The Time Doc's Interactive Metronome (IM)-Home guest posts

Most of my readers are aware of my interest in brain-clock based neurotechnologies, particularly as they relate to improving cognitive functioning. All posts related to this area of interest, as well as posts linking readers to other neuroscience developments, can be found here at the Brain Clock blog.

A few years back I also was a guest blogger (aka, the Time Doc) at the IM-Home blog. I frequently find myself directing people who contact me to some of those guest posts.  So, I've decided to provide a single link (click here) that will take readers to all my IM/brain clock guest posts. Enjoy.

I hope readers check out these posts and become more aware of the exciting neurotechnologies that are emerging based on the concept of temporal processing and the human brain clock.

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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)

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

Paving the Way to Treat Brain Rhythm Disorders | Neuroscience News

http://neurosciencenews.com/neural-oscillations-brain-disorders-2321/

How Band Class Alters the Teenage Brain

Kevin McGrew shared the story, How Band Class Alters the Teenage Brain, with you on Flipboard.

How Band Class Alters the Teenage Brain northwestern.edu EVANSTON, Ill. --- Music training, introduced as late as high school, may help improve the teenage brain's responses to sound and sharpen hearing and la... read more

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What Does It Mean to Be Musical?

Kevin McGrew shared the story, What Does It Mean to Be Musical?, with you on Flipboard.

What Does It Mean to Be Musical? creativitypost.com / Milena Z. Fisher, Ph.D. By Daniel J. Levitin | Apr 13, 2012 Synopsis Music can be seen as a model system for understanding gene x environment interactions and how these can inf... read more

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Diagnosing Alzheimer's Disease by Observing Brain Network Dy...

Kevin McGrew shared the story, Diagnosing Alzheimer's Disease by Observing Brain Network Dy..., with you on Flipboard.

Diagnosing Alzheimer's Disease by Observing Brain Network Dynamics neurosciencenews.com Various types of information can be ascertained by the way blood flows through the brain. When a region of the brain has been activated, blood flow incr... read more

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MRI studies point to brain connectivity changes in autism sp...

Kevin McGrew shared the story, MRI studies point to brain connectivity changes in autism sp..., with you on Flipboard.

MRI studies point to brain connectivity changes in autism spectrum disorders psypost.org Studies using magnetic resonance imaging (MRI) techniques are beginning to reveal differences in brain connectivity–the ways that different parts of the... read more

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