Assessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups

Background: Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. Method: Adult subjects (N = 2229; 56.2% male) aged 18–69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA-PGC PTSD sites underwent T1-weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel-wise (brainageR) and region-of-interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age − chronological age) controlling for chronological age, sex, and scan site. Results: BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three-way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages. Discussion: Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age-related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan

Neuroimaging-Based Classification of PTSD Using Data-Driven Computational Approaches:A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality.RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance.CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.</p

Assessment of Brain Age in Posttraumatic Stress Disorder: Findings from the ENIGMA PTSD and Brain Age Working Groups

Background Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. Method Adult subjects (N = 2229; 56.2% male) aged 18–69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA-PGC PTSD sites underwent T1-weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel-wise (brainageR) and region-of-interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age − chronological age) controlling for chronological age, sex, and scan site. Results BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three-way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages. Discussion Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age-related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan

A purple acid phosphatase from sweet potato contains an antiferromagnetically coupled binuclear Fe-Mn center

A purple acid phosphatase from sweet potato is the first reported example of a protein containing an enzymatically active binuclear Fe-Mn center. Multifield saturation magnetization data over a temperature range of 2 to 200 K indicates that this center is strongly antiferromagnetically coupled. Metal ion analysis shows an excess of iron over manganese. Low temperature EPR spectra reveal only resonances characteristic of high spin Fe(III) centers (Fe(III)-apo and Fe(III)-Zn(II)) and adventitious Cu(II) centers. There were no resonances from either Mn(II) or binuclear Fe-Mn centers. Together with a comparison of spectral properties and sequence homologies between known purple acid phosphatases, the enzymatic and spectroscopic data strongly indicate the presence of catalytic Fe(III)-Mn(II) centers in the active site of the sweet potato enzyme. Because of the strong antiferromagnetism it is likely that the metal ions in the sweet potato enzyme are linked via a mu -oxo bridge, in contrast to other known purple acid phosphatases in which a mu -hydroxo bridge is present. Differences in metal ion composition and bridging may affect substrate specificities leading to the biological function of different purple acid phosphatases

Mycorrhizal feedbacks influence global forest structure and diversity

One mechanism proposed to explain high species diversity in tropical systems is strong negative conspecific density dependence (CDD), which reduces recruitment of juveniles in proximity to conspecific adult plants. Although evidence shows that plant-specific soil pathogens can drive negative CDD, trees also form key mutualisms with mycorrhizal fungi, which may counteract these effects. Across 43 large-scale forest plots worldwide, we tested whether ectomycorrhizal tree species exhibit weaker negative CDD than arbuscular mycorrhizal tree species. We further tested for conmycorrhizal density dependence (CMDD) to test for benefit from shared mutualists. We found that the strength of CDD varies systematically with mycorrhizal type, with ectomycorrhizal tree species exhibiting higher sapling densities with increasing adult densities than arbuscular mycorrhizal tree species. Moreover, we found evidence of positive CMDD for tree species of both mycorrhizal types. Collectively, these findings indicate that mycorrhizal interactions likely play a foundational role in global forest diversity patterns and structure

The potential of ketamine for posttraumatic stress disorder: a review of clinical evidence.

Peer reviewed: TruePosttraumatic stress disorder (PTSD) is a devastating condition, for which there are few pharmacological agents, often with a delayed onset of action and poor efficacy. Trauma-focused psychotherapies are further limited by few trained providers and low patient engagement. This frequently results in disease chronicity as well as psychiatric and medical comorbidity, with considerable negative impact on quality of life. As such, off-label interventions are commonly used for PTSD, particularly in chronic refractory cases. Ketamine, an N-methyl-D-aspartate (NDMA) receptor antagonist, has recently been indicated for major depression, exhibiting rapid and robust antidepressant effects. It also shows transdiagnostic potential for an array of psychiatric disorders. Here, we synthesize clinical evidence on ketamine in PTSD, spanning case reports, chart reviews, open-label studies, and randomized trials. Overall, there is high heterogeneity in clinical presentation and pharmacological approach, yet encouraging signals of therapeutic safety, efficacy, and durability. Avenues for future research are discussed

The potential of ketamine for posttraumatic stress disorder: a review of clinical evidence.

Posttraumatic stress disorder (PTSD) is a devastating condition, for which there are few pharmacological agents, often with a delayed onset of action and poor efficacy. Trauma-focused psychotherapies are further limited by few trained providers and low patient engagement. This frequently results in disease chronicity as well as psychiatric and medical comorbidity, with considerable negative impact on quality of life. As such, off-label interventions are commonly used for PTSD, particularly in chronic refractory cases. Ketamine, an N-methyl-D-aspartate (NDMA) receptor antagonist, has recently been indicated for major depression, exhibiting rapid and robust antidepressant effects. It also shows transdiagnostic potential for an array of psychiatric disorders. Here, we synthesize clinical evidence on ketamine in PTSD, spanning case reports, chart reviews, open-label studies, and randomized trials. Overall, there is high heterogeneity in clinical presentation and pharmacological approach, yet encouraging signals of therapeutic safety, efficacy, and durability. Avenues for future research are discussed

The Opioid Abuse Risk Screener predicts aberrant same-day urine drug tests and 1-year controlled substance database checks: A brief report

The Opioid Abuse Risk Screener was developed to support well-informed decision-making in opioid analgesic prescribing by extending the breadth of psychiatric risk factors evaluated relative to other non–clinician-administered measures. We examined the preliminary predictive validity of the Opioid Abuse Risk Screener relative to the widely used Screener and Opioid Assessment for Patients with Pain–Revised in predicting aberrant urine drug tests and controlled substance database checks. The Opioid Abuse Risk Screener is significantly different from the Screener and Opioid Assessment for Patients with Pain–Revised in predicting aberrant same-day urine drug tests ( Z  = 2.912, p  = 0.0036) and controlled substance database checks within 1 year of assessment ( Z  = 3.731, p  = 0.0002). Promising preliminary analyses using machine learning methods are also discussed

Ketamine, but Not the NMDAR Antagonist Lanicemine, Increases Prefrontal Global Connectivity in Depressed Patients

Background Identifying the neural correlates of ketamine treatment may facilitate and expedite the development of novel, robust, and safe rapid-acting antidepressants. Prefrontal cortex (PFC) global brain connectivity with global signal regression (GBCr) was recently identified as a putative biomarker of major depressive disorder. Accumulating evidence have repeatedly shown reduced PFC GBCr in major depressive disorder, an abnormality that appears to normalize following ketamine treatment. Methods Fifty-six unmedicated participants with major depressive disorder were randomized to intravenous placebo (normal saline; n = 18), ketamine (0.5 mg/kg; n = 19), or lanicemine (100 mg; n = 19). PFC GBCr was computed using time series from functional magnetic resonance imaging scans that were completed at baseline, during infusion, and at 24-h posttreatment. Results Compared to placebo, ketamine significantly increased average PFC GBCr during infusion ( p  = 0.01) and at 24-h posttreatment ( p  = 0.02). Lanicemine had no significant effects on GBCr during infusion ( p  = 0.45) and at 24-h posttreatment ( p  = 0.23) compared to placebo. Average delta PFC GBCr (during minus baseline) showed a pattern of positively predicting depression improvement in participants receiving ketamine ( r  = 0.44; p  = 0.06; d  = 1.0) or lanicemine ( r  = 0.55; p  = 0.01; d  = 1.3) but not those receiving placebo ( r  = −0.1; p  = 0.69; d  = 0.02). Follow-up vertex-wise analyses showed ketamine-induced GBCr increases in the dorsolateral, dorsomedial, and frontomedial PFC during infusion and in the dorsolateral and dorsomedial PFC at 24-h posttreatment ( corrected p  < 0.05). Exploratory vertex-wise analyses examining the relationship with depression improvement showed positive correlation with GBCr in the dorsal PFC during infusion and at 24-h posttreatment but negative correlation with GBCr in the ventral PFC during infusion ( uncorrected p  < 0.01). Conclusions In a randomized placebo-controlled approach, the results provide the first evidence in major depressive disorder of ketamine-induced increases in PFC GBCr during infusion and suggest that ketamine’s rapid-acting antidepressant properties are related to its acute effects on prefrontal connectivity. Overall, the study findings underscore the similarity and differences between ketamine and another N-methyl-D-aspartate receptor antagonist while proposing a pharmacoimaging paradigm for the optimization of novel rapid-acting antidepressants prior to testing in costly clinical trials