Unraveling the spatiotemporal brain dynamics during a simulated reach-to-eat task

Abstract

The reach-to-eat task involves a sequence of action components including looking, reaching, grasping, and feeding. While cortical representations of individual action components have been mapped in human functional magnetic resonance imaging (fMRI) studies, little is known about the continuous spatiotemporal dynamics among these representations during the reach-to-eat task. In a periodic event-related fMRI experiment, subjects were scanned while they reached toward a food image, grasped the virtual food, and brought it to their mouth within each 16-s cycle. Fourier-based analysis of fMRI time series revealed periodic signals and noise distributed across the brain. Independent component analysis was used to remove periodic or aperiodic motion artifacts. Time-frequency analysis was used to analyze the temporal characteristics of periodic signals in each voxel. Circular statistics was then used to estimate mean phase angles of periodic signals and select voxels based on the distribution of phase angles. By sorting mean phase angles across regions, we were able to show the real-time spatiotemporal brain dynamics as continuous traveling waves over the cortical surface. The activation sequence consisted of approximately the following stages: (1) stimulus related activations in occipital and temporal cortices; (2) movement planning related activations in dorsal premotor and superior parietal cortices; (3) reaching related activations in primary sensorimotor cortex and supplementary motor area; (4) grasping related activations in postcentral gyrus and sulcus; (5) feeding related activations in orofacial areas. These results suggest that phase-encoded design and analysis can be used to unravel sequential activations among brain regions during a simulated reach-to-eat task.

Topological Maps and Brain Computations From Low to High

“…Because motor cortex controls a wider range of movements than the deep colliculus, it may consist of a patchwork quilt of multiple 2D vector maps. These results are broadly consistent with our finding of traveling waves and traveling bumps observed also in parietal and motor cortex in the phase-encoded reach-to-eat experiment ( Chen et al, 2018 ).…”

“…By applying the phase-encoded method to a more naturalistic reach-to-eat movement, it was possible to visualize the spatiotemporal unfolding of activity across map-containing areas. The unexpected result was that there was a spatially coherent traveling wave and bump activity that began in early visual areas and then swept over parietal cortex to the hand areas of somatomotor cortex eventually closing in on face somatomotor cortex ( Chen et al, 2018 ). This is strongly reminiscent of similar (though much faster) coherent waves of activity visualized in the barrel cortex of awake rodents during active whisking using voltage sensitive dyes ( Moldakarimov et al, 2018 ; see also Wu et al, 2008 , who describe similar waves visualized in slices).…”

Coordination of multiple joints increases bilateral connectivity with ipsilateral sensorimotor cortices

“…Both tasks evoked a common excitatory coupling pattern from secondary motor cortices (SMA and cPM) to motor cortex (cM1) in the contralateral hemisphere in the beta frequency band (13- 35 Hz). The involvement of secondary motor cortices falls in line with previous proposed roles for these regions: secondary motor areas feed into primary motor cortex during motor preparation and execution to shape motor output [27][28][29][30][31] . Such increased preparatory activity from contralateral secondary motor areas was observed in both distal hand tasks 32 as well as multi-joint movements involving the shoulder, such as reaching 33 .…”

Attention module improves both performance and interpretability of four‐dimensional functional magnetic resonance imaging decoding neural network

“…The first challenge is the abstraction of complex temporo‐spatial features within the fMRI time series. A fMRI time series is a four‐dimensional (4D) data that consists of three‐dimensional (3D) spatial and one‐dimensional (1D) temporal information, which means brain regions engage and disengage in time during coherent cognitive activity (Chen, Kreutz‐Delgado, Sereno, & Huang, 2019; Shine et al, 2016). Inspired by this, Mao et al (2019) developed a model of 3D CNN stacks and a long short‐term memory (LSTM) for spatial and temporal feature abstraction, respectively.…”