Identifying modular relations in complex brain networks

Kasper Winther Andersen; Morten Mørup; Hartwig Siebner; Kristoffer H. Madsen; Lars Kai Hansen

doi:10.1109/MLSP.2012.6349739

Identifying modular relations in complex brain networks

Kasper Winther Andersen, Morten Mørup, Hartwig Siebner, Kristoffer H. Madsen, Lars Kai Hansen

Copenhagen University Hospital Herlev and Gentofte

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

Abstract

We evaluate the infinite relational model (IRM) against two simpler alternative nonparametric Bayesian models for identifying structures in multi subject brain networks. The models are evaluated for their ability to predict new data and infer reproducible structures. Prediction and reproducibility are measured within the data driven NPAIRS split-half framework. Using synthetic data drawn from each of the generative models we show that the IRM model outperforms the two competing models when data contain relational structure. For data drawn from the other two simpler models the IRM does not overfit and obtains comparable reproducibility and predictability. For resting state functional magnetic resonance imaging data from 30 healthy controls the IRM model is also superior to the two simpler alternatives, suggesting that brain networks indeed exhibit universal complex relational structure in the population.

Original language	English
Title of host publication	2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Number of pages	6
Place of Publication	978-1-4673-1025-3
Publisher	IEEE
Publication date	2012
ISBN (Print)	978-1-4673-1024-6
DOIs	https://doi.org/10.1109/MLSP.2012.6349739
Publication status	Published - 2012
Event	2012 IEEE International Workshop on Machine Learning for Signal Processing - Santander, Spain Duration: 23 Oct 2012 → 26 Oct 2012 Conference number: 22 https://ieeexplore.ieee.org/xpl/conhome/6335571/proceeding

Conference

Conference	2012 IEEE International Workshop on Machine Learning for Signal Processing
Number	22
Country/Territory	Spain
City	Santander
Period	23/10/2012 → 26/10/2012
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6335571/proceeding

Series	Machine Learning for Signal Processing
ISSN	1551-2541

Keywords

Infinite Relational Model
Complex Networks
fMRI

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10.1109/MLSP.2012.6349739

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title = "Identifying modular relations in complex brain networks",

abstract = "We evaluate the infinite relational model (IRM) against two simpler alternative nonparametric Bayesian models for identifying structures in multi subject brain networks. The models are evaluated for their ability to predict new data and infer reproducible structures. Prediction and reproducibility are measured within the data driven NPAIRS split-half framework. Using synthetic data drawn from each of the generative models we show that the IRM model outperforms the two competing models when data contain relational structure. For data drawn from the other two simpler models the IRM does not overfit and obtains comparable reproducibility and predictability. For resting state functional magnetic resonance imaging data from 30 healthy controls the IRM model is also superior to the two simpler alternatives, suggesting that brain networks indeed exhibit universal complex relational structure in the population.",

keywords = "Infinite Relational Model, Complex Networks, fMRI",

author = "Andersen, {Kasper Winther} and Morten M{\o}rup and Hartwig Siebner and Madsen, {Kristoffer H.} and Hansen, {Lars Kai}",

year = "2012",

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note = "2012 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012 ; Conference date: 23-10-2012 Through 26-10-2012",

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Andersen, KW, Mørup, M, Siebner, H, Madsen, KH & Hansen, LK 2012, Identifying modular relations in complex brain networks. in 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 978-1-4673-1025-3, Machine Learning for Signal Processing, 2012 IEEE International Workshop on Machine Learning for Signal Processing, Santander, Spain, 23/10/2012. https://doi.org/10.1109/MLSP.2012.6349739

Identifying modular relations in complex brain networks. / Andersen, Kasper Winther; Mørup, Morten; Siebner, Hartwig et al.
2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). 978-1-4673-1025-3: IEEE, 2012. (Machine Learning for Signal Processing).

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

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Identifying modular relations in complex brain networks

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