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Conference proceeding
Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders
UNCERTAINTY FOR SAFE UTILIZATION OF MACHINE LEARNING IN MEDICAL IMAGING, AND GRAPHS IN BIOMEDICAL IMAGE ANALYSIS, UNSURE 2020, GRAIL 2020, v 12443, pp 131-141
01 Jan 2020
PMID: 34350428
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Advances in neuroimaging techniques such as diffusion MRI and functional MRI enabled evaluation of the brain as an information processing network that is called connectome. Connectomic analysis has led to numerous findings on the organization of the brain its pathological changes with diseases, providing imaging-based biomarkers that help in diagnosis and prognosis. A large majority of connectomic biomarkers benefit either from graph-theoretical measures that evaluate brain's network structure, or use standard metrics such as Euclidean distance or Pearson's correlation to show between-connectomes relations. However, such methods are limited in diagnostic evaluation of diseases, because they do not simultaneously measure the difference between individual connectomes, incorporate disease-specific patterns, and utilize network structure information. To address these limitations, we propose a graph matching based method to quantify connectomic similarity, which can be trained for diseases at functional systems level to provide a subject-specific biomarker assessing the disease. We validate our measure on a dataset of patients with traumatic brain injury and demonstrate that our measure achieves better separation between patients and controls compared to commonly used connectomic similarity measures. We further evaluate the vulnerability of the functional systems to the disease by utilizing the parameter tuning aspect of our method. We finally show that our similarity score correlates with clinical scores, highlighting its potential as a subject-specific biomarker for the disease.
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Details
- Title
- Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders
- Creators
- Rui Sherry Shen - University of PennsylvaniaJacob A. Alappatt - University of PennsylvaniaDrew Parker - University of PennsylvaniaJunghoon Kim - City College of New YorkRagini Verma - University of PennsylvaniaYusuf Osmanlioglu - Connect
- Contributors
- C H Sudre (Editor)H Fehri (Editor)T Arbel (Editor)C F Baumgartner (Editor)A Dalca (Editor)R Tanno (Editor)K VanLeemput (Editor)W M Wells (Editor)A Sotiras (Editor)B Papiez (Editor)E Ferrante (Editor)S Parisot (Editor)
- Publication Details
- UNCERTAINTY FOR SAFE UTILIZATION OF MACHINE LEARNING IN MEDICAL IMAGING, AND GRAPHS IN BIOMEDICAL IMAGE ANALYSIS, UNSURE 2020, GRAIL 2020, v 12443, pp 131-141
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature
- Number of pages
- 11
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:001115883100013
- Scopus ID
- 2-s2.0-85093120081
- Other Identifier
- 991021869012304721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods
- Engineering, Biomedical
- Radiology, Nuclear Medicine & Medical Imaging