Severino Mario, Schubert Julia J, Nordio Giovanna, Giacomel Alessio, Easmin Rubaida, Lao-Kaim Nick P, Selvaggi Pierluigi, Xu Zhilei, Pereira Joana B, Jauhar Sameer, Piccini Paola, Howes Oliver, Turkheimer Federico, Veronese Mattia
Department of Information Engineering, University of Padua, Padova, Italy.
Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Hum Brain Mapp. 2025 Jun 1;46(8):e70253. doi: 10.1002/hbm.70253.
Greater understanding of individual biological differences is essential for developing more targeted treatment approaches to complex brain disorders. Traditional analysis methods in molecular imaging studies have primarily focused on quantifying tracer binding in specific brain regions, often neglecting inter-regional functional relationships. In this study, we propose a statistical framework that combines molecular imaging data with perturbation covariance analysis to construct single-subject networks and investigate individual patterns of molecular alterations. This framework was tested on [18F]-DOPA PET imaging as a marker of the brain dopamine system in patients with Parkinson's Disease (PD) and schizophrenia to evaluate its ability to classify patients and characterize their disease severity. Our results show that single-subject networks effectively capture molecular alterations, differentiate individuals with heterogeneous conditions, and account for within-group variability. Moreover, the approach successfully distinguishes between preclinical and clinical stages of psychosis and identifies the corresponding molecular connectivity changes in response to antipsychotic medications. Mapping molecular imaging networks presents a new and powerful method for characterizing individualized disease trajectories as well as for evaluating treatment effectiveness in future research.
深入了解个体生物学差异对于开发针对复杂脑部疾病的更具针对性的治疗方法至关重要。分子成像研究中的传统分析方法主要集中于量化特定脑区的示踪剂结合，常常忽略区域间的功能关系。在本研究中，我们提出了一个统计框架，该框架将分子成像数据与微扰协方差分析相结合，以构建单受试者网络并研究分子改变的个体模式。该框架在[18F]-多巴正电子发射断层扫描（PET）成像上进行了测试，该成像作为帕金森病（PD）和精神分裂症患者脑多巴胺系统的标志物，以评估其对患者进行分类和表征其疾病严重程度的能力。我们的结果表明，单受试者网络有效地捕捉了分子改变，区分了具有不同病情的个体，并解释了组内变异性。此外，该方法成功地区分了精神病的临床前期和临床阶段，并确定了抗精神病药物治疗后相应的分子连接变化。绘制分子成像网络为表征个体疾病轨迹以及评估未来研究中的治疗效果提供了一种新的强大方法。
