Electroconvulsive therapy (ECT) is a well-established psychiatric treatment in which seizures are electrically induced in anesthetized patients for therapeutic effect mainly in various forms of mental depression. Electroencephalographic examination during seizures induced by ECT gives a very characteristic electroencephalogram (EEG) which however presents a large biological variation as well as nonstationarity patterns.
The EEG during seizures induced by ECT is very characteristic but shows a large biological variation as well as nonstationarity. Algorithm based in part of ictal EEG implemented in ECT equipment may be used to predict response of treatment and other potential clinical variables such as diagnosis [
This study is a part of a larger project in which the characteristics of ictal EEG after ECT stimulation in affective disorders will be analyzed with various mathematical methods (traditional and new methods) in relation to clinical and other biological variables.
In EEG signals the classical statistical approach of interpreting measurement errors as generators of uncertainty is not valid. For EEG most of the noise is due to model error which cannot be considered to be random. We have not seen any EEG model where residuals are completely randomly distributed around a fitted model. This may be due to dependence structures, but it is not clear how to estimate this dependency in nonstationary series. Moreover, there are usually strong individual responses which nowadays usually are taken into account via random parameters, for example, in mixed models. However, in this report we stay with classical spectral analysis and will not present results where individual variation is incorporated.
Major depression is a heterogeneous condition, and the search for neural correlates specific to clinically defined subtypes has been inconclusive. Nevertheless, functional neuroimaging studies of major depression have identified neurophysiologic abnormalities in multiple areas of the orbital and medial prefrontal cortex, anterior cingulum, and related parts of the striatum and thalamus [
The abnormality of the function in these cortical-subcortical circuits may depend on dysfunction in the major neuromodulating transmitters, imbalance in excitatory/inhibitory function, and lack of neurotrophic factors in turn causing decrease in synaptic plasticity [
Indication of ECT is pharmacological treatment resistant depression of various subtypes, that is, in this article referred to as psychotic depression, unipolar depression, and bipolar depression. In psychotic depression, patients experience delusions and/or hallucinations with colourings of low mood in addition to other depressive symptoms. In unipolar depression patients suffer from recurrent depressive episodes. In bipolar depression, patients suffer from a current depression that alternates with mania.
ECT devices provide the opportunity to monitor induced seizures with electroencephalography (EEG), electrocardiogram, and electromyogram. EEG is a powerful method that registers cortical electrical activity and may be used to monitor the course of events in a seizure.
In this treatment modality, electrodes are placed on the scalp, and a brief-pulse current is delivered, which depolarizes neurons, resulting in a generalized tonic-clonic seizure.
The course of the ictal EEG may be summarized as at least three events of distinct and sequential phase patterns. The first event contains high-voltage “sharp waves and spikes”, the second rhythmical “slow-waves”, and the third event an abrupt and well-defined ending, termed postictal suppression. This late phase is characterized by low amplitude and higher frequency [
Independent of electrode placement on the scalp has been shown to result in an increase in ictal slow wave delta power in the prefrontal cortex, with a stronger asymmetry seen with right unilateral ECT [
Most ECT studies have investigated the physiological mechanism of action in relation to clinical response [
The aim of this study was to analyze smooth density functions and time series of the seizure EEG, given in a clinical treatment setting, and relate these EEG characteristics to clinical subgroups of depressive disorder.
The research subjects consisted of 33 patients, 21 women and 12 men. Subjects were between the age 32 and 80. Two trained research raters used a structured interview (SCID-I, DSM-IV, 1994 [
Demographic variables of the subgroups of depression (psychotic depression (PD), Bipolar depression (BD), Unipolar depression (UD)): Sex (female (F), male (M)), Mean age
Group | Sex | Age | mC | Ohm |
---|---|---|---|---|
PD ( | F = 4, M = 3 | |||
BD ( | F = 3, M = 3 | |||
UD ( | F = 14, M = 6 |
Medication in individual patients. Labels: UD = unipolar depression; PD = unipolar depression with psychotic symptoms; BD = bipolar depression. Medication is indicated: 1 = with and 0 = without medication.
Patient ID | Group | Neuroleptics | Antidepressives |
---|---|---|---|
2 | BD | 1 | 0 |
13 | BD | 0 | 0 |
32 | BD | 0 | 0 |
121 | BD | 1 | 1 |
135 | BD | 0 | 0 |
146 | BD | 0 | 0 |
1 | PD | 0 | 0 |
15 | PD | 1 | 0 |
37 | PD | 0 | 0 |
41 | PD | 0 | 1 |
42 | PD | 0 | 0 |
122 | PD | 0 | 0 |
174 | PD | 0 | 0 |
8 | UD | 0 | 1 |
10 | UD | 0 | 0 |
16 | UD | 0 | 1 |
18 | UD | 0 | 0 |
19 | UD | 0 | 0 |
25 | UD | 0 | 1 |
27 | UD | 0 | 0 |
28 | UD | 0 | 0 |
30 | UD | 0 | 0 |
31 | UD | 0 | 0 |
34 | UD | 0 | 0 |
35 | UD | 0 | 0 |
39 | UD | 0 | 1 |
40 | UD | 0 | 0 |
45 | UD | 0 | 0 |
49 | UD | 0 | 0 |
74 | UD | 0 | 1 |
80 | UD | 0 | 0 |
97 | UD | 0 | 0 |
126 | UD | 0 | 1 |
All subjects received unilateral electrode placement according to the d’Elia method [
Two patients in the unipolar group were given thiopental sodium 1 mg/kg during anaesthesia. All others were given propofol 1.5–2.5 mg/kg. All patients received succinylcholin 1 mg/kg and ventilation until full saturation, that is, 100% oxygen by mask during anaesthesia.
The electrical stimulus intensity was determined by reference to a predefined chart, according to sex and age [
Two channels of EEG were recorded using an MECTA 5000Q device. Electrodes were placed on the left and right prefrontal area and correspondingly reference electrodes on the left and right processus mastoideus. The channels over the prefrontal areas are called Fp1 and Fp2, according to the 10–20 system. Disposable stick-on Ag/AgCl electrodes with conductive gel were used (SLE diagnostics). Before application of electrodes the sites were cleansed with alcohol and an abrasive cleanser (NUPREP, SLE diagnostics) to ensure low electrode/scalp impedance. Registration of EEG was performed before, during, and after ECT. Registration ceased approximately 30 seconds after the beginning of the postictal period. By sampling at a frequency rate of 128 Hz, the continuous EEG signals were recorded. The data from the digital port of the MECTA equipment were simultaneously recorded, transferred to computer hard disc, and saved in files together with stimulus parameters.
Artifacts were rejected manually; blind to subject and diagnosis by B.W. Movement artifacts were removed. All EEG recordings with alternating current (AC) disturbances were excluded from the analysis. When data was examined for artifacts, the definition of frequency bands was as follows: delta (
All calculations were performed on EEG signals collected directly after ECT stimulation and refer to the first ECT treatment. Collection was stopped 30 seconds postictally. EEG data can be difficult to interpret because there is no simple model for the dynamic variation of the signals. We have developed some specific statistical methods for the estimation which all are based on minor modifications of standard methods used in time series analysis and multivariate statistics [
The original EEG signals presented global as well as local trends. Therefore, in order to avoid any bias in the spectral estimates due to the presence of such trends the series were filtered by taking the first difference. After this preliminary operation, the series for each individual were Fourier transformed, and their spectral density functions were estimated.
In this study we found identifiable and significant differences in the EEG characteristics, from Fp1 and Fp2, respectively, as well as the first treatment, between the different subgroups of patients with major depression.
To illustrate how the smooth density functions are distributed for channel Fp1 of the first five seizures, we show the functions for one patient from each category, see Figures
Mean values and standard error of mean (SEM) of the principal components (PC), summarizing spectrum of smooth density distribution of psychotic and bipolar depressed patients (PD and BD, resp.). Unipolar depressed (UD) patients cannot be differed from either psychotic or bipolar depressed patients. Fp2 denotes the left fronto-orbital EEG electrode.
Variable | Group | Mean | SEM | |
---|---|---|---|---|
PC1 | PD | 7 | 0.65 | |
of the whole | UD | 20 | 0.29 | |
spectrum of sdf | BD | 6 | 0.43 | |
PC2 | PD | 7 | 0.29 | |
of the whole | UD | 20 | 0.32 | |
spectrum of sdf | BD | 6 | 0.60 | |
PC1 | PD | 7 | 1.08 | |
of selected | UD | 20 | 0.02 | 0.53 |
frequencies | BD | 6 | 0.62 |
(a) Smoothed spectral density functions (sdfs) of channel Fp1 (Pat. 42—Psychotic depressed), (b) sdfs of channel Fp1 (Pat. 80—Unipolar depressed), and (c) Sdfs of channel Fp1 (Pat. 2—Bipolar depressed).
In patients with psychotic depression we found a high occurrence of low frequencies in the EEG series. Most of the activity of the ictal EEG was found in the Delta and Theta frequency range (
In patients with unipolar depression, there was a more heterogeneous pattern in the frequencies of the ictal EEG. In almost all the seizures the highest peak of each smoothed spectral density function is located in the Gamma frequency range, more precisely around 47.3 Hz. However, considerable activity could be found in the Delta band where there were minor peaks, which were also characterized by decreasing amplitudes with increased number of treatments (see Figure
In patients with bipolar depression we found a high occurrence of high frequencies. Almost all activity was found in the Gamma range where the global maximum of each spectrum (at about 55 Hz) is usually followed by two minor peaks at about 47 and 51 Hz (see Figure
As a first step in finding out possible discriminatory variables between the three clinical groups, we stored the peak values of the spectral density functions of both prefrontal channels at 2.62, 2.87, 3.12, 3.25, 46.37, 46.62, 51.75, and 55.75 Hz. Thus, by selecting the most common frequencies from the EEG we totally had 16 observations, which came from both EEG channels.
Since these variables were highly correlated, we applied principal components analysis in order to avoid problems due to multicollinearity. The first six principal components were used in the multivariate analysis (see Figure
(a) Spectral density function of low-pass (I) and high-pass (II) series for a patient 1 (psychotic depression), (b) spectral density function of low-pass (I) and high-pass (II) series for a patient 19 (unipolar depression), and (c) spectral density function of low-pass (I) and high-pass (II) series for a patient 13 (bipolar depression).
Sex, age, and medication had no statistical influence when performing analysis of covariance.
Successively, in order to study in more detail the variation structure of the signals, we applied two different filters: a low-pass filter able to detect any possible difference within the Delta and Theta bands (0–8 Hz) and a high-pass filter in order to separate the Beta frequency band (more precisely the range 40–50 Hz). In both cases Yule-Walker filters of order eight were used.
Figures
Finally, regarding the high-pass-filtered series (graph at the bottom), we can see that there actually are differences in the distribution of the variation of the processes among the different groups. Bipolar depressed patients display an EEG spectrum with a unique high peak of the smooth density function at 46.5 Hz while the remains of the function seem to be caused mainly by unspecific noise. This frequency is found in the postictal suppression phase of the seizure EEG.
Also unipolar depressed patients tend to have spectra with the main peak located at the same frequency but smaller in amplitude while bipolar depressed patients manifest their peak at 55.75 Hz even though their Gamma activity is quite uniform within the band. Hence we recorded, for both channels, the values of the spectra of the low-pass series at 3.25 Hz and those of the high-pass series at 46.5. These values were then transformed, via principal components analysis, into new uncorrelated variables.
The differences between the EEG characteristics of the psychotic depression patient group and patients with bipolar disorder were significant. Linear discriminant analysis based on principal components of the obtained frequencies in the EEG signals gave almost perfect separation between the individuals belonging to different subgroups of depression. The means, standard errors of means, and approximate confidence limits of the principal components analysis of the frequencies are shown in Table
Comparisons between the different smooth density functions (sdfs) of the clinical groups as well as the selected frequencies were tested pairwise. The first components of the eight selected frequencies from both channels separated the two clinical groups. Also the first and second principal components of the frequencies of the whole sdfs differed significantly between psychotic depression and bipolar disorder.
With repeated ECT the differences in EEG characteristics between the clinical subgroups were reduced. In data from the first ECT session, the clinical subgrouping matched the classification aided by EEG characteristics in 94% of the patients; see Table
Results of linear discriminant analysis using variables from the post ECT EEG frequencies of the 1st seizure. The correct number of number of individuals in respective subgroups of depressed (True group = patients) is on top of the table. The number of classified individuals according to the discriminant function is shown in the left margin (assigned to group = classified). Subgroups of depression: psychotic depression (PD), bipolar depression (BD), and unipolar depression (UD).
Assigned to group | True group | |||
1 | 2 | 4 | Total | |
PD | 7 | 1 | 0 | |
UD | 0 | 18 | 0 | |
BD | 0 | 1 | 6 | |
Total | ||||
7 | 18 | 6 | ||
Proportion |
There is a difference between EEG characteristics for patients with unipolar depression, psychotic depression, and bipolar depression. With repeated ECT these differences tend to even out. The ictal EEG of the first ECT is the most informative, that is, it has the highest capability of discriminating clinical subgroups.
Several studies support a relationship between major depression with psychotic features and bipolar disorder [
There is considerable evidence from studies of different biological variables that point to distinct biological abnormalities in psychotic depression as compared with bipolar depression and unipolar depression [
This was an open study where treatment groups constitute of small and unequal sample sizes. Despite of no optimal study conditions, we saw significant differences between clinical subgroups. Many variables have been reported to influence EEG pattern. Patient medication and anaesthetic under treatment, patient age, ECT stimulus parameters, and the placement of EEG electrodes are all possible sources of variation. However, in our study, we saw no significant confounding of the results correlating with these variables, which may be due to the open study design.
It may be argued that the number of EEG placements were too low to quantify EEG patterns related to ECT. The rational behind use of two EEG scalp electrodes was that this study was an open study, in ECT practice. Using two EEG electrodes is of course a limitation when studying EEG characteristics, such as topographic and spatial properties. However, the standard placements of EEG electrodes during ECT are Fp1 and Fp2. Data from these placements are included in the treatment algorithm implemented in the electrical device. The algorithm was based on ictal EEG data from 28 electrode scalp sites from over 200 patients and has been tested on data over 80 patients, confirming validity [
Data from Fp1 and Fp2 sites added most of the statistical variation to the regression model. Therefore, we believe that, for our purpose, the choice of these two sites was sufficient when studying seizure EEG characteristics in relation to various clinical variables.
Another limitation in EEG studies to be considered is possible variation from EEG reference electrodes. We used standard ipsilateral electric references (i.e., bilateral processus mastoideus) according to Krystal et al. [
Multivariate analysis on small sample size has disadvantages when many variables relatively to the independent experimental units are observed. Therefore, we applied variable reducing techniques (PCA) to make it possible to compare the clinical groups in a more effective way. The new variables (principal components) formed by PCA are asymptotically uncorrelated, and they may be included in a multiple regression analysis. We had some indications that observations from Fp2 differentiated the clinical groups PD and BD but not those from Fp1. This finding may indicate a difference between PD and BD in how the epileptic seizure propagates.
It is conceivable that the use of EEG data can aid diagnosis and leads to separate treatment for clinical subgroups of depressed patients. Classifying clinical subgroups can lead to better prophylaxis for the individual patient. During a 25-year period, 25% of unipolar depression will change diagnosis to bipolar disorder [
In this study EEG indicates to be an instrument for classifying subgroups of depression at initiation of ECT. However, the results should be replicated in larger studies.
This work was supported by the COST Action BM0601 “NeuroMath”. Neither the COST Office nor any person acting on its behalf is responsible for the use which might be made of the information contained in this publication. The COST Office is not responsible for the external websites referred to in this publication. The authors thank Professor Lennart Wetterberg for valuable help with the manuscript clarifying many parts of it. They are also grateful to Dr. Jose Castillio-Restrepo who treated all patients and supervised and kept treatments of all patients under standard conditions.