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

• Published in: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2014; pp. 2046 - 2050
• Main Authors: Menon, Samir, Quigley, Paul, Yu, Michelle, Khatib, Oussama
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01-01-2014
• Subjects: Accuracy; Brain - physiology; Goals; Haptic interfaces; Head; Humans; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Motion; Noise; Noise measurement; Phantoms; Phantoms, Imaging; Task Performance and Analysis; Time Factors; Touch; Young Adult
• ISSN: 1094-687X; 1557-170X; 1558-4615
• DOI: 10.1109/EMBC.2014.6944018

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.