NeuroMorphix: A Novel Brain MRI Asymmetry-specific Feature Construction Approach For Seizure Recurrence Prediction

Seizure recurrence is an important concern after an initial unprovoked seizure; without drug treatment, it occurs within 2 years in 40-50% of cases. The decision to treat currently relies on predictors of seizure recurrence risk that are inaccurate, resulting in unnecessary, possibly harmful, treatment in some patients and potentially preventable seizures in others. Because of the link between brain lesions and seizure recurrence, we developed a recurrence prediction tool using machine learning and clinical 3T brain MRI. We developed NeuroMorphix, a feature construction approach based on MRI brain anatomy. Each of seven NeuroMorphix features measures the absolute or relative difference between corresponding regions in each cerebral hemisphere. FreeSurfer was used to segment brain regions and to generate values for morphometric parameters (8 for each cortical region and 5 for each subcortical region). The parameters were then mapped to whole brain NeuroMorphix features, yielding a total of 91 features per subject. Features were generated for a first seizure patient cohort (n = 169) categorised into seizure recurrence and non-recurrence subgroups. State-of-the-art classification algorithms were trained and tested using NeuroMorphix features to predict seizure recurrence. Classification models using the top 5 features, ranked by sequential forward selection, demonstrated excellent performance in predicting seizure recurrence, with area under the ROC curve of 88-93%, accuracy of 83-89%, and F1 score of 83-90%. Highly ranked features aligned with structural alterations known to be associated with epilepsy. This study highlights the potential for targeted, data-driven approaches to aid clinical decision-making in brain disorders.

Fractional order magnetic resonance fingerprinting in the human cerebral cortex

Mathematical models are becoming increasingly important in magnetic resonance imaging (MRI), as they provide a mechanistic approach for making a link between tissue microstructure and signals acquired using the medical imaging instrument. The Bloch equations, which describes spin and relaxation in a magnetic field, is a set of integer order differential equations with a solution exhibiting mono-exponential behaviour in time. Parameters of the model may be estimated using a non-linear solver, or by creating a dictionary of model parameters from which MRI signals are simulated and then matched with experiment. We have previously shown the potential efficacy of a magnetic resonance fingerprinting (MRF) approach, i.e. dictionary matching based on the classical Bloch equations, for parcellating the human cerebral cortex. However, this classical model is unable to describe in full the mm-scale MRI signal generated based on an heterogenous and complex tissue micro-environment. The time-fractional order Bloch equations has been shown to provide, as a function of time, a good fit of brain MRI signals. We replaced the integer order Bloch equations with the previously reported time-fractional counterpart within the MRF framework and performed experiments to parcellate human gray matter, which is cortical brain tissue with different cyto-architecture at different spatial locations. Our findings suggest that the time-fractional order parameters, {\alpha} and {\beta}, potentially associate with the effect of interareal architectonic variability, hypothetically leading to more accurate cortical parcellation.