The complexity of the human brain’s activity and connectivity varies over temporal scales and is altered in disease states such as schizophrenia. Using a multi-level analysis of spontaneous low-frequency fMRI data stretching from the activity of individual brain regions to the coordinated connectivity pattern of the whole brain, we investigate the role of brain signal complexity in schizophrenia. Specifically, we quantitatively characterize the univariate wavelet entropy of regional activity, the bivariate pairwise functional connectivity between regions, and the multivariate network organization of connectivity patterns. Our results indicate that univariate measures of complexity are less sensitive to disease state than higher level bivariate and multivariate measures. While wavelet entropy is unaffected by disease state, the magnitude of pairwise functional connectivity is significantly decreased in schizophrenia and the variance is increased. Furthermore, by considering the network structure as a function of correlation strength, we find that network organization specifically of weak connections is strongly correlated with attention, memory, and negative symptom scores and displays potential as a clinical biomarker, providing up to 75% classification accuracy and 85% sensitivity. We also develop a general statistical framework for the testing of group differences in network properties, which is broadly applicable to studies where changes in network organization are crucial to the understanding of brain function.

The present study shows convergent fMRI and DTI findings that are consistent with the disconnection hypothesis in schizophrenia, particularly in medial frontal regions, while adding some insight of the relationship between brain disconnectivity and behavior.

Major Depressive Disorder (MDD) begins frequently in adolescence and is associated with severe outcomes, but the developmental neurobiology of MDD is not well understood. Research in adults has implicated fronto-limbic neural networks in the pathophysiology of MDD, particularly in relation to the subgenual anterior cingulate cortex (ACC). Developmental changes in brain networks during adolescence highlight the need to examine MDD-related circuitry in teens separately from adults. Using resting state functional magnetic resonance imaging (fMRI), this study examined functional connectivity in adolescents with MDD (n=12) and healthy adolescents (n=14). Seed-based connectivity analysis revealed that adolescents with MDD have decreased functional connectivity in a subgenual ACC-based neural network that includes the supragenual ACC (BA 32), the right medial frontal cortex (BA 10), the left inferior (BA 47) and superior frontal cortex (BA 22), superior temporal gyrus (BA 22), and the insular cortex (BA 13). These preliminary data suggest that MDD in adolescence is associated with abnormal connectivity within neural circuits that mediate emotion processing. Future research in larger, un-medicated samples will be necessary to confirm this finding. We conclude that hypothesis-driven, seed-based analyses of resting state fMRI data hold promise for advancing our current understanding of abnormal development of neural circuitry in adolescents with MDD.

Objective-Major Depressive Disorder (MDD) occurs frequently in adolescents, but the neurobiology of depression in youth is poorly understood. Structural neuroimaging studies in both adult and pediatric populations have implicated fronto-limbic neural networks in the pathophysiology of MDD. Diffusion Tensor Imaging (DTI), which measures white matter (WM) microstructure, is a promising tool for examining neural connections and how they may be abnormal in MDD. Method-We used two separate approaches to analyze DTI data in adolescents with MDD (n=14) compared with healthy volunteers (n=14).Results-The first, hypothesis-driven approach was to use probabilistic tractography to delineate tracts arising from the subgenual anterior cingulate cortex (ACC). Adolescents with MDD demonstrated lower fractional anisotropy (FA) in the WM tract connecting subgenual ACC to amygdala in the right hemisphere. The second, exploratory approach was to conduct a voxel-wise comparison of FA. This analysis revealed ten clusters where adolescents with MDD had significantly lower (uncorrected) FA than the healthy group within WM tracts including right and left uncinate and supragenual cingulum.Conclusions-These preliminary data support the hypothesis that altered WM microstructure in fronto-limbic neural pathways may contribute to the pathophysiology of MDD in adolescents.

Short-term abstinent alcoholics have shown increased engagement of reward regions and reduced engagement of executive control regions. There is no report yet on whether these differences can predict relapse. This is the first study that investigates whether differences in resting-state networks can predict later relapse. Resting-state functional magnetic resonance imaging data were collected from 69 short-term abstinent alcoholics. Participants performed the affective go/no-go task outside of the scanner. At 6-month follow-up, participants were grouped as abstainers (N = 40; age: M = 46.70, standard deviation [SD] = 6.83) and relapsers (N = 29; age: M = 46.91, SD = 7.25). We examined baseline resting-state synchrony (RSS) using seed-based measures. Compared with abstainers, relapsers showed significantly decreased RSS within both the reward and executive control networks as well as within the visual network (P < 0.05). Lower RSS in relapsers could predict relapse (P < 0.05) and was significantly correlated with poor inhibitory control of emotional-laden stimuli (P < 0.017) and with alcohol use (P < 0.05). Results suggest that lower RSS during short-term abstinence may predict subsequent relapse. The association of lower RSS with poorer inhibitory control suggests that low RSS may constitute a faulty foundation for future responses to external cues, which can be manifested as the inability to inhibit behavior.