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Charles D. Smith

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3 papers

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YNIMG Journal 2019 Journal Article

Distinct patterns of default mode and executive control network circuitry contribute to present and future executive function in older adults

Christopher A. Brown
Frederick A. Schmitt
Charles D. Smith
Brian T. Gold

Executive function (EF) performance in older adults has been linked with functional and structural profiles within the executive control network (ECN) and default mode network (DMN), white matter hyperintensities (WMH) burden and levels of Alzheimer's disease (AD) pathology. Here, we simultaneously explored the unique contributions of these factors to baseline and longitudinal EF performance in older adults. Thirty-two cognitively normal (CN) older adults underwent neuropsychological testing at baseline and annually for three years. Neuroimaging and AD pathology measures were collected at baseline. Separate linear regression models were used to determine which of these variables predicted composite EF scores at baseline and/or average annual change in composite ΔEF scores over the three-year follow-up period. Results demonstrated that low DMN deactivation, high ECN activation and WMH burden were the main predictors of EF scores at baseline. In contrast, poor DMN and ECN WM microstructure and higher AD pathology predicted greater annual decline in EF scores. Subsequent mediation analysis demonstrated that DMN WM microstructure uniquely mediated the relationship between AD pathology and ΔEF. These results suggest that functional activation patterns within the DMN and ECN and WMHs contribute to baseline EF while structural connectivity within these networks impact longitudinal EF performance in older adults.

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YNICL Journal 2015 Journal Article

Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease

Joseph C. McBride
Xiaopeng Zhao
Nancy B. Munro
Gregory A. Jicha
Frederick A. Schmitt
Richard J. Kryscio
Charles D. Smith
Yang Jiang

Recently, Sugihara proposed an innovative causality concept, which, in contrast to statistical predictability in Granger sense, characterizes underlying deterministic causation of the system. This work exploits Sugihara causality analysis to develop novel EEG biomarkers for discriminating normal aging from mild cognitive impairment (MCI) and early Alzheimer's disease (AD). The hypothesis of this work is that scalp EEG based causality measurements have different distributions for different cognitive groups and hence the causality measurements can be used to distinguish between NC, MCI, and AD participants. The current results are based on 30-channel resting EEG records from 48 age-matched participants (mean age 75.7 years) - 15 normal controls (NCs), 16 MCI, and 17 early-stage AD. First, a reconstruction model is developed for each EEG channel, which predicts the signal in the current channel using data of the other 29 channels. The reconstruction model of the target channel is trained using NC, MCI, or AD records to generate an NC-, MCI-, or AD-specific model, respectively. To avoid over fitting, the training is based on the leave-one-out principle. Sugihara causality between the channels is described by a quality score based on comparison between the reconstructed signal and the original signal. The quality scores are studied for their potential as biomarkers to distinguish between the different cognitive groups. First, the dimension of the quality scores is reduced to two principal components. Then, a three-way classification based on the principal components is conducted. Accuracies of 95.8%, 95.8%, and 97.9% are achieved for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. This work presents a novel application of Sugihara causality analysis to capture characteristic changes in EEG activity due to cognitive deficits. The developed method has excellent potential as individualized biomarkers in the detection of pathophysiological changes in early-stage AD.

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YNIMG Journal 2010 Journal Article

Alterations in multiple measures of white matter integrity in normal women at high risk for Alzheimer's disease

Brian T. Gold
David K. Powell
Anders H. Andersen
Charles D. Smith

There is evidence that disruption of white matter (WM) microstructure is an early event in the course of Alzheimer's disease (AD). However, the neurobiological bases of WM microstructural declines in presymptomatic AD are unknown. In the present study we address this issue using a multimodal imaging approach to the study of presymptomatic AD. Participants were 37 high-risk (both family history of dementia and one or more APOE4 alleles) women and 20 low-risk (neither family history nor APOE4) women. Groups were matched for age, education, neuropsychological performance, and vascular factors that could affect white matter. Whole-brain analyses of diffusion tensor imaging data [including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (DA) and radial diffusivity (DR)] and volumetric comparisons of medial temporal lobe (MTL) structures were conducted. Results indicated equivalent entorhinal cortex and hippocampal volumes between risk groups. Nevertheless, the high risk group showed decreased microstructural integrity in WM tracts with direct and secondary connections to the MTL. The predominant alteration in WM integrity in the high AD-risk group was decreased FA not solely driven by either DA or DR changes alone in regions where no MD changes were observed. A second pattern observed in a smaller number of regions involved decreased FA and increased DR. These results suggest that disconnection of MTL-neocortical fiber pathways represents a very early event in the course of AD and suggest that demyelination may represent one contributing mechanism.

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