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Title

Gray matter network reorganization in multiple sclerosis from 7‐Tesla and 3‐Tesla MRI data

AuthorsGonzalez‐Escamilla, Gabriel; Ciolac, Dumitru; Santis, Silvia de; Radetz, Angela; Fleischer, Vinzenz; Droby, Amgad; Roebroeck, Alard; Meuth, Sven G.; Muthuraman, Muthuraman; Groppa, Sergiu
Issue Date2020
PublisherJohn Wiley & Sons
CitationAnnals of Clinical and Translational Neurology 7(4): 543-553 (2020)
Abstract[Objective]: The objective of this study was to determine the ability of 7T‐MRI for characterizing brain tissue integrity in early relapsing‐remitting MS patients compared to conventional 3T‐MRI and to investigate whether 7T‐MRI improves the performance for detecting cortical gray matter neurodegeneration and its associated network reorganization dynamics.
[Methods]: Seven early relapsing‐remitting MS patients and seven healthy individuals received MRI at 7T and 3T, whereas 30 and 40 healthy controls underwent separate 3T‐ and 7T‐MRI sessions, respectively. Surface‐based cortical thickness (CT) and gray‐to‐white contrast (GWc) measures were used to model morphometric networks, analyzed with graph theory by means of modularity, clustering coefficient, path length, and small‐worldness.
[Results]: 7T‐MRI had lower CT and higher GWc compared to 3T‐MRI in MS. CT and GWc measures robustly differentiated MS from controls at 3T‐MRI. 7T‐ and 3T‐MRI showed high regional correspondence for CT (r = 0.72, P = 2e‐78) and GWc (r = 0.83, P = 5.5e‐121) in MS patients. MS CT and GWc morphometric networks at 7T‐MRI showed higher modularity, clustering coefficient, and small‐worldness than 3T, also compared to controls.
[Interpretation]: 7T‐MRI allows to more precisely quantify morphometric alterations across the cortical mantle and captures more sensitively MS‐related network reorganization. Our findings open new avenues to design more accurate studies quantifying brain tissue loss and test treatment effects on tissue repair.
Publisher version (URL)https://doi.org/10.1002/acn3.51029
URIhttp://hdl.handle.net/10261/217841
DOI10.1002/acn3.51029
E-ISSN2328-9503
Appears in Collections:(IN) Artículos
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