New Miller Lab paper:
Jia, N., Brincat, S.L., Salazar-Gomez, A., Panko, M., Guenther, F. and Miller, E.K. (2017) Decoding of intended saccade direction in an oculomotor brain-computer interface. Journal of Neural Engineering, 2017. https://doi.org/10.1088/1741-2552/aa5a3e
Objective. To date, invasive brain-computer interface (BCI) research has largely focused on replacing lost limb functions using signals from of hand/arm areas of motor cortex. However, the oculomotor system may be better suited to BCI applications involving rapid serial selection from spatial targets, such as choosing from a set of possible words displayed on a computer screen in an augmentative and alternative communication (AAC) application. Here we aimed to demonstrate the feasibility of a BCI utilizing the oculomotor system. Approach. We developed a chronic intracortical BCI in monkeys to decode intended saccadic eye movement direction using activity from multiple frontal cortical areas. Main results. Intended saccade direction could be decoded in real time with high accuracy, particularly at contralateral locations. Accurate decoding was evident even at the beginning of the BCI session; no extensive BCI experience was necessary. High-frequency (80-500 Hz) local field potential magnitude provided the best performance, even over spiking activity, thus simplifying future BCI applications. Most of the information came from the frontal and supplementary eye fields, with relatively little contribution from dorsolateral prefrontal cortex. Significance. Our results support the feasibility of high-accuracy intracortical oculomotor BCIs that require little or no practice to operate and may be ideally suited for point and click computer operation as used in most current AAC systems.
The multidemand network is a set of frontoparietal areas in humans that are recruited for a wide range of cognitive-demanding tasks. Mitchell et al use FMRI connectivity analysis to identify a putative homolog in monkeys.
Siegel, M., Buschman, T.J., and Miller, E.K. (2015) Cortical information flow during flexible sensorimotor decisions. Science. 19 June 2015: 1352-1355.
During flexible behavior, multiple brain regions encode sensory inputs, the current task, and choices. It remains unclear how these signals evolve. We simultaneously recorded neuronal activity from six cortical regions (MT, V4, IT, LIP, PFC and FEF) of monkeys reporting the color or motion of stimuli. Following a transient bottom-up sweep, there was a top-down flow of sustained task information from frontoparietal to visual cortex. Sensory information flowed from visual to parietal and prefrontal cortex. Choice signals developed simultaneously in frontoparietal regions and travelled to FEF and sensory cortex. This suggests that flexible sensorimotor choices emerge in a frontoparietal network from the integration of opposite flows of sensory and task information.
Braunlich et al compared stimulus identity vs categorization tasks using fMRI in humans. They applied a Constrained Principal Components Analysis. They found evidence for two distinct frontoparietal networks. One that rapidly analyzes the stimuli and a second one that more slowly categorizes them.
Several lines of evidence suggests that searching a visual scene depends on an intrinsic periodicity. We scan the scene by moving the spotlight of attention at regular intervals. For example, Buschman and Miller (2009) found neurophysiological evidence in the frontal eye fields for regular shifts of attention at 25 Hz (i.e., every 40 ms). Dugue et al (2014) have now found evidence in humans using EEG recording and TMS stimulation in humans. They found successful search was associated with oscillations and phase resetting at 6 Hz. TMS applied at different intervals found disruption of search at a periodicity corresponding to 6 Hz. This was slower than reported by Buschman and Miller (2009), but that could be because Dugue et al used a more difficult search task.
Theta Oscillations Modulate Attentional Search Performance Periodically
Laura Dugué, Philippe Marque, and Rufin VanRullen Journal of Cognitive Neuroscience, 2014
For further reading:
Buschman, T.J. and Miller, E.K. (2009) Serial, covert, shifts of attention during visual search are reflected by the frontal eye fields and correlated with population oscillations. Neuron, 63: 386-396. View PDF »
Goal-direction and top-down control
Timothy J. Buschman and Earl K. Miller
We review the neural mechanisms that support top-down control of behavior. We suggest that goal-directed behavior utilizes two systems that work in concert. A basal ganglia-centered system quickly learns simple, fixed goal-directed behaviors while a prefrontal cortex-centered system gradually learns more complex (abstract or long-term) goal-directed behaviors. Interactions between these two systems allows top-down control mechanisms to learn how to direct behavior towards a goal but also how to guide behavior when faced with a novel situation.
Quentin et al examined the relationship between white matter connectivity between the frontal and parietal cortices and the improvement of visual perception by beta oscillatory synchrony between them. They used diffusion imaging to examine the white matter connectivity and used transcranial magnetic stimulation (TMS) over the right frontal eye fields (FEF) to induce beta oscillations. Individuals that showed greater perceptual improvement with the beta TMS also had stronger white matter connectivity.
Miller Lab alumnus Jon Wallis and crew studied two different types of cost-benefit decisions (delay vs effort). They found that different neurons in the dorsolateral prefrontal cortex, orbitofrontal, and anterior cingulate encoded the different types of decisions. Thus, rather than have neurons encode decisions on an abstract level, frontal cortex neurons encode stimuli based on their exact consequences.
More evidence for domain-general processing in higher-level cortex. Federenko et al tested human subjects with seven tasks with different cognitive demands. FMRI revealed overlapping activation zones in the frontal and parietal cortex. This is consistent with neurophysiological studies showing that many neurons in these areas are multifunctional. Rigotti et al recently demonstrated that these multifunctional “mixed selectivity” neurons provide the computational power needed for high-level cognition.
For further reading:
Rigotti, M., Barak, O., Warden, M.R., Wang, X., Daw, N.D., Miller, E.K., & Fusi, S. “The importance of mixed selectivity in complex cognitive tasks”. Nature, 497, 585-590, 2013 doi:10.1038/nature12160. View PDF
Miller, E.K. and Fusi, S. (2013) Limber neurons for a nimble mind. Neuron. 78:211-213. View PDF
Bea Luna and colleagues used graph theory to examine the development of functional hubs in the human brain. The hub architecture develops earlier, but connections between the hubs and “spokes” continue to develop and change into adulthood.
Max Riesenhuber and colleagues used EEG to examine the time course of shape and category signals in the human brain. Neural adaptation for category changes was seen in frontal cortex and then subsequently in temporal cortex. This supports the hypothesis that shape categories are formed by shape signals from temporal cortex that converge and form explicit category representations in frontal cortex. A late category signal in temporal cortex is consistent with category signals feeding back from frontal to temporal cortex.