Another nonlinear measure, L1, was calculated for all subjects at all electrodes. The evolving time was selected using the 1/e spectral frequency, and was approximately 180 to 240 msec. The calculation of L1 depended on the time over which the trajectory was evaluated. After 200 propagation steps, the values converged in an interval of ±0.92% around the final value of L1.
The average L1 values and standard deviations for all subjects at all electrodes are summarized in Table 2. It shows that the average L1 values were higher for the normal control subjects than for the AD patients (F7, T3, Fp1, P3, F8, T4, T6, Fp2, C4, P4, P < 0.01; F3, C3, F4, P < 0.01), just as the D2s were. The L1s of the AD patients in channels O1 and O2 were not significantly different from those of the normal control subjects. The differences between the values of L1 in the F7, T3, Fp1, P3, F8, T4, T6, Fp2, C4, and P4 channels were very significant—approximately 1.0 to 1.8 U. The results for L1 were consistent with those for D2, except in the F7 and O2 channels. AD patients had significantly lower L1 values than VaD patients (P < 0.001) in all channels. The L1 values for the VaD patients were higher than for the normal control subjects (F3, F4, F8, O1, O2, P < 0.01).
Our results indicate that AD patients have significantly lower D2 and L1 values than VaD patients and the age-matched healthy control subjects over all regions except at a few electrodes. By contrast, VaD patients have relatively higher D2 and L1 values than AD patients. VaD patients have an uneven distribution of D2 values over the channels than other groups, whereas AD patients have uniformly lower values of D2 and L1, indicating that EEGs in AD patients are less complex than those in other groups.
The finding that VaD patients have relatively higher values of D2 and L1 than AD patients and even normal control subjects, and have an uneven distribution of D2 values, may be in accord with previous findings (Johannesson et al., 1979; Saletu et al., 1991). Saletu et al. (1991) demonstrated the uneven distribution of EEG abnormality in multi-infarct dementia by multichannel EEG mapping. In mild cerebrovascular dementia, the EEG was often normal or slightly abnormal (Johannesson et al., 1979).
An uneven distribution of D2 values in VaD compared with those in AD may be the result of uneven neuronal pathology in VaD. VaD is a heterogeneous syndrome with various subtypes, including multi-infarct dementia, strategic single infarct dementia, small-vessel disease with dementia, hypoperfusion, and hemorrhagic dementia (Roman et al., 1993). VaD may exhibit cortical and/or subcortical involvement (Weiner et al., 1991). Several lines of studies using topographic EEG analysis showed the VaD group had more asymmetric findings than the AD group (Ding, 1990). Diffuse abnormality of EEG was found to increase in AD (Erkinjuntti et al., 1988). Conversely, clinical studies have linked signs and symptoms of VaD to patchy and irregular brain damage. Radiologic studies showed that VaD patients had central brain atrophy more often than AD patients or normal control subjects, indicating multiple small infarcts in the thalamus and other basal brain structures (Cumming and Beson, 1992). Fenton (1986) showed that VaD patients had the lowest coherence between the different cortical areas, indicating asymmetry of functioning, whereas patients with non-VaD had the greatest EEG coherence between the centroparietal and temporal regions within each hemisphere. The current study suggested that the distributions of lesions were more scattered in VaD than in AD. Therefore, an uneven distribution in the nonlinear measures in VaD patients may, to some extent, result from underlying etiologic heterogeneity. FIGURE
Similar disturbances in electrical activity may be caused by a variety of disease processes involving either damage to nerve cells or potentially reversible disturbances in cell metabolism resulting from metabolic insults. Hence, electrical patterns associated with brain dysfunction cannot be used to predict the exact nature of the underlying disease process (Fenton, 1986). Our results from the nonlinear analysis of the EEG suggest that the estimation of nonlinear measures like D2 and L1 may be helpful in diagnosing AD and VaD more accurately. Pritchard et al. (1994) found that the addition of nonlinear EEG measures improved the classification accuracy of the subjects as either AD patients or control subjects.
Several limitations of these findings merit consideration. The sample size was small, and the severity of cognitive dysfunction was not strictly controlled. The results obtained seem to depend on the clinical degree of dementia studied. The stage of dementia must affect the ability of the EEG to distinguish one type of dementia from the other or from control subjects. The relationship of EEG alterations to the severity of dementia raises an issue concerning the strict psychometric or neuropsychological evaluation in the nonlinear analysis of EEG. Additionally, we cannot explain why the VaD patients had higher D2 and L1 values in some channels than normal subjects. Our results, however, encourage further investigation of the complexity of electrocortical responses in brains injured by dementia, although the current study is quite preliminary.
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