Search

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 to understand 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.

The development of awake intraoperative brain-mapping procedures for resection of brain tumors is of growing interest in neuroscience, because it enables direct testing of brain tissue, previously only possible in non-human primates. In a recent study performed in this setting specific effects can be induced by direct electrical stimulation on different sectors of premotor cortex during the execution of a hand manipulation task. Specifically, direct electrical stimulation applied on a dorsal sector of precentral cortex led to an increase in motor unit recruitment in hand muscles during execution of a hand manipulation task (Recruitment sector). The opposite effect was elicited when electrical stimulation was delivered more ventrally on the precentral cortex (Suppression sector). We studied whether the different effects on motor behavior induced by direct electrical stimulation applied on the two sites of the precentral cortex underlie differences in their functional connectivity with other brain areas, measured using resting state fMRI. Using healthy adults scanned as part of the Human Connectome Project, we computed the functional connectivity of each sector used as seeds. The functional connectivity patterns of the two intraoperative seeds was similar but cross-comparison revealed that the left and right Recruitment sectors had stronger functional connections with the hand region of the sensorimotor cortex, while the right Suppression region displayed stronger functional connectivity with a bilateral set of parieto-frontal areas crucial for the integration of perceptual and cognitive hand-related sensorimotor processes required for goal-related hand actions. Our results suggest that analyzing data obtained in the intraoperative setting with resting state functional magnetic resonance imaging in healthy brains can yield useful insight into the roles of different premotor sectors directly involved in hand-object interaction.