Modeling the dynamics of disease states in depression

Major depressive disorder and bistability in an HPA-CNS toggle switch

Major depressive disorder (MDD) is the most common psychiatric disorder. It has a complex and heterogeneous etiology. Most treatments take weeks to show effects and work well only for a fraction of the patients. Thus, new concepts are needed to understand MDD and its dynamics. One of the strong correlates of MDD is increased activity and dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis which produces the stress hormone cortisol. Existing mathematical models of the HPA axis describe its operation on the scale of hours, and thus are unable to explore the dynamic on the scale of weeks that characterizes many aspects of MDD. Here, we propose a mathematical model of MDD on the scale of weeks, a timescale provided by the growth of the HPA hormone glands under control of HPA hormones. We add to this the mutual inhibition of the HPA axis and the hippocampus and other regions of the central nervous system (CNS) that forms a toggle switch. The model shows bistability between euthymic and depressed states, with a slow timescale of weeks in its dynamics. It explains why prolonged but not acute stress can trigger a self-sustaining depressive episode that persists even after the stress is removed. The model explains the weeks timescale for drugs to take effect, as well as the dysregulation of the HPA axis in MDD, based on gland mass changes. This understanding of MDD dynamics may help to guide strategies for treatment.

Mathematical models of cystic fibrosis as a systemic disease

Cystic fibrosis (CF) is widely known as a disease of the lung, even though it is in truth a systemic disease, whose symptoms typically manifest in gastrointestinal dysfunction first. CF ultimately impairs not only the pancreas and intestine but also the lungs, gonads, liver, kidneys, bones, and the cardiovascular system. It is caused by one of several mutations in the gene of the epithelial ion channel protein CFTR. Intense research and improved antimicrobial treatments during the past eight decades have steadily increased the predicted life expectancy of a person with CF (pwCF) from a few weeks to over 50 years. Moreover, several drugs ameliorating the sequelae of the disease have become available in recent years, and notable treatments of the root cause of the disease have recently generated substantial improvements in health for some but not all pwCF. Yet, numerous fundamental questions remain unanswered. Complicating CF, for instance in the lung, is the fact that the associated insufficient chloride secretion typically perturbs the electrochemical balance across epithelia and, in the airways, leads to the accumulation of thick, viscous mucus and mucus plaques that cannot be cleared effectively and provide a rich breeding ground for a spectrum of bacterial and fungal communities. The subsequent infections often become chronic and respond poorly to antibiotic treatments, with outcomes sometimes only weakly correlated with the drug susceptibility of the target pathogen. Furthermore, in contrast to rapidly resolved acute infections with a single target pathogen, chronic infections commonly involve multi-species bacterial communities, called "infection microbiomes," that develop their own ecological and evolutionary dynamics. It is presently impossible to devise mathematical models of CF in its entirety, but it is feasible to design models for many of the distinct drivers of the disease. Building upon these growing yet isolated modeling efforts, we discuss in the following the feasibility of a multi-scale modeling framework, known as template-and-anchor modeling, that allows the gradual integration of refined sub-models with different granularity. The article first reviews the most important biomedical aspects of CF and subsequently describes mathematical modeling approaches that already exist or have the potential to deepen our understanding of the multitude aspects of the disease and their interrelationships. The conceptual ideas behind the approaches proposed here do not only pertain to CF but are translatable to other systemic diseases. This article is categorized under: Congenital Diseases > Computational Models.

Dynamical systems in computational psychiatry: A toy-model to apprehend the dynamics of psychiatric symptoms

Introduction

These last years, scientific research focuses on the dynamical aspects of psychiatric disorders and their clinical significance. In this article, we proposed a theoretical framework formalized as a generic mathematical model capturing the heterogeneous individual evolutions of psychiatric symptoms. The first goal of this computational model based on differential equations is to illustrate the nonlinear dynamics of psychiatric symptoms. It offers an original approach to nonlinear dynamics to clinical psychiatrists.

Methods

In this study, we propose a 3+1 dimensions model (x, y, z + f) reproducing the clinical observations encountered in clinical psychiatry with: a variable modeling environmental noise (z) on the patient's internal factors (y) with its temporal specificities (f) and symptomatology (x). This toy-model is able to integrate empirical or simulated data from the influence of perceived environmental over time, their potential importance on the internal and subjective patient-specific elements, and their interaction with the apparent intensity of symptoms.

Results

Constrained by clinical observation of case formulations, the dynamics of psychiatric symptoms is studied through four main psychiatric conditions were modeled: i) a healthy situation, ii) a kind of psychiatric disorder evolving following an outbreak (i.e., schizophrenia spectrum), iii) a kind of psychiatric disorder evolving by kindling and bursts (e.g., bipolar and related disorders); iv) and a kind of psychiatric disorder evolving due to its high susceptibility to the environment (e.g., spersistent complex bereavement disorder). Moreover, we simulate the action of treatments on different psychiatric conditions.

Discussion

We show that the challenges of dynamical systems allow to understand the interactions of psychiatric symptoms with environmental, descriptive, subjective or biological variables. Although this non-linear dynamical model has limitations (e.g., explanatory scope or discriminant validity), simulations provide at least five main interests for clinical psychiatry, such as a visualization of the potential different evolution of psychiatric disorders, formulation of clinical cases, information about attracting states and bifurcations, or the possibility of a nosological refinement of psychiatric models (e.g., staging and symptom network models).

Mathematical Model of Interaction of Therapist and Patients with Bipolar Disorder: A Systematic Literature Review

Mood swings in patients with bipolar disorder (BD) are difficult to control and can lead to self-harm and suicide. The interaction between the therapist and BD will determine the success of therapy. The interaction model between the therapist and BD begins by reviewing the models that were previously developed using the Systematic Literature Review and Bibliometric methods. The limit of articles used was sourced from the Science Direct, Google Scholar, and Dimensions databases from 2009 to 2022. The results obtained were 67 articles out of a total of 382 articles, which were then re-selected. The results of the selection of the last articles reviewed were 52 articles. Using VOSviewer version 1.6.16, a visualization of the relationship between the quotes “model”, “therapy”, “emotions”, and “bipolar disorder” can be seen. This study also discusses the types of therapy that can be used by BD, as well as treatment innovations and the mathematical model of the therapy itself. The results of this study are expected to help further researchers to develop an interaction model between therapists and BD to improve the quality of life of BD.

Enactive and simondonian reflections on mental disorders

As an alternative to linear and unidimensional perspectives focused mainly on either organic or psychological processes, the enactive approach to life and mind-a branch of 4-E (embodied, embedded, enactive, extended) cognitive theories-offers an integrative framework to study mental disorders that encompasses and articulates organic, sensorimotor, and intersubjective dimensions of embodiment. These three domains are deeply entangled in a non-trivial manner. A question remains on how this systemic and multi-dimensional approach may be applied to our understanding of mental disorders and symptomatic behavior. Drawing on Gilbert Simondon's philosophy of individuation (focusing particularly on the concepts of tension, metastability, and preindividual), we provide some enactive conceptual tools to better understand the dynamic, interactive, and multi-dimensional nature of human bodies in mental disorders and psychopathological symptoms. One of such tools cursiva is sense-making, a key notion that captures the relational process of generating meaning by interacting with the sociomaterial environment. The article analyzes five aspects related to sense-making: temporality, adaptivity, the multiplicity of normativities it involves, the fundamental role of tension, and its participatory character. On this basis, we draw certain implications for our understanding of mental disorders and diverse symptoms, and suggest their interpretation in terms of difficulties to transform tensions and perform individuation processes, which result in a reduction of the field of potentialities for self-individuation and sense-making.

Markov modelling of treatment response in a 30-year cohort study of newly diagnosed epilepsy

People with epilepsy have variable and dynamic trajectories in response to antiseizure medications. Accurately modelling long-term treatment response will aid prognostication at the individual level and health resource planning at the societal level. Unfortunately, a robust model is lacking. We aimed to develop a Markov model to predict the probability of future seizure-freedom based on current seizure state and number of antiseizure medication regimens trialled. We included 1,795 people with newly diagnosed epilepsy who attended a specialist clinic in Glasgow, Scotland, between July 1982 and October 2012. They were followed up until October 2014 or death. We developed a simple Markov model, based on current seizure state only, and a more detailed model, based on both current seizure state and number of antiseizure medication regimens trialled. Sensitivity analyses were performed for the regimen-based states model to examine the effect of regimen changes due to adverse effects. The model was externally validated in a separate cohort of 455 newly diagnosis epilepsy patients seen in Perth, Australia, between May 1999 and May 2016. Our models suggested that once seizure-freedom was achieved, it was likely to persist, regardless of the number of antiseizure medications trialled to reach that point. The likelihood of achieving long-term seizure-freedom was highest with the first antiseizure medication regimen, at approximately 50%. The chance of achieving seizure-freedom fell with subsequent regimens. Fluctuations between seizure-free and not seizure-free states were highest earlier on, but decreased with chronicity of epilepsy. Seizure-freedom/recurrence risk tables were constructed with these probability data, similar to cardiovascular risk tables. Sensitivity analyses showed that the general trends and conclusions from the base model were maintained despite perturbing the model and input data with regimen changes due to adverse effects. Quantitative comparison with the external validation cohort showed excellent consistency at year 1, good at year 3 and moderate at year 5. Quantitative models, as used in this study, can provide pertinent clinical insights that are not apparent from simple statistical analysis alone. Attaining seizure freedom at any time in a patient's epilepsy journey will confer durable benefit. Seizure-freedom risk tables may be used to individualise the prediction of future seizure control trajectory.

A learning algorithm for predicting mental health symptoms and substance use

Learning health systems use data to generate knowledge that informs clinical care, but few studies have evaluated how to leverage patient-reported mental health symptoms and substance use data to make patient-specific predictions. We developed a general Bayesian prediction algorithm that uses self-reported psychiatric symptoms and substance use within a population to predict future symptoms and substance use for individuals in that population. We validated our approach in 2444 participants from two clinical cohorts - the National Network of Depression Centers and the Johns Hopkins HIV Clinical Cohort - by predicting symptoms of depression, anxiety, and mania as well as alcohol, heroin, and cocaine use and comparing our predictions to observed symptoms and substance use. When we dichotomized mental health symptoms as moderate-severe vs. none-mild, individual predictions yielded areas under the ROC curve (AUCs) of 0.84 [95% confidence interval 0.80-0.88] and 0.85 [0.82-0.88] for symptoms of depression in the two cohorts, AUCs of 0.84 [0.79-0.88] and 0.85 [0.82-0.88] for symptoms of anxiety, and an AUC of 0.77 [0.72-0.82] for manic symptoms. Predictions of substance use yielded an AUC of 0.92 [0.88-0.97] for heroin use, 0.90 [0.82-0.97] for cocaine use, and 0.90 [0.88-092] for alcohol misuse. This rigorous, mathematically grounded approach could provide patient-specific predictions at the point of care. It can be applied to other psychiatric symptoms and substance use indicators, and is customizable to specific health systems. Such approaches can realize the potential of a learning health system to transform ever-increasing quantities of data into tangible guidance for patient care.

Stability of clinically relevant depression symptoms in old-age across 11 cohorts: a multi-state study

AIMS

To study the temporal dynamics of depression symptom episodes in old-age and the related influence of risk factors.

METHODS

Data from 41 362 old adults (54.61% women; mean age = 75.30, SD = 6.20) from the Ageing Trajectories of Health - Longitudinal Opportunities and Synergies (ATHLOS) project were used. Depressive symptoms were followed over an 18-year period. A multi-state model, comprising three statuses (no depression, new clinically relevant episode of symptoms and episode persistence), was fitted. Multinomial regression was used to study the role of risk factors in status transition.

RESULTS

Almost 85% of participants showed no depression, but prevalence became lower over time (B = -0.25, P < 0.001). New episode point prevalence was over 5.30% with a significant probability of moving to persistence status (transition probability = 0.27). Episode persistence became evident in 9.86% of episode status transitions, with increasing rate over time (B = 0.54, P < 0.01). Loneliness was proven to be the strongest predictor of episode emergence (OR = 17.76) and persistence (OR = 5.93).

CONCLUSIONS

The course of depression tends to become chronic and unremitting in old-age. This study may help to plan interventions to tackle symptom escalation and risk factor influence.

The reduction of adult neurogenesis in depression impairs the retrieval of new as well as remote episodic memory

Major depressive disorder (MDD) is associated with an impairment of episodic memory, but the mechanisms underlying this deficit remain unclear. Animal models of MDD find impaired adult neurogenesis (AN) in the dentate gyrus (DG), and AN in DG has been suggested to play a critical role in reducing the interference between overlapping memories through pattern separation. Here, we study the effect of reduced AN in MDD on the accuracy of episodic memory using computational modeling. We focus on how memory is affected when periods with a normal rate of AN (asymptomatic states) alternate with periods with a low rate (depressive episodes), which has never been studied before. Also, unlike previous models of adult neurogenesis, which consider memories as static patterns, we model episodic memory as sequences of neural activity patterns. In our model, AN adds additional random components to the memory patterns, which results in the decorrelation of similar patterns. Consistent with previous studies, higher rates of AN lead to higher memory accuracy in our model, which implies that memories stored in the depressive state are impaired. Intriguingly, our model makes the novel prediction that memories stored in an earlier asymptomatic state are also impaired by a later depressive episode. This retrograde effect exacerbates with increased duration of the depressive episode. Finally, pattern separation at the sensory processing stage does not improve, but rather worsens, the accuracy of episodic memory retrieval, suggesting an explanation for why AN is found in brain areas serving memory rather than sensory function. In conclusion, while cognitive retrieval biases might contribute to episodic memory deficits in MDD, our model suggests a mechanistic explanation that affects all episodic memories, regardless of emotional relevance.

Predictive modeling in e-mental health: A common language framework

Recent developments in mobile technology, sensor devices, and artificial intelligence have created new opportunities for mental health care research. Enabled by large datasets collected in e-mental health research and practice, clinical researchers and members of the data mining community increasingly join forces to build predictive models for health monitoring, treatment selection, and treatment personalization. This paper aims to bridge the historical and conceptual gaps between the distant research domains involved in this new collaborative research by providing a conceptual model of common research goals. We first provide a brief overview of the data mining field and methods used for predictive modeling. Next, we propose to characterize predictive modeling research in mental health care on three dimensions: 1) time, relative to treatment (i.e., from screening to post-treatment relapse monitoring), 2) types of available data (e.g., questionnaire data, ecological momentary assessments, smartphone sensor data), and 3) type of clinical decision (i.e., whether data are used for screening purposes, treatment selection or treatment personalization). Building on these three dimensions, we introduce a framework that identifies four model types that can be used to classify existing and future research and applications. To illustrate this, we use the framework to classify and discuss published predictive modeling mental health research. Finally, in the discussion, we reflect on the next steps that are required to drive forward this promising new interdisciplinary field.

Psychotherapy Is Chaotic-(Not Only) in a Computational World

Objective: The aim of this article is to outline the role of chaotic dynamics in psychotherapy. Besides some empirical findings of chaos at different time scales, the focus is on theoretical modeling of change processes explaining and simulating chaotic dynamics. It will be illustrated how some common factors of psychotherapeutic change and psychological hypotheses on motivation, emotion regulation, and information processing of the client's functioning can be integrated into a comprehensive nonlinear model of human change processes.

Methods: The model combines 5 variables (intensity of emotions, problem intensity, motivation to change, insight and new perspectives, therapeutic success) and 4 parameters into a set of 5 coupled nonlinear difference equations. The results of these simulations are presented as time series, as phase space embedding of these time series (i.e., attractors), and as bifurcation diagrams.

Results: The model creates chaotic dynamics, phase transition-like phenomena, bi- or multi-stability, and sensibility of the dynamic patterns on parameter drift. These features are predicted by chaos theory and by Synergetics and correspond to empirical findings. The spectrum of these behaviors illustrates the complexity of psychotherapeutic processes.

Conclusion: The model contributes to the development of an integrative conceptualization of psychotherapy. It is consistent with the state of scientific knowledge of common factors, as well as other psychological topics, such as: motivation, emotion regulation, and cognitive processing. The role of chaos theory is underpinned, not only in the world of computer simulations, but also in practice. In practice, chaos demands technologies capable of real-time monitoring and reporting on the nonlinear features of the ongoing process (e.g., its stability or instability). Based on this monitoring, a client-centered, continuous, and cooperative process of feedback and control becomes possible. By contrast, restricted predictability and spontaneous changes challenge the usefulness of prescriptive treatment manuals or other predefined programs of psychotherapy.

Network destabilization and transition in depression: New methods for studying the dynamics of therapeutic change

The science of dynamic systems is the study of pattern formation and system change. Dynamic systems theory can provide a useful framework for understanding the chronicity of depression and its treatment. We propose a working model of therapeutic change with potential to organize findings from psychopathology and treatment research, suggest new ways to study change, facilitate comparisons across studies, and stimulate treatment innovation. We describe a treatment for depression that we developed to apply principles from dynamic systems theory and then present a program of research to examine the utility of this application. Recent methodological and technological developments are also discussed to further advance the search for mechanisms of therapeutic change.