The dynamic brain networks, MRI, and neuroimaging biomarkers

Processes in the cerebral cortex have the nonlinear and dynamic features. Accurate temporal coordination is necessary for the establishment of functional networks and assemblies. The brain is a complex and self-organized system with serial operations involved in highly interconnected networks. The cortical networks have a high-dimensional state space with non-stationary time series that are challenging to analyze (Singer, 2013).

Connectome refers to the connectivity among brain regions. The combination of such connectivity may lead to a complete picture of the entire brain networks. Magnetic resonance imaging (MRI), including structural MRI and diffusion MRI may provide the neuroimaging technique for the analysis of the connectome (Clayden, 2013). Such analyses may be useful for both clinical and non-clinical studies in neuroscience.

For example, mild cognitive impairment (MCI) has been considered a precursor for Alzheimer’s disease and a predictor for other neurodegenerative diseases. However, it is still difficult to have accurate and efficient diagnosis for MCI. Neuroimaging biomarkers have not been very well validated, compared to conventional biomarkers such as amyloid plaques, tau protein expression, and brain tissue atrophy. To solve the problem, a connectomes-scale analysis of structural and functional connectivity in MCI based on multimodal diffusion tensor imaging (DTI)/ functional magnetic resonance imaging (fMRI) datasets has been found useful (Zhu et al., 2013).

The analyses included DTI-derived structural profiles, the whole-brain functional connectivity, and a set of functional connectomes called “connectome signatures.” Most of the “connectome signatures” were obtained from the functional interactive networks such as the cognition-perception and cognition-action domains (Zhu et al., 2013). Specifically, the “connectome signatures” showed high MCI-vs-controls categorization accuracy. Such study indicated that functional “connectome signatures” may serve as neuroimaging biomarkers of MCI.


Clayden JD. Imaging connectivity: MRI and the structural networks of the brain. Funct Neurol. 2013 Jul/sep;28(3):197-203. doi: 10.11138/FNeur/2013.28.3.197.

Singer W. Cortical dynamics revisited. Trends Cogn Sci. 2013 Oct 16. doi:pii: S1364-6613(13)00210-6. 10.1016/j.tics.2013.09.006.

Zhu D, Li K, Terry DP, Puente AN, Wang L, Shen D, Miller LS, Liu T. Connectome-scale assessments of structural and functional connectivity in MCI. Hum Brain Mapp. 2013 Sep 30. doi: 10.1002/hbm.22373.

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