Global organization of neuronal activity only requires unstructured local connectivity

Modern electrophysiological recordings simultaneously capture single-unit spiking activities of hundreds of neurons spread across large cortical distances. Yet, this parallel activity is often confined to relatively low-dimensional manifolds. This implies strong coordination also among neurons that are most likely not even connected. Here, we combine in vivo recordings with network models and theory to characterize the nature of mesoscopic coordination patterns in macaque motor cortex and to expose their origin: We find that heterogeneity in local connectivity supports network states with complex long-range cooperation between neurons that arises from multi-synaptic, short-range connections. Our theory explains the experimentally observed spatial organization of covariances in resting state recordings as well as the behaviorally related modulation of covariance patterns during a reach-to-grasp task. The ubiquity of heterogeneity in local cortical circuits suggests that the brain uses the described mechanism to flexibly adapt neuronal coordination to momentary demands.

Sex differences in the alcohol-mediated modulation of BLA network states

About 85% of adults in the United States report drinking alcohol in their lifetime. Mood disorders, like generalized anxiety disorder and major depression, are highly comorbid with alcohol use. The basolateral amygdala (BLA) is an area of the brain that is heavily implicated in both mood disorders and alcohol use disorder. Importantly, modulation of BLA network/oscillatory states via parvalbumin-positive (PV) GABAergic interneurons has been shown to control the behavioral expression of fear and anxiety. Further, PV interneurons express a high density of δ-subunit-containing GABAA receptors (GABAARs), which are sensitive to low concentrations of alcohol. Our lab previously demonstrated that δ-subunit-containing GABAARs on PV interneurons in the BLA influence voluntary ethanol intake and anxiety-like behavior in withdrawal. Therefore, we hypothesized that the effects of alcohol may modulate BLA network states that have been associated with fear and anxiety behaviors via δ-GABAARs on PV interneurons in the BLA. Given the impact of ovarian hormones on the expression of δ-GABAARs, we examined the ability of alcohol to modulate local field potentials (LFPs) in the BLA from male and female C57BL/6J and Gabrd-/- mice after acute and repeated exposure to alcohol. Here, we demonstrate that acute and repeated alcohol can differentially modulate oscillatory states in male and female C57BL/6J mice, a process which involves δ-GABAARs. This is the first study to demonstrate that alcohol is capable of altering network states implicated in both anxiety and alcohol use disorders.

Distinct hippocampal network states support theta phase precession and theta sequences in CA1

The hippocampus likely uses temporal coding to represent complex memories via mechanisms such as theta phase precession and theta sequences. Theta sequences are rapid sweeps of spikes from multiple place cells, encoding past or planned trajectories or non-spatial information. Phase precession, the correlation between a place cell's theta firing phase and animal position has been suggested to facilitate sequence emergence. We find that CA1 phase precession varies strongly across cells and environmental contingencies. Phase precession depends on the CA1 network state, and is only present when the medium gamma oscillation (60-90 Hz, linked to Entorhinal inputs) dominates. Conversely, theta sequences are most evident for non-precessing cells or with leading slow gamma (20-45 Hz, linked to CA3 inputs). These results challenge the view that phase precession is the mechanism underlying the emergence of theta sequences and point at a 'dual network states' model for hippocampal temporal code, potentially supporting merging of memory and exogenous information in CA1.

Disruption in structural-functional network repertoire and time resolved subcortical-frontoparietal connectivity in disorders of consciousness

Understanding recovery of consciousness and elucidating its underlying mechanism is believed to be crucial in the field of basic neuroscience and medicine. Ideas such as the global neuronal workspace and the mesocircuit theory hypothesize that failure of recovery in conscious states coincide with loss of connectivity between subcortical and frontoparietal areas, a loss of the repertoire of functional networks states and metastable brain activation. We adopted a time-resolved functional connectivity framework to explore these ideas and assessed the repertoire of functional network states as a potential marker of consciousness and its potential ability to tell apart patients in the unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). In addition, prediction of these functional network states by underlying hidden spatial patterns in the anatomical network, i.e. so-called eigenmodes, were supplemented as potential markers. By analysing time-resolved functional connectivity from fMRI data, we demonstrated a reduction of metastability and functional network repertoire in UWS compared to MCS patients. This was expressed in terms of diminished dwell times and loss of nonstationarity in the default mode network and fronto-parietal subcortical network in UWS compared to MCS patients. We further demonstrated that these findings co-occurred with a loss of dynamic interplay between structural eigenmodes and emerging time-resolved functional connectivity in UWS. These results are, amongst others, in support of the global neuronal workspace theory and the mesocircuit hypothesis, underpinning the role of time-resolved thalamo-cortical connections and metastability in the recovery of consciousness.

Active neural coordination of motor behaviors with internal states

The brain continuously coordinates skeletomuscular movements with internal physiological states like arousal, but how is this coordination achieved? One possibility is that brain simply reacts to changes in external and/or internal signals. Another possibility is that it is actively coordinating both external and internal activities. We used functional ultrasound imaging to capture a large medial section of the brain, including multiple cortical and subcortical areas, in marmoset monkeys while monitoring their spontaneous movements and cardiac activity. By analyzing the causal ordering of these different time-series, we found that information flowing from the brain to movements and heart rate fluctuations were significantly greater than in the opposite direction. The brain areas involved in this external versus internal coordination were spatially distinct but also extensively interconnected. Temporally, the brain alternated between network states for this regulation. These findings suggest that the brain’s dynamics actively and efficiently coordinate motor behavior with internal physiology.

Similarity in evoked responses does not imply similarity in macroscopic network states across tasks

There is an ongoing debate as to whether cognitive processes arise from a group of functionally specialized brain modules (modularism) or as the result of a distributed nonlinear process (dynamical systems theory). The former predicts that tasks that recruit similar brain areas should have an equivalent degree of similarity in their connectivity. The latter allows for differential connectivity, even when the areas recruited are largely the same. Here we evaluated both views by comparing activation and connectivity patterns from a large sample of healthy subjects (N=242) that performed two executive control tasks, color-word Stroop task and Multi-Source Interference Task (MSIT), known to recruit similar brain areas. Using a measure of instantaneous connectivity based on edge time series as outcome variables, we estimated task-related network profiles as connectivity changes between incongruent and congruent information conditions. The degree of similarity of such profiles at the group level between both tasks was substantially smaller than their overlapping activation responses. A similar finding was observed at the subject level and when employing a different method for defining task-related connectivity. Our results are consistent with the perspective of the brain as a dynamical system, suggesting that task representations should be understood at both node and edge (connectivity) levels.

A deep learning framework for inference of single-trial neural population activity from calcium imaging with sub-frame temporal resolution

In many brain areas, neural populations act as a coordinated network whose state is tied to behavior on a moment-by-moment basis and millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe network-scale computation, as it can measure the activity of many individual neurons, monitor multiple layers simultaneously, and sample from identified cell types. However, estimating network states and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities, and limitations on temporal resolution. Here we describe RADICaL, a deep learning method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking activity for application to deconvolved calcium signals, whose statistics and temporal dynamics are quite distinct from electrophysiologically-recorded spikes. It incorporates a novel network training strategy that exploits the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers network states more accurately than previous methods, particularly for high-frequency components. In real 2p recordings from sensorimotor areas in mice performing a "water grab" task, RADICaL infers network states with close correspondence to single-trial variations in behavior, and maintains high-quality inference even when neuronal populations are substantially reduced.