Time-dependent spatially varying graphical models, with application to brain fMRI data analysis

Kristjan Greenewald; Seyoung Park; Shuheng Zhou; Alexander Giessing

Time-dependent spatially varying graphical models, with application to brain fMRI data analysis

Kristjan Greenewald, Seyoung Park, Shuheng Zhou, Alexander Giessing

Department of Applied Statistics

Research output: Contribution to journal › Conference article › peer-review

9 Citations (Scopus)

Abstract

In this work, we present an additive model for space-time data that splits the data into a temporally correlated component and a spatially correlated component. We model the spatially correlated portion using a time-varying Gaussian graphical model. Under assumptions on the smoothness of changes in covariance matrices, we derive strong single sample convergence results, confirming our ability to estimate meaningful graphical structures as they evolve over time. We apply our methodology to the discovery of time-varying spatial structures in human brain fMRI signals.

Original language	English
Pages (from-to)	5833-5841
Number of pages	9
Journal	Advances in Neural Information Processing Systems
Volume	2017-December
Publication status	Published - 2017
Event	31st Annual Conference on Neural Information Processing Systems, NIPS 2017 - Long Beach, United States Duration: 2017 Dec 4 → 2017 Dec 9

Bibliographical note

Publisher Copyright:
© 2017 Neural information processing systems foundation. All rights reserved.

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems
Signal Processing

Cite this

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title = "Time-dependent spatially varying graphical models, with application to brain fMRI data analysis",

abstract = "In this work, we present an additive model for space-time data that splits the data into a temporally correlated component and a spatially correlated component. We model the spatially correlated portion using a time-varying Gaussian graphical model. Under assumptions on the smoothness of changes in covariance matrices, we derive strong single sample convergence results, confirming our ability to estimate meaningful graphical structures as they evolve over time. We apply our methodology to the discovery of time-varying spatial structures in human brain fMRI signals.",

author = "Kristjan Greenewald and Seyoung Park and Shuheng Zhou and Alexander Giessing",

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year = "2017",

language = "English",

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T1 - Time-dependent spatially varying graphical models, with application to brain fMRI data analysis

AU - Greenewald, Kristjan

AU - Park, Seyoung

AU - Zhou, Shuheng

AU - Giessing, Alexander

PY - 2017

Y1 - 2017

N2 - In this work, we present an additive model for space-time data that splits the data into a temporally correlated component and a spatially correlated component. We model the spatially correlated portion using a time-varying Gaussian graphical model. Under assumptions on the smoothness of changes in covariance matrices, we derive strong single sample convergence results, confirming our ability to estimate meaningful graphical structures as they evolve over time. We apply our methodology to the discovery of time-varying spatial structures in human brain fMRI signals.

AB - In this work, we present an additive model for space-time data that splits the data into a temporally correlated component and a spatially correlated component. We model the spatially correlated portion using a time-varying Gaussian graphical model. Under assumptions on the smoothness of changes in covariance matrices, we derive strong single sample convergence results, confirming our ability to estimate meaningful graphical structures as they evolve over time. We apply our methodology to the discovery of time-varying spatial structures in human brain fMRI signals.

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M3 - Conference article

AN - SCOPUS:85047010140

SN - 1049-5258

VL - 2017-December

SP - 5833

EP - 5841

JO - Advances in Neural Information Processing Systems

JF - Advances in Neural Information Processing Systems

T2 - 31st Annual Conference on Neural Information Processing Systems, NIPS 2017

Y2 - 4 December 2017 through 9 December 2017

ER -

Time-dependent spatially varying graphical models, with application to brain fMRI data analysis

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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