Somatosensory Cortex Plays an Essential Role in Forelimb Motor Adaptation in Mice

Abstract

SUMMARY Our motor outputs are constantly re-calibrated to adapt to systematic perturbations. This motor adaptation is thought to depend on the ability to form a memory of a systematic perturbation, often called an internal model. However, the mechanisms underlying the formation, storage, and expression of such models remain unknown. Here, we developed a mouse model to study forelimb adaptation to force field perturbations. We found that temporally precise photoinhibition of somatosensory cortex (S1) applied concurrently with the force field abolished the ability to update subsequent motor commands needed to reduce motor errors. This S1 photoinhibition did not impair basic motor patterns, post-perturbation completion of the action, or their performance in a reward-based learning task. Moreover, S1 photoinhibition after partial adaptation blocked further adaptation, but did not affect the expression of already-adapted motor commands. Thus, S1 is critically involved in updating the memory about perturbation that is essential for forelimb motor adaptations.

Altered tuning in primary motor cortex does not account for behavioral adaptation during force field learning

“…Yet, there remains the possibility that some of the PD changes were the result of changes in short-latency proprioceptive feedback to M1 (Scott et al 2015). Indeed, it has been proposed that proprioceptive feedback to M1 is necessary to learn curl field perturbations (Wolpert et al 1995; Mathis et al 2017), and that the adapted internal model is shared for both feedforward control and feedback control (Wagner and Smith 2008). Although afferent inputs likely influence the shape of at least some tuning curves, this influence is equivalent to that of premotor inputs with respect to our argument about the role of M1 in short-term adaptation.…”

Spatiotemporal limits of optogenetic manipulations in cortical circuits

“…Both direct photoinhibition and ChR-assisted photoinhibition have been widely used in mice to reveal the involvement of brain areas and neuronal populations in specific phases of behavior (Goard et al, 2016;Guo et al, 2015;Guo et al, 2014b;Hanks et al, 2015;Kwon et al, 2016;Li et al, 2015;Li et al, 2016;Mathis et al, 2017;Morandell and Huber, 2017;Resulaj et al, 2018;Sachidhanandam et al, 2013). However, neural circuits have local and long-range connections (Harris and Shepherd, 2015;Hooks et al, 2013;Kato et al, 2017;Lefort et al, 2009;Mao et al, 2011;Ozeki et al, 2009;Xue et al, 2014).…”

Cell-type specific outcome representation in primary motor cortex

“…Of course the reverse possibility also exists that the outcome signals in cerebellum are dependent on M1 inputs. Other input sources such as S1 or VTA may also contribute to outcome signals in M1 (Hosp et al, 2011;Lacefield et al, 2019;Mao et al, 2011;Mathis et al, 2017;Molina-Luna et al, 2009;Schultz, 2000;Wickens et al, 2003).…”

“…Neuronal activity which convey reward, reward prediction errors, and performance error signals are ubiquitous in the brain and have been described in various brain regions including midbrain, cerebellum, and cortical regions (Amador et al, 2000;Amiez et al, 2006;Chen et al, 2017;Heffley et al, 2018;Heindorf et al, 2018;Isomura et al, 2013;Keller et al, 2012;Kostadinov et al, 2019;Krigolson and Holroyd, 2007;Laubach et al, 2000;Mathis et al, 2017;Matsumoto et al, 2007;Olson, 2004, 2005;Sajad et al, 2019;Schall et al, 2002;Schultz, 2000;Shadmehr and Krakauer, 2008;Stuphorn et al, 2000;Teichert et al, 2014;Wallis and Kennerley, 2010;Watabe-Uchida et al, 2017;Wickens et al, 2003). Such signals were shown to serve in error-based and reinforcement learning behavioral paradigms (Glascher et al, 2010;Mathis et al, 2017;Shadmehr and Krakauer, 2008;Wolpert et al, 2011) and such combined signals can enhance adaptation (Galea et al, 2015;Nikooyan and Ahmed, 2015).…”