From Neuroanatomy to Cognition

White Matter Fibres
The white matter was briefly mentioned in an earlier post, so this is sort of a continuation of the brain's gross anatomy. The white matter is located underneath the cortical gray matter and composed of fatty myelinated axons. It is an integral part of the central nervous system that transmits messages very rapidly. It basically has 3 types of fiber bundles: the projection fibers, commissural fibers, and association fibers.

1. Projection fibers are bi-directional, afferent, and efferent bundles. They appear as radiating bundles in the white matter that exit the cerebral cortex and converge towards the brainstem. One bundle carries visual information through the optic radiation. Near the subcortical nuclei, these axons form a compact band known as the internal capsule with anterior and posterior limbs. Afferent (sensory) fibers: mainly the thalamocortical bundles going to the various region of the cerebral cortex. The efferent fibers of the internal capsule arise from the cerebral cortex. They form various tracts, e.g. corticothalamic, corticobulbar, corticospinal, and corticopontine bundles. 
2. The axons part of the corpus callosum forms the commissural fibers. At different callosal segment, they have different connections: the rostrum (orbitofrontal), genu (frontal lobe), body (sensorimotor and posterior parietal), and splenium (posterior temporal and occipital). Other commissural fibers are the anterior commissure, connecting the olfactory system bilaterally.
3. The association fibers form the bi-directional cortico-cortical bridges connecting areas within the same hemisphere. They can be classified as short and long fasciculus:
        - Superior longitudinal fasciculus connects frontal and parietal lobes.
        - Occipito-frontal fasciculus connects frontal and occipital lobes.
        - Arcuate fasciculus connects the frontal with posterior temporal lobes.
        - Uncinate fasciculus connects orbitofrontal with anterior temporal lobes.
        - Inferior longitudinal fasciculus connects temporal and occipital lobes.
        - Extreme capsule fasciculus connects lateral temporal and lateral frontal lobes.

Of interest is the coronal section of the cerebral hemisphere from the insula moving inwards to the thalamus. The external capsule connects the motor cortex to the putamen and is unidirectional. The internal capsule connects specific thalamic nuclei to the specific cortical area and hence it is bidirectional.

The most common way to study the white matter is through MRI which can be observed well on T1-weighted, T2-weighted, and FLAIR sequences. More recently, scientists become more interested in modeling brain development over puberty and brain decline associated with aging. Fun facts: Gray matter volume increases in early childhood but declines after puberty. However, white matter volume progressively increases over time, supporting the concept of neural plasticity.

Cerebral organization
The cerebral cortex is organized into six layers that arise from the time of its development. This is the characteristic of the neocortex. Only the piriform cortex and the hippocampal formation, the oldest cortical structures phylogenetically or paleocortex or allocortex, do not exhibit this six-layer arrangement. The projection fibers are more deep-rooted, while the association and commissural fibers are more superficial. Three principal types of cells found in the cortex include the pyramidal, stellate, and fusiform neurons. Their fibers are arranged either tangentially or radially across layers.

Pyramidal cells, with a shape of a triangle with the top end going up to the surface (apical) and the horizontally running dendrites (basal), constitute the most in various cortical layers. The axons are either going down to the white matter (as projection fibers) or to other cortical areas (as association fibers). The biggest pyramidal cell, the Betz cell, is found only in Layer V of the precentral gyrus or motor cortex. Unlike pyramidal cells, granule or stellate cells are small, polygonal or triangular in shape. They are found in all layers, but especially numerous in Layer IV. Fusiform neurons are spindle-like cells found mostly in the deepest cortical layer, their long axis going vertically upward. Apart from these three types of cells, we encounter others, e.g. horizontal cells found mostly in the superficial layers. The works of Cajal and Golgi are crucial in deepening our understanding on these cells.

Fig-1: Six different cortical layers of the cerebral cortex, layer-I being the most superficial.

In brief, six-layered architecture can be described as follow:
a). Layer I (molecular layer), has few cell bodies, mostly axons, Layer II (external granular layer).
b). Layer III (external pyramidal layer), cells forming mainly association or commissural fibers.
c). Layer IV (internal granular layer), mainly the incoming afferent fibers from the thalamus.
d). Layer V (internal pyramidal layer), mainly efferent projection fibers.
e). Layer VI (multiform, fusiform layer).

What is the relationship between this architecture with the earlier functional lobes? Layer III plays a major role in cortico-cortical connections. Layer IV is predominant in sensory areas in the parietal and temporal lobes, e.g. the postcentral gyrus. These regions are granular. Layer V, on the other hand, is predominant in motor areas, e.g. precentral gyrus.

Fig-2: The distribution of different cortical composition: (1) Agranular; (2) Granular - frontal (dysgranular); (3) Granular - parietal; (4) Granular - occipital; and (5) Koniocortex. Only cortical motor areas are agranular.

Principal neurotransmitters

A variety of neurotransmitters is associated with neurons of the cerebral cortex. Among those, we have glutamate, aspartate, and γ-aminobutyric acid (GABA). Pyramidal cells are the main efferent neurons that are predominantly glutaminergic and are excitatory. Most interneurons within the cortex, however, are GABAergic and are inhibitory. They are bridging the afferent and efferent fibers together. Therefore the outputs of the cortex are modulated by a variety of cortical afferents via interneurons. 

A variety of neuropeptides or monoamines are also found in the cerebral cortex; they influence not only populations of neurons but also local metabolic activity and vascular smooth muscle. The most important monoamines in the cortex are (1) norepinephrine, which originates from the locus ceruleus of the pons and distributes sparsely to all cortical layers; (2) dopamine, which arises from the substantia nigra–pars compacta and the adjacent ventral tegmental area and is found in moderate amounts in layers I and VI and sparsely in layers II to V; and (3) serotonin, which arises from the raphe nuclei and distributes heavily to all cortical layers.

Cognition and the Brain
The study of human cognition and the brain is the heart of a classic science popularly known as neuropsychology. The interests existed since the time of Descartes, Gall, Broca, and so on, who studied the link between a neurological condition (e.g. lesions) and certain behavioral or psychological processes. A classic theory, phrenology, says that the brain is divided into discrete and unique areas responsible for a particular function only. The mastery of certain skills can be deduced by the bigger skeletal landmark of the head. An opposing view at that time held that there is no localization of brain functions and that the functions (what they called "Mind") are distributed across different parts of the brain. With more discoveries, modern neuroscience later thought that the brain is divided into many functional specialization. For example, one may use fMRI to elucidate brain areas associated with some behavioural tasks. One fundamental characteristic of the central nervous system is parallelism, that is, a large number of functions are simultaneously processed along two or more pathways. As a result, the damage of one pathway can allow other pathway to function, and that one brain function can be performed not only strictly by one area. 

Modern neuropsychology enjoys a multidisciplinary collaboration among cognitive scientists, physiologists, neuroscientists, and clinical psychologists. Originally, the field drew strong attention when Paul Broca came into contact with a patient undergoing a progressive speech disorder in 1861, who could only produce "tan". After the patient died, Broca found out that his inferior frontal gyrus (IFG) was damaged. Named after Broca, the type of such behavioral deficit linked to the damage of IFG is then called Broca's aphasia. Note: IFG is rostral to the mouth/orofacial musculature of the cortical motor area.

Fig-3: The difference between Broca's and Wernicke's aphasia together with affected areas on the left hemisphere.

Broca's finding was further developed with the findings of Carl Wernicke. He found that in a certain type of language disorder, the patients were able to produce speech but unable to comprehend the conversation. Called Wernicke's aphasia, the damage is found to be around the posterior part of the superior temporal gyrus (STG). This aphasia is not equal to deafness, for the person with Wernicke's aphasia is able to detect sound but unable to make sense of it. He further hypothesized that there is a link between IFG and STG and this is crucial in language. To be able to converse well, one has to first listen and understand the sentences one hears. Note: STG is near to the primary and secondary auditory cortex.

In the 1870s, John Hughlings Jackson proposed that the cerebral cortex is organized hierarchically and that some cortical areas are for higher-order functions (or cognitive) that are neither fully sensory nor motor. These brain areas are called association areas because they serve to associate sensory inputs to motor response and conduct mental processes related to sensorimotor behavior. The mental processes that Jackson attributed to these areas include interpretation of sensory information, the association of perceptions with previous experience, focusing of attention, and exploration of the environment. Jackson's finding is supported by clinical works. The major helps come from surgical rooms of patients with damage or lesion on the specific are, or people with underlying conditions. Other methods include experimental studies with monkeys and rats and the use of non-invasive brain imaging technology.

Before ending, I wish to mention major associative areas in the human brain important in cognition:

1. The posterior association area: the margin of the parietal, temporal, and occipital lobes. It integrates information from several sensory modalities such as vision, space, and body senses. It is also involved in language. Separate studies by Holmes and Luria on wounded soldiers found that bilateral injuries to the posterolateral parietal lobe yield to normal visual acuity but the soldiers were unable to scan visually or reach for an object of interest. They could not process together with the visual information when asked to describe in words what that they saw. This shows that the region is critical for integrating different sensory modalities and for using that integrated information to direct behavior. 
2. The anterior association area: the prefrontal region, rostral to postcentral gyrus. It is involved in the planning of action, shaping behavior, and judgment; a more popular term is the "Executive function". The most popular case showing how the injured prefrontal region leads to behavioral problems is perhaps of Phineas Gage. A series of clinical tests, e.g. the Tower of London test and the Wisconsin Card Sorting Test (WCST), can be used to diagnose people with neuropsychological disorders who have lost their executive functions, such as schizophrenia. WCST is primarily considered a test of executive functions, particularly abstract reasoning and cognitive flexibility in response to external changes.
3. The limbic association area: along the lower medial end of the cerebral hemisphere. It is for emotion, learning, and memory. Its involvement in learning and memory comes from the well-known study on patient H.M. by B. Milner in 1960s after both medial temporal lobes had been removed. She first demonstrated the remarkably selective role of this part of the brain in converting short-term into long-term memory. Studies in monkeys have helped establish that association areas in the medial temporal lobe, including the hippocampal formation, receive information from virtually every other association area. In other words, the hippocampal formation is able to sample the whole stream of ongoing cognitive activity and thereby relate different aspects of a single event so that they can be recalled as a coherent experience.

More recently, cognitive neuroscience is recognized as another separate field, combining neuroscience, neurophysiology, and psychology. Scientists now agree that the three areas (the triad) of executive function are working memory, flexible thinking, and inhibitory control.

Sensorimotor Control & Learning (Part I)

"Motor learning", the main theme of my current lab, lies at the intersection between motor behavior and neuropsychology of learning. It helps us to understand how a person acquires and learns new movements, e.g. to dance, play golf, or adopt a new language. The theme is studied depending on the organ where the voluntary movements are produced: the arm, legs, jaw, and eyes.

The current summary is based on modern studies from the 1990s by a group of engineers and computational scientists, outside the domain of neuropsychology and kinesiology. Emphasis will be made on the upper limb (arm reaching) as its core manipulation.

Components of Sensorimotor Control
First, gathering sensory information associated with the task. When one wants to move or perform a task with one's limb, visual information is gathered through saccades. This gaze behavior is also task-specific. Our brain appears to be able to filter irrelevant sensory inputs. For example: the study in inattentional blindness when one fails to notice prominent visual stimuli unrelated to the task one is attending. It is worth noting that sensory streams are temporally delayed and noisy.

Second, motor tasks involve a sequence of decision-making processes in the presence of delay and noise. Why does the noise come into play? Because both sensory and motor system are inherently noisy, arising naturally at the molecular, synaptic, and system levels. The final product is that trial-by-trial movement production to the same target in space is bound to exhibit some variability. These underlying risks can be viewed with the context of reward, especially during a period of motor learning. Even when a person faces the same motor learning task, one can be a risk-averse (exploitation) or risk-seeking (exploration).

Third, our nervous system can be modeled as a controller. Traditionally, there are two basic controllers for motor control and learning: the feedback and feedforward controller. Feedback control, as the name implies, refers to the control of voluntary movements using sensory feedback. In contrast, feedforward control doesn't depend on any feedback mechanisms. Given that our sensory inflow has an inherent delay (150 - 200 msec), it becomes unreliable to rely on for an accurate movement control. To meet the demand of the motor tasks, we often rely on feedforward control which is a predictive control. As we produce certain movement, we make also make prediction about the sensory consequences of that movement. This is done using of efference copy of the motor command and the difference between the predicted sensory consequence and the actual sensory feedback will be used in state estimation.

The last controller is related to the biomechanical properties of the body and the tools used. It is within the topic of arm impedance. Impedance control depends on a few factors such as arm stiffness, i.e. how springy the musculatures are. Like the internal model, impedance control is also inspired by concepts in engineering and biomechanics. Example: we can modify the way we grip a tool (hand stiffness, arm stiffness) produced through co-contraction of the opposing muscles. Although co-contraction can be a solution to the motor task, it is inherently unstable as the sensorimotor system is noisy.

Recent Techniques or Methodologies
We can study motor learning in various ways, It can be studied through behavioral studies and quantitative movement analysis. Modern motor learning literature typically includes three well-known behavioral paradigms:

1. Sequence learning: a type of motor skill learning where it employs serial reaction time tasks (SRTT). Here, a participant is asked to make a sequence of button pressing, key tap, or finger flexion. Motor performance is measured by the reaction or response time. It does not directly deal with the kinematic and dynamic features of motor learning. In more specific ways, learning is measured by the difference in reaction time between the random sequence and learned sequence.
2. Visuomotor rotation: this can be achieved by e.g., providing a set of prism worn by the participant or by a certain mechanism to distort the association between the visual feedback and the actual arm movement. The performance is measured by the movement deviation. This method introduces a mismatch between two related sensory inputs: visual and proprioception.
3. Force field paradigm: a participant performs reaching movement with a robotic manipulandum, capable of producing a velocity-dependent force that perturbs the movement trajectory. The presence of the force changes the dynamic of the motor task. The process of reaching motor performance signifies adaptation. The sudden removal of the force causes the trajectory to deflect to the opposite direction know as an after-effect. A more novel idea probes trial-by-trial performance in terms of the magnitude of the lateral force the participant produces. This is achieved by introducing catch trials in the form of force channels. This is the method used by Shadmehr and colleagues.

Lesion studies and non-invasive stimulation (TMS and tDCS) are able to complement the methods. Recently, neuroimaging methods are employed to learn regions of the brain associated with the behavioral tasks involved. Scientists employ engineering and computational modeling to represent the brain as a system or controller. In error-based learning, for example, motor adaptation can be captured by a linear time-invariant model (LTI). With this framework, in each trial, we learn new movements by employing an optimization algorithm (e.g. Kalman Filter).

Types of Motor Learning Processes
The processes of motor learning can be classified by the type of information the motor system learns.

1. Error-based learning: motor learning through the presence of an error, i.e. the discrepancy between the desired trajectory and the actual movement outcome. The term error also means the mismatch between the predicted sensory consequences and the observed sensory feedback. Three important behavioral paradigms to study this type of learning include visuomotor rotation, prism goggle, and force-field adaptation. Error reduction happens reasonably quick and this type of learning is known as adaptation. Improvements in adaptation reach plateau after 8-10 trials. We will focus more on this type of motor learning processes as it has been widely studied for the past decade.
2. Reinforcement learning: normally observed in a redundant system. This type of learning can happen even when there is no error involved. Learning is achieved through exploration by finding the best solution in the solution manifold. It is highly dependent on the available rewards, e.g. points, punishment, currency. A study by Izawa & Shadmehr (2011) shows that reinforcement learning, in some circumstances, can substitute for adaptation when there is uncertainty about, or no, sensory prediction error.
3. Use-dependent learning: not strictly a learning process, but rather, adaptation through repetitive movements. Movements to a certain direction are able to reduce variability in that direction and induce a bias towards this trained direction when reaching to other directions.
4. Learning by observation: typically it involves watching others doing the movements. This type stems from the findings of mirror neurons. Observational learning may include learning from predicting error by observing the action of others.
5. Structural learning: learning to extract common features of different task variants. When we know the underlying structure of the task, learning can be faster, e.g. learning to swing a tennis racket bears similarity with learning using a squash racket.

What is Internal Model?
Modern research of human motor behavior has been marked by the incorporation of control engineering theories, in particular, the concepts of the internal model (see: Jordan, 1995; Kawato et al. 1987). A controller G_c(s) is used to control the process G_p(s). A good controller should be able to represent the process to be controlled. It is said that the motor system is composed of the limbs (i.e. the plant) and the controller in the nervous system, the internal model.

The internal model is an approximation of the inverse dynamics of the system being controlled. It is a model that mimics the behavior of the natural process being controlled, which refers to our motor system. This model can be adapted at any time to a novel environment, making it a suitable computational model for motor learning (refer to studies by Shadmehr's group). There are 2 variants of the internal model: forward model and inverse model.

1. Forward model predicts sensory consequences from the efference copy generated during movement. Forward model is likened to a motor-to-sensory mapping. The efference copy is issued in conjunction with the motor command from the CNS. The model tries to anticipate the next state so that the movement goal is achieved and the error is minimized. 
2. Inverse model tries to approximate motor commands through an inverse transformation from the incoming sensory streams. This model is reactive rather than predictive. There is a close relationship between model (1) and (2).

The internal model is used to explain motor adaptation. As mentioned, it involves a decrease in sensory prediction error through trial-by-trial adjustments in the forward model. Accordingly, the update of the forward model is translated into an update of motor commands. Mathematically, the internal model is well captured by linear time-invariant (LTI) state-space models, which have sensory errors or perturbations as inputs, sensorimotor mappings as hidden variables, and the learned or adapted motor commands as the output.

Why can't we tickle ourselves? When we tickle our body, the central nervous system predicts the sensory consequence using the efference copy of "tickling". At the same time, there is this somatic sensation generated by the "tickling". As this sensation matches the predicted sensory consequence through the forward model, the comparator circuit in the CNS doesn't detect any mismatch.

Can Motor Learning Generalize?
After going through training of a task in one context or situation, a person is able to perform as well to a similar task but in a different context or situation. This concept is called generalization. When generalization is beneficial, it is usually termed transfer. Conversely, when it is detrimental, it is termed interference. Traditionally, the studies of generalization made use of dynamic or force field paradigm. The principles derived from those studies are associated with the concept of the internal model. On top of that, generalization is related to another concept called "motor memory". If interference occurs, the transfer of learning fails.

Using force field paradigm, it is thought that motor adaptation is able to generalize in the intrinsic coordinate system, i.e. based on internal muscular patterns of activity. A salient example of intrinsic transfer is when writing "9" by right and left hand. On the other hand, using the visuomotor paradigm (Krakauer et al., 2000), motor learning generalizes in the extrinsic coordinate system, i.e. based on the external spatial coordinate frame. Transfer in motor learning has also been studied in relevant to transfer across different movement direction. There is a limited transfer of dynamic (Gandolfo et al., 1996; Sainburg et al., 1999).

Transfer occurs in different configurations of the same arm (Shadmehr & Mussa-Ivaldi, 1994; Ghez et al., 2000; Malfait et al., 2002; Shadmehr & Moussavi, 2000). How about the interlimb transfer? Tranfer occurs from the dominant arm to the non-dominant arm and this happens in the extrinsic coordinate system (Criscimagna-Hemminger et al., 2003). The opposite is not true. Further, the interlimb transfer from the dominant to non-dominant hand occurs only when the force field is introduced abruptly (Malfait & Ostry, 2004). The gradual force field, on the other hand, does not cause the apparent interlimb transfer. It seems that interlimb transfer is regarded as a cognitive process.

The Concept of Motor Memory
The initial part of learning involves more cognitive processes, where one makes use of one's memory buffer to carry out and finish the task. This readily available, temporary buffer or space is known as working memory. A popular example of working memory is when you solve mathematical problems. The later part of learning is the period when motor performance stabilizes and involves consolidation, a term related to long-term storage of motor skills. This is why after a year of not playing the piano (or skiing), we are still able to play it as well. However, what is stored inside the memory (e.g. motor commands, task dynamic, somatic experience, etc.) is still debatable and the nature of consolidation is also conflicting, e.g. Caithness et al (2006).

Memories that can be consciously recalled are named declarative memories, e.g. memory of events, words, or facts. Conversely, memories on skills and knowledge to perform some particular actions are called procedural memories, e.g. the ability to walk or ski. Typically, the domain of motor learning deals with procedural memory, the nature of which is interesting to characterize. Smith et al. introduced a two-rate state-space model of the force-field adaptation. The model says that adaptation consists of fast learning with poor retention and slow learning with more stable retention. Krakauer & Shadmehr discuss whether the formation of such memory progresses over time from a labile state, which is susceptible to interference to a stable state, which is resistant to such interference.

The memory from experiences obtained from a rapidly changing environment leads to faster decaying and unstable motor memory. As opposed, more stable memory is achieved when exposed to a gradually changing environment. What happens when, after learning A, a person immediately learns B? Called retrograde interference, task B is able to disrupt the consolidation of A. This phenomenon does not appear after a longer period of training. There is even evidence suggesting sleeps enhance/improve consolidation. Sometimes, although we forget to do a certain task, a quick relearning is sufficient to meet the expected performance. Such a phenomenon is called saving, that is, faster relearning.

Lastly, the concepts of implicit and explicit processes have a place in the context of motor learning. Explicit processes require declarative knowledge of something. When a person learns to make a golf swing, voluntary explicit processes include, for example, adjustment to the weight of the golf stick or the knowledge on the target location. In contrast, proficiency in skill performance itself or the correct timing (or speed) involves implicit processes. In some conditions, implicit planning may override explicit strategies during a visuomotor adaptation task (Mazzoni & Krakauer, 2006). Scientists are still debating which of the two are dominant during motor learning, at different stages of learning.

The famous case of HM who couldn't recall practicing a mirror writing task but performed well in the task several days later prompts scientists to think that motor learning is purely implicit. Adaptation is seen as an implicit process, but it does not rule out the involvement of explicit processes. Such "cognitive" explicit processes are thought to serve as a form of learning strategy. Taylor and Ivry (2011) found two competing processes: explicit knowledge of target error and implicit knowledge of sensory prediction error (a la the usual adaptation mechanism). Keisler and Shadmehr used an interesting approach to examine declarative memory contribution to force-field adaptation. Subjects were adapted to force-A and then a brief exposure to force-B. After a 3-min interval, they experienced channel trials. At the same time, they had to memorize words in between. This memorization interfered with the memory of the second task B.

References  
[1]  Wolpert D.M., Diedrichsen J & Flanagan J.R. (2011). "Principles of sensorimotor learning". Nature Rev. Neurosci. 12: 739-751.
[2]  Krakauer, J. W. and P. Mazzoni (2011). "Human sensorimotor learning: adaptation, skill, and beyond." Curr  Opin Neurobiol, 21(4): 636-644.