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Thalamocortical dysrhythmia detected by machine learning

Author

Listed:
  • Sven Vanneste

    (The University of Texas at Dallas)

  • Jae-Jin Song

    (Seoul National University Bundang Hospital)

  • Dirk De Ridder

    (University of Otago)

Abstract

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.

Suggested Citation

  • Sven Vanneste & Jae-Jin Song & Dirk De Ridder, 2018. "Thalamocortical dysrhythmia detected by machine learning," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-02820-0
    DOI: 10.1038/s41467-018-02820-0
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    Cited by:

    1. Koch, Marco & Becker, Nicolas & Spinath, Frank M. & Greiff, Samuel, 2021. "Assessing intelligence without intelligence tests. Future perspectives," Intelligence, Elsevier, vol. 89(C).

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