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. 2015 Apr 30:245:58-63.
doi: 10.1016/j.jneumeth.2015.02.011. Epub 2015 Feb 20.

Predictive structural dynamic network analysis

Rong Chen  1 , Edward H Herskovits  2 ; Alzheimer's Disease Neuroimaging Initiative
Collaborators, Affiliations

Predictive structural dynamic network analysis

Rong Chen et al. J Neurosci Methods. .

Abstract

Background: Classifying individuals based on magnetic resonance data is an important task in neuroscience. Existing brain network-based methods to classify subjects analyze data from a cross-sectional study and these methods cannot classify subjects based on longitudinal data. We propose a network-based predictive modeling method to classify subjects based on longitudinal magnetic resonance data.

New method: Our method generates a dynamic Bayesian network model for each group which represents complex spatiotemporal interactions among brain regions, and then calculates a score representing that subject's deviation from expected network patterns. This network-derived score, along with other candidate predictors, are used to construct predictive models.

Results: We validated the proposed method based on simulated data and the Alzheimer's Disease Neuroimaging Initiative study. For the Alzheimer's Disease Neuroimaging Initiative study, we built a predictive model based on the baseline biomarker characterizing the baseline state and the network-based score which was constructed based on the state transition probability matrix. We found that this combined model achieved 0.86 accuracy, 0.85 sensitivity, and 0.87 specificity.

Comparison with existing methods: For the Alzheimer's Disease Neuroimaging Initiative study, the model based on the baseline biomarkers achieved 0.77 accuracy. The accuracy of our model is significantly better than the model based on the baseline biomarkers (p-value=0.002).

Conclusions: We have presented a method to classify subjects based on structural dynamic network model based scores. This method is of great importance to distinguish subjects based on structural network dynamics and the understanding of the network architecture of brain processes and disorders.

Keywords: Alzheimer's disease; Brain network; Dynamic Bayesian network; Magnetic resonance imaging; Predictive modeling.

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Figures

Figure 1
Figure 1
An example of a DBN modeling interactions among temporal processes
Figure 2
Figure 2
Image preprocessing
See this image and copyright information in PMC

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