Functional connectivity estimation over large networks at cellular resolution based on electrophysiological recordings and structural prior

Despite many structural and functional aspects of the brain organization have been extensively studied in neuroscience, we are still far from a clear understanding of the intricate structure-function interactions occurring in the multi-layered brain architecture, where billions of different neurons are involved. Although structure and function can individually convey a large amount of information, only a combined study of these two aspects can probably shade light on how brain circuits develop and operate at the cellular scale. Here, we propose a novel approach for refining functional connectivity estimates within neuronal networks using the structural connectivity as prior. This is done at the mesoscale, dealing with thousands of neurons while reaching, at the microscale, an unprecedented cellular resolution. The High-Density Micro Electrode Array (HD-MEA) technology, combined with fluorescence microscopy, offers the unique opportunity to acquire structural and functional data from large neuronal cultures approaching the granularity of the single cell. In this work, an advanced method based on probabilistic directional features and heat propagation is introduced to estimate the structural connectivity from the fluorescence image while functional connectivity graphs are obtained from the cross-correlation analysis of the spiking activity. Structural and functional information are then integrated by reweighting the functional connectivity graph based on the structural prior. Results show that the resulting functional connectivity estimates are more coherent with the network topology, as compared to standard measures purely based on cross-correlations and spatio-temporal filters. We finally use the obtained results to gain some insights on which features of the functional activity are more relevant to characterize actual neuronal interactions

INFERRING FUNCTIONAL NETWORK-BASED SIGNATURES VIA STRUCTURALLY- WEIGHTED LASSO MODEL

Most current research approaches for functional/effective connectivity analysis focus on pair-wise connectivity and cannot deal with network-scale functional interactions. In this paper, we propose a structurally-weighted LASSO (SW-LASSO) regression model to represent the functional interaction among multiple regions of interests (ROIs) based on resting state fMRI (R-fMRI) data. The structural connectivity constraints derived from diffusion tenor imaging (DTI) data will guide the selection of the weights which adjust the penalty levels of different coefficients corresponding to different ROIs. Using the Default Mode Network (DMN) as a test-bed, our results indicate that the learned SW-LASSO has good capability of differentiating Mild Cognitive Impairment (MCI) subjects from their normal controls and has promising potential to characterize the brain functions among different condition, thus serving as the functional network-based signature. Index Terms — Functional network-based signature, regression model relation between two regions. By using linear regression model, though, our proposed method could simultaneously characterize the interaction of one ROI with all other ROIs and has the capability of quantitatively measuring the percentage of contributions among different ROIs. The other advantage of our method is that it makes it possible to consider “directionality ” when studying functional interaction. In this paper, given structural connectivity constraint, we propose a novel structurally-weighted LASSO (SW-LASSO) regression model and use it to represent the functional interactions within the DMN based on R-fMRI data. The neuroscience basis behind using structural connectivity information as the weight to constrain the regression process is that if two regions have strong structural connections, they tend to have strong functional dependence between each other