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No part of this book may be reproduced or transmitted without publisher's prior permission. Violators will be prosecuted. 3/3/21, 2:50 AM- Pages - Jinal Shah 5803 Hunters Glen Dr Plainsboro, New Jersey 08536 March 1,2021 Patrick Bellgowan M.D Deputy Associate Director National Institute of Neurological Disorders and Stroke 6001 Executive Boulevard Suite 3309 Bethesda, MD Re: Advanced Research for the Causes of Chronic Traumatic Encephalopathy Dear Mr.Bellgowan, For the past few years, the National Football League (NFL) has been plagued with an issue: Chronic Traumatic Encephalopathy (CTE). In 2017, researchers Jesse Mez, Daniel H. Daneshvar, and Patrick Kiernan conducted a study titled Clinicopathological Evaluation of Chronic Traumatic Encephalopathy in Players of American Football.

In their study, the researchers (and the football world for that matter) were astonished by the results. The researchers discovered that out of 202 deceased NFL athletes, 87% (177 players) had CTE (Mez, Daneshvar, Kiernan, 2017). This discovery tore the football world apart. All of sudden NFL athletes such as John Urschel were retiring much earlier than expected (Hensley, 2017). Fast forward to 2021 and CTE remains a problem.

A big reason for this is the uncertainty surrounding the causes of CTE. For instance, in the past researchers claimed that repeated head trauma is the cause of CTE (Mez, Daneshvar, Kiernan, 2017). However, present research claims that this is false. Recent research shows that even players who have a history of hard tackles and numerous concussions show no signs of CTE (Golden, Zusman, 2019). What is even more conflicting is that a study conducted by Grant L Iverson, Teemu M Luoto, Pekka J Karhunen, and Rudolph J Castellani found signs of CTE in individuals who have had neither participated in contact sports nor have had significant head trauma (Iverson, Luoto, Pekka, Karhunen, Castellani, 2019).

This study shows that contrary to popular belief, repetitive head trauma may not be the most significant cause of CTE. These conflicting studies display the great uncertainty surrounding the causes of CTE. How can we possibly solve a problem for which we don’t know the causes? For this reason, I am requesting your help in conducting a study to answer the question what causes CTE? I understand that the National Institute of Neurological Disorders and Stroke (NINDS) prides itself in seeking fundamental knowledge about the brain and nervous system and using that knowledge to reduce the burden of neurological disease.

The NINDS is one of the few organizations that can provide appropriate funding and resources to aid in the understanding of the causes of CTE. Thus, I am seeking monetary support from you. Your support in this study will not only allow us to understand the causes of CTE, but also open new doors for further research for sustainable treatments. In the grand scheme of things, this study will be able to present opportunities on how to make the game of football (and all contact sports for that matter) safer for all. Plan of Action In order to fully understand the causes of CTE, I will need to have a team of researchers that will leverage data science and neuroscience.

These researchers will be operating out of their facilities that are specialized for their field of expertise. The neuroscientists will examine current NFL athletes and determine whether or not they show signs of CTE via Magnetic Resonance Imaging (MRI) scans. University of California Los Angeles (UCLA) researchers have identified that MRIs can be effectively used to diagnose CTE (Schnabel, 2016). These MRI scans will also allow the neuroscientists to compare the brains of non-CTE players with the brains of CTE players. Furthermore, these scans will allow the neuroscientists to compare the brains of CTE athletes with each other.

The data scientists, on the other hand, will collect and analyze various features such as position of the player, number of concussions, types of hits delivered, etc. Using these statistics, they will be able to effectively design machine learning models that can identify which features correlate the most with CTE. In order to validate the use of machine learning in this study, it is important to examine a study titled Machine Learning in Medicine: A Practical Introduction . The researchers, Jenni A M Sidey-Gibbons and Chris J Sidey-Gibbons, utilized machine learning techniques for “cancer diagnosis using descriptions of nuclei sampled from breast masses†(Sidey-Gibbons, Sidey-Gibbons, 2019). In the study, the researchers found that machine learning algorithms (specifically Support Vector Machines) were able to make the correct diagnosis at an astonishing 96% accuracy (Sidey-Gibbons, Sidey-Gibbons, 2019).

Evidently, machine learning will come to great use in this study. Combining the results that the data scientists produce with the results that the neuroscientists will bring to the table, we will effectively be able to identify the most important causations of CTE. My role in this study will be to work side by side with these different groups of specialists with the hope of developing a greater understanding of the causes of CTE. Models of Success The effectiveness of this research-based approach can be seen in Mercaptor Discoveries’ most recent research. Mercaptor Discoveries, a pharmaceutical firm, combined data science and neuroscience to produce a drug that could “halt the progression of the initial damage caused by concussions,†(Kelly, 2020).

What is also important to note is that Mercaptor Discoveries is using a data science + neuroscience driven approach. In an interview with Lisa Kelly of SBNation, Sara Isbell, CEO of Mercaptor Discoveries stated that “In Mercaptor’s most recent research, they have been applying their technology to the prevention and treatment of seizures, and they have efficacy data which proves their captons can be used to help seizure patients†(Kelly 2020). Mercaptor Discoveries’ data science + neuroscience-based approach is allowing them to be able to develop solutions to brain trauma. Another similar model of success comes from a study titled Leveraging Data Science for a Personalized Haemodialysis. In this study, researchers Miguel Hueso, Lluis de Haro, Jordi Calabia, Rafael Dal-Re, Cristian Tebe, Karina Gilbert, Josep M Cruzado, Alfredo Vellido leveraged data science to “facilitate precision and personalized medicine in nephrology†(Hueso, Haro, Calabia, Dal-Re, Tebe, Gilbert, Cruzado, Vellido, 2020).

This study is a prime example of the use of data science in medical fields. The researchers clearly state that “healthcare is quickly becoming data-dependent, and data science is a discipline that holds the promise of contributing to the development of personalized medicine†(Hueso, Haro, Calabia, Dal-Re, Tebe, Gilbert, Cruzado, Vellido, 2020). This conclusion makes it very clear that in order to understand any health-related topics such as CTE, it is important to utilize data science. Budget & Other Monetary Information The monetary aid that you will provide will translate into lab, equipment, salaries, and other miscellaneous resources. In terms of labs, this study will require multiple labs: some for neuroscience CTE research and some for data science CTE research.

Furthermore, the study will require willing participants from the NFL as well as brains of deceased NFL players. I would like to specify that I will require data collecting tools such as sensors & databases to help collect and store data for analysis. Additionally, I would also need MRI machines for scanning. With all these costs in mind, I estimate that the total cost of this research would be around 0,000. This number comes from a past study titled Chronic Traumatic Encephalopathy: Clinical Presentation and Biomarkers.

In total, the study cost around 2,379 (Stern, 2012). I anticipate that my study will require more money because of the fact that I will be utilizing more resources. Conclusion CTE is a big issue with a complex solution. Without fully understanding CTE, we will not be able to make the game of football safer for NFL athletes. Furthermore, we risk losing a sport that is highly integrated in American culture.

It is very important for us to understand the causes of CTE as once we understand these causes, we will be able to progress in finding sustainable treatments. Treatments such as Hyperbaric Oxygen Therapy have been slowed in progress due to the fact that we don’t understand what the true causes of CTE are (How Can NFL Players Treat Their CTE Symptoms, 2017). It is critical that we utilize data science & neuroscience to understand CTE so that we can make the game safer for NFL athletes. With that being said, I would like to formally invite you to my oral presentation of my proposal. My presentation will be located in Room 117 of Hickman Hall on the Cook/Douglas Campus of Rutgers University, New Brunswick, New Jersey.

The date and time of my presentation are as follows: 3/17/2021 at 7:30 PM. If you have any questions, comments, and/or concerns, please do not hesitate to contact me at ( or at [email protected] . I will gladly field any questions, comments, or concerns you may have regarding my study. I look forward to seeing you at my oral presentation and to finally connect. Let’s make the game of football safer together.

References Golden, C., & Zusman, M. (2019). Chronic Traumatic Encephalopathy (CTE) Impact on Brains, Emotions, and Cognition. Springer International Publishing. How Can NFL Players Treat Their CTE Symptoms? (2017, September 01). Hueso, M ., De Haro, L., Calabia, J., Dal-Re, R., Tebe, C., Gilbert, K., Cruzado, J.M ., & Vellido, A. (2020).

Leveraging Data Science for a Personalized Haemodialysis. Kidney Diseases (Basel ,Switzerland), 6(6), . Iverson, G.L., Luoto, T.M., Karhunen, P.J ., & Castellani, R.J. (2019). Mild Chronic Traumatic Encephalopathy Neuropathology in People with No Known Participation in Contact Sports or History of Repetitive Neurotrauma. Journal of Neuropathology and Experimental Neurology, 78(7), Kelly, L (2020, January 09).

Groundbreaking CTE Research may Save the Game of Football Mez, J., Daneshvar, D.H ,. & Kiernan, P.T (2017, July 25). Evaluation of Chronic Traumatic Encephalopathy in Football Players Schnabel, J. (2016, August 24). MRI Scan may help Diagnose Chronic Traumatic Encephalopathy, UCLA Researchers Report. Sidey-Gibbons, J ,. & Sidey-Gibbons, C.J . (2019). Machine Learning in Medicine: A Practical Introduction.

BMC Medical Research Methodology, 19(1), 64. Stern, R.A. (2012, May, 31). Chronic Traumatic Encephalopathy: Clinical Presentation and Biomarkers.

Paper for above instructions

Understanding the Causes of Chronic Traumatic Encephalopathy: A Research Proposal

Introduction

Chronic Traumatic Encephalopathy (CTE) has become an increasingly significant concern within contact sports, especially American football. Its association with long-term cognitive impairment, neurodegenerative diseases, and severe psychological complications has led researchers to extensively investigate its causes and implications. Despite past assertions pointing towards repetitive head trauma as the primary driver of CTE, recent studies have challenged this notion, revealing the need for comprehensive research to identify the true causative factors (Golden & Zusman, 2019; Iverson et al., 2019). This proposal seeks the funding and collaboration of the National Institute of Neurological Disorders and Stroke (NINDS) to conduct an innovative study combining data science and neuroscience to elucidate the underlying causes of CTE.

Background

In 2017, a pivotal study diagnosed CTE in 87% of deceased NFL players' brains, igniting a fervor in the sports community regarding player safety (Mez et al., 2017). Although early research centered on the correlation between repeated head trauma and CTE, newer evidence suggests that the relationship may not be so clear-cut. For instance, Iverson et al. (2019) found evidence of CTE in individuals without a history of significant head trauma or involvement in contact sports. This disconnect highlights the necessity for further investigation to decipher the factors contributing to CTE beyond mere physical impact.
The complexities of diagnosing CTE stem from its varied presentation, which includes mood disorders, cognitive decline, and neurobehavioral problems (Golden & Zusman, 2019). Therefore, understanding CTE necessitates an interdisciplinary approach.

Objectives

The primary aim of this research is to determine the causative factors of CTE through a collaborative model that integrates expertise in neuroscience and data science. Specific objectives include:
- To perform advanced neuroimaging (MRI scans) on current NFL players to observe potential indicators of CTE (Schnabel, 2016).
- To collect and analyze player data, including history of head trauma, position played, and other relevant variables.
- To develop predictive models that unveil the strongest correlations with CTE risk using machine learning techniques.

Methodology

Team Composition

To achieve our research objectives, we will assemble a multidisciplinary team comprising neuroscientists, data scientists, and experienced medical professionals. This collective will operate in specialized facilities equipped for neuroscience research and data analysis.

Neuroimaging

The neuroscientists will conduct MRI scans to evaluate current NFL players for signs of CTE. Research from UCLA has validated the efficacy of MRI in diagnosing CTE (Schnabel, 2016). The neuroimaging data will allow a detailed examination of physical brain changes, providing insight into potential neurological markers correlated with CTE (Golden & Zusman, 2019).

Data Analysis

Data scientists will gather and analyze player data that could correlate with CTE. This data will include:
- Player history of concussions.
- Type of positions played (e.g., quarterbacks vs. linemen).
- Incident logs of on-field impacts (i.e., frequency and severity of tackles).
Machine learning algorithms, such as Support Vector Machines and Random Forest classifiers, will be employed to detect patterns among the variables, enabling us to establish causative correlations and predictive risk models (Sidey-Gibbons & Sidey-Gibbons, 2019).

Models of Success

An analogous approach can be observed in Mercaptor Discoveries, where neuroscience and data science were harnessed to develop a drug that mitigates concussion-related damage (Kelly, 2020). This intersection of disciplines has proven beneficial in uncovering solutions for brain trauma. Further, the application of data science in personalized medicine as demonstrated by Hueso et al. (2020) illustrates the potential of data-driven methodologies in healthcare.
Moreover, the success of applying machine learning in medical diagnostics, as seen in various studies, affirms that data-centric approaches can yield high accuracy and effective predictive capabilities (Sidey-Gibbons & Sidey-Gibbons, 2019).

Budget and Resource Allocation

The financial requirements for this research are estimated to be around 0,000. This budget accounts for:
- Lab and equipment costs, including MRI machines.
- Salaries for researchers and technical staff.
- Data collection tools, such as sensors and databases necessary for comprehensive data analysis (Stern, 2012).

Conclusion

To address the critical issue of CTE and facilitate proactive measures for player safety, this research seeks to decipher the factors underpinning the condition. Understanding CTE is essential not only for advancing player health but also for preserving the integrity of the sport. By leveraging neuroscience and data science, this study will aim to reveal causative agents of CTE, paving the way for effective interventions and preventive strategies.
I invite NINDS to support this consequential study, which aims to make football safer and more sustainable for future generations. We look forward to collaborating in the oral presentation of this proposal and discussing its intricacies further.

References

1. Golden, C., & Zusman, M. (2019). Chronic Traumatic Encephalopathy (CTE) Impact on Brains, Emotions, and Cognition. Springer International Publishing.
2. Hueso, M., de Haro, L., Calabia, J., Dal-Re, R., Tebe, C., Gilbert, K., Cruzado, J.M., & Vellido, A. (2020). Leveraging Data Science for a Personalized Haemodialysis. Kidney Diseases (Basel, Switzerland), 6(6).
3. Iverson, G.L., Luoto, T.M., Karhunen, P.J., & Castellani, R.J. (2019). Mild Chronic Traumatic Encephalopathy Neuropathology in People with No Known Participation in Contact Sports or History of Repetitive Neurotrauma. Journal of Neuropathology and Experimental Neurology, 78(7).
4. Kelly, L. (2020). Groundbreaking CTE Research may Save the Game of Football.
5. Mez, J., Daneshvar, D.H., & Kiernan, P.T. (2017). Evaluation of Chronic Traumatic Encephalopathy in Football Players.
6. Schnabel, J. (2016). MRI Scan may help Diagnose Chronic Traumatic Encephalopathy, UCLA Researchers Report.
7. Sidey-Gibbons, J., & Sidey-Gibbons, C.J. (2019). Machine Learning in Medicine: A Practical Introduction. BMC Medical Research Methodology, 19(1), 64.
8. Stern, R.A. (2012). Chronic Traumatic Encephalopathy: Clinical Presentation and Biomarkers.
9. How Can NFL Players Treat Their CTE Symptoms? (2017).
10. Hueso, M., et al. (2020). Leveraging Data Science for Personalized Haemodialysis. Kidney Diseases (Basel, Switzerland), 6(6), 104-113.
This proposed research underscores the urgent need to further understand CTE, a concerning issue that affects not just players but the future of contact sports as a whole. Addressing these challenges through a collaborative and funded approach will benefit not only players but also the broader community engaged in sports and health sciences.