Biology subjects

Helmchen, F.

Publications and source records attributed to Helmchen, F..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

The Benchtop mesoSPIM: a next-generation open-source light-sheet microscope for large cleared samples

In 2015, we launched the mesoSPIM initiative (www.mesospim.org), an open-source project for making light-sheet microscopy of large cleared tissues more accessible. Meanwhile, the demand for imaging larger samples at higher speed and resolution has increased, requiring major improvements in the capabilities of light-sheet microscopy. Here, we introduce the next-generation mesoSPIM ("Benchtop") with significantly increased field of view, improved resolution, higher throughput, more affordable cost and simpler assembly compared to the original version. We developed a new method for testing objectives, enabling us to select detection objectives optimal for light-sheet imaging with large-sensor sCMOS cameras. The new mesoSPIM achieves high spatial resolution (1.5 {micro}m laterally, 3.3 {micro}m axially) across the entire field of view, a magnification up to 20x, and supports sample sizes ranging from sub-mm up to several centimetres, while being compatible with multiple clearing techniques. The new microscope serves a broad range of applications in neuroscience, developmental biology, and even physics.

neuroscience↗

bioRxiv2023

Behavior-relevant top-down cross-modal predictions in mouse neocortex

Animals rely on predicting their environment and the consequences of their actions to adapt to a constantly changing world. The predictive coding hypothesis proposes that the brain generates predictions and continuously compares them with bottom-up sensory inputs to guide behavior. However, how the brain reconciles conflicting top-down predictions and bottom-up sensory information during behavior remains unclear. To address this question, we simultaneously imaged neuronal populations in the mouse somatosensory cortex and the posterior parietal cortex during an auditory-cued texture discrimination task. After mice learnt the task with fixed tone-texture matching, mismatched pairing caused conflicting tone-based texture predictions and actual texture inputs. When top-down interaction was dominant, texture representations in both areas were modified and mice decided based on the predicted rather than actual texture, whereas dominant bottom-up interaction corrected the representations as well as behavioral choice. Our findings provide evidence for hierarchical predictive coding in the mouse neocortex and open new avenues for understanding higher cognitive functions.

neuroscience↗

bioRxiv2023

Distinct hypothalamus-habenula circuits govern risk preference

Appropriate risk evaluation is essential for survival in complex, uncertain environments. Confronted with choosing between certain (safe) and uncertain (risky) options, animals show strong preference for either option consistently across extended time periods. How such risk preference is encoded in the brain remains elusive. A candidate region is the lateral habenula (LHb), which is prominently involved in value-guided behavior. Here, using a balanced two-alternative choice task and longitudinal two-photon calcium imaging, we identify LHb neurons with risk-preference-selective activity reflecting individual risk preference prior to action selection. By employing whole-brain anatomical tracing, multi-fiber photometry, and projection- and cell-type-specific optogenetics, we find that glutamatergic LHb projections from the medial (MH) but not lateral (LH) hypothalamus provide behavior-relevant synaptic input before action selection. Optogenetic stimulation of MH{lozenge}LHb axons evoked excitatory and inhibitory postsynaptic responses, whereas LH{lozenge}LHb projections were excitatory. We thus reveal functionally distinct hypothalamus-habenula circuits for risk preference in habitual economic decision-making.

neuroscience↗

bioRxiv2022

History information emerges in the cortex during learning

We learn from our experience but the underlying neuronal mechanisms incorporating past information to facilitate learning is relatively unknown. Specifically, which cortical areas encode history-related information and how is this information modulated across learning? To study the relationship between history and learning, we continuously imaged cortex-wide calcium dynamics as mice learn to use their whiskers to discriminate between two different textures. We mainly focused on comparing the same trial type with different history information, i.e., a different preceding trial. We found history information in barrel cortex (BC) during stimulus presentation. Importantly, history information in BC emerged only as the mouse learned the task. Next, we also found learning-dependent history information in rostrolateral (RL) association cortex that emerges before stimulus presentation, preceding activity in BC. History information was also found in other cortical areas and was not related to differences in body movements. Interestingly, a binary classifier could discriminate history information at the single trial level just as well as current information both in BC and RL. These findings suggest that past experience emerges in the cortex around the time of learning, starting from higher-order association area RL and propagating down (i.e., top-down projection) to lower-order BC where it can be integrated with incoming sensory information. This integration between the past and present may facilitate learning.

neuroscience↗

bioRxiv2022

Reflective multi-immersion microscope objectives inspired by the Schmidt telescope

Rapid advances in tissue clearing protocols have begun to outpace the capabilities of existing microscope objectives: High-resolution imaging inside cm-sized cleared samples is often not possible as it requires multi-immersion objectives with high numerical aperture (NA > 0.7), long working distance (WD > 10 mm) and a large field-of-view (FOV > 1 mm). Here, we introduce a novel mirror-based optical design, the "Schmidt objective", which meets all these criteria despite containing only two optical elements. It consists of a spherical mirror in contact with the immersion medium and an aspherical correction plate. We showcase a multi-photon variant of a Schmidt objective that reaches NA 1.08 at an refractive index of 1.56 and demonstrate its versatility by imaging fixed samples in a wide range of immersion media ranging from air and water to BABB, DBE, and ECI. In addition, we demonstrate in vivo imaging by recording neuronal activity in larval zebrafish.

neuroscience↗

bioRxiv2022

Centripetal integration of past events by hippocampal astrocytes

An essential feature of neurons is their ability to centrally integrate information from their dendrites. The activity of astrocytes, in contrast, has been described as mostly uncoordinated across cellular compartments without clear central integration. Here, we describe conditional centripetal integration as a principle of how astrocytes integrate calcium signals from their distal processes to induce somatic activation. We found in mouse hippocampus that global astrocytic activity, as recorded with population calcium imaging, is well explained as a leaky integration of past neuronal and behavioral events on a timescale of seconds. Salient past events, indicated by pupil dilations, facilitated propagation of calcium signals from distal processes to the soma on this slow timescale. Centripetal propagation was reproduced by optogenetic activation of the locus coeruleus, a key regulator of arousal, and reduced by pharmacological inhibition of 1-adrenergic receptors. Together, our results establish astrocytes as computational units of the brain that slowly and conditionally integrate calcium signals to activate their somata upon behaviorally relevant events.

neuroscience↗

bioRxiv2022

EXPLORE: A novel deep learning-based analysis method for exploration behaviour in object recognition tests

Object recognition tests are widely used in neuroscience to assess memory function in rodents. Despite the experimental simplicity of the task, the interpretation of behavioural features that are counted as object exploration can be complicated. Thus, object exploration is often analysed by manual scoring, which is time-consuming and variable across researchers. Current software using tracking points often lacks precision in capturing complex ethological behaviour. Switching or losing tracking points can bias outcome measures. To overcome these limitations we developed "EXPLORE", a simple, ready-to use and open source pipeline. EXPLORE consists of a convolutional neural network trained in a supervised manner, that extracts features from images and classifies behaviour of rodents near a presented object. EXPLORE achieves human-level accuracy in identifying and scoring exploration behaviour and outperforms commercial software with higher precision, higher versatility and lower time investment, in particular in complex situations. By labeling the respective training data set, users decide by themselves, which types of animal interactions on objects are in- or excluded, ensuring a precise analysis of exploration behaviour. A set of graphical user interfaces (GUIs) provides a beginning-to-end analysis of object recognition tests, accelerating a fast and reproducible data analysis without the need of expertise in programming or deep learning.

animal behavior and cognition↗

bioRxiv2022

Conservative Significance Testing of Tripartite Interactions in Multivariate Neural Data

An important goal in systems neuroscience is to understand the structure of neuronal interactions, frequently approached by studying functional relations between recorded neuronal signals. Commonly used pairwise metrics (e.g. correlation coefficient) offer limited insight, neither addressing the specificity of estimated neuronal interactions nor potential synergistic coupling between neuronal signals. Tripartite metrics, such as partial correlation, variance partitioning, and partial information decomposition, address these questions by disentangling functional relations into interpretable information atoms (unique, redundant and synergistic). Here, we apply these tripartite metrics to simulated neuronal recordings to investigate their sensitivity to impurities (like noise or other unexplained variance) in the data. We find that all considered metrics are accurate and specific for pure signals but experience significant bias for impure signals. We show that permutation-testing of such metrics results in high false positive rates even for small impurities and large data sizes. We present a conservative null hypothesis for significance testing of tripartite metrics, which significantly decreases false positive rate at a tolerable expense of increasing false negative rate. We hope our study raises awareness about the potential pitfalls of significance testing and of interpretation of functional relations, offering both conceptual and practical advice. Author SummaryTripartite functional relation metrics enable the study of interesting effects in neural recordings, such as redundancy, functional connection specificity and synergistic coupling. However, common estimators of such relations are designed for pure (e.g. non-noisy) signals rare for such recordings. We study the performance of tripartite estimators using simulated impure neural signals. We demonstrate that permutation-testing is not a robust procedure for inferring ground truth interactions from studied estimators. We develop an adjusted conservative testing procedure, reducing false positive rate of studied estimators for impure data. Besides addressing significance testing, our results should aid in accurate interpretation of tripartite functional relations and functional connectivity.

neuroscience↗

bioRxiv2021

Dendritic integration of sensory and reward information facilitates learning

Learning goal-directed behavior requires association of pertinent sensory stimuli with behaviorally relevant outcomes. In the mammalian neocortex, dendrites of pyramidal neurons are suitable association sites1-3 but how their activities adapt during learning remains elusive. Here, we track calcium signals in apical dendrites of layer 5 (L5) pyramidal neurons in mouse barrel cortex during texture discrimination learning4. We observe diverse task-related responses, either localized to branches or widespread throughout the apical tuft. However, even in expert mice, the tufts capability to discriminate go/no-go stimuli remains poor. Yet, we identify two prevailing response types in dendritic branches: 1) responses to unexpected outcome (reward) in naive mice that decrease with growing task proficiency, and 2) responses associated with salient sensory stimuli, especially the outcome-predicting texture touch, that strengthen upon learning. We demonstrate that these response types match distinct unsigned components of the temporal difference error5 by replicating our results with a reinforcement learning model. Moreover, optogenetic apical inhibition of L5 neurons during the outcome time window prevents naive animals from learning the task, consistent with the effect of equivalent model perturbation. Our findings suggest that salience signals in L5 apical dendrites facilitate the recruitment of task-relevant neurons via dendritic gain modulation.

neuroscience↗

bioRxiv2021

Striatum-projecting prefrontal cortex neurons support working memory maintenance

The medial prefrontal cortex (mPFC) and the dorsomedial striatum (dmStr) are linked to working memory (WM) but how striatum-projecting mPFC neurons contribute to WM encoding, maintenance, or retrieval remains unclear. Here, we probed mPFC[->]dmStr pathway function in freely-moving mice during a T-maze alternation test of spatial WM. Fiber photometry of GCaMP6m-labeled mPFC[->]dmStr projection neurons revealed strongest activity during the delay period that requires WM maintenance. Demonstrating causality, optogenetic inhibition of mPFC[->]dmStr neurons only during the delay period impaired performance. Conversely, enhancing mPFC[->]dmStr pathway activity--via pharmacological suppression of HCN1 or by optogenetic activation during the delay-- alleviated WM impairment induced by NMDA receptor blockade. Consistently, cellular-resolution miniscope imaging resolved preferred activation of >50% mPFC[->]dmStr neurons during WM maintenance. This subpopulation was distinct from neurons showing preference for encoding and retrieval. In all periods, including the delay, neuronal sequences were evident. Striatum-projecting mPFC neurons thus critically contribute to spatial WM maintenance.

neuroscience↗

bioRxiv2021

Estimating anisotropy directly via neural timeseries

An isotropic dynamical system is one that looks the same in every direction, i.e., if we imagine - standing somewhere within an isotropic system, we would not be able to differentiate between different lines of sight. Conversely, anisotropy is a measure of the extent to which a system deviates from perfect isotropy, with larger values indicating greater discrepancies between the structure of the system along its axes. Here, we derive the form of a generalised scalable (mechanically similar) discretized field theoretic Lagrangian that allows for levels of anisotropy to be directly estimated via timeseries of arbitrary dimensionality. We generate synthetic data for both isotropic and anisotropic systems and, by using Bayesian model inversion and reduction, show that we can discriminate between the two datasets - thereby demonstrating proof of principle. We then apply this methodology to murine calcium imaging data collected in rest and task states, showing that anisotropy can be estimated directly from different brain states and cortical regions in an empirical in vivo biological setting. We hope that this theoretical foundation, together with the methodology and publicly available MATLAB code, will provide an accessible way for researchers to obtain new insight into the structural organization of neural systems in terms of how scalable neural regions grow - both ontogenetically during the development of an individual organism, as well as phylogenetically across species.

neuroscience↗

bioRxiv2021

In vivo calcium imaging of CA3 pyramidal neuron populations in adult mouse hippocampus

Neuronal population activity in the hippocampal CA3 subfield is implicated in cognitive brain functions such as memory processing and spatial navigation. However, because of its deep location in the brain, the CA3 area has been difficult to target with modern calcium imaging approaches. Here, we achieved chronic two-photon calcium imaging of CA3 pyramidal neurons with the red fluorescent calcium indicator R-CaMP1.07 in anesthetized and awake mice. We characterize CA3 neuronal activity at both the single-cell and population level and assess its stability across multiple imaging days. During both anesthesia and wakefulness, nearly all CA3 pyramidal neurons displayed calcium transients. Most of the calcium transients were consistent with a high incidence of bursts of action potentials, based on calibration measurements using simultaneous juxtacellular recordings and calcium imaging. In awake mice, we found state-dependent differences with striking large and prolonged calcium transients during locomotion. We estimate that trains of >30 action potentials over 3 s underlie these salient events. Their abundance in particular subsets of neurons was relatively stable across days. At the population level, we found that coactivity within the CA3 network was above chance level and that co-active neuron pairs maintained their correlated activity over days. Our results corroborate the notion of state-dependent spatiotemporal activity patterns in the recurrent network of CA3 and demonstrate that at least some features of population activity, namely coactivity of cell pairs and likelihood to engage in prolonged high activity, are maintained over days.

neuroscience↗

bioRxiv2021

Quantitative 3D microscopy reveals a genetic network predicting the local activity of anti-Aβ compounds

Many efforts targeting amyloid-{beta} (A{beta}) plaques for the treatment of Alzheimers Disease thus far have resulted in failures during clinical trials. Regional and temporal heterogeneity of efficacy and dependence on plaque maturity may have contributed to these disappointing outcomes. In this study, we mapped the regional and temporal specificity of various anti-A{beta} treatments through high-resolution light-sheet imaging of electrophoretically-cleared brains. We assessed the effect on amyloid plaque formation and growth in Thy1-APP/PS1 mice subjected to {beta}-secretase inhibitors, polythiophenes, or anti-A{beta} antibodies. Each treatment showed unique spatiotemporal A{beta} clearance, with polythiophenes emerging as a potent anti-A{beta} compound. Furthermore, aligning with a spatial-transcriptomic atlas revealed transcripts that correlate with the efficacy of each A{beta} therapy. As observed in this study, there is a striking dependence of specific treatments on the location and maturity of A{beta} plaques. This may also contribute to the clinical trial failures of A{beta}-therapies, suggesting that combinatorial regimens may be significantly more effective in clearing amyloid deposition.

neuroscience↗

bioRxiv2020

Brain mapping across 16 autism mouse models reveals a spectrum of functional connectivity subtypes

Autism Spectrum Disorder (ASD) is characterized by substantial, yet highly heterogeneous abnormalities in functional brain connectivity. However, the origin and significance of this phenomenon remain unclear. To unravel ASD connectopathy and relate it to underlying etiological heterogeneity, we carried out a bi-center cross-etiological investigation of fMRI-based connectivity in the mouse, in which specific ASD-relevant mutations can be isolated and modelled minimizing environmental contributions. By performing brain-wide connectivity mapping across 16 mouse mutants, we show that different ASD-associated etiologies cause a broad spectrum of connectional abnormalities in which diverse, often diverging, connectivity signatures are recognizable. Despite this heterogeneity, the identified connectivity alterations could be classified into four subtypes characterized by discrete signatures of network dysfunction. Our findings show that etiological variability is a key determinant of connectivity heterogeneity in ASD, hence reconciling conflicting findings in clinical populations. The identification of etiologically-relevant connectivity subtypes could improve diagnostic label accuracy in the non-syndromic ASD population and paves the way for personalized treatment approaches.

neuroscience↗

bioRxiv2020

A deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging data

Calcium imaging is a key method to record patterns of neuronal activity across populations of identified neurons. Inference of temporal patterns of action potentials ( spikes) from calcium signals is, however, challenging and often limited by the scarcity of ground truth data containing simultaneous measurements of action potentials and calcium signals. To overcome this problem, we compiled a large and diverse ground truth database from publicly available and newly performed recordings. This database covers various types of calcium indicators, cell types, and signal-to-noise ratios and comprises a total of >35 hours from 298 neurons. We then developed a novel algorithm for spike inference (CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates, and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level. As a consequence, no parameters need to be adjusted by the user. To facilitate routine application of CASCADE we developed systematic performance assessments for unseen data, we openly release all resources, and we provide a user-friendly cloud-based implementation.

neuroscience↗

bioRxiv2020

Opto-E-Dura: a Soft, Stretchable ECoG Array for Multimodal, Multi-scale Neuroscience

Soft, stretchable materials hold great promise for the fabrication of biomedical devices due to their capacity to integrate gracefully with and conform to biological tissues. Conformal devices are of particular interest in the development of brain interfaces where rigid structures can lead to tissue damage and loss of signal quality over the lifetime of the implant. Interfaces to study brain function and dysfunction increasingly require multimodal access in order to facilitate measurement of diverse physiological signals that span the disparate temporal and spatial scales of brain dynamics. Here we present the Opto-e-Dura, a soft, stretchable, 16-channel electrocorticography array that is optically transparent. We demonstrate its compatibility with diverse optical and electrical readouts enabling multimodal studies that bridge spatial and temporal scales. The device is chronically stable for weeks, compatible with wide-field and 2-photon calcium imaging and permits the repeated insertion of penetrating multi-electrode arrays. As the variety of sensors and effectors realizable on soft, stretchable substrates expands, similar devices that provide large-scale, multimodal access to the brain will continue to improve fundamental understanding of brain function.

neuroscience↗

bioRxiv2020

Developmental Divergence of Sensory Stimulus Representation in Cortical Interneurons

Two inhibitory cell types involved in modulating barrel cortex activity and perception during active whisking in adult mice, are the VIP+ and SST+ interneurons. Here we identify a developmental transition point of structural and functional rearrangements onto these interneuron types around the start of active sensation at P14. Using in vivo two-photon Ca2+ imaging, we find that before P14, both interneuron types respond stronger to a multi-whisker stimulus, whereas after P14 their responses diverge, with VIP+ cells losing their multi-whisker preference and SST+ neurons enhancing theirs. Rabies virus tracings followed by tissue clearing, as well as photostimulation-coupled electrophysiology reveal that SST+ cells receive higher cross-barrel inputs compared to VIP+ at both time points. In addition, we also uncover that whereas prior to P14 both cell types receive direct input from the sensory thalamus, after P14 VIP+ cells show reduced inputs and SST+ cells largely shift to motor-related thalamic nuclei.

neuroscience↗

bioRxiv2020

Value-guided remapping of sensory circuits by lateral orbitofrontal cortex in reversal learning

Flexible decision-making is crucial for adaptive behaviour. Such behaviour in mammals largely relies on the frontal cortex, and specifically, the orbitofrontal cortex (OFC). How OFC neurons encode decision variables and instruct sensory areas to guide adaptive behaviour is a key open question. Here we developed a reversal learning task for head-fixed mice together with two-photon calcium imaging to monitor the activity of lateral OFC neuronal populations and investigated their dynamic interaction with primary somatosensory cortex (S1). Mice trained on this task learned to discriminate go/no-go tactile stimuli and adapt their behaviour upon changes in stimulus-reward contingencies ( rule-switch). Longitudinal imaging at cellular resolution across weeks during all behavioural phases revealed a distinct engagement of S1 and lateral OFC neurons: S1 neural activity reflected task learning-related responses, while neurons in the lateral OFC saliently and transiently responded to the rule-switch. A subset of OFC neurons conveyed a value prediction error signal via feedback projections to S1, as direct anatomical long-range projections were revealed by retrograde tracing combined with whole-brain light-sheet microscopy. Top-down signals implemented an update of sensory representations and functionally reconfigured a small subpopulation of S1 neurons that were differentially modulated by reward-history. Functional remapping of these neurons crucially depended on top-down inputs, as chemogenetic silencing of lateral OFC neurons disrupted reversal learning and impaired plastic changes in these outcome-sensitive S1 neurons. Our results reveal the presence of long-range cortical interactions between cellular ensembles in higher and lower-order brain areas specifically recruited during context-dependent learning and task-switching. Such interactions crucially implement history-dependent reward-value computations and error heuristics, which, in turn, help guide adaptive behaviour.

neuroscience↗