Dataset Information

Cluster size statistic and cluster mass statistic: two novel methods for identifying changes in functional connectivity between groups or conditions.

ABSTRACT: Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods--the cluster size statistic (CSS) and cluster mass statistic (CMS)--are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity.

SUBMITTER: Ing A

PROVIDER: S-EPMC4048154 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Cluster size statistic and cluster mass statistic: two novel methods for identifying changes in functional connectivity between groups or conditions.

Ing Alex A Schwarzbauer Christian C

PloS one 20140606 6

Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome ...[more]

PMID: 24906136

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Project description:BackgroundAccurate identification of perturbed signaling pathways based on differentially expressed genes between sample groups is one of the key factors in the understanding of diseases and druggable targets. Most pathway analysis methods prioritize impacted signaling pathways by incorporating pathway topology using simple graph-based models. Despite their relative success, these models are limited in describing all types of dependencies and interactions that exist in biological pathways.ResultsIn this work, we propose a new approach based on the formal modeling of signaling pathways. Signaling pathways are formally modeled, and then model checking tools are applied to find the likelihood of perturbation for each pathway in a given condition. By adopting formal methods, various complex interactions among biological parts are modeled, which can contribute to reducing the false-positive rate of the proposed approach. We have developed a tool named Formal model checking based pathway analysis (FoPA) based on this approach. FoPA is compared with three well-known pathway analysis methods: PADOG, CePa, and SPIA on the benchmark of 36 GEO datasets from various diseases by applying the target pathway technique. This validation technique eliminates the need for possibly biased human assessments of results. In the cases that, there is no apriori knowledge of all relevant pathways, simulated false inputs (permuted class labels and decoy pathways) are chosen as a set of negative controls to test the false positive rate of the methods. Finally, to further evaluate the efficiency of FoPA, it is applied to a list of autism-related genes.ConclusionsThe results obtained by the target pathway technique demonstrate that FoPA is able to prioritize target pathways as well as PADOG but better than CePa and SPIA. Also, the false-positive rate of finding significant pathways using FoPA is lower than other compared methods. Also, FoPA can detect more consistent relevant pathways than other methods. The results of FoPA on autism-related genes highlight the role of "Renin-angiotensin system" pathway. This pathway has been supposed to have a pivotal role in some neurodegenerative diseases, while little attention has been paid to its impact on autism development so far.

Project description:Human gut microbiome dysbiosis has been associated with the onset of metabolic diseases and disorders. However, the critical factors leading to dysbiosis are poorly understood. In this study, we provide increasing evidence of the association of diet type and body mass index (BMI) and how they relatively influence the taxonomic structure of the gut microbiota with respect to the causation of gut microbiome dysbiosis. The study included randomly selected Alabama residents (n = 81), including females (n = 45) and males (n = 36). The demographics data included age (33 ± 13.3 years), height (1.7 ± 0.11 meters), and weight (82.3 ± 20.6 kg). The mean BMI was 28.3 ± 7.01, equating to an overweight BMI category. A cross-sectional case-control design encompassing the newly recognized effect size approach to bioinformatics analysis was used to analyze data from donated stool samples and accompanying nutrition surveys. We investigated the microbiome variations in the Bacteroidetes-Firmicutes ratio relative to BMI, food categories, and dietary groups at stratified abundance percentages of <20%, 20%, 30%, 40%, 50%, 60%, and ≥70%. We further investigated variation in the Firmicutes and Bacteroidetes phyla composition (at the genus and species level) in relation to BMI, food categories, and dietary groups (Westernized or healthy). The Pearson Correlation coefficient as an indication of effect size across Alpha diversity indices was used to test the hypothesis (H0 ): increased BMI has greater effect on taxonomic diversity than Westernized diet type, (Ha ): increased BMI does not have a greater effect on taxonomic diversity than Westernized diet type. In conclusion, we rejected the (H0 ) as our results demonstrated that Westernized diet type had an effect size of 0.22 posing a greater impact upon the gut microbiota diversity than an increased BMI with an effect size of 0.16. This implied Westernized diet as a critical factor in causing dysbiosis as compared to an overweight or obese body mass index.

Dataset Information

Cluster size statistic and cluster mass statistic: two novel methods for identifying changes in functional connectivity between groups or conditions.

Publications

Cluster size statistic and cluster mass statistic: two novel methods for identifying changes in functional connectivity between groups or conditions.

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