Timing Training in Female Soccer Players: Effects on Skilled Movement Performance and Brain Responses

Timing Training in Female Soccer Players: Effects on Skilled Movement Performance and Brain Responses. Frontiers in Human Neuroscience. Article link.

Marius Sommer, Charlotte K. Häger, Carl Johan Boraxbekk and Louise Rönnqvist

Abstract

Although trainers and athletes consider “good timing skills” critical for optimal sport
performance, little is known in regard to how sport-specific skills may benefit from timing training. Accordingly, this study investigated the effects of timing training on soccer skill performance and the associated changes in functional brain response in elite- and sub-elite female soccer players. Twenty-five players (mean age 19.5 years; active in the highest or second highest divisions in Sweden), were randomly assigned to either an experimental- or a control group. The experimental group (n = 12) was subjected to a 4-week program (12 sessions) of synchronized metronome training (SMT). We evaluated effects on accuracy and variability in a soccer cross-pass task. The associated brain response was captured by functional magnetic resonance imaging (fMRI) while watching videos with soccer-specific actions. SMT improved soccer cross-pass performance, with a significant increase in outcome accuracy, combined with a decrease in outcome variability. SMT further induced changes in the underlying brain response associated with observing a highly familiar soccer-specific action, denoted as decreased activation in the cerebellum post SMT. Finally, decreased cerebellar activation was associated with improved cross-pass performance and sensorimotor synchronization. These findings suggest a more efficient neural recruitment during action observation after SMT. To our knowledge, this is the first controlled study providing behavioral and neurophysiological evidence that timing training may positively influence soccer-skill, while strengthening the action-perception coupling via enhanced sensorimotor synchronization abilities, and thus influencing the underlying brain responses.

Conclusion

In summary, this is the first controlled study demonstrating that improved motor timing and multisensory integration, as an effect of SMT, also is associated with changes in functional brain response. The present study provides both behavioral and neurophysiological evidence that timing training positively influences soccer-skill, strengthens the action-perception coupling by means of enhanced sensorimotor synchronization abilities, and affect underlying brain responses. These findings are in accordance with the idea that SMT may result in increased brain communication efficiency and synchrony between brain regions (McGrew, 2013), which in the present study was evident by reduced activation within brain areas important for temporal planning, movement coordination and action recognition and understanding (cerebellum). Also, our results complement findings indicating that the cerebellum plays an important role in the action-perception coupling (Christensenetal.,2014),and confirm recent theories supporting a cognitive-perceptual role of the cerebellum (e.g., Roth et al., 2013).Probing the influence of timing training on the underlying brain activation during soccer specific action observation is an important approach as it provides a window into the brain plasticity associated with non-task specific (timing) training, and to the underlying brain activation of skilled performance. The present study suggests that the underlying brain activation during action observation, which is claimed to be important for action recognition and understanding (e.g., Rizzolatti and Craighero, 2004), may be influenced in other ways than through task-specific training (e.g., Calvo-Merino et al., 2005) or observational learning (e.g., Cross et al., 2013). Such knowledge of how SMT may alter brain activity within regions facilitating the action perception coupling is likely important for enhancing training techniques within sports, as well as for developing new rehabilitative techniques for many clinical populations.



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White matter matters: Changes in white matter tracts due to reading intervention

More research supporting “white matter matters”.




Rapid and widespread white matter plasticity during an intensive reading intervention

Nature Communications

Elizabeth Huber, Patrick M. Donnelly, Ariel Rokem & Jason D. Yeatman

ABSTRACT

White matter tissue properties are known to correlate with performance across domains ranging from reading to math, to executive function. Here, we use a longitudinal intervention design to examine experience-dependent growth in reading skills and white matter in grade school-aged, struggling readers. Diffusion MRI data were collected at regular intervals during an 8-week, intensive reading intervention. These measurements reveal large-scale changes throughout a collection of white matter tracts, in concert with growth in reading skill. Additionally, we identify tracts whose properties predict reading skill but remain fixed throughout the intervention, suggesting that some anatomical properties stably predict the ease with which a child learns to read, while others dynamically reflect the effects of experience. These results underscore the importance of considering recent experience when interpreting cross-sectional anatomy–behavior correlations. Widespread changes throughout the white matter may be a hallmark of rapid plasticity associated with an intensive learning experience.

Very interesting. The arcuate fasciculus tracts have also been implicated in higher order thinking (Gf) such as in the P-FIT model of intelligence. Also see white paper that implicates the AF in temporal processing “brain clock” timing interventions




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Higher intelligence related to more efficiently organized brains-bigger/larger/more not always better

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Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Article link.

Erhan Genç, Christoph Fraenz, Caroline Schlüter, Patrick Friedrich, Rüdiger Hossiep, Manuel C. Voelkle, Josef M. Ling, Onur Güntürkün, & Rex E. Jung

Abstract

Previous research has demonstrated that individuals with higher intelligence are more likely to have larger gray matter volume in brain areas predominantly located in parieto-frontal regions. These findings were usually interpreted to mean that individuals with more cortical brain volume possess more neurons and thus exhibit more computational capacity during reasoning. In addition, neuroimaging studies have shown that intelligent individuals, despite their larger brains, tend to exhibit lower rates of brain activity during reasoning. However, the microstructural architecture underlying both observations remains unclear. By combining advanced multi-shell diffusion tensor imaging with a culture-fair matrix-reasoning test, we found that higher intelligence in healthy individuals is related to lower values of dendritic density and arborization. These results suggest that the neuronal circuitry associated with higher intelligence is organized in a sparse and efficient manner, fostering more directed information processing and less cortical activity during reasoning.

From discussion

Taken together, the results of the present study contribute to our understanding of human intelligence differences in two ways. First, our findings confirm an important observation from previous research, namely, that bigger brains with a higher number of neurons are associated with higher intelligence. Second, we demonstrate that higher intelligence is associated with cortical mantles with sparsely and well-organized dendritic arbor, thereby increasing processing speed and network efficiency. Importantly, the findings obtained from our experimental sample were confirmed by the analysis of an independent validation sample from the Human Connectome Project25



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Human intelligence research four-levels of explanation: Connecting the dots - an Oldie-But-Goodie (OBG) post

Click on image to enlarge.

Research that falls under the breadth of the topic of human intelligence is extensive.

For decades I have attempted to keep abreast with intelligence-related research, particularly research that would help with the development, analysis, and interpretation of applied intelligence tests.   I frequently struggled with integrating research that focused on brain-behavior relations or networks, neural efficiency, etc.  I then rediscovered a simple three-level categorization of intelligence research by Earl Hunt.  I modified it into a four-level model, and the model is represented in the figure above.

In this "intelligent" testing series, primary emphasis will be on harnessing information from the top "psychometric level" of research to aid in test interpretation.  However, given the increased impact of cognitive neuropsychological research on test development, often one must turn to level 2 (information processing) to understand how to interpret specific tests.

This series will draw primarily from the first two levels, although there may be times were I import knowledge from the two brain-related levels.

To better understand this framework, and put the forthcoming information in this series in proper perspective, I would urge you to view the "connecting the dots" video PPT that I previously posted at this blog.

Here it is.  The next post will start into the psychometric level information that serves as the primary foundation of "intelligent" intelligence testing.

Temporal g and the temporal resoultion hypotheses support brain clock concept: An OBG post

[Double click on image to enlarge]

[This is an OBG post (oldie but goodie post) that was first posted June 29, 2009 at IQs Corner sister blog - the Brain Clock blog]

I've previously blogged, with considerable excitement, about recent research that has suggested that the temporal resolution of one's internal "brain clock" may be more closely associated with intelligence scholars search for the neural underpinnings of general intelligence (g). Traditionally, and overwhelmingly, intelligence scholars have studied and focused on mental reaction time, largely based on the seminal work of Arthur Jensen. Then, along came recent research led primarily by mental timing scholar Rammsayer and colleagues...research that suggested that temporal g (vs. reaction time g) may be more important in attempts to identify the underlying mechanism of neural efficiency.. the focus of the search for the "holy grail" of general intelligence for decades.

The following just published journal article continues to add to the evidence that temporal processing, temporal g, and/or temporal resolution, may be critically important in understanding human intellectual performance. Below is the article reference, abstract, and my paraphrased comments from a reading of the article.

• Troche, S. & Rammsayer, T. (2009). Temporal and non-temporal sensory discrimination and their predictions of capacity-and speed-related aspects of psychometric intelligence. Personality and Individual Differences,47, 52–57

Abstract

The temporal resolution power hypothesis explains individual differences in psychometric intelligence in terms of temporal acuity of the brain. This approach was supported by high correlations between temporal discrimination and psychometric intelligence. Psychometric intelligence, however, was frequently found to be related to non-temporal discrimination (e.g., frequency, intensity, brightness discrimination). The present study investigated 100 female and 100 male participants with the aim to elucidate the functional relations between psychometric intelligence and temporal and non-temporal discrimination ability. Supporting the assumption of dissociable mechanisms, non-temporal discrimination predicted directly capacity – but not speed-related aspects of psychometric intelligence whereas temporal discrimination predicted both aspects. A substantial correlation between temporal and non-temporal discrimination suggested that general discrimination ability might account for the relations of psychometric intelligence to temporal and non-temporal discrimination abilities. Findings point to an internal structure of general discrimination ability with some dimensions of discrimination more predictive to certain aspects of psychometric intelligence than others.

Introduction/background summary

The neural efficiency hypothesis, based on Jensen's model of neuronal oscillations, has stood front and center as the defacto explanation of individual differences in processing speed and psychometric intelligence. This model suggests that individuals differ in the rate of rate of oscillation between refractory and excitatory states of neurons. The efficiency of oscillation rate, in turn, determines the speed/efficiency of transmission of neurally encoded information. The bottom line is that individuals with higher neural oscillate rates are believed to process information more efficiently, which leads to better intellectual performance.

In contrast, according to the articles authors, the more recent "temporal resolution power (TRP) hypothesis also refers to a hypothetical oscillatory process in the brain to account for the relationship between efficiency and speed of information processing as well as psychometric intelligence (Rammsayer & Brandler, 2002, 2007). According to this view, higher neural temporal resolution leads to faster information processing and to better coordination of mental operations resulting in better performance on intelligence tests. Rammsayer and Brandler (2002) proposed that psychophysical timing tasks, assessing temporal sensitivity and timing accuracy, are the most direct behavioral measures of TRP. The TRP hypothesis has been supported by subsequent studies which found substantial correlations between psychometric intelligence and timing performance (Helmbold, Troche, & Rammsayer, 2006, 2007; Rammsayer &  Brandler, 2007)." Most of these studies have been described previously at the IQ Brain Clock blog under the label temporal g.

An important issue for the TRP hypothesis to address is the fact that the most frequently used mental timing tasks also imply some form of simple sensory discrimination (together with the timing component). In order for the TRP hypothesis to have merit, the model must address (explain) the established relation between sensory discrimination and psychometric (tested) intelligence not only for the temporal domain but also for other non-temporal sensory dimensions. As summarized by the author, "associations with psychometric intelligence were shown for color (r = .08 to r = .32; Acton & Schroeder, 2001), pitch (r = .42 to r = .54; Raz, Willerman, & Yama, 1987), or texture and shape in the tactile modality (r = .08 to r = .29; Stankov, Seizova-Cajic´, & Roberts, 2001)."

Purpose of study

The purpose of the current study was to disentangle the relations between temporal processing and sensory discrimination via the evaluation and testing of two different structural models. As described by the authors, "the first model expanded the investigation of Helmbold et al. (2006) to the level of latent variables by factorizing various non-temporal and temporal discrimination tasks. It is assumed that temporal and non-temporal discrimination abilities predict psychometric intelligence as two disocciable factors which, however, can be related to each other. The TRP hypothesis postulates that TRP affects both capacity- and speed-related aspects of psychometric intelligence (Helmbold & Rammsayer, 2006)."

Alternatively "Model 2 proceeds from Spearman’s (1904) assumption that a general discrimination ability predicts psychometric intelligence. In accordance with this view, temporal discrimination constitutes a factor indicsociable from non-temporal discrimination. In other words, temporal and non-temporal discrimination tasks build a common factor referred to as GDA."

Method summary

The subjects were 100 male and 100 female volunteers (18 to 30 years of age; mean ± SD = 22.2 ± 3.3 years). The sample comprised 93 university students, 89 vocational school students and apprentices, while the remaining participants were working individuals of different professions. All participants reported normal hearing and normal or corrected-to-normal sight. The authors employed structural equation modeling (SEM) methods to evaluate and compare the two models.

Capacity and speed components of psychometric IQ (g) were measured with 12 subtests of the Berlin model of intelligence structure (BIS) test (Jäger, Süß & Beauducel, 1997). Four temporal (temporal generalization, duration, temporal-order judgment, rhythm perception) and three non-temporal sensory discrimination tasks (pitch discrimination, intensity discrimination, rightness discrimination) were used to operationally define temporal processing and sensory discrimination, respectively.


Conclusions/discussion summary (emphasis added by blogmaster)

Evaluation and comparison of the two models suggested the following conclusions (as per the authors)

• The relation between non-temporal discrimination and speed was completely mediated by temporal discrimination. The association between temporal discrimination and capacity was twofold. There was a weak but reliable direct association as well as a stronger indirect relation mediated by non-temporal discrimination.

• Although Model 1 revealed a high correlation between temporal and non-temporal discrimination, the different relations of temporal and non-temporal discrimination to speed and capacity suggest that the two factors are disocciable. Our finding of a strong correlational link between temporal discrimination ability and psychometric intelligence is in line with the outcome of previous studies investigating the TRP hypothesis...according to this account, higher TRP entails increased speed and efficiency of information processing resulting in higher scores on both speed- and capacity-related intelligence tests. Thus, our finding that Model 1 fitted the data well is in line with the TRP hypothesis.

• The present results corroborate Helmbold and Rammsayer’s (2006) finding of a stronger relationship between temporal discrimination ability and capacity compared to speed. On the contrary, shared variance with non-temporal discrimination accounted for the association between capacity and temporal discrimination whereas the direct link between temporal discrimination and capacity was rather weak. Thus, the strong relation between TRP and psychometric intelligence is probably due to the fact that TRP, when measured as a factor derived from temporal discrimination tasks, taps both temporal and unspecific discrimination abilities. From this perspective, time-related aspects of TRP may account for the association to speed whereas rather unspecific discrimination-related aspects mainly account for the association with capacity.

• The more parsimonious Model 2 should be preferred over Model 1. Model 2 suggests that temporal and non-temporal discrimination tasks constitute a common factor of unspecific, general discrimination performance referred to as GDA. The close association between this factor and psychometric intelligence is supported by the outcome of previous studies.

• The finding, that both temporal and non-temporal discrimination share a common source, supports the notion that general discrimination ability is somehow associated with higher-order mental ability.

• The finding of a close association between GDA and psychometric intelligence suggests, that already at a very early sensory stage of information processing, higher neural efficiency can be observed as a correlate of psychometric intelligence

• The high correlations between GDA and speed- as well as capacity-related aspects of psychometric intelligence, as revealed by Model 2, emphasize the importance of sensory performance as a correlate of higher-order mental ability. Nevertheless, differential relations between temporal and non-temporal discrimination and aspects of psychometric intelligence, as suggested by Model 1, may help to elucidate the internal structure of GDA. This is, certain sensory processes appear to be more predictive for certain aspects of psychometric intelligence than others. Such a conclusion is in line with the results of Stankov et al. (2001) who reported differential relations between cognitive abilities and aspects of tactile and kinesthetic perceptual processing. In the face of the available data, mapping of differential relationships between distinct sensory performances and components of psychometric intelligence represent a promising strategy to further explore the significance of sensory processes for human mental abilities.

Bottom line: This study continues to support the importance of temporal g, temporal processing, or the TRP hypothesis in explaining neural efficiency, which in turn is believed to play a major role in facilitating better (higher) intellectual performance. Understanding the intenral IQ Brain Clock, and interventions/treatments that may help "fine tune" the brain clock (increase its timing resolution), appears an important avenue to pursue both for theoretical and applied (cognitive enhancement interventions) research. To pat myself on the back, I've previously summarized the potential link between increased resolution of the brain clock and higher cognitive functioning in prior professional presentations (click here to visit a SlideShare PPT show)

Technorati Tags: psychology, education, educational psychology, school psychology, neuroscience, neuropsychology, neurotechnology, Arthur Jensen, g, general intelligence, psychometric intelligence, temporal processing, temporal g, temporal resolution hypothesis, brain clock, IQ Brain Clock, sensory discrimination

New and emerging models of human intelligence (Conway and Kovacs): Comments and elaboration

A nice article that provides an overview of contemporary intelligence research. More importantly, the authors summarize and contrast the psychometric and information processing approaches to understanding human intelligence.

A few comments.  Also, click on any image to enlarge and make more readable.

First.  The CHC figure presented in the article is not a 100% accurate representation of the CHC model.  The figure in the article is most consistent with Jack Carroll's 1993 model.  His model was integrated with Cattell and Horn's models as the CHC model.  A recent chapter by Schneider and McGrew (2012)  provides the best summary of the "CHC" model.

Second. I have been a huge fan of Conway and Engle's executive attention model of working memory and love the figure explaining working memory and the focus of attention.  In fact, in a recent IM Keynote presentation I used a simpler version of this model to explain the importance of attentional control (AC; aka focus) in working memory, and in turn, it's role in understanding higher level cognition.  You can watch this material at the following YouTube video of the entire presentation.  You should start at approximately the 28 minute mark to see the relevant material.

Finally. The authors make the following statements in their "future directions"conclusion.  These points resonate to my thinking as recently outlined in a 4-level explanatory hierarchy for integrating different types of intelligence research.  That information is available in the last (brief) video (Human Intelligence Research:  Connecting the dots) at the end of this post.

Research Byte: Strong working memory (WM)--fluid intelligence (Gf) relationship not due to time allowed on both sets of tasks

Very good article that does not support Chuderski's research that had suggested a relationship between time on task (not the same as cognitive processing speed-Gs) and fluid reasoning or working memory. The current study reinforces the very high (but not 1.0) effect size from working memory to Gf. However, how much time an individual (at least for young adults) spends on working memory or fluid tasks does not explain the strong WM--Gf relation. Generalization to children and the elderly cannot be made without further research.

What I find particularly interesting is the authors hypothesis that one possible general mechanism explanation for the WM-->Gf link is temporal based processing of information. This is consistent with the temporal power resolution hypotheses (or temporal g) of Rammsayer and colleagues and a large body of research I have reported at the Brain Clock blog. If you visit that link, pay particular attention to the MindHub Pub2 that presents a three-level hypothesized model for understanding the IM effect. Note that at the lowest neurocognitive and biological level of intelligence research, I have hypothesized that temporal g (and not Jensen's reaction time g) may be one of the key domain-general mechanisms driving critical cognitive abilities, especially working memory and fluid intelligence.

As per the recent four-level reductionistic framework (see brief 10 minute video explanation) I have offered to organize intelligence related research (adapted from Earl Hunt's work), the current study links research at the psychometric, information processing, and neurocognitive and biological (neural efficiency) levels.

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Klingberg on working memory dev/trng, P-FIT model, neural/temporal efficiency, bran networks and cognitive development

Excellent article by Klingberg (2014) (copy with annnotated comments and links to other research) that brings together important constructs of working memory (Gwm), working memory training, brain networks and synchronization, white matter mattters, neuroal and temporal processing efficiency, and maturation and training effects on children's cognitive development.
The article does a good job of "connecting the dots" from many different programs of research.

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ADHD: And even MORE evidence suggestive of a brain network connectivity disorder

And more evidence for ADHD as being related to poor brain network connectivity. (click here for more posts) Click on images to enlarge.






And, again, this extant research is consistent with the three-level hypothesized explanation of the impact of certain brain training programs on controlled attention (click here for special white paper as well as on-line PPT modules and keynote video presentation of this model).




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ADHD as a brain network disorder: More evidence

It is becoming clear that ADHD is likely related to dysfunctional interactions between certain brain networks (click here for prior ADHD posts). The following two studies add to this growing literature on the importance of brain network connectivity.

This research is also consistent with my previously posted white-paper on brain networks, temporal processing (brain clock) and cognitive efficiency processing with a strong influence of white matter integrity (paper is written around explaining the efficacy of the IM intervention but can also be viewed as a three level explanation of how brain networks influence working memory, attentional control, and executive functioning).

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What is Interactive Metronome video: Brief appearance by blogmaster

Interactive Metronome has a new intro video ("What is Interactive Metronome") re: the neuro-timing intervention. The blogmaster makes a brief appearance (at approx. 45 seconds) discussing neural efficiency. Enjoy. It will make my mom proud :)

See my conflict of interest statement regarding my paid consulting role with IM.

 

 

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Research byte: Domain-specific expertise + neural efficiency = superior cognitive performance

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www.themindhub.com

Neural efficiency and general intelligence: Is it mental quickness or timing?

Today I made a post at IM-HOME discussing the neural efficiency = general intelligence hypothesis (Arthur Jensen) where I make the case that recent research suggests that temporal or timing g (the internal brain clock) is a more fundamental cause of neural efficiency than the dominant Jensen reaction time g hypothesis. Check it out.

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Technorati Tags: intelligence,IQ tests,IQ testing,IQ scores,CHC intelligence theory,CHC theory,Cattell-Horn-Carroll,human cognitive abilities,psychology,school psychology,individual differences,cognitive psychology,neuropsychology,neuroscience psychology,special education,educational psychology,psychometrics,psychological assessment,psychological measurement,IQs Corner,general intelligence,intelligence,IQ,Gs,Gt,brain clock,temporal g,reaction time,Arthur Jensen,neural efficiency

More supporting evidence IQ Brain Clock

Check out post at sister blog (IQ Brain Clock) re: more evidence supporting the concept and importance of an internal brain clock in intelligence behavior.

Technorati Tags: psychology, educational psychology, school psychology, cognition, intelligence, neuropsychology, neuroscience, IQ, intelligence, psychometrics, g, general intelligence, Aruthur Jensen, neural oscillations, neural efficiency, ISIR, brain clock, IQ Brain Clock, temporal processing, temporal g, temporal resolution power hypothesis

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.

Technorati Tags: psychology, educational psychology, school psychology, neuropsychology, neuroscience, IQ, intelligence, cognition, g, general intelligence, neural efficiency, neural oscillations