Causal hierarchy within the thalamo-cortical network in spike and wave discharges

Background: Generalised spike wave (GSW) discharges are the electroencephalographic (EEG) hallmark of absence seizures, clinically characterised by a transitory interruption of ongoing activities and impaired consciousness, occurring during states of reduced awareness. Several theories have been proposed to explain the pathophysiology of GSW discharges and the role of thalamus and cortex as generators. In this work we extend the existing theories by hypothesizing a role for the precuneus, a brain region neglected in previous works on GSW generation but already known to be linked to consciousness and awareness. We analysed fMRI data using dynamic causal modelling (DCM) to investigate the effective connectivity between precuneus, thalamus and prefrontal cortex in patients with GSW discharges. Methodology and Principal Findings: We analysed fMRI data from seven patients affected by Idiopathic Generalized Epilepsy (IGE) with frequent GSW discharges and significant GSW-correlated haemodynamic signal changes in the thalamus, the prefrontal cortex and the precuneus. Using DCM we assessed their effective connectivity, i.e. which region drives another region. Three dynamic causal models were constructed: GSW was modelled as autonomous input to the thalamus (model A), ventromedial prefrontal cortex (model B), and precuneus (model C). Bayesian model comparison revealed Model C (GSW as autonomous input to precuneus), to be the best in 5 patients while model A prevailed in two cases. At the group level model C dominated and at the population-level the p value of model C was ∼1. Conclusion: Our results provide strong evidence that activity in the precuneus gates GSW discharges in the thalamo-(fronto) cortical network. This study is the first demonstration of a causal link between haemodynamic changes in the precuneus - an index of awareness - and the occurrence of pathological discharges in epilepsy. © 2009 Vaudano et al

A Novel Joint Brain Network Analysis Using Longitudinal Alzheimer's Disease Data.

There is well-documented evidence of brain network differences between individuals with Alzheimer's disease (AD) and healthy controls (HC). To date, imaging studies investigating brain networks in these populations have typically been cross-sectional, and the reproducibility of such findings is somewhat unclear. In a novel study, we use the longitudinal ADNI data on the whole brain to jointly compute the brain network at baseline and one-year using a state of the art approach that pools information across both time points to yield distinct visit-specific networks for the AD and HC cohorts, resulting in more accurate inferences. We perform a multiscale comparison of the AD and HC networks in terms of global network metrics as well as at the more granular level of resting state networks defined under a whole brain parcellation. Our analysis illustrates a decrease in small-worldedness in the AD group at both the time points and also identifies more local network features and hub nodes that are disrupted due to the progression of AD. We also obtain high reproducibility of the HC network across visits. On the other hand, a separate estimation of the networks at each visit using standard graphical approaches reveals fewer meaningful differences and lower reproducibility

Brain covariance selection: better individual functional connectivity models using population prior

Spontaneous brain activity, as observed in functional neuroimaging, has been shown to display reproducible structure that expresses brain architecture and carries markers of brain pathologies. An important view of modern neuroscience is that such large-scale structure of coherent activity reflects modularity properties of brain connectivity graphs. However, to date, there has been no demonstration that the limited and noisy data available in spontaneous activity observations could be used to learn full-brain probabilistic models that generalize to new data. Learning such models entails two main challenges: i) modeling full brain connectivity is a difficult estimation problem that faces the curse of dimensionality and ii) variability between subjects, coupled with the variability of functional signals between experimental runs, makes the use of multiple datasets challenging. We describe subject-level brain functional connectivity structure as a multivariate Gaussian process and introduce a new strategy to estimate it from group data, by imposing a common structure on the graphical model in the population. We show that individual models learned from functional Magnetic Resonance Imaging (fMRI) data using this population prior generalize better to unseen data than models based on alternative regularization schemes. To our knowledge, this is the first report of a cross-validated model of spontaneous brain activity. Finally, we use the estimated graphical model to explore the large-scale characteristics of functional architecture and show for the first time that known cognitive networks appear as the integrated communities of functional connectivity graph.Comment: in Advances in Neural Information Processing Systems, Vancouver : Canada (2010

Bipartite Functional Fractionation within the Default Network Supports Disparate Forms of Internally Oriented Cognition.

Our understanding about the functionality of the brain's default network (DN) has significantly evolved over the past decade. Whereas traditional views define this network based on its suspension/disengagement during task-oriented behavior, contemporary accounts have characterized various situations wherein the DN actively contributes to task performance. However, it is unclear how different task-contexts drive componential regions of the DN to coalesce into a unitary network and fractionate into different subnetworks. Here we report a compendium of evidence that provides answers to these questions. Across multiple analyses, we found a striking dyadic structure within the DN in terms of the profiles of task-triggered fMRI response and effective connectivity, significantly extending beyond previous inferences based on meta-analysis and resting-state activities. In this dichotomy, one subset of DN regions prefers mental activities "interfacing with" perceptible events, while the other subset prefers activities "detached from" perceptible events. While both show a common "aversion" to sensory-motoric activities, their differential preferences manifest a subdivision that sheds light upon the taxonomy of the brain's memory systems. This dichotomy is consistent with proposals of a macroscale gradational structure spanning across the cerebrum. This gradient increases its representational complexity, from primitive sensory-motoric processing, through lexical-semantic representations, to elaborated self-generated thoughts.This research was funded by an MRC programme grant to MALR (MR/R023883/1) and a Sir Henry Wellcome Fellowship (201381/Z/16/Z) to RC

Changes in structural network topology correlate with severity of hallucinatory behavior in Parkinson's disease

Inefficient integration between bottom-up visual input and higher order visual processing regions is implicated in visual hallucinations in Parkinson's disease (PD). Here, we investigated white matter contributions to this perceptual imbalance hypothesis. Twenty-nine PD patients were assessed for hallucinatory behavior. Hallucination severity was correlated to connectivity strength of the network using the network-based statistic approach. The results showed that hallucination severity was associated with reduced connectivity within a subnetwork that included the majority of the diverse club. This network showed overall greater between-module scores compared with nodes not associated with hallucination severity. Reduced between-module connectivity in the lateral occipital cortex, insula, and pars orbitalis and decreased within-module connectivity in the prefrontal, somatosensory, and primary visual cortices were associated with hallucination severity. Conversely, hallucination severity was associated with increased between- and within-module connectivity in the orbitofrontal and temporal cortex, as well as regions comprising the dorsal attentional and default mode network. These results suggest that hallucination severity is associated with marked alterations in structural network topology with changes in participation along the perceptual hierarchy. This may result in the inefficient transfer of information that gives rise to hallucinations in PD. Author SummaryInefficient integration of information between external stimuli and internal perceptual predictions may lead to misperceptions or visual hallucinations in Parkinson's disease (PD). In this study, we show that hallucinatory behavior in PD patients is associated with marked alterations in structural network topology. Severity of hallucinatory behavior was associated with decreased connectivity in a large subnetwork that included the majority of the diverse club, nodes with a high number of between-module connections. Furthermore, changes in between-module connectivity were found across brain regions involved in visual processing, top-down prediction centers, and endogenous attention, including the occipital, orbitofrontal, and posterior cingulate cortex. Together, these findings suggest that impaired integration across different sides across different perceptual processing regions may result in inefficient transfer of information

Effective Connectivity of Functionally Anticorrelated Networks Under Lysergic Acid Diethylamide

Background: Classic psychedelic-induced ego dissolution involves a shift in the sense of self and a blurring of the boundary between the self and the world. A similar phenomenon is identified in psychopathology and is associated with the balance of anticorrelated activity between the default mode network, which directs attention inward, and the salience network, which recruits the dorsal attention network to direct attention outward. Methods: To test whether changes in anticorrelated networks underlie the peak effects of lysergic acid diethylamide (LSD), we applied dynamic causal modeling to infer effective connectivity of resting-state functional magnetic resonance imaging scans from a study of 25 healthy adults who were administered 100 μg of LSD or placebo. Results: We found that inhibitory effective connectivity from the salience network to the default mode network became excitatory, and inhibitory effective connectivity from the default mode network to the dorsal attention network decreased under the peak effect of LSD. Conclusions: The effective connectivity changes we identified may reflect diminution of the functional anticorrelation between resting-state networks that may be a key neural mechanism of LSD and underlie ego dissolution. Our findings suggest that changes to the sense of self and subject-object boundaries across different states of consciousness may depend upon the organized balance of effective connectivity of resting-state networks