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.

Identifying regions important for spreading and mediating perturbations is crucial to assess the susceptibilities of spatio-temporal complex systems such as the Earth's 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.

The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Theoretical interpretations considering the brain as a dynamical system suggest that prior to a seizure, recorded brain signals may exhibit critical slowing down, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we monitored key signatures of critical slowing down prior to seizures. The metrics used to detect critical slowing down fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing down is a reliable indicator that could be used in seizure forecasting algorithms.

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.

The mechanisms of seizure emergence, and the role of brief interictal epileptiform discharges (IEDs) in seizure generation are two of the most important unresolved issues in modern epilepsy research and clinical epileptology. Our study shows that the transition to seizure is not a sudden phenomenon, but a slow process characterized by the progressive loss of neuronal network resilience. From a dynamical perspective, the slow transition is governed by the principles of critical slowing, a robust natural phenomenon observable in systems characterized by transitions between contrasting dynamical regimes. In epilepsy, this complex process is modulated by the synchronous synaptic input from IEDs. IEDs are external perturbations that produce phasic changes in the slow transition process and can exert opposing effects on the dynamics of a seizuregenerating network, causing either stabilizing anti-seizure or destabilizing pro-seizure effects. We show that the multifaceted nature of IEDs is defined by the dynamical state of the seizuregenerating network at the moment of the discharge occurrence, not necessarily by the existence of distinct cellular mechanisms.

Abstract. The bias due to dynamical memory (serial correlations) in an association/dependence measure (absolute crosscorrelation) is demonstrated in model data and identified in time series of meteorological variables used for construction of climate networks. Accounting for such bias in inferring links of the climate network markedly changes the network topology and allows to observe previously hidden phenomena in climate network evolution.

No abstract

No abstract