fMRI resting state time series causality: comparison of Granger causality and phase slope index

Granger causality and Phase Slope Index (PSI) are recent approaches to measure how one signal depends on another, which gives an indication of information flow in complex systems. We show that the Granger causality and PSI mapping, voxel-by-voxel, for functional magnetic resonance imaging (fMRI) resting state data set. Slow fluctuations (< 0.1 Hz) in fMRI signal have been used to map several consistent resting state networks in the brain. The results demonstrate that PSI influence directions among reference regions and gray matter voxels were more consistent with the relevant previous studies compared with Granger causality. The PSI approach proposed is effective, computationally efficient, and easy to interpret

Granger causal time-dependent source connectivity in the somatosensory network

Peer reviewedPublisher PD

State space modeling of time-varying contemporaneous and lagged relations in connectivity maps

Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample

Multivariate Granger causality unveils directed parietal to prefrontal cortex connectivity during task-free MRI.

While a large body of research has focused on the study of functional brain "connectivity", few investigators have focused on directionality of brain-brain interactions which, in spite of the mostly bidirectional anatomical substrates, cannot be assumed to be symmetrical. We employ a multivariate Granger Causality-based approach to estimating directed in-network interactions and quantify its advantages using extensive realistic synthetic BOLD data simulations to match Human Connectome Project (HCP) data specification. We then apply our framework to resting state functional MRI (rs-fMRI) data provided by the HCP to estimate the directed connectome of the human brain. We show that the functional interactions between parietal and prefrontal cortices commonly observed in rs-fMRI studies are not symmetrical, but consists of directional connectivity from parietal areas to prefrontal cortices rather than vice versa. These effects are localized within the same hemisphere and do not generalize to cross-hemispheric functional interactions. Our data are consistent with neurophysiological evidence that posterior parietal cortices involved in processing and integration of multi-sensory information modulate the function of more anterior prefrontal regions implicated in action control and goal-directed behaviour. The directionality of functional connectivity can provide an additional layer of information in interpreting rs-fMRI studies both in health and disease

Forecasting brain activity based on models of spatiotemporal brain dynamics: A comparison of graph neural network architectures

Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph-structured signals like those observed in complex brain networks. In our study we compare different spatiotemporal GNN architectures and study their ability to model neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently often used for directed functional connectivity analysis. We show that by learning localized functional interactions on the anatomical substrate, GNN-based approaches are able to robustly scale to large network studies, even when available data are scarce. By including anatomical connectivity as the physical substrate for information propagation, such GNNs also provide a multimodal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatiotemporal dynamics in brain networks

Controlling for non-inhibitory processes in response inhibition research

Central to human adaptive behaviour is the ability to update one’s motor actions in the face of environmental changes, for which a key component is the ability to inhibit ongoing actions that are no longer appropriate. A substantial body of previous research has implicated the right inferior frontal gyrus (rIFG) and the pre-supplementary motor area (pre-SMA) as plausible sources of inhibitory control, but it remains unclear whether these regions host a specialised inhibitory control mechanism or instead support a more general system of action updating. This uncertainty stems from the limited number of studies that have controlled for non-inhibitory processes in response inhibition research. The overarching aim of this thesis was to resolve this ambiguity by studying behaviour, neurophysiology and neurochemistry during action updating in the presence and absence of inhibition. For the key experiments, detailed methods and hypotheses were pre-registered prior to data collection to minimise research bias and ensure transparent discrimination of confirmatory and exploratory inferences