Multiregional communication and the channel modulation hypothesis

Abstract

Multiregional communication is important to understanding the brain mechanisms supporting complex behaviors. Work in animals and human subjects shows that multiregional communication plays significant roles in cognitive function and is associated with neurological and neuropsychiatric disorders of brain function. Recent experimental advances enable empirical tests of the mechanisms of multiregional communication. Recent mechanistic insights into brain network function also suggest new therapies to treat disordered brain networks. Here, we discuss how to use the concept of communication channel modulation can help define and constrain what we mean by multiregional communication. We discuss behavioral and neurophysiological evidence for multiregional channels modulation. We then consider the role of causal manipulations and their implications for developing novel therapies based on multiregional communication.

Figure 1 –

(a) The communication channel is formed from anatomical projections from a sender (blue) to a receiver (red). As a result, activity in a population of sender neurons drives responses in receiver neurons (right). Note that due to local recurrence, not all neurons in the sender region need to innervate all neurons in the receiver region. The modulator network (black) contains neurons which send anatomical projections to the sender and/or receiver regions. Modulator network activity can alter the receiver response to input from the sender, and so modulate the communication channel. Note also that neurons in a given region of the modulator network need not project to the sender-receiver regions in the communication channel. (b) The modulator network may alter the receiver response due to an influence on the sender alone, (sender-dependent modulation) or due to an influence on the receiver (receiver-dependent modulation). (c) Channel modulation can support flexible behavior by opening the channel that communicates visual target information to guide a behavioral response (Behavior A) while closing the channel supporting communication between a distractor (S2) that would guide a different response (Behavior B).

Figure 2 –

Behavioral systems for multiregional communication. (a) Reach and saccade systems (including the posterior parietal cortex, PPC) work together to guide coordinated movements and independently to guide eye and hand movements. (b) Reaction times for the reach and saccade are correlated for coordinated movements. (c) Dual-task experimental designs can probe the mechanisms of communication across reach and saccade systems by manipulating the stimulus onset asynchrony (SOA) for the go cues for the reach and saccade. (d) Reaction time correlations for the reach and saccade decrease with increasing SOA demonstrating dual-task effects. Adapted from ref [8]. (e) Attention systems including the prefrontal cortex (PFC) can provide top-down modulation to sensory systems such as the visual via the visual thalamus (lateral geniculate nucleus, LGN) which increases its output gain to the visual cortex (primary visual cortex, V1). (f) Rodents can be trained to perform a form of dual-task paradigm by attending to either a visual or auditory cue. (g) Performance in the visual detection task decreased in cross-modal conditions (where auditory and visual cues compete for attention) compared to visual-cue only trials. Adapted from [10].

Figure 3 –

Evidence for network mechanisms of communication. (a) Model of sender-dependent modulation of communication in which the response at the receiver depends on the dynamics of activity at the sender. S denotes the Sender. R denotes the Receiver. M denotes the Modulator. (b), Example V2 neuron in which only two dimensions of V1 activity are needed to match the performance of the full model in predicting V2 response. Adapted from [29]. (c) Model of receiver-dependent modulation of communication in which the response at the receiver depends on the dynamics of the activity at the receiver prior to the arrival of inputs from the sender. S,R and M as in a). (d) Response to visual stimulation in V1 depends on the phase of the prestimulus delta band activity. Adapted from [30].

Figure 4 –

Modulator decoding for sender-receiver communication. (a) Schematic of predicting the channel state of communication from an orbitofrontal cortex (OFC) sender to a caudate nucleus (CN) receiver based on the neural activity recorded from a dorsal prefrontal cortex (dPFC) modulator. Anterior (A), posterior (P), dorsal (D), and ventral (V) directions are shown. (b) The pulse-by-pulse evoked CN receiver neural activity, in response to single microstimulation pulses (30 μA, 100 μs/phase) delivered at the OFC sender, are sorted by pulse-response latency (black crosses, “Hit”) using the stimulation-based accumulating log-likelihood ratio (stimAccLLR) method. Along with 60% “Hit” events, “Miss” events also observed. (c) Modulation spectrograms of dPFC modulator baseline LFP activity, computed as Z score magnitude of power difference between decoded “Hit” and “Miss” events in CN receiver responses. Contour shows significance from the permutation test (cluster-corrected; n = 10,000, p < 0.05, two-tailed test). Adapted from [37]