Sona, D. | Search bioRxiv

bioRxiv2018

Structurally Constrained Effective Brain Connectivity

The relationship between structure and function is of interest in many research fields involving the study of complex biological processes. In neuroscience in particular, the fusion of structural and functional data can help understanding the underlying principles of the operational networks in the brain. To address this issue, this paper proposes a constrained autoregressive model leading to a representation of "effective" connectivity that can be used to better understand how the structure modulates the function. Or simply, it can be used to find novel biomarkers characterizing groups of subjects. In practice, an initial structural connectivity representation is re-weighted to explain the functional co-activations. This is obtained by minimizing the reconstruction error of an autoregressive model constrained by the structural connectivity prior. The model has been designed to also include indirect connections, allowing to split direct and indirect components in the functional connectivity, and it can be used with raw and deconvoluted BOLD signal. The derived representation of dependencies was compared to the well known dynamic causal model, giving results closer to known ground-truth. Further evaluation of the proposed effective network was performed on two typical tasks. In a first experiment the direct functional dependencies were tested on a community detection problem, where the brain was partitioned using the effective networks across multiple subjects. In a second experiment the model was validated in a case-control task, which aimed at differentiating healthy subjects from individuals with autism spectrum disorder. Results showed that using effective connectivity leads to clusters better describing the functional interactions in the community detection task, while maintaining the original structural organization, and obtaining a better discrimination in the case-control classification task. HighlightsO_LIA method to combine structural and functional connectivity by using autoregressive model is proposed. C_LIO_LIThe autoregressive model is constrained by structural connectivity defining coefficients for Granger causality. C_LIO_LIThe usefulness of the generated effective connections is tested on simulations, ground-truth default mode network experiments, a classification and clustering task. C_LIO_LIThe method can be used for direct and indirect connections, and with raw and deconvoluted BOLD signal. C_LI

neuroscience↗

bioRxiv2018

Multi-Link Analysis: Brain Network Comparison via Sparse Connectivity Analysis

The analysis of the brain from a connectivity perspective is unveiling novel insights into brain structure and function. Discovery is, however, hindered by the lack of prior knowledge used to make hypotheses. On the other hand, exploratory data analysis is made complex by the high dimensionality of data. Indeed, in order to assess the effect of pathological states on brain networks, neuroscientists are often required to evaluate experimental effects in case-control studies, with hundreds of thousand connections.\n\nIn this paper, we propose an approach to identify the multivariate relationships in brain connections that characterise two distinct groups, hence permitting the investigators to immediately discover sub-networks that contain information about the differences between experimental groups. In particular, we are interested in data discovery related to connectomics, where the connections that characterize differences between two groups of subjects are found. Nevertheless, those connections not necessarily maximize accuracy in classification since this does not guarantee reliable interpretation of specific differences between groups. In practice, our method exploits recent machine learning techniques employing sparsity to deal with weighted networks describing the whole-brain macro connectivity. We evaluated our technique on functional and structural connectomes from human and mice brain data. In our experiments, we automatically identified disease-relevant connections in datasets with supervised and unsupervised anatomy-driven parcellation approaches, and by using high-dimensional datasets.

neuroscience↗