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The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis. Two clinically relevant subtypes of post-traumatic stress disorder and major depressive disorder have been identified via machine learning analyses of functional connectivity patterns in resting-state electroencephalography.

Peripheral Blood gene expression is widely used in the discovery of biomarkers and development of therapeutics. Recently, a spate of commercial blood collection and preservation systems have been introduced with proprietary variations that may differentially impact the transcriptomic profiles. Comparative analysis of these collection platforms will help optimize protocols to detect, identify, and reproducibly validate true biological variance among subjects. In the current study, we tested two recently introduced whole blood collection methods, RNAgard® and PAXgene® RNA, in addition to the traditional method of peripheral blood mononuclear cells (PBMCs) separated from whole blood and preserved in Trizol reagent. Study results revealed striking differences in the transcriptomic profiles from the three different methods that imply ex vivo changes in gene expression occurred during the blood collection, preservation, and mRNA extraction processes. When comparing the ability of the three preservation methods to accurately capture individuals' expression differences, RNAgard® outperformed PAXgene® RNA, and both showed better individual separation of transcriptomic profiles than PBMCs. Hence, our study recommends using a single blood collection platform, and strongly cautions against combining methods during the course of a defined study.

Posttraumatic stress disorder (PTSD) accounts for a substantial proportion of casualties among surviving soldiers of the Iraq and Afghanistan wars. Currently, the assessment of PTSD is based exclusively on symptoms, making it difficult to obtain an accurate diagnosis. This study aimed to find potential imaging markers for PTSD using structural, perfusion, and diffusion magnetic resonance imaging (MRI) together. Seventeen male veterans with PTSD (45 ± 14 years old) and 15 age-matched male veterans without PTSD had measurements of regional cerebral blood flow (rCBF) using arterial spin labeling (ASL) perfusion MRI. A slightly larger group had also measurements of white matter integrity using diffusion tensor imaging (DTI) with computations of regional fractional anisotropy (FA). The same subjects also had structural MRI of the hippocampal subfields as reported recently (W. Zhen et al. Arch Gen Psych 2010;67(3):296–303). On ASL-MRI, subjects with PTSD had increased rCBF in primarily right parietal and superior temporal cortices. On DTI, subjects with PTSD had FA reduction in white matter regions of the prefrontal lobe, including areas near the anterior cingulate cortex and prefrontal cortex as well as in the posterior angular gyrus. In conclusion, PTSD is associated with a systematic pattern of physiological and structural abnormalities in predominantly frontal lobe and limbic brain regions. Structural, perfusion, and diffusion MRI together may provide a signature for a PTSD marker.

Post-traumatic stress disorder (PTSD) is a mental disorder diagnosed by clinical interviews, self-report measures and neuropsychological testing. Traumatic brain injury (TBI) can have neuropsychiatric symptoms similar to PTSD. Diagnosing PTSD and TBI is challenging and more so for providers lacking specialized training facing time pressures in primary care and other general medical settings. Diagnosis relies heavily on patient self-report and patients frequently under-report or over-report their symptoms due to stigma or seeking compensation. We aimed to create objective diagnostic screening tests utilizing Clinical Laboratory Improvement Amendments (CLIA) blood tests available in most clinical settings. CLIA blood test results were ascertained in 475 male veterans with and without PTSD and TBI following warzone exposure in Iraq or Afghanistan. Using random forest (RF) methods, four classification models were derived to predict PTSD and TBI status. CLIA features were selected utilizing a stepwise forward variable selection RF procedure. The AUC, accuracy, sensitivity, and specificity were 0.730, 0.706, 0.659, and 0.715, respectively for differentiating PTSD and healthy controls (HC), 0.704, 0.677, 0.671, and 0.681 for TBI vs. HC, 0.739, 0.742, 0.635, and 0.766 for PTSD comorbid with TBI vs HC, and 0.726, 0.723, 0.636, and 0.747 for PTSD vs. TBI. Comorbid alcohol abuse, major depressive disorder, and BMI are not confounders in these RF models. Markers of glucose metabolism and inflammation are among the most significant CLIA features in our models. Routine CLIA blood tests have the potential for discriminating PTSD and TBI cases from healthy controls and from each other. These findings hold promise for the development of accessible and low-cost biomarker tests as screening measures for PTSD and TBI in primary care and specialty settings.

Circulating cell-free mitochondrial DNA (ccf-mtDNA) is a biomarker of cellular injury or cellular stress and is a potential novel biomarker of psychological stress and of various brain, somatic, and psychiatric disorders. No studies have yet analyzed ccf-mtDNA levels in post-traumatic stress disorder (PTSD), despite evidence of mitochondrial dysfunction in this condition. In the current study, we compared plasma ccf-mtDNA levels in combat trauma-exposed male veterans with PTSD ( n  = 111) with those who did not develop PTSD ( n  = 121) and also investigated the relationship between ccf mt-DNA levels and glucocorticoid sensitivity. In unadjusted analyses, ccf-mtDNA levels did not differ significantly between the PTSD and non-PTSD groups ( t  = 1.312, p  = 0.191, Cohen’s d = 0.172). In a sensitivity analysis excluding participants with diabetes and those using antidepressant medication and controlling for age, the PTSD group had lower ccf-mtDNA levels than did the non-PTSD group (F(1, 179) = 5.971, p  = 0.016, partial η 2  = 0.033). Across the entire sample, ccf-mtDNA levels were negatively correlated with post-dexamethasone adrenocorticotropic hormone (ACTH) decline ( r  = −0.171, p  = 0.020) and cortisol decline ( r  = −0.149, p  = 0.034) (viz., greater ACTH and cortisol suppression was associated with lower ccf-mtDNA levels) both with and without controlling for age, antidepressant status and diabetes status. Ccf-mtDNA levels were also significantly positively associated with IC 50-DEX (the concentration of dexamethasone at which 50% of lysozyme activity is inhibited), a measure of lymphocyte glucocorticoid sensitivity, after controlling for age, antidepressant status, and diabetes status ( β  = 0.142, p  = 0.038), suggesting that increased lymphocyte glucocorticoid sensitivity is associated with lower ccf-mtDNA levels. Although no overall group differences were found in unadjusted analyses, excluding subjects with diabetes and those taking antidepressants, which may affect ccf-mtDNA levels, as well as controlling for age, revealed decreased ccf-mtDNA levels in PTSD. In both adjusted and unadjusted analyses, low ccf-mtDNA levels were associated with relatively increased glucocorticoid sensitivity, often reported in PTSD, suggesting a link between mitochondrial and glucocorticoid-related abnormalities in PTSD.

Major depressive disorder (MDD) in the elderly is a risk factor for dementia, but the precise biological basis remains unknown, hampering the search for novel biomarkers and treatments. In this study, we performed metabolomics analysis of cerebrospinal fluid (CSF) from cognitively intact elderly patients (N = 28) with MDD and age- and gender-matched healthy controls (N = 18). The CSF levels of 177 substances were measured, while 288 substances were below the detection limit. Only ascorbic acid was significantly different, with higher levels in the MDD group at baseline. There were no correlations between CSF ascorbic acid levels and clinical variables in MDD patients at baseline. At the 3-year follow-up, there was no difference of CSF ascorbic acid levels between the two groups. There was a negative correlation between CSF ascorbic acid and CSF amyloid-β 42 levels in all subjects. However, there were no correlations between ascorbic acid and other biomarkers (e.g., amyloid-β 40 , total and phosphorylated tau protein). This preliminary study suggests that abnormalities in the transport and/or release of ascorbic acid might play a role in the pathogenesis of late-life depression.

We sought to find clinical subtypes of posttraumatic stress disorder (PTSD) in veterans 6–10 years post-trauma exposure based on current symptom assessments and to examine whether blood biomarkers could differentiate them. Samples were males deployed to Iraq and Afghanistan studied by the PTSD Systems Biology Consortium: a discovery sample of 74 PTSD cases and 71 healthy controls (HC), and a validation sample of 26 PTSD cases and 36 HC. A machine learning method, random forests (RF), in conjunction with a clustering method, partitioning around medoids, were used to identify subtypes derived from 16 self-report and clinician assessment scales, including the clinician-administered PTSD scale for DSM-IV (CAPS). Two subtypes were identified, designated S1 and S2, differing on mean current CAPS total scores: S2 = 75.6 (sd 14.6) and S1 = 54.3 (sd 6.6). S2 had greater symptom severity scores than both S1 and HC on all scale items. The mean first principal component score derived from clinical summary scales was three times higher in S2 than in S1. Distinct RFs were grown to classify S1 and S2 vs. HCs and vs. each other on multi-omic blood markers feature classes of current medical comorbidities, neurocognitive functioning, demographics, pre-military trauma, and psychiatric history. Among these classes, in each RF intergroup comparison of S1, S2, and HC, multi-omic biomarkers yielded the highest AUC-ROCs (0.819–0.922); other classes added little to further discrimination of the subtypes. Among the top five biomarkers in each of these RFs were methylation, micro RNA, and lactate markers, suggesting their biological role in symptom severity.

Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event 1 . These patients are at substantial psychiatric risk, with approximately 10–20% developing one or more disorders, including anxiety, depression or post-traumatic stress disorder (PTSD) 2 – 4 . At present, no accurate method exists to predict the development of PTSD symptoms upon ED admission after trauma 5 . Accurate risk identification at the point of treatment by ED services is necessary to inform the targeted deployment of existing treatment 6 – 9 to mitigate subsequent psychopathology in high-risk populations 10 , 11 . This work reports the development and validation of an algorithm for prediction of post-traumatic stress course over 12 months using two independently collected prospective cohorts of trauma survivors from two level 1 emergency trauma centers, which uses routinely collectible data from electronic medical records, along with brief clinical assessments of the patient’s immediate stress reaction. Results demonstrate externally validated accuracy to discriminate PTSD risk with high precision. While the predictive algorithm yields useful reproducible results on two independent prospective cohorts of ED patients, future research should extend the generalizability to the broad, clinically heterogeneous ED population under conditions of routine medical care. A machine-learning algorithm using electronic medical records and self-reported measures of stress at admission to the emergency department due to trauma can predict the risk and long-term trajectories of post-traumatic stress disorder in two independent cohorts.

It has been reported that posttraumatic stress disorder (PTSD) is associated with secondary spouse/partner (S/P) emotional distress and relationship violence. To investigate the relationships between PTSD, S/P emotional distress and relationship violence among police recruits using a prospective design. Two hypotheses were tested in 71 S/Ps: (1) Police officer reports of greater PTSD symptoms after 12 months of police service will be associated with greater secondary trauma symptoms among S/Ps; (2) Greater secondary trauma symptoms among S/Ps at 12 months will be associated with S/P reports of greater relationship violence. 71 police recruits and their S/Ps were assessed at baseline and 12 months after the start of police officer duty. Using linear and logistic regression, we analyzed explanatory variables for 12 month S/P secondary traumatic stress symptoms and couple violence, including baseline S/P variables and couple violence, as well as exposure and PTSD reports from both S/P and officer. S/P perception of officer PTSD symptoms predicted S/P secondary traumatic stress. OS/P secondary trauma was significantly associated with both total couple violence (.34, p = .004) and S/P to officer violence (.35, p = .003). Although results from this relatively small study of young police officers and their S/Ps must be confirmed by larger studies in general populations, findings suggest that S/P perception of PTSD symptoms may play a key role in the spread of traumatic stress symptoms across intimate partner relationships and intimate partner violence in the context of PTSD.

Background and Objective Posttraumatic stress disorder (PTSD) is a serious and frequently debilitating psychiatric condition that can occur in people who have experienced traumatic stressors, such as war, violence, sexual assault and other life‐threatening events. Treatment of PTSD and traumatic brain injury (TBI) in veterans is challenged by diagnostic complexity, partially due to PTSD and TBI symptoms overlap and to the fact that subjective self‐report assessments may be influenced by a patient's willingness to share their traumatic experiences and resulting symptoms. Corticotropin‐releasing factor (CRF) is one of the main mediators of hypothalamic pituitary adrenal (HPA)‐axis responses in stress and anxiety. Methods and Results We analyzed serum CRF levels in 230 participants including heathy controls (64), and individuals with PTSD (53), TBI (70) or PTSD + TBI (43) by enzyme immunoassay (EIA). Significantly lower CRF levels were found in both the PTSD and PTSD + TBI groups compared to healthy control (PTSD vs. Controls: P = 0.0014, PTSD + TBI vs. Controls: P = 0.0011) and chronic TBI participants (PTSD vs. TBI: P < 0.0001, PTSD + TBI vs. TBI: P < 0.0001), suggesting a PTSD‐related mechanism independent from TBI and associated with CRF reduction. CRF levels negatively correlated with PTSD severity on the Clinically Administered PTSD Scale (CAPS‐5) scale in the whole study group. Conclusions Hyperactivation of the HPA axis has been classically identified in acute stress. However, the recognized enhanced feedback inhibition of the HPA axis in chronic stress supports our findings of lower CRF in PTSD patients. This study suggests that reduced serum CRF in PTSD should be further investigated. Future validation studies will establish if CRF is a possible blood biomarker for PTSD and/or for differentiating PTSD and chronic TBI symptomatology. highlights The HPA axis is activated under acute stress conditions, but an enhanced feedback inhibition may be prevalent in chronic stress conditions such as PTSD. We observed a reduction in serum CRF levels in veterans with PTSD and PTSD + TBI, but not in veterans with chronic TBI alone. A serum CRF reduction may be indicative of CNS mechanisms specific to PTSD and should be further evaluated as a possible peripheral biomarker.