Neuroplasticity in the Intact Brain – Experience-dependent Learning and Neurobiological Substrates

By Heidi Reyst, Ph.D., CBIST
Rainbow Rehabilitation Centers

When psychologist William James wrote the word “plasticity” as a reference to the human cortex in his book The Principles of Psychology published in 1890, it was the first noted use of its kind. Specifically he wrote Plasticity “means the possession of a structure weak enough to yield to influence, but strong enough not to yield all at once. Organic matter, especially nervous tissue , seems endowed with a very extraordinary degree of plasticity of this sort” (cited in Pascual-Leone, Amedi, Fregni & Merabet, 2005).

In the context of the late 19th century this was not the prevailing wisdom (see previous article titled The Neuroplasticity Timeline–Localism, Holism and a truth somewhere in between), but rather a remarkable foreshadowing of 21st century understanding of what we now know to be “neuroplasticity.” While James’ description gives us a hint of what neuroplasticity is, by no means does it define nor explicate what it means at the biological level.

What is neuroplasticity?

Defining neuroplasticity has been a difficult issue for researchers and neuroscientists. While many definitions have been forwarded, no single one has been agreed upon. Stein (2012) speculated that this is the result of scientists not having a complete understanding of the brain and its functions. What is needed is one definition that encapsulates clearly what it is, and as clearly, what it is not. For example, some have defined neuroplasticity as only positive changes in behavior after central nervous system injury for cognitive, sensory or motor impairments (Stein, 2012). Others have observed that plasticity must be observed at the neuronal level, and not just behavioral change (Kleim, 2011; Warraich & Kleim, 2010). However, if we only define neuroplasticity as positive changes in behavior it does not allow for negative examples of neuroplasticity, like addictions. A definition that seems specific enough to allow for both positive and negative examples and is measurable at a biological (not just the behavioral) level is a hybrid definition taken from Kleim (2011) and Warraich & Kleim (2010), and Stein (2012).

Neuroplasticity is any enduring change in neuron structure or function that is observed either directly from measures of individual neurons (including changes in neuronal excitability, single unit action, dendritic arborization, spine density, or synapse number) or inferred from measures taken across populations of neurons that correlate with behavioral change.

This definition then includes any changes to neurons that are measurable and relate to change in behavior. As will be seen by investigating the biological substrates of neuroplasticity (the underlying morphological or biochemical changes), this definition fits a broad view as it relates to the intact brain as well as the injured brain.

As the previous article illustrates, neuroplasticity was neither a well-understood nor-well supported concept until the middle to late half of the 20th century. Various barriers contributed to the lack of support for the idea that the brain is dynamic, and capable of change in the form of reorganization. One significant barrier was the lack of technology that allowed scientists to see or measure change at the level of the neuron, and that contributed to the dogmatic adherence to the Doctrine of Localization— the idea that one location in the brain accounts for one specific function, and if that area is injured the function is thereby lost. Stein (2012) posited that what was missed by neuroscientists who were committed to localization was an explanation of how, after injury to the central nervous system, individuals were able to regain functions that were lost. For many years, recovery after injury was chalked up to compensation and not to any structural changes to the brain.

What 21st century technologies have afforded neuroscience is the ability to examine and measure the underlying brain structures and morphologies. In doing so we have learned a great deal about just how much our brain can “yield to influence.”

Experience-Dependent Neuroplasticity

Human behavior is guided by two key aspects—our nervous system and our experiences. These two aspects are tied directly together through the concept of neuroplasticity as Pascual-Leone, Amedi, Fregni & Merabet (2005) stated, “behavior will lead to changes in brain circuitry, just as changes in brain circuitry will lead to behavioral modifications”, (p. 379).

Sensory inputs and plasticity

Michael Merzenich and colleagues (Merzenich, Kaas, Wall, Sur , Nelson and Felleman; 1983) examined the primary sensory area (S1) in monkeys to see if the cortical areas which received sensory inputs changed if the inputs to those areas changed. To examine this, they obtained topographical motor maps to identify which areas of S1 received inputs from the specific cutaneous (skin) surfaces they were manipulating. Figure 1 shows an illustrative representation of a topographical map of the cortex. To begin, they mapped each monkey’s somatosensory cortex for the receptive fields that related to the nerves in their hand (radial, median, ulnar). This provided an exact map of areas in S1 containing receptors from each nerve (Figure 1). Once mapping was complete, they then surgically cut the median nerve. At various intervals, they remapped the same area of S1. Immediately after the nerve cut, they found there were no inputs to the cortical areas where the median nerve was previously represented. Thereafter, from two to nine months post nerve cut, they found the sensory inputs for the median nerve were completely taken over by inputs from the ulnar and radial nerves (Figure 2). As experience changed in the form of afferent inputs, the result to the sensory map was contraction of the sensory map for the median nerve, and expansion of the sensory map for the ulnar and radial nerves.Merzenich, Nelson, Stryker, Cynader, Schoppmann and Zook (1984) then examined changes to the S1 cortical map of monkeys after either one or two digits of their hand were surgically amputated. Like the previous experiments, each monkey’s S1 receptive fields were mapped to determine precisely which areas of S1 responded to inputs from each of the digits of their hand. After amputation of digit 3, or digits 2 and 3, the cortical field representations where the amputated digits had formerly occupied were gradually and nearly completely occupied by inputs from skin surfaces from the remaining digits and the palm area.

Thus, as the monkeys used their hands after the digit(s) were amputated, two corresponding changes transpired. One, the sensory inputs from the amputated digits ceased and the S1 receptors no longer registered inputs from those digit(s). Two, because sensory inputs were vacated from that cortical area, the inputs from those areas closest to the amputated fingers were then taken over by those vacated receptors.

Thus, as both studies found, as experience changed in the form of different patterns of input from the cutaneous surfaces, so too did the topographical representation of inputs to the S1 sensory map. Doidge (2007) eloquently pointed out the significance of Merzenich’s findings. “If the median nerve was cut, other nerves, still brimming with electrical input, would take over the unused map space to process their input. When it came to allocating brain-processing power, brain maps were governed by competition for precious resources and the principle of use it or lose it” (p. 59). In the intact brain, no cortical space goes unused, at least not for long.

Motor outputs and plasticity

Over a decade after evidence of experience- dependent plasticity in the sensory cortex was observed, Nudo, Milliken, Jenkins and Merzenich (1996) examined the effects of experience on the motor cortex. In one study, they mapped the primary motor area (M1) to determine which areas represented movements of the forelimb (which includes the digits, wrist, and forearm) in monkeys. They then trained the monkeys on different tasks that elicited movement of different parts of the forelimb. The results were consistent with what was found with sensory input changes, in that as experience in the form of movements changed, the M1 topographic map changed. In particular the studies found:

1. When the monkeys were trained on a task that required use of their digits, the motor map representation of their digits expanded, and the areas dedicated to the wrist/forearm contracted.
2. When the monkeys were trained on a task that required use of the forearm, the motor map representation of the forearm expanded, and the areas dedicated to the digits contracted.
3. Additionally, when movement combinations occurred (where muscles co-contracted together) they were represented within the same cortical area, suggesting “that the temporal correlation of movements (and presumably muscles) drives changes in cortical motor organization” (p.804). In other words, movements that occurred at the same time came to be represented in the cortex together.

Kleim, Barbay and Nudo (1998), using rats, found that training a skilled task (versus one only requiring movement) altered motor maps. They trained rats on a skilled reaching task that compared the motor maps of their brains to that of a control group of rats in an unskilled reaching condition. After the training period, motor maps were produced for the areas of the brain controlling the wrist, digit, elbow/shoulder and hind limb areas. They found that for the rats trained to reach and grasp in order to retrieve food pellets, they had significantly larger areas of motor map dedicated to wrist and digit movements than the rats who simply had to press a lever and obtain food.

Overall, the results of these studies demonstrated experience dependent learning in the M1 motor cortex. Like the somatosensory cortex, the motor cortex also demonstrates topographical map reorganization associated with learning. The previous data comes via rodent and primate modes. An important question is whether data regarding experiencedependent plasticity, which occurs in both the motor and sensory cortex, translates to humans.

A study by Pascual-Leone, Nguyet, Cohen, Brasil-Neto, Cammorota and Hallett (1995) used transcranial magnetic stimulation (a non-invasive method of mapping motor outputs) to examine the effects of training a fine motor task in humans. Like the rat and monkey models, they mapped the subjects M1 areas corresponding to finger extensor and flexor muscles, and then taught them a five-finger piano exercise. They practiced two hours daily for one week. Each day after testing, the subjects M1 maps for those muscles were re-mapped. They found that as performance improved (i.e., fewer errors), the size of the motor map increased.

They then divided subjects into two groups. The practice group continued daily practice with weekends off for four additional weeks and the non-practice group ceased practice. For the practice group, each week they were mapped on Fridays (after a week of practice) with a resulting temporary increase in motor maps. They were also mapped on Mondays (after the weekend break), with a resulting small but permanent increase from their baseline. Thus, each week these small incremental increases demonstrated overall improvement over the course of the study. The non-practice group motor maps remained at baseline after being remapped four weeks later.

This study indicated that experience dependent plasticity in the intact human cortex occurs. Collectively, the preceding studies also inform regarding the principles of topographical motor maps in that they are dynamic, greater skill is characterized by greater areas of cortical representation, and map topography equates with experience (Plowman and Kleim, 2010). Thus, topographic cortical maps increase in size as a result of experience. Warraich and Kleim (2010) noted that because these changes to the topographical maps remain long after learning or training, these changes “are encoded as enduring neurobiological changes in the CNS.”

Fast forward to today— 21st century technology

Since those early days of research in the 1980s, a neurotechnology evolution has allowed scientists to study the most sophisticated system of cells, networks, chemicals and molecules we know (i.e., the brain) at a remarkable level. These various technologies have allowed for a deeper look, thereby providing information at the structural level. Without these new technologies, one could only speculate that behavior (in the form of training or skill acquisition or learning) relates to changes in the brain, versus observing directly that behavior (or skill acquisition, or learning or training) results in anatomical level modifications.

In short, experience constantly changes both the structure and function of our entire nervous system, including the peripheral (spinal cord and nerves) and central (brain) throughout our lives (Kerr, Cheng and Jones (2011). Pascual- Leone, Amedi, Fregni & Merabet, (2005) noted what is now known almost universally today—that neuroplasticity is the normal, ongoing state of our nervous system.

“The brain, the source of human behavior, is by design molded by the environmental changes and pressures, physiologic modifications and pressures and experiences. This is the mechanism for growth and development-changes in the input of any neural system, or in the targets for demands of its efferent connections, lead to system reorganization that might be demonstrable at the level of behavior, anatomy, and physiology and down to the cellular and molecular level” (p. 379).

Neuroplasticity is therefore not a response to injury. Rather, it is a fundamental property of our nervous system.

The focus therefore turns to answering the question of how these skilled movements become neurobiologically encoded within our brain.

The Neurobiology of Neuroplasticity

Returning to the definition posed in the beginning of this article where neuroplasticity was described “as any enduring change in neuron structure or function,” provides direction as to where to turn to identify basic principles of neuroplasticity at an anatomical level. That direction points us squarely toward neurons. While there is neurogenesis (new neuron formation) in some brain regions after brain development is “complete,” e.g., the dentate gyrus, (Perederiey & Westbrook, 2013), it is generally not new neurons that impact cortical maps, but rather the strength of the connections between existing neurons that drives neuroplasticity. The main mechanism at the anatomical level driving neural connections is the synapse. As Figure 3 highlights, the modulator of motor map reorganization is synaptic change.

When brain morphology changes as a result of learning, synaptic change is often the controlling influence. Synaptic change can occur along two different lines. One is Synaptogenesis, and the other is Synaptic Plasticity. Synapse comes from the Greek word synapsis meaning “conjunction.” Neuroplasticity then involves a host of events that impact neuron to neuron structure, including:

• Synapses—changes in number†*
• Synapses—changes in strength†‡*
• Dendritic arborization (complexity of the dendritic tree)†*
• Dendritic spines—changes in density†‡*
• Synaptic receptors—changes in density†‡*
• Axonal arborization (sprouting)**
• Glial and neuron interactions***
• Vascular processes and angiogenesis (new blood vessel growth)***
• Cell proliferation (including neurogenesis)***

† these items relate substantially to the process of synaptogenesis

‡ these items relate substantially to the process of synaptic plasticity

*citation = Warraich & Kleim (2010); Plowman & Kleim (2011); Kleim (2011)

** citation = Perederiy & Westbrook (2013) *** citation = Kerr (2011)

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To understand the processes of Synaptogenesis and Synaptic Plasticity, the basic property of neurons and synapses must be understood. Figures 4 and 5 show the major components involved in neuronal connections.

Synaptogenesis

As a general rule, the greater the numbers of synapses within a grouping of neurons, the greater the speed and efficiency with which those neurons will communicate. Therefore, new synapse formation is a critical component to efficient and effective neuronal firing. And a key aspect of new synapse formation is the dendrite. Dendrites get their name from the Greek word déndron, meaning “tree.” This is largely due to their tree-like appearance, and likewise, dendrite growth is termed “dendritic arborization.”

There are a number of dendrite factors which affect synaptogenesis:

1. 1. The size and complexity of a dendritic arbor ultimately determine the volume of synapses, and therefore the number of receptive synaptic contacts a single neuron can make with other neurons (Koleske, 2013).
   2. Dendrites, as part of their makeup, can have from hundreds to thousands of dendritic spines on each dendrite with which to connect to other axons (Figure 6). These structures are on the receiving end of synaptic transmissions, and contain neurotransmitter receptors.
   3. Dendritic spines are highly excitable, and have the ability to change morphology in response to inputs i.e., experience. Thus, they are widely held to be critical to learning and memory. This particularly relates to the concept of Long Term Potentiation (LTP) which will be further discussed.
   4. Dendritic spines have a high morphological plasticity. They can change shape whereby the head grows larger and the neck becomes shorter (improving transmission efficiency) as a result of LTP stimulation (Bosch and Hayashi, 2011).
      

      Figure 6.

   5. Spine density increases with LTP as a result of a two-step process. In the first step, spines initially generate and retract in response to LTP, with no net change. In the second step, with repeated LTP, the volume of synapses was found to increase (Oe, Tominaga-Yoshino, Hasegawa and Ogura, 2013). This change in morphology is a key aspect of spine function (Yuste, Bonhoeffer; 2010) particularly as it relates to the maintenance and reorganization of neural circuits (Mizui, Kojima, 2013).
   6. As the spine density and individual spine size increases, so too does synapse strength (Mizui, Kojima, 2013).

How do new synapses form?

Synaptic formation is largely the byproduct of dendritic spines. There are three distinct types of spines:

1. Filopodia—long, thin spines which are highly motile (capable of movement)
2. Thin Spines—long thin spines containing a small spine head, and
3. Mushroom Spines—spines where the head diameter is much larger than the diameter of the neck (Figure 7).

The spine type critical to new synapse formation is the filopodia.

Filopodia are thought to reach out to axons, in essence to sample connections with other axons. They rapidly extend toward nearby axons, and actively pull axons toward dendrites, effectively increasing the likelihood of synapse formation (McCallister, 2000). If conditions are favorable, and they effectively initiate contact with an axon, this results in the formation of a new synapse (Toni et al., 2007). The filopodia then transform into a dendritic spine capable of synaptic transmission. For the formation of a new synapse to occur, the contact of the filopodia would lead to recruitment of synaptic vesicles and proteins to the presynaptic membrane (the axon), and neurotransmitter receptors to the postsynaptic area (the dendritic spine). Thus, with synaptogenesis, the creation of new neuron to neuron connections provides greater neural network connectivity, as more neurons connect to one another.

Synaptic plasticity

Synaptic plasticity relates to the strengthening or weakening of existing synapses in response to the level of activity of the synapse. This draws directly from Hebbian theory, where Abbott and Nelson (2000) noted in discussing Donald Hebb’s prevailing finding related to neuroplasticity where they “conjectured that synapses effective at evoking a response should grow stronger, but over time Hebbian plasticity has come to mean any long-lasting form of synaptic modification (strengthening or weakening) that is synapse specific and depends on correlations between pre- and postsynaptic firing” (Abbott and Nelson, 2000, p. 1178). From this idea came the maxim that “neurons that wire together fire together.” This strengthening of synapses is the result of LTP, where it is defined as “a longlasting strengthening of the response of a post-synaptic neuron to stimulation across the synapse that occurs with repeated stimulation” (www.merriam-webster.com). See Figure 8, for an overview of neuron-to-neuron connections. Conversely, the closely related concept of “neurons that fire out of sync lose their link” is Long-Term Depression (LTD). This is where there is a decrease in the efficacy of synapses as a result of decreased activity of neuron to neuron transmission.

Process of Long Term Potentiation

Ultimately, LTP (and LTD) changes the efficacy of a synapse. The process starts with NMDA receptor activation, which allows calcium (Ca2+) into the post synaptic neuron. The calcium influx activates the process of phosphorylation, which then results in rapid recruitment of AMPA receptors (a receptor for glutamate) to the synapse. With greater levels of AMPA receptors available, the release of glutamate by the axon terminal buttons causes a larger post-synaptic potential. Figure 9 highlights the process of LTP.

In recent work, Hill and Zito (2013) found that LTP enhanced the survival of dendritic spines, effectively taking new dendrites from a transient state to a longer persistent state. Spines enhanced by LTP were shown to have higher volumes than spines not stimulated via LTP. Their study also found that in essence, smaller, newer spines that underwent LTP increased volume and this volume provided a more stable state resulting in significantly higher rates of survivorship. Another factor likely impacting spine stability is that new spines tend to grow closer to synapses that have higher levels of activity, and that clustering of inputs from the dendrite arbor may result in stabilization.

Within the human cortex, 90% of excitatory synapses (axonal synapses that increase the likelihood of dendritic neurons firing) occur on dendritic spines (Harris & Kater, 1994). The importance of increased dendritic spines within the cortex cannot be understated. Greater volume of dendritic spines coupled with greater stabilization of dendrites within neurons results in more effective synapses. More effective synapses result in stronger linkages between neurons, and stronger neuronal connections result in faster, more effective processing. In the context of experience-dependent neuroplasticity, these neural network improvements equate to learning.

Research and synaptogenesis

Kleim et al. (2002) trained rats in a skilled or unskilled reaching task to examine if synaptic changes occurred in response to learned behavior. For rats trained in the skilled task, they found a statistically significant increase in the number of synapses per neuron (versus the untrained rats) in the area of the cortex directly related to the forelimb areas which were involved in the training. They noted that the results showed “that learning dependent synaptogenesis and functional reorganization are co-localized to specific regions of the cortex that mediate [relate to] the learned behavior” (p. 71).

Expanding on those findings, Kleim et al. (2004) then looked to see when, during the learning process, synaptogenesis actually occurred. Kleim and his colleagues, through previous research, found that motor skill learning involves two phases. In the first phase there is rapid performance improvement within the first few training sessions. In the second phase there are improvements, though less robust than in phase one, across multiple sessions.

Phase one changes involve activation of the cerebellum (which deals with motor control) and striatum (which plays a role in learning and motor control), and phase two changes activate the motor cortex (within the frontal lobe). Kleim et al. (2004) trained rats on a skilled reaching task and found that there was a statistically significant increase in reaching success at three days, seven days, and 10 days of training (see Table 1). When they examined changes to the motor map topography after three, seven and 10 days, they found that motor map reorganization did not change significantly until after day 10. When they examined when synaptogenesis occurred, they found that the number of synapses per neuron (those neurons related to the training) did not change until after seven and 10 days of training.

To summarize the work by Kleim and colleagues, they found:

1. Motor map changes and synaptogenesis occurred in areas of the brain which directly related to the learned behavior.
2. In phase one of learning, performance accuracy increased within three days of training, but there were no changes in synapse density in M1.
3. In phase two of learning accuracy continued to improve after seven and 10 days, and synapse density increased after seven and 10 days of training in M1.

Early in the article, it was noted that neuroplasticity is a fundamental property of our nervous system. The intrinsic factors that make neuroplasticity our baseline state are as follows: (see Figure 10 for an overview)

Experience (in the form of changes to sensory inputs or motor outputs) results in changes to our brain morphology.

• This experience changes our brain as a result of synaptogenesis (increase in neuron-to-neuron synapses) and synaptic plasticity (efficacy changes in existing synapses due to LTP).
• Synaptic changes result in cortical map reorganization.
• Cortical map reorganization results in learning, be it motor learning, sensory learning or otherwise.

Neuroplasticity as a fundamental property of the intact nervous system therefore provides us an ongoing substrate for learning. The question is whether or how this fundamental property changes after damage to the brain. That is the focus of the next article.

About the Author

Heidi Reyst Ph.D., CBIST
Vice President of Clinical Administration 

Dr. Reyst holds a Ph.D. in Applied Social Psychology from The George Washington University in Washington, D.C. She is a Certified Brain Injury Specialist Trainer, Academy of Certified Brain Injury Specialists, and has worked in various capacities within the field of brain injury rehabilitation since 1991. She currently oversees professional staff allocation, billing and service provision, professional staff training, accreditation readiness and outcomes management. Dr. Reyst is currently a member of the board of governors for the Academy of Certified Brain Injury Specialists, and is the Vice Chairperson for Information Management. She is a member of the American Psychological Association and is a frequent volunteer for the Brain Injury Association of Michigan.

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