and A. Tellegen (19X5') proposed a "consensual" structure of affect based on J. A. Russell's (1980) circumplcx. The authors" review of the literature indicates that this 2-factor model captures robust structural properties of self-rated mood. Nevertheless, the evidence also indicates that the circumplcx does not fit the data closely and needs to be refined. Most notably, the model's dimensions are not entirely independent: moreover, with the exception of Pleasantness-Unpleasantness, they are not completely bipolar. More generally, the data suggest a model that falls somewhere between classic simple structure and a true circumplex. The authors then examine two of the dimensions imbedded in this structure, which they label Negative Activation (NA) and Positive Activation (PA). The authors argue that PA and NA represent the subjective components of broader biobchavioral systems of approach and withdrawal, respectively. The authors conclude by demonstrating how this framework helps to clarify various affect-related phenomena, including circadian rhythms, sleep, and the mood disorders.

Schizophrenia is a psychotic disorder characterized by functional dysconnectivity or abnormal integration between distant brain regions. Recent functional imaging studies have implicated large-scale thalamo-cortical connectivity as being disrupted in patients. However, observed connectivity differences in schizophrenia have been inconsistent between studies, with reports of hyperconnectivity and hypoconnectivity between the same brain regions. Using resting state eyes-closed functional imaging and independent component analysis on a multi-site data that included 151 schizophrenia patients and 163 age- and gender matched healthy controls, we decomposed the functional brain data into 100 components and identified 47 as functionally relevant intrinsic connectivity networks. We subsequently evaluated group differences in functional network connectivity, both in a static sense, computed as the pairwise Pearson correlations between the full network time courses (5.4 minutes in length), and a dynamic sense, computed using sliding windows (44 s in length) and k-means clustering to characterize five discrete functional connectivity states. Static connectivity analysis revealed that compared to healthy controls, patients show significantly stronger connectivity, i.e., hyperconnectivity, between the thalamus and sensory networks (auditory, motor and visual), as well as reduced connectivity (hypoconnectivity) between sensory networks from all modalities. Dynamic analysis suggests that (1), on average, schizophrenia patients spend much less time than healthy controls in states typified by strong, large-scale connectivity, and (2), that abnormal connectivity patterns are more pronounced during these connectivity states. In particular, states exhibiting cortical–subcortical antagonism (anti-correlations) and strong positive connectivity between sensory networks are those that show the group differences of thalamic hyperconnectivity and sensory hypoconnectivity. Group differences are weak or absent during other connectivity states. Dynamic analysis also revealed hypoconnectivity between the putamen and sensory networks during the same states of thalamic hyperconnectivity; notably, this finding cannot be observed in the static connectivity analysis. Finally, in post-hoc analyses we observed that the relationships between sub-cortical low frequency power and connectivity with sensory networks is altered in patients, suggesting different functional interactions between sub-cortical nuclei and sensorimotor cortex during specific connectivity states. While important differences between patients with schizophrenia and healthy controls have been identified, one should interpret the results with caution given the history of medication in patients. Taken together, our results support and expand current knowledge regarding dysconnectivity in schizophrenia, and strongly advocate the use of dynamic analyses to better account for and understand functional connectivity differences.

The regional distribution of white matter (WM) abnormalities in schizophrenia remains poorly understood, and reported disease effects on the brain vary widely between studies. In an effort to identify commonalities across studies, we perform what we believe is the first ever large-scale coordinated study of WM microstructural differences in schizophrenia. Our analysis consisted of 2359 healthy controls and 1963 schizophrenia patients from 29 independent international studies; we harmonized the processing and statistical analyses of diffusion tensor imaging (DTI) data across sites and meta-analyzed effects across studies. Significant reductions in fractional anisotropy (FA) in schizophrenia patients were widespread, and detected in 20 of 25 regions of interest within a WM skeleton representing all major WM fasciculi. Effect sizes varied by region, peaking at (d=0.42) for the entire WM skeleton, driven more by peripheral areas as opposed to the core WM where regions of interest were defined. The anterior corona radiata (d=0.40) and corpus callosum (d=0.39), specifically its body (d=0.39) and genu (d=0.37), showed greatest effects. Significant decreases, to lesser degrees, were observed in almost all regions analyzed. Larger effect sizes were observed for FA than diffusivity measures; significantly higher mean and radial diffusivity was observed for schizophrenia patients compared with controls. No significant effects of age at onset of schizophrenia or medication dosage were detected. As the largest coordinated analysis of WM differences in a psychiatric disorder to date, the present study provides a robust profile of widespread WM abnormalities in schizophrenia patients worldwide. Interactive three-dimensional visualization of the results is available at www.enigma-viewer.org.

The authors investigated the stability of personality and trait affect in young adults. In Studies 1 and 2, young adults were retested on a Big Five personality measure and a trait affect inventory over a 2.5-year and a 2-month period, respectively. Results from Study 1 point to positive mean-level changes; participants scored higher on Extraversion, Openness, Agreeableness, and Conscientiousness at Time 2. Affectively, participants experienced less negative affect and more positive affect at Time 2. Results from both retests provide clear evidence of differential stability. Affective traits were consistently less stable than the Big Five. Other analyses suggest that life events influence the stability of affective traits more than the Big Five.

Big Five and affective traits were measured at three assessments when participants were on average 18, 21, and 24 years old. Rank-order stability analyses revealed that stability correlations tended to be higher across the second compared to the first retest interval; however, affective traits consistently were less stable than the Big Five. Median stability coefficients for the Big Five increased from .62 (Time 1 vs. Time 2) to .70 (Time 2 to Time 3); parallel increases also were observed for measures of negative affectivity (median rs=.49 and .55, respectively) and positive affectivity (median rs=.48 and .57, respectively). Growth curve analyses revealed significant change on each of the Big Five and affective traits, although many of the scales also showed significant variability in individual trajectories. Thus, rank-order stability is increasing for a range of personality traits, although there also is significant variability in change trajectories during young adulthood.