  Background: Positron Emission Tomography (PET) with 18F-fluorodeoxyglucose
(18F-FDG) reveals metabolic abnormalities in Parkinson's disease (PD) at a
systemic level. Previous metabolic connectome studies derived from groups of
patients have failed to identify the individual neurophysiological details. We
aim to establish an individual metabolic connectome method to characterize the
aberrant connectivity patterns and topological alterations of the
individual-level brain metabolic connectome and their diagnostic value in PD.
Methods: The 18F-FDG PET data of 49 PD patients and 49 healthy controls (HCs)
were recruited. Each individual's metabolic brain network was ascertained using
the proposed Jensen-Shannon Divergence Similarity Estimation (JSSE) method. The
intergroup difference of the individual's metabolic brain network and its
global and local graph metrics were analyzed to investigate the metabolic
connectome's alterations. The identification of the PD from HC individuals was
used by the multiple kernel support vector machine (MK-SVM) to combine the
information from connection and topological metrics. The validation was
conducted using the nest leave-one-out cross-validation strategy to confirm the
performance of the methods. Results: The proposed JSSE metabolic connectome
method showed the most involved metabolic motor networks were PUT-PCG, THA-PCG,
and SMA pathways in PD, which was similar to the typical group-level method,
and yielded another detailed individual pathological connectivity in ACG-PCL,
DCG-PHG and ACG pathways. These aberrant functional network measures exhibited
an ideal classification performance in the identifying of PD individuals from
HC individuals at an accuracy of up to 91.84%.
