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 not always better

Click on image to enlarge

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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Interactive Metronome (IM) is measuring and changing something real and important: An old-but-goodie (OBG) post

[This is an oldie-but-goodie (OGB) post that I originally posted as a guest blogger at the IM-HOME blog on Feb 2, 2012]

 

No human investigation can be called real science if it cannot be demonstrated mathematically

Leonardo da Vinci, Treatise on Painting (1651)

Progress in science depends on new techniques, new discoveries and new ideas, probably in that order Sydney Brenner (1980)

 

At the core of the IM intervention technology is a precise measurement system.  To users and clinicians the IM measurement system is transparent.  Yet, without the valid and precise measurement system, IM would not work.

In my “Brain or neural efficiency: Is it quickness or timing?” post, I advanced the hypothesis that the effectiveness of Interactive Metronome may be due to IM operating on a fundamental dimension of brain or neural efficiency, which intelligence scholars also relate to general intelligence (g).  I have also suggested that this mechanism improves control of attention and may allow individuals to “quiet a busy mind”and invoke “on-demand focus.”

As an applied intelligence test developer (click here), I have been intrigued by the underlying precise millisecond-based measurement system which is the heart of IM technology.  IM technology would not work if the underlying measurement system could not reliably measure differences in synchronized metronome tapping between individuals and changes within the same individual over repeated sessions. 

Wanting to know how precise the underlying IM measurement system is, I extracted the average millisecond scores from an unpublished 2003 Interactive Metronome document that reported average times for different age groups.  The sample consisted of the initial IM Long Form Assessment performance of 1,583 clinical and normal subjects ranging in age from 6 thru 60+.  It is important to note that the sample was not a nationally representative normal sample and was comprised of more clinical subjects receiving IM therapy.  Nevertheless, I wondered if this less-than-optimal set of data might demonstrate a pattern of increasingly shorter response times as individuals became older.  Why did I want to examine this?

Developmental increase in proficiency on tests and measures of human abilities is considered one form of evidence that a test or measurement system is reliably and validly measuring an important human ability.  In the case of intelligence, valid measures of cognitive abilities show developmental growth curves where the youngest subjects obtain the lowest raw scores and the average raw scores gradually increase with increasing age.  They eventually level out and then start a decline as old age sets in.  Below are growth curves from seven cognitive ability scores from the Woodcock-Johnson Battery—III, a test battery of which I am a co-author.  The important observation to note is that, despite the specific cognitive ability measure, all curves show low scores for the younger ages followed by acceleration of growth to a certain point.  Each curve then plateaus at a certain age range, after which age-related cognitive decline is noted, but at different rates for different abilities.  These curves are presented in the WJ III Technical Manual (McGrew & Woodcock, 1991) as a form of developmental validity evidence—which provides one piece of evidence that the WJ III tests are valid measures of different and important human intellectual abilities.

Scholars in intelligence have studied and postulated about the different rates of growth and decline for different abilities.  These are serious data about human intelligence and the measures used to capture differences in human abilities.  Within this context, I was ecstatic when I plotted the initial IM Long Form Assessment data (which is analogous to the first time a person is “tested” with the IM measurement system) and discovered the following plot.

 

The first thing the reader should note are the individual data points (the dots).  The points show some random “bouncing around” which we measurement folks call sampling error.  The critical point is that they follow a systematic trend that can be estimated by fitting a mathematical curve to the data points. This was the same procedure used to develop the WJ III cognitive curves in the first figure.  In the second figure, the IM timing curve is demarcated in red.  We who develop test norms and study human ability growth curves generate these smoothed growth curves as they are the best estimate of the real reality of the data if extremely large number of individuals had been tested at each age (there would be much less bounce).

One does not need to be a rocket scientist to interpret the smoothed IM growth curve.  Individuals at the youngest ages, on the average, show the largest millisecond discrepancy from the IM reference tone.  Then, with increasing age, the average IM target-to-response for individuals decreases systematically as children age.  At approximately 25 years of age the curve “bottoms out,” and then as individuals get older, IM millisecond timing scores increase (or get less accurate).  The systematic nature of this curve is amazing, considering it is based on a less-than-optimal sample for determining what constitutes average.

If the reader is having a hard time relating the IM timing curve to the WJ III cognitive ability curves, I have taken the liberty of simply rotating and flipping the IM timing accuracy growth curve in the figure below.  Vioila (aka, walla—“there it is”)!  The curve has the same general shape as the WJ III cognitive ability growth curves!  The reason for the difference between the WJ III growth curves and the first IM timing growth curve is that the meaning of high and low scores are reversed—higher IM times mean lower skilled performance while lower scores on the WJ III battery are associated with lower performance (and vice versa).

Readers who are parents may have seen similar growth curves during well-child visits with the family doctor.  Below are growth charts for weight and length for male children from birth to 36 years.  Although covering a much smaller age span than the WJ III cognitive and IM timing curves above, the shape of the curves is identical for the comparable age ranges (gradually increasing with age).  The middle dark line in each set (labeled 50 for 50^th percentile) is conceptually identical to the above single curve plots.  These physical measurement curves show the systematic and developmental nature of physical growth.

Why am I so excited about the IM timing growth curve?  Because it demonstrates, similar to the physical and intelligence growth curves, that the underlying measurement unit used as the core of IM therapy is measuring a human ability that follows a similar and expected developmental pattern.  Such curves are believed to be due, depending on the specific ability, to the influence of education and experiences as well as genetically-driven biological maturation of the central nervous system (CNS).  The IM timing curve is one form of evidence that the IM measurement system is measuring a fundamental human capacity.  This is extremely exciting!  It is one more piece of evidence that the IM core measurement technology is measuring and working on a core critical human ability. Coupled with other validity evidence previously discussed here and elsewhere, this additional piece of scientific evidence has convinced me that the IM measurement and intervention system is most likely measuring a fundamental aspect of the development of the central nervous system (e.g., neural efficiency).  The cognitive abilities I have suggested fall under the broad umbrella term of executive functions, and more specifically controlled attention (focus) and working memory.

A caveat before I close. The smoothed IM timing curve should not be used by IM providers to evaluate how typical, normal, or close-to-average a person is on their initial IM Long Form Assessment.  The mixed nature of the sample (normal and clinical subjects; more of the later) argues against such use.  Also, the curve only represents the average at each age and calculating and plotting the typical variability around the curve would also be necessary.  I deliberately left out the variability data curves so as not to encourage misuse of the information.

However, IM providers can evaluate their client’s performance by using the official IM Indicator Table.  A copy is reproduced below.  This table can be used to determine whether a client’s performance is in the “ballpark” for their age.  Providers simply locate the clients age in the row at the top then go down that column to find the millisecond score or range that includes their specific IM Long Form Assessment timing score.  The verbal description associated with each level (extremely deficient to exceptional) can be used to make quality of performance statements reflecting where an individual is at the time of the initial assessment.  The scores and labels should not be used for diagnostic purposes.  Instead, they can be used to describe, in approximate ball park terms, where an individual is at the time of the assessment when compared to others of the same age and to make comparisons about that same client’s performance over time.

Age	6	7 to 8	9 to 10	11 to 12	13 to 15	16+
Extreme Deficiency	280+	270+	260+	240+	215+	200+
Severe Deficiency	175-279	170-269	160-259	155-239	150-214	147-199
Below Average	120-174	90-169	80-159	75-154	72-149	70-146
Average	90-119	65-89	55-79	45-74	43-71	41-69
Above Average	56-89	45-64	38-54	36-44	33-42	30-40
Exceptional	40-55	32-44	28-37	26-35	23-32	22-29
Superior	Below 40	Below 32	Below 28	Below 26	Below 23	Below 22

In summary, I have traversed a number of empirical domains in my journey to understand IM.  The finding of such powerful and clear developmental evidence for the underlying IM measurement system is one of the final dots I connected which convinced me of the promise of IM.  The IM program is founded on a valid scientific measurement system of an important human cognitive ability (or constellation of related abilities).
 

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.

Click on images to enlarge.













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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).

Click on images to enlarge.














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White matter matters: Brain synchronization via the brain's communication subway system

White matter, in contrast to the grey squiggly mass (the cerebrum) that most people associate with the human brain, was for many years the research step-child to the cerebrum. That is no more. White matter, which has been called the brain's subway, super information system, or interstate highway communication system, now has a glass slipper. Research during the past decade has implicated white matter as performing the critical task of connecting and synchronizing different brain regions or networks so they can perform a wide variety of complex human cognitive or motor behaviors. The white matter system is considered the communication backbone system for the flow of information in the brain. Of particular interest (to me) is the parietal-frontal network, which is implicated as central to abstract human intelligence, fluid intelligence (Gf), working memory and attentional control (see prior posts re: the P-FIT model).

In a MindHub white paper I hypothesized that increasing white matter tract integrity may be a key mechanism behind the efficacy of the Interactive Metronome neuro-timing intervention (see figure below). I have gone as far as suggesting that the efficacy of many brain training/fitness programs may stem from a common domain-general effect--improving communication between and within various brain network(s) via more efficient white matter tract speed and communication. [Click on image to enlarge]

White matter integrity or dysfunction as been implicated in a wide variety of cognitive disorders or abilities, including cognitive control, math and intellectual giftedness, fluid intelligence or reasoning, processing speed, reading, decrease in cognitive functioning, meditation, working memory, vascular cognitive impairment, ADHD, autism, and cognitve and language maturation in infants. A sampling of recent white matter research article abstracts I have accumulated can be found by clicking here.
White matter matters!

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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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Interactive Metronome neurotechnology measurement system developmental validity evidence

I just guest posted some material at the IM-HOME blog re: developmental validity evidence for the timing measurement system that is at the core of the Interactive Metronome neurotechnology. There is clear validity evidence that the IM technology is measuring a valid construct of human cognitive abilities, which I have hypothesized to be in the domain of executive functions (esp. controlled attention and working memory). Interested readers should click here.





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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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