Showing posts with label brain networks. Show all posts
Showing posts with label brain networks. Show all posts

Wednesday, May 07, 2025

Research Byte: A #hierarchical model of early #brain #functional #network development - excellent #review #cognition #cognitive #brain networks #schoolpsychology

Click on image to enlarge for easy viewing

A good overview/review article of the evolution of brain networks with an excellent visual-graphic summary (I love good visual summaries, which I label in my blog as being a Gv Figure Hall of Fame)

A hierarchical model of early brain functional network development 
Wei Gao, Open access (you can download and read) in Trends in Cognitive Science

Abstract 

Functional brain networks emerge prenatally, grow interactively during the first years of life, and optimize both within-network topology and between-network interactions as individuals age. This review summarizes research that has characterized this process over the past two decades, and aims to link functional network growth with emerging behaviors, thereby developing a more holistic understanding of the developing brain and behavior from a functional network perspective. This synthesis suggests that the development of the brain's functional networks follows an overlapping hierarchy, progressing from primary sensory/motor to socioemotional-centered development and finally to higher-order cognitive/executive control networks. Risk-related alterations, resilience factors, treatment effects, and novel therapeutic opportunities are also dis-cussed to encourage the consideration of future imaging-assisted methods for identifying risks and interventions.

Sunday, February 23, 2025

Research Byte: Intrinsic #Brain Mapping of #Cognitive Abilities (as per #CHC): A Multiple-Dataset Study on #Intelligence and its Components (journal pre-proof)

 Click on image to enlarge for easy reading


A journal pre-proof copy of this article is available for download here.

Abstract

This study investigates how functional brain network features contribute to general intelligence and its cognitive components by analyzing three independent cohorts of healthy participants. Cognitive scores were derived from 1) the Wechsler Adult Intelligence Scale (WAIS-IV), 2) the Raven Standard Progressive Matrices (RPM), and 3) the NIH and Penn cognitive batteries from the Human Connectome Project. Factor analysis on the NIH and Penn cognitive batteries yielded latent variables that closely resembled the content of the WAIS-IV indices and RPM. We employed graph theory and a multi-resolution network analysis by varying the modularity parameter (γ) to investigate hierarchical brain-behavior relationships across different scales of brain organization. Brain-behavior associations were quantified using multi-level robust regression analyses to accommodate variability and confounds at the subject-level, node-level, and resolution-level. Our findings reveal consistent brain-behavior relationships across the datasets. Nodal efficiency in fronto-parietal sensorimotor regions consistently played a pivotal role in fluid reasoning, whereas efficiency in visual networks was linked to executive functions and memory. A broad, low-resolution 'task-positive' network emerged as predictive of full-scale IQ scores, indicating a hierarchical brain-behavior coding. Conversely, increased cross-network connections involving default mode and subcortical-limbic networks were associated with reductions in both general and specific cognitive performance. These outcomes highlight the relevance of network efficiency and integration, as well as of the hierarchical organization in supporting specific aspects of intelligence, while recognizing the inherent complexity of these relationships. Our multi-resolution network approach offers new insights into the interplay between multilayer network properties and the structure of cognitive abilities, advancing the understanding of the neural substrates of the intelligence construct.

Saturday, January 18, 2025

Research Byte: Dynamic switching between #brainnetworks (#executivecontrol #defaultmode) predicts #creative ability - attention #cognition #schoolpsychology #creativity


Dynamic switching between brain networks predicts creative ability.  

Qunlin et al.,(2025).   Click here read article and download PDF (Communications Biology) if you so desire.

Abstract

Creativity is hypothesized to arise from a mental state which balances spontaneous thought and cognitive control, corresponding to functional connectivity between the brain’s Default Mode (DMN) and Executive Control (ECN) Networks. Here, we conduct a large-scale, multi-center examination of this hypothesis. Employing a meta-analytic network neuroscience approach, we analyze resting-state fMRI and creative task performance across 10 independent samples from Austria, Canada, China, Japan, and the United States (N= 2433)—constituting the largest and most ethnically diverse creativity neuroscience study to date. Using time-resolved network analysis, we investigate the relationship between creativity (i.e., divergent thinking ability) and dynamic switching between DMN and ECN. We find that creativity, but not general intelligence, can be reliably predicted by the number of DMN-ECN switches. Importantly, we identify an inverted-U relationship between creativity and the
degree of balance between DMN-ECN switching, suggesting that optimal creative performance requires balanced brain network dynamics. Furthermore, an independent task-fMRI validation study (N= 31) demonstrates higher DMN-ECN switching during creative idea generation (compared to a control condition) and replicates the inverted-U relationship. Therefore, we provide robust evidence across multi-center datasets that creativity is tied to the capacity to dynamically switch between brain networks supporting spontaneous and controlled cognition.

Thursday, December 12, 2024

Research byte: Prediction of human #intelligence (#g #Gf #Gc) from #brain (#network) #connectivity - #CHC

Choosing explanation over performance: Insights from machine learning-based prediction of human intelligence from brain connectivity 

PNAS Nexus, Volume 3, Issue 12, December 2024, pgae519,
Online and PDF download available at this link:  https://doi.org/10.1093/pnasnexus/pgae519

Abstract

A growing body of research predicts individual cognitive ability levels from brain characteristics including functional brain connectivity. The majority of this research achieves statistically significant prediction performance but provides limited insight into neurobiological processes underlying the predicted concepts. The insufficient identification of predictive brain characteristics may present an important factor critically contributing to this constraint. Here, we encourage to design predictive modeling studies with an emphasis on interpretability to enhance our conceptual understanding of human cognition. As an example, we investigated in a preregistered study which functional brain connections successfully predict general, crystallized, and fluid intelligence in a sample of 806 healthy adults (replication: N = 322). The choice of the predicted intelligence component as well as the task during which connectivity was measured proved crucial for better understanding intelligence at the neural level. Further, intelligence could be predicted not solely from one specific set of brain connections, but from various combinations of connections with system-wide locations. Such partially redundant, brain-wide functional connectivity characteristics complement intelligence-relevant connectivity of brain regions proposed by established intelligence theories. In sum, our study showcases how future prediction studies on human cognition can enhance explanatory value by prioritizing a systematic evaluation of predictive brain characteristics over maximizing prediction performance (emphasis added).

Thursday, November 07, 2024

McGrew on #IQ scores: In what ways are a car engine, a starling bird #murmuration, and #g (general #intelligence) alike..how are they the same?

Kevin McGrew on IQ scores, borrowing from Detterman (2016) and McGrew et al., (2023)

“General intelligence (represented by a composite IQ score or the factor-analysis derived psychometirc g factor) is a fallible summary statistical (numerical) index of the efficiency of a complex system of dynamically interacting multiple brain networks.  Like the emergent statistical index of horsepower of a car engine, which does not represent a “thing” (a mechanism) in the engine, it reflects the current estimated efficiency of the processing of multiple interacting cognitive abilities and brain networks. It should not be interpreted as being the result of a single brain-based entity or mystical mental energy, as fixed, or reflecting biological/genetic destiny.  The manifest expression of this statistical emergent property index is also influenced by other non-cognitive (conative) (click for relevant article) traits and temporary states of the individual and current environmental variables” (K. McGrew, 11-07-24)


Question.  In what ways are a car engine, a starling bird murmuration, and general intelligence alike..how are they the same?  See slides and comments below for answer


 

(A starling bird murmuration)

Double click on larger more readable images 


















Saturday, September 29, 2018

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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Wednesday, July 18, 2018

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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Sunday, July 15, 2018

Excellent conceptual suggestion for organizing mind wandering research



Trends in Cognitive Sciences, June 2018, Vol. 22, No. 6

ABSTRACT

As empirical research on mind-wandering accelerates, we draw attention to an emerging trend in how mind-wandering is conceptualized. Previously articulated definitions of mind-wandering differ from each other in important ways, yet they also maintain overlapping characteristics. This conceptual structure suggests that mind-wandering is best considered from a family-resemblances perspective, which entails treating it as a graded, heterogeneous construct and clearly measuring and describing the specific aspect(s) of mind-wandering that researchers are investigating. We believe that adopting this family-resemblances approach will increase conceptual and methodological connections among related phenomena in the mind-wandering family and encourage a more nuanced and precise understanding of the many varieties of mind-wandering.

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Wednesday, May 16, 2018

Higher intelligence related to more efficiently organized brains-bigger/larger/more 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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Sunday, May 06, 2018

The salience brain network and personality (self-directedness; cognitive control)

Abstract:

A prevailing topic in personality neuroscience is the question how personality traits are
reflected in the brain. Functional and structural networks have been examined by functional and structural magnetic resonance imaging, however, the structural correlates of functionally defined networks have not been investigated in a personality context. By using the Temperament and Character Inventory (TCI) and Diffusion Tensor Imaging (DTI), the present study assesses in a sample of 116 healthy participants how personality traits proposed in the framework of the biopsychosocial theory on personality relate to white matter pathways delineated by functional network imaging. We show that the character trait self-directedness relates to the overall microstructural integrity of white matter tracts constituting the salience network as indicated by DTI-derived measures. Self-directedness has been proposed as the executive control component of personality and describes the tendency to stay focused on the attainment of long-term goals. The present finding corroborates the view of the salience network as an executive control network that serves maintenance of rules and task-sets to guide ongoing behavior.

Click here for info regarding one of the better brain network overview articles by Bressler and Menon.


Click on image to enlarge



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Saturday, March 17, 2018

The importance of differential psychology for school learning: 90% of school achievement variance is due to student characteristics

This is why the study of individual differences/differential psychology is so important. If you don’t want to read the article you can watch a video of Dr. Detterman where he summarizes his thinking and this paper.

Education and Intelligence: Pity the Poor Teacher because Student Characteristics are more Significant than Teachers or Schools. Article link.

Douglas K. Detterman

Case Western Reserve University (USA)

Abstract

Education has not changed from the beginning of recorded history. The problem is that focus has been on schools and teachers and not students. Here is a simple thought experiment with two conditions: 1) 50 teachers are assigned by their teaching quality to randomly composed classes of 20 students, 2) 50 classes of 20 each are composed by selecting the most able students to fill each class in order and teachers are assigned randomly to classes. In condition 1, teaching ability of each teacher and in condition 2, mean ability level of students in each class is correlated with average gain over the course of instruction. Educational gain will be best predicted by student abilities (up to r = 0.95) and much less by teachers' skill (up to r = 0.32). I argue that seemingly immutable education will not change until we fully understand students and particularly human intelligence. Over the last 50 years in developed countries, evidence has accumulated that only about 10% of school achievement can be attributed to schools and teachers while the remaining 90% is due to characteristics associated with students. Teachers account for from 1% to 7% of total variance at every level of education. For students, intelligence accounts for much of the 90% of variance associated with learning gains. This evidence is reviewed


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Sunday, November 05, 2017

Wig (2017, in press)-Segregated Systems of Human Brain Networks


Click on images to enlarge.

Article link.

This is an excellent and thought provoking brain network review that addresses the push-pull between optimal (and necessary) brain network segregation and more transient and fluid integration “on demand” to meet new task demands. Excellent summary.








ABSTRACT

The organization of the brain network enables its function. Evaluation of this
organization has revealed that large-scale brain networks consist of multiple segregated subnetworks of interacting brain areas. Descriptions of resting state network architecture have provided clues for understanding the functional significance of these segregated subnetworks, many of which corre-
spond to distinct brain systems. The present report synthesizes accumulating evidence to reveal how maintaining segregated brain systems renders the human brain network functionally specialized, adaptable to task demands, and largely resilient following focal brain damage. The organizational properties that support system segregation are harmonious with the properties that promote integration across the network, but confer unique and importantfeatures to the brain network that are central to its function and behavior.

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Wednesday, October 25, 2017

More support for P-FIT model of intelligence

Abstract

The authors describe the brain regions involved in the process of intelligence using as a basis, the models of the theory of frontoparietal integration (P-FIT Model). They also correlate the model described with functional areas of Brodmann, integrating them into the tertiary brain areas and address the subcortical structures involved in cognitive processes, including the memory. The studies performed by functional magnetic resonance, also unmask various regions related with intelligence, neither previously described by Brodmann nor even in conventional models of learning. The anterior insular cortex presents itself as the most recent tertiary area to be considered. Subcortical structures, when injured, mimick injuries to the cerebral cortex, demonstrating their great participation in cognition. The topographies of aphasia and the functioning mechanisms of the bearers of learning disorders, including dyslexic, dysgraphia and dyscalculic should be reconsidered. A better understanding of this topographic anatomy may clarify the mechanisms used in those individuals with cerebral lesions.

Click on images to enlarge.  Article link.







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Monday, December 05, 2016

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

Click on image to enlarge.

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

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

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

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

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

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



Thursday, March 31, 2016

Research Byte: Frontal and Parietal Cortices Show Different Spatiotemporal Dynamics across Problem-solving Stages--Is the P-FIT it?


Yet another study supporting the P-FIT neuro model of intelligence. Overview of P-FIT here. https://en.m.wikipedia.org/wiki/Parieto-frontal_integration_theory

I have previously provided an overview of the P-FIT model of intelligence at the Interactive Metronome-Home blog.

Frontal and Parietal Cortices Show Different Spatiotemporal Dynamics across Problem-solving Stages. - PubMed

Arithmetic problem-solving can be conceptualized as a multistage process ranging from task…

Read it on Flipboard


Read it on ncbi.nlm.nih.gov




Saturday, March 26, 2016

Researh Byte: Lower social intelligence results in greater brain network activity---when on is not socially confident, ones brain works harder

Contributions of self-report and performance-based individual differences measures of social cognitive ability to large-scale neural network functioning

  • Ryan Smith 
  • , Anna Alkozei
  • , William D. S. Killgore

Abstract

Adaptive social behavior appears to require flexible interaction between multiple large-scale brain networks, including the executive control network (ECN), the default mode network (DMN), and the salience network (SN), as well as interactions with the perceptual processing systems these networks function to modulate. Highly connected cortical “hub” regions are also thought to facilitate interactions between these networks, including the dorsolateral prefrontal cortex (DLPFC), dorsomedial prefrontal cortex (DMPFC), anterior cingulate cortex (ACC), and anterior insula (AI). However, less is presently known about the relationship between these network functions and individual differences in social-cognitive abilities. In the present study, 23 healthy adults (12 female) underwent functional magnetic resonance imaging (fMRI) while performing a visually based social judgment task (requiring the evaluation of social dominance in faces). Participants also completed both self-report and performance-based measures of emotional intelligence (EI), as well as measures of personality and facial perception ability. During scanning, social judgment, relative to a control condition involving simple perceptual judgment of facial features in the same stimuli, activated hub regions associated with each of the networks mentioned above (observed clusters included: bilateral DLPFC, DMPFC/ACC, AI, and ventral visual cortex). Interestingly, self-reported and performance-based measures of social-cognitive ability showed opposing associations with these patterns of activation. Specifically, lower self-reported EI and lower openness in personality both independently predicted greater activation within hub regions of the SN, DMN, and ECN (i.e., the DLPFC, DMPFC/ACC, and AI clusters); in contrast, in the same analyses greater scores on performance-based EI measures and on facial perception tasks independently predicted greater activation within hub regions of the SN and ECN (the DLPFC and AI clusters), and also in the ventral visual cortex. These findings suggest that lower confidence in one’s own social-cognitive abilities may promote the allocation of greater cognitive resources to, and improve the performance of, social-cognitive functions.

Keywords

Social Cognition Large-Scale Neural Networks Individual Differences Emotional Intelligence Social Visual Perception

Friday, February 26, 2016

White matter matters! 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!


Monday, January 11, 2016

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.