Christian Habeck, PhD - US grants

[unreadable] DESCRIPTION (provided by applicant): As clinical and cognitive neuroscience mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention. Multivariate techniques have many 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, on the other hand, cannot directly address functional connectivity in the brain. 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. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. We therefore propose a series of studies comparing multivariate approaches amongst each other and with traditional univariate approaches in dyadic reports and comprehensive review papers. For these studies we will use computer simulations as well as real-world neuroscience data sets. We will also extend and develop our own covariance approach further to enable adequate treatment of parametric within-subjects experimental designs and group-differences in one analysis step. Finally, we will provide a software analysis package that will integrate the most common features of multivariate approaches in a user-friendly manner. [unreadable] [unreadable]

PROJECT SUMMARY: This study focus on the optimal functional and structural imaging characterization of the cognitive aging and preclinical Alzheimer's disease (AD). It has been repeatedly demonstrated that performance across the age span on large batteries of diverse cognitive tests can be parsimoniously represented by a set of four reference abilities: episodic memory, perceptual speed, fluid ability, and vocabulary. Based on these findings, it has been argued that cognitive aging research should try to understand how aging impacts performance of this small set of reference abilities than focus on specific tasks. In contrast, neuroimaging researchers typically evaluate age differences in neural activation associated with the performance of a single specific task that may or may not be fully representative of these reference abilities. We have begun to identify the latent brain networks associated with each of the four reference abilities across adulthood. While undergoing functional imaging, we tested large group of healthy adults aged 20 to 80 with a series of 12 cognitive tasks that represent the four reference abilities (3 per construct). Using unique expertise in spatial covariance and other analyses of the fMRI imaging data, we have derived preliminary versions of the latent spatial, brain-wide fMRI networks that are associated with the latent cognitive structure of the reference abilities across adulthood. Successful identification of these reference ability neural networks may lead to a paradigm shift in research on the neural bases of age differences in cognition by focusing on the broad and replicable aspects common to several tasks rather than the possibly idiosyncratic features of individual tasks. We now propose to follow up this group at 5 years in order to begin to delineate how expression of these networks changes with aging and with the onset of mild cognitive impairment and AD. We will use multimodal imaging to evaluate potential mediators of age and dementia-related differences in the utilization of the networks. These include change in brain volume and cortical thickness; white matter hyperintensity burden; integrity of white matter tracts; resting CBF; and the default network. Importantly, we will use PET to assess amyloid burden. The proposed study will develop a completely new and more focused imaging approach to the study of cognitive aging and preclinical AD. It has the potential to provide key insights into the nature and causes of the neural changes that underlie cognitive aging and to more accurately describe the preclinical phase of AD.