Interactive Metronome (IM) efficacy studies: Randomized treatment control studies

In case you missed the prior post, if you are interested in IM efficacy studies (that used a randomized treatment control study design), you can find this info at this prior link.

An oldie-but-goodie (OBG) post.

White matter matters! Brain network communication and synchronization - An oldie-but-goodie (OBG) post

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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Your brain is a time machine: An oldie-but-goodie (OBG) post

This is an OBG (oldie-but-goodie) post I originally made on the IM-HOME blog

Time and space are the two fundamental dimensions of our lives. All forms of human behavior require us to process and understand information we receive from our environment in either spatial or temporal patterns. Even though mental timing (temporal processing) research is in a stage of infancy (when compared to spatial processing) important insights regarding the human brain clock have emerged.

Below is a list (albeit incomplete) of some of the major conclusions regarding the human brain clock. The sources for these statements come from my review of the temporal processing and brain clock literature during the past five years. Most of this information has been disseminated at the Brain Clock blog or the Brain Clock Evolving Web of Knowledge (EWOK). The goal of this post is to provide a Readers Digest summary of the major conclusions. This material can serve as a set of "talking points" at your next social event where you can impress your friends and family as you explain why you use the high-tech IM "clapper" (with a cowbell tone no less) either as a provider or as client.

Our brains measure time constantly. It's hard to find any complex human behavior where mental timing is not involved. Timing is required to walk, talk, perform complex movements and coordinate information flow across the brain for complex human thought. Think about moving your arm and hand to grasp a coffee cup. The messages to perform this task originate in your brain, which is not directly connected to your arm, hands and fingers. The ability to perform the necessary motor movements is possible only because the mind and extremities are connected via timing. Precisely timed neural messages connect your brain and extremities. You are a time machine.

Humans are remarkably proficient at internally perceiving and monitoring time to produce precisely timed behaviors and thinking. “We are aware of how long we have been doing a particular thing, how long it has been since we last slept, and how long it will be until lunch or dinner. We are ready, at any moment, to make complex movements requiring muscle coordination with microsecond accuracy, or to decode temporally complex auditory signals in the form of speech or music. Our timing abilities are impressive…” (Lewis & Walsh, 2005, p. 389).

To deal with time, humans have developed multiple timing systems that are active over more than 10 orders of magnitude with various degrees of precision (see figure below from Buhusi & Meck, 2005). These different timing systems can be classified into three general classes (viz., circadian, interval, and millisecond timing), each associated with different behaviors and brain structures and mechanisms. The fastest timing system (millisecond or interval timing) is involved in a numerous human behaviors such as speech and language, music perception and production, coordinated motor behaviors, attention, and thinking. This fast interval timing system is the most important timing system for understanding and diagnosing clinical disorders and for developing and evaluating effective treatment interventions for educational and rehabilitation settings. It is this timing system, and the relevant research, that is relevant to understanding Interactive Metronome. (Note.  See my conflict of interest statement at this blog.  I have an ongoing consulting relationship with IM).

Although there is consensus that the human brain contains some kind of clock, the jury is still out on the exact brain mechanisms and locations. It is also not clear whether there is one functional master clock or a series of clocks deployed in different brain areas. The areas of the brain most consistently associated with milli-second interval mental timing are the cerebellum, anterior cingulate, basal ganglia, the dorsolateral prefrontal cortex, right parietal cortex, motor cortex, and the frontal-striatal loop. That is a mouthful of technical brain terms. But, if you can memorize them and have them roll of your tongue with ease you will “shock and awe” your family and friends. Most of these areas of the brain are illustrated below. Now, if you really want to demonstrate your expertise, get your own illustrated “brain-in-a-pocket”. These images were generated by the free 3D Brain app available for your iPhone or iPad. Even cooler is the fact that you can rotate the images with your finger! You can give neuroanatomy lessons anytime…anywhere!

Research suggests that mental interval timing is controlled by two sub-systems. The automatic timing system processes discrete-event (discontinuous) timing in milliseconds. The cognitively-controlled timing system deals with continuous-event timing (in seconds) that requires controlled attention and working memory. Both systems are likely involved in IM. For example, the synchronized clapping requires motor planning and execution, functions most associated with the automatic timing system. However, the cognitive aspects of IM (focus, controlled attention, executive functions) invoke the cognitively controlled timing system. Aren’t these brain images awesome?

The dominant model in the brain clock research literature is that of a centralized internal clock that functions as per the pacemaker–accumulator model. Briefly, this is a model where an oscillator beating at a fixed frequency generates tics that are detected by a counter. For now I am just going to tease you with an image of this model. You can read more about this model at the Brain Clock blog.

Research suggests that the brain mechanisms underlying mental timing can be fine-tuned (modified) via experience and environmental manipulation. Modifiability of mental interval timing and subsequent transfer suggest a domain-general timing mechanism that, if harnessed via appropriately designed timing-based interventions, may improve human performance in a number of important cognitive and motor domains.

Mind wandering, the default network and controlled attention: An OBG video post

(This is an oldie-but-goodie blog post - OBG)

A few interesting video gems for your viewing.

First, forget multitasking and try mono-tasking.  Focus on just one thing...it may be beneficial.

Next, I have frequently blogged about the default mode or default brain network (Brain Clock posts; IM-HOME post).  The default mode (which is estimated to be active approximately 40% of our waking day) has been implicated in how our mind, when idling or resting, is very active--it does not rest while resting.  Difficulty quieting the default network has also been implicated in a variety of clinical disorders such as ADHD, Alzheimers, schizophrenia, and autism. This literature is now frequently referred to as mind wandering research (see Brain Clock mind wandering posts).  The following is a nice brief overview of the default brain network.

I have also suggested that some brain fitness technologies (Interactive Metronome in particular;  conflict of interest disclosure--I serve as a paid external consultant to IM regarding research) are achieving success by either directly or indirectly training controlled, focused attention, which requires shutting down and inhibiting the mind wandering predisposition of the default mode network.  I have posted both a set of PPT slides and the video of my recent IM keynote presentation at the Brain Clock blog where I presented the relevant research and hypotheses in detail.

Finally, a more lengthy, thought provoking video is presented last.  This video makes it clear that the brain is best conceptualized as an evolving interconnected network.

Enjoy.

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).
 

MindHub Pub #2: The Science Behind Interactive Metronome: An Integration of Brain Clock, Temporal Processing, Brain Network and Neurocognitive Research and Theory

[This is an "oldie but goodie" (OBG) post that was originally posted on March 5, 2013]

The second MindHub Pub working paper is now available:  The Science Behind Interactive Metronome:  An Integration of Brain Clock, Temporal Processing, Brain Network and Neurocognitive Research and Theory.  The PDF document can be viewed/downloaded by clicking here.

This working paper is an integration of research and theory that attempts to explain the science behind the positive outcomes of the Interactive Metronome rehabilitative and brain training neurotechnology (the IM effect).  A three-level explanatory model involving three different levels of brain and neurocognitive constructs (McGrew, 2012) is described.   The three-levels are presented in the visual summary in the figure below.  Although the text focuses on explaining the IM effect on cognitive functions (focus, controlled attention, working memory, executive functions), the three-level hypothesized model should be considered a general explanatory framework for understanding the positive IM effect in other human performance domains as well (e.g., recovery from stroke; gait; motor coordination).

The three-level model described here can also be viewed as an IM-free integration of research and theory that explains the relations between the temporal processing (temporal g) of the human brain clock (s), brain regions and networks, brain network communication and synchronization (the parietal-frontal integration theory of intelligence [P-FIT] in particular), and the neurocognitive constructs of attentional control (focus), working memory, and executive functioning.

[Click on image to enlarge]

Synchronized metronome tapping (SMT) and the automatic/controlled timing systems of the brain

[This is an OBG (oldie but goodie) post first posted November 4, 2006 - this new post has a few corrections and the fixing of some broken links]

In a prior post, I highlighted a distinction contemporary mental time-keeping researchers make regarding two general types of human timing systems. Briefly, the automatic timing system works in the millisecond range of time, is used in discrete-event (discontinuous) timing, and involves the cerebellum. This contrasts with the continuous-event, cognitively controlled timing system that requires attention and involves the basal ganglia and related cortical structures.

In their article, Lewis and Maill (2006) provide further clarification of how they perceive differences between these two mental timing systems. According to Lewis and Maill, "it is not any single characteristic, but rather a constellation of several characteristics which determines which timing system is recruited in any particular task." The three task characteristics isolated by these researchers are: (a) the duration measured, (b) whether or not the timed intervals were defined by movement and, (c) whether timing was continuous (e.g. an unbroken series of predictable intervals) or intermittent (e.g. broken into discrete measurements by the presence of unpredictable irregular intervals).

Lewis and Maill conclude that "our analysis showed that having any two out of the three characteristics associated with a task type (cognitive or automatic) dramatically increased the probability that the areas associated with that timing system would be recruited. Accordingly, we can think of any task having two or more cognitive attributes (e.g. measuring more than a second, discontinuously, and without relying upon movement) as a ‘cognitively controlled timing task’, and any task with two or more of the opposing characteristics as an ‘automatic timing task’."How does this apply to understanding the brain structures and functions involved in SMT (synchronized metronome tapping; e.g., Interactive Metronome)? [click here for from info on SMT and IM and my necessary conflict of interest disclosures...just follow the link trails].

Given my understanding (and one personal experience with an SMT intervention), I would hypothesize that SMT interventions most likely tap both the automatic and controlled cognitive timing systems (and related neurological structures and functions). SMT-based interventions typically involve a motor component (e.g., clapping hands together to the beat), a continuous tone interval, and require responding in terms of milliseconds. These characteristics definitely would be associated with the automatic timing system.

However, although an individual (during SMT training) is trying to synchronize their tapping in terms of milliseconds, the duration between the continuous tones is more in the range of a second or so. Also, especially during initial stages of SMT, an individual's working memory [see prior post on the pacemaker accumulator model of mental time-keeping] is particularly taxed as one monitors the SMT visual and/or auditory feedback provided, makes a decision about whether they are responding "too fast" or "too slow", and then consciously implements a correction to their "beat" behavior. These later characteristics are more characteristic of the cognitively controlled timing system.

So...it is my hypothesis that both the automatic and cognitively controlled timing systems of mental or interval time-keeping are involved with SMT-based interventions. It is possible that both are significantly active during early stages of SMT training and, with improvement and progress over time, the role of the cognitively controlled system decreases and the automatic system is more responsible. These are only hypothesis and need empirical study.

• Lewis, P. & Miall, C (2006). Remembering the time: a continuous clock. Trends in Cognitive Sciences, 10(9), 401-406.

Brain networks and fine tunning the networks: An OBG post

[This is an OBG (oldie but goodie) post first posted December 16, 2011]

Man has always known that the brain is the center of human behavior.  Early attempts at understanding which locations in the brain controlled different functions were non-scientific and included such practices as phrenology.  This pseudoscience believed that by feeling the bumps of a persons head it was possible to draw conclusions about specific brain functions and traits of the person.

(double click on any image to enlarge)

Eventually brain science revealed that different regions of the brain where specialized for different specific cognitive processes (but it was not related to the phrenological brain bump maps).  This has been called the modular or functional specialization view of the brain, which is grounded in the conclusion that different brain areas acted more-or-less as independent mechanisms for completing specific cognitive functions.

One of the most exciting developments in contemporary neuroscience is the recognition that the human brain processes information via different brain circuits or loops which at a higher level can be studied as large scale brain networks. Although the modular view still provides important brain insights, the accumulating evidence suggests that it has serious limitations and might in fact be misleading (Bresslor and Menon, 2010).  One of the best summaries of this cutting edge research is that by Bresslor and Menon.

Large scale brain network research suggests that cognitive functioning is the result of interactions or communication between different brain systems distributed throughout the brain. That is, when performing a particular task, just one isolated brain area is not working alone.  Instead, different areas of the brain, often far apart from each other within the geographic space of the brain, are communicating through a fast-paced synchronized set of brain signals.  These networks can be considered preferred pathways for sending signals back and forth to perform a specific set of cognitive or motor behaviors. 

To understand preferred neural pathways, think of walking on a college campus where there are paved sidewalks connecting different buildings that house specialized knowledge and activities.  If you have spent anytime on a college campus, one typically finds foot-worn short cuts in the grass that are the preferred (and more efficient) means by which most people move between building A and B.  The combined set of frequently used paved and unpaved pathways are the most efficient or preferred pathways for moving efficiently between buildings.  The human brain has developed preferred communication pathways that link together different brain circuits or loops in order to quickly and efficiently complete specific tasks. 

According to Bresslor and Menon (2010), “a large-scale functional network can therefore be defined as a collection of interconnected brain areas that interact to perform circumscribed functions.”  More importantly, component brain areas in these large-scale brain networks perform different roles.  Some act as controllers or task switchers that coordinate, direct and synchronize the involvement of other brain networks.  Other brain networks handle the flow of sensory or motor information and engage in conscious manipulation of the information in the form of “thinking.” 

As illustrated in the figure above, neuroscientists have identified a number of core brain network nodes or circuits.  The important new insight is that these various nodes or circuits are integrated together into a grander set of higher-level core functional brain networks.  Three important core networks are receiving considerable attention in explaining human behavior. 

Major functional brain networks

The default mode (DMN) or default brain network (shown in blue) is what your brain does when not engaged in specific tasks.  It is the busy or active part of your brain when you are mentally passive.  According to Bresslor and Brennon the “DMN is seen to collectively comprise an integrated system for autobiographical, self-monitoring and social cognitive functions.”  It has also been characterized as responsible for REST (rapid episodic spontaneous thinking).  In other words, this is the spontaneous mind wandering and internal self-talk and thinking we engage in when not working on a specific task or, when completing a task that is so automatized (e.g., driving a car) that our mind starts to wander and generate spontaneous thoughts.  As I have discussed previously (at IM-HOME blog), the default network is responsible for the unquiet or noisy mind.  And, it is likely that people differ in amount of spontaneous mind wandering (which can be both positive creative thinking or distracting thoughts), with some having a very unquiet mind that is hard to turn off, while others can turn off the inner thought generation and self-talk and display tremendous self-focus or controlled attention to perform a cognitively or motorically demanding task.  A very interesting discussion of the serendipitous discovery and explanation of the default brain network is in the following soon to be published scientific article.

The salience network (shown in yellow) is a controller or network switcher.  It monitors information from within (internal input) and from the external world arounding us, which is constantly bombarding us with information.  Think of the salience network as the air traffic controller of the brain.  Its job is to scan all information bombarding us from the outside world and also that from within our own brains.  This controller decides which information is most urgent, task relevant, and which should receive priority in the que of sending brain signals to areas of the brain for processing.  This controlling network must suppress either the default or executive networks depending on the task at hand.  It must suppress one, and activate the other.  Needless to say, this decision making and distribution of information must require exquisite and efficient neural timing as regulated by the brain clock(s).

Finally, the central-executive network (CEN; shown in red) “is engaged in higher-order cognitive and attentional control.”  In other words, when you must engage your conscious brain to work on a problem, place information in your working memory as you think, focus your attention on a task or problem, etc., you are  “thinking” and must focus your controlled attention.  As I understand this research, the salience or controller network is a multi-switching mechanism that is constantly initiating dynamic switching between the REST (sponatenous and often creative unique mind wandering) and thinking networks to best match the current demands you are facing.

According to Bresslor and Melon, not only is this large scale brain network helping us better understand normal cognitive and motor behavior, it is providing insights into clinical disorders of the brain.  Poor synchronization between the three major brain networks has been implicated in Alzheimer’s, schizophrenia, autism, the manic phase of bipolar and Parkinson’s (Bresslor and Melon, 2010), disorders that have all been linked to a brain or neural timing (i.e, the brain clock or clocks).  I also believe that ADHD would be implicated.  If the synchronized millisecond based communication between and within these large networks is compromised, and if the network traffic controller (the salience network) is disrupted in particular, efficient and normal cognition or motor behavior can be compromised.

I find this emerging research fascinating.  I believe it provides a viable working hypothesis to explain why different brain fitness or training neurotechnologies have shown promise in improving cognitive function in working memory, ADHD, and other clinical disorders.  It is my current hypothesis that various brain training technologies may focus on different psychological constructs (e.g., working memory; planning; focus or controlled attention), but their effectiveness may all be directly or indirectly facilitating the sychronization between the major brain networks.  More specifically, by strengthening the ability to invoke the salience or controller network, a person can learn to suppress, inhibit or silence the REST-producing default brain network more efficiently, long enough to exert more controlled attention or focus when invoking the thinking central executive network.  Collectively these brain fitness technologies may all improving the use of those abilities called executive function, or what I have called the personal brain manager.  Those technologies that focus on rhythm or brain timing are those I find most fascinating.  For example, the recent example of the use of melodic intonation therapy with Congresswoman Gabby Giffords (she suffered serious brain trauma due to a gun shot) demonstrates how rhythm-based brain timing therapies may help repair destroyed preferred and efficient neural pathways or, develop new pathways, much like the development of a new foot worn pathway in the grass on a college campus if a preferred pathway is disrupted by a new building, temporary work or rennovation, or some other destruction of a preferred and efficient network of movement path.

To understand the beauty of the synchronized brain, it is best to see the patterns of brain network connections in action.  Below is a video called the “Meditating Mind.”  I urge you to view the video for a number of reasons.  

A number of observations should be clear.  First, during the first part of the video the brain is seen as active even during a resting state.  This is visual evidence of the silent private dialogue (REST) of the default mode or network of the brain.  Next, the video mentions the rhythm of increased and decreased neural activation as the brain responds to no visual information or presentation of a video.  The changes in color and sound demonstrate the rich rhythmic synchronization of large and different parts of the brain, depending on whether the brain is engaged in a passive or active cognitive task.  The beauty of the rapidly changing and spreading communication should make it obvious that efficient rhythmic synchronization of timing of brain signals to and from different networks or circuits is critical to efficient brain functioning.

Finally, the contrast between the same brain under normal conditions and when engaged in a form of meditation is striking.  Clearly when this person’s brain is mediating, the brain is responding with a change in rates and frequency of brain network activation and synchrony.  As I described in my personal IM-HOME based experience post, mastering Interactive Metronome (IM) therapy requires “becoming one with the tone”…which sounds similar to the language of those who engage in various forms of meditation.  Could it be that the rhythmic demans of IM, which require an individual to “lock on” to the auditory tone and stay in that synchronized, rhythmic and repetitive state for as long as possible, might be similar to the underlying mechanics of some forms of meditation, which also seek to suppress irrelevant and distracting thoughts and eventually “let the mind go"---posibsly to follow a specific train of thought with complete and distraction free focus. 

Yes…this is speculation.  I am trying to connect research-based and personal experience dots.  It is exciting.  My IM-HOME based induce personal focus experience  makes sense from the perspective of the function and interaction between the three major large scale brain networks.

Temporal g and the temporal resolution power hypothesis (TRP): An OGB pst

[Double click on image to enlarge]

[This is an OBG post (oldie but goodie post) that was first posted June 29, 2009]

Temporal g...or...the temporal resolution power hypothesis (TRP). Lets hear it for the IQ Brain Clock!

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 suggestes 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/treatements 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)

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