Neuropsychological evaluation of blast-related concussion: Illustrating the challenges and complexities through OEF/OIF case studies

Background/objective: Soldiers of Operations Enduring Freedom (OEF) and Iraqi Freedom (OIF) sustain blast-related mild traumatic brain injury (concussion) with alarming regularity. This study discusses factors in addition to concussion, such as co-morbid psychological difficulty (e.g. post-traumatic stress) and symptom validity concerns that may complicate neuropsychological evaluation in the late stage of concussive injury. Case report: The study presents the complexities that accompany neuropsychological evaluation of blast concussion through discussion of three case reports of OEF/OIF personnel. Discussion: The authors emphasize uniform assessment of blast concussion, the importance of determining concussion severity according to acute-injury characteristics and elaborate upon non-concussion-related factors that may impact course of cognitive limitation. The authors conclude with a discussion of the need for future research examining the impact of blast concussion (particularly recurrent concussion) and neuropsychological performance

Evaluation Context Impacts Neuropsychological Performance of OEF/OIF Veterans with Reported Combat-Related Concussion

Although soldiers of Operations Iraqi Freedom (OIF) and Enduring Freedom (OEF) encounter combat-related concussion at an unprecedented rate, relatively few studies have examined how evaluation context, insufficient effort, and concussion history impact neuropsychological performances in the years following injury. The current study explores these issues in a sample of 119 U.S. veterans (OEF/OIF forensic concussion, n = 24; non-OEF/OIF forensic concussion, n = 20; OEF/OIF research concussion, n = 38; OEF/OIF research without concussion, n = 37). The OEF/OIF forensic concussion group exhibited significantly higher rates of insufficient effort relative to the OEF/OIF research concussion group, but a comparable rate of insufficient effort relative to the non-OEF/OIF forensic concussion group. After controlling for effort, the research concussion and the research non-concussion groups demonstrated comparable neuropsychological performance. Results highlight the importance of effort assessment among OEF/OIF and other veterans with concussion history, particularly in forensic contexts

Neuropsychological Outcomes of U.S. Veterans with Report of Remote Blast-Related Concussion and Current Psychopathology

This study explored whether remote blast-related MTBI and/or current Axis I psychopathology contribute to neuropsychological outcomes among OEF/OIF veterans with varied combat histories. OEF/OIF veterans underwent structured interviews to evaluate history of blast-related MTBI and psychopathology and were assigned to MTBI (n = 18), Axis I (n = 24), Co-morbid MTBI/Axis I (n = 34), or post-deployment control (n = 28) groups. A main effect for Axis I diagnosis on overall neuropsychological performance was identified (F(3,100) = 4.81; p = .004), with large effect sizes noted for the Axis I only (d = .98) and Co-morbid MTBI/Axis I (d = .95) groups relative to the control group. The latter groups demonstrated primary limitations on measures of learning/memory and processing speed. The MTBI only group demonstrated performances that were not significantly different from the remaining three groups. These findings suggest that a remote history of blast-related MTBI does not contribute to objective cognitive impairment in the late stage of injury. Impairments, when present, are subtle and most likely attributable to PTSD and other psychological conditions. Implications for clinical neuropsychologists and future research are discussed. (JINS, 2012, 18, 1–11

The Sensory Gating Inventory-Brief

The Sensory Gating Inventory (SGI) is a 36-item measure used to assess an individual’s subjective ability to modulate, filter, over-include, discriminate, attend to, and tolerate sensory stimuli. Due to its theoretical and empirical link with sensory processing deficits, this measure has been used extensively in studies of psychosis and other psychopathology. The current work fills a need within the field for a briefer measure of sensory gating aberrations that maintains the original measure’s utility. For this purpose, large samples (total n = 1552) were recruited from 2 independent sites for item reduction/selection and brief measure validation, respectively. These samples reflected subgroups of individuals with a psychosis-spectrum disorder, at high risk for a psychosis-spectrum disorder, nonpsychiatric controls, and nonpsychosis psychiatric controls. Factor analyses and item-response models were used to create the SGI-Brief (SGI-B; 10 Likert-rated items), a unidimensional self-report measure that retains the original SGI’s transdiagnostic (ie, present across disorders) utility and content breadth. Findings show that the SGI-B has excellent psychometric properties (alpha = 0.92) and demonstrates external validity through strong associations with measures of psychotic symptomatology, theoretically linked measures of personality (eg, perceptual dysregulation), and modest associations with laboratory-based sensory processing tasks in the auditory and visual domains on par with the original version. Accordingly, the SGI-B will be a valuable tool for dimensional and transdiagnostic examination of sensory gating abnormalities within clinical science research, while reducing administrator and participant burden

Age-dependent white matter disruptions after military traumatic brain injury: Multivariate analysis results from ENIGMA brain injury

Mild Traumatic brain injury (mTBI) is a signature wound in military personnel, and repetitive mTBI has been linked to age-related neurogenerative disorders that affect white matter (WM) in the brain. However, findings of injury to specific WM tracts have been variable and inconsistent. This may be due to the heterogeneity of mechanisms, etiology, and comorbid disorders related to mTBI. Non-negative matrix factorization (NMF) is a data-driven approach that detects covarying patterns (components) within high-dimensional data. We applied NMF to diffusion imaging data from military Veterans with and without a self-reported TBI history. NMF identified 12 independent components derived from fractional anisotropy (FA) in a large dataset (n = 1,475) gathered through the ENIGMA (Enhancing Neuroimaging Genetics through Meta-Analysis) Military Brain Injury working group. Regressions were used to examine TBI- and mTBI-related associations in NMF-derived components while adjusting for age, sex, post-traumatic stress disorder, depression, and data acquisition site/scanner. We found significantly stronger age-dependent effects of lower FA in Veterans with TBI than Veterans without in four components (q \u3c 0.05), which are spatially unconstrained by traditionally defined WM tracts. One component, occupying the most peripheral location, exhibited significantly stronger age-dependent differences in Veterans with mTBI. We found NMF to be powerful and effective in detecting covarying patterns of FA associated with mTBI by applying standard parametric regression modeling. Our results highlight patterns of WM alteration that are differentially affected by TBI and mTBI in younger compared to older military Veterans

Intrusive Traumatic Re-Experiencing Domain (ITRED) – Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium

Background Intrusive Traumatic Re-Experiencing Domain (ITRED) was recently introduced as a novel perspective on posttraumatic psychopathology, proposing to focus research of posttraumatic stress disorder (PTSD) on the unique symptoms of intrusive and involuntary re-experiencing of the trauma, namely, intrusive memories, nightmares, and flashbacks. The aim of the present study was to explore ITRED from a neural network connectivity perspective. Methods Data was collected from nine sites taking part in the ENIGMA-PTSD Consortium (n=584) and included itemized PTSD symptoms scores and resting-state functional connectivity (rsFC) data. We assessed the utility of rsFC in classifying PTSD, ITRED-only (no PTSD diagnosis), and Trauma-exposed (TE)-only (no PTSD or ITRED) groups using a machine learning approach, examining well-known networks implicated in PTSD. Random forest classification model was built on a training set using cross-validation (CV), and the averaged CV model performance for classification was evaluated using area-under-the-curve (AUC). The model was tested using a fully independent portion of the data (test dataset), and the test AUC was evaluated. Results RsFC signatures differentiated TE-only participants from PTSD and from ITRED-only participants at about 60% accuracy. Conversely, rsFC signatures did not differentiate PTSD from ITRED-only individuals (45% accuracy). Common features differentiating TE-only participants from PTSD and from ITRED-only participants mainly involved default mode network-related pathways. Some unique features, such as connectivity within the frontal-parietal network, differentiated TE-only participants from one group (PTSD or ITRED-only), but to a lesser extent from the other. Conclusion Neural network connectivity supports ITRED as a novel neurobiologically-based approach to classifying post-trauma psychopathology

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

Altered Small-World Brain Networks in Schizophrenia Patients during Working Memory Performance

Impairment of working memory (WM) performance in schizophrenia patients (SZ) is well-established. Compared to healthy controls (HC), SZ patients show aberrant blood oxygen level dependent (BOLD) activations and disrupted functional connectivity during WM performance. In this study, we examined the small-world network metrics computed from functional magnetic resonance imaging (fMRI) data collected as 35 HC and 35 SZ performed a Sternberg Item Recognition Paradigm (SIRP) at three WM load levels. Functional connectivity networks were built by calculating the partial correlation on preprocessed time courses of BOLD signal between task-related brain regions of interest (ROIs) defined by group independent component analysis (ICA). The networks were then thresholded within the small-world regime, resulting in undirected binarized small-world networks at different working memory loads. Our results showed: 1) at the medium WM load level, the networks in SZ showed a lower clustering coefficient and less local efficiency compared with HC; 2) in SZ, most network measures altered significantly as the WM load level increased from low to medium and from medium to high, while the network metrics were relatively stable in HC at different WM loads; and 3) the altered structure at medium WM load in SZ was related to their performance during the task, with longer reaction time related to lower clustering coefficient and lower local efficiency. These findings suggest brain connectivity in patients with SZ was more diffuse and less strongly linked locally in functional network at intermediate level of WM when compared to HC. SZ show distinctly inefficient and variable network structures in response to WM load increase, comparing to stable highly clustered network topologies in HC