Anxiety disorders are common in patients with MS, but are frequently overlooked and under-treated. Risk factors include being female, a co-morbid diagnosis of depression, and limited social support. Clinicians should evaluate all MS subjects for anxiety disorders, as they represent a treatable cause of disability in MS.

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Although TOM and psychotic symptoms are related to each other, antipsychotic treatment impacts each independently, suggesting a dissimilar cognitive or neurobiological substrate for the two.

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There is emerging evidence that identification and treatment of individuals in the prodromal or clinical high-risk (CHR) state for psychosis can reduce the probability that they will develop a psychotic disorder. Event-related brain potentials (ERPs) are a noninvasive neurophysiological technique that holds promise for improving our understanding of neurocognitive processes underlying the CHR state. We aimed to systematically review the current literature on cognitive ERP studies of the CHR population, in order to summarize and synthesize the results, and their implications for our understanding of the CHR state. Across studies, amplitudes of the auditory P300 and duration mismatch negativity (MMN) ERPs appear reliably reduced in CHR individuals, suggesting that underlying impairments in detecting changes in auditory stimuli are a sensitive early marker of the psychotic disease process. There are more limited data indicating that an earlier-latency auditory ERP response, the N100, is also reduced in amplitude, and in the degree to which it is modulated by stimulus characteristics, in the CHR population. There is also evidence that a number of auditory ERP measures (including P300, MMN and N100 amplitudes, and N100 gating in response to repeated stimuli) can further refine our ability to detect which CHR individuals are most at risk for developing psychosis. Thus, further research is warranted to optimize the predictive power of algorithms incorporating these measures, which could help efforts to target psychosis prevention interventions toward those most in need.

Functional brain networks emerge and dissipate over a primarily static anatomical foundation. The dynamic basis of these networks is inter-regional communication involving local and distal regions. It is assumed that inter-regional distances play a pivotal role in modulating network dynamics. Using three different neuroimaging modalities, 6 datasets were evaluated to determine whether experimental manipulations asymmetrically affect functional relationships based on the distance between brain regions in human participants. Contrary to previous assumptions, here we show that short- and long-range connections are equally likely to strengthen or weaken in response to task demands. Additionally, connections between homotopic areas are the most stable and less likely to change compared to any other type of connection. Our results point to a functional connectivity landscape characterized by fluid transitions between local specialization and global integration. This ability to mediate functional properties irrespective of spatial distance may engender a diverse repertoire of cognitive processes when faced with a dynamic environment.

By capturing the actions of distributed brain regions, neuroimaging can give unique insights into the networks underlying complex behavioral and cognitive functions. An approach to interpreting neuroimaging data grounded in emerging ideas in brain network theory is needed to better characterize these large-scale network dynamics. This paper focuses on three concepts germane to this approach to interpretation: "connectivity", "neural context", and "small-world properties". Measures of brain connectivity emphasize the combined action of areas. Functional connectivity analyses focus on interacting neural patterns, whereas effective connectivity analyses uncover directional influences between brain areas. The second concept, neural context, purports that a region's contribution to a function is more fully appreciated in relation to other coactive brain areas. The final concept is the extension of graph theory measures to the estimation of small-world properties. Measures such as clustering and path length can be used to infer the computational capacity of functional networks. These three constructs are central to the interpretation of neuroimaging data that will further unravel how brain network dynamics guide mental function, and are beginning to be applied to the study of neural disorders.

Aim The N400 event‐related potential is a neurophysiological index of cognitive processing of real‐world knowledge. In healthy populations, N400 amplitude is smaller in response to stimuli that are more related to preceding context. This ‘N400 semantic priming effect’ is thought to reflect activation of contextually related information in semantic memory (SM). N400 semantic priming deficits have been found in schizophrenia, and in patients at clinical high risk (CHR) for this disorder. Because this abnormality in processing relationships between meaningful stimuli could affect ability to navigate everyday situations, we hypothesized it would be associated with real‐world functional impairment in CHR patients. Second, we hypothesized it would correlate with global neurocognitive impairment in this group. Methods We measured N400 semantic priming in 35 CHR patients who viewed prime words each followed by a related or unrelated target word, at stimulus‐onset asynchrony (SOA) of 300 or 750 ms. We measured academic/occupational and social function with the global function (GF): Role and Social scales, and cognitive function with the MATRICS Consensus Cognitive Battery (MCCB). Results Decreased N400 semantic priming at the 300‐ms SOA correlated with lower GF:Role scores. Decreased N400 semantic priming at the 750‐ms SOA correlated with lower MCCB composite scores. Conclusions Deficits in activating contextually related concepts in SM over short time intervals may contribute to functional impairment in CHR patients. Furthermore, N400 priming deficits over longer intervals may be a biomarker of global cognitive dysfunction in this population. Longitudinal studies are needed to determine whether these deficits are associated with schizophrenia risk within this population.