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bioRxiv2019

Aging-related hypometabolism in the anterior cingulate cortex mediates the relationship between age vs. executive function but not vs. memory in cognitively intact elders

Elucidating the pathophysiology of cognitive decline during aging in those without overt neurodegeneration is a prerequisite to improved diagnosis, prevention, and treatment of cognitive aging. We showed previously the anterior cingulate cortex (ACC) and adjacent medial prefrontal cortex (mPFC) are centers for aging-related metabolic dysfunction that correlate with age-associated cognitive decline in healthy volunteers. Here, we examine using the extensive and well-characterized ADNI dataset the hypothesis that ACC metabolism in healthy seniors functions as a mediator in the relationship between age and executive function. In agreement with our previous findings, highly significant correlations arose between age and metabolism; metabolism and fluency; and age and fluency. These observations motivated a mediation model in which ACC metabolism mediates the relationship between age and fluency score. Significance of the indirect effect was examined by Sobel testing and bootstrapping. In these cognitively intact seniors with \"typical aging,\" there was neither a correlation between age and memory scores nor between ACC metabolism and memory scores. The metabolism in a control region, the primary motor cortex, showed no correlation with age or ACC metabolism. These findings motivate further research into aging-related ACC dysfunction to prevent, diagnose, and treat the decline in executive function associated with aging in the absence of known neurodegenerative diseases.\n\nSIGNIFICANCE STATEMENTThe pathophysiology of aging-related cognitive decline remains unclear but the anterior cingulate cortex (ACC), a major component of the anterior human attention system, shows decreasing metabolism that correlates with declining executive function despite otherwise intact cognition. Here, the relationships between ACC metabolism, age, executive function, and memory were examined using the large, public, ADNI database. Earlier findings were confirmed. In addition, ACC metabolism was found a mediator between age and executive function. In contrast, no correlation arose between memory and age or between memory and ACC metabolism. No correlations surfaced when using the metabolism of the right primary motor cortex as a control region. Development of preventive medicine and novel treatments will require elucidation of aging-related ACC pathophysiology requiring further research.

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↗