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Enhancement of structural and effective connectivity information by functional data and whole-brain models

Gustavo Deco (PI), Sebastian Idesis

Connectomics as one of the hot topics in neuroscience is based on the extraction of the underlying SC matrix. However, if SC is acquired by DTI/DSI tractography many links, in particular inter-hemispheric links, are missing. In databases like Cocomac, the distribution of the density of fibers weighting the coupling between different pairs of brain areas is also missing. With such a coarse description of the structural connectome, the fitting of the corresponding resting state FC matrix is very bad. We aim to enhance the quality of the experimentally obtained SC by using the FC to improve the underlying connectome using machine learning procedures and a whole-brain model linking structure and FC. This will be done using FC connectivity matrices from the human and the ferret brain (datasets from UKE-Engel, UKE-Zittel, BI and UNIPD). With this procedure we can not only enhance the underlying SC but also infer the local “effectivity” (i.e., synaptic conductivities of single fibers) of each single tract. In this way, we plan to derive a whole-brain “effective connectivity matrix” that we hypothesize will be more informative and in the case of human patients a much better biomarker in comparison with the traditional FC and SC. We aim to study how the SC of humans obtained with DTI/DSI tractography is improved, such that the corresponding resting FC obtained on the same group of humans is self-consistently maximally accounted. For the ferret brain, we will test this using SC obtained with anatomical tracing and DTI-based tractography. The improvements of the whole brain model based on this procedure, will be applied to the study of new causative model-base biomarkers for the diagnosis and even treatment design of neuropsychiatric diseases. Further more, we will consider also healthy populations and use this methodology for investigating normal aging, and brain differences in gender both under resting state and cognitive task conditions (using also standard data bases like HCP), and even more the differences in aging under the consideration of gender.