Date and time: 3 April 2025, 13:00-14:00 CET
Speaker: Dr. Satarupa Chakrabarti, Div. of Computational Science and Technology, EECS, KTH
Title: Extraction of Parkinson’s disease related temporal feature of brain activity
Where: Digital Futures hub, Osquars Backe 5, floor 2 at KTH main campus
Directions: https://www.digitalfutures.kth.se/contact/how-to-get-here/
OR Zoom: https://kth-se.zoom.us/j/69560887455
Host: Dr. Arvind Kumar, KTH
Bio: With postgraduation in Computer Science engineering, she undertook a Ph.D. degree in Computer Science from KIIT Deemed to be University, Bhubaneswar, India, where she defended her thesis related to designing a generalized epileptic seizure detection method with different feature-extracting techniques from biosensor data that showed the presence of temporal changes. She simultaneously worked as a Junior Research Fellow (JRF) with DST-SERB, India, in an interdisciplinary project (2018–2020). She has a strong and diverse background in research. She has previously worked as a postdoc fellow in the Space Physics Lab at the Indian Institute of Technology Roorkee.
Her primary research interests lie in the areas of biomedical engineering, signal processing, image processing, space physics, machine learning and deep learning. Now, Satarupa is a Digital Futures PostDoctoral Research Fellow at KTH, based in the Computational Brain Science group at the Division of Computational Science and Technology. Her research experiences involved collaboration with multidisciplinary teams, conducting research on various projects related to electrical engineering and space physics that led toward innovative, beneficial and cutting-edge endeavours.
Abstract: Parkinson’s Disease (PD) is the second most common neurodegenerative disorder with debilitating consequences. PD diagnosis is based on behavioral symptoms and chemical/genetic markers. Such a diagnosis can be erroneous and often does not capture the disease severity. Despite being a brain disease, the brain’s electrical activity (eg. fMRI, EEG/MEG) is completely ignored in PD diagnosis. This oversight is intriguing, considering experimental evidence indicating that PD-related changes profoundly impact the temporal dynamics of brain activity.
While biomarkers derived from brain activity usually focus on frequency domain parameters like oscillations and coherence, temporal features have been surprisingly neglected. In fact, except for certain forms of epilepsy, brain activity is not considered in the diagnosis of brain diseases. However, currently, brain activity-based biomarkers are not specific enough. This may be because these are defined in the frequency domain (e.g. oscillations and coherence). Unlike in epilepsy diagnosis, temporal features of brain activity are ignored. Therefore, I would like to change this in my project. I hypothesize that brain region-specific temporal features will provide highly specific information about the disease severity and can provide more precise diagnosis and prognosis of PD.
