Ebadi Aida, Allouch Sahar, Mheich Ahmad, Tabbal Judie, Kabbara Aya, Robert Gabriel, Lefebvre Aline, Iftimovici Anton, Rodríguez-Herreros Borja, Chabane Nadia, Hassan Mahmoud
MINDIG, F-35000, Rennes, France.
Service des Troubles du Spectre de l'Autisme et apparentés, Département de psychiatrie, Lausanne University Hospital (CHUV), Lausanne, Switzerland.
Transl Psychiatry. 2025 Jul 2;15(1):223. doi: 10.1038/s41398-025-03441-0.
Electroencephalography (EEG) has been thoroughly studied for decades in neurodevelopmental and psychiatric research. Yet its integration into clinical practice as a diagnostic/prognostic tool remains unachieved. We hypothesize that a key reason is the underlying patient's heterogeneity, overlooked in EEG research relying on a case-control approach. We combine high-density EEG with normative modeling to quantify this heterogeneity using two well-established and extensively investigated EEG characteristics -spectral power and functional connectivity- across a cohort of 1674 patients with attention-deficit/hyperactivity disorder, autism spectrum disorder, learning disorder, or anxiety, and 560 matched controls. Normative models showed that deviations from population norms among patients were highly heterogeneous and frequency-dependent. Deviation spatial overlap across patients did not exceed 40% and 24% for spectral and connectivity, respectively. Considering individual deviations in patients has significantly enhanced comparative analysis, and the identification of patient-specific markers has demonstrated a correlation with clinical assessments, representing a crucial step towards attaining precision psychiatry through EEG.
脑电图（EEG）在神经发育和精神疾病研究领域已被深入研究了数十年。然而，将其作为一种诊断/预后工具整合到临床实践中仍未实现。我们推测一个关键原因是潜在的患者异质性，在依赖病例对照方法的脑电图研究中被忽视了。我们将高密度脑电图与规范建模相结合，通过对1674名患有注意力缺陷多动障碍、自闭症谱系障碍、学习障碍或焦虑症的患者以及560名匹配对照组成的队列，利用两个成熟且经过广泛研究的脑电图特征——频谱功率和功能连接性，来量化这种异质性。规范模型表明，患者与总体规范的偏差高度异质且与频率相关。频谱和连接性方面患者间偏差的空间重叠分别不超过40%和24%。考虑患者的个体偏差显著增强了比较分析，并且特定患者标志物的识别已证明与临床评估相关，这代表了通过脑电图迈向精准精神病学的关键一步。
