Wrist and finger motor representations embedded in the cerebral and cerebellar resting-state activation.

Several functional magnetic resonance imaging (fMRI) studies have demonstrated that resting-state brain activity consists of multiple components, each corresponding to the spatial pattern of brain activity induced by performing a task. Especially in a movement task, such components have been shown to correspond to the brain activity pattern of the relevant anatomical region, meaning that the voxels of pattern that are cooperatively activated while using a body part (e.g., foot, hand, and tongue) also behave cooperatively in the resting state. However, it is unclear whether the components involved in resting-state brain activity correspond to those induced by the movement of discrete body parts. To address this issue, in the present study, we focused on wrist and finger movements in the hand, and a cross-decoding technique trained to discriminate between the multi-voxel patterns induced by wrist and finger movement was applied to the resting-state fMRI. We found that the multi-voxel pattern in resting-state brain activity corresponds to either wrist or finger movements in the motor-related areas of each hemisphere of the cerebrum and cerebellum. These results suggest that resting-state brain activity in the motor-related areas consists of the components corresponding to the elementary movements of individual body parts. Therefore, the resting-state brain activity possibly has a finer structure than considered previously.

Effective Augmentation of Creativity-Involving Productivity Consequent to Spontaneous Selectivity in Knowledge Acquisition.

The results of many studies have suggested that we actively select information from the environment. However, the functional consequences of such selectivity in knowledge acquisition remain unclear, even though it is a vital factor in determining the characteristics of our future knowledge and cognition. We hypothesized that spontaneous selectivity in knowledge acquisition results in effective augmentation of productivity, especially in creativity-demanding task. To test this, we conducted experiments in which subjects acquired novel compositional words during their rapid presentation, evaluated memory confidence rates for the acquired words, and then produced essays based on these words. First, in experiment 1, we showed that the level of confidence in the recognition memory for the words positively related with the length of the essays (a measure of creativity-involving productivity in quantity). Additionally, we found that the semantic distance from the essay to the components of the compositional word (a measure of creative-productivity in quality) was farther for the word with higher memory confidence than for the word with lower memory confidence, suggesting creative leaps when writing the former. While this result supported our hypothesis, it might also reflect better memory that was independent of spontaneous selection. Thus, in a different subject group, we conducted a similar experiment (experiment 2) in which two of the 20 compositional words were presented more often (five times per block) to force memorization. Again, consistent with our hypothesis, essays based on spontaneously memorized words (presented once per block) were significantly longer than those produced using the forcedly memorized words. Therefore, better memory per se did not explain the higher productivity. Instead, these results suggested that the higher creativity-involving productivity was consequent to spontaneous selectivity in the knowledge acquisition. Additionally, we propose a possible mechanism for the observed results based on the results of a neural network simulation. In this simulation, we found that novel information that was assigned to locations more easily accessible to the entire network was better assimilated and therefore selectively acquired. Based on this simulation, we moderately suggest that spontaneously acquired knowledge effectively confers productivity because it effectively activates large parts of the neural networks.

Revealing Relationships Among Cognitive Functions Using Functional Connectivity and a Large-Scale Meta-Analysis Database.

To characterize each cognitive function per se and to understand the brain as an aggregate of those functions, it is vital to relate dozens of these functions to each other. Knowledge about the relationships among cognitive functions is informative not only for basic neuroscientific research but also for clinical applications and developments of brain-inspired artificial intelligence. In the present study, we propose an exhaustive data mining approach to reveal relationships among cognitive functions based on functional brain mapping and network analysis. We began our analysis with 109 pseudo-activation maps (cognitive function maps; CFM) that were reconstructed from a functional magnetic resonance imaging meta-analysis database, each of which corresponds to one of 109 cognitive functions such as 'emotion,' 'attention,' 'episodic memory,' etc. Based on the resting-state functional connectivity between the CFMs, we mapped the cognitive functions onto a two-dimensional space where the relevant functions were located close to each other, which provided a rough picture of the brain as an aggregate of cognitive functions. Then, we conducted so-called conceptual analysis of cognitive functions using clustering of voxels in each CFM connected to the other 108 CFMs with various strengths. As a result, a CFM for each cognitive function was subdivided into several parts, each of which is strongly associated with some CFMs for a subset of the other cognitive functions, which brought in sub-concepts (i.e., sub-functions) of the cognitive function. Moreover, we conducted network analysis for the network whose nodes were parcels derived from whole-brain parcellation based on the whole-brain voxel-to-CFM resting-state functional connectivities. Since each parcel is characterized by associations with the 109 cognitive functions, network analyses using them are expected to inform about relationships between cognitive and network characteristics. Indeed, we found that informational diversities of interaction between parcels and densities of local connectivity were dependent on the kinds of associated functions. In addition, we identified the homogeneous and inhomogeneous network communities about the associated functions. Altogether, we suggested the effectiveness of our approach in which we fused the large-scale meta-analysis of functional brain mapping with the methods of network neuroscience to investigate the relationships among cognitive functions.

A Knowledge-Based Arrangement of Prototypical Neural Representation Prior to Experience Contributes to Selectivity in Upcoming Knowledge Acquisition.

Knowledge acquisition is a process in which one actively selects a piece of information from the environment and assimilates it with prior knowledge. However, little is known about the neural mechanism underlying selectivity in knowledge acquisition. Here we executed a 2-day human experiment to investigate the involvement of characteristic spontaneous activity resembling a so-called "preplay" in selectivity in sentence comprehension, an instance of knowledge acquisition. On day 1, we presented 10 sentences (prior sentences) that were difficult to understand on their own. On the following day, we first measured the resting-state functional magnetic resonance imaging (fMRI). Then, we administered a sentence comprehension task using 20 new sentences (posterior sentences). The posterior sentences were also difficult to understand on their own, but some could be associated with prior sentences to facilitate their understanding. Next, we measured the posterior sentence-induced fMRI to identify the neural representation. From the resting-state fMRI, we extracted the appearances of activity patterns similar to the neural representations for posterior sentences. Importantly, the resting-state fMRI was measured before giving the posterior sentences, and thus such appearances could be considered as preplay-like or prototypical neural representations. We compared the intensities of such appearances with the understanding of posterior sentences. This gave a positive correlation between these two variables, but only if posterior sentences were associated with prior sentences. Additional analysis showed the contribution of the entorhinal cortex, rather than the hippocampus, to the correlation. The present study suggests that prior knowledge-based arrangement of neural activity before an experience contributes to the active selection of information to be learned. Such arrangement prior to an experience resembles preplay activity observed in the rodent brain. In terms of knowledge acquisition, the present study leads to a new view of the brain (or more precisely of the brain's knowledge) as an autopoietic system in which the brain (or knowledge) selects what it should learn by itself, arranges preplay-like activity as a position for the new information in advance, and actively reorganizes itself.

Effect of Anserine/Carnosine Supplementation on Verbal Episodic Memory in Elderly People.

Our goal in this study was to determine whether or not anserine/carnosine supplementation (ACS) is capable of preserving cognitive function of elderly people. In a double-blind randomized controlled trial, volunteers were randomly assigned to an ACS or placebo group at a 1:1 ratio. The ACS group took 1.0 g of an anserine/carnosine (3:1) formula daily for 3 months. Participants were evaluated by psychological tests before and after the 3-month supplementation period. Thirty-nine healthy elderly volunteers (60-78 years old) completed the follow-up tests. Among the tests, delayed recall verbal memory assessed by the Wechsler Memory Scale-Logical Memory showed significant preservation in the ACS group, compared to the placebo group (p = 0.0128). Blood analysis revealed a decreased secretion of inflammatory cytokines, including CCL-2 and IL-8, in the ACS group. MRI analysis using arterial spin labeling showed a suppression in the age-related decline in brain blood flow in the posterior cingulate cortex area in the ACS group, compared to the placebo group (p = 0.0248). In another randomized controlled trial, delayed recall verbal memory showed significant preservation in the ACS group, compared to the placebo group (p = 0.0202). These results collectively suggest that ACS may preserve verbal episodic memory and brain perfusion in elderly people, although further study is needed.

Resting-state brain activity in the motor cortex reflects task-induced activity: A multi-voxel pattern analysis.

It has been suggested that resting-state brain activity reflects task-induced brain activity patterns. In this study, we examined whether neural representations of specific movements can be observed in the resting-state brain activity patterns of motor areas. First, we defined two regions of interest (ROIs) to examine brain activity associated with two different behavioral tasks. Using multi-voxel pattern analysis with regularized logistic regression, we designed a decoder to detect voxel-level neural representations corresponding to the tasks in each ROI. Next, we applied the decoder to resting-state brain activity. We found that the decoder discriminated resting-state neural activity with accuracy comparable to that associated with task-induced neural activity. The distribution of learned weighted parameters for each ROI was similar for resting-state and task-induced activities. Large weighted parameters were mainly located on conjunctive areas. Moreover, the accuracy of detection was higher than that for a decoder whose weights were randomly shuffled, indicating that the resting-state brain activity includes multi-voxel patterns similar to the neural representation for the tasks. Therefore, these results suggest that the neural representation of resting-state brain activity is more finely organized and more complex than conventionally considered.

Dendritic slow dynamics enables localized cortical activity to switch between mobile and immobile modes with noisy background input.

Mounting lines of evidence suggest the significant computational ability of a single neuron empowered by active dendritic dynamics. This motivates us to study what functionality can be acquired by a network of such neurons. The present paper studies how such rich single-neuron dendritic dynamics affects the network dynamics, a question which has scarcely been specifically studied to date. We simulate neurons with active dendrites networked locally like cortical pyramidal neurons, and find that naturally arising localized activity--called a bump--can be in two distinct modes, mobile or immobile. The mode can be switched back and forth by transient input to the cortical network. Interestingly, this functionality arises only if each neuron is equipped with the observed slow dendritic dynamics and with in vivo-like noisy background input. If the bump activity is considered to indicate a point of attention in the sensory areas or to indicate a representation of memory in the storage areas of the cortex, this would imply that the flexible mode switching would be of great potential use for the brain as an information processing device. We derive these conclusions using a natural extension of the conventional field model, which is defined by combining two distinct fields, one representing the somatic population and the other representing the dendritic population. With this tool, we analyze the spatial distribution of the degree of after-spike adaptation and explain how we can understand the presence of the two distinct modes and switching between the modes. We also discuss the possible functional impact of this mode-switching ability.