Hunsi Jayaprakash – What we can learn from forgetting


Hey everyone! My name is Hunsi Jayaprakash, and I am a rising senior. I am majoring in computer science and minoring in neuroscience. I am on the graduate track intending to pursue a career in the field of Computational Neuroscience. As the name suggests, I am interested in this interdisciplinary field because it involves the application of computer science, statistics, mathematics, and other fields to aid in answering some of the top questions in neuroscience.

One of my favorite things about the field of neuroscience is how neuroscience findings and breakthroughs have been evolving within the last decade with advancements in technology!

Outside of school and research, I am involved in cultural clubs on campus and am part of a Bollywood fusion dance team. It is not only a fun way to exercise, but I also enjoy meeting new people from diverse backgrounds. I am excited to be part of the Brackenridge Summer 2021 community and am looking forward to learning about research across a wide variety of disciplines.

My research: “Predicting Beta-Amyloid Accumulation based on Structural MR images: an investigation of Alzheimer’s disease.”

I am a part of Dr. Howard Aizenstein’ s research lab at the Geriatric Psychiatry Neuroimaging (GPN) lab and I work closely with the Alzheimer’s Disease Research Center (ADRC) in Pittsburgh. Alzheimer’s Disease (AD) is currently the sixth-leading cause of death in the United States, which has increased in prevalence due to an aging population. AD is a neurodegenerative disorder, which slowly degrades the integrity of the neural system. While memory loss is the most predominant symptom, AD also affects other cognitive domains, including language, visuomotor processing, attention, and executive functioning. This process starts decades before AD diagnosis and involves a neurotoxic protein, called beta-amyloid (Aβ). A buildup of Aβ is known to be a hallmark of AD pathology.

With the advancement of a radioactive tracer [e.g., Pittsburgh Component-B (PiB)] used in Positron Emission Tomography (PET) neuroimaging, we can estimate the level of Aβ accumulation as in-vivo. This advancement led to the discovery that Aβ can be present in the brain of cognitively normal older individuals. This state, characterized by an abnormal buildup of Aβ without the presence of cognitive deficits, is called the preclinical stage of AD. In previous studies, a strong correlation was reported between Aβ accumulation and neurodegeneration which eventually leads to cognitive decline. Thus, early detection is crucial in the preclinical stage.

Although a known cure for AD does not exist at present, early detection is important to ameliorate disease progression symptoms and identification of high-risk individuals. My project aims to build a novel algorithm using predictive learning algorithms to analyze Aβ accumulation based on non-invasive MR images. My research also offers to develop a more accurate model using machine learning methods for the early detection of individuals who are at high risk for advanced AD.

Brackenridge Fellowship and my Future Goals

I am excited to be part of the Brackenridge community this summer and pursue an area of research of my interest. In my future academic career, I hope to continue my research in graduate school and pursue a Ph.D. in computational neuroscience. I am grateful for this opportunity to be part of this collaborative setting and enhance my understanding of machine learning applications in neuroimaging. During this fellowship program, I am looking forward to learning about research across diverse fields of study and encouraging and facilitating an integrative conversation among the Brackenridge community.

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