Feature selective temporal prediction of Alzheimer’s disease progression using hippocampus surface morphometry

IntroductionPrediction of Alzheimer’s disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi‐task machine learning method (cFSGL) with a novel MR‐based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients.MethodsPrevious work has shown that a multi‐task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able to predict cognitive outcomes of ADNI subjects using FreeSurfer‐based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied a multivariate tensor‐based parametric surface analysis method (mTBM) to extract features from the hippocampal surfaces.ResultsWe combined mTBM features with traditional surface features such as middle axis distance, the Jacobian determinant as well as 2 of the Jacobian principal eigenvalues to yield 7 normalized hippocampal surface maps of 300 points each. By combining these 7 × 300 = 2100 features together with the previous ~350 features, we illustrate how this type of sparsifying method can be applied to an entire surface map of the hippocampus that yields a feature space that is 2 orders of magnitude larger than what was previously attempted.ConclusionsBy combining the power of the cFSGL multi‐task machine learning framework with the addition of AD sensitive mTBM feature maps of the hippocampus surface, we are able to improve the predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.In this work, we present our results of using machine learning to predict temporal behavior changes in Alzheimers Disease using entire topological feature maps of the hippocampus surface (2100 feature points). Our paper demonstrates that it is possible to use an entire topological map instead of just imaging derived volumetric measurements for predicting behavioral changes. We compare these results with previous results using only volumetric MR imaging features (309 features points) and show through repeated cross‐validation rounds that we are able to get better predictive power.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137757/1/brb3733_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137757/2/brb3733.pd

DTI based structural damage characterization for Disorders of Consciousness

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LANDMARK MATCHING ON THE SPHERE USING DISTANCE FUNCTIONS

Nonlinear registration of 3D surfaces is important in many medical imaging applications, including the mapping of longitudinal changes in anatomy, or of multi-subject functional MRI data to a canonical surface for comparison and integration. To register 3D surfaces, such as the cortical surface of the brain, one approach is to transform them first to planar or spherical objects. Internal landmarks can then be matched on these simpler parameter domains. Here we study the diffeomorphic matching of landmarks on the sphere. Our method builds on the level set technique of Leow et al. [1] for the plane. Both forward and backward matching terms are included, thus ensuring the invertibility of the representation. We demonstrate our technique on a pair of lines on the sphere. The overall approach improves on earlier work in cortical matching by allowing the matching energy to be relaxed along sulcal landmarks, minimizing distortion, and also enables point and curve landmarks to be aligned in the same general framework as densely-defined scalar fields, such as curvature or cortical thickness maps. 1

Impact of Early and Late Visual Deprivation on the Structure of the Corpus Callosum: A Study Combining Thickness Profile with Surface Tensor-Based Morphometry

Blindness represents a unique model to study how visual experience may shape the development of brain organization. Exploring how the structure of the corpus callosum (CC) reorganizes ensuing visual deprivation is of particular interest due to its important functional implication in vision (e.g. via the splenium of the CC). Moreover, comparing early versus late visually deprived individuals has the potential to unravel the existence of a sensitive period for reshaping the CC structure. Here, we develop a novel framework to capture a complete set of shape differences in the CC between congenitally blind (CB), late blind (LB) and sighted control (SC) groups. The CCs were manually segmented from T1-weighted brain MRI and modeled by 3D tetrahedral meshes. We statistically compared the combination of local area and thickness at each point between subject groups. Differences in area are found using surface tensor-based morphometry; thickness is estimated by tracing the streamlines in the volumetric harmonic field. Group differences were assessed on this combined measure using Hotelling’s T(2) test. Interestingly, we observed that the total callosal volume did not differ between the groups. However, our fine-grained analysis reveals significant differences mostly localized around the splenium areas between both blind groups and the sighted group (general effects of blindness) and, importantly, specific dissimilarities between the LB and CB groups, illustrating the existence of a sensitive period for reorganization. The new multivariate statistics also gave better effect sizes for detecting morphometric differences, relative to other statistics. They may boost statistical power for CC morphometric analyses

BOLD delay times using group delay in sickle cell disease

International audienceSickle cell disease (SCD) is an inherited blood disorder that effects red blood cells, which can lead to vasoocclu-sion, ischemia and infarct. This disease often results in neurological damage and strokes, leading to morbidity and mortality. Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for measuring and mapping the brain activity. Blood Oxygenation Level-Dependent (BOLD) signals contain also information about the neurovascular coupling, vascular reactivity, oxygenation and blood propagation. Temporal relationship between BOLD fluctuations in different parts of the brain provides also a mean to investigate the blood delay information. We used the induced desaturation as a label to profile transit times through different brain areas, reflecting oxygen utilization of tissue. In this study, we aimed to compare blood flow propagation delay time between these patients and healthy subjects in areas vascularized by anterior, middle and posterior cerebral arteries. At first, BOLD changes in these areas were almost simultaneous and shorter in the SCD patients, because of their increased brain blood flow. Secondly, the analysis of a patient with a stenosis on the anterior cerebral artery indicated that signal of the area vascularized by this artery lagged the MCA signal. These findings suggested that sickle cell disease causes blood propagation modifications, and these changes could be used as a biomarker of vascular damage

An experimental investigation of labeling efficiency for pseudo-continuous arterial spin labeling

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