Encoding the local connectivity patterns of fMRI for cognitive task and state classification.
In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher vectors (FV), vector of locally aggregated descriptors (VLAD) and bag-of-words (BoW) methods. We first obtain local descriptors, called mesh arc descriptors (MADs) from fMRI data, by forming l...
| Publicado en: | Brain Imaging & Behavior Vol. 13; no. 4; pp. 893 - 905 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137507312&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137507312 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Aug2019 vid: 13 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137507312 137507312 NLM29948907 10.1007/s11682-018-9901-5 NLM29948907 137507312 ppf: 893 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Encoding the local connectivity patterns of fMRI for cognitive task and state classification. aug: au: Onal Ertugrul, Itir Ozay, Mete Yarman Vural, Fatos T. affil: Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA sug: subj: Information Science Methods Brain Mapping Methods Nervous System Magnetic Resonance Imaging Methods Models, Theoretical Cluster Analysis Cognition Statistics Brain Physiology Brain Scales ab: In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher vectors (FV), vector of locally aggregated descriptors (VLAD) and bag-of-words (BoW) methods. We first obtain local descriptors, called mesh arc descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. Then, we extract a dictionary of relationships, called brain connectivity dictionary by fitting a generative Gaussian mixture model (GMM) to a set of MADs, and selecting codewords at the mean of each component of the mixture. Codewords represent connectivity patterns among anatomical regions. We also encode MADs by VLAD and BoW methods using k-Means clustering. We classify cognitive tasks using the Human Connectome Project (HCP) task fMRI dataset and cognitive states using the Emotional Memory Retrieval (EMR). We train support vector machines (SVMs) using the encoded MADs. Results demonstrate that, FV encoding of MADs can be successfully employed for classification of cognitive tasks, and outperform VLAD and BoW representations. Moreover, we identify the significant Gaussians in mixture models by computing energy of their corresponding FV parts, and analyze their effect on classification accuracy. Finally, we suggest a new method to visualize the codewords of the learned brain connectivity dictionary. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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