Alard Roebroeck

Mapping directed influence over the brain using Granger causality and fMRI

We propose Granger causality mapping (GCM) as an approach to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguishes it from other fMRI effective connectivity approaches that aim at testing or contrasting specific hypotheses about neuronal interactions. Instead, GCM relies on the concept of Granger causality to define the existence and direction of influence from information in the data. Temporal precedence information is exploited to compute Granger causality maps that identify voxels that are sources or targets of directed influence for any selected region-of-interest. We investigated the method by simulations and by application to fMRI data of a complex visuomotor task. The presented exploratory approach of mapping influences between a region of interest and the rest of the brain can form a useful complement to existing models of effective connectivity.

Effective connectivity: Influence, causality and biophysical modeling

This is the final paper in a Comments and Controversies series dedicated to “The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution”. We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener–Akaike–Granger–Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments.

Mapping the information flow from one brain to another during gestural communication

Both the putative mirror neuron system (pMNS) and the ventral medial prefrontal cortex (vmPFC) are deemed important for social interaction: the pMNS because it supposedly “resonates” with the actions of others, the vmPFC because it is involved in mentalizing. Strictly speaking, the resonance property of the pMNS has never been investigated. Classical functional MRI experiments have only investigated whether pMNS regions augment their activity when an action is seen or executed. Resonance, however, entails more than only “going on and off together”. Activity in the pMNS of an observer should continuously follow the more subtle changes over time in activity of the pMNS of the actor. Here we directly explore whether such resonance indeed occurs during continuous streams of actions. We let participants play the game of charades while we measured brain activity of both gesturer and guesser. We then applied a method to localize directed influences between the brains of the participants: between-brain Granger-causality mapping. Results show that a guessers brain activity in regions involved in mentalizing and mirroring echoes the temporal structure of a gesturers brain activity. This provides evidence for resonance theories and indicates a fine-grained temporal interplay between regions involved in motor planning and regions involved in thinking about the mental states of others. Furthermore, this method enables experiments to be more ecologically valid by providing the opportunity to leave social interaction unconstrained. This, in turn, would allow us to tap into the neural substrates of social deficits such as autism spectrum disorder.

The identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution

Functional magnetic resonance imaging (fMRI) is increasingly used to study functional connectivity in large-scale brain networks that support cognitive and perceptual processes. We face serious conceptual, statistical and data analysis challenges when addressing the combinatorial explosion of possible interactions within high-dimensional fMRI data. Moreover, we need to know, and account for, the physiological mechanisms underlying our signals. We argue here that (i) model selection procedures for connectivity should include consideration of more than just a few brain structures, (ii) temporal precedence - and causality concepts based on it - are essential in dynamic models of connectivity and (iii) undoing the effect of hemodynamics on fMRI data (by deconvolution) can be an important tool. However, it is crucially dependent upon assumptions that need to be verified.

Specialization in the default mode: Task-induced brain deactivations dissociate between visual working memory and attention

The idea of an organized mode of brain function that is present as default state and suspended during goal‐directed behaviors has recently gained much interest in the study of human brain function. The default mode hypothesis is based on the repeated observation that certain brain areas show task‐induced deactivations across a wide range of cognitive tasks. In this event‐related functional resonance imaging study we tested the default mode hypothesis by comparing common and selective patterns of BOLD deactivation in response to the demands on visual attention and working memory (WM) that were independently modulated within one task. The results revealed task‐induced deactivations within regions of the default mode network (DMN) with a segregation of areas that were additively deactivated by an increase in the demands on both attention and WM, and areas that were selectively deactivated by either high attentional demand or WM load. Attention‐selective deactivations appeared in the left ventrolateral and medial prefrontal cortex and the left lateral temporal cortex. Conversely, WM‐selective deactivations were found predominantly in the right hemisphere including the medial‐parietal, the lateral temporo‐parietal, and the medial prefrontal cortex. Moreover, during WM encoding deactivated regions showed task‐specific functional connectivity. These findings demonstrate that task‐induced deactivations within parts of the DMN depend on the specific characteristics of the attention and WM components of the task. The DMN can thus be subdivided into a set of brain regions that deactivate indiscriminately in response to cognitive demand (“the core DMN”) and a part whose deactivation depends on the specific task. Hum Brain Mapp, 2010.

Human cortical connectome reconstruction from diffusion weighted MRI: The effect of tractography algorithm

Reconstructing the macroscopic human cortical connectome by Diffusion Weighted Imaging (DWI) is a challenging research topic that has recently gained a lot of attention. In the present work, we investigate the effects of intra-voxel fiber direction modeling and tractography algorithm on derived structural network indices (e.g. density, small-worldness and global efficiency). The investigation is centered on three semi-independent distinctions within the large set of available diffusion models and tractography methods: i) single fiber direction versus multiple directions in the intra-voxel diffusion model, ii) deterministic versus probabilistic tractography and iii) local versus global measure-of-fit of the reconstructed fiber trajectories. The effect of algorithm and parameter choice has two components. First, there is the large effect of tractography algorithm and parameters on global network density, which is known to strongly affect graph indices. Second, and more importantly, there are remaining effects on graph indices which range in the tens of percent even when global density is controlled for. This is crucial for the sensitivity of any human structural network study and for the validity of study comparisons. We then investigate the effect of the choice of tractography algorithm on sensitivity and specificity of the resulting connections with a connectome dissection quality control (QC) approach. In this approach, evaluation of Tract Specific Density Coefficients (TSDCs) measures sensitivity while careful inspection of tractography path results assesses specificity. We use this to discuss interactions in the combined effects of these methods and implications for future studies.

The Brain's Intention to Imitate: The Neurobiology of Intentional versus Automatic Imitation

Whenever we observe a movement of a conspecific, our mirror neuron system becomes activated, urging us to imitate the observed movement. However, because such automatic imitation is not always appropriate, an inhibitive component keeping us from imitating everything we see seems crucial for an effective social behavior. This becomes evident from neuropsychological conditions like echopraxia, in which this suppression is absent. Here, we unraveled the neurodynamics underlying this proposed inhibition of automatic imitation by measuring and manipulating brain activity during the execution of a stimulus-response compatibility paradigm. Within the identified connectivity network, right middle/inferior frontal cortex sends neural input concerning general response inhibition to right premotor cortex, which is involved in automatic imitation. Subsequently, the fully prepared imitative response is sent to left opercular cortex that functions as a final gating mechanism for intentional imitation. We propose an informed neurocognitive model of inhibition of automatic imitation, suggesting a functional dissociation between automatic and intentional imitation.

Fighting food temptations: The modulating effects of short-term cognitive reappraisal, suppression and up-regulation on mesocorticolimbic activity related to appetitive motivation

The premise of cognitive therapy is that one can overcome the irresistible temptation of highly palatable foods by actively restructuring the way one thinks about food. Testing this idea, participants in the present study were instructed to passively view foods, up-regulate food palatability thoughts, apply cognitive reappraisal (e.g., thinking about health consequences), or suppress food palatability thoughts and cravings. We examined whether these strategies affect self-reported food craving and mesocorticolimbic activity as assessed by functional magnetic resonance imaging. It was hypothesized that cognitive reappraisal would most effectively inhibit the mesocorticolimbic activity and associated food craving as compared to suppression. In addition, it was hypothesized that suppression would lead to more prefrontal cortex activity, reflecting the use of more control resources, as compared to cognitive reappraisal. Self-report results indicated that up-regulation increased food craving compared to the other two conditions, but that there was no difference in craving between the suppression and cognitive reappraisal strategy. Corroborating self-report results, the neuroimaging results showed that up-regulation increased activity in important regions of the mesocorticolimbic circuitry, including the ventral tegmental area, ventral striatum, operculum, posterior insular gyrus, medial orbitofrontal cortex and ventromedial prefrontal cortex. Contrary to our hypothesis, suppression more effectively decreased activity in the core of the mesocorticolimbic circuitry (i.e., ventral tegmental area and ventral striatum) compared to cognitive reappraisal. Overall, the results support the contention that appetitive motivation can be modulated by the application of short-term cognitive control strategies.

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.

Histological Validation of DW-MRI Tractography in Human Postmortem Tissue

Despite several previous attempts, histological validation of diffusion-weighted magnetic resonance imaging (DW-MRI)-based tractography as true axonal fiber pathways remains difficult. In the present study, we establish a method to compare histological and tractography data precisely enough for statements on the level of single tractography pathways. To this end, we used carbocyanine dyes to trace connections in human postmortem tissue and aligned them to high-resolution DW-MRI of the same tissue processed within the diffusion tensor imaging (DTI) formalism. We provide robust definitions of sensitivity (true positives) and specificity (true negatives) for DTI tractography and characterize tractography paths in terms of receiver operating characteristics. With sensitivity and specificity rates of approximately 80%, we could show a clear correspondence between histological and inferred tracts. Furthermore, we investigated the effect of fractional anisotropy (FA) thresholds for the tractography and identified FA values between 0.02 and 0.08 as optimal in our study. Last, we validated the course of entire tractography curves to move beyond correctness determination based on pairs of single points on a tract. Thus, histological techniques, in conjunction with alignment and processing tools, may serve as an important validation method of DW-MRI on the level of inferred tractography projections between brain areas.