The effect of SOM size and similarity measure on identification of functional and anatomical regions in fMRI data

Patrick O’Driscoll; Erzsébet Merényi; Christof Karmonik; Robert Grossman

doi:10.1007/978-3-319-28518-4_22

The effect of SOM size and similarity measure on identification of functional and anatomical regions in fMRI data

Patrick O’Driscoll, Erzsébet Merényi, Christof Karmonik, Robert Grossman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

We demonstrate the advantage of larger SOMs than those typically used in the literature for clustering functional magnetic resonance images (fMRI). We also show the advantage of a connectivity similarity measure over distance measures for cluster discovery and extraction. We illustrate these points through maps generated from a multiple-subject investigation of the genesis of willed movement, where clusters of the fMRI time-courses signify functional (or anatomical) regions, and where accurate delineation of many clusters is critical for tracking the relationships of neural activities across space and time. While we do not provide an automated optimization of the SOM size it is clear that for this study increasing it up to 40 × 40 facilitates clearer discovery of more relevant clusters than from a 10 × 10 SOM (a size frequently used in the literature), and further increase has no benefits in our case despite using large data sets (all data from whole-brain scans). We offer insight through data characteristics and some objective justification.

Original language	English (US)
Title of host publication	Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016
Editors	Patrick O’Driscoll, Michael J. Mendenhall, Erzsébet Merényi
Publisher	Springer-Verlag
Pages	251-263
Number of pages	13
ISBN (Print)	9783319285177
DOIs	https://doi.org/10.1007/978-3-319-28518-4_22
State	Published - 2016
Event	11th International on Advances in Self-Organizing Maps and Learning Vector Quantization Workshop, WSOM 2016 - Houston, United States Duration: Jan 6 2016 → Jan 8 2016

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	428
ISSN (Print)	2194-5357

Other

Other	11th International on Advances in Self-Organizing Maps and Learning Vector Quantization Workshop, WSOM 2016
Country/Territory	United States
City	Houston
Period	1/6/16 → 1/8/16

Keywords

CONNvis
Cluster extraction
Conscience self-organizing map
Data-driven model
Functional magnetic resonance imaging
Willed movement

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science(all)

Access to Document

10.1007/978-3-319-28518-4_22

Cite this

O’Driscoll, P., Merényi, E., Karmonik, C., & Grossman, R. (2016). The effect of SOM size and similarity measure on identification of functional and anatomical regions in fMRI data. In P. O’Driscoll, M. J. Mendenhall, & E. Merényi (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016 (pp. 251-263). (Advances in Intelligent Systems and Computing; Vol. 428). Springer-Verlag. https://doi.org/10.1007/978-3-319-28518-4_22

The effect of SOM size and similarity measure on identification of functional and anatomical regions in fMRI data. / O’Driscoll, Patrick; Merényi, Erzsébet; Karmonik, Christof et al.
Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016. ed. / Patrick O’Driscoll; Michael J. Mendenhall; Erzsébet Merényi. Springer-Verlag, 2016. p. 251-263 (Advances in Intelligent Systems and Computing; Vol. 428).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

O’Driscoll, P, Merényi, E, Karmonik, C & Grossman, R 2016, The effect of SOM size and similarity measure on identification of functional and anatomical regions in fMRI data. in P O’Driscoll, MJ Mendenhall & E Merényi (eds), Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016. Advances in Intelligent Systems and Computing, vol. 428, Springer-Verlag, pp. 251-263, 11th International on Advances in Self-Organizing Maps and Learning Vector Quantization Workshop, WSOM 2016, Houston, United States, 1/6/16. https://doi.org/10.1007/978-3-319-28518-4_22

O’Driscoll P, Merényi E, Karmonik C, Grossman R. The effect of SOM size and similarity measure on identification of functional and anatomical regions in fMRI data. In O’Driscoll P, Mendenhall MJ, Merényi E, editors, Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016. Springer-Verlag. 2016. p. 251-263. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-28518-4_22

O’Driscoll, Patrick ; Merényi, Erzsébet ; Karmonik, Christof et al. / The effect of SOM size and similarity measure on identification of functional and anatomical regions in fMRI data. Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016. editor / Patrick O’Driscoll ; Michael J. Mendenhall ; Erzsébet Merényi. Springer-Verlag, 2016. pp. 251-263 (Advances in Intelligent Systems and Computing).

@inproceedings{d730a9ff18334fc3a7aa57db987429ee,

title = "The effect of SOM size and similarity measure on identification of functional and anatomical regions in fMRI data",

abstract = "We demonstrate the advantage of larger SOMs than those typically used in the literature for clustering functional magnetic resonance images (fMRI). We also show the advantage of a connectivity similarity measure over distance measures for cluster discovery and extraction. We illustrate these points through maps generated from a multiple-subject investigation of the genesis of willed movement, where clusters of the fMRI time-courses signify functional (or anatomical) regions, and where accurate delineation of many clusters is critical for tracking the relationships of neural activities across space and time. While we do not provide an automated optimization of the SOM size it is clear that for this study increasing it up to 40 × 40 facilitates clearer discovery of more relevant clusters than from a 10 × 10 SOM (a size frequently used in the literature), and further increase has no benefits in our case despite using large data sets (all data from whole-brain scans). We offer insight through data characteristics and some objective justification.",

keywords = "CONNvis, Cluster extraction, Conscience self-organizing map, Data-driven model, Functional magnetic resonance imaging, Willed movement",

author = "Patrick O{\textquoteright}Driscoll and Erzs{\'e}bet Mer{\'e}nyi and Christof Karmonik and Robert Grossman",

note = "Funding Information: This work was partially supported by the Program for Mind and Brain, Department of Neurosurgery, Houston Methodist Hospital. Figures are in color, request a color copy by email: patrick.odriscoll@rice.edu, erzsebet@rice.edu Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 11th International on Advances in Self-Organizing Maps and Learning Vector Quantization Workshop, WSOM 2016 ; Conference date: 06-01-2016 Through 08-01-2016",

year = "2016",

doi = "10.1007/978-3-319-28518-4_22",

language = "English (US)",

isbn = "9783319285177",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer-Verlag",

pages = "251--263",

editor = "Patrick O{\textquoteright}Driscoll and Mendenhall, {Michael J.} and Erzs{\'e}bet Mer{\'e}nyi",

booktitle = "Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016",

}

TY - GEN

T1 - The effect of SOM size and similarity measure on identification of functional and anatomical regions in fMRI data

AU - O’Driscoll, Patrick

AU - Merényi, Erzsébet

AU - Karmonik, Christof

AU - Grossman, Robert

N1 - Funding Information: This work was partially supported by the Program for Mind and Brain, Department of Neurosurgery, Houston Methodist Hospital. Figures are in color, request a color copy by email: patrick.odriscoll@rice.edu, erzsebet@rice.edu Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - We demonstrate the advantage of larger SOMs than those typically used in the literature for clustering functional magnetic resonance images (fMRI). We also show the advantage of a connectivity similarity measure over distance measures for cluster discovery and extraction. We illustrate these points through maps generated from a multiple-subject investigation of the genesis of willed movement, where clusters of the fMRI time-courses signify functional (or anatomical) regions, and where accurate delineation of many clusters is critical for tracking the relationships of neural activities across space and time. While we do not provide an automated optimization of the SOM size it is clear that for this study increasing it up to 40 × 40 facilitates clearer discovery of more relevant clusters than from a 10 × 10 SOM (a size frequently used in the literature), and further increase has no benefits in our case despite using large data sets (all data from whole-brain scans). We offer insight through data characteristics and some objective justification.

AB - We demonstrate the advantage of larger SOMs than those typically used in the literature for clustering functional magnetic resonance images (fMRI). We also show the advantage of a connectivity similarity measure over distance measures for cluster discovery and extraction. We illustrate these points through maps generated from a multiple-subject investigation of the genesis of willed movement, where clusters of the fMRI time-courses signify functional (or anatomical) regions, and where accurate delineation of many clusters is critical for tracking the relationships of neural activities across space and time. While we do not provide an automated optimization of the SOM size it is clear that for this study increasing it up to 40 × 40 facilitates clearer discovery of more relevant clusters than from a 10 × 10 SOM (a size frequently used in the literature), and further increase has no benefits in our case despite using large data sets (all data from whole-brain scans). We offer insight through data characteristics and some objective justification.

KW - CONNvis

KW - Cluster extraction

KW - Conscience self-organizing map

KW - Data-driven model

KW - Functional magnetic resonance imaging

KW - Willed movement

UR - http://www.scopus.com/inward/record.url?scp=84955294573&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84955294573&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-28518-4_22

DO - 10.1007/978-3-319-28518-4_22

M3 - Conference contribution

AN - SCOPUS:84955294573

SN - 9783319285177

T3 - Advances in Intelligent Systems and Computing

SP - 251

EP - 263

BT - Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016

A2 - O’Driscoll, Patrick

A2 - Mendenhall, Michael J.

A2 - Merényi, Erzsébet

PB - Springer-Verlag

T2 - 11th International on Advances in Self-Organizing Maps and Learning Vector Quantization Workshop, WSOM 2016

Y2 - 6 January 2016 through 8 January 2016

ER -

The effect of SOM size and similarity measure on identification of functional and anatomical regions in fMRI data

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this