In functional magnetic resonance imaging (fMRI) studies, performance of unilateral hand movements is associated with primary motor cortex activity ipsilateral to the moving hand (M1ipsi), in addition to contralateral activity (M1contra). The magnitude of M1ipsi activity increases with the demand on precision of the task. However, it is unclear how demand-dependent increases in M1ipsi recruitment relate to the control of hand movements. To address this question, we used fMRI to measure blood oxygenation level-dependent (BOLD) activity during performance of a task that varied in demand on precision. Participants (N = 23) manipulated an MRI-compatible joystick with their right or left hand to move a cursor into targets of different sizes (small, medium, large, extra-large). Performance accuracy, movement time, and number of velocity peaks scaled with target size, whereas reaction time, maximum velocity, and initial direction error did not. In the univariate analysis, BOLD activation in M1contra and M1ipsi was higher for movements to smaller targets. Representational similarity analysis, corrected for mean activity differences, revealed multivoxel BOLD activity patterns during movements to small targets were most similar to those for medium targets and least similar to those for extra-large targets. Only models that varied with demand (target size, performance accuracy, and number of velocity peaks) correlated with the BOLD dissimilarity patterns, though differently for right and left hands. Across individuals, M1contra and M1ipsi similarity patterns correlated with each other. Together, these results suggest that increasing demand on precision in a unimanual motor task increases M1 activity and modulates M1 activity patterns.

Neurophysiology and neuroimaging evidence shows that the brain represents multiple environmental and body-related features to compute transformations from sensory input to motor output. However, it is unclear how these features interact during goal-directed movement. To investigate this issue, we examined the representations of sensory and motor features of human hand movements within the left-hemisphere motor network. In a rapid event-related fMRI design, we measured cortical activity as participants performed right-handed movements at the wrist, with either of two postures and two amplitudes, to move a cursor to targets at different locations. Using a multivoxel analysis technique with rigorous generalization tests, we reliably distinguished representations of task-related features (primarily target location, movement direction, and posture) in multiple regions. In particular, we identified an interaction between target location and movement direction in the superior parietal lobule, which may underlie a transformation from the location of the target in space to a movement vector. In addition, we found an influence of posture on primary motor, premotor, and parietal regions. Together, these results reveal the complex interactions between different sensory and motor features that drive the computation of sensorimotor transformations.

Anticipatory load forces for dexterous object manipulation in humans are modulated based on visual object property cues, sensorimotor memories of previous experiences with the object, and, when digit positioning varies from trial to trial, the integrating of this sensed variability with force modulation. Studies of the neural representations encoding these anticipatory mechanisms have not considered these mechanisms separately from each other or from feedback mechanisms emerging after lift onset. Here, representational similarity analyses of fMRI data were used to identify neural representations of sensorimotor memories and the sensing and integration of digit position. Cortical activity and movement kinematics were measured as 20 human subjects (11 women) minimized tilt of a symmetrically shaped object with a concealed asymmetric center of mass (CoM, left and right sided). This task required generating compensatory torques in opposite directions, which, without helpful visual CoM cues, relied primarily on sensorimotor memories of the same object and CoM. Digit position was constrained or unconstrained, the latter of which required modulating forces beyond what can be recalled from sensorimotor memories to compensate for digit position variability. Ventral premotor (PMv), somatosensory, and cerebellar lobule regions (CrusII, VIIIa) were sensitive to anticipatory behaviors that reflect sensorimotor memory content, as shown by larger voxel pattern differences for unmatched than matched CoM conditions. Cerebellar lobule I-IV, Broca area 44, and PMv showed greater voxel pattern differences for unconstrained than constrained grasping, which suggests their sensitivity to monitor the online coincidence of planned and actual digit positions and correct for a mismatch by force modulation. To pick up a water glass without slipping, tipping, or spilling requires anticipatory planning of fingertip load forces before the lift commences. This anticipation relies on object visual properties (e.g., mass/mass distribution), sensorimotor memories built from previous experiences (especially when object properties cannot be inferred visually), and online sensing of where the digits are positioned. There is limited understanding of how the brain represents each of these anticipatory mechanisms. We used fMRI measures of regional brain patterns and digit position kinematics before lift onset of an object with nonsalient visual cues specifically to isolate sensorimotor memories and integration of sensed digit position with force modulation. In doing so, we localized neural representations encoding these anticipatory mechanisms for dexterous object manipulation.

Purpose Heating of gradient coils and passive shim components is a common cause of instability in the B 0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites. Method A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC). Results Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI. Discussion This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher ...

Visual processing for perception and for action is thought to be mediated by two specialized neural pathways. Using a visuomotor decision-making task, we show that participants differentially utilized online perceptual decision-making in reaching and interception and that eye movements necessary for perception influenced motor decision strategies. These results provide evidence that task complexity modulates how pathways processing perception versus action information interact during the visual control of movement.

Reach-to-grasp is an evolutionarily conserved motor function that is adversely impacted following stroke and traumatic brain injury (TBI). Non-invasive brain stimulation (NIBS) methods, such as transcranial magnetic stimulation and transcranial direct current stimulation, are promising tools that could enhance functional recovery of reach-to-grasp post-brain injury. Though the rodent literature provides a causal understanding of post-injury recovery mechanisms, it has had a limited impact on NIBS protocols in human research. The high degree of homology in reach-to-grasp circuitry between humans and rodents further implies that the application of NIBS to brain injury could be better informed by findings from pre-clinical rodent models and neurorehabilitation research. Here, we provide an overview of the advantages and limitations of using rodent models to advance our current understanding of human reach-to-grasp function, cortical circuitry, and reorganization. We propose that a cross-species comparison of reach-to-grasp recovery could provide a mechanistic framework for clinically efficacious NIBS treatments that could elicit better functional outcomes for patients.

According to Fitts' Law, the time (MT) to move to a target is a linear function of the logarithm of the ratio between the target's distance and width. Although Fitts' Law accurately predicts MTs for direct movements, it does not accurately predict MTs for indirect movements, as when an obstacle intrudes on the direct movement path. To address this limitation, Jax et al. (2007) added an obstacle-intrusion term to Fitts' Law. They accurately predicted MTs around obstacles in two-dimensional (2-D) workspaces, but their model had one more parameter than Fitts' Law did and was merely descriptive. In this study, we addressed these concerns by turning to the mechanistic, posture-based (PB) movement planning model. The PB-based model accounted for almost as much MT variance in a 3-D movement task as the model of Jax et al., with only two parameters, the same number of parameters as in Fitts' Law.

Precise left-hand movements take longer than right-hand movements (for right-handers). To quantify how left-hand movements are affected by task difficulty and phase of movement control, we manipulated the difficulty of repetitive speeded aiming movements while participants used the left or right hand. We observed left-hand costs in both initial impulse and current control phases of movement. While left-hand cost during the initial impulse phase was small, left-hand cost during the current control phase varied from 10 to 60 ms, in direct proportion to the movement's difficulty as quantified by Fitts' law (0.77 < R² < 0.99, across three experiments). In particular, in comparison with a difficult task for the right hand (Fitts' ID(R) = 6.6), the left hand's task would have to be made easier by 0.5 bits (ID(L) = 6.1) to be performed as quickly. The left-hand cost may reflect the time required for callosal transfer of information between the left and right hemispheres during the current control phase of precision left-hand movements or reflect movement control differences in the current control phase of movement that are inherent to the hemispheres. Overall, the present results support multiphase models of movement generation, in which separate specialized processes contribute to the launching and completion of precision hand movements.