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Difference between revisions of "Encrypted code: Hyperexcitability of the trigeminal system"
Encrypted code: Hyperexcitability of the trigeminal system (view source)
Revision as of 16:56, 19 October 2024
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'''Abstract:'''This section introduces the Cognitive Neural Network (CNN) as an analytical tool for complex clinical diagnoses, particularly in the context of the patient known as "Bruxer," who suffers from severe nocturnal and diurnal bruxism. Building on the method described in the "Encrypted Code: Ephaptic Transmission" chapter, the CNN is used here to refine and decrypt the machine language of the Central Nervous System (CNS) and provide a clearer diagnostic framework. The case study emphasizes the importance of distinguishing between dental and neurological contexts, with a focus on trigeminal system excitability and hyperexcitability. | |||
The diagnostic process begins by assessing the patient's clinical data, including a jaw jerk test that reveals slight asymmetry in amplitude, and progresses to using the CNN to evaluate specific PubMed results related to bruxism and the trigeminal system. The analysis highlights the relevance of electrophysiological tests, such as the recovery cycle of the masseter inhibitory reflex (rcMIR), in identifying hyperexcitability in the trigeminal motor system. | |||
Following the rcMIR analysis, a brain MRI confirms the presence of a pineal cavernoma, providing a definitive diagnosis. The chapter concludes that bruxism is not solely a dental issue but involves CNS hyperexcitability, suggesting that it may be a form of orofacial dystonia. The final considerations propose that bruxism, when accompanied by orofacial pain (OP), should be viewed as a potential manifestation of central nervous system dysfunction, with implications for broader neurophysiological assessments and treatment. | |||
==Introduction== | ==Introduction== | ||
We have therefore reached the section of the Cognitive Neural Network' abbreviated to 'RNC' presented for the diagnosis of the case of our 'Mary Poppins' in the chapter 'Encrypted code: Ephaptic transmission' and which we will propose again as a diagnostic model to accustom the reader to the procedure , simple, intuitive but essential in clinical cases of complex diagnosis such as our patient 'Bruxer'. Our starting point, therefore, is the point of arrival of the phase preceding the 'RNC', ie the discriminatory phase of the contexts ('''<math>\tau</math>''' Coherence Demarcator). The low diagnostic weight derived from the neurological assertions <math>\Im_n\cup0,33 | We have therefore reached the section of the Cognitive Neural Network' abbreviated to 'RNC' presented for the diagnosis of the case of our 'Mary Poppins' in the chapter 'Encrypted code: Ephaptic transmission' and which we will propose again as a diagnostic model to accustom the reader to the procedure , simple, intuitive but essential in clinical cases of complex diagnosis such as our patient 'Bruxer'. Our starting point, therefore, is the point of arrival of the phase preceding the 'RNC', ie the discriminatory phase of the contexts ('''<math>\tau</math>''' Coherence Demarcator). The low diagnostic weight derived from the neurological assertions <math>\Im_n\cup0,33 | ||