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Meditation is a specific consciousness state in which deep relaxation and increased internalized attention coexist. There have been various neurophysiological studies on meditation. However, the personal predispositions/traits that characterize the properties of meditation have not been adequately studied. We analyzed changes in neurophysiological parameters [EEG coherence and autonomic nervous activity using heart rate variability (HRV) as an index] during Zen meditation, and evaluated the results in association with trait anxiety (assessed by Spielberger’s State-Trait Anxiety Inventory) in 22 healthy adults who had not previously practiced any form of meditation. During meditation, in terms of mean values in all subjects, an increase in slow alpha interhemispheric EEG coherence in the frontal region, an increase in high-frequency (HF) power (as a parasympathetic index of HRV), and a decrease in the ratio of low-frequency to HF power (as a sympathetic index of HRV) were observed. Further evaluation of these changes in individuals showed a negative correlation between the percent change (with the control condition as the baseline) in slow alpha interhemispheric coherence reflecting internalized attention and the percent change in HF reflecting relaxation. The trait anxiety score was negatively correlated with the percent change in slow alpha interhemispheric coherence in the frontal region and was positively correlated with the percent change in HF. These results suggest that lower trait anxiety more readily induces meditation with a predominance of internalized attention, while higher trait anxiety more readily induces meditation with a predominance of relaxation.

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Multifractal analysis based on generalized concepts of fractals has been applied to evaluate biological tissues composed of complex structures. This type of analysis can provide a precise quantitative description of a broad range of heterogeneous phenomena. Previously, we applied multifractal analysis to describe heterogeneity in white matter signal fluctuation on T2-weighted MR images as a new method of texture analysis and established Da as the most suitable index for evaluating white matter structural complexity (Takahashi et al. J. Neurol. Sci., 2004; 225: 33À37). Considerable evidence suggests that pathophysiological processes occurring in deep white matter regions may be partly responsible for cognitive deterioration and dementia in elderly subjects. We carried out a multifractal analysis in a group of 36 healthy elderly subjects who showed no evidence of atherosclerotic risk factors to examine the microstructural changes of the deep white matter on T2-weighted MR images. We also performed conventional texture analysis, i.e., determined the standard deviation of signal intensity divided by mean signal intensity (SD/MSI) for comparison with multifractal analysis. Next, we examined the association between the findings of these two types of texture analysis and the ultrasonographically measured intima -media thickness (IMT) of the carotid arteries, a reliable indicator of early carotid atherosclerosis. The severity of carotid IMT was positively associated with Da in the deep white matter region. In addition, this association remained significant after excluding 12 subjects with visually detectable deep white matter hyperintensities on MR images. However, there was no significant association between the severity of carotid IMTand SD/MSI. These results indicate the potential usefulness of applying multifractal analysis to conventional MR images as a new approach to detect the microstructural changes of apparently normal white matter during the early stages of atherosclerosis. D

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