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2022; 13: 827536.
2022 年； 13：827536。
Published online 2022 Aug 3. doi: 10.3389/fpsyt.2022.827536
2022 年 8 月 3 日線上發布。
PMCID: PMC9381950
PMCID： PMC9381950
PMID: 35990051
電話號碼： 35990051

Theta oscillations: A rhythm difference comparison between major depressive disorder and anxiety disorder
Theta 振盪：重度憂鬱症和焦慮症之間的節律差異比較

Yu Zhang,corresponding author 1 , 2 , 3 , * Lei Lei, 1 Ziwei Liu, 1 , 2 , 3 Mingxue Gao, 1 , 2 , 3 Zhifen Liu, 1 , 2 Ning Sun, 1 , 2 Chunxia Yang, 1 , 2 Aixia Zhang, 1 , 2 Yikun Wang, 2 and Kerang Zhangcorresponding author 1 , 2 , *
張宇, corresponding author 1 , 2 , 3 , *雷雷, 1劉紫薇, 1 , 2 , 3高明學, 1 , 2 , 3劉志芬, 1 , 2孫寧, 1 , 2楊春霞, 1 , 2張愛霞, 1 , 22王一琨、 2和張克讓 corresponding author 1 , 2 , *
Author information Article notes Copyright and License information PMC Disclaimer
作者資訊文章註釋版權和許可資訊PMC 免責聲明

Associated Data 相關數據

Supplementary Materials 補充資料
Data Availability Statement
數據可用性聲明

Abstract 抽象的

Background 背景

Due to substantial comorbidities of major depressive disorder (MDD) and anxiety disorder (AN), these two disorders must be distinguished. Accurate identification and diagnosis facilitate effective and prompt treatment. EEG biomarkers are a potential research hotspot for neuropsychiatric diseases. The purpose of this study was to investigate the differences in EEG power spectrum at theta oscillations between patients with MDD and patients with AN.
由於重度憂鬱症（MDD）和焦慮症（AN）存在大量合併症，因此必須區分這兩種疾病。準確的識別和診斷有助於有效、及時的治療。腦電圖生物標記是神經精神疾病的潛在研究熱點。本研究的目的是調查 MDD 患者和 AN 患者在 θ 振盪時腦電圖功率譜的差異。

Methods 方法

Spectral analysis was used to study 66 patients with MDD and 43 patients with AN. Participants wore 16-lead EEG caps to measure resting EEG signals. The EEG power spectrum was measured using the fast Fourier transform. Independent samples t-test was used to analyze the EEG power values of the two groups, and p < 0.05 was statistically significant.
光譜分析用於研究 66 名 MDD 患者和 43 名 AN 患者。參與者配戴 16 導程腦電圖帽來測量靜止腦電圖訊號。使用快速傅立葉變換測量腦電圖功率譜。以獨立樣本t檢定分析兩組EEG功率值， p < 0.05有統計學意義。

Results 結果

EEG power spectrum of the MDD group significantly differed from the AN group in the theta oscillation on 4–7 Hz at eight electrode points at F3, O2, T3, P3, P4, FP1, FP2, and F8.
MDD 組的 EEG 功率譜與 AN 組在 F3、O2、T3、P3、P4、FP1、FP2 和 F8 8 個電極點的 4-7 Hz θ 振盪方面有顯著差異。

Conclusion 結論

Participants with anxiety demonstrated reduced power in the prefrontal cortex, left temporal lobe, and right occipital regions. Confirmed by further studies, theta oscillations could be another biomarker that distinguishes MDD from AN.
患有焦慮症的參與者表現出前額葉皮質、左顳葉和右枕葉區域的力量下降。進一步研究證實，theta 振盪可能是區分 MDD 和 AN 的另一個生物標記。

Keywords: major depressive disorder, anxiety disorder, EEG power spectrum, diagnosis, biomarker
關鍵字:重度憂鬱症, 焦慮症, 腦電圖功率譜, 診斷, 生物標記

Introduction 介紹

As two major mental illnesses in society, major depressive disorder (MDD) and anxiety disorder (AN) are normally expressed as “low mood”, “unhappy”, “nervous”, or “anxious”. Both diseases are widely prevalent in the general population and primary health care (, ) and are difficult to distinguish (). According to studies, 39% of people with a generalized anxiety disorder (GAD) also met the criteria for MDD (). About 85% of people with MDD can also have significant anxiety symptoms, and up to 90% of people with AN may acquire depression as a co-disease (). Therefore, it is critical to distinguish AN from MDD, as the two require distinct interventions. However, no definite diagnostic distinction exists between the two diseases (, ). The indicators of related anxiety and depression scale are subjective and imprecise. Therefore, there is an urgent need to discriminate between two groups using alternative methods. MDD and AN are two neuropsychiatric disorders (, ) that require a positive diagnosis to facilitate proper treatment.
作為社會上的兩大精神疾病，重度憂鬱症（MDD）和焦慮症（AN）通常表現為「情緒低落」、「不快樂」、「緊張」或「焦慮」。這兩種疾病在普通人群和初級衛生保健中廣泛流行 ( , )，並且很難區分 ( )。研究表明，39% 的廣泛性焦慮症 (GAD) 患者也符合 MDD 的標準 ( )。大約 85% 的 MDD 患者也會出現明顯的焦慮症狀，高達 90% 的 AN 患者可能會合併憂鬱症 ( )。因此，區分 AN 和 MDD 至關重要，因為兩者需要不同的介入措施。然而，這兩種疾病之間並沒有明確的診斷差異 ( , )。相關焦慮、憂鬱量表指標具有主觀性、不精確性。因此，迫切需要使用替代方法來區分兩個群體。 MDD 和 AN 是兩種神經精神疾病 ( , )，需要積極的診斷才能進行適當的治療。

EEG biomarkers have become an emerging topic in the neuropsychiatric field (). EEG development exhibits potential desirable application in the medical diagnosis of MDD (). In 2020, Trambaiolli et al. () demonstrated that by incorporating neurophysiological biomarkers, EEG can be used to predict MDD and AN as measured by widely used questionnaires. EEG biomarkers are objective indicators of a patient's medical state that are sensitive and specific to a given pathology (). The time-series changes in resting-state EEG signals were relatively simple, and the changes in time-varying signals in the stimulus or response-locking trials were negligible. Therefore, it is advisable to use the method of spectrum analysis to decompose the complex time history waveform into several single harmonics through Fourier transform to obtain the frequency structure of the signal and the information of each harmonic and phase ().
腦電圖生物標記已成為神經精神病學領域的新興議題（ ）。腦電圖的發展在 MDD 的醫學診斷中表現出潛在的理想應用（ ）。 2020 年，Trambaiolli 等人。 ( ) 證明，透過結合神經生理學生物標記物，EEG 可用於預測廣泛使用的問卷測量的 MDD 和 AN。腦電圖生物標記是患者醫療狀態的客觀指標，對給定的病理學敏感且具有特異性 ( )。靜止態腦電圖訊號的時間序列變化相對簡單，刺激或反應鎖定試驗中時變訊號的變化可以忽略不計。因此，建議採用頻譜分析的方法，透過傅立葉變換將複雜的時程波形分解為多個單次諧波，以獲得訊號的頻率結構以及各諧波和相位的資訊（ ）。

Dell'Acqua et al. () indicated that increased theta frequency in patients with depression may be associated with positive symptoms such as restlessness. Shabah et al. () demonstrated in multiple rat experiments that hippocampal theta modulates the behavioral inhibitory system that controls anxiety without false positives (even with sedatives) or negatives (even with drugs not effective for panic or depression), and in subsequent volunteer trials reached the same conclusion. Right frontal theta rhythm is positively correlated with neuroticism and trait anxiety and may serve as a biomarker for anxiety disorders. Frontal midline theta (FM-θ) which has already been suggested as a potential marker of anxiety, may help distinguish anxiety symptoms in pleasant or unpleasant tasks and is enhanced after anxiety symptoms are relieved (). At the same time, previous research also contemplates whether FM-θ in resting EEG can be used as a biomarker to distinguish between MDD and AN. Therefore, this study explores this inference with great interest.
戴爾阿誇等人。 ( ) 顯示憂鬱症患者 θ 頻率增加可能與煩躁等陽性症狀相關。沙巴等人。 ( ) 在多項大鼠實驗中證明，海馬theta 調節控制焦慮的行為抑制系統，沒有假陽性（即使使用鎮靜劑）或陰性（即使使用對恐慌或抑鬱無效的藥物），並且在隨後的志願者試驗中得出了相同的結論。右額葉θ節律與神經質和特質焦慮呈正相關，可作為焦慮症的生物標記。額葉中線θ（FM-θ）已被認為是焦慮的潛在標誌，可能有助於區分愉快或不愉快任務中的焦慮症狀，並且在焦慮症狀緩解後增強（ ）。同時，先前的研究也思考靜止腦電圖中的FM-θ是否可以作為區分MDD和AN的生物標記。因此，本研究饒有興趣地探討了這個推論。

To sum up, EEG can be used to distinguish between MDD and AN. In that context, theta oscillations may be a potential discriminating indicator, but it has not been thoroughly studied at present. Therefore, this study aims to explore the differences in the theta oscillations EEG power spectrum at 4–7 Hz by collecting resting-state EEG of the two diseases.
綜上所述，腦電圖可以用來區分MDD和AN。在這種情況下，theta 振盪可能是潛在的判別指標，但目前尚未得到徹底研究。因此，本研究旨在透過採集兩種疾病的靜息態腦電圖來探討4~7 Hz θ振盪腦電圖功率譜的差異。

Materials and methods 材料和方法

Subjects 科目

The participants for this study included eligible 109 outpatients and inpatients, including 66 patients with MDD and 43 patients with AN. The patients were informed about the routine examination required before enrollment and any issues that required care. They were asked to sign an informed consent before enrollment. All patients included in the study met the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) (). MDD is diagnosed according to the “Depressive Disorders” chapter, requiring patients to meet the criteria for single-episode major depressive disorder. Enrolled patients with anxiety disorders met the DSM-V diagnostic criteria for unspecified anxiety disorder. According to DSM-V, it must be ensured that patients are healthy, and have no bad habits (no long-term history of smoking and drinking). Exclusion criteria included neurological trauma, neurocognitive impairment, taking any psychotropic drugs (), participation in clinical trials within 3 months, pregnancy or contraindications, taking central stimulants, and unable to cooperate with the completion of EEG collection.
本研究的參與者包括 109 名符合條件的門診患者和住院患者，其中 66 名 MDD 患者和 43 名 AN 患者。患者在入組前被告知需要進行的常規檢查以及需要護理的任何問題。他們被要求在入組前簽署知情同意書。研究中納入的所有患者均符合《精神疾病診斷與統計手冊》第五版（DSM-V）（ ）的診斷標準。 MDD的診斷依據「憂鬱症」章節，要求患者符合單期重度憂鬱症的標準。入組的焦慮症患者符合 DSM-V 未特指焦慮症的診斷標準。根據DSM-V，必須確保患者身體健康，無不良嗜好（無長期吸菸、飲酒史）。排除標準包括神經外傷、神經認知障礙、服用任何精神藥物（ ）、3個月內參加臨床試驗、懷孕或有禁忌症、服用中樞興奮劑、無法配合完成腦電圖採集。

All participants completed a demographic questionnaire. Two trained evaluators independently scored patients according to the 17-item Hamilton Depression Scale (HAMD-17) () and the 14-item Hamilton Anxiety Scale (HAMA-14) (). They ensured that the HAMD-17 scores in the depression group were greater than 17 points and the HAMA-14 scores were not higher than 7 points. Participants with at least 2 weeks of depressed mood, sleep disturbance, poor appetite, and even self-harm thoughts or behaviors were assigned to one group. In the anxiety group, the HAMA-14 score of the anxiety group was greater than 14 points twice, and the HAMD-17 score was not higher than 7 points. Participants who scored at least 14 points and had psychentonia, somatotonia, and autonomic nerve dysfunction symptoms for at least several months were assigned to the AN group.
所有參與者都完成了人口統計調查問卷。兩位訓練有素的評估員根據 17 項漢密爾頓憂鬱量表 (HAMD-17) ( ) 和 14 項漢密爾頓焦慮量表 (HAMA-14) ( ) 對患者進行獨立評分。他們確保憂鬱症組的HAMD-17得分大於17分，HAMA-14得分不高於7分。至少有兩週情緒低落、睡眠障礙、食慾不佳、甚至有自殘想法或行為的參與者被分配到一組。焦慮組中，焦慮組HAMA-14評分兩次大於14分，HAMD-17評分不高於7分。得分至少 14 分且患有精神緊張、軀體緊張和自主神經功能障礙症狀至少幾個月的參與者被分配到 AN 組。

Demographics are listed in Table 1. The age of the MDD group ranged between 20 and 60, while in the AN group it ranged between 18 and 65. Participants who completed the study's eligibility requirements and volunteered to participate in the clinical study were included.
表 1列出了人口統計資料。 MDD組的年齡在20歲到60歲之間，而AN組的年齡在18歲到65歲之間。

Table 1 表格1

Demographic characteristics and scale scores of patients.
患者的人口統計學特徵和量表評分。

MDD (n = 66)
MDD（ n = 66） 		AN (n = 43)
AN（ n = 43） 		X2/t X 2 /噸 p-value p值
Gender (M/F) 性別（男/女）18/4814/290.5880.256a 0.256a
Age (years) 年齡（歲）40.67 ± 1.62 40.67±1.6249 ± 1.80 49±1.80−3.3560.154b 0.154b
Education 教育4.48 ± 0.18 4.48±0.183.84 ± 0.21 3.84±0.212.3130.151b 0.151b
HAMD-1716.45 ± 0.58 16.45±0.588.70 ± 0.57 8.70±0.579.1150.209b 0.209b
HAMA-148.02 ± 0.47 8.02±0.4719.23 ± 1.00 19.23±1.00−11.3010.000b*
0.000b *
ap-value for chi-square test.
卡方檢定的 p值。
bp-value for double sample t-test.
b雙樣本 t 檢定的 p 值。
*p < 0.05.
* p< 0.05。

Age-related differences were not very significant between the two groups (F = 2.062, P = 0.154). The education distribution between MDD group and AN group suggested no large difference (F = 2.092, P = 0.151). The gender disparity between two groups was not significant (F = 1.303, P = 0.256). As we did not observe any significant differences in age, gender, and education between two groups, the influence of demographics to the groups difference was negligible. The average score of HAMD-17 was basically identical (F = 0.159, P = 0.209). Research reveals that HAMD-17 score in the normal population is high. Many people with AN may acquire depression symptom as a co-disease. The average HAMD-17 score of the MDD group is higher than that of the AN group. In a state of HAMA scale, scores have significant differences between MDD group and AN group (F = 15.79, P = 0.000). The average HAMA-14 score of AN group was obviously higher than that of MDD group.
兩組之間與年齡相關的差異並不非常顯著（ F = 2.062， P = 0.154）。 MDD組與AN組的教育程度分佈無顯著差異（ F =2.092， P =0.151）。兩組間性別差異不顯著（ F =1.303， P =0.256）。由於我們沒有觀察到兩組之間的年齡、性別和教育程度有任何顯著差異，因此人口統計對組間差異的影響可以忽略不計。 HAMD-17 的平均得分基本上相同（ F = 0.159， P = 0.209）。研究表明，正常人群的 HAMD-17 得分較高。許多患有 AN 的人可能會出現憂鬱症狀作為合併疾病。 MDD組的平均HAMD-17得分高於AN組。在HAMA量表狀態下，MDD組與AN組評分有顯著差異（ F =15.79， P =0.000）。 AN組HAMA-14平均分數明顯高於MDD組。

Resting-state EEG collecting
靜息態腦電圖擷取

Before the experiment, participants were instructed to ensure they had enough sleep the night before their date and to avoid alcohol, caffeine, nicotine, and neuro stimulants. Upon arrival, they first signed the informed consent and were then administered the HAMA score. To maintain the study's data quality, participants were made to sit in a quiet, confined, non-direct light source stimulation environment. After sensors were installed, EEG was recorded over a 5-min period for each participant. During the experiment, participants were required to remain still with eyes open, minimizing significant facial and eye movements. Using an electrical cap at 16 scalp locations, electroencephalograms (EEG) of participants were recorded. Sixteen lateral electrode pairs (FP1, FP2, F3, F4, F7, F8, C3, C4, P3, P4, T3, T4, T5, T6, O1, and O2) were recorded, with the mastoid electrode (A1-A2) serving as the reference electrode.
在實驗之前，參與者被要求確保約會前一天晚上有足夠的睡眠，並避免飲酒、咖啡因、尼古丁和神經興奮劑。抵達後，他們首先簽署知情同意書，然後進行 HAMA 評分。為了維持研究的數據質量，參與者被要求坐在安靜、受限、非直接光源刺激的環境中。安裝感測器後，在 5 分鐘內記錄每位參與者的腦電圖。在實驗過程中，參與者被要求睜著眼睛保持靜止，盡量減少臉部和眼睛的明顯移動。在 16 個頭皮位置使用電帽記錄參與者的腦電圖 (EEG)。記錄十六個橫向電極對（FP1、FP2、F3、F4、F7、F8、C3、C4、P3、P4、T3、T4、T5、T6、O1 和 O2），乳突電極（A1-A2）作為參考電極。

Data preprocessing 資料預處理

The sample rate was 256 Hz, with pass filtering of 0.1–70 Hz. Additionally, ECG records were used to investigate heart rate variability for eliminating ECG interference. The data were averaged for each electrode recording period, and the absolute power was calculated for each band. Data were segmented into three second segments. The electroencephalogram was manually corrected using independent component analysis (ICA) to remove components associated with artifacts such as blinking, eye movements, and sport-related artifacts (–). The investigation excluded frequencies other than 0.1–70 Hz on any channel.
取樣率為 256 Hz，帶通濾波為 0.1–70 Hz。此外，心電圖記錄也用於研究心率變異性，以消除心電圖幹擾。對每個電極記錄週期的數據進行平均，並計算每個條帶的絕對功率。數據被分為三秒段。使用獨立成分分析（ICA）腦電圖進行手動校正，以消除與眨眼、眼球運動和運動相關偽影等偽影相關的成分（ ）。調查排除了任何頻道上 0.1–70 Hz 以外的頻率。

Frequency-domain analysis
頻域分析

The fast Fourier transform (FFT) () was used to transform these EEG signals from the time domain to the frequency domain, and the obtained frequency domain sampling sequence was properly converted and processed by EEGLAB to obtain the frequency spectrum of the signal, which was convenient to analyze the characteristics of the signal. A comparative study was undertaken to determine the differences in EEG power at each electrode site in theta oscillations (4–7 Hz) in anxiety and depression patients. In the frequency domain, linear time-invariant systems were generally applicable. The time variable in the resting state was negligible, indicating that we could calculate the energy difference between the two using frequency domain analysis. The complex data preprocessing steps were implemented in EEGLAB (, ).
利用快速傅立葉變換（FFT）（ ）將這些腦電訊號從時域轉換到頻域，將得到的頻域取樣序列透過EEGLAB進行適當的轉換與處理，得到訊號的頻譜，即方便分析訊號的特性。進行了一項比較研究，以確定焦慮症和憂鬱症患者的 θ 振盪（4-7 Hz）中每個電極部位的 EEG 功率差異。在頻域中，線性時不變系統普遍適用。靜息狀態下的時間變數可以忽略不計，這表示我們可以使用頻域分析來計算兩者之間的能量差異。複雜的資料預處理步驟在 EEGLAB 中實現 ( , )。

Statistical analysis 統計分析

The clinical data were statistically analyzed using SPSS 26.0. The measurement data were expressed in the form of “mean ± standard deviation (x ± s)”, and the results were expressed as percentages (%). The chi-square test was used to compare the gender component in the two groups, and the two-sample t-test was used to compare the age, education level, and anxiety and depression scales of the two groups. EEG data were evaluated by independent samples t-test to evaluate the differences between groups in Weighted Phase-Lag Index (wPLI) (), p < 0.05 indicated statistical significance.
臨床資料採用SPSS 26.0進行統計分析。測定數據以「平均值±標準差（x±s）」的形式表示，結果以百分比（%）表示。以卡方檢定比較兩組性別組成，以雙樣本t檢定比較兩組年齡、文化程度、焦慮憂鬱量表。 EEG數據透過獨立樣本t檢定來評估加權相位滯後指數（wPLI）（ ）組之間的差異， p ％3C 0.05表明統計顯著性。

Results 結果

EEG analysis revealed differences between two groups in theta oscillations of 4–7 Hz at eight electrode sites (FP1, FP2, F3, O2, T3, P3, P4, and F8). Calculated using eight different homologous electrodes in theta power, these values are projected to cover the cerebral hemisphere. Power spectrum energy values are depicted in different colors on the topographic map. Accordingly, the theta band of 4–7 Hz can be plotted for the two groups (Figure 1). P-values of the region between the eight electrode positions indicate significance. To represent p-values between two groups, regions formed were divided by different colors. Figure 2 depicts the p-value of power spectral density between the two groups which can be utilized to obtain a more intuitive sense of their differences. Concerning the theta oscillations of 4–7 Hz, significant differences were found in green areas (P < 0.05).
EEG 分析揭示了兩組在 8 個電極位點（FP1、FP2、F3、O2、T3、P3、P4 和 F8）的 4-7 Hz θ 振盪之間的差異。使用八個不同的同源電極以 θ 功率計算，這些值預計會覆蓋大腦半球。功率譜能量值在地形圖上以不同顏色表示。因此，可以為兩組繪製 4-7 Hz 的 theta 頻帶（圖 1 ）。八個電極位置之間的區域的P值表示顯著性。為了表示兩組之間的p值，形成的區域以不同的顏色劃分。圖 2描繪了兩組之間功率譜密度的p值，可用於更直觀地了解它們的差異。關於 4–7 Hz 的 theta 振盪，在綠色區域發現顯著差異 (P < 0.05)。

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Topographic map of EEG power spectrum in the theta band for depression group (MDD) and anxiety group (AN). (A) The EEG power spectrum topography map of MDD group. (B) The EEG power spectrum topography map of AN group. The diagram above shows the difference in the energy distribution between the two groups. The blue part represents low EEG power, and the red part represents high EEG power. The darker the blue, the lower the power, and the darker the red, the higher the power.
憂鬱組（MDD）與焦慮組（AN）θ波段腦電圖功率譜地形圖。 (A) MDD組腦電功率譜地形圖。 (B) AN組腦電功率譜地形圖。上圖顯示了兩組之間能量分佈的差異。藍色部分代表低腦電功率，紅色部分代表高腦電功率。藍色越深，功率越低，紅色越深，功率越高。

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Topographic map of P-values between the two groups. Green areas represent significant differences (P < 0.05).
兩組之間P值的地形圖。綠色區域代表顯著差異（ P < 0.05）。

The topographic map (Figure 2) illustrates differences in the regions of the prefrontal cortex (FP1, FP2, F3), left temporal lobe (T3), and right occipital regional (P3, P4, and O2). Among them, the difference is most obvious in the right occipital region (P3, P4, and O2). In the distributions in the left occipital cortex region (T5), little difference was observed between the two groups. The two groups exhibited a similar topographic distribution of the power spectrum of the two bands. Figure 1 depicts the EEG power spectrum distribution of anxiety patients, which is lower than depression patients. The two groups revealed higher value distribution in the posterior region, a lower value distribution in the central temporal region, and a slightly higher value distribution in the frontal region. Compared with depression patients, anxiety patients have asymmetrical distribution power of theta power on 4–7 Hz in the rear region. Significant differences between the two groups were more obvious on the left temporal lobe than on the right rear (Figure 2). Demographic differences between the two groups of patients were previously ruled out. Therefore, the above conclusion is logical.
地形圖（圖2 ）顯示了前額葉皮質（FP1、FP2、F3）、左顳葉（T3）和右枕葉區域（P3、P4和O2）的差異。其中，右枕區（P3、P4、O2）差異最為明顯。在左側枕葉皮質區域（T5）的分佈中，兩組之間幾乎沒有觀察到差異。兩組的兩個頻帶的功率譜表現出相似的地形分佈。圖1描繪了焦慮症患者的腦電圖功率譜分佈，其低於憂鬱症患者。兩組顯示後部區域的數值分佈較高，顳部區域的數值分佈較低，額葉區域的數值分佈稍高。與憂鬱症患者相比，焦慮症患者後部4~7Hz θ功率分佈不對稱。兩組之間的顯著差異在左顳葉比右後部更明顯（圖2 ）。先前排除了兩組患者之間的人口統計學差異。因此，上述結論是合乎邏輯的。

In eight bands, statistically significant differences in power spectral density were observed. Three electrodes in the prefrontal area (FP1, FP2, and F3) on theta frequency between two groups varies markedly (P < 0.05). The power of the depression group was obviously higher than the anxiety group (Figures 3A–C). The left temporal lobe, particularly at electrode T3, differed between the two groups (Figure 3H), whereas the right temporal lobe differences were found in the F8 electrode (Figure 3D). Differences were identified between the two groups in the temporal lobe. The power of the depression group revealed significantly stronger responses than the anxiety group, as evidenced by right occipital regional responses (Figures 3E–G). In the posterior occipital region, the energy difference between the two groups increased as the frequency increased and the difference between the two groups was most obvious at 7 Hz.
在八個頻段中，觀察到功率譜密度有統計上的顯著差異。前額葉區域的三個電極（FP1、FP2 和 F3）在 θ 頻率上兩組間差異顯著（ P < 0.05）。憂鬱組的力量明顯高於焦慮組（圖3A-C ）。左顳葉，特別是在電極 T3 處，兩組之間存在差異（圖 3H ），而右顳葉在 F8 電極中存在差異（圖 3D ）。兩組之間的顳葉有差異。憂鬱組的力量顯示出比焦慮組明顯更強的反應，右枕骨區域反應證明了這一點（圖 3E-G ）。在後枕葉區域，兩組之間的能量差異隨著頻率的增加而增加，兩組之間的差異在7 Hz時最為明顯。

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(A–H) The power spectral density (PSD) for the different electrode sites according depressive group (MDD) and anxiety group (AN). The horizontal axis represents the EEG frequency, the vertical axis represents the EEG PSD, the red line represents MDD group, and the green line represents AN group.
(A-H)根據憂鬱組 (MDD) 和焦慮組 (AN) 不同電極部位的功率譜密度 (PSD)。橫軸代表EEG頻率，縱軸代表EEG PSD，紅線代表MDD組，綠線代表AN組。

Discussion 討論

The aim of distinguishing MDD and AN is to better understand EEG biomarkers and facilitate the early diagnosis and effective treatment of both these diseases (). EEG frequencies (alpha, beta, theta, and delta) are potential biomarkers of depression (). MDD and AN differ in theta oscillations (4–7 Hz) and the results show that there are differences in the eight electrodes of F3, O2, T3, P3, P4, FP1, FP2, and F8. These eight different electrode points may represent the prefrontal cortex, left temporal, and right occipital regions.
區分 MDD 和 AN 的目的是為了更好地了解腦電圖生物標記物，並促進這兩種疾病的早期診斷和有效治療（ ）。腦電圖頻率（α、β、θ 和 δ）是憂鬱症的潛在生物標記 ( )。 MDD和AN在θ振盪（4-7Hz）上有差異，結果顯示F3、O2、T3、P3、P4、FP1、FP2和F8這8個電極有差異。這八個不同的電極點可能代表前額葉皮質、左顳葉和右枕葉區域。

Differences in brain regions
大腦區域的差異

Patients with mental illness experience altered brain activity. Patients with anxiety and those with depression showed different levels of activation and inhibition of brain activity in different brain regions. Hayata et al. () studied the difference in cerebral hemispheric activity in anxiety and depression using different brain activity pattern models of distinct regions. Bruder et al. () also came to a similar conclusion. As early as 1984, Robinson et al. () reported that damage to the left frontal lobe can lead to depression, and when the left brain is injured, the closer the lesion is to the frontal lobe, the more severe the depressive symptoms. Subsequent studies delved deeper into the link between prefrontal brain activity and depression. Hu et al. () found that MDD patients had significantly lower activation in the right prefrontal cortex. The right frontal lobe brain activity in persons with depression is reduced compared with the left side, and the approach motivation is weakened (). Research by Mohamed Nour et al. () on the prefrontal cortex also suggests that changes in brain electrical activity may be closely related to emotional processing, social interaction, and cognition, and that low activation in this area may be associated with lower thresholds for sad and depressive experiences (). Emotional behavior is associated with asymmetrical frontal lobe activation, negative emotion, or dragging behavior with right frontal lobe activation, and positive emotion or approach behavior with left frontal lobe activation (). The different changes in electrical activity in the bilateral prefrontal cortex caused by anxiety and depression may explain the differences in the EEG power of the prefrontal cortex between the two groups of patients in the results of this study.
患有精神疾病的患者會經歷大腦活動的改變。焦慮症患者和憂鬱症患者的不同大腦區域表現出不同程度的大腦活動活化和抑制。早田等人。 ( )使用不同區域的不同大腦活動模式模型研究了焦慮和憂鬱時大腦半球活動的差異。布魯德等。 ( )也得出了類似的結論。早在 1984 年，Robinson 等人。 ( )報告，左額葉損傷可導致憂鬱症，而當左腦損傷時，病變部位距離額葉越近，憂鬱症狀越嚴重。隨後的研究更深入研究了前額葉腦活動與憂鬱症之間的關聯。胡等人。 ( ) 發現 MDD 患者的右前額葉皮質活化程度明顯較低。憂鬱症患者的右額葉大腦活動較左側減少，接近動機減弱（ ）。穆罕默德·努爾等人的研究。 （ ）對前額葉皮質的影響也表明，腦電活動的變化可能與情緒處理、社交互動和認知密切相關，並且該區域的低激活可能與悲傷和抑鬱經歷的閾值較低有關（ ） 。情緒行為與不對稱額葉活化、負向情緒或右額葉活化的拖拉行為有關，以及與左額葉活化的正向情緒或接近行為相關（ ）。 焦慮和憂鬱引起的雙側前額葉皮質電活動的不同變化可能解釋了本研究結果中兩組患者前額葉皮質腦電圖功率的差異。

Previous neuroimaging studies have shown that the temporal lobe can act as a network node in anxiety disorders (–). Anxiety leads to redistribution of regional cerebral blood flow (rCBF) (), which decreases relative to left frontal EEG activity reflecting decreased approach motivation or increased withdrawal tendencies (). Additionally, activation of marked brain activity was decreased in the right temporal lobe in patients with major depressive disorder (). The difference in the temporal lobe EEG power in this study is related to the functional loss caused by the decreased left hemisphere cerebral blood flow in anxiety patients and the weakened right temporal lobe electrical activity in depressed patients. Asymmetries of brain activity in different brain regions (especially in the region of prefrontal cortex and left temporal lobe) in patients with depression and anxiety are associated with specific symptom characteristics of the disease.
先前的神經影像學研究表明顳葉可以充當焦慮症的網路節點（ ）。焦慮會導致區域腦血流 (rCBF) 的重新分佈 ( )，相對於左額葉腦電圖活動的減少反映了接近動機的降低或退縮傾向的增加 ( )。此外，重度憂鬱症患者右顳葉顯著大腦活動的活化減少（ ）。本研究中顳葉腦電圖功率的差異與焦慮症患者左半球腦血流量減少和憂鬱症患者右顳葉電活動減弱導致的功能喪失有關。憂鬱和焦慮患者不同大腦區域（特別是前額葉皮質和左顳葉區域）大腦活動的不對稱性與疾病的特定症狀特徵有關。

There are few previous studies on depression and anxiety disorders in the occipital area, and only a few research suggest that the generation of anxiety may be related to the changes in the brain electrical activity in the occipital area (). The connection between mental illness and the occipital area needs to be further verified.
以往關於枕區憂鬱症和焦慮症的研究很少，只有少數研究顯示焦慮的產生可能與枕區腦電活動的變化有關（ ）。精神疾病與枕區的關聯有待進一步驗證。

Study in theta oscillations
θ 振盪研究

Theta rhythm can be a good indicator in diagnostic tools (–). As two major psychiatric illnesses, anxiety is inextricably linked to depression (). This study revealed that almost 50% of adults with a 12-month history of GAD met the criteria for lifelong major depressive disorder, compared to only 7.4% of those without GAD (). Theta bands associated with emotional processing () are well relative to depression and healthy controls (). Studies indicate that a higher baseline theta activity is associated with greater improvement in depression (, ). Additionally, frontal theta asymmetry is also a potential biomarker of depression (, ). In 2000, Suetsugi et al. () suggested that frontal midline theta is a reliable measure of anxiety and that low levels of theta are associated with higher levels of anxiety. In other words, the theta power distribution at the group level was negatively correlated with the severity of anxiety attacks. Therefore, theta oscillations may be a biomarker for differentiating depression and anxiety disorders.
Theta 節律可以作為診斷工具的良好指標 ( – )。作為兩種主要的精神疾病，焦慮與憂鬱有著千絲萬縷的關係（ ）。這項研究表明，幾乎 50% 有 12 個月 GAD 病史的成年人符合終身重度憂鬱症的標準，而沒有 GAD 病史的成年人中只有 7.4% ( )。與情緒處理相關的 Theta 帶 ( ) 與憂鬱症和健康對照組 ( ) 密切相關。研究表明，較高的基線 θ 活動與憂鬱症的改善程度相關 ( , )。此外，額葉θ不對稱也是憂鬱症的潛在生物（ ）。 2000 年，Suetsugi 等人。 ( )顯示額葉中線θ是衡量焦慮的可靠指標，低程度的θ與較高程度的焦慮相關。換句話說，群體層面的θ功率分佈與焦慮發作的嚴重程度呈負相關。因此，θ振盪可能是區分憂鬱症和焦慮症的生物標記。

As one of the biomarkers of EEG, the relationship between theta oscillations and mental disorders that affect brain activity is still worthy of further study. Previous research has indicated a variation in EEG activity on theta frequency in patients with depression (). Depression has been demonstrated to increase theta band activity in occipital and parietal regions (), which can reflect a decreased cortical activation in these brain regions. Meanwhile, theta oscillations may be associated with negative clinical symptoms in patients with anxiety and depression, such as seeking multiple ways to engage in suicidal or self-harming behaviors (, ). Theta relative power in the central frontal region (F3, FZ, FCZ, and CZ) was significantly higher in the group with high Scale for Suicidal Ideation (SSI) scores than with low SSI scores (). Obviously, the severity of depressive symptoms is proportional to theta power. In the theta band, anxiety increased connectivity between the right frontal and central areas and right temporal and left occipital areas (). Theta rhythm may be closely related to the electrical activity in the medial prefrontal cortex (), which has been linked to various neurological and psychiatric disorders, including MDD and AN ().
作為腦電圖的生物標記之一，θ振盪與影響大腦活動的精神障礙之間的關係仍值得進一步研究。先前的研究表明，憂鬱症患者的腦電圖活動在θ頻率上存在差異（ ）。憂鬱症已被證明會增加枕葉和頂葉區域的 θ 帶活動（ ），這可以反映這些大腦區域的皮質活化減少。同時，θ 振盪可能與焦慮和憂鬱患者的負面臨床症狀有關，例如尋求多種方式進行自殺或自殘行為 ( , )。在自殺意念量表 (SSI) 評分高的組別中，中央額葉區域（F3、FZ、FCZ 和 CZ）的 Theta 相對功率顯著高於 SSI 評分低的組別 ( )。顯然，憂鬱症狀的嚴重程度與 theta 功率成正比。在 θ 帶中，焦慮增加了右額葉和中央區域以及右顳葉和左枕葉區域之間的連結性 ( )。 Theta 節律可能與內側前額葉皮質的電活動密切相關 ( )，而內側前額葉皮質的電活動與各種神經和精神疾病有關，包括 MDD 和 AN ( )。

In conclusion, theta oscillation might be of some value in distinguishing MDD and AN; the prefrontal lobe, left temporal lobe, and right occipital lobe are significantly different in patients with anxiety and those with depression. However, further studies are needed to confirm the importance of the EEG biomarker theta oscillation in psychiatric disorders.
綜上所述，theta振盪對於區分MDD和AN可能具有一定的價值；焦慮症患者和憂鬱症患者的前額葉、左顳葉和右枕葉有顯著差異。然而，還需要進一步的研究來證實腦電圖生物標記θ振盪在精神疾病中的重要性。

Limitations 限制

Certain limitations of the results should be considered. On the one hand, in terms of data, the sample size was limited and the number of EEG leads were insufficient. The small sample size may have had limitations in terms of experimental age and gender. The EEG data that were collected was from a single lead, affecting the experimental results. At this stage, some of the more advanced EEG research leads have reached 256 leads. The increase in the number of EEG leads can increase the accuracy of brain region localization and is also critical for further exploration of diseases. At the same time, the influence of the style of the electrode cap and the material of the EEG paste on the research results cannot be ignored. On the other hand, most clinical diagnostic tools rely on medical history collection and self-reporting. Although medical history and scales have been used as the basis for clinical diagnosis for many years, there are still some deficiencies, such as insufficient sensitivity and specificity. Finally, this study is limited to the generalized level of psychiatric disorders and did not conduct research at the subtype level.
應考慮結果的某些限制。一方面，數據方面，樣本數有限，腦電圖導極數量不足。小樣本量可能在實驗年齡和性別方面有其限制。採集的腦電圖數據來自單導聯，影響了實驗結果。現階段，一些較先進的腦電圖研究導極已達到256導極。腦電圖導聯數量的增加可以提高大腦區域定位的準確性，對於進一步探索疾病也至關重要。同時，電極帽的樣式和腦電貼的材質對研究結果的影響也不容忽視。另一方面，大多數臨床診斷工具依賴病史收集和自我報告。儘管病史和量表多年來一直被用作臨床診斷的依據，但仍存在一些不足，例如敏感性和特異性不足。最後，本研究僅限於精神疾病的廣義水平，並未進行亞型水平的研究。

To address the shortcomings of existing research, future research should first expand the sample size, choose EEG caps with more leads, test experimental equipment multiple times, such as EEG caps of different materials and EEG heights, and then select EEG data. Future research can also explore the relationship between disease subtypes, such as the difference between depression with or without anxiety symptoms. The relationship between EEG biomarkers and psychiatric disorders other than EEG biomarker theta oscillations, which was mainly explored in this study, can also be explored.
針對現有研究的不足，未來的研究應先擴大樣本量，選擇導極更多的腦電帽，多次測試實驗設備，如不同材質的腦電帽和腦電高度，然後選擇腦電數據。未來的研究還可以探索疾病亞型之間的關係，例如伴隨或不伴隨焦慮症狀的憂鬱症之間的差異。本研究主要探討的腦電圖生物標記與精神疾病之間的關係也可以探討，而不是腦電圖生物標記θ振盪。

Conclusion 結論

Compared to the depression group, the anxiety group demonstrated a decrease in power. The differences between the two groups are concentrated in the prefrontal cortex, left temporal lobe, and right occipital regions, affecting emotional regulation and cognitive functions. Theta frequencies enable early identification of depressive and anxious symptoms, as well as effective and objective treatment monitoring. Additional research is warranted to explore whether other EEG biomarkers would be beneficial in clinical studies.
與憂鬱組相比，焦慮組表現出力量下降。兩組之間的差異集中在前額葉皮質、左顳葉和右枕葉區域，影響情緒調節和認知功能。 Theta 頻率能夠及早識別憂鬱和焦慮症狀，以及有效和客觀的治療監測。需要進行更多研究來探索其他腦電圖生物標記是否有益於臨床研究。

Data availability statement
數據可用性聲明

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
支持本文結論的原始數據將由作者毫無保留地提供。

Ethics statement 道德聲明

The studies involving human participants were reviewed and approved by the First Hospital Committee of Shanxi Medical University. The patients/participants provided their written informed consent to participate in this study.
涉及人類參與者的研究得到了山西醫科大學第一醫院委員會的審查和批准。患者/參與者提供了參與本研究的書面知情同意書。

Author contributions 作者貢獻

YZ: study design, data curation, formal analysis, and writing—original draft. LL: investigation, validation, and writing—review and editing. ZiL and MG: investigation, software, and writing—review and editing. ZhL and NS: investigation, resources, and writing—review and editing. CY and AZ: carried out recruitment, clinical treatment, and writing—review and editing. YW: investigation and writing—review and editing. KZ: funding acquisition, advisors, overall oversight, and writing—review and editing. All authors contributed to the article and approved the submitted version.
YZ：研究設計、資料管理、形式分析與撰寫初稿。 LL：調查、驗證和寫作——審查和編輯。 ZiL 和 MG：調查、軟體和寫作——審查和編輯。 ZhL 和 NS：調查、資源和寫作——評論和編輯。 CY和AZ：進行招募、臨床治療、寫作審查和編輯。 YW：調查和寫作——評論和編輯。 KZ：資金獲取、顧問、整體監督以及寫作審查和編輯。所有作者都對本文做出了貢獻並批准了提交的版本。

Funding 資金

This study was supported by the National Key Research and Development Program of China (2016YFC1307103), the National Natural Science Foundation of China (No. 81471379), the National Natural Science Foundation of China (81701345), and the Natural Science Foundation of Shanxi Province for Youths (201601D021151).
該研究獲得國家重點研發計劃（2016YFC1307103）、國家自然科學基金（81471379）、國家自然科學基金（81701345）和山西省自然科學基金的資助青少年 (201601D021151)。

Conflict of interest 利益衝突

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
作者聲明，該研究是在不存在任何可能被視為潛在利益衝突的商業或財務關係的情況下進行的。

Publisher's note 出版商備註

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
本文所表達的所有主張僅代表作者的主張，不一定代表其附屬組織的主張，也不代表出版商、編輯和審稿人的主張。本文中可能評估的任何產品或其製造商可能提出的聲明均未得到出版商的保證或認​​可。

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