Neural assemblies revealed by inferred connectivity-based models of prefrontal cortex recordings

Where is the pain? Spatial patterns of pain co-occurrence in a population-based study of 4833 pain drawings incorporating network analysis

ABSTRACT

Spatial pain patterns are widely used as diagnostic tools, yet population-level estimates, such as the prevalence of pain in specific body regions and likelihood of their co-occurrence, are lacking. Despite this, bilateral limb pain is considered relatively uncommon. Baseline data from a population-based Danish cohort were analysed. Twenty-one pain drawing regions, coded as binary "pain"/"no-pain" variables, were entered into an Ising model. Conditional dependencies between pairs of painful regions were quantified, while accounting for the pain state of other regions. Four-week prevalence of pain was also calculated for body regions. Of 4833 analysed pain drawings, 34.7% (1676) reported bilateral (upper or lower) limb pain and 32.3% (1561) reported symmetrical (mirrored) bilateral limb pain. Strongest positive edge weights of the Ising model were between mirrored contralateral regions; the strongest being between left and right hips (mean: 3.86, 95% confidence interval: 3.84-3.87). Next strongest edge weights were between spatially adjacent ipsilateral regions; the strongest being between the right hip and right buttock (mean: 2.72, 95% confidence interval: 2.71-2.74). Negative edge weights, indicating inhibitory relationships, were consistently seen between nonmirrored contralateral regions, the strongest being between regions adjacent to their mirrored contralateral counterparts. In conclusion, bilateral limb pain, particularly in mirrored regions, is more prevalent than previously thought. Pain co-occurrence is facilitated between mirrored contralateral regions and, to a lesser degree, between adjacent ipsilateral regions. An inhibitory effect occurs between nonmirrored contralateral regions, diminishing with increasing distance from the mirrored region. Potential inhibition between mirrored contralateral regions is likely overshadowed by the more dominant facilitation.

Lattice physics approaches for neural networks

Modern neuroscience has evolved into a frontier field that draws on numerous disciplines, resulting in the flourishing of novel conceptual frames primarily inspired by physics and complex systems science. Contributing in this direction, we recently introduced a mathematical framework to describe the spatiotemporal interactions of systems of neurons using lattice field theory, the reference paradigm for theoretical particle physics. In this note, we provide a concise summary of the basics of the theory, aiming to be intuitive to the interdisciplinary neuroscience community. We contextualize our methods, illustrating how to readily connect the parameters of our formulation to experimental variables using well-known renormalization procedures. This synopsis yields the key concepts needed to describe neural networks using lattice physics. Such classes of methods are attention-worthy in an era of blistering improvements in numerical computations, as they can facilitate relating the observation of neural activity to generative models underpinned by physical principles.

Functional coupling networks inferred from prefrontal cortex activity show experience-related effective plasticity

Functional coupling networks are widely used to characterize collective patterns of activity in neural populations. Here, we ask whether functional couplings reflect the subtle changes, such as in physiological interactions, believed to take place during learning. We infer functional network models reproducing the spiking activity of simultaneously recorded neurons in prefrontal cortex (PFC) of rats, during the performance of a cross-modal rule shift task (task epoch), and during preceding and following sleep epochs. A large-scale study of the 96 recorded sessions allows us to detect, in about 20% of sessions, effective plasticity between the sleep epochs. These coupling modifications are correlated with the coupling values in the task epoch, and are supported by a small subset of the recorded neurons, which we identify by means of an automatized procedure. These potentiated groups increase their coativation frequency in the spiking data between the two sleep epochs, and, hence, participate to putative experience-related cell assemblies. Study of the reactivation dynamics of the potentiated groups suggests a possible connection with behavioral learning. Reactivation is largely driven by hippocampal ripple events when the rule is not yet learned, and may be much more autonomous, and presumably sustained by the potentiated PFC network, when learning is consolidated.

Cell assemblies coding for memories are widely believed to emerge through synaptic modification resulting from learning, yet their identification from activity is very arduous. We propose a functional-connectivity-based approach to identify experience-related cell assemblies from multielectrode recordings in vivo, and apply it to the prefrontal cortex activity of rats recorded during a task epoch and the preceding and following sleep epochs. We infer functional couplings between the recorded cells in each epoch. Comparisons of the functional coupling networks across the epochs allow us to identify effective potentiation between the two sleep epochs. The neurons supporting these potentiated interactions strongly coactivate during the task and subsequent sleep epochs, but not in the preceding sleep, and, hence, presumably belong to an experience-related cell assembly. Study of the reactivation of this assembly in response to hippocampal ripple inputs suggests possible relations between the stage of behavorial learning and memory consolidation mechanisms.

Functional connectivity models for decoding of spatial representations from hippocampal CA1 recordings

Hippocampus stores spatial representations, or maps, which are recalled each time a subject is placed in the corresponding environment. Across different environments of similar geometry, these representations show strong orthogonality in CA3 of hippocampus, whereas in the CA1 subfield a considerable overlap between the maps can be seen. The lower orthogonality decreases reliability of various decoders developed in an attempt to identify which of the stored maps is active at the moment. Especially, the problem with decoding emerges with a need to analyze data at high temporal resolution. Here, we introduce a functional-connectivity-based decoder, which accounts for the pairwise correlations between the spiking activities of neurons in each map and does not require any positional information, i.e. any knowledge about place fields. We first show, on recordings of hippocampal activity in constant environmental conditions, that our decoder outperforms existing decoding methods in CA1. Our decoder is then applied to data from teleportation experiments, in which an instantaneous switch between the environment identity triggers a recall of the corresponding spatial representation . We test the sensitivity of our approach on the transition dynamics between the respective memory states (maps). We find that the rate of spontaneous state shifts (flickering) after a teleportation event is increased not only within the first few seconds as already reported, but this instability is sustained across much longer (> 1 min.) periods.