Biology subjects

Hafting, T.

Publications and source records attributed to Hafting, T..

9 recordsLinked to original sources

bioRxiv2026

Perineuronal nets reflect a continuum of fast-spiking specialization in adult parvalbumin interneurons

Perineuronal nets (PNNs) preferentially enwrap parvalbumin (PV) interneurons, regulating plas-ticity and circuit function. The molecular differences between PNN-positive and PNN-negative PV neurons remain unknown. We combined Xenium spatial transcriptomics with PNN-labeling in adult mouse cortex (378,349 cells) and found that 97% of PNNs enwrap PV neurons; sub-stantially higher than previous estimates. PNN status reflected a transcriptional continuum rather than discrete subtypes. A classifier trained on Xenium data (AUC = 0.87) applied to Allen Brain scRNA-seq data, enabled genome-wide analysis of 34,326 cortical PV neurons. PNN-positive PV neurons expressed mature fast-spiking markers: Kv3 channels, mature NMDA subunits (Grin2a), fast-kinetic GABA-A receptors (Gabra1), oxidative phosphorylation genes, and gap junctions (Gjd2). PNN-negative PV neurons expressed neuropeptides and GABA-A subunits typical of Sst interneurons, suggesting a transcriptomic boundary between cell types and potentially ele-vated plasticity. This establishes PNN status as a molecular correlate of PV cell specialization, with implications for therapeutic strategies targeting cortical plasticity.

neuroscience↗

bioRxiv2024

Task and Behavior-Related Variables Are Encoded by the Postrhinal and Medial Entorhinal Cortex During Non-Spatial Associative Learning

The medial entorhinal cortex (MEC) is pivotal in spatial computations and episodic memory. In particular, an animals position can be decoded from the activity of entorhinal grid cells. However, it remains elusive whether MEC could play a more general role in different types of associative learning and how the representations develop during the learning process. It has been shown that the postrhinal cortex (POR), which is directly connected to MEC, integrates visual stimuli with salient outcomes. Here, we use a non-spatial visual association task to investigate whether MEC neurons represent low-level visual cues during learning. Using a Go/NoGo visual association task, we recorded neural activity in MEC and POR throughout the learning phase as mice associated drifting gratings with rewarded, aversive, or neutral outcomes. Our findings reveal that the neural tuning curves in both the POR and MEC change with the learning of the task. From the start of training, the POR neurons exhibited response tuning to the visual cues, and the tuning was stable to cue orientations during learning. In contrast, MEC neurons did not initially respond very strongly to visual cues but developed a robust tuning toward the rewarded trials. While the MECs representation of visual information was limited, it encoded other task elements. A large fraction of the neurons formed distinct functional clusters that were either activated or suppressed by reward-related behavior. Remarkably, these clusters segregated anatomically in MEC and maintained strong within-cluster correlations before and after training. Notably, although the same functional clusters were apparent in the POR, they did not show any anatomical structure as in the MEC. Task reversal induced significant changes in network responses across both regions, with a decrease in overall task-responsive neurons but a slight increase in stimulus representation. Strikingly, information about the choice to lick emerged with learning in both brain areas, and most significantly within the functional cell clusters representing reward consumption and plus-cue stimulus. Our results demonstrate that although neurons in MEC and POR develop behavior-modulated tuning during learning of a non-spatial visual association task, the MEC exhibits stronger within-cluster correlations and anatomical organization. Conversely, the POR population exhibits less structural organization and more specific stimulus-tuning, which is reflective of being a higher visual association area. Our findings reveal that the MEC can encode task- and behavior-related variables beyond spatial information.

neuroscience↗

bioRxiv2024

Cortical Reactivations Modulated by Local InhibitoryCircuits Mediate Memory Consolidation

Highly salient events activate neurons across various brain regions. During subsequent rest or sleep, the activity patterns of these neurons often correlate with those observed during the preceding experience. Growing evidence suggests that these reactivations play a crucial role in memory consolidation, the process by which experiences are solidified in cortical networks for long-term storage. Here, we demonstrate that reactivations in the lateral visual cortex are vital for the consolidation of visual association learning. By employing longitudinal two-photon Ca2+ imaging alongside paired LFP recordings in the hippocampus and cortex, we show that targeted manipulation of PV+ inhibitory neurons in the lateral visual cortex after daily training selectively attenuated cue-specific reactivations and learning, with no apparent effect on normal network function during training. In contrast, reactivations in the control group were biased towards salient cues, aligned with learning process and persisted for hours after training had ended. Overall, our results underscore a crucial role for cortical reactivations in memory consolidation.

neuroscience↗

bioRxiv2024

Deciphering the Role of Aggrecan in Parvalbumin Interneurons: Unexpected Outcomes from a Conditional ACAN Knockout That Eliminates WFA+ Perineuronal Nets

The transition from juvenile to adult is accompanied by the maturation of inhibitory parvalbumin-positive (PV+) neurons and reduced plasticity. This transition involves the formation of perineuronal nets (PNNs), a dense configuration of the extracellular matrix that predominantly envelops parvalbumin-positive (PV+) neurons. Aggrecan, a proteoglycan encoded by the ACAN gene, has been shown to have a key role in the PNNs as knock-out of ACAN in the adult brain reactivates juvenile plasticity, but the contribution of different cell populations is unknown Here, we establish and characterize a mouse model in which ACAN is selectively knocked out (KO) in PV+ neurons (ACANflx/PVcre). Moreover, we develop a viral tool to perform similar cell-type directed KO in adult mice. Both models are compared with the traditional method of PNN removal, namely enzymatic degradation of PNNs with Chondroitinase ABC (chABC). We show that PV+ neurons in adult ACANflx/PVcre mice do not produce PNNs that are labeled by Wisteria floribunda agglutinin (WFA), the most commonly used PNN marker. Surprisingly, electrophysiological properties of PV+ interneurons in the visual cortex (V1) and ocular dominance plasticity of adult ACANflx/PVcre mice were similar to controls. In contrast, AAV-mediated ACAN knockout in adult mice increased ocular dominance plasticity. Moreover, in vivo chABC treatment of KO mice resulted in reduced firing rate of PV+ cells and increased frequency of spontaneous excitatory postsynaptic currents (sEPSC), a phenotype associated with chABC treatment of WT animals. This suggests compensatory mechanisms in the germline KO. Indeed, qPCR of bulk tissue indicates that other PNN components are expressed at higher levels in the KO animals. Finally, we perform memory-and behavioral testing to see if the lack of ACAN from PV+ neurons throughout development affected stereotypic behaviors and memory processing. ACANflx/PVcre mice have learning and memory abilities similar to controls, but use bold search strategies during navigation in the Morris water maze. The low level of anxiety-related behavior is confirmed in an open field and zero maze, where they spent nearly twice the time in open areas.

neuroscience↗

bioRxiv2019

Perineuronal nets stabilize the grid cell network

Grid cells are part of a widespread network that supports navigation and spatial memory. Stable grid patterns appear late in development, in concert with extracellular matrix aggregates termed perineuronal nets (PNNs) that condense around inhibitory neurons. To reveal the relationship between stable spatial representations and the presence of PNNs we recorded from populations of neurons in adult rats. We show that removal of PNNs leads to lower inhibitory spiking activity, and reduces grid cells ability to create stable representations of a novel environment. Furthermore, in animals with disrupted PNNs, exposure to a novel arena corrupted the spatiotemporal relationships within grid cell modules, and the stored representations of a familiar arena. Finally, we show that PNN removal in entorhinal cortex distorted spatial representations in downstream hippocampal neurons. Together this work suggests that PNNs provide a key stabilizing element for the grid cell network.

neuroscience↗

bioRxiv2018

Inferring causal connectivity from pairwise recordings and optogenetics

To understand the neural mechanisms underlying brain function, neuroscientists aim to quantify causal interactions between neurons, for instance by perturbing the activity of neuron A and measuring the effect on neuron B. Recently, manipulating neuron activity using light-sensitive opsins, optogenetics, has increased the specificity of neural perturbation. However, using widefield optogenetic interventions, multiple neurons are usually perturbed, producing a confound - any of the stimulated neurons can have affected the postsynaptic neuron making it challenging to discern which neurons produced the causal effect. Here, we show how such confounds produce large biases in interpretations. We explain how confounding can be reduced by combining instrumental variables (IV) and difference in differences (DiD) techniques from econometrics. Combined, these methods can estimate (causal) effective connectivity by exploiting the weak, approximately random signal resulting from the interaction between stimulation and the absolute refractory period of the neuron. In simulated neural networks, we find that estimates using ideas from IV and DiD outperform naive techniques suggesting that methods from causal inference can be useful to disentangle neural interactions in the brain.

neuroscience↗

bioRxiv2018

Open source modules for tracking animal behavior and closed-loop stimulation based on Open Ephys and Bonsai

ObjectiveA major goal in systems neuroscience is to determine the causal relationship between neural activity and behavior. To this end, methods that combine monitoring neural activity, behavioral tracking, and targeted manipulation of neurons in closed-loop are powerful tools. However, commercial systems that allow these types of experiments are usually expensive and rely on non-standardized data formats and proprietary software which may hinder user-modifications for specific needs. In order to promote reproducibility and data-sharing in science, transparent software and standardized data formats are an advantage. Here, we present an open source, low-cost, adaptable, and easy to set-up system for combined behavioral tracking, electrophysiology and closed-loop stimulation.\n\nApproachBased on the Open Ephys system (www.open-ephys.org) we developed multiple modules to include real-time tracking and behavior-based closed-loop stimulation. We describe the equipment and provide a step-by-step guide to set up the system. Combining the open source software Bonsai (bonsai-rx.org) for analyzing camera images in real time with the newly developed modules in Open Ephys, we acquire position information, visualize tracking, and perform tracking-based closed-loop stimulation experiments. To analyze the acquired data we provide an open source file reading package in Python.\n\nMain resultsThe system robustly visualizes real-time tracking and reliably recovers tracking information recorded from a range of sampling frequencies (30-1000Hz). We combined electrophysiology with the newly-developed tracking modules in Open Ephys to record place cell and grid cell activity in the hippocampus and in the medial entorhinal cortex, respectively. Moreover, we present a case in which we used the system for closed-loop optogenetic stimulation of entorhinal grid cells.\n\nSignificanceExpanding the Open Ephys system to include animal tracking and behavior-based closed-loop stimulation extends the availability of high-quality, low-cost experimental setup within standardized data formats serving the neuroscience community.

neuroscience↗

bioRxiv2018

Experimental Directory Structure (Exdir): An alternative to HDF5 without introducing a new file format

Natural sciences generate an increasing amount of data in a wide range of formats developed by different research groups and commercial companies. At the same time there is a growing desire to share data along with publications in order to enable reproducible research. Open formats have publicly available specifications which facilitate data sharing and reproducible research. Hierarchical Data Format 5 (HDF5) is a popular open format widely used in neuroscience, often as a foundation for other, more specialized formats. However, drawbacks related to HDF5s complex specification have initiated a discussion for an improved replacement. We propose a novel alternative, the Experimental Directory Structure (Exdir), an open standard for data storage in experimental pipelines which amends drawbacks associated with HDF5 while retaining its advantages. HDF5 stores data and metadata in a hierarchy within a complex binary file which, among other things, is not human-readable, not optimal for version control systems, and lacks support for storing raw data. Exdir, one the other hand, uses file system directories to represent the hierarchy, with metadata stored in human-readable YAML files, datasets stored in binary NumPy files, and raw data stored directly in subdirectories. Furthermore, storing data in multiple files makes it easier to track for version control systems. Exdir is not a file format in itself, but a standard for organizing files in a directory structure. Exdir uses the same abstractions as HDF5 and is compatible with the HDF5 Abstract Data Model. Several research groups are already using data stored in a directory hierarchy as an alternative to HDF5, but no common standard exists in the field. This complicates and limits the opportunity for data sharing and development of common tools for reading, writing, and analyzing data. Exdir facilitates improved data storage, data sharing, reproducible research, and novel insight from interdisciplinary collaboration. With the publication of Exdir, we invite the scientific community to join the development to create an open standard that will serve as many needs as possible and that will serve as a foundation for open access to and exchange of data.\n\nSIGNIFICANCE STATEMENTExperimental Directory Structure (Exdir) is a proposal to standardize a storage solution that has become an increasingly popular alternative to Hierarchical Data Format 5 (HDF5), namely to use directories to define a hierarchy, store data in binary files, and metadata in text files. While this strategy is deployed locally by several research groups, no common standard exists. We envision the establishment of such a standard and present Exdir to the community as a starting point.

bioinformatics↗

bioRxiv2018

Firing-Rate Based Network Modeling of the dLGN Circuit: Effects of Cortical Feedback on Spatiotemporal Response Properties of Relay Cells

Visual signals originating in the retina pass through the dorsal geniculate nucleus (dLGN), the visual part of thalamus, on the way to the visual cortex. This is however not a simple feedforward flow of information: there is a significant feedback from cortical cells back to both relay cells and interneurons in the dLGN. Despite four decades of experimental and theoretical studies, the functional role of this feedback is still debated. Here we use a firing-rate model, the extended difference-of-gaussians (eDOG) model, to explore cortical feedback effects on visual responses of dLGN relay cells. For this model the responses are found by direct evaluation of two- or three-dimensional integrals allowing for fast and comprehensive studies of putative effects of different candidate organizations of the cortical feedback. Our analysis identifies a special mixed configuration of excitatory and inhibitory cortical feedback which seems to best account for available experimental data. This configuration consists of a slow (long-delay) and spatially widespread inhibitory feedback, combined with a fast (short-delayed) and spatially narrow excitatory feedback, where (iii) the excitatory/inhibitory ON-ON connections are accompanied respectively by inhibitory/excitatory OFF-ON connections, i.e. following a phase-reversed arrangement. The recent development of optogenetic and pharmacogenetic methods has provided new tools for more precise manipulation and investigation of the thalamocortical circuit, in particular for mice. Such data will expectedly allow the eDOG model to be better constrained by data from specific animal model systems than has been possible until now for cat. We have therefore made the Python tool pyLGN which allows for easy adaptation of the eDOG model to new situations.\n\nAuthor SummaryOn route from the retina to primary visual cortex, visually evoked signals have to pass through the dorsal lateral geniculate nucleus (dLGN). However, this is not an exclusive feed forward flow of information as feedback exists from neurons in the cortex back to both relay cells and interneurons in the dLGN. The functional role of this feedback remains mostly unresolved. Here, we use a firing-rate model, the extended difference-of-gaussians (eDOG) model, to explore cortical feedback effects on visual responses of dLGN relay cells. Our analysis indicates that a particular mix of excitatory and inhibitory cortical feedback agrees best with available experimental observations. In this configuration ON-center relay cells receive both excitatory and (indirect) inhibitory feedback from ON-center cortical cells (ON-ON feedback) where the excitatory feedback is fast and spatially narrow while the inhibitory feedback is slow and spatially widespread. In addition to the ON-ON feedback, the connections are accompanied by OFF-ON connections following a so-called phase-reversed (push-pull) arrangement. To facilitate further applications of the model, we have made the Python tool pyLGN which allows for easy modification and evaluation of the a priori quite general eDOG model to new situations.

neuroscience↗