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

Controlling for lesions, kinematics and physiological noise: impact on fMRI results of spastic post-stroke patients

Functional magnetic resonance imaging (fMRI) is a widely used technique for assessing brain function in both healthy and pathological populations. Some factors, such as motion, physiological noise and lesion presence, can contribute to signal change and confound the fMRI data, but fMRI data processing techniques have been developed to correct for these confounding effects. Fifteen spastic subacute stroke patients underwent fMRI while performing a highly controlled task (i.e. passive extension of their affected and unaffected wrists). We investigated the impact on activation maps of lesion masking during preprocessing and first- and second-level analyses, and of adding wrist extension amplitudes and physiological data as regressors using the Statistical Parametric Mapping toolbox (SPM12).

We observed a significant decrease in sensorimotor region activation after the addition of lesion masks and movement/physiological regressors during the processing of stroke patients’ fMRI data. Our results demonstrate that:•

The unified segmentation routine results in good normalization accuracy when dealing with stroke lesions regardless of their size;

•

Adding a group lesion mask during the second-level analysis seems to be a suitable option when none of the patients have lesions in target regions. Otherwise, no masking is acceptable;

•

Movement amplitude is a significant contributor to the sensorimotor activation observed during passive wrist extension in spastic stroke patients;

•

Movement features and physiological noise are relevant factors when interpreting for sensorimotor activation in studies of the motor system in patients with brain lesions. They can be added as nuisance covariates during large patient groups’ analyses.

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: 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.