Identification of Functional Clusters in the Striatum Using Infinite Relational Modeling

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

doi:10.1007/978-3-642-34713-9_29

Identification of Functional Clusters in the Striatum Using Infinite Relational Modeling

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

Copenhagen University Hospital Herlev and Gentofte

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

Abstract

In this paper we investigate how the Infinite Relational Model can be used to infer functional groupings of the human striatum using resting state fMRI data from 30 healthy subjects. The Infinite Relational Model is a non-parametric Bayesian method for infering community structure in complex networks. We visualize the solution found by performing evidence accumulation clustering on the maximum a posterior solutions found in 100 runs of the sampling scheme. The striatal groupings found are symmetric between hemispheres indicating that the model is able to group voxels across hemispheres, which are involved in the same neural computations. The reproducibility of the groupings found are assessed by calculating mutual information between half splits of the subject sample for various hyperparameter values. Finally, the model's ability to predict unobserved links is assessed by randomly treating links and non-links in the graphs as missing. We find that the model is performing well above chance for all subjects.

Original language	English
Title of host publication	Machine Learning and Interpretation in Neuroimaging : International Workshop, MLINI 2011, Held at NIPS 2011, Sierra Nevada, Spain, December 16-17, 2011, Revised Selected and Invited Contributions
Publisher	Springer
Publication date	2012
Pages	226-233
ISBN (Print)	978-3-642-34712-2
ISBN (Electronic)	978-3-642-34713-9
DOIs	https://doi.org/10.1007/978-3-642-34713-9_29
Publication status	Published - 2012
Event	International Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI 2011) - Granada, Spain Duration: 16 Dec 2011 → 17 Dec 2011 https://sites.google.com/site/mlini2011/home

Workshop

Workshop	International Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI 2011)
Country/Territory	Spain
City	Granada
Period	16/12/2011 → 17/12/2011
Internet address	https://sites.google.com/site/mlini2011/home

Series	Lecture Notes in Computer Science
Volume	7263
ISSN	0302-9743

Keywords

Complex networks
Graph theory
Infinite relational Model
Basal ganglia
Striatum

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-642-34713-9_29

OpenUrl availability

Full text

Cite this

Andersen, K. W., Madsen, K. H., Siebner, H., Hansen, L. K., & Mørup, M. (2012). Identification of Functional Clusters in the Striatum Using Infinite Relational Modeling. In Machine Learning and Interpretation in Neuroimaging: International Workshop, MLINI 2011, Held at NIPS 2011, Sierra Nevada, Spain, December 16-17, 2011, Revised Selected and Invited Contributions (pp. 226-233). Springer. https://doi.org/10.1007/978-3-642-34713-9_29

Andersen, Kasper Winther ; Madsen, Kristoffer Hougaard ; Siebner, Hartwig et al. / Identification of Functional Clusters in the Striatum Using Infinite Relational Modeling. Machine Learning and Interpretation in Neuroimaging: International Workshop, MLINI 2011, Held at NIPS 2011, Sierra Nevada, Spain, December 16-17, 2011, Revised Selected and Invited Contributions. Springer, 2012. pp. 226-233 (Lecture Notes in Computer Science, Vol. 7263).

@inproceedings{7f448e6486d849428be4e8bc358b3a2e,

title = "Identification of Functional Clusters in the Striatum Using Infinite Relational Modeling",

abstract = "In this paper we investigate how the Infinite Relational Model can be used to infer functional groupings of the human striatum using resting state fMRI data from 30 healthy subjects. The Infinite Relational Model is a non-parametric Bayesian method for infering community structure in complex networks. We visualize the solution found by performing evidence accumulation clustering on the maximum a posterior solutions found in 100 runs of the sampling scheme. The striatal groupings found are symmetric between hemispheres indicating that the model is able to group voxels across hemispheres, which are involved in the same neural computations. The reproducibility of the groupings found are assessed by calculating mutual information between half splits of the subject sample for various hyperparameter values. Finally, the model's ability to predict unobserved links is assessed by randomly treating links and non-links in the graphs as missing. We find that the model is performing well above chance for all subjects.",

keywords = "Complex networks, Graph theory, Infinite relational Model, Basal ganglia, Striatum",

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

year = "2012",

doi = "10.1007/978-3-642-34713-9_29",

language = "English",

isbn = "978-3-642-34712-2",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "226--233",

booktitle = "Machine Learning and Interpretation in Neuroimaging",

note = "International Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI 2011) ; Conference date: 16-12-2011 Through 17-12-2011",

url = "https://sites.google.com/site/mlini2011/home",

}

Andersen, KW, Madsen, KH, Siebner, H, Hansen, LK & Mørup, M 2012, Identification of Functional Clusters in the Striatum Using Infinite Relational Modeling. in Machine Learning and Interpretation in Neuroimaging: International Workshop, MLINI 2011, Held at NIPS 2011, Sierra Nevada, Spain, December 16-17, 2011, Revised Selected and Invited Contributions. Springer, Lecture Notes in Computer Science, vol. 7263, pp. 226-233, International Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI 2011), Granada, Spain, 16/12/2011. https://doi.org/10.1007/978-3-642-34713-9_29

Identification of Functional Clusters in the Striatum Using Infinite Relational Modeling. / Andersen, Kasper Winther; Madsen, Kristoffer Hougaard; Siebner, Hartwig et al.
Machine Learning and Interpretation in Neuroimaging: International Workshop, MLINI 2011, Held at NIPS 2011, Sierra Nevada, Spain, December 16-17, 2011, Revised Selected and Invited Contributions. Springer, 2012. p. 226-233 (Lecture Notes in Computer Science, Vol. 7263).

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

TY - GEN

T1 - Identification of Functional Clusters in the Striatum Using Infinite Relational Modeling

AU - Andersen, Kasper Winther

AU - Madsen, Kristoffer Hougaard

AU - Siebner, Hartwig

AU - Hansen, Lars Kai

AU - Mørup, Morten

PY - 2012

Y1 - 2012

N2 - In this paper we investigate how the Infinite Relational Model can be used to infer functional groupings of the human striatum using resting state fMRI data from 30 healthy subjects. The Infinite Relational Model is a non-parametric Bayesian method for infering community structure in complex networks. We visualize the solution found by performing evidence accumulation clustering on the maximum a posterior solutions found in 100 runs of the sampling scheme. The striatal groupings found are symmetric between hemispheres indicating that the model is able to group voxels across hemispheres, which are involved in the same neural computations. The reproducibility of the groupings found are assessed by calculating mutual information between half splits of the subject sample for various hyperparameter values. Finally, the model's ability to predict unobserved links is assessed by randomly treating links and non-links in the graphs as missing. We find that the model is performing well above chance for all subjects.

AB - In this paper we investigate how the Infinite Relational Model can be used to infer functional groupings of the human striatum using resting state fMRI data from 30 healthy subjects. The Infinite Relational Model is a non-parametric Bayesian method for infering community structure in complex networks. We visualize the solution found by performing evidence accumulation clustering on the maximum a posterior solutions found in 100 runs of the sampling scheme. The striatal groupings found are symmetric between hemispheres indicating that the model is able to group voxels across hemispheres, which are involved in the same neural computations. The reproducibility of the groupings found are assessed by calculating mutual information between half splits of the subject sample for various hyperparameter values. Finally, the model's ability to predict unobserved links is assessed by randomly treating links and non-links in the graphs as missing. We find that the model is performing well above chance for all subjects.

KW - Complex networks

KW - Graph theory

KW - Infinite relational Model

KW - Basal ganglia

KW - Striatum

U2 - 10.1007/978-3-642-34713-9_29

DO - 10.1007/978-3-642-34713-9_29

M3 - Article in proceedings

SN - 978-3-642-34712-2

T3 - Lecture Notes in Computer Science

SP - 226

EP - 233

BT - Machine Learning and Interpretation in Neuroimaging

PB - Springer

T2 - International Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI 2011)

Y2 - 16 December 2011 through 17 December 2011

ER -

Andersen KW, Madsen KH, Siebner H, Hansen LK , Mørup M. Identification of Functional Clusters in the Striatum Using Infinite Relational Modeling. In Machine Learning and Interpretation in Neuroimaging: International Workshop, MLINI 2011, Held at NIPS 2011, Sierra Nevada, Spain, December 16-17, 2011, Revised Selected and Invited Contributions. Springer. 2012. p. 226-233. (Lecture Notes in Computer Science, Vol. 7263). doi: 10.1007/978-3-642-34713-9_29

Identification of Functional Clusters in the Striatum Using Infinite Relational Modeling

Abstract

Workshop

Keywords

UN SDGs

Access to Document

OpenUrl availability

Fingerprint

Cite this