Disrupted brain network topology in Parkinson’s disease: a longitudinal magnetoencephalography study

Abstract

Although alterations in resting-state functional connectivity between brain regions have previously been reported in Parkinson's disease, the spatial organization of these changes remains largely unknown. Here, we longitudinally studied brain network topology in Parkinson's disease in relation to clinical measures of disease progression, using magnetoencephalography and concepts from graph theory. We characterized whole-brain functional networks by means of a standard graph analysis approach, measuring clustering coefficient and shortest path length, as well as the construction of a minimum spanning tree, a novel approach that allows a unique and unbiased characterization of brain networks. We observed that brain networks in early stage untreated patients displayed lower local clustering with preserved path length in the delta frequency band in comparison to controls. Longitudinal analysis over a 4-year period in a larger group of patients showed a progressive decrease in local clustering in multiple frequency bands together with a decrease in path length in the alpha2 frequency band. In addition, minimum spanning tree analysis revealed a decentralized and less integrated network configuration in early stage, untreated Parkinson's disease that also progressed over time. Moreover, the longitudinal changes in network topology identified with both techniques were associated with deteriorating motor function and cognitive performance. Our results indicate that impaired local efficiency and network decentralization are very early features of Parkinson's disease that continue to progress over time, together with reductions in global efficiency. As these network changes appear to reflect clinically relevant phenomena, they hold promise as markers of disease progression.

The trees and the forest: Characterization of complex brain networks with minimum spanning trees

“…The studies of Boersma et al suggested that more regular networks with higher clustering and longer path length correspond with more line like MSTs with longer diameter and smaller leaf number, while more random networks show low clustering and short path length and the corresponding MSTs shorter diameters and higher leaf numbers. However, an opposite pattern was shown in the study of Olde Dubbelink et al (2013). In this study, a lower clustering coefficient and shorter path length were associated with a more line like MST with large diameter and lower leaf number.…”

“…In those studies where connectivity data or conventional graph analysis showed an effect, an effect was always present in the MST analysis as well (Boersma et al, 2011;2013;Olde Dubbelink et al, 2013;van Dellen et al, 2013). In other studies MST analyses succeed in demonstrating an experimental effect while other methods were less successful (Demuru et al, 2013;Lee et al, 2006).…”

“…This could explain why a simple subgraph of a weighted network can still capture relevant changes in the case of disease or disturbed consciousness. It might also explain why MST changes correlate with cognition and motor function (Demuru et al, 2013;Olde Dubbelink et al, 2013). One very interesting empirical observation is that the MST seems to consist of a stable core and more variable periphery (Lee et al, 2006).…”

“…In a longitudinal study of resting-state functional brain network organization in Parkinson patients and healthy controls conventional graph analysis based upon weighted normalised clustering coefficient and path length showed for the Parkinson patients a loss of clustering which progressed over time, and a shorter path length at later stages (Olde Dubbelink et al, 2013). Minimum spanning tree analysis revealed a shift from more integrated ("star like") to more disintegrated ("path like") networks in Parkinson patients, and these changes also progressed over time.…”

Neurological Diseases from a Systems Medicine Point of View

“…Here, the disturbance of these mapped circuits may be assessed using the technique of recording neuronal activity in local brain areas, called electroencephalography ( EEG) . EEG allows to obtain a good temporal resolution when measuring brain activity during epileptic seizures [ 167 ], or permits longitudinal tracking of the disruption of disease-relevant brain circuits, as shown for PD [ 168 ].…”