Addressing brain metabolic connectivity in treatment-resistant schizophrenia: a novel graph theory-driven application of 18F-FDG-PET with antipsychotic dose correction - Giuseppe De Simone; Felice Iasevoli; Annarita Barone; Valeria Gaudieri; Alberto Cuocolo; Mariateresa Ciccarelli; Sabina Pappatà; Andrea de Bartolomeis (Schizophrenia)
Abstract Few studies using Positron Emission Tomography with 18F-fluorodeoxyglucose (18F-FDG-PET) have examined the neurobiological basis of antipsychotic resistance in schizophrenia, primarily focusing on metabolic activity, with none investigating connectivity patterns. Here, we aimed to explore differential patterns of glucose metabolism between patients and controls (CTRL) through a graph theory-based approach and network comparison tests. PET scans with 18F-FDG were obtained by 70 subjects, 26 with treatment-resistant schizophrenia (TRS), 28 patients responsive to antipsychotics (nTRS), and 16 CTRL. Relative brain glucose metabolism maps were processed in the automated anatomical labeling (AAL)-Merged atlas template. Inter-subject connectivity matrices were derived using Gaussian Graphical Models and group networks were compared through permutation testing. A logistic model based on machine-learning was employed to estimate the association between the metabolic signals of brain regions and treatment resistance. To account for the potential influence of antipsychotic medication, we incorporated chlorpromazine equivalents as a covariate in the network analysis during partial correlation calculations. Additionally, the machine-learning analysis employed medication dose-stratified folds. Global reduced connectivity was detected in the nTRS (p-value = 0.008) and TRS groups (p-value = 0.001) compared to CTRL, with prominent alterations localized in the frontal lobe, Default Mode Network, and dorsal dopamine pathway. Disruptions in frontotemporal and striatal-cortical connectivity were detected in TRS but not nTRS patients. After adjusting for antipsychotic doses, alterations in the anterior cingulate, frontal and temporal gyri, hippocampus, and precuneus also emerged. The machine-learning approach demonstrated an accuracy ranging from 0.72 to 0.8 in detecting the TRS condition.