Anil Rao

Longitudinal and cross-sectional analysis of atrophy in Alzheimer's disease: Cross-validation of BSI, SIENA and SIENAX

Brain volume loss (atrophy) is widely used as a marker of disease progression. Atrophy has been measured with a variety of methods, some estimating atrophy rate from two temporally separated scans, and others estimating atrophy state from a single scan. Three popular tools for measuring brain atrophy are BSI and SIENA (rate) and SIENAX (state). Previous papers have shown BSI and SIENA to have similar accuracy, but no work has carefully compared both methods using the same data set. Here we compare these methods, using data from patients with Alzheimers disease and age-matched controls. We also compare the SIENA longitudinal measure with atrophy state estimated by SIENAX using just the earliest scan taken from each subject. We show strong correspondence and similar sensitivity to atrophy between all 3 measures.

Anatomically-distinct genetic associations of APOE epsilon4 allele load with regional cortical atrophy in Alzheimer's disease.

APOE epsilon4 is the best-established genetic risk factor for sporadic Alzheimers disease (AD). However, while homozygotes show greater disease susceptibility and earlier age of onset than heterozygotes, they may not show faster rates of clinical progression. We hypothesize that there are differential APOE epsilon4 allele-load dependent influences on neuropathology across the brain. Our aim was to define the relationship between APOE epsilon4 allele load and regionally-specific brain cortical atrophy in Alzheimers Disease (AD). For this reason voxel-based morphometry (VBM) was performed using T1-weighted MR images from 83 AD patients, contrasting regional cortical grey matter by APOE epsilon4 load according to either dominant or genotypic models. Patients fulfilled NINCDS-ADRDA criteria and were genotyped for APOE epsilon4 (15 epsilon4/epsilon4, 39 epsilon4/- and 29-/-). We observed that grey matter volume (GMV) decreased additively with increasing allele load in the medial (MTL) and anterior temporal lobes bilaterally. By contrast, a 2 degree-of-freedom genotypic model suggested a dominant effect of the APOE epsilon4 allele in the left temporal lobe. Brain regions showing a significant APOE epsilon4 allele load effect on GMV in AD included only some of those typically described as having greatest amyloid plaque deposition and atrophy. Temporal regions appeared to show a dominant effect of APOE epsilon4 allele load instead of the additive effect previously strongly associated with age of onset. Regional variations with allele load may be related to different mechanisms for effects of APOE epsilon4 load on susceptibility and disease progression.

Brain Microstructure Reveals Early Abnormalities more than Two Years prior to Clinical Progression from Mild Cognitive Impairment to Alzheimer's Disease

Diffusion imaging is a promising marker of microstructural damage in neurodegenerative disorders, but interpretation of its relationship with underlying neuropathology can be complex. Here, we examined both volumetric and brain microstructure abnormalities in 13 amnestic patients with mild cognitive impairment (MCI), who progressed to probable Alzheimers disease (AD) no earlier than 2 years after baseline scanning, in order to focus on early, and hence more sensitive, imaging markers. We compared them to 22 stable amnestic MCI patients with similar cognitive performance and episodic memory impairment but who did not show progression of symptoms for at least 3 years. Significant group differences were mainly found in the volume and microstructure of the left hippocampus, while white matter group differences were also found in the body of the fornix, left fimbria, and superior longitudinal fasciculus (SLF). Diffusion index abnormalities in the SLF were the sign of a subtle microstructural injury not detected by standard atrophy measures in the corresponding gray matter regions. The microstructural measure obtained in the left hippocampus using diffusion imaging showed the most substantial differences between the two groups and was the best single predictor of future progression to AD. An optimal prediction model (91% accuracy, 85% sensitivity, 96% specificity) was obtained by combining MRI measures and CSF protein biomarkers. These results highlight the benefit of using the information of brain microstructural damage, in addition to traditional gray matter volume, to detect early, subtle abnormalities in MCI prior to clinical progression to probable AD and, in combination with CSF markers, to accurately predict such progression.

Construction of a Statistical Model for Cardiac Motion Analysis Using Nonrigid Image Registration

In this paper we present a new technique for tracking the movement of the myocardium using a statistical model derived from the motion fields in the hearts of several healthy volunteers. To build the statistical model we tracked the motion of the myocardium in 17 volunteers using a nonrigid registration technique based on free-form deformations and mapped the motion fields obtained into a common reference coordinate system. A principal component analysis (PCA) was then performed on the motion fields to extract the major modes of variation in the fields between the successive time frames. The modes of variation obtained were then used to parametrize the free-form deformations and build our statistical model. The results of using our model to track the motion of the heart in normal volunteers are also presented.

Spatial transformation of motion and deformation fields using nonrigid registration

In this paper, we present a technique that can be used to transform the motion or deformation fields defined in the coordinate system of one subject into the coordinate system of another subject. Such a transformation accounts for the differences in the coordinate systems of the two subjects due to misalignment and size/shape variation, enabling the motion or deformation of each of the subjects to be directly quantitatively and qualitatively compared. The field transformation is performed by using a nonrigid registration algorithm to determine the intersubject coordinate system mapping from the first subject to the second subject. This fixes the relationship between the coordinate systems of the two subjects, and allows us to recover the deformation/motion vectors of the second subject for each corresponding point in the first subject. Since these vectors are still aligned with the coordinate system of the second subject, the inverse of the intersubject coordinate mapping is required to transform these vectors into the coordinate system of the first subject, and we approximate this inverse using a numerical line integral method. The accuracy of our numerical inversion technique is demonstrated using a synthetic example, after which we present applications of our method to sequences of cardiac and brain images.

Hierarchical statistical shape analysis and prediction of sub-cortical brain structures.

In this paper, we present the application of two multivariate statistical techniques to investigate how different structures within the brain vary statistically relative to each other. The first of these techniques is canonical correlation analysis which extracts and quantifies correlated behaviour between two sets of vector variables. The second technique is partial least squares regression which determines the best factors within a first set of vector variables for predicting a vector variable from a second set. We describe how these techniques can be used to quantify and predict correlated behaviour in sub-cortical structures within the brain using 3D MR images.

Multiple Sclerosis Lesion Segmentation Using Dictionary Learning and Sparse Coding

The segmentation of lesions in the brain during the development of Multiple Sclerosis is part of the diagnostic assessment for this disease and gives information on its current severity. This laborious process is still carried out in a manual or semiautomatic fashion by clinicians because published automatic approaches have not been universal enough to be widely employed in clinical practice. Thus Multiple Sclerosis lesion segmentation remains an open problem. In this paper we present a new unsupervised approach addressing this problem with dictionary learning and sparse coding methods. We show its general applicability to the problem of lesion segmentation by evaluating our approach on synthetic and clinical image data and comparing it to state-of-the-art methods. Furthermore the potential of using dictionary learning and sparse coding for such segmentation tasks is investigated and various possibilities for further experiments are discussed.

Structural brain changes in patients with recurrent major depressive disorder presenting with anxiety symptoms.

Major depressive disorder (MDD) presents with extensive clinical heterogeneity. In particular, overlap with anxiety symptoms is common during depressive episodes and as a comorbid disorder. The aim of this study was to test for morphological brain differences between patients having a history of recurrent MDD with, and without, anxiety symptoms (MDD+A and MDD−A).

Comparison of Cardiac Motion Across Subjects Using Non-rigid Registration

We present a novel technique that enables a direct quantitative comparison of cardiac motion derived from 4D MR image sequences to be made either within or across patients. This is achieved by registering the images that describe the anatomy of both subjects and then using the computed transformation to map the motion fields of each subject into the same coordinate system. The motion fields are calculated by registering each of the frames in a sequence of tagged short-axis MRI images to the end-diastolic frame using a non-rigid registration technique based on multi-level free-form deformations. The end-diastolic untagged short-axis images acquired shortly after the tagged images were obtained are registered using non-rigid registration to determine an inter-subject mapping, which is used to transform the motion fields of one of the subjects into the coordinate system of the other, which is thus our reference coordinate system. The results show the transformed myocardial motion fields of a series of volunteers, and clearly demonstrate the potential of the proposed technique.

Hierarchical Manifold Learning for Regional Image Analysis

We present a novel method of hierarchical manifold learning which aims to automatically discover regional properties of image datasets. While traditional manifold learning methods have become widely used for dimensionality reduction in medical imaging, they suffer from only being able to consider whole images as single data points. We extend conventional techniques by additionally examining local variations, in order to produce spatially-varying manifold embeddings that characterize a given dataset. This involves constructing manifolds in a hierarchy of image patches of increasing granularity, while ensuring consistency between hierarchy levels. We demonstrate the utility of our method in two very different settings: 1) to learn the regional correlations in motion within a sequence of time-resolved MR images of the thoracic cavity; 2) to find discriminative regions of 3-D brain MR images associated with neurodegenerative disease.