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.

The prefrontal cortex plays a critical role in recollecting the temporal context of past events. The present study used eventrelated functional magnetic resonance imaging (fMRI) and explored the neural correlates of temporal-order retrieval during a recency judgment paradigm. In this paradigm, after study of a list of words presented sequentially, subjects were presented with two of the studied words simultaneously and were asked which of the two words was studied more recently. Two types of such retrieval trials with varied (high and low) levels of demand for temporal-order retrieval were intermixed and compared using event-related fMRI. The intraparadigm comparison of high versus low demand trials revealed brain regions with activation that was modulated on the basis of demand for temporal-order retrieval. Multiple lateral prefrontal regions including the middle and inferior lateral prefrontal cortex were prominently activated. Activation was also observed in the anterior prefrontal cortex and the medial temporal cortex, regions well documented to be related to memory retrieval in general. The modulation of brain activity in these regions suggests a detailed pathway that is engaged during recency judgment.Key words: recency; prefrontal; memory; retrieval; context; fMRIThe prefrontal cortex has been implicated in several types of mnemonic functions (Stuss and Benson, 1986;Fuster, 1997). Among them is recollection of the temporal context of past events, an ability that has most often been tested using recency judgment paradigms in which two events are to be judged as to which has occurred more recently (Yntema and Trask, 1963). Since the initial report in Milner (1971), several neuropsychological studies of humans and monkeys have provided evidence that damage to the lateral prefrontal cortex impairs temporal-order retrieval and that the effect of damage is greater in retrieving the temporal order of past events than in retrieving the past events themselves (Shimamura et al., 1990;Milner et al., 1991;Petrides, 1991;Butters et al., 1994). Previous neuroimaging studies investigating recency judgment used this temporal-order versus item retrieval contrast and revealed prefrontal activation associated with temporal-order retrieval relative to item retrieval (Eyler Zorrilla et al., 1996;Cabeza et al., 1997Cabeza et al., , 2000.The contrast of the dichotomous temporal-order versus item retrieval is useful in detecting functional characteristics that are differential among particular brain regions, as is most typically used in the demonstration of double dissociation between regions. However, this approach leaves unspecified the brain activity related to temporal-order retrieval itself at a whole-brain level because, for instance, it is possible that brain activity common to temporal-order and item retrieval is subtracted out even when the activity is related to temporal-order retrieval. An alternative approach complements the previous approach and allows us to uncover the whole neural correlates of temporal-order retr...

Background and Purpose-Inertial force of the bloodstream results in the local elevation of intravascular pressure secondary to flow impact. Previous studies suggest that this "impacting force" and the local pressure elevation at the aneurysm may have a large contribution to the development of cerebral aneurysms. The goal of the present study is to evaluate how the bloodstream impacting force and the local pressure elevation at the aneurysm influences the rupture of cerebral aneurysms. Methods-A total of 29 aneurysms were created in 26 patient-specific vessel models, and computer simulations were used to calculate pressure distributions around the vessel branching points and the aneurysms. Results-Direct impact of the parent artery bloodstream resulted in local elevation in pressure at branch points, and bends in arteries (231.2Ϯ198.1 Pa; 100 Paϭ0.75 mm Hg). The bloodstream entered into the aneurysm with a decreased velocity after it impacted on the branching points or bends. Thus, the flow impact at the aneurysm occurred usually weakly. At the top or the rupture point of the aneurysm, the flow velocity was always delayed. The local pressure elevation at the aneurysm was 119.3Ϯ91.2 Pa. Conclusions-The pressure elevation at the area of flow impact and at the aneurysm constituted only 1% to 2% of the peak intravascular pressure. The results suggest that the bloodstream impacting force and the local pressure elevation at the aneurysm may have less contribution to the rupture of cerebral aneurysms than was expected previously.