The Hierarchical Taxonomy of Psychopathology (HiTOP) is a scientific effort to address shortcomings of traditional mental disorder diagnoses, which suffer from arbitrary boundaries between ...psychopathology and normality, frequent disorder co‐occurrence, heterogeneity within disorders, and diagnostic instability. This paper synthesizes evidence on the validity and utility of the thought disorder and detachment spectra of HiTOP. These spectra are composed of symptoms and maladaptive traits currently subsumed within schizophrenia, other psychotic disorders, and schizotypal, paranoid and schizoid personality disorders. Thought disorder ranges from normal reality testing, to maladaptive trait psychoticism, to hallucinations and delusions. Detachment ranges from introversion, to maladaptive detachment, to blunted affect and avolition. Extensive evidence supports the validity of thought disorder and detachment spectra, as each spectrum reflects common genetics, environmental risk factors, childhood antecedents, cognitive abnormalities, neural alterations, biomarkers, and treatment response. Some of these characteristics are specific to one spectrum and others are shared, suggesting the existence of an overarching psychosis superspectrum. Further research is needed to extend this model, such as clarifying whether mania and dissociation belong to thought disorder, and explicating processes that drive development of the spectra and their subdimensions. Compared to traditional diagnoses, the thought disorder and detachment spectra demonstrated substantially improved utility: greater reliability, larger explanatory and predictive power, and higher acceptability to clinicians. Validated measures are available to implement the system in practice. The more informative, reliable and valid characterization of psychosis‐related psychopathology offered by HiTOP can make diagnosis more useful for research and clinical care.
Problematic alcohol use is a serious threat to the behavioral health of active-duty Service Members (ADSM), resulting in numerous calls from governmental agencies to better understand mechanistic ...factors contributing to alcohol misuse within the military. Alcohol use motives are reliable predictors of alcohol-related behaviors and are considered malleable targets for prevention and intervention efforts. However, empirical research indicates that drinking motives vary across contextually distinct populations. Although some research has been conducted among veteran and reservist populations, limited work has been specifically focused on ADSM and no research has evaluated motives and alcohol metrics among ADSM based on military rank. Participants for the current study included 682 ADSM recruited from a large military installation in the U.S. Structural equation modeling evaluated associations between four drinking motives (i.e., enhancement, social, conformity, coping) and three alcohol misuse metrics (i.e., alcohol frequency, binge frequency, alcohol problems). Three models were evaluated: one full (combined) model and two separate models based on military rank – junior enlisted (i.e., E1–E4) and non-commissioned officers (NCOs) (i.e., E5–E9). Results for junior enlisted ADSM indicated that coping and enhancement motives were most strongly associated with all alcohol misuse metrics. However, among NCOs, results indicated that alcohol problems were only associated with coping motives. Notably, results also indicated that alcohol use motives accounted for substantively more variance across all alcohol-related metrics among NCOs. Findings generally support extant military-related literature indicating use of alcohol for coping (e.g., with anxiety) as the motivation most consistently associated with increased alcohol misuse. However, novel findings highlight enhancement motives – using alcohol to attain some positive internal reward – as another, often stronger, motivation impacting alcohol use outcomes. Further, findings highlight notable distinctions between alcohol use motives (i.e., coping vs. enhancement) and the impact of alcohol use motives (i.e., effect size) on alcohol metrics between junior enlisted and NCOs.
•Generally, coping and enhancement motives were most strongly linked to alcohol misuse.•Enhancement motives impacted junior enlisted alcohol use more than coping motives.•Only coping motives were associated with alcohol problems among NCOs.•Alcohol outcomes of NCOs were more impacted by motives than junior enlisted ADSM.
For decades confirmatory factor analysis (CFA) has been the preeminent method to study the underlying structure of posttraumatic stress disorder (PTSD); however, methodological limitations of CFA ...have led to the emergence of other analytic approaches. In particular, network analysis has become a gold standard to investigate the structure and relationships between PTSD symptoms. A key methodological limitation, however, which has significant clinical implications, is the lack of data on the potential impact of item order effects on the conclusions reached through network analyses.
The current study, involving a large sample (
= 5055) of active duty army soldiers following deployment to Iraq, assessed the vulnerability of network analyses and prevalence rate to item order effects. This was done by comparing symptom networks of the DSM-IV PTSD checklist items to these same items distributed in random order. Half of the participants rated their symptoms on traditionally ordered items and half the participants rated the same items, but in random order and interspersed between items from other validated scales. Differences in prevalence rate and network composition were examined.
The prevalence rate differed between the ordered and random item samples. Network analyses using the ordered survey closely replicated the conclusions reached in the existing network analyses literature. However, in the random item survey, network composition differed considerably.
Order effects appear to have a significant impact on conclusions reached from PTSD network analysis. Prevalence rates were also impacted by order effects. These findings have important diagnostic and clinical treatment implications.
Deep neural networks (DNN) are increasingly being used in neuroimaging research for the diagnosis of brain disorders and understanding of human brain. Despite their impressive performance, their ...usage in medical applications will be limited unless there is more transparency on how these algorithms arrive at their decisions. We address this issue in the current report. A DNN classifier was trained to discriminate between healthy subjects and those with posttraumatic stress disorder (PTSD) using brain connectivity obtained from functional magnetic resonance imaging data. The classifier provided 90% accuracy. Brain connectivity features important for classification were generated for a pool of test subjects and permutation testing was used to identify significantly discriminative connections. Such heatmaps of significant paths were generated from 10 different interpretability algorithms based on variants of layer-wise relevance and gradient attribution methods. Since different interpretability algorithms make different assumptions about the data and model, their explanations had both commonalities and differences. Therefore, we developed a consensus across interpretability methods, which aligned well with the existing knowledge about brain alterations underlying PTSD. The confident identification of more than 20 regions, acknowledged for their relevance to PTSD in prior studies, was achieved with a voting score exceeding 8 and a family-wise correction threshold below 0.05. Our work illustrates how robustness and physiological plausibility of explanations can be achieved in interpreting classifications obtained from DNNs in diagnostic neuroimaging applications by evaluating convergence across methods. This will be crucial for trust in AI-based medical diagnostics in the future.
The extent to which multiple past concussions are associated with lingering symptoms or mental health problems in military service members is not well understood. The purpose of this study was to ...examine the association between lifetime concussion history, cognitive functioning, general health, and psychological health in a large sample of fit-for-duty U.S. Army soldiers preparing for deployment. Data on 458 active-duty soldiers were collected and analyzed. A computerized cognitive screening battery (CNS-Vital Signs(®)) was used to assess complex attention (CA), reaction time (RT), processing speed (PS), cognitive flexibility (CF), and memory. Health questionnaires included the Neurobehavioral Symptom Inventory (NSI), PTSD Checklist-Military Version (PCL-M), Zung Depression and Anxiety Scales (ZDS; ZAS), Perceived Stress Scale (PSS), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and the Alcohol Use and Dependency Identification Test (AUDIT). Soldiers with a history of multiple concussions (i.e., three or more concussions) had significantly greater post-concussive symptom scores compared with those with zero (d=1.83, large effect), one (d=0.64, medium effect), and two (d=0.64, medium effect) prior concussions. Although the group with three or more concussions also reported more traumatic stress symptoms, the results revealed that traumatic stress was a mediator between concussions and post-concussive symptom severity. There were no significant differences on neurocognitive testing between the number of concussions. These results add to the accumulating evidence suggesting that most individuals recover from one or two prior concussions, but there is a greater risk for ongoing symptoms if one exceeds this number of injuries.
The concept of resilience is embedded within military culture and professional identity. To date, temporal changes in individuals' perceptions of their own resilience have not been systematically ...assessed in highstakes occupational contexts, like the military. The current study examined change in selfreported resilience over time by: (1) examining the longitudinal measurement invariance of the Brief Resilience Scale (BRS); (2) assessing the longitudinal pattern of resilience across a combat deployment cycle; and (3) examining predictors of postdeployment resilience and change in resilience scores across time. U.S. Army soldiers assigned to a combat brigade completed a survey at four time points over the course of a deployment cycle: (a) prior to deployment to Afghanistan; (b) during deployment; (c) immediately following return to home station; and (d) approximately 2-3 months thereafter. The longitudinal measurement invariance of the BRS was established. Growth curve modeling indicated that, on average, self-reported resilience decreased across the deployment cycle, but there was considerable individual variation in the rate of change. Of note, loneliness, as measured during deployment, predicted the rate of change in self-reported resilience over time. Results have implications for the longitudinal analysis of resilience and for the development of interventions with military personnel.
There are growing concerns about the generalizability of machine learning classifiers in neuroimaging. In order to evaluate this aspect across relatively large heterogeneous populations, we ...investigated four disorders: Autism spectrum disorder (
N
= 988), Attention deficit hyperactivity disorder (
N
= 930), Post-traumatic stress disorder (
N
= 87) and Alzheimer’s disease (
N
= 132). We applied 18 different machine learning classifiers (based on diverse principles) wherein the training/validation and the hold-out test data belonged to samples with the same diagnosis but differing in either the age range or the acquisition site. Our results indicate that overfitting can be a huge problem in heterogeneous datasets, especially with fewer samples, leading to inflated measures of accuracy that fail to generalize well to the general clinical population. Further, different classifiers tended to perform well on different datasets. In order to address this, we propose a consensus-classifier by combining the predictive power of all 18 classifiers. The consensus-classifier was less sensitive to unmatched training/validation and holdout test data. Finally, we combined feature importance scores obtained from all classifiers to infer the discriminative ability of connectivity features. The functional connectivity patterns thus identified were robust to the classification algorithm used, age and acquisition site differences, and had diagnostic predictive ability in addition to univariate statistically significant group differences between the groups. A MATLAB toolbox called Machine Learning in NeuroImaging (MALINI), which implements all the 18 different classifiers along with the consensus classifier is available from Lanka et al. (
2019
) The toolbox can also be found at the following URL:
https://github.com/pradlanka/malini
.
Brain functioning relies on various segregated/specialized neural regions functioning as an integrated-interconnected network (i.e., metastability). Various psychiatric and neurologic disorders are ...associated with aberrant functioning of these brain networks. In this study, we present a novel framework integrating the strength and temporal variability of metastability in brain networks. We demonstrate that this approach provides novel mechanistic insights which enables better imaging-based predictions. Using whole-brain resting-state fMRI and a graph-theoretic framework, we integrated strength and temporal-variability of complex-network properties derived from effective connectivity networks, obtained from 87 U.S. Army soldiers consisting of healthy combat controls (
= 28), posttraumatic stress disorder (PTSD;
= 17), and PTSD with comorbid mild-traumatic brain injury (mTBI;
= 42). We identified prefrontal dysregulation of key subcortical and visual regions in PTSD/mTBI, with all network properties exhibiting lower variability over time, indicative of poorer flexibility. Larger impairment in the prefrontal-subcortical pathway but not prefrontal-visual pathway differentiated comorbid PTSD/mTBI from the PTSD group. Network properties of the prefrontal-subcortical pathway also had significant association (
= 0.56) with symptom severity and neurocognitive performance; and were also found to possess high predictive ability (81.4% accuracy in classifying the disorders, explaining 66-72% variance in symptoms), identified through machine learning. Our framework explained 13% more variance in behaviors compared to the conventional framework. These novel insights and better predictions were made possible by our novel framework using static and time-varying network properties in our three-group scenario, advancing the mechanistic understanding of PTSD and comorbid mTBI. Our contribution has wide-ranging applications for network-level characterization of healthy brains as well as mental disorders.
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