Ultra-high resolution 7 Tesla fMRI, pushing the resolution to cortical layers.

Functional magnetic resonance imaging (fMRI) has been instrumental for studying cortical function in humans, revealing functional organization across brain regions. Now, higher field strengths are opening the possibility to differentiate functional activation across cortical layers, paving the way towards understanding the mechanisms of human cortical circuitry and dysfunction.

Objectives The key objectives of this studentship are to develop ultra-high field structural and functional MR imaging and advanced data analysis methods to (a) measure fine-grain function and functional connectivity in cortical and subcortical sensory regions (at the scale of ~1 mm); (b) develop novel fMRI techniques and analysis methods for differentiating functional activity across cortical layers (at the scale of < 1 mm); and (c) measure myelination patters at ultra-high spatial resolution (< 1 mm). These methods will be applied to study somatosensory and auditory processing, and to investigate how function and structure are re-organized in the brains of individual patients with peripheral somatosensory or auditory loss.

The student will acquire key skills for measuring high-quality, ultra-high-resolution fMRI data (< 750µm isotropic resolution) in a short acquisition time (TR), using simultaneous multi-slice (SMS) imaging techniques and surface coils, and develop techniques for measuring layer-specific functional activity. The student will also develop alternative functional measures to the BOLD signal, such as arterial spin labelling (ASL) and vascular space occupancy (VASO), and use ultra-high-resolution anatomical imaging to assess cortical gyration and myelination. To address these aims, the student will learn advanced image analysis techniques (such as weakly-supervised machine learning methods for classification and pattern recognition, of particular importance for developing cortical laminar fMRI segmentation).

This new cross-disciplinary project will be supervised by a cross-disciplinary team comprising Prof Francis and Dr Sanchez-Panchuelo (Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy) who have expertise in high-resolution MRI and fMRI techniques and somatosensory processing. Dr Chen (School of Computer Science) brings new research expertise to this project on image processing, computer vision and machine learning for medical image analysis. This will be particularly applicable to develop a unified/generic framework for addressing segmentation of high resolution fMRI data and cortical layers. Dr Krumbholz (School of Medicine), leads research on the study of mechanisms of hearing and hearing impairment, and methods will be applied to patient populations. Dr Kevin Aquino, Monash University, Australia will collaborate through the GAME (Gobal Alliance for Medical Excellence) consortium alliance, and has experience in layer-specific imaging.