The brain’s capacity to reorganize and adapt after damage is known as neuroplasticity or brain plasticity.
Take-home Messages
- What It Is: Brain plasticity, also known as neuroplasticity, is the brain’s biological, chemical, and physical capacity to reorganize its structure and function.
- Why It Happens: Neuroplasticity occurs due to learning, experience, and memory formation or due to damage to the brain.
- Synaptic Pruning: Learning and new experiences cause new neural pathways to strengthen, whereas neural pathways used infrequently become weak and eventually die. This process is called synaptic pruning.
- Not Just Childhood: Although traditionally associated with changes in childhood, recent research indicates that mature brains continue to show plasticity due to learning.
- Protective Effects: Neuroplasticity provides protective effects in managing traumas during human development (Cioni et al., 2011). Also, learning music or second languages can increase neuroplasticity (Herholtz & Zatorre, 2012).
- Stroke Recovery: Plasticity allows the brain to cope better with the indirect effects of brain damage resulting from
inadequate blood supply following a stroke. - Adaptive Purpose: Fundamentally, the nervous system needs to rearrange itself to adapt to its unfolding situation. The genes program the body to have neuroplasticity so that animals can survive in unpredictable environments.
Neuroplasticity, also called brain plasticity, refers to the capacity of the brain to change and adapt in structure and function in response to learning and experience.
The brain possesses a remarkable ability to rewire itself. These changes range from individual neuron pathways making new connections to systematic adjustments like cortical remapping. This is when one brain area takes over for another.
This occurs in all healthy people, especially children, after various problems like brain injuries.
Early Theories
Early experimental work on neuroplasticity was conducted by an eighteenth-century Italian scientist, Michele Malacarne, who discovered that animals made to learn tasks would develop larger brain structures (Rosenzweig, 1996).
The first theoretical notions of neural plasticity were developed in the nineteenth century by William James, a psychology pioneer. James wrote about this topic in his 1890 book The Principles of Psychology (James, 1890).
Not everyone agreed the adult brain could change at all. In the twentieth century, the pioneering neuroanatomist Santiago Ramón y Cajal argued the opposite: that nerve paths, once mature, were fixed for good.
As Cajal wrote, in the adult brain, nerve paths are “something fixed, ended, immutable. Everything may die, nothing may be regenerated” (Cajal, 1928; Fuchs & Flügge, 2014).
Modern Theories
Progress of the idea – modern theories: Modern experimental instruments like imaging tools have yielded enough information to develop improved theories.
Scientists now think that neuroplasticity occurs throughout all life stages, with extensive capacities from childhood development to healing diseases (Doidge, 2007).
The brain can rearrange itself in terms of the functions it carries out and the basic underlying structure (Zilles, 1992).
Functional Plasticity
Functional Recovery After Brain Trauma
Functional plasticity is the brain’s ability to move functions from a damaged area of the brain after trauma, to other undamaged areas. Existing neural pathways that are inactive or used for other purposes take over and carry out functions lost because of the injury.
After a brain injury, such as an accident or stroke, the unaffected brain areas can adapt and take over the functions of the affected parts. This process varies in speed, but it can be fast in the first few weeks (phase of spontaneous recovery) then it becomes slower.
It can be helped by rehabilitation, and the nature of rehabilitation programs varies with the type of injury, from retraining some types of movement to speech therapy.
There are ways through which brain plasticity can enable brain-damaged people to regain some of their past capacities. Each of the approaches through which the nervous system adapts its functionality has differences in how it occurs and in which patients it occurs.
Axonal sprouting
Functional plasticity can occur through a process termed axonal sprouting, where undamaged axons grow new nerve endings to reconnect the neurons, whose links were severed through damage.
Undamaged axons can also sprout nerve endings and connect with other undamaged nerve cells, thus making new links and new neural pathways to accomplish what was a damaged function.
Homologous Area Adaptation
Although each brain hemisphere has its own functions, if one brain hemisphere is damaged, the intact hemisphere can sometimes take over some of the functions of the damaged one.
In homologous area adaptation, brain-behavior becomes active in the equivalent part on the opposite side of the brain from where it usually occurs (Grafman, 2000). If it normally occurs on the right side, it would move to the left side, and vice versa. The two sides essentially swap jobs.
This functional neuroplasticity occurs more often in children than in adults. Shifting over a module to the opposite side displaces some of the functionality that was originally there.
As a result, the two functions may become less effective, contaminating each other.
Cross-Modal Reassignment
Cross-modal reassignment occurs when the brain uses an area that would normally process one type of sensory information for a different type instead. For example, it might repurpose an area built for sight to process sound.
When a brain region stops receiving its usual input, it can be repurposed for another sense. This often happens after a person becomes blind. The visual area may then process touch instead.
This lets some blind people “see” Braille text with their fingers (Grafman, 2000).
Other blind people learn to reuse their visual cortex for sound. This produces a form of “echolocation” that helps them navigate.
Thaler, Arnott, and Goodale (2011) used fMRI to test this. They found the clicks and echoes are processed mainly in the visual cortex, not the auditory cortex, even though the information arrives through hearing.
Map Expansion
In map expansion, the brain notices that a certain area gets extensive use, so it expands this area (Grafman, 2000).
This is comparable to how the body can notice that certain muscles get more use (such as those involved in an often-played sport), then grows those muscles larger.
When a person often engages in an activity or experience, this produces enlargement of the associated brain region.
Brain growth occurs right away, so neuroscientists can detect it through brain imaging technologies while it occurs (Grafman, 2000).
Compensatory Masquerade
Compensatory masquerade involves the brain reusing a component to conduct a mental operation other than what it would typically do.
For example, suppose a person suffers a brain injury with some functionality lost. In that case, they may reuse a different method. They might, for instance, find their way by remembering directions instead of relying on their sense of location (Grafman, 2000).
Evidence For Functional Plasticity
Homologous Adaptation: A Case Example
Case studies of stroke survivors show the brain can rewire itself after damage. Undamaged brain sites take over the functions of the damaged ones.
Neurons next to a damaged site can take over at least some of what was lost. Grafman (2000) reports one clear case.
A youth with a right parietal lobe injury ended up with the left parietal lobe taking over some right-side functions. The shift came at a cost. The youth then had difficulty with tasks normally handled by the left side, because right-side equivalents had taken over left-side brain resources (Grafman, 2000).
Neuronal Unmasking:
Wall, Barlow, and Gaze (1977) discovered that the brain contains dormant, or “silent,” synapses. These are connections that exist anatomically but produce no effect under normal conditions.
When brain damage removes a region’s usual input, these dormant synapses can switch on. This process is called neuronal unmasking. It opens functional connections to parts of the brain that were not normally engaged, letting them take over some of the lost function.
Case studies of stroke survivors support this picture. Neurons next to a damaged site can take over at least some of its lost functions. This fits local unmasking and sprouting rather than a wholesale relocation of function across the brain.
Cortical Remapping: Merzenich et al. (1984)
Cortical remapping is the largest-scale form of brain plasticity. It happens when one brain area takes over the job that a different, damaged area used to do. This is the “cortical remapping” named earlier in this article.
Merzenich et al. (1984): Remapping the Hand
The clearest early demonstration came from Merzenich et al. (1984), who studied the cortical map of the hand in adult owl monkeys.
Aim: To find out whether, and how quickly, the brain’s map of the hand reorganises after losing the input from one finger.
Method: Using electrodes inserted into the cortex, researchers first mapped which spot responded to touch on each finger of eight anaesthetised owl monkeys. They then surgically removed the third (middle) finger.
Method (follow-up): Sixty-two days later, they mapped the same cortical area again, using the same method.
Results: The first mapping showed five separate cortical areas, one per finger. Amputation changed that map. After the amputation, the areas for the two neighbouring fingers had spread into the now-unused territory that used to belong to the missing finger.
Results (unchanged areas): The areas for the two outer fingers were unchanged.
Conclusion: The brain’s sensory map is not fixed. Cortical remapping had occurred within just 62 days in adult monkeys, with neighbouring brain areas expanding into territory that lost its input.
Evaluation: Mapping the same animals before and after a precisely controlled injury is strong evidence the reorganisation was actually caused by the lost input.
The sample was small and non-human, though. A single surgical cut is also a poor model for the slower, messier damage caused by a human stroke.
This finding mattered well beyond owl monkeys. It gave later human research on amputees, stroke survivors, and blind Braille readers a concrete mechanism to look for behind their own, less direct, evidence.
When Cortical Remapping Goes Wrong: Phantom-Limb Pain
Cortical remapping is not always helpful, though. When a limb is amputated, the cortical territory that used to represent it does not simply go quiet.
Signals from neighbouring body parts invade the vacated territory, the same basic process Merzenich et al. demonstrated in owl monkeys losing a digit. In some amputees, this invasion is experienced as a continuing, sometimes painful, sensation that the missing limb is still present, known as phantom-limb pain.
The map itself is not lost, though. Cortical remapping does not mean the brain’s map disappears altogether. Localisation of function still holds at any given moment: a brain area still does a specific job. Function moves; capacity does not.
The distinction is subtle but important.
What plasticity changes is which job an area does over time, not whether any given area does a job at all. Cortical remapping is the clearest illustration of the brain reassigning territory, not of brain tissue losing its function entirely.
Structural Plasticity
How Experience Changes Brain Plasticity
Structural neuroplasticity is the brain’s ability to change its physical structure as a result of learning, involving reshaping individual neurons (nerve cells).
During infancy, the brain experiences rapid growth in the number of synaptic connections.
As each neuron matures, it sends out multiple branches; this increases the number of synaptic contacts from neuron to neuron. At birth, each neuron in the cerebral cortex has approximately 2,500 synapses.
By the time a child is three years old, the number of synapses is approximately 15,000 (Gopnick et al. 1999).
As we mature, the connections we do not use are deleted, and the ones we use frequently are strengthened, called neural pruning (Purcell & Zukerman, 2011). This process continues throughout our life.
While plasticity occurs throughout life, it is especially relevant during the early “critical years,” when brain plasticity enables the senses, language, and other skills to develop.
Developmental plasticity
Part of the development of the vision system is genetically hardwired. However, another part of this development depends on neuroplasticity. As a child grows, the incoming information from light sources, such as light reflected off the faces of caregivers, provides necessary cues for the brain to adjust its growth patterns.
The equivalent plasticity-based growth also occurs with the other senses, calibrating the young person to local conditions. Pruning works the same way.
Synapses multiply very fast in infancy, at a rate of up to 40,000 a second, until close to two years of age. Pruning then takes over, eliminating synapses at up to 100,000 a second until the end of puberty and removing around half of those first formed.
Kolb and Fantie (1989) disagree. They argue this pruning is not simply loss. It refines an over-connected network into a more efficient, specialised one.
A clear example of this refinement comes from language. Werker and Tees (1992) found that very young infants can tell apart speech sounds from any human language, including contrasts English speakers never learn to hear.
By around one year old, this broad ability narrows to the sounds of the infant’s own language, as the underlying neural tuning is shaped by exposure.
This narrowing is a hallmark of a critical period: a developmental window in which a lack of the right input can cause a permanent loss of ability.
The development of language reveals even more about neuroplasticity. Again, part of this functionality is genetically hardwired, but part depends on environmental feedback. An individual has certain nerve cells programmed to become grammar modules.
For these to function correctly, they require the input of specific grammatical rules from a culture, such as the rules of English or Spanish. Thus, neuroplasticity enables the brain to process language.
How Does Neuroplasticity Work?
At the most basic level, it starts with the production of a new nerve cell (neurogenesis). Then, individual neurons develop new connections with each other.
A neuron works by sending or receiving electrochemical signals from other neurons in the brain.
The way that individual neurons connect to each other controls how the signals get sent. It works much like the routing of messages over the internet, or instructions moving through a computer processor. Connections form a network.
As each neuron develops connections to others, this results in growing clusters of cells. The neurons can adjust the level or strength of the signal with connecting neurons.
This ongoing process provides fine-tuning of the neural architecture. Neuroplasticity uses cascades of electrochemical signals that unfold as a result of the expression of genetic codes through cell signal molecules (Flavell & Greenberg, 2008).
Rewiring larger regions, reorganizing the nervous system at multiple levels
Neurons work together at several different levels. Not only individual cells but even clumps within brain regions can grow in greater or lower density.
As cells grow or die in different regions, the relative densities vary. Such variations can provide an even broader adjustment or neuroplasticity in the brain than individual nerve cell connections. This adjustment can be substantial.
When nerve bundles become broken, through injury or surgery, the brain can regrow these elements (Doidge, 2007). Surprisingly, the brain can reconnect itself in an efficient manner even to deal with sizable upsets. It operates like a plant, able to regrow around lost parts. This regrowth starts immediately.
The brain acts fast.
The brain can regrow after injury, starting instantly at the molecular level in the injured area (Wall & Wang, 2002). Gradually, the repairs extend through subcortical layers, reaching larger-scale cortical levels of the brain.
This growth occurs throughout the nervous system, including the spine and distributed branches, not only in the brain.
Recurring synaptic connections grow more efficient (cell assembly theory)
Nerve cells work by producing electrochemical activity in the synapses, which are gaps connecting the cells together.
As a synaptic connection fires more often, it grows more efficient in what is known as “cell assembly theory.” A phrase describing this phenomenon is “cells that fire together wire together” (Lowel, 1992). The underlying mechanism is called long-term potentiation.
This process repeats constantly.
The nerve connections grow stronger when one cell fires before the other rather than when they both fire simultaneously. Sequential firing produces a causal relationship, enabling the nervous system to learn. Timing matters.
As a comparison, internet search engines track which sites link directly to which other sites. The combined directional links of billions of sites produce an efficient map of the internet.
The combined directional links of billions of neurons produce an efficient map of the body and its environment.
Evidence for Structural Plasticity
The famous study by Maguire et al. (2000) demonstrates structural brain plasticity in a real-world setting. They studied 16 London taxi drivers and found more grey matter in the posterior hippocampus than in a control group. This area is involved in short-term memory and spatial navigation.
Total hippocampal volume did not differ between the two groups. Grey matter had instead been redistributed: more towards the back of the hippocampus in drivers, less towards the front.
The size of this shift correlated with years spent driving a taxi (Maguire et al., 2000). Because taxi drivers cannot be randomly assigned to their job, the study is a quasi-experiment, so the finding is technically correlational rather than fully causal.
This idea has direct experimental support. Draganski et al. (2004) tested whether learning a new motor skill, juggling, changes the brain’s physical structure.
Aim: To test whether learning a new motor skill changes the brain’s physical structure.
Method: Volunteers with no juggling experience were randomly split into a juggling group and a non-juggling control group. All were scanned by MRI three times.
Method (timing): The first scan came before training, the second after three months of learning to juggle, and the third after three more months without practice.
Results: Jugglers gained grey matter. By the second scan they had significantly more than controls, mainly in areas that process visual movement.
Results (follow-up): Three months later, without practice, some of this growth had reversed.
Conclusion: Learning a skill grows the brain tissue that supports it. That growth is not permanent once practice stops.
Evaluation: This was a genuine experiment. Random allocation supports a causal claim, though the sample was small and self-selected.
A second, very different study extended this idea. Draganski et al. (2006) scanned medical students before and after they crammed for an exam.
They found grey-matter growth in the parietal cortex that, unlike the juggling effect, did not reverse three months later.
Further support comes from Mechelli et al. (2004). Learning a second language increases the density of grey matter in the left inferior parietal cortex.
How much reorganisation occurs depends on the fluency reached and the age the second language was learned. Fluency matters.
With age, neuroplasticity generally declines. Mahncke et al. (2006) tested this with a computer-based cognitive training program in older adults with memory impairment.
In a randomised, controlled study, the trained group improved significantly more than the control group. Structured training can partly offset this age-related decline.
Learning and new experiences cause new neural pathways to strengthen, whereas neural pathways which are used infrequently become weak and eventually die.
Boyke et al. (2008) found that even at 60+, learning a new skill (juggling again) resulted in increased neural growth, this time in the visual cortex.
Kühn et al. (2014) found that playing video games for 30 minutes a day increased grey matter in the cortex, hippocampus, and cerebellum. The effect was consistent.
The complex demands of a video game, such as spatial navigation, planning, and fast decision-making, may explain why.
Lutz et al. (2004) compared eight experienced Tibetan Buddhist meditators with 10 people who had never meditated.
Gamma-wave brain activity, linked to coordinating activity across brain regions, was much higher in the meditators, both at rest and during meditation itself. The pattern held up.
This suggests sustained meditation practice can produce lasting changes in how the brain coordinates its own activity.
Kempermann et al. (1998) found that mice housed in more complex environments showed an increase in new neurons compared to a control group living in simple cages.
Changes were particularly clear in the hippocampus, which is linked to memory and spatial navigation.
Critical Evaluation
Neuroplasticity can explain a broad range of facts about the structure and function of the brain. This notion does, however, have some constraints.
These involve the gradual decline of neuroplasticity with age and certain restrictions regarding how much neural plasticity is possible, even in young, healthy people. Also, scientists have yet to learn many critical aspects of neuroplasticity.
The limits of brain plasticity (decline with age, biological constraints)
Neuroplasticity can only go so far. Non-human animals show many areas of brain plasticity. However, their brains cannot reshape themselves enough to learn a human language or perform advanced mathematics.
Neuroplasticity works on biologically available material, which imposes limitations like only adjusting the specific neural substrate of a cognitive function or adapting a brain function somewhat for a season. Plasticity has real limits.
The limits are real.
In people whose brains reuse large regions for different operations, this capacity can only work for specific types of processing. Blind people whose vision centres become useful for touch or sound are one example. Colour vision is the exception.
Even people blind from birth cannot reuse their color-detection cells for touch. Unlike geometry-detection cells, these have hard coding for visual input that cannot be repurposed (Grafman, 2000). Not every limit is about age.
Even in healthy individuals, neuroplasticity declines with age (Lu et al., 2004). Over the years, as the body becomes less flexible, so does the brain. Youth brings flexibility.
Much neuroplasticity is geared towards enabling younger people to develop an understanding and capacity to act within their surroundings. This stabilizes to some extent in adulthood, even declining in the elderly.
One can see the decline of neuroplasticity in how older people become more fixed in their ways while younger people learn rapidly.
What don’t we know about brain plasticity?
Neuroplasticity has grown over recent centuries into a topic of considerable interest to scientists but remains poorly understood. The brain imaging tools for conducting studies on this topic are still young. As such, much knowledge has yet to be found.
Scientists remain unsure about many of the mechanisms underlying brain plasticity (Grafman, 2000). While a few of the processes have been studied in molecular detail, others have not, and the conceptual understanding of how they take place is a source of ignorance.
Therefore, the technical underpinnings of neuroplasticity represent an area of active interest in which further investigations are being carried out.
Neuroplasticity is the brain’s freedom to readjust its own routing. It does this to enable more effective responses to injury, or to everyday challenges, and it has been shown to occur across a wide range of animal species.
However, we still only know some of the brain regions where it occurs. We also know only some of the mechanisms behind it, and some of its costs and benefits.
Animal adaptations
Animals other than humans exhibit neuroplasticity as much as humans do, or in some cases, even more.
Animals evolved to survive over periods of years through recurring cycles of weather and other environmental conditions. As such, some species exhibit neuroplasticity in cyclical patterns (Nottebohm, 1981). The seasons shape the brain.
Brain regions that navigate the environment, like the hippocampus, often grow during mating season (Nottebohm, 1981). Also, some birds’ brain centers for singing mating songs grow during this period.
Other animals’ brains develop differently depending on the season, such as adjusting when to lay more eggs or become pregnant (Wayne et al., 1998).
Behavioral or environmental modification
Several alterations to one’s behaviors or environmental conditions can affect the brain’s structure. These include sports or other physical activities, meditation, using drugs, or pollution.
When a person becomes physically active, this affects the brain and the rest of the body. Exercise helps the brain directly.
Aerobic activities like running or bicycling increase the brain’s rate of producing neurons (Gomez-Pinilla & Hillman, 2013). This results in better awareness of one’s surroundings, decision-making, affect, and other mental functions. Not every benefit is physical.
Meditation practices, which involve focusing attention, can also invoke neuroplasticity (Lutz et al., 2004). In this case, the brain increases its capacity for controlling emotions or awareness. Focus itself reshapes the brain.
Not every change is beneficial, though.
Drugs, including alcohol, illicit drugs, and certain medications, can affect the brain (Ganguly & Poo, 2013). These substances often produce chemical adjustments in the brain, which can last well beyond the duration of drug use, even for decades. Not all of this damage heals.
In some cases, like heavy drinking, entire brain regions can restructure themselves to recover lost functionality.
The brain responds to the environment as the main part of its normal function. As such, the brain can also undergo harm from environmental problems. Pollution is one such harm.
Air pollution, heavy metals, and other pollutants can inhibit brain development. For example, air pollution can destroy brain cells, reducing cognitive function (Calderón-Garcidueñas, 2002).
Contemporary Research
Recent studies complicate a simple “more experience means more plasticity” picture, and put commercial brain-training claims to a harder test.
Does More Experience Always Mean More Plasticity?
Gallo et al. (2025) scanned 69 young Russian-English bilingual adults. They modelled the relationship between daily second-language use and hippocampal grey matter.
They found an inverted-U pattern in the left hippocampus (p = .019): volume rose with moderate daily use, then fell again at the highest levels of use.
The authors call this “dynamic restructuring.” An early phase of growth gives way to an efficiency-driven pruning phase once a skill is well learned.
This echoes, over years rather than months, the same expand-then-partly-reverse pattern Draganski et al. (2004) found in jugglers above.
Making Rehabilitation Feedback Contingent on the Brain’s Own Activity
Kim et al. (2025) tested a brain-computer interface (BCI) for stroke rehabilitation. Twenty-five people with chronic stroke took part.
They were randomly split into two groups. Both received electrical stimulation to the affected wrist across 20 sessions.
In one group, stimulation triggered only when the BCI detected the right brain pattern for the intended movement. In the other, stimulation ran on the same schedule regardless of what the brain was doing. Only the trigger differed.
Wrist strength improved significantly more in the contingent-feedback group (p = .036). Brain activity tracked the gain, too: the size of the connectivity change correlated with the size of the strength gain (r = .608, p = .027).
The benefit came from the brain’s own activity, not the stimulation alone.
The Limits of Commercial Brain-Training Claims
Simons et al. (2016) reviewed the peer-reviewed evidence behind commercial “brain-training” programs, which often cite neuroplasticity to justify their claims.
They applied strict criteria to separate well-controlled studies from weaker ones. Near transfer (getting better at the trained task) was judged separately from far transfer (broader gains in everyday cognition).
Near transfer was consistently strong. Far transfer was weak and inconsistent.
Once poorly controlled studies were set aside, there was little evidence that training on one task produces broad gains in intelligence or everyday functioning.
Genuine neuroplasticity is not in doubt. But the field, as a whole, had overstated how far a narrow, gamified task can generalise.
References
Boyke, J., Driemeyer, J., Gaser, C., Büchel, C., & May, A. (2008). Training-induced brain structure changes in the elderly. Journal of Neuroscience, 28 (28), 7031–7035. https://doi.org/10.1523/JNEUROSCI.0742-08.2008
Bruel-Jungerman, E., Davis, S., & Laroche, S. (2007). Brain Plasticity Mechanisms and Memory: A Party of Four. The Neuroscientist, 13 (5), 492–505. https://doi.org/10.1177/1073858407302725
Cajal, S. Ramón y. (1928). Degeneration and Regeneration of the Nervous System (R. M. May, Trans.). Oxford University Press.
Calderón-Garcidueñas, L., Azzarelli, B., Acuna, H., Garcia, R., Gambling, T. M., Osnaya, N., Monroy, S., Del Rosario Tizapantzi, M., Carson, J. L., Villarreal-Calderon, A., & Rewcastle, B. (2002). Air Pollution and Brain Damage. Toxicologic Pathology, 30 (3), 373–389. https://doi.org/10.1080/01926230252929954
Cioni, G., D’Acunto, G., & Guzzetta, A. (2011). Perinatal brain damage in children. Progress in Brain Research, 139–154. https://doi.org/10.1016/b978-0-444-53884-0.00022-1
Craik, F. I. M., & Bialystok, E. (2006). Cognition through the lifespan: mechanisms of change. Trends in Cognitive Sciences, 10 (3), 131–138. https://doi.org/10.1016/j.tics.2006.01.007
Doidge, N. (2007). The Brain That Changes Itself: Stories of Personal Triumph from the Frontiers of Brain Science (Reprint ed.). Penguin Books.
Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., & May, A. (2004). Neuroplasticity: Changes in grey matter induced by training. Nature, 427 (6972), 311–312. https://doi.org/10.1038/427311a
Draganski, B., Gaser, C., Kempermann, G., Kuhn, H. G., Winkler, J., Büchel, C., & May, A. (2006). Temporal and spatial dynamics of brain structure changes during extensive learning. Journal of Neuroscience, 26 (23), 6314–6317. https://doi.org/10.1523/JNEUROSCI.4628-05.2006
Flavell, S. W., & Greenberg, M. E. (2008). Signaling Mechanisms Linking Neuronal Activity to Gene Expression and Plasticity of the Nervous System. Annual Review of Neuroscience, 31 (1), 563–590. https://doi.org/10.1146/annurev.neuro.31.060407.125631
Freed, W., de Medinaceli, L., & Wyatt, R. (1985). Promoting functional plasticity in the damaged nervous system. Science, 227 (4694), 1544–1552. https://doi.org/10.1126/science.3975624
Fuchs, E., & Flügge, G. (2014). Adult Neuroplasticity: More Than 40 Years of Research. Neural Plasticity, 2014, 1–10. https://doi.org/10.1155/2014/541870
Gallo, F., Voits, T., Rothman, J., Abutalebi, J., Shtyrov, Y., & Myachykov, A. (2025). Experience-dependent neuroplasticity in the hippocampus of bilingual young adults. eNeuro, 12 (6), Article ENEURO.0128-25.2025. https://doi.org/10.1523/ENEURO.0128-25.2025
Ganguly, K., & Poo, M.-M. (2013). Activity-Dependent Neural Plasticity from Bench to Bedside. Neuron, 80 (3), 729–741. https://doi.org/10.1016/j.neuron.2013.10.028
Grafman, J. (2000). Conceptualizing functional neuroplasticity. Journal of Communication Disorders, 33 (4), 345–356. https://doi.org/10.1016/s0021-9924(00)00030-7
Herholz, S. C., & Zatorre, R. J. (2012). Musical Training as a Framework for Brain Plasticity: Behavior, Function, and Structure. Neuron, 76 (3), 486–502. https://doi.org/10.1016/j.neuron.2012.10.011
James, W. (1890). The Principles of Psychology, Vols. 1-2.
Kempermann, G., Brandon, E. P., & Gage, F. H. (1998). Environmental stimulation of 129/SvJ mice causes increased cell proliferation and neurogenesis in the adult dentate gyrus. Current Biology, 8 (16), 939–944. https://doi.org/10.1016/S0960-9822(07)00377-6
Kim, M. S., Park, H., Kwon, I., An, K.-O., Kim, H., Park, G., Hyung, W., Im, C.-H., & Shin, J.-H. (2025). Efficacy of brain-computer interface training with motor imagery-contingent feedback in improving upper limb function and neuroplasticity among persons with chronic stroke: A double-blinded, parallel-group, randomized controlled trial. Journal of NeuroEngineering and Rehabilitation, 22 (1), Article 1. https://doi.org/10.1186/s12984-024-01535-2
Kolb, B., & Fantie, B. (1989). Development of the child’s brain and behavior. In C. R. Reynolds & E. Fletcher-Janzen (Eds.), Handbook of Clinical Child Neuropsychology (pp. 17–39). Plenum Press.
Lowel, S. (1992, January 10). Selection of intrinsic horizontal connections in the visual cortex by correlated neuronal activity. Science. https://science.sciencemag.org/content/255/5041/209.long
Lu, T., Pan, Y., Kao, S.-Y., Li, C., Kohane, I., Chan, J., & Yankner, B. A. (2004). Gene regulation and DNA damage in the ageing human brain. Nature, 429 (6994), 883–891. https://doi.org/10.1038/nature02661
Lutz, A., Greischar, L. L., Rawlings, N. B., Ricard, M., & Davidson, R. J. (2004). Long-term meditators self-induce high-amplitude gamma synchrony during mental practice. Proceedings of the National Academy of Sciences, 101 (46), 16369–16373. https://doi.org/10.1073/pnas.0407401101
Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S. J., & Frith, C. D. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences, 97 (8), 4398–4403. https://doi.org/10.1073/pnas.070039597
Mahncke, H. W., Connor, B. B., Appelman, J., Ahsanuddin, O. N., Hardy, J. L., Wood, R. A., Joyce, N. M., Boniske, T., Atkins, S. M., & Merzenich, M. M. (2006). Memory enhancement in healthy older adults using a brain plasticity-based training program: A randomized, controlled study. Proceedings of the National Academy of Sciences, 103 (33), 12523–12528. https://doi.org/10.1073/pnas.0605194103
Mechelli, A., Crinion, J. T., Noppeney, U., O’Doherty, J., Ashburner, J., Frackowiak, R. S., & Price, C. J. (2004). Neurolinguistics: Structural plasticity in the bilingual brain. Nature, 431 (7010), 757. https://doi.org/10.1038/431757a
Merzenich, M. M., Nelson, R. J., Stryker, M. P., Cynader, M. S., Schoppmann, A., & Zook, J. M. (1984). Somatosensory cortical map changes following digit amputation in adult monkeys. Journal of Comparative Neurology, 224 (4), 591–605. https://doi.org/10.1002/cne.902240408
Nottebohm, F. (1981). A brain for all seasons: cyclical anatomical changes in song control nuclei of the canary brain. Science, 214 (4527), 1368–1370. https://doi.org/10.1126/science.7313697
Purcell, A., & Zukerman, W. (2011, August 17). Brain’s synaptic pruning continues into your 20s. New Scientist. https://www.newscientist.com/article/dn20803-brains-synaptic-pruning-continues-into-your-20s/
Rosenzweig, M. R. (1996). Aspects of the Search for Neural Mechanisms of Memory. Annual Review of Psychology, 47 (1), 1–32. https://doi.org/10.1146/annurev.psych.47.1.1
Simons, D. J., Boot, W. R., Charness, N., Gathercole, S. E., Chabris, C. F., Hambrick, D. Z., & Stine-Morrow, E. A. L. (2016). Do “brain-training” programs work? Psychological Science in the Public Interest, 17 (3), 103–186. https://doi.org/10.1177/1529100616661983
Thaler, L., Arnott, S. R., & Goodale, M. A. (2011). Neural correlates of natural human echolocation in early and late blind echolocation experts. PLOS ONE, 6 (5), e20162. https://doi.org/10.1371/journal.pone.0020162
Wall, P. D., Barlow, H. B., & Gaze, R. M. (1977). The presence of ineffective synapses and the circumstances which unmask them. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 278 (961), 361–372. https://doi.org/10.1098/rstb.1977.0048
Wall, J. T., Xu, J., & Wang, X. (2002). Human brain plasticity: an emerging view of the multiple substrates and mechanisms that cause cortical changes and related sensory dysfunctions after injuries of sensory inputs from the body. Brain Research Reviews, 39 (2–3), 181–215. https://doi.org/10.1016/s0165-0173(02)00192-3
Wayne, N. L., Kim, Y. J., & Yong-Montenegro, R. J. (1998). Seasonal Fluctuations in the Secretory Response of Neuroendocrine Cells of Aplysia californicato Inhibitors of Protein Kinase A and Protein Kinase C. General and Comparative Endocrinology, 109 (3), 356–365. https://doi.org/10.1006/gcen.1997.7040
Werker, J. F., & Tees, R. C. (1992). The organization and reorganization of human speech perception. Annual Review of Neuroscience, 15, 377–402. https://doi.org/10.1146/annurev.ne.15.030192.002113
Zilles, K. (1992). Neuronal plasticity as an adaptive property of the central nervous system. Annals of Anatomy – Anatomischer Anzeiger, 174 (5), 383–391. https://doi.org/10.1016/s0940-9602(11)80255-4
