A chemogenetic approach for dopamine imaging with tunable sensitivity.

Genetically-encoded dopamine (DA) sensors enable high-resolution imaging of DA release, but their ability to detect a wide range of extracellular DA levels, especially tonic versus phasic DA release, is limited by their intrinsic affinity. Here we show that a human-selective dopamine receptor positive allosteric modulator (PAM) can be used to boost sensor affinity on-demand. The PAM enhances DA detection sensitivity across experimental preparations (in vitro, ex vivo and in vivo) via one-photon or two-photon imaging. In vivo photometry-based detection of optogenetically-evoked DA release revealed that DETQ administration produces a stable 31 minutes window of potentiation without effects on animal behavior. The use of the PAM revealed region-specific and metabolic state-dependent differences in tonic DA levels and enhanced single-trial detection of behavior-evoked phasic DA release in cortex and striatum. Our chemogenetic strategy can potently and flexibly tune DA imaging sensitivity and reveal multi-modal (tonic/phasic) DA signaling across preparations and imaging approaches.

Sampling bias corrections for accurate neural measures of redundant, unique, and synergistic information.

Shannon Information theory has long been a tool of choice to measure empirically how populations of neurons in the brain encode information about cognitive variables. Recently, Partial Information Decomposition (PID) has emerged as principled way to break down this information into components identifying not only the unique information carried by each neuron, but also whether relationships between neurons generate synergistic or redundant information. While it has been long recognized that Shannon information measures on neural activity suffer from a (mostly upward) limited sampling estimation bias, this issue has largely been ignored in the burgeoning field of PID analysis of neural activity. We used simulations to investigate the limited sampling bias of PID computed from discrete probabilities (suited to describe neural spiking activity). We found that PID suffers from a large bias that is uneven across components, with synergy by far the most biased. Using approximate analytical expansions, we found that the bias of synergy increases quadratically with the number of discrete responses of each neuron, whereas the bias of unique and redundant information increase only linearly or sub-linearly. Based on the understanding of the PID bias properties, we developed simple yet effective procedures that correct for the bias effectively, and that improve greatly the PID estimation with respect to current state-of-the-art procedures. We apply these PID bias correction procedures to datasets of 53117 pairs neurons in auditory cortex, posterior parietal cortex and hippocampus of mice performing cognitive tasks, deriving precise estimates and bounds of how synergy and redundancy vary across these brain regions.

Cortico-cortical transfer of socially derived information gates emotion recognition.

Emotion recognition and the resulting responses are important for survival and social functioning. However, how socially derived information is processed for reliable emotion recognition is incompletely understood. Here, we reveal an evolutionarily conserved long-range inhibitory/excitatory brain network mediating these socio-cognitive processes. Anatomical tracing in mice revealed the existence of a subpopulation of somatostatin (SOM) GABAergic neurons projecting from the medial prefrontal cortex (mPFC) to the retrosplenial cortex (RSC). Through optogenetic manipulations and Ca2+ imaging fiber photometry in mice and functional imaging in humans, we demonstrate the specific participation of these long-range SOM projections from the mPFC to the RSC, and an excitatory feedback loop from the RSC to the mPFC, in emotion recognition. Notably, we show that mPFC-to-RSC SOM projections are dysfunctional in mouse models relevant to psychiatric vulnerability and can be targeted to rescue emotion recognition deficits in these mice. Our findings demonstrate a cortico-cortical circuit underlying emotion recognition.

Early IGF-1 receptor inhibition in mice mimics preterm human brain disorders and reveals a therapeutic target.

Besides recent advances in neonatal care, preterm newborns still develop sex-biased behavioral alterations. Preterms fail to receive placental insulin-like growth factor-1 (IGF-1), a major fetal growth hormone in utero, and low IGF-1 serum levels correlate with preterm poor neurodevelopmental outcomes. Here, we mimicked IGF-1 deficiency of preterm newborns in mice by perinatal administration of an IGF-1 receptor antagonist. This resulted in sex-biased brain microstructural, functional, and behavioral alterations, resembling those of ex-preterm children, which we characterized performing parallel mouse/human behavioral tests. Pharmacological enhancement of GABAergic tonic inhibition by the U.S. Food and Drug Administration-approved drug ganaxolone rescued functional/behavioral alterations in mice. Establishing an unprecedented mouse model of prematurity, our work dissects the mechanisms at the core of abnormal behaviors and identifies a readily translatable therapeutic strategy for preterm brain disorders.

Engineering memory with an extrinsically disordered kinase.

Synaptic plasticity plays a crucial role in memory formation by regulating the communication between neurons. Although actin polymerization has been linked to synaptic plasticity and dendritic spine stability, the causal link between actin polymerization and memory encoding has not been identified yet. It is not clear whether actin polymerization and structural changes in dendritic spines are a driver or a consequence of learning and memory. Using an extrinsically disordered form of the protein kinase LIMK1, which rapidly and precisely acts on ADF/cofilin, a direct modifier of actin, we induced long-term enlargement of dendritic spines and enhancement of synaptic transmission in the hippocampus on command. The activation of extrinsically disordered LIMK1 in vivo improved memory encoding and slowed cognitive decline in aged mice exhibiting reduced cofilin phosphorylation. The engineered memory by an extrinsically disordered LIMK1 supports a direct causal link between actin-mediated synaptic transmission and memory.

Sensitive multicolor indicators for monitoring norepinephrine in vivo.

Genetically encoded indicators engineered from G-protein-coupled receptors are important tools that enable high-resolution in vivo neuromodulator imaging. Here, we introduce a family of sensitive multicolor norepinephrine (NE) indicators, which includes nLightG (green) and nLightR (red). These tools report endogenous NE release in vitro, ex vivo and in vivo with improved sensitivity, ligand selectivity and kinetics, as well as a distinct pharmacological profile compared with previous state-of-the-art GRABNE indicators. Using in vivo multisite fiber photometry recordings of nLightG, we could simultaneously monitor optogenetically evoked NE release in the mouse locus coeruleus and hippocampus. Two-photon imaging of nLightG revealed locomotion and reward-related NE transients in the dorsal CA1 area of the hippocampus. Thus, the sensitive NE indicators introduced here represent an important addition to the current repertoire of indicators and provide the means for a thorough investigation of the NE system.

ASTRA: a deep learning algorithm for fast semantic segmentation of large-scale astrocytic networks.

Changes in the intracellular calcium concentration are a fundamental fingerprint of astrocytes, the main type of glial cell. Astrocyte calcium signals can be measured with two-photon microscopy, occur in anatomically restricted subcellular regions, and are coordinated across astrocytic networks. However, current analytical tools to identify the astrocytic subcellular regions where calcium signals occur are time-consuming and extensively rely on user-defined parameters. These limitations limit reproducibility and prevent scalability to large datasets and fields-of-view. Here, we present Astrocytic calcium Spatio-Temporal Rapid Analysis (ASTRA), a novel software combining deep learning with image feature engineering for fast and fully automated semantic segmentation of two-photon calcium imaging recordings of astrocytes. We applied ASTRA to several two-photon microscopy datasets and found that ASTRA performed rapid detection and segmentation of astrocytic cell somata and processes with performance close to that of human experts, outperformed state-of-the-art algorithms for the analysis of astrocytic and neuronal calcium data, and generalized across indicators and acquisition parameters. We also applied ASTRA to the first report of two-photon mesoscopic imaging of hundreds of astrocytes in awake mice, documenting large-scale redundant and synergistic interactions in extended astrocytic networks. ASTRA is a powerful tool enabling closed-loop and large-scale reproducible investigation of astrocytic morphology and function.

SmaRT2P: a software for generating and processing smart line recording trajectories for population two-photon calcium imaging.

Two-photon fluorescence calcium imaging allows recording the activity of large neural populations with subcellular spatial resolution, but it is typically characterized by low signal-to-noise ratio (SNR) and poor accuracy in detecting single or few action potentials when large number of neurons are imaged. We recently showed that implementing a smart line scanning approach using trajectories that optimally sample the regions of interest increases both the SNR fluorescence signals and the accuracy of single spike detection in population imaging in vivo. However, smart line scanning requires highly specialised software to design recording trajectories, interface with acquisition hardware, and efficiently process acquired data. Furthermore, smart line scanning needs optimized strategies to cope with movement artefacts and neuropil contamination. Here, we develop and validate SmaRT2P, an open-source, user-friendly and easy-to-interface Matlab-based software environment to perform optimized smart line scanning in two-photon calcium imaging experiments. SmaRT2P is designed to interface with popular acquisition software (e.g., ScanImage) and implements novel strategies to detect motion artefacts, estimate neuropil contamination, and minimize their impact on functional signals extracted from neuronal population imaging. SmaRT2P is structured in a modular way to allow flexibility in the processing pipeline, requiring minimal user intervention in parameter setting. The use of SmaRT2P for smart line scanning has the potential to facilitate the functional investigation of large neuronal populations with increased SNR and accuracy in detecting the discharge of single and few action potentials.

Network States Classification based on Local Field Potential Recordings in the Awake Mouse Neocortex.

Recent studies using intracellular recordings in awake behaving mice revealed that cortical network states, defined based on membrane potential features, modulate sensory responses and perceptual outcomes. Single-cell intracellular recordings are difficult and have low yield compared to extracellular recordings of population signals, such as local field potentials (LFPs). However, it is currently unclear how to identify these behaviorally-relevant network states from the LFP. We used simultaneous LFP and intracellular recordings in the somatosensory cortex of awake mice to design a network state classification from the LFP, the Network State Index (NSI). We used the NSI to analyze the relationship between single-cell (intracellular) and population (LFP) signals over different network states of wakefulness. We found that graded levels of population signal faithfully predicted the levels of single-cell depolarization in nonrhythmic regimes whereas, in δ ([2-4 Hz]) oscillatory regimes, the graded levels of rhythmicity in the LFP mapped into a stereotypical oscillatory pattern of membrane potential. Finally, we showed that the variability of network states, beyond the occurrence of slow oscillatory activity, critically shaped the average correlations between single-cell and population signals. Application of the LFP-based NSI to mouse visual cortex data showed that this index increased with pupil size and during locomotion and had a U-shaped dependence on population firing rates. NSI-based characterization provides a ready-to-use tool to understand from LFP recordings how the modulation of local network dynamics shapes the flexibility of sensory processing during behavior.

Paradoxical somatodendritic decoupling supports cortical plasticity during REM sleep.

Rapid eye movement (REM) sleep is associated with the consolidation of emotional memories. Yet, the underlying neocortical circuits and synaptic mechanisms remain unclear. We found that REM sleep is associated with a somatodendritic decoupling in pyramidal neurons of the prefrontal cortex. This decoupling reflects a shift of inhibitory balance between parvalbumin neuron-mediated somatic inhibition and vasoactive intestinal peptide-mediated dendritic disinhibition, mostly driven by neurons from the central medial thalamus. REM-specific optogenetic suppression of dendritic activity led to a loss of danger-versus-safety discrimination during associative learning and a lack of synaptic plasticity, whereas optogenetic release of somatic inhibition resulted in enhanced discrimination and synaptic potentiation. Somatodendritic decoupling during REM sleep promotes opposite synaptic plasticity mechanisms that optimize emotional responses to future behavioral stressors.

A deep-learning approach for online cell identification and trace extraction in functional two-photon calcium imaging.

In vivo two-photon calcium imaging is a powerful approach in neuroscience. However, processing two-photon calcium imaging data is computationally intensive and time-consuming, making online frame-by-frame analysis challenging. This is especially true for large field-of-view (FOV) imaging. Here, we present CITE-On (Cell Identification and Trace Extraction Online), a convolutional neural network-based algorithm for fast automatic cell identification, segmentation, identity tracking, and trace extraction in two-photon calcium imaging data. CITE-On processes thousands of cells online, including during mesoscopic two-photon imaging, and extracts functional measurements from most neurons in the FOV. Applied to publicly available datasets, the offline version of CITE-On achieves performance similar to that of state-of-the-art methods for offline analysis. Moreover, CITE-On generalizes across calcium indicators, brain regions, and acquisition parameters in anesthetized and awake head-fixed mice. CITE-On represents a powerful tool to speed up image analysis and facilitate closed-loop approaches, for example in combined all-optical imaging and manipulation experiments.

Complementary encoding of spatial information in hippocampal astrocytes.

Calcium dynamics into astrocytes influence the activity of nearby neuronal structures. However, because previous reports show that astrocytic calcium signals largely mirror neighboring neuronal activity, current information coding models neglect astrocytes. Using simultaneous two-photon calcium imaging of astrocytes and neurons in the hippocampus of mice navigating a virtual environment, we demonstrate that astrocytic calcium signals encode (i.e., statistically reflect) spatial information that could not be explained by visual cue information. Calcium events carrying spatial information occurred in topographically organized astrocytic subregions. Importantly, astrocytes encoded spatial information that was complementary and synergistic to that carried by neurons, improving spatial position decoding when astrocytic signals were considered alongside neuronal ones. These results suggest that the complementary place dependence of localized astrocytic calcium signals may regulate clusters of nearby synapses, enabling dynamic, context-dependent variations in population coding within brain circuits.

A genetically encoded sensor for in vivo imaging of orexin neuropeptides.

Orexins (also called hypocretins) are hypothalamic neuropeptides that carry out essential functions in the central nervous system; however, little is known about their release and range of action in vivo owing to the limited resolution of current detection technologies. Here we developed a genetically encoded orexin sensor (OxLight1) based on the engineering of circularly permutated green fluorescent protein into the human type-2 orexin receptor. In mice OxLight1 detects optogenetically evoked release of endogenous orexins in vivo with high sensitivity. Photometry recordings of OxLight1 in mice show rapid orexin release associated with spontaneous running behavior, acute stress and sleep-to-wake transitions in different brain areas. Moreover, two-photon imaging of OxLight1 reveals orexin release in layer 2/3 of the mouse somatosensory cortex during emergence from anesthesia. Thus, OxLight1 enables sensitive and direct optical detection of orexin neuropeptides with high spatiotemporal resolution in living animals.

Stimulus Feature-Specific Control of Layer 2/3 Subthreshold Whisker Responses by Layer 4 in the Mouse Primary Somatosensory Cortex.

In the barrel field of the rodent primary somatosensory cortex (S1bf), excitatory cells in layer 2/3 (L2/3) display sparse firing but reliable subthreshold response during whisker stimulation. Subthreshold responses encode specific features of the sensory stimulus, for example, the direction of whisker deflection. According to the canonical model for the flow of sensory information across cortical layers, activity in L2/3 is driven by layer 4 (L4). However, L2/3 cells receive excitatory inputs from other regions, raising the possibility that L4 partially drives L2/3 during whisker stimulation. To test this hypothesis, we combined patch-clamp recordings from L2/3 pyramidal neurons in S1bf with selective optogenetic inhibition of L4 during passive whisker stimulation in both anesthetized and awake head-restrained mice. We found that L4 optogenetic inhibition did not abolish the subthreshold whisker-evoked response nor it affected spontaneous membrane potential fluctuations of L2/3 neurons. However, L4 optogenetic inhibition decreased L2/3 subthreshold responses to whisker deflections in the preferred direction, and it increased L2/3 responses to stimuli in the nonpreferred direction, leading to a change in the direction tuning. Our results contribute to reveal the circuit mechanisms underlying the processing of sensory information in the rodent S1bf.

Optogenetic strategies for high-efficiency all-optical interrogation using blue-light-sensitive opsins.

All-optical methods for imaging and manipulating brain networks with high spatial resolution are fundamental to study how neuronal ensembles drive behavior. Stimulation of neuronal ensembles using two-photon holographic techniques requires high-sensitivity actuators to avoid photodamage and heating. Moreover, two-photon-excitable opsins should be insensitive to light at wavelengths used for imaging. To achieve this goal, we developed a novel soma-targeted variant of the large-conductance blue-light-sensitive opsin CoChR (stCoChR). In the mouse cortex in vivo, we combined holographic two-photon stimulation of stCoChR with an amplified laser tuned at the opsin absorption peak and two-photon imaging of the red-shifted indicator jRCaMP1a. Compared to previously characterized blue-light-sensitive soma-targeted opsins in vivo, stCoChR allowed neuronal stimulation with more than 10-fold lower average power and no spectral crosstalk. The combination of stCoChR, tuned amplified laser stimulation, and red-shifted functional indicators promises to be a powerful tool for large-scale interrogation of neural networks in the intact brain.

Computation of the electroencephalogram (EEG) from network models of point neurons.

The electroencephalogram (EEG) is a major tool for non-invasively studying brain function and dysfunction. Comparing experimentally recorded EEGs with neural network models is important to better interpret EEGs in terms of neural mechanisms. Most current neural network models use networks of simple point neurons. They capture important properties of cortical dynamics, and are numerically or analytically tractable. However, point neurons cannot generate an EEG, as EEG generation requires spatially separated transmembrane currents. Here, we explored how to compute an accurate approximation of a rodent's EEG with quantities defined in point-neuron network models. We constructed different approximations (or proxies) of the EEG signal that can be computed from networks of leaky integrate-and-fire (LIF) point neurons, such as firing rates, membrane potentials, and combinations of synaptic currents. We then evaluated how well each proxy reconstructed a ground-truth EEG obtained when the synaptic currents of the LIF model network were fed into a three-dimensional network model of multicompartmental neurons with realistic morphologies. Proxies based on linear combinations of AMPA and GABA currents performed better than proxies based on firing rates or membrane potentials. A new class of proxies, based on an optimized linear combination of time-shifted AMPA and GABA currents, provided the most accurate estimate of the EEG over a wide range of network states. The new linear proxies explained 85-95% of the variance of the ground-truth EEG for a wide range of network configurations including different cell morphologies, distributions of presynaptic inputs, positions of the recording electrode, and spatial extensions of the network. Non-linear EEG proxies using a convolutional neural network (CNN) on synaptic currents increased proxy performance by a further 2-8%. Our proxies can be used to easily calculate a biologically realistic EEG signal directly from point-neuron simulations thus facilitating a quantitative comparison between computational models and experimental EEG recordings.

Extended field-of-view ultrathin microendoscopes for high-resolution two-photon imaging with minimal invasiveness.

Imaging neuronal activity with high and homogeneous spatial resolution across the field-of-view (FOV) and limited invasiveness in deep brain regions is fundamental for the progress of neuroscience, yet is a major technical challenge. We achieved this goal by correcting optical aberrations in gradient index lens-based ultrathin (≤500 µm) microendoscopes using aspheric microlenses generated through 3D-microprinting. Corrected microendoscopes had extended FOV (eFOV) with homogeneous spatial resolution for two-photon fluorescence imaging and required no modification of the optical set-up. Synthetic calcium imaging data showed that, compared to uncorrected endoscopes, eFOV-microendoscopes led to improved signal-to-noise ratio and more precise evaluation of correlated neuronal activity. We experimentally validated these predictions in awake head-fixed mice. Moreover, using eFOV-microendoscopes we demonstrated cell-specific encoding of behavioral state-dependent information in distributed functional subnetworks in a primary somatosensory thalamic nucleus. eFOV-microendoscopes are, therefore, small-cross-section ready-to-use tools for deep two-photon functional imaging with unprecedentedly high and homogeneous spatial resolution.

Temporal Sharpening of Sensory Responses by Layer V in the Mouse Primary Somatosensory Cortex.

The timing of stimulus-evoked spikes encodes information about sensory stimuli. Here we studied the neural circuits controlling this process in the mouse primary somatosensory cortex. We found that brief optogenetic activation of layer V pyramidal cells just after whisker deflection modulated the membrane potential of neurons and interrupted their long-latency whisker responses, increasing their accuracy in encoding whisker deflection time. In contrast, optogenetic inhibition of layer V during either passive whisker deflection or active whisking decreased accuracy in encoding stimulus or touch time, respectively. Suppression of layer V pyramidal cells increased reaction times in a texture discrimination task. Moreover, two-color optogenetic experiments revealed that cortical inhibition was efficiently recruited by layer V stimulation and that it mainly involved activation of parvalbumin-positive rather than somatostatin-positive interneurons. Layer V thus performs behaviorally relevant temporal sharpening of sensory responses through circuit-specific recruitment of cortical inhibition.

High-Accuracy Detection of Neuronal Ensemble Activity in Two-Photon Functional Microscopy Using Smart Line Scanning.

Two-photon functional imaging using genetically encoded calcium indicators (GECIs) is one prominent tool to map neural activity. Under optimized experimental conditions, GECIs detect single action potentials in individual cells with high accuracy. However, using current approaches, these optimized conditions are never met when imaging large ensembles of neurons. Here, we developed a method that substantially increases the signal-to-noise ratio (SNR) of population imaging of GECIs by using galvanometric mirrors and fast smart line scan (SLS) trajectories. We validated our approach in anesthetized and awake mice on deep and dense GCaMP6 staining in the mouse barrel cortex during spontaneous and sensory-evoked activity. Compared to raster population imaging, SLS led to increased SNR, higher probability of detecting calcium events, and more precise identification of functional neuronal ensembles. SLS provides a cheap and easily implementable tool for high-accuracy population imaging of neural GCaMP6 signals by using galvanometric-based two-photon microscopes.