Figure 1: Definition of functional and effective connectivity. (A) Classification of causal (directional link), indirect (multi-neurons pathway) and apparent (functional coupling due to common input) connectivity. (B) Classification of most common connectivity inference methods in terms of causality and detection of direct links. On the x -axes, the graph shows a scale of causality which refers to the ability of a given connectivity method to infer or not the directionality of the functional connections between neurons. On the y-axes, the graph visually quantifies the capabilities of one approach to detect direct links between neurons by identifying and discarding multi-neurons connections and apparent ones. Indicators that are acausal and do not infer direct links can only report about functional connectivity (light yellow); indicators which contain information about direction of interaction and direct neuron-to-neuron communication are close to the inference of effective connectivity (orange color). Most common model-free techniques are indicated with black dots. Model-based methods are reported with blue triangles and their ability to infer effective connectivity is negatively weighted by the impossibility to test their performances. The super-selective correlation approach we propose is reported in black like the other model-free methods; a diamond signal is used to emphasize the fact that, although being model-free, it aims at inferring effective connections. The graph visually summarizes results of comparisons between connectivity methods from review papers [14, 76, 19] and do not contain precise quantitative information about the differences.
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