Convolutional Sparse Coded Dynamic Brain Functional Connectivity
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Convolutional Sparse Coded Dynamic Brain Functional Connectivity Jin Yan1 · Yingying Zhu1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Functional brain network has been widely studied in many previous work for brain disorder diagnosis and brain network analysis. However, most previous work focus on static dynamic brain network research. Lots of recent work reveals that the brain shows dynamic activity even in resting state. Such dynamic brain functional connectivity reveals discriminative patterns for identifying many brain disorders. Current sliding window based dynamic brain connectivity framework are not easy to be applied to real clinical applications due to many issues: First, how to set up the optimal sliding window size and how to determine the threshold for the brain connectivity patterns. Secondly, how to represent the high dimensional dynamic brain connectivity pattern in a low dimensional representations for diagnosis purpose. Last, how to deal with the different length dynamic brain network patterns especially when the raw data are of different length. In order to address all those above issues, we proposed a new framework, which employs multiple scale sliding windows and automatically learns a sparse and low ran dynamic brain functional connectivity patterns from raw fMRI data. Furthermore, we are able to measure different length dynamic brain functional connectivity patterns in an equal space by learning a sparse coded convolutional filters. We have evaluated our method with state of the art dynamic brain network methods and the results demonstrated the strong potential of our methods for brain disorder diagnosis in real clinical applications. Keywords Functional magnetic resonance images · Convolutional sparse coding · Dynamic brain network · Computer assisted diagnosis
* Yingying Zhu [email protected] Jin Yan [email protected] 1
University of Texas at Arlington, Arlington, TX, USA
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J. Yan, Y. Zhu
1 Introduction Functional magnetic resonance imaging (fMRI) provides a non-invasive way to examine human brain activity. This imaging technique is often referred to as blood oxygenation level dependent (BOLD) imaging [1] because it measures changes of cerebral blood oxygenation that are closely related to neuronal activity [2]. Traditionally fMRI has been used to examine brain activation patterns in health [3–5] and disease [6–8] during the performance of a cognitive or motor task. However, recent studies have begun to use resting-state fMRI (rs-fMRI) to measure regional interactions that occur when a subject is not performing an explicit task [9, 10]. In resting state, fluctuations in spontaneous neural activity are thought to underlie the spontaneous BOLD signal fluctuations. Synchrony, or correlation, between the fluctuations among regions are used to assess inter-regional functional connectivity (FC) in human brain [11, 12]. Many works have been done to extract the functional brain networks from fMRI data. Those works can be divid
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