Abstract
Scaled Subprofile Model of Principal Component Analysis (SSM-PCA) is a multivariate statistical method and has been widely used in Positron Emission Tomography (PET). Recently, SSM-PCA has been applied to discriminate patients with Parkinson's disease and healthy controls with Amplitude of Low Frequency Fluctuation (ALFF) from Resting-State Functional Magnetic Resonance Imaging (RS-fMRI). As RS-fMRI scans are more readily available than PET scans, it is important to investigate the intra- and inter-scanner reliability of SSM-PCA in RS-fMRI. A RS-fMRI dataset with Eyes Open (EO) and Eyes Closed (EC) conditions was obtained in 21 healthy subjects (21.8 ± 1.8 years old, 11 females) on 3 visits (V1, V2, and V3), with V1 and V2 (mean interval of 14 days apart) on one scanner and V3 (about 8 months from V2) on a different scanner. To simulate between-group analysis in conventional SSM-PCA studies, 21 subjects were randomly divided into two groups, i.e., EC-EO group (EC ALFF map minus EO ALFF map, n = 11) and EO-EC group (n = 10). A series of covariance patterns and their expressions were derived for each visit. Only the expression of the first pattern showed significant differences between the two groups for all the visits (p = 0.012, 0.0044, and 0.00062 for V1, V2, and V3, respectively). This pattern, referred to as EOEC-pattern, mainly involved the sensorimotor cortex, superior temporal gyrus, frontal pole, and visual cortex. EOEC-pattern's expression showed fair intra-scanner reliability (ICC = 0.49) and good inter-scanner reliability (ICC = 0.65 for V1 vs. V2 and ICC = 0.66 for V2 vs. V3). While the EOEC-pattern was similar with the pattern of conventional unpaired T-test map, the two patterns also showed method-specific regions, indicating that SSM-PCA and conventional T-test are complementary for neuroimaging studies.
Highlights
Identification of reproducible and region-specific effects that characterize normal or diseased brain state is one of the most important goals of brain functional imaging studies
For Scaled Subprofile Model of Principal Component Analysis (SSM-PCA), we found that only one study measured the test-retest reliability with Positron Emission Tomography (PET) images from two groups of subjects (Ma et al, 2007)
GIS1 was named as the EOEC-pattern map for V1–3
Summary
Identification of reproducible and region-specific effects that characterize normal or diseased brain state is one of the most important goals of brain functional imaging studies. The Scaled Subprofile Model of Principal Component Analysis (SSM-PCA) is one of the earliest multivariate data analytic techniques that are available to recognize significant group-dependent and regionspecific effects (Moeller et al, 1987; Moeller and Strother, 1991; Alexander and Moeller, 1994; Eidelberg, 2009). The SSMPCA is one form of regional covariance analysis, identifying functional interaction patterns among brain regions that are spatially distributed throughout the brain (Moeller et al, 1987). Each pattern is expressed in each subject with a Subject Scaling Factor (SSF), which can be prospectively assessed and compared between groups and validated with disease severity and neuropsychological test scores (Alexander and Moeller, 1994; Eidelberg, 2009)
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