Alpha frequency (AF) was one of the first measures of the EEG that has been investigated in the search for the neurobiological basis of intelligence. Many of the early studies that visually analysed the EEG traces, calculated the AF by counting the number of alpha waves in small samples of the EEG trace and calculating the mean AF. Once again, the results of these early studies were mixed.
Knott, Friedman, & Bardsley (1942) reported finding a positive relationship (r = +0.5) between intelligence and alpha frequency while eyes closed in eight year old children, but no relationship in twelve year old children. Ellingson & Lathrop (1973) reported finding a positive, but not significant, relationship in a small sample of psychiatric patients. Although they found no evidence of a relationship in a sample of Down's syndrome patients (aged 13-42 years; Ellingson & Lathrop, 1973). Shagass (1946) also reported finding no relationship between the occipital alpha frequency when resting with eyes closed and scores on a group ability test in a large sample of aircrew candidates. In contrast, Mundy-Castle (1958) found a series of moderate strength positive correlations between mean alpha frequency when relaxed with eyes closed and the general IQ, verbal IQ, and practical IQ scales on the South African version of the Wechsler-Bellevue Intelligence Scale in a sample of adults.
More recent studies have used computers to analyse the EEG activity. These later studies used a slightly different measure of alpha frequency known as the peak AF, which can be defined as the peak frequency of the alpha rhythm, or the frequency in the traditional alpha rhythm range (i.e. 8-13 Hz) that shows the largest power estimate. (Klimesch, 1999). AF has the advantage of being not as susceptible to the influence of extracerebral factors such as skull thickness or conductance as the alpha amplitude and the alpha power measures of EEG, and therefore its variation seems to result directly from the variation in brain function (Anokhin & Vogel, 1996).
Gasser, Von Lucadou-Muiller, Verleger and Bacher (1983) investigated the relationship between intelligence scores and EEG parameters in a group of normal and a group of mildly mentally retarded 10-13 year old children. They reported a low to moderate strength positive correlation between IQ scores and the peak alpha frequency over the parietal and occipital areas for the mentally retarded children, but not the normal children.
Anokhin and Vogel (1996) have also reported that higher IQ scores were associated with an increased AF, but this time in a sample of normal, healthy adults. They hypothesised that the pattern of the correlations they found implied that non-verbal inductive reasoning abilities as assessed by Ravens Standard Progressive Matrices (SPM) may be related to neurophysiological properties of the frontal areas of the brain, whereas, the significant correlations between AF and the verbal tasks showed a more diffuse topographical distribution. Neubauer, Sange, and Pfurtscheller (1999) also found that higher IQ scores were associated with a higher individual peak alpha frequency, although the correlations did not reach significance.
Studies have also investigated the relationship between intelligence and frequency measures in other EEG bands, both while relaxing with eyes closed or open, or while performing a cognitive task. For example, Giannitrapani (1969) investigated the average EEG frequency in a sample of normal, healthy adults while resting with their eyes closed and while performing mental arithmetic. They reported finding interactions between the average EEG frequency, IQ group and hemisphere, with the average EEG frequency in the left frontal area greater than in the right frontal area for both the average and high IQ groups. In the parietal area, the high IQ showed greater average EEG frequency in the left, while the average group displayed greater average EEG frequency in the right. In the occipital area, the opposite was found with the high IQ group showing greater average EEG frequency in the right occipital area, and the average IQ group showing greater average EEG frequency in the left occipital area. Giannitrapani also reported finding a moderate to strong positive correlation between IQ and the average EEG frequency during mental arithmetic (a positive relationship was also seen during the resting condition, but it was not significant). The study also found that participants with a higher IQ tended to show the smallest difference between the average EEG frequency during the resting and thinking states. Fischer, Hunt and Randhawa (1982) investigated EEG parameters while resting in groups of children. They also found that higher scores on reasoning and mathematical ability tests were associated with relatively a higher resting EEG frequency in the left hemisphere than in the right hemisphere, but only in a sample of academically talented children, not in the academically handicapped group. Fischer et al. also investigated the ratio of the average alpha frequency to the average overall frequency. While they found no relationship between the alpha ratio measure and performance on various cognitive tests for the academically talented group, there were significant correlations in the academically handicapped group. The findings indicated that for the academically handicapped group, a decrease in the average alpha frequency in relation to the average overall frequency was associated with better scores on a reasoning test and lower scores on the mathematics and reading tests.
Marosi et al. (1999) took a different approach and investigated the mean frequency in four EEG bands, delta, theta, alpha and beta, while the subjects were resting with their eyes open. The strength and direction of the relationships depended on the location the EEG was recorded from, the EEG bandwidth, and the scale on the Wechsler Adult Intelligence Scale (WAIS). They found that generally, as IQ scores increased, the mean delta and theta frequencies decreased and the mean alpha and beta frequencies increased. Marosi et al. further concluded that broad band measurements are not an adequate tool in the study of certain abilities as broad bands dilute the effect of IQ when it occurs in a narrower frequency range.
At present, only a hypothetical explanation of the AF-intelligence relationship can be proposed based on the possible role of the alpha rhythm for information processing in cortical networks (Klimesch, Schimke & Pfurtscheller, 1993; Lebdev, 1990) and on individual differences in cortical arousal level (Golubeva, 1980; Robinson, 1993). Alpha rhythm emerges as a result of synchronous oscillations of synaptic potentials in large populations of neurons (primarily pyramidal cells) spread throughout the cortex. Although the exact mechanisms of alpha rhythm generation and its functional meaning are not understood completely so far, there is increasing evidence that synchronized oscillatory activity in the cerebral cortex is essential for spatiotemporal coordination and integration of activity of anatomically distributed but functionally related neural elements. Recent experimental and simulation studies of information processing in neuronal networks suggest that synchronized oscillatory activity in cell assemblies plays a key role in encoding, storage, and retrieval of information in the brain. Whereas information seems to be encoded by the temporal sequence of action potentials, synchronized periodic fluctuations of membrane excitability enable temporally and spatially structured co-activation of cells in an assembly (Birmbaumer, Elbert, Canavan & Rockstrih, 1990; Buzasaki & Chrobak, 1995; Lidman & Idiart, 1995). As a consequence, the duration of the cycle of the dominant cerebral rhythm may limit the capacity for storage, transfer, and retrieval of info in individual brains.
There are also some indications that AF is related to the level of cortical arousal in both state and trait aspect. AF increases with mental activity compared to rest. Increasing cognitive task difficulty leads to right hemispheric as well as bilateral alpha acceleration (Earle, 1988); a decrease in individual AF is always related to a drop in performance on memory tasks (Klimesch, Schimke & Pfurtscheller,
1993). Resting AF is higher in persons with a higher level of tonic cortical arousal regarded as a stable individual train and assessed using either EEG or EP measures. Moreover, it correlates positively with indices of mental activity level, academic performance in high school students, as well as performance on some memory tasks (Golubeva, 1980). It may also be hypothesised that individuals with a higher level of cortical arousal would also show a better performance on intelligence tests.
Regarding hypothetical neuroanatomical features underlying stable individual differences in AF, the degree of myelination could play an important role. AR results from cyclic excitations in cortico-cortical and thalamo-cortical circuits involving certain numbers of interneurons. It can be argued that the duration of a cycle would be shorter with greater axonal and dendritic conduction velocity (given the same or a similar number of interneurons) and, hence, the freq of the resulting rhythm would be higher. R. Miller (1994) provided evidence that ax-onal conduction delay in cortico-cortical connections, rather than synaptic delay, is the major factor limiting EEG propagation velocity. In turn, conduction velocity in cortico-cortical connections is mainly determined by the degree of axonal myelination. This interpretation of the AF-intelligence relationship also seems to be consistent with the brain myelination hypothesis of intelligence (E.M. Miller,
The findings of several experiments suggest that alpha frequency is an indicator of the speed of cognition and memory performance in particular. Early findings reported by Surwillo (1961, 1963a, 1963b, 1964a, 2964b, 1971) indicate that alpha frequency is significantly correlated with the speed of information processing as measured by reaction times (RT). Subjects with high alpha frequency show fast reaction times (RTs), whereas slow subjects have low alpha frequency (see Klimasch et al, 1996). These findings are in good agreement with the results from a variety of experiments from the laboratory of Klimasch which revealed that the alpha frequency of good memory performers is about 1 Hz higher than that of age-matched samples of bad performers (Klimesch, 1996; Klimesch, 1997; Klimesch et al, 1990a; Klimesch et al, 1990b; Klimesch et al, 1993a, Klimesch et al, 1993b). Because good performers are faster in retrieving information from memory than bad performers (Klimesch, 1994), these data indicate that alpha frequency is related to the speed of information processing or RT. These results also suggest that alpha frequency should be related to individual differences in intelligence which is an assertion that is supported by the data in this area of research (Anokhin & Vogel, 1996). All of these findings are based on inter-individual differences in alpha frequency. In, contrast, intra-individual differences or task related shifts in alpha frequency appear not to be related to the speed of information processing (Klimesch et al, 1996) because as asymmetric desynchronization in the broad alpha band
(favouring the lower or upper band) will lead to a shift in power and thus to a distorted estimate of alpha frequency.
In summarizing, the reported findings suggest that alpha frequency is an indicator of cognition and memory performance. This conclusion is also supported by the fact that alpha frequency increases from early childhood to adulthood and then decreases with age over the remaining life span in a similar way as brain volume and general cognitive performance (e.g. Bigler et al, 1995; Willerman etal, 1991)
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