New articles on q-bio


[1] 2010.15191

Chronic, cortex-wide imaging of specific cell populations during behavior

Measurements of neuronal activity across brain areas are important for understanding the neural correlates of cognitive and motor processes like attention, decision-making, and action selection. However, techniques that allow cellular resolution measurements are expensive and require a high degree of technical expertise, which limits their broad use. Widefield imaging of genetically encoded indicators is a high throughput, cost effective, and flexible approach to measure activity of specific cell populations with high temporal resolution and a cortex-wide field of view. Here we outline our protocol for assembling a widefield setup, a surgical preparation to image through the intact skull, and imaging neural activity chronically in behaving, transgenic mice that express a calcium indicator in specific subpopulations of cortical neurons. Further, we highlight a processing pipeline that leverages novel, cloud-based methods to analyze large-scale imaging datasets. The protocol targets labs that are seeking to build macroscopes, optimize surgical procedures for long-term chronic imaging, and/or analyze cortex-wide neuronal recordings.


[2] 2010.15214

Inference of ventricular activation properties from non-invasive electrocardiography

The realisation of precision cardiology requires novel techniques for the non-invasive characterisation of individual patients' cardiac function to inform therapeutic and diagnostic decision-making. The electrocardiogram (ECG) is the most widely used clinical tool for cardiac diagnosis. Its interpretation is, however, confounded by functional and anatomical variability in heart and torso. In this study, we develop new computational techniques to estimate key ventricular activation properties for individual subjects by exploiting the synergy between non-invasive electrocardiography and image-based torso-biventricular modelling and simulation. More precisely, we present an efficient sequential Monte Carlo approximate Bayesian computation-based inference method, integrated with Eikonal simulations and torso-biventricular models constructed based on clinical cardiac magnetic resonance (CMR) imaging. The method also includes a novel strategy to treat combined continuous (conduction speeds) and discrete (earliest activation sites) parameter spaces, and an efficient dynamic time warping-based ECG comparison algorithm. We demonstrate results from our inference method on a cohort of twenty virtual subjects with cardiac volumes ranging from 74 cm3 to 171 cm3 and considering low versus high resolution for the endocardial discretisation (which determines possible locations of the earliest activation sites). Results show that our method can successfully infer the ventricular activation properties from non-invasive data, with higher accuracy for earliest activation sites, endocardial speed, and sheet (transmural) speed in sinus rhythm, rather than the fibre or sheet-normal speeds.


[3] 2010.15272

The distribution of inhibitory neurons in the C. elegans connectome facilitates self-optimization of coordinated neural activity

The nervous system of the nematode soil worm Caenorhabditis elegans exhibits remarkable complexity despite the worm's small size. A general challenge is to better understand the relationship between neural organization and neural activity at the system level, including the functional roles of inhibitory connections. Here we implemented an abstract simulation model of the C. elegans connectome that approximates the neurotransmitter identity of each neuron, and we explored the functional role of these physiological differences for neural activity. In particular, we created a Hopfield neural network in which all of the worm's neurons characterized by inhibitory neurotransmitters are assigned inhibitory outgoing connections. Then, we created a control condition in which the same number of inhibitory connections are arbitrarily distributed across the network. A comparison of these two conditions revealed that the biological distribution of inhibitory connections facilitates the self-optimization of coordinated neural activity compared with an arbitrary distribution of inhibitory connections.


[4] 2010.15308

Short term memory by transient oscillatory dynamics in recurrent neural networks

Despite the importance of short-term memory in cognitive function, how the input information is encoded and sustained in neural activity dynamics remains elusive. Here, by training recurrent neural networks to short-term memory tasks and analyzing the dynamics, the characteristic of the short-term memory mechanism was obtained in which the input information was encoded in the amplitude of transient oscillation, rather than the stationary neural activities. This transient orbit was attracted to a slow manifold, which allowed for the discarding of irrelevant information. Strong contraction to the manifold results in the noise robustness of the transient orbit, accordingly to the memory. The generality of the result and its relevance to neural information processing were discussed.


[5] 2010.15334

Bifurcation of the neuronal population dynamics of the modified theta model: transition to macroscopic gamma oscillation

Interactions of inhibitory neurons produce gamma oscillations (30--80 Hz) in the local field potential, which is known to be involved in functions such as cognition and attention. In this study, the modified theta model is considered to investigate the theoretical relationship between the microscopic structure of inhibitory neurons and their gamma oscillations under a wide class of distribution functions of tonic currents on individual neurons. The stability and bifurcation of gamma oscillations for the Vlasov equation of the model is investigated by the generalized spectral theory. It is shown that as a connection probability of neurons increases, a pair of generalized eigenvalues crosses the imaginary axis twice, which implies that a stable gamma oscillation exists only when the connection probability has a value within a suitable range. On the other hand, when the distribution of tonic currents on individual neurons is the Lorentzian distribution, the Vlasov equation is reduced to a finite dimensional dynamical system. The bifurcation analyses of the reduced equation exhibit equivalent results with the generalized spectral theory. It is also demonstrated that the numerical computations of neuronal population follow the analyses of the generalized spectral theory as well as the bifurcation analysis of the reduced equation.


[6] 2010.15493

An introduction to the mathematical modelling of iPSCs

The aim of this chapter is to convey the importance and usefulness of mathematical modelling as a tool to achieve a deeper understanding of stem cell biology. We introduce key mathematical concepts (random walk theory, differential equations and agent-based modelling) which form the basis of current descriptions of induced pluripotent stem cells. We hope to encourage a meaningful dialogue between biologists and mathematicians and highlight the value of such an interdisciplinary approach.


[7] 2010.15693

The soil seed bank can buffer long-term compositional changes in annual plant communities

Ecological theory predicts that the soil seed bank stabilises the composition of annual plant communities in the face of environmental variability. However, long-term data on the community dynamics in the seed bank and the standing vegetation are needed to test this prediction. We tested the hypothesis that the composition of the seed bank undergoes lower temporal variability than the standing vegetation in a nine-year study in Mediterranean, semi-arid, and arid ecosystems. The composition of the seed bank was estimated by collecting soil cores from the studied sites on an annual basis. Seedling emergence under optimal watering conditions was measured in each soil core for three consecutive years, to account for seed dormancy. In all sites, the composition of the seed bank differed from the vegetation throughout the years. Small-seeded and dormant-seeded species had a higher frequency in the seed bank than in the standing vegetation. In contrast, functional group membership (grasses vs. forbs) did not explain differences in species frequency between the seed bank and the vegetation after controlling for differences between grasses and forbs in seed mass and seed dormancy. Contrary to predictions, the magnitude of year-to-year variability (the mean compositional dissimilarity between consecutive years) was not lower in the seed bank than in the vegetation in all sites. However, long-term compositional trends in the seed bank were weaker than in the vegetation in the Mediterranean and semi-arid sites. In the arid site where year-to-year variability was highest, no long-term trends were observed. Overall, the effect of the seed bank on the temporal variability of the vegetation in annual communities depends on site conditions and time scale. While the year-to-year variability of the seed bank is similar to the vegetation, the soil seed bank can buffer long-term trends.


[8] 2010.15573

Quantum-like modeling in biology with open quantum systems and instruments

We present the novel approach to mathematical modeling of information processes in biosystems. It explores the mathematical formalism and methodology of quantum theory, especially quantum measurement theory. This approach is known as {\it quantum-like} and it should be distinguished from study of genuine quantum physical processes in biosystems (quantum biophysics, quantum cognition). It is based on quantum information representation of biosystem's state and modeling its dynamics in the framework of theory of open quantum systems. This paper starts with the non-physicist friendly presentation of quantum measurement theory, from the original von Neumann formulation to modern theory of quantum instruments. Then, latter is applied to model combinations of cognitive effects and gene regulation of glucose/lactose metabolism in Escherichia coli bacterium. The most general construction of quantum instruments is based on the scheme of indirect measurement, in that measurement apparatus plays the role of the environment for a biosystem. The biological essence of this scheme is illustrated by quantum formalization of Helmholtz sensation-perception theory. Then we move to open systems dynamics and consider quantum master equation, with concentrating on quantum Markov processes. In this framework, we model functioning of biological functions such as psychological functions and epigenetic mutation.


[9] 2010.15594

Shared Space Transfer Learning for analyzing multi-site fMRI data

Multi-voxel pattern analysis (MVPA) learns predictive models from task-based functional magnetic resonance imaging (fMRI) data, for distinguishing when subjects are performing different cognitive tasks -- e.g., watching movies or making decisions. MVPA works best with a well-designed feature set and an adequate sample size. However, most fMRI datasets are noisy, high-dimensional, expensive to collect, and with small sample sizes. Further, training a robust, generalized predictive model that can analyze homogeneous cognitive tasks provided by multi-site fMRI datasets has additional challenges. This paper proposes the Shared Space Transfer Learning (SSTL) as a novel transfer learning (TL) approach that can functionally align homogeneous multi-site fMRI datasets, and so improve the prediction performance in every site. SSTL first extracts a set of common features for all subjects in each site. It then uses TL to map these site-specific features to a site-independent shared space in order to improve the performance of the MVPA. SSTL uses a scalable optimization procedure that works effectively for high-dimensional fMRI datasets. The optimization procedure extracts the common features for each site by using a single-iteration algorithm and maps these site-specific common features to the site-independent shared space. We evaluate the effectiveness of the proposed method for transferring between various cognitive tasks. Our comprehensive experiments validate that SSTL achieves superior performance to other state-of-the-art analysis techniques.


[10] 2010.15612

Inference of joint conformational distributions from separately-acquired experimental measurements

Many biomolecules have flexible structures, requiring distributional estimates of their conformations. Experiments to acquire distributional data typically measure pairs of labels separately, losing information on the joint distribution. These data are assumed independent when estimating the conformational ensemble. We developed a method to estimate the true joint distribution from separately acquired measurements, testing it on two biological systems. This method accurately reproduces the joint distribution where known and generates testable predictions about complex conformational ensembles.