Three different similarity measures were evaluated with FSNA to form subsets of nodes and experiments.

The RDNA algorithm was modified to provide multiple subnets or maximal cliques for meta-analysis networks. Java-based applications were developed for both RDNA and FSNA. Two versions of these data were evaluated, one using a more strict ALE threshold (P < 0.01) with a 13-node meta-analysis network, and the other a more lax threshold (P < 0.05) with a 22-node network. Crucial to understanding how the brain works is connectivity, and the centerpiece of brain connectivity is the connectome, a comprehensive description of how neurons and brain regions are connected. These two network analysis methods were evaluated using data from activation likelihood estimation (ALE)-based meta-analysis of the Stroop paradigm. The first letters indicate the days the poster is displayed: MT Monday, June 7 and Tuesday, June 8 WTh Wednesday, June 9 and Thursday, June 10 The. Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA. The first, called replicator dynamics network analysis (RDNA), analyzes co-occurrence of activations, whereas the second, called fractional similarity network analysis (FSNA), uses binary pattern matching to form similarity subnets. Having first begun in July 2009, the Human Connectome Project (HCP) is a joint undertaking by the National Institutes of Health (NIH) as well as four universities and one hospital. Each poster will be displayed for two days. Towards a brain-based predictome of mental illness. Two new network analysis algorithms have been adapted for use with meta-analysis networks. Using the meta-analysis network as a standardized data structure, network analysis algorithms can examine complex interrelationships and resolve hidden details. The high information content in large data sets from voxel-based meta-analyses is complex, making it hard to readily resolve details.

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