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Subject-Specific Brain Activity Analysis in fMRI Data Using Merge Trees
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-0632-1545
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-5220-633X
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-5352-1086
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2022 (English)In: 2022 IEEE WORKSHOP ON TOPOLOGICAL DATA ANALYSIS AND VISUALIZATION (TOPOINVIS 2022), IEEE , 2022, p. 113-123Conference paper, Published paper (Refereed)
Abstract [en]

We present a method for detecting patterns in time-varying functional magnetic resonance imaging (fMRI) data based on topological analysis. The oxygenated blood flow measured by fMRI is widely used as an indicator of brain activity. The signal is, however, prone to noise from various sources. Random brain activity, physiological noise, and noise from the scanner can reach a strength comparable to the signal itself. Thus, extracting the underlying signal is a challenging process typically approached by applying statistical methods. The goal of this work is to investigate the possibilities of recovering information from the signal using topological feature vectors directly based on the raw signal without medical domain priors. We utilize merge trees to define a robust feature vector capturing key features within a time step of fMRI data. We demonstrate how such a concise feature vector representation can be utilized for exploring the temporal development of brain activations, connectivity between these activations, and their relation to cognitive tasks.

Place, publisher, year, edition, pages
IEEE , 2022. p. 113-123
Keywords [en]
fMRI data analysis; data abstraction; temporal data; feature detection; merge tree; computational topology-based techniques
National Category
Signal Processing Computer Sciences Human Computer Interaction
Identifiers
URN: urn:nbn:se:liu:diva-191883DOI: 10.1109/TopoInVis57755.2022.00018ISI: 000913326500012ISBN: 9781665493543 (electronic)ISBN: 9781665493550 (print)OAI: oai:DiVA.org:liu-191883DiVA, id: diva2:1738849
Conference
IEEE VIS Workshop on Topological Data Analysis and Visualization (TopoInVis), Oklahoma City, OK, oct 17, 2022
Note

Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation; SeRC (Swedish e-Science Research Center); ELLIIT environment for strategic research in Sweden; Swedish Research Council (VR) [2019-05487]

Available from: 2023-02-23 Created: 2023-02-23 Last updated: 2023-06-09

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Rasheed, FarhanJönsson, DanielMasood, Talha BinHotz, Ingrid

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Rasheed, FarhanJönsson, DanielNilsson, EmmaMasood, Talha BinHotz, Ingrid
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Media and Information TechnologyFaculty of Science & EngineeringCenter for Medical Image Science and Visualization (CMIV)
Signal ProcessingComputer SciencesHuman Computer Interaction

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