A Novel Approach to Identifying a Neuroimaging Biomarker for Patients With Serious Mental Illness

Topological data analysis for revealing dynamic brain reconfiguration in MEG data

In recent years, the focus of the functional connectivity community has shifted from stationary approaches to the ones that include temporal dynamics. Especially, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)) with high temporal resolution and good spatial coverage have made it possible to measure the fast alterations in the neural activity in the brain during ongoing cognition. In this article, we analyze dynamic brain reconfiguration using MEG images collected from subjects during the rest and the cognitive tasks. Our proposed topological data analysis method, called Mapper, produces biomarkers that differentiate cognitive tasks without prior spatial and temporal collapse of the data. The suggested method provides an interactive visualization of the rapid fluctuations in electrophysiological data during motor and cognitive tasks; hence, it has the potential to extract clinically relevant information at an individual level without temporal and spatial collapse.

History of childhood physical abuse is associated with gut microbiota diversity among adult psychiatric inpatients

BACKGROUND

Traumatic life events are associated with the development of psychiatric and chronic medical illnesses. This exploratory study examined the relationship between traumatic life events and the gut microbiota among adult psychiatric inpatients.

METHODS

105 adult psychiatric inpatients provided clinical data and a single fecal sample shortly after admission. A modified version of the Stressful Life Events Screening Questionnaire was used to quantify history of traumatic life events. 16S rRNA gene sequencing was used to analyze the gut microbial community.

RESULTS

Gut microbiota diversity was not associated with overall trauma score or any of the three trauma factor scores. Upon item-level analysis, history of childhood physical abuse was uniquely associated with beta diversity. Linear Discriminant Analysis Effect Size (LefSe) analyses revealed that childhood physical abuse was associated with abundance of distinct bacterial taxa associated with inflammation.

LIMITATIONS

This study did not account for dietary differences, though diet was highly restricted as all participants were psychiatric inpatients. Absolute variance accounted for by the taxa was small though practically meaningful. The study was not powered for full subgroup analysis based on race and ethnicity.

CONCLUSIONS

This study is among the first to demonstrate a relationship between childhood physical abuse and gut microbiota composition among adult psychiatric patients. These findings suggest that early childhood adverse events may have long-conferred systemic consequences. Future efforts may target the gut microbiota for the prevention and/or treatment of psychiatric and medical risk associated with traumatic life events.

Differential co-expression networks of the gut microbiota are associated with depression and anxiety treatment resistance among psychiatric inpatients

BACKGROUND

Comorbid anxiety and depression are common and are associated with greater disease burden than either alone. Our recent efforts have identified an association between gut microbiota dysfunction and severity of anxiety and depression. In this follow-up, we applied Differential Co-Expression Analysis (DiffCoEx) to identify potential gut microbiota biomarker(s) candidates of treatment resistance among psychiatric inpatients.

METHODS

In a sample of convenience, 100 psychiatric inpatients provided clinical data at admission and discharge; fecal samples were collected early during the hospitalization. Whole genome shotgun sequencing methods were used to process samples. DiffCoEx was used to identify clusters of microbial features significantly different based on treatment resistance status. Once overlapping features were identified, a knowledge-mining tool was used to review the literature using a list of microbial species/pathways and a select number of medical subject headlines (MeSH) terms relevant for depression, anxiety, and brain-gut-axis dysregulation. Network analysis used overlapping features to identify microbial interactions that could impact treatment resistance.

RESULTS

DiffCoEx analyzed 10,403 bacterial features: 43/44 microbial features associated with depression treatment resistance overlapped with 43/114 microbial features associated with anxiety treatment resistance. Network analysis resulted in 8 biological interactions between 16 bacterial species. Clostridium perfringens evidenced the highest connection strength (0.95). Erysipelotrichaceae bacterium 6_1_45 has been most widely examined, is associated with inflammation and dysbiosis, but has not been associated with depression or anxiety.

CONCLUSION

DiffCoEx potentially identified gut bacteria biomarker candidates of depression and anxiety treatment-resistance. Future efforts in psychiatric microbiology should examine the mechanistic relationship of identified pro-inflammatory species, potentially contributing to a biomarker-based algorithm for treatment resistance.

Topological data analysis in biomedicine: A review

Significant technological advances made in recent years have shepherded a dramatic increase in utilization of digital technologies for biomedicine- everything from the widespread use of electronic health records to improved medical imaging capabilities and the rising ubiquity of genomic sequencing contribute to a "digitization" of biomedical research and clinical care. With this shift toward computerized tools comes a dramatic increase in the amount of available data, and current tools for data analysis capable of extracting meaningful knowledge from this wealth of information have yet to catch up. This article seeks to provide an overview of emerging mathematical methods with the potential to improve the abilities of clinicians and researchers to analyze biomedical data, but may be hindered from doing so by a lack of conceptual accessibility and awareness in the life sciences research community. In particular, we focus on topological data analysis (TDA), a set of methods grounded in the mathematical field of algebraic topology that seeks to describe and harness features related to the "shape" of data. We aim to make such techniques more approachable to non-mathematicians by providing a conceptual discussion of their theoretical foundations followed by a survey of their published applications to scientific research. Finally, we discuss the limitations of these methods and suggest potential avenues for future work integrating mathematical tools into clinical care and biomedical informatics.

Improving Psychiatric Care Through Integrated Digital Technologies

This manuscript provides an overview of our efforts to implement an integrated electronic monitoring and feedback platform to increase patient engagement, improve care delivery and outcome of treatment, and alert care teams to deterioration in functioning. Patients First utilizes CareSense, a digital care navigation and data collection system, to integrate traditional patient-reported outcomes monitoring with novel biological monitoring between visits to provide patients and caregivers with real-time feedback on changes in symptoms such as stress, anxiety, and depression. The next stage of project development incorporates digital therapeutics (computerized therapeutic interventions) for patients, and video resources for primary care physicians and nurse practitioners who serve as the de facto front line for psychiatric care. Integration of the patient-reported outcomes monitoring with continuous biological monitoring, and digital supports is a novel application of existing technologies. Video resources pushed to care providers whose patients trigger a symptom severity alert is, to our knowledge, an industry first.

"Operator syndrome": A unique constellation of medical and behavioral health-care needs of military special operation forces

OBJECTIVE

U.S. military special operation forces represent the most elite units of the U.S. Armed Forces. Their selection is highly competitive, and over the course of their service careers, they experience intensive operational training and combat deployment cycles. Yet, little is known about the health-care needs of this unique population.

METHOD

Professional consultations with over 50 special operation forces operators (and many spouses or girlfriends) over the past 6 years created a naturalistic, observational base of knowledge that allowed our team to identify a unique pattern of interrelated medical and behavioral health-care needs.

RESULTS

We identified a consistent pattern of health-care difficulties within the special operation forces community that we and other special operation forces health-care providers have termed "Operator Syndrome." This includes interrelated health and functional impairments including traumatic brain injury effects; endocrine dysfunction; sleep disturbance; obstructive sleep apnea; chronic joint/back pain, orthopedic problems, and headaches; substance abuse; depression and suicide; anger; worry, rumination, and stress reactivity; marital, family, and community dysfunction; problems with sexual health and intimacy; being "on guard" or hypervigilant; memory, concentration, and cognitive impairments; vestibular and vision impairments; challenges of the transition from military to civilian life; and common existential issues.

CONCLUSIONS

"Operator Syndrome" may be understood as the natural consequences of an extraordinarily high allostatic load; the accumulation of physiological, neural, and neuroendocrine responses resulting from the prolonged chronic stress; and physical demands of a career with the military special forces. Clinical research and comprehensive, intensive immersion programs are needed to meet the unique needs of this community.

The gut microbiota is associated with psychiatric symptom severity and treatment outcome among individuals with serious mental illness

BACKGROUND

Emerging evidence implicates the gut microbiota in central nervous system functioning via its effects on inflammation, the hypothalamic-pituitary axis, and/or neurotransmission. Our understanding of the cellular underpinnings of the brain-gut relationship is based almost exclusively on animal models with some small-scale human studies. This study examined the relationship between the gut microbiota and psychiatric symptom severity and treatment response among inpatients with serious mental illness.

METHOD

We collected data from adult inpatients (N = 111). Measures of diagnoses, suicide severity, trauma, depression, and anxiety were collected shortly after admission, while self-collected fecal swabs were collected early in the course of hospitalization and processed using 16S rRNA gene sequencing and whole genome shotgun sequencing methods.

RESULTS

Results indicate that depression and anxiety severity shortly after admission were negatively associated with bacterial richness and alpha diversity. Additional analyses revealed a number of bacterial taxa associated with depression and anxiety severity. Gut microbiota richness and alpha diversity early in the course of hospitalization was a significant predictor of depression remission at discharge.

CONCLUSIONS

This study is among the first to demonstrate a gut microbiota relationship with symptom severity among psychiatric inpatients as well as a relationship to remission of depression post-treatment. These findings are consistent with animal models and limited human studies as well as with the broader literature implicating inflammation in the pathophysiology of depression. These findings offer the foundation for further studies of novel therapeutic approaches to the treatment, prevention of, or recurrence of serious mental illness.

Generating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis

In this article, we present an open source neuroinformatics platform for exploring, analyzing, and validating distilled graphical representations of high-dimensional neuroimaging data extracted using topological data analysis (TDA). TDA techniques like Mapper have been recently applied to examine the brain's dynamical organization during ongoing cognition without averaging data in space, in time, or across participants at the outset. Such TDA-based approaches mark an important deviation from standard neuroimaging analyses by distilling complex high-dimensional neuroimaging data into simple-yet neurophysiologically valid and behaviorally relevant-representations that can be interactively explored at the single-participant level. To facilitate wider use of such techniques within neuroimaging and general neuroscience communities, our work provides several tools for visualizing, interacting with, and grounding TDA-generated graphical representations in neurophysiology. Through Python-based Jupyter notebooks and open datasets, we provide a platform to assess and visualize different intermittent stages of Mapper and examine the influence of Mapper parameters on the generated representations. We hope this platform could enable researchers and clinicians alike to explore topological representations of neuroimaging data and generate biological insights underlying complex mental disorders.

Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics

The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.

Increased habenular connectivity in opioid users is associated with an α5 subunit nicotinic receptor genetic variant

BACKGROUND AND OBJECTIVES

Opioid use disorder (OUD) is a chronic disorder with relapse based on both desire for reinforcement (craving) and avoidance of withdrawal. The aversive aspect of dependence and relapse has been associated with a small brain structure called the habenula, which expresses large numbers of both opioid and nicotinic receptors. Additionally, opioid withdrawal symptoms can be induced in opioid-treated rodents by blocking not only opioid, but also nicotinic receptors. This receptor co-localization and cross-induction of withdrawal therefore might lead to genetic variation in the nicotinic receptor influencing development of human opioid dependence through its impact on the aversive components of opioid dependence.

METHODS

We studied habenular resting state functional connectivity with related brain structures, specifically the striatum. We compared abstinent psychiatric patients who use opioids (N = 51) to psychiatric patients who do not (N = 254) to identify an endophenotype of opioid use that focused on withdrawal avoidance and aversion rather than the more commonly examined craving aspects of relapse.

RESULTS

We found that habenula-striatal connectivity was stronger in opioid-using patients. Increased habenula-striatum connectivity was observed in opioid-using patients with the low risk rs16969968 GG genotype, but not in patients carrying the high risk AG or AA genotypes.

CONCLUSIONS

We propose that increased habenula-striatum functional connectivity may be modulated by the nicotinic receptor variant rs16969968 and may lead to increased opioid use.

SCIENTIFIC SIGNIFICANCE

Our data uncovered a promising brain target for development of novel anti-addiction therapies and may help the development of personalized therapies against opioid abuse. (Am J Addict 2017;26:751-759).