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Extracting BOLD signals based on time-constrained multiset canonical correlation analysis for brain functional network estimation and classification
- Source :
- Brain research. 1775
- Publication Year :
- 2021
-
Abstract
- Brain functional network (BFN), usually estimated from blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI), has been proven to be a powerful tool to study the organization of the brain and discover biomarkers for diagnosis of brain disorders. Prior to BFN estimation and classification, extracting representative BOLD signals from brain regions of interest (ROIs) is a critical step. Traditional extraction methods include averaging, peaking operation and dimensionality reduction, often leading to signal cancellation and information loss. In this paper, we propose a novel method, namely time-constrained multiset canonical correlation analysis (TMCCA), to extract representative BOLD signals for subsequent BFN estimation and classification. Different from traditional methods that equally treat all BOLD signals in a ROI, the proposed method assigns weights to different BOLD signals, and learns the optimal weights to make the extracted representative signals jointly maximize the multiple correlations between ROIs. Importantly, time-constraint is incorporated into our proposed method, which can effectively encode nonlinear relationship among BOLD signals. To evaluate the effectiveness of the proposed method, the extracted BOLD signals is used to estimate BFN and, in turn, identify brain disorders, including mild cognitive impairment (MCI) and autistic spectrum disorder (ASD). Experimental results demonstrate that our proposed TMCCA can lead to better performance than traditional methods.
- Subjects :
- Time constrained
Computer science
Autism Spectrum Disorder
ENCODE
Functional networks
medicine
Humans
Cognitive Dysfunction
Molecular Biology
Multiset
Brain Mapping
Blood-oxygen-level dependent
medicine.diagnostic_test
business.industry
General Neuroscience
Dimensionality reduction
Brain
Pattern recognition
Magnetic Resonance Imaging
nervous system
Canonical Correlation Analysis
Neurology (clinical)
Artificial intelligence
Nerve Net
Canonical correlation
business
Functional magnetic resonance imaging
psychological phenomena and processes
Developmental Biology
Subjects
Details
- ISSN :
- 18726240
- Volume :
- 1775
- Database :
- OpenAIRE
- Journal :
- Brain research
- Accession number :
- edsair.doi.dedup.....1cec4dffc60ef7a985453c69f5bb18ea