Causal Structure for the Healthy Longevity Based on the Socioeconomic Status, Healthy Diet and Lifestyle, and Three Health Dimensions, in Japan

This chapter aims to clarify the causal relationship between healthy life expectancy, socioeconomic status, dietary habits, lifestyle habits, and three health factors, as indicated by the WHO. In addition, the annual income threshold for couples to maintain a certain number of survival days will be clarified. Of the 16,462 elderly people aged 65 and over, 13,195 were included in the self-assessment questionnaire survey conducted in September 2001. A follow-up survey was completed in 2004, and 8,162 survivors were followed until August 2007. From a cross-lagged effects variation model, causal relationships were analyzed using longitudinal survival days between 2004 and 2007. After estimating a best-fit model, we discovered that current dietary and lifestyle habits did not determine healthy longevity. However, the survival days were more directly affected by three health-related dimensions three years earlier based on educational attainment and previous annual income indirectly. This study suggests that it might be of great importance for elderly individuals to emphasize income maintenance rather than focusing on diet and lifestyle improvements. In addition to showing a statistically significant relationship between income and survival days, we clarified that there is a threshold for income to maintain a certain number of survival days. For the elderly, it was 4.5 million yen(3,462 US $) for both sexes as a marital yearly income

Human premotor areas parse sequences into their spatial and temporal features.

Skilled performance is characterized by precise and flexible control of movement sequences in space and time. Recent theories suggest that integrated spatio-temporal trajectories are generated by intrinsic dynamics of motor and premotor networks. This contrasts with behavioural advantages that emerge when a trained spatial or temporal feature of sequences is transferred to a new spatio-temporal combination arguing for independent neural representations of these sequence features. We used a new fMRI pattern classification approach to identify brain regions with independent vs integrated representations. A distinct regional dissociation within motor areas was revealed: whereas only the contralateral primary motor cortex exhibited unique patterns for each spatio-temporal sequence combination, bilateral premotor areas represented spatial and temporal features independently of each other. These findings advocate a unique function of higher motor areas for flexible recombination and efficient encoding of complex motor behaviours

A Neurodynamic Account of Spontaneous Behaviour

The current article suggests that deterministic chaos self-organized in cortical dynamics could be responsible for the generation of spontaneous action sequences. Recently, various psychological observations have suggested that humans and primates can learn to extract statistical structures hidden in perceptual sequences experienced during active environmental interactions. Although it has been suggested that such statistical structures involve chunking or compositional primitives, their neuronal implementations in brains have not yet been clarified. Therefore, to reconstruct the phenomena, synthetic neuro-robotics experiments were conducted by using a neural network model, which is characterized by a generative model with intentional states and its multiple timescales dynamics. The experimental results showed that the robot successfully learned to imitate tutored behavioral sequence patterns by extracting the underlying transition probability among primitive actions. An analysis revealed that a set of primitive action patterns was embedded in the fast dynamics part, and the chaotic dynamics of spontaneously sequencing these action primitive patterns was structured in the slow dynamics part, provided that the timescale was adequately set for each part. It was also shown that self-organization of this type of functional hierarchy ensured robust action generation by the robot in its interactions with a noisy environment. This article discusses the correspondence of the synthetic experiments with the known hierarchy of the prefrontal cortex, the supplementary motor area, and the primary motor cortex for action generation. We speculate that deterministic dynamical structures organized in the prefrontal cortex could be essential because they can account for the generation of both intentional behaviors of fixed action sequences and spontaneous behaviors of pseudo-stochastic action sequences by the same mechanism

Changes in Cerebral Hemodynamics during Complex Motor Learning by Character Entry into Touch-Screen Terminals

Introduction Studies of cerebral hemodynamics during motor learning have mostly focused on neurorehabilitation interventions and their effectiveness. However, only a few imaging studies of motor learning and the underlying complex cognitive processes have been performed. Methods We measured cerebral hemodynamics using near-infrared spectroscopy (NIRS) in relation to acquisition patterns of motor skills in healthy subjects using character entry into a touchscreen terminal. Twenty healthy, right-handed subjects who had no previous experience with character entry using a touch-screen terminal participated in this study. They were asked to enter the characters of a randomly formed Japanese syllabary into the touchscreen terminal. All subjects performed the task with their right thumb for 15 s alternating with 25 s of rest for 30 repetitions. Performance was calculated by subtracting the number of incorrect answers from the number of correct answers, and gains in motor skills were evaluated according to the changes in performance across cycles. Behavioral and oxygenated hemoglobin concentration changes across task cycles were analyzed using Spearman\u27s rank correlations. Results Performance correlated positively with task cycle, thus confirming motor learning. Hemodynamic activation over the left sensorimotor cortex (SMC) showed a positive correlation with task cycle, whereas activations over the right prefrontal cortex (PFC) and supplementary motor area (SMA) showed negative correlations. Conclusions We suggest that increases in finger momentum with motor learning are reflected in the activity of the left SMC. We further speculate that the right PFC and SMA were activated during the early phases of motor learning, and that this activity was attenuated with learning progress