Odors Pulsed at Wing Beat Frequencies are Tracked by Primary Olfactory Networks and Enhance Odor Detection

Each down stroke of an insect's wings accelerates axial airflow over the antennae. Modeling studies suggest that this can greatly enhance penetration of air and air-born odorants through the antennal sensilla thereby periodically increasing odorant-receptor interactions. Do these periodic changes result in entrainment of neural responses in the antenna and antennal lobe (AL)? Does this entrainment affect olfactory acuity? To address these questions, we monitored antennal and AL responses in the moth Manduca sexta while odorants were pulsed at frequencies from 10–72 Hz, encompassing the natural wingbeat frequency. Power spectral density (PSD) analysis was used to identify entrainment of neural activity. Statistical analysis of PSDs indicates that the antennal nerve tracked pulsed odor up to 30 Hz. Furthermore, at least 50% of AL local field potentials (LFPs) and between 7–25% of unitary spiking responses also tracked pulsed odor up to 30 Hz in a frequency-locked manner. Application of bicuculline (200 μM) abolished pulse tracking in both LFP and unitary responses suggesting that GABAA receptor activation is necessary for pulse tracking within the AL. Finally, psychophysical measures of odor detection establish that detection thresholds are lowered when odor is pulsed at 20 Hz. These results suggest that AL networks can respond to the oscillatory dynamics of stimuli such as those imposed by the wing beat in a manner analogous to mammalian sniffing

Antennal lobe representations are optimized when olfactory stimuli are periodically structured to simulate natural wing beat effects

Animals use behaviors to actively sample the environment across a broad spectrum of sensory domains. These behaviors discretize the sensory experience into unique spatiotemporal moments, minimize sensory adaptation, and enhance perception. In olfaction, behaviors such as sniffing, antennal flicking, and wing beating all act to periodically expose olfactory epithelium. In mammals, it is thought that sniffing enhances neural representations; however, the effects of insect wing beating on representations remain unknown. To determine how well the antennal lobe (AL) produces odor dependent representations when wing beating effects are simulated, we used extracellular methods to record neural units and local field potentials (LFPs) from moth AL. We recorded responses to odors presented as prolonged continuous stimuli or periodically as 20 and 25 Hz pulse trains designed to simulate the oscillating effects of wing beating around the antennae during odor guided flight. Using spectral analyses, we show that ~25% of all recorded units were able to entrain to “pulsed stimuli”; this includes pulsed blanks, which elicited the strongest overall entrainment. The strength of entrainment to pulse train stimuli was dependent on molecular features of the odorants, odor concentration, and pulse train duration. Moreover, units showing pulse tracking responses were highly phase locked to LFPs during odor stimulation, indicating that unit-LFP phase relationships are stimulus-driven. Finally, a Euclidean distance-based population vector analysis established that AL odor representations are more robust, peak more quickly, and do not show adaptation when odors were presented at the natural wing beat frequency as opposed to prolonged continuous stimulation. These results suggest a general strategy for optimizing olfactory representations, which exploits the natural rhythmicity of wing beating by integrating mechanosensory and olfactory cues at the level of the AL

Assessing Transcriptome Quality in Patch-Seq Datasets

Patch-seq, combining patch-clamp electrophysiology with single-cell RNA-sequencing (scRNAseq), enables unprecedented access to a neuron's transcriptomic, electrophysiological, and morphological features. Here, we present a re-analysis of five patch-seq datasets, representing cells from ex vivo mouse brain slices and in vitro human stem-cell derived neurons. Our objective was to develop simple criteria to assess the quality of patch-seq derived single-cell transcriptomes. We evaluated patch-seq transcriptomes for the expression of marker genes of multiple cell types, benchmarking these against analogous profiles from cellular-dissociation based scRNAseq. We found an increased likelihood of off-target cell-type mRNA contamination in patch-seq cells from acute brain slices, likely due to the passage of the patch-pipette through the processes of adjacent cells. We also observed that patch-seq samples varied considerably in the amount of mRNA that could be extracted from each cell, strongly biasing the numbers of detectable genes. We developed a marker gene-based approach for scoring single-cell transcriptome quality post-hoc. Incorporating our quality metrics into downstream analyses improved the correspondence between gene expression and electrophysiological features. Our analysis suggests that technical confounds likely limit the interpretability of patch-seq based single-cell transcriptomes. However, we provide concrete recommendations for quality control steps that can be performed prior to costly RNA-sequencing to optimize the yield of high-quality samples

Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses : A cross-sectional study of 89,205 participants from the UK Biobank

Funding Information: The authors acknowledge Milos Milic for data curation assistance. MW and SJT acknowledge support from the Kavli Foundation, Krembil Foundation, CAMH Discovery Fund, the McLaughlin Foundation, NSERC (RGPIN-2020-05834 and DGECR-2020-00048) and CIHR (NGN-171423). DF is supported by the Michael and Sonja Koerner Foundation New Scientist Program, Krembil Foundation, CAMH Discovery Fund, and the McLaughlin Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This research was conducted under the auspices of UK Biobank application 61530, ?Multimodal subtyping of mental illness across the adult lifespan through integration of multi-scale whole-person phenotypes?. The authors acknowledge Milos Milic for data curation assistance. This research was conducted under the auspices of UK Biobank application 61530, ?Multimodal subtyping of mental illness across the adult lifespan through integration of multi-scale whole-person phenotypes.? Publisher Copyright: Copyright: © 2021 Wainberg et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background Sleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale. Here, we aimed to examine the association of accelerometer-derived sleep measures with psychiatric diagnoses and polygenic risk scores in a large community-based cohort. Methods and findings In this post hoc cross-sectional analysis of the UK Biobank cohort, 10 interpretable sleep measures—bedtime, wake-up time, sleep duration, wake after sleep onset, sleep efficiency, number of awakenings, duration of longest sleep bout, number of naps, and variability in bedtime and sleep duration—were derived from 7-day accelerometry recordings across 89,205 participants (aged 43 to 79, 56% female, 97% self-reported white) taken between 2013 and 2015. These measures were examined for association with lifetime inpatient diagnoses of major depressive disorder, anxiety disorders, bipolar disorder/mania, and schizophrenia spectrum disorders from any time before the date of accelerometry, as well as polygenic risk scores for major depression, bipolar disorder, and schizophrenia. Covariates consisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index (an indicator of socioeconomic status), and the top 10 genotype principal components. We found that sleep pattern differences were ubiquitous across diagnoses: each diagnosis was associated with a median of 8.5 of the 10 accelerometer-derived sleep measures, with measures of sleep quality (for instance, sleep efficiency) generally more affected than mere sleep duration. Effect sizes were generally small: for instance, the largest magnitude effect size across the 4 diagnoses was β = −0.11 (95% confidence interval −0.13 to −0.10, p = 3 × 10−56, FDR = 6 × 10−55) for the association between lifetime inpatient major depressive disorder diagnosis and sleep efficiency. Associations largely replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self-reported sleep properties. Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry. Conclusions In this study, we observed that sleep pattern differences are a transdiagnostic feature of individuals with lifetime mental illness, suggesting that they should be considered regardless of diagnosis. Accelerometry provides a scalable way to objectively measure sleep properties in psychiatric clinical research and practice, even across tens of thousands of individuals.Peer reviewe

Distinctive biophysical features of human cell-types: insights from studies of neurosurgically resected brain tissue

Electrophysiological characterization of live human tissue from epilepsy patients has been performed for many decades. Although initially these studies sought to understand the biophysical and synaptic changes associated with human epilepsy, recently, it has become the mainstay for exploring the distinctive biophysical and synaptic features of human cell-types. Both epochs of these human cellular electrophysiological explorations have faced criticism. Early studies revealed that cortical pyramidal neurons obtained from individuals with epilepsy appeared to function “normally” in comparison to neurons from non-epilepsy controls or neurons from other species and thus there was little to gain from the study of human neurons from epilepsy patients. On the other hand, contemporary studies are often questioned for the “normalcy” of the recorded neurons since they are derived from epilepsy patients. In this review, we discuss our current understanding of the distinct biophysical features of human cortical neurons and glia obtained from tissue removed from patients with epilepsy and tumors. We then explore the concept of within cell-type diversity and its loss (i.e., “neural homogenization”). We introduce neural homogenization to help reconcile the epileptogenicity of seemingly “normal” human cortical cells and circuits. We propose that there should be continued efforts to study cortical tissue from epilepsy patients in the quest to understand what makes human cell-types “human”

Neurodata Without Borders: Creating a Common Data Format for Neurophysiology

The Neurodata Without Borders (NWB) initiative promotes data standardization in neuroscience to increase research reproducibility and opportunities. In the first NWB pilot project, neurophysiologists and software developers produced a common data format for recordings and metadata of cellular electrophysiology and optical imaging experiments. The format specification, application programming interfaces, and sample datasets have been released

NeuroElectro: a community database on the electrophysiological diversity of mammalian neuron types

<p>A poster presented at Cosyne 2013 on the creation of a database called NeuroElectro (neuroelectro.org) that organizes information on the electrophysiological diversity of neuron types in the brain.</p

Understanding the Form and Function of Neuronal Physiological Diversity

<p>For decades electrophysiologists have recorded and characterized the biophysical properties of a rich diversity of neuron types. This diversity of neuron types is critical for generating functionally important patterns of brain activity and implementing neural computations. In this thesis, I developed computational methods towards quantifying neuron diversity and applied these methods for understanding the functional implications of within-type neuron variability and across-type neuron diversity.</p> <p>First, I developed a means for defining the functional role of differences among neurons of the same type. Namely, I adapted statistical neuron models, termed generalized linear models, to precisely capture how the membranes of individual olfactory bulb mitral cells transform afferent stimuli to spiking responses. I then used computational simulations to construct virtual populations of biophysically variable mitral cells to study the functional implications of within-type neuron variability. I demonstrate that an intermediate amount of intrinsic variability enhances coding of noisy afferent stimuli by groups of biophysically variable mitral cells. These results suggest that within-type neuron variability, long considered to be a disadvantageous consequence of biological imprecision, may serve a functional role in the brain.</p> <p>Second, I developed a methodology for quantifying the rich electrophysiological diversity across the majority of the neuron types throughout the mammalian brain. Using semi-automated text-mining, I built a database, Neuro- Electro, of neuron type specific biophysical properties extracted from the primary research literature. This data is available at http://neuroelectro.org, which provides a publicly accessible interface where this information can be viewed. Though the extracted physiological data is highly variable across studies, I demonstrate that knowledge of article-specific experimental conditions can significantly explain the observed variance. By applying simple analyses to the dataset, I find that there exist 5-7 major neuron super-classes which segregate on the basis of known functional roles. Moreover, by integrating the NeuroElectro dataset with brain-wide gene expression data from the Allen Brain Atlas, I show that biophysically-based neuron classes correlate highly with patterns of gene expression among voltage gated ion channels and neurotransmitters. Furthermore, this work lays the conceptual and methodological foundations for substantially enhanced data sharing in neurophysiological investigations in the future.</p

Agile text mining with Sherlok

The successful development of an intelligent text mining application requires the collaboration of two main stakeholders: subject matter experts and text miners. In this paper, we describe a new methodology, agile text mining to improve that collaboration. Agile text mining is characterized by short development cycles, frequent tasks redefinition and continuous performance monitoring through integration tests. We introduce Sherlok, a system supporting the development of agile text mining applications and present an application to extract mention of neurons from a very large corpus of scientific articles. The resulting code and models are publicly available