Neuronal Activity Distributed in Multiple Cortical Areas during Voluntary Control of the Native Arm or a Brain-Computer Interface

Abstract

Voluntary control of visually-guided upper extremity movements involves neuronal activity in multiple areas of the cerebral cortex. Studies of brain-computer interfaces (BCIs) that use spike recordings for input, however, have focused largely on activity in the region from which those neurons that directly control the BCI, which we call BCI units, are recorded. We hypothesized that just as voluntary control of the arm and hand involves activity in multiple cortical areas, so does voluntary control of a BCI. In two subjects ( Macaca mulatta ) performing a center-out task both with a hand-held joystick and with a BCI directly controlled by four primary motor cortex (M1) BCI units, we recorded the activity of other, non-BCI units in M1, dorsal premotor cortex (PMd) and ventral premotor cortex (PMv), primary somatosensory cortex (S1), dorsal posterior parietal cortex (dPPC), and the anterior intraparietal area (AIP). In most of these areas, non-BCI units were active in similar percentages and at similar modulation depths during both joystick and BCI trials. Both BCI and non-BCI units showed changes in preferred direction (PD). Additionally, the prevalence of effective connectivity between BCI and non-BCI units was similar during both tasks. The subject with better BCI performance showed increased percentages of modulated non-BCI units with increased modulation depth and increased effective connectivity during BCI as compared with joystick trials; such increases were not found in the subject with poorer BCI performance. During voluntary, closed-loop control, non-BCI units in a given cortical area may function similarly whether the effector is the native upper extremity or a BCI-controlled device.

Timescales of Local and Cross-Area Interactions during Neuroprosthetic Learning

“…During "brain control," actuator movements are causally linked to an ensemble of neurons in M1, "direct neurons" (Taylor et al, 2002). Such direct neurons change their firing properties during neuroprosthetic learning (Ganguly et al, 2011;Arduin et al, 2013;Gulati et al, 2014Gulati et al, , 2017Athalye et al, 2017Athalye et al, , 2018; in contrast, the vast majority of neurons in the larger M1 network, "indirect neurons," generally become less task coupled (Fetz, 2007;Ganguly et al, 2011;Koralek et al, 2012Koralek et al, , 2013Arduin et al, 2013;Clancy et al, 2014;Gulati et al, 2017;Silversmith et al, 2021; but see Liu and Schieber, 2020). It remains unknown how neural activity in M2, i.e., indirect neurons which shape M1, plays a role in neuroprosthetic learning.…”

Cyclic, Condition-Independent Activity in Primary Motor Cortex Predicts Corrective Movement Behavior

“…For monkey Q, two 32-channel arrays and one 16-channel array in M1 were used. The location of the implanted arrays, spanning the forelimb representation in M1, have been previously reported ( Liu and Schieber, 2020 , their Fig. 2) and spanned the forelimb area of M1.…”

Cyclic, condition-independent activity in primary motor cortex predicts corrective movement behavior

“…The location of the implanted arrays, spanning the forelimb representation in M1, have been previously reported (Fig. 2 of Liu and Schieber, 2020) and spanned the forelimb area of M1. Intracortical microstimulation on single electrodes with a current up to a maximum of 100 µA (12 biphasic pulses, 0.2ms pulse width per phase, 3ms interpulse interval) with the animal lightly anesthetized with ketamine evoked a variety of forelimb movements.…”