P123. Correlation between EEG and clinical symptoms in depression

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Introduction

The underlying neurophysiological characteristics in Major Depressive Disorders(MDD) have been examined intensively for the past decades. Some studies have indicated pattern of electroencephalography(EEG) activity that distinguishes MDD patients from healthy subjects. However, the correlation between either clinical symptoms such as psychometric scales or sleeping disturbances and EEG activity were seldom investigated to date. Therefore this study aims to examine the correlation between depressive and psychomotor symptoms as well as sleeping disturbances with EEG power values and with current source density (CSD) analysis.

Methods

20 patients with MDD (mean age ± SD: 47 ± 12.57; female: 13) were compared with 20 age matched healthy subjects (mean age ± SD: 51 ± 10.5; female: 12). A 10 minutes eyes-closed resting state EEG (31 channels, additional ECG channel, 10/20 system) was applied to all participants. Impedances were kept below 5kΩ. EEG data were pre-processed using Brain Vision Analyzer (Brain Products, Gilching). EEG raw data were manually cleaned from movement artefacts. ECG and ocular artefacts were excluded via independent component analysis. Artefact free EEG was filtered with 0.5Hz high pass and 50Hz low pass and average was set as reference. EEG power and asymmetry indices were calculated from 8 minutes of artefact free data for each frequency and pooled over 6 electrode sites. One minute of artefact free data in each participant were exported for CSD analysis. Voxelwise CSD power was calculated for each frequency with Low Resolution Electromagnetic Tomography (LORETA) and pooled for cortical brodmann areas with relation to executive, attentional, motor and emotional functions. EEG power, asymmetry and CSD power were the subjects of Pearson’s correlation analyses to examine the interconnection with depressive symptoms (BDI, HDRS), psychomotor activity (Motor Agitation and Retardation Scale), psychomotor speed (TMT A), executive functions (TMT B, Stroop), Pittsburgh Sleep Quality Intervention (PSQI) and measurement of motor activity via actigraphy.

Results

There were significant positive correlations between TMT A and B and EEG power in beta and gamma bands widespread from central to posterior regions (p).

Conclusion

Our results suggested that EEG power in beta and gamma frequency bands over central to posterior regions as well as higher power in delta and beta CSD in frontal regions can possibly predict the decreased performances in neuropsychological tests related to psychomotor speed and divided attention in MDD group. Sleeping disturbances are associated with higher power in beta and gamma EEG frequencies over temporoparietal regions. These findings confirm that depressive symptoms in patients with MDD have neurofunctional expressions in the EEG domain and may suggest further that cognitive and sleep disturbances are associated with activity in distinct neurofunctional areas.

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