Publications by authors named "Helmut Laufs"

77 Publications

Nonequilibrium brain dynamics as a signature of consciousness.

Phys Rev E 2021 Jul;104(1-1):014411

Physics Department, University of Buenos Aires, and Buenos Aires Physics Institute, Buenos Aires 1428, Argentina.

The cognitive functions of human and nonhuman primates rely on the dynamic interplay of distributed neural assemblies. As such, it seems unlikely that cognition can be supported by macroscopic brain dynamics at the proximity of equilibrium. We confirmed this hypothesis by investigating electrocorticography data from nonhuman primates undergoing different states of unconsciousness (sleep, and anesthesia with propofol, ketamine, and ketamine plus medetomidine), and functional magnetic resonance imaging data from humans, both during deep sleep and under propofol anesthesia. Systematically, all states of reduced consciousness unfolded at higher proximity to equilibrium compared to conscious wakefulness, as demonstrated by the computation of entropy production and the curl of probability flux in phase space. Our results establish nonequilibrium macroscopic brain dynamics as a robust signature of consciousness, opening the way for the characterization of cognition and awareness using tools from statistical mechanics.
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http://dx.doi.org/10.1103/PhysRevE.104.014411DOI Listing
July 2021

Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness.

PLoS Comput Biol 2021 Jul 27;17(7):e1009139. Epub 2021 Jul 27.

Department of Physics, University of Buenos Aires, Intendente Güiraldes 2160-Ciudad Universitaria-Buenos Aires, Argentina.

Consciousness transiently fades away during deep sleep, more stably under anesthesia, and sometimes permanently due to brain injury. The development of an index to quantify the level of consciousness across these different states is regarded as a key problem both in basic and clinical neuroscience. We argue that this problem is ill-defined since such an index would not exhaust all the relevant information about a given state of consciousness. While the level of consciousness can be taken to describe the actual brain state, a complete characterization should also include its potential behavior against external perturbations. We developed and analyzed whole-brain computational models to show that the stability of conscious states provides information complementary to their similarity to conscious wakefulness. Our work leads to a novel methodological framework to sort out different brain states by their stability and reversibility, and illustrates its usefulness to dissociate between physiological (sleep), pathological (brain-injured patients), and pharmacologically-induced (anesthesia) loss of consciousness.
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http://dx.doi.org/10.1371/journal.pcbi.1009139DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8315553PMC
July 2021

Decoding brain states on the intrinsic manifold of human brain dynamics across wakefulness and sleep.

Commun Biol 2021 07 9;4(1):854. Epub 2021 Jul 9.

Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK.

Current state-of-the-art functional magnetic resonance imaging (fMRI) offers remarkable imaging quality and resolution, yet, the intrinsic dimensionality of brain dynamics in different states (wakefulness, light and deep sleep) remains unknown. Here we present a method to reveal the low dimensional intrinsic manifold underlying human brain dynamics, which is invariant of the high dimensional spatio-temporal representation of the neuroimaging technology. By applying this intrinsic manifold framework to fMRI data acquired in wakefulness and sleep, we reveal the nonlinear differences between wakefulness and three different sleep stages, and successfully decode these different brain states with a mean accuracy across participants of 96%. Remarkably, a further group analysis shows that the intrinsic manifolds of all participants share a common topology. Overall, our results reveal the intrinsic manifold underlying the spatiotemporal dynamics of brain activity and demonstrate how this manifold enables the decoding of different brain states such as wakefulness and various sleep stages.
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http://dx.doi.org/10.1038/s42003-021-02369-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270946PMC
July 2021

Noise-driven multistability vs deterministic chaos in phenomenological semi-empirical models of whole-brain activity.

Chaos 2021 Feb;31(2):023127

Buenos Aires Physics Institute and Physics Department, University of Buenos Aires, Buenos Aires 1428, Argentina.

An outstanding open problem in neuroscience is to understand how neural systems are capable of producing and sustaining complex spatiotemporal dynamics. Computational models that combine local dynamics with in vivo measurements of anatomical and functional connectivity can be used to test potential mechanisms underlying this complexity. We compared two conceptually different mechanisms: noise-driven switching between equilibrium solutions (modeled by coupled Stuart-Landau oscillators) and deterministic chaos (modeled by coupled Rossler oscillators). We found that both models struggled to simultaneously reproduce multiple observables computed from the empirical data. This issue was especially manifested in the case of noise-driven dynamics close to a bifurcation, which imposed overly strong constraints on the optimal model parameters. In contrast, the chaotic model could produce complex behavior over a range of parameters, thus being capable of capturing multiple observables at the same time with good performance. Our observations support the view of the brain as a non-equilibrium system able to produce endogenous variability. We presented a simple model capable of jointly reproducing functional connectivity computed at different temporal scales. Besides adding to our conceptual understanding of brain complexity, our results inform and constrain the future development of biophysically realistic large-scale models.
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http://dx.doi.org/10.1063/5.0025543DOI Listing
February 2021

Generative Embeddings of Brain Collective Dynamics Using Variational Autoencoders.

Phys Rev Lett 2020 Dec;125(23):238101

Physics Department, University of Buenos Aires and Buenos Aires Physics Institute, Buenos Aires 1428, Argentina.

We consider the problem of encoding pairwise correlations between coupled dynamical systems in a low-dimensional latent space based on few distinct observations. We use variational autoencoders (VAEs) to embed temporal correlations between coupled nonlinear oscillators that model brain states in the wake-sleep cycle into a two-dimensional manifold. Training a VAE with samples generated using two different parameter combinations results in an embedding that encodes the repertoire of collective dynamics, as well as the topology of the underlying connectivity network. We first follow this approach to infer the trajectory of brain states measured from wakefulness to deep sleep from the two end points of this trajectory; then, we show that the same architecture was capable of representing the pairwise correlations of generic Landau-Stuart oscillators coupled by complex network topology.
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http://dx.doi.org/10.1103/PhysRevLett.125.238101DOI Listing
December 2020

Signature of consciousness in brain-wide synchronization patterns of monkey and human fMRI signals.

Neuroimage 2021 02 1;226:117470. Epub 2020 Nov 1.

Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats, Barcelona, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Melbourne, Clayton, Australia.

During the sleep-wake cycle, the brain undergoes profound dynamical changes, which manifest subjectively as transitions between conscious experience and unconsciousness. Yet, neurophysiological signatures that can objectively distinguish different consciousness states based are scarce. Here, we show that differences in the level of brain-wide signals can reliably distinguish different stages of sleep and anesthesia from the awake state in human and monkey fMRI resting state data. Moreover, a whole-brain computational model can faithfully reproduce changes in global synchronization and other metrics such as functional connectivity, structure-function relationship, integration and segregation across vigilance states. We demonstrate that the awake brain is close to a Hopf bifurcation, which naturally coincides with the emergence of globally correlated fMRI signals. Furthermore, simulating lesions of individual brain areas highlights the importance of connectivity hubs in the posterior brain and subcortical nuclei for maintaining the model in the awake state, as predicted by graph-theoretical analyses of structural data.
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http://dx.doi.org/10.1016/j.neuroimage.2020.117470DOI Listing
February 2021

Connectivity dynamics from wakefulness to sleep.

Neuroimage 2020 10 17;220:117047. Epub 2020 Jun 17.

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA.

Interest in time-resolved connectivity in fMRI has grown rapidly in recent years. The most widely used technique for studying connectivity changes over time utilizes a sliding windows approach. There has been some debate about the utility of shorter versus longer windows, the use of fixed versus adaptive windows, as well as whether observed resting state dynamics during wakefulness may be predominantly due to changes in sleep state and subject head motion. In this work we use an independent component analysis (ICA)-based pipeline applied to concurrent EEG/fMRI data collected during wakefulness and various sleep stages and show: 1) connectivity states obtained from clustering sliding windowed correlations of resting state functional network time courses well classify the sleep states obtained from EEG data, 2) using shorter sliding windows instead of longer non-overlapping windows improves the ability to capture transition dynamics even at windows as short as 30 ​s, 3) motion appears to be mostly associated with one of the states rather than spread across all of them 4) a fixed tapered sliding window approach outperforms an adaptive dynamic conditional correlation approach, and 5) consistent with prior EEG/fMRI work, we identify evidence of multiple states within the wakeful condition which are able to be classified with high accuracy. Classification of wakeful only states suggest the presence of time-varying changes in connectivity in fMRI data beyond sleep state or motion. Results also inform about advantageous technical choices, and the identification of different clusters within wakefulness that are separable suggest further studies in this direction.
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http://dx.doi.org/10.1016/j.neuroimage.2020.117047DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7753906PMC
October 2020

Modeling regional changes in dynamic stability during sleep and wakefulness.

Neuroimage 2020 07 11;215:116833. Epub 2020 Apr 11.

Department of Physics, University of Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina. Electronic address:

Global brain states are frequently placed within a unidimensional continuum by correlational studies, ranging from states of deep unconsciousness to ordinary wakefulness. An alternative is their multidimensional and mechanistic characterization in terms of different cognitive capacities, using computational models to reproduce the underlying neural dynamics. We explore this alternative by introducing a semi-empirical model linking regional activation and long-range functional connectivity in the different brain states visited during the natural wake-sleep cycle. Our model combines functional magnetic resonance imaging (fMRI) data, in vivo estimates of structural connectivity, and anatomically-informed priors to constrain the independent variation of regional activation. The best fit to empirical data was achieved using priors based on functionally coherent networks, with the resulting model parameters dividing the cortex into regions presenting opposite dynamical behavior. Frontoparietal regions approached a bifurcation from dynamics at a fixed point governed by noise, while sensorimotor regions approached a bifurcation from oscillatory dynamics. In agreement with human electrophysiological experiments, sleep onset induced subcortical deactivation with low correlation, which was subsequently reversed for deeper stages. Finally, we introduced periodic forcing of variable intensity to simulate external perturbations, and identified the key regions relevant for the recovery of wakefulness from deep sleep. Our model represents sleep as a state with diminished perceptual gating and the latent capacity for global accessibility that is required for rapid arousals. To the extent that the qualitative characterization of local dynamics is exhausted by the dichotomy between unstable and stable behavior, our work highlights how expanding the model parameter space can describe states of consciousness in terms of multiple dimensions with interpretations given by the choice of anatomically-informed priors.
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http://dx.doi.org/10.1016/j.neuroimage.2020.116833DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7894985PMC
July 2020

Magnetic Measurement of Electrically Evoked Muscle Responses With Optically Pumped Magnetometers.

IEEE Trans Neural Syst Rehabil Eng 2020 03 20;28(3):756-765. Epub 2020 Jan 20.

Objective: Electroneurography has been an essential method for assessing peripheral nerve disorders for decades. During this procedure, a nerve is briefly electrically excited, and nerve conduction properties are identified by indirect means from the behavior of the innervated muscle. The magnetic field of the resulting muscle response can also be recorded by novel, uncooled magnetometers, which have become very attractive for different medical applications over recent years. These highly sensitive magnetometers are called optically pumped magnetometers.

Methods: We performed unaveraged and averaged magnetic signal detection of electrically evoked muscle responses using optically pumped magnetometers. We then discussed the suitability of this procedure for clinical applications in the context of diagnostic value and in direct comparison with the current electrical gold standard.

Results: The magnetic detection of muscle responses is possible using optically pumped magnetometers. Our magnetic results (averaged and unaveraged) closely match those from electrical measurements.

Conclusion: Optically pumped magnetometers provide an alternative, contactless technology for electrode-based motor studies, but they are currently not ready for routine clinical use. This costly technology requires additional earth magnetic shielding because this is a prerequisite for proper operation. Currently, there are no diagnostic advantages over electrical measurements. Additionally, the required measurement setup and procedure are much more complicated.

Significance: In contrast to already published proof-of-principle studies for magnetomyography, we report in detail the results of the magnetic measurements of electrically evoked muscle responses in a shielded environment by applying supramaximal stimulation and finally validate our findings with electroneurography data as a reference.
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http://dx.doi.org/10.1109/TNSRE.2020.2968148DOI Listing
March 2020

Signal Modeling and Simulation of Temporal Dispersion and Conduction Block in Motor Nerves.

IEEE Trans Biomed Eng 2020 07 20;67(7):2094-2102. Epub 2019 Nov 20.

Objective: Electroneurography is a well-established diagnostic test for supporting the diagnosis of disorders of myelinated peripheral nerves. Neurophysiological quantities are automatically calculated and are used to determine the pathology of the nerve (axonal damage) or its sheath (myelin damage). Specific differential diagnostic criteria are derived from time-domain normative data, which result primarily from a computer simulation in the early 1990s based on animal data, namely rats. However, the rat signals studied differ significantly from those of humans because of anatomical differences.

Methods: We present a model-based simulation of nerve conduction in healthy and pathological motor nerves. In contrast to earlier simulations, the present model is based on motor unit action potentials extracted from real human measurements facilitating the generation of realistic signals, starting from a conduction velocity distribution. In addition to the modeling of healthy nerves, we model a hereditary peripheral nerve disease as well as an acute and a chronic inflammatory demyelinating condition.

Results: Quantitative signal differences based on standard variables in the time-domain are presented. The findings for the demyelinating conditions demonstrate amplitude reductions of 71% and 65% between the distal and proximal responses, which result from an increase in the variance of the nerve fiber conduction velocities.

Conclusion: The simulation results closely match those of empirical measurements, indicating that the signal model captures relevant pathological mechanisms. An amplitude reduction of more than 50% in demyelinating conditions is in accordance with routine measurements and shows that temporal dispersion is quite well-modeled compared to previous simulation models.

Significance: The simulation outcomes can serve as the basis for an improved pathophysiological understanding of peripheral nerve disorders and should aid neurophysiologists to refine their diagnostic armamentarium resulting in a more precise differential diagnosis.
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http://dx.doi.org/10.1109/TBME.2019.2954592DOI Listing
July 2020

Awakening: Predicting external stimulation to force transitions between different brain states.

Proc Natl Acad Sci U S A 2019 09 19;116(36):18088-18097. Epub 2019 Aug 19.

Department of Psychiatry, University of Oxford, Oxford OX3 7JX, United Kingdom;

A fundamental problem in systems neuroscience is how to force a transition from one brain state to another by external driven stimulation in, for example, wakefulness, sleep, coma, or neuropsychiatric diseases. This requires a quantitative and robust definition of a brain state, which has so far proven elusive. Here, we provide such a definition, which, together with whole-brain modeling, permits the systematic study in silico of how simulated brain stimulation can force transitions between different brain states in humans. Specifically, we use a unique neuroimaging dataset of human sleep to systematically investigate where to stimulate the brain to force an awakening of the human sleeping brain and vice versa. We show where this is possible using a definition of a brain state as an ensemble of "metastable substates," each with a probabilistic stability and occurrence frequency fitted by a generative whole-brain model, fine-tuned on the basis of the effective connectivity. Given the biophysical limitations of direct electrical stimulation (DES) of microcircuits, this opens exciting possibilities for discovering stimulation targets and selecting connectivity patterns that can ensure propagation of DES-induced neural excitation, potentially making it possible to create awakenings from complex cases of brain injury.
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http://dx.doi.org/10.1073/pnas.1905534116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6731634PMC
September 2019

EEG-correlated fMRI of human alpha (de-)synchronization.

Clin Neurophysiol 2019 08 24;130(8):1375-1386. Epub 2019 May 24.

Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Schleusenweg 2-16, 60528 Frankfurt am Main, Germany; Department of Neurology, Christian-Albrechts-University Kiel, Arnold-Heller-Str. 3, Haus 41, 24105 Kiel, Germany.

Objectives: We investigated blood oxygenation level-dependent (BOLD) brain activity changes in wakefulness and light sleep and in relation to those associated with the posterior alpha rhythm, the most prominent feature of the clinical EEG. Studies have reported different sets of brain regions changing their oxygen consumption with waxing and waning alpha oscillations. Here, we hypothesize that these dissimilar activity patterns reflect different wakefulness-dependent brain states.

Methods: We recorded BOLD signal changes and electroencephalography (EEG) simultaneously in 149 subjects at rest. Based on American Academy of Sleep Medicine criteria, we selected subjects exhibiting wakefulness or light sleep (N1). We identified brain regions in which BOLD signal changes correlated with (i) clinical sleep stages, (ii) alpha band power and (iii) a multispectral EEG index, respectively.

Results: During light sleep, we found increased BOLD activity in parieto-occipital regions. In wakefulness compared to light sleep, we revealed BOLD signal increases in the thalamus. The multispectral EEG-index revealed hippocampal activity changes in light sleep not reported before.

Conclusion: Changes in alpha oscillations reflect different brain states associated with different levels of wakefulness and thalamic activity. We can link the previously described parieto-occipital pattern to drowsiness. Additionally, in that stage, we identify hippocampal activity fluctuations.

Significance: Thalamic activity varies with early changes of wakefulness, which is important to consider in resting state experiments. The EEG-indexed activation of the hippocampus during light sleep suggests that memory encoding might already take place during this early stage of sleep.
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http://dx.doi.org/10.1016/j.clinph.2019.04.715DOI Listing
August 2019

Low-Frequency Oscillations Code Speech during Verbal Working Memory.

J Neurosci 2019 08 13;39(33):6498-6512. Epub 2019 Jun 13.

Department of Neurology, Goethe University, 60528 Frankfurt, Germany,

The way the human brain represents speech in memory is still unknown. An obvious characteristic of speech is its evolvement over time. During speech processing, neural oscillations are modulated by the temporal properties of the acoustic speech signal, but also acquired knowledge on the temporal structure of language influences speech perception-related brain activity. This suggests that speech could be represented in the temporal domain, a form of representation that the brain also uses to encode autobiographic memories. Empirical evidence for such a memory code is lacking. We investigated the nature of speech memory representations using direct cortical recordings in the left perisylvian cortex during delayed sentence reproduction in female and male patients undergoing awake tumor surgery. Our results reveal that the brain endogenously represents speech in the temporal domain. Temporal pattern similarity analyses revealed that the phase of frontotemporal low-frequency oscillations, primarily in the beta range, represents sentence identity in working memory. The positive relationship between beta power during working memory and task performance suggests that working memory representations benefit from increased phase separation. Memory is an endogenous source of information based on experience. While neural oscillations encode autobiographic memories in the temporal domain, little is known on their contribution to memory representations of human speech. Our electrocortical recordings in participants who maintain sentences in memory identify the phase of left frontotemporal beta oscillations as the most prominent information carrier of sentence identity. These observations provide evidence for a theoretical model on speech memory representations and explain why interfering with beta oscillations in the left inferior frontal cortex diminishes verbal working memory capacity. The lack of sentence identity coding at the syllabic rate suggests that sentences are represented in memory in a more abstract form compared with speech coding during speech perception and production.
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http://dx.doi.org/10.1523/JNEUROSCI.0018-19.2019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697399PMC
August 2019

Spatio-Temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks.

Front Neurosci 2018 20;12:600. Epub 2018 Sep 20.

The Mind Research Network, Albuquerque, NM, United States.

We introduce a novel recurrent neural network (RNN) approach to account for temporal dynamics and dependencies in brain networks observed via functional magnetic resonance imaging (fMRI). Our approach directly parameterizes temporal dynamics through recurrent connections, which can be used to formulate blind source separation with a conditional (rather than marginal) independence assumption, which we call RNN-ICA. This formulation enables us to visualize the temporal dynamics of both first order (activity) and second order (directed connectivity) information in brain networks that are widely studied in a static sense, but not well-characterized dynamically. RNN-ICA predicts dynamics directly from the recurrent states of the RNN in both task and resting state fMRI. Our results show both task-related and group-differentiating directed connectivity.
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http://dx.doi.org/10.3389/fnins.2018.00600DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158311PMC
September 2018

EEG Microstate Sequences From Different Clustering Algorithms Are Information-Theoretically Invariant.

Front Comput Neurosci 2018 27;12:70. Epub 2018 Aug 27.

Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany.

We analyse statistical and information-theoretical properties of EEG microstate sequences, as seen through the lens of five different clustering algorithms. Microstate sequences are computed for = 20 resting state EEG recordings during wakeful rest. The input for all clustering algorithms is the set of EEG topographic maps obtained at local maxima of the spatial variance. This data set is processed by two classical microstate clustering algorithms (1) atomize and agglomerate hierarchical clustering (AAHC) and (2) a modified K-means algorithm, as well as by (3) K-medoids, (4) principal component analysis (PCA) and (5) fast independent component analysis (Fast-ICA). Using this technique, EEG topographies can be substituted with microstate labels by competitive fitting based on spatial correlation, resulting in a symbolic, non-metric time series, the microstate sequence. Microstate topographies and symbolic time series are further analyzed statistically, including static and dynamic properties. Static properties, which do not contain information about temporal dependencies of the microstate sequence include the maximum similarity of microstate maps within and between the tested clustering algorithms, the global explained variance and the Shannon entropy of the microstate sequences. Dynamic properties are sensitive to temporal correlations between the symbols and include the mixing time of the microstate transition matrix, the entropy rate of the microstate sequences and the location of the first local maximum of the autoinformation function. We also test the Markov property of microstate sequences, the time stationarity of the transition matrix and detect periodicities by means of time-lagged mutual information. Finally, possible long-range correlations of microstate sequences are assessed via Hurst exponent estimation. We find that while static properties partially reflect properties of the clustering algorithms, information-theoretical quantities are largely invariant with respect to the clustering method used. As each clustering algorithm has its own profile of computational speed, ease of implementation, determinism vs. stochasticity and theoretical underpinnings, our results convey a positive message concerning the free choice of method and the comparability of results obtained from different algorithms. The invariance of these quantities implies that the tested properties are algorithm-independent, inherent features of resting state EEG derived microstate sequences.
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http://dx.doi.org/10.3389/fncom.2018.00070DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6119811PMC
August 2018

Human non-REM sleep and the mean global BOLD signal.

J Cereb Blood Flow Metab 2019 11 3;39(11):2210-2222. Epub 2018 Aug 3.

Department of Radiology, Washington University, Saint Louis, MO, USA.

A hallmark of non-rapid eye movement (REM) sleep is the decreased brain activity as measured by global reductions in cerebral blood flow, oxygen metabolism, and glucose metabolism. It is unknown whether the blood oxygen level dependent (BOLD) signal undergoes similar changes. Here we show that, in contrast to the decreases in blood flow and metabolism, the mean global BOLD signal increases with sleep depth in a regionally non-uniform manner throughout gray matter. We relate our findings to the circulatory and metabolic processes influencing the BOLD signal and conclude that because oxygen consumption decreases proportionately more than blood flow in sleep, the resulting decrease in paramagnetic deoxyhemoglobin accounts for the increase in mean global BOLD signal.
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http://dx.doi.org/10.1177/0271678X18791070DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6827126PMC
November 2019

Information-Theoretical Analysis of EEG Microstate Sequences in Python.

Front Neuroinform 2018 1;12:30. Epub 2018 Jun 1.

Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany.

We present an open-source Python package to compute information-theoretical quantities for electroencephalographic data. Electroencephalography (EEG) measures the electrical potential generated by the cerebral cortex and the set of spatial patterns projected by the brain's electrical potential on the scalp surface can be clustered into a set of representative maps called EEG microstates. Microstate time series are obtained by competitively fitting the microstate maps back into the EEG data set, i.e., by substituting the EEG data at a given time with the label of the microstate that has the highest similarity with the actual EEG topography. As microstate sequences consist of non-metric random variables, e.g., the letters A-D, we recently introduced information-theoretical measures to quantify these time series. In wakeful resting state EEG recordings, we found new characteristics of microstate sequences such as periodicities related to EEG frequency bands. The algorithms used are here provided as an open-source package and their use is explained in a tutorial style. The package is self-contained and the programming style is procedural, focusing on code intelligibility and easy portability. Using a sample EEG file, we demonstrate how to perform EEG microstate segmentation using the modified K-means approach, and how to compute and visualize the recently introduced information-theoretical tests and quantities. The time-lagged mutual information function is derived as a discrete symbolic alternative to the autocorrelation function for metric time series and confidence intervals are computed from Markov chain surrogate data. The software package provides an open-source extension to the existing implementations of the microstate transform and is specifically designed to analyze resting state EEG recordings.
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http://dx.doi.org/10.3389/fninf.2018.00030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5992993PMC
June 2018

Occupation-Associated Fatal Limbic Encephalitis Caused by Variegated Squirrel Bornavirus 1, Germany, 2013.

Emerg Infect Dis 2018 06;24(6):978-987

Limbic encephalitis is commonly regarded as an autoimmune-mediated disease. However, after the recent detection of zoonotic variegated squirrel bornavirus 1 in a Prevost's squirrel (Callosciurus prevostii) in a zoo in northern Germany, we retrospectively investigated a fatal case in an autoantibody-seronegative animal caretaker who had worked at that zoo. The virus had been discovered in 2015 as the cause of a cluster of cases of fatal encephalitis among breeders of variegated squirrels (Sciurus variegatoides) in eastern Germany. Molecular assays and immunohistochemistry detected a limbic distribution of the virus in brain tissue of the animal caretaker. Phylogenetic analyses demonstrated a spillover infection from the Prevost's squirrel. Antibodies against bornaviruses were detected in the patient's cerebrospinal fluid by immunofluorescence and newly developed ELISAs and immunoblot. The putative antigenic epitope was identified on the viral nucleoprotein. Other zoo workers were not infected; however, avoidance of direct contact with exotic squirrels and screening of squirrels are recommended.
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http://dx.doi.org/10.3201/eid2406.172027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6004865PMC
June 2018

Mutual information identifies spurious Hurst phenomena in resting state EEG and fMRI data.

Phys Rev E 2018 Feb;97(2-1):022415

Department of Neurology and Brain Imaging Center, Goethe University, Schleusenweg 2-16, 60528 Frankfurt am Main, Germany.

Long-range memory in time series is often quantified by the Hurst exponent H, a measure of the signal's variance across several time scales. We analyze neurophysiological time series from electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) resting state experiments with two standard Hurst exponent estimators and with the time-lagged mutual information function applied to discretized versions of the signals. A confidence interval for the mutual information function is obtained from surrogate Markov processes with equilibrium distribution and transition matrix identical to the underlying signal. For EEG signals, we construct an additional mutual information confidence interval from a short-range correlated, tenth-order autoregressive model. We reproduce the previously described Hurst phenomenon (H>0.5) in the analytical amplitude of alpha frequency band oscillations, in EEG microstate sequences, and in fMRI signals, but we show that the Hurst phenomenon occurs without long-range memory in the information-theoretical sense. We find that the mutual information function of neurophysiological data behaves differently from fractional Gaussian noise (fGn), for which the Hurst phenomenon is a sufficient condition to prove long-range memory. Two other well-characterized, short-range correlated stochastic processes (Ornstein-Uhlenbeck, Cox-Ingersoll-Ross) also yield H>0.5, whereas their mutual information functions lie within the Markovian confidence intervals, similar to neural signals. In these processes, which do not have long-range memory by construction, a spurious Hurst phenomenon occurs due to slow relaxation times and heteroscedasticity (time-varying conditional variance). In summary, we find that mutual information correctly distinguishes long-range from short-range dependence in the theoretical and experimental cases discussed. Our results also suggest that the stationary fGn process is not sufficient to describe neural data, which seem to belong to a more general class of stochastic processes, in which multiscale variance effects produce Hurst phenomena without long-range dependence. In our experimental data, the Hurst phenomenon and long-range memory appear as different system properties that should be estimated and interpreted independently.
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http://dx.doi.org/10.1103/PhysRevE.97.022415DOI Listing
February 2018

A combinatorial framework to quantify peak/pit asymmetries in complex dynamics.

Sci Rep 2018 02 23;8(1):3557. Epub 2018 Feb 23.

School of Mathematical Sciences, Queen Mary University of London, E14NS, London, United Kingdom.

We explore a combinatorial framework which efficiently quantifies the asymmetries between minima and maxima in local fluctuations of time series. We first showcase its performance by applying it to a battery of synthetic cases. We find rigorous results on some canonical dynamical models (stochastic processes with and without correlations, chaotic processes) complemented by extensive numerical simulations for a range of processes which indicate that the methodology correctly distinguishes different complex dynamics and outperforms state of the art metrics in several cases. Subsequently, we apply this methodology to real-world problems emerging across several disciplines including cases in neurobiology, finance and climate science. We conclude that differences between the statistics of local maxima and local minima in time series are highly informative of the complex underlying dynamics and a graph-theoretic extraction procedure allows to use these features for statistical learning purposes.
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http://dx.doi.org/10.1038/s41598-018-21785-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5824940PMC
February 2018

Decoding Pedophilia: Increased Anterior Insula Response to Infant Animal Pictures.

Front Hum Neurosci 2017 23;11:645. Epub 2018 Jan 23.

Department of Psychiatry, Social Psychiatry and Psychotherapy, Section of Clinical Psychology and Sexual Medicine, Hannover Medical School, Hannover, Germany.

Previous research found increased brain responses of men with sexual interest in children (i.e., pedophiles) not only to pictures of naked children but also to pictures of child faces. This opens the possibly that pedophilia is linked (in addition to or instead of an aberrant sexual system) to an over-active nurturing system. To test this hypothesis we exposed pedophiles and healthy controls to pictures of infant and adult animals during functional magnetic resonance imaging of the brain. By using pictures of infant animals (instead of human infants), we aimed to elicit nurturing processing without triggering sexual processing. We hypothesized that elevated brain responses to nurturing stimuli will be found - in addition to other brain areas - in the anterior insula of pedophiles because this area was repeatedly found to be activated when adults see pictures of babies. Behavioral ratings confirmed that pictures of infant or adult animals were not perceived as sexually arousing neither by the pedophilic participants nor by the heathy controls. Statistical analysis was applied to the whole brain as well as to the anterior insula as region of interest. Only in pedophiles did infants relative to adult animals increase brain activity in the anterior insula, supplementary motor cortex, and dorsolateral prefrontal areas. Within-group analysis revealed an increased brain response to infant animals in the left anterior insular cortex of the pedophilic participants. Currently, pedophilia is considered the consequence of disturbed sexual or executive brain processing, but details are far from known. The present findings raise the question whether there is also an over-responsive nurturing system in pedophilia.
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http://dx.doi.org/10.3389/fnhum.2017.00645DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5778266PMC
January 2018

Non-linear Relationship between BOLD Activation and Amplitude of Beta Oscillations in the Supplementary Motor Area during Rhythmic Finger Tapping and Internal Timing.

Front Hum Neurosci 2017 30;11:582. Epub 2017 Nov 30.

Cognitive Neuroscience Group, Department of Neurology, Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany.

Functional imaging studies using BOLD contrasts have consistently reported activation of the supplementary motor area (SMA) both during motor and internal timing tasks. Opposing findings, however, have been shown for the modulation of beta oscillations in the SMA. While movement suppresses beta oscillations in the SMA, motor and non-motor tasks that rely on internal timing increase the amplitude of beta oscillations in the SMA. These independent observations suggest that the relationship between beta oscillations and BOLD activation is more complex than previously thought. Here we set out to investigate this rapport by examining beta oscillations in the SMA during movement with varying degrees of internal timing demands. In a simultaneous EEG-fMRI experiment, 20 healthy right-handed subjects performed an auditory-paced finger-tapping task. Internal timing was operationalized by including conditions with taps on every fourth auditory beat, which necessitates generation of a slow internal rhythm, while tapping to every auditory beat reflected simple auditory-motor synchronization. In the SMA, BOLD activity increased and power in both the low and the high beta band decreased expectedly during each condition compared to baseline. Internal timing was associated with a reduced desynchronization of low beta oscillations compared to conditions without internal timing demands. In parallel with this relative beta power increase, internal timing activated the SMA more strongly in terms of BOLD. This documents a task-dependent non-linear relationship between BOLD and beta-oscillations in the SMA. We discuss different roles of beta synchronization and desynchronization in active processing within the same cortical region.
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http://dx.doi.org/10.3389/fnhum.2017.00582DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5714933PMC
November 2017

Perturbation of whole-brain dynamics in silico reveals mechanistic differences between brain states.

Neuroimage 2018 04 7;169:46-56. Epub 2017 Dec 7.

Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Denmark; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; Institut d'études avancées de Paris, France. Electronic address:

Human neuroimaging research has revealed that wakefulness and sleep involve very different activity patterns. Yet, it is not clear why brain states differ in their dynamical complexity, e.g. in the level of integration and segregation across brain networks over time. Here, we investigate the mechanisms underlying the dynamical stability of brain states using a novel off-line in silico perturbation protocol. We first adjust a whole-brain computational model to the basal dynamics of wakefulness and deep sleep recorded with fMRI in two independent human fMRI datasets. Then, the models of sleep and awake brain states are perturbed using two distinct multifocal protocols either promoting or disrupting synchronization in randomly selected brain areas. Once perturbation is halted, we use a novel measure, the Perturbative Integration Latency Index (PILI), to evaluate the recovery back to baseline. We find a clear distinction between models, consistently showing larger PILI in wakefulness than in deep sleep, corroborating previous experimental findings. In the models, larger recoveries are associated to a critical slowing down induced by a shift in the model's operation point, indicating that the awake brain operates further from a stable equilibrium than deep sleep. This novel approach opens up for a new level of artificial perturbative studies unconstrained by ethical limitations allowing for a deeper investigation of the dynamical properties of different brain states.
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http://dx.doi.org/10.1016/j.neuroimage.2017.12.009DOI Listing
April 2018

Advanced CT for diagnosis of seizure-related stroke mimics.

Eur Radiol 2018 May 7;28(5):1791-1800. Epub 2017 Dec 7.

Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Arnold-Heller-Str. 3, 24105, Kiel, Germany.

Background And Purpose: It is assumed that up to 30 % of clinically diagnosed acute ischaemic strokes (AIS) are actually stroke mimics (SM). Our aim was to evaluate the usefulness of advanced CT including CT angiography (CTA) and CT perfusion (CTP) findings when distinguishing AIS from seizure-related SM.

Methods: Over a 22-month period data were gathered of patients who presented to our stroke centre with AIS-like symptoms and were examined immediately with an advanced CT, analysed and evaluated by two experienced neuroradiologists who preferred SM rather than AIS. All these patients additionally received electroencephalography and follow-up imaging. CTA was the important feature to exclude vessel occlusion or haemodynamic relevant stenosis. Perfusion patterns were retrospectively analysed qualitatively.

Results: The most common perfusion abnormality was cortical hyperperfusion (22/37 [59.5 %] patients) followed by a hypoperfusion pattern with a cortical-subcortical involvement (15/37 [40.5 %] patients) without evidence of vessel occlusion or stenosis. Seizure-related hyper- and hypoperfusion patterns typically crossed the normal anatomical vascular territories boundaries.

Conclusion: Beyond its use in core and penumbra estimation, advanced CT provides important information to emergency physicians in the difficult clinical diagnosis when differentiating between AIS and seizure-related symptoms with an important impact on therapeutic decision-making.

Key Points: • Advanced CT helps to differentiate between ischaemic strokes and stroke mimics. • Seizure-related perfusion patterns are distinct from ischaemia hypoperfusion. • Advanced CT could improve rapid adequate treatment for AIS and seizure events.
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http://dx.doi.org/10.1007/s00330-017-5174-4DOI Listing
May 2018

Resting-state fMRI in sleeping infants more closely resembles adult sleep than adult wakefulness.

PLoS One 2017 17;12(11):e0188122. Epub 2017 Nov 17.

Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America.

Resting state functional magnetic resonance imaging (rs-fMRI) in infants enables important studies of functional brain organization early in human development. However, rs-fMRI in infants has universally been obtained during sleep to reduce participant motion artifact, raising the question of whether differences in functional organization between awake adults and sleeping infants that are commonly attributed to development may instead derive, at least in part, from sleep. This question is especially important as rs-fMRI differences in adult wake vs. sleep are well documented. To investigate this question, we compared functional connectivity and BOLD signal propagation patterns in 6, 12, and 24 month old sleeping infants with patterns in adult wakefulness and non-REM sleep. We find that important functional connectivity features seen during infant sleep closely resemble those seen during adult sleep, including reduced default mode network functional connectivity. However, we also find differences between infant and adult sleep, especially in thalamic BOLD signal propagation patterns. These findings highlight the importance of considering sleep state when drawing developmental inferences in infant rs-fMRI.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0188122PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5693436PMC
December 2017

Novel Intrinsic Ignition Method Measuring Local-Global Integration Characterizes Wakefulness and Deep Sleep.

eNeuro 2017 Sep-Oct;4(5). Epub 2017 Sep 22.

Department of Psychiatry, University of Oxford, Oxford OX3 7JX, United Kingdom.

A precise definition of a brain state has proven elusive. Here, we introduce the novel local-global concept of intrinsic ignition characterizing the dynamical complexity of different brain states. Naturally occurring intrinsic ignition events reflect the capability of a given brain area to propagate neuronal activity to other regions, giving rise to different levels of integration. The ignitory capability of brain regions is computed by the elicited level of integration for each intrinsic ignition event in each brain region, averaged over all events. This intrinsic ignition method is shown to clearly distinguish human neuroimaging data of two fundamental brain states (wakefulness and deep sleep). Importantly, whole-brain computational modelling of this data shows that at the optimal working point is found where there is maximal variability of the intrinsic ignition across brain regions. Thus, combining whole brain models with intrinsic ignition can provide novel insights into underlying mechanisms of brain states.
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http://dx.doi.org/10.1523/ENEURO.0106-17.2017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5617208PMC
May 2018

On wakefulness fluctuations as a source of BOLD functional connectivity dynamics.

Sci Rep 2017 07 19;7(1):5908. Epub 2017 Jul 19.

Department of Neurology and Brain Imaging Center, Goethe University Frankfurt am Main, Schleusenweg 2-16, 60528, Frankfurt am Main, Germany.

Human brain dynamics and functional connectivity fluctuate over a range of temporal scales in coordination with internal states and environmental demands. However, the neurobiological significance and consequences of functional connectivity dynamics during rest have not yet been established. We show that the coarse-grained clustering of whole-brain dynamic connectivity measured with magnetic resonance imaging reveals discrete patterns (dynamic connectivity states) associated with wakefulness and sleep. We validate this using EEG in healthy subjects and patients with narcolepsy and by matching our results with previous findings in a large collaborative database. We also show that drowsiness may account for previous reports of metastable connectivity states associated with different levels of functional integration. This implies that future studies of transient functional connectivity must independently monitor wakefulness. We conclude that a possible neurobiological significance of dynamic connectivity states, computed at a sufficiently coarse temporal scale, is that of fluctuations in wakefulness.
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http://dx.doi.org/10.1038/s41598-017-06389-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5517577PMC
July 2017

Increased Stability and Breakdown of Brain Effective Connectivity During Slow-Wave Sleep: Mechanistic Insights from Whole-Brain Computational Modelling.

Sci Rep 2017 07 5;7(1):4634. Epub 2017 Jul 5.

Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Calle Ramón Trias Fargas 25-27, 08005, Barcelona, Spain.

Recent research has found that the human sleep cycle is characterised by changes in spatiotemporal patterns of brain activity. Yet, we are still missing a mechanistic explanation of the local neuronal dynamics underlying these changes. We used whole-brain computational modelling to study the differences in global brain functional connectivity and synchrony of fMRI activity in healthy humans during wakefulness and slow-wave sleep. We applied a whole-brain model based on the normal form of a supercritical Hopf bifurcation and studied the dynamical changes when adapting the bifurcation parameter for all brain nodes to best match wakefulness and slow-wave sleep. Furthermore, we analysed differences in effective connectivity between the two states. In addition to significant changes in functional connectivity, synchrony and metastability, this analysis revealed a significant shift of the global dynamic working point of brain dynamics, from the edge of the transition between damped to sustained oscillations during wakefulness, to a stable focus during slow-wave sleep. Moreover, we identified a significant global decrease in effective interactions during slow-wave sleep. These results suggest a mechanism for the empirical functional changes observed during slow-wave sleep, namely a global shift of the brain's dynamic working point leading to increased stability and decreased effective connectivity.
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http://dx.doi.org/10.1038/s41598-017-04522-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5498661PMC
July 2017

Mapping visual dominance in human sleep.

Neuroimage 2017 04 21;150:250-261. Epub 2017 Feb 21.

Department of Radiology, Washington University, Saint Louis, MO 63110, USA; Department of Neurology, Washington University, Saint Louis, MO 63110, USA; Department of Neuroscience, Washington University, Saint Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University, Saint Louis, MO 63110, USA. Electronic address:

Sleep is a universal behavior, essential for humans and animals alike to survive. Its importance to a person's physical and mental health cannot be overstated. Although lateralization of function is well established in the lesion, split-brain and task based neuroimaging literature, and more recently in functional imaging studies of spontaneous fluctuations of the fMRI BOLD signal during wakeful rest, it is unknown if these asymmetries are present during sleep. We investigated hemispheric asymmetries in the global brain signal during non-REM sleep. Here we show that increasing sleep depth is accompanied by an increasing rightward asymmetry of regions in visual cortex including primary bilaterally and in the right hemisphere along the lingual gyrus and middle temporal cortex. In addition, left hemisphere language regions largely maintained their leftward asymmetry during sleep. Right hemisphere attention related regions expressed a more complicated relation with some regions maintaining a rightward asymmetry while this was lost in others. These results suggest that asymmetries in the human brain are state dependent.
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http://dx.doi.org/10.1016/j.neuroimage.2017.02.053DOI Listing
April 2017

Human cortical-hippocampal dialogue in wake and slow-wave sleep.

Proc Natl Acad Sci U S A 2016 11 17;113(44):E6868-E6876. Epub 2016 Oct 17.

Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110;

Declarative memory consolidation is hypothesized to require a two-stage, reciprocal cortical-hippocampal dialogue. According to this model, higher frequency signals convey information from the cortex to hippocampus during wakefulness, but in the reverse direction during slow-wave sleep (SWS). Conversely, lower-frequency activity propagates from the information "receiver" to the "sender" to coordinate the timing of information transfer. Reversal of sender/receiver roles across wake and SWS implies that higher- and lower-frequency signaling should reverse direction between the cortex and hippocampus. However, direct evidence of such a reversal has been lacking in humans. Here, we use human resting-state fMRI and electrocorticography to demonstrate that δ-band activity and infraslow activity propagate in opposite directions between the hippocampus and cerebral cortex. Moreover, both δ activity and infraslow activity reverse propagation directions between the hippocampus and cerebral cortex across wake and SWS. These findings provide direct evidence for state-dependent reversals in human cortical-hippocampal communication.
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http://dx.doi.org/10.1073/pnas.1607289113DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5098641PMC
November 2016
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