Abstract
Whole brain dynamics intuitively depend upon the internal wiring of the brain; but to which extent the individual structural connectome constrains the corresponding functional connectome is unknown, even though its importance is uncontested. After acquiring structural data from individual mice, we virtualized their brain networks and simulated in silico functional MRI data. Theoretical results were validated against empirical awake functional MRI data obtained from the same mice. We demonstrate that individual structural connectomes predict the functional organization of individual brains. Using a virtual mouse brain derived from the Allen Mouse Brain Connectivity Atlas, we further show that the dominant predictors of individual structure-function relations are the asymmetry and the weights of the structural links. Model predictions were validated experimentally using tracer injections, identifying which missing connections (not measurable with diffusion MRI) are important for whole brain dynamics in the mouse. Individual variations thus define a specific structural fingerprint with direct impact upon the functional organization of individual brains, a key feature for personalized medicine.
Highlights
Whole brain dynamics intuitively depend upon the internal wiring of the brain; but to which extent the individual structural connectome constrains the corresponding functional connectome is unknown, even though its importance is uncontested
We extracted Structural connectivity (SC) from diffusion MRI (dMRI) data to build individual virtual brains, which were imported into The Virtual Mouse Brain (TVMB), the extension of the open source neuroinformatic platform The Virtual Brain (TVB) [9] designed for accommodating large-scale simulations and analysis in the mouse, to generate in silico blood oxygenation level-dependent (BOLD) activity [21] using the reduced Wong Wang model [14, 23]
We argue that the observed difference in predictive power (PP) between deterministic and probabilistic processed connectomes depends on the proportion of false negative (FN) and false positive (FP) connections introduced by the 2 different algorithms
Summary
Whole brain dynamics intuitively depend upon the internal wiring of the brain; but to which extent the individual structural connectome constrains the corresponding functional connectome is unknown, even though its importance is uncontested. After acquiring structural data from individual mice, we virtualized their brain networks and simulated in silico functional MRI data. Model predictions were validated experimentally using tracer injections, identifying which missing connections (not measurable with diffusion MRI) are important for whole brain dynamics in the mouse. We use The Virtual Brain (TVB), which allows building individual brain network models based on structural data [9] This brain network modeling approach operationalizes the functional consequences of structural network variations [10, 11] and allows us to systematically investigate SC–FC relations in individual human brains [12,13,14,15]. Focusing our attention on simulating mouse brain dynamics, we can use this detailed connectome to explore which missing features in the dMRI account for individual SC– FC relations. We show how limitations of connectome reconstruction with the diffusion-MRI method restrict our comprehension of the structural–functional relation
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