Structural Connectivity Alterations in Amyotrophic Lateral Sclerosis: A Graph Theory Based Imaging Study

• Published in: Frontiers in neuroscience Vol. 13; p. 1044
• Main Authors: Fortanier, Etienne, Grapperon, Aude-Marie, Le Troter, Arnaud, Verschueren, Annie, Ridley, Ben, Guye, Maxime, Attarian, Shahram, Ranjeva, Jean-Philippe, Zaaraoui, Wafaa
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 02.10.2019; Frontiers; Frontiers Media S.A
• Subjects: ALS; Amyotrophic lateral sclerosis; Bioengineering; Brain; connectivity; Disease; DTI; Engineering Sciences; graph theory; hub; Imaging; Life Sciences; Magnetic resonance imaging; Medical imaging; MRI; Neural networks; Neurodegeneration; Neurodegenerative diseases; Neuroimaging; Neuromuscular diseases; Neuroscience; Nodes; Postcentral gyrus; Precentral gyrus; Psychiatry; Schizophrenia; Signal and Image processing; Studies; Substantia alba; France; ALS; DTI; connectivity; hub; MRI; graph theory
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2019.01044

Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive neurodegenerative disorder. Diffusion magnetic resonance imagining (MRI) studies have consistently showed widespread alterations in both motor and non-motor brain regions. However, connectomics and graph theory based approaches have shown inconsistent results. Hub-centered lesion patterns and their impact on local and large-scale brain networks remain to be established. The objective of this work is to characterize topological properties of structural brain connectivity in ALS using an array of local, global and hub-based network metrics. Magnetic resonance imagining data were acquired from 25 patients with ALS and 26 age-matched healthy controls. Structural network graphs were constructed from diffusion tensor MRI. Network-based statistics (NBS) and graph theory metrics were used to compare structural networks without regions of interest. Patients with ALS exhibited global network alterations with decreased global efficiency (Eglob) ( = 0.03) and a trend of reduced whole brain mean degree ( = 0.05) compared to controls. Six nodes showed significantly decreased mean degree in ALS: left postcentral gyrus, left interparietal and transverse parietal sulcus, left calcarine sulcus, left occipital temporal medial and lingual sulcus, right precentral gyrus and right frontal inferior sulcus ( < 0.01). Hub distribution was comparable between the two groups. There was no selective hub vulnerability or topological reorganization centered on these regions as the hub disruption index (κ) was not significant for the relevant metrics (degree, local efficiency and betweenness centrality). Using NBS, we identified an impaired motor subnetwork of 11 nodes and 10 edges centered on the precentral and the paracentral nodes ( < 0.01). Significant clinical correlations were identified between degree in the frontal area and the disease progression rate of ALS patients ( < 0.01). Our study provides evidence that alterations of structural connectivity in ALS are primarily driven by node degree and white matter tract degeneration within an extended network around the precentral and the paracentral areas without hub-centered reorganization.