Making the Invisible Visible: a Multi-Scale Imaging Approach to Detect and Characterise Cortical Pathology

Many diseases of the brain, including epilepsy, dementia, multiple sclerosis & mental health disorders, involve its outermost layer, the cortex. A key challenge in using conventional MRI as part of their diagnosis, or to study their pathophysiology, is sensitivity, i.e., cortical abnormalities may be small and/or subtle in their morphology & therefore missed. Even if abnormal signal is detected, it is impossible to say what drives such signal changes, e.g., differences in cell size/shape/density. Currently such information can only be obtained by cutting out the tissue & examining it under a microscope.

Recent advances in MRI physics, however, hold the promise of detecting & characterising heretofore invisible tissue abnormalities directly in the cortex. Ultra-strong magnetic fields give much higher resolution images, while ultra-strong 'gradients' provide sensitivity to tissue 'microstructure' properties such as cell density, size & shape that cannot be seen on conventional MRI. Such technologies have been applied to white matter, but their use in cortex remains largely unexplored. Here, we will provide a proof-of-principle that, by combining the latest in MRI hardware, physics, microscopy, mathematical modelling & artificial intelligence (AI), we will not only be able to see cortical abnormalities in more patients than ever before, but also obtain the same kind of information about cellular make-up that would otherwise require invasive biopsy.

To this end, beginning with existing microscopy datasets, we will build ultra-realistic 3D computational models of cortical tissue & change their properties to mimic what we see in disease, & learn how this would change the signals from the MRI scanners under different settings. This will allow us to select the scanner settings that maximise sensitivity to disease & to learn which parts of our mathematical models are most informative about the pathology, e.g., accounting for cell size/shape/density.

Using AI, we will combine the spatial resolution from ultra-strong magnets & the microstructural sensitivity from ultra-strong gradients, to create new 'hybrid' MRI images with unprecedented detail. With a fully optimised MRI protocol & models that capture the key cortical features that are otherwise 'invisible' on conventional MRI, we will scan healthy individuals to learn how much typical variation there is in each feature. We hypothesise that cortical pathology will lead to some model parameters falling outside of this normative range, allowing us to detect them automatically.

To test our hypothesis & validate our approach, we will trial our technique in patients with a form of epilepsy that is associated with highly localised abnormalities in the structure of the cortex, called 'focal cortical dysplasia' (FCD), prior to surgery to remove epileptogenic tissue. This tissue will undergo prolonged imaging in an experimental scanner with even greater sensitivity to differences in tissue microstructure than human MRI scanners. Using AI, we will use these more detailed images to enhance the detail of the images collected in the living human brain. Using microscopy of the sample, we will then produce a histological 'ground truth' and, again using AI, update our models & acquisition protocol to maximise sensitivity & accuracy of our pipeline. Finally, we will test our approach on patients with no visible disease on conventional MRI (but where symptoms are consistent with cortical abnormality). Where abnormal tissue is predicted, we will attempt validation with electrical recordings & microscopy of any resected tissue.

Ultimately, the detection of pathology invisible to standard clinical MRI may direct more accurate network interrogation thereby improving surgical outcomes, expand the population of patients suitable for surgery, & yield insight into associated cognitive and behavioural co-morbidities in people with diseases affecting the cortex.

The biological problem we address is how to make 'invisible' cortical pathology detectable via MRI. Revealing a fuller extent of cortical pathology combined with 'virtual histopathology' will transform clinical neuroscience research by providing earlier, and more complete maps of cortical pathology and allowing their tracking over time across a disease spectrum from epilepsy, multiple sclerosis & dementia to brain injury and mental health disorders. Using AI-based approaches, we aim to break free from the standard imaging research paradigm of making statistical inferences on patients vs controls at the group level, and provide diagnostic performance on an individual level. This step-change will aid mechanistic understanding, provide earlier biomarkers of disease modification & stratify patients for personalised medicine.

Until now quantitative MRI has focussed mostly on white matter, where modelling of microstructural architectures (e.g., oriented axons) is relatively tractable. Apart from volumetric analysis, the more technically challenging multi-scale interrogation of cortex has lagged behind, yet cortical pathology is a critical determinant of neurological disease. By coupling in vivo and ex vivo state-of-the-art multi-scale MRI, tissue microscopy, in silico modelling & AI-based techniques, we are now well-poised to reveal and characterize (via 'virtual histopathology') cortical pathologies, invisible to current MR techniques.

Generating AI-optimised MRI sequences mapped to tissue structure obtained from selected neurosurgical samples mandates a concerted multi-disciplinary effort that could not be achieved by a single research PI/group independently. Our proposal assembles the new collaborations, expertise & specialised equipment to achieve our goal. We will provide proof-of-concept using focal cortical dysplasias generating seizures as the model disease, but our vision is that the approach will have wide-ranging impact across cortical diseases.