Croarkin, Paul E

Background: Cardiac biomarkers are recognized as potential indicators for brain function, emotion regulation, and psychiatric risk. While research in adults has suggested associations between cardiac variables of autonomic function and neural changes, little is known about these relationships in children and young adults with psychiatric conditions. This systematic review aimed to examine the evidence linking cardiac biomarkers with structural, functional, and connectivity-based brain changes in this population. Methods: A systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Multiple databases were searched for studies examining associations between any cardiac biomarker and neuroimaging findings in individuals under 21 years old with psychiatric diagnoses. After screening and full-text review, 11 eligible studies were included. Results: The included studies investigating a range of psychiatric conditions and cardiac biomarkers were associated with brain changes across three domains: structure, connectivity, and neural responses. Associations were most consistent between higher vagal tone and structural and functional integrity of emotion-regulating regions such as the prefrontal cortex, amygdala, and insula. However, findings were heterogeneous and potentially moderated by symptom severity or environmental stressors. Conclusions: This review supports the potential of cardiac biomarkers, particularly high-frequency heart rate variability and root mean square of the successive differences, as proxies of brain changes in youth with psychiatric disorders. While promising, current evidence is too limited and variable for clinical applications. Future research should prioritize large-scale, longitudinal studies using harmonized protocols and wearable technologies to validate these indices as translational tools in child and adolescent psychiatry.

Introduction Psychiatric disorders have their genesis in early life. Standard screening approaches during well child visits for pathological infant distress, maternal depression, and dysfunctional parenting behaviors are likely inadequate. Microbiome measures and infant vocalizations have promise as scalable psychiatric biomarkers for infants. The purpose of this study was to examine associations among infant gut colonization based on microbiome measurements with maternal distress and maternal depressive symptoms in a sample of infants. Methods This study sought to examine infant microbiome correlates of infant distress, parent-infant interactions, maternal distress, and maternal depressive symptoms. We collected ( N = 31) microbiome samples, infant vocalizations during vaccination, and behavioral measures during a 4 month well child visit (WCV) and did a battery of clinical assessments to assess for maternal depression, parent-child interactions, family characteristics and family stress. Whole-genome SHOTGUN sequencing was utilized to identify three types of associations: alpha-diversity using Shannon and Inverse-Simpson indexes, beta-diversity using Bray-Curtis and Jaccard distances, and differential abundance using LinDA. Spectral measures of infant cries were also modeled to assess potential relationships with clinical assessments and the microbiome. Results There were 19 phyla, 417 genera, and 1246 species identified with taxonomic classification. Maternal distress as measured by PHQ-9 scores obtained when infants were 2 months old were associated with 4 bacterial species ( Actinomyces johnsonii, Bilophila wadsworthia, Clostridium dakarense and Ruminococcus flavefaciens ; FDR < 0.1) and beta-diversity ( p = 0.006-BC; p = 0.005-Jaccard). Infant cries with greater high frequency band power ( p < 0.03) and a greater high-to-mid frequency ratio–metrics ( p < 0.05) were associated with altered α-diversity of the microbiome. No correlations were present between maternal PHQ-9 at 4 months, PSI-IV and microbiome diversity. Conclusion The present findings suggest that an infant stress (assessed by quality of crying) is associated with lower microbiome diversity. Decreased diversity reflects an unhealthy microbiome. Parental depressive symptoms may also influence infant microbiome. Future interventional studies focused on the quality of the infant-caregiver relationship should examine related changes in intestinal microbiota.

ABSTRACT Major depressive disorder (MDD) is a substantial public health challenge. Pharmacogenomics (PGx), which identifies genetic variations that predict drug treatment outcomes, may have utility for clinical practice, but adequate representation of all populations is needed. As precision medicine in psychiatry moves towards the use of Artificial Intelligence (AI) and Machine Learning (ML) to predict treatment outcomes using PGx data, representation of diverse populations will be especially important in order to mitigate algorithmic bias and achieve equitable and generalizable findings. This work sought to quantify population diversity in pharmacogenomic studies of MDD through a systematic review. Data from 390 MDD antidepressant PGx studies were extracted from 5739 articles screened. Studies summarized were predominantly conducted in Europe, East Asia, and North America. Across all global studies, the study population comprised 57.3% White, 36.4% Asian, 1.7% Black, 3.5% Hispanic/Latino, and 0.1% Native American or Indigenous participants. Only sixty‐three (16.2%) studies included Black or Latino/Hispanic patients. Additionally, Black, Asian, Hispanic/Latino, and American Indian/Alaska Native populations were statistically underrepresented in U.S. study populations when compared to national census data, while Asian and Black populations were underrepresented in the United Kingdom. The overrepresentation of participants from a limited number of countries combined with the underrepresentation of Black and Hispanic/Latino populations could impact the extent to which pharmacogenomic testing and associated AI/ML‐based PGx tools could individualize antidepressant medication regimens for treating MDD.

ABSTRACTPharmacogenetics is a promising strategy to facilitate individualized care for patients with Major Depressive Disorder (MDD). Research is ongoing to identify the optimal genetic markers for predicting outcomes to antidepressant therapies. The primary aim of this systematic review was to summarize antidepressant pharmacogenetic studies to enhance understanding of the genes, variants, datatypes/methodologies, and outcomes investigated in the context of MDD. The secondary aim was to identify clinical genetic panels indicated for antidepressant prescribing and summarize their genes and variants. Screening of N = 5793 articles yielded N = 390 for inclusion, largely comprising adult (≥ 18 years) populations. Top‐studied variants identified in the search were discussed and compared with those represented on the N = 34 clinical genetic panels that were identified. Summarization of articles revealed sources of heterogeneity across studies and low rates of replicability of pharmacogenetic associations. Heterogeneity was present in outcome definitions, treatment regimens, and differential inclusion of mediating variables in analyses. Efficacy outcomes (i.e., response, remission) were studied at greater frequency than adverse‐event outcomes. Studies that used advanced analytical approaches, such as machine learning, to integrate variants with complimentary biological datatypes were fewer in number but achieved higher rates of significant associations with treatment outcomes than candidate variant approaches. As large biological datasets become more prevalent, machine learning will be an increasingly valuable tool for parsing the complexity of antidepressant response. This review provides valuable context and considerations surrounding pharmacogenetic associations in MDD which will help inform future research and translation efforts for guiding antidepressant care.

Abstract Temporal lobe epilepsy is a common neurological disease characterized by recurrent seizures that often originate within limbic networks involving amygdala and hippocampus. The limbic network is involved in crucial physiologic functions involving memory, emotion and sleep. Temporal lobe epilepsy is frequently drug-resistant, and people often experience comorbidities related to memory, mood and sleep. Deep brain stimulation targeting the anterior nucleus of the thalamus (ANT-DBS) is an established therapy for temporal lobe epilepsy. However, the optimal stimulation parameters and their impact on memory, mood and sleep comorbidities remain unclear. We used an investigational brain sensing-stimulation implanted device to accurately track seizures, interictal epileptiform spikes (IES), and memory, mood and sleep comorbidities in five ambulatory subjects. Wireless streaming of limbic network local field potentials (LFPs) and subject behaviour were captured on a mobile device integrated with a cloud environment. Automated algorithms applied to the continuous LFPs were used to accurately cataloged seizures, IES and sleep-wake brain state. Memory and mood assessments were remotely administered to densely sample cognitive and behavioural response during ANT-DBS in ambulatory subjects living in their natural home environment. We evaluated the effect of continuous low-frequency and duty cycle high-frequency ANT-DBS on epileptiform activity and memory, mood and sleep comorbidities. Both low-frequency and high-frequency ANT-DBS paradigms reduced seizures. However, continuous low-frequency ANT-DBS showed greater reductions in IES, electrographic seizures and better sleep and memory outcomes. These results highlight the potential of synchronized brain sensing and dense behavioural tracking during ANT-DBS for optimizing neuromodulation therapy. While studies with larger patient numbers are needed to validate the benefits of low-frequency ANT-DBS, these findings are potentially translatable to individuals currently implanted with ANT-DBS systems.