A recent paradigm shift in systems neuroscience is the division of the human brain into functional networks. Functional networks are collections of brain regions with strongly correlated activity both at rest and during cognitive tasks, and each network is believed to implement a different aspect of cognition. Here, we propose that anxiety disorders and high trait anxiety are associated with a particular pattern of functional network dysfunction: increased functioning of the cingulo-opercular and ventral attention networks as well as decreased functioning of the fronto-parietal and default mode networks. This functional network model can be used to differentiate the pathology of anxiety disorders from other psychiatric illnesses such as major depression and provides targets for novel treatment strategies.

Statistical methods designed for categorical data were used to perform confirmatory factor analyses and item response theory (IRT) analyses of the Fear of Negative Evaluation scale (FNE; D. Watson & R. Friend, 1969) and the Brief FNE (BFNE; M. R. Leary, 1983). Results suggested that a 2-factor model fit the data better for both the FNE and the BFNE, although the evidence was less strong for the FNE. The IRT analyses indicated that although both measures had items with good discrimination, the FNE items discriminated only at lower levels of the underlying construct, whereas the BFNE items discriminated across a wider range. Convergent validity analyses indicated that the straightforwardly-worded items on each scale had significantly stronger relationships with theoretically related measures than did the reverse-worded items. On the basis of all analyses, usage of the straightforwardly-worded BFNE factor is recommended for the assessment of fear of negative evaluation.

The use of unreliable measures constitutes a threat to our understanding of psychopathology, because advancement of science using both behavioral and biologically-oriented measures can only be certain if such measurements are reliable. Two pillars of NIMH’s portfolio – the Research Domain Criteria (RDoC) initiative for psychopathology and the target engagement initiative in clinical trials – cannot succeed without measures that possess the high reliability necessary for tests involving mediation and selection based on individual differences. We focus on the historical lack of reliability of attentional bias measures as an illustration of how reliability can pose a threat to our understanding. Our own data replicate previous findings of poor reliability for traditionally-used scores, which suggests a serious problem with the ability to test theories regarding attentional bias. This lack of reliability may also suggest problems with the assumption (in both theory and the formula for the scores) that attentional bias is consistent and stable across time. In contrast, measures accounting for attention as a dynamic process in time show good reliability in our data. The field is sorely in need of research reporting findings and reliability for attentional bias scores using multiple methods, including those focusing on dynamic processes over time. We urge researchers to test and report reliability of all measures, considering findings of low reliability not just as a nuisance but as an opportunity to modify and improve upon the underlying theory. Full assessment of reliability of measures will maximize the possibility that RDoC (and psychological science more generally) will succeed.

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