Thinking, Walking, Talking: The Development of Integratory Brain Function
Gerry Leisman1, Ahmed A. Moustafa2*, Tal Shafir3
1The National Institute for Brain and Rehabilitation Sciences, Israel, 2Department of Veterans Affairs,
New Jersey Health Care System & School of Social Sciences and Psychology & Marcs Institute for Brain
and Behaviour, University of Western Sydney, Australia, 3Haifa Univ, Israel
Submitted to Journal:Frontiers in Public Health
Specialty Section:Child Health and Human Development
Article type:Review Article
Received on:27 Oct 2015
Frontiers website link:www.frontiersin.org
In review Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or financial
relationships that could be construed as a potential conflict of interest
Author contribution statement
All 3 authors wrote the review.
motor processes, Cognitive Processes, Executive Function, Prefrontal Cortex, Cerebellum, Basal Ganglia, premotor cortex.
Word count: 107
In this article, we argue that motor and cognitive processes are not separate, but most likely share similar evolutionary history.
This is supported by clinical and neural data showing that some brain regions play a similar function in both motor and cognitive
functions. In addition, we also argue that cognitive processes play a role for the generation of complex motor output, which is
supported by data from patients with stroke and Parkinson’s disease. Further, we also review data that motor processes can
contribute to cognitive function, as found by many training programs. Thus, motor and cognitive processes have dynamical
bidirectional relationships, which we explain in this article.
(Authors are required to state the ethical considerations of their study in the manuscript including for cases
where the study was exempt from ethical approval procedures.)
Did the study presented in the manuscript involve human or animal subjects: No
Thinking, Walking, Talking: The Development of Integratory Brain
Gerry Leisman,1,2 Ahmed A. Moustafa,3 Tal Shafir4
1The National Institute for Brain and Rehabilitation Sciences, Nazareth, Israel
2Universidad de Ciencias Médicas de la Habana, Facultad Manuel Fajardo
3School of Social Sciences and Psychology, Marcs Institute for Brain and
Behaviour, University of Western Sydney, Sydney, Australia
4Graduate School of Creative Arts Therapies, Faculty of Social Welfare and Health
Sciences, University of Haifa, Israel.
To whom correspondence should be addressed,
Ahmed A. Moustafa,
School of Social Sciences and Psychology & Marcs Institute for Brain and Behaviour,
Western Sydney University, Sydney, NSW.
Email: [email protected]
In this article, we argue that motor and cognitive processes are not separate, but most
likely share similar evolutionary history. This is supported by clinical and neural data
showing that some brain regions play a similar function in both motor and cognitive
functions. In addition, we also argue that cognitive processes play a role for the
generation of complex motor output, which is supported by data from patients with
stroke and Parkinson’s disease. Further, we also review data that motor processes can
contribute to cognitive function, as found by many training programs. Thus, motor
and cognitive processes have dynamical bidirectional relationships, which we explain
in this article.
Key words: motor processes, cognitive processes, executive function, prefrontal
cortex, cerebellum, basal ganglia, premotor cortex.
In an attempt to better understand the relationship between motor and
cognitive functions and the reasons for their linkage, it is important to note that in
humans, bipedalism was a major reason for human neocortical evolution. As bipedal
locomotion is phylogenetically the most sophisticated and complex movement and
characteristically human (even though birds on the ground, some mammals, and
primates possess that function as well), humans are dedicated to this mode of
locomotion. Birds have a larger encephalization index than do their reptile cousins,
with that difference being explainable by bipedalism (Harcourt-Smith, 2010).
Bipedalism in humans is both constant and employs an upright spine, unlike other
organisms with that skill. On this basis, we can conclude that bipedalism was
partially responsible for the development of the large human brain.
This occurred, we postulate, because of the human bipedal posture’s unique
ability to harness gravitational forces through the now upright postural motor system.
Bipedalism has been utilized as a power source to maintain a genetic mutation, having
created larger pools of neurons. It is argued that the same evolutionary process has
allowed us to develop the binding of the motor system into synchronous, rhythmic,
purposeful movement, which expanded to eventually allow for cognitive binding or
Postural muscles, we claim, were the main conduit for this motor and
cognitive binding to evolve and continue to exist (for a more comprehensive review
of the nature of evolutionary brain development, posture, brain size and the
implications for limitations of the pelvis as well as the genetic implications, the reader
is referred to Falk et al., 2012; Dennis et al., 2012; as well as Melillo & Leisman,
2009). Deviations from normal postural development or from normal levels of
postural activity can disrupt or delay cerebellar and cortical maturation and may
disrupt the underlying oscillatory timing mechanisms on which motor and cognitive
binding is based (Vercruyssen & Simmonton, 1994; Alperin et al., 2005; Dijkstra et
al., 2005; Leisman et al., 2014). As a result, cognition, more likely, evolved
secondarily and in parallel to the evolution of motricity. We will explore the
relationship between cognitive and motor functions from evolutionary, physiological,
biomechanical, cognitive, and clinical perspectives.
We will demonstrate that cognitive and motor functions are arguably part of
the same function, even though they have been historically viewed as separate. They
both evolved in parallel as a product of the evolution of sophisticated complex
movement. The same underlying mechanisms that evolved to enable more complex
coordinated movements were adapted and utilized to effect more sophisticated
cognitive processes.(Schmahmann, 1996; Schack, 2004; Melillo & Leisman, 2009;
Koziol et al., 2014; Leisman et al., 2014a).
As a result, there exists an overlap of cognitive and motor functions based on
the articulation of cerebellum, basal ganglia, and frontal lobes, which are brain areas
known to control motor and non-motor intentional and executive function. Most
developmental disabilities have as their most common symptom, motor
incoordination or clumsiness, especially of posture and gait. Impulse control, either
inhibited or facilitated, and judgment disorders can all be attributed to dysfunction of
this network and its control of motor and non-motor behavior. This spectrum of
disorders all involve disruption primarily of what is known as executive processes
which are functions attributed to the frontal lobe.
Motor-Cognitive Interactions: Thinking About Moving
Whether one moves or one is planning to move, or thinking about someone else
moving, overlapping neural networks are activated. Motor-cognitive interactions
involve mental processing in which the motor system draws on stored information to
plan and produce action, as well as to anticipate, predict, and interpret the actions of
others. Reasoning and problem solving rely on these cognitive-motor interactions
(Schmahmann, 1996; Schmahmann & Caplan, 2006; Leisman et al., 2014a)
One may never have thought about how one plans and controls movement, but
we know that actions such as we might see when playing the violin or throwing a ball,
writing, or eating with a knife and fork, are not simply reflexes. Movement is not only
simply triggered by an external stimulus such as what one does upon touching a hot
stove. Movement is the result of a series of mental processes. These mental processes
can be used cognitively even when no movement results.
Many assume that there is continuity between planning and enactment, with
movement being a voluntary displacement of a body part in physical space, whereas
an action consists of a series of movements that must be accomplished in order to
reach a goal. Actions are planned with respect to a specific goal. For example, if you
are thirsty and want to take a sip of coffee, you might look at your coffee mug, reach
toward it, wrap your fingers around the handle, lift the mug, and bring it to your lips.
These motor actions implicitly involve various cognitive functions that allow
successful motor performance.
Motor cognition (i.e., cognitive processes that underlie complex motor output)
encompasses all the mental processes involved in the planning, preparation, and
production of our own actions, as well as the mental processes involved in
anticipating, predicting, and interpreting the actions of others. Motor-cognitive
interactions can be best understood through the perception-action cycle involving the
transformation of perceived patterns of intended movement into coordinated patterns
of actual movement. For example, toddlers who do not perform this function well, or
some of those who have suffered a stroke impairing their ability to descend a flight of
stairs with automaticity, will descend one step at a time. We, on the other hand,
descend one step and “know” that the remaining steps each possess the same riserheight
as the preceding step. We casually notice and compute how high each step in a
stairway rises, and we lower our feet accordingly (Gibson, 1966).
Even the seemingly simple movement planning required for descending a
staircase – unconsciously determining when and how low to lower one’s feet – relies
on a sophisticated set of neural processes. Evolutionarily speaking, perception exists
not just to recognize objects and events, but also to provide guidance and feedback for
our movements, so that a given movement is efficient and successful in its aim.
In addition, it is not just that perception exists partly in the service of planning
movements; our movements allow us to perceive, which in turn allows us to plan our
subsequent movements. Thus, the relationship between motor and perceptual
processes is dynamical. This is how normally functioning adults and not toddlers or
some post-stroke patients possess automaticity in descending a staircase. Animals
move so that they can obtain food, and eat so that they can then move; they move so
that they can perceive, and perceive so that they can move. Perception and action are
mutually intertwined and interdependent – and motor cognition lies at the heart of how
the two interact. We plan so that we will reach an action goal, and what we perceive
lets us know whether we are getting closer to that goal, or are on the wrong track. The
mediating link between perception and action involves representation or shared
coding in our brains of perception and action, and the contents of both perceptions
and intentions – mental plans designed to achieve a goal through action – depends on
neural processes with both perceptual and motor aspects (Haggard, 2005).
SPECIALIZATION OF COGNITIVE AND MOTOR BRAIN REGIONS
A Non-Localizationist Description of the Neuroscience of Motor Processing
Motor cognition is localized in brain areas responsible for movement control.
Motor processes in the brain are supported by various control centers, with the
principal area consisting of M1, which is the “lowest level” motor area for the control
of fine motor movements, and with fibers directly innervating the muscles
themselves. In addition, the premotor area (PM) is involved in setting up programs for
specific sequences of movements (and sends input to M1), and the supplementary
motor area (SMA) is involved in setting up and executing action plans. Thus, these
areas are often regarded as forming a hierarchy, with M1 at the bottom and SMA at
the top. For our present purposes we cannot go too far wrong by considering the areas
as processing increasingly abstract sorts of information, from specific movements
(M1) to less precisely specified sets of movements (PM), to overarching plans for
action (SMA). These three areas are illustrated in Fig. 1.
Figure 1. Motor areas in the frontal lobe. The premotor cortex on the lateral surface of the brain is
divided into the dorsal and ventral premotor areas (PMd and PMv) and the supplementary motor
cortex on the medial wall of the brain can be divided into the supplementary motor and presupplementary motor areas (SMA and pre-SMA). The premotor cortex below the superior frontal
sulcus is typically considered PMv whereas premotor cortex above this anatomical landmark is
typically considered PMd. The vertical anterior-commissural line is often used to denote the boundary
between SMA and pre-SMA. One can further divide PMd according to a rostral subdivision located
along the superior frontal gyrus and a caudal subdivision located along the precentral gyrus. There
also exists two cingulate motor areas (RCZa and RZp) anterior to the vertical anterior-commissural
line and one cingulate motor area (CCZ) posterior to the vertical anterior-commissural line. This
parcellation of non-primary motor areas in the human was proposed by Picard and Strick (2001).
Several studies have compared neuronal activity in M1, PM, and SMA during
the preparation of motor responses to investigate the distinction between processing
of tasks instigated by an external stimulus (for example, reaching to turn off one’s
alarm clock) and ones that are internally initiated (for example, setting one’s alarm
clock). In the latter case, one needs to plan the movement sequence in advance; in the
former case one does not, as the movements are executed in response to the external
stimulus. Mushiake and colleagues (1991) recorded single-cell activity in the M1,
PM, and SMA of monkeys immediately before and while they were carrying out a
sequential motor task. The key to the experiment was that a movement sequence was
either visually triggered (VT) or internally triggered (IT). In the VT condition,
monkeys were required to touch three pads on a panel as they were illuminated in a
random sequence. In the IT condition the monkeys were required to remember a
predetermined sequence and press it on a keypad without visual guidance.
The results showed that most M1 neurons exhibited similar activity during
both pre-movement and movement periods, in both the IT and VT conditions. This
makes sense, because the same movements ultimately were produced in the two
conditions. However, in SMA, more neurons were active in the IT condition than in
the VT condition during both the pre-movement and movement periods, which
suggests that having to formulate a plan involves SMA. In contrast, in PM more
neurons were active during the VT than the IT condition in both periods, which
suggests that this area is involved in setting up specific movement sequences. These
findings show that motor production as a whole – both pre-movement and movement –
exists at a number of levels of processing; moreover, neural processing differs when
one formulates a plan in advance and when one simply responds to an environmental
cue. The discovery that these three brain areas operate on increasingly more specific
information might suggest that the areas always operate strictly in sequence;
specifically, it might be tempting to think that SMA finishes processing and only then
directs PM, which finishes its processing and only then in turn directs M1. But this
apparently is not the case. Instead, other neural evidence suggests that the three brain
areas do not always operate in this sequence, but instead interact in complex ways.
Nevertheless, different brain regions play different roles in the conception,
initiation, and control of action. We have already seen that the SMA is involved in the
organization of motor sequences based on plans, and that PM is involved in the
preparation of a specific action. In addition, the prefrontal cortex and basal ganglia
are involved in the initiation and in the temporal organization of action and the
cerebellum is involved in the temporal control of action sequences (cf. Leisman et al.,
2012a; 2014a; Leisman & Melillo, 2013). All these regions show anticipatory activity
in relation to a forthcoming action. In fact, connections from one area to another
typically are mirrored by feedback connections from the “receiving” area to the
“sending” one; information runs in both directions presumably allowing the areas to
coordinate their processing.
In short, motor cognition relies on a multicomponent system, with many
distinct processes that occur simultaneously, and these processes occur in different
brain regions that support different neural networks.
Functional Networks of the Brain
Early childhood is marked by a lack of localized brain function and over the lifespan
human skills become controlled by regional centers as a way of effecting better and
optimized cognitive and motor performance. Connections between different parts of
the brain increase throughout childhood and well into adulthood. The resulting
increase in connectivity shapes how well different parts of the brain work in tandem
(Melillo & Leisman, 2009). Research is finding that the extent of connectivity is
related to growth in intellectual capacities such as memory and reading ability. The
rate of regional brain development differs from one individual to the next and within
the same individual (Melillo & Leisman, 2009; Leisman, et al., 2015).
Infancy is characterized by clumsy and non-optimized motor behavior and
similar less integrated cognitive performance. In the same way an adult being fully
fluent in a late-acquired second language is less optimized or efficient in that second
language compared to his or her mother tongue, as rated by functional connectivities
(cf. Leisman et al., 2012a; Leisman & Melillo, 2015). Localization of function in the
brain is the result of a need for automated and optimized performance in the adult
requiring efficient local rather than multi-focal control (Bullmore & Sporns, 2009;
2012; Melillo & Leisman, 2009; Leisman & Melillo, 2015; Leisman et al., 2014b;
Gilchreist, 2012; 2015). This is most clearly exemplified by multi brain regions
necessary for the control of language in late bilinguals rather than circumscribed local
control regions in early bilinguals, with the latter presenting with language control
centers in the same region for both languages, and distributed control noticed in late
bilinguals after language has become localized developmentally in all cases.
The concept of ‘functional specialization’ assumes that a cortical area is
specialized for certain aspects of cognitive and or motor function, allowing for the
anatomical segregation of an area from its surrounding cortex. For example, the
posterior wall of the precentral gyrus contains a microstructural entity, labeled
‘Brodmann’s area 4’ due to its distinct cytoarchitecture (Brodmann, 1999). Otfried
Foerster was one of the first scientists to note that within this area ‘stimulation of a
given focus produces a single isolated movement of the corresponding part of the
body’ (Foerster, 1936, p. 137). Since then an overwhelming number of studies have
used cortical stimulation approaches or functional neuroimaging techniques, and
investigated in great detail the functional properties of that area, which was later
termed ‘primary motor cortex’ (M1) (Penfield and Rasmussen, 1952; Fink et al.,
1997; Hallett, 2000; Schieber, 2000; Dum and Strick, 2002).
However, localizing activity in a distinct cortical region does not explain how
spatially distributed areas are bound together for mediating and/or sustaining a
perceptual or motor process. Functional specialization is therefore only meaningful in
the context of ‘functional integration’ (Leisman and Koch, 2009; Koch & Leisman,
2012; Friston, 1994). The concept of functional integration assumes that sensory,
motor or cognitive processes rely on context-dependent interactions between different
brain regions mediated by specific anatomical connections (Friston, 2002). For
example, activity in M1 might be driven by facilitatory or inhibitory influences from
premotor areas that themselves interact with activity in prefrontal, posterior-parietal
or sensory areas (Rizzolatti et al., 1998; Pascual-Leone et al., 2000; Grefkes et al.,
2010). It is conceivable, however, that the spatial separation of brain areas within or
between functional networks might also constitute an important mechanism
preventing potential interference during processing of competing information or tasks
(Gee et al., 2011). Furthermore, other concepts of brain organization, such as the
theory of inter-hemispheric rivalry and competitive feedback inhibition (Kinsbourne,
2006), the concept of oscillatory patterns for supporting, propagating and
coordinating cross-neuronal interactions (Llinas et al., 1999; Buzsaki and Draguhn,
2004; Logothetis et al., 2007; Hoerzer et al., 2010), the universal control system
theory (Kazantsev et al., 2003), and the concept of synaptic homoeostasis for the
stabilization of neuronal circuits (Turrigiano, 2007) all underpin the relevance of a
network perspective for describing and explaining brain function. Hence, a
connectivity-based system perspective seems to be much closer to the neurobiology
underlying brain function under both physiological and pathological conditions
compared with approaches assigning specific behaviors (or clinical symptoms) to
anatomically segregated regions. Further, functional deficits in cognitive-motor
function are more likely the result from problems in the functional networks rather
than dysfunction in a localized area, the former resulting in disorders of optimization
and efficiency rather than in a complete loss of function.
In an attempt to understand the nature of integratory function of the brain, we
can start by observing that although the left hemisphere is nominally dedicated to the
control of the language function in most individuals, patients with right hemisphere
damage have difficulty understanding linguistic units that have more than one
meaning, and they fail to use broad contextual information in the interpretation of
connected discourse (Chiarello, 1991).
Studies of event-related potentials in intact individuals show that the right
hemisphere is activated by semantic processing whereas the left is activated primarily
by syntax processing (Neville et al., 1991). Studies of the language abilities of
patients who have had their left hemispheres entirely removed because of severe
pathology similarly suggest that the right hemisphere can support many language
functions but that the left is necessary for normal syntax. Together these findings
suggest that for adults the right hemisphere is involved in semantics and pragmatics
but syntax is the province of the left hemisphere. There are three major
distinguishable components of syntax that relate to motor function: 1) the principal
categories of words (nouns and verbs, with the dependent categories of adjectives and
adverbs), 2) ordering of words, including sub-ordering, that is, the clustering of words
within a larger order, and 3) function words (including sub-words e.g. morphemes
such as terminations of abstract nouns, verb inflections etc.). The syntax of a language
results from the co-operation and interaction of these three components. A motor
theory of language has motor programs and the principles for combining motor
programs as the underlying structure of language. There also exists a close link
between motor control (action organization) and perception (the organization of
vision). For each of the three components in syntax, the relation to motor theory may
take the form of: 1) a relation directly with the organization of action (referred to by
one writer as ‘the grammar of action’); 2) a relation directly with the organization of
perception (referred to by Gregory years ago as ‘the grammar of vision’ (Gregory,
1972, p. 622). Vision, of course, is motor-based; the eye sees by the combination of
saccades and fixations plus a constant (structural) tremor, which appears to play an
essential role in maintaining vision.
In children, the right hemisphere seems to be more important for language
acquisition than for language processing. If brain damage is suffered in infancy prior
to language acquisition, right hemisphere damage is more detrimental to future
language acquisition than left hemisphere damage (Stileset al., 1998; Herschensohn,
2007). Another difficulty with concluding that the left hemisphere is the language
organ is that although the left hemisphere is primarily responsible for language, it is
not dedicated specifically to language. It has been suggested that the left hemisphere
is specialized for executing well-practiced routines (cf. Mills, Coffey-Corina, &
The fewer brain regions necessary for the control of an operation, the more
optimized or efficient the function, but that is not to say that there is no further control
possible if those areas are destroyed or dysfunctional. If that were the case, then there
would be no basis for rehabilitation and recovery after stroke for example (Leisman &
Melillo, 2015; Leisman et al., 2014a). Specialization is necessitated by the brain’s
need to optimize control. Integratory function within system is still an issue of
coupling as represented in Fig. 2 where we can see effective connectivity of motor
network during unimanual hand movements (c.f., Grefkes et al., 2008) compared with
inefficiencies associated with individuals post-stroke.
Networks of Cognitive Movement Interaction
One of the primary functions of neurological development of the nervous
system is the integration of developing systems so that function will be localized
for more efficiency. But that is not to say that the system must work by localized
control (Land, 2006; Yarbus, 1967). For example, the languages that are learned in
early childhood prior to the development of Broca’s and Wernicke’s areas, with
their nominal control of expressive and receptive language respectively, are
learned fast as a consequence of the exuberant neuronal connectivities present
in early childhood development (Leisman & Melillo, 2015; Leisman et al., 2014a).
These abundant connectivities in childhood allow for the rapid
acquisition of knowledge. The system of exuberant connectivities in childhood
renders the nervous system less optimized than the adult brain-state and its
resultant localization of function. When that now optimized localization of
function has developed, the number of potential connectivities is significantly
reduced. Specialization of cortical regions optimizes the system but does so by
concentrating the networks in a circumscribed area allowing for more effective
temporal as well as spatially represented responses. In short, more potential
connectivities in early childhood will lead to greater automatization of skilldevelopment
and localized function in the normal adult and less of an ability of
the adult to acquire information with as much ease as in early childhood.
The concept of ‘‘cortical efficiency’’ that has been described elsewhere
(Falkmer & Gregersen, 2005; Norton & Stark, 1971; Duchowski, 2003; von
Helmholtz, 1925; Gilchreist, 2012; 2015) indicates that higher ability in a cognitive
task is associated with more efficient neural processing and not necessarily with
a particular brain region that is involved in that processing. Intuitively, we would
expect higher performance to correlate with more activity. For the cerebral
cortex, however, the contrary was found to be the case. Higher performance in
several behavioral tasks, including verbal (Demir et al., 2014), numeric, figural,
and spatial reasoning (Posner et al., 1980; Triesman & Gelade, 1980) is consistent
with the reduced consumption of energy in several cortical areas. This
phenomenon has also been studied with EEG techniques in different frequency
bands. The amount of a background power (7.5–12.5 Hz) decreases during
cognitive activity compared with the resting state. This decrease has been
observed to correlate with higher performance in subjects with higher IQ scores
or with higher performance after training, indicating a more efficient processing
strategy for a cognitive task (Nodine & Krundel, 1987).
The function of neurological development in childhood is to facilitate the
dynamic creation of functional specialization. As these abilities can be changed,
they are therefore plastic. This localization of function is not the explanation of
how cognitive processes are controlled in the brain, but rather the end-result of
training. The efficiency of cognitive function is directly related to the
effectiveness of network organization in the neocortex and can be measured
now: fewer brain regions necessary to accomplish a single task in one individual
compared to another for the same task is a measure of efficiency.
Both cognitive and motor functions require the learning of sequential
actions. These sequences are most optimized with control by specialized
networks mediated by both executive function and automaticity. The learning of
complex sequences requires adequately functioning executive processes (e.g.
those involved with error monitoring or motor program structuring). Structural
complexity remains the same for any sequence. Activations at varying levels of
complexity have demonstrated overlap in the supplementary motor cortex
(Brown et al., 2006) and other brain regions, such as the cerebellum, basal ganglia
pre-motor cortex, thalamus, ventro-lateral pre-motor cortex, and precuneus,
with increased activations at increased levels of complexity (Leisman et al.,
Executive function and action intersect and cooperate with each other
(Leisman et al., 2014a). Useful actions and automatisms are initially acquired during
childhood and youth, and continue to be acquired throughout the course of life, by
means of incidental experience and by formal education. There is obvious advantage
to automate actions such as walking down a flight of stairs as compared to, playing a
tennis match, or the violin. Voluntary (i.e., cognitively interacting) vs. automated
action are described in Fig. 3.
To understand better how connectivity analysis can allow us to examine
processing efficiencies in cognitive motor interaction, we must pin down the concept
of brain connectivities (Horwitz, 2003). Brain connectivity can be divided into three
main concepts: (a) anatomical (or structural) connectivity measured in terms of
physical (and chemical) connections between neuronal populations or individual
neurons, (b) “functional” connectivity by which we mean the statistical similarity
between activities in distributed neuronal populations, and (c) “effective”
connectivity, which speaks to the directed influence the activity of one region exerts
onto another region’s activity in a given context (Sporns, 2010).
This distinction is useful for our discussion in that the measurement
instruments and data analytical tools at our disposal have mainly focused on each
aspect separately (Guye et al., 2008). Effective connectivity can be defined as the
influence one node of neurons exerts over another (Buchel & Friston, 1997). Effective
connectivity is inferred using a model of neuronal integration by estimating model
parameters that best explain observed fMRI or EEG/MEG signals. Effective
connectivity is important in analysis of functional integration as the underlying model
defines the mechanisms of neuronal coupling. It is exemplified in Fig 4..
Because of the linkage between motor and cognitive function, it is our
contention that inactivity has an effect of rending an individual’s cognitive as well as
motor performance less efficient or utilizing significantly decreased modes of
functional and effective connectivities and exercise has the converse effect (Leisman
et al 2014a; Castelli et al., 2007: Chaddock et al., 2010a; 2010b; Pontifex et al., 2013;
Kamijo et al., 2012; Raine et al., 2013). Fig. 5 demonstrates significant motor system
activation with different action verbs using qEEG (Pulvermüller, Härle, & Hummel,
2001) and supporting the application of the relationship between language function
with action visualized or actualized. Pulvermüller et al. (2011) recorded brain
electrical activity elicited by visually presented words using current source densities.
Verbs referring to actions usually performed with different body parts were
compared. Significant topographical differences in brain activity elicited by verb
types were found starting at 250 ms after word onset. At the vertex, close to the
cortical representation of the leg, leg-related verbs (for example, to walk) produced
strongest ingoing currents, whereas for face-related verbs (for example, to talk) the
most ingoing activity was seen at more lateral electrodes placed over the left Sylvian
fissure, close to the representation of the articulators. Action words caused differential
activation along the motor strip, with strongest in-going activity occurring close to the
cortical representation of the body parts primarily used for carrying out the actions to
which the verbs refer.
What trauma or disease can do to cognitive-motor interaction is to render the
networks between the two sets of skills less efficient in ways similar to the
inefficiencies seen in early child. The concept of “re-habilitation” speaks to that
notion with habilitating for a second time. Figs. 2 and 6 demonstrate connectivity
parameters between nodes of a motor network that changes the efficiency of the
network as a consequence of stroke.
Most systems in the body possess a dynamic interaction through feedback
between different brain regions that involve feedback connections. In applying this
notion to cognitive-motor interaction, one should be able to effect motor performance
by cognitive imagery and cognitive performance by motor and movement exercise.
Motor imagery is viewed as a window to cognitive-motor processes and particularly
to motor control. Mental simulation theory (Jeannerod, 2001) stresses that cognitive
motor processes such as motor imagery and action observation share the same
representations as motor execution. Munzert et al., (2009) overviewed motor imagery
studies that support and extend predictions from mental simulation theory. They noted
that motor areas in the brain play an important role in motor imagery. The literature
supports the notion that mental training procedures can be applied as a therapeutic
tool in motor rehabilitation (Cho et al., 2012).
The Effects of Motor Processes on Cognitive Function
We know that cognitive exercises and motor imagery can effect overall motor
performance (Leisman, 2012; Leisman et al. 2012b), but does the reverse hold true?
Can motor training and exercise affect cognitive performance? The “mind” and its
attendant cognitive abilities is no longer conceived of as a set of logical/abstract
functions, but rather as a biological system rooted in bodily experience and
interconnected with bodily action and interaction with other individuals. From this
perspective, action and representation are no longer interpreted in terms of the classic
physical–mental dichotomy, but are closely interconnected. Acting in the world,
interacting with objects and individuals in it, representing the world, perceiving it,
categorizing it, and understanding its significance are different levels of the same
relational link that exists between organisms and the local environments in which they
operate, think, and live. Research on canonical and mirror neurons reinterprets the
motor system’s role within the entire schema of the central nervous system and is
particularly important for going beyond the mind–body dichotomy between thought
and action. Internal simulation of motor acts during imagery or observation of others’
movements enable social cognition, empathy and understanding of others’ intentions
and emotions (Gallese and Sinigaglia, 2011), as well as affect one’s own emotions
(Shafir et al, 2013). But beyond the cognitive-motor interaction at the brain level,
movement itself can affect cognition.
A meta-analytic study was conducted (Garbarini & Adenzato, 2004) to
examine the hypothesis that aerobic fitness training enhances the cognitive vitality of
healthy but sedentary older adults. Eighteen intervention studies published between
1966 and 2001 were entered into the analysis. It was determined that fitness training
has robust benefits for cognition, with the largest fitness-induced benefits occurring
for executive-control processes. The results support the notion that cognitive function
and neural plasticity is maintained throughout the lifespan and that a relationship
exists between fitness and cognitive function.
Physical activity improves cognitive function and brain plasticity (Ratey et
al., 2011). The significance of this relationship is even more important than ever
given the increase in aging populations with declining health and cognitive functions.
Kramer and Erickson (2007a; 2007b) evaluated the hypothesis that physical activity
and exercise might serve to protect, and also enhance, cognitive and brain function
across the adult lifespan. They critically reviewed the literature of the effects of
physical activity and exercise on cognition, brain function and brain structure of
adults, including epidemiological or prospective observational studies, randomized
human clinical interventions and non-human animal studies. They noted that the
literature supports the claim that physical activity enhances cognitive and brain
function, and protects against the development of neurodegenerative diseases. The
research, however, has been unclear in determining how much exercise is needed and
how long these benefits can last. A recent study conducted at the University of
Adelaide in Australia (McDonnell et al., 2013) suggests that one 30-minute session of
vigorous exercise can lead to changes in the brain that make it more “plastic,”
including improvements in memory and motor skill coordination.
Hillman et al. (2009a) reported on the effects of acute moderate treadmill walking on
behavioral and neuroelectric indices of the cognitive control of attention and on
aspects of cognition involved in school-based academic performance. The resting
session consisted of cognitive testing followed by a cardiorespiratory fitness
assessment to determine aerobic fitness. The exercise session consisted of 20 min of
walking on a motor-driven treadmill at 60 percent of estimated maximum heart rate
followed by cognitive testing once heart rate returned to within 10% of pre-exercise
levels. The results indicated an improvement in response accuracy; larger P3
amplitude represented in Fig. 7, and better performance on academic achievement
tests following aerobic exercise relative to the resting session. Collectively, these
findings indicate that aerobic exercise improves attention and academic performance.
These data suggest that single bouts of exercise affect specific underlying processes
that support cognitive health and may be necessary for effective functioning across
Transcranial magnetic stimulation (TMS) is widely used to study the
properties of corticospinal pathways. In recent years, it has also been used to study
cortical reorganization in response to interventions such as amputation, afferent
stimulation, motor learning, cortical and spinal lesions, ischaemia and limb
immobilization (reviewed in Cohen et al. 1998). Smith and colleagues (2014)
recruited a small group of adults in their late 20s and early 30s who were asked to ride
exercise bikes for a period of 30 minutes. Changes in the brain directly after the
exercise session were monitored and again 15 minutes later. Cortical stimulusresponse
curves [90%-150% resting motor threshold (RMT)] were investigated as
well as short-interval intracortical inhibition (SICI) before and at 0 and 15 min
following 30 min of ergometer cycling at low-moderate or moderate-high intensity.
Results showed that one 30-minute session of physical activity could improve the
brain’s plasticity, or its ability to change physically, functionally, and chemically.
Positive changes in the brain were sustained 15-minutes after exercising. Plasticity in
the brain is important for learning, memory and motor skill coordination (Leisman
2011). The more ‘plastic’ the brain becomes, the more it can modify the number and
strength of connections between nerve cells and different brain areas. This exerciserelated
change in the brain may, in part, explain why physical activity has a positive
effect on memory and higher-level functions.
A similar study conducted at the University of Illinois at Urbana-Champaign
found that regular exercise benefits the brain’s supply of white matter, facilitating
connectivities between different regions of grey matter in the cerebrum. Researchers
assessed the link between physical fitness and the brain in 24 9- and 10-year-olds.
Children who were more physically fit had thicker and denser white matter, meaning
they had a greater capacity for memory, attention span, and cognitive efficiency
(Chaddock-Heyman et al., 2014).
Exercise may be an effective approach to preserve and improve cognitive
function and brain health prior to the time that individuals reach the “Fourth Age.”
For example, randomized clinical trials have found that moderate-intensity exercise
Other studies demonstrated that higher fitness levels and greater amounts of physical
activity are associated with greater gray matter volumes (Erickson et al., 2010;
Weinstein et al., 2012), white matter integrity (Tian et al., 2014), reduced brain
atrophy rates (Gow et al., 2012; Smith et al., 2014), improved memory and executive
function (Kramer et al., 1999; Colcombe and Kramer, 2003; Smith et al., 2010)
increased prefrontal cortex (Colcombe et al., 2006) and hippocampal volume
(Erickson et al., 2011), as well as brain network connectivity (Leisman & Melillo,
2015; Voss et al., 2010a; 2010b) and reduced risk of cognitive impairment and
dementia (Larson et al., 2006; Sofi et al., 2011). (For an extensive review of the
effects of physical activity across the life span see Voelcker-Rehage & Niemann
The mechanisms by which exercise improves or maintains brain health in
humans remain poorly understood, but likely include changes in inflammation, insulin
resistance, central changes in serotonin, dopamine, or other neurotransmitters, as well
as increased expression of BDNF and other neurotrophic factors (Ratey et al., 2011;
Silverman & Deuster 2014))
It has been known that individuals who are markedly late in achieving
developmental motor milestones are at high risk for subsequent cognitive impairment
(Murray et al., 2006; 2007). The mechanisms underlying infant and adult motor and
cognitive associations remain poorly characterized and we have attempted here to
create a basis for the characterization of motor cognitive interactions. The neural
systems that subserve motor development in infancy also contribute to the
development and operation of cognitive processes later in life. Factors related to
efficiencies in such systems may be reflected in both rapid motor developments early
in life and subsequently in improved cognitive functions throughout the life span
(Murray et al., 2006; Ridler et al., 2006). However, a number of questions remain
concerning the specificity of associations between early motor development and later
cognitive functions as well as between adult motor activity and cognitive function,
which, if they could be answered, could shed light on the reasons behind the
associations. For example, is early motor development associated with other
developmental domains, such as language? Are cognitive-motor interactions confined
to specific domains of cognition (e.g., executive function), or do these interactions
also apply to general intellectual function? Does the association between motor and
cognitive function in infancy continue into adulthood and aging?
Murray and colleagues (2007) examined these questions in a large British
general population birth cohort in which measurements were available for
development in language and motor domains in infancy, general intellectual function
in childhood and adolescence, and specific neuropsychological function (e.g., verbal
fluency, executive/frontal lobe function) in adulthood. These authors (Murray et al.,
2007) noted that faster attainment of motor developmental milestones is related to
better adult cognitive performance in some domains, such as executive function.
The developing infant is concerned with navigating to items of interest and
exploring the environment, ultimately to develop a sense of self, independent of the
environment to which he or she is circumnavigating. The central idea concerns the
influence on a proceeding (or currently planned) muscular act (Taanila et al., 2005).
That influence stems from motivation-triggered anticipation of the act’s outcome, and
it is conjectured to prevail only if “consciousness” is present (Ridler et al., 2006).
When a child attempts its first step, prior attainment of the balanced upright
position will have involved failed attempts, with attendant pain. What leads to
discomfort will have been stored as memory of possible sensory feedback resulting
from certain self-paced movements. Likewise, the fact that specific muscular
movements can achieve forward motion will already be part of a repertoire accessible
to the child. Ultimately, the child hits upon the correct combination and timing of
elemental movements and the first successful steps are taken. That consolidation into
a more complex motor pattern is temporarily deposited in explicit memory (Squire,
1992), and subsequently transferred to long-term implicit memory (Schacter, 1990),
probably during the frequent periods of sleep (Leisman, 2013a; 2013b), characteristic
of infancy. Soon, the toddler is able to walk without concentrating on every step, and
more complicated foot-related scenarios will enjoy brief sojourns at the center of the
The system conjures up a simulated probable outcome of the intended motor
pattern, and vetoes it if the prognosis is adverse. The simulated outcome lies below
the threshold for actual movement, and the mimicking requires two-way interaction
between the nervous system and the spindles (Proske et al., 2000) associated with the
skeletal musculature, particularly when the muscles are already in the process of
doing something else. The interplay provides the basis of sensation, this always being
in the service of anticipation. We can think without acting, act without thinking, act
while thinking about that act, and act while thinking about something else.
Our acts can be composite, several muscular patterns being activated
concurrently, though we appear not to be able to simultaneously maintain two streams
of thought. (Cotterill, 2001) When we think about one thing while doing something
else, it is always our thoughts, which are the focus of attention. This suggests that
there are least two thresholds, the higher associated with overt movement and the
lower with thought. Assuming that the signals underlying competing potential
thoughts must race each other to a threshold (Bundesen, 1988), it may be significant
that cortical and thalamic projections form no strong loops (Crick and Koch, 1998).
The presence of strong loops could make overt movement too automatic. We can now
add a second possible penalty; thoughts might otherwise establish themselves by
default. One should note that overt movement and mere imagery-that is, covert
preparations for movement, appear to involve identical areas (Jeannerrod, 1999).
The bottleneck in sensory processing (Broadbent, 1958) arises because
planning of movement is forced to avoid potential conflict between the individual
muscles. Because we learn about the world only through our actual or simulated
muscular movements, this is postulated to produce the unity of conscious experience.
Intelligence then becomes a measure of the facility for consolidating elementary
movements (overt or covert) into more complex motor patterns, while creativity is the
capacity for probing novel consolidations of motor responses.
Thoughts, according to this scheme, are merely simulated interactions with the
environment, and their ultimate function is the addition of new implicit memories,
new standard routes from sensory input to permit motor output or new optimized
complex reflexes. The duality of routes could well underlie the interplay between
explicit and implicit in brain function.
The child public health implications of cognitive motor interaction are
significant. There exists a pandemic of physical inactivity among all age groups.
Recent reports forecast that inactivity will continue to rise throughout the
industrialized world over the next few decades (Ng & Popkin, 2012). Although the
consequences of physical inactivity on health are well known, its effects on cognitive
and brain health are only beginning to emerge.
Kamijo et al., (2012) investigated inhibitory control and spatial working
memory in a large sample of preadolescents whose aerobic fitness was determined
using the PACER test. Importantly, even though using a field test of aerobic fitness,
the investigators found a significant relation between children’s cognitive skills,
working memory, in particular, and fitness in general that previous investigations had
uncovered using primarily laboratory measures (Hillman at al., 2008; 2009b).
Castelli et al. (2007), in a relatively large sample, found a relationship between
physical fitness and achievement test performance in third-fifth graders. Their
“Fitnessgram” was based on aerobic capacity (PACER), muscle (strength and
flexibility), and the participants’ body-mass index. The results are represented in Fig.
Kamijo et al., (2012) had examined the relationship between physical exercise and
academic achievement as did Hillman et al. (2008) with a summary of the results
reflected in Figs 9(A) and (B).
Figure 9. Effects of exercise on (A) cognitive performance. The significant results of a
meta-analysis of the effects of fitness training on four different cognitive tasks. (B)
Effects of Academic Skills (from Hillman et al., 2008).
In further evidence of the the significant effects of obesity and adiposity on
academic performance, Kamijo et al. (2012) noted significant relationships between
adiposity, cognition, and achievement as reflected in Fig. 10.
Figure 10. Adiposity, cognition and academic achievement. The relationship between
abdominal fat mass (in Kg) on cognitive inhibitory control, working memory, and
academic achievement (N=122 children between the ages of 7-9 years controlled for
age, sex, fitness, Socio-economic-status, and IQ) (from Kamijo et al., 2012).
In Fig. 11, physical motor activity effects on brain at baseline are noted in contrast to the effects of walking in excess of 72 streets with the differences being represented in greater gray matter volume. Of interest is the fact that Cotman and
Berchtold (20012) have found that with exercise, brain-derived neurotrophic factor
In review 19 (BDNF) that encourages the growth and differentiation of synapses in the
hippocampus and basal forebrain and which is vital to learning, memory and thinking,
is significantly increased in rodents. Of interest is the fact that although BDNF
increases in mouse hippocampus after 7 days of volunteer wheel running compared to
sedentary mice (Cotman & Berchtold, 2002), discontinuation of the exercise
according to (Nishijima & Llorens-Martin, 2013) reverses the increased cell number
as well as the cognitive gain in rodents.
Figure 11. Walking associated with gray matter volume increases in specific brain
regions. (A) Demonstrates the effects of physical activity on the brain at baseline. (B)
Demonstrates the effects of walking greater than 72 streets showing greater gray
We can conclude then that movement facilitates cognition throughout the life
span. Children as well as adults need to move to get smarter and maintain optimum
cognitive and physical health and exercise can facilitate cognitive and neurological
Aguiar, A.S. Jr., Castro, A.A., Moreira, E.L., Glaser, V., Santos, A.R., Tasca, C.I.,
(2011). Short bouts of mild-intensity physical exercise improve spatial
learning and memory in aging rats: involvement of hippocampal plasticity via
AKT, CREB and BDNF signaling. Mech. Ageing Dev.132, 560–567.
Alperin, N., Lee, S.H., Sivaramakrishnan, A., Hushek, S.G. (2005). Quantifying the
effect of posture on intracranial physiology in humans by MRI flow studies. J.
Magn. Reson. Imaging. 22,591–596.
Baker, L.D., Frank, L.L., Foster-Schubert, K., Green, P. S., Wilkinson, C.W.,
McTiernan, A. (2010). Effects of aerobic exercise on mild cognitive
impairment: a controlled trial. Arch. Neurol. 67, 71–79.
Bechara, R.G., Kelly, A.M. (2013). Exercise improves object recognition memory
and induces BDNF expression and cell proliferation in cognitively enriched
rats. Behav. Brain Res. 245, 96–100.
Broadbent, D.E. (1958). Perception and Communication. Oxford University Press,
Brodmann, K. (1999). Brodmann’s localisation in the cerebral cortex / (translated
with editorial notes and an introduction by Garey, L.J.). London, Imperial
Brown, S., Martinez, M.J., Parsons, L.M. (2006). Music and language side by side in
the brain: a PET study of the generation of melodies and sentences. Eur. J.
Neurosci. 23(10), 2791-2803.
Büchel, C. and Friston, K. J. (1997). Effective connectivity and neuroimaging. In
SPMcourse, short course notes, chapter 6. Institute of Neurology, Wellcome
Department of Cognitive Neurology, London, UK.
http://www.fil.ion.ucl.ac.uk/spm/course/notes97/Ch6.pdf. (downloaded 09-
Bullmore, E., Sporns, O. (2009). Complex brain networks: graph theoretical analysis
of structural and functional systems. Nat. Rev. Neurosci. 10(3),186-98. doi:
10.1038/nrn2575. Epub 2009 Feb 4.
Bullmore, E,, Sporns, O. (2012). The economy of brain network organization. Nat.
Rev. Neurosci. 13(5):336-349.
Bundesen, C., (1998). A computational theory of visual attention. Phil. Trans. R. Soc.
Lond. B. 353, 1271–1281.
Buzsaki, G., Draguhn, A. (2004). Neuronal oscillations in cortical networks. Science.
Castelli, D.M., Hillman, C.H., Buck, S.M., Erwin, H.E. (2007). Physical fitness and
academic achievement in third- and fifth-grade students. J. Sport Exerc.
Chaddock,L., Erickson, K.I., Prakash, R.S., Van Patter, M., Voss, M.W., Pontifex,
M.B., Raine, L.B., Hillman, C.H., Kramer, A.F. (2010a). Basal ganglia
volume is associated with aerobic fitness in preadolescent children. Dev.
Chaddock, L., Erickson, K.I., Prakash, R.S., Kim, J.S., Voss, M.W., Vanpatter, M.,
Pontifex, M.B., Raine, L.B., Konkel, A., Hillman, C.H., Cohen, N.J., Kramer,
A.F. (2010b). A neuroimaging investigation of the association between
aerobic fitness, hippocampal volume, and memory performance in
preadolescent children. Brain Res. 1358:172-83.
Chaddock-Heyman, L., Erickson, K.I., Holtrop, J.L., Voss, M.W., Pontifex, M.B.,
Raine, L.B., Hillman, C.H., Kramer, A.F. (2014). Aerobic fitness is associated
with greater white matter integrity in children. Front. Hum.
Neurosci. 8:584. doi: 10.3389/fnhum.2014.00584
Chiarello, C. (1991). On codes of meaning and the meaning of codes:semantic access
and retrieval within and between hemispheres. In: M.J. Beeman, C. Chiarello
(Eds.) Right hemisphere language comprehension: perspectives from
cognitive neuroscience. Mahwah, NJ, Erlbaum.
Cho H-y, Kim J-s, Lee G-C (2012) Effects of motor imagery training on balance and
gait abilities in post-stroke patients: a randomized controlled trial. Clinical
Coelho, F.G.D.M., Gobbi, S., Andreatto, C.A.A., Corazza, D.I., Pedroso, R.V.,
Santos-Galduróz, R.F. (2013). Physical exercise modulates peripheral levels
of brain-derived neurotrophic factor (BDNF): a systematic review of
experimental studies in the elderly. Arch. Gerontol. Geriatr. 56, 10–15.
Cohen, L.G., Ziemann, U., Chen, R., Classen, J., Hallett, M., Gerloff, C. Butefisch, C.
(1998). Studies of neuroplasticity with transcranial magnetic stimulation. J.
Clin. Neurophysiol. 15, 305–324.
Colcombe S, Kramer AF, (2003). Fitness Effects on the Cognitive Function of Older
Adults: A Meta-Analytic Study. Psychol. Sci. 14, 125-130.
Colcombe, S.J., Erickson, K.I., Scalf, P.E., Kim J.S., Prakash, R., McAuley, E.
(2006). Aerobic exercise training increases brain volume in aging humans. J.
Gerontol. A Biol. Sci. Med. Sci. 61, 1166–1170.
Cotman, C.W., Berchtold, N.C. (2002). Exercise: a behavioral intervention to
enhance brain health and plasticity. Trends Neurosci. 25, 295-301.
Cotterill, R.M.J. (2001). Cooperation of the basal ganglia, cerebellum, sensory
cerebrum and hippocampus: possible implications for cognition,
consciousness, intelligence and creativity. Prog. Neurobiol. 64, 1-33.
Crick, F., Koch, C. (1998). Constraints on cortical and thalamic projections: the nostrong-
loop hypothesis. Nature, 391, 245–250.
Demir, Ö.E., Prado, J., Booth, J.R. (2014). The differential role of verbal and spatial
working memory in the neural basis of arithmetic. Dev. Neuropsychol. 39(6),
Dennis, M.Y., Nuttle, X., Sudmant, P.H., Antonacci, F., Graves, T.A., Nefedov, M.,
Rosenfeld, J.A., Sajjadian, S., Malig, M., Kotkiewicz, H., Curry, C.J., Shafer,
S., Shaffer, L.G., de Jong, P.J., Wilson, R.K., Eichler, E.E. (2012). Evolution
of human-specific neural SRGAP2 genes by incomplete segmental
duplication. Cell. 149(4), 912-922. doi: 10.1016/j.cell.2012.03.033. Epub
2012 May 3.
Dijkstra, K., Kaschak, M.P., Zwaan, R.A. (2007). Body posture facilitates retrieval of
autobiographical memories. Cognition. 102, 139–149.
Duchowski, A.T. (2003). Eye tracking methodology theory and practice. New York,
Dum, R.P., Strick, P.L., (2002). Motor areas in the frontal lobe of the
primate. Physiol. Behav. 77, 677-682.
Erickson, K.I., Raji, C.A., Lopez, O.L., Becker, J.T., Rosano, C., Newman, A.B.
(2010). Physical activity predicts gray matter volume in late adulthood: the
cardiovascular health study. Neurology. 75, 1415–1422.
Erickson, K.I., Voss, M.W., Prakash, R.S., Basak, C., Szabo, A., Chaddock, L.
(2011). Exercise training increases size of hippocampus and improves
memory. Proc. Natl. Acad. Sci. U.S.A. 108, 3017–3022.
Eriksen, B.A., Eriksen, C.W. (1974). Effects of noise letters upon the identification
of a target letter in a nonsearch task. Percept. Psychophysics. 16(1), 143-149.
Falk, D., Zollikofer, C.P., Morimoto, N., Ponce de León, M.S. (2012). Metopic suture
of Taung (Australopithecus africanus) and its implications for hominin brain
evolution. Proc. Nat. Acad. Sci. U.S.A. 109(22), 8467-8470. doi:
10.1073/pnas.1119752109. Epub 2012 May 7.
Falkmer, T., Gregersen, N.A. (2005). Comparison of eye movement behaviour of
inexperienced and experienced drivers in real traffic environments. Optom.
Vis. Sci. 82, 332-339.
Fink, G.R., Frackowiak, R.S., Pietrzyk, U., Passingham, R.E. (1997). Multiple
nonprimary motor areas in the human cortex. J. Neurophysiol. 77, 2164-2174.
Fitzsimmons, N.A., Lebedev, M.A., Peikon, I.D., Nicolelis, M.A.L. (2009).
Extracting kinematic parameters for monkey bipedal walking from cortical
neuronal ensemble activity. Front. Integrat. Neurosci. 3, 3.
Foerster, O. (1936). The motor cortex in man in the light of Hughling Jackson’s
doctrines. Brain. 59,135-59.
Friston, K.J. (1994). Functional and effective connectivity in neuroimaging: a
synthesis. Hum. Brain Mapp. 2, 56-78.
Friston, K. (2002). Beyond phrenology: what can neuroimaging tell us about
distributed circuitry? Ann. Rev. Neurosci. 25,221-50.
Gallese, V., Sinigaglia, C. (2011). What is so special about embodied simulation?
Trends Cog. Sciences 15, 512-519.
Garbarini, F., Adenzato, M. (2004). At the root of embodied cognition: cognitive
science meets neurophysiology. Brain Cogn. 56, 100-106.
Gee D.G., Biswal. B.B., Kelly. C., Stark. D.E., Margulies. D.S., Shehzad. Z. (2011).
Low frequency fluctuations reveal integrated and segregated processing
among the cerebral hemispheres. Neuroimage. 54, 517-527.
Gibson, J.J. (1966). The senses considered as perceptual systems. Oxford, UK,
Gilchriest, J.A. (2012). A method for quatifying visual search scanpath efficiency.
Funct. Neurol. Rehab. Ergon. 1(2), 181-196.
Grefkes, C., Nowak, D.A., Eickhoff, S.B., Dafotakis, M., Kust, J., Karbe, H., Fink,
G.R. (2008). Cortical connectivity after subcortical stroke assessed with
functional magnetic resonance imaging. Ann. Neurol. 63, 236-246.
Grefkes, C., Nowak, D.A., Wang, L.E., Dafotakis, M., Eickhoff, S.B., Fink, G.R.
(2010). Modulating cortical connectivity in stroke patients by rTMS assessed
with fMRI and dynamic causal modeling. Neuroimage. 50, 234-243.
Gregory, R.L. (1972). Eye and Brain. London: Weidenfeld & Nicolson.
Gómez-Pinilla, F., Ying, Z., Roy, R.R., Molteni, R., Edgerton, V.R.
(2002). Voluntary exercise induces a BDNF-mediated mechanism that
promotes neuroplasticity. J. Neurophysiol. 88, 2187–2195.
Gow, A.J., Bastin, M.E., Maniega, S.M., Hernández, M.C.V., Morris, Z., Murray, C.
(2012). Neuroprotective lifestyles and the aging brain: activity, atrophy, and
white matter integrity. Neurology. 79, 1802–1808.
Guye, M., Bartolomei, F., Ranjeva, J.P. (2008). Imaging structural and functional
connectivity: towards a unified definition of human brain
organization? Curr. Opin. Neurol. 21, 393–403.
Haggard, P. (2005). Conscious intention and motor cognition. Trends Cogn. Sci.
Hallett, M. (2000). Transcranial magnetic stimulation and the human
brain. Nature. 406:147-150.
Harcourt-Smith, W.H.E. (2010). The first hominins and the origins of bipedalism.
Evolution: Education and Outreach. 3(3), 333-340.
Von Helmholtz, H. (1925). Handbuch der physiologischen optik [Treatise on
Physiological Optics Vol. III], (translated from the German). Rochester, NY,
The Optical Society of America.
Herschensohn, J. (2007). Language development and age. Cambridge, UK,
Cambridge University Press.
Hillman, C.H., Erickson, K.I., Kramer, A.F. (2008). Be smart, exercise your heart:
exercise effects on brain and cognition. Nature Rev. Neurosci. 9, 58–65.
Hillman, C.H., Buck, S.M., Themanson, J.R., Pontifex, M.B., Castelli, D.M. (2009).
Aerobic fitness and cognitive development: event-related brain potential and
task performance indices of executive control in preadolescent children. Dev.
Psychol. 45, 114–129.
Hillman, C.H., Pontifex, M.B., Raine, L.B., Castelli, D.M., Hall, E.E., Kramer, A.F.
(2009). The effect of acute treadmill walking on cognitive control and
academic achievement in preadolescent children. Neuroscience. 159, 1044-
Hoerzer, G.M., Liebe, S., Schloegl, A., Logothetis, N.K., Rainer, G. (2010). Directed
coupling in local field potentials of macaque v4 during visual short-term
memory revealed by multivariate autoregressive models. Front. Comput.
Neurosci. 4, 14.
Horwitz, B. (2003). The elusive concept of brain connectivity. Neuroimage. 19, 466–
Jeannerod, M. (1999). To act or not to act: perspectives on the representation of
actions. Quarterly J. Exp. Psychol. 52A, 1–29.
Jeannerod, M. (2001). Neural simulation of action: a unifying mechanism for motor
cognition. Neuroimage. 14, 103–109.
Kamijo, K., Khan, N.A., Pontifex, M.B., Scudder, M.R., Drollette, E.S., Raine, L.B.,
Evans, E.M., Castelli, D.M., Hillman, C.H. (2012). The relation of adiposity
to cognitive control and scholastic achievement in preadolescent children.
Obesity (Silver Spring). 20(12):2406-11.
Kazantsev, V.B., Nekorkin, V.I., Makarenko, V.I., Llinas, R. (2003). Olivo-cerebellar
cluster-based universal control system. Proc. Natl. Acad. Sci. U.S.A. 100,
Kinsbourne, M. (2006). From unilateral neglect to the brain basis of
consciousness. Cortex. 42, 869-74.
Knaepen, K., Goekint, M., Heyman, E.M., Meeusen, R. (2010). Neuroplasticityexercise-
induced response of peripheral brain-derived neurotrophic factor: a
systematic review of experimental studies in human subjects. Sports Med. 40,
Koch, P., Leisman, G. (2012). Computational model of attention brain function.
Funct. Neurol. Rehab. Ergon. 2(4), 353-363.
Koziol, L.F., Budding, D., Andreasen, N. D’Arrigo, S., Bulgheroni, S., Imamizu, H.,
Ito, M., Manto, M., Marvel, C., Parker, K., Pezzulo, G., Ramnani, N., Riva,
D., Schmahmann, J., Vandervert, L., Yamazaki, T. Consensus paper: the
cerebellum’s role in movement and cognition. Cerebellum. 13:151–177.
Kramer, A.F., Hahn, S., Cohen, N.J., Banich, M.T., McAuley, E., Harrison, C.R.
(1999). Ageing, fitness and neurocognitive function. Nature 400, 418–419.
Kramer, A.F., Erickson, K.I. (2007a). Capitalizing on cortical plasticity: influence of
physical activity on cognition and brain function. Trends Cogn Sci. 11(8),
Kramer, A.F., Erickson, K.I. (2007b).Effects of physical activity on cognition, wellbeing,
and brain: human interventions. Alzheimers Dement. 3(2 Suppl), S45-
Land, M.F. (2006). Eye movements and control of actions in everyday life. Prog.
Retin. Eye Res. 25, 296-324.
Larson, E.B., Wang, L., Bowen, J.D., McCormick, W.C., Teri, L., Crane, P.
(2006). Exercise is associated with reduced risk for incident dementia among
persons 65 years of age and older. Ann. Intern. Med. 144, 73–81.
Leisman, G. (2011). Brain networks, plasticity, and functional connectivities inform
current directions in functional neurology and rehabilitation. Funct. Neurol.
Rehab. Ergon. 1(2), 315-356.
Leisman, G. (2012). The brain on art: auditory, visual, spatial aesthetic, and artistic
training facilitates brain plasticity. Funct. Neurol. Rehab. Ergon. 2(3), 251-
Leisman, G. (2013a). Optimization methodology and functional connectivities inform
the cognitive modifiability in the rehabilitation of developmental language
difficulties. In: Proceedings of the Jerusalem International Conference on
Neural Plasticity and Cognitive Modifiability, Pianoro, Medimond.
Leisman, G. (2013b). If it’s localization then there is no development, education, and
rehabilitation: Neuroeducation needs to be about building networks. Funct.
Neurol. Rehab. Ergon. 3, 329-340.
Leisman, G. (2015). Cognitive Rehabilitation in Developmental Disabilities. In: S.
Misciagna (Ed.). Handbook of Cognitive Rehabilitation. Secaucus, NJ: Austin
Leisman, G. and Koch, P. (2009). Networks of conscious experience: computational
neuroscience in an understanding of life, death, and consciousness. Rev.
Neurosci. 20(3), 151-176.
Leisman, G., Melillo, R., Carrick, F.R. (2012a). Clinical motor and cognitive
neurobehavioral relationships in the basal ganglia. In: A.F.A. Fernando, C.
Barrios Bauer (Eds.). Basal ganglia: an integrative view. Rijeka, Croatia:
Intech, pp. 1-30.
Leisman, G., Melillo, R., Mualem, R., Machado, C. (2012b). The effect of music
training and production on functional brain organization and cerebral
asymmetry. In: Kravchuk, T., Groysman, A., Soddu, C., Colabella, E.,
Leisman G. (Eds.) Art, Science and Technology. Milano, Italy, Domus
Argenia. Pp. 133-139
Leisman, G., Melillo, R. (2013). The Basal Ganglia: Motor and Cognitive
Relationships in a Clinical Neurobehavioral Context Rev. Neurosci. 24(1), 9-
Leisman, G., Braun-Benjamin, O., Melillo, R. (2014a). Cognitive-motor interactions
of the basal ganglia in development. Front. Sys. Neurosci. 8:16.
Leisman, G., Rodriguez-Rojas, R. Batista, K, Carballo, M Morales, J. M., Iturria, Y.,
Machado, C. Measurement of Axonal Fiber Connectivity in Consciousness
Evaluation. (2014b). Proceedings of the 2014 IEEE 28th Convention of
Electrical and Electronics Engineers in Israel, IEEE: Minneapolis, MN,
2014. IEEE Cat. No: CFP14417-CDR; ISBN: 978-1-4799-5987-7; DOI:
Leisman, G., Melillo, R. (2015). The plasticity of brain networks as a basis for a
science of nervous system rehabilitation. Intern. J. Neurorehabil. 2:2, 155.
Leisman, G., Mualem, R., Mougrabi, S.K. The neurological development of the child
with educational enrichment in mind. Psicología Educativa. 2015 [In Press].
Li, Y., Luikart, B.W., Birnbaum, S., Chen, J., Kwon, C.H., Kernie, S.G. (2008). TrkB
regulates hippocampal neurogenesis and governs sensitivity to antidepressive
treatment. Neuron. 59, 399–412.
Llinas, R.R., Ribary, U., Jeanmonod, D., Kronberg, E., Mitra, P.P.(1999). Thalamocortical
dysrhythmia: A neurological and neuropsychiatric syndrome
characterized by magnetoencephalography. Proc. Natl. Acad. Sci. U.S.A. 96,
Logothetis, N.K., Kayser, C., Oeltermann, A. (2007). In vivo measurement of cortical
impedance spectrum in monkeys: implications for signal
propagation. Neuron. 55, 809-823.
Marais, L., Stein, D.J., Daniels, W.M. (2009). Exercise increases BDNF levels in the
striatum and decreases depressive-like behavior in chronically stressed
rats. Metab. Brain Dis. 24, 587–597.
McDonnell, M.N., Buckley, J.D., Opie, G.M., Ridding, M.C., Semmler, J.G. (2013).
A single bout of aerobic exercise promotes motor cortical neuroplasticity. J.
Appl. Physiol. (1985). 114(9):1174-1182.
Melillo, R., Leisman, G. (2009) Neurobehavioral disorders of childhood: an
evoloutionary approach. New York, NY: Springer, 2009.
Mills, D. L., Coffey-Corina, S., & Neville, H. J. (1997). Language comprehension and
cerebral specialization from 13 to 20 months. Dev. Neuropsychol. 13, 397–
Munzert, J., Lorey, B., Zentgraf, K. (2009). Cognitive motor processes: the role of
motor imagery in the study of motor representations. Brain Res Rev. 60(2),
Murray, G.K., Veijola, J., Moilanen, K. (2006). Infant motor development is
associated with adult cognitive categorisation in a longitudinal birth cohort
study. J. Child Psychol. Psychiatry. 47, 25–29.
Murray, G.K., Jones, P., Kuh, D., Richards, M. (2007). Infant developmental
milestones and subsequent cognitive function. Ann. Neurol. 62(2), 128-136.
Mushiake, H., Inase, M., Tanji, J. (1991). Neuronal activity in the primate premotor,
supplementary, and precentral motor cortex during visually guided and
internally determined sequential movements. J. Neurophysiol. 66(3), 705-718.
Neeper, S.A., Gómez-Pinilla, F., Choi, J., Cotman, C.W. (1996). Physical activity
increases mRNA for brain-derived neurotrophic factor and nerve growth
factor in rat brain. Brain Res. 726, 49–56.
Neville, H., Nicol, J.L., Barss, A., Forster, K.I., Garrett, M.F. (1991). Syntactically
based sentence processing classes: evidence from event-related brain
potentials. J. Cogn. Neurosci. 3(2),151-165.
Ng, S.W., Popkin, B.M. (2012). Time use and physical activity: a shift away from
movement across the globe. Obes Rev. 13, 659-680.
Nishijima, T., Llorens-Martin, M. (2013). Cessation of voluntary wheel running
increases anxiety-like behavior and impairs adult hippocampal neurogenesis in
mice. Behav. Brain Res. 245, 34-41.
Nodine, C.F., Krundel, H.L. (1987). The cognitive side of visual search. In: O’Regan,
J., Levy-Schoen A., (Eds.) Eye movements: from physiology to cognition.
Amsterdam, Elsevier, pp. 573-582.
Norton, D., Stark, L. (1971). Eye movements and visual perception. Sci. Amer. 224,
Pascual-Leone, A., Walsh, V., Rothwell, J. (2000). Transcranial magnetic stimulation
in cognitive neuroscience: virtual lesion, chronometry, and functional
connectivity. Curr. Opin. Neurobiol. 10, 232-237.
Penfield, W., Rasmussen, T. (1952). The cerebral cortex of man. New
Picard, N., Strick, P. L. (2001). Imaging the premotor areas. Curr. Opin.
Neurobiol. 11, 663–672.
Pontifex, M.B., Saliba, B.J., Raine, L.B., Picchietti, D.L., Hillman, C.H. (2013).
Exercise improves behavioral, neurocognitive, and scholastic performance in
children with attention-deficit/hyperactivity disorder. J. Pediatr. 162(3):543-
Posner, M.I., Snyder, C.R.R., Davidson, B.J. (1980). Attention and the detection of
signals. Exp Psychol Gen. 109(2), 160-174.
Proske, U., Wise, A.K., Gregory, J.E. (2000)The role of muscle receptors in the
detectin of movements. Prog. Neurobiol. 60, 85-96.
Pulvermüller, F., Härle, M., Hummel, F. (2001). Walking or talking?: behavioral and
neurophysiological correlates of action verb processing. Brain Lang. 78, 143–
Pulvermüller, F., Kiff, J., Shtyrov, Y. (2012). Can language-action links explain
language laterality?: an ERP study of perceptual and articulatory learning of
novel pseudowords. Cortex. 48(7), 871-881. Epub 2011 Feb 13.
Raine, L.B., Lee, H.K., Saliba, B.J., Chaddock-Heyman, L., Hillman, C.H., Kramer,
A.F. (2013). The influence of childhood aerobic fitness on learning and
memory. PLoS One. 8(9):e72666. doi: 10.1371/journal.pone.0072666.
Ratey, J. J., & Loehr, J. E. (2011). The positive impact of physical activity on
cognition during adulthood: a review of underlying mechanisms, evidence and
recommendations. Reviews in the Neurosciences, 22(2), 171-185.
Ridler, K., Veijola, J.M., Tanskanen, P. (2006). Fronto-cerebellar systems are
associated with infant motor and adult executive functions in healthy adults
but not in schizophrenia. Proc. Natl. Acad. Sci. U.S.A. 103, 15651–15656.
Rizzolatti, G., Luppino, G., Matelli, M. (1998). The organization of the cortical
motor system: new concepts. Electroenceph. Clin. Neurophysiol. 106, 283-
Schack, T. (2004). The cognitive architecture of complex movement. Intern. J. Sport
Exer. Psychol. 2(4).
Schieber, M. (2000). New views of the primary motor cortex. Neuroscientist. 6:380-
Schmahmann, J.D. (1996). From movement to thought: anatomic substrates of the
cerebellar contribution to cognitive processing Hum. Brain Map. 4,174-198.
Schmahmann, J.D., Caplan, D. (2006). Cognition, emotion and the cerebellum.
Shafir, T, Taylor, S.F., Atkinson, A.P., Langenecker, S.A., Zubieta, J-K. (2013).
Emotion regulation through execution, observation, and imagery of emotional
movements. Brain Cognit. 82, 219-227.
Silverman, M. N., & Deuster, P. A. (2014). Biological mechanisms underlying the
role of physical fitness in health and resilience. Interface Focus, 4(5). doi:
Smith, P.J., Blumenthal, J.A., Hoffman, B.M., Cooper, H., Strauman, T.A., Welsh-
Bohmer, K. (2010). Aerobic exercise and neurocognitive performance: a
meta-analytic review of randomized controlled trials. Psychosom. Med. 72,
Smith, A.E., Goldsworthy, M.R., Garside, T., Wood, F.M., Ridding, M.C. (2014).
The influence of a single bout of aerobic exercise on short-interval
intracortical excitability. Exp Brain Res. 232, 1875-1882.
Sofi, F., Valecchi, D., Bacci, D., Abbate, R., Gensini, G.F., Casini, A.
(2011). Physical activity and risk of cognitive decline: a meta-analysis of
prospective studies. J. Intern. Med. 269, 107–117.
Sporns, O. (2010). Networks of the brain. Cambridge, MA, MIT Press.
Stiles, J., Bates, E., Thal, D., Trauner, D., Reilly, J. (1998). Linguistic, cognitive and
affective development in children with pre- and perinatal focal brain injury: A
ten-year overview from the San Diego Longitudinal Project. In: C. Rovee
Collier, L. Lipsit, H. Hayne (Eds.), Advances in infancy research. Norwood,
NJ, Ablex, pp. 131-163.
Stranahan, A.M., Lee, K., Martin, B., Maudsley, S., Golden, E., Cutler, R.G.
(2009). Voluntary exercise and caloric restriction enhance hippocampal
dendritic spine density and BDNF levels in diabetic mice. Hippocampus.19,
Ströhle, A., Stoy, M., Graetz, B., Scheel, M., Wittmann, A., Gallinat, J. (2010). Acute
exercise ameliorates reduced brain-derived neurotrophic factor in patients with
panic disorder. Psychoneuroendocrinology. 35, 364–368.
Taanila, A., Murray, G.K., Jokelainen, J. (2005). Infant developmental milestones: a
31-year follow-up. Dev. Med. Child Neurol. 47, 581–586.
Tian, Q., Erickson, K.I., Simonsick, E.M., Aizenstein, H.J., Glynn, N.W., Boudreau,
R.M. (2014). Physical activity predicts microstructural integrity in memoryrelated
networks in very old adults. J. Gerontol. A Biol. Sci. Med. Sci. 69,
Treisman, A., Gelade, G. (1980). A feature integration theory of attention. Cog.
Psychol. 12, 97-136.
Turrigiano, G. (2007). Homeostatic signaling: the positive side of negative
feedback. Curr. Opin. Neurobiol. 17, 318-324.
Van Praag, H., Shubert, T., Zhao, C., Gage, F.H. (2005). Exercise enhances learning
and hippocampal neurogenesis in aged mice. J. Neurosci. 25, 8680–8685.
Vercruyssen, M., Simonton, K. (1994). Effects of posture on mental performance: we
think faster on our feet than on our seat. In: R. Lueder, K. Noro (Eds.) Hard
facts about soft machines: the ergonomics of seating. Bristol, PA, Taylor &
Francis, pp. 119-132.
Voelcker-Rehage, C., & Niemann, C. (2013). Structural and functional brain changes
related to different types of physical activity across the life span. Neuroscience
& Biobehavioral Reviews, 37(9), 2268-2295.
Voss, M.W., Prakash, R.S., Erickson, K.I., Basak, C., Chaddock, L., Kim J.S.
(2010a). Plasticity of brain networks in a randomized intervention trial of
exercise training in older adults. Front. Aging Neurosci. 2:32.
Voss, M.W., Erickson, K.I., Prakash, R.S., Chaddock, L., Malkowski, E., Alves, H.
(2010b). Functional connectivity: a source of variance in the association
between cardiorespiratory fitness and cognition? Neuropsychologia. 48, 1394–
Voss, M.W., Erickson, K.I., Prakash, R.S., Chaddock, L., Kim, J.S., Alves, H.
(2013). Neurobiological markers of exercise-related brain plasticity in older
adults. Brain Behav. Immun. 28, 90–99.
Wang, L., Yu, C., Chen, H., Qin, W., He, Y., Fan, F., Zhang, Y., Wang, M., Li, K.,
Zang, Y., Woodward, T.S., Zhu, C. (2010). Dynamic functional reorganization
of the motor execution network after stroke. Brain. 133(Pt 4), 1224-1238.
Epub 2010 Mar 30.
Westlake, K.P., Nagarajan, S.S. (2011). Functional connectivity in relation to motor
performance and recovery after stroke. Front. Syst. Neurosci. 5:8. doi:
Weinstein, A.M., Voss, M.W., Prakash, R.S., Chaddock, L., Szabo, A., White, S.M.
(2012). The association between aerobic fitness and executive function is
mediated by prefrontal cortex volume. Brain Behav. Immun. 26, 811–819.
Yarbus, A.L. (1967). Eye movements and vision. New York, NY, Plenum.