Imaging markers of mild cognitive impairment: Multivariate analysis of CBF SPECT
Introduction
Mild cognitive impairment (MCI) is conceptualized as a boundary state between aging and dementia [1]. The term refers to individuals whose memory or other cognitive abilities are not normal, but do not meet conventional criteria for dementia. Most of the MCI subjects will progress to dementia, especially Alzheimer's disease (AD), at a rate of 10–15% per year, as compared to the normal aging who convert at a rate of 1–2% per year [15].
The current MCI research mainly concerns the early diagnosis of dementia and pharmacologic intervention. Thus, a thorough understanding of genesis and natural history of Alzheimer disease is necessary, which will lead to a number of potential clinical applications, including the diagnosis, treatment, and prevent of the onset and progression of the disease. Single photon emission computed tomography (SPECT) has been shown to be useful for the diagnosis of AD [9]. Reduced regional cerebral blood flow (rCBF) in parietal lobe and parieto-temporal association cortex are the typical findings in early AD. In MCI, SPECT has also been shown to be able to predict conversion to AD. A decreased rCBF in parietal lobe, posterior cingulate and precuni was found to be related to the future development of dementia [10], [11], [13]. Regarding to the longitudinal rCBF changes of MCI, selective rCBF reduction was observed in the left hippocampus and parahippocampus gyrus [13]. Concerning the neuropsychological evaluation, MCI had impaired cognitive function at baseline and declined significantly faster concerning episodic memory, semantic memory and perceptual speed during the follow-up, but working memory was spared [3].
In brain imaging data analysis, multivariate analysis techniques have recently received increasing attention. The technique is based upon principal component analysis (PCA), which evaluates correlation of activation across brain regions. Scaled subprofile model (SSM) analysis is one type of multivariate analyses and could be applied in the cross-sectional study to identify the covariate pathological brain networks in diseased groups. Ordinal trends (OrT) analysis is the method of choice in the analysis of neuroimaging data from experiments with parametric designs involving two or more task condition [6], [8]. It focuses on neural processes for which the associated covariance patterns exhibit ordinal trends, i.e., the subject scores increase monotonically with changes in experimental conditions or at different time points. While originally developed for the study of time series fMRI data, this approach can also be used to model the specific network changes that occur in PET and SPECT studies of disease progression, as well as serial imaging studies of treatment effects and medication washout in the resting condition.
The results of multivariate analysis can be more easily interpreted as a signature of neuronal networks. Univariate approaches, on the other hand, cannot directly address functional connectivity in the brain. The multivariate 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.
Based on this background, we aimed to examine the natural history of MCI using SPECT and neuropsychology and evaluated baseline differences and longitudinal progression of MCI. SSM analyses were applied in SPECT data among the groups of PMCI, SMCI and normal controls at the initial investigation, and OrT analysis was performed in PMCI and SMCI, both separately and combined, to model the disease progression. Neuropsychological tests were correlated with covariate pattern expression.
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Subjects
Thirty-nine MCI subjects and twenty controls were evaluated. The MCI subjects were selected from those individuals consecutively investigated for suspected dementia at the Geriatric Clinic, Karolinska University Hospital. The control subjects were recruited through advertisements in the press, the Swedish Pensioner Society and a Driving and Aging project.
All subjects underwent SPECT and neuropsychological examination at the initial investigation. The MCI subjects were followed clinically for an
Group comparison of neuropsychology at baseline
The results of group comparison of neuropsychological tests among PMCI, SMCI and controls at baseline are presented in Table 1. PMCI performed significantly worse concerning episodic memory, visuospatial function and MMSE as compared to SMCI at baseline.
Longitudinal changes of neuropsychological tests
PMCI had significantly decreased MMSE and episodic memory during the follow-up (MMSE—baseline: 25.7 ± 2.6 (mean ± S.D.), follow-up: 23.2 ± 4.6 (mean ± S.D.), p = 0.0224; recognition words D′—baseline: 2.1 ± 1.0 (mean ± S.D.), follow-up: 1.7 ± 0.9 (mean ± S.D.), p =
Discussion
The results of SSM analysis revealed the PMCI related covariate patterns, which differentiate the rCBF differences between PMCI and SMCI, as well as PMCI and controls at the initial investigation. The scores of the covariate pattern expression were associated with the decline of the cognitive function at baseline. The PMCI related pattern was represented by the hypoperfusion in parietal lobe associated with elevated rCBF in frontal lobe and cerebellum as compared with SMCI at baseline, which is
Acknowledgments
This work was supported by the Swedish Medical Research Council, the Swedish Council for Social Research, the Gamla Tjänarinnor Foundation, the Greta Lindenau-Hansell's Foundation, Alzheimerfonden, the Karolinska Institute and the Swedish Society of Medicine, Nuclear Diagnostics Ltd, London, England and SADF. We are grateful to Toni Flanagan for the assistance in preparing the manuscript.
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