دانلود مقاله ISI انگلیسی شماره 33337
عنوان فارسی مقاله

نوسانات آلفا به عنوان یک ارتباط اضطراب خصلتی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
33337 2004 14 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Alpha oscillations as a correlate of trait anxiety
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Journal of Psychophysiology, Volume 53, Issue 2, July 2004, Pages 147–160

کلمات کلیدی
نوار مغزی - نوسانات مغزی - شخصیت - اضطراب - افسردگی -
پیش نمایش مقاله
پیش نمایش مقاله نوسانات آلفا به عنوان یک ارتباط اضطراب خصلتی

چکیده انگلیسی

The associations among psychometric measures of anxiety and depression and individually adjusted electroencephalogram (EEG) spectral power measures registered in resting condition and during experimental settings were investigated in 30 males aged 18–25 years. During all stages of registration, Taylor Manifest Anxiety and Spielberger state anxiety (SA) and trait anxiety (TA) scores were positively related to alpha and negatively to delta relative power with these relations being independent of cortical site. Within-subject estimate of the strength of reciprocal relationship between alpha and delta oscillations (alpha–delta anticorrelation, or ADA) was positively related to trait anxiety and depression. Three minutes after an alarming event (unexpected loud sound), a further increase of alpha power was observed. In low-anxiety subjects, this increase was mostly associated with fast alpha (alpha 3), whereas in high-anxiety ones, it was mainly linked to slow alpha (alpha 2). SA mediated relationship between TA and EEG power, while ADA and alpha band reactivity showed trait-like features being associated with TA even after accounting for SA. These findings are interpreted as an indication of higher vigilance and higher reactivity of alpha system in anxious individuals.

مقدمه انگلیسی

Basar (1998), in his fundamental book on brain oscillations, points out that a great change is taking place in neuroscience due to the fact that brain scientists have recognized the importance of oscillatory phenomena and the functional electroencephalogram (EEG). A particularly important landmark in this book is the emphasis given to the alphas (i.e., distributed oscillatory processes in the 10-Hz frequency range). This great change has been fostered by development of new methodological approaches including “methods of thoughts” (Basar, 1998). Brain oscillations recorded in a form of spontaneous or evoked EEG could be considered as a kind of message carrying important information about intrinsic modes of brain activity. Given potential importance of this message, usage of EEG in psychology research seems inevitable. Knyazev and Slobodskaya (2003) proposed an evolutionary-based interpretation of brain oscillations relevant for research of EEG correlates of personality. Using as a starting point the concept of triune brain introduced by MacLean (1985), Knyazev and Slobodskaya (2003) suggested that delta, theta, and alpha oscillations reflect activities of three hierarchical philogenetic brain systems. Delta oscillations are linked with the most ancient system, which was dominant in reptilian brain. Theta oscillations dominate in lower mammals. Alpha oscillations are manifestations of activities of the newest system, which dominates in adult humans. The three hierarchical phylogenetic systems fulfill parallel processing, and their contribution to resulting behavior could differ in different individuals. The strength of reciprocal relationships between these systems and the relative prevalence of some oscillations over others relate to stable behavioral patterns relevant to personality and psychopathology. According to MacLean's concept, the reptilian brain consists of the brainstem, cerebellum, and basal ganglia. Superimposed on this brain in the course of evolution is the brain of lower mammals, which added the limbic system. Finally, the third brain appeared in advanced mammals and primates in the form of added neocortex. It is important to emphasize that we do not equate exactly the three oscillatory systems with anatomical structures included in the three brains of the MacLean model; we just borrow his evolutionary idea. It should be kept in mind that the MacLean model is a theoretical construct, whereas the three oscillatory systems are empirical entities. So we could just look for existing data about evolution and distribution of these systems. Comparative studies by Bullock indicate that the most striking evolutionary puzzle is a general consistent difference in the power spectrum of EEG between all vertebrates and all invertebrates. This difference concerns the synchrony of slow waves (<50 Hz), which is dramatically higher in vertebrates. Invertebrates have much more obvious unit spiking than vertebrates, but much less relative amplitude of slow waves (Bullock, 1993). Among vertebrates, the degree of synchronization also increases during evolution. There is an evidence of less synchrony or more rapid coherence decline with distance in reptiles, amphibians, and fish than in mammals (Bullock, 1997). Beyond this, the power spectra look alike in all the vertebrates, falling quite steeply on each side of a maximum around 5–15 Hz (Bullock, 1993). Oscillations of delta, theta, and alpha frequencies could be found in each vertebrate (Basar, 1998). But there is an important distinction between reptiles, lower mammals, and humans in what frequency dominates in the scalp EEG. Alpha is the dominant frequency in adult humans, while theta dominates in the EEG of lower mammals (Klimesch, 1999) and delta in the reptilian EEG Gaztelu et al., 1991 and Gonzalez et al., 1999. That means that all three oscillatory systems were acquired early in the evolution of vertebrates, but they further developed with different rates. Development of delta system peaks in reptiles, theta in mammals, and alpha in primates. The three oscillatory systems do not have to exactly match anatomically the three brains of the MacLean model. They might be selectively distributed over the entire brain Basar, 1998 and Basar, 1999, although the main populations of neurons representing these systems must be located within respective brains. Following MacLean, one might speculate about physiological and behavioral correlates of these systems. The most ancient delta system deals with internally driven behavior oriented to acquisition of biologically important goals, such as survival, physical maintenance, dominance, and mating. Among environmental signals, this system mostly recognizes those relevant to biologically important motives or current goals. Interestingly, randomly distributed splashes of delta activity have been repeatedly registered during presentation of target stimuli in P300 experimental paradigm (Basar, 1998). Increase in delta power might be expected in states of increased biological motivation (e.g., sexual). Indeed, Schutter and van Honk (in press) have recently shown that in healthy male volunteers, administration of testosterone, which presumably enhances sexual motivation, significantly increases the delta power. In children, prevalence of delta oscillations has been shown to relate to parent, teacher, and self-ratings of conduct disorder Knyazev et al., 2002b and Knyazev et al., 2003, which is also in line with the above reasoning. According to MacLean, the reptilian brain is active, even in deep sleep, since it controls autonomic functions, such as breathing and heartbeat. That explains why delta is the most salient rhythm during the slow-wave sleep. Theta system operates in close conjunction with the delta system, but it is linked with more flexible behavior regulation, which implies the matching of internal drives with acquired during lifetime experience. Alpha system is engaged in perception and recognition of environmental patterns. Increase of alpha activity during sensory stimulation is linked to specific sensory cortices (Basar, 1998). Both theta and alpha oscillations have been associated with memory processes, but Klimesch (1999) emphasizes crucial distinction between the two in what kind of memory they are linked with. While theta is associated with contextual memory, alpha is engaged in semantic memory processes. These two kinds of memory differ not only in temporal characteristics but also mostly in their content. Contextual memory stores sensual images. All mammals including humans share this kind of memory. Semantic memory is a store of knowledge, which is enormously developed in human beings. Prevalence of alpha oscillations and enhanced anticorrelation between alpha and delta bands has been shown to relate to behavioral inhibition (BI) and trait anxiety Knyazev et al., 2002a and Knyazev and Slobodskaya, 2003. Why is alpha activity enhanced in anxious individuals? Gray and other researchers of behavioral inhibition have recognized long ago that activity of the behavioral inhibition system (BIS) is linked with permanent scanning of environment in search of potentially threatening factors. Particularly, novelty is also a BIS-relevant stimulus as it signifies a potential threat (Gray, 1987). It is clear then that in anxious individuals, a system dealing with perception and recognition of environmental patterns should be more prepared for information processing especially in a new or “strange” environment, such as psychophysiological laboratory. One may argue that alpha power is in opposite relation to the activity level; therefore, if the alpha system is expected to be more active in anxious individuals, that would show itself in less alpha power. Such train of thought made Eysenck and Eysenck (1985) predict that extraverts should have more alpha power, since their theory posits that extraverts are less aroused than introverts. But most meta-analysts and reviewers of the relevant literature note equivocal relations between extraversion and EEG measures Gale, 1983, O'Gorman, 1984 and Bartussek, 1984. It is scarcely surprising because intraindividual regularities may not be automatically extended to interindividual differences. When different states of an individual are considered, alpha oscillations are indeed more synchronous during relatively inactive states, but that does not necessarily mean that an individual with more alpha power is more relaxed than an individual with less alpha power when both of them are measured in similar conditions. For example, it is well known that heart rate tends to increase with increasing muscle activity. Therefore, it could be taken as a measure of muscle activity. Now if we compare two Ss and find that one of them has slower heart rate than another one, we might conclude that the current level of muscle activity is higher in the latter subject. But actually, if we measure heart rate in a stayer even during moderate muscle activity (e.g., mounting a stairway), it might be found to be slower than the heart rate of an individual with low fitness measured in a resting state. Besides, it should be kept in mind that EEG power reflects the number of neurons that discharge synchronously (Klimesch, 1999). Therefore, different and sometimes conflicting reasons may lead to the same result (i.e., increased alpha power). As has been noted elsewhere Knyazev et al., in press a and Knyazev et al., in press b, the total power of a given rhythm in the site of registration depends on the mean power of oscillation and the number of currently active individual oscillators belonging to this oscillatory system, on a degree of their synchrony and on reciprocal relationships between oscillatory systems. Short-lasting changes of alpha power may mostly depend on a degree of alpha synchrony, whereas long-lasting changes may reflect a changing number of active alpha oscillators. In our opinion, interindividual differences of alpha power should be considered as differences in alpha system preparedness for information processing, with higher power being an indicator of higher vigilance. There are several recent reports of alpha synchronization in tasks associated with the maintenance of attention during anticipation of visual events (e.g., Fernandez et al., 1998 and Orekhova et al., 2001). There is another important question. Personality characteristics such as trait anxiety are stable over time and should have some structural basis. Brain oscillatory activity reflected in EEG is very labile and strongly depends on an individual's state. How can these two be compared? Yet, test–retest reliability of resting EEG has been shown to be comparable with that of personality questionnaires (Kondacs and Szabo, 1999), thus demonstrating trait-like properties of the labile EEG signal. We consider that possible correlations between the two domains should reflect correlation between the individuals' traits and states. For example, in the same environment, an anxious individual should probably show more state anxiety. Therefore, we suggest that the relationship between EEG power and trait anxiety is mediated by state anxiety. The present study aims to replicate previous findings and to overcome some their limitations. The adult sample of studies by Knyazev et al., 2002a and Knyazev et al., 2003 and Knyazev and Slobodskaya (2003) was mainly females. EEG recordings were made only in resting conditions and from a limited number (six) of head sites. In the present study, we aimed to test whether association between the personality trait of behavioral inhibition and EEG found previously in resting females could be reproduced in males in different stages of a psychophysiological experiment, particularly under condition of unexpected arousing stimulus (alarm). We also sought to investigate the cortical specificity of EEG–personality relationship and to test whether the relationship between trait anxiety and EEG is mediated by state anxiety.

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