PurposeDiffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat’s robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI.ApproachAs a baseline, we match N=358 participants from two sites to create a “silver standard” that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i) βAGE, the linear regression coefficient of the relationship between FA and age; (ii) γ^sf*, the ComBat-estimated site-shift; and (iii) δ^sf*, the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions.ResultsComBat remains well behaved for βAGE when N>162 and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable.ConclusionPrior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds.
Sleep disturbances are commonly reported among patients with Alzheimer’s Disease (AD). Further, the disruption of subcortical areas such as the Basal Forebrain (BF) and its constituent Nucleus Basalis of Meynert (NBM), which play an important role in maintaining wakefulness or alertness (also known as vigilance), occurs early in AD. In this study, we delineate vigilance-linked fMRI patterns in an aging population and determine how these patterns relate to subcortical integrity and cognition. We used fMRI data from the Vanderbilt Memory and Aging Project dataset, consisting of 49 MCI patients and 75 healthy controls. Since external measures of vigilance are not present during fMRI, we used a data-driven technique for extracting vigilance information directly from fMRI data. With this approach, we derived subject-specific spatial maps reflecting a whole-brain activity pattern that is correlated with vigilance. We first assessed the relationships between cognitive measures (subject memory composite and executive function scores) and structural measures (BF and NBM volumes obtained from subject-specific segmentation methods) using Pearson correlations. BF and NBM volumes were found to be significantly correlated with memory composite in MCI subjects and with executive function in HCs. We then performed a mediation analysis to evaluate how NBM volume may mediate fMRI-derived vigilance effects on memory composite scores in MCI subjects. fMRI vigilance activity and memory composite were significantly associated in the hippocampus, posterior cingulate cortex, and anterior cingulate cortex, regions involved in the default-mode and salience networks. These results suggest that cognitive decline in AD may be linked with both subcortical structural changes and vigilance-related fMRI signals, opening new directions for potential functional biomarkers in pathological aging populations.
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