Biomedical subjects

Cornelis J Stam

Publications and source records attributed to Cornelis J Stam.

36 records · Page 2Linked to original sources

PubMed2005

A method for detection of Alzheimer's disease using ICA-enhanced EEG measurements.

OBJECTIVE: Many researchers have studied automatic EEG classification and recently a lot of work has been done on artefact-removal from EEG data using independent component analyses (ICA). However, demonstrating that a ICA-processed multichannel EEG measurement becomes more interpretable compared to the raw data (as is usually done in work on ICA-processing of EEG data) does not yet prove that detection of (incipient) anomalies is also better possible after ICA-processing. The objective of this study is to show that ICA-preprocessing is useful when constructing a detection system for Alzheimer's disease. METHODS AND MATERIAL: The paper describes a method for detection of EEG patterns indicative of Alzheimer's disease using automatic pattern recognition techniques. Our method incorporates an artefact removal stage based on ICA prior to automatic classification. The method is evaluated on measurements of a length of 8s from two groups of patients, where one group is in an initial stage of the disease (28 patients), whereas the other group is in a more progressed stage (15 patients). Both setups include a control group that should be classified as normal (10 and 21, respectively). RESULTS: Our final classification results for the group with severe Alzheimer's disease are comparable to the best results from literature. We show that ICA-based reduction of artefacts improves classification results for patients in an initial stage. CONCLUSION: We conclude that a more robust detection of Alzheimer's disease related EEG patterns may be obtained by employing ICA as ICA based pre-processing of EEG data can improve classification results for patients in an initial stage of Alzheimer's disease.

Algorithms↗

PubMed2004

Neural networks involved in mathematical thinking: evidence from linear and non-linear analysis of electroencephalographic activity.

Using linear and non-linear methods, electroencephalographic (EEG) signals were measured at various brain regions to provide information regarding patterns of local and coordinated activity during performance of three arithmetic tasks (number comparison, single-digit multiplication, and two-digit multiplication) and two control tasks that did not require arithmetic operations. It was hypothesized that these measures would reveal the engagement of local and increasingly complex cortical networks as a function of task specificity and complexity. Results indicated regionally increased neuronal signalling as a function of task complexity at frontal, temporal and parietal brain regions, although more robust task-related changes in EEG-indices of activation were derived over the left hemisphere. Both linear and non-linear indices of synchronization among EEG signals recorded from over different brain regions were consistent with the notion of more "local" processing for the number comparison task. Conversely, multiplication tasks were associated with a widespread pattern of distant signal synchronizations, which could potentially indicate increased demands for neural networks cooperation during performance of tasks that involve a greater number of cognitive operations.

Adult↗

PubMed2004

Different EEG frequency band synchronization during nocturnal frontal lobe seizures.

OBJECTIVE: In this article we describe the course of synchronization between different EEG channels during nocturnal seizures in one patient with nocturnal frontal lobe epilepsy (NFLE). METHODS: The functional interactions between the different EEG channels during the nocturnal seizures were analyzed by means of the so-called synchronization likelihood (SL). SL is a measure of the dynamical interdependencies between a time series (EEG channel) and one or more other time series. In contrast to coherence, SL measures linear as well as non-linear interdependencies and it can do so as a function of time, making it suitable for non-stationary time series. RESULTS: The main result of our single-patient study is the demonstration of a significant hyper-synchronization during NFLE seizures in the 8-12 Hz band which seems to be stopped by an increase in synchronization in the 0.5-4 Hz band, towards the end of each ictal episode. CONCLUSIONS: We suggest that a self-inhibiting complex mechanism might be responsible for the termination of ictal episodes which might take place at the level of the cortical layers and might involve mainly pyramidal neurons. SIGNIFICANCE: This study shows that advanced EEG analysis methods can help the current understanding of ictal manifestations of NFLE.

Adult↗

PubMed2004

Abnormal EEG synchronisation in heavily drinking students.

OBJECTIVE: In alcoholics, grey and white brain matter is damaged. In addition, functional brain connectivity as measured by EEG coherence is abnormal. We investigated whether heavily drinking students, although drinking for a shorter period than alcoholics, already show differences in functional connectivity compared to light-drinking controls. METHODS: EEG was recorded in 11 light and 11 heavy male student drinkers during eyes closed, and eyes closed plus mental rehearsal of pictures. Functional connectivity was assessed with the Synchronisation Likelihood method. RESULTS: Heavily drinking students had more synchronisation in the theta (4-8 Hz) and gamma (30-45 Hz) band than lightly drinking students during eyes closed, both with and without a mental-rehearsal task. CONCLUSIONS: Heavy student drinkers have increases in EEG synchronisation that are indicative of changes in hippocampal-neocortical connectivity. SIGNIFICANCE: Heavy student drinkers show differences in functional connectivity as compared to their lightly drinking counterparts, even though they have a relatively short drinking history.

Adult↗

PubMed2004

A role for dopamine in the processing of drug cues in heroin dependent patients.

Drug and alcohol dependence are associated with enhanced attention for drug-related stimuli. This cognitive processing bias has been suggested to be related to craving and to represent one of the core mechanisms of addition. The present study tests the hypothesis that enhanced attention for heroin cues is mediated by the dopaminergic system using haloperidol as dopamine antagonist. In a double blind, randomized crossover design, 17 detoxified heroin dependent patients received a single oral dose of haloperidol 2 mg and placebo. Patients performed an Emotional Stroop Task to assess the cognitive processing of drug cues under both conditions. In addition, self-reported craving was assessed. In the haloperidol condition, patients performed better on the Emotional Stroop Task than in the placebo condition. However, no effect of haloperidol on subjective craving was found. These findings provide preliminary indications that attentional bias in heroin dependent humans is mediated by dopaminergic mechanisms.

Adult↗

PubMed2004

Electroencephalographic power and coherence analyses suggest altered brain function in abstinent male heroin-dependent patients.

Previous studies have shown that drug abuse is associated with altered brain function. However, studies of heroin abuse-related brain dysfunctions are scarce. Electroencephalographic (EEG) power and coherence analyses are two important tools for examining the effects of drugs on brain function. In the present study, we compared EEG power and coherence measures of 18 abstinent heroin-dependent subjects with those of 12 healthy control subjects. Furthermore, within the heroin group, associations between heroin use in the past, heroin craving and these EEG measures were studied. The results show that heroin-dependent subjects have increased relative beta-2 power and increased left intrahemispheric gamma coherence compared with control subjects. Furthermore, coherence measures showed correlations with clinical variables. These EEG abnormalities may reflect underlying changes in brain function due to long-term drug abuse and premorbid characteristics.

Adult↗

PubMed2004

Two new neurophysiological indices of cocaine craving: evoked brain potentials and cue modulated startle reflex.

Craving for cocaine is one of the hallmarks of cocaine dependence. One of the problems with craving is its measurement. Traditional psychophysiological indices such as skin conductance and heart rate have yielded contradictory results. These measures of craving were found to correlate only moderately with self-reported craving. In the present study, event-related brain potentials (ERPs) and the cue modulated startle response (CMSR) are evaluated as indices for cocaine craving. Twenty-one abstinent cocaine-dependent subjects were divided into high and low cravers group based on the median split of self-reported craving scores. ERPs and CMSR were measured when subjects watched neutral, pleasant, unpleasant and cocaine-related pictures. Overall, it was found that cocaine-dependent subjects showed augmented slow-positive waves (SPWs) of the ERP on the cocaine pictures compared to neutral pictures. Only high cravers showed larger SPWs on the cocaine cues, suggesting an association between cue-elicited SPWs and self-reported cocaine craving. By contrast to the ERP measures, CMSR did not differentiate between cocaine pictures and neutral pictures. In addition, no differences between the low- and high cravers on the CMSR measure were found. The present results show that the evoked-potentials paradigm provides promising results to index cue-elicited craving. The use of startle modulation deserves further investigation.

Adult↗

PubMed2003

Neurophysiological evidence for abnormal cognitive processing of drug cues in heroin dependence.

RATIONALE: Recent studies provide evidence for specific aspects of cue processing in addictive disorders. OBJECTIVE: The present study employs event related potentials (ERPs) to investigate heroin related visual information processing. METHODS: Neutral and heroin related pictures were presented to 19 male abstinent heroin dependent patients and 14 male healthy controls. RESULTS: Patients exhibited larger slow positive wave (SPW) components of the ERP on heroin related pictures than on neutral pictures. Within healthy control subjects there was no difference on the SPW between neutral and heroin pictures. Within heroin dependent patients, mean SPW response to heroin pictures was correlated with post-experiment craving. CONCLUSION: This study provides neurophysiological evidence that information processing of drug-related information is abnormal in heroin dependent patients. The results provide further evidence for the cognitive and neurobiological accounts of substance dependence such as the incentive-sensitization theory.

Behavior, Addictive↗

PubMed2003

Nonlinear synchronization in EEG and whole-head MEG recordings of healthy subjects.

According to Friston, brain dynamics can be modelled as a large ensemble of coupled nonlinear dynamical subsystems with unstable and transient dynamics. In the present study, two predictions from this model (the existence of nonlinear synchronization between macroscopic field potentials and itinerant nonlinear dynamics) were investigated. The dependence of nonlinearity on the method of measuring brain activity (EEG vs. MEG) was also investigated. Dataset I consisted of 10 MEG recordings in 10 healthy subjects. Dataset II consisted of simultaneously recorded MEG (126 channels) and EEG (19 channels) in 5 healthy subjects. Nonlinear coupling was assessed with the synchronization likelihood S and dynamic itinerancy with the synchronization entropy Hs. Significance was assessed with a bootstrap procedure ("surrogate data testing"), comparing S and Hs with their distribution under the null hypothesis of stationary, linear dynamics. Significant nonlinear synchronization was detected in 14 of 15 subjects. The nonlinear dynamics were associated with a high index of itinerant behaviour. Nonlinear interdependence was significantly more apparent in MEG data than EEG. Synchronous oscillations in MEG and EEG recordings contain a significant nonlinear component that exhibits characteristics of unstable and itinerant behaviour. These findings are in line with Friston's proposal that the brain can be conceived as a large ensemble of coupled nonlinear dynamical subsystems with labile and unstable dynamics. The spatial scale and physical properties of MEG acquisition may increase the sensitivity of the data to underlying nonlinear structure.

Adult↗

PubMed2003

Seizure detection in the neonatal EEG with synchronization likelihood.

OBJECTIVE: To investigate whether epileptic seizure activity can be distinguished from non-epileptic background activity in the neonatal electroenceplalogram (EEG), using synchronization likelihood as a measure of synchronization between EEG channels. METHODS: Forty-two 21s EEG epochs and two complete EEGs from 21 different neonatal patients in a 12-channel bipolar recording were studied (AD-conversion 16bit; sample frequency 200Hz; filter setting 0.5-30Hz). For EEG of each patient, we selected one epoch with epileptic discharges and one without. Synchronization was calculated in all epochs. In two complete EEGs, synchronization was calculated and correlated with a visual scoring of the EEG. RESULTS: Synchronization likelihood was higher in all the epochs with epileptic seizures as compared to the epochs without epileptic activity (P<0.01). When synchronization likelihood exceeded 0.11, the sensitivity for the presence of epileptic activity was 0.85 (95% confidence limits [CL(95)]=0.69-1) and the specificity was 0.75 (CL(95)=0.56-0.94).Analysis of EEG score and synchronization likelihood of two complete EEGs revealed a high correlation between the occurrence of epileptic seizures and elevated synchronization likelihood (Spearman r=0.707, P<0.001). CONCLUSIONS: The results of this study demonstrate that synchronization likelihood is a potential tool in the automatic monitoring of high-risk infants for epileptic activity on neonatal wards.

Cortical Synchronization↗

PubMed2003

Nonlinear EEG analysis during sleep in premature and full-term newborns.

OBJECTIVE: Recently it has been shown that the adult sleep EEG is mostly determined by high-dimensional, linear dynamics with the exception of the A phase of the cyclic alternating pattern which displays more synchronized nonlinear dynamics. It is not known how these two different types of brain dynamics develop in early life; for this reason the aim of this study was that to extend the nonlinear analysis to the EEG during sleep recorded in premature and full-term newborns. METHODS: EEG epochs were chosen from a total of 24 polygraphic recordings from 14 babies (9 males and 5 females) aged between 33 weeks 3 days and 4 months conceptional age. All subjects were neurologically normal and showed normal psychomotor development at follow-up. A total of 243 artifact-free epochs was chosen during active sleep (AS, 74 total epochs), quiet sleep (QS, 76 total epochs) and indeterminate sleep (IS, 93 total epochs). The dynamic properties of the EEG were assessed by means of the nonlinear cross prediction test which uses 3 different 'model' time series in order to predict nonlinearly the original data set (Pred, Ama, and Tir). Pred is a measure of the predictability of the time series, and Ama and Tir are measures of asymmetry, indicating nonlinear structure. RESULTS: Our results show that the structure of sleep EEG in newborns is significantly different from that of adults, it cannot be distinguished from that of high-dimensional noise in the majority of epochs, and shows a tendency to become nonlinear in nature, mostly during QS, in a small percentage of the epochs analyzed. CONCLUSIONS: These findings can be interpreted as the effect of immature synaptic interconnections between neurons in the newborn brain.

Aging↗

PubMed2003

Changes in linear and nonlinear EEG measures as a function of task complexity: evidence for local and distant signal synchronization.

The purpose of the present study was threefold: First, to replicate previous findings of changes in local gamma band power as a function of the complexity of a visuo-semantic processing task, second, to extend these findings in tasks delivered in the auditory modality, and third to explore the use of non-linear algorithms as indices of complexity and distant synchronization in the EEG signal. EEG was recorded from 28 scalp locations as participants performed three visual discrimination tasks designed to tap into increasingly more complex operations regularly involved in the recognition of living animate objects. Two auditory processing tasks involving the same stimuli, but requiring no semantic processing, served as controls. The degree of complexity of the semantic decision was associated with the predicted changes in local gamma power, as well as with broadband changes in the non-linear predictability of the signal (an index derived using an artificial neural network algorithm). These changes were observed at all scalp regions, a finding consistent with the wide cortical distribution of component processes involved in the tasks. In addition, the synchronization between temporal and parieto-occipital electrodes and the remaining recording sites was highest in the gamma bands and lowest in the alpha bands for the task that required the most complex visuo-semantic decision. This trend reversed with reduced task complexity, consistent with the view that multidimensional semantic decisions require the involvement of distributed cortical networks in auditory and visual association areas and in the frontal lobes.

Acoustic Stimulation↗

PubMed2003

Localization of slow wave activity in patients with tumor-associated epilepsy.

OBJECTIVE: Brain tumors are frequently accompanied by abnormal low frequency magnetic activity (ALFMA). The prevalence and clinical meaning of ALFMA are not well known, although a relation with epileptic brain tissue has been suggested. We studied the prevalence, characteristics and clinical correlates of ALFMA in 20 patients with brain tumors. METHODS: In 20 patients with clinical seizures due to a supratentorial tumor, MEG was performed, followed by MR imaging. MEG signals were band pass-filtered (1-4 Hz); the sources of this activity were localized and projected onto the MRI of the patient. RESULTS: Peritumoral ALFMA could be detected in 13 of 20 patients. A pattern of ALFMA distribution around the tumor could be recognized. In eight cases ALFMA also appeared to be localized within the tumor. In three cases ALFMA was also detected in peritumoral white matter. CONCLUSIONS: Automatic detection of abnormal delta-activity in patients with a brain tumor and seizures can be performed in a clinical setting. When detected, ALFMA is mostly present in circumscribed regions around the tumor. Presence of ALFMA within the tumor might be an important warning signal for the neurosurgeon that the tumor area comprises functional brain tissue.

Adult↗

PubMed2002

Non-linear EEG measures during sleep: effects of the different sleep stages and cyclic alternating pattern.

The objective of this work was to study the non-linear aspects of sleep EEG, taking into account the different sleep stages and the peculiar organization of its phasic events in ordered sequences (CAP) by applying a series of new non-linear measures (non-linear cross prediction or NLCP), which appear more reliable for the detection and characterization of non-linear structures in experimental data than the commonly used correlation dimension. Eight healthy subjects aged 18-20 years participated in this study. Polysomnography was performed in all subjects; signals were sampled at 128 Hz and stored on hard disk. The C3 or C4 derivation was used for all the subsequent computational steps, which were performed on EEG epochs (4096 data points) selected from sleep stage 2 (S2) and slow-wave sleep (SWS), in both CAP and non-CAP (NCAP) conditions. Also, epochs from sleep stage 1 (S1), REM and wakefulness preceding sleep were recorded. The dynamic properties of the EEG were assessed by means of the non-linear cross-prediction test, which uses three different 'model' time series in order to predict non-linearly the original data set (Pred, Ama, and Tir). Pred is a measure of the predictability of the time series, and Ama and Tir are measures of asymmetry, indicating non-linear structure. The non-linear measures applied in this study indicate that sleep EEG tends to show non-linear structure only during CAP periods, both during S2 and SWS. Moreover, during CAP periods, non-linearity can only be detected during the phase A1 subtypes (and partially A2) of CAP. The A3 phases show characteristics of non-stationarity and bear some resemblance to wakefulness. Based on the results of this study, sleep might be considered as a dynamically evolving sequence of different states of the EEG, which we could track by detecting non-linearity, mostly in association with CAP. Our results clearly show that detectable non-linearity in the EEG is closely related to the occurrence of the phase A of CAP.

Adolescent↗

PubMed2002

Ongoing electroencephalographic signal study of simple arithmetic using linear and non-linear measures.

This study examined patterns of variation in the ongoing electroencephalogram during performance of three visual tasks. One task required exact arithmetic calculations on Arabic numerals. A second task involved pseudoword processing as a control for the verbal (phonological) component that is, by definition, part of arithmetic tasks. A third task primarily required visual/graphemic processing, which is also involved in the other two tasks. Spectral power in the alpha range was lowest during the pseudoword task, followed by power during the arithmetic task, and finally by power in the visual task, indicating more prominent desynchronization during engagement in the analysis of word-like printed material. Finally, linear (power in the gamma range) and non-linear measures (correlation dimension) provided evidence of predominant right hemisphere engagement during the arithmetic task.

Adult↗

PubMed2002

Variability of EEG synchronization during a working memory task in healthy subjects.

Working memory is associated with an increase in EEG theta synchronization and a decrease in lower alpha band synchronization. We investigated whether such changes in mean synchronization level are accompanied by changes in small scale fluctuations of synchronization. EEGs (19 channels; average reference; sample frequency 250 Hz) were recorded in 21 healthy subjects (12 males; mean age 62.5 years; S.D. 2.1) at rest and during a visual working memory condition. EEG synchronization was computed in six frequency bands (2-6; 6-10; 10-14; 14-18; 18-22; 22-50 Hz) using the synchronization likelihood. Variability of the synchronization was quantified with synchronization entropy. During the working memory condition synchronization increased in the 2-6 Hz band, and decreased in the 6-10, 14-18 and 18-22 Hz bands. Working memory was associated with increased variability in the 2-6 Hz band, and decreased variability in the 6-10 Hz band and, to a lesser extent, in the 14-18 and 18-22 Hz bands. Working memory is accompanied not only by characteristic changes in the mean level of interactions between neural networks, but also by changes in small scale fluctuations in such interactions. Strong, but rapidly fluctuating coupling between neural systems might provide a mechanism to optimize the balance between local differentiation and global integration of brain activity.

Adult↗

PubMed2002

Generalized synchronization of MEG recordings in Alzheimer's Disease: evidence for involvement of the gamma band.

The purpose of this study was to investigate interdependencies in whole-head magnetoencephalography (MEG) of Alzheimer patients and healthy control subjects. Magnetoencephalograms were recorded in 20 Alzheimer patients (11 men; mean age, 69.0 years [standard deviation, 8.2 years]); Mini-Mental State Examination score, 21.3 points; range, 15 to 27 points) and 20 healthy control subjects (9 men; mean age, 66.4 years [standard deviation, 9.0 years]) during a no-task eyes-closed condition with a 151 channel whole-head MEG system. Synchronization likelihood (a new measure for linear as well as nonlinear interdependencies between signals) and coherence were computed for each channel in different frequency bands (2 to 6, 6 to 10, 10 to 14, 14 to 18, 18 to 22, 22 to 40 Hz). Synchronization was lower in Alzheimer patients in the upper alpha band (10 to 14 Hz), the upper beta band (18 to 22 Hz), and the gamma band (22 to 40 Hz). In contrast, coherence did not show significant group differences at the p<0.05 level. The synchronization likelihood showed a spatial pattern (high synchronization central, parietal and right frontal; low synchronization, occipital and temporal). This study confirms a widespread loss of functional interactions in the alpha and beta bands, and provides the first evidence for loss of gamma band synchronization in Alzheimer's disease. Synchronization likelihood may be more sensitive to detect such changes than the commonly used coherence analysis.

Aged↗

PubMedDate not supplied

A novel method for the topographic analysis of neural activity reveals formation and dissolution of 'Dynamic Cell Assemblies'.

The study of synchronous oscillations in neural systems is a very active area of research. However, cognitive function may depend more crucially upon a dynamic alternation between synchronous and desynchronous activity rather than synchronous behaviour per se. The principle aim of this study is to develop and validate a novel method of quantifying this complex process. The method permits a direct mapping of phase synchronous dynamics and desynchronizing bursts in the spatial and temporal domains. Two data sets are analyzed: Numeric data from a model of a sparsely coupled neural cell assembly and experimental data consisting of scalp-recorded EEG from 40 human subjects. In the numeric data, the approach enables the demonstration of complex relationships between cluster size and temporal duration that cannot be detected with other methods. Dynamic patterns of phase-clustering and desynchronization are also demonstrated in the experimental data. It is further shown that in a significant proportion of the recordings, the pattern of dynamics exhibits nonlinear structure. We argue that this procedure provides a 'natural partitioning' of ongoing brain dynamics into topographically distinct synchronous epochs which may be integral to the brain's adaptive function. In particular, the character of transitions between consecutive synchronous epochs may reflect important aspects of information processing and cognitive flexibility.

Adaptation, Ocular↗