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The aim of this study is to determine whether multiple sclerosis (MS) is associated with changes in global degree rank order disruption index (kD), a functional connectivity-based measure representing the overall distribution of nodal degree centrality, and its relationship between the clinical presentation of MS (global disability, cognitive and motor performance). The performance of kD will be assessed in comparison to established magnetic resonance imaging (MRI) biomarkers in MS (regional degree centrality, structural MRI with volumetry, diffusion-weighted imaging [DWI], lesion mapping) in order to assess the potential clinical utility of kD. To this end, a pre-existing dataset of 64 people with MS and 65 healthy control subjects will be evaluated in this secondary analysis.

Bernstein Conference 2022 abstract. http://bernstein-conference.de

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 of the brain tissue and activity. We capture the organization of white matter fibers acquired by diffusion-weighted imaging using probabilistic diffusion tractography. By segmenting the results of tractography into larger anatomical units, it is possible to draw inferences about the structural relationships between these parts of the system. This pipeline results in a structural connectivity matrix, which contains an estimate of connection strength among all regions. However, raw data processing is complex, computationally intensive, and requires expert quality control, which may be discouraging for researchers with less experience in the field. We thus provide brain structural connectivity matrices in a form ready for modelling and analysis and thus usable by a wide community of scientists. The presented dataset contains brain structural connectivity matrices together with the underlying raw diffusion and structural data, as well as basic demographic data of 88 healthy subjects.

Bernstein Conference 2021 abstract. http://bernstein-conference.de

In a coupled system, predictive information flows from the causing to the caused variable. The amount of transferred predictive information can be quantified through the use of transfer entropy or, for Gaussian variables, equivalently via Granger causality. It is natural to expect and has been repeatedly observed that a tight coupling does not permit to reconstruct a causal connection between causing and caused variables. Here, we show that for a model of interacting social groups, carried from the master equation to the Fokker&ndash, Planck level, a residual predictive information flow can remain for a pair of uni-directionally coupled variables even in the limit of infinite coupling strength. We trace this phenomenon back to the question of how the synchronizing force and the noise strength scale with the coupling strength. A simplified model description allows us to derive analytic expressions that fully elucidate the interplay between deterministic and stochastic model parts.

Supplemental material, JOP849814_supplemental_material for Cannabis-induced altered states of consciousness are associated with specific dynamic brain connectivity states by Yuliya Zaytseva, Jiří Horáček, Jaroslav Hlinka, Iveta Fajnerová, Renata Androvičová, Jaroslav Tintěra, Virginio Salvi, Marie Balíková, Tomáš Hložek, Filip Španiel and Tomáš Páleníček in Journal of Psychopharmacology

Brýle pro rozšířenou realitu (augmented reality, AR) v kombinaci s GPS navigací představují rychle se vyvíjející technologii, která umožňuje externalizovat navigační doevednosti. S ohledem na očekávané každodenní užívání této technologie by měl být prozkoumán její vliv na neuropolastické změny mozku a navigační dovednosti uživatelů. Tato stuje si bere za cíl zhodnotit možné změny ve funkční konektivitě (functional connectivity, FC) hipokampu a ostatních oblastí mozku, které se podílejí na navigačních dovednostech. Účastníci studie, celkem 33, podstoupili dvě klidová měření funkční konektivity pomocí magnetické rezonance (resting state functional magneticresonance imaging, rsfMRI), první před začátkem studie, druhé po třech mesících. Po dobu těchto tří měsíců požívali učastníci v experimentální skupině, 17 účastníků, AR brýle (Vuzix M100) vybavené GPS navigačním systémem při pěším pohybu v reálném prostředí. Kontrolní skupina, 16 účastníků, žádné podobné zařízení k navigaci při pohybu pěšky během tohoto období nepoužívala. Data z rsfMRI byla analyzována seed-drive přístupem. Model virtuálního města byl použit k otestování navigačních dovedností účasntíků a to před i po používání AR brýlí. Při prvním měření jsme zaznamenali silné provázání pravého a levého hipokampu (p < 0.05, se FDR korekcí). Malé změny ve vzájemné funkční konektivitě obou polovin hipokampu, v jejich konektiviě s temporálními oblastmi mozku a s mozečkem (0.05, bez FRD korekce) byly pozorovány u obou skupin. Experimentální skupina vykazovala znížení funkční Augmented reality (AR) glasses with GPS navigation represent the rapidly evolving technology which spares (and externalizes) navigational capacities. Regarding the expected everyday usage of this device, its impact on neuroplastic brain changes and navigation abilities should be evaluated. This study aimed to assess possible changes in functional connectivity (FC) of hippocampus and other brain regions involved in spatial navigation. Thirty-three healthy participants completed two resting state functional magnetic resonance imaging (rsfMRI) measurements at the baseline and after 3 months. For this period, the experimental group (n = 17) has had used AR device (Vuzix M100) with incorporated GPS guidance system during navigation in real world. Participants from the control group (n = 16) have not used any GPS device while navigating during walking. The rsfMRI FC of right and left hippocampi was analyzed using a seed-driven approach. Virtual city task was used to test navigational abilities both before and after the usage of AR device. We identified strong functional coupling of right and left hippocampi at the baseline (p < 0.05, FDR corrected). Mild changes in bilateral hippocampal FC (p < 0.05, FDR uncorrected) were observed in both assessed groups mainly between the bilateral hippocampi and between each hippocampus and temporal regions and cerebellum. However, the experimental group showed FC decrease after three months of using GPS navigation implemented in AR glasses in contrast to FC increase in the control group without such intervention. Importantly, no effect of intervention on navigational abilities was observed. Our observation supports the assumption that externalization of spatial navigation to technological device (GPS in AR glasses) can decrease the functional coupling between hippocampus and associated brain regions. Considering some limitations of the present study, further studies should elucidate the mechanism of the observed changes and their impact on cognitive abilities.

An increasing number of studies is currently focusing on ‘personality neuroscience’, a term labeling the research aimed at neuroimaging correlates of inter-individual temperament and character variability. Among other methods, a graph theoretical analysis of the functional connectivity in resting state functional magnetic resonance imaging data was applied in a recent study by Gao et al. (2013, Frontiers in Human Neuroscience). This paper aims to replicate this study and extends the original statistical methods in order to demonstrate the effect of multiple comparison problem. In contrast to the original study, five personality dimensions were obtained in the revised ‘Big Five’ Personality Inventory. Using a larger sample (84 subjects) and an equivalent data analysis procedure, we obtained widely disagreeing results compared to the original study. While the Gao et al. reported a range of significant correlations between personality dimensions and some of the network metrics, we failed to replicate any significant correlations when FDR testing was applied. These results demonstrate that as with other neuroimaging studies, appropriate control of multiple comparison problem should be meticulously applied in order to prevent such false alarms in research into neurological substrates of personality differences. Of course, we do not attempt to disprove the existence of some link between personality and brain’s intrinsic functional architecture. Nevertheless, this link is very likely much more subtle and elusive than was suggested in previous studies.

Ultraslow fluctuations (0.01-0.1 Hz) are a feature of intrinsic brain activity of as yet unclear origin. We propose a candidate mechanism based on retrograde endocannabinoid signaling in a synaptically coupled network of excitatory neurons. This is known to cause depolarization-induced suppression of excitation (DISE), which we model phenomenologically. We construct emergent network oscillations in a globally coupled network and show that for strong synaptic coupling DISE can lead to a synchronized population burst at the frequencies of resting brain rhythms. © 2010 The American Physical Society.

The study of spontaneous brain activity using functional Magnetic Resonance Imaging (fMRI) is a relatively young and rapidly developing field born in the mid-nineties. So far, sufficiently solid foundations have been established, mainly in validating the neuronal origin of a significant component of observed low-frequency fluctuations in the 'resting state' fMRI signal. Nevertheless, the field is still facing several major challenges. This thesis first reviews the current state of knowledge and subsequently proceeds to present original research results that are directed towards overcoming these challenges.\ud The first challenge stems from the indirect nature of the fMRI recordings, obscuring the interpretation in terms of the underlying neuronal activity. Two investigations related to this are presented. First, I show that increased head-movement, epiphenomenal to altered states of consciousness, can lead to spurious increases in low-frequency fluctuations in fMRI signal. This may adversely affect inferences on the underlying neurophysiological processes. Second, I demonstrate a direct electrophysiological correlate of increased synchronisation of fMRI activity in areas of the much studied default-mode network. By directly studying electrophysiological correlates of fMRI-based functional connectivity, this study took a pioneering approach to confirming the biological validity of the fMRI functional connectivity concept.\ud Another widely debated question within the field is the optimal method for extracting relevant information from the extreme volumes of neuroimaging data. I present an investigation providing insights and practical recommendations for this question, based on assessing the interdependence information neglected by the commonly used linear correlation for fMRI functional connectivity studies. The results suggest that in typical resting state data, the nonlinear contributions to instantaneous connectivity are negligible.\ud The third major challenge of the field is the integration of the experimental evidence into theoretical models of spontaneous brain activity. In the last part of this thesis, such models are discussed in detail, focusing on the two crucial features of observed spontaneous brain activity: functional connectivity and low-frequency fluctuations. Two specific mechanisms for emergence of the latter are proposed, depending either on the local synchronisation dynamics or the regulatory action of particular neuromodulators. \ud The thesis concludes with discussion of the questions arising from the presented results in the context of the most recent development in the wider field.

In bioinformatics, regularized linear discriminant analysis is commonly used as a tool for supervised classification problems tailormade for high-dimensional data with the number of variables exceeding the number of observations. However, its various available versions are too vulnerable to the presence of outlying measurements in the data. In this paper, we exploit principles of robust statistics to propose new versions of regularized linear discriminant analysis suitable for highdimensional data contaminated by (more or less) severe outliers. The work exploits a regularized version of the minimum weighted covariance determinant estimator, which is one of highly robust estimators of multivariate location and scatter. The performance of the novel classification methods is illustrated on real data sets with a detailed analysis of data from brain activity research.