scholarly journals Inversion of a large-scale circuit model reveals a cortical hierarchy in the dynamic resting human brain

2019 ◽  
Vol 5 (1) ◽  
pp. eaat7854 ◽  
Author(s):  
Peng Wang ◽  
Ru Kong ◽  
Xiaolu Kong ◽  
Raphaël Liégeois ◽  
Csaba Orban ◽  
...  
Keyword(s):  
Large Scale ◽  
Meta Analysis ◽  
Circuit Model ◽  
Model Parameters ◽  
Optimization Approach ◽  
Functional Magnetic Resonance ◽  
Cortical Hierarchy ◽  
Out Of Sample ◽  
External Stimuli ◽  
Recurrent Connections

We considered a large-scale dynamical circuit model of human cerebral cortex with region-specific microscale properties. The model was inverted using a stochastic optimization approach, yielding markedly better fit to new, out-of-sample resting functional magnetic resonance imaging (fMRI) data. Without assuming the existence of a hierarchy, the estimated model parameters revealed a large-scale cortical gradient. At one end, sensorimotor regions had strong recurrent connections and excitatory subcortical inputs, consistent with localized processing of external stimuli. At the opposing end, default network regions had weak recurrent connections and excitatory subcortical inputs, consistent with their role in internal thought. Furthermore, recurrent connection strength and subcortical inputs provided complementary information for differentiating the levels of the hierarchy, with only the former showing strong associations with other macroscale and microscale proxies of cortical hierarchies (meta-analysis of cognitive functions, principal resting fMRI gradient, myelin, and laminar-specific neuronal density). Overall, this study provides microscale insights into a macroscale cortical hierarchy in the dynamic resting brain.

Predicting Spring Tornado Activity in the Central Great Plains by 1 March

2014 ◽  
Vol 142 (1) ◽  
pp. 259-267 ◽  
Author(s):  
James B. Elsner ◽  
Holly M. Widen
Keyword(s):  
Great Plains ◽  
Large Scale ◽  
Negative Binomial ◽  
Gulf Of Alaska ◽  
Model Parameters ◽  
Climate Data ◽  
Convective Storms ◽  
Bayesian Formulation ◽  
Out Of Sample ◽  
Western Caribbean

Abstract The authors illustrate a statistical model for predicting tornado activity in the central Great Plains by 1 March. The model predicts the number of tornado reports during April–June using February sea surface temperature (SST) data from the Gulf of Alaska (GAK) and the western Caribbean Sea (WCA). The model uses a Bayesian formulation where the likelihood on the counts is a negative binomial distribution and where the nonstationarity in tornado reporting is included as a trend term plus first-order autocorrelation. Posterior densities for the model parameters are generated using the method of integrated nested Laplacian approximation (INLA). The model yields a 51% increase in the number of tornado reports per degree Celsius increase in SST over the WCA and a 15% decrease in the number of reports per degree Celsius increase in SST over the GAK. These significant relationships are broadly consistent with a physical understanding of large-scale atmospheric patterns conducive to severe convective storms across the Great Plains. The SST covariates explain 11% of the out-of-sample variability in observed F1–F5 tornado reports. The paper demonstrates the utility of INLA for fitting Bayesian models to tornado climate data.

scholarly journals A dynamical systems framework to uncover the drivers of large-scale cortical activity

2019 ◽  
Author(s):  
Arian Ashourvan ◽  
Sérgio Pequito ◽  
Maxwell Bertolero ◽  
Jason Z. Kim ◽  
Danielle S. Bassett ◽  
...  
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Intrinsic Activity ◽  
Model Parameters ◽  
Sensory Stimuli ◽  
Human Connectome Project ◽  
External Stimuli ◽  
And Task

ABSTRACTA fundamental challenge in neuroscience is to uncover the principles governing complex interactions between the brain and its external environment. Over the past few decades, the development of functional neuroimaging techniques and tools from graph theory, network science, and computational neuroscience have markedly expanded opportunities to study the intrinsic organization of brain activity. However, many current computational models are fundamentally limited by little to no explicit assessment of the brain’s interactions with external stimuli. To address this limitation, we propose a simple scheme that jointly estimates the intrinsic organization of brain activity and extrinsic stimuli. Specifically, we adopt a linear dynamical model (intrinsic activity) under unknown exogenous inputs (e.g., sensory stimuli), and jointly estimate the model parameters and exogenous inputs. First, we demonstrate the utility of this scheme by accurately estimating unknown external stimuli in a synthetic example. Next, we examine brain activity at rest and task for 99 subjects from the Human Connectome Project, and find significant task-related changes in the identified system, and task-related increases in the estimated external inputs showing high similarity to known task regressors. Finally, through detailed examination of fluctuations in the spatial distribution of the oscillatory modes of the estimated system during the resting state, we find an apparent non-stationarity in the profile of modes that span several brain regions including the visual and the dorsal attention systems. The results suggest that these brain structures display a time-varying relationship, or alternatively, receive non-stationary exogenous inputs that can lead to apparent system non-stationarities. Together, our embodied model of brain activity provides an avenue to gain deeper insight into the relationship between cortical functional dynamics and their drivers.

scholarly journals T156. FUNCTIONAL CONNECTIVITY AND RISK OF PSYCHOSIS: AN ACTIVATION LIKELIHOOD ESTIMATION (ALE) META-ANALYSIS OF FUNCTIONAL MAGNETIC RESONANCE IMAGING STUDIES

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S290-S290
Author(s):  
Lorenzo Del Fabro ◽  
André Schmidt ◽  
Giuseppe Delvecchio ◽  
Armando D’Agostino ◽  
Stefan Borgwardt ◽  
...  
Keyword(s):  
At Risk ◽  
High Risk ◽  
Resting State ◽  
Large Scale ◽  
Meta Analysis ◽  
A Priori ◽  
Resting State Fmri ◽  
Healthy Controls ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging

Abstract Background Disrupted communication involving large-scale neural networks is hypothesized to underlie the pathophysiology of schizophrenia, as demonstrated by impaired resting-state functional connectivity (rsFC). Seed-based functional magnetic resonance imaging (fMRI) studies in subjects at increased risk of developing psychosis have begun to identify abnormalities in rsFC, although reported findings remain mixed. The aim of this study was to conduct a meta-analysis of seed-based resting-state fMRI studies to test whether high-risk subjects show rsFC alterations relative to healthy controls within and between the default mode network (DMN), control executive network (CEN), and salience network (SN). Methods A literature search was performed to identify seed-based resting-state fMRI studies comparing subjects with genetic risk factors, psychotic-like experiences, and clinical high-risk for psychosis to healthy controls. Then, coordinates of seed regions were extracted and categorized into networks by their location within a priori templates. Activation likelihood estimate (ALE) analysis examined the reported coordinates for hypo-connectivity and hyper-connectivity with each a priori network. Results The meta-analysis included 15 studies (774 subjects at risk, 628 healthy controls) on clinical high-risk for psychosis, 6 studies (123 subjects at risk, 147 healthy controls) on psychotic-like experiences, and 5 studies (173 subjects at risk, 256 healthy controls) on genetic risk factors of developing psychosis. We found specific patterns of hypo- and hyper-connectivity within and between large-scale networks. Our results showed that subjects with high-risk for psychosis were characterized by hypo-connectivity within the SN and CEN and hyper-connectivity within the DMN and CEN. Network seeds in the DMN, CEN, and SN displayed hyper-connectivity with regions in other networks. The DMN seeds displayed hypo-connectivity with regions in the CEN, while CEN and SN seeds displayed hypo-connectivity with regions in the DMN. Discussion This meta-analysis provides evidence that subjects at risk for psychosis present distinctive abnormalities of hyper- and hypo-connectivity within and between the DMN, CEN and SN, particularly implicating network dys-connectivity as a core deficit underlying the psychopathology of psychosis in the preclinical phase. More studies are needed to investigate whether subjects at risk to develop psychosis present patterns of dysfunction between the rsFC of healthy subjects and that of patients with established psychosis.

Relationship between total plasma homocysteine and the risk of aneurysms – a meta-analysis

VASA ◽  
2020 ◽  
pp. 1-6
Author(s):  
Hanji Zhang ◽  
Dexin Yin ◽  
Yue Zhao ◽  
Yezhou Li ◽  
Dejiang Yao ◽  
...  
Keyword(s):  
Large Scale ◽  
Meta Analysis ◽  
Single Species ◽  
Total Plasma ◽  
Control Groups ◽  
Randomized Controlled ◽  
Randomized Controlled Studies ◽  
The Difference ◽  
The Relationship ◽  
Healthy Participants

Summary: Our meta-analysis focused on the relationship between homocysteine (Hcy) level and the incidence of aneurysms and looked at the relationship between smoking, hypertension and aneurysms. A systematic literature search of Pubmed, Web of Science, and Embase databases (up to March 31, 2020) resulted in the identification of 19 studies, including 2,629 aneurysm patients and 6,497 healthy participants. Combined analysis of the included studies showed that number of smoking, hypertension and hyperhomocysteinemia (HHcy) in aneurysm patients was higher than that in the control groups, and the total plasma Hcy level in aneurysm patients was also higher. These findings suggest that smoking, hypertension and HHcy may be risk factors for the development and progression of aneurysms. Although the heterogeneity of meta-analysis was significant, it was found that the heterogeneity might come from the difference between race and disease species through subgroup analysis. Large-scale randomized controlled studies of single species and single disease species are needed in the future to supplement the accuracy of the results.

Identification of and Correction for Publication Bias: Comment

2019 ◽  
Author(s):  
Amanda Kvarven ◽  
Eirik Strømland ◽  
Magnus Johannesson
Keyword(s):  
Publication Bias ◽  
False Positive ◽  
Large Scale ◽  
Meta Analysis ◽  
False Positive Rate ◽  
Effect Sizes ◽  
Replication Studies ◽  
Moderate Reduction ◽  
Positive Rate ◽  
Meta Analyses

Andrews & Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.

Association between ALDH2 Gene Polymorphism and Late-onset Alzheimer Disease: An Up-to-date Meta-analysis

2020 ◽  
Vol 17 (2) ◽  
pp. 105-111
Author(s):  
Haitao Liu ◽  
Wei Ge ◽  
Wei Chen ◽  
Xue Kong ◽  
Weiming Jian ◽  
...  
Keyword(s):  
Gene Polymorphism ◽  
Large Scale ◽  
Late Onset ◽  
Meta Analysis ◽  
Case Control ◽  
Cochrane Library ◽  
Positive Trend ◽  
Case Control Studies ◽  
China National Knowledge Infrastructure ◽  
Aldh2 Gene

Objectives: Previous case-control studies have focused on the relationship between ALDH2 gene polymorphism and late-onset Alzheimer's Disease (LOAD), but no definite unified conclusion has been reached. Therefore, the correlation between ALDH2 Glu504Lys polymorphism and LOAD remains controversial. To analyze the correlation between ALDH2 polymorphism and the risk of LOAD, we implemented this up-to-date meta-analysis to assess the probable association. Methods: Studies were searched through China National Knowledge Infrastructure (CNKI), VIP Database for Chinese Technical Periodicals, China Biology Medicine, PubMed, Cochrane Library, Clinical- Trials.gov, Embase, and MEDLINE from January 1, 1994 to December 31, 2018, without any restrictions on language and ethnicity. Results: Five studies of 1057 LOAD patients and 1136 healthy controls met our criteria for the analysis. Statistically, the ALDH2 GA/AA genotype was not linked with raising LOAD risk (odds ratio (OR) = 1.48, 95% confidence interval (CI) = 0.96-2.28, p = 0.07). In subgroup analysis, the phenomenon that men with ALDH2*2 had higher risk for LOAD (OR = 1.72, 95%CI = 1.10-2.67, p = 0.02) was observed. Conclusions: This study comprehends only five existing case-control studies and the result is negative. The positive trend might appear when the sample size is enlarged. In the future, more large-scale casecontrol or cohort studies should be done to enhance the association between ALDH2 polymorphism and AD or other neurodegenerative diseases.

Serum Copper Level and Polycystic Ovarian Syndrome: A Meta-Analysis

2021 ◽  
pp. 1-8
Author(s):  
Qingtao Jiang ◽  
Feng Zhang ◽  
Lei Han ◽  
Baoli Zhu ◽  
Xin Liu
Keyword(s):  
Large Scale ◽  
Polycystic Ovarian Syndrome ◽  
Meta Analysis ◽  
Serum Copper ◽  
Copper Level ◽  
Serum Copper Level ◽  
Copper Status ◽  
Plasma Copper ◽  
Mean Differences ◽  
Ovarian Syndrome

<b><i>Introduction:</i></b> The association of serum copper with polycystic ovarian syndrome (PCOS) has been studied for years, but no definite conclusion is drawn. Therefore, we conducted a meta-analysis to investigate serum copper concentrations in PCOS subjects compared with healthy controls. <b><i>Methods:</i></b> Electronic search was performed in PubMed, Google Scholar, and Scopus up to June 30, 2020, without any restriction. Standardized mean differences (SMDs) with corresponding 95% CIs in serum copper levels were employed with random-effects model. <i>I</i><sup>2</sup> was applied to evaluate heterogeneity among studies. <b><i>Results:</i></b> Nine studies, measuring plasma copper levels in 1,168 PCOS patients and 1,106 controls, were included. Pooled effect size suggested serum copper level was significantly higher in women with PCOS (SMD = 0.51 μg/mL, 95% CI = [0.30, 0.72], <i>p</i> &#x3c; 0.0001). The overall heterogeneity was not connected with subgroups of the country, but derived from the opposite result of 1 study. <b><i>Conclusion:</i></b> Our research generally indicated circulating copper level in PCOS sufferers was significantly higher than normal controls. Large-scale studies are still needed to elucidate the clear relation between copper status and etiology of PCOS.

scholarly journals Efficacy and safety of Si-Jun-Zi-Tang-based therapies for functional (non-ulcer) dyspepsia: a meta-analysis of randomized controlled trials

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yaping Wang ◽  
Bin Liu ◽  
Xiuqiong Fu ◽  
Tiejun Tong ◽  
Zhiling Yu
Keyword(s):  
Relapse Rate ◽  
Large Scale ◽  
Primary Outcome ◽  
Response Rate ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Efficacy And Safety ◽  
History Of ◽  
Non Ulcer Dyspepsia

Abstract Background The traditional Chinese medicine formula Si-Jun-Zi-Tang (SJZT) has a long history of application in the treatment of functional dyspepsia (non-ulcer dyspepsia, FD)-like symptoms. SJZT-based therapies have been claimed to be beneficial in managing FD. This study aimed to assess the efficacy and safety of SJZT-based therapies in treating FD by meta-analysis. Methods Systematic searches for RCTs were conducted in seven databases (up to February 2019) without language restrictions. Data were analyzed using Cochrane RevMan software version 5.3.0 and Stata software version 13.1, and reported as relative risk (RR) or odds ratio (OR) with 95% confidence intervals (CIs). The primary outcome was response rate and the secondary outcomes were gastric emptying, quality of life, adverse effects and relapse rate. The quality of evidence was evaluated according to criteria from the Cochrane risk of bias. Results A total of 341 potentially relevant publications were identified, and 12 RCTs were eligible for inclusion. For the response rate, there was a statically significant benefit in favor of SJZT-based therapies (RR = 1.23; 95% CI 1.17 to 1.30). However, the benefit was limited to modified SJZT (MSJZT). The relapse rate of FD patients received SJZT-based therapies was lower than that of patients who received conventional medicines (OR = 0.23; 95% CI 0.10 to 0.51). No SJZT-based therapies-related adverse effect was reported. Conclusion SJZT-based prescriptions may be effective in treating FD and no serious side-effects were identified, but the effect on response rate appeared to be limited to MSJZT. The results should be interpreted with caution as all the included studies were considered at a high risk of bias. Standardized, large-scale and strictly designed RCTs are needed to further validate the benefits of SJZT-based therapies for FD management. Trial registration Systematic review registration: [PROSPERO registration: CRD42019139136].

scholarly journals Why ability point estimates can be pointless: a primer on using skill measures from large-scale assessments in secondary analyses

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Clemens M. Lechner ◽  
Nivedita Bhaktha ◽  
Katharina Groskurth ◽  
Matthias Bluemke
Keyword(s):  
Measurement Error ◽  
Statistical Models ◽  
Test Scores ◽  
Large Scale ◽  
Equation Modeling ◽  
Model Parameters ◽  
Advantages And Disadvantages ◽  
Point Estimates ◽  
Secondary Analyses ◽  
Large Scale Assessments

AbstractMeasures of cognitive or socio-emotional skills from large-scale assessments surveys (LSAS) are often based on advanced statistical models and scoring techniques unfamiliar to applied researchers. Consequently, applied researchers working with data from LSAS may be uncertain about the assumptions and computational details of these statistical models and scoring techniques and about how to best incorporate the resulting skill measures in secondary analyses. The present paper is intended as a primer for applied researchers. After a brief introduction to the key properties of skill assessments, we give an overview over the three principal methods with which secondary analysts can incorporate skill measures from LSAS in their analyses: (1) as test scores (i.e., point estimates of individual ability), (2) through structural equation modeling (SEM), and (3) in the form of plausible values (PVs). We discuss the advantages and disadvantages of each method based on three criteria: fallibility (i.e., control for measurement error and unbiasedness), usability (i.e., ease of use in secondary analyses), and immutability (i.e., consistency of test scores, PVs, or measurement model parameters across different analyses and analysts). We show that although none of the methods are optimal under all criteria, methods that result in a single point estimate of each respondent’s ability (i.e., all types of “test scores”) are rarely optimal for research purposes. Instead, approaches that avoid or correct for measurement error—especially PV methodology—stand out as the method of choice. We conclude with practical recommendations for secondary analysts and data-producing organizations.

Sign in / Sign up

Export Citation Format

Share Document