The entropic brain: effortful speech reflcted in organisation of the network graph

Abstract

Uncertainty of incoming information increases the amount of mental effort. Scientists might have been interested in quantifying the amount of this mental effort, as evidenced by the great amount of research devoted to computing the energy used to deal with the uncertainty of an event. One of these measures is Shannon’s entropy, which was initially used as a measure of the level of uncertainty with respect to the outcome of an event (Shannon, 1948). As defined by Shannon, entropy is expected to increase as the level of disorder (uncertainty or task complexity) increases. Therefore, to evaluate how the brain deals with entropy, one could either locate the focal area showing activity that is positively correlated with the degree of uncertainty or consider information transfer. We speculated that a functionally effective brain would modulate its network to fit to the varying entropy, since computation in the brain is probabilistic. Therefore, we put forward two hypotheses: 1) the functional brain architecture will reflect the varying entropy, and 2) the entropy will be modality-specific. In order to test these hypotheses, we estimated several network properties of the magnetoencephalography time-series signals obtained from healthy monolingual listeners. We particularly focused on whether entropy reflects the modality-specific processing load, especially when the degrees of the processing load of the two events were similar in terms of the rate of accuracy and the length of response time between tasks. In order to manipulate both entropy and modality, we varied the processing complexity (easy vs. difficult) in either a linguistic or non-linguistic (pitch change detection vs. word intelligibility test) task. Using graph-theoretical measures, the global organisation measures of the network, such as its small-worldness, correlation coefficient, global efficiency, and characteristic path-lengths, were compared within the network extracted as a set of 78 brain atlas nodes. The results showed a significant main effect of task complexity on the brain network properties, demonstrating that task load is indeed ubiquitous regardless of task modality. Equally importantly, we also found a pronounced task-specific difference in the network properties between linguistic and non-linguistic modalities. Regardless of modality, in the effortful tasks, the characteristic path-length and the correlation-coefficient were significantly larger, whereas the linguistic tasks resulted in significantly higher small-worldness, with the hubs located at the usual language nodes. Our findings collectively suggest that task load is ubiquitous but is also modality-specific in the brain network properties, as evidenced by the specific network graph measures.