الفهرس 
• Physiological Foundations of Quantitative EEG AnalysisOnly 14 pages are availabe for public view
 |  | from | 174 |  |  |
| | | from | 174 | | |
Abstract
In the diseased brain normal mechanisms of EEG rhythms may be impaired and the rhythms may (1) become slower in frequency (so-called EEG slowing), (2) may appear in unusual places (e.g., alpha rhythms at temporal areas), (3) may become higher in amplitude (the phenomenon called hypersynchronization) and in more synchronicity with other areas (the phenomenon called hypercoherence), (4) in some severe cases (characterized by disconnection of cortical areas from subcortical structures due to stroke, trauma, or tumor) a separate slow rhythm in delta frequency (1–3 Hz) may appear In some cases normal synchronization mechanisms may be enhanced and spike or spike/slow wave patterns appear indicating a so-called focus in the human brain which in some situations may cause a seizurer. Normative databases help an electroencephalographer to recognize those abnormal patterns and to assess the level of statistical significance of the abnormality. 3D location of generators of EEG rhythms can be assessed by different techniques such as dipole approximation and low resolution electromagnetic tomography (LORETA). The frequency range below 0.1 Hz corresponds to so-called deco-second (with periods of tens of seconds) oscillations. Infra-slow potentials appear to be associated with slow metabolic processes. When these processes are measured by recording local blood flow and/or by recording concentration of local oxygen the most striking feature of dynamics of these two metabolic parameters is the appearance of infra-slow quasi-periodic fluctuations. Nowadays infra-slow oscillations are detected by fMRI studies and are associated with oscillations in local blood flow. Slow waves are in the range of 0.1–1 Hz. Slow waves are present in EEG during all states from the deep sleep to the state of focused attention. They dominate EEG recordings in deep sleep giving it the name “slow wave sleep. ” Slow oscillations are characterized by rhythmic cycles of cortical membrane depolarization (so-called UP states) following by hyperpolarization (so-called DOWN) states. UP states are associated with increase of discharge rate of a group of cortical neurons while DOWN states are associated with decrease of neuronal spiking. Delta oscillations of sleep (within the range 1–4 Hz) are generated by interplay of two ion currents (and consequently two types of ion channels) of the thalamic neurons projecting to the corresponding cortical areas. The interplay of the excitatory and inhibitory ion currents in the thalamic membrane is responsible for generation of so-called Ca ++ spikes – rebound depolarization in response to prolong hyperpolarization.
During relaxed wakefulness in eyes closed condition, the human brain exhibits several types of distinct rhythmic electrical activity in the alpha frequency band (8–13 Hz). At least three main types of alpha rhythmicity are separated. They are: (1) the posterior alpha rhythms, (2) the Rolandic or mu-rhythm, (3) midtemporal, these oscillations are driven by rhythmic
activity from thalamic nuclei: each rhythm having an origin in a corresponding thalamic nucleus. The frequency of the occipital alpha rhythm slightly changes with age reaching its maximum at the age of 15–20 years old, posterior alpha rhythms are suppressed in response to visual stimulation while Rolandic rhythms respond by desynchronization (decrease of amplitude) to actual or imaginary actions. Alpha rhythms must be separated and distinguished from sleep spindles. These two distinct categories of rhythms are observed in different states (sleep versus wakefulness), have different spatial distribution (sleep spindles have broad central distribution while alpha rhythms are located near the primary sensory cortical areas), different frequencies (sleep spindles are about 13–14 Hz in contrast to alpha frequencies that vary between 8 and 13 Hz). As far as alpha rhythms concerns, the mechanisms of their generation are still poorly understood. The power of alpha activity is inversely correlated to metabolic function of the corresponding cortical area giving rise to a functional explanation of alpha rhythms as idling rhythms of the cortex.
There are several types of rhythms in the beta band. To reflect this heterogeneity, the beta band is conventionally divided into the following sub-bands: low beta – from 13 to 20 Hz, high beta – from 21 to 30 Hz, gamma activity – from 31Hz and higher. Sometimes, a special type “40 Hz activity ” is separated in addition to the listed one. Networks of inhibitory interneurons have shown both theoretically and experimentally to be crucially involved in generating beta rhythms. The involvement of inhibitory neurons is supported by the sensitivity of beta rhythms to GABAergic agonists – pharmaceuticals that mimic the action of GABA, GABA agonists such as barbiturates and benzodiazepines increase the power of high frequency bands. In the normal brain, beta activity was shown to be positively correlated with metabolic activity in the cortical area underlying the recording electrode.
Theta rhythm is the “rhythm with a frequency of 4 to under 8 Hz. ” Japanese scientists were the first who induced the EEG theta activity by administering a mental task consisting of continuous arithmetic addition. This theta activity was a train of rhythmic waves at a frequency of 6–7 Hz with maximum amplitude around the frontal midline and was labeled as the frontal midline theta rhythm. The frontal midline theta rhythm is often associated with the hippocampal theta rhythms. It was hypothesized that the hippocampal theta oscillations are involved in memory encoding and retrieval. Recordings in human hippocampus are available only in rare cases of stereotactic operations in epileptic patients with depth electrodes implanted for diagnostic purposes. The frontal midline theta shows individual differences and is related to certain personality traits. The frontal midline theta correlates with changes in anxiety levels induced by anti-anxiety drugs.
In the normal brain excitation and inhibition within the cortex are well balanced, If the balance is disrupted so that excitation exceeds inhibition the cortex starts producing abnormal patterns called paroxysms. These abnormalities in majority of cases can be recorded from the scalp by conventional EEG in a form of specific electrographic patterns. There are several types of paroxysmal events, the most common of them are spikes, sharp waves, spike-slow wave complexes. Using modern techniques of electromagnetic tomography, such as LORETA or dipole approximations, focuses can be localized within the cortex with a good precision.
Methods of analysis of back ground EEG can be classified according to anatomical locations, tools, mathematical programes.
1-Anatomical localization:
cA. anatomical locations
B. brodman’s areas
2- Tools
A.10–20 international system of electrode placement
B. electrodes
3- Mathematical programes
A. amplifiers
B. EEG digitizing
C. montages
D. fourier analysis (Linear Analysis of EEGs)
E. EEG mapping
F. filtering
G. bispectrum
H. coherence
I. event-related desynchronization
J. wavelet transformation
k. LORETA
In the context of application of QEEG in some neurological diseases, in persons with pure epileptic predisposition LORETA results showed that Statistically significant decrease of alpha activity in the medial and lateral parts of the cortex above the level of basal ganglia, being more widespread in the right hemisphere than in the left. However, maximal alpha difference was found in the left precuneus, (BA)7. An overall tendency for bilaterally increased delta and theta activity was found in the persons with epileptic predisposition as compared to the controls. Greatest delta differences were found bilaterally in the medial and basal prefrontal cortex (gyrus rectus, orbitofrontal gyrus, anterior-basal parts of the medial and middle frontal gyri, anterior cingulate, subcallosal gyrus). These areas correspond to BAs 10, 11, 34. Greatest theta differences were found in about the same parts of the frontal cortex and in the nearby basal temporal cortex (uncus, parahippocampal gyrus; BAs 20, 28, 36) and the anterior edge of the right insula, BA 13.
Analysis of background EEG activity in patients with juvenile myoclonic epilepsy revealed that, in the UM group, AP delta abnormal Z scores were identified in frontotemporal and occipital leads. In AP alpha and beta bands an increase in Z scores was encountered in frontoparietal leads. In addition, the AP theta Z scores were below −1.96 and distributed in all regions. In the M group, AP beta Z scores were above 1.96 in frontoparietal leads. The AP delta increased above 1.96 in frontotemporal and occipital leads in six patients of eleven. The AP alpha showed an abnormal decrease in Z scores.
Quantitative analysis of the background activity in BREC disclosed reduction of the power mainly in the alpha and beta ranges, a significant power increase was detected in the delta and theta ranges in almost all positions . These results did not change when patients with neurological abnormalities or on medication were excluded from the analysis. These lines of evidence have been interpreted as secondary to the presence of epileptiform discharges, leading even to a suggestion that medication should be tried in children with rolandic spikes, with or without seizures.
QEEG spectral analysis of PSE patients during IPS found to produce an enhancement of synchronization, mainly in the beta and gamma range, provided that the photic stimulation induces a PPR.
A statistically significant correlation between a quantitative EEG this finding supports the value of EEG as a bedside monitoring tool in ischemic stroke regardless of lesion localization. Furthermore, pdBSI indicated the presence of a recent ischemic lesion with a higher accuracy than the NIHSS score at admission and predicted definite stroke in patients with a NIHSS score of 0. pdBSI emerged as statistically significant parameters for definite stroke in LACS and in POCS over DTABR. In addition to pdBSI correlated significantly with infarct volume also has a Correlation with functional status as it is notived that DTABR & pdBSI were significantly correlated with the mRS score at month 6. In patients presenting with a lacunar syndrome, EEG (delta + theta)/(alpha + beta) ratio obtained in the subacute phase correlated with lesion volume and predicted definite stroke and unfavourable functional outcome one week after stroke. These finding may have an impact on stroke care. Further research is necessary to explore and confirm the additional predictive value of EEG parameters on clinically relevant outcome measures, especially neurological deterioration, spontaneous neurological improvement and death in POCS and LACS as was shown in ACS.
During the attentional processing, changes in the high EEG spectrum (beta-2 and gamma) in MS patients exhibit physiological alterations that are not normally detected by spontaneous EEG analysis. The different spectral pattern between pathological and controls groups could represent specific changes for the RRMS patients, indicative of compensatory mechanisms or cortical excitatory states representative of some phases during the RRMS course that are not present in the BMS group. Nearly half (42%) the RRMS patients showed a statistically significant increase of two or more standard deviations (SD) compared to the control mean value for the beta-2 and gamma bands ( p = 0.004). These alterations were localized to the anterior regions of the right hemisphere, and bilaterally to the posterior areas of the scalp. None of the BMS patients or control subjects had values outside the range of ± 2 SD. There were no significant correlations between these values and the other variables analysed (age, EDSS, years of evolution or behavioural performance).
The analysis of QEEGs revealed significantly lower values of alpha/slow wave power ratios and the mean frequency of waves from all derivations in the whole group of patients with AD compared with the whole group with SVD and with the control group. The mean frequencies of waves in all derivations were also analysed in AD subgroups. Significant differences were found among all the investigated subgroups. However the mean frequency parameter showed the lowest significance in AD groups with moderate and marked degree of dementia, it was somewhat higher in the group of patients with mild degree of dementia and had the highest value in the control group . In AD the alpha/slow wave power ratios were significantly lower in subgroups with moderate and marked dementia (AD II, AD III) compared with the subgroup with mild dementia (AD I). However there were no essential differences between subgroups with AD with moderate (AD II) and marked dementia (AD III). The most significant difference in alpha/ theta wave power ratios was found between AD I and the control group. Although there were significant differences between all groups and subgroups, except AD II and AD III subgroups, the most significant differences were noted for occipital derivations . What was the most important was to find QEEG parameters that could differentiate between the subgroups with AD and SVD with the same level of cognitive impairment. The mean frequency from temporal derivations T3, T4 (decreased in AD) was found to be a differentiating parameter between the subgroups with AD and those with SVD with mild cognitive impairment. Similar differences were detected when comparing subgroups with AD and SVD with moderate dementia (AD II and SVD II) , Alpha/delta wave power ratio (lower in AD) was found to be another differentiating parameter between subgroups with AD and those with SVD with a moderate degree of dementia (AD II and SVD II) , a significant correlation between the severity of dementia on the MMSE scale and all calculated QEEG parameters was established in the AD group and also in SVD . The alpha/slow wave power ratios and the mean frequencies from all and temporal derivations (T3, T4) decreased in parallel to the progress of dementia.
The hazard of developing dementia in PD was 13 times higher for those with low background rhythm frequency (lower than the grand median of 8.5 Hz) than for those with high background rhythm frequency (p < 0.001). Hazard ratios (HRs) were also significant for > median theta bandpower (HR = 3.0; p = 0.004) compared to below, and for certain neuropsychological measures. The HRs for delta, alpha, and beta bandpower as well as baseline demographic and clinical characteristics were not significant.
QEEG features that distinguished between HD subjects and healthy controls were examined in relation to illness severity, using UHDRS subscales, as well as to the number of CAG repeats in the HD cohort. HD subjects showed a global increase in delta power as compared to controls, even when examining unmedicated HD subjects only, or premanifest HD subjects. HD subjects also showed loss of the normal anterior-posterior (AP) gradient of relative alpha and delta power. Relative alpha AP gradient loss was associated with lower TFC and greater cognitive dysfunction. Relative delta AP gradient loss was associated with lower TFC, more severe motor symptoms, and greater number of CAG repeats. Overall, results suggest that QEEG power measures may capture perturbations of brain function that are related to functional status as well as to underlying genetic repeat expansion in HD. Pilot data in premanifest HD subjects are consistent with the hypothesis that brain functional abnormalities may be detectable even in premanifest gene carriers. Cross-sectional findings suggest that QEEG measures may be biomarkers of HD progression.
Unremitting fatigue and unrefreshing sleep, the waking manifestations of CFS, may be the consequence of impaired sleep homeostasis rather than a primary sleep disorder. In persons with CFS, delta power was diminished during slow wave sleep, but elevated during both stage 1 and REM. Alpha power was reduced during stage 2, slow wave, and REM sleep. Those with CFS also had significantly lower theta, gamma, and beta spectral power during stage 2, Slow Wave Sleep, and REM
To conclude, QEEG is a useful tool for diagnosis, differential diagnosis, prognosis and even for therapy (as in neurofeedback),it is not expensive and not invasive, however it is mandatory to imply this technique on much more number of patients and normal persons to can say we have a valid data