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Principal Investigator  
Principal Investigator's Name: Yasma Esteitieh
Institution: Malmö Borgarskola
Department: IB
Country:
Proposed Analysis: Our study looks into using Topological Data Analysis to study a degenerative disease. More specifically, a question we are interested in is: ‘Can topological data analysis of subjective cognitive decline, MCI, and AD fMRI data predict Alzheimer's disease?’ Neurodegenerative diseases have an effect on the brain, and specifically, Alzheimer’s disease (AD), is a neurodegenerative disease that is the most common type of dementia. Early prediction of AD before the onset of symptoms is a significant and interesting problem to address. It offers the potential for intervention to interrupt the disease before dementia symptoms start. Synaptic activity is affected during neurodegenerative disease progression and is therefore critical to study when looking at AD. Topological Data Analysis has already generated both promising applications and results in neuroscience. We will be applying topological data analysis and, more specifically, persistent homology to subjective cognitive decline, mild cognitive impairment and Alzheimer’s disease fMRI data to study early-onset changes in synaptic connectivity. Functional MRI scans can detect synaptic connectivity, and in AD patients, some of these connections differ from normal patterns. Therefore, using TDA, we can analyze fMRI data of patients' pre-development of AD and compare it to healthy patients' fMRI data. This could potentially identify early signs of brain degenerative problems and be used as a predictor of AD. We need fMRI data in the form of weights, specifically matrices with weights on entries.
Additional Investigators  
Investigator's Name: Ran Levi
Proposed Analysis: Topological Data Analysis (Persistent Homology) on MRI data