Biology subjects

Sakaki, M.

Publications and source records attributed to Sakaki, M..

3 recordsLinked to original sources

bioRxiv2022

Heart Rate Variability Covaries with Amygdala Functional Connectivity During Voluntary Emotion Regulation

The Neurovisceral Integration Model posits that shared neural networks support the effective regulation of emotions and heart rate, with heart rate variability (HRV) serving as an objective, peripheral index of prefrontal inhibitory control. Prior neuroimaging studies have predominantly examined both HRV and associated neural functional connectivity at rest, as opposed to contexts that require active emotion regulation. The present study sought to extend upon previous resting-state functional connectivity findings, examining HRV and corresponding amygdala functional connectivity during a cognitive reappraisal task. Seventy adults (52 old and 18 young adults, 18-84 years, 51% male) received instructions to cognitively reappraise negative and neutral affective images during functional MRI scanning. HRV measures were derived from a finger pulse signal throughout the scan. During the task, young adults exhibited a significant inverse association between HRV and amygdala-medial prefrontal cortex (mPFC) functional connectivity, in which higher HRV was correlated with weaker amygdala-mPFC coupling, whereas old adults displayed a slight positive, albeit non-significant correlation. Furthermore, voxelwise whole-brain functional connectivity analyses showed that higher HRV was linked to weaker right amygdala-posterior cingulate cortex connectivity across old and young adults, and in old adults, higher HRV positively correlated with stronger right amygdala - right ventrolateral prefrontal cortex connectivity. Collectively, these findings highlight the importance of assessing HRV and neural functional connectivity during active regulatory contexts to further identify neural concomitants of HRV and adaptive emotion regulation.

neuroscience↗

bioRxiv2019

Motivated for near impossibility: How task type and reward modulates intrinsic motivation and the striatal activation for an extremely difficult task.

Economic and decision-making theories suppose that people would disengage from a task with near zero success probability, because this implicates little normative utility values. However, humans are often motivated for an extremely challenging task, even without any extrinsic incentives. The current study aimed to address the nature of this challenge-based motivation and its neural correlates. We found that, when participants played a skill-based task without extrinsic incentives, their task enjoyment increased as the chance of success decreased, even if the task was almost impossible to achieve. However, such challenge-based motivation was not observed when participants were rewarded for the task or the reward was determined in a probabilistic manner. The activation in the ventral striatum/pallidum tracked the pattern of task enjoyment. These results suggest that people are intrinsically motivated to challenge a nearly impossible task, but only when the task requires certain skills and extrinsic rewards are unavailable.

neuroscience↗

bioRxiv2019

Critical brain dynamics and human intelligence

According to the critical brain hypothesis, the brain is considered to operate near criticality and realize efficient neural computations. Despite the prior theoretical and empirical evidence in favor of the hypothesis, no direct link has been provided between human cognitive performance and the neural criticality. Here we provide such a key link by analyzing resting-state dynamics of functional magnetic resonance imaging (fMRI) networks at a whole-brain level. We develop a data-driven analysis method, inspired from statistical physics theory of spin systems, to map out the whole-brain neural dynamics onto a phase diagram. Using this tool, we show evidence that neural dynamics of human participants with higher fluid intelligence quotient scores are closer to a critical state, i.e., the boundary between the paramagnetic phase and the spin-glass (SG) phase. The present results are consistent with the notion of "edge-of-chaos" neural computation.

neuroscience↗