Journal of Clinical EEG & Neuroscience, July 2005
Table of Contents
In a recent article the authors presented a comprehensive review of research performed on computational modeling of Alzheimer’s disease (AD) and its markers with a focus on computer imaging, classification models, connectionist neural models, and biophysical neural models. The popularity of imaging techniques for detection and diagnosis of possible AD stems from the relative ease with which neurological markers can be converted to visual markers. However, due to the expense of specialized experts and equipment involved in the use of imaging techniques, a subject of significant research interest is detecting markers in EEGs obtained from AD patients. In this article, the authors present a state-of-the-art review of models of computation and analysis of EEGs for diagnosis and detection of AD. This review covers three areas: time-frequency analysis, wavelet analysis, and chaos analysis. The vast number of physiological parameters involved in the poorly understood processes responsible for AD yields a large combination of parameters that can be manipulated and studied. A combination of parameters from different investigation modalities seems to be more effective in increasing the accuracy of detection and diagnosis.
In this study, we hypothesized that a quantitative EEG (qEEG) method for measuring EEG variability combined with specific psychophysical tasks could improve the classification accuracy of subjects with normal aging vs. mild cognitive impairment (MCI) or mild dementia due to Alzheimer’s Disease and Related Disorders (ADRD). The cross-sectional sample consisted of 48 subjects (32 normal aging and 16 ADRD: n=3 mild dementia, n=13 MCI FAST stage 3).
An optimal qEEG cutoff value for the delayed recognition memory tasks correctly discriminated 30 of the 32 normal aging subjects (94% specificity) and 14 of 16 MCI-to-mild ADRD subjects (88% sensitivity). On the other hand, the application of this qEEG measure to EEG data recorded while subjects performed a SFM task did not distinguish between ADRD and normal aging any better than chance.
In conclusion, this qEEG measure is specific to the psychophysical task being performed by the subject. When it was combined with delayed recognition memory tasks, it yielded results that are comparable to the accuracies reported by PET scan studies of normal aging vs. AD with mild cognitive impairment. These results warrant further evaluation.
Ring Chromosome 20 with Nonconvulsive Status Epilepticus: Electroclinical Correlation of a Rare Epileptic Syndrome
The electroclinical features of two Thai women with ring chromosome 20 and nonconvulsive status epilepticus (NCSE) were studied. Both have also had generalized tonic-clonic seizures and complex partial seizures of varying frequencies since adolescence. Their intellectual functions were normal. Twenty-four-hour video/EEG telemetry recorded during the NCSE showed fluctuating consciousness between overt unresponsiveness and normal awareness. The EEG consisted of long-lasting generalized rhythmic 3-5 Hz sharp or slow waves with a few spikes, lasting several days. Despite the continuous discharges, the patients had relatively subtle clinical episodes of seizures, during which they were sometimes responsive to verbal stimuli. Intravenous antiepileptic drugs (AED) had little effect on the rhythmic EEG. No lesion in their MRIs contributed to NCSE. Ring chromosome 20 was found in 20% of female karyotype in both patients [46,XX,r(20) (p13 q13)/46,XX] but were negative in four healthy siblings. Oral AEDs decreased more than 75% of the overt CPS episodes in both patients at 22 and 26 months of follow-up but had no effect on the natural history of electrical NCSE. The patients’ daily activities were minimally affected by the ongoing electrical discharges.
These are the first two cases reported of ring chromosome 20 with NCSE in Thailand. Our patients present a rather benign and pharmacologically responsive course probably because of the low percentage of r(20) mosaicism. The electroclinical correlations in our cases raise the possibility that the mechanism of continuous rhythmic waves in this syndrome may be unrelated to epilepsy. Assessing the severity of this syndrome using both clinical seizures and EEG is crucial.
This report deals with a newly described ictal pattern, called the initial ictal slow shift (IS)2. This pattern may be seen in subdural records as the first sign of an ictal event, occurring before the later typical rhythms of a seizure state appear. A positive shift, very similar in appearance from one seizure to another, usually lasted for 1-2 sec, followed by a negativity for 7-9 sec that included the typical rhythmical discharges. At times, a negative shift occurred first, seen up to 15 mV very high in amplitude, for a few seconds before the typical ictal rhythms were seen. Scalp records may also demonstrate slow shifts, and examples are shown of the typical 3/sec bilateral spike and wave (S+W) complexes of absence seizures. A slow shift occasionally appeared 1 sec before the onset of these complexes, but more often a few seconds after the onset. Finally, after the end of the S+W complexes, a positive shift, for as long as 5-6 sec may occur, up to 600 µV in amplitude. These shifts could relate to data showing that patients are not really back to a normal responsiveness for at least 5 sec after the end of the S+W complexes (see Discussion).
The extent of medical follow-up of abnormal screening EEGs secured from psychiatric patients, particularly those reporting slow wave dysrhythmias as the single finding, still varies widely. From an earlier routine EEG screening program for psychiatric inpatients, 103 consecutive cases of abnormal EEGs with generalized, focal, or paroxysmal slowing as the only EEG finding were identified. Despite suggestions for medical follow-up, less than half (44.6%) of the patients received subsequent study. However, 74.2% of patients considered to be without organic pathophysiology at the time of the EEG had positive organic findings on medical follow-up.
Seventy-four participants (aged 20-82 years) went through a continuous performance recognition memory task with multiple repetitions of words and non-words while ERPs were recorded from the scalp. The old/new ERP effect (the difference in activation to stimuli correctly recognized as old and stimuli correctly recognized as new) for words but not non-words declined with increasing age in a linear pattern, but the relationship between the old/new effect and age varied throughout the ERP time window. Differences in topography between age groups were manifested in a frontal shift in activation for older age groups. Further, the data point to differences in semantic versus non-semantic processing across the adult life span, and it is concluded that specific cognitive memory processes are differentially involved at different ages.
Approximate Entropy of the Electroencephalogram in Healthy Awake Subjects and Absence Epilepsy Patients
The approximate entropy (ApEn) of signals in the electroencephalogram (EEG) was evaluated in 8 healthy volunteers and in 10 patients with absence epilepsy, both during seizure-free and seizure intervals. We estimated the nonlinearity of each 3-sec EEG segment using surrogate data methods. The mean (± SD) ApEn in EEG was 0.83 ± 0.22 in healthy subjects awake with eyes closed. It was significantly lower during epileptic seizures (0.48 ± 0.05) than during seizure-free intervals (0.80 ± 0.13) (P<0.001). Nonlinearity was clearly detected in EEG signals from epileptic patients during seizures but not during seizure-free intervals or in EEG signals from healthy subjects. The ApEn of EEG signals estimated over consecutive intervals could serve to determine pathological brain activity such as that occurring during absence epilepsy.
Somatosensory Evoked Potential and EEG in Children With Focal Idiopathic Epilepsies With and Without Evoked Spikes by Tapping of the Feet or Hands
The characteristics of SEP cortical components were studied in 40 children with focal idiopathic epilepsies of childhood. Twenty children had focal idiopathic epilepsies and evoked spikes (FIE-ES) on the EEG and 20 had benign focal epilepsy of childhood with centrotemporal spikes (CTE) but without evoked spikes (ES). These data were compared with those of a control group of 20 normal children.
N35 high-amplitude component was more frequent in the CTE group than in the control group (p<0.001). P98 showed high amplitude in 50% of the children of FIE-ES group, and in none of the CTE and control groups (p<0.001). The P98 high amplitude component was more common in the FIE-ES group than in the other groups, and so was the N35 high amplitude component in the CTE group. Lateralization of high-amplitude components of N35 in the CTE group and of P98 in FIE group was not correlated with lateralization of epileptiform activity and evoked spikes.
Based on our findings, there are SEPs cortical component differences in childhood focal idiopathic epilepsies according to occurrence or absence of ES.
We describe the clinical and electroencephalographic features of a comatose patient with severe anoxic encephalopathy who experienced acute reflex myoclonus precipitated by passive eye opening/closure and painful stimulation.
Acute stimulus-sensitive postanoxic myoclonus is an underdiagnosed epileptic condition. Shortly after the anoxic insult, the diagnosis should be based on EEG evaluation and various types of stimulation. These should include passive eye opening/closure and painful stimuli.
The objective of our study was to investigate the effect of topiramate (TPM) on the Lyapunov exponent of EEG by means of quantitative pharmacoelectroencephalography (QPEEG) and nonlinear analysis methods. One dose of TPM was administrated to epileptics and healthy adults. EEG samples were obtained prior to and at regular intervals (at 0.5, 1, 2, 4, 6, 8, 12, 24 hours) within the 24 hours following the administration of TPM. EEG activity was processed with nonlinear analysis methods. The Lyapunov exponent of the scalp areas was calculated through 60 s epochs without artifacts after each recording. The statistical difference between baseline predrug assessment and each postdrug control was calculated by computing the paired t test. Results showed that the Lyapunov exponent increased first, then decreased, then increased finally. We conclude that TPM can change the complexity of EEG.