Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder

Posttraumatic stress disorder (PTSD) is a heterogeneous condition associated with a range of brain imaging abnormalities. Early life stress (ELS) contributes to this heterogeneity, but we do not know how a history of ELS influences traditionally defined brain signatures of PTSD. Here, we used a novel machine learning method – evolving partitions to improve classification (EPIC) – to identify shared and unique structural neuroimaging markers of ELS and PTSD in 97 combat-exposed military veterans. METHODS: We used EPIC with repeated cross-validation (CV) to determine how combinations of cortical thickness, surface area, and subcortical brain volumes could contribute to classification of PTSD (n = 40) versus controls (n = 57), and classification of ELS within the PTSD (ELS+ n = 16; ELS− n = 24) and control groups (ELS+ n = 16; ELS− n = 41). Additional inputs included intracranial volume, age, sex, adult trauma, and depression. RESULTS: On average, EPIC classified PTSD with 69% accuracy (SD = 5%), and ELS with 64% accuracy in the PTSD group (SD = 10%), and 62% accuracy in controls (SD = 6%). EPIC selected unique sets of individual features that classified each group with 75–85% accuracy in post hoc analyses; combinations of regions marginally improved classification from the individual atlas-defined brain regions. Across analyses, surface area in the right posterior cingulate was the only variable that was repeatedly selected as an important feature for classification of PTSD and ELS. CONCLUSIONS: EPIC revealed unique patterns of features that distinguished PTSD and ELS in this sample of combat-exposed military veterans, which may represent distinct biotypes of stress-related neuropathology

Imaging Frontostriatal Function in Ultra-High-Risk, Early, and Chronic Schizophrenia During Executive Processing

Individuals experiencing prodromal symptoms of schizophrenia (ultra-high-risk group) demonstrate impaired performance on tasks of executive function, attention, and working memory. The neurobiological underpinnings of such executive deficits in ultra-high-risk individuals remains unclear

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

Neurosteroids and Self-Reported Pain in Veterans Who Served in the U.S. Military after September 11, 2001

Nearly half of Operation Enduring Freedom / Operation Iraqi Freedom (OEF/OIF) veterans experience continued pain post-deployment. Several investigations report analgesic effects of allopregnanolone and other neurosteroids in animal models, but few data are currently available focusing on neurosteroids in clinical populations. Allopregnanolone positively modulates GABAA receptors and demonstrates pronounced analgesic and anxiolytic effects in rodents, yet studies examining the relationship between pain and allopregnanolone in humans are limited. We thus hypothesized that endogenous allopregnanolone and other neurosteroid levels may be negatively correlated with self-reported pain symptoms in humans

Serotonin transporter gene polymorphisms and brain function during emotional distraction from cognitive processing in posttraumatic stress disorder

BACKGROUND: Serotonergic system dysfunction has been implicated in posttraumatic stress disorder (PTSD). Genetic polymorphisms associated with serotonin signaling may predict differences in brain circuitry involved in emotion processing and deficits associated with PTSD. In healthy individuals, common functional polymorphisms in the serotonin transporter gene (SLC6A4) have been shown to modulate amygdala and prefrontal cortex (PFC) activity in response to salient emotional stimuli. Similar patterns of differential neural responses to emotional stimuli have been demonstrated in PTSD but genetic factors influencing these activations have yet to be examined. METHODS: We investigated whether SLC6A4 promoter polymorphisms (5-HTTLPR, rs25531) and several downstream single nucleotide polymorphisms (SNPs) modulated activity of brain regions involved in the cognitive control of emotion in post-9/11 veterans with PTSD. We used functional MRI to examine neural activity in a PTSD group (n = 22) and a trauma-exposed control group (n = 20) in response to trauma-related images presented as task-irrelevant distractors during the active maintenance period of a delayed-response working memory task. Regions of interest were derived by contrasting activation for the most distracting and least distracting conditions across participants. RESULTS: In patients with PTSD, when compared to trauma-exposed controls, rs16965628 (associated with serotonin transporter gene expression) modulated task-related ventrolateral PFC activation and 5-HTTLPR tended to modulate left amygdala activation. Subsequent to combat-related trauma, these SLC6A4 polymorphisms may bias serotonin signaling and the neural circuitry mediating cognitive control of emotion in patients with PTSD. CONCLUSIONS: The SLC6A4 SNP rs16965628 and 5-HTTLPR are associated with a bias in neural responses to traumatic reminders and cognitive control of emotions in patients with PTSD. Functional MRI may help identify intermediate phenotypes and dimensions of PTSD that clarify the functional link between genes and disease phenotype, and also highlight features of PTSD that show more proximal influence of susceptibility genes compared to current clinical categorizations

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

Linking Symptom Inventories using Semantic Textual Similarity

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment

Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder:Results from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA). METHODS: Neuroimaging and clinical data were aggregated from 29 research sites in >1,300 PTSD cases and >2,000 trauma-exposed controls (age 6.2-85.2 years) by the ENIGMA-PGC PTSD working group. Cortical regions in the network were rank-ordered by effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2 to 148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared to the mean SC of 5,000 randomly generated n-region networks. RESULTS: Patients with PTSD, relative to non-PTSD controls, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex and age modulated covariance differences of PTSD-related structural networks. CONCLUSIONS: Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The structural covariance networks that are perturbed in PTSD comport with converging evidence from resting state functional connectivity networks and networks impacted by inflammatory processes, and stress hormones in PTSD