Robustness of Representative Signals Relative to Data Loss
Using Atlas-Based Parcellations

J 2018

Robustness of Representative Signals Relative to Data Loss Using Atlas-Based Parcellations

GAJDOŠ, Martin, Eva VÝTVAROVÁ, Jan FOUSEK, Martin LAMOŠ, Michal MIKL et. al.

Basic information

Original name

Robustness of Representative Signals Relative to Data Loss Using Atlas-Based Parcellations

Authors

GAJDOŠ, Martin (203 Czech Republic, belonging to the institution), Eva VÝTVAROVÁ (203 Czech Republic, belonging to the institution), Jan FOUSEK (203 Czech Republic, belonging to the institution), Martin LAMOŠ (203 Czech Republic, belonging to the institution) and Michal MIKL (203 Czech Republic, guarantor, belonging to the institution)

Edition

BRAIN TOPOGRAPHY, DORDRECHT, SPRINGER, 2018, 0896-0267

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30103 Neurosciences

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

Impact factor

Impact factor: 3.104

RIV identification code

RIV/00216224:14740/18:00101793

Organization unit

Central European Institute of Technology

DOI

http://dx.doi.org/10.1007/s10548-018-0647-6

UT WoS

000440763900004

Keywords in English

Parcellation; fMRI; Atlas; Representative signal; Coverage

Abstract

V originále

Parcellation-based approaches are an important part of functional magnetic resonance imaging data analysis. They are a necessary processing step for sorting data in structurally or functionally homogenous regions. Real functional magnetic resonance imaging datasets usually do not cover the atlas template completely; they are often spatially constrained due to the physical limitations of MR sequence settings, the inter-individual variability in brain shape, etc. When using a parcellation template, many regions are not completely covered by actual data. This paper addresses the issue of the area coverage required in real data in order to reliably estimate the representative signal and the influence of this kind of data loss on network analysis metrics. We demonstrate this issue on four datasets using four different widely used parcellation templates. We used two erosion approaches to simulate data loss on the whole-brain level and the ROI-specific level. Our results show that changes in ROI coverage have a systematic influence on network measures. Based on the results of our analysis, we recommend controlling the ROI coverage and retaining at least 60% of the area in order to ensure at least 80% of explained variance of the original signal.

Links

GA14-33143S, research and development project

Name: Vliv fyziologických procesů na reliabilitu a časovou proměnlivost konektivity v lidském mozku měřené pomocí fMRI

Investor: Czech Science Foundation

LM2015062, research and development project

Name: Národní infrastruktura pro biologické a medicínské zobrazování

Investor: Ministry of Education, Youth and Sports of the CR

LQ1601, research and development project

Name: CEITEC 2020 (Acronym: CEITEC2020)

Investor: Ministry of Education, Youth and Sports of the CR

Citovat

GAJDOŠ, Martin, Eva VÝTVAROVÁ, Jan FOUSEK, Martin LAMOŠ and Michal MIKL. Robustness of Representative Signals Relative to Data Loss Using Atlas-Based Parcellations. BRAIN TOPOGRAPHY. DORDRECHT: SPRINGER, 2018, vol. 31, No 5, p. 767-779. ISSN 0896-0267. Available from: https://dx.doi.org/10.1007/s10548-018-0647-6.

@article{1507857,
   author = {Gajdoš, Martin and Výtvarová, Eva and Fousek, Jan and Lamoš, Martin and Mikl, Michal},
   article_location = {DORDRECHT},
   article_number = {5},
   doi = {http://dx.doi.org/10.1007/s10548-018-0647-6},
   keywords = {Parcellation; fMRI; Atlas; Representative signal; Coverage},
   language = {eng},
   issn = {0896-0267},
   journal = {BRAIN TOPOGRAPHY},
   title = {Robustness of Representative Signals Relative to Data Loss Using Atlas-Based Parcellations},
   volume = {31},
   year = {2018}
}

TY  - JOUR
ID  - 1507857
AU  - Gajdoš, Martin - Výtvarová, Eva - Fousek, Jan - Lamoš, Martin - Mikl, Michal
PY  - 2018
TI  - Robustness of Representative Signals Relative to Data Loss Using Atlas-Based Parcellations
JF  - BRAIN TOPOGRAPHY
VL  - 31
IS  - 5
SP  - 767-779
EP  - 767-779
PB  - SPRINGER
SN  - 08960267
KW  - Parcellation
KW  - fMRI
KW  - Atlas
KW  - Representative signal
KW  - Coverage
N2  - Parcellation-based approaches are an important part of functional magnetic resonance imaging data analysis. They are a necessary processing step for sorting data in structurally or functionally homogenous regions. Real functional magnetic resonance imaging datasets usually do not cover the atlas template completely; they are often spatially constrained due to the physical limitations of MR sequence settings, the inter-individual variability in brain shape, etc. When using a parcellation template, many regions are not completely covered by actual data. This paper addresses the issue of the area coverage required in real data in order to reliably estimate the representative signal and the influence of this kind of data loss on network analysis metrics. We demonstrate this issue on four datasets using four different widely used parcellation templates. We used two erosion approaches to simulate data loss on the whole-brain level and the ROI-specific level. Our results show that changes in ROI coverage have a systematic influence on network measures. Based on the results of our analysis, we recommend controlling the ROI coverage and retaining at least 60% of the area in order to ensure at least 80% of explained variance of the original signal.
ER  -

GAJDOŠ, Martin, Eva VÝTVAROVÁ, Jan FOUSEK, Martin LAMOŠ and Michal MIKL. Robustness of Representative Signals Relative to Data Loss Using Atlas-Based Parcellations. \textit{BRAIN TOPOGRAPHY}. DORDRECHT: SPRINGER, 2018, vol.~31, No~5, p.~767-779. ISSN~0896-0267. Available from: https://dx.doi.org/10.1007/s10548-018-0647-6.

Detailed Information on Publication Record