scholarly journals Emergence of Canonical Functional Networks from the Structural Connectome

2020 ◽  
Author(s):  
Xihe Xie ◽  
Pablo F. Damasceno ◽  
Chang Cai ◽  
Srikantan Nagarajan ◽  
Ashish Raj
Keyword(s):  
Spatial Patterns ◽  
Linear Models ◽  
Signal Transmission ◽  
Functional Networks ◽  
Inference Procedure ◽  
Linear Superposition ◽  
Structural Connectome ◽  
Complex Laplacian ◽  
Valued Graph ◽  
Complex Valued

AbstractHow do functional brain networks emerge from the underlying wiring of the brain? We examine how resting-state functional activation patterns emerge from the underlying connectivity and length of white matter fibers that constitute its “structural connectome”. By introducing realistic signal transmission delays along fiber projections, we obtain a complex-valued graph Laplacian matrix that depends on two parameters: coupling strength and oscillation frequency. This complex Laplacian admits a complex-valued eigen-basis in the frequency domain that is highly tunable and capable of reproducing the spatial patterns of canonical functional networks without requiring any detailed neural activity modeling. Specific canonical functional networks can be predicted using linear superposition of small subsets of complex eigenmodes. Using a novel parameter inference procedure we show that the complex Laplacian outperforms the real-valued Laplacian in predicting functional networks. The complex Laplacian eigenmodes therefore constitute a tunable yet parsimonious substrate on which a rich repertoire of realistic functional patterns can emerge. Although brain activity is governed by highly complex nonlinear processes and dense connections, our work suggests that simple extensions of linear models to the complex domain effectively approximate rich macroscopic spatial patterns observable on BOLD fMRI.

scholarly journals Emergence of Canonical Functional Networks from the Structural Connectome

NeuroImage ◽  
2021 ◽  
pp. 118190
Author(s):  
Xihe Xie ◽  
Chang Cai ◽  
Pablo F. Damasceno ◽  
Srikantan Nagarajan ◽  
Ashish Raj
Keyword(s):  
Functional Networks ◽  
Structural Connectome

Patterns of human–crocodile conflict in Queensland: a review of historical estuarine crocodile (Crocodylus porosus) management

2017 ◽  
Vol 44 (4) ◽  
pp. 281 ◽  
Author(s):  
M. L. Brien ◽  
C. M. Gienger ◽  
C. A. Browne ◽  
M. A. Read ◽  
M. J. Joyce ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Public Safety ◽  
Linear Models ◽  
East Coast ◽  
Adult Males ◽  
Wet Season ◽  
Temporal And Spatial Patterns ◽  
Crocodylus Porosus ◽  
Temporal And Spatial ◽  
The Government

Context In Queensland, the management of estuarine crocodiles (Crocodylus porosus) by the government is important for ensuring public safety, especially along the populated east coast, where there is a large human population. Aims The present study aimed to determine historical, temporal and spatial patterns of human–crocodile conflict in Queensland. Methods The study used Queensland Government records of estuarine crocodile attacks (1971–2015), sightings by the general public (2003–2015), and removals and relocations for management purposes (1985–2015) to develop General Linear Models describing historical, temporal and spatial patterns. Key results The highest number of attacks, sightings, removals and relocations occurred along the populated east coast between Townsville and the Daintree during wet season months (November–February). There have been 35 crocodile attacks in Queensland since 1971 (total 0.8 per year; fatal 0.3 per year), mostly involving local people or regular visitors (77.1%), specifically adult males (71.4%; mean age 44). There has been an increase in the rate of crocodile attacks over time, with an average of 1.3 per year since 1996, most of which were non-fatal (84%). The number of crocodile sightings has been increasing annually (with a mean of 348 per year since 2011), while the number of crocodiles removed or relocated for management purposes (n = 608) has fluctuating widely each year (range 1–57). Conclusions The level of human–crocodile conflict in Queensland is increasing, and this is likely to be a consequence of increasing human and crocodile populations. While conflict is highest during the wet season, estuarine crocodiles pose a threat to public safety year round. Implications With the increase in conflict, the ongoing management of estuarine crocodiles, through targeted removals in and around areas of higher human habitation and through education, is essential for ensuring public safety into the future.

scholarly journals Fine-scale spatial patterns of wildlife disease are common and understudied

2020 ◽  
Author(s):  
Gregory F Albery ◽  
Amy R Sweeny ◽  
Daniel J Becker ◽  
Shweta Bansal
Keyword(s):  
Spatial Patterns ◽  
Spatial Data ◽  
Disease Ecology ◽  
Linear Models ◽  
Wild Animal ◽  
Environmental Drivers ◽  
Fine Scale ◽  
Wild Mammals ◽  
Transmission Modes ◽  
Susceptibility Effects

AbstractAll pathogens are heterogeneous in space, yet little is known about the prevalence and scale of this spatial variation, particularly in wild animal systems. To address this question, we conducted a broad literature search to identify datasets involving diseases of wild mammals in spatially distributed contexts. Across 31 such final datasets featuring 89 replicates and 71 host-parasite combinations, only 51% had previously been used to test spatial hypotheses. We analysed these datasets for spatial dependence within a standardised modelling framework using Bayesian linear models. We detected spatial autocorrelation in 44/89 model replicates (54%) across 21/31 datasets (68%), spread across parasites of all groups and transmission modes. Surprisingly, although larger sampling areas more easily detected spatial patterns, even some very small study areas (under 0.01km2) exhibited substantial spatial heterogeneity. Parasites of all transmission modes had easily detectable spatial patterns, implying that structured contact networks and susceptibility effects are likely as important in spatially structuring disease as are environmental drivers of transmission efficiency. Our findings imply that fine-scale spatial patterns of infection often manifest in wild animal systems, whether or not the aim of the study is to examine environmentally varying processes. Given the widespread nature of these findings, studies should more frequently record and analyse spatial data, facilitating development and testing of spatial hypotheses in disease ecology.

Spatial patterns of wildfire ignitions in south-eastern Australia

2015 ◽  
Vol 24 (8) ◽  
pp. 1098 ◽  
Author(s):  
Kathryn M. Collins ◽  
Owen F. Price ◽  
Trent D. Penman
Keyword(s):  
Spatial Patterns ◽  
Linear Models ◽  
Important Variable ◽  
Explanatory Variables ◽  
Climate Change Effects ◽  
South Eastern ◽  
Management Actions ◽  
South Wales ◽  
Eastern Australia ◽  
South Eastern Australia

Wildfires can have devastating effects on life, property and the environment. Official inquiries following major damaging fires often recommend management actions to reduce the risk of future losses from wildfires. Understanding where wildfires are most likely to occur in the landscape is essential to determining where wildfires pose the greatest risk to people and property. We investigated the spatial patterns of wildfire ignitions at a bioregional scale in New South Wales and Victoria using generalised linear models. We used a combination of social and biophysical variables and examined whether different categories of ignitions respond to different explanatory variables. Human-caused ignitions are the dominant source of ignitions for wildfires in south-eastern Australia and our results showed that for such ignitions, population density was the most important variable for the spatial pattern of ignitions. In future years, more ignitions are predicted in the coastal and hinterland areas due to population increases and climate change effects.

scholarly journals Equilibrium Stochastic Delay Processes

2022 ◽  
Author(s):  
Viktor Holubec ◽  
Artem Ryabov ◽  
Sarah A. M. Loos ◽  
Klaus Kroy
Keyword(s):  
Linear Models ◽  
Linear Time ◽  
Brownian Particle ◽  
Signal Transmission ◽  
Point Of View ◽  
Thermal Bath ◽  
Temporal Delay ◽  
Science And Engineering ◽  
Stochastic Delay ◽  
Starting Point

Abstract Stochastic processes with temporal delay play an important role in science and engineering whenever finite speeds of signal transmission and processing occur. However, an exact mathematical analysis of their dynamics and thermodynamics is available for linear models only. We introduce a class of stochastic delay processes with nonlinear time-local forces and linear time-delayed forces that obey fluctuation theorems and converge to a Boltzmann equilibrium at long times. From the point of view of control theory, such ``equilibrium stochastic delay processes'' are stable and energetically passive, by construction. Computationally, they provide diverse exact constraints on general nonlinear stochastic delay problems and can, in various situations, serve as a starting point for their perturbative analysis. Physically, they admit an interpretation in terms of an underdamped Brownian particle that is either subjected to a time-local force in a non-Markovian thermal bath or to a delayed feedback force in a Markovian thermal bath. We illustrate these properties numerically for a setup familiar from feedback cooling and point out experimental implications.

Quadrature subunits in directionally selective simple cells: Spatiotemporal interactions

1997 ◽  
Vol 14 (2) ◽  
pp. 357-371 ◽  
Author(s):  
Robert C. Emerson
Keyword(s):  
Time Course ◽  
Striate Cortex ◽  
Linear Models ◽  
Linear Filter ◽  
Nonlinear Interactions ◽  
Linear Superposition ◽  
Cortical Cells ◽  
Simple Cells ◽  
Preferred Direction ◽  
Branch Model

AbstractI explore here whether linear mechanisms can explain directional selectivity (DS) in simple cells of the cat's striate cortex, a question suggested by a recent upswing in popularity of linear DS models. I chose a simple cell with a space-time inseparable receptive field (RF), i.e. one that shows gradually shifting latency across space, as the RF type most likely to depend on linear mechanisms of DS. However, measured responses of the cell to a moving bar were less modulated, and extended over a larger spatial region than predicted by two different popular “linear” models. They also were more DS in exhibiting a higher ratio of total spikes for the preferred direction. Each of the two models used for comparison has a single “branch” with a single spatiotemporally inseparable linear filter followed by a threshold, hence, a “1-branch” model. Nonlinear interactions between pairs of bars in a 2-bar linear superposition test of the cell also disagreed in time-course with those of the 1-branch models. The only model whose 1-bar and 2-bar predictions matched the measured cell (including a complete “4-branch” motion energy model that matches complex cells) has two branches that differ in phase by about 90 deg, i.e. in quadrature. Each branch has its own threshold that helps define the preceding spatiotemporal unit as a subunit even after the outputs of the two branches are summed. As subunit phases differ by only 90 deg, flashing bar responses of the 2-subunit model are similar to those of the 1-subunit model. Therefore, the number of subunits is hidden from view when testing with a conventional stationary bar. In summary, movement responses and nonlinear interactions between pairs of bars in the measured cell matched those of the 2-subunit model, while they disagreed with the popular 1-subunit model. Thus, multiple nonlinear subunits appear to be necessary for DS, even in simple cortical cells.

scholarly journals Bayesian Sparse Estimation Using Double Lomax Priors

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Xiaojing Gu ◽  
Henry Leung ◽  
Xingsheng Gu
Keyword(s):  
Bayesian Inference ◽  
Latent Variable ◽  
Linear Models ◽  
Model Identification ◽  
Ar Model ◽  
Smoothing Effect ◽  
Inference Procedure ◽  
Sparse Estimation ◽  
Automatic Relevance Determination ◽  
Convergence Performance

Sparsity-promoting prior along with Bayesian inference is an effective approach in solving sparse linear models (SLMs). In this paper, we first introduce a new sparsity-promoting prior coined as Double Lomax prior, which corresponds to a three-level hierarchical model, and then we derive a full variational Bayesian (VB) inference procedure. When noninformative hyperprior is assumed, we further show that the proposed method has one more latent variable than the canonical automatic relevance determination (ARD). This variable has a smoothing effect on the solution trajectories, thus providing improved convergence performance. The effectiveness of the proposed method is demonstrated by numerical simulations including autoregressive (AR) model identification and compressive sensing (CS) problems.

scholarly journals Predictive Statistical Representations of Observed and Simulated Rainfall Using Generalized Linear Models

2019 ◽  
Vol 32 (11) ◽  
pp. 3409-3427
Author(s):  
Junho Yang ◽  
Mikyoung Jun ◽  
Courtney Schumacher ◽  
R. Saravanan
Keyword(s):  
Logistic Regression ◽  
Spatial Patterns ◽  
Generalized Linear Models ◽  
Statistical Models ◽  
Large Scale ◽  
Linear Models ◽  
Probability Distributions ◽  
Environmental Parameters ◽  
Radar Observations ◽  
Satellite Radar

Abstract This study explores the feasibility of predicting subdaily variations and the climatological spatial patterns of rain in the tropical Pacific from atmospheric profiles using a set of generalized linear models: logistic regression for rain occurrence and gamma regression for rain amount. The prediction is separated into different rain types from TRMM satellite radar observations (stratiform, deep convective, and shallow convective) and CAM5 simulations (large-scale and convective). Environmental variables from MERRA-2 and CAM5 are used as predictors for TRMM and CAM5 rainfall, respectively. The statistical models are trained using environmental fields at 0000 UTC and rainfall from 0000 to 0600 UTC during 2003. The results are used to predict 2004 rain occurrence and rate for MERRA-2/TRMM and CAM5 separately. The first EOF profile of humidity and the second EOF profile of temperature contribute most to the prediction for both statistical models in each case. The logistic regression generally performs well for all rain types, but does better in the east Pacific compared to the west Pacific. The gamma regression produces reasonable geographical rain amount distributions but rain rate probability distributions are not predicted as well, suggesting the need for a different, higher-order model to predict rain rates. The results of this study suggest that statistical models applied to TRMM radar observations and MERRA-2 environmental parameters can predict the spatial patterns and amplitudes of tropical rainfall in the time-averaged sense. Comparing the observationally trained models to models that are trained using CAM5 simulations points to possible deficiencies in the convection parameterization used in CAM5.

scholarly journals Data-driven approaches for Tau-PET imaging biomarkers in Alzheimer’s disease

2018 ◽  
Author(s):  
Jacob W. Vogel ◽  
Niklas Mattsson ◽  
Yasser Iturria-Medina ◽  
T. Olof Strandberg ◽  
Michael Schöll ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Spatial Patterns ◽  
Clustering Algorithm ◽  
Linear Models ◽  
Data Driven ◽  
Imaging Biomarkers ◽  
Discovery Sample ◽  
Topographical Distribution ◽  
Tau Pet

ABSTRACTPrevious positron emission tomography (PET) studies have quantified filamentous tau pathology using regions-of-interest (ROIs) based on observations of the topographical distribution of neurofibrillary tangles in post-mortem tissue. However, such approaches may not take full advantage of information contained in neuroimaging data. The present study employs an unsupervised data-driven method to identify spatial patterns of tau-PET distribution, and to compare these patterns to previously published “pathology-driven” ROIs. Tau-PET patterns were identified from a discovery sample comprised of 123 normal controls and patients with mild cognitive impairment or Alzheimer’s disease (AD) dementia from the Swedish BioFINDER cohort, who underwent [18F]AV1451 PET scanning. Associations with cognition were tested in a separate sample of 90 individuals from ADNI. BioFINDER [18F]AV1451 images were entered into a robust voxelwise stable clustering algorithm, which resulted in five clusters. Mean [18F]AV1451 uptake in the data-driven clusters, and in 35 previously published pathology-driven ROIs, was extracted from ADNI [18F]AV1451 scans. We performed linear models comparing [18F]AV1451 signal across all 40 ROIs to several tests of global cognition, adjusting for age, sex and education. Two data-driven ROIs consistently demonstrated the strongest or near-strongest effect sizes across all cognitive tests. Inputting all regions plus demographics into a feature selection routine resulted in selection of two ROIs (one data-driven, one pathology-driven) and education, which together explained 28% of the variance of a global cognitive composite score. Our findings suggest that [18F]AV1451-PET data naturally clusters into spatial patterns that are biologically meaningful and that may offer advantages as clinical tools.

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