Troyer, A. K. | Search bioRxiv

bioRxiv2019

Sex differences in cortisol and memory following acute social stress in amnestic mild cognitive impairment

ObjectiveOlder adults with amnestic mild cognitive impairment (aMCI) develop Alzheimers type dementia approximately ten times faster annually than the normal population. Adrenal hormones are associated with aging and cognition. We investigated the relationship between acute stress, cortisol, and memory function in aMCI with an exploratory analysis of sex. MethodSalivary cortisol was sampled diurnally and during two test sessions, one session with the Trier Social Stress Test (TSST), to explore differences in the relationship between cortisol and memory function in age-normal cognition (NA) and aMCI. Participants with aMCI (n=6 women, 9 men; mean age=75) or similarly aged NA (n=9 women, 7 men, mean age=75) were given tests of episodic, associative, and spatial working memory with a psychosocial stressor (TSST) in the second session. ResultsThe aMCI group performed worse on the memory tests than NA as expected, and males with aMCI had elevated cortisol levels on test days. Immediate episodic memory was enhanced by social stress in NA but not in the aMCI group, indicating that stress-induced alterations in memory are different in individuals with aMCI. High cortisol was associated with impaired performance on episodic memory in aMCI males only. Cortisol in Session 1 moderated the relationship with spatial working memory, whereby higher cortisol was associated with worse performance in NA, but better spatial working memory in aMCI. In addition, effects of aMCI on perceived anxiety in response to stress exposure were moderated by stress-induced cortisol in a sex-specific manner. ConclusionsWe show effects of aMCI on Test Session cortisol levels and effects on perceived anxiety, and stress-induced impairments in memory in males with aMCI in our exploratory sample. Future studies should explore sex as a biological variable as our findings suggests that effects at the confluence of aMCI and stress can be obfuscated without sex as a consideration.

neuroscience↗

bioRxiv2018

Generalization of the minimum covariance determinant algorithm for categorical and mixed data types

The minimum covariance determinant (MCD) algorithm is one of the most common techniques to detect anomalous or outlying observations. The MCD algorithm depends on two features of multivariate data: the determinant of a matrix (i.e., geometric mean of the eigenvalues) and Mahalanobis distances (MD). While the MCD algorithm is commonly used, and has many extensions, the MCD is limited to analyses of quantitative data and more specifically data assumed to be continuous. One reason why the MCD does not extend to other data types such as categorical or ordinal data is because there is not a well-defined MD for data types other than continuous data. To address the lack of MCD-like techniques for categorical or mixed data we present a generalization of the MCD. To do so, we rely on a multivariate technique called correspondence analysis (CA). Through CA we can define MD via singular vectors and also compute the determinant from CAs eigenvalues. Here we define and illustrate a generalized MCD on categorical data and then show how our generalized MCD extends beyond categorical data to accommodate mixed data types (e.g., categorical, ordinal, and continuous). We illustrate this generalized MCD on data from two large scale projects: the Ontario Neurodegenerative Disease Research Initiative (ONDRI) and the Alzheimers Disease Neuroimaging Initiative (ADNI), with genetics (categorical), clinical instruments and surveys (categorical or ordinal), and neuroimaging (continuous) data. We also make R code and toy data available in order to illustrate our generalized MCD.

bioinformatics↗