Evidence for a characteristic EEG frequency response to thiopental

Personalized Medicine in ADHD and Depression: Use of Pharmaco-EEG

This chapter summarises recent developments on personalised medicine in psychiatry with a focus on ADHD and depression and their associated biomarkers and phenotypes. Several neurophysiological subtypes in ADHD and depression and their relation to treatment outcome are reviewed. The first important subgroup consists of the 'impaired vigilance' subgroup with often-reported excess frontal theta or alpha activity. This EEG subtype explains ADHD symptoms well based on the EEG Vigilance model, and these ADHD patients responds well to stimulant medication. In depression this subtype might be unresponsive to antidepressant treatments, and some studies suggest these depressive patients might respond better to stimulant medication. Further research should investigate whether sleep problems underlie this impaired vigilance subgroup, thereby perhaps providing a route to more specific treatments for this subgroup. Finally, a slow individual alpha peak frequency is an endophenotype associated with treatment resistance in ADHD and depression. Future studies should incorporate this endophenotype in clinical trials to investigate further the efficacy of new treatments in this substantial subgroup of patients.

Analytical methods to differentiate similar electroencephalographic spectra: neural network and discriminant analysis

Differences in electroencephalographic (EEG) power spectra obtained under similar, but not identical, conditions may be difficult to discern using standard techniques. Statistical analysis may not be useful because of the large number of comparisons necessary. Visual recognition of differences also may be difficult. A new technique, neural network analysis, has been used successfully in other problems of pattern recognition and classification. We examined a number of methods of classifying similar EEG data: standard statistical analysis (analysis of variance), visual recognition, discriminant analysis, and neural network analysis. Twenty-nine volunteers received either thiopental (n = 9), midazolam (n = 10), or propofol (n = 10) in sedative doses in 3 different studies. These drugs produced very similar changes in the EEG power spectra. Except for beta 2 power during thiopental infusion, differences between drugs could not be detected using analysis of variance. Visual categorization was correct in 72% of the baseline EEGs, 70% of thiopental EEGs, 27% of propofol EEGs, and 46% of midazolam EEGs. A classification neural network (Learning Vector Quantization network) containing a Kohonen hidden layer was able to successfully classify 57 of 58 EEG samples (of 4 minutes' duration). Discriminant analysis had a similar rate of success. This level of performance was achieved by dividing the EEG power spectrum from 1 to 30 Hz into 15 2-Hz bandwidths. When the EEG power spectrum was divided into the "classical" frequency bandwidths (alpha, beta 1, beta 2, theta, delta), both neural network and discriminant analysis performance deteriorated. By training the network using only certain inputs we were able to identify drug-specific bandwidths that seemed to be important in correct classification. We conclude that propofol, thiopental, and midazolam produce different effects on the EEG and that both neural network and discriminant analysis are useful in identifying these differences. We also conclude that EEG spectra should be analyzed without using classical EEG bands (alpha, beta, etc.). Additionally, neural networks can be used to identify frequency bands that are "important" in specific drug effects on the EEG. Once a classification algorithm is obtained using either a neural network or discriminant analysis, it could be used as an on-line monitor to recognize drug-specific EEG patterns.

Quantitative electroencephalographic profile of 3-(4-hydroxy-1-piperidinyl)-6-(2,4-dichlorophenyl)-pyridazine (SR 41378) in the rat

Quantitative electroencephalographic (QEEG) analysis was performed in rats following the oral administration of SR 41378 [3-(4-hydroxy-1-piperidinyl)-6-(2,4-dichlorophenyl)-pyridazine], a novel aminopyridazine derivative, which has been shown to possess anticonvulsant, antianxiety and hypnotic activities in mice and rats. The EEG effects of SR 41378 (10, 30 and 100 mg/kg) were compared to those of secobarbital (30 and 60 mg/kg) and diazepam (1, 3 and 10 mg/kg). SR 41378 and secobarbital increased the power of the middle-frequencies (8-16 Hz) of the EEG, reduced that of 4-8 Hz (theta) activities and did not affect 1-4 Hz (delta) activities. Diazepam also increased the power of middle-frequency activities and decreased that of both delta and theta activities. Quantitative EEG profiles were calculated from the mean integrated power (MIP) of selected frequency bands. The QEEG profile of SR 41378 was found to share common characteristics with those of secobarbital and diazepam: dose-dependent decrease of theta band MIP and increase of 8-20 Hz (middle beta bands) MIP. However, both SR 41378 and secobarbital induced a reduction of the 28-32 Hz (fast beta bands) MIP, whereas diazepam diminished the delta band. These results suggest that SR 41378, a novel chemical structure, shares common psychotropic properties with barbiturates and benzodiazepines.

Extraction of clinical information from electroencephalographic background activity: the combined use of brain electrical activity mapping and intravenous sodium thiopental

Traditional visual inspection of electroencephalographic (EEG) tracings and computer-assisted topographic mapping were compared in their abilities to detect and locate supratentorial lesions following intravenous sodium thiopental administration. Of the 13 subjects, 8 had atrophic and 5 had mass lesions, all defined by computed tomographic scan and 11 confirmed at operation. EEGs made before and after thiopental administration were evaluated separately from topographic maps of statistical difference between EEGs at multiple frequency ranges made before and after thiopental injection. Topographic mapping of statistical difference accurately detected all thirteen lesions, whereas EEG detected eight. In addition to demonstrating reduced beta production overlying structural abnormalities, topographic mapping revealed regionally augmented beta, especially over irritative lesions. Moreover, localization was possible with the topographic method after the first thiopental injection, whereas a second injection was required for EEG localization by visual inspection. Changes in slow (delta) activity were also useful in delineation of atrophic lesions, in which delta was usually augmented but occasionally regionally reduced. Multielectrode studies with topographic mapping appear essential in delineating cerebral abnormalities, because both slow and fast activities may be increased or reduced over such areas. The relative response of EEG background activity to thiopental at different frequencies may assist lesion characterization as well as localization.