Investigation of the dynamics underlying periodic complexes in the EEG

Periodic complexes (PC), occurring lateralised or diffuse, are relatively rare EEG phenomena which reflect acute severe brain disease. The pathophysiology is still incompletely understood. One hypothesis suggested by the alpha rhythm model of Lopes da Silva is that periodic complexes reflect limit cycle dynamics of cortical networks caused by excessive excitatory feedback. We examined this hypothesis by applying a recently developed technique to EEGs displaying periodic complexes and to periodic complexes generated by the model. The technique, non-linear cross prediction, characterises how well a time series can be predicted, and how much amplitude and time asymmetry is present. Amplitude and time asymmetry are indications of non-linearity. In accordance with the model, most EEG channels with PC showed clear evidence of amplitude and time asymmetry, pointing to non-linear dynamics. However, the non-linear predictability of true PC was substantially lower than that of PC generated by the model. Furthermore, no finite value for the correlation dimension could be obtained for the real EEG data, whereas the model time series had a dimension slighter higher than one, consistent with a limit cycle attractor. Thus we can conclude that PC reflect non-linear dynamics, but a limit cycle attractor is too simple an explanation. The possibility of more complex (high dimensional and spatio-temporal) non-linear dynamics should be investigated. Received: 26 February 1998 / Accepted in revised form: 24 August 1998     

Nonlinearity in normal human EEG: cycles,temporal asymmetry,nonstationarity and randomness,not chaos

Two-hour vigilance and sleep electroencephalogram (EEG) recordings from five healthy volunteers were analyzed using a method for identifying nonlinearity and chaos which combines the redundancy–linear redundancy approach with the surrogate data technique. A nonlinear component in the EEG was detected, however, inconsistent with the hypothesis of low-dimensional chaos. A possibility that a temporally asymmetric process may underlie or influence the EEG dynamics was indicated. A process that merges nonstationary nonlinear deterministic oscillations with randomness is proposed for an explanation of observed properties of the analyzed EEG signals. Taking these results into consideration, the use of dimensional and related chaos-based algorithms in quantitative EEG analysis is critically discussed. Received: 25 September 1994 / Accepted in revised form: 10 July 1996     

Non-linear analysis of the rhythmic activity in rodent brains

This paper discusses the employment of non-parametric non-linear prediction algorithms to investigate non-linear dynamics in the rhythmic brain activity of rats. Three algorithms (Sugihara-May Simplex, K-neighbour and Casdagli's) were tested yielding similar prediction results which--when subject to a suitable bootstrap based t-tests--revealed that the theta waves recorded in rat brains cannot have their intrinsic non-linearity dismissed at a significance of 0.05.     

Testing procedures for non-stationarity and non-linearity in physiological signals

Most of the physiological signals (EEG, ECG, blood flow, human gait, etc.) characterize by complex dynamics including both non-stationarities and non-linearities. These time series resemble red noise with long-range correlation and 1/(f beta) power spectrum. A question arises as to how to distinguish the characteristics of the process underlying the signal dynamics from the properties of the observed time series. The classical methods to determine possible non-linear (chaotic) dynamics (e.g. correlation dimension) often fail in such signals because of relatively short data records containing stochastic components and non-stationarities. We report an application of several approaches, aimed at (1) determining of the non-stationarities in the signals and (2) testing whether non-linear dynamics exists. Assessment of the intrinsic correlation properties of the dynamic process and distinguishing the same from external trends was performed using singular spectra and detrended fluctuation analysis. The existence of non-linear dynamics was tested by correlation dimension (modified algorithm of re-embedding) and by correlation integrals of real and surrogate data. The correlation integrals of real signal and surrogate data sets were statistically compared using Kolmogorov-Smirnov (K-S) test. The procedures were tested on EEG and laser-Doppler (LD) blood flow. Our suggestion is that no one approach taken alone is the best for our aims. Instead, a battery of methods should be used.     

Revisited measles and chickenpox dynamics through orthogonal transformation

The question addressed is whether or not childhood epidemics such as measles and chickenpox are characterized by low-dimensional chaos. We propose a new method for the detection and extraction of hidden periodic components embedded in an irregular cyclical series, and study the characterization of the epidemiological series in terms of the characteristic features or periodicity attributes of the extracted components. It is shown that the measles series possesses two periodic components each having a period of one year. Both the periodic components have time-varying pattern, and the process is nonlinear and deterministic; there is no evidence of strong chaoticity in the measles dynamics. The chickenpox series has one seasonal component with stable pattern, and the process is deterministic but linear, and hence non-chaotic. We also propose surrogate generators based on null hypotheses relating to the variability of the periodicity attributes to analyse the dynamics in the epidemic series. The process dynamics is also studied using seasonally forced SEIR epidemic model, and the characterization performance of the proposed schemes is assessed.     

The first Chinese case of Creutzfeldt-Jakob disease patient with R208H mutation in PRNP

A case of Creutzfeldt-Jakob disease (CJD) with a rare mutation of the prion protein (PrP) gene (PRNP) at codon 208 (R208H), while the codon 129 was a methionine homozygous genotype is reported. The patient initial displayed hand tremor, dizziness and progressive cognitive dysfunction. Subsequently, other symptoms gradually appeared, including cerebellar ataxia and mental disorder. No periodic activity was recorded at electroencephalography (EEG) and 14-3-3 protein in cerebrospinal fluid was negative. Total clinical course was about 4 months. Retrospective investigation of this family across seven generations did not figure out clear family history. However, genetic analyses revealed six first-degree family members with the R208H allele.     

A Chinese Creutzfeldt-Jakob disease patient with E196K mutation in PRNP

Genetic Creutzfeldt-Jakob disease (gCJD) is caused by a range of mutations in the prion protein gene (PRNP) and account for approximately 10–15% of overall human prion diseases worldwide. They are different with disease onset, disease duration, clinical signs and diagnostic findings. Here we reported a 71 year-old female with an E196K mutation in one PRNP allele, while the codon 129 was a methionine homozygous genotype. The patient started with non-specific symptoms, but displayed rapidly progressive disturbances of speech, memory, cognitive and physical movement. No periodic activity was recorded at electroencephalography (EEG) during the entire disease course. Retrospective investigation of her family members did not reveal similar neurological disorders. Total clinical course was about seven months.Key words: Creutzfeldt-Jakob disease, PRNP, E196K     

The first Chinese case of Creutzfeldt-Jakob disease patient with R208H mutation in PRNP

A case of Creutzfeldt-Jakob disease (CJD) with a rare mutation of the prion protein (PrP) gene (PRNP) at codon 208 (R208H), while the codon 129 was a methionine homozygous genotype is reported. The patient initial displayed hand tremor, dizziness and progressive cognitive dysfunction. Subsequently, other symptoms gradually appeared, including cerebellar ataxia and mental disorder. No periodic activity was recorded at electroencephalography (EEG) and 14-3-3 protein in cerebrospinal fluid was negative. Total clinical course was about four months. Retrospective investigation of this family across seven generations did not figure out clear family history. However, genetic analyses revealed six first-degree family members with the R208H allele.Key words: creutzfeldt-Jakob disease, PRNP, R208H     

Bispectral analysis does not differentiate between anaesthesia EEG and a linear random process]

Bispectral analysis of the electroencephalogram (EEG) has been used to monitor depth of anaesthesia. In the majority of publications this has involved the use of the so called BIS-Index TM (Aspect Medical Systems, Inc.). The exact relationship of this index to such bispectral parameters as the bispectrum and bicoherence has not yet been reported. If the EEG is considered as a linear random process, bicoherence is trivial, i.e. it is independent of the EEG frequency. The aim of this study was to determine the proportion of EEG epochs with non-trivial bicoherence during isoflurane/N20 anaesthesia. We reanalyzed 25.5 hours of digitalised EEG signal from 9 patients undergoing gynaecological surgery. The test developed by Hinich for Gaussian distribution and linearity was then applied. The test was validated using various synthetic surrogate data: Gaussian random data, the z-component of the Lorenz attractor, the phase randomized EEG and the phase randomized z-component of the Lorenz attractor. The percentage of epochs (8.192 s, 1024 data points) with non-trivial bicoherence was: Lorenz data 95.4%, phase randomized Lorenz data 9.4%, synthetic Gaussian data 14.8%, original EEG 9.1%, phase randomized EEG 5.1%. The original EEG data were not found to contain a higher percentage of epochs with non-trivial bicoherence than the phase randomized data and the synthetic Gaussian data. We conclude that bispectral analysis does not substantially add to the information obtained with other methods of quantitative EEG analysis.     

Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson’s disease

Parkinson’s disease motor symptoms are associated with an increase in subthalamic nucleus beta band oscillatory power. However, these oscillations are phasic, and there is a growing body of evidence suggesting that beta burst duration may be of critical importance to motor symptoms. This makes insights into the dynamics of beta bursting generation valuable, in particular to refine closed-loop deep brain stimulation in Parkinson’s disease. In this study, we ask the question “Can average burst duration reveal how dynamics change between the ON and OFF medication states?”. Our analysis of local field potentials from the subthalamic nucleus demonstrates using linear surrogates that the system generating beta oscillations is more likely to act in a non-linear regime OFF medication and that the change in a non-linearity measure is correlated with motor impairment. In addition, we pinpoint the simplest dynamical changes that could be responsible for changes in the temporal patterning of beta oscillations between medication states by fitting to data biologically inspired models, and simpler beta envelope models. Finally, we show that the non-linearity can be directly extracted from average burst duration profiles under the assumption of constant noise in envelope models. This reveals that average burst duration profiles provide a window into burst dynamics, which may underlie the success of burst duration as a biomarker. In summary, we demonstrate a relationship between average burst duration profiles, dynamics of the system generating beta oscillations, and motor impairment, which puts us in a better position to understand the pathology and improve therapies such as deep brain stimulation.     

The effect of slow allosteric transitions in a coupled biochemical oscillator model.

The effect of slowed allosteric transitions in a coupled biochemical oscillator model showing complex dynamic behavior is investigated. When the allosteric transitions are sufficiently fast one can obtain a low-dimensional asymptotic approximation for the dynamics of the species that evolve on a slow time-scale. Such low-dimensional models are common in studies of biological control systems and little attention has, so far, been given to the dynamic effect of the large number of species usually eliminated from more biochemically detailed models. Here we investigate the dynamic effect of explicit inclusion of allosteric transitions having finite time-scales of equilibration. It is found that slowed allosteric transitions suppress complex dynamic modes such a bursting, quasi-periodicity and chaos. The effect arises as the enzyme of consideration becomes trapped in an active state where it is unable to respond to changes in effector concentration on the time-scale necessary to support the modes of complex dynamics. Slow allosteric transitions may be favourable in biological systems in which complex oscillations are not desirable but which, at the same time, may benefit from the presence of positive feedbacks. Our findings suggest that slow allosteric transitions and finite internal rates in general may contribute significantly to the dynamics of biological control mechanisms.     

Is breathing in infants chaotic? Dimension estimates for respiratory patterns during quiet sleep

Wedescribe an analysis of dynamic behavior apparent in times-seriesrecordings of infant breathing during sleep. Three principal techniqueswere used: estimation of correlation dimension, surrogate dataanalysis, and reduced linear (autoregressive) modeling (RARM). Correlation dimension can be used to quantify the complexity of timeseries and has been applied to a variety of physiological andbiological measurements. However, the methods most commonly used toestimate correlation dimension suffer from some technical problems thatcan produce misleading results if not correctly applied. We used a newtechnique of estimating correlation dimension that has fewer problems.We tested the significance of dimension estimates by comparingestimates with artificial data sets (surrogate data). On the basis ofthe analysis, we conclude that the dynamics of infant breathing duringquiet sleep can best be described as a nonlinear dynamic system withlarge-scale, low-dimensional and small-scale, high-dimensionalbehavior; more specifically, a noise-driven nonlinear system with atwo-dimensional periodic orbit. Using our RARM technique, we identifiedthe second period as cyclic amplitude modulation of the same period asperiodic breathing. We conclude that our data are consistent withrespiration being chaotic.

     

Methods of non-linear dynamics in estimation of electroencephalograms of healthy people and of patients with epilepsy

A possibility is discussed of use of methods of non-linear dynamics for analysis of spontaneous EEG and if the EEG caused by low acoustic stimuli in healthy people and in patients with epilepsy. A use of methods of non-linear dynamics--the fractal dimension of EEG--in clinical practice and in research is described.     

Dynamic processes in the human EEG

The possible mechanisms which determine the temporal dynamics of discrete narrow-band spectral components of human EEG recorded by a single electrode in the state of rest were analyzed. The dynamics of short-segment spectra was observed by application of Fast Fourier Transform (FFT) to 5-s EEG epochs successively shifted by 0.32 s. For each subject the matrices were formed and presented in a graphic mode. Matrix rows represented the number of points in each short-segment spectrum, and the columns represented the number of short-segment spectra. The columns reflect the amplitude dynamics of a given frequency, and power transition between the columns reflects the frequency dynamics. The most common type of the amplitude dynamics consisted in short (2-8 s) periods of stable activity of the discrete spectral components replaced by symmetrical bifurcation or confluence of spectral peaks. The obtained results suggest by the presence of both additive and multiplicative mechanisms of oscillatory interactions in the EEG. More detailed analysis of the amplitude-modulated EEG processes is provided by application of some additive features of the FFT to both EEG and computer-simulated signals.     

Measuring the dissimilarity between EEG recordings through a non-linear dynamical system approach

A new measure of dissimilarity between two EEG segments is proposed. It is derived from the application of the mathematical concept of distance between series of one-step predictions according to the estimated non-linear autoregressive functions. The non-linear autoregressive estimation is performed by non-parametric regression using kernel estimators. The possibility of applying this measure for automatic classification of EEG segments is explored. For this purpose multidimensional scaling and cluster analyses are applied on the basis of the calculated dissimilarity measures. In particular, its application to different EEG segments with delta activity and also with alpha waves reveals high agreement with visual classification by EEG specialists.     

The epidemiological, clinical, and laboratory features of sporadic Creutzfeldt-Jakob disease patients in China: surveillance data from 2006 to 2010

Background

Creutzfeldt-Jakob disease (CJD) is a rare, rapidly progressive fatal central nervous system disorder, which consists of three main catalogues: sporadic, familial, and iatrogenic CJD.

Methodology/Principal Findings

In China, the surveillance for CJD started in 2006, covering 12 provincial Centers for Disease Control and Prevention (CDCs) and 15 hospitals. From 2006 to 2010, 624 suspected patients were referred to China CJD surveillance. The epidemiological, clinical and laboratory features of sporadic CJD (sCJD) were analysed. Both groups of probable and possible sCJD showed highest incidences in the population of 60 to 69 year-olds. The most common presenting symptoms were progressive dementia and mental-related symptoms (neurological symptoms including sleeping turbulence, depression, anxiety and stress). Among the four main clinical manifestations, myoclonus was more frequently observed in the probable sCJD patients. About 2/3 of probable sCJD cases showed positive 14-3-3 in CSF and/or periodic sharp wave complexes (PSWC) in electroencephalography (EEG). The presence of myoclonus was significantly closely related with the appearance of PSWC in EEG. Polymorphisms of codon 129 in PRNP of the notified cases revealed a highly predominant M129M genotype in Han Chinese. Among 23 genetic human prion diseases, ten were D178N/M129M Fatal familial insomnia (FFI) and five were T188K genetic CJD (gCJD), possibly indicating a special distribution of gCJD-related mutations in Han Chinese.

Conclusion

From the period of 2006 to 2010, 261 patients were diagnosed as sCJD and 23 patients were diagnosed as genetic human prion diseases in China. The epidemiological, clinical and laboratory analysis data were consistent with the characteristics of sporadic CJD, which provide insight into the features of CJD in China.     

术中皮质脑电图监测癫痫病灶切除13例分析

目的:探讨继发性癫痫术中运用皮质脑电图监测切除癫痫病灶的疗效。方法:对13例继发性癫痫患者术前经多次常规脑电图、24h动态脑电图检查定位并联合CT、MRI等检查结果,确定癫痫病灶的准确位置。在皮质脑电图精确定位监测下手术切除致痫灶。结果:13例癫痫患者均通过皮质脑电图监测,准确定位,切除致痫灶,切除病灶后的棘波、尖波,棘、尖慢复合波减少或完全消失。结论:利用皮质脑电图监测手术切除痫灶是治疗继发性癫痫最有效的方法之一。     

The influence of visual imagery experience on EEG spatial organization

The comparison of EEG spatial organization between groups of 23 students of graphic arts department ("professional" subjects) and 39 subjects of another specialization ("non-professional" subjects) was made in order to find EEG correlates of visual imagery experience. Changes in the spatial organization of biopotentials (spatial synchronization and spatial disorder, spectral power and coherence) were analyzed while subjects mentally composed visual images from two simple elements, right angle and oblique line. The total number of elements presented for the image composition increased with each subsequent task (in total, four tasks were presented) from the number adequate to simultaneous perception and conscious processing (less than 7 +/- 2) to a much higher number. Intergroup differences, especially, in the degree of the spatial disorder (non-linear processes), were most evident under conditions when the subjects operated with a greater number of elements (tasks 3 and 4). This parameter increased more rapidly in "professionals" than in "non-professionals". These changes were most pronounced in the right anterior cortex. In "non-professional" subjects, spatial synchronization (linear processes) increased in the right posterior area. In "professional" subjects, coherence and spectral power increased in a greater number of narrow EEG frequency subbands than in "non-professional" subjects. The findings suggest that the imagery performance in subjects with visual imagery experience involves complicated neurodynamic processes such as non-linear dynamics and numerous EEG spatial resonance systems.     

Parental generalized EEG alpha activity predisposes to spike wave discharges in offspring

Familial and twin studies have shown that the individual variability of the normal human electroencephalogram (EEG) is largely genetically determined. In epileptology, these genetic parameters of the EEG background activity are almost totally neglected. The aim of the present study has been to investigate whether a special genetic type of background activity might be related to the pathogenesis of epilepsy. EEG recordings of parents of 257 epileptic children were evaluated retrospectively. Some 156 healthy adults served as controls. Special attention was paid to alpha activity extending to the frontal region, both in bipolar and in referential recordings (Alpha I). Alpha I was found significantly more often in parents of children with primary generalized epilepsy (18%) compared with parents of children with focal epilepsy (8%) or controls (9%). In a second step, parental EEGs of children with different EEG patterns associated with epilepsy were studied. Alpha I was found significantly more often in parents of children with focal sharp waves and generalized spikes and waves (26%) than in parents of probands with focal sharp waves without additional generalized spikes and waves (8%) or in controls (9%). Parents of probands with theta rhythms and spikes and waves had alpha I significantly more often (18%) than parents of probands with theta rhythms without additional spikes and waves (8%) or controls (9%). The findings reveal a clear correlation between the type of EEG background activity in parents and the EEG characteristics in their children, thus pointing to common mechanisms.     

Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages

ABSTRACT

We compared performance in deriving sleep variables by both Fitbit Charge 2?, which couples body movement (accelerometry) and heart rate variability (HRV) in combination with its proprietary interpretative algorithm (IA), and standard actigraphy (Motionlogger® Micro Watch Actigraph: MMWA), which relies solely on accelerometry in combination with its best performing ‘Sadeh’ IA, to electroencephalography (EEG: Zmachine® Insight+ and its proprietary IA) used as reference. We conducted home sleep studies on 35 healthy adults, 33 of whom provided complete datasets of the three simultaneously assessed technologies. Relative to the Zmachine EEG method, Fitbit showed an overall Kappa agreement of 54% in distinguishing wake/sleep epochs and sensitivity of 95% and specificity of 57% in detecting sleep epochs. Fitbit, relative to EEG, underestimated sleep onset latency (SOL) by ~11 min and overestimated sleep efficiency (SE) by ~4%. There was no statistically significant difference between Fitbit and EEG methods in measuring wake after sleep onset (WASO) and total sleep time (TST). Fitbit showed substantial agreement with EEG in detecting rapid eye movement and deep sleep, but only moderate agreement in detecting light sleep. The MMWA method showed 51% overall Kappa agreement with the EEG one in detecting wake/sleep epochs, with sensitivity of 94% and specificity of 53% in detecting sleep epochs. MMWA, relative to EEG, underestimated SOL by ~10 min. There was no significant difference between Fitbit and MMWA methods in amount of bias in estimating SOL, WASO, TST, and SE; however, the minimum detectable change (MDC) per sleep variable with Fitbit was better (smaller) than with MMWA, respectively, by ~10 min, ~16 min, ~22 min, and ~8%. Overall, performance of Fitbit accelerometry and HRV technology in conjunction with its proprietary IA to detect sleep vs. wake episodes is slightly better than wrist actigraphy that relies solely on accelerometry and best performing Sadeh IA. Moreover, the smaller MDC of Fitbit technology in deriving sleep parameters in comparison to wrist actigraphy makes it a suitable option for assessing changes in sleep quality over time, longitudinally, and/or in response to interventions.