Locality Preserving Projection of Functional Connectivity for Regression

Abstract

In this chapter, we proposed a pattern regression framework to predict individuals brain ages from the whole-brain resting-state functional connectivity MRI (rs-fcMRI). In the framework, a supervised locality preserving projections (LPP) algorithm was employed to learn a low-dimensional representation of brain development from many individuals at different ages, and locally adjusted support vector regression (LASVR) method was developed in the manifold coordinate space for making continuously valued predictions about the functional development levels of individual brains. We aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8–79 years) of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system, while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brain functional development levels. This study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI.