here, we use continuous theta-burst transcranial magnetic stimulation (cTBS) to test this model causally. cTBS was applied to three LPFC sites and a control site in counterbalanced sessions. Behavioral modulations resulting from cTBS were largely predicted by information flow within the previously estimated DCM. However, cTBS to caudal LPFC unexpectedly impaired processes that are presumed to involve rostral LPFC. Adding a pathway from caudal to mid-rostral LPFC significantly improved the model fit and accounted for the observed behavioral findings. These data provide causal evidence for LPFC dynamics supporting cognitive control and demonstrate the utility of combining DCM with causal manipulations to test and refine models of cognition.

Embedding Task-Based Neural Models into a Connectome-Based Model of the Cerebral Cortex

A number of recent efforts have used large-scale, biologically realistic, neural models to help understand the neural basis for the patterns of activity observed in both resting state and task-related functional neural imaging data. An example of the former is The Virtual Brain (TVB) software platform, which allows one to apply large-scale neural modeling in a whole brain framework. TVB provides a set of structural connectomes of the human cerebral cortex, a collection of neural processing units for each connectome node, and various forward models that can convert simulated neural activity into a variety of functional brain imaging signals. In this paper, we demonstrate how to embed a previously or newly constructed task-based large-scale neural model into the TVB platform. We tested our method on a previously constructed large-scale neural model (LSNM) of visual object processing that consisted of interconnected neural populations that represent, primary and secondary visual, inferotemporal, and prefrontal cortex. Some neural elements in the original model were “non-task-specific” (NS) neurons that served as noise generators to “task-specific” neurons that processed shapes during a delayed match-to-sample (DMS) task. We replaced the NS neurons with an anatomical TVB connectome model of the cerebral cortex comprising 998 regions of interest interconnected by white matter fiber tract weights. We embedded our LSNM of visual object processing into corresponding nodes within the TVB connectome. Reciprocal connections between TVB nodes and our task-based modules were included in this framework. We ran visual object processing simulations and showed that the TVB simulator successfully replaced the noise generation originally provided by NS neurons; i.e., the DMS tasks performed with the hybrid LSNM/TVB simulator generated equivalent neural and fMRI activity to that of the original task-based models. Additionally, we found partial agreement between the functional connectivities using the hybrid LSNM/TVB model and the original LSNM. Our framework thus presents a way to embed task-based neural models into the TVB platform, enabling a better comparison between empirical and computational data, which in turn can lead to a better understanding of how interacting neural populations give rise to human cognitive behaviors.

Reappraising the Functional Implications of the Primate Visual Anatomical Hierarchy

The primate visual system has been shown to be organized into an anatomical hierarchy by the application of a few principled criteria. It has been widely assumed that cortical visual processing is also hierarchical, with the anatomical hierarchy providing a defined substrate for clear levels of hierarchical function. A large body of empirical evidence seemed to support this assumption, including the general observations that functional properties of visual neurons grow progressively more complex at progressively higher levels of the anatomical hierarchy. However, a growing body of evidence, including recent direct experimental comparisons of functional properties at two or more levels of the anatomical hierarchy, indicates that visual processing neither is hierarchical nor parallels the anatomical hierarchy. Recent results also indicate that some of the pathways of visual information flow are not hierarchical, so that the anatomical hierarchy cannot be taken as a strict flowchart of visual information either. Thus, while the sustaining strength of the notion of hierarchical processing may be that it is rather simple, its fatal flaw is that it is overly simplistic. NEUROSCIENTIST 13(5):416—421, 2007. DOI: 10.1177/1073858407305201

The hierarchical organization of the lateral prefrontal cortex

Higher-level cognition depends on the lateral prefrontal cortex (LPFC), but its functional organization has remained elusive. An influential proposal is that the LPFC is organized hierarchically whereby progressively rostral areas of the LPFC process/represent increasingly abstract information facilitating efficient and flexible cognition. However, support for this theory has been limited. Here, human fMRI data revealed rostral/caudal gradients of abstraction in the LPFC. Dynamic causal modeling revealed asymmetrical LPFC interactions indicative of hierarchical processing. Contrary to dominant assumptions, the relative strength of efferent versus afferent connections positioned mid LPFC as the apex of the hierarchy. Furthermore, cognitive demands induced connectivity modulations towards mid LPFC consistent with a role in integrating information for control operations. Moreover, the strengths of these dynamics were related to trait-measured higher-level cognitive ability. Collectively, these results suggest that the LPFC is hierarchically organized with the mid LPFC positioned to synthesize abstract and concrete information to control behavior.

DOI:http://dx.doi.org/10.7554/eLife.12112.001

Part of the brain called the lateral prefrontal cortex has a critical role in many of the processes seen as hallmarks of human cognition, such as reasoning, planning and problem-solving. Individuals with damage to the lateral prefrontal cortex are disorganized and easily distracted, and may show behaviors that are inappropriate for their context. However, the involvement of the lateral prefrontal cortex in such a wide range of processes makes it difficult to study. This in turn presents a significant roadblock to a full understanding of cognition and human intelligence.

Of particular interest is whether the lateral prefrontal cortex has a hierarchical organization wherein a region coordinates the roles of other regions, much like the chief executive of a company. Therefore, Nee and D’Esposito set out to map how the lateral prefrontal cortex is organized, and how its different regions communicate with each other to support complex cognition.

Brain imaging revealed that the rear (posterior) part of the lateral prefrontal cortex processes an individual’s current situation, while the front (anterior) prepares for future situations. Areas in the middle process both types of information. These central areas appear to be highly influential as they have stronger connections to the anterior and posterior regions than vice versa.

In cognitively demanding situations, the middle areas receive input from both anterior regions (potentially about future needs) and posterior regions (potentially about current needs). By combining the two sets of information, the middle areas can select behaviors that take into account both present circumstances and longer-term goals. With this strategic overview, the middle areas of the lateral prefrontal cortex are well positioned to play the part of the brain’s chief executive.

Future experiments should test whether the interactions observed between the different regions of the lateral prefrontal cortex are essential for complex planning and thinking. Additional work in animals would improve our understanding of the mechanisms underlying these interactions. Finally, studying how these interactions are altered in disorders such as schizophrenia, where the lateral prefrontal cortex shows abnormal activity, might pave the way for more effective treatments.

DOI:http://dx.doi.org/10.7554/eLife.12112.002

Test-retest reliability of effective connectivity in the face perception network

Computational approaches have great potential for moving neuroscience toward mechanistic models of the functional integration among brain regions. Dynamic causal modeling (DCM) offers a promising framework for inferring the effective connectivity among brain regions and thus unraveling the neural mechanisms of both normal cognitive function and psychiatric disorders. While the benefit of such approaches depends heavily on their reliability, systematic analyses of the within-subject stability are rare. Here, we present a thorough investigation of the test-retest reliability of an fMRI paradigm for DCM analysis dedicated to unraveling intra- and interhemispheric integration among the core regions of the face perception network. First, we examined the reliability of face-specific BOLD activity in 25 healthy volunteers, who performed a face perception paradigm in two separate sessions. We found good to excellent reliability of BOLD activity within the DCM-relevant regions. Second, we assessed the stability of effective connectivity among these regions by analyzing the reliability of Bayesian model selection and model parameter estimation in DCM. Reliability was excellent for the negative free energy and good for model parameter estimation, when restricting the analysis to parameters with substantial effect sizes. Third, even when the experiment was shortened, reliability of BOLD activity and DCM results dropped only slightly as a function of the length of the experiment. This suggests that the face perception paradigm presented here provides reliable estimates for both conventional activation and effective connectivity measures. We conclude this paper with an outlook on potential clinical applications of the paradigm for studying psychiatric disorders. Hum Brain Mapp 37:730-744, 2016. © 2015 Wiley Periodicals, Inc.

Functional Magnetic Resonance Imaging Methods

Since its inception in 1992, Functional Magnetic Resonance Imaging (fMRI) has become an indispensible tool for studying cognition in both the healthy and dysfunctional brain. FMRI monitors changes in the oxygenation of brain tissue resulting from altered metabolism consequent to a task-based evoked neural response or from spontaneous fluctuations in neural activity in the absence of conscious mentation (the "resting state"). Task-based studies have revealed neural correlates of a large number of important cognitive processes, while fMRI studies performed in the resting state have demonstrated brain-wide networks that result from brain regions with synchronized, apparently spontaneous activity. In this article, we review the methods used to acquire and analyze fMRI signals.

Granger causality network reconstruction of conductance-based integrate-and-fire neuronal systems

Reconstruction of anatomical connectivity from measured dynamical activities of coupled neurons is one of the fundamental issues in the understanding of structure-function relationship of neuronal circuitry. Many approaches have been developed to address this issue based on either electrical or metabolic data observed in experiment. The Granger causality (GC) analysis remains one of the major approaches to explore the dynamical causal connectivity among individual neurons or neuronal populations. However, it is yet to be clarified how such causal connectivity, i.e., the GC connectivity, can be mapped to the underlying anatomical connectivity in neuronal networks. We perform the GC analysis on the conductance-based integrate-and-fire (I&F) neuronal networks to obtain their causal connectivity. Through numerical experiments, we find that the underlying synaptic connectivity amongst individual neurons or subnetworks, can be successfully reconstructed by the GC connectivity constructed from voltage time series. Furthermore, this reconstruction is insensitive to dynamical regimes and can be achieved without perturbing systems and prior knowledge of neuronal model parameters. Surprisingly, the synaptic connectivity can even be reconstructed by merely knowing the raster of systems, i.e., spike timing of neurons. Using spike-triggered correlation techniques, we establish a direct mapping between the causal connectivity and the synaptic connectivity for the conductance-based I&F neuronal networks, and show the GC is quadratically related to the coupling strength. The theoretical approach we develop here may provide a framework for examining the validity of the GC analysis in other settings.

Time-variant partial directed coherence for analysing connectivity: a methodological study

For the past decade, the detection and quantification of interactions within and between physiological networks has become a priority-in-common between the fields of biomedicine and computer science. Prominent examples are the interaction analysis of brain networks and of the cardiovascular-respiratory system. The aim of the study is to show how and to what extent results from time-variant partial directed coherence analysis are influenced by some basic estimator and data parameters. The impacts of the Kalman filter settings, the order of the autoregressive (AR) model, signal-to-noise ratios, filter procedures and volume conduction were investigated. These systematic investigations are based on data derived from simulated connectivity networks and were performed using a Kalman filter approach for the estimation of the time-variant multivariate AR model. Additionally, the influence of electrooculogram artefact rejection on the significance and dynamics of interactions in 29 channel electroencephalography recordings, derived from a photic driving experiment, is demonstrated. For artefact rejection, independent component analysis was used. The study provides rules to correctly apply particular methods that will aid users to achieve more reliable interpretations of the results.

Multi-site reproducibility of prefrontal-hippocampal connectivity estimates by stochastic DCM

This study examined the reproducibility of prefrontal-hippocampal connectivity estimates obtained by stochastic dynamic causal modeling (sDCM). 180 healthy subjects were measured by functional magnetic resonance imaging (fMRI) during a standard working memory N-Back task at three different sites (Mannheim, Bonn, Berlin; each with 60 participants). The reproducibility of regional activations in key regions for working memory (dorsolateral prefrontal cortex, DLPFC; hippocampal formation, HF) was evaluated using conjunction analyses across locations. These analyses showed consistent activation of right DLPFC and deactivation of left HF across all three different sites. The effective connectivity between DLPFC and HF was analyzed using a simple two-region sDCM. For each subject, we evaluated sixty-seven alternative sDCMs and compared their relative plausibility using Bayesian model selection (BMS). Across all locations, BMS consistently revealed the same winning model, with the 2-Back working memory condition as driving input to both DLPFC and HF and with a connection from DLPFC to HF. Statistical tests on the sDCM parameter estimates did not show any significant differences across the three sites. The consistency of both the BMS results and model parameter estimates indicates the reliability of sDCM in our paradigm. This provides a basis for future genetic and clinical studies using this approach.

A modular approach to language production: models and facts

Numerous cortical disorders affect language. We explore the connection between the observed language behavior and the underlying substrates by adopting a neurocomputational approach. To represent the observed trajectories of the discourse in patients with disorganized speech and in healthy participants, we design a graphical representation for the discourse as a trajectory that allows us to visualize and measure the degree of order in the discourse as a function of the disorder of the trajectories. Our work assumes that many of the properties of language production and comprehension can be understood in terms of the dynamics of modular networks of neural associative memories. Based upon this assumption, we connect three theoretical and empirical domains: (1) neural models of language processing and production, (2) statistical methods used in the construction of functional brain images, and (3) corpus linguistic tools, such as Latent Semantic Analysis (henceforth LSA), that are used to discover the topic organization of language. We show how the neurocomputational models intertwine with LSA and the mathematical basis of functional neuroimaging. Within this framework we describe the properties of a context-dependent neural model, based on matrix associative memories, that performs goal-oriented linguistic behavior. We link these matrix associative memory models with the mathematics that underlie functional neuroimaging techniques and present the "functional brain images" emerging from the model. This provides us with a completely "transparent box" with which to analyze the implication of some statistical images. Finally, we use these models to explore the possibility that functional synaptic disconnection can lead to an increase in connectivity between the representations of concepts that could explain some of the alterations in discourse displayed by patients with schizophrenia.

Temporal microstructure of cortical networks (TMCN) underlying task-related differences

Neuro-electromagnetic recording techniques (EEG, MEG, iEEG) provide high temporal resolution data to study the dynamics of neurocognitive networks: large scale neural assemblies involved in task-specific information processing. How does a neurocognitive network reorganize spatiotemporally on the order of a few milliseconds to process specific aspects of the task? At what times do networks segregate for task processing, and at what time scales does integration of information occur via changes in functional connectivity? Here, we propose a data analysis framework-Temporal microstructure of cortical networks (TMCN)-that answers these questions for EEG/MEG recordings in the signal space. Method validation is established on simulated MEG data from a delayed-match to-sample (DMS) task. We then provide an example application on MEG recordings during a paired associate task (modified from the simpler DMS paradigm) designed to study modality specific long term memory recall. Our analysis identified the times at which network segregation occurs for processing the memory recall of an auditory object paired to a visual stimulus (visual-auditory) in comparison to an analogous visual-visual pair. Across all subjects, onset times for first network divergence appeared within a range of 0.08-0.47 s after initial visual stimulus onset. This indicates that visual-visual and visual auditory memory recollection involves equivalent network components without any additional recruitment during an initial period of the sensory processing stage which is then followed by recruitment of additional network components for modality specific memory recollection. Therefore, we propose TMCN as a viable computational tool for extracting network timing in various cognitive tasks.

Neural correlates of multisensory perceptual learning

The brain's ability to bind incoming auditory and visual stimuli depends critically on the temporal structure of this information. Specifically, there exists a temporal window of audiovisual integration within which stimuli are highly likely to be perceived as part of the same environmental event. Several studies have described the temporal bounds of this window, but few have investigated its malleability. Recently, our laboratory has demonstrated that a perceptual training paradigm is capable of eliciting a 40% narrowing in the width of this window that is stable for at least 1 week after cessation of training. In the current study, we sought to reveal the neural substrates of these changes. Eleven human subjects completed an audiovisual simultaneity judgment training paradigm, immediately before and after which they performed the same task during an event-related 3T fMRI session. The posterior superior temporal sulcus (pSTS) and areas of auditory and visual cortex exhibited robust BOLD decreases following training, and resting state and effective connectivity analyses revealed significant increases in coupling among these cortices after training. These results provide the first evidence of the neural correlates underlying changes in multisensory temporal binding likely representing the substrate for a multisensory temporal binding window.

Neural correlates of multisensory perceptual learning

The brain's ability to bind incoming auditory and visual stimuli depends critically on the temporal structure of this information. Specifically, there exists a temporal window of audiovisual integration within which stimuli are highly likely to be perceived as part of the same environmental event. Several studies have described the temporal bounds of this window, but few have investigated its malleability. Recently, our laboratory has demonstrated that a perceptual training paradigm is capable of eliciting a 40% narrowing in the width of this window that is stable for at least 1 week after cessation of training. In the current study, we sought to reveal the neural substrates of these changes. Eleven human subjects completed an audiovisual simultaneity judgment training paradigm, immediately before and after which they performed the same task during an event-related 3T fMRI session. The posterior superior temporal sulcus (pSTS) and areas of auditory and visual cortex exhibited robust BOLD decreases following training, and resting state and effective connectivity analyses revealed significant increases in coupling among these cortices after training. These results provide the first evidence of the neural correlates underlying changes in multisensory temporal binding likely representing the substrate for a multisensory temporal binding window.

Causal interactions in attention networks predict behavioral performance

Lesion and functional brain imaging studies have suggested that there are two anatomically nonoverlapping attention networks. The dorsal frontoparietal network controls goal-oriented top-down deployment of attention; the ventral frontoparietal network mediates stimulus-driven bottom-up attentional reorienting. The interaction between the two networks and its functional significance has been considered in the past but no direct test has been carried out. We addressed this problem by recording fMRI data from human subjects performing a trial-by-trial cued visual spatial attention task in which the subject had to respond to target stimuli in the attended hemifield and ignore all stimuli in the unattended hemifield. Correlating Granger causal influences between regions of interest with behavioral performance, we report two main results. First, stronger Granger causal influences from the dorsal attention network (DAN) to the ventral attention network (VAN), i.e., DAN→VAN, are generally associated with enhanced performance, with right intraparietal sulcus (IPS), left IPS, and right frontal eye field being the main sources of behavior-enhancing influences. Second, stronger Granger causal influences from VAN to DAN, i.e., VAN→DAN, are generally associated with degraded performance, with right temporal-parietal junction being the main sources of behavior-degrading influences. These results support the hypothesis that signals from DAN to VAN suppress and filter out unimportant distracter information, whereas signals from VAN to DAN break the attentional set maintained by the dorsal attention network to enable attentional reorienting.

A short history of causal modeling of fMRI data

Twenty years ago, the discovery of the blood oxygen level dependent (BOLD) contrast and invention of functional magnetic resonance imaging (MRI) not only allowed for enhanced analyses of regional brain activity, but also laid the foundation for novel approaches to studying effective connectivity, which is essential for mechanistically interpretable accounts of neuronal systems. Dynamic causal modeling (DCM) and Granger causality (G-causality) modeling have since become the most frequently used techniques for inferring effective connectivity from fMRI data. In this paper, we provide a short historical overview of these approaches, describing milestones of their development from our subjective perspectives.

Critical comments on dynamic causal modelling

Dynamic causal modelling (DCM) (Friston et al., 2003) is a technique designed to investigate the influence between brain areas using time series data obtained by EEG/MEG or functional magnetic resonance imaging (fMRI). The basic idea is to fit various models to time series data, and select one of those models using Bayesian model comparison. Here, we present a critical evaluation of DCM in which we show that DCM can be challenged on several grounds. We will discuss three main points relating to combinatorial explosion, the validity of the model selection procedure, and problems with respect to model validation.

Underconnected, but how? A survey of functional connectivity MRI studies in autism spectrum disorders

Growing consensus suggests that autism spectrum disorders (ASD) are associated with atypical brain networks, thus shifting the focus to the study of connectivity. Many functional connectivity studies have reported underconnectivity in ASD, but results in others have been divergent. We conducted a survey of 32 functional connectivity magnetic resonance imaging studies of ASD for numerous methodological variables to distinguish studies supporting general underconnectivity (GU) from those not consistent with this hypothesis (NGU). Distinguishing patterns were apparent for several data analysis choices. The study types differed significantly with respect to low-pass filtering, task regression, and whole-brain field of view. GU studies were more likely to examine task-driven time series in regions of interest, without the use of low-pass filtering. Conversely, NGU studies mostly applied task regression (for removal of activation effects) and low-pass filtering, testing for correlations across the whole brain. Results thus suggest that underconnectivity findings may be contingent on specific methodological choices. Whereas underconnectivity reflects reduced efficiency of within-network communication in ASD, diffusely increased functional connectivity can be attributed to impaired experience-driven mechanisms (e.g., synaptic pruning). Both GU and NGU findings reflect important aspects of network dysfunction associated with sociocommunicative, cognitive, and sensorimotor impairments in ASD.

A role of diffusion tensor imaging in movement disorder surgery

The safe and reversible nature of deep brain stimulation (DBS) has allowed movement disorder neurosurgery to become commonplace throughout the world. Fundamental understanding of individual patient’s anatomy is critical for optimizing the effects and side effects of DBS surgery. Three patients undergoing stereotactic surgery for movement disorders, at the institution’s intraoperative magnetic resonance imaging operating suite, were studied with fiber tractography. Stereotactic targets and fiber tractography were determined on preoperative magnetic resonance imagings using the Schaltenbrand–Wahren atlas for definition in the BrainLab iPlan software (BrainLAB Inc., Feldkirchen, Germany). Subthalamic nucleus, globus pallidus interna, and ventral intermediate nucleus targets were studied. Diffusion tensor imaging parameters used ranged from 2 to 8 mm for volume of interest in the x/y/z planes, fiber length was kept constant at 30 mm, and fractional anisotropy threshold varied from 0.20 to 0.45. Diffusion tensor imaging tractography allowed reliable and reproducible visualization and correlation between frontal eye field, premotor, primary motor, and primary sensory cortices via corticospinal tracts and corticopontocerebellar tracts. There is an apparent increase in the number of cortical regions targeted by the fiber tracts as the region of interest is enlarged. This represents a possible mechanism of the increased effects and side effects observed with higher stimulation voltages. Currently available diffusion tensor imaging techniques allow potential methods to characterize the effects and side effects of DBS. This technology has the potential of being a powerful tool to optimize DBS neurosurgery.

Functional connectivity and neurological recovery

Modern theories of brain function emphasize the importance of distributed functional networks and synchronized activity within and between networks in mediating cognitive functions. This view highlights the importance of considering brain-behavior relationships after focsal lesions not only as the result of local structural damage but also as a more widespread alteration of the physiological state of networks connected to the lesion. Recent findings demonstrate coherent activity in large-scale brain networks not only during task performance, but also, surprisingly, at rest in the absence of stimuli, tasks, or overt responses. Moreover, breakdown of coherent activity at rest, even in regions that are structurally intact, correlates with behavioral deficits and with their recovery after injury. This network perspective is fundamental to understand not only healthy brain function, but also the pathophysiology of brain injuries, mechanisms of functional recovery, and the basis for novel interventions for therapy.

Connectivity Analysis is Essential to Understand Neurological Disorders

Neurological and neuropsychiatric disorders are major causes of morbidity worldwide. A systems level analysis including functional and structural neuroimaging is particularly useful when the pathology leads to disorders of higher order cognitive functions in human patients. However, an analysis that is restricted to regional effects is impoverished and insensitive, compared to the analysis of distributed brain networks. We discuss the issues to consider when choosing an appropriate connectivity method, and compare the results from several different methods that are relevant to fMRI and PET data. These include psychophysiological interactions in general linear models, structural equation modeling, dynamic causal modeling, and independent components analysis. The advantages of connectivity analysis are illustrated with a range of structural and neurodegenerative brain disorders. We illustrate the sensitivity of these methods to the presence or severity of disease and/or treatment, even where analyses of voxel-wise activations are insensitive. However, functional and structural connectivity methods should be seen as complementary to, not a substitute for, other imaging and behavioral approaches. The functional relevance of changes in connectivity, to motor or cognitive performance, are considered alongside the complex relationship between structural and functional changes and neuropathology. Finally some of the problems associated with connectivity analysis are discussed. We suggest that the analysis of brain connectivity is an essential complement to the analysis of regionally specific dysfunction, in order to understand neurological and neuropsychiatric disease, and to evaluate the mechanisms of effective therapies.

Estimation of directional coupling between cortical areas using Near-Infrared Spectroscopy (NIRS)

This study invesitigated the feasibility of measuring directional coupling between cortical areas with near-infrared spectroscopy (NIRS). Cerebral hemodynamic responses were recorded at the primary somatosensory cortex (S1), secondary somatosensory cortex (S2), and primary motor cortex (M1) regions of the rat barrel cortex during electrical stimulation of rat whiskers. Deoxyhemoglobin concentration changes were calculated from NIRS recordings and the Granger causality based on the multivariate autoregressive (MVAR) model was used to estimate the effective causal connectivity among S1, S2, and M1. The estimated causality patterns of seven rats showed consistent unidirectional coupling between the somatosensory areas and the motor areas (S1 and S2-->M1), which coincided well with our hypothesis because the rats' motor function was completely anesthetized. Our preliminary results suggest that cortico-cortical directional coupling can be successfully investigated with NIRS.

Semantic domain-specific functional integration for action-related vs. abstract concepts

A central topic in cognitive neuroscience concerns the representation of concepts and the specific neural mechanisms that mediate conceptual knowledge. Recently proposed modal theories assert that concepts are grounded on the integration of multimodal, distributed representations. The aim of the present work is to complement the available neuropsychological and neuroimaging evidence suggesting partially segregated anatomo-functional correlates for concrete vs. abstract concepts, by directly testing the semantic domain-specific patterns of functional integration between language and modal semantic brain regions. We report evidence from a functional magnetic resonance imaging study, in which healthy participants listened to sentences with either an action-related (actions involving physical entities) or an abstract (no physical entities involved) content. We measured functional integration using dynamic causal modeling, and found that the left superior temporal gyrus was more strongly connected: (1) for action-related vs. abstract sentences, with the left-hemispheric action representation system, including sensorimotor areas; (2) for abstract vs. action-related sentences, with left infero-ventral frontal, temporal, and retrosplenial cingulate areas. A selective directionality effect was observed, with causal modulatory effects exerted by perisylvian language regions on peripheral modal areas, and not vice versa. The observed condition-specific modulatory effects are consistent with embodied and situated language processing theories, and indicate that linguistic areas promote a semantic content-specific reactivation of modal simulations by top-down mechanisms.

Cerebral localization of functions and the neurology of language: fact versus fiction or is it something else?

Over the last 15 years there has been a burgeoning number of publications using functional brain imaging (>40,000 articles based on an ISI/Web of Science search) to localize behavioral and cognitive processes to specific areas in the human brain that are often not confirmed by traditional, lesion-based studies. Thus, there is a need to reassess what cerebral localization of functions is and is not. Otherwise, there is no rational way to interpret the escalating claims of localization in the functional imaging literature that is taking on the appearance of neurophysiologic "phrenology". This article will present arguments to suggest that functional localization in the brain is a robust but very dynamic, four-dimensional process. It is a learned phenomenon driven over time by large-scale, spatially distributed, neural networks seeking to efficiently maximize the processing, storage, and manipulation of information for cognitive and behavioral operations. Because of historical considerations and space limitations, the main focus will be on localization of language-related functions whose theoretical neurological basis can be generalized for any complex cognitive-behavioral function.

The timing of emotional discrimination in human amygdala and ventral visual cortex

Models of visual emotional perception suggest a reentrant organization of the ventral visual system with the amygdala. Using focused functional magnetic resonance imaging in humans with a sampling rate of 100 ms, here we determine the relative timing of emotional discrimination in amygdala and ventral visual cortical structures during emotional perception. Results show that amygdala and inferotemporal visual cortex differentiate emotional from nonemotional scenes approximately 1 s before extrastriate occipital cortex, whereas primary occipital cortex shows consistent activity across all scenes. This pattern of discrimination is consistent with a reentrant organization of emotional perception in visual processing, in which transaction between rostral ventral visual cortex and amygdala originates the identification of emotional relevance.

Ten simple rules for dynamic causal modeling

Dynamic causal modeling (DCM) is a generic Bayesian framework for inferring hidden neuronal states from measurements of brain activity. It provides posterior estimates of neurobiologically interpretable quantities such as the effective strength of synaptic connections among neuronal populations and their context-dependent modulation. DCM is increasingly used in the analysis of a wide range of neuroimaging and electrophysiological data. Given the relative complexity of DCM, compared to conventional analysis techniques, a good knowledge of its theoretical foundations is needed to avoid pitfalls in its application and interpretation of results. By providing good practice recommendations for DCM, in the form of ten simple rules, we hope that this article serves as a helpful tutorial for the growing community of DCM users.

Dynamic Causal Modeling applied to fMRI data shows high reliability

Sensitivity, specificity, and reproducibility are vital to interpret neuroscientific results from functional magnetic resonance imaging (fMRI) experiments. Here we examine the scan-rescan reliability of the percent signal change (PSC) and parameters estimated using Dynamic Causal Modeling (DCM) in scans taken in the same scan session, less than 5 min apart. We find fair to good reliability of PSC in regions that are involved with the task, and fair to excellent reliability with DCM. Also, the DCM analysis uncovers group differences that were not present in the analysis of PSC, which implies that DCM may be more sensitive to the nuances of signal changes in fMRI data.

Assessing neuronal networks: understanding Alzheimer's disease

Findings derived from neuroimaging of the structural and functional organization of the human brain have led to the widely supported hypothesis that neuronal networks of temporally coordinated brain activity across different regional brain structures underpin cognitive function. Failure of integration within a network leads to cognitive dysfunction. The current discussion on Alzheimer's disease (AD) argues that it presents in part a disconnection syndrome. Studies using functional magnetic resonance imaging, positron emission tomography and electroencephalography demonstrate that synchronicity of brain activity is altered in AD and correlates with cognitive deficits. Moreover, recent advances in diffusion tensor imaging have made it possible to track axonal projections across the brain, revealing substantial regional impairment in fiber-tract integrity in AD. Accumulating evidence points towards a network breakdown reflecting disconnection at both the structural and functional system level. The exact relationship among these multiple mechanistic variables and their contribution to cognitive alterations and ultimately decline is yet unknown. Focused research efforts aimed at the integration of both function and structure hold great promise not only in improving our understanding of cognition but also of its characteristic progressive metamorphosis in complex chronic neurodegenerative disorders such as AD.

Large-scale neural model validation of partial correlation analysis for effective connectivity investigation in functional MRI

Recent studies of functional connectivity based upon blood oxygen level dependent functional magnetic resonance imaging have shown that this technique allows one to investigate large-scale functional brain networks. In a previous study, we advocated that data-driven measures of effective connectivity should be developed to bridge the gap between functional and effective connectivity. To attain this goal, we proposed a novel approach based on the partial correlation matrix. In this study, we further validate the use of partial correlation analysis by employing a large-scale, neurobiologically realistic neural network model to generate simulated data that we analyze with both structural equation modeling (SEM) and the partial correlation approach. Unlike real experimental data, where the interregional anatomical links are not necessarily known, the links between the nodes of the network model are fully specified, and thus provide a standard against which to judge the results of SEM and partial correlation analyses. Our results show that partial correlation analysis from the data alone exhibits patterns of effective connectivity that are similar to those found using SEM, and both are in agreement with respect to the underlying neuroarchitecture. Our findings thus provide a strong validation for the partial correlation method.

How well does structural equation modeling reveal abnormal brain anatomical connections? An fMRI simulation study

Many brain disorders result from alterations in the strength of anatomical connectivity between different brain regions. This study investigates whether such alterations can be revealed by examining differences in interregional effective connectivity between patient and normal subjects. We applied one prominent effective connectivity method - Structural Equation Modeling (SEM) - to simulated functional MRI (fMRI) timeseries from a neurobiologically realistic network model in which the anatomical connectivity is known and can be manipulated. These timeseries were simulated for two task conditions, a delayed match-to-sample (DMS) task and passive-viewing, and for "normal subjects" and "patients" who had one weakened anatomical connection in the neural network model. SEM results were compared between task conditions as well as between groups. A significantly reduced effective connectivity corresponding to the weakened anatomical connection during the DMS task was found. We also obtained a significantly reduced set of effective connections in the patient networks for anatomical connections "downstream" from the weakened linkage. However, some "upstream" effective connections were significantly larger in the patient group relative to normals. Finally, we found that of the SEM model measures we examined, the total error variance was the best at distinguishing a patient network from a normal network. These results suggest that caution is necessary in applying effective connectivity methods to fMRI data obtained from non-normal populations, and emphasize that functional interactions among network elements can appear as abnormal even if only part of a network is damaged.

From loci to networks and back again: anomalies in the study of autism

Recent developments in functional imaging as well as the emergence of new anatomical imaging techniques suited for the study of white matter have shifted investigational paradigms from a localized to a more holistic network approach. Aside from detecting local activity, functional MRI can be applied to the study of connectivity. However, the concept of "functional connectivity" remains broad, and specific designs and analyses may affect the results. In addition, connectivity cannot be viewed in isolation. Rather, from a developmental perspective, connectivity and local cortical architecture are intimately related. Therefore, combined approaches examining local organization and connectivity are the most promising avenues for elucidating disturbances of neurofunctional organization in developmental disorders. Here this approach is illustrated via data obtained from autism research that suggest impaired local cortical architecture and reduced long-range connectivity between cerebral regions.

How does reward expectation influence cognition in the human brain?

The prospect of reward changes how we think and behave. We investigated how this occurs in the brain using a novel continuous performance task in which fluctuating reward expectations biased cognitive processes between competing spatial and verbal tasks. Critically, effects of reward expectancy could be distinguished from induced changes in task-related networks. Behavioral data confirm specific bias toward a reward-relevant modality. Increased reward expectation improves reaction time and accuracy in the relevant dimension while reducing sensitivity to modulations of stimuli characteristics in the irrelevant dimension. Analysis of functional magnetic resonance imaging data shows that the proximity to reward over successive trials is associated with increased activity of the medial frontal cortex regardless of the modality. However, there are modality-specific changes in brain activity in the lateral frontal, parietal, and temporal cortex. Analysis of effective connectivity suggests that reward expectancy enhances coupling in both early visual pathways and within the prefrontal cortex. These distributed changes in task-related cortical networks arise from subjects' representations of future events and likelihood of reward.

Imaging structural and functional connectivity: towards a unified definition of human brain organization?

PURPOSE OF REVIEW

Diffusion tractography and functional/effective connectivity MRI provide a better understanding of the structural and functional human brain connectivity. This review will underline the major recent methodological developments and their exceptional respective contributions to physiological and pathophysiological studies in vivo. We will also emphasize the benefits provided by computational models of complex networks such as graph theory.

RECENT FINDINGS

Imaging structural and functional brain connectivity has revealed the complex brain organization into large-scale networks. Such an organization not only permits the complex information segregation and integration during high cognitive processes but also determines the clinical consequences of alterations encountered in development, ageing, or neurological diseases. Recently, it has also been demonstrated that human brain networks shared topological properties with the so-called 'small-world' mathematical model, allowing a maximal efficiency with a minimal energy and wiring cost.

SUMMARY

Separately, magnetic resonance tractography and functional MRI connectivity have both brought new insights into brain organization and the impact of injuries. The small-world topology of structural and functional human brain networks offers a common framework to merge structural and functional imaging as well as dynamical data from electrophysiology that might allow a comprehensive definition of the brain organization and plasticity.

Negation in the brain: modulating action representations

Sentential negation is a universal syntactic feature of human languages that reverses the truth value expressed by a sentence. An intriguing question concerns what brain mechanisms underlie our ability to represent and understand the meaning of negative sentences. We approach this issue by investigating action-related language processing and the associated neural representations. Using functional magnetic resonance imaging we measured brain activity in 18 healthy subjects during passive listening of sentences characterized by a factorial combination of polarity (affirmative vs. negative) and concreteness (action-related vs. abstract). Negation deactivated cortical areas and the left pallidum. Compared to abstract sentences, action-related sentences activated the left-hemispheric action-representation system. Crucially, the polarity by concreteness interactions showed that the activity within the action-representation system was specifically reduced for negative action-related vs. affirmative action-related sentences (compared to abstract sentences). Accordingly, functional integration within this system as measured by Dynamic Causal Modeling was specifically weaker for negative action-related than for affirmative action-related sentences. This modulation of action representations indicates that sentential negation transiently reduces the access to mental representations of the negated information.

Cognitive neuroscience of aging

The number of reports on the cognitive neuroscience of aging has increased in recent years, and most of these studies have found many similarities in the patterns of activity in young and old adults, indicating that basic neural mechanisms are maintained into older age. Despite these overall similarities, older adults often have less activity in some regions, such as medial temporal areas during memory processing and visual regions across a variety of cognitive domains. It seems clear that age reductions in cognitive function can be tied, at least in part, to these reductions in brain activity. On the other hand, older adults typically also overrecruit some brain areas, mainly the ventral or dorsal prefrontal cortex during memory tasks, as well as both the frontal and parietal regions during tasks engaging cognitive control processes, such as attention. Sometimes this overrecruitment appears to be in response to altered function in other brain regions and is often seen in those older adults who perform better on the task at hand. These findings have provided rather convincing support for the idea that overrecruitment can be compensatory in the elderly. Nevertheless, not all age increases can be interpreted as compensatory, and some are more indicative of neural inefficiency. The challenge facing future research will be to understand the task conditions that promote compensation in older adults, the role of the various brain areas in aiding cognitive function, and how these compensatory mechanisms can be elicited to enhance quality of life in the elderly.