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Graph Deep Learning for Multimodal N...

Qu, Gang,

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Graph Deep Learning for Multimodal Neuroimaging Study /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Graph Deep Learning for Multimodal Neuroimaging Study // Gang Qu.
Author:	Qu, Gang,
Description:	1 electronic resource (153 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
Contained By:	Dissertations Abstracts International86-01B.
Subject:	Bioengineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31292438
ISBN:	9798383201930

Graph Deep Learning for Multimodal Neuroimaging Study /
Qu, Gang,

Graph Deep Learning for Multimodal Neuroimaging Study /Gang Qu. - 1 electronic resource (153 pages)

Source: Dissertations Abstracts International, Volume: 86-01, Section: B.

This dissertation presents an advanced foray into neuroimaging, utilizing graph deep learning (GDL) methodologies to elucidate the complex functional organization of the human brain. Emphasizing the pivotal role of multimodal studies in neuroscience, we demonstrate the transformative potential of these approaches in understanding cerebral functionality.Central to this exploration is the application of Geometric Deep Learning, with a particular emphasis on Graph Neural Networks (GNNs). This innovative approach addresses the challenges inherent in traditional neuroimaging methods. The thesis delineates the development and application of sophisticated graph models, such as subject-to-subject graphs, functional connectivity graphs, and the Gated Graph Transformer (GGT). These models are strategically employed to enhance predictive analytics, focusing on cognitive assessment and neural biomarker discovery. The investigation further explores the amalgamation of diverse imaging modalities and paradigms, accentuating their essential contribution to a holistic understanding of human cognition and brain development, which augments predictive precision and facilitates the identification of crucial biomarkers, thereby enriching our comprehension of brain dynamics.Empirically validated through comprehensive simulations and analysis of extensive neuroimaging datasets, the findings of this research not only align with but also expand upon the existing body of neurological research. In adherence to the principles of open science, the methodologies and tools crafted in this research are conscientiously disseminated, guaranteeing reproducibility and fostering continued scholarly inquiry.In conclusion, this dissertation represents a substantial scholarly advancement in neuroimaging, evidencing the profound capability of graph deep learning to unravel the complexities of cerebral function. It lays a robust groundwork for future multimodal and multiparadigm neuroimaging research explorations.

English

ISBN: 9798383201930Subjects--Topical Terms:

657580
Bioengineering.
Subjects--Index Terms:

Graph deep learning

Graph Deep Learning for Multimodal Neuroimaging Study /
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520 $a This dissertation presents an advanced foray into neuroimaging, utilizing graph deep learning (GDL) methodologies to elucidate the complex functional organization of the human brain. Emphasizing the pivotal role of multimodal studies in neuroscience, we demonstrate the transformative potential of these approaches in understanding cerebral functionality.Central to this exploration is the application of Geometric Deep Learning, with a particular emphasis on Graph Neural Networks (GNNs). This innovative approach addresses the challenges inherent in traditional neuroimaging methods. The thesis delineates the development and application of sophisticated graph models, such as subject-to-subject graphs, functional connectivity graphs, and the Gated Graph Transformer (GGT). These models are strategically employed to enhance predictive analytics, focusing on cognitive assessment and neural biomarker discovery. The investigation further explores the amalgamation of diverse imaging modalities and paradigms, accentuating their essential contribution to a holistic understanding of human cognition and brain development, which augments predictive precision and facilitates the identification of crucial biomarkers, thereby enriching our comprehension of brain dynamics.Empirically validated through comprehensive simulations and analysis of extensive neuroimaging datasets, the findings of this research not only align with but also expand upon the existing body of neurological research. In adherence to the principles of open science, the methodologies and tools crafted in this research are conscientiously disseminated, guaranteeing reproducibility and fostering continued scholarly inquiry.In conclusion, this dissertation represents a substantial scholarly advancement in neuroimaging, evidencing the profound capability of graph deep learning to unravel the complexities of cerebral function. It lays a robust groundwork for future multimodal and multiparadigm neuroimaging research explorations.
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653 $a Neuroimaging
653 $a Functional magnetic resonance imaging
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653 $a Multimodal neuroimaging
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