Explaining the neural activity distribution associated with discrete movement sequences: Evidence for parallel functional systems

Cognitive, Affective, & Behavioral Neuroscience (2019) 19:138–153 https://doi.org/10.3758/s13415-018-00651-6 Explaining the neural activity distribution associated with discrete movement sequences: Evidence for parallel functional systems Willem B. Verwey 1,2 & Anne-Lise Jouen 3 & Peter F. Dominey 3 & Jocelyne Ventre-Dominey 3 Published online: 7 November 2018 # The Author(s) 2018 Abstract To explore the effects of practice we scanned participants with fMRI while they were performing four-key unfamiliar and familiar sequences, and compared the associated activities relative to simple control sequences. On the basis of a recent cognitive model of sequential motor behavior (C-SMB), we propose that the observed neural activity would be associated with three functional networks that can operate in parallel and that allow (a) responding to stimuli in a reaction mode, (b) sequence execution using spatial sequence representations in a central-symbolic mode, and (c) sequence execution using motor chunk representations in a chunking mode. On the basis of this model and findings in the literature, we predicted which neural areas would be active during execution of the unfamiliar and familiar keying sequences. The observed neural activities were largely in line with our predictions, and allowed functions to be attributed to the active brain areas that fit the three above functional systems. The results corroborate C-SMB’s assumption that at advanced skill levels the systems executing motor chunks and translating key-specific stimuli are racing to trigger individual responses. They further support recent behavioral indications that spatial sequence representations continue to be used. Keywords Discrete sequence production task . Sequence learning . Execution modes . fMRI Introduction An important current research issue concerns the way in which people control habitual movement sequences like writing one’s signature and typing one’s name. Over the years, this issue has been addressed with numerous behavioral and imaging studies (for recent reviews, see Abrahamse, Ruitenberg, De Kleine, & Verwey, 2013; Ashby, Turner, & Horvitz, 2010; Diedrichsen & Kornysheva, 2015; Hardwick, Rottschy, Miall, & Eickhoff, 2013; Keele, Ivry, Mayr, Hazeltine, & Heuer, 2003; Penhune, 2013; Penhune & Steele, 2012; Verwey, Shea, & Wright, 2015). Meta-analyses of imaging studies show that motor control and motor learning are generally associated with increased * Willem B. Verwey 1 Department of Cognitive Psychology and Ergonomics, University of Twente, Twente, The Netherlands 2 Human Performance Laboratories, Department of Health and Kinesiology, Texas A&M University, College Station, TX, USA 3 INSERM U846, Stem Cell and Brain Research Institute, Bron, France activity in the primary motor cortex (M1), the dorsal premotor cortex, the primary somatosensory cortex (S1), the superior parietal lobule, the supplementary motor areas (SMAproper and preSMA), the putamen, the thalamus, and multiple cerebellar nuclei (Hardwick et al., 2013; Laird et al., 2011; Toro, Fox, & Paus, 2008). Laird et al. (2011, also see Ray et al., 2013) distinguished three motor networks with different functions: (a) a network including M1, S1, and the cerebellum that is responsible for executing hand and finger movements like finger tapping, grasping, and pointing; (b) the medial superior parietal cortex that extends this M1-S1-cerebellar network and that supports the execution of more complicated motor skills like drawing and reaching; and (c) a network consisting of premotor and supplementary motor cortices that is involved in preparing and executing fixed movement sequences and their timing. These meta-analyses synthesize the commonalities across many tasks, but they do not give detailed information on the functional contribution of each of these brain structures to motor behavior in specific tasks, and exactly how practice affects the associated neural activity patterns. For that reason, there is a need for studies addressing more specifically the function of individual brain regions in motor tasks. Motivated by recent developments, we believe that a detailed understanding of the neural system requires insights from Cogn Affect Behav Neurosci (2019) 19:138–153 cognitive task models (Berlot, Popp, & Diedrichsen, 2018; Cookson, Hazeltine, & Schumacher, 2016; Forstmann, Wagenmakers, Eichele, Brown, & Serences, 2011; Krakauer, Ghazanfar, Gomez-Marin, MacIver, & Poeppel, 2018; Love, 2016). The reason is that these cognitive models distinguish separable processes that most likely emerge from activity in different neural networks. We report here an imaging study in which participants performed a Discrete Sequence Production (DSP) task (Verwey, 1999). This task is interesting for imaging research because extensive behavioral study has produced detailed cognitive models (Abrahamse et al., 2013; Verwey, 2001; Verwey et al., 2015). Also, unlike many other motor tasks, the DSP task lends itself to scrutiny in MRI scanners because it involves movements that give little motion artefacts. Participants in DSP experiments practice two short (Bdiscrete^) key-pressing sequences separated by a clear break. While practicing in an initial phase in which participants react to two series of key-specific stimuli, the task turns into a twochoice reaction time task in which each response consists of a familiar keying sequence. From a behavioral perspective these discrete movement sequences are interesting because the resulting motor representations are believed to produce the building blocks of complex, hierarchically controlled motor skills (Balleine, Dezfouli, Ito, & Doya, 2015; Cisek & Kalaska, 2010; Park, Wilde, & Shea, 2004; Shea & Kovacs, 2013; for recent real world examples, see Arnold, Wing, & Rotshtein, 2017; Thompson, McColeman, Stepanova, & Blair, 2017; Yamaguchi, Crump, & Logan, 2012). Recent interest comes from robot designers who are inspired by the way evolution shaped human motor control when they develop algorithms for motor learning and control in robots (Kupferberg et al., 2011; J. Peters, Mülling, Kober, Nguyen-Tuong, & Krömer, 2011). C-SMB: A cognitive model for sequencing behavior In the present study we assessed the neural activity associated with the learning and execution of keying sequences in the DSP task, and we interpreted that activity in terms of the execution modes proposed by the Cognitive framework for Sequential Motor Behavior (C-SMB, Verwey et al., 2015; an extension of the Dual Processor Model, Abrahamse et al., 2013; Verwey, 2001) .1 This is a useful undertaking because C-SMB provides indications as to why imaging of different motor sequencing 1 We here focus on representations in discrete familiar motor sequences that have a clear start and end. These representations are believed to play a limited role in cycling tasks like the finger opposition and the serial reaction time tasks (Diedrichsen & Kornysheva, (...truncated)