Biology subjects

McInnis, M.

Publications and source records attributed to McInnis, M..

3 recordsLinked to original sources

bioRxiv2023

Longitudinal hair cortisol in bipolar disorder and a mechanism based on HPA dynamics

Bipolar disorder (BD) is a dynamic disease in which mania, depression and mixed states vary on a timescale of months to years. BD patients characteristically exhibit elevated levels of the hormone cortisol. Persistently elevated cortisol can also cause mood episodes in a substantial fraction of the general population. Although BD is a dynamic disease that is related to cortisol, longitudinal cortisol dynamics in BD have rarely been studied. Here we use hair to measure past cortisol where each cm of hair corresponds to a month of growth. Cortisol was measured in 12 cm hair samples from people with BD (n=26) and controls (n=59), corresponding to one year of cortisol data. We found that hair cortisol exhibited a frequency spectrum with enhanced year-scale fluctuations whose amplitude was about 4-fold higher on average in BD compared to controls. Cortisol in the proximal 2 cm hair segment correlated with mood scales that report on mood in the past two months. In line with the notion that cortisol correlates with mood, we find that the mean frequency spectrum of depression (n=266) and mania (n=273) scores from a large longitudinal study of BD is similar to the hair cortisol spectrum from the present cohort. Taken together, these results suggest a mechanism for BD as the intersection of two neuropsychological traits: cortisol-induced mood episodes (CIM) and high emotional reactivity (ER). High ER causes fluctuations in which cortisol is elevated for months, as shown by a mathematical model of the hypothalamic-pituitary-adrenal (HPA) axis that regulates cortisol. In individuals with CIM, the magnitude of these persistent cortisol fluctuations can be high enough to trigger mood episodes. Thus, this study combines longitudinal cortisol measurements and mathematical modeling to provide a potential mechanistic link between the timescales of cortisol and moods in BD.

systems biology↗

bioRxiv2019

Gene-Set Enrichment with Mathematical Biology (GEMB)

Gene-set analyses measure the association between a disease of interest and a set of genes related to a biological pathway. These analyses often incorporate gene network properties to account for the differential contributions of each gene. Extending this concept further, mathematical models of biology can be leveraged to define gene interactions based on biophysical principles by predicting the effects of genetic perturbations on a particular downstream function. We present a method that combines gene weights from model predictions and gene ranks from genome-wide association studies into a weighted gene-set test. Using publicly-available summary data from the Psychiatric Genetics Consortium (n=41,653; ~9) million SNPs), we examine an a priori hypothesis that intracellular calcium ion concentrations contribute to bipolar disorder. In this case study, we are able to strengthen inferences from a P-value of 0.081 to 1.7x10-4 by moving from a general calcium signaling pathway to a specific model-predicted function.

genetics↗

bioRxiv2017

Harmonization of cortical thickness measurements across scanners and sites

With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such unwanted sources of variation, which we refer to as \"scanner effects\", can hinder the detection of imaging features associated with clinical covariates of interest and cause spurious findings. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements, across a total of 11 scanners. We propose a set of general tools for visualizing and identifying scanner effects that are generalizable to other modalities. We then propose to use ComBat, a technique adopted from the genomics literature and recently applied to diffusion tensor imaging data, to combine and harmonize cortical thickness values across scanners. We show that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses. We also show that ComBat is useful for combining imaging data with the goal of studying life-span trajectories in the brain.

neuroscience↗