Metabolic Imaging And Intelligence

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There have not been a large number of studies conducted investigating intelligence using metabolic imaging techniques such as positron emission tomography (PET) or functional magnetic resonance imaging (fMRI). Unfortunately the large sample required to encompass a broad range of intelligence quotients makes these techniques very costly, and the invasive nature of the PET technique makes it difficult to obtain a representative sample.

Metabolic imaging techniques such as PET and fMRI have the advantage of excellent spatial resolution. This enables researchers to investigate if the biological basis of intelligence manifests itself in the amount of cortical activation during a task or at rest, or the areas of the brain recruited to complete a task, or a combination of both.

Chase et al. (1984) used PET to investigate the resting metabolic rate of a sample of Alzheimer's Disease patients and a small selection of normal control subjects. They reported moderately strong positive correlations between the full score, verbal and performance IQ on the WAIS-R and cortical glucose metabolic rate (GMR). It was also reported that the verbal subtests correlated more highly with glucose use in the left parasylvian area, while performance subtests correlated more strongly with glucose use in the right posterior parietal area.

In contrast to Chase et al. (1984) study, the majority of the studies investigating the metabolic correlates of intelligence have investigated the topography of changes in metabolic activity during an intelligence, reasoning or ability tests. Generally, these studies have reported a negative relationship between performance and overall cortical GMR during the Raven's Advanced Progressive Matrices (APM) (Haier et al., 1988), a visuo-spatial motor task (the tetris game) (Haier et al., 1992a; 1992b), and a verbal fluency test (Parks et al., 1988).

Haier et al. (1988) reported a moderate to strong negative correlation between performance on the APM and the absolute cortical GMR, suggesting that those subjects who scored lower on the APM required more cortical activity to perform the task than those who scored higher on the APM. Haier et al. (1988) hypothesised that this may have been because the lower IQ subjects use inefficient neural circuitry to solve the problem, either because they do not have the correct circuits or they cannot or do not access the correct circuits. In contrast, those subjects who perform better on the APM can access the most efficient circuits or my have more efficient circuits generally, and therefore use less energy (see Figure 5).

A similar results was obtained by Parks et al. (1988) during a PET study of verbal fluency. Parks et al. reported a moderate strength negative relationship between performance on a verbal fluency test and overall cortical GMR in the frontal, temporal and parietal regions during the verbal fluency task. In a similar vein to Haier et al. (1988), they hypothesised that it was possible that those who performed well on the verbal fluency task used more efficient strategies than those who found the verbal fluency task more difficult. Therefore, those who found the task difficult had to work harder and therefore showed greater activation. These hypotheses gained further support from a series of studies by Haier et al. (1992a, 1992b), who reported that the cortical GMR during a visuo-spatial motor task (the tetris game) decreased after practice. The greatest decrease in cortical GMR was seen in the subjects who improved the most after practice on the task (Haier et al, 1992) and those with the highest scores on the Ravens APM (Haier et al., 1992).

Larson et al. (1995) pointed out that in standard intelligence tests everyone receives the same items in the same order. Typically, some low-aptitude participants will experience difficulty with most of the items, whereas many of the high-aptitude participants will excel. From this, it is quite logical to assume that low-aptitude participants may be required to expend more effort (p269). Therefore, Larson et al. (1995) conducted a study where the cognitive task (the backward digit span) was adapted for each subject so as to have the subject performing at the 90% (easy) or 75% (hard) accuracy. It was proposed that this would remove the confounding factor of the low aptitude subject expending more mental effort than the high aptitude subject. In contrast to the earlier studies, Larson et al. reported that subjects

Figure 5. Cortical GMR using Positron Emission Tomography during the Raven Advanced Progressive Matrices (APM). Glucose uptake in a low APM (left) and high APM (right) participants. From Haier et al. (1998).

with a higher APM score tended to show higher cortical GMR during the backward digit span task than the subjects with the lower APM scores. It was also reported that for the average APM group, cortical GMR decreased during the more difficult task, while the high APM group showed an increased cortical GMR during the harder task. Larson et al. suggested that the differences in cortical GMR during the harder task between the two groups may be the result of group differences in the problem solving strategies utilised.

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