Neural efficiency, executive function and intelligence (g, IQ): An embarrasment of riches

I give up. I don't have the time, or maybe the neural efficiency, to read, digest, integrate, and summarize a wave of recent research articles dealing with the concept of neural efficiency (oscillations) and intelligence. That being said, I'm simply going to post the references and abstracts. Maybe an interested IQ's Corner blog reader would be interested in reading these articles and attempting to summarize (via a guest blog post)...something I had hoped to do.

When less is more and when more is more: The mediating roles of capacity and speed in brain-behavior efficiency (Bart Rypma and Vivek Prabhakaran). Intelligence 37 (2009) 207–22.

An enduring enterprise of experimental psychology has been to account for individual differences in human performance. Recent advances in neuroimaging have permitted testing of hypotheses regarding the neural bases of individual differences but this burgeoning literature has been characterized by inconsistent results. We argue that careful design and analysis of neuroimaging studies is required to separate individual differences in processing capacity from individual differences in processing speed to account for these differences in the literature. We utilized task designs which permitted separation of processing capacity influences on brainbehavior relationships from those related to processing speed. In one set of studies, participants performed verbal delayed-recognition tasks during blocked and event-related fMRI scanning. The results indicated that those participants with greater working memory (WM) capacity showed greater prefrontal cortical activity, strategically capitalized on the additional processing time available in the delay period, and evinced faster WM-retrieval rates than low-capacity participants. In another study, participants performed a digit-symbol substitution task (DSST) designed to minimize WM storage capacity requirements and maximize processing speed requirements during fMRI scanning. In some prefrontal cortical (PFC) brain regions, participants with faster processing speed showed less PFC activity than slower

Neuroanatomical correlates of intelligence (Eileen Luders, Katherine L. Narr, Paul M. Thompson and Arthur W. Toga). Intelligence 37 (2009) 156–163.

With the advancement of image acquisition and analysis methods in recent decades, unique opportunities have emerged to study the neuroanatomical correlates of intelligence. Traditional approaches examining global measures have been complemented by insights from more regional analyses based on pre-defined areas. Newer state-of-the-art approaches have further enhanced our ability to localize the presence of correlations between cerebral characteristics and intelligence with high anatomic precision. These in vivo assessments have confirmed mainly positive correlations, suggesting that optimally increased brain regions are associated with better cognitive performance. Findings further suggest that the models proposed to explain the anatomical substrates of intelligence should address contributions from not only (pre)frontal regions, but also widely distributed networks throughout the whole brain.

Exploring possible neural mechanisms of intelligence differences using processing speed and working memory tasks: An fMRI study (Gordon D. Waiter, Ian J. Deary, Roger T. Staff, Alison D. Murray, Helen C. Fox, John M. Starr and Lawrence J. Whalley). Intelligence 37 (2009) 199–206

To explore the possible neural foundations of individual differences in intelligence test scores, we examined the associations between Raven's Matrices scores and two tasks that were administered in a functional magnetic resonance imaging (fMRI) setting. The two tasks were an n-back working memory (N = 37) task and inspection time (N = 47). The subjects were members of the Aberdeen Birth Cohort 1936, aged in their mid–late 60s when tested for this study. Performance on both tasks was correlated significantly with scores on Raven's Matrices. In the inspection time task there were regions with significant correlations between the neural activity (BOLD response) and performance but not between BOLD response and scores on Raven's Matrices. In the working memory task there were no significant correlations between BOLD response and either performance or scores on Raven's Matrices. Moreover, there was almost no mediation of the Raven's Matrices versus n-back and inspection time scores correlations by the respective BOLD response. These findings partially replicate important aspects of a prominent report in this field [Gray, J.R., Chabris, C.F., & Braver, T.S. (2003). Neural mechanisms of general fluid intelligence. Nature Neuroscience, 6, 316–322.], but have also extended the those finding into both a unique population and a novel functional task.

Intelligence and neural efficiency: Measures of brain activation versus measures of functional connectivity in the brain (Aljoscha C. Neubauer and Andreas Fink). Intelligence 37 (2009) 223–229

The neural efficiency hypothesis of intelligence suggests a more efficient use of the cortex (or even the brain) in brighter as compared to less intelligent individuals. This has been shown in a series of studies employing different neurophysiological measurement methods and a broad range of different cognitive task demands. However, most of the studies dealing with the brain–IQ relationship used parameters of absolute or relative brain activation such as the eventrelated (de-)synchronization of EEG alpha activity, allowing for interpretations in terms of more or less brain activation when individuals are confronted with cognitively demanding tasks. In order to investigate the neural efficiency hypothesis more thoroughly, we also used measures that inform us about functional connectivity between different brain areas (or functional coupling, respectively) when engaged in cognitive task performance. Analyses reveal evidence that higher intelligence is associated with a lower brain activation (or a lower ERD, respectively) and a stronger phase locking between short-distant regions of the frontal cortex.

Gray matter and intelligence factors: Is there a neuro-g? (Richard J. Haier, Roberto Colom, David H. Schroeder, Christopher A. Condon, Cheuk Tang, Emily Eaves and Kevin Head). Intelligence 37 (2009) 136–144

Heterogeneous results among neuro-imaging studies using psychometric intelligence measures may result from the variety of tests used. The g-factor may provide a common metric across studies. Here we derived a g-factor from a battery of eight cognitive tests completed by 6929 young adults, 40 of whom also completed structural MRI scans. Regional gray matter (GM) was determined using voxel-based-morphometry (VBM) and correlated to g-scores. Results showed correlations distributed throughout the brain, but there was limited overlap with brain areas identified in a similar study that used a different battery of tests to derive g-scores. Comparable spatial scores (with g variance removed) also were derived from both batteries, and there was considerable overlap in brain areas where GM was correlated to the respective spatial scores. The results indicate that g-scores derived from different test batteries do not necessarily have equivalent neuro-anatomical substrates, suggesting that identifying a “neurog” will be difficult. The neuro-anatomical substrate of a spatial factor, however, appears more consistent and implicates a distributed network of brain areas that may be involved with spatial ability. Future imaging studies directed at identifying the neural basis of intelligence may benefit from usinga psychometric test battery chosen with specific criteria.

Intergenerational transmission of neuropsychological executive functioning ("Jennifer M. Jester, Joel T. Nigg, Leon I. Puttler, Jeffrey C. Long, Hiram E. Fitzgerald and Robert A. Zucker"). Brain and Cognition 70 (2009) 145–153

Relationships between parent and child executive functioning were examined, controlling for the critical potential confound of IQ, in a family study involving 434 children (130 girls and 304 boys) and 376 parents from 204 community recruited families at high risk for the development of substance use disorder. Structural equation modeling found evidence of separate executive functioning and intelligence (IQ) latent variables. Mother’s and father’s executive functioning were associated with child’s executive functioning (beta = 0.34 for father–child and 0.51 for mother–child), independently of parental IQ, which as expected was associated with child’s IQ (beta = 0.52 for father–child and 0.54 for mother–child). Familial correlations also showed a significant relationship of executive functioning between parents and offspring. These findings clarify that key elements of the executive functioning construct are reliably differentiable from IQ, and are transmitted in families. This work supports the utility of the construct of executive function in further study of the mechanisms and etiology of externalizing psychopathologies.

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