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Abstract Plasticity and homeostatic mechanisms allow neural networks to maintain proper function while responding to physiological challenges. Despite previous work investigating morphological and synaptic effects of brain-derived neurotrophic factor (BDNF), the most prevalent growth factor in the central nervous system, how exposure to BDNF manifests at the network level remains unknown. Here we report that BDNF treatment affects rodent hippocampal network dynamics during development and recovery from glutamate-induced excitotoxicity in culture. Importantly, these effects are not obvious when traditional activity metrics are used, so we delve more deeply into network organization, functional analyses, and in silico simulations. We demonstrate that BDNF partially restores homeostasis by promoting recovery of weak and medium connections after injury. Imaging and computational analyses suggest these effects are caused by changes to inhibitory neurons and connections. From our in silico simulations, we find that BDNF remodels the network by indirectly strengthening weak excitatory synapses after injury. Ultimately, our findings may explain the difficulties encountered in preclinical and clinical trials with BDNF and also offer information for future trials to consider. 

Abstract Measures of functional connectivity have played a central role in advancing our understanding of how information is transmitted and processed within the brain. Traditionally, these studies have focused on identifying redundant functional connectivity, which involves determining when activity is similar across different sites or neurons. However, recent research has highlighted the importance of also identifying synergistic connectivity—that is, connectivity that gives rise to information not contained in either site or neuron alone. Here, we measured redundant and synergistic functional connectivity between neurons in the mouse primary auditory cortex during a sound discrimination task. Specifically, we measured directed functional connectivity between neurons simultaneously recorded with calcium imaging. We used Granger Causality as a functional connectivity measure. We then used Partial Information Decomposition to quantify the amount of redundant and synergistic information about the presented sound that is carried by functionally connected or functionally unconnected pairs of neurons. We found that functionally connected pairs present proportionally more redundant information and proportionally less synergistic information about sound than unconnected pairs, suggesting that their functional connectivity is primarily redundant. Further, synergy and redundancy coexisted both when mice made correct or incorrect perceptual discriminations. However, redundancy was much higher (both in absolute terms and in proportion to the total information available in neuron pairs) in correct behavioural choices compared to incorrect ones, whereas synergy was higher in absolute terms but lower in relative terms in correct than in incorrect behavioural choices. Moreover, the proportion of redundancy reliably predicted perceptual discriminations, with the proportion of synergy adding no extra predictive power. These results suggest a crucial contribution of redundancy to correct perceptual discriminations, possibly due to the advantage it offers for information propagation, and also suggest a role of synergy in enhancing information level during correct discriminations. 

Analyzing the functional connectivity of the brain is an enormous challenge, as deciphering functional connectivity requires knowledge of functional responses and connections. One promising strategy is analyzing the spatial pattern of activity correlations across cell populations. In the primary auditory cortex (A1), cells respond to different sound features. On the large scale, there exists a tonotopic map, which is fractured at the small scale, raising the question of whether functional connections are spatially ordered or disordered. To test whether functional connectivity on a local and a global scale is also disordered, we first designed a robust statistical model to estimate parameters and test for the significance of the estimated correlation maps. We developed an inference method that allows efficient model fitting and statistical testing to project the correlation maps to 2D space. We then performed in vivo two-photon calcium imaging in layer 2/3 of A1 with pure tones (PT) or a combination of two tones (TT; harmonically related or not). We found that the spatial patterns of signal correlations (SCs) depend on the type of sound stimuli that were presented. The functional 2D maps of PT-driven SCs are more restricted to local neurons than TT signal correlations which showed more global textures. 2D SC patterns for harmonic stimuli showed spatially distinct relationships. TT SCs revealed spatially precise functional connectivity between harmonically related neurons. Thus, even though the frequency preference of neighboring neurons in A1 is functionally diverse, the functional connection pattern of these neurons is functionally precise and harmonically related. 

Characterizing neuronal responses to natural stimuli remains a central goal in sensory neuroscience. In auditory cortical neurons, the stimulus selectivity of elicited spiking activity is summarized by a spectrotemporal receptive field (STRF) that relates neuronal responses to the stimulus spectrogram. Though effective in characterizing primary auditory cortical responses, STRFs of non-primary auditory neurons can be quite intricate, reflecting their mixed selectivity. The complexity of non-primary STRFs hence impedes understanding how acoustic stimulus representations are transformed along the auditory pathway. Here, we focus on the relationship between ferret primary auditory cortex (A1) and a secondary region, dorsal posterior ectosylvian gyrus (PEG). We propose estimating receptive fields in PEG with respect to a well-established high-dimensional computational model of primary-cortical stimulus representations. These “cortical receptive fields” (CortRF) are estimated greedily to identify the salient primary-cortical features modulating spiking responses and in turn related to corresponding spectrotemporal features. Hence, they provide biologically plausible hierarchical decompositions of STRFs in PEG. Such CortRF analysis was applied to PEG neuronal responses to speech and temporally orthogonal ripple combination (TORC) stimuli and, for comparison, to A1 neuronal responses. CortRFs of PEG neurons captured their selectivity to more complex spectrotemporal features than A1 neurons; moreover, CortRF models were more predictive of PEG (but not A1) responses to speech. Our results thus suggest that secondary-cortical stimulus representations can be computed as sparse combinations of primary-cortical features that facilitate encoding natural stimuli. Thus, by adding the primary-cortical representation, we can account for PEG single-unit responses to natural sounds better than bypassing it and considering as input the auditory spectrogram. These results confirm with explicit details the presumed hierarchical organization of the auditory cortex. 

Functional connectivity is among the widely used metrics to assess the network-level attributes of brain function. While most existing analysis frameworks assume static functional connectivity during the course of an experiment, to capture neural dynamics over short time scales, a time-varying notion of functional connectivity is required. By revealing how neural networks reconfigure in response to changing external stimuli, internal states, and task demands, time-varying functional connectivity can be leveraged to study flexible cognition, such as working memory, attention, and decision-making. A major challenge in estimating time-varying functional connectivity from high-dimensional neural is the associated computational complexity. Existing methods trade off accuracy for computational efficiency, especially in applications that require real-time or near real-time processing. Here, we build on existing work using covariance-domain state-space models and introduce a framework based on variational inference that allows low-complexity estimation of time-varying functional connectivity and construction of confidence intervals. We validate the performance of the proposed method using simulation studies. Our results reveal significant gains in computational complexity compared to existing methods, while maintaining high accuracy. 

Understanding how networks of neurons transmit information is crucial to uncovering the underlying mechanisms of brain function. A common measure of communication in neuronal networks is functional connectivity. But, due to the presence of many latent confounding factors in existing experimental paradigms, functional connectivity estimates do not allow a direct interpretation of causal interactions in a network. Here, we aim at addressing this challenge using a quasi-experimental approach, namely Instrumental Variables, in a concurrent optogenetic stimulation of two-photon calcium imaging paradigm. We propose a methodology based on variational inference that allows estimating the spiking activity from blurred and noisy two-photon observations. We then use maximum likelihood estimation to construct a statistical testing framework that allows to distinguish between direct and confounding pairwise effects, by taking a set of random stimulation patterns as the instrumental variables. We demonstrate the utility of our approach using simulated data and compare its performance with existing work. Our results show that the proposed method can achieve high sensitivity and specificity in functional network discovery in presence of confounding effects and using a limited number of stimulation patterns and trials. 

Central in the study of population codes, coordinated ensemble spiking activity is widely observable in neural recordings with hypothesized roles in robust stimulus representation, interareal communication, and learning and memory formation. Model-free measures of synchrony characterize coherent pairwise activity but not higher-order interactions, a limitation transcended by statistical models of ensemble spiking activity. However, existing model-based analyses often impose assumptions about the relevance of higher-order interactions and require repeated trials to characterize dynamics in the correlational structure of ensemble activity. To address these shortcomings, we propose an adaptive greedy filtering algorithm based on a discretized mark point-process model of ensemble spiking and a corresponding statistical inference framework to identify significant higher-order coordination. In the course of developing a precise statistical test, we show that confidence intervals can be constructed for greedily estimated parameters. We demonstrate the utility of our proposed methods on simulated neuronal assemblies. Applied to multi-electrode recordings from human and rat cortical assemblies, our proposed methods provide new insights into the dynamics underlying localized population activity during transitions between brain states. 

Optogenetic stimulation has opened up a new avenue to probe neuronal circuitry at high spatiotemporal resolutions. A key challenge in optogenetic stimulation is deciding which subset out of thousands of neurons should be stimulated to elicit a desired network activation or affect behavior. In this work, we introduce a reinforcement learning approach to adaptively narrow down the multitude of stimulation possibilities and robustly identify Granger causal networks that underlie neuronal activity. We use realistic simulations with different underlying circuitry to show the effectiveness of reinforcement learning in identifying an optimal policy for selecting stimulation targets. 

Extracting directional connectivity in a neuronal ensemble from spiking observations is a key challenge in understanding the circuit mechanisms of brain function. Existing methods proceed in two stages, by first estimating the latent processes that govern spiking, followed by characterizing connectivity using said estimates. As such, the extracted networks in the second stage are highly sensitive to the accuracy of the estimates in the first stage. In this work, we introduce a framework to directly extract Granger causal links from spiking observations, without requiring intermediate time-domain estimation, by explicitly modeling the endogenous and exogenous latent processes that underlie spiking activity. Our proposed method integrates several techniques such as point processes, state-space modeling and Pólya-Gamma augmentation. We demonstrate the utility of our proposed approach using simulated data and application to real data from the rat brain during sleep.