Protocol for quantitative characterization of human retinotopic maps using quasiconformal mapping.

High-field functional magnetic resonance imaging generates in vivo retinotopic maps, but quantifying them remains challenging. Here, we present a pipeline based on conformal geometry and Teichmüller theory for the quantitative characterization of human retinotopic maps. We describe steps for cortical surface parameterization and surface-spline-based smoothing. We then detail Beltrami coefficient-based mapping, which provides a quantitative and re-constructible description of the retinotopic maps. This framework has been successfully used to analyze the Human Connectome Project's V1 retinotopic maps. For complete details on the use and execution of this protocol, please refer to Ta et al. (2022).1.

Improved Prediction of Amyloid-ß and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding.

BACKGROUND: Amyloid-ß (Aß) plaques and tau protein tangles in the brain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative ('N') biomarkers comprise the "ATN framework" of AD. Current methods to detect Aß/tau pathology include cerebrospinal fluid (invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (promising but mainly still in development). OBJECTIVE: To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements. METHODS: With MRI-based hippocampal multivariate morphometry statistics (MMS) features, we apply our Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) method combined with the ridge regression model to individual amyloid/tau measure prediction. RESULTS: We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer's Disease Neuroimaging Initiative. Each subject has one pair consisting of a PET image and MRI scan, collected at about the same time. Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics. CONCLUSION: The MMS-based PASCP-MP is an efficient tool that can bridge hippocampal atrophy with amyloid and tau pathology and thus help assess disease burden, progression, and treatment effects.