Biology subjects

Battaglia, F. P.

Publications and source records attributed to Battaglia, F. P..

13 recordsLinked to original sources

bioRxiv2025

Power Pixels: a turnkey pipeline for processing of Neuropixel recordings

There are many open-source tools available for the processing of neuronal data acquired using Neuropixels probes. Each of these tools, focuses on a part of the process from raw data to single neuron activity. For example, SpikeInterface is an incredibly useful Python module for pre-processing and spike sorting of individual recordings. However, there are more steps in between raw data and spikes, such as synchronization of spike times between probes and histological reconstruction of probe insertions. Therefore, we developed Power Pixels, combining the functionality of several packages into one integrated pipeline, which may be run in any lab workflow. It includes pre-processing, spike sorting, neuron-level quality control metrics, synchronization between multiple probes, compression of raw data, and ephys-to-histology alignment. Integrating all these steps into one pipeline greatly simplifies Neuropixels data processing, especially for novel users who might struggle to find their way around all the available code and tools. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/661890v6_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@91f8b2org.highwire.dtl.DTLVardef@15e9c92org.highwire.dtl.DTLVardef@794868org.highwire.dtl.DTLVardef@80d867_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

Rat anterior cingulate neurons responsive to rule or strategy changes are modulated by the hippocampal theta rhythm and sharp-wave ripples

To better understand neural processing during adaptive learning of stimulus-response-reward contingencies, we recorded synchrony of neuronal activity in anterior cingulate cortex (ACC) with hippocampal rhythms in male rats acquiring and switching between spatial and visual discrimination tasks in a Y-maze. ACC population and single unit activity responded shortly after task rule changes, or just before the rats adopted different task strategies. Hippocampal theta oscillations (associated with memory encoding) modulated an elevated proportion of rule-change responsive neurons (70%), but other neurons that were correlated with strategy-change, strategy value, and reward-rate were not. However, hippocampal sharp wave-ripples modulated significantly higher proportions of rule-change, strategy-change and reward-rate responsive cells during post-session sleep but not pre-session sleep. This suggests an underestimated mechanism for hippocampal mismatch and contextual signals to facilitate ACC detection of contingency changes for cognitive flexibility, a function that is attenuated after it is damaged.

neuroscience↗

bioRxiv2023

Direct Entorhinal Control of CA1 Temporal Coding

During behavior, hippocampal neurons fire in consistent theta sequences, organized by the theta rhythm, which have been linked to predictive coding of future actions. The mechanisms of sequence generation are yet unclear, but in the hippocampal CA1 subfield, are thought to involve both major input streams into CA1 neurons, from CA3 pyramidal neurons and directly from entorhinal cortex. We disentangled the role of these two afferent input with highly specific optogenetic inhibition limited to the direct entorhinal afferents of CA1, thereby leaving the rest of the hippocampal-entorhinal circuit intact. While CA1 spatial firing properties were largely unaffected, theta phase precession was largely abolished. Surprisingly, while theta phase precession is thought to generate theta sequences, theta sequences were actually strengthened when it was suppressed. These results suggest that sequence generation is internal to the hippocampus, while the entorhinal inputs may act as a supervisory signal driving learning and representational updates.

neuroscience↗

bioRxiv2022

Pynapple: a toolbox for data analysis in neuroscience.

Datasets collected in neuroscientific studies are of ever-growing complexity, often combining high dimensional time series data from multiple data acquisition modalities. Handling and manipulating these various data streams in an adequate programming environment is crucial to ensure reliable analysis, and to facilitate sharing of reproducible analysis pipelines. Here, we present Pynapple, the PYthon Neural Analysis Package, a lightweight python package designed to process a broad range of time-resolved data in systems neuroscience. The core feature of this package is a small number of versatile objects that support the manipulation of any data streams and task parameters. The package includes a set of methods to read common data formats and allows users to easily write their own. The resulting code is easy to read and write, avoids low-level data processing and other error-prone steps, and is open source. Libraries for higher-level analyses are developed within the Pynapple framework but are contained within in a collaborative repository of specialized and continuously updated analysis routines. This provides flexibility while ensuring long-term stability of the core package. In conclusion, Pynapple provides a common framework for data analysis in neuroscience. HighlightsO_LIAn open-source framework for data analysis in systems neuroscience. C_LIO_LIEasy-to-use object-oriented programming for data manipulation. C_LIO_LIA lightweight and standalone package ensuring long-term backward compatibility. C_LI

neuroscience↗

bioRxiv2022

Coexisting persistent and dynamic representations of stimulus, time and trial outcome emerge in the hippocampus-prefrontal circuitry during learning of a temporal association

Temporal associations between sensory stimuli separated in time rely on the interaction between the hippocampus and medial prefrontal cortex (mPFC). However, it is not known how changes in their neural activity support the emergence of temporal association learning. Here, we use simultaneous electrophysiological recordings in the hippocampal CA1 region and mPFC of mice to elucidate the neural dynamics underlying memory formation in an auditory trace conditioning task. We found that in both areas conditioned (CS+/CS-) and unconditioned stimuli (US) evoked similar temporal sequences of neural responses that progressively diverged during learning. Additionally, persistent CS representations emerged in mPFC after learning, supported by CS+ coding states whose transient reactivation reliably predicted lick onset and behavioral performance on single trials. These results show that coordination of temporal sequences in CA1 and persistent activity in mPFC may underlie temporal association learning, and that transient reactivations of engrams in mPFC predict the animal behavior. HighlightsO_LITemporal representation of CS+, but not CS- and US, strengthens in CA1 after learning. C_LIO_LISimilarity between stimulus and reward temporal representations decrease with learning, but is recovered in error trials. C_LIO_LIRepresentation of stimulus identity is strong and stable in PFC since stimulus onset, while it only emerges in CA1 during trace period. C_LIO_LINeural states defined on faster time-scales reveal the emergence of CS coding states in PFC whose onset predicts lick times and task performance. C_LIO_LIPFC-CA1 states do not increase coordination during late CS+ stimulus and trace. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/497926v2_fig0.gif" ALT="Figure 0"> View larger version (32K): org.highwire.dtl.DTLVardef@93f7a7org.highwire.dtl.DTLVardef@6804a0org.highwire.dtl.DTLVardef@107a6a2org.highwire.dtl.DTLVardef@14cfb23_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 0:C_FLOATNO Graphical Abstract C_FIG

neuroscience↗

bioRxiv2021

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

Theta sequences and phase precession shape the activity time course of hippocampal place cells. Theta sequences are rapid sweeps of spikes from multiple cells, tracing trajectories from past to future. Phase precession is the correlation between a place cells theta firing phase and animal position. Here we present an analysis of the strongly variable character of these features in CA1. We identify a cell group that do not phase precess but reliably express theta sequences. The cells that do phase precess only do so when the medium gamma oscillations (60-90 Hz, linked to Entorhinal inputs) dominates but then they show less theta sequences. The same cells express more sequences but not precession when slow gamma (20-45 Hz, linked to CA3 inputs) dominates. Moreover, sequences occur independently in the two groups. Our results challenge a causal relationship between precession and sequences and highlights the highly heterogeneous character of the hippocampal output.

neuroscience↗

bioRxiv2021

The Hybrid Drive: a chronic implant device combining tetrode arrays with silicon probes for layer-resolved ensemble electrophysiology in freely moving mice

Understanding the function of brain cortices requires simultaneous investigation at multiple spatial and temporal scales and to link neural activity to an animals behavior. A major challenge is to measure within- and across-layer information in actively behaving animals, in particular in mice that have become a major species in neuroscience due to an extensive genetic toolkit. Here we describe the Hybrid Drive, a new chronic implant for mice that combines tetrode arrays to record within-layer information with silicon probes to simultaneously measure across-layer information. The design of our device combines up to 14 tetrodes and 2 silicon probes, that can be arranged in custom arrays to generate unique areas-specific (and multi-area) layouts. We show that large numbers of neurons and layer-resolved local field potentials can be recorded from the same brain region across weeks without loss in electrophysiological signal quality. The drives lightweight structure ({approx} 3.5 g) leaves animal behavior largely unchanged during a variety of experimental paradigms. We demonstrate how the data collected with the Hybrid Drive allow state-of-the-art analysis in a series of experiments linking the spiking activity of CA1 pyramidal layer neurons to the oscillatory activity across hippocampal layers. Our new device fits a gap in the existing technology and increases the range and precision of questions that can be addressed about neural computations in freely behaving mice.

neuroscience↗

bioRxiv2020

Learning differentially shapes prefrontal and hippocampal activity patterns during classical conditioning

The ability to use sensory cues to inform goal directed actions is a critical component of intelligent behavior. To study how sound cues are translated into anticipatory licking during classical appetitive conditioning, we employed high-density electrophysiological recordings from the hippocampal CA1 area and the prefrontal cortex (PFC). CA1 and PFC neurons undergo distinct learning dependent changes at the single cell level and maintain representations of cue identity during anticipatory behavior at the population level. In addition, reactivation of task-related neuronal assemblies during hippocampal awake Sharp-Wave Ripples (aSWR) changed within individual sessions in CA1 and over the course of multiple sessions in PFC. Despite both areas being highly engaged and synchronized during the task, we found no evidence for coordinated single cell or assembly activity during conditioning trials or aSWR.

neuroscience↗

bioRxiv2020

A geometry of spike sequences: Fast, unsupervised discovery of high-dimensional neural spiking patterns based on optimal transport theory

Neural coding and memory formation depend on temporal spiking sequences that span high-dimensional neural ensembles. The unsupervised discovery and characterization of these spiking sequences requires a suitable dissimilarity measure to spiking patterns, which can then be used for clustering and decoding. Here, we present a new dissimilarity measure based on optimal transport theory called SpikeShip, which compares multineuron spiking patterns based on all the relative spike-timing relationships among neurons. SpikeShip computes the optimal transport cost to make all the relative spike-timing relationships (across neurons) identical between two spiking patterns. We show that this transport cost can be decomposed into a temporal rigid translation term, which captures global latency shifts, and a vector of neuron-specific transport flows, which reflect inter-neuronal spike timing differences. SpikeShip can be effectively computed for high-dimensional neuronal ensembles, has a low (linear) computational cost that has the same order as the spike count, and is sensitive to higher-order correlations. Furthermore SpikeShip is binless, can handle any form of spike time distributions, is not affected by firing rate fluctuations, can detect patterns with a low signal-to-noise ratio, and can be effectively combined with a sliding window approach. We compare the advantages and differences between SpikeShip and other measures like SPIKE and Victor-Purpura distance. We applied SpikeShip to large-scale Neuropixel recordings during spontaneous activity and visual encoding. We show that high-dimensional spiking sequences detected via SpikeShip reliably distinguish between different natural images and different behavioral states. These spiking sequences carried complementary information to conventional firing rate codes. SpikeShip opens new avenues for studying neural coding and memory consolidation by rapid and unsupervised detection of temporal spiking patterns in high-dimensional neural ensembles.

neuroscience↗

bioRxiv2019

Experience-dependent changes to cortico-hippocampal networks during NREM sleep

Memory reactivation during NonREM-ripples is thought to communicate new information to a systems-wide network. Cortical high frequency events have also been described that co-occur with ripples. Focusing on NonREM sleep after different behaviors, both hippocampal ripples and parietal high frequency oscillations were detected. A bimodal frequency distribution was observed in the parietal high frequency events, faster and slower, with increases in prefrontal directionality measured by Granger causality analysis specifically seen during the fast parietal oscillations. Furthermore, fast events activated prefrontal-parietal cortex whereas slow events activated hippocampal-parietal areas. Finally, there was a learning-induced increase in both number and size of fast high frequency events. These patterns were not seen after novelty exposure or foraging, but occurred after the learning of a new goal location in a maze. Disruption of either sleep or hippocampal ripples impaired long-term memory consistent with these having a role in memory consolidation.

neuroscience↗

bioRxiv2019

Reduced firing rates of pyramidal cells in frontal cortex of APP/PS1 can be restored by acute treatment with levetiracetam

In recent years aberrant neural oscillations in various cortical areas have emerged as a common physiological hallmark across mouse models of amyloid pathology and patients with Alzheimers disease. However, much less is known about the underlying effect of amyloid pathology on single cell activity. Here, we used high density silicon probe recordings from frontal cortex area of 9 months old APP/PS1 mice to show that resting state Local Field Potential (LFP) power in the theta and beta band is increased in transgenic animals, while single cell firing rates, specifically of putative pyramidal cells, are significantly reduced. At the same time, these sparsely firing pyramidal cells phase-lock their spiking activity more strongly to the ongoing theta and beta rhythms. Furthermore, we demonstrated that the anti-epileptic drug, levetiracetam, can restore principal cell firing rates back to control levels. Overall, our results highlight reduced firing rates of cortical pyramidal cells as a symptom of amyloid pathology and indicate that lifting cortical inhibition might contribute to the beneficial effects of levetiracetam on AD patients.\n\nO_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

neuroscience↗

bioRxiv2018

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.\n\nAuthor summaryThe 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.

neuroscience↗

bioRxiv2017

Impaired hippocampal representation of place in the Fmr1-knockout mouse model of Fragile X syndrome

Fragile X syndrome (FXS) is an X-chromosome linked intellectual disability and the most common genetic cause of autism spectrum disorder (ASD). Building upon demonstrated deficits in neuronal plasticity and spatial memory in FXS, we investigated how spatial information processing is affected in vivo in an FXS mouse model (Fmr1-KO). Healthy hippocampal neurons (so-called place cells) exhibit place-related activity during spatial exploration, and the stability of these spatial representations can be taken as an index of memory function. We find impaired stability and reduced specificity of Fmr1-KO spatial representations. This is a potential biomarker for the cognitive dysfunction observed in FXS, informative on the ability to integrate sensory information into an abstract representation and successfully retain this conceptual memory. Our results provide key insight into the biological mechanisms underlying cognitive disabilities in FXS and ASD, paving the way for a targeted approach to remedy these.

animal behavior and cognition↗