TY - GEN
T1 - Tensor-based fusion of EEG and FMRI to understand neurological changes in schizophrenia
AU - Acar, Evrim
AU - Levin-Schwartz, Yuri
AU - Calhoun, Vince D.
AU - Adali, Tulay
N1 - Funding Information:
This work was funded in part by the Danish Council for Independent Research - Technology and Production Sciences Program under the projects 11-116328 and 11-120947.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/25
Y1 - 2017/9/25
N2 - Neuroimaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide information about neurological functions in complementary spatiotemporal resolutions; therefore, fusion of these modalities is expected to provide better understanding of brain activity. In this paper, we jointly analyze fMRI and EEG data collected during an auditory oddball task with the goal of capturing brain activity patterns that differ between patients with schizophrenia and healthy controls. Rather than selecting a single electrode or matricizing the third-order tensor that can be naturally used to represent multi-channel EEG signals, we preserve the multi-way structure of EEG data and use a coupled matrix and tensor factorization (CMTF) model to jointly analyze fMRI and EEG signals. Our analysis reveals that (i) joint analysis of EEG and fMRI using a CMTF model can capture meaningful temporal and spatial signatures of patterns that behave differently in patients and controls, and (ii) these differences and the interpretability of the associated components increase by including multiple electrodes from frontal, motor and parietal areas, but not necessarily by including all electrodes in the analysis.
AB - Neuroimaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide information about neurological functions in complementary spatiotemporal resolutions; therefore, fusion of these modalities is expected to provide better understanding of brain activity. In this paper, we jointly analyze fMRI and EEG data collected during an auditory oddball task with the goal of capturing brain activity patterns that differ between patients with schizophrenia and healthy controls. Rather than selecting a single electrode or matricizing the third-order tensor that can be naturally used to represent multi-channel EEG signals, we preserve the multi-way structure of EEG data and use a coupled matrix and tensor factorization (CMTF) model to jointly analyze fMRI and EEG signals. Our analysis reveals that (i) joint analysis of EEG and fMRI using a CMTF model can capture meaningful temporal and spatial signatures of patterns that behave differently in patients and controls, and (ii) these differences and the interpretability of the associated components increase by including multiple electrodes from frontal, motor and parietal areas, but not necessarily by including all electrodes in the analysis.
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U2 - 10.1109/ISCAS.2017.8050303
DO - 10.1109/ISCAS.2017.8050303
M3 - Conference contribution
AN - SCOPUS:85032670577
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - IEEE International Symposium on Circuits and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 50th IEEE International Symposium on Circuits and Systems, ISCAS 2017
Y2 - 28 May 2017 through 31 May 2017
ER -