The metabolic topography of essential blepharospasm

Hutchinson, Michael; Nakamura, Tomoya; Moeller, James R.; Antonini, Angelo; Belakhlef, Abdelfatihe; Dhawan, Vijay; Eidelberg, David

doi:10.1212/wnl.55.5.673

Cited by 144 publications

(104 citation statements)

References 14 publications

Supporting

Mentioning

103

Contrasting

Order By: Relevance

“…Derivation of AD-related pattern-To identify network-correlates of early dementia and age, we employed the Scaled Subprofile Model (SSM), a covariance-analysis method (Alexander et al, 1999;Moeller et al, 1987) that has been used previously in resting imaging studies of normal aging and a variety of neurological diseases (Alexander et al, 1999;Hutchinson et al, 2000;Moeller et al, 1996;Nakamura et al, 2001). This analysis was applied to the FDG-PET images acquired for AD and control subjects.…”

Section: Pca Approachmentioning

confidence: 99%

Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease

et al. 2008

View full text Add to dashboard Cite

We performed univariate and multivariate discriminant analysis of FDG-PET scans to evaluate their ability to identify Alzheimer's disease (AD). FDG-PET scans came from two sources: 17 AD patients and 33 healthy elderly controls were scanned at the University of Michigan; 102 early AD patients and 20 healthy elderly controls were scanned at the Technical University of Munich, Germany. We selected a derivation sample of 20 AD patients and 20 healthy controls matched on age with the remainder divided into 5 replication samples. The sensitivity and specificity of diagnostic ADmarkers and threshold criteria from the derivation sample were determined in the replication samples. Although both univariate and multivariate analyses produced markers with high classification accuracy in the derivation sample, the multivariate marker's diagnostic performance in the replication samples was superior. Further, supplementary analysis showed its performance to be unaffected by the loss of key regions. Multivariate measures of AD utilize the covariance structure of imaging data and provide complementary, clinically relevant information that may be superior to univariate measures.

show abstract

Section: Pca Approachmentioning

confidence: 99%

Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease

et al. 2008

View full text Add to dashboard Cite

show abstract

“…To identify CBF network correlates of early dementia, we employed an analysis technique that originated from the Scaled Subprofile Model, a covariance analysis method (Moeller et al, 1987) that has been used previously in resting imaging studies of normal aging and a variety of neurologic diseases (Hutchinson et al, 2000;Moeller et al, 1999;Nakamura et al, 2001). We summarize the steps of this algorithm in detail; no room has been left for arbitrary decisions on the part of the analyst:…”

Section: Covariance Analysismentioning

confidence: 99%

Multivariate and Univariate Analysis of Continuous Arterial Spin Labeling Perfusion MRI in Alzheimer's Disease

Asllani

Habeck

Scarmeas

et al. 2007

J Cereb Blood Flow Metab

150

132

View full text Add to dashboard Cite

Continuous arterial spin labeling (CASL) magnetic resonance imaging (MRI) was combined with multivariate analysis for detection of an Alzheimer's disease (AD)-related cerebral blood flow (CBF) covariance pattern. Whole-brain resting CBF maps were obtained using spin echo, echo planar imaging (SE-EPI) CASL in patients with mild AD (n = 12, age = 70.7 ± 8.7 years, 7 males, modified Mini-Mental State Examination (mMMS) = 38.7/57 ± 11.1) and age-matched healthy controls (HC) (n = 20; age = 72.1 ± 6.5 years, 8 males). A covariance pattern for which the mean expression was significantly higher (P < 0.0005) in AD than in HC was identified containing posterior cingulate, superior temporal, parahippocampal, and fusiform gyri, as well as thalamus, insula, and hippocampus. The results from this analysis were supplemented with those from the more standard, region of interest (ROI) and voxelwise, univariate techniques. All ROIs (17/hemisphere) showed significant decrease in CBF in AD (P < 0.001 for all ROIs, a corrected = 0.05). The area under the ROC curve for discriminating AD versus HC was 0.97 and 0.94 for covariance pattern and gray matter ROI, respectively. Fewer areas of depressed CBF in AD were detected using voxelwise analysis (corrected, P < 0.05). These areas were superior temporal, cingulate, middle temporal, fusiform gyri, as well as inferior parietal lobule and precuneus. When tested on extensive split-half analysis to map out the replicability of both multivariate and univariate approaches, the expression of the pattern from multivariate analysis was superior to that of the univariate.

show abstract

“…To identify network-correlates of early dementia, we employed an analysis technique that originated from the Scaled Subprofile Model (SSM), a covariance-analysis method (1,25) that has been used previously in resting imaging studies of normal aging and a variety of neurological diseases (26)(27)(28)(29). SSM involved a principal components analysis of the scans from the control and AD groups, and captured the major sources of between-and within-group variation, producing a series of principal components (PCs).…”

Section: Multivariate Data Analysismentioning

confidence: 99%

Neural network approaches and their reproducibility in the study of verbal working memory and Alzheimer’s disease

Habeck¹,

Stern²

2007

Clinical Neuroscience Research

View full text Add to dashboard Cite

As clinical and cognitive neurosciences mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention because they have attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, in contrast, cannot directly address functional connectivity in the brain. Apart from this conceptual difference, the covariance approach can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent, and often overly conservative, corrections for voxel-wise multiple comparisons. Multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. We provide two examples that illustrate different uses of multivariate techniques in cognitive and clinical neuroscience. We hope this contribution helps facilitate wider dissemination of these techniques in the research community.

show abstract

The metabolic topography of essential blepharospasm

Cited by 144 publications

References 14 publications

Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease

Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease

Multivariate and Univariate Analysis of Continuous Arterial Spin Labeling Perfusion MRI in Alzheimer's Disease

Neural network approaches and their reproducibility in the study of verbal working memory and Alzheimer’s disease

Contact Info

Product

Resources

About