Brain Arousal as Measured by EEG-Assessment Differs Between Children and Adolescents With Attention-Deficit/Hyperactivity Disorder (ADHD) and Depression

Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning

Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person's psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, practical, and automatic diagnosis technologies. Recently, electroencephalogram (EEG) signals have been widely applied for DD diagnosis, but mainly with high-density EEG, which can severely limit the efficiency of the EEG data acquisition and reduce the practicability of diagnostic techniques. The current study attempts to achieve accurate and practical DD diagnoses based on combining frontal six-channel electroencephalogram (EEG) signals and deep learning models. To this end, 10 min clinical resting-state EEG signals were collected from 41 DD patients and 34 healthy controls (HCs). Two deep learning models, multi-resolution convolutional neural network (MRCNN) combined with long short-term memory (LSTM) (named MRCNN-LSTM) and MRCNN combined with residual squeeze and excitation (RSE) (named MRCNN-RSE), were proposed for DD recognition. The results of this study showed that the higher EEG frequency band obtained the better classification performance for DD diagnosis. The MRCNN-RSE model achieved the highest classification accuracy of 98.48 ± 0.22% with 8-30 Hz EEG signals. These findings indicated that the proposed analytical framework can provide an accurate and practical strategy for DD diagnosis, as well as essential theoretical and technical support for the treatment and efficacy evaluation of DD.

Possible Effects of Bright Light Therapy on Electroencephalogram-Vigilance in the Treatment of Depression in Adolescents: A Pilot Study

BACKGROUND

Chronotherapeutic treatments for depression, such as bright light therapy (BLT), are non-invasive and produce almost no side effects. However, study evidence for reliable neurobiological changes associated with treatment response is still rare. Several studies using EEG-vigilance indicate higher arousal and a later decline during resting state in adult depressive patients compared to healthy controls. To our knowledge, there are no study reports on EEG-vigilance in depressive youth to date.

METHODS

A total of 11 adolescents with depression receiving BLT were compared to 11 age and gender-matched patients with depression receiving treatment as usual (TAU). The BLT was administered in the morning for 2 weeks on five consecutive days per week. The depressive symptomatology was assessed using the Beck Depression Inventory (BDI-II) and the resting state electroencephalogram (EEG) of 20 min was recorded. EEG and BDI-II were assessed before and after 10 days of treatment. Vigilance level and vigilance decline were estimated using the VIGALL toolbox.

RESULTS

Brain arousal increased after 10 days of bright light therapy in adolescents with depression. Severe depressive symptoms were associated with higher brain arousal levels; the BDI-II sum score correlated negatively with the amount of drowsiness.

LIMITATIONS

The sample size was small and participants' brain arousal at baseline was not matched and differed between BLT and TAU groups.

CONCLUSION

The BLT might have an additional effect on brain arousal. EEG-vigilance seems to be a reliable and valid marker for neurobiological changes that are probably associated with depression and its treatment and, therefore, might be of clinical relevance.