Biology subjects

Dard, R. F.

Publications and source records attributed to Dard, R. F..

6 recordsLinked to original sources

bioRxiv2026

CICADA: A unified framework for NWB-based neurophysiological data analysis

Neurophysiology datasets are becoming increasingly complex, combining behavioral measurements with high-dimensional neuronal activity recordings coming from optical and/or electrophysiological measurements. The Neurodata Without Borders (NWB) standard has emerged in the community as the format of record. While standardized and widely used preprocessing tools generating NWB files have been developed, extensible frameworks for scientific analysis downstream of the NWB ecosystem are still under-represented. We present CICADA, a Python framework dedicated to analysis of neurophysiological data in the standardized NWB format. The toolbox is built as three hierarchically-organized packages: cicada-nwb (NWB access layer), cicada-analysis (plugin-based analysis engine and tool library), and cicada-gui (PyQt5 desktop application at the head of the pipeline). Beyond this architectural separation, CICADA is built around a central design principle: supporting a continuum from turnkey use to full modularity. Researchers can use the complete GUI-driven cicada-gui workflow without writing code, programmatically use existing analysis plugins from cicada-analysis, contribute to new analysis plugins, reuse utilities from cicada-tools, or build entirely custom pipelines on top of the cicada-nwb access layer alone. The same analysis plugin runs identically in interactive GUI and parameter-configured headless modes, enabling reproducible multi-session, multi-animal group analyses. We illustrate the versatility of CICADA with example analyses of behavioral, calcium imaging (two-photon and widefield) and extracellular electrophysiology datasets from rodent laboratories. CICADA is open source, actively maintained, and designed so that any laboratory can contribute at any level of the stack without modifying the core framework.

neuroscience↗

bioRxiv2025

Distinct developmental trajectories of externally- and internally-generated hippocampal sequences and assemblies

The hippocampus generates cognitive maps through two distinct mechanisms: external representations anchored to environmental landmarks and internal sequences integrating self-referenced information. In adult CA1, these functions segregate radially, with deep cells processing external cues and superficial cells handling internal representations. This anatomical organization suggests that developmental processes constrain the formation of these functionally distinct circuits. However, the developmental timeline for the emergence of these two types of hippocampal representations remains unknown. Using longitudinal two-photon calcium imaging in head-fixed mice during the third postnatal week, we investigated when internal versus external hippocampal representations emerge. We recorded from deep and superficial CA1 layers while mice ran on treadmills that are bare or enriched with tactile cues, addressing the challenge of tracking cells across sessions. Internal sequences emerge during the mid-fourth postnatal week, following the development of cue-based representations. These sequences initially encode elapsed time before evolving to integrate run distance, arising after rest cell assemblies as spatial coding and functional connectivity networks stabilize. These findings identify the mid fourth postnatal week as a critical developmental milestone marking the establishment of a balanced integration between external landmark-based and internal self-referenced spatial representations. This transition period may constitute a vulnerable developmental window during which disruptions could predispose to disorders characterised by altered cognitive maps. HighlightsO_LIP24 is critical for CA1 development C_LIO_LICue-based representations stabilize by P24 C_LIO_LIInternal sequences emerge at P24, encoding run duration before distance C_LIO_LICell assemblies form before internal sequences C_LI

neuroscience↗

bioRxiv2022

Cortical neuronal assemblies coordinate with EEG microstate dynamics during resting wakefulness.

The disruption of cortical assembly activity has been associated with anesthesia-induced loss of consciousness. However, information about the relationship between cortical assembly activity and the variations in consciousness associated with natural vigilance states is currently lacking. To address this, we performed vigilance state-specific clustering analysis on 2-photon calcium imaging data from sensorimotor cortex in combination with global EEG microstate analysis derived from multi-EEG signals obtained from widespread cortical locations. Our analysis revealed no difference in the structure of assembly activity during quiet wakefulness (QW), NREMs, or REMs, despite the latter two vigilance states being associated with significantly reduced levels of consciousness relative to QW. However, we found a significant coordination between global EEG microstate dynamics and local cortical assembly activity during periods of QW, but not sleep. These results suggest that the coordination of cortical assembly activity with global brain dynamics could be a key factor of sustained conscious experience.

neuroscience↗

bioRxiv2022

Extrinsic control of the early postnatal CA1 hippocampal circuits

The adult CA1 region of the hippocampus produces coordinated neuronal dynamics with minimal reliance on its extrinsic inputs. In contrast, the neonatal CA1 is tightly linked to externally-generated sensorimotor activity but the circuit mechanisms underlying early synchronous activity in CA1 remain unclear. Here, using a combination of in vivo and ex vivo circuit mapping, calcium imaging and electrophysiological recordings in mouse pups, we show that early dynamics in the ventro-intermediate CA1 are under the mixed influence of entorhinal (EC) and thalamic (VMT) inputs. Both VMT and EC can drive internally-generated synchronous events ex vivo. However, movement-related population bursts detected in vivo are exclusively driven by the EC. These differential effects on synchrony reflect the different intrahippocampal targets of these inputs. Hence, cortical and subcortical pathways act differently on the neonatal CA1, implying distinct contributions to the development of the hippocampal microcircuit and related cognitive maps.

neuroscience↗

bioRxiv2021

The rapid developmental rise of somatic inhibition disengages hippocampal dynamics from self-motion

Early electrophysiological brain oscillations recorded in preterm babies and newborn rodents are initially mostly ignited by bottom-up sensorimotor activity and only later can detach from external inputs. This is a hallmark of most developing brain areas including the hippocampus, which in the adult brain, functions in integrating external inputs onto internal dynamics. Such developmental disengagement from external inputs is likely a fundamental step for the proper development of cognitive internal models. Despite its importance, the developmental timeline and circuit basis for this disengagement remain unknown. To address this issue, we have investigated the daily evolution of CA1 dynamics and underlying circuits during the first two postnatal weeks of mouse development using two-photon calcium imaging in non-anesthetized pups. We show that the first postnatal week ends with an abrupt shift in the representation of self-motion in CA1. Indeed, most CA1 pyramidal cells switch from activated to inhibited by self-generated movements at the end of the first postnatal week whereas the majority of GABAergic neurons remain positively modulated throughout this period. This rapid switch occurs within two days and follows the rapid anatomical and functional surge of local somatic GABAergic innervation. The observed change in dynamics is consistent with a two-population model undergoing a strengthening of inhibition. We propose that this abrupt developmental transition inaugurates the emergence of internal cognitive models.

neuroscience↗

bioRxiv2019

DeepCINAC: a deep-learning-based Python toolbox for inferring calcium imaging neuronal activity based on movie visualization

Two-photon calcium imaging is now widely used to infer neuronal dynamics from changes in fluorescence of an indicator. However, state of the art computational tools are not optimized for the reliable detection of fluorescence transients from highly synchronous neurons located in densely packed regions such as the CA1 pyramidal layer of the hippocampus during early postnatal stages of development. Indeed, the latest analytical tools often lack proper benchmark measurements. To meet this challenge, we first developed a graphical user interface allowing for a precise manual detection of all calcium transients from imaged neurons based on the visualization of the calcium imaging movie. Then, we analyzed the movies using a convolutional neural network with an attention process and a bidirectional long-short term memory network. This method is able to reach human performance and offers a better F1 score (harmonic mean of sensitivity and precision) than CaImAn to infer neural activity in the developing CA1 without any user intervention. It also enables automatically identifying activity originating from GABAergic neurons. Overall, DeepCINAC offers a simple, fast and flexible open-source toolbox for processing a wide variety of calcium imaging datasets while providing the tools to evaluate its performance. Significance statementInferring neuronal activity from calcium imaging data remains a challenge due to the difficulty in obtaining a ground truth using patch clamp recordings and the problem of finding optimal tuning parameters of inference algorithms. DeepCINAC offers a flexible, fast and easy-to-use toolbox to infer neuronal activity from any kind of calcium imaging dataset through visual inspection.

neuroscience↗