Neural manifold analysis of brain circuit dynamics in health and disease

Recent developments in experimental neuroscience make it possible to simultaneously record the activity of thousands of neurons. However, the development of analysis approaches for such large-scale neural recordings have been slower than those applicable to single-cell experiments. One approach that has gained recent popularity is neural manifold learning. This approach takes advantage of the fact that often, even though neural datasets may be very high dimensional, the dynamics of neural activity tends to traverse a much lower-dimensional space. The topological structures formed by these low-dimensional neural subspaces are referred to as “neural manifolds”, and may potentially provide insight linking neural circuit dynamics with cognitive function and behavioral performance. In this paper we review a number of linear and non-linear approaches to neural manifold learning, including principal component analysis (PCA), multi-dimensional scaling (MDS), Isomap, locally linear embedding (LLE), Laplacian eigenmaps (LEM), t-SNE, and uniform manifold approximation and projection (UMAP). We outline these methods under a common mathematical nomenclature, and compare their advantages and disadvantages with respect to their use for neural data analysis. We apply them to a number of datasets from published literature, comparing the manifolds that result from their application to hippocampal place cells, motor cortical neurons during a reaching task, and prefrontal cortical neurons during a multi-behavior task. We find that in many circumstances linear algorithms produce similar results to non-linear methods, although in particular cases where the behavioral complexity is greater, non-linear methods tend to find lower-dimensional manifolds, at the possible expense of interpretability. We demonstrate that these methods are applicable to the study of neurological disorders through simulation of a mouse model of Alzheimer’s Disease, and speculate that neural manifold analysis may help us to understand the circuit-level consequences of molecular and cellular neuropathology

Multiphoton minimal inertia scanning for fast acquisition of neural activity signals

Objective: Multi-photon laser scanning microscopy provides a powerful tool for monitoring the spatiotemporal dynamics of neural circuit activity. It is, however, intrinsically a point scanning technique. Standard raster scanning enables imaging at subcellular resolution; however, acquisition rates are limited by the size of the field of view to be scanned. Recently developed scanning strategies such as Travelling Salesman Scanning (TSS) have been developed to maximize cellular sampling rate by scanning only select regions in the field of view corresponding to locations of interest such as somata. However, such strategies are not optimized for the mechanical properties of galvanometric scanners. We thus aimed to develop a new scanning algorithm which produces minimal inertia trajectories, and compare its performance with existing scanning algorithms. Approach: We describe here the Adaptive Spiral Scanning (SSA) algorithm, which fits a set of near-circular trajectories to the cellular distribution to avoid inertial drifts of galvanometer position. We compare its performance to raster scanning and TSS in terms of cellular sampling frequency and signal-to-noise ratio (SNR). Main Results: Using surrogate neuron spatial position data, we show that SSA acquisition rates are an order of magnitude higher than those for raster scanning and generally exceed those achieved by TSS for neural densities comparable with those found in the cortex. We show that this result also holds true for in vitro hippocampal mouse brain slices bath loaded with the synthetic calcium dye Cal-520 AM. The ability of TSS to "park" the laser on each neuron along the scanning trajectory, however, enables higher SNR than SSA when all targets are precisely scanned. Raster scanning has the highest SNR but at a substantial cost in number of cells scanned. To understand the impact of sampling rate and SNR on functional calcium imaging, we used the Crame ́r-Rao Bound on evoked calcium traces recorded simultaneously with electrophysiology traces to calculate the lower bound estimate of the spike timing occurrence. Significance: The results show that TSS and SSA achieve comparable accuracy in spike time estimates compared to raster scanning, despite lower SNR. SSA is an easily implementable way for standard multi-photon laser scanning systems to gain temporal precision in the detection of action potentials while scanning hundreds of active cells

On the origin of tropospheric ozone and NOx over the tropical South Pacific

The budgets of ozone and nitrogen oxides (NOx = NO + NO2) in the tropical South Pacific troposphere are analyzed by photochemical point modeling of aircraft observations at 0-12 km altitude from the Pacific Exploratory Mission-Tropics A campaign flown in September-October 1996. The model reproduces the observed NO2/NO concentration ratio to within 30% and has similar success in simulating observed concentrations of peroxides (H2O2, CH3OOH), lending confidence in its use to investigate ozone chemistry. It is found that chemical production of ozone balances only half of chemical loss in the tropospheric column over the tropical South Pacific. The net loss is 1.8 x 1011 molecules cm-2 s-1. The missing source of ozone is matched by westerly transport of continental pollution into the region. Independent analysis of the regional ozone budget with a global three-dimensional model corroborates the results from the point model and reveals the importance of biomass burning emissions in South America and Africa for the ozone budget over the tropical South Pacific. In this model, biomass burning increases average ozone concentrations by 7-8 ppbv throughout the troposphere. The NOx responsible for ozone production within the South Pacific troposphere below 4 km can be largely explained by decomposition of peroxyacetylnitrate (PAN) transported into the region with biomass burning pollution at higher altitudes. Copyright 1999 by the American Geophysical Union

Applications of Information Theory to Analysis of Neural Data

Information theory is a practical and theoretical framework developed for the study of communication over noisy channels. Its probabilistic basis and capacity to relate statistical structure to function make it ideally suited for studying information flow in the nervous system. It has a number of useful properties: it is a general measure sensitive to any relationship, not only linear effects; it has meaningful units which in many cases allow direct comparison between different experiments; and it can be used to study how much information can be gained by observing neural responses in single trials, rather than in averages over multiple trials. A variety of information theoretic quantities are commonly used in neuroscience - (see entry "Definitions of Information-Theoretic Quantities"). In this entry we review some applications of information theory in neuroscience to study encoding of information in both single neurons and neuronal populations.Comment: 8 pages, 2 figure

Palladium-catalyzed heteroallylation of unactivated alkenes – synthesis of citalopram

A palladium-catalyzed difunctionalization of unactivated alkenes with tethered nucleophiles is reported. The versatile reaction occurs with simple allylic halides and can be carried out under air. The methodology provides rapid access to a wide array of desirable heterocyclic targets, as illustrated by a concise synthesis of the widely prescribed antidepressant citalopram

Gatekeeping in health care

We study the competitive effects of restricting direct access to secondary care by gatekeeping, focusing on the informational role of general practitioners (GPs). In the secondary care market there are two hospitals choosing quality and specialization. Patients, who are ex ante uninformed, can consult a GP to receive an (imperfect) diagnosis and obtain information about the secondary care market. We show that hospital competition is amplified by higher GP attendance but dampened by improved diagnosing accuracy. Therefore, compulsory gatekeeping may result in excessive quality competition and too much specialization, unless the mismatch costs and the diagnosing accuracy are sufficiently high. Second-best price regulation makes direct regulation of GP consultation redundant, but will generally not implement first-best

Fuzzy Fibers: Uncertainty in dMRI Tractography

Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI) allows for noninvasive reconstruction of fiber bundles in the human brain. In this chapter, we discuss sources of error and uncertainty in this technique, and review strategies that afford a more reliable interpretation of the results. This includes methods for computing and rendering probabilistic tractograms, which estimate precision in the face of measurement noise and artifacts. However, we also address aspects that have received less attention so far, such as model selection, partial voluming, and the impact of parameters, both in preprocessing and in fiber tracking itself. We conclude by giving impulses for future research

Altered channel gating mechanism for CFTR inhibition by a high-affinity thiazolidinone blocker

AbstractThe thiazolidinone CFTRinh-172 was identified recently as a potent and selective blocker of the cystic fibrosis transmembrane conductance regulator (CFTR) Cl− channel. Here, we characterized the CFTRinh-172 inhibition mechanism by patch-clamp and short-circuit analysis using cells stably expressing wild-type and mutant CFTRs. CFTRinh-172 did not alter CFTR unitary conductance (8 pS), but reduced open probability by >90% with Ki≈0.6 μM. This effect was due to increased mean channel closed time without changing mean channel open time. Short-circuit current experiments indicated similar CFTRinh-172 inhibitory potency (Ki≈0.5 μM) for inhibition of Cl− current in wild-type, G551D, and G1349D CFTR; however, Ki was significantly reduced to 0.2 μM for ΔF508 CFTR. Our studies provide evidence for CFTR inhibition by CFTRinh-172 by a mechanism involving altered CFTR gating