Mahdi Alizadeh1,2, Arichena R Manmatharayan3, Therese Johnston4, Sara Thalheimer5, Margaret Finley6, Megan Detloff7, Ashwini Sharan5, James Harrop5, Andrew Newburg8, Laura Krisa4, Feroze B Mohamed5. 1. Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA. Mahdi.Alizadeh.2@jefferson.edu. 2. Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, USA. Mahdi.Alizadeh.2@jefferson.edu. 3. Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, USA. 4. Department of Physical Therapy, Jefferson College of Rehabilitation Sciences, Thomas Jefferson University, Philadelphia, PA, USA. 5. Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA. 6. Department of Physical Therapy & Rehabilitation Science, Drexel University, Philadelphia, PA, USA. 7. Department of Neurobiology & Anatomy, Marion Murray Spinal Cord Research Center, College of Medicine, Drexel University, Philadelphia, PA, USA. 8. Marcus Institute of Integrative Health-Myrna Brind Center, Marcus Institute, Thomas Jefferson University, Villanova, PA, USA.
Abstract
STUDY DESIGN: Retrospective study. OBJECTIVES: We aimed to characterize the convergent disruptions of the structural connectivity based on network modeling technique (i.e., graph theory) to identify significant changes in network organization/reorganization between uninjured and chronic spinal cord injury (SCI) participants. SETTING: USA. METHODS: Ten adult participants including 4 with chronic SCI and 6 uninjured were scanned using a multi-shell diffusion imaging on a 3.0 T MR scanner. Whole brain structural connectivity matrix was estimated by performing the quantification of the number of white matter fibers (called edges) connecting each possible pair of brain region (called nodes). Brain regions were defined according to Desikan-Killiany cortical atlas. Using connectivity matrix, connectivity strength as well as six different graph theoretical measurements were computed for each participant. They include: (1) global efficiency; (2) local efficiency; (3) degree; (4) betweenness centrality; (5) average shortest length and (6) clustering coefficient. Finally network based statistics was applied to extract nodes/connections with significant differences between groups (uninjured vs SCI). RESULTS: The SCI group showed significant decreases in betweenness centrality in the left precentral gyrus (T-score=2.98, p value=0.02), and the right caudal middle frontal gyrus (score = 2.35, p value=0.047). It also showed significant decrease in left transverse temporal gyrus (T-score=2.36, p value=0.046) in clustering coefficient. In addition, altered regions in the occipital and parietal lobe were also identified. CONCLUSION: These results suggest that not only local but also global alterations of the white matter occur after SCI. The proposed modeling technique has the potential to serve as a screening tool to identify any areas of the brain affected after SCI.
STUDY DESIGN: Retrospective study. OBJECTIVES: We aimed to characterize the convergent disruptions of the structural connectivity based on network modeling technique (i.e., graph theory) to identify significant changes in network organization/reorganization between uninjured and chronic spinal cord injury (SCI) participants. SETTING: USA. METHODS: Ten adult participants including 4 with chronic SCI and 6 uninjured were scanned using a multi-shell diffusion imaging on a 3.0 T MR scanner. Whole brain structural connectivity matrix was estimated by performing the quantification of the number of white matter fibers (called edges) connecting each possible pair of brain region (called nodes). Brain regions were defined according to Desikan-Killiany cortical atlas. Using connectivity matrix, connectivity strength as well as six different graph theoretical measurements were computed for each participant. They include: (1) global efficiency; (2) local efficiency; (3) degree; (4) betweenness centrality; (5) average shortest length and (6) clustering coefficient. Finally network based statistics was applied to extract nodes/connections with significant differences between groups (uninjured vs SCI). RESULTS: The SCI group showed significant decreases in betweenness centrality in the left precentral gyrus (T-score=2.98, p value=0.02), and the right caudal middle frontal gyrus (score = 2.35, p value=0.047). It also showed significant decrease in left transverse temporal gyrus (T-score=2.36, p value=0.046) in clustering coefficient. In addition, altered regions in the occipital and parietal lobe were also identified. CONCLUSION: These results suggest that not only local but also global alterations of the white matter occur after SCI. The proposed modeling technique has the potential to serve as a screening tool to identify any areas of the brain affected after SCI.
Authors: Raffaele Nardone; Yvonne Höller; Francesco Brigo; Martin Seidl; Monica Christova; Jürgen Bergmann; Stefan Golaszewski; Eugen Trinka Journal: Brain Res Date: 2013-02-08 Impact factor: 3.252
Authors: Michel R T Sinke; Willem M Otte; Daan Christiaens; Oliver Schmitt; Alexander Leemans; Annette van der Toorn; R Angela Sarabdjitsingh; Marian Joëls; Rick M Dijkhuizen Journal: Brain Struct Funct Date: 2018-02-20 Impact factor: 3.270