Exercise Intervention in PTSD: A Narrative Review and Rationale for Implementation

Posttraumatic stress disorder (PTSD) is a prominent mental health problem in veteran and community populations. There is accumulating evidence to suggest that aerobic exercise may serve as an effective treatment option for individuals with PTSD. The purpose of this review is to summarize the existing literature exploring aerobic exercise and PTSD and briefly discuss potential mechanisms of PTSD symptom reduction. A search of electronic databases and reference sections of relevant articles published through October 1, 2018 revealed 19 relevant studies that examined aerobic exercise and PTSD symptomatology. A narrative review of extant studies provides encouraging evidence that aerobic exercise interventions alone or as an adjunct to standard treatment may positively impact PTSD symptoms. Potential mechanisms by which aerobic exercise could exert a positive impact in PTSD include exposure and desensitization to internal arousal cues, enhanced cognitive function, exercise-induced neuroplasticity, normalization of hypothalamic pituitary axis (HPA) function, and reductions in inflammatory markers. Randomized clinical trials and translational neuroscience approaches are required to clarify the efficacy of exercise intervention for PTSD and elucidate potential mechanisms of exercise-induced PTSD symptom reduction

A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes

Large databases of high-resolution structural MR images are being assembled to quantitatively examine the relationships between brain anatomy, disease progression, treatment regimens, and genetic influences upon brain structure. Quantifying brain structures in such large databases cannot be practically accomplished by expert neuroanatomists using hand-tracing. Rather, this research will depend upon automated methods that reliably and accurately segment and quantify dozens of brain regions. At present, there is little guidance available to help clinical research groups in choosing such tools. Thus, our goal was to compare the performance of two popular and fully automated tools, FSL/FIRST and FreeSurfer, to expert hand tracing in the measurement of the hippocampus and amygdala. Volumes derived from each automated measurement were compared to hand tracing for percent volume overlap, percent volume difference, across-sample correlation, and 3-D group-level shape analysis. In addition, sample size estimates for conducting between-group studies were computed for a range of effect sizes. Compared to hand tracing, hippocampal measurements with FreeSurfer exhibited greater volume overlap, smaller volume difference, and higher correlation than FIRST, and sample size estimates with FreeSurfer were closer to hand tracing. Amygdala measurement with FreeSurfer was also more highly correlated to hand tracing than FIRST, but exhibited a greater volume difference than FIRST. Both techniques had comparable volume overlap and similar sample size estimates. Compared to hand tracing, a 3-D shape analysis of the hippocampus showed FreeSurfer was more accurate than FIRST, particularly in the head and tail. However, FIRST more accurately represented the amygdala shape than FreeSurfer, which inflated its anterior and posterior surfaces

The ENIGMA sports injury working group - an international collaboration to further our understanding of sport-related brain injury

Sport-related brain injury is very common, and the potential long-term effects include a wide range of neurological and psychiatric symptoms, and potentially neurodegeneration. Around the globe, researchers are conducting neuroimaging studies on primarily homogenous samples of athletes. However, neuroimaging studies are expensive and time consuming, and thus current findings from studies of sport-related brain injury are often limited by small sample sizes. Further, current studies apply a variety of neuroimaging techniques and analysis tools which limit comparability among studies. The ENIGMA Sports Injury working group aims to provide a platform for data sharing and collaborative data analysis thereby leveraging existing data and expertise. By harmonizing data from a large number of studies from around the globe, we will work towards reproducibility of previously published findings and towards addressing important research questions with regard to diagnosis, prognosis, and efficacy of treatment for sport-related brain injury. Moreover, the ENIGMA Sports Injury working group is committed to providing recommendations for future prospective data acquisition to enhance data quality and scientific rigor

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts

BACKGROUND: Incorporating genomic data into risk prediction has become an increasingly useful approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. METHODS: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. RESULTS: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p-0.003), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. CONCLUSION: Results, especially those from the eMRS, reinforce earlier findings that methylation and trauma are interconnected and can be leveraged to increase the correct classification of those with vs. without PTSD. Moreover, our models can potentially be a valuable tool in predicting the future risk of developing PTSD. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting the condition and, relatedly, improve their performance in independent cohorts

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts

BACKGROUND: Incorporating genomic data into risk prediction has become an increasingly popular approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. METHODS: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. RESULTS: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p=0.006), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. CONCLUSION: The inclusion of exposure variables adds to the predictive power of MRS. Classification-based MRS may be useful in predicting risk of future PTSD in populations with anticipated trauma exposure. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting PTSD and, relatedly, improve their performance in independent cohorts

Epigenome-wide association studies identify novel DNA methylation sites associated with PTSD: A meta-analysis of 23 military and civilian cohorts

BACKGROUND: The occurrence of post-traumatic stress disorder (PTSD) following a traumatic event is associated with biological differences that can represent the susceptibility to PTSD, the impact of trauma, or the sequelae of PTSD itself. These effects include differences in DNA methylation (DNAm), an important form of epigenetic gene regulation, at multiple CpG loci across the genome. Moreover, these effects can be shared or specific to both central and peripheral tissues. Here, we aim to identify blood DNAm differences associated with PTSD and characterize the underlying biological mechanisms by examining the extent to which they mirror associations across multiple brain regions. METHODS: As the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup, we conducted the largest cross-sectional meta-analysis of epigenome-wide association studies (EWASs) of PTSD to date, involving 5077 participants (2156 PTSD cases and 2921 trauma-exposed controls) from 23 civilian and military studies. PTSD diagnosis assessments were harmonized following the standardized guidelines established by the PGC-PTSD Workgroup. DNAm was assayed from blood using either Illumina HumanMethylation450 or MethylationEPIC (850K) BeadChips. A common QC pipeline was applied. Within each cohort, DNA methylation was regressed on PTSD, sex (if applicable), age, blood cell proportions, and ancestry. An inverse variance-weighted meta-analysis was performed. We conducted replication analyses in tissue from multiple brain regions, neuronal nuclei, and a cellular model of prolonged stress. RESULTS: We identified 11 CpG sites associated with PTSD in the overall meta-analysis (1.44e-09 < p < 5.30e-08), as well as 14 associated in analyses of specific strata (military vs civilian cohort, sex, and ancestry), including CpGs in AHRR and CDC42BPB. Many of these loci exhibit blood-brain correlation in methylation levels and cross-tissue associations with PTSD in multiple brain regions. Methylation at most CpGs correlated with their annotated gene expression levels. CONCLUSIONS: This study identifies 11 PTSD-associated CpGs, also leverages data from postmortem brain samples, GWAS, and genome-wide expression data to interpret the biology underlying these associations and prioritize genes whose regulation differs in those with PTSD

A review of cardiorespiratory fitness-related neuroplasticity in the aging brain

The literature examining the relationship between cardiorespiratory fitness and the brain in older adults has increased rapidly, with 30 of 34 studies published since 2008. Here we review cross-sectional and exercise intervention studies in older adults examining the relationship between cardiorespiratory fitness and brain structure and function, typically assessed using Magnetic Resonance Imaging (MRI). Studies of patients with Alzheimer’s disease are discussed when available. The structural MRI studies revealed a consistent positive relationship between cardiorespiratory fitness and brain volume in cortical regions including anterior cingulate, lateral prefrontal, and lateral parietal cortex. Support for a positive relationship between cardiorespiratory fitness and medial temporal lobe volume was less consistent, although evident when a region-of-interest approach was implemented. In fMRI studies, cardiorespiratory fitness in older adults was associated with activation in similar regions as those identified in the structural studies, including anterior cingulate, lateral prefrontal, and lateral parietal cortex, despite heterogeneity among the functional tasks implemented. This comprehensive review highlights the overlap in brain regions showing a positive relationship with cardiorespiratory fitness in both structural and functional imaging modalities. The findings suggest that aerobic exercise and cardiorespiratory fitness contribute to healthy brain aging, although additional studies in Alzheimer’s disease are needed

Emotion and Cognition Interactions in PTSD: A Review of Neurocognitive and Neuroimaging Studies

Posttraumatic stress disorder (PTSD) is a psychiatric syndrome that develops after exposure to terrifying and life-threatening events including warfare, motor-vehicle accidents, and physical and sexual assault. The emotional experience of psychological trauma can have long-term cognitive effects. The hallmark symptoms of PTSD involve alterations to cognitive processes such as memory, attention, planning and problem solving, underscoring the detrimental impact that negative emotionality has on cognitive functioning. As such, an important challenge for PTSD researchers and treatment providers is to understand the dynamic interplay between emotion and cognition. Contemporary cognitive models of PTSD theorize that a preponderance of information processing resources are allocated towards threat detection and interpretation of innocuous stimuli as threatening, narrowing one’s attentional focus at the expense of other cognitive operations. Decades of research have shown support for these cognitive models of PTSD using a variety of tasks and methodological approaches. The primary goal of this review is to summarize the latest neurocognitive and neuroimaging research of emotion-cognition interactions in PTSD. To directly assess the influence of emotion on cognition and vice versa, the studies reviewed employed challenge tasks that included both cognitive and emotional components. The findings provide evidence for memory and attention deficits in PTSD that are often associated with changes in functional brain activity. The results are reviewed to provide future directions for research that may direct better and more effective treatments for PTSD

Inflammatory biomarkers link perceived stress with metabolic dysregulation

Objective: Perceived stress has been identified as a risk factor for metabolic syndrome. However, the intermediate pathways underlying this relationship are not well understood. Inflammatory responses may be one process by which stress leads to metabolic dysregulation. Prior work has shown that chronic stress is associated with elevated systemic inflammation and that altered inflammatory activity contributes to the pathogenesis of metabolic syndrome. The current analyses tested this hypothesis by examining inflammation as a pathway by which perceived stress affects metabolic health. Methods: Data from the Midlife in the United States Study (MIDUS) (N = 648; Mean age = 52.3) provided measures of perceived stress, inflammatory biomarkers [C-reactive protein (CRP), interleukin-6 (IL-6), E-selectin, fibrinogen, intracellular adhesion molecule-1 (ICAM-1)] and metabolic health markers. Confirmatory factor analysis (CFA) was used to confirm the fit of a hierarchical model of metabolic syndrome in our sample. Structural equation modeling (SEM) was used to test the assumption that inflammation mediates the association between perceived stress and the latent factor representing metabolic syndrome. Results: The CFA of metabolic syndrome demonstrated excellent goodness of fit to our sample [CFI = 0.97, TLI = 0.95, RMSEA = 0.06, SMSR = 0.05]. Mediation analysis with SEM revealed that the indirect pathway linking stress to metabolic dysregulation through inflammation was significant [B = 0.08, SE = 0.01, z = 3.69, p < .001, 95% confidence interval CI (0.04, 0.13)]. Conclusions: These results suggest that inflammatory biomarkers are a viable explanatory pathway for the relationship between perceived stress and metabolic health consequences. Interventions that target psychosocial stress may serve as cost-effective and accessible treatment options for mitigating inflammatory health risks

Partial Least Squares analysis of Alzheimer’s disease biomarkers, modifiable health variables, and cognition in older adults with Mild Cognitive Impairment

Objective: To identify novel associations between modifiable physical and health variables, Alzheimer’s disease (AD) biomarkers, and cognitive function in a cohort of older adults with Mild Cognitive Impairment (MCI). Method: Metrics of cardiometabolic risk, stress, inflammation, neurotrophic/growth factors, AD, and cognition were assessed in 155 MCI participants (Mean age = 74.2 years) from the Alzheimer’s Disease Neuroimaging Initiative. Partial Least Squares analysis was employed to examine associations among these physiological variables and cognition. Results: Latent variable 1 revealed a unique combination of AD biomarkers, neurotrophic/growth factors, including brain-derived neurotrophic factor, and education that were significantly associated with specific domains of cognitive function, including episodic memory, executive function, and processing speed, representing 47.9% of the covariance in the data. Age, BMI, and metrics tapping working memory, language or premorbid IQ were not significant. Conclusions: Our data-driven analysis highlights the significant relationships between metrics associated with AD-pathology, neuroprotection, and neuroplasticity with tasks requiring fluid (episodic memory and executive function) rather than crystallized (premorbid IQ and language) ability. These data also indicate that biological metrics are more strongly associated with episodic memory, executive function, and processing speed than chronological age in older adults with MCI