scholarly journals Model-based detection of putative synaptic connections from spike recordings with latency and type constraints

2020 ◽  
Author(s):  
Naixin Ren ◽  
Shinya Ito ◽  
Hadi Hafizi ◽  
John M. Beggs ◽  
Ian H. Stevenson
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Large Scale ◽  
Hypothesis Test ◽  
Detection Methods ◽  
Multielectrode Array ◽  
Synaptic Connections ◽  
Model Framework ◽  
Synapse Detection

AbstractDetecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. While previous methods often treat the detection of each putative connection as a separate hypothesis test, here we develop a modeling approach that infers synaptic connections while incorporating circuit properties learned from the whole network. We use an extension of the Generalized Linear Model framework to describe the cross-correlograms between pairs of neurons and separate correlograms into two parts: a slowly varying effect due to background fluctuations and a fast, transient effect due to the synapse. We then use the observations from all putative connections in the recording to estimate two network properties: the presynaptic neuron type (excitatory or inhibitory) and the relationship between synaptic latency and distance between neurons. Constraining the presynaptic neuron’s type, synaptic latencies, and time constants improves synapse detection. In data from simulated networks, this model outperforms two previously developed synapse detection methods, especially on the weak connections. We also apply our model to in vitro multielectrode array recordings from mouse somatosensory cortex. Here our model automatically recovers plausible connections from hundreds of neurons, and the properties of the putative connections are largely consistent with previous research.New & NoteworthyDetecting synaptic connections using large-scale extracellular spike recordings is a difficult statistical problem. Here we develop an extension of a Generalized Linear Model that explicitly separates fast synaptic effects and slow background fluctuations in cross-correlograms between pairs of neurons while incorporating circuit properties learned from the whole network. This model outperforms two previously developed synapse detection methods in the simulated networks, and recovers plausible connections from hundreds of neurons in in vitro multielectrode array data.

scholarly journals Model-based detection of putative synaptic connections from spike recordings with latency and type constraints

2020 ◽  
Vol 124 (6) ◽  
pp. 1588-1604
Author(s):  
Naixin Ren ◽  
Shinya Ito ◽  
Hadi Hafizi ◽  
John M. Beggs ◽  
Ian H. Stevenson
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Large Scale ◽  
Statistical Problem ◽  
Detection Methods ◽  
Multielectrode Array ◽  
Synaptic Connections ◽  
Synaptic Effects ◽  
Synapse Detection

Detecting synaptic connections using large-scale extracellular spike recordings is a difficult statistical problem. Here, we develop an extension of a generalized linear model that explicitly separates fast synaptic effects and slow background fluctuations in cross-correlograms between pairs of neurons while incorporating circuit properties learned from the whole network. This model outperforms two previously developed synapse detection methods in the simulated networks and recovers plausible connections from hundreds of neurons in in vitro multielectrode array data.

scholarly journals Exploring the Association between Compliance with Measures to Prevent the Spread of COVID-19 and Big Five Traits with Bayesian Generalized Linear Model

2021 ◽  
Author(s):  
Hyemin Han
Keyword(s):  
Regression Model ◽  
Linear Model ◽  
Big Five ◽  
Generalized Linear Model ◽  
Regression Models ◽  
Large Scale ◽  
Negative Association ◽  
Data Driven ◽  
Big Five Traits ◽  
Bayesian Generalized Linear Model

Research has examined the association between people’s compliance with measures to prevent the spread of COVID-19 and personality traits. However, previous studies were conducted with relatively small-size datasets and employed frequentist analysis that does not allow data-driven model exploration. To address the limitations, a large-scale international dataset, COVIDiSTRESS Global Survey dataset, was explored with Bayesian generalized linear model that enables identification of the best regression model. The best regression models predicting participants’ compliance with Big Five traits were explored. The findings demonstrated first, all Big Five traits, except extroversion, were positively associated with compliance with general measures and distancing. Second, neuroticism, extroversion, and agreeableness were positively associated with the perceived cost of complying with the measures while conscientiousness showed negative association. The findings and the implications of the present study were discussed.

scholarly journals Efficient approaches for large-scale GWAS with genotype uncertainty

2021 ◽  
Author(s):  
Emil Jørsboe ◽  
Anders Albrechtsen
Keyword(s):  
Linear Model ◽  
Allele Frequency ◽  
Latent Variable ◽  
Statistical Power ◽  
Large Scale ◽  
Association Studies ◽  
Genetic Data ◽  
Model Framework ◽  
The Individual ◽  
Genotype Probabilities

Abstract Association studies using genetic data from SNP-chip-based imputation or low-depth sequencing data provide a cost-efficient design for large-scale association studies. We explore methods for performing association studies applicable to such genetic data and investigate how using different priors when estimating genotype probabilities affects the association results. Our proposed method, ANGSD-asso’s latent model, models the unobserved genotype as a latent variable in a generalized linear model framework. The software is implemented in C/C++ and can be run multi-threaded. ANGSD-asso is based on genotype probabilities, which can be estimated using either the sample allele frequency or the individual allele frequencies as a prior. We explore through simulations how genotype probability-based methods compare with using genetic dosages. Our simulations show that in a structured population using the individual allele frequency prior has better power than the sample allele frequency. In scenarios with sequencing depth and phenotype correlation ANGSD-asso’s latent model has higher statistical power and less bias than using dosages. Adding additional covariates to the linear model of ANGSD-asso’s latent model has higher statistical power and less bias than other methods that accommodate genotype uncertainty, while also being much faster. This is shown with imputed data from UK Biobank and simulations.

Prosodic parallelism explaining morphophonological variation in German

Linguistics ◽  
2015 ◽  
Vol 53 (3) ◽  
Author(s):  
Richard Wiese ◽  
Augustin Speyer
Keyword(s):  
Target Word ◽  
Empirical Evidence ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Large Scale ◽  
Mixed Effects ◽  
Final Position ◽  
Chi Square ◽  
Early New High German ◽  
Chi Square Tests

AbstractWords in German show several instances of a seemingly optional schwa-zero alternation, both in relation with inflected forms as well as in the final position of stems and simplex words, as inLarge-scale corpora are used as the main source of evidence for the verification or falsification of the hypothesis. A diverse set of nouns and adverbs involving schwa-zero alternations were studied in appropriate phrasal contexts, both from present-day Standard German and from Early New High German. Based on comprehensive corpus counts, these phrases are tested for the hypothesis of prosodic parallelism. A series of chi square tests and a generalized linear model with mixed effects demonstrate statistically that the prosodic shapes of the target word and its adjacent form are not independent of each other. The focus of the paper is on empirical evidence for

scholarly journals Instantaneous Geometric Rates via Generalized Linear Models

2017 ◽  
Vol 17 (2) ◽  
pp. 358-371 ◽  
Author(s):  
Andrea Discacciati ◽  
Matteo Bottai
Keyword(s):  
Clinical Trial ◽  
Randomized Clinical Trial ◽  
Linear Model ◽  
Generalized Linear Models ◽  
Generalized Linear Model ◽  
Renal Carcinoma ◽  
Linear Models ◽  
Rate Model ◽  
Model Framework ◽  
Link Functions

The instantaneous geometric rate represents the instantaneous probability of an event of interest per unit of time. In this article, we propose a method to model the effect of covariates on the instantaneous geometric rate with two models: the proportional instantaneous geometric rate model and the proportional instantaneous geometric odds model. We show that these models can be fit within the generalized linear model framework by using two nonstandard link functions that we implement in the user-defined link programs log_igr and logit_igr. We illustrate how to fit these models and how to interpret the results with an example from a randomized clinical trial on survival in patients with metastatic renal carcinoma.

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