In this resting-state EEG study, we used EEG-based connectomics and feature extraction strategies to examine illness duration and brain function in AD. The significant findings are as follows: first, compared to GAD patients, PSD and FE values were significantly increased in AS patients, especially in the alpha2 frequency range. Second, the two groups' functional connections of the brain's hemispheres were distinct. In all frequency bands, more connectivity and more activity in the bilateral cerebral hemispheres of the brain can be observed in AS patients. This phenomenon is evident in the beta-frequency band, which is thought to be associated with cognitive function. Third, we used machine learning to detect the classification accuracy of multidimensional EEG characteristics, finding more accurate classification effects.
Our most important result is that both groups' PSD and FE in the alpha2 band differed significantly. Power values for PSD reflect the complexity of brain activity. A similar study, based on relevant dimensional analysis, also indicated an increased complexity of brain electrical characteristics in patients with GAD[26]. This phenomenon may be explained by an increase in the internal cognitive processing of GAD patients in the process of non-specific information processing in the form of diffuse negative bias[27]. Previous studies have confirmed that prefrontal high-beta current density changes are consistent with a reduction in comorbid anxiety[28]. The complexity of entropy is also associated with the quantity of "information" that the signal carries[29]. Higher entropy levels have been associated with a healthy state in which the nervous system can respond and adapt to dynamic changes. However, lower entropy values are closely related to pathological conditions and forfeit the ability to respond to nervous system changes[30]. In addition, the decrease in the relative power of the slow-wave frequency band (theta and alpha1) and the significant increase in the electromagnetic power (β) of the fast-waves frequencies reflect the activation of negative emotions in the brain[15]. One possible explanation is that in the processing of non-specific information, GAD patients are concerned about increased cognitive processing manifested by dispersive negative biases and mental fluctuations[27]. The core symptoms of GAD patients are persistent excessive worry and anxiety about the things around them, and the prolonged disease process can be seen to disrupt the work of the brain, which suggests the nervous mechanisms of the GAD disease. There is still a shortage of research on the length and duration of anxiety disorder, and our research has filled this gap. Significant differences in EEG rhythms between AS and GAD provide a theoretical basis for further exploration of the development of AD in brain imaging.
This paper evaluates the weakness of brain functional connections by calculating the PLI in two groups of AD patients, with the advantage of overcoming the volume-transmitted problems in the process of cerebroelectric collection[31]. Our previous analysis of the GAD and the cerebro-electric characteristics of healthy people showed a decrease in the functional connection between the GAD area and other regions of the brain[25]. Lower FCs are associated with higher characteristic anxiety[32], and researchers have observed alpha connectivity disorders[33]. Instead, in resting brain electromagnetic studies, increased theta rhythm fluctuation consistency compared to HC indicates higher connectivity of social anxiety disorder[34]. In this study, we found that the functional connectivity of the left brain is significantly lower in GAD patients, who has been sick longer, especially in the beta frequency. Varieties of psychiatric, cognitive, and behavioral phenotypes have been related to brain functional connectivity. Coherent intrinsic brain activity is significant for healthy brain function. FC of the sensory system was reduced in highly anxious individuals[35]. Another significant result is that the functional connectivity in the four-band rhythms is more complex in AS ones, and it runs through the left and right hemispheres. Functional connections between the left and right cerebral hemispheres increased more in AS patients than those with GAD. It does not mean any functional connections between the brain and the body in GAD patients. However, as the disease progressed, functional connections weakened. This result revealed that after controlling for age and Hamilton Anxiety Scale score, patterns of EEG functional connectivity decrease as the disease progresses if untreated, which is consistent with the results of Carmen Andreescu, MD et al. 's study on functional connectivity of GAD in the whole life cycle[36]. There is now increasing evidence to support the view that anxiety disorder is associated with abnormal communication between brain regions. Our research shows that the decreased connectivity between the left and right hemispheres of the brain may be seen in untreated patients with a long course of GAD. It indicates that the brain function of GAD patients is impaired.
The onset of illness can often be hard to be detected for patients in clinical practice. However, anxiety disorders are treated differently based on their disease course[14], so the time when the illness began is crucial. Machine learning can extract information based on a certain amount of data and predict diagnosis through complex algorithms[23]. So far, machine learning has been used to diagnose schizophrenia[37], major depression[38], bipolar disorder[24]. GAD is characterized by persistent and excessive anxiety for at least 6 months. Four machine learning methods have proven the accuracy of the classification criteria and achieved excellent classification performance, and the results showed that the 6-month classification of the course of disease showed a good accuracy (all equal or above 0.97) in machine learning. This indicates that six months later, the brain function of AD patients was impaired. Therefore, it is necessary and correct to classify patients with AD using cerebrospinal characteristics for the duration of their illness, while demonstrating the advantages of machine learning in classifying diseases.
Overall, based on machine learning methods, we divided AD patients into two groups on the basis of whether the course of the disease had reached six months and obtained a higher classification accuracy. By analyzing the results of brain electrodes and calculating machine learning accuracy, we found that the power spectrum and fuzzy entropy in AS patients, especially in the Alpha2 band, were significantly higher than those with GAD. In addition, the functional connectivity between the left and right hemispheres of the GAD patients was significantly weakened compared with that of the AS group, that is, the connectivity between the left and right hemispheres was decreased. This indicates that the structure and function of the brain are increasingly affected as the duration of the disease is prolonged. Therefore, the DSM-Ⅴ diagnostic criteria set the course criteria for GAD to meet the duration of six months of illness is reasonable and important for further treatment. In the future, studies of prolonged course of disease that lead to impaired brain function in patients with anxiety disorder deserve more attention.