Severity of Depression Predicts Remission Rates Using Transcranial Magnetic Stimulation

Background: Multiple factors likely impact response and remission rates in the treatment of depression with repetitive transcranial magnetic stimulation (rTMS). Notably the role of symptom severity in outcomes with rTMS is poorly understood.Objective/Hypothesis: This study investigated the predictors of achieving remission in patients suffering from depression who receive ≥3 rTMS treatments per week. Methods: Available data on 41 patients treated at Walter Reed National Military Medical Center from 2009 to 2014 were included for analysis. Patients received a range of pulse sequences from 3,000 to 5,000 with left sided or bilateral coil placement. Primary outcome measures were total score on the Patient Health Questionnaire (PHQ-9) or the Quick Inventory of Depressive Symptomatology—Self Rated (QIDS-SR). Remission was defined as a total score less than five, and response was defined as a 50% decrease in the total score on both outcome metrics. Outcomes in patients diagnosed as suffering from mild or moderate depression were compared to those suffering from severe depression. Results: Of the 41 patients receiving treatment, 16 reached remission by the end of treatment. Remission rate was associated with the initial severity of depression, with patients with mild or moderate depression reaching remission at a significantly higher rate than those with severe depression. Total number of rTMS sessions or length of treatment were not predictors of remission. Conclusion: Patients with a baseline level of depression characterized as mild or moderate had significantly better outcomes following rTMS compared to patients with severe depression

Simulated Military Operational Stress Negatively Impacts Psychomotor Vigilance and Neurocognitive Biomarkers in Men and Women

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Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning

Many brain-based disorders are traditionally diagnosed based on clinical interviews and behavioral assessments, which are recognized to be largely imperfect. Therefore, it is necessary to establish neuroimaging-based biomarkers to improve diagnostic precision. Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising technique for the characterization and classification of varying disorders. However, most of these classification methods are supervised, i.e., they require a priori clinical labels to guide classification. In this study, we adopted various unsupervised clustering methods using static and dynamic rs-fMRI connectivity measures to investigate whether the clinical diagnostic grouping of different disorders is grounded in underlying neurobiological and phenotypic clusters. In order to do so, we derived a general analysis pipeline for identifying different brain-based disorders using genetic algorithm-based feature selection, and unsupervised clustering methods on four different datasets; three of them—ADNI, ADHD-200, and ABIDE—which are publicly available, and a fourth one—PTSD and PCS—which was acquired in-house. Using these datasets, the effectiveness of the proposed pipeline was verified on different disorders: Attention Deficit Hyperactivity Disorder (ADHD), Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), Post-Traumatic Stress Disorder (PTSD), and Post-Concussion Syndrome (PCS). For ADHD and AD, highest similarity was achieved between connectivity and phenotypic clusters, whereas for ASD and PTSD/PCS, highest similarity was achieved between connectivity and clinical diagnostic clusters. For multi-site data (ABIDE and ADHD-200), we report site-specific results. We also reported the effect of elimination of outlier subjects for all four datasets. Overall, our results suggest that neurobiological and phenotypic biomarkers could potentially be used as an aid by the clinician, in additional to currently available clinical diagnostic standards, to improve diagnostic precision. Data and source code used in this work is publicly available at https://github.com/xinyuzhao/identification-of-brain-based-disorders.git

Refinement of Saliva MicroRNA Biomarkers for Sports-Related Concussion

Purpose Recognizing sport-related concussion (SRC) is challenging and relies heavily on subjective symptom reports. An objective, biological marker could improve recognition and understanding of SRC. There is emerging evidence that salivary micro-ribonucleic acids (miRNAs) may serve as biomarkers of concussion; however, it remains unclear whether concussion-related miRNAs are impacted by exercise. We sought to determine whether 40 miRNAs previously implicated in concussion pathophysiology were affected by participation in a variety of contact and non-contact sports. Our goal was to refine a miRNA-based tool capable of identifying athletes with SRC without the confounding effects of exercise. Methods This case-control study harmonized data from concussed and non-concussed athletes recruited across 10 sites. Levels of salivary miRNAs within 455 samples from 314 individuals were measured with RNA sequencing. Within-subjects testing was used to identify and exclude miRNAs that changed with either: (a) a single episode of exercise (166 samples from 83 individuals) or (b) season-long participation in contact sports (212 samples from 106 individuals). The miRNAs that were not impacted by exercise were interrogated for SRC diagnostic utility using logistic regression (172 samples from 75 concussed and 97 non-concussed individuals). Results Two miRNAs (miR-532-5p, miR-182-5p) decreased (adjusted p \u3c 0.05) after a single episode of exercise, and 1 miRNA (miR-4510) increased only after contact sports participation. Twenty-three miRNAs changed at the end of a contact sports season. Two of these miRNAs (miR-26b-3p, miR-29c-3p) were associated (R \u3e 0.5; adjusted p \u3c 0.05) with the number of head impacts sustained in a single football practice. Among the 15 miRNAs not confounded by exercise or season-long contact sports participation, 11 demonstrated a significant difference (adjusted p \u3c 0.05) between concussed and non-concussed participants, and 6 displayed moderate ability (AUC \u3e 0.70) to identify concussion. A single ratio (miR-27a-5p/miR-30a-3p) displayed the highest accuracy (AUC = 0.810, sensitivity = 82.4%, specificity = 73.3%) for differentiating concussed and non-concussed participants. Accuracy did not differ between participants with SRC and non-SRC (z = 0.5, p = 0.60). Conclusion Salivary miRNA levels may accurately identify SRC when not confounded by exercise. Refinement of this approach in a large cohort of athletes could eventually lead to a non-invasive, sideline adjunct for SRC assessment

Saliva microRNA Biomarkers of Cumulative Concussion

Recurrent concussions increase risk for persistent post-concussion symptoms, and may lead to chronic neurocognitive deficits. Little is known about the molecular pathways that contribute to persistent concussion symptoms. We hypothesized that salivary measurement of microribonucleic acids (miRNAs), a class of epitranscriptional molecules implicated in concussion pathophysiology, would provide insights about the molecular cascade resulting from recurrent concussions. This hypothesis was tested in a case-control study involving 13 former professional football athletes with a history of recurrent concussion, and 18 age/sex-matched peers. Molecules of interest were further validated in a cross-sectional study of 310 younger individuals with a history of no concussion (n = 230), a single concussion (n = 56), or recurrent concussions (n = 24). There was no difference in neurocognitive performance between the former professional athletes and their peers, or among younger individuals with varying concussion exposures. However, younger individuals without prior concussion outperformed peers with prior concussion on three balance assessments. Twenty salivary miRNAs differed (adj. p \u3c 0.05) between former professional athletes and their peers. Two of these (miR-28-3p and miR-339-3p) demonstrated relationships (p \u3c 0.05) with the number of prior concussions reported by younger individuals. miR-28-3p and miR-339-5p may play a role in the pathophysiologic mechanism involved in cumulative concussion effects

Levels of Arachidonic Acid–Derived Oxylipins and Anandamide Are Elevated Among Military <i>APOE</i> ɛ4 Carriers With a History of Mild Traumatic Brain Injury and Post-Traumatic Stress Disorder Symptoms

Currently approved blood biomarkers detect intracranial lesions in adult patients with mild to moderate traumatic brain injury (TBI) acutely post-injury. However, blood biomarkers are still needed to help with a differential diagnosis of mild TBI (mTBI) and post-traumatic stress disorder (PTSD) at chronic post-injury time points. Owing to the association between phospholipid (PL) dysfunction and chronic consequences of TBI, we hypothesized that examining bioactive PL metabolites (oxylipins and ethanolamides) would help identify long-term lipid changes associated with mTBI and PTSD. Lipid extracts of plasma from active-duty soldiers deployed to the Iraq/Afghanistan wars (control = 52, mTBI = 21, PTSD = 34, and TBI + PTSD = 13) were subjected to liquid chromatography/mass spectrometry analysis to examine oxylipins and ethanolamides. Linear regression analyses followed by post hoc comparisons were performed to assess the association of these lipids with diagnostic classifications. Significant differences were found in oxylipins derived from arachidonic acid (AA) between controls and mTBI, PTSD, and mTBI + PTSD groups. Levels of AA-derived oxylipins through the cytochrome P450 pathways and anandamide were significantly elevated among mTBI + PTSD patients who were carriers of the apolipoprotein E E4 allele. These studies demonstrate that AA-derived oxylipins and anandamide may be unique blood biomarkers of PTSD and mTBI + PTSD. Further, these AA metabolites may be indicative of an underlying inflammatory process that warrants further investigation. Future validation studies in larger cohorts are required to determine a potential application of this approach in providing a differential diagnosis of mTBI and PTSD in a clinical setting

Diagnosing Mild Traumatic Brain Injury Using Saliva RNA Compared to Cognitive and Balance Testing

BACKGROUND: Early, accurate diagnosis of mild traumatic brain injury (mTBI) can improve clinical outcomes for patients, but mTBI remains difficult to diagnose because of reliance on subjective symptom reports. An objective biomarker could increase diagnostic accuracy and improve clinical outcomes. The aim of this study was to assess the ability of salivary noncoding RNA (ncRNA) to serve as a diagnostic adjunct to current clinical tools. We hypothesized that saliva ncRNA levels would demonstrate comparable accuracy for identifying mTBI as measures of symptom burden, neurocognition, and balance. METHODS: This case‐control study involved 538 individuals. Participants included 251 individuals with mTBI, enrolled ≤14 days postinjury, from 11 clinical sites. Saliva samples (n = 679) were collected at five time points (≤3, 4‐7, 8‐14, 15‐30, and 31‐60 days post‐mTBI). Levels of ncRNAs (microRNAs, small nucleolar RNAs, and piwi‐interacting RNAs) were quantified within each sample using RNA sequencing. The first sample from each mTBI participant was compared to saliva samples from 287 controls. Samples were divided into testing (n = 430; mTBI = 201 and control = 239) and training sets (n = 108; mTBI = 50 and control = 58). The test set was used to identify ncRNA diagnostic candidates and create a diagnostic model. Model accuracy was assessed in the naïve test set. RESULTS: A model utilizing seven ncRNA ratios, along with participant age and chronic headache status, differentiated mTBI and control participants with a cross‐validated area under the curve (AUC) of .857 in the training set (95% CI, .816‐.903) and .823 in the naïve test set. In a subset of participants (n = 321; mTBI = 176 and control = 145) assessed for symptom burden (Post‐Concussion Symptom Scale), as well as neurocognition and balance (ClearEdge System), these clinical measures yielded cross‐validated AUC of .835 (95% CI, .782‐.880) and .853 (95% CI, .803‐.899), respectively. A model employing symptom burden and four neurocognitive measures identified mTBI participants with similar AUC (.888; CI, .845‐.925) as symptom burden and four ncRNAs (.932; 95% CI, .890‐.965). CONCLUSION: Salivary ncRNA levels represent a noninvasive, biologic measure that can aid objective, accurate diagnosis of mTBI

Reconceptualising product life-cycle theory as stakeholder engagement with non-profit organisation

The paper critically re-examines product life-cycle (PLC) theory, developed over fifty years ago. Despite prevalence in marketing pedagogy and continued popularity within empirical research, PLC is seldom challenged. The paper identifies the organisation-centric construct underpinning the theory and highlights a disconnection between PLC theory and the recent academic insight around customer engagement. It reconceptualises the life-cycle concept based on engagement between stakeholder and non-profit organisation (NPO), structured upon both the market orientation and social exchange constructs. The revised framework maps stakeholder engagement with the NPO through the five stages of incubation, interaction, involvement, immersion, and incapacitation. The paper concludes with identifying a roadmap for future empirical research to develop and validate the re-envisaged conceptual model. The methodology used is narrative literature review supported by secondary research from specialist practitioner reports