Single‐Subject Network Analysis of FDOPA PET in Parkinson's Disease and Psychosis Spectrum

• Published in: HUMAN BRAIN MAPPING Vol. 46; no. 8; pp. e70253 - n/a
• Main Authors: 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, Angelescu, Ilinca, Bloomfield, Micheal, Bonoldi, Ilaria, Borgan, Faith, Dahoun, Tarik, Enrico, D’Ambrosio, Demjaha, Arsime, Donocik, Jecek, Egerton, Alice, Kaar, Stephen, Kim, Euitae, Kim, Seoyoung, Maccabe, James, Matthews, Julian, McCutcheon, Robert, McGuire, Philip, Nosarti, Chiara, Nour, Matthew M., Rogdaki, Maria, Rutigliano, Grazia, Shatalina, Ekaterina, Talbot, Peter S, Vano, Luke
• Format: Journal Article Publication
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.06.2025
• Subjects: Aged; Antipsychotic drugs; Brain; Brain - diagnostic imaging; Brain - metabolism; Dihydroxyphenylalanine - analogs & derivatives; Diseases; Entacapone; FDOPA; Female; Humans; Male; Middle Aged; molecular connectivity; network analysis; Parkinson Disease - diagnostic imaging; Parkinson Disease - drug therapy; Parkinson Disease - metabolism; Parkinson's disease; PET; PET imaging; Positron-Emission Tomography - methods; Psychotic Disorders - diagnostic imaging; Psychotic Disorders - drug therapy; Psychotic Disorders - metabolism; Radiopharmaceuticals; Schizophrenia; Schizophrenia - diagnostic imaging; Schizophrenia - drug therapy; Schizophrenia - metabolism; Tracers (Biology); FDOPA; network analysis; Parkinson's disease; schizophrenia; molecular connectivity; PET
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.70253

ABSTRACT 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. We propose a statistical framework combining molecular imaging and perturbation covariance analysis to build single‐subject networks, identifying molecular alterations in brain disorders. This method tests whether it can characterize altered molecular patterns and distinguish disorders through classification.