Jaroslav Hlinka

Disconnection as a mechanism for cognitive dysfunction in multiple sclerosis

Disconnection of cognitively important processing regions by injury to the interconnecting white matter provides a potential mechanism for cognitive dysfunction in multiple sclerosis. The contribution of tract-specific white matter injury to dysfunction in different cognitive domains in patients with multiple sclerosis has not previously been studied. We apply tract-based spatial statistics (TBSS) to diffusion tensor imaging (DTI) in a cohort of multiple sclerosis patients to identify loci where reduced white matter tract fractional anisotropy (FA) predicts impaired performance in cognitive testing. Thirty-seven multiple sclerosis patients in remission (median age 43.5 years; Expanded Disability Status Scale range 1.5-6.5; 35 relapsing remitting, two secondary-progressive) underwent 3 T MRI including high-resolution DTI. Multiple sclerosis patients underwent formal testing of performance in multiple cognitive domains. Normalized cognitive scores were used for voxel-wise statistical analysis using TBSS, while treating age as a covariate of no interest. Permutation-based inference on cluster size (t > 2, P <0.05 corrected) was used to correct for multiple comparisons. Statistical mapping revealed differential patterns of FA reduction for tests of sustained attention, working memory and processing speed, visual working memory and verbal learning and recall. FA was not associated with frontal lobe function or visuospatial perception. Cognitively relevant tract localizations only partially overlapped with areas of high FLAIR lesion probability, confirming the contribution of normal-appearing white matter abnormality to cognitive dysfunction. Of note, tract localizations showing significant associations with cognitive impairment were found to interconnect cortical regions thought to be involved in processing in these cognitive domains, or involve possible compensatory processing pathways. This suggests that TBSS reveals functionally relevant tract injury underlying cognitive dysfunction in patients with multiple sclerosis.

Functional connectivity in resting-state fMRI: is linear correlation sufficient?

Functional connectivity (FC) analysis is a prominent approach to analyzing fMRI data, especially acquired under the resting state condition. The commonly used linear correlation FC measure bears an implicit assumption of Gaussianity of the dependence structure. If only the marginals, but not all the bivariate distributions are Gaussian, linear correlation consistently underestimates the strength of the dependence. To assess the suitability of linear correlation and the general potential of nonlinear FC measures, we present a framework for testing and estimating the deviation from Gaussianity by means of comparing mutual information in the data and its Gaussianized counterpart. We apply this method to 24 sessions of human resting state fMRI. For each session, matrix of connectivities between 90 anatomical parcel time series is computed using mutual information and compared to results from its multivariate Gaussian surrogate that conserves the correlations but cancels any nonlinearity. While the group-level tests confirmed non-Gaussianity in the FC, the quantitative assessment revealed that the portion of mutual information neglected by linear correlation is relatively minor-on average only about 5% of the mutual information already captured by the linear correlation. The marginality of the non-Gaussianity was confirmed in comparisons using clustering of the parcels-the disagreement between clustering obtained from mutual information and linear correlation was attributable to random error. We conclude that for this type of data, practical relevance of nonlinear methods trying to improve over linear correlation might be limited by the fact that the data are indeed almost Gaussian.

Slow EEG pattern predicts reduced intrinsic functional connectivity in the default mode network: An inter-subject analysis

The last two decades have witnessed great progress in mapping neural networks associated with task-induced brain activation. More recently, identification of resting state networks (RSN) paved the way to investigate spontaneous task-unrelated brain activity. The cardinal features characterising RSN are low-frequency fluctuations of blood oxygenation level dependent (BOLD) signals synchronised between spatially distinct, but functionally connected brain areas. Simultaneous EEG/fMRI has been previously deployed to study the neurophysiological signature of RSN by comparing EEG power with BOLD amplitudes. We hypothesised that band-limited EEG power may be directly related to network-specific functional connectivity (FC) of BOLD signal time courses. Hence, we studied the association between individual EEG signature and FC in a core RSN, the so-called default mode network (DMN). Combined EEG/fMRI data of 20 healthy volunteers collected during a 15-minute rest period were analysed. Using an inter-subject analysis design, we demonstrated a network and frequency specific relation between RSN FC and EEG. In a multiple regression model, EEG band-powers explained 70% of DMN FC variance, with significant partial correlations of DMN FC to delta (r=-0.73) and beta (r=0.53) power. The identified EEG pattern has been previously associated with increased alertness. Conversely, an established EEG-derived sedation index (spectral edge frequency SEF95) closely correlated with DMN FC. The study presents an approach that opens a new perspective to EEG/fMRI correlation. Direct evidence was provided for a distinct neurophysiological correlate of DMN FC. This finding further validates the biological relevance of network-specific intrinsic FC and provides an initial neurophysiological basis for interpreting studies of DMN FC alterations.

Identifying causal gateways and mediators in complex spatio-temporal systems.

Identifying regions important for spreading and mediating perturbations is crucial to assess the susceptibilities of spatio-temporal complex systems such as the Earths climate to volcanic eruptions, extreme events or geoengineering. Here a data-driven approach is introduced based on a dimension reduction, causal reconstruction, and novel network measures based on causal effect theory that go beyond standard complex network tools by distinguishing direct from indirect pathways. Applied to a data set of atmospheric dynamics, the method identifies several strongly uplifting regions acting as major gateways of perturbations spreading in the atmosphere. Additionally, the method provides a stricter statistical approach to pathways of atmospheric teleconnections, yielding insights into the Pacific–Indian Ocean interaction relevant for monsoonal dynamics. Also for neuroscience or power grids, the novel causal interaction perspective provides a complementary approach to simulations or experiments for understanding the functioning of complex spatio-temporal systems with potential applications in increasing their resilience to shocks or extreme events.

Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information

Across geosciences, many investigated phenomena relate to specific complex systems consisting of intricately intertwined interacting subsystems. Such dynamical complex systems can be represented by a directed graph, where each link denotes an existence of a causal relation, or information exchange between the nodes. For geophysical systems such as global climate, these relations are commonly not theoretically known but estimated from recorded data using causality analysis methods. These include bivariate nonlinear methods based on information theory and their linear counterpart. The trade-off between the valuable sensitivity of nonlinear methods to more general interactions and the potentially higher numerical reliability of linear methods may affect inference regarding structure and variability of climate networks. We investigate the reliability of directed climate networks detected by selected methods and parameter settings, using a stationarized model of dimensionality-reduced surface air temperature data from reanalysis of 60-year global climate records. Overall, all studied bivariate causality methods provided reproducible estimates of climate causality networks, with the linear approximation showing

Non-linear dependence and teleconnections in climate data: sources, relevance, nonstationarity

Quantification of relations between measured variables of interest by statistical measures of dependence is a common step in analysis of climate data. The choice of dependence measure is key for the results of the subsequent analysis and interpretation. The use of linear Pearson’s correlation coefficient is widespread and convenient. On the other side, as the climate is widely acknowledged to be a nonlinear system, nonlinear dependence quantification methods, such as those based on information-theoretical concepts, are increasingly used for this purpose. In this paper we outline an approach that enables well informed choice of dependence measure for a given type of data, improving the subsequent interpretation of the results. The presented multi-step approach includes statistical testing, quantification of the specific non-linear contribution to the interaction information, localization of areas with strongest nonlinear contribution and assessment of the role of specific temporal patterns, including signal nonstationarities. In detail we study the consequences of the choice of a general nonlinear dependence measure, namely mutual information, focusing on its relevance and potential alterations in the discovered dependence structure. We document the method by applying it to monthly mean temperature data from the NCEP/NCAR reanalysis dataset as well as the ERA dataset. We have been able to identify main sources of observed non-linearity in inter-node couplings. Detailed analysis suggested an important role of several sources of nonstationarity within the climate data. The quantitative role of genuine nonlinear coupling at monthly scale has proven to be almost negligible, providing quantitative support for the use of linear methods for monthly temperature data.

Diffusion tensor imaging and MR morphometry of the central auditory pathway and auditory cortex in aging.

Age-related hearing loss (presbycusis) is caused mainly by the hypofunction of the inner ear, but recent findings point also toward a central component of presbycusis. We used MR morphometry and diffusion tensor imaging (DTI) with a 3T MR system with the aim to study the state of the central auditory system in a group of elderly subjects (>65years) with mild presbycusis, in a group of elderly subjects with expressed presbycusis and in young controls. Cortical reconstruction, volumetric segmentation and auditory pathway tractography were performed. Three parameters were evaluated by morphometry: the volume of the gray matter, the surface area of the gyrus and the thickness of the cortex. In all experimental groups the surface area and gray matter volume were larger on the left side in Heschls gyrus and planum temporale and slightly larger in the gyrus frontalis superior, whereas they were larger on the right side in the primary visual cortex. Almost all of the measured parameters were significantly smaller in the elderly subjects in Heschls gyrus, planum temporale and gyrus frontalis superior. Aging did not change the side asymmetry (laterality) of the gyri. In the central part of the auditory pathway above the inferior colliculus, a trend toward an effect of aging was present in the axial vector of the diffusion (L1) variable of DTI, with increased values observed in elderly subjects. A trend toward a decrease of L1 on the left side, which was more pronounced in the elderly groups, was observed. The effect of hearing loss was present in subjects with expressed presbycusis as a trend toward an increase of the radial vectors (L2L3) in the white matter under Heschls gyrus. These results suggest that in addition to peripheral changes, changes in the central part of the auditory system in elderly subjects are also present; however, the extent of hearing loss does not play a significant role in the central changes.

Small-world topology of functional connectivity in randomly connected dynamical systems.

Characterization of real-world complex systems increasingly involves the study of their topological structure using graph theory. Among global network properties, small-world property, consisting in existence of relatively short paths together with high clustering of the network, is one of the most discussed and studied. When dealing with coupled dynamical systems, links among units of the system are commonly quantified by a measure of pairwise statistical dependence of observed time series (functional connectivity). We argue that the functional connectivity approach leads to upwardly biased estimates of small-world characteristics (with respect to commonly used random graph models) due to partial transitivity of the accepted functional connectivity measures such as the correlation coefficient. In particular, this may lead to observation of small-world characteristics in connectivity graphs estimated from generic randomly connected dynamical systems. The ubiquity and robustness of the phenomenon are documented by an extensive parameter study of its manifestation in a multivariate linear autoregressive process, with discussion of the potential relevance for nonlinear processes and measures.

The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks.

In recent years, there has been an increasing interest in the study of large-scale brain activity interaction structure from the perspective of complex networks, based on functional magnetic resonance imaging (fMRI) measurements. To assess the strength of interaction (functional connectivity, FC) between two brain regions, the linear (Pearson) correlation coefficient of the respective time series is most commonly used. Since a potential use of nonlinear FC measures has recently been discussed in this and other fields, the question arises whether particular nonlinear FC measures would be more informative for the graph analysis than linear ones. We present a comparison of network analysis results obtained from the brain connectivity graphs capturing either full (both linear and nonlinear) or only linear connectivity using 24 sessions of human resting-state fMRI. For each session, a matrix of full connectivity between 90 anatomical parcel time series is computed using mutual information. For comparison, connectivity matrices obtained for multivariate linear Gaussian surrogate data that preserve the correlations, but remove any nonlinearity are generated. Binarizing these matrices using multiple thresholds, we generate graphs corresponding to linear and full nonlinear interaction structures. The effect of neglecting nonlinearity is then assessed by comparing the values of a range of graph-theoretical measures evaluated for both types of graphs. Statistical comparisons suggest a potential effect of nonlinearity on the local measures-clustering coefficient and betweenness centrality. Nevertheless, subsequent quantitative comparison shows that the nonlinearity effect is practically negligible when compared to the intersubject variability of the graph measures. Further, on the group-average graph level, the nonlinearity effect is unnoticeable.

Active optical control of the terahertz reflectivity of high-resistivity semiconductors.

We study theoretically and demonstrate experimentally light-controllable terahertz reflectivity of high-resistivity semiconductor wafers. Photocarriers created by interband light absorption form a thin conducting layer at the semiconductor surface, which allows the terahertz reflectivity of the element to be tuned between antireflective (R <3%) and highly reflective (R >85%) limits by means of the intensity and wavelength of the optical illumination.