Simultaneous cortical, subcortical, and brainstem mapping of sensory activation.

Non-painful tactile sensory stimuli are processed in the cortex, subcortex, and brainstem. Recent functional magnetic resonance imaging (fMRI) studies have highlighted the value of whole-brain, systems-level investigation for examining pain processing. However, whole-brain fMRI studies are uncommon, in part due to challenges with signal to noise when studying the brainstem. Furthermore, the differentiation of small sensory brainstem structures such as the cuneate and gracile nuclei necessitates high resolution imaging. To address this gap in systems-level sensory investigation, we employed a whole-brain, multi-echo fMRI acquisition at 3T with multi-echo independent component analysis (ME-ICA) denoising and brainstem-specific modeling to enable detection of activation across the entire sensory system. In healthy participants, we examined patterns of activity in response to non-painful brushing of the right hand, left hand, and right foot, and found the expected lateralization, with distinct cortical and subcortical responses for upper and lower limb stimulation. At the brainstem level, we were able to differentiate the small, adjacent cuneate and gracile nuclei, corresponding to hand and foot stimulation respectively. Our findings demonstrate that simultaneous cortical, subcortical, and brainstem mapping at 3T could be a key tool to understand the sensory system in both healthy individuals and clinical cohorts with sensory deficits.

Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA.

Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson's disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired BOLD signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models' performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example in a chronic stroke cohort with varying stroke location and degree of tissue damage.

Development of a Training Game to Coordinate Torques Produced Between Arms.

The ability of individuals to accurately judge the forces that they generate is integral to seamlessly controlling their movements during everyday life. Individuals with chronic hemiparetic stroke have been shown to be impaired when matching forces between arms; this impairment may make activities as simple as carrying a tray challenging. Our goal was to develop a training protocol that individuals with stroke could use to improve their accuracy in judging the torques that they generate between arms. We designed a torque coordination game for this goal and tested its feasibility in six individuals without neurological impairments. Participants interacted with an instrumented isometric device at each arm and received automated audiovisual cues in response to the torques that they generated about each elbow joint. During the game, the participant's task was to keep a launched ball on its planned course. The participant achieved this task by sequentially applying required elbow torques at the correct times to close a left flap using the left arm and a right flap using the right arm. Participants performed this task 20 times when initiating with their left arm and 20 times when initiating with their right arm. Results indicate that all participants had a success rate in the range of 60% to 80% regardless of the arm dominance of the leading arm. Additionally, all participants anecdotally reported the game to be intuitive, and they provided an average difficulty rating that indicated the task was relatively easy to learn (i.e., 3 out of 10). Based on these findings, we conclude that this game may be suitable, enjoyable, and motivational for training coordination of torques between arms in individuals with stroke.