Multivariate Analysis of Electrophysiological Signals Reveals the Temporal Properties of Visuomotor Computations for Precision Grips

Abstract

The frontoparietal networks underlying grasping movements have been extensively studied, especially using fMRI. Accordingly, whereas much is known about their cortical locus much less is known about the temporal dynamics of visuomotor transformations. Here, we show that multivariate EEG analysis allows for detailed insights into the time course of visual and visuomotor computations of precision grasps. Male and female human participants first previewed one of several objects and, upon its reappearance, reached to grasp it with the thumb and index finger along one of its two symmetry axes. Object shape classifiers reached transient accuracies of 70% at ϳ105 ms, especially based on scalp sites over visual cortex, dropping to lower levels thereafter. Grasp orientation classifiers relied on a system of occipital-to-frontal electrodes. Their accuracy rose concurrently with shape classification but ramped up more gradually, and the slope of the classification curve predicted individual reaction times. Further, cross-temporal generalization revealed that dynamic shape representation involved early and late neural generators that reactivated one another. In contrast, grasp computations involved a chain of generators attaining a sustained state about 100 ms before movement onset. Our results reveal the progression of visual and visuomotor representations over the course of planning and executing grasp movements.

Concurrent Multimodal Data Acquisition During Brain Scanning is within Reach

“…This is possible by running multivariate analysis correlating individual participants' trial-by-trial fluctuations in neural activity associated with specific kinematics (Gu, Wood, Gribble, & Corneil, 2016) and eye-movements. Although similar explorations have been conducted, the current methods improve spatial resolution previously obtained with combined EEG & motor tracking (Amengual et al, 2014;Guo et al, 2019;Sburlea & Muller-Putz, 2018), and temporal resolution obtained with combined fMRI and motor tracking (Budisavljevic et al, 2017;Di Bono et al, 2017;Filimon, Nelson, Huang, & Sereno, 2009). Furthermore, correlating individual neural signature variations to individual kinematic profiles will provide an intrinsic link between neural population activity and specific aspects of motor control that is currently lacking (Lehky & Sereno, 2019).…”

“…This will provide a more accurate multimodal view of grasping processes (Betti, Castiello, & Begliomini, 2021). Subsequently, associated brain analysis may classify neural signatures into those more related to visual properties, hand movements, or eye movements, and outline which factors influence their individual contributions and interactions (Guo, Nestor, Nemrodov, Frost, & Niemeier, 2019). Overall, these methods improve previous visuomotor research in three main ways.…”

Grasping with a twist: Dissociating action goals from motor actions in human frontoparietal circuits

“…They also provide a valuable complement for EEG studies that reveal temporal coding with even finer temporal resolution but poorer spatial resolution. An EEG study by (Guo et al, 2019) showed that grasp orientation (defined by instruction rather than object attributes) can be classified during both a visual preview and action execution, with similar representations between the two phases; moreover, their most informative electrodes were over left caudal parietal cortex, though source localization indicated diverse potential sources (that may include cIPS, SPL, and EBA).…”

“…The purpose of the current study was to investigate how isolated and sequential actions unfold differently on a moment-to-moment basis. The temporal unfolding of brain activation during actions has been largely overlooked (or only studied with EEG, without localizing the specific brain regions involved, e.g., Guo et al, 2019). Most studies that have used multivoxel pattern analysis (MVPA) to investigate hand actions have averaged data within time bins for planning and execution (e.g., Gallivan et al, 2011).…”

Multivariate Analysis of Electrophysiological Signals Reveals the Time Course of Precision Grasps Programs: Evidence for Nonhierarchical Evolution of Grasp Control

“…Turella et al (2016) identified action planning processes ;750 ms after object presentation and upstream from effector-related processes. Guo et al (2019) revealed the time course of object shape and grasp orientation representations during grasp planning and execution.…”