Non-invasive staining of tissue microstructure in temporal lobe epilepsy using in- vivo MRI

Ex-vivo histology is the gold standard to investigate human brain microstructure.

However, its invasive nature precludes its use in monitoring disease progression and the investigation of the pathophysiological origin of neurological disorders.

MRStain will address this shortcoming by exploiting the sensitivity of the Magnetic Resonance Imaging (MRI) signal to estimate aggregated histological metrics in the human brain non-invasively.

Like established histology staining methods (e.g., myelin-basic protein), MRStain will be sensitive to changes in cellular populations, axons, myelin, and iron.

This will be achieved by augmenting the MRI measurements with computational biophysical models, which can disentangle tissue metrics at the micron-scale using the macroscopic spatial resolution (14 mm) of MRI.

However, the clinical use of these models has not been employed because their validity and generalizability across disease trajectories has yet to be tested against the ex-vivo histological gold standard.This project will address this shortcoming by generating a globally unique multi-modal dataset that combines novel in-vivo and ex-vivo MRI techniques with biophysical models as well as cutting-edge large-scale 3D histology.

The project will benefit from a unique translational university hospital environment where large sections of freshly excised brain tissue from drug-resistant temporal lobe epilepsy patients (TLE, 80 sections of about 3 x 2 x cm^3) can be examined, enabling in-vivo MRI-based biophysical tissue parameters to be validated against their histological gold standard.

I will develop an MRStain model to identify TLE relevant changes that will achieve a paradigm shift in how epilepsy is treated by identifying target brain areas for surgery that will help to predict seizure-free outcome after surgery.

The final validated MRStain models will also pave the way for similar noninvasive investigations of other neuropsychiatric diseases with unprecedented precision.