Thalamocortical dysrhythmia detected by machine learning

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.
Thalamocortical dysrhythmia has been proposed to occur in a number of neurological and psychiatric disorders. Here, the authors use a data-driven approach to demonstrate thalamocortical dysrhythmia occurs in individuals with Parkinson’s disease, neuropathic pain, tinnitus, and depression.