Inhibition of inappropriate responses is an essential executive function needed for adaptation to changing environments. In stop-signal tasks, which are often used to investigate response inhibition, subjects make "go" responses while they prepare to stop at a suddenly given "stop" signal. However, the preparatory processes ongoing before response inhibition have rarely been investigated, and it remains unclear how the preparation contributes to response inhibition. In the present study, a stop-signal task was designed so that the extent of the preparation could be estimated using behavioral and neuroimaging measures. Specifically, in addition to the conventional go trials where preparation to stop was required ("uncertain-go" trials), another type of go trial was introduced where a stop-signal was never given and such preparation was unnecessary ("certain-go" trials). An index reflecting the "preparation cost" was then calculated by subtracting the reaction times in the certain-go trials from those in the uncertain-go trials. It was revealed that the stop signal reaction time, a common index used to evaluate the efficiency of response inhibition, decreased as the preparation cost increased, indicating greater preparation supports more efficient inhibition. In addition, imaging data showed that response inhibition recruited frontoparietal regions (the contrast "stop vs uncertain-go") and that preparation recruited most of the inhibition-related frontoparietal regions (the contrast "uncertain-go vs certain-go"). It was also revealed that the inhibition-related activity declined as the preparation cost increased. These behavioral and imaging results suggest preparation makes a complementary contribution to response inhibition during performance of a stop-signal task.

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The contribution of the right inferior frontal cortex to response inhibition has been demonstrated by previous studies of neuropsychology, electrophysiology, and neuroimaging. The inferior frontal cortex is also known to be activated during processing of infrequent stimuli such as stimulus-driven attention. Response inhibition has most often been investigated using the go/no-go task, and the no-go trials are usually given infrequently to enhance prepotent response tendency. Thus, it has not been clarified whether the inferior frontal activation during the go/no-go task is associated with response inhibition or processing of infrequent stimuli. In the present functional magnetic resonance imaging study, we employed not only frequent-go trials but also infrequent-go trials that were presented as infrequently as the no-go trials. The imaging results demonstrated that the posterior inferior frontal gyrus (pIFG) was activated during response inhibition as revealed by the no-go vs. infrequent-go trials, whereas the inferior frontal junction (IFJ) region was activated primarily during processing of infrequent stimuli as revealed by the infrequent-go versus frequent-go trials. These results indicate that the pIFG and IFJ within the inferior frontal cortex are spatially close but are associated with different cognitive control processes in the go/no-go paradigm.

The resting-state human brain networks underlie fundamental cognitive functions and consist of complex interactions among brain regions. However, the level of complexity of the resting-state networks has not been quantified, which has prevented comprehensive descriptions of the brain activity as an integrative system. Here, we address this issue by demonstrating that a pairwise maximum entropy model, which takes into account region-specific activity rates and pairwise interactions, can be robustly and accurately fitted to resting-state human brain activities obtained by functional magnetic resonance imaging. Furthermore, to validate the approximation of the resting-state networks by the pairwise maximum entropy model, we show that the functional interactions estimated by the pairwise maximum entropy model reflect anatomical connexions more accurately than the conventional functional connectivity method. These findings indicate that a relatively simple statistical model not only captures the structure of the resting-state networks but also provides a possible method to derive physiological information about various large-scale brain networks.

During rest, the human brain performs essential functions such as memory maintenance, which are associated with resting-state brain networks (RSNs) including the default-mode network (DMN) and frontoparietal network (FPN). Previous studies based on spiking-neuron network models and their reduced models, as well as those based on imaging data, suggest that resting-state network activity can be captured as attractor dynamics, i.e., dynamics of the brain state toward an attractive state and transitions between different attractors. Here, we analyze the energy landscapes of the RSNs by applying the maximum entropy model, or equivalently the Ising spin model, to human RSN data. We use the previously estimated parameter values to define the energy landscape, and the disconnectivity graph method to estimate the number of local energy minima (equivalent to attractors in attractor dynamics), the basin size, and hierarchical relationships among the different local minima. In both of the DMN and FPN, low-energy local minima tended to have large basins. A majority of the network states belonged to a basin of one of a few local minima. Therefore, a small number of local minima constituted the backbone of each RSN. In the DMN, the energy landscape consisted of two groups of low-energy local minima that are separated by a relatively high energy barrier. Within each group, the activity patterns of the local minima were similar, and different minima were connected by relatively low energy barriers. In the FPN, all dominant local minima were separated by relatively low energy barriers such that they formed a single coarse-grained global minimum. Our results indicate that multistable attractor dynamics may underlie the DMN, but not the FPN, and assist memory maintenance with different memory states.