Background
Accurate parcellation of the cerebral cortex in an individual is a guide to its underlying organization. The most promising in vivo quantitative magnetic resonance (MR)-based microstructural cortical mapping methods are yet to achieve a level of parcellation accuracy comparable to quantitative histology.
Methods
We scanned 6 participants using a 3D echo-planar imaging MR fingerprinting (EPI-MRF) sequence on a 7T Siemens scanner. After projecting MRF signals to the individual-specific inflated model of the cortical surface, normalized autocorrelations of MRF residuals of vertices of 8 microstructurally distinct areas (BA1, BA2, BA4a, BA6, BA44, BA45, BA17, and BA18) from 3 cortical regions were used as feature vector inputs into linear support vector machine (SVM), radial basis function SVM (RBF-SVM), random forest, and k-nearest neighbors supervised classification algorithms. The algorithms' prediction performance was compared using: (i) features from each vertex or (ii) features from neighboring vertices.
Results
The neighborhood-based RBF-SVM classifier achieved the highest prediction score of 0.85 for classification of MRF residuals in the central region from a held-out participant.
Conclusions
We developed an automated method of cortical parcellation using a combination of MR fingerprinting residual analysis and machine learning classification. Our findings provide the basis for employing unsupervised learning algorithms for whole-cortex structural parcellation in individuals.
Accurate characterisation of the microstructure of the human cerebral cortex is important for a number of applications. It guides anatomical parcellation, the functional correlates of which inform neurosurgical decision making. It also enables detection of subtle abnormalities such as focal cortical dysplasia (FCD), an important cause of epilepsy. Many studies have been Publications included in this thesis Conference abstract Shahrzad Moeiniyan Bagheri, Viktor Vegh, David Reutens, "Towards in-vivo voxel-wise parcellation of human brain cortex".
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