Synergy quantization for multi-modal neuroimage classification by cross-view pattern analysis

Sidong Liu*, Weidong Cai, Sonia Pujol, Ron Kikinis, Dagan Feng

*Corresponding author for this work

Research output: Contribution to conferenceAbstractpeer-review



Recent research indicates that using multi-modal data, such as PET/CT, MRI/EEG and DTI/fMRI, could achieve better performance than using single-modal data. To quantitatively analyze the synergy between multi-modal biomarkers, we proposed a novel cross-view pattern analysis method in this study.


We investigated two types of neuroimaging biomarkers derived from the T1-weighted MRI and the FDG-PET data of 331 ADNI subjects. We first identified the disease-relevant patterns of each biomarker by applying ANOVA on the regions of interest across the whole brain, and then quantified the synergy between these two biomarkers based on the relative entropy of their patterns. To validate the effectiveness of proposed method, we designed a set of experiments to distinguish patients with Alzheimer’s Disease (AD) or Mild Cognitive Impairment (MCI) from the cognitively normal controls (NC). We further tested the null hypothesis of no correlation against the alternative that there was a nonzero correlation between the true classification performance and the predicted synergy.


The patterns of these two biomarkers varied dramatically. The PET biomarker showed a more spreading pattern than the MRI biomarker and achieved higher precision for NC (53.3%) and MCI subjects (64.0%), whereas MRI biomarker had better performance on AD patient classification (67.6%). Combining the multi-modal biomarkers could achieve better results (63.9% for NC, 64.5% for MCI and 80.6% for AD) as compared to either PET or MRI biomarker. Cross-validation showed that the performance improvements were closely correlated to the predicted synergy.


We proposed a cross-view neuroimaging pattern analysis method to quantify the synergy in multi-modal data. The pilot experiment results showed that our method could reliably predict the synergy between different biomarkers.


Multi-modal data are playing a more important role in medical image analysis, but it is challenging to take full advantage of them. Our cross-view pattern analysis method showed a great potential to evaluate the synergy between different modalities and predict their performance.
Original languageEnglish
Number of pages1
Publication statusPublished - 13 Nov 2014
Externally publishedYes
EventIEEE EMBS BRAIN Grand Challenges Conference - Washington DC, United States
Duration: 13 Nov 201414 Nov 2014


ConferenceIEEE EMBS BRAIN Grand Challenges Conference
Country/TerritoryUnited States
CityWashington DC


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