In the present study, brain activations were measured using positron emission tomography (PET) over the course of practice. Fourteen right-handed participants were scanned during six 1-min periods of practice tracing a cutout maze design with their eyes closed. Practice-related decreases were found in the right premotor and posterior parietal cortex and left cerebellum, increases in the supplementary motor area (SMA) and primary motor cortex. The decrease in right premotor activity and the increase in SMA was significantly correlated with a decrease in the number of stops, implying involvement in learning and storing the movement sequence. The significant correlation between decreases in errors and left cerebellar and right posterior parietal activity suggests a role in accuracy. Involvement of the primary motor cortex in motor execution is suggested by the correlation of increased activation and movement speed. These results suggest that different neural structures (involving a premotor-parietal-cerebellar circuit) play a role in a sequential maze learning task.
Hanneke I. van Mier, Joel S. Perlmutter and Steven E. Petersen
Edwin M. Robertson
The concept of canonical representations within the motor system has been both supported and refuted using a variety of behavioral studies. Here, based upon neurophysiological data, I discuss the relationship amongst those neuronal substrates of action and the behavioral components of a movement. A novel view of reaching and grasping has been proposed which predicts that movements with similar kinematic and dynamic properties have a similar representation within the nervous system (Smeets & Brenner, 1999). However this is broadly inconsistent with a variety of neurophysiological findings that emphasize the independence amongst representations of action.
Calligraphic writing presents many challenges for motor control, including: learning and recall of stroke sequences; critical timing of stroke onsets and durations; fine control of grip and contact forces; and letterform invariance under size scaling, which entails fine control of stroke directions and amplitudes during recruitment and derecruitment of musculoskeletal degrees of freedom. Experimental and computational studies in behavioral neuroscience have progressed toward explaining the learning, planning, and control exercised in tasks that share features with calligraphic writing and drawing. This article highlights component operations ranging from parallel sequence representations to fine force control. Treated in succession are: competitive queuing models of sequence representation, performance, learning, and recall; letter size scaling and motor equivalence; cursive handwriting models in which sensory-motor transformations are performed by circuits that learn inverse differential kinematic mappings; and fine-grained control of timing and transient forces by circuit models that learn to solve inverse dynamics problems.
Jeroen B.J. Smeets and Eli Brenner
We agree with Robertson that our new view on grasping is a description of motor behavior rather than an exploration into the nature of the neural processing underlying this behavior. However, neurophysiologists might be inspired by our new view to ask other questions, perform other experiments, and analyze these differently. In this way, they could generate new insights about the neural control of grasping.
Louisa D. Raisbeck, Jed A. Diekfuss, Dustin R. Grooms and Randy Schmitz
.1 −82.1 −4.58 2 Lateral occipital cortex Left 473 <.001 −54 −72 6 4.01 −50.1 −79.1 −1.27 3 Postcentral gyrus Heschl’s gyrus Precentral gyrus Left 301 <.001 −58 −14 20 3.54 53.7 −18 19.7 4 Cerebellum: IX, VIIb Left 265 .01 −30 −65 −54 3.35 −14 −52.2 −48 Note: Brain regions with significant activation
Takeaki Ando, Shannon Gehr, Melanie L. McGrath and Adam B. Rosen
The purpose of this report is to present the case of a National Junior Collegiate Athletic Association football player diagnosed with Chiari malformation postconcussion. A Chiari malformation is characterized by the cerebellum presenting below the level of the foramen. The uniqueness of this case stems from the patient’s health history, length of symptoms, and diagnosis. The effectiveness of treatment options, and the primary means to reduce the risk of catastrophic head injury in those with Chiari malformations are debatable. Clinicians should be familiar with the potential for the presence of a Chiari malformation with persistent symptoms postconcussion.
Bettina Brendel, Michael Erb, Axel Riecker, Wolfgang Grodd, Hermann Ackermann and Wolfram Ziegler
The present study combines functional magnetic resonance imaging (fMRI) and reaction time (RT) measurements to further elucidate the influence of syllable frequency and complexity on speech motor control processes, i.e., overt reading of pseudowords. Tying in with a recent fMRI-study of our group we focused on the concept of a mental syllabary housing syllable sized ready-made motor plans for high- (HF), but not low-frequency (LF) syllables. The RT-analysis disclosed a frequency effect weakened by a simultaneous complexity effect for HF-syllables. In contrast, the fMRI data revealed no effect of syllable frequency, but point to an impact of syllable structure: Compared with CV-items, syllables with a complex onset (CCV) yielded higher hemodynamic activation in motor “execution” areas (left sensorimotor cortex, right inferior cerebellum), which is at least partially compatible with our previous study. We discuss the role of the syllable in speech motor control.
Jay P. Mehta, Matthew D. Verber, Jon A. Wieser, Brian D. Schmit and Sheila M. Schindler-Ivens
We used functional magnetic resonance imaging (fMRI) to record human brain activity during slow (30 RPM), fast (60 RPM), passive (30 RPM), and variable rate pedaling. Ten healthy adults participated. After identifying regions of interest, the intensity and volume of brain activation in each region was calculated and compared across conditions (p < .05). Results showed that the primary sensory and motor cortices (S1, M1), supplementary motor area (SMA), and cerebellum (Cb) were active during pedaling. The intensity of activity in these areas increased with increasing pedaling rate and complexity. The Cb was the only brain region that showed significantly lower activity during passive as compared with active pedaling. We conclude that M1, S1, SMA, and Cb have a role in modifying continuous, bilateral, multijoint lower extremity movements. Much of this brain activity may be driven by sensory signals from the moving limbs.
Semyon Slobounov, Tao Wu and Mark Hallett
Human upright posture is a product of a complex dynamic system that relies on integration of input from multimodal sensory sources. Extensive research has explored the role of visual, vestibular, and somatosensory systems in the control of upright posture. However, the role of higher cognitive function in a participant’s assessment of postural stability has been less studied. In previous research, we showed specific neural activation patterns in EEG associated with recognition of unstable postures in young healthy participants. Similar EEG patterns have been recently observed in regulation of posture equilibrium in dynamic stances. This article evaluates participants’ postural stability in dynamic stances and neural activation patterns underlying visual recognition of unstable postures using event-related functional MRI (fMRI). Our results show that the “stable” participants were successful in recognition of unstable postures of a computer-animated body model and experienced egocentric motion. Successful recognition of unstable postures in these participants induces activation of distinct areas of the brain including bilateral parietal cortex, anterior cingulate cortex, and bilateral cerebellum. In addition, significant activation is observed in basal ganglia (caudate nucleus and putamen) but only during perception of animated postures. Our findings suggest the existence of modality-specific distributed activation of brain areas responsible for detection of postural instability.
Victoria Galea, Robyn Traynor and Michael Pierrynowski
& Hazeltine, 1995 ; Semjen, Schulze, & Vorberg, 2000 ; Wing, 2002 ). Recent evidence points to the likely organization by more than one central timekeeper ( Repp, 2005 ; Repp & Su, 2013 ). The cerebellum may be uniquely involved with this form of control ( Ivry, Keele, & Diener, 1988 ; Spencer, Ivry