A5040682292- Functional Brain Connectivity Studies
- Neural dynamics and brain function
- Advanced Neuroimaging Techniques and Applications
- Complex Systems and Time Series Analysis
- EEG and Brain-Computer Interfaces
- Advanced MRI Techniques and Applications
- Complex Network Analysis Techniques
- Mental Health Research Topics
- Nonlinear Dynamics and Pattern Formation
- Climate variability and models
- Neuroscience and Neuropharmacology Research
- Schizophrenia research and treatment
- Health, Environment, Cognitive Aging
- Health Systems, Economic Evaluations, Quality of Life
- stochastic dynamics and bifurcation
- Opinion Dynamics and Social Influence
- Topological and Geometric Data Analysis
- Neural Networks and Applications
- Multiple Sclerosis Research Studies
- Gene Regulatory Network Analysis
- Advanced Memory and Neural Computing
- Cognitive Abilities and Testing
- Control Systems and Identification
- Ecosystem dynamics and resilience
- Advanced Causal Inference Techniques
National Institute of Mental Health
2016-2025
Czech Academy of Sciences, Institute of Computer Science
2016-2025
Czech Academy of Sciences
2016-2025
National Institute of Public Health
2022
National Institute of Mental Health
2019-2021
Charles University
2017
Czech Academy of Sciences, Institute of Psychology
2017
University of Würzburg
2016
Queen's Medical Centre
2010
University of Nottingham
2008-2010
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 tract-specific different domains patients with sclerosis has not previously been studied. We apply tract-based spatial statistics (TBSS) diffusion tensor imaging (DTI) cohort identify loci where reduced tract fractional anisotropy (FA) predicts impaired performance testing. Thirty-seven...
Abstract Identifying regions important for spreading and mediating perturbations is crucial to assess the susceptibilities of spatio-temporal complex systems such as Earth’s climate volcanic eruptions, extreme events or geoengineering. Here a data-driven approach introduced based on dimension reduction, causal reconstruction, novel network measures effect theory that go beyond standard tools by distinguishing direct from indirect pathways. Applied data set atmospheric dynamics, method...
Abstract The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting loss of consciousness, injury even death. Theoretical interpretations considering as a dynamical system suggest that prior seizure, recorded signals may exhibit critical slowing down, warning signal preceding many transitions systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we monitored...
Across geosciences, many investigated phenomena relate to specific complex systems consisting of intricately intertwined interacting subsystems. Such dynamical can be represented by a directed graph, where each link denotes an existence causal relation, or information exchange between the nodes. For geophysical such as global climate, these relations are commonly not theoretically known but estimated from recorded data using causality analysis methods. These include bivariate nonlinear...
We study patterns of partial synchronization in a network FitzHugh-Nagumo oscillators with empirical structural connectivity measured human subjects. report the spontaneous occurrence phenomena that closely resemble ones seen during epileptic seizures humans. In order to obtain deeper insights into interplay between dynamics and topology, we perform long-term simulations oscillatory on different paradigmatic structures: random networks, regular nonlocally coupled ring networks fractal...
Abstract. The bias due to dynamical memory (serial correlations) in an association/dependence measure (absolute cross-correlation) is demonstrated model data and identified time series of meteorological variables used for construction climate networks. Accounting such inferring links the network markedly changes topology allows observe previously hidden phenomena evolution.
Complex spatiotemporal patterns, called chimera states, consist of coexisting coherent and incoherent domains can be observed in networks coupled oscillators. The interplay synchrony asynchrony complex brain is an important aspect studies both function disease. We analyse the collective dynamics FitzHugh-Nagumo neurons motivated by its potential application to epileptology epilepsy surgery. compare two topologies: empirical structural neural connectivity derived from diffusion-weighted...
Abstract The human brain represents a complex computational system, the function and structure of which may be measured using various neuroimaging techniques focusing on separate properties tissue activity. We capture organization white matter fibers acquired by diffusion-weighted imaging probabilistic diffusion tractography. By segmenting results tractography into larger anatomical units, it is possible to draw inferences about structural relationships between these parts system. This...
Abstract Modern imaging methods allow a non‐invasive assessment of both structural and functional brain connectivity. This has lead to the identification disease‐related alterations affecting The mechanism how such in connectivity arise structured network interacting neural populations is as yet poorly understood. Here we use modeling approach explore way which this can highlight important role that local population dynamics have shaping emergent spatial patterns. for taken be Wilson–Cowan...
Electroencephalography (EEG) signals recorded during simultaneous functional magnetic resonance imaging (fMRI) are contaminated by strong artifacts. Among these, the ballistocardiographic (BCG) artifact is most challenging, due to its complex spatio-temporal dynamics associated with ongoing cardiac activity. The presence of BCG residuals in EEG data may hide true, or generate spurious correlations between and fMRI time-courses. Here, we propose an adaptive Optimal Basis Set (aOBS) method for...
Abstract Air temperature variability on different time scales exhibits recurring patterns and quasi‐oscillatory phenomena. Climate oscillations with the period about 7–8 years have been observed in many instrumental records Europe. Although these are weak if considering their amplitude, they might nonnegligible influence shorter due to cross‐scale interactions recently by Paluš (2014). In order quantify influence, we propose a simple conditional mean approach which estimates effect of cycle...
Early diagnosis of schizophrenia could improve the outcome illness. Unlike classical between-group comparisons, machine learning can identify subtle disease patterns on a single subject level, which help realize potential MRI in establishing psychiatric diagnosis. Machine has previously been predominantly tested gray-matter structural or functional data. In this paper we used classifier to differentiate patients with first episode schizophrenia-spectrum disorder (FES) from healthy controls...
Developing sensitive and reliable methods to distinguish normal abnormal brain states is a key neuroscientific challenge. Topological Data Analysis, despite its relative novelty, already generated many promising applications, including in neuroscience. We conjecture prominent tool of persistent homology may benefit from going beyond analysing structural functional connectivity effective graphs capturing the direct causal interactions or information flows. Therefore, we assess potential...
In recent years, there has been an increasing interest in the study of large-scale brain activity interaction structure from perspective complex networks, based on functional magnetic resonance imaging (fMRI) measurements. To assess strength (functional connectivity, FC) between two regions, linear (Pearson) correlation coefficient respective time series is most commonly used. Since a potential use nonlinear FC measures recently discussed this and other fields, question arises whether...
Abstract While brain imaging tools like functional magnetic resonance (fMRI) afford measurements of whole-brain activity, it remains unclear how best to interpret patterns found amid the data’s apparent self-organization. To clarify activity support function, one might identify metric spaces that optimally distinguish states across experimentally defined conditions. Therefore, present study considers relative capacities several disambiguate states. One fundamental space interprets fMRI data...
Longitudinal neuroimaging studies offer valuable insight into intricate dynamics of brain development, ageing, and disease progression over time. However, prevailing analytical approaches rooted in our understanding population variation are primarily tailored for cross-sectional studies. To fully harness the potential longitudinal data, we have to develop refine methodologies that adapted designs, considering complex interplay between individual dynamics.We build on normative modelling...
Background Detrimental effects of misinformation were observed during the COVID-19 pandemic. Presently, amid Russia’s military aggression in Ukraine, another wave is spreading on web and impacting our daily lives, with many citizens politicians embracing Russian propaganda narratives. Despite lack an objective connection between these 2 societal issues, anecdotal observations suggest that supporters regarding (BM-C) have also adopted about war Ukraine (BM-U) while sharing similar media use...