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Functional neuroimaging data embodies a massive multiple testing problem, where 100,000 correlated test statistics must be assessed. The familywise error rate, the chance of any false positives is the standard measure of Type I errors in multiple testing. In this paper we review and evaluate three approaches to thresholding images of test statistics:(More)
Because of their increased sensitivity to spatially extended signals, cluster-size tests are widely used to detect changes and activations in brain images. However, when images are nonstationary, the cluster-size distribution varies depending on local smoothness. Clusters tend to be large in smooth regions, resulting in increased false positives, while in(More)
Cluster size tests used in analyses of brain images can have more sensitivity compared to intensity based tests. The random field (RF) theory has been widely used in implementation of such tests, however the behavior of such tests is not well understood, especially when the RF assumptions are in doubt. In this paper, we carried out a simulation study of(More)
In a massively univariate analysis of brain image data, statistical inference is typically based on intensity or spatial extent of signals. Voxel intensity-based tests provide great sensitivity for high intensity signals, whereas cluster extent-based tests are sensitive to spatially extended signals. To benefit from the strength of both, the intensity and(More)
Histopathology is insufficient to predict disease progression and clinical outcome in lung adenocarcinoma. Here we show that gene-expression profiles based on microarray analysis can be used to predict patient survival in early-stage lung adenocarcinomas. Genes most related to survival were identified with univariate Cox analysis. Using either two(More)
OBJECTIVE A single institution's experience with CT-guided percutaneous radiofrequency ablation of biopsy-proven renal cell carcinomas (RCCs) was studied to determine the disease-free survival and complication rate. MATERIALS AND METHODS Data from 125 RCCs in 104 patients treated with curative intent was reviewed. Radiofrequency ablation treatments were(More)
In recent years, multiple brain MR imaging modalities have emerged; however, analysis methodologies have mainly remained modality-specific. In addition, when comparing across imaging modalities, most researchers have been forced to rely on simple region-of-interest type analyses, which do not allow the voxel-by-voxel comparisons necessary to answer more(More)
Small-world networks are a class of networks that exhibit efficient long-distance communication and tightly interconnected local neighborhoods. In recent years, functional and structural brain networks have been examined using network theory-based methods, and consistently shown to have small-world properties. Moreover, some voxel-based brain networks(More)
In diffusion tensor imaging (DTI), interpreting changes in terms of fractional anisotropy (FA) and mean diffusivity or axial (D(||)) and radial (D(⊥)) diffusivity can be ambiguous. The main objective of this study was to gain insight into the heterogeneity of age-related diffusion changes in human brain white matter by analyzing relationships between the(More)
In Alzheimer's disease (AD), atrophy negatively impacts cognition while in healthy adults, inverse relationships between brain volume and cognition may occur. We investigated correlations between gray matter volume and cognition in elderly controls, AD and mild cognitive impairment (MCI) patients with memory and executive deficits. AD demonstrated(More)