G. Di Scala*, M. Dupuy*, E. Guillaud, E. Doat, E. Barse, B. Dillhareguy, F.A.M. Jean, M. Audiffren, J.R. Cazalets & S. Chanraud.
Experimental Aging Research, DOI: 10.1080/0361073X.2018.1560108, Feb 2019 : 45(1) : 41-56.
ABSTRACT
Background/Study context: Posture and gait are complex sensorimotor functions affected by age. These difficulties are particularly apparent when performing cognitively demanding tasks. Characterizing the functional organization of brain networks involved in these associations remains a challenge because of the incompatibility of brain imagery techniques with gross body movements. The present study aimed at testing whether resting-state functional connectivity of sensorimotor networks is associated with posture and gait performance recorded offline, in young and older adults.
Methods: Young (n = 12, mean = 24.1 y/o) and older (n = 14, mean = 65.6 y/o) healthy adults were tested for stability of their posture and gait. Four hours later, anatomical and functional brain imaging data were collected with Magnetic Resonance Imaging (MRI). Bilateral precentral and postcentral gyri were used as seeds in a graph theory analysis focused on global and local efficiency . The possible association between these data and posture and gait performance was examined.
Results: Both samples presented similar sensorimotor graphs, but with different global and local efficiencies (small world properties). The association between the networks’ graph measures and posture and gait performance also differed across groups: local efficiency was
correlated with gait stability in challenging conditions in older adults, but not in young adults.
Conclusion: This exploratory study suggests that combining analyses of functional networks and offline body movement may provide important information about motor function. In older adults, the association between graph properties of the sensorimotor network and gait performance in challenging conditions may be indicative of compensatory processes. Prospective studies involving more subjects with a larger age range are warranted.
More :
• Alexandre F (2013) Comprendre les réseaux cérébraux, rapport de recherche RR 8219, INRIA, pp 21 <HAL 00783331>.
• Bassett DS & Sporns O (2017) Network neuroscience. Nature Neuroscience, 20(3), 353-364.
And :
The authors wish to express how pleased they were to interact with the members of the PAM (Plate-forme d’Analyse du Mouvement). They look forward to other collaborations.
Graph theory describes networks as ensembles of nodes linked by edges.
Global and local efficiency are metrics of the network measuring its ability to transmit information globally and locally, respectively. Global efficiency: mean minimum number of links required to connect each pair of nodes. Local efficiency: average global efficiency of the subgraph composed of neighboring nodes of a node of interest, excluding that node itself.
