Delineating the whole brain BOLD response to passive movement kinematics

Abstract

The field of brain-machine interfaces (BMIs) has made great advances in recent years, converting thought to movement, with some of the most successful implementations measuring directly from the motor cortex. However, the ability to record from additional regions of the brain could potentially improve flexibility and robustness of use. In addition, BMIs of the future will benefit from integrating kinesthesia into the control loop. Here, we examine whether changes in passively induced forefinger movement amplitude are represented in different regions than forefinger velocity via a MR compatible robotic manipulandum. Using functional magnetic resonance imaging (fMRI), five healthy participants were exposed to combinations of forefinger movement amplitude and velocity in a factorial design followed by an epoch-based analysis. We found that primary and secondary somatosensory regions were activated, as well as cingulate motor area, putamen and cerebellum, with greater activity from changes in velocity compared to changes in amplitude. This represents the first investigation into whole brain response to parametric changes in passive movement kinematics. In addition to informing BMIs, these results have implications towards neural correlates of robotic rehabilitation.

Design and Evaluation of a Cable-Driven fMRI-Compatible Haptic Interface to Investigate Precision Grip Control

“…This could be increased by selecting more powerful motors and encoders, as well as by increasing transmission stiffness. Nontheless, the bandwidth requirement was met [36], while also outperforming other devices interacting with human finger motion in the MRI scanner [19], [20]. Transmission elasticity also influences the structural stiffness of the system.…”

“…The relatively large Z-width identified for our device is comparable to the Z-width of other non-MR-compatible haptic devices [59] (very few research papers report Z-width measurements, and no references could be found for MR-compatible devices), and allows for future rendering of virtual objects with a wide range of mechanical properties, ranging from close to transparent (∼ 0.2 N friction force) to relatively stiff (3.3 N/mm) objects, and viscosities (viscous force fields) up to 85 N/(m/s). The static friction is lower (in particular due to cable transmission and friction compensation using the MR-compatible force sensor [42] attached at the output) than in the devices we previously developed [20] and also compared to other MRI-compatible haptic systems [60], which display higher output friction (∼ 3 N) even after performing friction compensation using force sensors at the output. No literature was found on MRI-compatible grasping devices that report static friction.…”

“…The dexterity of the fingers together with the small range of motion and relatively small interaction forces during object manipulation (limiting possible movement artifacts) make the hand the ideal body part for motor control studies during fMRI. A simple 1-DOF prototype force feedback device based on piezoelectric actuation was proposed for virtual reality (VR) – fMRI in [19], while in [20] a MRI-compatible forefinger manipulator actuated via a hydrostatic transmission was used to examine brain changes induced by passive forefinger movements. However, due to their inherent design characteristics (low output force [19] and low force control bandwidth [20]), these devices cannot match the dynamic capabilities of the human fingers.…”

Quantitative Testing of fMRI-Compatibility of an Electrically Active Mechatronic Device for Robot-Assisted Sensorimotor Protocols

“…Functional Magnetic Resonance Imaging (fMRI) has been used extensively to study functional brain processes [1], [2], enabling localization of task-related brain activation [3], the analysis of intrinsic and/or task-related fluctuations [4], [5] and causal coupling among brain signals [6] or, more recently, as a tool to provide on-line feedback on neural activity to improve task learning [7]. Tasks studied through fMRI include primary sensory functions, attention and recognition, word processing, and motor tasks [8].…”

“…In the pursuit of combining accurate kinesthetic feedback during sensorimotor protocols with simultaneous observation of brain activity via fMRI, researchers have endeavored to build MR-compatible haptic devices to support unconstrained hand movements [12], [21]- [23], hand/finger grasp [7], [24], [25], and foot movements [13], [26]. Such devices need to adhere to strict requirements for MR-compatibility, such as i) avoiding the use of magnetic materials to prevent distortion of the static magnetic field, ii) minimizing electromagnetic signals in both the radio-frequency (RF) and audible range to avoid interference with the RF-transceiver and gradients subsystems used in MRI, and iii) limiting use of in-bore electrical conductors to avoid coupling of eddy currents and Different no.…”