杉山 太成

<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Background</jats:title> <jats:p>Gait—a frequently performed activity of daily living—is thought to reflect multiple dimensions of an individual’s physical and cognitive status. Individuals with frailty or mild cognitive impairment (MCI) show decreased gait speed. However, previous studies have not simultaneously considered both statuses, although they frequently co-occur and may act as confounders. The direct association between frailty and gait is well-understood. In contrast, the association between cognitive decline—independent of physical function—and decreased gait speed, as well as the relationship among these three factors (frailty, cognitive decline, and gait speed), is not fully understood.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>This study examined the effect of MCI on gait speed after accounting for frailty. Older individuals were categorized as (1) frailty with MCI, (2) frailty without MCI, (3) pre-frailty with MCI, (4) pre-frailty without MCI, (5) non-frailty with MCI, and (6) non-frailty without MCI. Frailty was assessed using the Kihon checklist and MCI using the Montreal Cognitive Assessment. Participants completed a 10-m walk test under two conditions: comfortable walking and fast walking. Two types of analyses were conducted: mediation analysis and two-way analysis of covariance (ANCOVA).</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>Mediation analysis supported independent relationships between frailty and MCI status and gait speed, suggesting a direct association between MCI and gait speed, even when accounting for frailty. In addition, two-way analysis of covariance indicated significant main effects of both frailty and MCI on gait speed, with no significant interaction between them under the two walking conditions.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>These findings suggest that the observed association between MCI and gait speed is largely independent from frailty status, providing additional evidence supporting the association between cognitive function and gait performance.</jats:p> </jats:sec>

<jats:p>Meta-learning enables us to learn how to learn the same or similar tasks more efficiently. Decision-making literature theorizes that a prefrontal network, including the orbitofrontal and anterior cingulate cortices, underlies meta-learning of decision making by reinforcement learning. Recently, computationally similar meta-learning has been theorized and empirically demonstrated in motor adaptation. However, it remains unclear whether meta-learning of motor adaptation also relies on a prefrontal network. Considering hierarchical information flow from the prefrontal to motor cortices, this study explores whether meta-learning is processed in the dorsolateral prefrontal cortex (DLPFC) or in the dorsal premotor cortex (PMd), which is situated upstream of the primary motor cortex, but downstream of the DLPFC. Transcranial magnetic stimulation (TMS) was delivered to either PMd or DLPFC during a motor meta-learning task, in which human participants were trained to regulate the rate and retention of motor adaptation to maximize rewards. While motor adaptation itself was intact, TMS to PMd, but not DLPFC, attenuated meta-learning, impairing the ability to regulate motor adaptation to maximize rewards. Further analyses revealed that TMS to PMd attenuated meta-learning of memory retention. These results suggest that meta-learning of motor adaptation relies more on the premotor area than on a prefrontal network. Thus, while PMd is traditionally viewed as crucial for planning motor actions, this study suggests that PMd is also crucial for meta-learning of motor adaptation, processing goal-directed planning of how long motor memory should be retained to fit the long-term goal of motor adaptation.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Humans and animals develop learning-to-learn strategies throughout their lives to accelerate learning. One theory suggests that this is achieved by a metacognitive process of controlling and monitoring learning. Although such learning-to-learn is also observed in motor learning, the metacognitive aspect of learning regulation has not been considered in classical theories of motor learning. Here, we formulated a minimal mechanism of this process as reinforcement learning of motor learning properties, which regulates a policy for memory update in response to sensory prediction error while monitoring its performance. This theory was confirmed in human motor learning experiments, in which the subjective sense of learning-outcome association determined the direction of up- and down-regulation of both learning speed and memory retention. Thus, it provides a simple, unifying account for variations in learning speeds, where the reinforcement learning mechanism monitors and controls the motor learning process.</jats:p>