Chen, K., Reiman, E. M., Huan, Z., Caselli, R. J., Bandy, D., Ayutyanont, N., & Alexander, G. E. (2009). Linking functional and structural brain images with multivariate network analyses: A novel application of the partial least square method. NeuroImage, 47(2), 602-610.
PMID: 19393744;PMCID: PMC2700206;Abstract:
In this article, we introduce a multimodal multivariate network analysis to characterize the linkage between the patterns of information from the same individual's complementary brain images, and illustrate its potential by showing its ability to distinguish older from younger adults with greater power than several previously established methods. Our proposed method uses measurements from every brain voxel in each person's complementary co-registered images and uses the partial least square (PLS) algorithm to form a combined latent variable that maximizes the covariance among all of the combined variables. It represents a new way to calculate the singular value decomposition from the high-dimensional covariance matrix in a computationally feasible way. Analyzing fluorodeoxyglucose positron emission tomography (PET) and volumetric magnetic resonance imaging (MRI) images, this method distinguished 14 older adults from 15 younger adults (p = 4e- 12) with no overlap between groups, no need to correct for multiple comparisons, and greater power than the univariate Statistical Parametric Mapping (SPM), multimodal SPM or multivariate PLS analysis of either imaging modality alone. This technique has the potential to link patterns of information among any number of complementary images from an individual, to use other kinds of complementary complex datasets besides brain images, and to characterize individual state- or trait-dependent brain patterns in a more powerful way. © 2009 Elsevier Inc. All rights reserved.
Alexander, G. E., & Moeller, J. R. (1994). Application of the scaled subprofile model to functional imaging in neuropsychiatric disorders: A principal component approach to modeling brain function in disease. Human Brain Mapping, 2(1-2), 79-94.
Abstract:
Recent advances in functional neuroimaging have presented a challenge to traditional statistical methods in characterizing the effects of neuropsychiatric illness on brain function. The most common approach for analyzing regional group differences has relied on t-tests with significance thresholds selected to reduce the potential effect of multiple statistical tests. Regional covariance analysis offers an alternative to this threshold-based, group difference approach by identifying the functional interactions among brain regions that can be spatially distributed throughout the brain. The Scaled Subprofile Model (SSM) is one form of regional covariance analysis that has been applied to the study of patient groups. Based on a modified principal component analysis, the SSM offers a method for modeling regionally specific patterns of brain function whose expression can be evaluated between groups and validated against clinical measures of patient disease severity and neuropsychological test scores. We review the application of the SSM, to date, in studies of the effects of neurological and psychiatric illness on brain function, including a discussion of SSM methodology and its application to the study of resting state functional neuroimaging in patient groups. SSM analyses applied to studies of Alzheimer's disease, Parkinson's disease, major depressive disorder, AIDS dementia complex, and neoplastic disease each identified functionally specific topographic effects that were associated with clinical disease severity. The results of the SSM analyses suggest that neuropsychiatric disorders may alter functional networks or systems of neural activity in ways that can be expressed as regional covariance patterns in resting functional imaging data. © 1994 Wiley-Liss, Inc.
Huang, W., Alexander, G. E., Daly, E. M., Shetty, H. U., Krasuski, J. S., Rapoport, S. I., & Schapiro, M. B. (1999). High brain myo-inositol levels in the predementia phase of Alzheimer's disease in adults with Down's syndrome: A 1H MRS study. American Journal of Psychiatry, 156(12), 1879-1886.
PMID: 10588400;Abstract:
Objective: An extra portion of chromosome 21 in Down's syndrome leads to a dementia in later life that is phenotypically similar to Alzheimer's disease. Down's syndrome therefore represents a model for studying preclinical stages of Alzheimer's disease. Markers that have been investigated in symptomatic Alzheimer's disease are myo-inositol and N- acetylaspartate. The authors investigated whether abnormal brain levels of myo-inositol and other metabolites occur in the preclinical stages of Alzheimer's disease associated with Down's syndrome. Method: The authors used 1H magnetic resonance spectroscopy (MRS) with external standards to measure absolute brain metabolite concentrations in 19 nondemented adults with Down's syndrome and 17 age- and sex-matched healthy comparison subjects. Results: Concentrations of myo-inositol and choline-containing compounds were significantly higher in the occipital and parietal regions of the adults with Down's syndrome than in the comparison subjects. Within the Down's syndrome group, older subjects (42-62 years, N=11) had higher myo-inositol levels than younger subjects (28-39 years, N=8). Older subjects in both groups had lower N-acetylaspartate levels than the respective younger subjects, although this old-young difference was not greater in the Down's syndrome group. Conclusions: The approximately 50% higher level of myo-inositol in Down's syndrome suggests a gene dose effect of the extra chromosome 21, where the human osmoregulatory sodium/myo-inositol cotransporter gene is located. The even higher myoinositol level in older adults with Down's syndrome extends to the predementia phase earlier findings of high myo-inositol levels in symptomatic Alzheimer's disease.
Burns, C. M., Chen, K., Kaszniak, A. W., Lee, W., Alexander, G. E., Bandy, D., Fleisher, A., Caselli, R. J., & Reiman, E. R. (2013). Higher serum glucose levels are associated with cerebral hypometabolism in Alzheimer's regions. Neurology, 80, 1557-64.
Alexander, G. E., Bergfield, K. L., Chen, K., Reiman, E. M., Hanson, K. D., Lin, L., Bandy, D., Caselli, R. J., & Moeller, J. R. (2012). Gray matter network associated with risk for Alzheimer's disease in young to middle-aged adults. Neurobiology of Aging, 33(12), 2723-2732.
PMID: 22405043;PMCID: PMC3398228;Abstract:
The apolipoprotein E (APOE) ε4 allele increases the risk for late-onset Alzheimer's disease (AD) and age-related cognitive decline. We investigated whether ε4 carriers show reductions in gray matter volume compared with ε4 non-carriers decades before the potential onset of AD dementia or healthy cognitive aging. Fourteen cognitively normal ε4 carriers, aged 26 to 45 years, were compared with 10 age-matched, ε4 non-carriers using T1-weighted volumetric magnetic resonance imaging (MRI) scans. All had reported first- or second-degree family histories of dementia. Group differences in gray matter were tested using voxel-based morphometry (VBM) and a multivariate model of regional covariance, the Scaled Subprofile Model (SSM). A combination of the first two SSM MRI gray matter patterns distinguished the APOE ε4 carriers from non-carriers. This combined pattern showed gray matter reductions in bilateral dorsolateral and medial frontal, anterior cingulate, parietal, and lateral temporal cortices with covarying relative increases in cerebellum, occipital, fusiform, and hippocampal regions. With these gray matter differences occurring decades before the potential onset of dementia or cognitive aging, the results suggest longstanding, gene-associated differences in brain morphology that may lead to preferential vulnerability for the later effects of late-onset AD or healthy brain aging. © 2012 Elsevier Inc..
