Biology subjects

Hagenauer, M. H.

Publications and source records attributed to Hagenauer, M. H..

4 recordsLinked to original sources

bioRxiv2017

Investigating circadian rhythmicity in pain sensitivity using a neural circuit model for spinal cord processing of pain

Primary processing of painful stimulation occurs in the dorsal horn of the spinal cord. In this article, we introduce mathematical models of the neural circuitry in the dorsal horn responsible for processing nerve fiber inputs from noxious stimulation of peripheral tissues and generating the resultant pain signal. The differential equation models describe the average firing rates of excitatory and inhibitory interneuron populations, as well as the wide dynamic range (WDR) neurons whose output correlates with the pain signal. The temporal profile of inputs on the different afferent nerve fibers that signal noxious and innocuous stimulation and the excitability properties of the included neuronal populations are constrained by experimental results. We consider models for the spinal cord circuit in isolation and when top-down inputs from higher brain areas that modulate pain processing are included. We validate the models by replicating experimentally observed phenomena of A fiber inhibition of pain and wind-up. We then use the models to investigate mechanisms for the observed phase shift in circadian rhythmicity of pain that occurs with neuropathic pain conditions. Our results suggest that changes in neuropathic pain rhythmicity can occur through dysregulation of inhibition within the dorsal horn circuit.

synthetic biology↗

bioRxiv2017

The Modulation of Pain by Circadian and Sleep-Dependent Processes: A Review of the Experimental Evidence

This proceedings paper is the first in a series of three papers developing mathematical models for the complex relationship between pain and the sleep-wake cycle. Here, we briefly review what is known about the relationship between pain and the sleep-wake cycle in humans and laboratory rodents in an effort to identify constraints for the models. While it is well accepted that sleep behavior is regulated by a daily (circadian) timekeeping system and homeostatic sleep drive, the joint modulation of these two primary biological processes on pain sensitivity has not been considered. Under experimental conditions, pain sensitivity varies across the 24 h day, with highest sensitivity occurring during the evening in humans. Pain sensitivity is also modulated by sleep behavior, with pain sensitivity increasing in response to the build up of homeostatic sleep pressure following sleep deprivation or sleep disruption. To explore the interaction between these two biological processes using modeling, we first compare the magnitude of their effects across a variety of experimental pain studies in humans. To do this comparison, we normalize the results from experimental pain studies relative to the range of physiologicallymeaningful stimulation levels. Following this normalization, we find that the estimated impact of the daily rhythm and of sleep deprivation on experimental pain measurements is surprisingly consistent across different pain modalities. We also review evidence documenting the impact of circadian rhythms and sleep deprivation on the neural circuitry in the spinal cord underlying pain sensation. The characterization of sleep-dependent and circadian influences on pain sensitivity in this review paper is used to develop and constrain the mathematical models introduced in the two companion articles.

neuroscience↗

bioRxiv2017

A Two-Process Model for Circadian and Sleep-dependent Modulation of Pain Sensitivity

Pain sensitivity is strongly modulated by time of day and by prior sleep behavior. These two factors, governed by the circadian rhythm and homeostatic sleep drive, respectively, likewise dictate the timing and duration of sleep. The fields of sleep and circadian research have identified much of the physiology underlying the circadian rhythm and homeostatic sleep drive with mathematical modeling playing an important role in understanding how these two processes interact to affect sleep behavior. We hypothesize that the daily rhythm of pain sensitivity and its sleep-dependent modulation reflect an interaction of the circadian rhythm and homeostatic sleep drive. To investigate this hypothesis, we adapt the formalism of a classic mathematical model for the regulation of sleep behavior by the circadian rhythm and homeostatic sleep drive, called the Two Process model, to simulate the interaction of these two processes on pain sensitivity. To construct the model, we utilize data from experimental reports on the daily rhythmicity of pain sensitivity in humans to define a \"daily pain sensitivity\" function. We decompose this function into two processes: a sleep-dependent process S(t) that follows the homeostatic sleep drive and a circadian process C(t) that is dictated by the circadian rhythm. By simulating different sleep schedules with the original Two Process model, we compute changes in the sleep-dependent process S(t) that modulates pain sensitivity. By combining S(t) with the circadian process C(t), our model predicts resultant changes in the daily pain sensitivity rhythm. We illustrate model predictions for changes in pain sensitivity due to sleep deprivation, sleep restriction and shift work schedules. We believe that this model may be a useful tool for pain management by providing predictions of the variations in pain sensitivity due to changing sleep schedules.

neuroscience↗

bioRxiv2016

INFERENCE OF CELL TYPE COMPOSITION FROM HUMAN BRAIN TRANSCRIPTOMIC DATASETS ILLUMINATES THE EFFECTS OF AGE, MANNER OF DEATH, DISSECTION, AND PSYCHIATRIC DIAGNOSIS

Psychiatric illness is unlikely to arise from pathology occurring uniformly across all cell types in affected brain regions. Despite this, transcriptomic analyses of the human brain have typically been conducted using macro-dissected tissue due to the difficulty of performing single-cell type analyses with donated post-mortem brains. To address this issue statistically, we compiled a database of several thousand transcripts that were specifically-enriched in one of 10 primary cortical cell types in previous publications. Using this database, we predicted the relative cell type composition for 833 human cortical samples using microarray or RNA-Seq data from the Pritzker Consortium (GSE92538) or publicly-available databases (GSE53987, GSE21935, GSE21138, CommonMind Consortium). These predictions were generated by averaging normalized expression levels across transcripts specific to each cell type using our R-package BrainInABlender (validated and publicly-released: https://github.com/hagenaue/BrainInABlender). Using this method, we found that the principal components of variation in the datasets strongly correlated with the neuron to glia ratio of the samples.\n\nThis variability was not simply due to dissection - the relative balance of brain cell types appeared to be influenced by a variety of demographic, pre- and post-mortem variables. Prolonged hypoxia around the time of death predicted increased astrocytic and endothelial gene expression, illustrating vascular upregulation. Aging was associated with decreased neuronal gene expression. Red blood cell gene expression was reduced in individuals who died following systemic blood loss. Subjects with Major Depressive Disorder had decreased astrocytic gene expression, mirroring previous morphometric observations. Subjects with Schizophrenia had reduced red blood cell gene expression, resembling the hypofrontality detected in fMRI experiments. Finally, in datasets containing samples with especially variable cell content, we found that controlling for predicted sample cell content while evaluating differential expression improved the detection of previously-identified psychiatric effects. We conclude that accounting for cell type can greatly improve the interpretability of transcriptomic data.

bioinformatics↗