Background This article introduces a novel index aimed at uncovering specific brain connectivity patterns associated with Alzheimer's disease (AD), defined according to neuropsychological patterns. Methods Electroencephalographic (EEG) recordings of 370 people, including 170 healthy subjects and 200 mild-AD patients, were acquired in different clinical centres using different acquisition equipment by harmonising acquisition settings. The study employed a new derived Small World (SW) index, SWcomb, that serves as a comprehensive metric designed to integrate the seven SW parameters, computed across the typical EEG frequency bands. The objective is to create a unified index that effectively distinguishes individuals with a neuropsychological pattern compatible with AD from healthy ones. Results Results showed that the healthy group exhibited the lowest SWcomb values, while the AD group displayed the highest SWcomb ones. Conclusions These findings suggest that SWcomb index represents an easy-to-perform, low-cost, widely available and non-invasive biomarker for distinguishing between healthy individuals and AD patients.

中文翻译：

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区分阿尔茨海默型痴呆症和健康受试者的小世界衍生指数


背景本文介绍了一种新颖的指数，旨在揭示与阿尔茨海默病 (AD) 相关的特定大脑连接模式，该模式根据神经心理学模式进行定义。方法通过协调采集设置，在不同的临床中心使用不同的采集设备采集 370 人的脑电图 (EEG) 记录，其中包括 170 名健康受试者和 200 名轻度 AD 患者。该研究采用了新派生的小世界 (SW) 指数 SWcomb，它作为一个综合指标，旨在整合跨典型脑电图频段计算的七个 SW 参数。目标是创建一个统一的指数，有效区分具有与 AD 相关的神经心理学模式的个体与健康个体。结果结果显示，健康组的SWcomb值最低，而AD组的SWcomb值最高。结论 这些研究结果表明，SWcomb 指数是一种易于执行、低成本、广泛使用且非侵入性的生物标志物，可用于区分健康个体和 AD 患者。