Using focal cooling to link neural dynamics and behavior

Abstract

Establishing a causal link between neural function and behavioral output has remained a challenging problem. Commonly used perturbation techniques enable unprecedented control over intrinsic activity patterns and can effectively identify crucial circuit elements important for specific behaviors. However, these approaches may severely disrupt activity, precluding an investigation into the behavioral relevance of moment-to-moment neural dynamics within a specified brain region. Here we discuss the application of mild focal cooling to slow down intrinsic neural circuit activity while preserving its overall structure. Using network modeling and examples from multiple species, we highlight the power and versatility of focal cooling for understanding how neural dynamics control behavior and argue for its wider adoption within the systems neuroscience community.

Figure 1.. Two Approaches for Perturbing Neural Activity and Behavior

(A) Spike raster plot of eight simultaneously recorded neurons (left). The proportion of the trial is color-coded, transitioning from orange to green throughout its duration. Dimensionality-reduced neural activity plotted in state-space (middle). The black line describes the example trial at left. Since individual trials are not identical in this network, the standard network structure is determined by the correlated population activity, and its thickness reflects trial-by-trial variability. In this case, a causal relationship exists between the joint spiking activity of the network and a measurable behavioral output (right). (B) Neural activity from (A) is interrupted in the middle of the trial, which causes the network to deviate from the normal activity patterns, leading to a concomitant behavioral disruption. (C) A manipulation that changes the temporal dynamics of neural activity while preserving the overall structure of the population activity can maintain but temporally distort behavioral output.

Figure 2.. Temperature-induced Manipulation of Neural Dynamics in an Isolated Circuit

(A) Simultaneous intracellular recordings from three interconnected neurons (LP, PD and PY) from the crab somatogastric ganglion. Activity recorded from three complete pyloric rhythm cycles are plotted at 23°C (top) and 15°C (middle). Artificially stretched traces at 23°C (bottom) closely match profile of 15°C traces. Inset: simplified diagram of pyloric circuit. Replotted data from (Tang et al., 2010). (B) Data from (A) depicted in a state-space plot (z-scored) forms a simple 3D trajectory at 23°C (top) and 15°C (middle). Both trajectories (23°C black, 15°C blue) occupy similar space when plotted together (bottom).

Figure 3.. Biophysical Mechanisms of Cooling.

At left, a schematic of a synaptically connected pair of neurons with numbers indicating four sites at which temperature-sensitive processes are occurring as illustrated in the accompanying examples: (1) Frequency of subthreshold membrane potentials from a guinea pig inferior olivary neuron decreases with cooling, as indicated with power spectra at right. Adapted from (Lampl and Yarom, 1997). (2) Evoked spikes of a guinea pig CA1 pyramidal neuron are slower and wider at lower temperatures (Injected current: 120 ms, 0.85 nA). Scale bar: 20 mV. Adapted from (Thompson et al., 1985). (3) Relationship between brain temperature and conduction latency in 8 unmyelinated axons from the Dutch belted rabbit. Adapted from (Swadlow et al., 1981). (4) Difference in timing between presynaptic calcium current and postsynaptic current (i.e., synaptic delay) at two different temperatures. Adapted from (Sabatini and Regehr, 1996).

Figure 4.. Change to Neural Dynamics and Behavior from Cooling Zebra Finch HVC

(A) Schematic of recording array in the forebrain nucleus HVC of the zebra finch. (B) Spectrogram of a zebra finch song motif consisting of syllables A through E (top). Spike times of 20 simultaneously recorded HVC premotor neurons in which activity from each neuron separated vertically (middle) or collapsed into a single row (bottom). Syllables shaded in grey. Sonograms here and below display frequencies from 0.5 to 8.0 kHz. Adapted from (Egger et al., 2020). (C) Spike times of the same neurons as in (B) and syllable durations during 14 song renditions, sorted by increasing duration. Adapted from (Egger et al., 2020). (D) Context-dependent increase in brain temperature upon exposure to a female (top). Numbers correspond to spectrograms from a male zebra finch singing in isolation (undirected song) or to a female (directed song) (bottom). Adapted from (Aronov and Fee, 2012). (E) In the context of naturally occurring brain temperature variability, song motif duration is inversely proportional to HVC temperature. Adapted from (Aronov and Fee, 2012). (F) HVC temperature can be selectively controlled using a chronically implanted Peltier device. Measuring temperature levels 0.5 mm below the surface of the cooling probe demonstrates that applied current (0.5A and −1.0A) leads to heating and cooling, respectively. Adapted from (Long and Fee, 2008). (G) Representative sonograms at a range of different experimentally determined HVC temperatures. For reference, a spectrogram of a control song linearly stretched to duration of motif produced at coldest temperature (below). Adapted from (Long and Fee, 2008).

Figure 5.. Manipulations in a Recurrent Neural Network

(A) Schematic of our timing circuit model which consists of N = 800 rate-based neurons forming an all-to-all recurrent neural network with random synaptic weights drawn from a normal distribution scaled by a factor $g/\sqrt{N}$ with g set to 1.5 (Sussillo and Abbott, 2009). Additionally, all neurons synapse onto a single output unit. Recurrent and output weights are updated using the FORCE algorithm (Sussillo and Abbott, 2009) to learn a desired output pattern (Laje and Buonomano, 2013). (B) Activity of 4 example neurons after learning (top). Network activity structure visualized using the first two principal components (PC; bottom left) and a learned output signal (bottom right). (C) Simulated large-scale manipulations and their effects on neuronal activity (left), network dynamics (center), and output (right). Shown here are responses to activation (top row, shaded red area) and persistent (bottom row, shaded grey area) inactivation of 20% of neurons within the simulated network. (D) Simulated cooling manipulation through three monotonically increasing levels of focal cooling. Mild cooling was modeled by augmenting network dynamics by a factor Q: $\tau \frac{d\overrightarrow{x}}{dt}=-\overrightarrow{x}$. Here, τ = 10ms, W_rec is the recurrent weight matrix, and $\overrightarrow{x}$ and $\overrightarrow{r}$ are vectors of length N describing the internal dynamics and firing rates of each neuron. Temperature dependence of Q was modeled using Q₁₀ = 2. Note that this simplified rate model does not account for axonal conduction delays (Egger et al., 2020; Swadlow et al., 1981).

Figure 6.. Use of Cooling to Infer Hierarchical Organization of Timing Circuits.

(A) Picture of a male plainfin midshipman fish and accompanying sound waveform indicating a grunt train. Adapted from (Brantley and Bass, 1994). (B) Simplified diagram of part of the vocal control circuit in the male plainfin midshipman fish. VPP: vocal prepacemaker nucleus; VPN: vocal pacemaker nucleus; MN: vocal motoneurons. Right: Cartoon of intracellular membrane potential at different timescales in VPP (top) and VPN neurons (bottom). Adapted from (Chagnaud et al., 2011). (C) Simple model of a hierarchically organized timing circuit. A recurrent neural network (top) produces an output that switches between a low and a persistent high level, which serves as input drive to a pattern-generating circuit (bottom) that produces bursts during the time the input is at a high level. For simplicity, we modeled the pattern-generating circuit (Izhikevich, 2003) with parameters a = 0.02, b = 0.2, c = −50mV and d = 2.0. Temperature dependence was modeled by scaling the time derivatives of the membrane potential v and the internal state variable u by a factor Q with Q₁₀ = 2. (D)–(F) Cooling both the recurrent neural network (upstream circuit) and the pattern generator (downstream) leads to a decrease in interburst interval and an expansion of the burst epoch (D). Cooling either the upstream or downstream network individually can selectively affect one of these two variables (i.e., interburst interval or burst epoch) without changing the other (E and F).

Figure 7.. Temporal and Spatial Segregation of Function Revealed by Cooling.

(A) Anatomical location of the orofacial motor cortex (OMC) in the singing mouse (S. teguina) as determined by intracortical microstimulation. Adapted from (Okobi et al., 2019). (B) Spectrograms of example S. teguina songs during control and cooling sessions. Adapted from (Okobi et al., 2019). (C) A schematic depicting a hierarchical song control network in S. teguina with an upstream OMC region coupled with a subcortical note generating circuit. Cooling OMC increases song duration without modifying note duration, supporting this model of a separation of timescales. Modified from (Okobi et al., 2019). (D) Intraoperative mild cooling of human cortical surface can affect speech timing. Surface rendering of brain structure indicating two cooling sites. The duration of the speech task (i.e., ‘Monday, Tuesday, Wednesday, Thursday, Friday’) is significantly expanded when Site #2 is cooled with no effect of cooling Site #1 nearby. Adapted from (Long et al., 2016). (E) Intraoperative cooling can affect speech quality. Brain surface rending with four cooling sites (top). While cooling each site, participants were asked to recite the days of the week (‘o’ symbols) or a string of numbers (‘+’ symbols) (middle). The estimated temperature change at each site (bottom). Site #4 had a strong relationship between temperature and speech quality. Adapted from (Long et al., 2016). (F) Brain regions that affect speech quality and timing are spatially segregated in the human brain. Significant timing (upper left, yellow) and quality sites (upper right, blue) are provided as well as a higher spatial resolution map of cooling-induced changes. Adapted from (Long et al., 2016).