Literature DB >> 33985346

Developing Precision Invasive Neuromodulation for Psychiatry.

Nicholas L Bormann1, Nicholas T Trapp1, Nandakumar S Narayanan1, Aaron D Boes1.   

Abstract

Psychiatric conditions are common and often disabling. Although great strides have been made in alleviating symptoms with pharmacotherapy and psychotherapeutic approaches, many patients continue to have severe disease burden despite the best therapies available. One of the pervasive challenges to improving treatment is that present diagnostic and therapeutic strategies lag behind our modern conceptualization of the pathophysiology of these disorders. Psychiatric symptoms manifest through activity in specific neural circuits; thus, therapies capable of modulating these circuits are attractive. The investigators reviewed recent advances that facilitate treating medically refractory psychiatric disorders with intracranial neuromodulation in a way that intervenes more directly with the underlying pathophysiology. Specifically, they reviewed the prospects for using intracranial multielectrode arrays to record brain activity with high spatiotemporal resolution and identify circuit-level electrophysiological correlates of symptoms. A causal relationship of circuit electrophysiology to symptoms could then be established by modulating the circuits to disrupt the symptoms. Personalized therapeutic neuromodulation strategies can then proceed in a rational manner with stimulation protocols informed by the underlying circuit-based pathophysiology of the most bothersome symptoms. This strategy would enhance current methods in neurotherapeutics by identifying individualized anatomical targets with symptom-specific precision, circumventing many of the limitations inherent in modern psychiatric nosology and treatment.

Entities:  

Keywords:  Deep Brain Stimulation; Epilepsy; Mood Disorders; Neuromodulation; Neurostimulation; Psychiatry

Year:  2021        PMID: 33985346     DOI: 10.1176/appi.neuropsych.20100268

Source DB:  PubMed          Journal:  J Neuropsychiatry Clin Neurosci        ISSN: 0895-0172            Impact factor:   2.198


  1 in total

1.  A pilot study of machine learning of resting-state EEG and depression in Parkinson's disease.

Authors:  Arturo I Espinoza; Patrick May; Md Fahim Anjum; Arun Singh; Rachel C Cole; Nicholas Trapp; Soura Dasgupta; Nandakumar S Narayanan
Journal:  Clin Park Relat Disord       Date:  2022-09-27
  1 in total

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