Static and dynamic electroencephalographic studies in Alzheimer's disease

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Static and dynamic electroencephalographic studies in Alzheimer's disease Summary of thesis Zoltán Hidasi MD Semmelweis University Doctoral School of Mental Health Supervisor: Péter Rajna MD, PhD,

1 Static and dynamic electroencephalographic studies in Alzheimer's disease Summary of thesis Zoltán Hidasi MD Semmelweis University Doctoral School of Mental Health Supervisor: Péter Rajna MD, PhD, DSc Opponents: Zsuzsanna Arányi MD, PhD István Kondákor MD, PhD President of final committee: Ilona Huszár MD, PhD Members of final committee: Zsuzsanna Czenner, PhD Péter Tariska MD, PhD Budapest 2008

2 INTRODUCTION Remarkable development was observed in the diagnostics of Alzheimer's disease (AD) in the past decades. Improving diagnostic approaches, based on studies dealing with genetic, molecular biological and pathological aspects of AD, also imply newer therapeutic possibilities. Thus an increasing need for early and accurate diagnosis of AD has emerged. Electrophysiological methods traditionally play primary role in neuropsychiatric diagnosis, although comparing to other diagnostic approaches, their importance seem to decline. Electrophysiological methods are in continuous development, whereas this improvement is rather observed in research. The use of the new results in everyday praxis is still not widely experienced. Electroencephalography (EEG) is traditionally performed on awake, calm, immobile subjects, lying with the eyes closed. Activity registered in this state is referred as static EEG. Every other situation used for EEG registration aims to cause changes compared to the resting EEG, followed by the analysis of the changes themselves and their dynamics. Changes on resting (static) EEG caused by different interventions, and the registration of those changes is interpreted as dynamic EEG. Increasing significance of functional imaging methods underlines the importance of recently developed dynamic EEG methods both in research and in clinical diagnosis. According to some authors, the analysis of the spontaneous EEG recorded at rest without perceptive-cognitive demand is not sensitive enough to produce significant group differences in spectral EEG parameters in AD patients, therefore the increasing importance of dynamic EEG methods is also observable in the field of AD. Previous studies deal with the EEG recorded during cognitive tasks in AD patients, although the registration of those EEG sections is difficult in most cases. Evaluation of the EEG activity registered after the cognitive performance can also be informative in AD. Dynamic EEG methods are suitable for providing neurophysiologyc/neurocognitive information about brain functioning in normal and pathological conditions. Moreover, these methods provide data about functions not detectable with other diagnostic methods in AD. In the presence of new diagnostic and therapeutic possibilities, there is an increased need for dynamic EEG approaches useful in the everyday clinical practice. In my thesis, I focused on the evaluation of simple but informative EEG situations, appropriate quantitative EEG measures and task situations suitable for the investigation of AD, and the comparison of static and dynamic EEG methods in AD. 2

3 AIMS The basic rationale of the work was the assumption that the short term after-effects of mental effort as manifested in the EEG would be substantially different in AD patients compared to those observed in control subjects and as such, can quantitatively be presented. Thus, it was assumed that 1) the state of the subject as reflected by the electrophysiological activity recorded immediately after the performance of a cognitive task reflects the consequences of the effort required by the task and 2) that the fixed duration and level of difficulty of the task represented an approximately equal load for all subjects within the groups making the interindividual comparison of the effect of the task possible. Our primary hypothesis was that differences observed in the electrophysiological parameters (spectral parameters and changes of coherence in the present study) between the before-task, and after-task conditions will be markedly different in AD patients compared to those seen in controls. We expected to find less prominent changes in the AD patients than those seen in normal controls since the available cognitive capacity is presumably decreased in AD. In addition, the neural circuity involved in the cooperative activity of interactive neuronal systems during cognitive processes is presumably impaired in AD depending upon the stage of the disease. Differences were presumed both in spectral and in coherence values, according to previous studies. Group differences especially in alpha and theta frequencies were expected, with different changes in the fast (alpha, beta) and slow (delta, theta) frequency domains. The sensitivity of spectral and coherence values were thought to be different, we expected more pronounced group differences in the coherence measures, because of the presumed functional disconnection in AD. METHODS Patients and subjects The group of AD patients consisted of 8 women and 6 men. Their mean age was 67.4 years (range 58-79), and their average MMS score was (range: 16-24). The group of control participants consisted of 6 women and 4 men. Their mean age was 67.2 years (range: 55-78), average MMS score was 29.8 (range: 29-30). Inclusion criteria for the AD group were based on those defined by NINCDS- ADRDA, score on mini mental state (MMS) between 15 and 24 (inclusive), age 50 years or older, and stable medication history for one month prior to the beginning of the study. 3

4 Inclusion criteria for the healthy control group were: age > 50 years, absence of any neurological and psychiatric diseases and no treatment with drugs of psychotropic effect. EEG recording The EEG was recorded by 19 Ag-AgCl electrodes placed according to the international system using BRAINLIFT 21-5 (Medicor, Micromed, Hungary) amplifiers (sampling rate: 200 Hz, bandpass: Hz). Of the continuously recorded EEG 2560 ms (512 data points) long EEG-epochs were made and subjected to automatic artifact screening, during which epochs exceeding +/- 70 μv were rejected. Visual artifact screening was also performed to ensure the epochs were free of other types of artifacts (muscle activity, etc.) as well. Data acquisition started with the recording of spontaneous resting EEG for two minutes with eyes closed ( before-task period ). Following this, a 20-second recording was performed with eyes open for the stabilization of vigilance level. This was followed by performing the cognitive task (reverse counting) lasting for 45 seconds. Another 2-minute EEG recording epoch followed which was the EEG section evaluated for immediate post task changes ( after-task period ). The session ended by an epoch lasting for 20 s with eyes open and then hyperventilation for 3 minutes. The analyses were performed in the following frequency bands (delta: Hz, theta: 4-8 Hz, alpha1: 8-11 Hz, alpha2:11-14 Hz, beta Hz, beta2: Hz). Relative frequency spectra were calculated by means of Fast Fourier Transform for the above bands by the Neuroscan 4.3 software. Coherence was computed with the same software for interhemispheric and intrahemispheric short-range and long-range electrode pairs. Cognitive task: reverse counting The subjects had to count backwards from 100 by 7 for 45 seconds. The subjects performed the task with eyes closed, and listed aloud the results of each step of the counting. Perfect performance was not the goal during reverse counting, a continuous mental effort was achieved in all subjects. Statistical analysis Individual means for the relative spectra were calculated by averaging the values measured for each EEG channel for the given frequency bands. Power spectra and alpha peak frequencies were measured separately at the O1 and O2 electrodes. Group statistics were 4

5 computed based on these averages. Two-way Multivariate Analyses of Variance MANOVAs) were calculated in Group (AD vs. Control) x Condition (Before vs. After task) design for the relative power and coherence values on each and every frequency band in both conditions. Following MANOVAs ANOVAs were also calculated in the same two-way and one-way between groups (AD vs. Control) designs to reveal between group differences for each variable. RESULTS Frequency spectrum analysis Significant differences were found in the theta band between the two groups. The relative theta was higher in the AD group compared to the controls both before and after the calculation task (F(1,22)=10.49; p<0.004 and F(1,22)=6.99; p<0.02, respectively). Compared to the control group, a marginally significant decrease of the beta1 band was found in the patients before the task (F(1,22)=3.59; p<0.07). The same tendency was found following performance of the task, but in this case the difference was not significant. A significant Group x Condition interaction was found in the alpha2 band (F(1,22)=4.2594, p<0.05). Following the task, the amount of alpha2 band increased in the AD patients whereas it showed a small, non-significant decrease in the controls. The same tendency could be seen in the beta1 band (F(1, 22)=3.3559, p<0.08). In the delta band a marginally significant interaction was observed (F(1, 22)=2.8615, p<0.10). After the completion of the task, the delta power increased in the controls, but decreased in the AD group. No such interaction was found in the theta, beta2, and alpha1 bands. Although alpha power measured on the O1 and O2 electrodes was not different in the two groups, a significantly lower alpha frequency peak was found in AD than that seen in the controls (F(1, 22)=5.3127, p<0.05). However, no significant Group x Condition interaction of the alpha peak frequency as a result of task performance was observed. Coherence Significant group-differences were found in the apha1, alpha2 and beta2 bands in both conditions. Compared to that seen in the controls coherence was lower in AD in the alpha1 band but only in the after task condition. Coherence was higher in the alpha2 and beta2 bands in AD in both conditions. Significant Group x Condition interactions were found in the alpha1 frequency band for the majority of intrahemispheric long-range electrode-pairs: in the controls coherence 5

6 increased in all considered electrode-pairs as a result of the calculation task which was significant at the F4-T4, F3-T5, F3-P3, F4-P4, F3-O1, F4-O2, T4-O2 electrode-pairs. Contrary to this, no significant increase was found in the AD group following task execution. Furthermore, coherence showed a small (non-significant) decrease after the task The Group X Condition interaction in the MANOVA performed on the 12 long-range electrode pairs proved to be significant (F(1,22)=8.11, p<0.01). In the Control group the coherence between long-range intrahemispheric electrode-pairs was significantly higher in the after task period but it was slightly lower in the AD group. CONCLUSIONS Electrophysiological parameters calculated from epochs following the completion of a cognitive task are able to differentiate patients with AD from normal controls with the use of cognitive EEG method. Not only EEG sections registered during cognitive task as seen in the literature, but the comparison of pre-task and post-task activity provides data about cognitive functions and their neurophysiologic aspects in AD. The task used in our study (mental calculation, reverse counting), as a complex cognitive task, probably by involving several cognitive neuronal networks is suitable for inducing significant differences between the groups investigated. Regarding spectral parameters there was an increase in fast (alpha2 and beta1), and a decrease in slow (delta) frequencies in the AD group following the completion of the cognitive task. These measures changed in the opposite way in the control group after the cognitive performance. The increase of the slow and decrease of fast frequency activity - occurring during the task is a well known phenomenon in task-related EEG studies in healthy subjects, this is in harmony with our findings in the control population. As these changes in our case occurred after the cognitive task, it is remarkable, that in healthy controls the changes are similar to those, experienced during the task. The consequences of the cognitive task were different in delta, alpha2 and beta1 frequencies regarding spectral parameters in the two groups. This is probably related to the difference of widespread activation of cognitive neuronal networks, induced by the task. This phenomenon is remarkable especially regarding future studies in this field. 6

7 The significant differences between the two groups of subjects regarding before- and aftertask conditions according to our results was found in the alpha2 band, where the increase of the relative power in the AD group might be a correlate of a rebound effect, which was more pronounced than that seen in the control group. The higher amount of relative alpha2 band seen in AD patients following the task may correspond to a long-lasting effect of the increased effort invested in the execution of the task. This effect may represent some kind of cognitive capacity characterizing in the AD group. Mobilization of this reserve capacity might correspond to the preserved cognitive abilities in AD of mild and moderate degree, represented by the patients in the present study. In coherence values the most prominent differences between AD and controls were found in the alpha1 band, for most of the of long-range electrode-pairs. Coherence values increased in the control population at these electrode-pairs, as they did not change in the AD group. In our case, these differences were proved after the cognitive performance. The differences in alpha1 coherence values in long-range intrahemispheric coherence was unrelated to circumscribed cortical areas, thus indicate the diffuse impairment of long cortical connections in AD. These coherence changes may have become apparent as a result of the demand represented by the cognitive task, involving multiple neuronal systems. Our findings are consistent with the neocortical disconnection hypothesis of AD, related to the loss of structural and functional integrity of long cortico-cortical tracts. Increase of alpha1 coherence represents the preserved functional connectivity of cortical areas induced by the task in healthy controls. Alpha1 coherence remains unchanged in AD group after the cognitive task, representing the impairment of this functional connectivity. Differences were found between the two groups in beta2 coherence both before and after task performance. These differences occurred in the long-range comparisons where higher beta2 coherence values were observed in the AD group than in the control group, irrespective of task performance. Neurophysiologic background of these findings is unknown. The findings in spectral power and coherence values seem not to be closely related to each other. Differences in coherence between the controls and AD patients were found to be more conspicuous than those seen for the spectral measures. As the underlying neurophysiologic mechanisms of the findings related to the differences between the controls and AD patients 7

8 are not clear, the two types of analyses may reflect different, perhaps complementary pathophysiological aspects. In the comparison of dynamic EEG methods, we concluded that spectral differences appeared between AD and control groups with the use of cognitive task, whereas they did not appear with sensory stimulus (eye-opening). Concerning coherence, differences appeared also as the effect of eye-opening between the two groups, though the differences were more consistent and unambiguous (changes in long-range intrahemispheric coherence in alpha1 band) with the help of cognitive task situation. Cognitive task was generally found more sensitive in differentiation of AD group, than the use of sensory stimulus, regarding dynamic EEG methodology, with special importance of coherence measures. Another possibility for increasing sensitivity of EEG studies is the use of methods based on nonlinear complexity analysis (Omega-complexity and synchronization likelihood in our case). We can conclude, that dynamic EEG approach definitely provides extra information about AD group, compared to traditional, static EEG methods. This extra information is related to adaptation of brain mechanisms, functional changes in response to cognitive demand, that can also be detected after the cognitive load. These new data have special importance concerning early diagnosis, therapeutic approaches, prognostic aspects and the follow-up of long-term course. The neurophysiologic and neuropathologic background, generators related to upper mentioned quantitative EEG parameters and their changes is not clear. Further evaluation of EEG data recorded following task performance in a larger pool of patients is needed, both for the better understanding of neurophysiologic processes underlying the mechanisms of the disease and for ultimate diagnostic purposes in everyday clinical practice. Functional imaging methods and their combination with dynamic EEG procedures are capable of increasing both sensitivity and specificity of the individual methods. Combination of the evaluation of several electrophysiological parameters also seems to be a useful possibility. In general there is an increased need for dynamic EEG methods in everyday neuropsychiatric diagnostics. These methods are capable to provide more functional information about the activity of the brain both in physiological and pathological conditions. The method presented in my work (with several other procedures) might be suitable for a 8

9 widespread use in the clinical practice. The variability and pronounced practical importance of dynamic EEG approaches probably lead to their appearance in everyday practice in the close future. PUBLICATIONS Publications related to the thesis: Rajna Péter, Hidasi Zoltán, Waldemar Szelenberger. Eseményfüggő EEG- és kiváltottválaszvizsgálatok a klinikai gyakorlatban. Clin Neurosci/Ideggyógyászati Szemle. 2005;58(11-12): Czigler Balázs, Csikós Dóra, Gaál Zsófia Anna, Csibri Éva, Kiss Éva, Hidasi Zoltán, Salacz Pál, Molnár Márk. Kvantitatív EEG Alzheimer-kórban: spektrális-, koherencia- és komplexitás-jellemzők. Psychiat Hung. 2006;21(4): Zoltán Hidasi, Balázs Czigler, Pál Salacz, Éva Csibri, Márk Molnár. Changes of EEG spectra and coherence following performance in a cognitive task in Alzheimer's disease. International Journal of Psychophysiology 2007;65: Balázs Czigler, Dóra Csikós, Zoltán Hidasi, Zsófia Anna Gaál, Éva Csibri, Éva Kiss, Pál Salacz, Márk Molnár. Quantitative EEG in early Alzheimer s patients power spectrum and complexity features, International Journal of Psychophysiology. 2008; doi: /j.ijpsycho Publications not related to the thesis: Csibri É, Hidasi Z, Rajna P. Dementia diagnózis és terápia. Családorvosi Fórum 2002;4: Csibri É, Hidasi Z, Salacz P, Rajna P. Alzheimer kór; jelen és jövő. Családorvosi Fórum. 2002;9:

10 Salacz Pál, Hidasi Zoltán, Jekkel Éva, Rásonyi Tamás, Csukly Gábor, Csibri Éva. Enyhe kognitív zavarok. Orvostovábbképző szemle 2006;Különszám: Hidasi Zoltán. Droghasználat mellett kialakuló pszichózis. Addictologia Hungarica. 2005;4: Hidasi Z: Pszichiátria gyakorlatok fogorvostan-hallgatóknak. Egyetemi jegyzet. Semmelweis Kiadó, Budapest, Abstracts related to the thesis: Rajna P, Csibri É, Szelenberger W, Hidasi Z: Cognitive EEG test applicable also in dementia. Clin Neurosci/Ideggyógyászati Szemle. 2003;56(9-10): Hidasi Z, Csibri É, Salacz P, Szuromi B, Jekkel É, Rajna P Dynamic EEG protocol in Alzheimer s disease. Clin Neurosci/Ideggyógyászati Szemle.2004;57(9-10): Hidasi Z, Czigler B, Csibri É, Molnár M. Changes in EEG coherence after cognitive task in Alzheimer s disease. Clin Neurosci/Ideggyógyászati Szemle. 2005;58(9-10):

Source localisation in the clinical practice: spontaneous EEG examinations with LORETA. Ph.D. thesis. Márton Tamás Tóth M.D.

Source localisation in the clinical practice: spontaneous EEG examinations with LORETA Ph.D. thesis Márton Tamás Tóth M.D. Department of Neurology, University of Pécs Leader of project:: Prof. István Kondákor,