Meta-analytic Networks Research Articles

Fear of missing out (FOMO) associates with reduced cortical thickness in core regions of the posterior default mode network and higher levels of problematic smartphone and social media use

Background and aimsFear of missing out (FOMO) promotes the desire or urge to stay continuously connected with a social reference group and updated on their activities, which may result in escalating and potentially addictive smartphone and social media use. The present study aimed to determine whether the neurobiological basis of FOMO encompasses core regions of the reward circuitry or social brain, and associations with levels of problematic smartphone or social media use. MethodsWe capitalized on a dimensional neuroimaging approach to examine cortical thickness and subcortical volume associations in a sample of healthy young individuals (n = 167). Meta-analytic network and behavioral decoding analyses were employed to further characterize the identified regions. ResultsHigher levels of FOMO associated with lower cortical thickness in the right precuneus. In contrast, no associations between FOMO and variations in striatal morphology were observed. Meta-analytic decoding revealed that the identified precuneus region exhibited a strong functional interaction with the default mode network (DMN) engaged in social cognitive and self-referential domains. Discussion and conclusionsTogether the present findings suggest that individual variations in FOMO are associated with the brain structural architecture of the right precuneus, a core hub within a large-scale functional network resembling the DMN and involved in social and self-referential processes. FOMO may promote escalating social media and smartphone use via social and self-referential processes rather than reward-related processes per se.

The origins of evil: From lesions to the functional architecture of the antisocial brain

In the past decades, a growing body of evidence has suggested that some individuals may exhibit antisocial behaviors following brain lesions. Recently, some authors have shown that lesions underpinning antisocial behaviors may disrupt a particular brain network during resting-state. However, it remains unknown whether these brain lesions may alter specific mental processes during tasks. Therefore, we conducted meta-analytic co-activation analyses on lesion masks of 17 individuals who acquired antisocial behaviors following their brain lesions. Each lesion mask was used as a seed of interest to examine their aberrant co-activation network using a database of 143 whole-brain neuroimaging studies on antisocial behaviors (n = 5,913 subjects). We aimed to map the lesion brain network that shows deficient activity in antisocial population against a null distribution derived from 655 control lesions. We further characterized the lesion-based meta-analytic network using term-based decoding (Neurosynth) as well as receptor/transporter density maps (JuSpace). We found that the lesion meta-analytic network included the amygdala, orbitofrontal cortex, ventro- and dorso-medial prefrontal cortex, fusiform face area, and supplementary motor area (SMA), which correlated mainly with emotional face processing and serotoninergic system (5-HT1A and 5-HTT). We also investigated the heterogeneity in co-activation networks through data-driven methods and found that lesions could be grouped in four main networks, encompassing emotional face processing, general emotion processing, and reward processing. Our study shows that the heterogeneous brain lesions underpinning antisocial behaviors may disrupt specific mental processes, which further increases the risk for distinct antisocial symptoms. It also highlights the importance and complexity of studying brain lesions in relationship with antisocial behaviors.

Coordinate-Based Network Mapping of Brain Structure in Major Depressive Disorder in Younger and Older Adults: A Systematic Review and Meta-Analysis.

Structural neuroimaging findings in younger and older adults with major depressive disorder (MDD) are highly heterogeneous, possibly as a result of methodological limitations, lack of distinction between MDD and late-life depression (LLD), or clinical moderators. Using a novel meta-analytic network mapping approach, the authors sought to identify the circuits affected in different clinical subtypes of MDD. The authors identified all voxel-based and surface-based morphometry studies published through October 2020 that compared younger adults with MDD or older adults with LLD to nonpsychiatric control participants. An activation likelihood estimation (ALE) analysis and a novel coordinate-based network mapping approach were used to identify brain circuits affected in MDD and LLD. Meta-regressions examined the impact of age at onset in older patients with LLD and treatment with antidepressants in younger patients with MDD. The authors analyzed 145 comparisons from 143 articles, including a total of 14,318 participants (MDD: N=6,362; LLD: N=535; control subjects: N=7,421). Significant ALE results confirmed previous findings implicating the left and right parahippocampus and anterior cingulate in MDD and the anterior cingulate in LLD. In contrast, coordinate-based network mapping showed differences in the frontoparietal, dorsal attention, and visual networks both in MDD and LLD. Meta-regressions showed that late onset was significantly associated with widespread structural abnormalities in LLD, and treatment with antidepressants showed a significant association with abnormalities in the anterior cingulate (Brodmann's area 32) and dorsolateral prefrontal cortex (Brodmann's area 9) in MDD. These findings help to clarify the shared circuitry of depression across the adult lifespan and highlight some unique circuitry relevant to late-onset depression, which may explain some of the risk for cognitive decline and dementia.

Single-cell co-expression analysis reveals that transcriptional modules are shared across cell types in the brain.

Gene-gene relationships are commonly measured via the co-variation of gene expression across samples, also known as gene co-expression. Because shared expression patterns are thought to reflect shared function, co-expression networks describe functional relationships between genes, including co-regulation. However, the heterogeneity of cell types in bulk RNA-seq samples creates connections in co-expression networks that potentially obscure co-regulatory modules. The brain initiative cell census network (BICCN) single-cell RNA sequencing (scRNA-seq) datasets provide an unparalleled opportunity to understand how gene-gene relationships shape cell identity. Comparison of the BICCN data (500,000 cells/nuclei across 7 BICCN datasets) with that of bulk RNA-seq networks (2,000 mouse brain samples across 52 studies) reveals a consistent topology reflecting a shared co-regulatory signal. Differential signals between broad cell classes persist in driving variation at finer levels, indicating that convergent regulatory processes affect cell phenotype at multiple scales.

Meta-Analytic Network Retrospective of Public Transport and Its Effects on The Governance of Health

Meta-Analytic Network Retrospective of Public Transport and Its Effects on The Governance of Health

A meta-analytic approach to mapping co-occurrent grey matter volume increases and decreases in psychiatric disorders

Numerous studies have investigated grey matter (GM) volume changes in diverse patient groups. Reports of disorder-related GM reductions are common in such work, but many studies also report evidence for GM volume increases in patients. It is unclear whether these GM increases and decreases are independent or related in some way. Here, we address this question using a novel meta-analytic network mapping approach. We used a coordinate-based meta-analysis of 64 voxel-based morphometry studies of psychiatric disorders to calculate the probability of finding a GM increase or decrease in one region given an observed change in the opposite direction in another region. Estimating this co-occurrence probability for every pair of brain regions allowed us to build a network of concurrent GM changes of opposing polarity. Our analysis revealed that disorder-related GM increases and decreases are not independent; instead, a GM change in one area is often statistically related to a change of opposite polarity in other areas, highlighting distributed yet coordinated changes in GM volume as a function of brain pathology. Most regions showing GM changes linked to an opposite change in a distal area were located in salience, executive-control and default mode networks, as well as the thalamus and basal ganglia. Moreover, pairs of regions showing coupled changes of opposite polarity were more likely to belong to different canonical networks than to the same one. Our results suggest that regional GM alterations in psychiatric disorders are often accompanied by opposing changes in distal regions that belong to distinct functional networks.

Do I feel or do I know? Neuroimaging meta-analyses on the multiple facets of empathy

Empathy is a multidimensional construct including affective and cognitive components while maintaining the distinction between one-self and others. Our meta-analyses focused on shared and distinct networks underlying cognitive (taking somebody else's perspective in emotional/painful situations) and affective (self-referentially feeling somebody else's emotions/pain) empathy for various states including painful and emotional situations. Furthermore, a comparison with direct pain experience was carried out.For cognitive empathy, consistent activation in the anterior dorsal medial frontal gyrus (dmPFG) and the supramarginal gyrus (SMG) occurred. For affective empathy, convergent activation of the posterior dmPFG and the inferior frontal gyrus (IFG) was found. Consistent activation of the anterior insula (AI), the anterior dmPFG and the SMG was observed for empathy for pain, while convergent recruitment of the temporo-parietal junction, precuneus, posterior dmPFG, and the IFG was revealed in the meta-analysis across empathy for emotion experiments. The AI and the dmPFG/mid-cingulate cortex (MCC) showed overlapping as well as distinct neural activation for pain processing and empathy for pain.Taken together, we were able to show difference in the meta-analytic networks across cognitive and affective empathy as well as for pain and empathy processing. Based on the current results, distinct functions along the midline structures of the brain during empathy processing are apparent. Our data are lending further support for a multidimensional concept of empathy.

Comparison of metabolite networks from four German population-based studies.

BackgroundMetabolite networks are suggested to reflect biological pathways in health and disease. However, it is unknown whether such metabolite networks are reproducible across different populations. Therefore, the current study aimed to investigate similarity of metabolite networks in four German population-based studies.MethodsOne hundred serum metabolites were quantified in European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam (n = 2458), EPIC-Heidelberg (n = 812), KORA (Cooperative Health Research in the Augsburg Region) (n = 3029) and CARLA (Cardiovascular Disease, Living and Ageing in Halle) (n = 1427) with targeted metabolomics. In a cross-sectional analysis, Gaussian graphical models were used to construct similar networks of 100 edges each, based on partial correlations of these metabolites. The four metabolite networks of the top 100 edges were compared based on (i) common features, i.e. number of common edges, Pearson correlation (r) and hamming distance (h); and (ii) meta-analysis of the four networks.ResultsAmong the four networks, 57 common edges and 66 common nodes (metabolites) were identified. Pairwise network comparisons showed moderate to high similarity (r = 63–0.96, h = 7–72), among the networks. Meta-analysis of the networks showed that, among the 100 edges and 89 nodes of the meta-analytic network, 57 edges and 66 metabolites were present in all the four networks, 58–76 edges and 75–89 nodes were present in at least three networks, and 63–84 edges and 76–87 edges were present in at least two networks. The meta-analytic network showed clear grouping of 10 sphingolipids, 8 lyso-phosphatidylcholines, 31 acyl-alkyl-phosphatidylcholines, 30 diacyl-phosphatidylcholines, 8 amino acids and 2 acylcarnitines.ConclusionsWe found structural similarity in metabolite networks from four large studies. Using a meta-analytic network, as a new approach for combining metabolite data from different studies, closely related metabolites could be identified, for some of which the biological relationships in metabolic pathways have been previously described. They are candidates for further investigation to explore their potential role in biological processes.

PRM2 - Considerations in Meta-Analytic Network Design: The Case of Platinum Doublet Chemotherapies in Advanced Non-Small Cell Lung Cancer (Advnsclc)

PRM2 - Considerations in Meta-Analytic Network Design: The Case of Platinum Doublet Chemotherapies in Advanced Non-Small Cell Lung Cancer (Advnsclc)

Network meta-analysis, electrical networks and graph theory.

Network meta-analysis is an active field of research in clinical biostatistics. It aims to combine information from all randomized comparisons among a set of treatments for a given medical condition. We show how graph-theoretical methods can be applied to network meta-analysis. A meta-analytic graph consists of vertices (treatments) and edges (randomized comparisons). We illustrate the correspondence between meta-analytic networks and electrical networks, where variance corresponds to resistance, treatment effects to voltage, and weighted treatment effects to current flows. Based thereon, we then show that graph-theoretical methods that have been routinely applied to electrical networks also work well in network meta-analysis. In more detail, the resulting consistent treatment effects induced in the edges can be estimated via the Moore-Penrose pseudoinverse of the Laplacian matrix. Moreover, the variances of the treatment effects are estimated in analogy to electrical effective resistances. It is shown that this method, being computationally simple, leads to the usual fixed effect model estimate when applied to pairwise meta-analysis and is consistent with published results when applied to network meta-analysis examples from the literature. Moreover, problems of heterogeneity and inconsistency, random effects modeling and including multi-armed trials are addressed. Copyright © 2012 John Wiley & Sons, Ltd.