Hiroyuki Tsubomi

Neural limits to representing objects still within view

Visual working memory is an online workspace for temporarily representing visual information from the environment. The two most prevalent empirical characteristics of working memory are that it is supported by sustained neural activity over a delay period and it has a severely limited capacity for representing multiple items simultaneously. Traditionally, such delay activity and capacity limits have been considered to be exclusive for maintaining information about objects that are no longer visible to the observers. Here, by contrast, we provide both neurophysiological and psychophysical evidence that the sustained neural activity and capacity limits for items that are continuously visible to the human observer are indistinguishable from those measured for items that are no longer visible. This holds true even when the observers know that the objects will not disappear from the visual field. These results demonstrate that our explicit representation of objects that are still “in view” is far more limited than previously assumed.

Larger right posterior parietal volume in action video game experts: a behavioral and voxel-based morphometry (VBM) study.

Recent studies suggest that action video game players exhibit superior performance in visuospatial cognitive tasks compared with non-game players. However, the neural basis underlying this visuospatial cognitive performance advantage remains largely unknown. The present human behavioral and imaging study compared gray matter volume in action video game experts and non-experts using structural magnetic resonance imaging and voxel-based morphometry analysis. The results revealed significantly larger gray matter volume in the right posterior parietal cortex in experts compared with non-experts. Furthermore, the larger gray matter volume in the right posterior parietal cortex significantly correlated with individual performance in a visual working memory task in experts. These results suggest that differences in brain structure may be linked to extensive video game play, leading to superior visuospatial cognitive performance in action video game experts.

Connectivity and signal intensity in the parieto-occipital cortex predicts top-down attentional effect in visual masking: An fMRI study based on individual differences

Top-down attention affects even the early stages of visual processing. For example, several studies have reported that instructions prior to the presentation of visual stimuli can both enhance and reduce visual masking. The finding that top-down processing influences perceptual processing is called the attentional effect. However, the magnitude of the attentional effect differs between individuals, and how these differences relate to brain activation remains to be explained. One possibility would be that activation intensity predicts the magnitude of the attentional effect. Another possible explanation would be that effective connectivity among activated areas determines the attentional effect. In the present study, we used structural equation modeling to analyze individual differences in the attentional effect on visual masking, in relation to the signal and connectivity strength of activated brain regions prior to presentation of the visual stimuli. The results showed that signal intensity was positively correlated with attentional effect in the occipital areas, but not in fronto-parietal areas, and the effect was also positively correlated with connective efficiency from the right intraparietal sulcus (IPS) to the bilateral fusiform gyrus (GF). Furthermore, a higher degree of effective connections from the right IPS to the GF led to greater neural activity in the GF. We therefore propose that the effective modulator in the parietal areas and strong activation in the visual areas together and in cooperation predict higher attentional effects in visual processing.

A Comparison of Actual and Artifactual Features Based on Fractal Analyses: Resting-State MEG Data

Future standardized system for distinguishing actual and artifactual magnetoencephalogram (MEG) data is an essential tool. In this paper, we proposed the quantitative parameters based on fractal dimension (FD) analyses in which the FD may convey different features before and after artifact removal. The six FD algorithms based on time-series computation, namely, box-counting method (BCM), variance fractal dimension (VFD), Higuchi’s method (HM), Kazt’s method (KM), detrended fluctuation analysis (DFA), and modified zero-crossing rate (MZCR) were compared. These approaches measure nonlinear-behavioral responses in the resting-state MEG data. Experimental results showed that the FD value of actual MEG was increased statistically in comparison with the artifactual MEG. The DFA and the HM present a best performance for analyzing simulated data and resting-state MEG data, respectively.

Development of visual working memory and distractor resistance in relation to academic performance

Visual working memory (VWM) enables active maintenance of goal-relevant visual information in a readily accessible state. The storage capacity of VWM is severely limited, often as few as 3 simple items. Thus, it is crucial to restrict distractor information from consuming VWM capacity. The current study investigated how VWM storage and distractor resistance develop during childhood in relation to academic performance in the classroom. Elementary school children (7- to 12-year-olds) and adults (total N=140) completed a VWM task with and without visual/verbal distractors during the retention period. The results showed that VWM performance with and without distractors developed at similar rates until reaching adult levels at 10years of age. In addition, higher VWM performance without distractors was associated with higher academic scores in literacy (reading and writing), mathematics, and science for the younger children (7- to 9-year-olds), whereas these academic scores for the older children (10- to 12-year-olds) were associated with VWM performance with visual distractors. Taken together, these results suggest that VWM storage and distractor resistance develop at a similar rate, whereas their contributions to academic performance differ with age.

Neural Mechanisms of Individual Differences in Working Memory Capacity: Observations From Functional Neuroimaging Studies

Working memory capacity (WMC) indicates an individual’s capability of executive attentional control, which is thought to be critical for general fluid intelligence. Individual variability in WMC has been attributed to the function of the lateral prefrontal cortex (lPFC); however, it is still less clear how the lPFC contributes to individual differences in WMC. Referring to functional neuroimaging studies, we consider three possible neural mechanisms. First, greater task-related activity of the lPFC predicts higher WMC across tasks. Second, a specific task-related functional connectivity also predicts higher WMC. The lPFC consistently forms a part of the connectivity while the coupled region varies depending on tasks. Thus, WMC is reflected by not a fixed but flexible connectivity regulated by the lPFC. Third, distinctive intrinsic connectivity even during resting state is also responsible for individual differences in WMC, with the lPFC seated at a critical hub within the network. These three neural mechanisms differentially contribute to WMC, and therefore, complementarily explain individual differences in WMC.

Brain Network Efficiency and Intelligent Scores of Children

Graph theory is recently becoming a popular method to study brain functional networks and evaluate efficiency of brain function. However, only a few studies have focused on children. One of the main reasons is that children’s data are usually contaminated by artifacts. We propose to construct brain graphs after applying independent component analysis (ICA) to remove artifacts in the magnetoencephalography (MEG) data of children. The clustering coefficient and the harmonic average path length are calculated from artifact-free MEG data. Analysis showed that certain values of the clustering coefficient and harmonic average path length denoted more intelligent performance, as assessed by the Kaufman Assessment Battery for Children, which may indicate better network efficiency.