Directed causality of the human electrocorticogram during dexterous movement

Abstract

While significant strides have been made in designing brain-machine interfaces for use in humans, efforts to decode truly dexterous movements in real time have been hindered by difficulty extracting detailed movement-related information from the most practical human neural interface, the electrocorticogram (ECoG). We explore a potentially rich, largely untapped source of movement-related information in the form of cortical connectivity computed with time-varying dynamic Bayesian networks (TV-DBN). We discover that measures of connectivity between ECoG electrodes derived from the local motor potential vary with dexterous movement in 65% of movement-related electrode pairs tested, and measures of connectivity derived from spectral features vary with dexterous movement in 76%. Due to the large number of features generated with connectivity methods, the TV-DBN a promising tool for dexterous decoding.

Cortical subnetwork dynamics during human language tasks

“…This difference in subnetwork dynamics—between intra-pSTG FNCs that are preferential for the external speech cue, and FNCs with SMC–Wernicke’s area interactions with stronger activation during the subject’s own verbal response—is consistent with recent studies suggesting functional distinctions between processing external and self-generated speech (Towle et al, 2008; Chang et al, 2013; Kort et al, 2014). The inclusion of SMC-Wernicke interactions in these FNCs, as well as in the red response-aligned FNCs from picture naming, is consistent with previous studies in nonhuman primates showing motor-sensory interactions during vocalization (Eliades and Wang, 2003); these interactions may reflect auditory feedback for motor control (Houde and Chang, 2015) and/or feedforward motor inputs for auditory state prediction (Hickok, 2012). Interestingly, subject 5’s SMC-Wernicke FNCs (red and pink in Figure 4) involved distinct sites superior to those implicated in her cue-preferential intra-pSTG FNC (green); this correlates well with predictions of a spatially distinct “sensorimotor interface” for speech (Hickok et al, 2003; Hickok and Poeppel, 2007; Hickok et al, 2009).…”

“…Previous studies using ERI estimates found that many of the interactions between ECoG sites tend to modulate together over the course of a task (Korzeniewska et al, 2008, 2011; Benz et al, 2012a). We therefore hypothesized that subnetworks corresponding to distinct computational stages could be identified as groups of interactions with similarly timed task-related activation, which we term “function al network components” (FNCs).…”