TY - JOUR
T1 - Quantifying the Interaction and Contribution of Multiple Datasets in Fusion
T2 - Application to the Detection of Schizophrenia
AU - Levin-Schwartz, Yuri
AU - Calhoun, Vince D.
AU - Adali, Tulay
N1 - Funding Information:
Manuscript received December 9, 2016; revised February 23, 2017; accepted February 28, 2017. Date of publication March 6, 2017; date of current version June 29, 2017. This work was supported in part by the National Institute of Biomedical Imaging and Bioengineering under Grant R01 EB 005846 and National Science Foundation-Computing and Communication Foundations under Grant 1618551. Asterisk indicates corresponding author.
Publisher Copyright:
© 1982-2012 IEEE.
PY - 2017/7
Y1 - 2017/7
N2 - The extraction of information from multiple sets of data is a problem inherent to many disciplines. This is possible by either analyzing the data sets jointly as in data fusion or separately and then combining as in data integration. However, selecting the optimal method to combine and analyze multiset data is an ever-present challenge. The primary reason for this is the difficulty in determining the optimal contribution of each data set to an analysis as well as the amount of potentially exploitable complementary information among data sets. In this paper, we propose a novel classification rate-based technique to unambiguously quantify the contribution of each data set to a fusion result as well as facilitate direct comparisons of fusion methods on real data and apply a new method, independent vector analysis (IVA), to multiset fusion. This classification rate-based technique is used on functional magnetic resonance imaging data collected from 121 patients with schizophrenia and 150 healthy controls during the performance of three tasks. Through this application, we find that though optimal performance is achieved by exploiting all tasks, each task does not contribute equally to the result and this framework enables effective quantification of the value added by each task. Our results also demonstrate that data fusion methods are more powerful than data integration methods, with the former achieving a classification rate of 73.5 % and the latter achieving one of 70.9 %, a difference which we show is significant when all three tasks are analyzed together. Finally, we show that IVA, due to its flexibility, has equivalent or superior performance compared with the popular data fusion method, joint independent component analysis.
AB - The extraction of information from multiple sets of data is a problem inherent to many disciplines. This is possible by either analyzing the data sets jointly as in data fusion or separately and then combining as in data integration. However, selecting the optimal method to combine and analyze multiset data is an ever-present challenge. The primary reason for this is the difficulty in determining the optimal contribution of each data set to an analysis as well as the amount of potentially exploitable complementary information among data sets. In this paper, we propose a novel classification rate-based technique to unambiguously quantify the contribution of each data set to a fusion result as well as facilitate direct comparisons of fusion methods on real data and apply a new method, independent vector analysis (IVA), to multiset fusion. This classification rate-based technique is used on functional magnetic resonance imaging data collected from 121 patients with schizophrenia and 150 healthy controls during the performance of three tasks. Through this application, we find that though optimal performance is achieved by exploiting all tasks, each task does not contribute equally to the result and this framework enables effective quantification of the value added by each task. Our results also demonstrate that data fusion methods are more powerful than data integration methods, with the former achieving a classification rate of 73.5 % and the latter achieving one of 70.9 %, a difference which we show is significant when all three tasks are analyzed together. Finally, we show that IVA, due to its flexibility, has equivalent or superior performance compared with the popular data fusion method, joint independent component analysis.
KW - Functional magnetic resonance imaging (fMRI)
KW - ICA
KW - IVA
KW - data fusion
KW - schizophrenia
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U2 - 10.1109/TMI.2017.2678483
DO - 10.1109/TMI.2017.2678483
M3 - Article
C2 - 28287964
AN - SCOPUS:85028418743
SN - 0278-0062
VL - 36
SP - 1385
EP - 1395
JO - IEEE transactions on medical imaging
JF - IEEE transactions on medical imaging
IS - 7
M1 - 7872466
ER -