scholarly journals Convergent neural representations of acute nociceptive pain in healthy volunteers: A large-scale fMRI meta-analysis

2019 ◽  
Author(s):  
Anna Xu ◽  
Bart Larsen ◽  
Erica B. Baller ◽  
J. Cobb Scott ◽  
Vaishnavi Sharma ◽  
...  
Keyword(s):  
Acute Pain ◽  
Error Control ◽  
Large Scale ◽  
Meta Analysis ◽  
Brain Regions ◽  
Type I ◽  
List Type ◽  
Midcingulate Cortex ◽  
Core Set ◽  
Healthy Participants

ABSTRACTCharacterizing a reliable, pain-related neural signature is critical for translational applications. Many prior fMRI studies have examined acute pain-related brain activation in healthy participants. However, synthesizing these data to identify convergent patterns of activation can be challenging due to the heterogeneity of experimental designs and samples. To address this challenge, we conducted a comprehensive meta-analysis of fMRI studies of stimulus-induced pain in healthy participants. Following pre-registration, two independent reviewers evaluated 4,927 abstracts returned from a search of 8 databases, with 222 fMRI experiments meeting inclusion criteria. We analyzed these experiments using Activation Likelihood Estimation with rigorous type I error control (voxel height p < 0.001, cluster p < 0.05 FWE-corrected) and found a convergent, largely bilateral pattern of pain-related activation in the secondary somatosensory cortex, insula, midcingulate cortex, and thalamus. Notably, these regions were consistently recruited regardless of stimulation technique, location of induction, and participant sex. These findings suggest a highly-conserved core set of pain-related brain areas, encouraging applications as a biomarker for novel therapeutics targeting acute pain.HIGHLIGHTSPain stimulation recruits a core set of pain-related brain regions.This core set includes thalamus, SII, insula and mid-cingulate cortex.These regions were recruited regardless of stimulus modality and stimulus location.

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.

scholarly journals Convergent neural representations of experimentally-induced acute pain in healthy volunteers: A large-scale fMRI meta-analysis

2020 ◽  
Vol 112 ◽  
pp. 300-323 ◽  
Author(s):  
Anna Xu ◽  
Bart Larsen ◽  
Erica B. Baller ◽  
J. Cobb Scott ◽  
Vaishnavi Sharma ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Acute Pain ◽  
Large Scale ◽  
Meta Analysis ◽  
Neural Representations ◽  
Experimentally Induced

scholarly journals Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model

PLoS Genetics ◽  
2021 ◽  
Vol 17 (11) ◽  
pp. e1009922
Author(s):  
Zhaotong Lin ◽  
Yangqing Deng ◽  
Wei Pan
Keyword(s):  
Large Scale ◽  
Type I Error ◽  
Mendelian Randomization ◽  
Meta Analysis ◽  
Type I ◽  
Perturbation Scheme ◽  
Analysis Model ◽  
Component Mixture ◽  
Inverse Variance ◽  
Summary Data

With the increasing availability of large-scale GWAS summary data on various traits, Mendelian randomization (MR) has become commonly used to infer causality between a pair of traits, an exposure and an outcome. It depends on using genetic variants, typically SNPs, as instrumental variables (IVs). The inverse-variance weighted (IVW) method (with a fixed-effect meta-analysis model) is most powerful when all IVs are valid; however, when horizontal pleiotropy is present, it may lead to biased inference. On the other hand, Egger regression is one of the most widely used methods robust to (uncorrelated) pleiotropy, but it suffers from loss of power. We propose a two-component mixture of regressions to combine and thus take advantage of both IVW and Egger regression; it is often both more efficient (i.e. higher powered) and more robust to pleiotropy (i.e. controlling type I error) than either IVW or Egger regression alone by accounting for both valid and invalid IVs respectively. We propose a model averaging approach and a novel data perturbation scheme to account for uncertainties in model/IV selection, leading to more robust statistical inference for finite samples. Through extensive simulations and applications to the GWAS summary data of 48 risk factor-disease pairs and 63 genetically uncorrelated trait pairs, we showcase that our proposed methods could often control type I error better while achieving much higher power than IVW and Egger regression (and sometimes than several other new/popular MR methods). We expect that our proposed methods will be a useful addition to the toolbox of Mendelian randomization for causal inference.

scholarly journals The Neural Basis of Motivational Influences on Cognitive Control

2017 ◽  
Author(s):  
Cameron Parro ◽  
Matthew L Dixon ◽  
Kalina Christoff
Keyword(s):  
Cognitive Control ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Meta Analysis ◽  
Premotor Cortex ◽  
Likelihood Estimation ◽  
Brain Regions ◽  
Control Mechanisms ◽  
Neural Basis ◽  
The Brain

AbstractCognitive control mechanisms support the deliberate regulation of thought and behavior based on current goals. Recent work suggests that motivational incentives improve cognitive control, and has begun to elucidate the brain regions that may support this effect. Here, we conducted a quantitative meta-analysis of neuroimaging studies of motivated cognitive control using activation likelihood estimation (ALE) and Neurosynth in order to delineate the brain regions that are consistently activated across studies. The analysis included functional neuroimaging studies that investigated changes in brain activation during cognitive control tasks when reward incentives were present versus absent. The ALE analysis revealed consistent recruitment in regions associated with the frontoparietal control network including the inferior frontal sulcus (IFS) and intraparietal sulcus (IPS), as well as consistent recruitment in regions associated with the salience network including the anterior insula and anterior mid-cingulate cortex (aMCC). A large-scale exploratory meta-analysis using Neurosynth replicated the ALE results, and also identified the caudate nucleus, nucleus accumbens, medial thalamus, inferior frontal junction/premotor cortex (IFJ/PMC), and hippocampus. Finally, we conducted separate ALE analyses to compare recruitment during cue and target periods, which tap into proactive engagement of rule-outcome associations, and the mobilization of appropriate viscero-motor states to execute a response, respectively. We found that largely distinct sets of brain regions are recruited during cue and target periods. Altogether, these findings suggest that flexible interactions between frontoparietal, salience, and dopaminergic midbrain-striatal networks may allow control demands to be precisely tailored based on expected value.

scholarly journals Intracranial brain stimulation modulates fMRI-based network switching

2021 ◽  
Author(s):  
Mangor Pedersen ◽  
Andrew Zalesky
Keyword(s):  
Clinical Efficacy ◽  
Brain Stimulation ◽  
Large Scale ◽  
Brain Networks ◽  
Network Models ◽  
Brain Regions ◽  
Brain Network ◽  
Intracranial Stimulation ◽  
List Type ◽  
Network Modelling

SummaryThe extent to which resting-state fMRI (rsfMRI) reflects direct neuronal changes remains unknown. Using 160 simultaneous rsfMRI and intracranial brain stimulation recordings acquired in 26 individuals with epilepsy (with varying electrode locations), we tested whether brain networks dynamically change during intracranial brain stimulation, aiming to establish whether switching between brain networks is reduced during intracranial brain stimulation. As the brain spontaneously switches between a repertoire of intrinsic functional network configurations and the rate of switching is typically increased in brain disorders, we hypothesised that intracranial stimulation would reduce the brain’s switching rate, thus potentially normalising aberrant brain network dynamics. To test this hypothesis, we quantified the rate that brain regions changed networks over time in response to brain stimulation, using network switching applied to multilayer modularity analysis of time-resolved rsfMRI connectivity. Network switching was significantly decreased during epochs with brain stimulation compared to epochs with no brain stimulation. The initial stimulation onset of brain stimulation was associated with the greatest decrease in network switching, followed by a more consistent reduction in network switching throughout the scans. These changes were most commonly observed in cortical networks spatially distant from the stimulation targets. Our results suggest that neuronal perturbation is likely to modulate large-scale brain networks, and multilayer network modelling may be used to inform the clinical efficacy of brain stimulation in neurological disease.HighlightsrsfMRI network switching is attenuated during intracranial brain stimulationStimulation-induced switching is observed distant from electrode targetsOur results are validated across a range of network parametersNetwork models may inform clinical efficacy of brain stimulation

scholarly journals eQTL mapping using allele-specific gene expression

2021 ◽  
Author(s):  
Vasyl Zhabotynsky ◽  
Licai Huang ◽  
Paul Little ◽  
Yijuan Hu ◽  
Fernando F Pardo Manuel de Villena ◽  
...  
Keyword(s):  
Gene Expression ◽  
Error Control ◽  
Large Scale ◽  
Type I Error ◽  
Computational Cost ◽  
Specific Gene ◽  
Linear Regression Method ◽  
Type I ◽  
Specific Gene Expression ◽  
Allele Specific

Using information from allele-specific gene expression (ASE) can substantially improve the power to map gene expression quantitative trait loci (eQTLs). However, such practice has been limited, partly due to high computational cost and the requirement to access raw data that can take a large amount of storage space. To address these computational challenges, we have developed a computational framework that uses a statistical method named TReCASE as its computational engine, and it is computationally feasible for large scale analysis. We applied it to map eQTLs in 28 human tissues using the data from the Genotype-Tissue Expression (GTEx) project. Compared with a popular linear regression method that does not use ASE data, TReCASE can double the number of eGenes (i.e., genes with at least one significant eQTL) when sample size is relatively small, e.g., $n=200$. We also demonstrated how to use the ASE data that we have collected to study dynamic eQTLs whose effect sizes vary with respect to another variable, such as age. We find the majority of such dynamic eQTLs are due to some underlying latent factors, such as cell type proportions. We further compare TReCASE versus another method RASQUAL. TReCASE is ten times or more faster than RASQUAL and it provides more robust type I error control.

scholarly journals Altered Temporal Variability of Local and Large-scale Resting-state Brain Functional Connectivity Patterns in Schizophrenia and Bipolar Disorder

2020 ◽  
Author(s):  
Yicheng Long ◽  
Zhening Liu ◽  
Calais Kin-yuen Chan ◽  
Guowei Wu ◽  
Zhimin Xue ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Functional Connectivity ◽  
Clinical Features ◽  
Resting State ◽  
Large Scale ◽  
Brain Regions ◽  
Type I ◽  
Healthy Controls ◽  
Resting State Functional Connectivity ◽  
Brain Functional Connectivity

AbstractSchizophrenia and bipolar disorder share some common clinical features and are both characterized by aberrant resting-state functional connectivity (FC). However, little is known about the common and specific aberrant features of the dynamic FC patterns in these two disorders. In this study, we explored the differences in dynamic FC among schizophrenia patients (n = 66), type I bipolar disorder patients (n = 53) and healthy controls (n = 66), by comparing temporal variabilities of FC patterns involved in specific brain regions and large-scale brain networks. Compared with healthy controls, both patient groups showed significantly increased regional FC variabilities in subcortical areas including the thalamus and basal ganglia, as well as increased inter-network FC variability between the thalamus and sensorimotor areas. Specifically, more widespread changes were found in the schizophrenia group, involving increased FC variabilities in sensorimotor, visual, attention, limbic and subcortical areas at both regional and network levels, as well as decreased regional FC variabilities in the default-mode areas. The observed alterations shared by schizophrenia and bipolar disorder may help to explain their overlapped clinical features; meanwhile, the schizophrenia-specific abnormalities in a wider range may support that schizophrenia is associated with more severe functional brain deficits than bipolar disorder.

scholarly journals Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Solveig K. Sieberts ◽  
◽  
Thanneer M. Perumal ◽  
Minerva M. Carrasquillo ◽  
Mariet Allen ◽  
...  
Keyword(s):  
Large Scale ◽  
Posterior Probability ◽  
Meta Analysis ◽  
Tissue Expression ◽  
Brain Regions ◽  
Research Community ◽  
High Quality ◽  
Post Mortem Brain ◽  
Cohort Analyses ◽  
Proof Of Principle

Abstract The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes). We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. Additionally, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a resource for use by the research community. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975).

scholarly journals COMBINING MACHINE LEARNING WITH AUTOMATED NEMATODE LIFESPAN ANALYSIS TO IDENTIFY MODIFIERS OF ALZHEIMER’S DISEASE

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S96-S96
Author(s):  
Joshua Russell ◽  
Matt Kaeberlein
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Meta Analysis ◽  
Brain Regions ◽  
Data Sets ◽  
Data Types ◽  
C Elegans

Abstract Here we present new computational and experimental methods to leverage the gene expression and neuropathology data collected from several large-scale studies of Alzheimer’s disease . These data sets include diverse data types, including transcriptomics, neuropathology phenotypes such as quantification of amyloid beta plaques and tau tangles in different brain regions, as well as assessments of dementia prior to death. This meta-analysis is a complex undertaking because the available data are from different studies and/or brain regions involving study-specific confounders and/or region-specific biological processes. We have therefore taken neural network and probabilistic computational approaches that reduce the data dimensionality, allowing statistical comparison across all brain samples. These approaches identify gene expression changes that are significantly associated with clinical and neuropathological assessment of Alzheimer’s disease. We then conduct in vivo validation of the genes through genetic screening of C. elegans models of Alzheimer's disease utilizing our automated robotic lifespan analysis platform. This approach allows for the greater leverage of existing Alzheimer’s disease biobank data to identify deep genetic signatures that could help identify new clinical gene-expression markers and pharmacological targets for Alzheimer’s disease.

scholarly journals Diurnal brain temperature rhythms and mortality after brain injury: a prospective and retrospective cohort study

2021 ◽  
Author(s):  
Nina M Rzechorzek ◽  
Michael J Thrippleton ◽  
Francesca M Chappell ◽  
Grant Mair ◽  
Ari Ercole ◽  
...  
Keyword(s):  
Intensive Care ◽  
Brain Temperature ◽  
Brain Regions ◽  
Time Of Day ◽  
Clinical Settings ◽  
List Type ◽  
Brain Injured ◽  
Injured Patients ◽  
Healthy Participants ◽  
Deep Brain

ABSTRACTObjectiveTo determine the clinical relevance of brain temperature (TBr) variation in patients after traumatic brain injury (TBI).DesignCohort study with prospective (healthy participant) and retrospective (TBI patient) arms.SettingSingle neuroimaging site in the UK (prospective arm); intensive care sites contributing to the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) High Resolution ICU (HR ICU) Sub-Study (retrospective arm).Participants40 healthy adults aged 20-40 years recruited for non-invasive brain thermometry and all patients up to May 2020 that had TBr measured directly and were not subjected to Targeted Temperature Management (TTM).Main outcome measuresA diurnal change in TBr (healthy participants); death in intensive care (patients).ResultsIn healthy participants, mean TBr (38.5 SD 0.4°C) was higher than oral temperature (36.0 SD 0.5°C), and 0.36°C higher in luteal females relative to follicular females and males (95% confidence interval 0.17 to 0.55, P=0.0006 and 0.23 to 0.49, P<0.0001, respectively). TBr increased with age, most notably in deep brain regions (0.6°C over 20 years; 0.11 to 1.07, P=0.0002). The mean maximal spatial TBr range was 2.41 (SD 0.46)°C, with highest temperatures in the thalamus. TBr varied significantly by time of day, especially in deep brain regions (0.86°C; 0.37 to 1.26, P=0.0001), and was lowest in the late evening. Diurnal TBr in cortical white matter across participants ranged from 37.0 to 40.3°C. In TBI patients (n=114), mean TBr (38.5 SD 0.8°C) was significantly higher than body temperature (TBo 37.5 SD 0.5°C; P<0.0001) and ranged from 32.6 to 42.3°C. Only 25/110 patients displayed a diurnal temperature rhythm; TBr amplitude was reduced in older patients (P=0.018), and 25/113 patients died in intensive care. Lack of a daily TBr rhythm, or an age increase of 10 years, increased the odds of death 12-fold and 11-fold, respectively (OR for death with rhythm 0.09; 0.01 to 0.84, P=0.035 and for death with ageing by 1 year 1.10; 1.05 to 1.16, P=0.0002). Mean TBr was positively associated with survival (OR for death 0.45 for 1°C increase; 0.21 to 0.96, P=0.040).ConclusionsHealthy TBr exceeds TBo and varies by sex, age, menstrual cycle, brain region, and time of day. Our 4-dimensional reference resource for healthy TBr can guide interpretation of TBr data in multiple clinical settings. Daily temperature variation is frequently disrupted or absent in TBI patients, in which TBr variation is of greater prognostic use than absolute TBr. Older TBI patients lacking a daily TBr rhythm are at greatest risk of death in intensive care. Appropriately controlled trials are needed to confirm the predictive power of TBr rhythmicity in relation to patient outcome, as well as the clinical utility of TTM protocols in brain-injured patients.RegistrationUK CRN NIHR CPMS 42644; ClinicalTrials.gov number, NCT02210221.SUMMARY BOXWhat is already known on this topicBrain temperature (TBr) can be measured directly in brain-injured patients via intracranial probe, but this method cannot be used in healthy individuals.TBr can be measured non-invasively using magnetic resonance spectroscopy (MRS), but this method is not appropriate for most brain-injured patients.Since physiological reference ranges for TBr in health have not been established, the clinical relevance of TBr variation in patients is unknown, and the use of TTM in neurocritical care remains controversial.What this study addsA reference map for healthy adult TBr at three clinically-relevant time points that can guide interpretation of TBr measured directly, or by MRS, in multiple clinical settings.Our results suggest that loss of diurnal TBr rhythmicity after TBI increases the odds of intensive care death 12-fold; some TTM strategies may be clinically inappropriate.

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