Abstract #0653

The BigMac dataset: ultra-high angular resolution diffusion imaging and multi-contrast microscopy of a whole macaque brain

Amy FD Howard¹, Saad Jbabdi¹, Alexandre A Khrapitchev², Jerome Sallet³, Greg Daubney³, Jeroen Mollink^1,4, Connor Scott⁵, Nicola Sibson², and Karla L Miller¹

¹FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, ²CR-UK/MRC Oxford Institute for Radiation Oncology, Department of Oncology, University of Oxford, Oxford, United Kingdom, ³Wellcome Centre for Integrative Neuroimaging, Experimental Psychology, Medical Sciences Division, University of Oxford, Oxford, United Kingdom, ⁴Department of Anatomy, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands, ⁵Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

Diffusion MRI has the ability to reveal the complex connectivity of the human brain. However, the link between the diffusion signal and the underlying tissue microstructure remains elusive. To drive diffusion MRI validation, we present BigMac: a unique dataset which combines ultra-high angular resolution diffusion MRI with microscopy throughout an adult macaque brain.

With this dataset we ask how under-sampling q-space biases our reconstruction of the ‘true’ diffusion profile. Our results indicate that the error associated with interpolating under-sampled data decays exponentially with the angular resolution of sampling, and that high angular resolution is necessary to characterise acutely crossing fibres.

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