Influence of planar manipulandum to the hand trajectory during point to point movement

Haptic fMRI: Reliability and performance of electromagnetic haptic interfaces for motion and force neuroimaging experiments

Haptic interfaces compatible with functional magnetic resonance imaging (Haptic fMRI) promise to enable rich motor neuroscience experiments that study how humans perform complex manipulation tasks. Here, we present a large-scale study (176 scans runs, 33 scan sessions) that characterizes the reliability and performance of one such electromagnetically actuated device, Haptic fMRI Interface 3 (HFI-3). We outline engineering advances that ensured HFI-3 did not interfere with fMRI measurements. Observed fMRI temporal noise levels with HFI-3 operating were at the fMRI baseline (0.8% noise to signal). We also present results from HFI-3 experiments demonstrating that high resolution fMRI can be used to study spatio-temporal patterns of fMRI blood oxygenation dependent (BOLD) activation. These experiments include motor planning, goal-directed reaching, and visually-guided force control. Observed fMRI responses are consistent with existing literature, which supports Haptic fMRI's effectiveness at studying the brain's motor regions.

Haptic fMRI: combining functional neuroimaging with haptics for studying the brain's motor control representation

A challenging problem in motor control neuroimaging studies is the inability to perform complex human motor tasks given the Magnetic Resonance Imaging (MRI) scanner's disruptive magnetic fields and confined workspace. In this paper, we propose a novel experimental platform that combines Functional MRI (fMRI) neuroimaging, haptic virtual simulation environments, and an fMRI-compatible haptic device for real-time haptic interaction across the scanner workspace (above torso ∼ .65×.40×.20m(3)). We implement this Haptic fMRI platform with a novel haptic device, the Haptic fMRI Interface (HFI), and demonstrate its suitability for motor neuroimaging studies. HFI has three degrees-of-freedom (DOF), uses electromagnetic motors to enable high-fidelity haptic rendering (>350Hz), integrates radio frequency (RF) shields to prevent electromagnetic interference with fMRI (temporal SNR >100), and is kinematically designed to minimize currents induced by the MRI scanner's magnetic field during motor displacement (<2cm). HFI possesses uniform inertial and force transmission properties across the workspace, and has low friction (.05-.30N). HFI's RF noise levels, in addition, are within a 3 Tesla fMRI scanner's baseline noise variation (∼.85±.1%). Finally, HFI is haptically transparent and does not interfere with human motor tasks (tested for .4m reaches). By allowing fMRI experiments involving complex three-dimensional manipulation with haptic interaction, Haptic fMRI enables-for the first time-non-invasive neuroscience experiments involving interactive motor tasks, object manipulation, tactile perception, and visuo-motor integration.

Haptic fMRI: using classification to quantify task-correlated noise during goal-directed reaching motions

Neuroimaging artifacts in haptic functional magnetic resonance imaging (Haptic fMRI) experiments have the potential to induce spurious fMRI activation where there is none, or to make neural activation measurements appear correlated across brain regions when they are actually not. Here, we demonstrate that performing three-dimensional goal-directed reaching motions while operating Haptic fMRI Interface (HFI) does not create confounding motion artifacts. To test for artifacts, we simultaneously scanned a subject's brain with a customized soft phantom placed a few centimeters away from the subject's left motor cortex. The phantom captured task-related motion and haptic noise, but did not contain associated neural activation measurements. We quantified the task-related information present in fMRI measurements taken from the brain and the phantom by using a linear max-margin classifier to predict whether raw time series data could differentiate between motion planning or reaching. fMRI measurements in the phantom were uninformative (2σ, 45-73%; chance=50%), while those in primary motor, visual, and somatosensory cortex accurately classified task-conditions (2σ, 90-96%). We also localized artifacts due to the haptic interface alone by scanning a stand-alone fBIRN phantom, while an operator performed haptic tasks outside the scanner's bore with the interface at the same location. The stand-alone phantom had lower temporal noise and had similar mean classification but a tighter distribution (bootstrap Gaussian fit) than the brain phantom. Our results suggest that any fMRI measurement artifacts for Haptic fMRI reaching experiments are dominated by actual neural responses.

Haptic fMRI: accurately estimating neural responses in motor, pre-motor, and somatosensory cortex during complex motor tasks

Haptics combined with functional magnetic resonance imaging (Haptic fMRI) can non-invasively study how the human brain coordinates movement during complex manipulation tasks, yet avoiding associated fMRI artifacts remains a challenge. Here, we demonstrate confound-free neural activation measurements using Haptic fMRI for an unconstrained three degree-of-freedom motor task that involves planning, reaching, and visually guided trajectory tracking. Our haptic interface tracked subjects' hand motions, velocities, and accelerations (sample-rate, 350Hz), and provided continuous realtime visual feedback. During fMRI acquisition, we achieved uniform response latencies (reaching, 0.7-1.1s; tracking, 0.4-0.65s); minimized hand jitter (<;8mm); and ensured reliable motion trajectories (tracking, <;7mm root-mean-square error). In addition, our protocol decorrelated head motion from both hand speed (r=-0.03) and acceleration (r=-0.025), which reliably produced low head motion levels (<;0.4mm/s between scan volumes) and a low fMRI temporal noise-to-signal ratio (<;1%) across thirty-five scan runs. Our results address the primary outstanding Haptic fMRI confounds: motion induced low spatial-frequency magnetic field changes, which correlate neural activation across cortex; unreliable motions and response latencies, which reduce statistical power; and task-correlated head motion, which causes spurious fMRI activation. Haptic fMRI can thus reliably elicit and localize heterogeneous neural activation for different tasks in motor (movement), pre-motor (planning), and somatosensory (limb displacement) cortex, demonstrating that it is feasible to use the technique to study how the brain achieves three dimensional motor control.

Control of robot assistant for rehabilitation of upper extremities

The assisted movement in humans with paresis of upper extremities is becoming popular for neurorehabilitation. We propose a novel method for trajectory selection and assistance control. This paper presents simulation of a planar two degrees of freedom robot that assists horizontal movement of the hand. The control assumes that during the exercise the hand needs to follow healthy alike trajectories. The robot is assumed to provide minimal assistance and operate as a teacher of the movement.