Angular Central Gaussian and Watson Mixture Models for Assessing Dynamic Functional Brain Connectivity During a Motor Task

Anders S. Olsen; Emil Ortvald; Kristoffer H. Madsen; Mikkel N. Schmidt; Morten Mørup

doi:10.1109/ICASSPW59220.2023.10193021

Angular Central Gaussian and Watson Mixture Models for Assessing Dynamic Functional Brain Connectivity During a Motor Task

Anders S. Olsen
, Emil Ortvald
, Kristoffer H. Madsen
, Mikkel N. Schmidt
, Morten Mørup

Copenhagen University Hospital Amager and Hvidovre

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

The development of appropriate models for dynamic functional connectivity is imperative to gain a better understanding of the brain both during rest and while performing a task. Leading eigenvector dynamics analysis is among the favored methods for assessing frame-wise connectivity, but eigenvectors are distributed on the sign-symmetric unit hypersphere, which is typically disregarded during modeling. Here we develop both mixture model and Hidden Markov model formulations for two sign-symmetric spherical statistical distributions and display their performance on synthetic data and task-fMRI data involving a finger-tapping task.

Original language	English
Title of host publication	Proceedings of the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)
Number of pages	5
Publisher	IEEE
Publication date	2023
ISBN (Print)	979-8-3503-0262-2
ISBN (Electronic)	979-8-3503-0261-5
DOIs	https://doi.org/10.1109/ICASSPW59220.2023.10193021
Publication status	Published - 2023
Event	2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops - Rhodes Island, Greece Duration: 4 Jun 2023 → 10 Jun 2023

Conference

Conference	2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops
Country/Territory	Greece
City	Rhodes Island
Period	04/06/2023 → 10/06/2023

Keywords

Dynamic functional connectivity
Leading eigenvector dynamics analysis
Watson
Angular Central Gaussian
Hidden Markov models

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10.1109/ICASSPW59220.2023.10193021

FulltextAccepted author manuscript, 2.08 MB

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Olsen, A. S., Ortvald, E., Madsen, K. H., Schmidt, M. N., & Mørup, M. (2023). Angular Central Gaussian and Watson Mixture Models for Assessing Dynamic Functional Brain Connectivity During a Motor Task. In Proceedings of the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) IEEE. https://doi.org/10.1109/ICASSPW59220.2023.10193021

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title = "Angular Central Gaussian and Watson Mixture Models for Assessing Dynamic Functional Brain Connectivity During a Motor Task",

abstract = "The development of appropriate models for dynamic functional connectivity is imperative to gain a better understanding of the brain both during rest and while performing a task. Leading eigenvector dynamics analysis is among the favored methods for assessing frame-wise connectivity, but eigenvectors are distributed on the sign-symmetric unit hypersphere, which is typically disregarded during modeling. Here we develop both mixture model and Hidden Markov model formulations for two sign-symmetric spherical statistical distributions and display their performance on synthetic data and task-fMRI data involving a finger-tapping task.",

keywords = "Dynamic functional connectivity, Leading eigenvector dynamics analysis, Watson, Angular Central Gaussian, Hidden Markov models",

author = "Olsen, \{Anders S.\} and Emil Ortvald and Madsen, \{Kristoffer H.\} and Schmidt, \{Mikkel N.\} and Morten M{\o}rup",

year = "2023",

doi = "10.1109/ICASSPW59220.2023.10193021",

language = "English",

isbn = "979-8-3503-0262-2",

booktitle = "Proceedings of the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)",

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note = "2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW ; Conference date: 04-06-2023 Through 10-06-2023",

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Olsen, AS, Ortvald, E, Madsen, KH , Schmidt, MN & Mørup, M 2023, Angular Central Gaussian and Watson Mixture Models for Assessing Dynamic Functional Brain Connectivity During a Motor Task. in Proceedings of the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). IEEE, 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, Rhodes Island, Greece, 04/06/2023. https://doi.org/10.1109/ICASSPW59220.2023.10193021

Angular Central Gaussian and Watson Mixture Models for Assessing Dynamic Functional Brain Connectivity During a Motor Task. / Olsen, Anders S.; Ortvald, Emil; Madsen, Kristoffer H. et al.
Proceedings of the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). IEEE, 2023.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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T1 - Angular Central Gaussian and Watson Mixture Models for Assessing Dynamic Functional Brain Connectivity During a Motor Task

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AU - Ortvald, Emil

AU - Madsen, Kristoffer H.

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AU - Mørup, Morten

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AB - The development of appropriate models for dynamic functional connectivity is imperative to gain a better understanding of the brain both during rest and while performing a task. Leading eigenvector dynamics analysis is among the favored methods for assessing frame-wise connectivity, but eigenvectors are distributed on the sign-symmetric unit hypersphere, which is typically disregarded during modeling. Here we develop both mixture model and Hidden Markov model formulations for two sign-symmetric spherical statistical distributions and display their performance on synthetic data and task-fMRI data involving a finger-tapping task.

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KW - Leading eigenvector dynamics analysis

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KW - Hidden Markov models

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DO - 10.1109/ICASSPW59220.2023.10193021

M3 - Article in proceedings

SN - 979-8-3503-0262-2

BT - Proceedings of the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)

PB - IEEE

T2 - 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops

Y2 - 4 June 2023 through 10 June 2023

ER -

Angular Central Gaussian and Watson Mixture Models for Assessing Dynamic Functional Brain Connectivity During a Motor Task

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