Simultaneous Low-rank Component and Graph Estimation for High-dimensional Graph Signals: Application to Brain Imaging

Li, Rui; Nejati, Hossein; Safavi, Seyed Hamid; Cheung, Ngai-Man

doi:10.48550/arxiv.1609.08221

Cited by 2 publications

(3 citation statements)

References 22 publications

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“…Most brain regions did display very significant excursion percentages: considering alignment, occipital (nodes 21-25), parietal (nodes 18 and 19) and temporal (nodes 29-33) regions were the strongest contributors, while for liberality, key areas were located in temporal (nodes [29][30][31][32][33], subcortical (nodes 34,[36][37][38][39] or frontal (nodes 1-9) regions. Figure 8B displays the anatomical location of the main contributing regions.…”

Section: A Resolving Excursions In Alignment or Liberality Regimesmentioning

confidence: 99%

“…The theory of GSP has been growing rapidly in recent years, with development in areas including sampling theory [24], [25], stationarity [26], [27] and uncertainty [28]- [31], filtering [32]- [34], directed graphs [35], and dictionary learning [36]. Applications have been spanning many areas including neuroscience [14], [37], imaging [38], [39], medical imaging [40], video [41], online learning [42], and rating prediction [43]- [45].…”

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confidence: 99%

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A Graph Signal Processing Perspective on Functional Brain Imaging

et al. 2018

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Modern neuroimaging techniques provide us with unique views on brain structure and function; i.e., how the brain is wired, and where and when activity takes place. Data acquired using these techniques can be analyzed in terms of its network structure to reveal organizing principles at the systems level. Graph representations are versatile models where nodes are associated to brain regions and edges to structural or functional connections. Structural graphs model neural pathways in white matter that are the anatomical backbone between regions. Functional graphs are built based on functional connectivity, which is a pairwise measure of statistical interdependency between activity traces of regions. Therefore, most research to date has focused on analyzing these graphs reflecting structure or function.Graph signal processing (GSP) is an emerging area of research where signals recorded at the nodes of the graph are studied atop the underlying graph structure. An increasing number of fundamental operations have been generalized to the graph setting, allowing to analyze the signals from a new viewpoint. Here, we review GSP for brain imaging data and discuss their potential to integrate brain structure, contained in the graph itself, with brain function, residing in the graph signals. We review how brain activity can be meaningfully filtered based on concepts of spectral modes derived from brain structure. We also derive other operations such as surrogate data generation or decompositions informed by cognitive systems. In sum, GSP offers a novel framework for the analysis of brain imaging data.

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Section: A Resolving Excursions In Alignment or Liberality Regimesmentioning

confidence: 99%

mentioning

confidence: 99%

A Graph Signal Processing Perspective on Functional Brain Imaging

et al. 2018

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“…Other studies have applied GSP techniques to EEG/MEG signals, for instance for noise suppression [8], dimensionality reduction [7], [8], [10] and classification [7], [10]. The authors of the latter article compared classification accuracy when building graphs using different connectivity measures.…”

Section: Introductionmentioning

confidence: 99%

Evaluating graph signal processing for neuroimaging through classification and dimensionality reduction

Ménoret

Farrugia

Pasdeloup

et al. 2017

2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals. In the present work, we apply dimensionality reduction techniques based on graph representations of the brain to decode brain activity from real and simulated fMRI datasets. We introduce seven graphs obtained from a) geometric structure and/or b) functional connectivity between brain areas at rest, and compare them when performing dimension reduction for classification. We show that mixed graphs using both a) and b) offer the best performance. We also show that graph sampling methods perform better than classical dimension reduction including Principal Component Analysis (PCA) and Independent Component Analysis (ICA).

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Simultaneous Low-rank Component and Graph Estimation for High-dimensional Graph Signals: Application to Brain Imaging

Cited by 2 publications

References 22 publications

A Graph Signal Processing Perspective on Functional Brain Imaging

A Graph Signal Processing Perspective on Functional Brain Imaging

Evaluating graph signal processing for neuroimaging through classification and dimensionality reduction

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