EEG microstate periodicity explained by rotating phase patterns of resting-state alpha oscillations.

Spatio-temporal patterns in electroencephalography (EEG) can be described by microstate analysis, a discrete approximation of the continuous electric field patterns produced by the cerebral cortex. Resting-state EEG microstates are largely determined by alpha frequencies (8-12 Hz) and we recently demonstrated that microstates occur periodically with twice the alpha frequency. To understand the origin of microstate periodicity, we analyzed the analytic amplitude and the analytic phase of resting-state alpha oscillations independently. In continuous EEG data we found rotating phase patterns organized around a small number of phase singularities which varied in number and location. The spatial rotation of phase patterns occurred with the underlying alpha frequency. Phase rotors coincided with periodic microstate motifs involving the four canonical microstate maps. The analytic amplitude showed no oscillatory behaviour and was almost static across time intervals of 1-2 alpha cycles, resulting in the global pattern of a standing wave. In n=23 healthy adults, time-lagged mutual information analysis of microstate sequences derived from amplitude and phase signals of awake eyes-closed EEG records showed that only the phase component contributed to the periodicity of microstate sequences. Phase sequences showed mutual information peaks at multiples of 50 ms and the group average had a main peak at 100 ms (10 Hz), whereas amplitude sequences had a slow and monotonous information decay. This result was confirmed by an independent approach combining temporal principal component analysis (tPCA) and autocorrelation analysis. We reproduced our observations in a generic model of EEG oscillations composed of coupled non-linear oscillators (Stuart-Landau model). Phase-amplitude dynamics similar to experimental EEG occurred when the oscillators underwent a supercritical Hopf bifurcation, a common feature of many computational models of the alpha rhythm. These findings explain our previous description of periodic microstate recurrence and its relation to the time scale of alpha oscillations. Moreover, our results corroborate the predictions of computational models and connect experimentally observed EEG patterns to properties of critical oscillator networks.

Capturing the non-stationarity of whole-brain dynamics underlying human brain states.

Brain dynamics depicts an extremely complex energy landscape that changes over time, and its characterisation is a central unsolved problem in neuroscience. We approximate the non-stationary landscape sustained by the human brain through a novel mathematical formalism that allows us characterise the attractor structure, i.e. the stationary points and their connections. Due to its time-varying nature, the structure of the global attractor and the corresponding number of energy levels changes over time. We apply this formalism to distinguish quantitatively between the different human brain states of wakefulness and different stages of sleep, as a step towards future clinical applications.

Information-theoretical analysis of resting state EEG microstate sequences - non-Markovianity, non-stationarity and periodicities.

We present an information-theoretical analysis of temporal dependencies in EEG microstate sequences during wakeful rest. We interpret microstate sequences as discrete stochastic processes where each state corresponds to a representative scalp potential topography. Testing low-order Markovianity of these discrete sequences directly, we find that none of the recordings fulfils the Markov property of order 0, 1 or 2. Further analyses show that the microstate transition matrix is non-stationary over time in 80% (window size 10 s), 60% (window size 20 s) and 44% (window size 40 s) of the subjects, and that transition matrices are asymmetric in 14/20 (70%) subjects. To assess temporal dependencies globally, the time-lagged mutual information function (autoinformation function) of each sequence is compared to the first-order Markov model defined by the classical transition matrix approach. The autoinformation function for the Markovian case is derived analytically and numerically. For experimental data, we find non-Markovian behaviour in the range of the main EEG frequency bands where distinct periodicities related to the subject's EEG frequency spectrum appear. In particular, the microstate clustering algorithm induces frequency doubling with respect to the EEG power spectral density while the tail of the autoinformation function asymptotically reaches the first-order Markov confidence interval for time lags above 1000 ms. In summary, our results show that resting state microstate sequences are non-Markovian processes which inherit periodicities from the underlying EEG dynamics. Our results interpolate between two diverging models of microstate dynamics, memoryless Markov models on one side, and long-range correlated models on the other: microstate sequences display more complex temporal dependencies than captured by the transition matrix approach in the range of the main EEG frequency bands, but show finite memory content in the long run.

Analytical and empirical fluctuation functions of the EEG microstate random walk - Short-range vs. long-range correlations.

We analyze temporal autocorrelations and the scaling behaviour of EEG microstate sequences during wakeful rest. We use the recently introduced random walk approach and compute its fluctuation function analytically under the null hypothesis of a short-range correlated, first-order Markov process. The empirical fluctuation function and the Hurst parameter H as a surrogate parameter of long-range correlations are computed from 32 resting state EEG recordings and for a set of first-order Markov surrogate data sets with equilibrium distribution and transition matrices identical to the empirical data. In order to distinguish short-range correlations (H ≈ 0.5) from previously reported long-range correlations (H > 0.5) statistically, confidence intervals for H and the fluctuation functions are constructed under the null hypothesis. Comparing three different estimation methods for H, we find that only one data set consistently shows H > 0.5, compatible with long-range correlations, whereas the majority of experimental data sets cannot be consistently distinguished from Markovian scaling behaviour. Our analysis suggests that the scaling behaviour of resting state EEG microstate sequences, though markedly different from uncorrelated, zero-order Markov processes, can often not be distinguished from a short-range correlated, first-order Markov process. Our results do not prove the microstate process to be Markovian, but challenge the approach to parametrize resting state EEG by single parameter models.

Working memory performance of early MS patients correlates inversely with modularity increases in resting state functional connectivity networks.

Multiple sclerosis (MS) is an autoimmune inflammatory demyelinating and neurodegenerative disorder of the central nervous system characterized by multifocal white matter brain lesions leading to alterations in connectivity at the subcortical and cortical level. Graph theory, in combination with neuroimaging techniques, has been recently developed into a powerful tool to assess the large-scale structure of brain functional connectivity. Considering the structural damage present in the brain of MS patients, we hypothesized that the topological properties of resting-state functional networks of early MS patients would be re-arranged in order to limit the impact of disease expression. A standardized dual task (Paced Auditory Serial Addition Task simultaneously performed with a paper and pencil task) was administered to study the interactions between behavioral performance and functional network re-organization. We studied a group of 16 early MS patients (35.3±8.3 years, 11 females) and 20 healthy controls (29.9±7.0 years, 10 females) and found that brain resting-state networks of the MS patients displayed increased network modularity, i.e. diminished functional integration between separate functional modules. Modularity correlated negatively with dual task performance in the MS patients. Our results shed light on how localized anatomical connectivity damage can globally impact brain functional connectivity and how these alterations can impair behavioral performance. Finally, given the early stage of the MS patients included in this study, network modularity could be considered a promising biomarker for detection of earliest-stage brain network reorganization, and possibly of disease progression.

Classification of EEG abnormalities in partial epilepsy with simultaneous EEG-fMRI recordings.

Scalp EEG recordings and the classification of interictal epileptiform discharges (IED) in patients with epilepsy provide valuable information about the epileptogenic network, particularly by defining the boundaries of the "irritative zone" (IZ), and hence are helpful during pre-surgical evaluation of patients with severe refractory epilepsies. The current detection and classification of epileptiform signals essentially rely on expert observers. This is a very time-consuming procedure, which also leads to inter-observer variability. Here, we propose a novel approach to automatically classify epileptic activity and show how this method provides critical and reliable information related to the IZ localization beyond the one provided by previous approaches. We applied Wave_clus, an automatic spike sorting algorithm, for the classification of IED visually identified from pre-surgical simultaneous Electroencephalogram-functional Magnetic Resonance Imagining (EEG-fMRI) recordings in 8 patients affected by refractory partial epilepsy candidate for surgery. For each patient, two fMRI analyses were performed: one based on the visual classification and one based on the algorithmic sorting. This novel approach successfully identified a total of 29 IED classes (compared to 26 for visual identification). The general concordance between methods was good, providing a full match of EEG patterns in 2 cases, additional EEG information in 2 other cases and, in general, covering EEG patterns of the same areas as expert classification in 7 of the 8 cases. Most notably, evaluation of the method with EEG-fMRI data analysis showed hemodynamic maps related to the majority of IED classes representing improved performance than the visual IED classification-based analysis (72% versus 50%). Furthermore, the IED-related BOLD changes revealed by using the algorithm were localized within the presumed IZ for a larger number of IED classes (9) in a greater number of patients than the expert classification (7 and 5, respectively). In contrast, in only one case presented the new algorithm resulted in fewer classes and activation areas. We propose that the use of automated spike sorting algorithms to classify IED provides an efficient tool for mapping IED-related fMRI changes and increases the EEG-fMRI clinical value for the pre-surgical assessment of patients with severe epilepsy.

[Hirayama disease in Germany: case reports and review of the literature]. / Hirayama-Syndrom in Deutschland : Fallberichte und Literaturübersicht.

Hirayama disease is a juvenile benign distal upper limb muscular atrophy rarely observed in Europe, usually monomelic involving C7-Th1 innervated muscles. It is characterized by insidious onset and a self-limited course within a few years. The pathogenesis of this mostly sporadic disease is not fully clarified. Cervical flexion myelopathy with mechanical ischemic damage of spinal motoneurons is the best established pathogenetic hypothesis, but neurodegenerative and autoimmune causes are also debated. Typically, young men of Asian origin are affected. Here we describe three German Caucasian patients with Hirayama disease and provide an up-to-date review of the literature.

Imaging haemodynamic changes related to seizures: comparison of EEG-based general linear model, independent component analysis of fMRI and intracranial EEG.

BACKGROUND: Simultaneous EEG-fMRI can reveal haemodynamic changes associated with epileptic activity which may contribute to understanding seizure onset and propagation. METHODS: Nine of 83 patients with focal epilepsy undergoing pre-surgical evaluation had seizures during EEG-fMRI and analysed using three approaches, two based on the general linear model (GLM) and one using independent component analysis (ICA): The results were compared with intracranial EEG. RESULTS: The canonical GLM analysis revealed significant BOLD signal changes associated with seizures on EEG in 7/9 patients, concordant with the seizure onset zone in 4/7. The Fourier GLM analysis revealed changes in BOLD signal corresponding with the results of the canonical analysis in two patients. ICA revealed components spatially concordant with the seizure onset zone in all patients (8/9 confirmed by intracranial EEG). CONCLUSION: Ictal EEG-fMRI visualises plausible seizure related haemodynamic changes. The GLM approach to analysing EEG-fMRI data reveals localised BOLD changes concordant with the ictal onset zone when scalp EEG reflects seizure onset. ICA provides additional information when scalp EEG does not accurately reflect seizures and may give insight into ictal haemodynamics.

The avalanche-like behaviour of large-scale haemodynamic activity from wakefulness to deep sleep.

Increasing evidence suggests that responsiveness is associated with critical or near-critical cortical dynamics, which exhibit scale-free cascades of spatio-temporal activity. These cascades, or 'avalanches', have been detected at multiple scales, from in vitro and in vivo microcircuits to voltage imaging and brain-wide functional magnetic resonance imaging (fMRI) recordings. Criticality endows the cortex with certain information-processing capacities postulated as necessary for conscious wakefulness, yet it remains unknown how unresponsiveness impacts on the avalanche-like behaviour of large-scale human haemodynamic activity. We observed a scale-free hierarchy of co-activated connected clusters by applying a point-process transformation to fMRI data recorded during wakefulness and non-rapid eye movement (NREM) sleep. Maximum-likelihood estimates revealed a significant effect of sleep stage on the scaling parameters of the cluster size power-law distributions. Post hoc statistical tests showed that differences were maximal between wakefulness and N2 sleep. These results were robust against spatial coarse graining, fitting alternative statistical models and different point-process thresholds, and disappeared upon phase shuffling the fMRI time series. Evoked neural bistabilities preventing arousals during N2 sleep do not suffice to explain these differences, which point towards changes in the intrinsic dynamics of the brain that could be necessary to consolidate a state of deep unresponsiveness.

Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep.

The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle. Notably, our results reveal key trajectories to switch within and between EEG-based sleep stages, while highlighting the heterogeneities of stage N1 sleep and wakefulness before and after sleep.

Combined EEG-fMRI and tractography to visualise propagation of epileptic activity.

In a patient with refractory temporal lobe epilepsy, EEG-fMRI showed activation in association with left anterior temporal interictal discharges, in the left temporal, parietal and occipital lobes. Dynamic causal modelling suggested propagation of neural activity from the temporal focus to the area of occipital activation. Tractography showed connections from the site of temporal lobe activation to the site of occipital activation. This demonstrates the principle of combining EEG-fMRI and tractography to delineate the pathways of propagation of epileptic activity.

Converging PET and fMRI evidence for a common area involved in human focal epilepsies.

OBJECTIVES: Experiments in animal models have identified specific subcortical anatomic circuits, which are critically involved in the pathogenesis and control of seizure activity. However, whether such anatomic substrates also exist in human epilepsy is not known. METHODS: We studied 2 separate groups of patients with focal epilepsies arising from any cortical location using either simultaneous EEG-fMRI (n = 19 patients) or [¹¹C]flumazenil PET (n = 18). RESULTS: Time-locked with the interictal epileptiform discharges, we found significant hemodynamic increases common to all patients near the frontal piriform cortex ipsilateral to the presumed cortical focus. GABA(A) receptor binding in the same area was reduced in patients with more frequent seizures. CONCLUSIONS: Our findings of cerebral blood flow and GABAergic changes, irrespective of where interictal or ictal activity occurs in the cortex, suggest that this area of the human primary olfactory cortex may be an attractive new target for epilepsy therapy, including neurosurgery, electrical stimulation, and focal drug delivery.

Recent advances in recording electrophysiological data simultaneously with magnetic resonance imaging.

Simultaneous recording of brain activity by different neurophysiological modalities can yield insights that reach beyond those obtained by each technique individually, even when compared to those from the post-hoc integration of results from each technique recorded sequentially. Success in the endeavour of real-time multimodal experiments requires special hardware and software as well as purpose-tailored experimental design and analysis strategies. Here, we review the key methodological issues in recording electrophysiological data in humans simultaneously with magnetic resonance imaging (MRI), focusing on recent technical and analytical advances in the field. Examples are derived from simultaneous electroencephalography (EEG) and electromyography (EMG) during functional MRI in cognitive and systems neuroscience as well as in clinical neurology, in particular in epilepsy and movement disorders. We conclude with an outlook on current and future efforts to achieve true integration of electrical and haemodynamic measures of neuronal activity using data fusion models.

Independent component analysis of interictal fMRI in focal epilepsy: comparison with general linear model-based EEG-correlated fMRI.

The general linear model (GLM) has been used to analyze simultaneous EEG-fMRI to reveal BOLD changes linked to interictal epileptic discharges (IED) identified on scalp EEG. This approach is ineffective when IED are not evident in the EEG. Data-driven fMRI analysis techniques that do not require an EEG derived model may offer a solution in these circumstances. We compared the findings of independent components analysis (ICA) and EEG-based GLM analyses of fMRI data from eight patients with focal epilepsy. Spatial ICA was used to extract independent components (IC) which were automatically classified as either BOLD-related, motion artefacts, EPI-susceptibility artefacts, large blood vessels, noise at high spatial or temporal frequency. The classifier reduced the number of candidate IC by 78%, with an average of 16 BOLD-related IC. Concordance between the ICA and GLM-derived results was assessed based on spatio-temporal criteria. In each patient, one of the IC satisfied the criteria to correspond to IED-based GLM result. The remaining IC were consistent with BOLD patterns of spontaneous brain activity and may include epileptic activity that was not evident on the scalp EEG. In conclusion, ICA of fMRI is capable of revealing areas of epileptic activity in patients with focal epilepsy and may be useful for the analysis of EEG-fMRI data in which abnormalities are not apparent on scalp EEG.

Impact of interictal epileptic activity on normal brain function in epileptic encephalopathy: an electroencephalography-functional magnetic resonance imaging study.

Using electroencephalography (EEG) in combination with functional magnetic resonance imaging (fMRI), we studied a 9.5-year-old girl who developed cognitive and behavioral regression in association with intense interictal bilaterally synchronous epileptic discharges (IBSEDs) both during the awake state and during sleep. During runs of IBSEDs, EEG-fMRI demonstrated deactivations in the lateral and medial frontoparietal cortices, posterior cingulate gyrus, and cerebellum together with focal relative activations in the right frontal, parietal, and temporal cortices. The deactivations probably reflect the repercussion of the interictal epileptic activity on normal brain function which might cause the neuropsychological regression by inducing repetitive interruptions of neurophysiological function resulting in a chronic state of specific psychomotor impairment. The relative activations could possibly indicate the source of epileptic activity rapidly spreading to other brain regions.

Where the BOLD signal goes when alpha EEG leaves.

Previous studies using simultaneous EEG and fMRI recordings have yielded discrepant results regarding the topography of brain activity in relation to spontaneous power fluctuations in the alpha band of the EEG during eyes-closed rest. Here, we explore several possible explanations for this discrepancy by re-analyzing in detail our previously reported data. Using single subject analyses as a starting point, we found that alpha power decreases are associated with fMRI signal increases that mostly follow two distinct patterns: either 'visual' areas in the occipital lobe or 'attentional' areas in the frontal and parietal lobe. On examination of the EEG spectra corresponding to these two fMRI patterns, we found greater relative theta power in sessions yielding the 'visual' fMRI pattern during alpha desynchronization and greater relative beta power in sessions yielding the 'attentional' fMRI pattern. The few sessions that fell into neither pattern featured the overall lowest theta and highest beta power. We conclude that the pattern of brain activation observed during spontaneous power reduction in the alpha band depends on the general level of brain activity as indexed over a broader spectral range in the EEG. Finally, we relate these findings to the concepts of 'resting state' and 'default mode' and discuss how - as for sleep - EEG-based criteria might be used for staging brain activity during wakefulness.

fMRI temporal clustering analysis in patients with frequent interictal epileptiform discharges: comparison with EEG-driven analysis.

Temporal clustering analysis (TCA) is an exploratory data-driven technique that has been proposed for the analysis of resting fMRI to localise epileptiform activity without need for simultaneous EEG. Conventionally, fMRI of epileptic activity has been limited to those patients with subtle clinical events or frequent interictal epileptiform EEG discharges, requiring simultaneous EEG recording, from which a linear model is derived to make valid statistical inferences from the fMRI data. We sought to evaluate TCA by comparing the results with those of EEG correlated fMRI in eight selected cases. Cases were selected with clear epileptogenic localisation or lateralisation on the basis of concordant EEG and structural MRI findings, in addition to concordant activations seen on EEG-derived fMRI analyses. In three, areas of activation were seen with TCA but none corresponding to the electro-clinical localisation or activations obtained with EEG driven analysis. Temporal clusters were closely coincident with times of maximal head motion. We feel this is a serious confound to this approach and recommend that interpretation of TCA that does not address motion and physiological noise be treated with caution. New techniques to localise epileptogenic activity with fMRI alone require validation with an appropriate independent measure. In the investigation of interictal epileptiform activity, this is best done with simultaneous EEG recording.

Leishmania promastigotes release a granulocyte chemotactic factor and induce interleukin-8 release but inhibit gamma interferon-inducible protein 10 production by neutrophil granulocytes.

Recent data from our laboratory suggest that neutrophil granulocytes (polymorphonuclear leukocytes [PMN]) can serve as host cells for Leishmania major in the early phase of infection. In line with these findings, an early influx of PMN to the infected tissues was shown by others to be associated with susceptibility to infection with L. major. The mechanisms underlying the initial PMN recruitment to the site of infection is poorly understood. In the present study we investigated whether Leishmania can influence PMN migration. Supernatants of Leishmania promastigotes were tested for their chemotactic activity using an in vitro chemotaxis assay. All Leishmania species tested (L. major, L. aethiopica, and L. donovani) displayed a marked chemotactic effect on human PMN. However, no effect on the migration of macrophages and NK cells was observed. Checkerboard analysis revealed that the observed PMN migration was due to chemotaxis rather than chemokinesis. Most of the chemotactic activity was found in fractions containing molecules with sizes between 10 and 50 kDa. Pretreatment of PMN with N-formyl-methionyl-leucyl-phenylalanine blocked the chemotactic activity of Leishmania supernatants up to 75%. In addition, we found that leishmanial contact induced the release of interleukin-8 (IL-8) and inhibited the production of gamma interferon-inducible protein 10 (IP-10) by PMN. These data suggest that infection with Leishmania promastigotes leads to PMN accumulation via the production of a chemotactic factor by the parasites, and this effect is amplified by the induction of IL-8 production in PMN. On the other hand, the inhibition of IP-10 production can lead to prevention of NK cell activation.

Hemi-spinal cord infarction due to vertebral artery dissection in congenital afibrinogenemia.

Isolated ischemia of the cervical spinal cord is an uncommon but increasingly recognized complication of vertebral artery dissection (VAD). The authors report a young patient with congenital afibrinogenemia, who developed VAD with extensive unilateral spinal cord infarction, probably caused by local compression of spinal radicular feeders at their origin by vertebral artery hematoma.

EEG-correlated fMRI of human alpha activity.

Electroencephalography-correlated functional magnetic resonance imaging (EEG/fMRI) can be used to identify blood oxygen level-dependent (BOLD) signal changes associated with both physiological and pathological EEG events. Here, we implemented continuous and simultaneous EEG/fMRI to identify BOLD signal changes related to spontaneous power fluctuations in the alpha rhythm (8-12 Hz), the dominant EEG pattern during relaxed wakefulness. Thirty-two channels of EEG were recorded in 10 subjects during eyes-closed rest inside a 1.5-T magnet resonance (MR) scanner using an MR-compatible EEG recording system. Functional scanning by echoplanar imaging covered almost the entire cerebrum every 4 s. Off-line MRI artifact subtraction software was applied to obtain continuous EEG data during fMRI acquisition. The average alpha power over 1-s epochs was derived at several electrode positions using a Fast Fourier Transform. The power time course was then convolved with a canonical hemodynamic response function, down-sampled, and used for statistical parametric mapping of associated signal changes in the image time series. At all electrode positions studied, a strong negative correlation of parietal and frontal cortical activity with alpha power was found. Conversely, only sparse and nonsystematic positive correlation was detected. The relevance of these findings is discussed in view of the current theories on the generation and significance of the alpha rhythm and the related functional neuroimaging findings.