Multi-delay arterial spin labelling-based metabolic connectomes reveals topological alterations in Parkinson's disease.

Authors

Madhu Thakur, Shaohua Ding, Xiang Lu, Beilei Chen, Qi Yan, Daming Shen, Hongying Zhang, Song'an Shang, Xin Zhou

Published in

Magnetic resonance imaging. Pages 110762. Aug 01, 2026. Epub Aug 01, 2026.

Abstract

Parkinson's disease (PD) is characterized by widespread brain network dysfunction. Although metabolic abnormalities have been reported, network-level alterations in metabolic connectomes remain insufficiently understood.
To investigate topological alterations of metabolic connectomes in PD using hemodynamic metrics of cerebral blood flow (CBF), arterial cerebral blood volume (aCBV) and arterial transit time (ATT) from multi-delay arterial spin labelling (mASL).
This prospective study included 52 patients with PD and 55 HCs. Metabolic connectomes were constructed using perfusion parameters that derived from uncorrected CBF (un-CBF) from single-delay ASL and corrected CBF (c-CBF), aCBV and ATT from mASL. The global, nodal, and connection-level topological properties were analysed and compared between groups.
Metabolic connectome in PD patients showed increased normalized clustering coefficient and small-worldness, with reduced characteristic path length under corrected CBF and aCBV, indicating enhanced global integration. Under un-corrected CBF and ATT no global alterations were observed. At the nodal level, PD patient showed increased centrality and efficiency in sensorimotor regions, including the supplementary motor area, postcentral gyrus, and paracentral lobule. Connection-level analysis revealed strengthened covariance within the sensorimotor network and between sensorimotor, temporal, frontal, and subcortical regions, with more widespread alterations in aCBV and ATT-derived connectomes.
Our findings evidenced that PD is associated with widespread topological reorganization of metabolic connectomes and supported mASL as an advanced approach for detecting network dysfunction in PD.

PMID:
42542219
Bibliographic data and abstract were imported from PubMed on 02 Aug 2026.

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