Biomedical subjects

C J Stam

Publications and source records attributed to C J Stam.

At least 19 recordsLinked to original sources

PubMed2000

Brain dynamics in theta and alpha frequency bands and working memory performance in humans.

In this study non-linear and linear global electroencephalogram (EEG) changes during a visual working memory task were studied using a separate analysis of theta, lower alpha and upper alpha band filtered data. EEGs were recorded in 21 healthy subjects (62.5 year; SD 2.1; 12 females, nine males) during an eyes-closed no-task condition and a working memory condition. Coarse-grained dimension was estimated for both conditions from spatially embedded EEG data filtered in the theta band and both alpha bands. Linear measures of coupling and mean amplitude were also computed. During the working memory condition lower alpha band dimension increased. Linear analysis showed alpha1 band desynchronization. Female subjects had a higher dimension in the theta band as well as more desynchronization in the theta and alpha1 band. Working memory capacity correlated with a lower theta band dimension during the no-task condition in female subjects. The increase in alpha1 band complexity can be interpreted as increased desynchronization corresponding with attentional processes. Higher complexity/desynchronization in females seems to be a more structural phenomenon and may be more intimately related to task performance.

Alpha Rhythm↗

PubMed2000

Changes in functional coupling between neural networks in the brain during maturation revealed by omega complexity.

To study age-dependent changes in coupling between cortical neural networks we applied a new method (omega complexity) to determine overall coherence of EEGs of 34 subjects ranging in age from 3 months to 16 years. We found that the functional coupling between different brain regions is low at birth and increases significantly in the first two decades of life. We suggest that this coupling depends critically upon the system of associational and callosal fibers which is unmyelinated at birth, and which only finishes myelinization in the second or third decade. Thus age-dependant changes in omega complexity may reflect maturation of brain structures underlying higher cerebral functions. If these results can be replicated, preferably in prospective, cohort rather than transectional type studies, omega complexity might prove to be clinically useful as an objective, quantitative measure of brain maturation.

Adolescent↗

PubMed2000

Nonlinearity in human resting, eyes-closed EEG: an in-depth case study.

The question of nonlinearity in the human electroencephalogram (EEG) is important, since linear methods of EEG analysis are more well-developed and computationally faster than nonlinear methods. Furthermore, the presence or absence of nonlinearity has important theoretical implications for understanding the nature of the brain's oscillatory activity. Using a linear summary measure as a control, we report a failure to reject the null hypothesis of a (largely) stationary linear-Gaussian process for normal, resting, eyes-closed EEG from a single participant. We found significant evidence of nonlinearity at two occipital sites (O1 and O2) where the 8-12.5 Hz alpha rhythm was prominent. However, this element of nonlinear structure appeared trivial, as (1) we found no evidence of time irreversibility at these loci, and (2) best-fitting linear models accounted on-average for over 94% of the variance in the data with nonlinear modeling doing no better. Half of the remaining variance could be accounted for by nonstationarity. While our findings technically apply only to the one individual tested, his EEG was typical of those seen under the conditions that we employed.

Alpha Rhythm↗

PubMed1999

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.

Acute Disease↗

PubMed1999

Dynamics underlying rhythmic and non-rhythmic variants of abnormal, waking delta activity.

OBJECT: We applied a new test, nonlinear cross prediction (NLCP), to investigate whether or polymorphic delta activity (PDA) and frontal intermittent rhythmic delta activity (FIRDA) reflect linear or nonlinear brain dynamics. Furthermore realistic models were constructed to explain the dynamical properties of PDA and FIRDA. METHODS: Forty-nine EEG time series with FIRDA and 40 time series with PDA were studied with the NLCP algorithm. This characterizes a time series in terms of its predictability, amplitude asymmetry, and time asymmetry, with the latter two measures reflecting nonlinearity. Parameters of an EEG model proposed by Lopes da Silva were adjusted to obtain time series resembling PDA and FIRDA. RESULTS: FIRDA was more predictable than PDA. Most PDA segments could not be distinguished from linearly filtered noise. In contrast, FIRDA activity showed strong evidence of nonlinear dynamics. These dynamical properties of PDA and FIRDA could be reproduced by the Lopes da Silva model. PDA and FIRDA reflect a point attractor and a limit cycle attractor, respectively, perturbed by dynamical noise. CONCLUSION: Experimental analysis and modeling of the data suggest that PDA and FIRDA reflect fundamentally different types of brain dynamics. While PDA is filtered noise, reflecting low-level, random input to cortical networks, FIRDA may reflect limit-cycle oscillations due to increased excitation.

Adult↗

PubMed1999

Decrease of non-linear structure in the EEG of Alzheimer patients compared to healthy controls.

OBJECTIVE: Non-linear EEG analysis can provide information about the functioning of neural networks that cannot be obtained with linear analysis. The correlation dimension (D2) is considered to be a reflection of the complexity of the cortical dynamics underlying the EEG signal. The presence of non-linear dynamics can be determined by comparing the D2 calculated from original EEG data with the D2 from phase-randomized surrogate data. METHODS: In a prospective study, we used this method in order to investigate non-linear structure in the EEG of Alzheimer patients and controls. Twenty-four patients (mean age 75.6 years) with 'probable Alzheimer's disease' (NINCDS-ADRDA criteria) and 22 controls (mean age 70.3 years) were examined. D2 was calculated from original and surrogate data at 16 electrodes and in three conditions: with eyes open, eyes closed and during mental arithmetic. RESULTS: D2 was significantly lower in the Alzheimer patients compared to controls (P = 0.023). The difference between original and surrogate data was significant in both groups, implicating that non-linear dynamics play a role in the D2 value. Moreover, this difference between original and surrogate data was smaller in the patient group. D2 increased with activation, but not significantly more in controls than in patients. CONCLUSIONS: In conclusion, we found decreased dimensional complexity in the EEG of Alzheimer patients. This decrease seems to be attributable at least partially to different non-linear EEG dynamics. Because of this, non-linear EEG analysis could be a useful tool to increase our insight into brain dysfunction in Alzheimer's disease.

Aged↗

PubMed1999

Dynamics of the human alpha rhythm: evidence for non-linearity?

OBJECT: For a better understanding of the physiological mechanisms responsible for alpha rhythms it is important to know whether non-linear processes play a role in their generation. We used non-linear forecasting in combination with surrogate data testing to investigate the prevalence and nature of alpha rhythm non-linearity, based on EEG recordings from humans. We interpreted these findings using computer simulations of the alpha rhythm model of Lopes da Silva et al. (1974). METHODS: EEGs were recorded at 02 and O1 in 60 healthy subjects (30 males; 30 females; age: 49.28 years; range 11-84) during a resting eyes-closed state. Four artefact-free epochs (2.5 s; sample frequency 200 Hz) from each subject were tested for non-linearity using a non-linear prediction statistic and phase-randomized surrogate data. A similar type of analysis was done on the output of the alpha model for different values of input. RESULTS: In the 480 (60 subjects, 2 derivations, 4 blocks) epochs studied, the null hypothesis that the alpha rhythms can result from linearly filtered noise, could be rejected in 6 cases (1.25%). The alpha model showed a bifurcation from a point attractor to a limit cycle at an input pulse density of 615 pps. Non-linearity could only be detected in the model output close to and beyond this bifurcation point. The sources of the non-linearity are the sigmoidal relationships between average membrane potential and output pulse density of the various cells of the neuronal populations. CONCLUSION: The alpha rhythm is a heterogeneous entity dynamically: 98.75% of the epochs (type I alpha) cannot be distinguished from filtered noise. Apparently, during these epochs the activity of the brain has such a high complexity that it cannot be distinguished from a random process. In 1.25% of the epochs (type II alpha) non-linearity was found which may be explained by dynamics in the vicinity of a bifurcation to a limit cycle. There is thus experimental evidence from the point of view of dynamics for the existence of the two types of alpha rhythm and the bifurcation predicted by the model.

Adolescent↗

PubMed1999

EEG correlates of cerebral engagement in reading tasks.

This study evaluated the utility of electroencephalographic (EEG) measures as indices of regional cerebral engagement activation during reading in neurologically intact adult volunteers. EEG was recorded from 16 scalp locations as participants performed four visual detection tasks designed to tap into increasingly more complex operations regularly involved in reading, namely visual-spatial, orthographic, phonological, and semantic. EEG records were quantified using power spectrum measures in four frequency bands (delta, theta, alpha, beta1, and beta2), in addition to a non-linear estimate of signal complexity (prediction error). Results showed that (1) changes in spectral power between pairs of reading tasks, and (2) regional variations in EEG measures for each task, were restricted to signals recorded over the left hemisphere. These findings are in agreement with knowledge regarding left hemisphere involvement in higher level component processes of reading.

Adolescent↗

PubMed1999

Nonlinear EEG analysis in early Alzheimer's disease.

Nonlinear EEG analysis attempts to characterize the dynamics of neural networks in the brain. Abnormalities in nonlinear EEG measures have been found repeatedly in Alzheimer's disease (AD). The present study was undertaken to investigate whether these abnormalities could already be found in the early stage of AD. In a representative sample of 49 community-dwelling elderly, Alzheimer's disease was diagnosed in 7 subjects. Correlation dimension (D2) and nonlinear prediction were measured at 16 electrodes and in two different activational states. Also, 10 surrogate data sets were generated for each EEG epoch in order to investigate the presence of nonlinear dynamics. Differences between nonlinear statistics derived from original and from surrogate data sets were expressed as Z-scores. We found lower D2 and higher predictability in the demented subjects compared to the normal subjects. The results obtained with the Z-scores pointed to changed nonlinear dynamics in frontal and temporal areas in demented subjects. However, the major differences between demented and healthy subjects are not due to nonlinearity. From this it appears that linear dynamics change first in the course of AD, followed by changes in nonlinear dynamics.

Aged↗

PubMed1998

Use of non-linear EEG analysis to study abnormal brain dynamics in deaf human subjects.

We compared the cortical dynamics of deaf subjects to those of control subjects at rest with eyes closed and during reading with the help of a non-linear prediction statistic. This method is suitable for short-term noisy time series such as electroencephalographic signals. Furthermore, we used surrogate data to test for non-linear dynamics underlying the electroencephalographic time series recorded. Our results indicate that significant non-linearity accompanies cortical activation during reading. This is more diffuse in deaf subjects and could be due to the widespread reorganization of their cerebral cortex. Predictability was lower in deaf subjects at rest, which indicates their increased 'readiness' in the resting condition. Finally, our results indicate that normal and deaf subjects differ significantly in terms of cortical dynamics.

Adult↗

PubMed1998

Nonlinear analysis of EEG in septic encephalopathy.

Electroencephalograms (EEG) were recorded in fourteen patients who experienced a severe septic encephalopathy (SE). EEG analysis included visual inspection, spectral analysis and a recently developed nonlinear analysis (the Kaplan test). All EEGs showed decreased fast activity and an increase of slow wave activity on visual inspection. There was a nonsignificant trend of negative correlation between the spectral EEG analysis and the severity of the acute systemic illness (based on the sum score of 14 variables known as APACHE II score) (standard coefficient = -0.43, p = 0.118). However, a much more pronounced and significant negative correlation was observed between the Kaplan test and the APACHE II score (standard coefficient = -0.94, p = 0.005). The EEG abnormalities seen in these patients were independent of the sedation level. Neither the EEG parameters, nor the APACHE II score, predicted outcome. Nonlinear analysis is more powerful than spectral analysis to extract clinical relevant information from EEGs in patients who experience a severe SE. The nonlinear EEG analysis suggest that brain dynamics in SE may be characterized by a shift into a fundamentally different level of cortical information exchange which can be summarized in nonlinear terminology as a loss of deterministic structure in the EEG.

APACHE↗

PubMed1998

Modeling the temporal fluctuations of the cerebral blood flow velocity waveforms using surrogate data testing.

The objective of this study is to find out which mathematical model best explains the temporal fluctuations of the axial blood flow velocity waveforms in the basal arteries of the brain. Blood flow velocity time series were sampled by transcranial Doppler (TCD) examination of the middle cerebral arteries in 10 healthy volunteers. A recently developed mathematical test (surrogate data analysis) was used to examine whether the spectral Doppler maximum waveform consistent with some prespecified model (null hypothesis). We tested four different null hypothesis. 1. Uncorrelated white noise. 2. Linearly filtered noise. 3. Linearly filtered noise with a static nonlinear amplitude transformation. 4. Noisy nonlinear limit cycle. All null hypotheses except the last one could be rejected. We conclude that the TCD waveforms are best described as nonlinear limit cycle with some percentage of noise, either dynamical and/or observational, which is uncorrelated from one single oscillation to the next. These results are a strong argument to perform nonlinear analysis in future TCD studies in order to obtain a better understanding of the cerebral hemodynamics.

Adult↗

PubMed1998

Usefulness of non-linear EEG analysis.

Spectral analysis methods are useful for the evaluation of EEG signals. Nevertheless, they refer only to the frequency domain and ignore any potentially interesting phase information. Analytical methods based upon the theory of nonlinear dynamics provides this and additional information. We used both methods to evaluate the EEG signals of volunteers performing two distinct mental arithmetic tasks. We extracted the power spectrum, the coherence and nonlinear parameters (dimension, the first Lyapunov exponent, the Kolmogorov entropy, the mutual dimension and the dimensions based upon spatial embedding of the original data as well as their surrogates). We found that 1) the spatial embedding dimension differed from that of the surrogates, indicating nonlinearity, 2) there were differences between the two arithmetic tasks, and 3) the spectral and nonlinear methods differ in terms of the information they provide. Our results indicate that nonlinear analysis methods can be useful despite the fact that they are still at an early stage of development.

Adult↗

PubMed1998

Preliminary report of detecting microembolic signals in transcranial Doppler time series with nonlinear forecasting.

BACKGROUND AND PURPOSE: Most algorithms used for automatic detection of microembolic signals (MES) are based on power spectral analysis of the Doppler shift. However, controversies exist as to whether these algorithms can replace the human expert. Therefore, a different algorithm was applied that takes advantage of the periodicity of the MES. This so-called nonlinear forecasting (NLF) is able to detect periodicity in a time series, and it is hypothesized that this technique has the potential to detect MES. Moreover, because of the lack of prominent periodicity in both the normal Doppler signals (DS) and movement artifacts (MA), the NLF has a potential to differentiate MES from normal blood flow variations and MA. METHODS: Twenty single MES and 100 MA were selected by 2 human experts. NLF was applied to MES and MA and compared with 200 randomly chosen DS. NLF resulted in a so-called prediction value that ranges from + 1 in signals with prominent periodicity to 0 in signals that lack periodicity. RESULTS: NLF revealed that MES are more predictable than the normal Doppler signals (prediction [MES]=0.829+/-0.084 versus prediction [DS]= -0.060+/-0.228; P<0.0001). Moreover, MES are more predictable than the MA (prediction [MA]=-0.034+/-0.223; P<0.0001). No difference in prediction could be found between DS and MA. CONCLUSIONS: This preliminary report shows that MES can be separated from DS and MA by NLF. Research is needed as to whether this technology can be further developed for automatic detection of MES.

Artifacts↗

PubMed1998

Electroencephalographic signal analysis and desynchronization effect caused by two differing mental arithmetic skills.

Using several electroencephalographic signal analysis methods, it is possible to detect activated cortical areas during cognitive processes. These methods are of interest for neuropsychological studies and in an attempt to develop neurophysiological methods that can be used in the clinic. We used two distinct methods to study two different mental arithmetic tasks. Our purpose was to test the hypothesis that there are different desynchronization effects during the two distinct cognitive processes and to compare the two methods used. The first method concerned EEG signal band reactivity changes. The bands were obtained using spectral analysis. The second method is a new one for estimation of the whole EEG signal complexity (ASE = Acceleration Spectrum Entropy). We estimated the EEG signal bands and the ASE changes during two arithmetical tasks in 24 subjects and compared them with the values of the resting state. The ASE method and the EEG band reactivity method could distinguish between the resting condition and the tasks thus demonstrating the usefulness of the ASE method to study cognitive processes. Furthermore, the two tasks affected differently the power spectrum values of the delta, theta and the alpha bands thus indicating the involvement of different brain mechanisms.

Adult↗

PubMed1998

Nonlinear dynamical analysis of periodic lateralized epileptiform discharges.

Nonlinear time series analysis can be used to investigate the dynamics underlying the generation of EEG signal. In the present study we used this approach to study the pathophysiology of PLEDs. We calculated the correlation dimension D2 of an EEG with typical PLEDs, and compared the results with those obtained for surrogate data. These surrogate data have the same power spectrum and amplitude distribution as the original EEG data, but are otherwise random. By construction, such surrogate data can be described by a linear model. Our results showed that D2 estimations for PLEDs were low, on the order of one, and that the results for EEG and the surrogate data were clearly different, indicating that the EEG with PLEDs reflects nonlinear dynamics of the underlying neural networks.

Electroencephalography↗

PubMed1997

Non-linear analysis of the electroencephalogram in Creutzfeldt-Jakob disease.

Creutzfeldt-Jakob disease is a rare, neurological, dementing disorder characterised by periodic sharp waves in the electroencephalogram (EEG). Non-linear analysis of these EEG changes may provide insight into the abnormal dynamics of cortical neural networks in this disorder. Babloyantz et al. have suggested that the periodic sharp waves reflect low-dimensional chaotic dynamics in the brain. In the present study this hypothesis was re-examined using newly developed techniques for non-linear time series analysis. We analysed the EEG of a patient with autopsy-proven Creutzfeldt-Jakob disease using the method of non-linear forecasting as introduced by Sugihara and May, and we tested for non-linearity with amplitude-adjusted, phase-randomised surrogate data. Two epochs with generalised periodic sharp waves showed clear evidence for non-linearity. These epochs could be predicted better and further ahead in time than most of the irregular background activity. Testing against cycle-randomised surrogate data and close inspection of the periodograms showed that the non-linearity of the periodic sharp waves may be better explained by quasi-periodicity than by low-dimensional chaos. The EEG further displayed at least one example of a sudden, large qualitative change in the dynamics, highly suggestive of a bifurcation. The presence of quasi-periodicity and bifurcations strongly argues for the use of a non-linear model to describe the EEG in Creutzfeldt-Jakob disease.

Creutzfeldt-Jakob Syndrome↗

PubMed1997

The electroencephalogram during normal third trimester pregnancy and six months postpartum.

In 1942 Gibbs and Reid described a slowing of the electroencephalogram (EEG) at the end of a normal pregnancy. To the best of our knowledge this is the only report that addresses the modification of the EEG in normal pregnancy. We performed a spectral multichannel EEG analysis and revealed no differences during third trimester pregnancy and six months postpartum. Therefore EEG changes seen during pregnancy, which were previously regarded as 'subtle changes of pregnancy', may turn out to be clinically relevant changes which indicate either pre-existing EEG dysfunction or EEG abnormalities in the context of a pregnancy-related disorder.

Analysis of Variance↗