It might intrigue you to know that ROSI is undergoing a renovation of sorts – with a new name to boost, as voted by students (in other words, prepare for the worst).
The surprising results of a new research study bring happiness and hope to many people worldwide, but warn others to be careful. On October 19th, the journal Nature published the outcome of an experiment studying the stability of intelligence in teenagers.
After the second set of IQ tests and MRI scans, it was clear that brain plasticity, the ability of the human brain to change as the result of one’s experiences, had been severely underestimated. Admittedly, this was a considerably small research study; there is no guarantee that this sample group is a true representation of the rest of the population. We will be provided with an authorization token (please note: passwords are not shared with us) and will sync your accounts for you. For more than two decades, there have been extensive studies of experience-based neural plasticity exploring effective applications of brain plasticity for cognitive and motor development. Functional decline is evident in cognitive, motor, social, psychological, physical domains. The gradual age-related decline in neural-behavioral functionality considerably impacts adults aged 65 or older (Yan et al., 1998, 2000).
The increasing number of older adults and those who develop MCI or AD pose great challenges to our society (Brookmeyer et al., 2007). Neural plasticity (also known as neuroplasticity, brain plasticity, cortical plasticity, cortical re-mapping) is an inherent characteristic or ability for lifelong skills learning and relearning. Experience-dependent changes in the lower neocortical regions can reshape the activation patterns and the anatomy of the cerebral cortex (Wall et al., 2002).
Sensory networks may be immutable beyond the critical periods of cortical plasticity (Ponti et al., 2008). The next question concerns the length of learning that can induce activity-dependent neural plasticity in macro- or micro-structures. Therefore, activity-dependent neural plasticity can be induced by both lengthy-extensive and brief-intensive practice (Ziemann et al., 2004). Mental exercise is also reported to have a positive impact on long-term neural activities (Lutz et al., 2004). Evidence shows that experience-dependent neural plasticity can be stimulated both by physical and mental practice to different extents, depending on the training contents and the abilities to test.
Motor learning is conceptualized as the process of deliberate or goal-directed practice that results in long-lasting performance stabilization and adaptation (Schmidt and Lee, 2005).
Motor learning takes place when a learner repeatedly and actively engages in physical or mental practice of a given skill or a set of skills (Schmidt and Bjork, 1992). Finally, we discuss how and why physical practice and mental training improve motor skills and brain fitness based on the concept of brain plasticity.
Environmental factors such as exercise, training, injuries and rehabilitation change neural plasticity by varying degrees in different cortical regions. Furthermore, computer-based practice significantly enhances cognitive rehabilitation in healthy older adults; the beneficial effects of training can be sustained for 3 months.
Due to brain plasticity, human intelligence can change by enormous amounts within teenage years. Based on the study, early intervention in the form of enriching education should greatly help these children lead better lives in their adult years. This means that you will not need to remember your user name and password in the future and you will be able to login with the account you choose to sync, with the click of a button.
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Research suggests that human brains continuously undergo structural reorganization and functional changes in response to stimulations or training.
However, experiences still enhance cognitive and motor functions via synaptogenesis and neurogenesis in certain brain areas, such as the hippocampus and cerebellum. Developed brains may restructure themselves due to extensive or rigorous skill learning lasting months or following a short period of practice (Taubert et al., 2010).
In learning a complex visuomotor task, older and younger learners showed similar increases in corticomotor excitability after several minutes of training, reflecting similar cognitive-motor plasticity of the two age groups (Cirillo et al., 2011).
For example, adaptive working memory training increased neural efficiency in working memory tasks in older adults (Brehmer et al., 2011). Compared to physical training, greater improvements in executive functions were observed after cognitive training (Gajewski and Falkenstein, 2012). Researchers of on-line learning primarily examine how learners of different ages or skill levels, in different learning conditions, acquire a variety of motor or cognitive skills during practice. The second question is whether sleep-based off-line motor learning enhances brain functionality of older adults. Different neural mechanisms for over-day and over-night off-line learning modes may exist (Robertson et al., 2004, 2005). Two essential questions need to be answered: what are the neural changes associated with motor learning?
An animal study suggested that physical exercise or motor learning can increase the thickness of the motor cortex (Anderson et al., 2002). As a result of long-term professional training, the cerebral structures and functions of expert musicians show significant changes. Functional capacity increases during childhood and young adulthood and decreases in older adulthood.
This means that, while it is likely that we can gain IQ points during this window of opportunity, we can lose IQ points just as easily.
The debate is essentially this: how much of who we are as human beings is due to heredity (nature) and how much is due to the environment (nurture)?

IQ, also known as intelligence quotient, is a measure of relative intelligence determined by a standardized test. Moreover, there was a correlation between the IQ tests and the MRI scans; the subjects with IQ changes also had changes in the physical structure of their brains. There is most certainly more to the concept of brain plasticity than was previously thought, over and beyond normal development. From a developmental point of view, the assumption of lifespan brain plasticity has been extended to older adults in terms of the benefits of cognitive training and physical therapy. These areas of decline collectively cause neurodegenerative disorders like mild cognitive impairment (MCI), Alzheimer’s disease (AD), or AD-related dementia.
Actively engaging in physical, motor or intellectual exercises, or deliberately receiving multisensory stimulations, can prevent functional decline and preserve cognitive functions. We summarize recent knowledge of brain plasticity and highlight the applications of developmental studies on cognitive training and movement therapy. Over the past few decades, researchers have made substantial efforts and progress in understanding the brain mechanisms of functional changes and structural reorganizations in people at varying developmental stages. When describing neural structures and functions, the cortical sensory organizations are usually portrayed as mappings where specific sensory inputs project to given cortical locations to form sensory-based neural representations (Buonomano and Merzenich, 1998). In contrast, experiences may induce fewer changes during non-critical periods of brain development.
Brain plasticity may be an age-related, age-dependent, or age-independent developmental process (Kolb et al., 1998).
A remarkable increase of gray matter volume was observed in the prefrontal, frontal and parietal regions while the learners were acquiring a balance-control skill.
When younger adults actively engaged in a learning and memory task for months, the size of gray matter expanded remarkably in the posterior and lateral parietal sites (Draganski et al., 2006). Working memory training induced white matter increases that were correlated with performance improvements in older adults (Engvig et al., 2012).
Cardiovascular and coordination training led to differential changes in sensorimotor and visual-spatial networks (Voelcker-Rehage et al., 2011). If on-line learning occurs, skill improvements can be observed during the practice phase and can be captured in immediate or delayed retention or transfer tests.
For cognitively unhealthy older learners, longer intervals (6, 12 s) reduced the beneficial effect of KR on on-line learning to a greater degree than on the learning of their healthy counterparts and younger adults. The third question is how physical exercise, motor practice and skill learning improve motor performance and brain fitness based on the neural plasticity mechanisms aforementioned.
Contrary to our expectation that sleep-based learning would be more advantageous than on-line learning, older learners showed small gains in skill retrieval after sleep (Yan et al., 2009). An increased density of blood vessels and the development of synapses in the cerebellum were observed following physical exercise (Black et al., 1990).
The cortical alterations may start in the early stages of training, or life, and develop continuously in higher regions later, reflecting rapid and remarkable changes in the critical period of neural plasticity (Schlaug, 2001). Injury studies support the notion of structural and functional reversibility of the higher regions of CNS.
Finally, the links between physical exercise and brain function in older adults will be highlighted. The slope of functional changes largely depends on individual differences in genetics, personality, motivation, lifestyle, socio-cultural background, exercise opportunities and learning experiences. The down-turning paths are for normal aging, cognitive aging, and successful aging in motor and cognitive functions. Are abstract human traits such as intelligence, personality, aggression and sexual orientation influenced by our genes or by our experiences?
MRI scans, or magnetic resonance imaging scans, are special radiological tests that use magnetic waves to create pictures of an area of the human body; in this case, pictures of grey matter in the brain.
While there is a chance of greatly increasing IQ during teenage years, there is also a chance of greatly decreasing IQ.
To summarize recent developments, first, we introduce the concept of neural plasticity from a developmental perspective.
The compromised functionality reduces the quality of life and causes great concerns in the geriatric and gerontological communities.
Brain plasticity is a lifetime developmental process and continues to play a significant role in older adulthood. We discuss the close relationship between neural plasticity, motor learning and cognitive aging. When an individual acquires novel skills or information, the newly obtained experience will alter the neural maps, networks, pathways or circuits made up of countless neurons and synapses (Wall et al., 2002). The associated neurons of a given response will be activated simultaneously in response to similar stimuli in the future. Young children may gain more from skill learning during the age-sensitive periods than their older peers (Ito, 2004).
Changes in fractional anisotropy of the related white matter were observed following skill learning. Task-specific memory capacity, developed via a dynamic process of information encoding and retrieval, results in greater functional plasticity in the multi-level memory networks.
On-line learning (also known as within-session practice-dependent acquisition) occurs when a learner practices a given skill or a set of skills. Poorer attention, concentration and working memory, and decreased information processing speeds in older adults may be the reasons.
Perhaps off-line learning is more profitable than on-line learning in motor learning in older learners. From a biological viewpoint, skills learning or relearning is associated with neural plasticity for the survival and development of a species.

Physical exercise contributes to memory formation and increases the number of neurons and synapses (dentate gyrus) in both young and old mice (van Praag et al., 1999a,b, 2005). For instance, certain therapeutic treatments or training can repair or recover the vanished, or reduced, brain functions caused by injury (Stein and Hoffman, 2003; Wieloch and Nikolich, 2006). Variations in the curves are collective or interactive outcomes of these biological and environmental factors. There has been strong evidence for both sides of the debate; however, a recent research study’s shocking results have managed to tip the scales in nurture’s favour. This means that fourth grade geniuses should not get too comfortable with their intelligence just yet; they should still be keeping their minds active by seeking new information and learning new skills. Secondly, we note that motor learning often refers to deliberate practice and the resulting performance enhancement and adaptability.
Cognitive and motor activities have to be intellectually stimulating and physically appropriate to bring about maximal benefits to the aging brain (Colcombe et al., 2003, 2006).
The goal is to integrate empirical results focusing on motor skills acquisition and the benefits of motor practice or exercise in older adults. Neural plasticity is, therefore, a biological foundation of the learning brain (Taubert et al., 2010). Developing brains may have a greater potential to be trained or are more flexible in skills learning than developed brains (Kolb, 2000; Bunge and Wright, 2007).
Understanding experience-related neural plasticity within a developmental framework along with the optimal timing of skills training or rehabilitation for individuals at different ages certainly has clinical and educational relevance. Even minor adjustments in the training experience result in structural modifications and the rebuilding of functional capacities. The benefits can be transferred to non-trained sustained attention and working memory tasks (Anguera et al., 2013). Upon completion of the task-specific practice, a learner continues to acquire or stabilize a given skill or a set of skills. Given the potential value of plasticity-based skill learning and the contribution of motor practice to functional neural regeneration, these studies have important implications for older adults. The slope of functional change is subject to a number of biological and environmental factors. We discuss the close interplay between neural plasticity, motor learning and cognitive aging. Behavioral improvements are likely to occur while having an active lifestyle that is a key to reduce the negative impact of cognitive-motor aging on daily functions and to enhance the quality of life.
Furthermore, a few weeks of training in mice led to the formation and removal of synapses, and a heightened level of synapse turnover.
Sport-specific experiences and the resultant neural enhancements contribute to an effective use of sensory information in judgments or decision-making and better motor memory consolidation in expert players.
In particular, motor learning would be more pronounced after an optimal post-practice interval during which a learner is sleeping or napping. Older learners might fail to register the given KR or keep the action-related information active for a long period of time.
Motor learning results in neurobehavioral changes, making the neural system shift from a conscious control (central, command-driven, top-down) to an unconscious (peripheral, feedback-based, button-up) control mode; the neural representations of the skill are formed during this process (Willingham, 1998).
Thirdly, we review research on motor skill acquisition in older adults with, and without, impairments relative to aging-related cognitive decline.
These changes may collectively cause an adaptive remapping of neural pathways (Trachtenberg et al., 2002).
This mode of motor learning refers to off-line learning (also known as between-session practice-independent performance enhancement). The flexible or changeable nature of cortical reorganization is the basis of skill learning. The plasticity-based motor learning or training should integrate multiple tasks or activities to be implemented at multiple skill levels (fundamental, daily, motor, and special skills) and with multisensory inputs (Nojima et al., 2012). Finally, to enhance future research and application, we highlight the implications of neural plasticity in skills learning and cognitive rehabilitation for the aging population. When a given stimulus is repeatedly and intentionally coupled with the consistent activation patterns of a set of neurons during the critical period, the neural representation of that stimulus is reinforced (Zhou and Merzenich, 2007).
In retention tests, older adults with intact cognitive and motor functions outperformed those who had relatively poorer scores in an eye-hand-coordination test (finger tapping), the Mini Mental State Exam (MMSE), and the Trail Making Test (TMT-A and B).
For example, during the 24 h post-practice interval, there was no control over the activities or schedules of the learners, which might have affected the learning of older adults more than that of their younger peers (Brown et al., 2009).
Individual differences in cognitive and motor functions in older adults should be considered when designing and implementing training programs.
Interactions of cognitive and motor aging, environment and neural processes collectively contribute to the observed differences in motor learning between older and younger adults. Greater individual differences in the cognitive and motor capacities of older adults may also hinder their on- and off-line motor learning (Yan et al., 2009).
Future research to identify major barriers and facilitators to brain fitness and motor functionality in older adults is integral to cognitive-motor skill learning and rehabilitation. In the case of cognitively healthy older adults, without additional practice, their newly acquired motor experiences or memories could be retrievable for a minimum of 2 years after learning (Smith et al., 2005). An apparent challenge now lies in the search for an optimal therapeutic approach to enhance skills learning in older adults. Thus, aging is not necessarily associated with memory deterioration, but aging-related cognitive-motor dysfunction plays a central role in the decline of on-line motor learning.

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