Structural equation modeling of treatment-related changes in neural connectivity for youth with PTSD.

BACKGROUND: Previous studies suggest that improvement in symptoms of posttraumatic stress disorder (PTSD) is accompanied by changes in neural connectivity, however, few studies have investigated directional (effective) connectivity. The current study assesses treatment-related changes in effective connectivity in youth with PTSD undergoing Trauma-Focused Cognitive Behavioral Therapy (TF-CBT). METHODS: Functional MRI scans before and after 16 weeks of TF-CBT for 20 youth with PTSD, or the same time interval for 20 healthy controls (HC) were included in the analysis. Structural equation modeling was used to model group differences in directional connectivity at baseline, and changes in connectivity from pre- to post-treatment. RESULTS: At baseline, the PTSD group, relative to the HC group, had significantly greater connectivity in the path from dorsal cingulate to anterior cingulate and from dorsal cingulate to posterior cingulate corticies. From pre- to post-treatment, connectivity in these paths decreased significantly in the PTSD group, as did connectivity from right hippocampus to left superior temporal gyrus. Connectivity from the left amygdala to the lateral orbital frontal cortex was significantly lower in PTSD vs HC at baseline, but did not change from pre- to post-treatment. CONCLUSION: Although based on a small sample, these results converge with previous studies in suggesting a central role for the dorsal cingulate cortex in PTSD symptoms. The direction of this connectivity suggests that the dorsal cingulate is the source of modulation of anterior and posterior cingulate cortex during trauma-focused cognitive behavioral therapy.

Metabolic syndrome predictors of brain gray matter volume in an age-stratified community sample of 776 Mexican- American adults: Results from the genetics of brain structure image archive.

Introduction: This project aimed to investigate the association between biometric components of metabolic syndrome (MetS) with gray matter volume (GMV) obtained with magnetic resonance imaging (MRI) from a large cohort of community-based adults (n = 776) subdivided by age and sex and employing brain regions of interest defined previously as the "Neural Signature of MetS" (NS-MetS). Methods: Lipid profiles, biometrics, and regional brain GMV were obtained from the Genetics of Brain Structure (GOBS) image archive. Participants underwent T1-weighted MR imaging. MetS components (waist circumference, fasting plasma glucose, triglycerides, HDL cholesterol, and blood pressure) were defined using the National Cholesterol Education Program Adult Treatment Panel III. Subjects were grouped by age: early adult (18-25 years), young adult (26-45 years), and middle-aged adult (46-65 years). Linear regression modeling was used to investigate associations between MetS components and GMV in five brain regions comprising the NS-MetS: cerebellum, brainstem, orbitofrontal cortex, right insular/limbic cluster and caudate. Results: In both men and women of each age group, waist circumference was the single component most strongly correlated with decreased GMV across all NS-MetS regions. The brain region most strongly correlated to all MetS components was the posterior cerebellum. Conclusion: The posterior cerebellum emerged as the region most significantly associated with MetS individual components, as the only region to show decreased GMV in young adults, and the region with the greatest variance between men and women. We propose that future studies investigating neurological effects of MetS and its comorbidities-namely diabetes and obesity-should consider the NS-MetS and the differential effects of age and sex.

Psychometric properties of the Disability Identity Development Scale: Confirmatory factor and bifactor analyses.

PURPOSE/OBJECTIVE: This work provides comprehensive analyses targeting the factor structure and dimensionality of the Disability Identity Development Scale (DIDS). In Forber-Pratt et al., 2020, disability was defined broadly to include individuals with visible or hidden disabilities across many disability groups (i.e., physical, intellectual, learning, or chronic illness). RESEARCH METHOD/DESIGN: Retained items from previous exploratory factor analysis were administered to a sample of adults with disabilities (n = 1,126) ranging in age from 18-78 years. Confirmatory factor analytics (CFA) including traditional CFA, and bifactor confirmatory analyses were used to examine the dimensionality and structure of the DIDS. RESULTS: Traditional CFA provided lack of evidence in support of the oblique four-factor structure previously reported. Bifactor confirmatory analysis revealed items on the DIDS are consistent with unidimensional, and to a lesser degree multidimensional solutions (i.e., items lacked a level of content diversity to substantiate a complex, reliable multifactor structure). DISCUSSION/CONCLUSION: Analytic results on the DIDS revealed reasonable psychometric properties as a measure of disability identity. Our results support using a sum or total score of disability identity. Results of this work are an important contribution to a growing body of literature supporting, and investigating, disability identity development. Furthermore, the DIDS measure with its resulting composite score of disability identity has the potential to inform clinicians in the field of rehabilitation psychology as well as informing future targeted interventions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Using the Schmahmann Syndrome Scale to Assess Cognitive Impairment in Young Adults with Metabolic Syndrome: a Hypothesis-Generating Report.

The posterior cerebellum is the most significantly compromised brain structure in individuals with metabolic syndrome (MetS) (Hum Brain Mapp 40(12):3575-3588, 2019). In light of this, we hypothesized that cognitive decline reported in patients with MetS is likely related to posterior cerebellar atrophy. In this study, we performed a post hoc analyses using T1-weighted magnetic resonance imaging (MRI), diffusion tensor imaging (DTI) in the form of voxel-wise tract-based spatial statistics (TBSS), biometric, and psychometric data from young participants with (n = 52, aged 18-35 years) and without MetS (n = 52, aged 18-35 years). To test the predictive value of components of the Schmahmann syndrome scale (SSS), also known as the cerebellar cognitive affective syndrome scale, we used structural equation modeling to adapt available psychometric scores in our participant sample to the SSS and compare them to the composite score of all psychometric data available. Our key findings point to a statistically significant correlation between TBSS fractional anisotropy (FA) values from DTI and adapted SSS psychometric scores in individuals with MetS (r2 = .139, 95% CI = 0.009, .345). This suggests that the SSS could be applied to assess cognitive and likely neuroanatomical effects associated with MetS. We strongly suggest that future work aimed at investigating the neurocognitive effects of MetS and related comorbidities (i.e., dyslipidemia, diabetes, obesity) would benefit from implementing and further exploring the validity of the SSS in this patient population.

Why Temporal Persistence of Biometric Features, as Assessed by the Intraclass Correlation Coefficient, Is So Valuable for Classification Performance.

It is generally accepted that relatively more permanent (i.e., more temporally persistent) traits are more valuable for biometric performance than less permanent traits. Although this finding is intuitive, there is no current work identifying exactly where in the biometric analysis temporal persistence makes a difference. In this paper, we answer this question. In a recent report, we introduced the intraclass correlation coefficient (ICC) as an index of temporal persistence for such features. Here, we present a novel approach using synthetic features to study which aspects of a biometric identification study are influenced by the temporal persistence of features. What we show is that using more temporally persistent features produces effects on the similarity score distributions that explain why this quality is so key to biometric performance. The results identified with the synthetic data are largely reinforced by an analysis of two datasets, one based on eye-movements and one based on gait. There was one difference between the synthetic and real data, related to the intercorrelation of features in real data. Removing these intercorrelations for real datasets with a decorrelation step produced results which were very similar to that obtained with synthetic features.

Revisiting Reliability: Using Sampling Utterances and Grammatical Analysis Revised (SUGAR) to Compare 25- and 50-Utterance Language Samples.

Purpose The goal of this study was to determine whether the results obtained from a 25-utterance conversational language sample were as reliable as those obtained from a 50-utterance sample. Method Robust conversational language samples from 220 children with typically developing language (106 boys, 114 girls) ranging in age from 3;2 to 7;10 (years;months) were collected. The language samples were randomly assigned to one of two conditions: a 25-utterance condition and a 50-utterance condition. Transcripts were examined for three metrics, including mean length of utteranceSUGAR, words per sentence, and clauses per sentence. Results Data were analyzed using two methods. A linear mixed-model analysis was used to assess absolute and relative reliability, and the Bland-Altman procedure was used to assess absolute reliability and clinical acceptability. Results of the mixed-model analysis indicated that mean length of utteranceSUGAR and words per sentence demonstrated relative reliability; however, none of the metrics demonstrated absolute reliability. In contrast, results of the Bland-Altman scatter plots indicated that all three metrics demonstrated absolute reliability because 94%-96% of participants' scores fell within the limits of agreement. Taken together, the results suggested that the statistically significant differences indicated by the mixed-model analysis were not clinically significant. Conclusion These results highlighted the importance of using different methods of analysis in studies of reliability. The findings indicated that reliable language sample results can be obtained from 25-utterance samples. Furthermore, by including practices already in use (e.g., collecting samples ≤ 50 utterances) and including only minimal changes to current practices, the methods used in this study are feasible for school-based clinicians, could be easily integrated into clinical practice, and could increase the use of evidence-based assessment practices in schools.

Initial factor exploration of disability identity.

PURPOSE/OBJECTIVE: This article describes the initial factor exploration of disability identity and preliminary psychometric characteristics based on an adult self-report tool. Disability was defined broadly, and the sample included individuals with visible and/or hidden disabilities across many disability groups (i.e., physical, intellectual, learning, mental illness). METHOD: Items were developed (n = 102) and a pilot measure was administered to a sample of adults with disabilities (n = 566). An exploratory factor analysis (EFA) using the mean and variance adjusted weighted least squares (WLSMV) estimator was conducted. The resulting items were sent to expert reviewers for evaluation. RESULTS: Following the exploratory analyses, 37 items were retained that made up four factors: internal beliefs about own disability and the disability community, anger and frustration with disability experiences, adoption of disability community values, and contribution to the disability community. The pilot measure aligned well with the theoretical framework that guided its development. DISCUSSION/CONCLUSION: This factor exploration is a contribution to a growing body of literature supporting, and investigating, disability identity development. This work presents a more comprehensive understanding of disability identity development. Armed with a better understanding, this will serve as a basis to inform future scale development and validation. After this validation work is completed, there is the potential to apply findings to tailor interventions and clinical work, so that psychologists and rehabilitation professionals may be better prepared to meet the developmental needs of disabled clients. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Network Alterations in Comorbid Chronic Pain and Opioid Addiction: An Exploratory Approach.

The comorbidity of chronic pain and opioid addiction is a serious problem that has been growing with the practice of prescribing opioids for chronic pain. Neuroimaging research has shown that chronic pain and opioid dependence both affect brain structure and function, but this is the first study to evaluate the neurophysiological alterations in patients with comorbid chronic pain and addiction. Eighteen participants with chronic low back pain and opioid addiction were compared with eighteen age- and sex-matched healthy individuals in a pain-induction fMRI task. Unified structural equation modeling (SEM) with Lagrange multiplier (LM) testing yielded a network model of pain processing for patient and control groups based on 19 a priori defined regions. Tests of differences between groups on specific regression parameters were determined on a path-by-path basis using z-tests corrected for the number of comparisons. Patients with the chronic pain and addiction comorbidity had increased connection strengths; many of these connections were interhemispheric and spanned regions involved in sensory, affective, and cognitive processes. The affected regions included those that are commonly altered in chronic pain or addiction alone, indicating that this comorbidity manifests with neurological symptoms of both disorders. Understanding the neural mechanisms involved in the comorbidity is crucial to finding a comprehensive treatment, rather than treating the symptoms individually.

A neural signature of metabolic syndrome.

That metabolic syndrome (MetS) is associated with age-related cognitive decline is well established. The neurobiological changes underlying these cognitive deficits, however, are not well understood. The goal of this study was to determine whether MetS is associated with regional differences in gray-matter volume (GMV) using a cross-sectional, between-group contrast design in a large, ethnically homogenous sample. T1-weighted MRIs were sampled from the genetics of brain structure (GOBS) data archive for 208 Mexican-American participants: 104 participants met or exceeded standard criteria for MetS and 104 participants were age- and sex-matched metabolically healthy controls. Participants ranged in age from 18 to 74 years (37.3 ± 13.2 years, 56.7% female). Images were analyzed in a whole-brain, voxel-wise manner using voxel-based morphometry (VBM). Three contrast analyses were performed, a whole sample analysis of all 208 participants, and two post hoc half-sample analyses split by age along the median (35.5 years). Significant associations between MetS and decreased GMV were observed in multiple, spatially discrete brain regions including the posterior cerebellum, brainstem, orbitofrontal cortex, bilateral caudate nuclei, right parahippocampus, right amygdala, right insula, lingual gyrus, and right superior temporal gyrus. Age, as shown in the post hoc analyses, was demonstrated to be a significant covariate. A further functional interpretation of the structures exhibiting lower GMV in MetS reflected a significant involvement in reward perception, emotional valence, and reasoning. Additional studies are needed to characterize the influence of MetS's individual clinical components on brain structure and to explore the bidirectional association between GMV and MetS.

The hippocampal network model: A transdiagnostic metaconnectomic approach.

Purpose: The hippocampus plays a central role in cognitive and affective processes and is commonly implicated in neurodegenerative diseases. Our study aimed to identify and describe a hippocampal network model (HNM) using trans-diagnostic MRI data from the BrainMap® database. We used meta-analysis to test the network degeneration hypothesis (NDH) (Seeley et al., 2009) by identifying structural and functional covariance in this hippocampal network. Methods: To generate our network model, we used BrainMap's VBM database to perform a region-to-whole-brain (RtWB) meta-analysis of 269 VBM experiments from 165 published studies across a range of 38 psychiatric and neurological diseases reporting hippocampal gray matter density alterations. This step identified 11 significant gray matter foci, or nodes. We subsequently used meta-analytic connectivity modeling (MACM) to define edges of structural covariance between nodes from VBM data as well as functional covariance using the functional task-activation database, also from BrainMap. Finally, we applied a correlation analysis using Pearson's r to assess the similarities and differences between the structural and functional covariance models. Key findings: Our hippocampal RtWB meta-analysis reported consistent and significant structural covariance in 11 key regions. The subsequent structural and functional MACMs showed a strong correlation between HNM nodes with a significant structural-functional covariance correlation of r = .377 (p = .000049). Significance: This novel method of studying network covariance using VBM and functional meta-analytic techniques allows for the identification of generalizable patterns of functional and structural abnormalities pertaining to the hippocampus. In accordance with the NDH, this framework could have major implications in studying and predicting spatial disease patterns using network-based assays.

Confirmatory Factor Analysis Alternative: Free, Accessible CBID Software.

New software that performs Classical and Bayesian Instrument Development (CBID) is reported that seamlessly integrates expert (content validity) and participant data (construct validity) to produce entire reliability estimates with smaller sample requirements. The free CBID software can be accessed through a website and used by clinical investigators in new instrument development. Demonstrations are presented of the three approaches using the CBID software: (a) traditional confirmatory factor analysis (CFA), (b) Bayesian CFA using flat uninformative prior, and (c) Bayesian CFA using content expert data (informative prior). Outcomes of usability testing demonstrate the need to make the user-friendly, free CBID software available to interdisciplinary researchers. CBID has the potential to be a new and expeditious method for instrument development, adding to our current measurement toolbox. This allows for the development of new instruments for measuring determinants of health in smaller diverse populations or populations of rare diseases.

Psychological Distress in Healthy Low-Risk First-Time Mothers during the Postpartum Period: An Exploratory Study.

Psychological distress, defined as depression, anxiety, and insomnia in this study, can occur following the birth of a baby as new mothers, in addition to marked physiological changes, are faced with adapting to new roles and responsibilities. We investigated the cooccurrence of stress, depression, anxiety, and insomnia in mothers during the postpartum period; tested the feasibility of study methods and procedures for use in this population; and identified new mothers interest in using cranial electrotherapy stimulation (CES) as an intervention for reducing psychological distress. We recruited healthy, low-risk, English speaking first-time mothers, ages 18-32 years, with healthy babies (N = 33), within 12 months of an uncomplicated birth. Participants completed the PSS, HAM-D14, HAM-A17, and PSQI19. No problems were encountered with study procedures. Mothers reported a high interest (4.9) in the potential use of CES to treat or prevent the occurrence of psychological distress. All participants (N = 33) reported moderate levels of depression and anxiety, while 75.8% (n = 25) reported insomnia. PSS scores were within the norms for healthy women. Further research is recommended to investigate if our findings can be replicated or if different patterns of associations emerge. Implications for clinical practice are addressed.

A novel method for expediting the development of patient-reported outcome measures and an evaluation across several populations.

Item response theory (IRT) models provide an appropriate alternative to the classical ordinal confirmatory factor analysis (CFA) during the development of patient-reported outcome measures (PROMs). Current literature has identified the assessment of IRT model fit as both challenging and underdeveloped (Sinharay & Johnson, 2003; Sinharay, Johnson, & Stern, 2006). This study evaluates the performance of Ordinal Bayesian Instrument Development (OBID), a Bayesian IRT model with a probit link function approach, through applications in two breast cancer-related instrument development studies. The primary focus is to investigate an appropriate method for comparing Bayesian IRT models in PROMs development. An exact Bayesian leave-one-out cross-validation (LOO-CV) approach (Vehtari & Lampinen, 2002) is implemented to assess prior selection for the item discrimination parameter in the IRT model and subject content experts' bias (in a statistical sense and not to be confused with psychometric bias as in differential item functioning) toward the estimation of item-to-domain correlations. Results support the utilization of content subject experts' information in establishing evidence for construct validity when sample size is small. However, the incorporation of subject experts' content information in the OBID approach can be sensitive to the level of expertise of the recruited experts. More stringent efforts need to be invested in the appropriate selection of subject experts to efficiently use the OBID approach and reduce potential bias during PROMs development.

Progressive Bidirectional Age-Related Changes in Default Mode Network Effective Connectivity across Six Decades.

The default mode network (DMN) is a set of regions that is tonically engaged during the resting state and exhibits task-related deactivation that is readily reproducible across a wide range of paradigms and modalities. The DMN has been implicated in numerous disorders of cognition and, in particular, in disorders exhibiting age-related cognitive decline. Despite these observations, investigations of the DMN in normal aging are scant. Here, we used blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) acquired during rest to investigate age-related changes in functional connectivity of the DMN in 120 healthy normal volunteers comprising six, 20-subject, decade cohorts (from 20-29 to 70-79). Structural equation modeling (SEM) was used to assess age-related changes in inter-regional connectivity within the DMN. SEM was applied both using a previously published, meta-analytically derived, node-and-edge model, and using exploratory modeling searching for connections that optimized model fit improvement. Although the two models were highly similar (only 3 of 13 paths differed), the sample demonstrated significantly better fit with the exploratory model. For this reason, the exploratory model was used to assess age-related changes across the decade cohorts. Progressive, highly significant changes in path weights were found in 8 (of 13) paths: four rising, and four falling (most changes were significant by the third or fourth decade). In all cases, rising paths and falling paths projected in pairs onto the same nodes, suggesting compensatory increases associated with age-related decreases. This study demonstrates that age-related changes in DMN physiology (inter-regional connectivity) are bidirectional, progressive, of early onset and part of normal aging.

A novel method for expediting the development of patient-reported outcome measures and an evaluation of its performance via simulation.

BACKGROUND: Developing valid and reliable patient-reported outcome measures (PROMs) is a critical step in promoting patient-centered health care, a national priority in the U.S. Small populations or rare diseases often pose difficulties in developing PROMs using traditional methods due to small samples. METHODS: To overcome the small sample size challenge while maintaining psychometric soundness, we propose an innovative Ordinal Bayesian Instrument Development (OBID) method that seamlessly integrates expert and participant data in a Bayesian item response theory (IRT) with a probit link model framework. Prior distributions obtained from expert data are imposed on the IRT model parameters and are updated with participants' data. The efficiency of OBID is evaluated by comparing its performance to classical instrument development performance using actual and simulation data. RESULTS AND DISCUSSION : The overall performance of OBID (i.e., more reliable parameter estimates, smaller mean squared errors (MSEs) and higher predictive validity) is superior to that of classical approaches when the sample size is small (e.g. less than 100 subjects). Although OBID may exhibit larger bias, it reduces the MSEs by decreasing variances. Results also closely align with recommendations in the current literature that six subject experts will be sufficient for establishing content validity evidence. However, in the presence of highly biased experts, three experts will be adequate. CONCLUSIONS: This study successfully demonstrated that the OBID approach is more efficient than the classical approach when the sample size is small. OBID promises an efficient and reliable method for researchers and clinicians in future PROMs development for small populations or rare diseases.

Response to Sijtsma and van der Ark (2015): "Conceptions of reliability revisited and practical recommendations".

Sijtsma and van der Ark present a broad set of models and methods for reliability estimation, and their discussion of similarities and differences provides clear information for nurse researchers to move forward in their instrument development projects. In particular, we applaud the authors' clear exposition of the factor analytic model and its utility for providing a framework for unifying reliability and validity. However, we do not want to be constrained only to the point estimates. We also need to ascertain the uncertainty in the point estimate-usually in the form of a 95% confidence interval-or, as the Bayesians refer to, a credible interval. Another issue not discussed by Sijtsma and van der Ark is conditional standard errors of measurement along the score scale measuring latent traits or true scores. In our response, practical tools for estimating intervals and a brief discussion of conditional standard errors of measurement are presented.

Thalamic functional connectivity predicts seizure laterality in individual TLE patients: application of a biomarker development strategy.

Noninvasive markers of brain function could yield biomarkers in many neurological disorders. Disease models constrained by coordinate-based meta-analysis are likely to increase this yield. Here, we evaluate a thalamic model of temporal lobe epilepsy that we proposed in a coordinate-based meta-analysis and extended in a diffusion tractography study of an independent patient population. Specifically, we evaluated whether thalamic functional connectivity (resting-state fMRI-BOLD) with temporal lobe areas can predict seizure onset laterality, as established with intracranial EEG. Twenty-four lesional and non-lesional temporal lobe epilepsy patients were studied. No significant differences in functional connection strength in patient and control groups were observed with Mann-Whitney Tests (corrected for multiple comparisons). Notwithstanding the lack of group differences, individual patient difference scores (from control mean connection strength) successfully predicted seizure onset zone as shown in ROC curves: discriminant analysis (two-dimensional) predicted seizure onset zone with 85% sensitivity and 91% specificity; logistic regression (four-dimensional) achieved 86% sensitivity and 100% specificity. The strongest markers in both analyses were left thalamo-hippocampal and right thalamo-entorhinal cortex functional connection strength. Thus, this study shows that thalamic functional connections are sensitive and specific markers of seizure onset laterality in individual temporal lobe epilepsy patients. This study also advances an overall strategy for the programmatic development of neuroimaging biomarkers in clinical and genetic populations: a disease model informed by coordinate-based meta-analysis was used to anatomically constrain individual patient analyses.

The neural changes in connectivity of the voice network during voice pitch perturbation.

Voice control is critical to communication. To date, studies have used behavioral, electrophysiological and functional data to investigate the neural correlates of voice control using perturbation tasks, but have yet to examine the interactions of these neural regions. The goal of this study was to use structural equation modeling of functional neuroimaging data to examine network properties of voice with and without perturbation. Results showed that the presence of a pitch shift, which was processed as an error in vocalization, altered connections between right STG and left STG. Other regions that revealed differences in connectivity during error detection and correction included bilateral inferior frontal gyrus, and the primary and pre motor cortices. Results indicated that STG plays a critical role in voice control, specifically, during error detection and correction. Additionally, pitch perturbation elicits changes in the voice network that suggest the right hemisphere is critical to pitch modulation.

Is the Polar F6 heart rate monitor less accurate during aerobic bench stepping because of arm movements?

Because of the well-documented linear relationship between heart rate and oxygen consumption (VO2), heart rate is commonly used to estimate energy expenditure during exercise. However, previous research suggests that heart rate increases without a concomitant rise in VO2 when arm movements are added to exercise. If so, this could impact the accuracy of heart rate monitors in estimating energy expenditure during combined arm and leg exercise. This study compared the cardiorespiratory responses to a bench step aerobics routine performed with and without arm movements and evaluated whether the accuracy of the Polar F6 heart rate monitor in predicting energy expenditure was impacted by the inclusion of arm movements. Thirty-two women performed the same routine with and without arm movements while stepping up and down off of a 15.24-cm bench at a cadence of 128 b·min-1. Heart rate and VO2 increased, whereas oxygen pulse (VO2·heart rate-1) decreased when arm movements were added (p < 0.001). However, the differences between the energy expenditure estimated by the Polar F6 heart rate monitor and the energy expenditure measured by indirect calorimetry were similar during the same aerobic bench stepping routine performed with and without arms (Δ∼2 kCal·min-1, p ≥ 0.05). Results confirm that arm movements during aerobic bench stepping elicit a disproportionate rise in heart rate relative to V[Combining Dot Above]O2. However, results do not support that these movements increase the prediction error in energy expenditure, as the Polar F6 heart rate monitor over predicted energy expenditure when arm movements were involved and when they were not involved.

Military service member and veteran self reports of efficacy of cranial electrotherapy stimulation for anxiety, posttraumatic stress disorder, insomnia, and depression.

Cranial electrotherapy stimulation (CES) is being prescribed for service members and veterans for the treatment of anxiety, posttraumatic stress disorder (PTSD), insomnia and depression. The purpose of this study was to examine service members' and veterans' perceptions of the effectiveness and safety of CES treatment. Service members and veterans (N=1,514) who had obtained a CES device through the Department of Defense or Veterans Affairs Medical Center from 2006-2011 were invited to participate in the web based survey via email. One hundred fifty-two participants returned questionnaires. Data were analyzed using descriptive statistics. Participants reported clinical improvement of 25% or more from using CES for anxiety (66.7%), PTSD (62.5%), insomnia (65.3%) and depression (53.9%). The majority of these participants reported clinical improvement of 50% or more. Respondents also perceived CES to be safe (99.0%). Those individuals who were not taking any prescription medication rated CES more effective than the combined CES and prescription medication group. CES provides service members and veterans with a safe, noninvasive, nondrug, easy to use treatment for anxiety, PTSD, insomnia, and depression that can be used in the clinical setting or self-directed at home.