The brain is a highly complex network of nonlinear systems with internal dynamic states that are not easily quantified. As a result, it is essential to understand the properties of the connectivity network linking disparate parts of the brain used in complex cognitive processes, such as working memory. Working memory is the system in control of temporary retention and online organization of thoughts for successful goal directed behavior. Individuals exhibit a typically small capacity limit on the number of items that can be simultaneously retained in working memory. To modify network connections and thereby augment working memory capacity, researchers have targeted brain areas using a variety of noninvasive stimulation interventions. However, few existing methods take advantage of the brain's own structure to actively generate and entrain internal oscillatory modulations in locations deep within the auditory pathways. One technique is known as binaural beats, which arises from the brain's interpretation of two pure tones, with a small frequency mismatch, delivered independently to each ear. The mismatch between these tones is perceived as a so-called beat frequency which can be used to modulate behavioral performance and cortical connectivity. Currently, all binaural stimulation therapeutic systems are open-loop "one-size-fits-all" approaches. However, these methods can prove not as effective because each person's brain responds slightly differently to exogenous stimuli. Therefore, the driving motivation for developing a closed-loop stimulation system is to help populations with large individual variability. One such example is persons with mild cognitive impairment (MCI), which causes cognitive impairments beyond those expected based on age. Therefore, applying a closed-loop binaural beat control system to increase the cognitive load level to people with MCI could potentially maintain their quality of life. In this dissertation, I will present a comparison of algorithms to determine brain connectivity, results of open-loop based binaural stimulation, the development of a closed-loop brain network simulation platform, and finally an experimental study to determine the effectiveness of closed-loop control to modulate brain networks hence influencing cognitive abilities. / Ph. D. / In order to do complex tasks, such as creating a memory, multiple regions of the brain must interact to become a network. Specifically for this work, we are looking at working memory which is the system that allows us to remember and manipulate information in the presence of additional incoming information. Working memory capacity, which is the number of items we can remember, is dependent upon synchronization between particular regions of the brain, particularly the frontal and parietal lobes. Higher synchronization means that people will, on average, respond with higher accuracy during a working memory task. To modify the connections in the network and thereby augment working memory capacity, a non-invasive brain stimulation technique called binaural beats can be used. Binaural beats take advantage of the brain’s response to two pure tones, delivered independently to each ear, when those tones have a small frequency mismatch. The mismatch between the tones is interpreted as a beat frequency, which may act to synchronize brain waves. This research seeks to answer the question of whether binaural beats can be used to identify and control working memory. Currently, nearly all therapeutic stimulation systems are open-loop “one-size-fits-all” approaches. However, these methods can prove not as effective because each person’s brain responds slightly differently to external stimuli. Therefore, the driving motivation for developing a closed-loop stimulation system is to help populations with large individual variability. One such example is persons with mild cognitive impairment (MCI) which is considered a precursor to Alzheimer’s. Therefore, applying a closed-loop binaural beat control system to increase the cognitive load level to people with MCI could potentially maintain their quality of life. In this dissertation, we have showed that we can successfully increase the connectivity in the brain using binaural beats in a closed-loop system.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/83341 |
| Date | 17 May 2018 |
| Creators | Beauchene, Christine Elizabeth |
| Contributors | Mechanical Engineering, Abaid, Nicole, Leonessa, Alexander, Moran, Rosalyn J., Diana, Rachel A., Southward, Steve C. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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