Eva Výtvarová

Large-scale cortico-subcortical functional networks in focal epilepsies: The role of the basal ganglia

Objectives The aim was to describe the contribution of basal ganglia (BG) thalamo-cortical circuitry to the whole-brain functional connectivity in focal epilepsies. Methods Interictal resting-state fMRI recordings were acquired in 46 persons with focal epilepsies. Of these 46, 22 had temporal lobe epilepsy: 9 left temporal (LTLE), 13 right temporal (RTLE); 15 had frontal lobe epilepsy (FLE); and 9 had parietal/occipital lobe epilepsy (POLE). There were 20 healthy controls. The complete weighted network was analyzed based on correlation matrices of 90 and 194 regions. The network topology was quantified on a global and regional level by measures based on graph theory, and connection-level changes were analyzed by the partial least square method. Results In all patient groups except RTLE, the shift of the functional network topology away from random was observed (normalized clustering coefficient and characteristic path length were higher in patient groups than in controls). Links contributing to this change were found in the cortico-subcortical connections. Weak connections (low correlations) consistently contributed to this modification of the network. The importance of regions changed: decreases in the subcortical areas and both decreases and increases in the cortical areas were observed in node strength, clustering coefficient and eigenvector centrality in patient groups when compared to controls. Node strength decreases of the basal ganglia, i.e. the putamen, caudate, and pallidum, were displayed in LTLE, FLE, and POLE. The connectivity within the basal ganglia–thalamus circuitry was not disturbed; the disturbance concerned the connectivity between the circuitry and the cortex. Significance Focal epilepsies affect large-scale brain networks beyond the epileptogenic zones. Cortico-subcortical functional connectivity disturbance was displayed in LTLE, FLE, and POLE. Significant changes in the resting-state functional connectivity between cortical and subcortical structures suggest an important role of the BG and thalamus in focal epilepsies.

22-Effect of spatial smoothing on graph analysis of fMRI data

Introduction Smoothing is used to increase SNR. Here we examine it’s effect of smoothing on graph analysis of fMRI data. Methods We used fMRI data of 30 subjects from HCP (Human Connectome Project; 3T; motor task). We preprocessed the data using realign and unwarp, spatial normalization, filtering and smoothing with FWHM = {none, 3, 5, 8 and 11} mm, parcellated according AAL atlas, used mean and first eigenvector as representative signal. Finally, we performed graph analysis using average node strength, characteristic path length, lambda, efficiency, clustering coefficient, and gamma. Results Increase of FWHM is related to increase of clustering coefficient, node strength and efficiency, and to decrease of path length. Gamma and lambda are relatively not influenced by size of FMWH kernel. With low FHWM or no smoothing and using mean is the node strength higher and path length shorter. Discussion Increase of FWHM increases correlations in the network, i.e. weights in graph. Therefore node strength increases and characteristic path length decreases. Clustering coefficient also reflects increasing weights between neighboring nodes. Increase of correlation coefficients with higher FWHM is more prominent when using first eigenvector as representative signal. We recommend to smooth fMRI data consistently across study.

16-Investigating modularity and its capacity as a marker of neurodegenerative diseases

Early-stage detection of different kinds of dementia and cognitive impairments is an extremely important task. The neuroscience community is devoting great effort into finding potential markers of disease that would be noninvasive, easy to establish, and stable. In this paper, we evaluate different community detection algorithms and their strengths to discriminate between health and Parkinson’s disease (PD) and mild cognitive impairment preceding Alzheimer’s disease (AD-MCI). On a dataset of 50 controls (34 women; 66.74 ± 7.35 years) and 70 patients (35 women; 66.71 ± 9.44 years), the resting-state fMRI was measured. The data were preprocessed by 16 different variants of preprocessing, functional network for each subject and each preprocessing variant constructed and evaluated by diverse algorithms for modularity computation. The modularity coefficients reflect the ability of a network to form clusters. They were used as a classifier. We measured an increased modularity coefficient with 81.8% accuracy of classifying PD versus controls and 76.2% accuracy of classifying AD-MCI versus controls. Significantly higher modularity coefficient values were measured when the random matrix theory decomposition was adapted for network construction. These results were observed on networks of 82 nodes based on AAL atlas and 317 nodes based on multimodal parcellation atlas.

Robustness of Representative Signals Relative to Data Loss Using Atlas-Based Parcellations

Parcellation-based approaches are an important part of functional magnetic resonance imaging data analysis. They are a necessary processing step for sorting data in structurally or functionally homogenous regions. Real functional magnetic resonance imaging datasets usually do not cover the atlas template completely; they are often spatially constrained due to the physical limitations of MR sequence settings, the inter-individual variability in brain shape, etc. When using a parcellation template, many regions are not completely covered by actual data. This paper addresses the issue of the area coverage required in real data in order to reliably estimate the representative signal and the influence of this kind of data loss on network analysis metrics. We demonstrate this issue on four datasets using four different widely used parcellation templates. We used two erosion approaches to simulate data loss on the whole-brain level and the ROI-specific level. Our results show that changes in ROI coverage have a systematic influence on network measures. Based on the results of our analysis, we recommend controlling the ROI coverage and retaining at least 60% of the area in order to ensure at least 80% of explained variance of the original signal.

Investigating Community Detection Algorithms and their Capacity as Markers of Brain Diseases

In this paper, we present a workflow for evaluating resting-state brain functional connectivity with different community detection algorithms and their strengths to discriminate between health and Parkinsons disease (PD) and mild cognitive impairment preceding Alzheimers disease (AD-MCI). We further analyze the complexity of particular pipeline steps aiming to provide guidelines for both execution on computing infrastructure and further optimization efforts. On a dataset of 50 controls and 70 patients we measured an increased modularity coefficient with 81.8% accuracy of classifying PD versus controls and 76.2% accuracy of classifying AD-MCI versus controls. Significantly higher modularity coefficient values were measured when the random matrix theory decomposition was adapted for network construction. These results were observed on networks of 82 nodes based on AAL atlas and 317 nodes based on multimodal parcellation atlas.

Agent-Based Modelling And Simulation For The Geospatial Network Model Of The Roman World

In this paper we present a computational environment called LINUM for agent-based modelling, based on a geospatial transport model similar to the one implemented in ORBIS, The Stanford Geospatial Network Model of the Roman World. We provide the implementation of the transport model, and tools for creating and analyzing agent-based models around it. We also provide web-based visualization of the results of the simulation, and enable the user to generate time-collapsed static network for further analysis by the means of complex networks measures. The functionality of the environment is demonstrated on a model of diffusion process on the transport network.