Topic: functional principal component analysis

Patients with Alzheimer's disease (AD) often display disrupted resting-state brain functional connectivity compared to healthy individuals, as observed in resting-state fMRI studies. Understanding which brain regions contribute to this disruption is vital for understanding disease progression.We propose the Functional Connectivity Curve Detection model (FunCurvDtx) to identify key brain regions for classifying AD versus normal subjects based on functional connectivity. FunCurvDtx combines functional principal component analysis (fPCA) and group penalized logistic regression to analyze function

Imaging genetics has advanced to uncover genetic variants associated with imaging phenotypes characterized by imaging data, which can be considered functional. Functional data refers to data that are inherently functions, where each observation represents a curve, or some other form of continuous variation over a domain.In this study, we propose a novel approach, the functional sequence kernel association test (fSKAT), aimed at identifying single-nucleotide polymorphisms (SNPs) associated with functional phenotypes. To address the infinite space of functional data, we employ functional princip