Mesocircuit mechanisms in the diagnosis and treatment of disorders of consciousness

神经科学 意识 背景(考古学) 意识障碍 丘脑 机制(生物学) 心理学 彗差(光学) 生物神经网络 唤醒 持续植物状态 最小意识状态 生物 物理 古生物学 光学 量子力学
作者
Nicholas D. Schiff
出处
期刊:Presse Medicale [Elsevier BV]
卷期号:52 (2): 104161-104161 被引量:33
标识
DOI:10.1016/j.lpm.2022.104161
摘要

The ‘mesocircuit hypothesis’ proposes mechanisms underlying the recovery of consciousness following severe brain injuries. The model builds up from a single premise that multifocal brain injuries resulting in coma and subsequent disorders of consciousness produce widespread neuronal death and dysfunction. Considering the general properties of cortical, thalamic, and striatal neurons, a lawful and specific circuit-level mechanism is constructed based on these known anatomical and physiological specializations of neuronal subtypes. The mesocircuit model generates many testable predictions at the mesocircuit, local circuit, and cellular level across multiple cerebral structures to correlate diagnostic measurements and interpret therapeutic interventions. The anterior forebrain mesocircuit is integrally related to the frontal-parietal network, another network demonstrated to show strong correlation with levels of recovery in disorders of consciousness. A further extension known as the “ABCD” model has been used to examine interaction of these models in recovery of consciousness using electrophysiological data types. Many studies have examined predictions of the mesocircuit model; here we first present the model and review the accumulated evidence for several predictions of model across multiple stages of recovery function in human subjects. Recent studies linking the mesocircuit model, the ABCD model, and interactions with the frontoparietal network are reviewed. Finally, theoretical implications of the mesocircuit model at the neuronal level are considered to interpret recent studies of deep brain stimulation in the central lateral thalamus in patients recovering from coma and in new experimental models in the context of emerging understanding of neuronal and local circuit mechanisms underlying conscious brain states.
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