Imaging studies of major depressive disorder (MDD) have demonstrated enhanced resting-state activity of the amygdala as well as exaggerated reactivity to negative emotional stimuli relative to healthy controls. However, the abnormalities in the intrinsic connectivity of the amygdala in MDD still remain unclear. As the resting-state activity and functional connectivity (RSFC) reflect fundamental brain processes, we compared the RSFC of the amygdala between unmedicated MDD patients and healthy controls. Seventy-four subjects, 55 adults meeting the DSM IV criteria for MDD and 19 healthy controls, underwent a resting state 3-T functional magnetic resonance imaging (fMRI) scan. An amygdala seed-based low frequency RSFC map for the whole brain was generated for each group. Compared with healthy controls, MDD patients showed a wide-spread reduction in the intrinsic connectivity of the amygdala with a variety of brain regions involved in emotional processing and regulation, including the ventrolateral prefrontal cortex, insula, caudate, middle and superior temporal regions, occipital cortex, and cerebellum, as well as increased connectivity with the bilateral temporal poles (p< 0.05 corrected). The increase in the intrinsic connectivity of amygdala with the temporal poles was inversely correlated with symptom severity and anxiety scores. Although the directionality of connections between regions cannot be inferred from temporal correlations, the reduced intrinsic connectivity of the amygdala predominantly with regions involved in emotional processing may reflect impaired bottom-up signaling for top-down cortical modulation of limbic regions leading to abnormal affect regulation in MDD.

This study investigated the effects of a high-intensity cycling exercise on changes in spectral and temporal aspects of electroencephalography (EEG) measured from 10 experienced cyclists. Cyclists performed a maximum aerobic power test on the first testing day followed by a time-to-exhaustion trial at 85% of their maximum power output on 2 subsequent days that were separated by ∼48 h. EEG was recorded using a 64-channel system at 500 Hz. Independent component (IC) analysis parsed the EEG scalp data into maximal ICs. An equivalent current dipole model was calculated for each IC, and results were clustered across subjects. A time-frequency analysis of the identified electrocortical clusters was performed to investigate the magnitude and timing of event-related spectral perturbations. Significant changes (P < 0.05) in electrocortical activity were found in frontal, supplementary motor and parietal areas of the cortex. Overall, there was a significant increase in EEG power as fatigue developed throughout the exercise. The strongest increase was found in the frontal area of the cortex. The timing of event-related desynchronization within the supplementary motor area corresponds with the onset of force production and the transition from flexion to extension in the pedaling cycle. The results indicate an involvement of the cerebral cortex during the pedaling task that most likely involves executive control function, as well as motor planning and execution.

Growing evidence documents the potential of machine learning for developing brain based diagnostic methods for major depressive disorder (MDD). As symptom severity may influence brain activity, we investigated whether the severity of MDD affected the accuracies of machine learned MDD-vs-Control diagnostic classifiers. Forty-five medication-free patients with DSM-IV defined MDD and 19 healthy controls participated in the study. Based on depression severity as determined by the Hamilton Rating Scale for Depression (HRSD), MDD patients were sorted into three groups: mild to moderate depression (HRSD 14-19), severe depression (HRSD 20-23), and very severe depression (HRSD ≥ 24). We collected functional magnetic resonance imaging (fMRI) data during both resting-state and an emotional-face matching task. Patients in each of the three severity groups were compared against controls in separate analyses, using either the resting-state or task-based fMRI data. We use each of these six datasets with linear support vector machine (SVM) binary classifiers for identifying individuals as patients or controls. The resting-state fMRI data showed statistically significant classification accuracy only for the very severe depression group (accuracy 66%, p = 0.012 corrected), while mild to moderate (accuracy 58%, p = 1.0 corrected) and severe depression (accuracy 52%, p = 1.0 corrected) were only at chance. With task-based fMRI data, the automated classifier performed at chance in all three severity groups. Binary linear SVM classifiers achieved significant classification of very severe depression with resting-state fMRI, but the contribution of brain measurements may have limited potential in differentiating patients with less severe depression from healthy controls.

Objectives: To determine time to vigabatrin (VGB, Sabril; Lundbeck, Deerfield, IL) induced retinal damage in children with infantile spasms (IS) and to identify risk factors for VGB-induced retinal damage (VGB-RD). Methods: Observational cohort study including 146 participants (68 female, 81 male) with IS, an age-specific epilepsy syndrome of early infancy, treated with VGB. Participants ranged from 3 to 34.9 months of age (median 7.6 months). The median duration of VGB treatment was 16 months (range 4.6–78.5 months). Electroretinograms (ERGs) were performed according to the Standards of the International Society for Clinical Electrophysiology of Vision. Inclusion required baseline (pre-VGB or within 4 weeks of starting VGB treatment) and at least 2 follow-up ERGs. Significant reduction from baseline of the 30-Hz ERG flicker amplitude on 2 consecutive visits identified VGB-RD. Kaplan-Meier survival analyses depicted the effect of duration of VGB on VGB-RD. Results: These data represent the largest survival analysis of children treated with VGB who did not succumb to retinal toxicity during the study. Thirty of the 146 participants (21%) showed VGB-RD. The ERG amplitude reduced with duration of VGB treatment (p = 0.0004) with no recovery after VGB cessation. With 6 and 12 months of VGB treatment, 5.3% and 13.3%, respectively, developed VGB-RD. There was neither effect of age of initiation of VGB treatment nor sex of the child on survival statistics and no significant effect of cumulative dosage on the occurrence of VGB-RD. Conclusions: Minimizing VGB treatment to 6 months will reduce the prevalence of VGB-RD in patients with IS.

Significance: Sleep and wake states are regulated by a variety of mechanisms. One such important system is the circadian clock, which provides temporal structure to sleep and wake. Conversely, changes in behavioral state can influence the phase of the circadian clock. Here we demonstrate that the level of wakefulness is critical for arousal to reset circadian clock phase. We then show that treatments that produce arousal and reset the circadian clock activate the basal forebrain. Finally, we demonstrate that cholinergic input from the basal forebrain is both necessary and sufficient for eliciting this arousal-induced resetting of the circadian clock. These results establish a functional link between the major forebrain arousal center and the circadian system.