Neonatal and Pediatric Medicine
Open Access

Mu rhythm; Alpha rhythm; Aging; Older adults; Movement; Action observation; EEG

Introduction

EEG measures electrophysiological activity in the brain by placing multiple electrodes on the scalp. 1929, Berger. We report for the first time potentials measurable as transient changes on the scalp. The following year, he discovered that certain events could disrupt ongoing Alpha activity [1]. The phenomenon of these event-related changes is observed with a certain frequency in ongoing EEG activity. When the eyes are closed, alpha wave activity (8–13 Hz) predominates in the posterior region [2]. Visual stimulation causes reinforcement and inhibition of activity. Alpha rhythm is thought to represent a cortical “idle state” that facilitates cortical activation by a variety of stimuli. The generation of these alpha oscillations is associated with the interaction of the thalamus and cortex. Changes in parietal beta activity (13–25 Hz) during movement-related tasks include performing movements or observing images and movements. In patients with prodromal AD, increased delta oscillations (1–4 Hz) appear to occur in the occipital region, indicating impaired synaptic transmission [3]. Increased theta output (4–8 Hz) is often interpreted as decreased alpha rhythms and correlates with decreased cognitive processing speed. It has been reported that increased theta oscillations in the posterior region may indicate early neurodegeneration.

The reduction in power in some frequency bands is called Event- Related Asynchronous (ERD) and the increase in power is called Event-Related Synchronous (ERS). ERD and ERS can be measured with EEG and MEG recordings. MEG measures the weak magnetic fields generated by currents within bundles of synchronized neurons in the cortex, grooves, scalp, and skull. The advantage of EEG is that it is easy to measure and is significantly less costly than other methods of studying brain function, but both EEG and MEG can be used to measure responses of cortical activity at rest and during sensory or motor tasks [4]. Neurophysiological techniques such as EEG and MEG are useful for understanding the mechanisms underlying age-related changes and pathological aging in the elderly. EEG and MEG can measure changes in brain activity instantaneously (in milliseconds) during a motor task, whereas functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) are evaluation-based. Therefore, it usually takes 6-7 seconds or more to process brain activity [5]. Therefore, it is difficult to analyze brain activity during motor tasks from a temporal perspective using these techniques. High-resolution techniques are essential for studying changes in brain electrical activity in age-related cognitive processes [6].

Activity is observed at upper alpha and lower beta frequencies before and after spontaneous exercise. Upper alpha band activity is predominantly between 8 and 13 Hz and is referred to as ‘mu’ activity. Activity in the low-beta band is primarily around 13–25 Hz, but varies between studies. Alpha ERD reflects activation from motor readiness to completion, while alpha ERS indicates cortical idleness or inhibition of sensorimotor cortical activation. However, scientists disagree on the definition of mu rhythm and the beta frequency band [7]. He claims that mu rhythm has two main components, 10 Hz and 20 Hz. However, some studies have divided hyperactivity and hypoactivity into muism and betaism. Analysis diversity is an issue when using ANOVA, as many of the relevant adjustments are confirmed without multiple comparisons. Hobson and Bishop point to possible functional differences between the two frequency bands [8].

When exercise begins, μ-ERD occurs bilaterally and extensively, whereas β-ERD becomes bilateral and more limited. Desynchronization of both rhythms during spontaneous movement or observation of movement has been suggested to be correlated with activity in the sensorimotor cortex. Both activities show ERS for a few seconds after the move is complete. The beta ERS is more clearly observed than the mu ERS, as the beta ERS exhibits increased power over baseline when the mu rhythm still exhibits an asynchronous low power pattern [9]. The period of beta ERS after the move is called “beta rebound”. Post-exercise mu-ERS is interpreted as active inhibition or inactivation of neuronal processes, whereas beta-ERS refers to the ‘idling’ of motor cortex neurons. The post exercise 20 Hz rhythmic synchrony corresponds to a period of reduced excitability in the corticospinal pathway. Furthermore, it supports the notion that ERS is associated with idle time of neurons in the motor cortex. Beta rebound is believed to be involved in information processes for evaluating predicted and executed moves. The alpha ERD indicates that cortical areas are activated during task execution, processing sensory and cognitive information, or generating motor commands. Furthermore, an increase in ERD is interpreted as either larger neural networks or more cell assemblies are involved in information processing. Therefore, the size and size of the μ ERD can indicate the size of the neural network recruited to perform the task [10].

ERS Decrease

Older people show beta rebound after exercise, which is significantly reduced. Reported beta ERS after motor changes during distal, proximal, and goal-directed motor tasks in older adults. A visuomotor study in the elderly found that β-ERS was slower than and half as intense as in the young. Reported that beta-ERS was significantly lower in the elderly than in the young after contraction or relaxation tasks. In the Go/Nogo study, young people showed a beta rebound after go cues, but this was not the case in older people. Postexercise beta-rebound reflects the processing of sensory information by afferent somatosensory neurons. Decreased beta-ERS in the elderly is reportedly caused by reduced and impaired reafferent sensory input; given that beta rebound is related to somatosensory input. In other words, decreased beta rebound reflects impaired sensory integration. Beta His rebound is now thought to be more detailed and related to movement prediction and performance processing. The increase in beta ERS has improved the status quo, eliminating the need to change the instruction set last minute. Therefore, decreased beta-rebound in older adults also suggests longer exercise assessments.

Murhythm during action observation in older adults

The sensorimotor cortex is prone to aging and brain activity increases during exercise. We noted the paucity of muism studies conducted during observed activities in adolescents through the elderly. He investigated whether mu rhythms changed with age in a behavioral observation task in 301 subjects (from age 10 to his age of 86) and found two results. First, alpha band activity increases from adulthood to old age. Second, beta frequency activity decreases after age 60. Distinct patterns of alpha and beta activity indicate age-dependent developmental trajectories. These modulations of alpha and beta activity are related to previous reports of hyperactivity of sensorimotor cortex during exercise in the elderly. In other words, these results led him to hypothesize that brain hyperactivation in older people is compensatory. Note that this is the first study to measure changes in alpha rhythm over a lifetime. Another hypothesis is that mu-ERD is increased in older adults if the observed effect reflects that of NMS expertise. In other words, the increased activation of motor regions in older adults may reflect each adult’s experience and expertise in the observed movements. We investigated whether mu rhythm was influenced by exercise experience and observational experience, and found that exercise experience had the greatest mu ERD compared with observers and novices. These findings lead to the fact that mu sensorimotor rhythms are regulated by agency skills, with older people possessing significantly more skills than younger people. Several studies have shown similar results, reporting that movement experiences (dancing acts) modulate rhythm. Similar results were obtained in fMRI studies. Experienced dancers reported the greatest premotor and parietal activity when watching videos of them dancing. Due to their long routines, older people may experience more and more behaviors with age.

As mentioned above, few studies have been conducted on mu rhythm during behavioral observation in older adults. We found only one study of murism in which he used behavioral observations from adolescence to old age. Currently, there are few studies on mourithm in behavioral observation of the elderly.

Discussion

Brain over-activation in older adults

We argue that these overall results are relevant to the compensatory and general interpretation of brain hyperactivation in older adults. Compensation means that additional activation compensates for agerelated cognitive decline. The phenomenon of widespread cortical activation observed in the elderly is consistent with those reported in fMRI studies. Older adults showed increased general activity in the anterior/prefrontal cortex of the brain, particularly the prefrontal and premotor cortices. It has been reported that functional reorganization of the central nervous system in the elderly causes brain hyperactivation. For example, older adults with slower reaction times did not differ in activation patterns from younger ones, but older adults with faster movements showed greater motor cortex recruitment than younger ones. In addition, it was reported that older subjects showed greater increases in posterior cerebellar and pre-supplementary motor area activity when performing several complex motor tasks automatically. Hyperactivation of prefrontal and sensorimotor cortical regions during motor tasks reflects additional use of neural resources for cognitive control and sensory processing. Why do older adults become hyperactive in motor and cognitive tasks? One hypothesis is the concept of ‘functional compensation’. Additional supplementation is provided to compensate for age-related brain degeneration to maintain athletic performance. Another hypothesis, called ‘dedifferentiation’, suggests that hyperactivation may be due to an inability to select appropriate brain regions. We tested two hypotheses to examine the relationship between performance and brain activity in a complex limb coordination task. As a result, we observed a relationship between the level of brain activation and exercise capacity in the elderly. This report provided strong evidence for the functional compensation hypothesis. Recently, the study of behavioral mechanisms in the elderly has been addressed, and several studies support the compensatory hypothesis. We examined brain activity measured by fMRI during a force adaptation task. The results showed that sensory attenuation increased with age and was associated with decreased gray matter volume and functional connectivity of the premotor cortex. We tested how motor strategies change with age by moving tasks with objects of varying weights. They found that older adults moved objects faster and spent more time accelerating than younger adults. The authors concluded that older subjects rely on predictive processes to compensate for reduced sensory sensitivity.

Conclusion

Anatomic, clinical and technological considerations

This review focused on age-related changes in alpha rhythm in motor tasks and behavioral observations. There are far fewer alpharhythmic studies, especially behavioral observational studies, in older adults than those in young people and young children. A review of these studies found that compared to younger individuals, older individuals exhibited changes in alpha activity during voluntary exercise that corresponded to the following four characteristics. B. Increased ERD, early onset and late termination, symmetrical ERD pattern, increased recruitment of cortical regions, and significantly reduced beta rebound. We hypothesize that this represents a price to pay for increased use of neural resources to maintain better athletic performance. We also know that alpha rhythm patterns in behavioral observations change with age, and that alpha rhythms have different developmental trajectories. In addition, we also found several studies reporting increased recruitment of cortical regions in the elderly. Older people have the highest percentage of people who need rehabilitation. Elucidating motor alpha rhythms associated with long-term effects of behavioral learning, imitation, and behavioral observation in the elderly provides a basis for better presentation of rehabilitation intervention tasks.

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