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Abstract #2425

Novel subject-specific method of visualising group differences from multiple DTI metrics without averaging

Eryn E Kwon1,2,3, Maryam Tayebi1,3, Joshua McGeown1,2, Matthew McDonald1,2, Patrick McHugh1,4, Paul Condron1,2, Leigh Potter1,5, Davidson Taylor1,6, Jerome Maller7, Miao Qiao8, Alan Wang1,2,3, Poul Nielsen3,8, Justin Fernandez1,3,8, Miriam Scadeng1,2, Samantha Holdsworth1,2, and Vickie Shim1,3
1Mātai Medical Research Institute, Tairāwhiti/Gisborne, New Zealand, 2Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland, Auckland, New Zealand, 3Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand, 4Tūranga Health, Tūranganui-a-kiwa, Tairāwhiti/Gisborne, New Zealand, 5Ngāti Porou, Ngāti Kahungunu, Rongomaiwahine, Rongowhakaata, Tairāwhiti/Gisborne, New Zealand, 6Ngai Tāmanuhiri, Rongowhakaata, Ngāti Porou, Tairāwhiti/Gisborne, New Zealand, 7General Electric Healthcare, Victoria, Australia, 8Department of Engineering Science, Faculty of Engineering, University of Auckland, Auckland, New Zealand

Synopsis

Keywords: Data Analysis, Data Analysis, DTI, diffusion MRI, PCA, Mesh-fitting, subject-specific model, data aggregationAveraging is commonly used for data reduction/aggregation to summarise such high-dimensional data, resulting in information loss. However, individual variability makes group-wise comparisons difficult without data reduction/aggregation. To address these issues, we developed a novel technique that integrates diffusion tensor (DTI) metrics along the whole volumes of the fibre bundle using a mesh-fitting technique. Using the right Corticospinal Tract (rCST) as an example, we demonstrate the utility of the method in detecting differences in DTI metrics of contact-sports and non-contact-sports athletes.

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Keywords