Department of Psychiatry, Ghent University Hospital (UZ Gent), Corneel Heymanslaan 10, 9000, Ghent, Belgium.
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Sci Rep. 2022 Oct 7;12(1):16903. doi: 10.1038/s41598-022-20274-9.
Major Depressive Disorder (MDD) is a widespread mental illness that causes considerable suffering, and neuroimaging studies are trying to reduce this burden by developing biomarkers that can facilitate detection. Prior fMRI- and neurostimulation studies suggest that aberrant subgenual Anterior Cingulate (sgACC)-dorsolateral Prefrontal Cortex (DLPFC) functional connectivity is consistently present within MDD. Combining the need for reliable depression markers with the electroencephalogram's (EEG) high clinical utility, we investigated whether aberrant EEG sgACC-DLPFC functional connectivity could serve as a marker for depression. Source-space Amplitude Envelope Correlations (AEC) of 20 MDD patients and 20 matched controls were contrasted using non-parametric permutation tests. In addition, extracted AEC values were used to (a) correlate with characteristics of depression and (b) train a Support Vector Machine (SVM) to determine sgACC-DLPFC connectivity's discriminative power. FDR-thresholded statistical maps showed reduced sgACC-DLPFC AEC connectivity in MDD patients relative to controls. This diminished AEC connectivity is located in the beta-1 (13-17 Hz) band and is associated with patients' lifetime number of depressive episodes. Using extracted sgACC-DLPFC AEC values, the SVM achieved a classification accuracy of 84.6% (80% sensitivity and 89.5% specificity) indicating that EEG sgACC-DLPFC connectivity has promise as a biomarker for MDD.
重度抑郁症(MDD)是一种广泛存在的精神疾病,会给患者带来极大的痛苦。神经影像学研究正在通过开发有助于检测的生物标志物来努力减轻这一负担。先前的 fMRI 和神经刺激研究表明,异常的前扣带回皮质下部(sgACC)-背外侧前额叶皮质(DLPFC)功能连接在 MDD 中始终存在。将对可靠的抑郁标志物的需求与脑电图(EEG)的高临床效用相结合,我们研究了异常 EEG sgACC-DLPFC 功能连接是否可以作为抑郁的标志物。使用非参数置换检验对比了 20 名 MDD 患者和 20 名匹配对照者的源空间幅度包络相关性(AEC)。此外,提取的 AEC 值用于(a)与抑郁特征相关,(b)训练支持向量机(SVM)以确定 sgACC-DLPFC 连接的判别能力。经 FDR 阈值处理的统计图显示,与对照组相比,MDD 患者的 sgACC-DLPFC AEC 连接减少。这种减少的 AEC 连接位于β-1(13-17 Hz)频段,与患者一生中抑郁发作的次数有关。使用提取的 sgACC-DLPFC AEC 值,SVM 实现了 84.6%的分类准确率(80%的敏感性和 89.5%的特异性),表明 EEG sgACC-DLPFC 连接有望成为 MDD 的生物标志物。