Predominance of Movement Speed Over Direction in Neuronal Population Signals of Motor Cortex: Intracranial EEG Data and A Simple Explanatory Model

Abstract

How neuronal activity of motor cortex is related to movement is a central topic in motor neuroscience. Motor-cortical single neurons are more closely related to hand movement velocity than speed, that is, the magnitude of the (directional) velocity vector. Recently, there is also increasing interest in the representation of movement parameters in neuronal population activity, such as reflected in the intracranial EEG (iEEG). We show that in iEEG, contrasting to what has been previously found on the single neuron level, speed predominates over velocity. The predominant speed representation was present in nearly all iEEG signal features, up to the 600–1000 Hz range. Using a model of motor-cortical signals arising from neuronal populations with realistic single neuron tuning properties, we show how this reversal can be understood as a consequence of increasing population size. Our findings demonstrate that the information profile in large population signals may systematically differ from the single neuron level, a principle that may be helpful in the interpretation of neuronal population signals in general, including, for example, EEG and functional magnetic resonance imaging. Taking advantage of the robust speed population signal may help in developing brain–machine interfaces exploiting population signals.

Mapping ECoG channel contributions to trajectory and muscle activity prediction in human sensorimotor cortex

“…In the current study, weights for the δ, θ and γ bands were higher than those of the other bands. This result is consistent with previous studies decoding for arm movement 7 , finger movement 14 , velocity 37 , and grasp type 38 .…”

“…Previous studies reported that weight values from δ and γ bands were effective and informative in trajectory decoding 36 37 38 . Crone et al .…”

Cortical and subcortical areas involved in the regulation of reach movement speed in the human brain: An fMRI study

“…Interestingly, trial-wise variations of single neuron activations in M1 and PMd in the preparatory phase predicted some of the speed variability in the executed reach movement (Churchland, Afshar, & Shenoy, 2006a). In contrast to the monkey single-cell studies, Hammer et al (2016) recorded iEEG in and around human hand/arm motor cortex from large neuronal populations and found that single iEEG-electrode time series' correlated stronger with the undirected speed of movement than directed velocity. In contrast to the monkey single-cell studies, Hammer et al (2016) recorded iEEG in and around human hand/arm motor cortex from large neuronal populations and found that single iEEG-electrode time series' correlated stronger with the undirected speed of movement than directed velocity.…”

“…We believe that these directions might not be sensitive enough to assess directional tuning with fMRI, as neurons with different preferred directions might fall within the same voxel and cancel each other out. According to Hammer et al (2016), directional tuning can be better characterized on the micro level, but its specificity degrades when the brain activity is recorded from larger neuronal populations (i.e., above 10,000 neurons) which is the case in the standard fMRI studies where each voxel represents the activation of approximately 500,000 neurons. Accordingly, two fMRI adaptation studies (Eisenberg et al, 2010;Fabbri et al, 2010) found orientation tuning in motor cortices when subjects repeatedly performed movements in the same direction.…”

“…Using information based analyses, Golub et al (2014) found that the single trial activation of small populations of motor cortical neurons conveyed much more information about endpoint movement direction than speed. This difference between studies may be due to the different population sizes that were used for speed prediction (Hammer et al, 2016) in the different studies or due to sampling from different motor cortical areas with different functional properties. This difference between studies may be due to the different population sizes that were used for speed prediction (Hammer et al, 2016) in the different studies or due to sampling from different motor cortical areas with different functional properties.…”