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Background Abnormalities in the 40-Hz auditory steady-state response (ASSR) of the gamma range have been reported in schizophrenia (SZ) and are regarded as important pathophysiological features. Many of the previous studies reported diminished gamma oscillations in SZ, although some studies reported increased spontaneous gamma oscillations. Furthermore, brain morphological correlates of the gamma band ASSR deficits have rarely examined. We investigated different measures of the 40-Hz ASSR and their association with brain volumes and psychological measures of SZ. Methods The 40-Hz ASSR was measured for 80 dB click sounds (1 ms, 500-ms trains at 40-Hz, with 3050 to 3500 inter-train interval) using electroencephalography with 64 electrodes in 33 patients with SZ (male: 16, female: 17 (age range: 21–60)) and 30 healthy controls (HCs) (male: 13, female: 17 (age range: 23–64)). Four gamma oscillation measures (evoked power, spontaneous oscillations (baseline and total power), and inter-trial phase coherence (ITC)) were assessed. The source activities of the ASSR were also analyzed. Brain volumes were assessed using high-resolution magnetic resonance imaging and voxel-based morphometry and superior temporal gyrus (STG) volume measures were obtained. Results Patients with SZ had larger total and evoked powers and higher ITC than HCs. Both groups showed significantly different association between mean evoked power and right STG volume. In HCs but not SZ, mean evoked power showed significant positive correlation with right STG volume. In addition, the two groups showed significantly different association between verbal fluency and mean evoked power. High evoked power was significantly correlated with poor verbal fluency in SZ. Conclusions The current study found increased gamma oscillation in SZ and suggests significant involvement of the STG in gamma oscillations.

BackgroundElectroencephalogram (EEG)-based brain network analysis is a useful biological correlate reflecting brain function. Sensor-level network analysis might be contaminated by volume conduction and does not explain regional brain characteristics. Source-level network analysis could be a useful alternative. We analyzed EEG-based source-level network in major depressive disorder (MDD).MethodResting-state EEG was recorded in 87 MDD and 58 healthy controls, and cortical source signals were estimated. Network measures were calculated: global indices (strength, clustering coefficient (CC), path length (PL), and efficiency) and nodal indices (eigenvector centrality and nodal CC) in six frequency. Correlation analyses were performed between network indices and symptom scales.ResultsAt the global level, MDD showed decreased strength, CC in theta and alpha bands, and efficiency in alpha band, while enhanced PL in alpha band. At nodal level, eigenvector centrality of alpha band showed region dependent changes in MDD. Nodal CCs of alpha band were reduced in MDD and were negatively correlated with depression and anxiety scales.ConclusionDisturbances in EEG-based brain network indices might reflect altered emotional processing in MDD. These source-level network indices might provide useful biomarkers to understand regional brain pathology in MDD.

Heart rate variability (HRV) reflects beat-to-beat variability in the heart rate due to the dynamic interplay of the sympathetic and parasympathetic nervous systems. HRV is considered an index of the functional status of the autonomic nervous system. A decrease in HRV is thus observed in individuals with autonomic dysfunction. Abnormal HRV has been reported in a range of mental disorders. In this review, we give an overview of HRV in patients with major depressive disorder (MDD), schizophrenia, and posttraumatic stress disorder (PTSD), one of whose core symptoms is cognitive dysfunction. The association between HRV and cognitive function is highlighted in this review. This review consists of three main sections. In the first section, we examine how HRV in patients with MDD, schizophrenia, and PTSD is characterized, and how it is different when compared to that in healthy controls. In the second section, beyond the heart itself, we discuss the intimate connection between the heart and the brain, focusing on how HRV interacts with quantitative electroencephalography (qEEG) in the context of physiological changes in the sleep cycle. Lastly, we finish the review with the examination of the association between HRV and cognitive function. The overall findings indicate that the reduction in HRV is one of main manifestations in MDD, schizophrenia, and PTSD, and also more generally HRV is closely linked to the change in qEEG and also to individual differences in cognitive performance.