Francesco P. Battaglia

How to detect the Granger-causal flow direction in the presence of additive noise?

Granger-causality metrics have become increasingly popular tools to identify directed interactions between brain areas. However, it is known that additive noise can strongly affect Granger-causality metrics, which can lead to spurious conclusions about neuronal interactions. To solve this problem, previous studies have proposed the detection of Granger-causal directionality, i.e. the dominant Granger-causal flow, using either the slope of the coherency (Phase Slope Index; PSI), or by comparing Granger-causality values between original and time-reversed signals (reversed Granger testing). We show that for ensembles of vector autoregressive (VAR) models encompassing bidirectionally coupled sources, these alternative methods do not correctly measure Granger-causal directionality for a substantial fraction of VAR models, even in the absence of noise. We then demonstrate that uncorrelated noise has fundamentally different effects on directed connectivity metrics than linearly mixed noise, where the latter may result as a consequence of electric volume conduction. Uncorrelated noise only weakly affects the detection of Granger-causal directionality, whereas linearly mixed noise causes a large fraction of false positives for standard Granger-causality metrics and PSI, but not for reversed Granger testing. We further show that we can reliably identify cases where linearly mixed noise causes a large fraction of false positives by examining the magnitude of the instantaneous influence coefficient in a structural VAR model. By rejecting cases with strong instantaneous influence, we obtain an improved detection of Granger-causal flow between neuronal sources in the presence of additive noise. These techniques are applicable to real data, which we demonstrate using actual area V1 and area V4 LFP data, recorded from the awake monkey performing a visual attention task.

Light distribution and thermal effects in the rat brain under optogenetic stimulation

Optical brain stimulation gained a lot of attention in neuroscience due to its superior cell-type specificity. In the design of illumination strategies, predicting the light propagation in a specific tissue is essential and requires knowledge of the optical properties of that tissue. We present the estimated absorption and reduced scattering in rodent brain tissue using non-destructive contact spatially resolved spectroscopy (cSRS). The obtained absorption and scattering in the cortex, hippocampus and striatum are similar, but lower than in the thalamus, leading to a less deep but broader light penetration profile in the thalamus. Next, the light distribution was investigated for different stimulation protocols relevant for fiber-optic based optogenetic experiments, using Monte Carlo simulation. A protocol specific analysis is proposed to evaluate the potential of thermally induced side effects.

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. Author Summary 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.

The yin and yang of memory consolidation: hippocampal and neocortical

While hippocampal and cortical mechanisms of memory consolidation have long been studied, their interaction is poorly understood. We sought to investigate potential interactions with respect to trace dominance, strengthening, and interference associated with postencoding novelty or sleep. A learning procedure was scheduled in a watermaze that placed the impact of novelty and sleep in opposition. Distinct behavioural manipulations—context preexposure or interference during memory retrieval—differentially affected trace dominance and trace survival, respectively. Analysis of immediate early gene expression revealed parallel up-regulation in the hippocampus and cortex, sustained in the hippocampus in association with novelty but in the cortex in association with sleep. These findings shed light on dynamically interacting mechanisms mediating the stabilization of hippocampal and neocortical memory traces. Hippocampal memory traces followed by novelty were more dominant by default but liable to interference, whereas sleep engaged a lasting stabilization of cortical traces and consequent trace dominance after preexposure.

Inferred Model of the Prefrontal Cortex Activity Unveils Cell Assemblies and Memory Replay

Cell assemblies are thought to be the units of information representation in the brain, yet their detection from experimental data is arduous. Here, we propose to infer effective coupling networks and model distributions for the activity of simultaneously recorded neurons in prefrontal cortex, during the performance of a decision-making task, and during preceding and following sleep epochs. Our approach, inspired from statistical physics, allows us to define putative cell assemblies as the groups of co-activated neurons in the models of the three recorded epochs. It reveals the existence of task-related changes of the effective couplings between the sleep epochs. The assemblies which strongly coactivate during the task epoch are found to replay during subsequent sleep, in correspondence to the changes of the inferred network. Across sessions, a variety of different network scenarios is observed, providing insight in cell assembly formation and replay. Author Summary Memories are thought to be represented in the brain through groups of coactivating neurons, the so-called cell assemblies. We propose an approach to identify cell assemblies from multi-electrode recordings of neural activity in vivo, and apply it to the prefrontal cortex activity of a behaving rat. Our statistical physics inspired approach consists in inferring effective interactions between the recorded cells, which reproduce the correlations in their spiking activities. The analysis of the effective interaction networks and of the model distributions allows us to identify cell assemblies, which strongly co-activate when the rat is learning, and also during subsequent sleep. Our approach is thus capable of providing detailed insights in cell-assembly formation and replay, crucial for memory consolidation.

Inferred network from prefrontal cortex activity of rats unveils cell assemblies

We analyzed recordings of prefrontal cortex activity of a rat in three different phases: while the animal faces a task in which a rule has to be learned and during the previous and subsequent sleep phases. We inferred an Ising model (characterized by binary variables and local fields and couplings as parameters) from the recorded spiking frequencies and pairwise correlations between neurons. We have shown how the inferred model can be used to deepen the analysis of the recordings, unveiling the presence of highly coordinated groups of neurons (cell assemblies), that is neurons that are activated together and synchronously inhibit the activity of other specific neurons. To identify the coactivated groups, we found the maxima of the log-likelihood of a configuration of neurons (stable states), defined as the sum of all the fields and couplings relative to the active neurons, performing an ascent dynamics on the energy landscape. When the model is inferred from the activity binned into 10 ms time bins, the only stable state is the one with all silent neurons. By adding an external input into the model and slowly increasing its value, stable states with more and more active neurons appear (see Figure ​Figure1A),1A), starting from the neurons with higher spiking frequency. Remarkably, the curves in Figure ​Figure1A1A show large jumps at specific values of the input strength, corresponding to the co-activation of strongly interconnected neurons, which not necessarily have high average activity. These highly synchronized neurons have been found from the models of both the awake and sleep epochs (see Figure ​Figure1B),1B), and are partially shared between different phases. Figure 1 Co-activation of neurons as a function of the input strength and of the time bin. A: Number of active neurons A(H) in the stable states vs external input H. Parameters (fields and couplings) of the Ising model were inferred from the activity of the Maze ... We investigated the meaning of the external input parameter, discovering that it carries information on the time scale at which we observe correlations between neurons, namely the time bin width. In fact, the two curves of Figure ​Figure1A,1A, which refer to the model inferred from the neuronal activity binned into two different time bins (10 ms and 30 ms), overlap by applying a translation of log(30 ms/10 ms) in the input strength. The fact that at Δt = 30 ms the first co-activated group appears for a small input strength H~1 means that the group is likely to be co-activated in a 30 ms time scale. Neurons found in activated and inhibited groups extracted from our model correspond to large entries in the two principal eigenvectors of the Pearson correlation matrix obtained from the recorded activity. In particular the 1st component has large and positive entries on both activated groups, and the 2nd component shows negative entries on the 1st group, and positive ones on the 2nd group which entails that the groups can activate together or not. Moreover the activation of a group causes the inhibition of another group, which has also large entries on the 1st and 2nd components but with opposite signs. The sign of the components therefore refects in an intricate manner the activation-inhibition relationships between different groups.

Closed-loop optical stimulation and recording system with GPU-based real-time spike sorting

Closed-loop brain computer interfaces are rapidly progressing due to their applications in fundamental neuroscience and prosthetics. For optogenetic experiments, the integration of optical stimulation and electrophysiological recordings is emerging as an imperative engineering research topic. Optical stimulation does not only bring the advantage of cell-type selectivity, but also provides an alternative solution to the electrical stimulation-induced artifacts, a challenge in closedloop architectures. A closed-loop system must identify the neuronal signals in real-time such that a strategy is selected immediately (within a few milliseconds) for delivering stimulation patterns. Real-time spike sorting poses important challenges especially when a large number of recording channels are involved. Here we present a prototype allowing simultaneous optical stimulation and electro-physiological recordings in a closed-loop manner. The prototype was implemented with online spike detection and classification capabilities for selective cell stimulation. Real-time spike sorting was achieved by computations with a high speed, low cost graphic processing unit (GPU). We have successfully demonstrated the closed-loop operation, i.e. optical stimulation in vivo based on spike detection from 8 tetrodes (32 channels). The performance of GPU computation in spike sorting for different channel numbers and signal lengths was also investigated.

Memory Consolidation, Replay, and Cortico-Hippocampal Interactions

Memory consolidation depends on the exchange of information between the hippocampus and the neocortex. The interaction between these two structures is based on dynamical processes such as oscillations, taking place during active behavior as well as sleep. Memory replay, that is, the reactivation, during sleep or other off-line periods, of the same configurations of neural activity that occurred during experience, is thought to be a key mechanism for memory consolidation. We review here the physiology of cortico-hippocampal interaction during sleep, as well as some results on cortical replay and its relationship with hippocampal activity.

Inferred Ising model unveils potentiation of pairwise neural interactions and replay of rule-learning related neural activity

In a recent experiment [1] the prefrontal cortex activity of rats was measured using multi-electtrode recordings during the awake epoch and during the previous and subsequent slow wave sleep (SWS) periods. During the awake epoch the animal faces a task, such as following a light in a Y-shaped maze, where rule learning is rewarded with food. Through the analysis of the recorded activity by means of Principal Component Analysis, the replay of the activity during the SWS after the task was shown to occur. Here we re-analyze those data with an Ising model inference algorithm (the Selective Cluster Expansion, introduced in [2]) and we show how valuable informations can be extracted from the inferred parameters in the context of neural activity replay and neuroplasticity. We start by binning, with a fixed bin-width of 10 ms, the recording of spiking times and by computing the set of probabilities that a single neuron is active in a single time-bin and the probabilities that a couple of neurons

A novel distance measure for the unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles

Temporally ordered multi-neuron patterns likely encode information in the brain. We introduce an unsupervised method, SPOTDisClust (Spike Pattern Optimal Transport Dissimilarity Clustering), for their detection from high-dimensional neural ensembles. SPOTDisClust measures similarity between two ensemble spike patterns by determining the minimum transport cost of transforming their corresponding normalized cross-correlation matrices into each other (SPOTDis). Then, it performs density-based clustering based on the resulting inter-pattern dissimilarity matrix. SPOTDisClust does not require binning and can detect complex patterns (beyond sequential activation) even when high levels of out-of-pattern “noise” spiking are present. Our method handles efficiently the additional information from increasingly large neuronal ensembles and can detect a number of patterns that far exceeds the number of recorded neurons. In an application to neural ensemble data from macaque monkey V1 cortex, SPOTDisClust can identify different moving stimulus directions on the sole basis of temporal spiking patterns. Author summary The brain encodes information by ensembles of neurons, and recent technological developments allow researchers to simultaneously record from over thousands of neurons. Neurons exhibit spontaneous activity patterns, which are constrained by experience and development, limiting the portion of state space that is effectively visited. Patterns of spontaneous activity may contribute to shaping the synaptic connectivity matrix and contribute to memory consolidation, and synaptic plasticity formation depends crucially on the temporal spiking order among neurons. Hence, the unsupervised detection of spike sequences is a sine qua non for understanding how spontaneous activity contributes to memory formation. Yet, sequence detection presents major methodological challenges like the sparsity and stochasticity of neuronal output, and its high dimensionality. We propose a dissimilarity measure between neuronal patterns based on optimal transport theory, determining their similarity from the pairwise cross-correlation matrix, which can be taken as a proxy of the “trace” that is left on the synaptic matrix. We then perform unsupervised clustering and visualization of patterns using density clustering on the dissimilarity matrix and low-dimensional embedding techniques. This method does not require binning of spike times, is robust to noise, jitter and rate fluctuations, and can detect more patterns than the number of neurons.