Chunk #24 — Mature Network Architecture Develops Via Segregation and Integration — Developmental Changes in Functional Relationships Observed with Support Vector Machines
Development via integration and segregation can also be seen using a methodologically distinct analysis utilizing support vector machines. Dosenbach and colleagues used a support vector machine analysis to both determine whether children and adult rs-fcMRI scans can be separated into two groups by the machine and on what basis that separation is made. Support vector machine analyses learn to make group assignments using measurements (features) from many examples of each group. In this case the machine was given rs-fcMRI correlation values for region pairs (the features) for both children and adults (the assignment groups). When a new person is added, the machine can use its pattern of features to assign it to the child or adult group. The machine can also report which features (pair correlation values) were most useful in making the assignment. When dividing children and adults, the support vector machine was 91% accurate. In addition to classification, SVM regression was used to predict an individual's relative brain maturity on a functional connectivity maturation index (See Fig. 4). The functional maturation curve, derived from SVM regression, accounted for
