The Human Motor System Supports Sequence-Specific Representations over Multiple Training-Dependent Timescales

Abstract

Motor sequence learning is associated with increasing and decreasing motor system activity. Here, we ask whether sequence-specific activity is contingent upon the time interval and absolute amount of training over which the skill is acquired. We hypothesize that within each motor region, the strength of any sequence representation is a non-linear function that can be characterized by 3 timescales. We had subjects train for 6 weeks and measured brain activity with functional magnetic resonance imaging. We used repetition suppression (RS) to isolate sequence-specific representations while controlling for effects related to kinematics and general task familiarity. Following a baseline training session, primary and secondary motor regions demonstrated rapidly increasing RS. With continued training, there was evidence for skill-specific efficiency, characterized by a dramatic decrease in motor system RS. In contrast, after performance had reached a plateau, further training led to a pattern of slowly increasing RS in the contralateral sensorimotor cortex, supplementary motor area, ventral premotor cortex, and anterior cerebellum consistent with skill-specific specialization. Importantly, many motor areas show changes involving more than 1 of these 3 timescales, underscoring the capacity of the motor system to flexibly represent a sequence based on the amount of prior experience.

Circuit Mechanisms of Sensorimotor Learning

“…This phenomenon is echoed by several studies that suggest an overall reduced level of cortical activation during execution of a highly practiced motor behavior (Jenkins et al, 1994; Ma et al, 2010; Picard et al, 2013; Toni et al, 1998; Ungerleider et al, 2002; Wymbs and Grafton, 2015). For example, fMRI measurements in humans showed that professional piano players recruit smaller regions of cortex than control subjects when performing a complex finger movement (Krings et al, 2000).…”

Motor Learning in Stroke

“…Thus, arbitrary model selection could lead to differing results 28 . Furthermore, there is considerable evidence that the relationship between training and performance is non-linear; as people become increasingly skilled they require more training to achieve small gains in performance 31 . Here we avoided this conceptual pitfall by comparing absolute levels of performance, rather than normalized changes.…”

The Striatum: Where Skills and Habits Meet

“…A key advance in recent human brain-imaging methods is the ability to map activity that corresponds to a specific sequence or skill using either machine learning or repetition suppression methods (Wiestler and Diedrichsen 2013;Wymbs and Grafton 2014). These show that the degree to which different cortical areas represent a specific skill depends in large part on the depth of training experience, and not simply on time .…”

“…Ultimately, a central feature of motor skill is the ability to guide actions without explicit memory or external stimuli through the creation of direct motormotor associations. Not surprisingly, skill-specific changes also emerge within motor cortex (Karni et al 1995;Wymbs and Grafton 2014). Thus, across habits and skills, different sorts of automaticity are gained.…”