Scaled Subprofile Model: A Statistical Approach to the Analysis of Functional Patterns in Positron Emission Tomographic Data

Moeller, James R.; Strother, Stephen C.; Sidtis, John J.; Rottenberg, David A.

doi:10.1038/jcbfm.1987.118

Cited by 206 publications

(143 citation statements)

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“…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

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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.

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Section: Pca Approachmentioning

confidence: 99%

Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease

et al. 2008

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“…incomplete review of landmark articles see (1)(2)(3)(4)). Despite widespread interest in functional connectivity and the interaction between different brain areas, it is somewhat surprising that multivariate approaches have not yet quite enjoyed the success and the widespread usage of massively univariate techniques.…”

Section: Introductionmentioning

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

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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.

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“…These programs also typically include methods for presentation or display of results to help visualize the observed significant effects. Interest in the use of multivariate statistical techniques with neuroimaging data has grown dramatically in recent years and such methods are increasingly being used to characterize the regional patterns or distributed networks of brain responses to stimulation and for group differences in structure and function (Moeller et al, 1987;Habeck et al, 2003;Alexander et al, 1999Alexander et al, , 2006Smith et al, 2006;Grady et al, 2001;McIntosh et al, 1992McIntosh et al, , 1996Friston et al, 1993Friston et al, , 2003McKeown et al, 1998). These latter methods typically assess aspects of the covariance patterns in neuroimaging data to identify regional interactions and their relation to behavior or group membership (Alexander and Moeller, 1994).…”

Section: Statistical Methods For Image Analysismentioning

confidence: 99%

“…These latter methods typically assess aspects of the covariance patterns in neuroimaging data to identify regional interactions and their relation to behavior or group membership (Alexander and Moeller, 1994). The multivariate approaches include, for example, methods such as the scaled subprofile model (SSM; Moeller et al, 1987) and other principal component-based methods (Friston et al, 1993), independent component analyses (McKeown et al, 1998), structural equation modeling (Mcintosh et al, 1992), dynamic causal modeling (Friston et al, 2003), ordinal trend analysis (Habeck et al, 2005), and partial-least squares analysis (PLS; Mcintosh et al, 1996).…”

Section: Statistical Methods For Image Analysismentioning

confidence: 99%

Neuroimaging: Overview of Methods and Applications

Ryan¹,

Alexander²

2008

Handbook of Physiological Research Methods in Health Psychology

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IntroductionThe past few decades have seen enormous advances in the ability to visualize and measure aspects of brain anatomy and physiology in the living human being. The recent developments in neuroimaging techniques that provide measures of brain structure and function, as well as cerebral activity during cognitive processes such as perception, attention, and memory have changed the face of cognitive neuroscience, and have allowed us to ask questions about the relation between the brain and behavior that were simply not possible only a decade or two ago. As these techniques have become more accessible and affordable for research use, they have been increasingly applied to a variety of research questions, having the potential to greatly enhance our understanding of the normal brain and its development throughout the lifespan. Even areas of research that have traditionally not been linked to neuroscience, such as social psychology, economics, and education are increasingly making use of neuroimaging methods.From the beginning, evaluating the effects of health-related factors, such as aging, neurological and psychiatric illness, and brain injury have been an important and rapidly growing part of the neuroimaging field. Health psychology provides a potentially unique perspective in the application of neuroimaging methods studying how brainbehavior relationships are altered by disease states and other health-related factors.Neuroimaging provides a set of tools that can address a wide variety of questions. For example, researchers might address how neural systems important for specific cognitive processes are altered by the presence, progression, and treatment of a disease; test the effect of health factors such as genetic risk on regional patterns of 3 brain structure and function; identify subgroups for which treatment or prevention therapies are most effective; or determine the effect of individual differences on the expression, severity, and progression of brain diseases. To utilize these techniques effectively, however, those new to the neuroimaging field and unfamiliar with its rapidly evolving technology are often faced with the daunting task of figuring out where and how to begin.The purpose of this chapter is to provide an overview of the major neuroimaging methods and their applications to health psychology research. Since a detailed and complete description of the methods and general applications for neuroimaging in health-related research is well beyond the scope of this chapter, we have focused on the description of methods and applications that we believe will provide a useful starting point for researchers who are thinking of applying such neuroimaging techiques to their own areas of health-related psychology.We begin with a brief review of the major structural and functional neuroimaging research methods that are commonly in use, including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and diffusion-weight...

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Scaled Subprofile Model: A Statistical Approach to the Analysis of Functional Patterns in Positron Emission Tomographic Data

Cited by 206 publications

References 43 publications

Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease

Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease

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

Neuroimaging: Overview of Methods and Applications

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