Agent-based modeling of the central amygdala and pain using cell-type specific physiological parameters

Chronic Neuropathic Pain and Comorbid Depression Syndrome: From Neural Circuit Mechanisms to Treatment

Chronic neuropathic pain and comorbid depression syndrome (CDS) is a major worldwide health problem that affects the quality of life of patients and imposes a tremendous socioeconomic burden. More than half of patients with chronic neuropathic pain also suffer from moderate or severe depression. Due to the complex pathogenesis of CDS, there are no effective therapeutic drugs available. The lack of research on the neural circuit mechanisms of CDS limits the development of treatments. The purpose of this article is to provide an overview of the various circuits involved in CDS. Notably, activating some neural circuits can alleviate pain and/or depression, while activating other circuits can exacerbate these conditions. Moreover, we discuss current and emerging pharmacotherapies for CDS, such as ketamine. Understanding the circuit mechanisms of CDS may provide clues for the development of novel drug treatments for improved CDS management.

Improved Efficiency and Sensitivity Analysis of 3-D Agent-based Model for Pain-related Neural Activity in the Amygdala

Neuropathic pain is caused by nerve injury and involves brain areas such as the central nucleus of the amygdala (CeA). We developed the first 3-D agent-based model (ABM) of neuropathic pain-related neurons in the CeA using NetLogo3D. The execution time of a single ABM simulation using realistic parameters (e.g., 13,000 neurons and 22,000+ neural connections) is an important factor in the model's usability. In this paper, we describe our efforts to improve the computational efficiency of our 3-D ABM, which resulted in a 28% reduction in execution time on average for a typical simulation. With this upgraded model, we performed one- and two-parameter sensitivity analyses to study the sensitivity of model output to variability in several key parameters along the anterior to posterior axis of the CeA. These results highlight the importance of computational modeling in exploring spatial and cell-type specific properties of brain regions to inform future wet lab experiments.

Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.

An Agent-Based Model to Reproduce the Boolean Logic Behaviour of Neuronal Self-Organised Communities through Pulse Delay Modulation and Generation of Logic Gates

The human brain is arguably the most complex "machine" to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain's structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence frameworks inspired by its logical functioning. In this article, an approach to model some aspects of the brain learning and signal processing is presented, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model to demonstrate how dynamic neuroplasticity, neural inhibition and neuron migration can reshape the brain's logical connectivity to synchronise signal processing and obtain certain target latencies. This work showcases the importance of dynamic logical and biophysical remodelling in brain plasticity. Combining mathematical (agents, graph theory, topology and backpropagation) and biomedical ingredients (metastability, neuroplasticity and migration), these preliminary results prove complex brain phenomena can be reproduced-under pertinent simplifications-via affordable computations, which can be construed as a starting point for more ambitiously accurate simulations.

The role of pain modulation pathway and related brain regions in pain

Pain is a multifaceted process that encompasses unpleasant sensory and emotional experiences. The essence of the pain process is aversion, or perceived negative emotion. Central sensitization plays a significant role in initiating and perpetuating of chronic pain. Melzack proposed the concept of the "pain matrix", in which brain regions associated with pain form an interconnected network, rather than being controlled by a singular brain region. This review aims to investigate distinct brain regions involved in pain and their interconnections. In addition, it also sheds light on the reciprocal connectivity between the ascending and descending pathways that participate in pain modulation. We review the involvement of various brain areas during pain and focus on understanding the connections among them, which can contribute to a better understanding of pain mechanisms and provide opportunities for further research on therapies for improved pain management.

Exercise therapy for chronic pain: How does exercise change the limbic brain function?

We are exposed to various external and internal threats which might hurt us. The role of taking flexible and appropriate actions against threats is played by "the limbic system" and at the heart of it there is the ventral tegmental area and nucleus accumbens (brain reward system). Pain-related fear causes excessive excitation of amygdala, which in turn causes the suppression of medial prefrontal cortex, leading to chronification of pain. Since the limbic system of chronic pain patients is functionally impaired, they are maladaptive to their situations, unable to take goal-directed behavior and are easily caught by fear-avoidance thinking. We describe the neural mechanisms how exercise activates the brain reward system and enables chronic pain patients to take goal-directed behavior and overcome fear-avoidance thinking. A key to getting out from chronic pain state is to take advantage of the behavioral switching function of the basal nucleus of amygdala. We show that exercise activates positive neurons in this nucleus which project to the nucleus accumbens and promote reward behavior. We also describe fear conditioning and extinction are affected by exercise. In chronic pain patients, the fear response to pain is enhanced and the extinction of fear memories is impaired, so it is difficult to get out of "fear-avoidance thinking". Prolonged avoidance of movement and physical inactivity exacerbate pain and have detrimental effects on the musculoskeletal and cardiovascular systems. Based on the recent findings on multiple bran networks, we propose a well-balanced exercise prescription considering the adherence and pacing of exercise practice. We conclude that therapies targeting the mesocortico-limbic system, such as exercise therapy and cognitive behavioral therapy, may become promising tools in the fight against chronic pain.

Developing a 3-D computational model of neurons in the central amygdala to understand pharmacological targets for pain

Neuropathic and nociplastic pain are major causes of pain and involve brain areas such as the central nucleus of the amygdala (CeA). Within the CeA, neurons expressing protein kinase c-delta (PKCδ) or somatostatin (SST) have opposing roles in pain-like modulation. In this manuscript, we describe our progress towards developing a 3-D computational model of PKCδ and SST neurons in the CeA and the use of this model to explore the pharmacological targeting of these two neural populations in modulating nociception. Our 3-D model expands upon our existing 2-D computational framework by including a realistic 3-D spatial representation of the CeA and its subnuclei and a network of directed links that preserves morphological properties of PKCδ and SST neurons. The model consists of 13,000 neurons with cell-type specific properties and behaviors estimated from laboratory data. During each model time step, neuron firing rates are updated based on an external stimulus, inhibitory signals are transmitted between neurons via the network, and a measure of nociceptive output from the CeA is calculated as the difference in firing rates of pro-nociceptive PKCδ neurons and anti-nociceptive SST neurons. Model simulations were conducted to explore differences in output for three different spatial distributions of PKCδ and SST neurons. Our results show that the localization of these neuron populations within CeA subnuclei is a key parameter in identifying spatial and cell-type pharmacological targets for pain.

Synaptic plasticity in two cell types of central amygdala for regulation of emotion and pain

The amygdala is a critical brain site for regulation of emotion-associated behaviors such as pain and anxiety. Recent studies suggest that differential cell types and synaptic circuits within the amygdala complex mediate interacting and opposing effects on emotion and pain. However, the underlying cellular and circuit mechanisms are poorly understood at present. Here we used optogenetics combined with electrophysiological analysis of synaptic inputs to investigate pain-induced synaptic plasticity within the amygdala circuits in rats. We found that 50% of the cell population in the lateral division of the central nucleus of the amygdala (CeAl) received glutamate inputs from both basolateral amygdala (BLA) and from the parabrachial nucleus (PBN), and 39% of the remaining CeAl cells received glutamate inputs only from PBN. Inflammatory pain lasting 3 days, which induced anxiety, produced sensitization in synaptic activities of the BLA-CeAl-medial division of CeA (CeAm) pathway primarily through a postsynaptic mechanism. Moreover, in CeAl cells receiving only PBN inputs, pain significantly augmented the synaptic strength of the PBN inputs. In contrast, in CeAl cells receiving both BLA and PBN inputs, pain selectively increased the synaptic strength of BLA inputs, but not the PBN inputs. Electrophysiological analysis of synaptic currents showed that the increased synaptic strength in both cases involved a postsynaptic mechanism. These findings reveal two main populations of CeAl cells that have differential profiles of synaptic inputs and show distinct plasticity in their inputs in response to anxiety-associated pain, suggesting that the specific input plasticity in the two populations of CeAl cells may encode a different role in amygdala regulation of pain and emotion.

Mentoring Undergraduate Research in Mathematical Modeling

In writing about undergraduate research in mathematical modeling, I draw on my 31 years as a mathematics professor at the University of Nebraska-Lincoln, where I mentored students in honors' theses, REU groups, and research done in a classroom setting, as well as my prior experience. I share my views on the differences between research at the undergraduate and professional levels, offer advice for undergraduate mentoring, provide suggestions for a variety of ways that students can disseminate their research, offer some thoughts on mathematical modeling and how to explain it to undergraduates, and discuss the challenges involved in broadening research participation to include early career students and mid-tier students and how to deliver a research experience in a classroom setting. While different situations pose different challenges, different problems require different approaches, and different experiences lead to different conclusions, it is my hope that my experiences will be of broad value to a wide audience.

Integration of Adenylate Kinase 1 with Its Peptide Conformational Imprint

In the present study, molecularly imprinted polymers (MIPs) were used as a tool to grasp a targeted α-helix or β-sheet of protein. During the fabrication of the hinge-mediated MIPs, elegant cavities took shape in a special solvent on quartz crystal microbalance (QCM) chips. The cavities, which were complementary to the protein secondary structure, acted as a peptide conformational imprint (PCI) for adenylate kinase 1 (AK1). We established a promising strategy to examine the binding affinities of human AK1 in conformational dynamics using the peptide-imprinting method. Moreover, when bound to AK1, PCIs are able to gain stability and tend to maintain higher catalytic activities than free AK1. Such designed fixations not only act on hinges as accelerators; some are also inhibitors. One example of PCI inhibition of AK1 catalytic activity takes place when PCI integrates with an AK1_9-23 β-sheet. In addition, conformation ties, a general MIP method derived from random-coil AK1_133-144 in buffer/acetonitrile, are also inhibitors. The inhibition may be due to the need for this peptide to execute conformational transition during catalysis.