Searching the Resource Information Network
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
Often derived from partial correlations or many pairwise analyses, covariance networks represent the inter-relationships among regions and can reveal important topological structures in brain measures from healthy and pathological subjects. However both approaches are not consistent network estimators and are sensitive to the value of the tuning parameters. Here, we propose a consistent covariance network estimator by maximising the network likelihood (MNL) which is robust to the tuning parameter. We validate the consistency of our algorithm theoretically and via a simulation study, and contrast these results against two well-known approaches: the graphical LASSO (gLASSO) and Pearson pairwise correlations (PPC) over a range of tuning parameters. The MNL algorithm had a specificity equal to and greater than 0.94 for all sample sizes in the simulation study, and the sensitivity was shown to increase as the sample size increased. The gLASSO and PPC demonstrated a specificity-sensitivity trade-off over a range of values of tuning parameters highlighting the discrepancy in the results for misspecified values. Application of the MNL algorithm to the case study data showed a loss of connections between healthy and impaired groups, and improved ability to identify between lobe connectivity in contrast to gLASSO networks. In this work, we propose the MNL algorithm as an effective approach to find covariance brain networks, which can inform the organisational features in brain-wide analyses, particularly for large sample sizes.
Pubmed ID: 30001336
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Database of the results of the ADNI study. ADNI is an initiative to develop biomarker-based methods to detect and track the progression of Alzheimer's disease (AD) that provides access to qualified scientists to their database of imaging, clinical, genomic, and biomarker data.
View all literature mentionsA public charity whose mission is to support the NIH in its mission to improve health, by forming and facilitating public-private partnerships for biomedical research and training. Its vision is Building Partnerships for Discovery and Innovation to Improve Health. The FNIH draws together the world''s foremost researchers and resources, pressing the frontier to advance critical discoveries. They are recognized as the number-one medical research charity in the countryleveraging support, and convening high level partnerships, for the greatest impact on the most urgent medical challenges we face today. Grants are awarded as part of a public-private partnership with the National Heart, Lung, and Blood Institute (NHLBI) on behalf of The Heart Truth in support of women''s heart health education and research. Funding for the Community Action Program is provided by the FNIH through donations from individuals and corporations including The Heart Truth partners Belk Department Stores, Diet Coke, and Swarovski. Successful biomedical research relies upon the knowledge, training and dedication of those who conduct it. Bringing multiple disciplines to bear on health challenges requires innovation and collaboration on the part of scientists. Foundation for NIH partnerships operate in a variety of ways and formats to recruit, train, empower and retain their next generation of researchers. From lectures and multi-week courses, to scholarships and awards through fellowships and residential training programs, their programs respond to the needs of scientists at every level and stage in their careers.
View all literature mentions