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Matsuyoshi, D.

Publications and source records attributed to Matsuyoshi, D..

6 recordsLinked to original sources

Dopamine shapes brain metastate dynamics

Dopamines influence on large-scale network dynamics, especially on the default mode network (DMN), remains uncertain, as fMRI studies have produced mixed results. One likely contributor to these discrepancies is reliance on traditional functional connectivity analyses, which typically derive a single metric (e.g., the Pearson correlation coefficient) from the entire time series and thus fail to capture network dynamics. To address this issue, we combined a dopaminergic challenge (mazindol, a dopamine transporter [DAT] reuptake inhibitor), PET, resting-state fMRI, and hidden Markov modeling (HMM) to characterize time-varying alterations in human large-scale functional networks following acute DAT blockade. We found that mazindol-induced increases in endogenous dopamine altered the balance between the brains functional "metastates," two recurrent higher-order network configurations that each encompass multiple HMM-derived brain states. Mazindol increased the time participants spent in an internally oriented cognitive metastate and decreased the time spent in a sensorimotor-perceptual metastate, with the DMN showing the most pronounced lengthening. In exploratory analyses, declines in [{superscript 1}{superscript 1}C]raclopride binding, a PET index of D2 dopamine receptor availability reflecting increased striatal extracellular dopamine levels, tended to show a positive correlation with the prolongation of these cognitive states. These findings indicate that dopamine is closely linked to shifts from sensorimotor and perceptual to cognitive brain metastates, potentially underpinning the prioritization of internally oriented over externally driven psychological processes. Our results highlight the importance of dynamic, time-resolved connectivity approaches for understanding neuromodulatory actions in the human brain and suggest that dopamine helps regulate the dynamic balance between functionally competing large-scale brain networks.

neuroscience↗

Superiority illusion in older adults: volume and functional connectivity of the precuneus

AimPositive thinking, which has been known to extend the lifespan, tends to increase with age. One specific form of positive thinking, the superiority illusion (SI), involves the belief that one is better than others. Despite its potential benefits in aging, the neural basis of the SI in elderly populations remains underexplored. MethodsThis study combined a behavioral task, voxel-based morphometry (VBM), and resting-state functional connectivity (rsFC) analyses to investigate the neural substrates of the SI in a cohort of 100 participants, including young (N = 33), middle-aged (N = 33), and older adults (N = 34). ResultsOur findings indicated that higher SI scores in older adults were correlated with greater gray matter volume in the right precuneus and stronger rsFC between the right precuneus and the left lateral occipital cortex. However, these correlations were not evident in younger and middle-aged groups. ConclusionsOur findings underscore the importance of the right precuneus and its connectivity in the manifestation of the SI, particularly in older adults, highlighting its potential role in adaptive aging processes.

neuroscience↗

A neuroimaging dataset during sequential color qualia similarity judgments with and without reports

Recent neuroscientific research has advanced our understanding of consciousness, yet the connection between specific qualitative aspects of consciousness, known as "qualia," and particular brain regions or networks remains elusive. Traditional methods that rely on verbal descriptions from participants pose challenges in neuroimaging studies. To address this, our group has introduced a novel "qualia structure" paradigm that leverages exhaustive, structural, and relational comparisons among qualia instead of verbal reports. In this study, we present the first fMRI dataset that captures relational similarity judgments among two out of nine color qualia per trial from 35 participants. This dataset also includes a "no-report" condition in half of the trials to assess the impact of overt reporting. Additionally, each participants color discriminability was evaluated with a hue test conducted outside the scanner. Our data offer valuable insights into the brain functions associated with color qualia and contribute to a deeper understanding of the neural foundations of consciousness.

neuroscience↗

Gaze inversion effect: Sex invariance and difference in gaze direction sensitivity

Humans are adept at distinguishing individual faces, yet inversion dramatically impairs this ability. This face inversion effect is remarkably robust across observers, but evidence is mixed as to whether inversion also impairs the perception of facial parts, particularly the eye region. Some studies have shown that featural processing is preserved or even enhanced when faces are inverted, whereas others have reported clear inversion-related impairments in feature-based judgements. These mixed findings may reflect limited statistical power, unbalanced participant sex ratios, and heterogeneous task designs. To address these issues, we examined how strongly face inversion affects sensitivity to gaze direction in a well-powered, sex-balanced sample. A total of 190 participants judged whether the eyes in briefly presented upright or inverted faces were looking directly at them or not. Inversion reliably reduced sensitivity to gaze direction, yielding a medium-to-large effect size. Females showed modestly higher overall sensitivity than males (a small-to-medium effect), whereas the inversion effect was highly similar for females and males. These findings show that brief gaze judgements are not immune to inversion, even in a task that could in principle be based largely on eye-region information. They provide quantitative constraints that models of gaze perception should accommodate, including the largely sex-invariant inversion effect.

neuroscience↗

Machine learning unveils latent architecture of superiority illusion that predicts visual illusion perception and metacognitive performance

Humans are typically inept at evaluating their abilities and predispositions, while often disregarding such lack of metacognitive insight into their capacities and even augmenting (albeit illusorily) self-evaluation such that they should have more desirable traits than an average peer. This superiority illusion helps maintain a healthy mental state. However, the scope and range of its influence on broader human behavior, especially perceptual tasks, remain elusive. As belief shapes the way people perceive and recognize, the illusory self-superiority belief potentially regulates our perceptual and metacognitive performance. In this study, we used hierarchical Bayesian estimation and machine learning of signal detection theoretic measures to understand how superiority illusion influences visual perception and metacognition for Ponzo illusion. Our results demonstrated that superiority illusion correlated with visual illusion magnitude and metacognitive performance. Next, we used machine learning with a relaxed elastic net and unveiled the latent architecture that underlies the correlations. We revealed that the "extraversion" superiority dimension tapped into visual illusion magnitude and metacognitive ability. In contrast, the "honesty-humility" and "neuroticism" dimensions were only predictive of visual illusion magnitude and metacognitive ability, respectively. These results suggest common and distinct influences of superiority features on perceptual sensitivity and metacognition. Our findings contribute to the accumulating body of evidence indicating that the superiority illusion leverage is far-reaching, even to visual perception. Significance StatementsPeople have a cognitive bias to overestimate their abilities above the mean (superiority illusion) and thereby help maintain a healthy mental state. In this work, we show that the influences of superiority illusion are more extensive than previously thought. We find that superiority illusion correlated with visual illusion magnitude and metacognitive performance. Furthermore, using hierarchical Bayesian estimation and machine learning, we unveil the latent architecture (i.e., overlapping yet dissociable superiority features) that predicts visual illusion magnitude and metacognitive performance. These findings suggest that superiority illusion is a cardinal cognitive bias that involves a vast assortment of behavior as an illusion is an efficient and adaptive tool for humans to somehow thrive in a world of ambiguity.

neuroscience↗

Huge intrinsic correlation between developmental prosopagnosia questionnaires: A comment on Shah et al. (2015)

The 20-item prosopagnosia index (PI20) is a self-report measure of face recognition ability, which is aimed to assess the risk for developmental prosopagnosia (DP), developed by Shah, Gaule, Sowden, Bird, and Cook (2015). Although they validated PI20 in several ways and it may serve as a quick and cost-effective measure for estimating DP risk (Livingston & Shah, in press; Shah et al., 2015), they did not formally evaluate its validity against a pre-existing alternative questionnaire (Kennerknecht et al., 2008) even though they criticized the weak relationship of the pre-existing questionnaire to actual behavioral face recognition performance. Thus, we administered the questionnaires to a large population (N = 855) and found a very strong correlation (r = 0.82 [95% confidence interval: 0.80, 0.84]), a principal component that accounted for more than 90% of the variance, and comparable reliability between the questionnaires. These results show unidimensionality and equivalence between the two questionnaires, or at least, a very strong common latent factor underlying them. The PI20 may not be greater than the pre-existing questionnaire; the two questionnaires measured essentially the same trait. The intrinsic equivalence between the questionnaires necessitates a revision of the view that the PI20 overcomes the weakness of the pre-existing questionnaire. Because both questionnaires contained unreliable items, we suggest, instead of using either questionnaire alone, that selection of a set of items with high reliability may offer a more robust approach to capture face recognition ability.

neuroscience↗