Cortico-subcortical β burst dynamics underlying movement cancellation in humans

Abstract

Dominant neuroanatomical models hold that humans regulate their movements via loop-like cortico-subcortical networks, including the subthalamic nucleus (STN), thalamus, and sensorimotor cortices (SMC). Inhibitory commands across these networks are purportedly sent via transient, burst-like signals in the β frequency (15-29Hz). However, since human depth-recording studies are typically limited to one recording site, direct evidence for this proposition is hitherto lacking. Here, we present simultaneous multi-site depth-recordings from SMC and either STN or thalamus in humans performing the stop-signal task. In line with their purported function as inhibitory signals, subcortical β-bursts were increased on successful stop-trials and were followed within 50ms by increased β-bursting over SMC. Moreover, between-site comparisons (including in a patient with simultaneous recordings from all three sites) confirmed that β-bursts in STN precede thalamic β-bursts. This provides first empirical evidence for the role of β-bursts in conveying inhibitory commands along long-proposed cortico-subcortical networks underlying movement regulation in humans.

Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson’s disease

“…realistic transient beta oscillations, is consistent with the theory that the STN-GPe loop could play a predominant role in the generation of pathological beta activity [55][56][57]. In line with this, STN beta bursts have recently been shown to precede thalamic and cortical bursts during movement cancellation [58]. However, there is no consensus on how exaggerated beta oscillations arise, and other theories include excessive beta oscillations originating in the longer cortico-BG-thalamic loop [35,59], or directly within interconnected populations of striatal neurons [60].…”

The Pause-then-Cancel model of human action-stopping: Theoretical considerations and empirical evidence

“…Moreover, Jana and colleagues (2020) found that beta bursts over frontal cortex were followed within 20ms by broad skeleto-motor suppression and within 40ms by outright stopping detectable at the motor effector. A third study has similarly demonstrated the beta burst dynamics related to action stopping in the human subcortex: successful stop trials showed increased beta burst rates in both STN and motor thalamus, and -just like the scalp-recorded fronto-central beta bursts in Wessel (2020), STN bursts were immediately followed by increases in sensorimotor beta bursting (Diesburg et al, 2021). In sum, local field potential recordings, measured both intracranially and on the scalp, consistently support the notion that beta band activity across the nodes of the purported inhibitory cortico-basal ganglia circuitry may be an index of motor inhibition during action stopping.…”

“…Furthermore, increases in average beta during stop trials have been observed in regions associated with both processes -STN (Wessel et al, 2016;Bartoli et al, 2018;Ghahremani et al, 2018;Wessel et al, 2019), rIFC (Swann et al, 2009), and preSMA (Swann et al, 2012), and at their potential projection sites on the scalp (Wagner et al, 2016;Castiglione et al, 2019;Wessel, 2020). Moreover, recent work has demonstrated prominent increases in beta burst rates during stopping in both STN and motor thalamus at later latencies following SSRT (Diesburg et al, 2021). Hence, beta power may be a signature of inhibitory network-wide communication in both the cortico-basal-ganglia pathways which underlie action stopping, especially given that stimulation in the STN changes the scalp profile of average beta band activity (Swann et al, 2011).…”

Cortico-subcortical β burst dynamics underlying movement cancellation in humans

“…Preprocessing and analysis of LFP data were conducted using custom MATLAB scripts. Data and analysis code for this study can be found on Dryad at https://datadryad.org/stash/dataset/doi :10.5061/dryad.gf1vhhmq0 ( Diesburg et al, 2021 ). Electrical line noise from the operating room environment was filtered from the data using EEGLAB’s ( Delorme and Makeig, 2004 ) cleanline function after which the recordings were down-sampled to 1000 Hz for analysis.…”