Gender classification using mesh networks on multiresolution multitask fMRI data

Autor: Fatos T. Yarman Vural, Itir Onal Ertugrul, Mete Ozay
Rok vydání: 2019
Předmět:
Male
Discrete wavelet transform
Elementary cognitive task
Databases
Factual
Computer science
Cognitive Neuroscience
Multiresolution analysis
Mesh networking
Wavelet Analysis
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
050105 experimental psychology
Machine Learning
03 medical and health sciences
Behavioral Neuroscience
Cellular and Molecular Neuroscience
0302 clinical medicine
Discriminative model
Connectome
Humans
0501 psychology and cognitive sciences
Radiology
Nuclear Medicine and imaging
Polygon mesh
Sex Characteristics
Human Connectome Project
business.industry
05 social sciences
Brain
Pattern recognition
Magnetic Resonance Imaging
Psychiatry and Mental health
Task (computing)
Neurology
Multivariate Analysis
Female
Neurology (clinical)
Artificial intelligence
business
030217 neurology & neurosurgery
Zdroj: Brain Imaging and Behavior. 14:460-476
ISSN: 1931-7565
1931-7557
DOI: 10.1007/s11682-018-0021-z
Popis: Brain connectivity networks have been shown to represent gender differences under a number of cognitive tasks. Recently, it has been conjectured that fMRI signals decomposed into different resolutions embed different types of cognitive information. In this paper, we combine multiresolution analysis and connectivity networks to study gender differences under a variety of cognitive tasks, and propose a machine learning framework to discriminate individuals according to their gender. For this purpose, we estimate a set of brain networks, formed at different resolutions while the subjects perform different cognitive tasks. First, we decompose fMRI signals recorded under a sequence of cognitive stimuli into its frequency subbands using Discrete Wavelet Transform (DWT). Next, we represent the fMRI signals by mesh networks formed among the anatomic regions for each task experiment at each subband. The mesh networks are constructed by ensembling a set of local meshes, each of which represents the relationship of an anatomical region as a weighted linear combination of its neighbors. Then, we estimate the edge weights of each mesh by ridge regression. The proposed approach yields 2CL functional mesh networks for each subject, where C is the number of cognitive tasks and L is the number of subband signals obtained after wavelet decomposition. This approach enables one to classify gender under different cognitive tasks and different frequency subbands. The final step of the suggested framework is to fuse the complementary information of the mesh networks for each subject to discriminate the gender. We fuse the information embedded in mesh networks formed for different tasks and resolutions under a three-level fuzzy stacked generalization (FSG) architecture. In this architecture, different layers are responsible for fusion of diverse information obtained from different cognitive tasks and resolutions. In the experimental analyses, we use Human Connectome Project task fMRI dataset. Results reflect that fusing the mesh network representations computed at multiple resolutions for multiple tasks provides the best gender classification accuracy compared to the single subband task mesh networks or fusion of representations obtained using only multitask or only multiresolution data. Besides, mesh edge weights slightly outperform pairwise correlations between regions, and significantly outperform raw fMRI signals. In addition, we analyze the gender discriminative power of mesh edge weights for different tasks and resolutions.
Databáze: OpenAIRE
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