Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France.
Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli, Italy; Department of Motor Sciences and Wellness, University of Naples "Parthenope", Italy.
Neuroimage. 2021 Sep;238:118253. doi: 10.1016/j.neuroimage.2021.118253. Epub 2021 Jun 9.
Brain connectome fingerprinting is rapidly rising as a novel influential field in brain network analysis. Yet, it is still unclear whether connectivity fingerprints could be effectively used for mapping and predicting disease progression from human brain data. We hypothesize that dysregulation of brain activity in disease would reflect in worse subject identification. We propose a novel framework, Clinical Connectome Fingerprinting, to detect individual connectome features from clinical populations. We show that "clinical fingerprints" can map individual variations between elderly healthy subjects and patients with mild cognitive impairment in functional connectomes extracted from magnetoencephalography data. We find that identifiability is reduced in patients as compared to controls, and show that these connectivity features are predictive of the individual Mini-Mental State Examination (MMSE) score in patients. We hope that the proposed methodology can help in bridging the gap between connectivity features and biomarkers of brain dysfunction in large-scale brain networks.
脑连接组指纹识别技术作为一种新兴的脑网络分析领域正在迅速崛起。然而，目前尚不清楚连接组指纹是否可以有效地用于从人脑数据中映射和预测疾病进展。我们假设疾病中大脑活动的失调将反映在更差的个体识别上。我们提出了一种新的框架，即临床连接组指纹识别，用于从临床人群中检测个体连接组特征。我们表明，“临床指纹”可以映射出从脑磁图数据中提取的功能连接组中，老年健康受试者和轻度认知障碍患者之间的个体差异。我们发现，与对照组相比，患者的可识别性降低，并表明这些连接特征可以预测患者的个体简易精神状态检查（MMSE）评分。我们希望所提出的方法可以帮助弥合大规模脑网络中连接特征和大脑功能障碍生物标志物之间的差距。
