Morphometry Based on Effective and Accurate Correspondences of Localized Patterns (MEACOLP)

Local features in volumetric images have been used to identify correspondences of localized anatomical structures for brain morphometry. However, the correspondences are often sparse thus ineffective in reflecting the underlying structures, making it unreliable to evaluate specific morphological differences. This paper presents a morphometry method (MEACOLP) based on correspondences with improved effectiveness and accuracy. A novel two-level scale-invariant feature transform is used to enhance the detection repeatability of local features and to recall the correspondences that might be missed in previous studies. Template patterns whose correspondences could be commonly identified in each group are constructed to serve as the basis for morphometric analysis. A matching algorithm is developed to reduce the identification errors by comparing neighboring local features and rejecting unreliable matches. The two-sample t-test is finally adopted to analyze specific properties of the template patterns. Experiments are performed on the public OASIS database to clinically analyze brain images of Alzheimer's disease (AD) and normal controls (NC). MEACOLP automatically identifies known morphological differences between AD and NC brains, and characterizes the differences well as the scaling and translation of underlying structures. Most of the significant differences are identified in only a single hemisphere, indicating that AD-related structures are characterized by strong anatomical asymmetry. In addition, classification trials to differentiate AD subjects from NC confirm that the morphological differences are reliably related to the groups of interest

Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion

We propose a novel classification framework to precisely identify individuals with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) from normal controls (NC). The proposed method combines three different features from structural MR images: gray-matter volume, gray-level cooccurrence matrix, and Gabor feature. These features can obtain both the 2D and 3D information of brains, and the experimental results show that a better performance can be achieved through the multifeature fusion. We also analyze the multifeatures combination correlation technologies and improve the SVM-RFE algorithm through the covariance method. The results of comparison experiments on public Alzheimer’s Disease Neuroimaging Initiative (ADNI) database demonstrate the effectiveness of the proposed method. Besides, it also indicates that multifeatures combination is better than the single-feature method. The proposed features selection algorithm could effectively extract the optimal features subset in order to improve the classification performance

The Influence of Spatial Registration on Detection of Cerebral Asymmetries Using Voxel-Based Statistics of Fractional Anisotropy Images and TBSS

The sensitivity of diffusion tensor imaging (DTI) for detecting microstructural white matter alterations has motivated the application of voxel-based statistics (VBS) to fractional anisotropy (FA) images (FA-VBS). However, detected group differences may depend on the spatial registration method used. The objective of this study was to investigate the influence of spatial registration on detecting cerebral asymmetries in FA-VBS analyses with reference to data obtained using Tract-Based Spatial Statistics (TBSS). In the first part of this study we performed FA-VBS analyses using three single-contrast and one multi-contrast registration: (i) whole-brain registration based on T2 contrast, (ii) whole-brain registration based on FA contrast, (iii) individual-hemisphere registration based on FA contrast, and (iv) a combination of (i) and (iii). We then compared the FA-VBS results with those obtained from TBSS. We found that the FA-VBS results depended strongly on the employed registration approach, with the best correspondence between FA-VBS and TBSS results when approach (iv), the “multi-contrast individual-hemisphere” method was employed. In the second part of the study, we investigated the spatial distribution of residual misregistration for each registration approach and the effect on FA-VBS results. For the FA-VBS analyses using the three single-contrast registration methods, we identified FA asymmetries that were (a) located in regions prone to misregistrations, (b) not detected by TBSS, and (c) specific to the applied registration approach. These asymmetries were considered candidates for apparent FA asymmetries due to systematic misregistrations associated with the FA-VBS approach. Finally, we demonstrated that the “multi-contrast individual-hemisphere” approach showed the least residual spatial misregistrations and thus might be most appropriate for cerebral FA-VBS analyses

In vivo detection of cerebral tau pathology in long-term survivors of traumatic brain injury

Traumatic brain injury (TBI) can trigger progressive neurodegeneration, with tau pathology seen years after a single moderate-severe TBI. Identifying this type of posttraumatic pathology in vivo might help to understand the role of tau pathology in TBI pathophysiology. We used flortaucipir positron emission tomography (PET) to investigate whether tau pathology is present many years after a single TBI in humans. We examined PET data in relation to markers of neurodegeneration in the cerebrospinal fluid (CSF), structural magnetic resonance imaging measures, and cognitive performance. Cerebral flortaucipir binding was variable, with many participants with TBI showing increases in cortical and white matter regions. At the group level, flortaucipir binding was increased in the right occipital cortex in TBI when compared to healthy controls. Flortaucipir binding was associated with increased total tau, phosphorylated tau, and ubiquitin carboxyl-terminal hydrolase L1 CSF concentrations, as well as with reduced fractional anisotropy and white matter tissue density in TBI. Apolipoprotein E (APOE) ε4 genotype affected the relationship between flortaucipir binding and time since injury, CSF β amyloid 1–42 (Aβ42) concentration, white matter tissue density, and longitudinal Mini-Mental State Examination scores in TBI. The results demonstrate that tau PET is a promising approach to investigating progressive neurodegeneration associated with tauopathy after TBI

A Probabilistic Approach To Non-Rigid Medical Image Registration

Non-rigid image registration is an important tool for analysing morphometric differences in subjects with Alzheimer's disease from structural magnetic resonance images of the brain. This thesis describes a novel probabilistic approach to non-rigid registration of medical images, and explores the benefits of its use in this area of neuroimaging. Many image registration approaches have been developed for neuroimaging. The vast majority suffer from two limitations: Firstly, the trade-off between image fidelity and regularisation requires selection. Secondly, only a point-estimate of the mapping between images is inferred, overlooking the presence of uncertainty in the estimation. This thesis introduces a novel probabilistic non-rigid registration model and inference scheme. This framework allows the inference of the parameters that control the level of regularisation, and data fidelity in a data-driven fashion. To allow greater flexibility, this model is extended to allow the level of data fidelity to vary across space. A benefit of this approach, is that the registration can adapt to anatomical variability and other image acquisition differences. A further advantage of the proposed registration framework is that it provides an estimate of the distribution of probable transformations. Additional novel contributions of this thesis include two proposals for exploiting the estimated registration uncertainty. The first of these estimates a local image smoothing filter, which is based on the registration uncertainty. The second approach incorporates the distribution of transformations into an ensemble learning scheme for statistical prediction. These techniques are integrated into standard frameworks for morphometric analysis, and are demonstrated to improve the ability to distinguish subjects with Alzheimer's disease from healthy controls

Analyzing Heterogeneity In Neuroimaging With Probabilistic Multivariate Clustering Approaches

Automated quantitative neuroimaging analysis methods have been crucial in elucidating normal and pathological brain structure and function, and in building in vivo markers of disease and its progression. Commonly used methods can identify and precisely quantify subtle and spatially complex imaging patterns of brain change associated with brain diseases. However, the overarching premise of these methods is that the disease group is a homogeneous entity resulting from a single, unifying pathophysiological process that has a single imaging signature. This assumption ignores ample evidence for the heterogeneous nature of neurodegenerative diseases and neuropsychiatric disorders, resulting in incomplete or misleading descriptions. Accurate characterization of heterogeneity is important for deepening our understanding of neurobiological processes, thus leading to improved disease diagnosis and prognosis. In this thesis, we leveraged machine learning techniques to develop novel tools that can analyze the heterogeneity in both cross-sectional and longitudinal neuroimaging studies. Specifically, we developed a semi-supervised clustering method for characterizing heterogeneity in cross-sectional group comparison studies, where normal and patient populations are modeled as high-dimensional point distributions, and heterogeneous disease effects are captured by estimating multiple transformations that align the two distributions, while accounting for the effect of nuisance covariates. Moreover, toward dissecting the heterogeneity in longitudinal cohorts, we proposed a method which simultaneously fits multiple population longitudinal multivariate trajectories and clusters subjects into subgroups. Longitudinal trajectories are modeled using spatiotemporally regularized cubic splines, while clustering is performed by assigning subjects to the subgroup whose population trajectory best fits their data. The proposed tools were extensively validated using synthetic data. Importantly, they were applied to study the heterogeneity in large clinical neuroimaging cohorts. We identified four disease subtypes with distinct imaging signatures using data from Alzheimer’s Disease Neuroimaging Initiative, and revealed two subgroups with different longitudinal patterns using data from Baltimore Longitudinal Study on Aging. Critically, we were able to further characterize the subgroups in each of the studies by performing statistical analyses evaluating subgroup differences with additional information such as neurocognitive data. Our results demonstrate the strength of the developed methods, and may pave the road for a broader understanding of the complexity of brain aging and Alzheimer’s disease

Computer aided diagnosis in temporal lobe epilepsy and Alzheimer's dementia

Computer aided diagnosis within neuroimaging must rely on advanced image processing techniques to detect and quantify subtle signal changes that may be surrogate indicators of disease state. This thesis proposes two such novel methodologies that are both based on large volumes of interest, are data driven, and use cross-sectional scans: appearance-based classification (ABC) and voxel-based classification (VBC).The concept of appearance in ABC represents the union of intensity and shape information extracted from magnetic resonance images (MRI). The classification method relies on a linear modeling of appearance features via principal components analysis, and comparison of the distribution of projection coordinates for the populations under study within a reference multidimensional appearance eigenspace. Classification is achieved using forward, stepwise linear discriminant analyses, in multiple cross-validated trials. In this work, the ABC methodology is shown to accurately lateralize the seizure focus in temporal lobe epilepsy (TLE), differentiate normal aging individuals from patients with either Alzheimer's dementia (AD) or Mild Cognitive Impairment (MCI), and finally predict the progression of MCI patients to AD. These applications demonstrated that the ABC technique is robust to different signal changes due to two distinct pathologies, to low resolution data and motion artifacts, and to possible differences inherent to multi-site acquisition.The VBC technique relies on voxel-based morphometry to identify regions of grey and white matter concentration differences between co-registered cohorts of individuals, and then on linear modeling of variables extracted from these regions. Classification is achieved using linear discriminant analyses within a multivariate space composed of voxel-based morphometry measures related to grey and white matter concentration, along with clinical variables of interest. VBC is shown to increase the accuracy of prediction of one-year clinical status from three to four out of five TLE patients having undergone selective amygdalo-hippocampectomy. These two techniques are shown to have the necessary potential to solve current problems in neurological research, assist clinical physicians with their decision-making process and influence positively patient management

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

Mechanisms of voice processing in dementia

Perception of nonverbal vocal information is essential in our daily lives. Patients with degenerative dementias commonly have difficulty with such aspects of vocal communication; however voice processing has seldom been studied in these diseases. This thesis comprises a series of linked studies of voice processing in canonical dementias: Alzheimer’s disease, behavioural variant frontotemporal dementia, semantic dementia and progressive nonfluent aphasia. A series of neuropsychological tests were developed to examine perceptual and semantic stages of voice processing and to assess two aspects of accent processing: comprehension of foreign accented speech and recognition of regional and foreign accents; patient performance was referenced to healthy control subjects. Neuroanatomical associations of voice processing performance were assessed using voxel based morphometry. Following a symptom-led approach, a syndrome of progressive associative phonagnosia was characterised in two detailed case studies. Following a disease-led approach, this work was extended systematically to cohorts of patients representing the target diseases and assessing voice processing in relation to other aspects of person recognition (faces and names). This work provided evidence for separable profiles of voice processing impairment in different diseases: associative deficits were particularly severe in semantic dementia, whilst perceptual deficits showed relative specificity for Alzheimer’s disease. Neuroanatomical associations were identified for voice recognition in the right temporal pole and anterior fusiform gyrus, and for voice discrimination in the right inferior parietal lobe. The final phase of this work addressed the neuropsychological and neuroanatomical basis of accent processing, as an important dimension of nonverbal vocal analysis that is not dependent on voice identity. This work provides evidence for impaired processing of accents in progressive nonfluent aphasia and Alzheimer’s with neuroanatomical associations in the anterior and superior temporal lobe. The thesis contributes new information about voice processing in the degenerative dementias and furthers our understanding of the mechanisms of human voice analysis