Months-long tracking of neuronal ensembles spanning multiple brain areas with Ultra-Flexible Tentacle Electrodes.

We introduce Ultra-Flexible Tentacle Electrodes (UFTEs), packing many independent fibers with the smallest possible footprint without limitation in recording depth using a combination of mechanical and chemical tethering for insertion. We demonstrate a scheme to implant UFTEs simultaneously into many brain areas at arbitrary locations without angle-of-insertion limitations, and a 512-channel wireless logger. Immunostaining reveals no detectable chronic tissue damage even after several months. Mean spike signal-to-noise ratios are 1.5-3x compared to the state-of-the-art, while the highest signal-to-noise ratios reach 89, and average cortical unit yields are ~1.75/channel. UFTEs can track the same neurons across sessions for at least 10 months (longest duration tested). We tracked inter- and intra-areal neuronal ensembles (neurons repeatedly co-activated within 25 ms) simultaneously from hippocampus, retrosplenial cortex, and medial prefrontal cortex in freely moving rodents. Average ensemble lifetimes were shorter than the durations over which we can track individual neurons. We identify two distinct classes of ensembles. Those tuned to sharp-wave ripples display the shortest lifetimes, and the ensemble members are mostly hippocampal. Yet, inter-areal ensembles with members from both hippocampus and cortex have weak tuning to sharp wave ripples, and some have unusual months-long lifetimes. Such inter-areal ensembles occasionally remain inactive for weeks before re-emerging.

Deviance Distraction and Stimulus-Specific Adaptation in the Somatosensory Cortex Reduce with Experience.

Automatic detection of a surprising change in the sensory input is a central element of exogenous attentional control. Stimulus-specific adaptation (SSA) is a potential neuronal mechanism detecting such changes and has been robustly described across sensory modalities and different instances of the ascending sensory pathways. However, little is known about the relationship of SSA to perception. To assess how deviating stimuli influence target signal detection, we used a behavioral cross-modal paradigm in mice and combined it with extracellular recordings from the primary somatosensory whisker cortex. In this paradigm, male mice performed a visual detection task while task-irrelevant whisker stimuli were either presented as repetitive "standard" or as rare deviant stimuli. We found a deviance distraction effect on the animals' performance: Faster reaction times but worsened target detection was observed in the presence of a deviant stimulus. Multiunit activity and local field potentials exhibited enhanced neuronal responses to deviant compared with standard whisker stimuli across all cortical layers, as a result of SSA. The deviant-triggered behavioral distraction correlated with these enhanced neuronal deviant responses only in the deeper cortical layers. However, the layer-specific effect of SSA on perception reduced with increasing task experience as a result of statistical distractor learning. These results demonstrate a layer-specific involvement of SSA on perception that is susceptible to modulation over time.SIGNIFICANCE STATEMENT Detecting sudden changes in our immediate environment is behaviorally relevant and important for efficient perceptual processing. However, the connection between the underpinnings of cortical deviance detection and perception remains unknown. Here, we investigate how the cortical representation of deviant whisker stimuli impacts visual target detection by recording local field potential and multiunit activity in the primary somatosensory cortex of mice engaged in a cross-modal visual detection task. We find that deviant whisker stimuli distract animals in their task performance, which correlates with enhanced neuronal responses for deviants in a layer-specific manner. Interestingly, this effect reduces with the increased experience of the animal as a result of distractor learning on statistical regularities.

Bayesian surprise shapes neural responses in somatosensory cortical circuits.

Numerous psychophysical studies show that Bayesian inference governs sensory decision-making; however, the specific neural circuitry underlying this probabilistic mechanism remains unclear. We record extracellular neural activity along the somatosensory pathway of mice while delivering sensory stimulation paradigms designed to isolate the response to the surprise generated by Bayesian inference. Our results demonstrate that laminar cortical circuits in early sensory areas encode Bayesian surprise. Systematic sensitivity to surprise is not identified in the somatosensory thalamus, rather emerging in the primary (S1) and secondary (S2) somatosensory cortices. Multiunit spiking activity and evoked potentials in layer 6 of these regions exhibit the highest sensitivity to surprise. Gamma power in S1 layer 2/3 exhibits an NMDAR-dependent scaling with surprise, as does alpha power in layers 2/3 and 6 of S2. These results show a precise spatiotemporal neural representation of Bayesian surprise and suggest that Bayesian inference is a fundamental component of cortical processing.

Projections from the five divisions of the orbital cortex to the thalamus in the rat.

The orbital cortex (ORB) of the rat consists of five divisions: the medial (MO), ventral (VO), ventrolateral (VLO), lateral (LO), and dorsolateral (DLO) orbital cortices. No previous report has comprehensively examined and compared projections from each division of the ORB to the thalamus. Using the anterograde anatomical tracer, Phaseolus vulgaris leucoagglutinin, we describe the efferent projections from the five divisions of the ORB to the thalamus in the rat. We demonstrated that, with some overlap, each division of the ORB distributed in a distinct (and unique) manner to nuclei of the thalamus. Overall, ORB projected to a relatively restricted number of sites in the thalamus, and strikingly distributed entirely to structures of the medial/midline thalamus, while completely avoiding lateral regions or principal nuclei of the thalamus. The main termination sites in the thalamus were the paratenial nucleus (PT) and nucleus reuniens (RE) of the midline thalamus, the medial (MDm) and central (MDc) divisions of the mediodorsal nucleus, the intermediodorsal nucleus, the central lateral, paracentral, and central medial nuclei of the rostral intralaminar complex and the submedial nucleus (SM). With some exceptions, medial divisions of the ORB (MO, VO) mainly targeted "limbic-associated" nuclei such as PT, RE, and MDm, whereas lateral division (VLO, LO, DLO) primarily distributed to "sensorimotor-associated" nuclei including MDc, SM, and the rostral intralaminar complex. As discussed herein, the medial/midline thalamus may represent an important link (or bridge) between the orbital cortex and the hippocampus and between the ORB and medial prefrontal cortex. In summary, the present results demonstrate that each division of the orbital cortex projects in a distinct manner to nuclei of the thalamus which suggests unique functions for each division of the orbital cortex.

Machine learning reveals interhemispheric somatosensory coherence as indicator of anesthetic depth.

The goal of this study was to identify features in mouse electrocorticogram recordings that indicate the depth of anesthesia as approximated by the administered anesthetic dosage. Anesthetic depth in laboratory animals must be precisely monitored and controlled. However, for the most common lab species (mice) few indicators useful for monitoring anesthetic depth have been established. We used electrocorticogram recordings in mice, coupled with peripheral stimulation, in order to identify features of brain activity modulated by isoflurane anesthesia and explored their usefulness in monitoring anesthetic depth through machine learning techniques. Using a gradient boosting regressor framework we identified interhemispheric somatosensory coherence as the most informative and reliable electrocorticogram feature for determining anesthetic depth, yielding good generalization and performance over many subjects. Knowing that interhemispheric somatosensory coherence indicates the effectively administered isoflurane concentration is an important step for establishing better anesthetic monitoring protocols and closed-loop systems for animal surgeries.

Deep-learning based identification, tracking, pose estimation, and behavior classification of interacting primates and mice in complex environments.

The quantification of behaviors of interest from video data is commonly used to study brain function, the effects of pharmacological interventions, and genetic alterations. Existing approaches lack the capability to analyze the behavior of groups of animals in complex environments. We present a novel deep learning architecture for classifying individual and social animal behavior, even in complex environments directly from raw video frames, while requiring no intervention after initial human supervision. Our behavioral classifier is embedded in a pipeline (SIPEC) that performs segmentation, identification, pose-estimation, and classification of complex behavior, outperforming the state of the art. SIPEC successfully recognizes multiple behaviors of freely moving individual mice as well as socially interacting non-human primates in 3D, using data only from simple mono-vision cameras in home-cage setups.

An optimized registration workflow and standard geometric space for small animal brain imaging.

The reliability of scientific results critically depends on reproducible and transparent data processing. Cross-subject and cross-study comparability of imaging data in general, and magnetic resonance imaging (MRI) data in particular, is contingent on the quality of registration to a standard reference space. In small animal MRI this is not adequately provided by currently used processing workflows, which utilize high-level scripts optimized for human data, and adapt animal data to fit the scripts, rather than vice-versa. In this fully reproducible article we showcase a generic workflow optimized for the mouse brain, alongside a standard reference space suited to harmonize data between analysis and operation. We introduce four separate metrics for automated quality control (QC), and a visualization method to aid operator inspection. Benchmarking this workflow against common legacy practices reveals that it performs more consistently, better preserves variance across subjects while minimizing variance across sessions, and improves both volume and smoothness conservation RMSE approximately 2-fold. We propose this open source workflow and the QC metrics as a new standard for small animal MRI registration, ensuring workflow robustness, data comparability, and region assignment validity, all of which are indispensable prerequisites for the comparability of scientific results across experiments and centers.

Non-invasive molecularly-specific millimeter-resolution manipulation of brain circuits by ultrasound-mediated aggregation and uncaging of drug carriers.

Non-invasive, molecularly-specific, focal modulation of brain circuits with low off-target effects can lead to breakthroughs in treatments of brain disorders. We systemically inject engineered ultrasound-controllable drug carriers and subsequently apply a novel two-component Aggregation and Uncaging Focused Ultrasound Sequence (AU-FUS) at the desired targets inside the brain. The first sequence aggregates drug carriers with millimeter-precision by orders of magnitude. The second sequence uncages the carrier's cargo locally to achieve high target specificity without compromising the blood-brain barrier (BBB). Upon release from the carriers, drugs locally cross the intact BBB. We show circuit-specific manipulation of sensory signaling in motor cortex in rats by locally concentrating and releasing a GABAA receptor agonist from ultrasound-controlled carriers. Our approach uses orders of magnitude (1300x) less drug than is otherwise required by systemic injection and requires very low ultrasound pressures (20-fold below FDA safety limits for diagnostic imaging). We show that the BBB remains intact using passive cavitation detection (PCD), MRI-contrast agents and, importantly, also by sensitive fluorescent dye extravasation and immunohistochemistry.

An Automated Open-Source Workflow for Standards-Compliant Integration of Small Animal Magnetic Resonance Imaging Data.

Large-scale research integration is contingent on seamless access to data in standardized formats. Standards enable researchers to understand external experiment structures, pool results, and apply homogeneous preprocessing and analysis workflows. Particularly, they facilitate these features without the need for numerous potentially confounding compatibility add-ons. In small animal magnetic resonance imaging, an overwhelming proportion of data is acquired via the ParaVision software of the Bruker Corporation. The original data structure is predominantly transparent, but fundamentally incompatible with modern pipelines. Additionally, it sources metadata from free-field operator input, which diverges strongly between laboratories and researchers. In this article we present an open-source workflow which automatically converts and reposits data from the ParaVision structure into the widely supported and openly documented Brain Imaging Data Structure (BIDS). Complementing this workflow we also present operator guidelines for appropriate ParaVision data input, and a programmatic walk-through detailing how preexisting scans with uninterpretable metadata records can easily be made compliant after the acquisition.

Engineering brain activity patterns by neuromodulator polytherapy for treatment of disorders.

Conventional drug screens and treatments often ignore the underlying complexity of brain network dysfunctions, resulting in suboptimal outcomes. Here we ask whether we can correct abnormal functional connectivity of the entire brain by identifying and combining multiple neuromodulators that perturb connectivity in complementary ways. Our approach avoids the combinatorial complexity of screening all drug combinations. We develop a high-speed platform capable of imaging more than 15000 neurons in 50ms to map the entire brain functional connectivity in large numbers of vertebrates under many conditions. Screening a panel of drugs in a zebrafish model of human Dravet syndrome, we show that even drugs with related mechanisms of action can modulate functional connectivity in significantly different ways. By clustering connectivity fingerprints, we algorithmically select small subsets of complementary drugs and rapidly identify combinations that are significantly more effective at correcting abnormal networks and reducing spontaneous seizures than monotherapies, while minimizing behavioral side effects. Even at low concentrations, our polytherapy performs superior to individual drugs even at highest tolerated concentrations.

Combinatorial programming of human neuronal progenitors using magnetically-guided stoichiometric mRNA delivery.

Identification of optimal transcription factor expression patterns to direct cellular differentiation along a desired pathway presents significant challenges. We demonstrate massively combinatorial screening of temporally-varying mRNA transcription factors to direct differentiation of neural progenitor cells using a dynamically-reconfigurable magnetically-guided spotting technology for localizing mRNA, enabling experiments on millimetre size spots. In addition, we present a time-interleaved delivery method that dramatically reduces fluctuations in the delivered transcription factor copy numbers per cell. We screened combinatorial and temporal delivery of a pool of midbrain-specific transcription factors to augment the generation of dopaminergic neurons. We show that the combinatorial delivery of LMX1A, FOXA2 and PITX3 is highly effective in generating dopaminergic neurons from midbrain progenitors. We show that LMX1A significantly increases TH-expression levels when delivered to neural progenitor cells either during proliferation or after induction of neural differentiation, while FOXA2 and PITX3 increase expression only when delivered prior to induction, demonstrating temporal dependence of factor addition.

Brain activity patterns in high-throughput electrophysiology screen predict both drug efficacies and side effects.

Neurological drugs are often associated with serious side effects, yet drug screens typically focus only on efficacy. We demonstrate a novel paradigm utilizing high-throughput in vivo electrophysiology and brain activity patterns (BAPs). A platform with high sensitivity records local field potentials (LFPs) simultaneously from many zebrafish larvae over extended periods. We show that BAPs from larvae experiencing epileptic seizures or drug-induced side effects have substantially reduced complexity (entropy), similar to reduced LFP complexity observed in Parkinson's disease. To determine whether drugs that enhance BAP complexity produces positive outcomes, we used light pulses to trigger seizures in a model of Dravet syndrome, an intractable genetic epilepsy. The highest-ranked compounds identified by BAP analysis exhibit far greater anti-seizure efficacy and fewer side effects during subsequent in-depth behavioral assessment. This high correlation with behavioral outcomes illustrates the power of brain activity pattern-based screens and identifies novel therapeutic candidates with minimal side effects.

A Hippocampal Model for Behavioral Time Acquisition and Fast Bidirectional Replay of Spatio-Temporal Memory Sequences.

The hippocampus is known to play a crucial role in the formation of long-term memory. For this, fast replays of previously experienced activities during sleep or after reward experiences are believed to be crucial. But how such replays are generated is still completely unclear. In this paper we propose a possible mechanism for this: we present a model that can store experienced trajectories on a behavioral timescale after a single run, and can subsequently bidirectionally replay such trajectories, thereby omitting any specifics of the previous behavior like speed, etc, but allowing repetitions of events, even with different subsequent events. Our solution builds on well-known concepts, one-shot learning and synfire chains, enhancing them by additional mechanisms using global inhibition and disinhibition. For replays our approach relies on dendritic spikes and cholinergic modulation, as supported by experimental data. We also hypothesize a functional role of disinhibition as a pacemaker during behavioral time.

Automated deep-phenotyping of the vertebrate brain.

Here, we describe an automated platform suitable for large-scale deep-phenotyping of zebrafish mutant lines, which uses optical projection tomography to rapidly image brain-specific gene expression patterns in 3D at cellular resolution. Registration algorithms and correlation analysis are then used to compare 3D expression patterns, to automatically detect all statistically significant alterations in mutants, and to map them onto a brain atlas. Automated deep-phenotyping of a mutation in the master transcriptional regulator fezf2 not only detects all known phenotypes but also uncovers important novel neural deficits that were overlooked in previous studies. In the telencephalon, we show for the first time that fezf2 mutant zebrafish have significant patterning deficits, particularly in glutamatergic populations. Our findings reveal unexpected parallels between fezf2 function in zebrafish and mice, where mutations cause deficits in glutamatergic neurons of the telencephalon-derived neocortex.

Guided Homing of Cells in Multi-Photon Microfabricated Bioscaffolds.

Tissues contain exquisite vascular microstructures, and patterns of chemical cues for directing cell migration, homing, and differentiation for organ development and function. 3D microfabrication by multi-photon photolithography is a flexible, high-resolution tool for generating 3D bioscaffolds. However, the combined fabrication of scaffold microstructure simultaneously with patterning of cues to create both geometrically and chemically defined microenvironments remains to be demonstrated. This study presents a high-speed method for micron-resolution fabrication of scaffold microstructure and patterning of protein cues simultaneously using native scaffold materials. By the simultaneous microfabrication of arbitrary microvasculature geometries, and patterning selected regions of the microvasculature with the homing ligand P-selectin, this study demonstrates adhesion, rolling, and selective homing of cells in defined 3D regions. This novel ability to generate high-resolution geometries replete with patterned cues at high speed enables the construction of biomimetic microenvironments for complex 3D assays of cellular behavior.

Organ-targeted high-throughput in vivo biologics screen identifies materials for RNA delivery.

Therapies based on biologics involving delivery of proteins, DNA, and RNA are currently among the most promising approaches. However, although large combinatorial libraries of biologics and delivery vehicles can be readily synthesized, there are currently no means to rapidly characterize them in vivo using animal models. Here, we demonstrate high-throughput in vivo screening of biologics and delivery vehicles by automated delivery into target tissues of small vertebrates with developed organs. Individual zebrafish larvae are automatically oriented and immobilized within hydrogel droplets in an array format using a microfluidic system, and delivery vehicles are automatically microinjected to target organs with high repeatability and precision. We screened a library of lipid-like delivery vehicles for their ability to facilitate the expression of protein-encoding RNAs in the central nervous system. We discovered delivery vehicles that are effective in both larval zebrafish and rats. Our results showed that the in vivo zebrafish model can be significantly more predictive of both false positives and false negatives in mammals than in vitro mammalian cell culture assays. Our screening results also suggest certain structure-activity relationships, which can potentially be applied to design novel delivery vehicles.

mRNA-engineered mesenchymal stem cells for targeted delivery of interleukin-10 to sites of inflammation.

Mesenchymal stem cells (MSCs) are promising candidates for cell-based therapy to treat several diseases and are compelling to consider as vehicles for delivery of biological agents. However, MSCs appear to act through a seemingly limited "hit-and-run" mode to quickly exert their therapeutic impact, mediated by several mechanisms, including a potent immunomodulatory secretome. Furthermore, MSC immunomodulatory properties are highly variable and the secretome composition following infusion is uncertain. To determine whether a transiently controlled antiinflammatory MSC secretome could be achieved at target sites of inflammation, we harnessed mRNA transfection to generate MSCs that simultaneously express functional rolling machinery (P-selectin glycoprotein ligand-1 [PSGL-1] and Sialyl-Lewis(x) [SLeX]) to rapidly target inflamed tissues and that express the potent immunosuppressive cytokine interleukin-10 (IL-10), which is not inherently produced by MSCs. Indeed, triple-transfected PSGL-1/SLeX/IL-10 MSCs transiently increased levels of IL-10 in the inflamed ear and showed a superior antiinflammatory effect in vivo, significantly reducing local inflammation following systemic administration. This was dependent on rapid localization of MSCs to the inflamed site. Overall, this study demonstrates that despite the rapid clearance of MSCs in vivo, engineered MSCs can be harnessed via a "hit-and-run" action for the targeted delivery of potent immunomodulatory factors to treat distant sites of inflammation.

Synchronous symmetry breaking in neurons with different neurite counts.

As neurons develop, several immature processes (i.e., neurites) grow out of the cell body. Over time, each neuron breaks symmetry when only one of its neurites grows much longer than the rest, becoming an axon. This symmetry breaking is an important step in neurodevelopment, and aberrant symmetry breaking is associated with several neuropsychiatric diseases, including schizophrenia and autism. However, the effects of neurite count in neuronal symmetry breaking have never been studied. Existing models for neuronal polarization disagree: some predict that neurons with more neurites polarize up to several days later than neurons with fewer neurites, while others predict that neurons with different neurite counts polarize synchronously. We experimentally find that neurons with different neurite counts polarize synchronously. We also show that despite the significant differences among the previously proposed models, they all agree with our experimental findings when the expression levels of the proteins responsible for symmetry breaking increase with neurite count. Consistent with these results, we observe that the expression levels of two of these proteins, HRas and shootin1, significantly correlate with neurite count. This coordinated symmetry breaking we observed among neurons with different neurite counts may be important for synchronized polarization of neurons in developing organisms.

Ultra-rapid laser protein micropatterning: screening for directed polarization of single neurons.

Protein micropatterning is a powerful tool for studying the effects of extracellular signals on cell development and regeneration. Laser micropatterning of proteins is the most flexible method for patterning many different geometries, protein densities, and concentration gradients. Despite these advantages, laser micropatterning remains prohibitively slow for most applications. Here, we take advantage of the rapid multi-photon induced photobleaching of fluorophores to generate sub-micron resolution patterns of full-length proteins on polymer monolayers, with sub-microsecond exposure times, i.e. one to five orders of magnitude faster than all previous laser micropatterning methods. We screened a range of different PEG monolayer coupling chemistries, chain-lengths and functional caps, and found that long-chain acrylated PEG monolayers are effective at resisting non-specific protein adhesion, while permitting efficient cross-linking of biotin-4-fluorescein to the PEG monolayers upon exposure to femtosecond laser pulses. We find evidence that the dominant photopatterning chemistry switches from a two-photon process to three- and four-photon absorption processes as the laser intensity increases, generating increasingly volatile excited triplet-state fluorophores, leading to faster patterning. Using this technology, we were able to generate over a hundred thousand protein patterns with varying geometries and protein densities to direct the polarization of hippocampal neurons with single-cell precision. We found that certain arrays of patterned triangles as small as neurite growth cones can direct polarization by impeding the elongation of reverse-projecting neurites, while permitting elongation of forward-projecting neurites. The ability to rapidly generate and screen such protein micropatterns can enable discovery of conditions necessary to create in vitro neural networks with single-neuron precision for basic discovery, drug screening, as well as for tissue scaffolding in therapeutics.