Biology subjects

Tench, C.

Publications and source records attributed to Tench, C..

6 recordsLinked to original sources

bioRxiv2022

The central autonomic system revisited - convergent evidence for a regulatory role of the insular and midcingulate cortex from neuroimaging meta-analyses

The autonomic nervous system regulates dynamic body adaptations to internal and external environment changes. Capitalizing on two different algorithms (Analysis of Brain Coordinates and GingerALE) that differ in empirical assumptions, we scrutinized the meta-analytic convergence of human neuroimaging studies investigating the neural basis of peripheral autonomic signal processing. Among the selected studies, we identified 42 records reporting 44 different experiments and testing 792 healthy individuals. The results of the two different algorithms converge in identifying the bilateral dorsal anterior insula and midcingulate cortex as the critical areas of the central autonomic system (CAN). However, whereas the bilateral dorsal anterior insula appears to be involved in processing autonomic nervous system signals regardless of task type, activity in the midcingulate cortex appears to be primarily engaged in processing autonomic signals during cognitive tasks and task-free conditions. Applying an unbiased approach, we were able to identify a single functionally condition-independent circuit that supports CAN activity. Although partially overlapping with the salience network, this functional circuit includes, in addition to the bilateral insular cortex and midcingulate cortex, the bilateral inferior parietal lobules and small clusters in the bilateral middle frontal gyrus. Our results do not support the hypothesis of divergent pathways for the sympathetic and parasympathetic systems or a robust involvement of the default mode network, particularly during parasympathetic activity. However, these results may be due to the relatively low number of studies investigating the parasympathetic system (12%), making our results more consistent with the central processing network of sympathetic activity. Remarkably, the critical regions of the CAN observed in this meta-analysis are among the most reported co-activated areas in neuroimaging studies and have been repeatedly shown as being dysregulated across different mental and neurological disorders. This suggests that the central dynamic interaction maintaining bodily homeostasis reported in several brain imaging studies may be associated with increased autonomic nervous system engagement and that disruptions in this interplay may underpin unspecific pathological symptoms across mental and neurological disorders.

neuroscience↗

bioRxiv2020

Coordinate Density Analysis of neuroimaging studies

Functional MRI and voxel-based morphometry (VBM) are important approaches to testing hypotheses in neuroscience, helping us to understand neurological disease, and brain function and development. However, they are technically challenging with no one optimal generalisable method, and the multiple popular techniques have been shown to produce different results. Furthermore, results may be sensitive to settings, such as smoothing or statistical thresholding, that can be difficult to optimise per hypothesis. It is useful, therefore, to be able to meta-analyse published results from such studies that tested a similar hypothesis potentially using different analysis methods, scanners, and protocols as well as different subjects. Coordinate based meta-analysis (CBMA) offers this using only commonly reported summary results. It is the aim of CBMA to find those results that indicate replicable effects across studies. However, just like the multiple analysis methods offered for neuroimaging, there are now multiple CBMA algorithms each with specific features and empirical parameters/assumptions. Results derived from CBMA are inevitably conditional on the algorithm used, so conclusions are clearer when the analysis approach is easy to understand. With this in mind a new CBMA method (Analysis of Brain Coordinates; ABC) is presented, with the aim of being easy to interpret by eliminating empirical assumptions where possible and by relating statistical thresholding directly to replication of effect.

neuroscience↗

bioRxiv2020

Gray Matter alterations in MS and CIS: a Coordinate based Meta-analysis and regression

The purpose of this coordinate based meta-analysis (CBMA) was to summarise the available evidence related to regional grey matter (GM) changes in patients with multiple sclerosis (MS) and clinically isolated syndrome (CIS). CBMA is a way to find the consistent results across multiple independent studies that are otherwise not easily comparable due to methodological differences. The coordinate based random effect size (CBRES) meta-analysis method utilizes the reported coordinates (foci of the clusters of GM loss) and Z score standardised by number of subjects, controlling type I error rate by false cluster discovery rate (FCDR). Thirty-four published articles reporting forty-five independent studies using voxel-based morphometry (VBM) for the assessment of GM atrophy between MS or CIS patients and healthy controls were identified from electronic databases. The primary meta-analysis identified clusters of spatially consistent cross-study reporting of GM atrophy; subgroup analyses and meta-regression were also performed. This meta-analysis demonstrates consistent areas of GM loss in MS or CIS, in the form of significant clusters. Some clusters also demonstrate correlation with disease duration.

neuroscience↗

bioRxiv2018

Coordinate based meta-analysis of networks in neuroimaging studies

Meta-analysis of published neuroimaging results is commonly performed using coordinate based meta-analysis (CBMA). Most commonly CBMA algorithms detect spatial clustering of reported coordinates across multiple studies by assuming that results relating to the common hypothesis fall in similar anatomical locations. The null hypothesis is that studies report uncorrelated results, which is simulated by random coordinates. It is assumed that multiple clusters are independent yet it is likely that multiple results reported per study are not, and in fact represent a network effect. Here the multiple reported effect sizes (reported peak Z scores) are assumed multivariate normal, and maximum likelihood used to estimate the parameters of the covariance matrix. The hypothesis is that the effect sizes are correlated. The parameters are covariance of effect size, considered as edges of a network, while clusters are considered as nodes. In this way coordinate based meta-analysis of networks (CBMAN) estimates a network of reported meta-effects, rather than multiple independent effects (clusters).\n\nCBMAN uses only the same data as CBMA, yet produces extra information in terms of the correlation between clusters. Here it is validated on numerically simulated data, and demonstrated on real data used previously to demonstrate CBMA. The CBMA and CBMAN clusters are similar, despite the very different hypothesis.

neuroscience↗

bioRxiv2018

Coordinate based meta-analysis of whole-brain voxel-based morphometry studies does not show evidence of grey matter loss specific to PTSD

Neuroimaging studies have detected structural alteration in post-traumatic stress disorder (PTSD), but findings are inconsistent. This might be explained by heterogeneity between subjects with PTSD in terms of common comorbidities such as depressive and anxiety disorders and also in traumatic experience. Despite this, coordinate based meta-analysis (CBMA) has been used to try and identify localised grey matter changes, and does suggest some PTSD specific pathology. However, there are multiple technical issues that make the meta-analytic evidence questionable, warranting a re-evaluation.\n\nA literature search for voxel-based morphometry studies was performed. Only whole-brain studies using subjects with a current diagnosis of PTSD, and having a comparison group of either healthy or trauma exposed controls, were included. Twenty one voxel-based morphometry studies met the inclusion criteria. CBMA was performed to identify altered grey matter (GM) structures.\n\nUsing a novel coordinate based random effect size meta-analysis, no grey matter structure was identified as being consistently altered in PTSD compared to controls. This was also verified using the activation likelihood estimate algorithm.\n\nThere is no evidence, from CBMA, of consistent localised grey matter changes specific to PTSD. Inconsistency may reflect true heterogeneity in PTSD pathology or methodological issues with imaging and/or analysis, limiting the detection of PTSD specific pathology.

neuroscience↗

bioRxiv2016

Coordinate Based Random Effect Size meta-analysis of neuroimaging studies

1Low power in neuroimaging studies can make them difficult to interpret, and Coordinate based meta- analysis (CBMA) may go some way to mitigating this issue. CBMA has been used in many analyses to detect where published functional MRI or voxel-based morphometry studies testing similar hypotheses report significant summary results (coordinates) consistently. Only the reported coordinates and possibly t statistics are analysed, and statistical significance of clusters is determined by coordinate density.\n\nHere a method of performing coordinate based random effect size meta-analysis and meta-regression is introduced. The algorithm (ClusterZ) analyses both coordinates and reported t statistic or Z score, standardised by the number of subjects. Statistical significance is determined not by coordinate density, but by a random effects meta-analyses of reported effects performed cluster-wise using standard statistical methods and taking account of censoring inherent in the published summary results. Type 1 error control is achieved using the false cluster discovery rate (FCDR), which is based on the false discovery rate. This controls both the family wise error rate under the null hypothesis that coordinates are randomly drawn from a standard stereotaxic space, and the proportion of significant clusters that are expected under the null. Such control is vital to avoid propagating and even amplifying the very issues motivating the meta-analysis in the first place. ClusterZ is demonstrated on both numerically simulated data and on real data from reports of grey matter loss in multiple sclerosis (MS) and syndromes suggestive of MS, and of painful stimulus in healthy controls. The software implementation is available to download and use freely.

neuroscience↗