作为阿尔茨海默病早期生物标志物的 15-29Hz 高功率瞬态贝塔事件特征:一项脑电图研究

Danylyna Shpakivska-Bilan, Gianluca Susi, David Zhou, Jesus Cabrera Alvarez, Blanca P Carvajal, Ernesto Pereda, Maria Eugenia Lopez, Ricardo Bruna, Fernando Maestu, Stephanie R Jones
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摘要

从轻度认知功能障碍(MCI)患者的 M/EEG 记录中观察到的一种典型模式是大脑振荡活动持续减慢。振荡减慢的定义并不精确,因为它们是时间和频带的平均值,掩盖了信号中的精细结构和疾病的潜在可靠生物标志物。最近的研究表明,高平均频带功率可能来自功率的瞬时增加,称为 "事件 "或 "爆发"。为了更好地了解 AD 进展过程中的 MEG 振荡减慢,我们分析了高功率振荡事件的特征及其与认知能力下降之间的关系。我们对年龄匹配的 MCI 患者的 MEG 静息态振荡进行了登记,这些患者在 2.5 年的时间内转为(CONV,41 人)或未转为(NOCONV,44 人)AD。为了区分未来的CONV和NOCONV,我们对前扣带回(ACC)和楔前区(PC)α和β波段瞬时高功率事件的速率、持续时间、频率跨度和功率进行了表征。结果显示,阿尔法和贝塔波段的静息态功率都有类似事件的模式,但只有贝塔波段的特征可预测向注意力缺失症的转化,尤其是在PC中。具体来说,与 NOCONV 相比,PC 中 CONV 的 beta 事件数量较少,同时事件功率较低,事件持续时间呈缩短趋势(p < 0.05)。ACC 中的β事件持续时间也明显较短(p < 0.01)。此外,CONV 中贝塔事件表达的减少预示着平均相对贝塔功率值的降低、AD 转换概率的增加以及认知能力的下降。我们的研究为重新解释 M/EEG 减慢和检查 beta 事件特征铺平了道路,将其作为 AD 连续体的一种新的生物标志物,并与神经变性中的抑制性认知控制理论建立了潜在联系。这些结果可能会使我们更接近于了解该疾病的神经机制,从而有助于指导新的疗法。
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High power transient 15-29Hz beta event features as early biomarkers of Alzheimer's Disease conversion: a MEG study
A typical pattern observed in M/EEG recordings of Mild Cognitive Impairment (MCI) patients progressing to Alzheimer's disease is a continuous slowing of brain oscillatory activity. Definitions of oscillatory slowing are imprecise, as they average across time and frequency bands, masking the finer structure in the signal and potential reliable biomarkers of the disease. Recent studies show that high averaged band power can result from transient increases in power, termed 'events' or 'bursts'. To better understand MEG oscillatory slowing in AD progression, we analyzed features of high-power oscillatory events and their relationship to cognitive decline. MEG resting-state oscillations were registered in age-matched patients with MCI who later convert (CONV, N=41) or do not convert (NOCONV, N=44) to AD, in a period of 2.5 years. To distinguish future CONV from NOCONV, we characterised the rate, duration, frequency span and power of transient high-power events in the alpha and beta band in anterior cingulate (ACC) and precuneus (PC). Results revealed event-like patterns in resting-state power in both the alpha and beta-bands, however only beta-band features were predictive of conversion to AD, particularly in PC. Specifically, compared to NOCONV, CONV had a lower number of beta events, along with lower power events and a trend toward shorter duration events in PC (p < 0.05). Beta event durations were also significantly shorter in ACC (p < 0.01). Further, this reduced expression of beta events in CONV predicted lower values of mean relative beta power, increased probability of AD conversion, and poorer cognitive performance. Our work paves the way for reinterpreting M/EEG slowing and examining beta event features as a new biomarker along the AD continuum, and a potential link to theories of inhibitory cognitive control in neurodegeneration. These results may bring us closer to understanding the neural mechanisms of the disease that help guide new therapies.
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