TY - GEN
T1 - Identification of multimodal MRI and EEG biomarkers using joint-ICA and divergence criteria
AU - Calhoun, V.
AU - Silva, R.
AU - Liu, J.
PY - 2007/12/1
Y1 - 2007/12/1
N2 - The acquisition of multiple brain imaging types for a given study is a very common practice. However these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform joint independent component analysis across image modalities, including structural MRI data, functional MRI activation data and EEG data, and to visualize the results via a joint histogram visualization technique. Evaluation of which combination of fused data is most useful is determined by using several information theoretic divergence measures. We demonstrate our method on a data set composed of functional MRI data from two tasks, structural MRI data, and EEG data collected on patients with schizophrenia and healthy controls. Our method provides a way to improve feature selection and even preprocessing. We show that combining data types can improve our ability to distinguish differences between groups.
AB - The acquisition of multiple brain imaging types for a given study is a very common practice. However these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform joint independent component analysis across image modalities, including structural MRI data, functional MRI activation data and EEG data, and to visualize the results via a joint histogram visualization technique. Evaluation of which combination of fused data is most useful is determined by using several information theoretic divergence measures. We demonstrate our method on a data set composed of functional MRI data from two tasks, structural MRI data, and EEG data collected on patients with schizophrenia and healthy controls. Our method provides a way to improve feature selection and even preprocessing. We show that combining data types can improve our ability to distinguish differences between groups.
UR - http://www.scopus.com/inward/record.url?scp=48149101988&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=48149101988&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2007.4414298
DO - 10.1109/MLSP.2007.4414298
M3 - Conference contribution
AN - SCOPUS:48149101988
SN - 1424415667
SN - 9781424415663
T3 - Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP
SP - 151
EP - 156
BT - Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP
T2 - 17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007
Y2 - 27 August 2007 through 29 August 2007
ER -