Thalamocortical dysrhythmia (TCD) is a model proposed to explain divergent neurological disorders. It is characterized by a common oscillatory pattern in which resting-state alpha activity is replaced by cross-frequency coupling of low- and high-frequency oscillations. We undertook a data-driven approach using support vector machine learning for analyzing resting-state electroencephalography oscillatory patterns in patients with Parkinson's disease, neuropathic pain, tinnitus, and depression. We show a spectrally equivalent but spatially distinct form of TCD that depends on the specific disorder. However, we also identify brain areas that are common to the pathology of Parkinson's disease, pain, tinnitus, and depression. This study therefore supports the validity of TCD as an oscillatory mechanism underlying diverse neurological disorders.

中文翻译：

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通过机器学习检测到的丘脑皮质心律失常。

丘脑皮质节律不齐（TCD）是一种用于解释发散性神经系统疾病的模型。它的特征在于常见的振荡模式，其中静止状态的α活动被低频和高频振荡的交叉频率耦合所代替。我们采用支持向量机学习的数据驱动方法来分析帕金森氏病，神经性疼痛，耳鸣和抑郁症患者的静息状态脑电图振荡模式。我们显示了取决于特定疾病的TCD的光谱等效但在空间上不同的形式。但是，我们还确定了帕金森氏病，疼痛，耳鸣和抑郁症病理常见的大脑区域。因此，这项研究支持TCD作为各种神经系统疾病潜在的振荡机制的有效性。