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Computational Methods for Neuroscien...

Liu, Yikang,

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Computational Methods for Neuroscience Discovery with Neuroimaging Data /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational Methods for Neuroscience Discovery with Neuroimaging Data // Yikang Liu.
Author:	Liu, Yikang,
Description:	1 electronic resource (171 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
Contained By:	Dissertations Abstracts International83-06B.
Subject:	Neurosciences. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28929342
ISBN:	9798494498052

Computational Methods for Neuroscience Discovery with Neuroimaging Data /
Liu, Yikang,

Computational Methods for Neuroscience Discovery with Neuroimaging Data /Yikang Liu. - 1 electronic resource (171 pages)

Source: Dissertations Abstracts International, Volume: 83-06, Section: B.

Resting-state functional magnetic resonance imaging (rsfMRI) is a non-invasive method to study brain function and organization. The last decades have seen a dramatic growth of human rsfMRI studies, public datasets, and data analysis software, which advanced our understanding of neuroscience and brain diseases. However, studies and resources of rodent rsfMRI are still lacking, despite its essential role in translational research. In this dissertation, we first present an open database of rsfMRI data collected from 90 awake rats with a well-established awake imaging paradigm that avoids anesthesia interference, together with a preprocessing pipeline optimized for rat data. Based on this dataset, we propose two methods termed SHERM and fastClean towards automated preprocessing of rodent rsfMRI data. First, SHERM targets rodent brain extraction, which is an essential step to aid with rsfMRI image registration. Current methods usually require manual adjustment of input parameters due to widely different image qualities and/or contrasts. SHERM, however, only requires a brain template mask as the input and is shown to automatically and reliably extract the brain tissue in both rat and mouse MRI images. fastClean is an unsupervised deep learning method that removes rsfMRI artifacts induced by the scanner, head motion, and nonneural physiological noise. Existing machine learning methods either perform unsatisfactorily in low-dimensional rodent data or suffer from long online training. With an efficient network architecture and meta-learning techniques, fastClean generates equivalently clean or cleaner data in minutes on both rodent and human datasets. Finally, we systematically investigated the spatiotemporal dynamics of spontaneous brain activity in the awake rat brain with a graph-based data mining method. We found that brain activity traverse among multiple resting-state functional connectivity patterns with nonrandom and reproducible sequential orders and time delays, revealed a network structure of these transition paths, and showed prominent brain regions involved and their temporal evolutions during the propagation of spontaneous brain activity. Taken together, this iv dissertation presents multiple computational methods for rsfMRI studies, demonstrates their contribution to automatic data preprocessing, data cleaning, and spatiotemporal neural pattern discovery, and advances our understanding of network organization and dynamics of the awake rat brain.

English

ISBN: 9798494498052Subjects--Topical Terms:

588700
Neurosciences.
Subjects--Index Terms:

Resting-state functional magnetic resonance imaging

Computational Methods for Neuroscience Discovery with Neuroimaging Data /
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300 $a 1 electronic resource (171 pages)
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520 $a Resting-state functional magnetic resonance imaging (rsfMRI) is a non-invasive method to study brain function and organization. The last decades have seen a dramatic growth of human rsfMRI studies, public datasets, and data analysis software, which advanced our understanding of neuroscience and brain diseases. However, studies and resources of rodent rsfMRI are still lacking, despite its essential role in translational research. In this dissertation, we first present an open database of rsfMRI data collected from 90 awake rats with a well-established awake imaging paradigm that avoids anesthesia interference, together with a preprocessing pipeline optimized for rat data. Based on this dataset, we propose two methods termed SHERM and fastClean towards automated preprocessing of rodent rsfMRI data. First, SHERM targets rodent brain extraction, which is an essential step to aid with rsfMRI image registration. Current methods usually require manual adjustment of input parameters due to widely different image qualities and/or contrasts. SHERM, however, only requires a brain template mask as the input and is shown to automatically and reliably extract the brain tissue in both rat and mouse MRI images. fastClean is an unsupervised deep learning method that removes rsfMRI artifacts induced by the scanner, head motion, and nonneural physiological noise. Existing machine learning methods either perform unsatisfactorily in low-dimensional rodent data or suffer from long online training. With an efficient network architecture and meta-learning techniques, fastClean generates equivalently clean or cleaner data in minutes on both rodent and human datasets. Finally, we systematically investigated the spatiotemporal dynamics of spontaneous brain activity in the awake rat brain with a graph-based data mining method. We found that brain activity traverse among multiple resting-state functional connectivity patterns with nonrandom and reproducible sequential orders and time delays, revealed a network structure of these transition paths, and showed prominent brain regions involved and their temporal evolutions during the propagation of spontaneous brain activity. Taken together, this iv dissertation presents multiple computational methods for rsfMRI studies, demonstrates their contribution to automatic data preprocessing, data cleaning, and spatiotemporal neural pattern discovery, and advances our understanding of network organization and dynamics of the awake rat brain.
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653 $a Resting-state functional magnetic resonance imaging
653 $a Meta-learning techniques
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