Multimodal Discrimination between Normal Aging, Mild Cognitive Impairment and Alzheimer's Disease and Prediction of Cognitive Decline

Corinna Bauer, Howard Cabral, Ronald Killiany
<span title="2018-02-06">2018</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/abr6zdbfebblxlzh436eqtiagi" style="color: black;">Diagnostics</a> </i> &nbsp;
Alzheimer's Disease (AD) and mild cognitive impairment (MCI) are associated with widespread changes in brain structure and function, as indicated by magnetic resonance imaging (MRI) morphometry and 18-fluorodeoxyglucose position emission tomography (FDG PET) metabolism. Nevertheless, the ability to differentiate between AD, MCI and normal aging groups can be difficult. Thus, the goal of this study was to identify the combination of cerebrospinal fluid (CSF) biomarkers, MRI morphometry, FDG PET
more &raquo; ... etabolism and neuropsychological test scores to that best differentiate between a sample of normal aging subjects and those with MCI and AD from the Alzheimer's Disease Neuroimaging Initiative. The secondary goal was to determine the neuroimaging variables from MRI, FDG PET and CSF biomarkers that can predict future cognitive decline within each group. To achieve these aims, a series of multivariate stepwise logistic and linear regression models were generated. Combining all neuroimaging modalities and cognitive test scores significantly improved the index of discrimination, especially at the earliest stages of the disease, whereas MRI gray matter morphometry variables best predicted future cognitive decline compared to other neuroimaging variables. Overall these findings demonstrate that a multimodal approach using MRI morphometry, FDG PET metabolism, neuropsychological test scores and CSF biomarkers may provide significantly better discrimination than any modality alone. positron emission tomography (PET) tracers and concentrations of biomarkers in cerebrospinal fluid (CSF). Pathologically, AD and amnestic MCI are characterized by the presence of intracellular neurofibrillary tangles (NFTs) and extracellular amyloid plaques. The NFTs are composed of insoluble hyperphosphorylated tau protein and reduce the integrity of the cytoskeleton, such that neurons are dysfunctional. Ultimately, this leads to synaptic and neuronal loss [5, 6] . Amyloid plaques are extracellular, composed of insoluble fibrils of amyloid-beta (Aβ) and may be related to the hypometabolism that is observed using 18-fluorodeoxyglucose PET (FDG PET) [6] . In AD and MCI, NFTs accumulate in the locus coeruleus, hippocampus, entorhinal cortex, amygdala and other limbic areas that are important for memory [7, 8] . As Alzheimer's dementia progresses, the NFTs affect more neocortical areas, resulting in deficits in other cognitive domains [7, [9] [10] [11] . On the other hand, amyloid plaques tend to accumulate more in the association cortices first and affect hippocampal structures only as the disease progresses [9, 12] . Because both NFT density [13] and the extent of amyloid distribution [14] are related to the severity of impaired cognition, it may be possible to monitor the degree of dementia via CSF biomarkers of amyloid and tau pathologies, namely total tau (tTau), hyperphosphorylated tau (pTau) and 16] . These biomarkers are able to identify AD in its early stages with fairly high accuracy [17] and increased levels of pTau and tTau have been observed in AD compared to normal aging [18, 19] . Furthermore, CSF samples from both MCI and AD subjects show decreased concentrations of Aβ-42 [19] , which may reflect an increased deposition of Aβ in aggregated plaques in the brain [20] . It is evident that increasing concentrations of tau and amyloid in the CSF may be indicative of further progression along the spectrum of Alzheimer's dementia. Accumulation of AD pathology can have multiple consequences, including a disruption of synaptic function that may be indirectly measured via changes in glucose metabolism. FDG PET, a glucose analogue, is typically used as a marker of synaptic function, as metabolic changes are closely tied to glucose consumption [21] . There is a relatively consistent pattern of decreased metabolism that occurs in AD. The regions that tend to show hypometabolism are the posterior cingulate/retrosplenial cortex and the cortical structures in the parieto-temporal junction, such as the angular gyrus and precuneus [22] [23] [24] [25] [26] . Some studies also indicate a decrease in hippocampal and entorhinal metabolism [6, 27, 28] , although this is not consistently observed. There is a less consistent pattern for MCI [29] . Neuronal loss may also occur as a result of AD pathology. This loss is visible in vivo through MRI changes in cortical surface area, thickness, or volume of the cortical structures. Such morphometric changes have consistently been observed in MCI and AD, with the earliest detectable changes occurring in the entorhinal cortex hippocampus, spreading outward to other cortical and subcortical structures [30] [31] [32] . Ultimately, the accumulation of pathologies and the resultant changes in synaptic function and neuronal loss manifests as cognitive deficits. Initially, difficulties with memory tasks are often observed, followed by deficits in executive function and ultimately affecting visuospatial abilities and attention in the later stages of the disease [33, 34] . The cognitive deficits seen throughout mild cognitive impairment (MCI) and AD correlate with the degree of pathology in post-mortem tissue analysis as well as in vivo imaging measures [5, [35] [36] [37] . However, many of these studies only report one or two tests in the same subjects, thus we do not have a complete picture as to the neural correlates of the wide range of neuropsychological functions in normal aging, MCI and AD. Each of the four modalities (CSF biomarkers, FDG-PET, MRI morphometry and neuropsychological evaluations) discussed above may be useful for discriminating normal aging, MCI and AD. Since each of the modalities is to an extent independent of the others, it is conceivable that this combination would provide better discrimination than any individual method on its own. In the current study, we explored this concept using a data-driven approach, which may provide additional variables not typically included in an a priori analysis. Because each of these factors can provide unique information that can influence diagnosis as a whole, we also examined if the ability to differentiate groups is
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