The Statistical Analysis of the Varying Brain

Oliver Y. Chen; Duy Thanh Vu; Gilbert Greub; Hengyi Cao; Xingru He; Yannick Muller; Constantinos Petrovas; Haochang Shou; Viet Dung Nguyen; Bangdong Zhi; Laurent Perez; Jean Louis Raisaro; Guy Nagels; Maarten De Vos; Wei He; Raphael Gottardo; Palie Smart; Marcus Munafo; Giuseppe Pantaleo

doi:10.1109/SSP53291.2023.10208029

The Statistical Analysis of the Varying Brain

Oliver Y. Chen, Duy Thanh Vu, Gilbert Greub, Hengyi Cao, Xingru He, Yannick Muller, Constantinos Petrovas, Haochang Shou, Viet Dung Nguyen, Bangdong Zhi, Laurent Perez, Jean Louis Raisaro, Guy Nagels, Maarten De Vos, Wei He, Raphael Gottardo, Palie Smart, Marcus Munafo, Giuseppe Pantaleo

Vice Chancellor's Office

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

Abstract

We present here a systematical approach to studying the varying brain. We first distinguish different types of brain variability and provide examples for them. Next, we show classical analysis of covariance (ANCOVA) as well as advanced residual analysis via statistical- and deep-learning aim to decompose the total variance of the brain or behaviour data into explainable variance components. Additionally, we discuss innate and acquired brain variability. For varying big brain data, we define the neural law of large numbers and discuss methods for extracting representations from large-scale, potentially high-dimensional brain data. Finally, we examine the gut-brain axis, an often lurking, yet important, source of brain variability.

Original language	English
Title of host publication	Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023
Publisher	IEEE
Pages	700-704
Number of pages	5
ISBN (Electronic)	9781665452458
DOIs	https://doi.org/10.1109/SSP53291.2023.10208029
Publication status	Published - 9 Aug 2023
Event	22nd IEEE Statistical Signal Processing Workshop, SSP 2023 - Hanoi, Viet Nam Duration: 2 Jul 2023 → 5 Jul 2023

Publication series

Name	IEEE Workshop on Statistical Signal Processing Proceedings
Volume	2023-July

Conference

Conference	22nd IEEE Statistical Signal Processing Workshop, SSP 2023
Country/Territory	Viet Nam
City	Hanoi
Period	2/07/23 → 5/07/23

Keywords

acquired variability
ANCOVA
Bayesian brain
Brain variability
gut-brain axis
high-dimensional data
innate variability
residual learning

ASJC Scopus subject areas

Electrical and Electronic Engineering
Applied Mathematics
Signal Processing
Computer Science Applications

Access to Document

10.1109/SSP53291.2023.10208029

Cite this

Chen, O. Y., Thanh Vu, D., Greub, G., Cao, H., He, X., Muller, Y., Petrovas, C., Shou, H., Nguyen, V. D., Zhi, B., Perez, L., Raisaro, J. L., Nagels, G., De Vos, M., He, W., Gottardo, R., Smart, P., Munafo, M., & Pantaleo, G. (2023). The Statistical Analysis of the Varying Brain. In Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023 (pp. 700-704). (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2023-July). IEEE. https://doi.org/10.1109/SSP53291.2023.10208029

The Statistical Analysis of the Varying Brain. / Chen, Oliver Y.; Thanh Vu, Duy; Greub, Gilbert et al.
Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023. IEEE, 2023. p. 700-704 (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2023-July).

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

Chen, OY, Thanh Vu, D, Greub, G, Cao, H, He, X, Muller, Y, Petrovas, C, Shou, H, Nguyen, VD, Zhi, B, Perez, L, Raisaro, JL, Nagels, G, De Vos, M, He, W, Gottardo, R, Smart, P, Munafo, M & Pantaleo, G 2023, The Statistical Analysis of the Varying Brain. in Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023. IEEE Workshop on Statistical Signal Processing Proceedings, vol. 2023-July, IEEE, pp. 700-704, 22nd IEEE Statistical Signal Processing Workshop, SSP 2023, Hanoi, Viet Nam, 2/07/23. https://doi.org/10.1109/SSP53291.2023.10208029

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abstract = "We present here a systematical approach to studying the varying brain. We first distinguish different types of brain variability and provide examples for them. Next, we show classical analysis of covariance (ANCOVA) as well as advanced residual analysis via statistical- and deep-learning aim to decompose the total variance of the brain or behaviour data into explainable variance components. Additionally, we discuss innate and acquired brain variability. For varying big brain data, we define the neural law of large numbers and discuss methods for extracting representations from large-scale, potentially high-dimensional brain data. Finally, we examine the gut-brain axis, an often lurking, yet important, source of brain variability.",

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AU - Petrovas, Constantinos

AU - Shou, Haochang

AU - Nguyen, Viet Dung

AU - Zhi, Bangdong

AU - Perez, Laurent

AU - Raisaro, Jean Louis

AU - Nagels, Guy

AU - De Vos, Maarten

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AB - We present here a systematical approach to studying the varying brain. We first distinguish different types of brain variability and provide examples for them. Next, we show classical analysis of covariance (ANCOVA) as well as advanced residual analysis via statistical- and deep-learning aim to decompose the total variance of the brain or behaviour data into explainable variance components. Additionally, we discuss innate and acquired brain variability. For varying big brain data, we define the neural law of large numbers and discuss methods for extracting representations from large-scale, potentially high-dimensional brain data. Finally, we examine the gut-brain axis, an often lurking, yet important, source of brain variability.

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The Statistical Analysis of the Varying Brain

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Keywords

ASJC Scopus subject areas

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