A regulatory pathway model of neuropsychological disruption in Havana syndrome

Introduction

In 2016 diplomatic personnel serving in Havana, Cuba, began reporting audible sensory phenomena paired with onset of complex and persistent neurological symptoms consistent with brain injury. The etiology of these Anomalous Health Incidents (AHI) and subsequent symptoms remains unknown. This report investigates putative exposure-symptom pathology by assembling a network model of published bio-behavioral pathways and assessing how dysregulation of such pathways might explain loss of function in these subjects using data available in the published literature. Given similarities in presentation with mild traumatic brain injury (mTBI), we used the latter as a clinically relevant means of evaluating if the neuropsychological profiles observed in Havana Syndrome Havana Syndrome might be explained at least in part by a dysregulation of neurotransmission, neuro-inflammation, or both.

Method

Automated text-mining of >9,000 publications produced a network consisting of 273 documented regulatory interactions linking 29 neuro-chemical markers with 9 neuropsychological constructs from the Brief Mood Survey, PTSD Checklist, and the Frontal Systems Behavior Scale. Analysis of information flow through this network produced a set of regulatory rules reconciling to within a 6% departure known mechanistic pathways with neuropsychological profiles in N = 6 subjects.

Results

Predicted expression of neuro-chemical markers that jointly satisfy documented pathways and observed symptom profiles display characteristically elevated IL-1B, IL-10, NGF, and norepinephrine levels in the context of depressed BDNF, GDNF, IGF1, and glutamate expression (FDR < 5%). Elevations in CRH and IL-6 were also predicted unanimously across all subjects. Furthermore, simulations of neurological regulatory dynamics reveal subjects do not appear to be "locked in" persistent illness but rather appear to be engaged in a slow recovery trajectory.

Discussion

This computational analysis of measured neuropsychological symptoms in Havana-based diplomats proposes that these AHI symptoms may be supported in part by disruption of known neuroimmune and neurotransmission regulatory mechanisms also associated with mTBI.

A - 6 Applying Artificial Intelligence Language Models for Knowledge Integration of Neuroimaging, Health Behavioral Assessment, and Clinical Intervention: Potential Impact on Brain Health in Special Operators

OBJECTIVE

Specialized military groups like Explosive Ordnance Disposal (EOD) personnel can suffer allostatic overload leading to complex health outcomes characterized as Operator Syndrome (OS). Here we (1) evaluate Brodmann's Areas (BA) brain regions associated with OS-related behavioral health symptoms then (2) explore how DoD/VA-recommended clinical interventions relate to these brain regions using large-scale automated text mining.

DATA SELECTION

Neuroimaging reported by Brodmann's Areas and 9 behavioral symptom measures and constructs of interest were extracted from full-text peer-reviewed publications using both rule-based and generative AI natural language processing (NLP) engines. The relationships linking BA regions with behavioral symptoms and the 7 interventions were assessed, with a focus on post-traumatic stress disorder (PTSD) and traumatic brain injury (TBI) severity.

DATA SYNTHESIS

The network connected behavioral health symptoms and 39 of 52 BA regions through 131 interactions. Co-occurring PTSD and TBI associations to BA (counting ≥3 citations) were detected with the precentral gyrus (BA4), premotor cortex and supplementary motor cortex (BA6), middle temporal gyrus (BA21), superior temporal gyrus (BA22), angular gyrus (BA39), and dorsolateral prefrontal cortex (BA46). Behavioral interventions of Cognitive Processing Therapy (CPT), Prolonged Exposure Therapy, Cognitive Behavioral Therapy were found to modulate BA 6, with CPT alone also modulating BA 46. Only the pharmacotherapy fluoxetine (SSRI) related to BA6.

CONCLUSIONS

NLP offers a rapid means of exploring complex injuries in Veterans. Future investigations will test these models using acquired data from EOD veterans and specific neuroimaging modalities (e.g., BOLD fMRI) to determine how they might improve understanding of health sequelae of environmental exposures and inform intervention.

Mapping the network biology of metabolic response to stress in posttraumatic stress disorder and obesity

The co-occurrence of stress-induced posttraumatic stress disorder (PTSD) and obesity is common, particularly among military personnel but the link between these conditions is unclear. Individuals with comorbid PTSD and obesity manifest other physical and psychological problems, which significantly diminish their quality of life. Current understanding of the pathways connecting stress to PTSD and obesity is focused largely on behavioral mediators alone with little consideration of the biological regulatory mechanisms that underlie their co-occurrence. In this work, we leverage prior knowledge to systematically highlight such bio-behavioral mechanisms and inform on the design of confirmatory pilot studies. We use natural language processing (NLP) to extract documented regulatory interactions involved in the metabolic response to stress and its impact on obesity and PTSD from over 8 million peer-reviewed papers. The resulting network describes the propagation of stress to PTSD and obesity through 34 metabolic mediators using 302 documented regulatory interactions supported by over 10,000 citations. Stress jointly affected both conditions through 21 distinct pathways involving only two intermediate metabolic mediators out of a total of 76 available paths through this network. Moreover, oxytocin (OXT), Neuropeptide-Y (NPY), and cortisol supported an almost direct propagation of stress to PTSD and obesity with different net effects. Although stress upregulated both NPY and cortisol, the downstream effects of both markers are reported to relieve PTSD severity but exacerbate obesity. The stress-mediated release of oxytocin, however, was found to concurrently downregulate the severity of both conditions. These findings highlight how a network-informed approach that leverages prior knowledge might be used effectively in identifying key mediators like OXT though experimental verification of signal transmission dynamics through each path will be needed to determine the actual likelihood and extent of each marker’s participation.

Network Modeling of Complex Time-Dependent Changes in Patient Adherence to Adjuvant Endocrine Treatment in ER+ Breast Cancer

Early patient discontinuation from adjuvant endocrine treatment (ET) is multifactorial and complex: Patients must adapt to various challenges and make the best decisions they can within changing contexts over time. Predictive models are needed that can account for the changing influence of multiple factors over time as well as decisional uncertainty due to incomplete data. AtlasTi8 analyses of longitudinal interview data from 82 estrogen receptor-positive (ER+) breast cancer patients generated a model conceptualizing patient-, patient-provider relationship, and treatment-related influences on early discontinuation. Prospective self-report data from validated psychometric measures were discretized and constrained into a decisional logic network to refine and validate the conceptual model. Minimal intervention set (MIS) optimization identified parsimonious intervention strategies that reversed discontinuation paths back to adherence. Logic network simulation produced 96 candidate decisional models which accounted for 75% of the coordinated changes in the 16 network nodes over time. Collectively the models supported 15 persistent end-states, all discontinued. The 15 end-states were characterized by median levels of general anxiety and low levels of perceived recurrence risk, quality of life (QoL) and ET side effects. MIS optimization identified 3 effective interventions: reducing general anxiety, reinforcing pill-taking routines, and increasing trust in healthcare providers. Increasing health literacy also improved adherence for patients without a college degree. Given complex regulatory networks’ intractability to end-state identification, the predictive models performed reasonably well in identifying specific discontinuation profiles and potentially effective interventions.

Increasing Resilience to Traumatic Stress: Understanding the Protective Role of Well-Being

The brain maintains homeostasis in part through a network of feedback and feed-forward mechanisms, where neurochemicals and immune markers act as mediators. Using a previously constructed model of biobehavioral feedback, we found that in addition to healthy equilibrium another stable regulatory program supported chronic depression and anxiety. Exploring mechanisms that might underlie the contributions of subjective well-being to improved therapeutic outcomes in depression, we iteratively screened 288 candidate feedback patterns linking well-being to molecular signaling networks for those that maintained the original homeostatic regimes. Simulating stressful trigger events on each candidate network while maintaining high levels of subjective well-being isolated a specific feedback network where well-being was promoted by dopamine and acetylcholine, and itself promoted norepinephrine while inhibiting cortisol expression. This biobehavioral feedback mechanism was especially effective in reproducing well-being's clinically documented ability to promote resilience and protect against onset of depression and anxiety.