Shape analysis of the human brain.

Autism is a complex developmental disability that has dramatically increased in prevalence, having a decisive impact on the health and behavior of children. Methods used to detect and recommend therapies have been much debated in the medical community because of the subjective nature of diagnosing autism. In order to provide an alternative method for understanding autism, the current work has developed a 3-dimensional state-of-the-art shape based analysis of the human brain to aid in creating more accurate diagnostic assessments and guided risk analyses for individuals with neurological conditions, such as autism. Methods: The aim of this work was to assess whether the shape of the human brain can be used as a reliable source of information for determining whether an individual will be diagnosed with autism. The study was conducted using multi-center databases of magnetic resonance images of the human brain. The subjects in the databases were analyzed using a series of algorithms consisting of bias correction, skull stripping, multi-label brain segmentation, 3-dimensional mesh construction, spherical harmonic decomposition, registration, and classification. The software algorithms were developed as an original contribution of this dissertation in collaboration with the BioImaging Laboratory at the University of Louisville Speed School of Engineering. The classification of each subject was used to construct diagnoses and therapeutic risk assessments for each patient. Results: A reliable metric for making neurological diagnoses and constructing therapeutic risk assessment for individuals has been identified. The metric was explored in populations of individuals having autism spectrum disorders, dyslexia, Alzheimers disease, and lung cancer. Conclusion: Currently, the clinical applicability and benefits of the proposed software approach are being discussed by the broader community of doctors, therapists, and parents for use in improving current methods by which autism spectrum disorders are diagnosed and understood

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Detecting abnormalities in two-dimensional (2D) and three-dimensional (3D) medical structures is among the most interesting and challenging research areas in the medical imaging field. Obtaining the desired accurate automated quantification of abnormalities in medical structures is still very challenging. This is due to a large and constantly growing number of different objects of interest and associated abnormalities, large variations of their appearances and shapes in images, different medical imaging modalities, and associated changes of signal homogeneity and noise for each object. The main objective of this dissertation is to address these problems and to provide proper mathematical models and techniques that are capable of analyzing low and high resolution medical data and providing an accurate, automated analysis of the abnormalities in medical structures in terms of their area/volume, shape, and associated abnormal functionality. This dissertation presents different preliminary mathematical models and techniques that are applied in three case studies: (i) detecting abnormal tissue in the left ventricle (LV) wall of the heart from delayed contrast-enhanced cardiac magnetic resonance images (MRI), (ii) detecting local cardiac diseases based on estimating the functional strain metric from cardiac cine MRI, and (iii) identifying the abnormalities in the corpus callosum (CC) brain structure—the largest fiber bundle that connects the two hemispheres in the brain—for subjects that suffer from developmental brain disorders. For detecting the abnormal tissue in the heart, a graph-cut mathematical optimization model with a cost function that accounts for the object’s visual appearance and shape is used to segment the the inner cavity. The model is further integrated with a geometric model (i.e., a fast marching level set model) to segment the outer border of the myocardial wall (the LV). Then the abnormal tissue in the myocardium wall (also called dead tissue, pathological tissue, or infarct area) is identified based on a joint Markov-Gibbs random field (MGRF) model of the image and its region (segmentation) map that accounts for the pixel intensities and the spatial interactions between the pixels. Experiments with real in-vivo data and comparative results with ground truth (identified by a radiologist) and other approaches showed that the proposed framework can accurately detect the pathological tissue and can provide useful metrics for radiologists and clinicians. To estimate the strain from cardiac cine MRI, a novel method based on tracking the LV wall geometry is proposed. To achieve this goal, a partial differential equation (PDE) method is applied to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. The main advantage of the proposed tracking method over traditional texture-based methods is its ability to track the movement and rotation of the LV wall based on tracking the geometric features of the inner, mid-, and outer walls of the LV. This overcomes noise sources that come from scanner and heart motion. To identify the abnormalities in the CC from brain MRI, the CCs are aligned using a rigid registration model and are segmented using a shape-appearance model. Then, they are mapped to a simple unified space for analysis. This work introduces a novel cylindrical mapping model, which is conformal (i.e., one to one transformation and bijective), that enables accurate 3D shape analysis of the CC in the cylindrical domain. The framework can detect abnormalities in all divisions of the CC (i.e., splenium, rostrum, genu and body). In addition, it offers a whole 3D analysis of the CC abnormalities instead of only area-based analysis as done by previous groups. The initial classification results based on the centerline length and CC thickness suggest that the proposed CC shape analysis is a promising supplement to the current techniques for diagnosing dyslexia. The proposed techniques in this dissertation have been successfully tested on complex synthetic and MR images and can be used to advantage in many of today’s clinical applications of computer-assisted medical diagnostics and intervention

Motion processing deficits in migraine are related to contrast sensitivity

Background: There are conflicting reports concerning the ability of people with migraine to detect and discriminate visual motion. Previous studies used different displays and none adequately assessed other parameters that could affect performance, such as those that could indicate precortical dysfunction. Methods: Motion-direction detection, discrimination and relative motion thresholds were compared from participants with and without migraine. Potentially relevant visual covariates were included (contrast sensitivity; acuity; stereopsis; visual discomfort, stress, triggers; dyslexia). Results: For each task, migraine participants were less accurate than a control group and had impaired contrast sensitivity, greater visual discomfort, visual stress and visual triggers. Only contrast sensitivity correlated with performance on each motion task; it also mediated performance. Conclusions: Impaired performance on certain motion tasks can be attributed to impaired contrast sensitivity early in the visual system rather than a deficit in cortical motion processing per se. There were, however, additional differences for global and relative motion thresholds embedded in noise, suggesting changes in extrastriate cortex in migraine. Tasks to study the effects of noise on performance at different levels of the visual system and across modalities are recommended. A battery of standard visual tests should be included in any future work on the visual system and migraine

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Functional Magnetic Resonance Imaging

"Functional Magnetic Resonance Imaging - Advanced Neuroimaging Applications" is a concise book on applied methods of fMRI used in assessment of cognitive functions in brain and neuropsychological evaluation using motor-sensory activities, language, orthographic disabilities in children. The book will serve the purpose of applied neuropsychological evaluation methods in neuropsychological research projects, as well as relatively experienced psychologists and neuroscientists. Chapters are arranged in the order of basic concepts of fMRI and physiological basis of fMRI after event-related stimulus in first two chapters followed by new concepts of fMRI applied in constraint-induced movement therapy; reliability analysis; refractory SMA epilepsy; consciousness states; rule-guided behavioral analysis; orthographic frequency neighbor analysis for phonological activation; and quantitative multimodal spectroscopic fMRI to evaluate different neuropsychological states

Processing of structural neuroimaging data in young children:bridging the gap between current practice and state-of-the-art methods

The structure of the brain is subject to very rapid developmental changes during early childhood. Pediatric studies based on Magnetic Resonance Imaging (MRI) over this age range have recently become more frequent, with the advantage of providing in vivo and non-invasive high-resolution images of the developing brain, toward understanding typical and atypical trajectories. However, it has also been demonstrated that application of currently standard MRI processing methods that have been developed with datasets from adults may not be appropriate for use with pediatric datasets. In this review, we examine the approaches currently used in MRI studies involving young children, including an overview of the rationale for new MRI processing methods that have been designed specifically for pediatric investigations. These methods are mainly related to the use of age-specific or 4D brain atlases, improved methods for quantifying and optimizing image quality, and provision for registration of developmental data obtained with longitudinal designs. The overall goal is to raise awareness of the existence of these methods and the possibilities for implementing them in developmental neuroimaging studies

A CAD system for early diagnosis of autism using different imaging modalities.

The term “autism spectrum disorder” (ASD) refers to a collection of neuro-developmental disorders that affect linguistic, behavioral, and social skills. Autism has many symptoms, most prominently, social impairment and repetitive behaviors. It is crucial to diagnose autism at an early stage for better assessment and investigation of this complex syndrome. There have been a lot of efforts to diagnose ASD using different techniques, such as imaging modalities, genetic techniques, and behavior reports. Imaging modalities have been extensively exploited for ASD diagnosis, and one of the most successful ones is Magnetic resonance imaging(MRI),where it has shown promise for the early diagnosis of the ASD related abnormalities in particular. Magnetic resonance imaging (MRI) modalities have emerged as powerful means that facilitate non-invasive clinical diagnostics of various diseases and abnormalities since their inception in the 1980s. After the advent in the nineteen eighties, MRI soon became one of the most promising non- invasive modalities for visualization and diagnostics of ASD-related abnormalities. Along with its main advantage of no exposure to radiation, high contrast, and spatial resolution, the recent advances to MRI modalities have notably increased diagnostic certainty. Multiple MRI modalities, such as different types of structural MRI (sMRI) that examines anatomical changes, and functional MRI (fMRI) that examines brain activity by monitoring blood flow changes,have been employed to investigate facets of ASD in order to better understand this complex syndrome. This work aims at developing a new computer-aided diagnostic (CAD) system for autism diagnosis using different imaging modalities. It mainly relies on making use of structural magnetic resonance images for extracting notable shape features from parts of the brainthat proved to correlate with ASD from previous neuropathological studies. Shape features from both the cerebral cortex (Cx) and cerebral white matter(CWM)are extracted. Fusion of features from these two structures is conducted based on the recent findings suggesting that Cx changes in autism are related to CWM abnormalities. Also, when fusing features from more than one structure, this would increase the robustness of the CAD system. Moreover, fMRI experiments are done and analyzed to find areas of activation in the brains of autistic and typically developing individuals that are related to a specific task. All sMRI findings are fused with those of fMRI to better understand ASD in terms of both anatomy and functionality,and thus better classify the two groups. This is one aspect of the novelty of this CAD system, where sMRI and fMRI studies are both applied on subjects from different ages to diagnose ASD. In order to build such a CAD system, three main blocks are required. First, 3D brain segmentation is applied using a novel hybrid model that combines shape, intensity, and spatial information. Second, shape features from both Cx and CWM are extracted and anf MRI reward experiment is conducted from which areas of activation that are related to the task of this experiment are identified. Those features were extracted from local areas of the brain to provide an accurate analysis of ASD and correlate it with certain anatomical areas. Third and last, fusion of all the extracted features is done using a deep-fusion classification network to perform classification and obtain the diagnosis report. Fusing features from all modalities achieved a classification accuracy of 94.7%, which emphasizes the significance of combining structures/modalities for ASD diagnosis. To conclude, this work could pave the pathway for better understanding of the autism spectrum by finding local areas that correlate to the disease. The idea of personalized medicine is emphasized in this work, where the proposed CAD system holds the promise to resolve autism endophenotypes and help clinicians deliver personalized treatment to individuals affected with this complex syndrome

Sparse feature learning for image analysis in segmentation, classification, and disease diagnosis.

The success of machine learning algorithms generally depends on intermediate data representation, called features that disentangle the hidden factors of variation in data. Moreover, machine learning models are required to be generalized, in order to reduce the specificity or bias toward the training dataset. Unsupervised feature learning is useful in taking advantage of large amount of unlabeled data, which is available to capture these variations. However, learned features are required to capture variational patterns in data space. In this dissertation, unsupervised feature learning with sparsity is investigated for sparse and local feature extraction with application to lung segmentation, interpretable deep models, and Alzheimer\u27s disease classification. Nonnegative Matrix Factorization, Autoencoder and 3D Convolutional Autoencoder are used as architectures or models for unsupervised feature learning. They are investigated along with nonnegativity, sparsity and part-based representation constraints for generalized and transferable feature extraction

The Role of the Lateral Geniculate Nucleus in Developmental Dyslexia: Evidence From Multi-Modal Magnetic Resonance Imaging

The ability to read proficiently is key to social participation and an important premise for individual well-being and vocational success. Individuals with developmental dyslexia, a highly prevalent neurodevelopmental disorder affecting hundreds of millions of children and adults worldwide, face severe and persistent difficulties in attaining adequate reading levels. Despite years of extensive research efforts to elucidate the neurobiological origin of this disorder, its exact etiology remains unclear to date. In this context, most neuroimaging research on dyslexia in humans has focused on the cerebral cortex and has identified alterations in a distributed left-lateralized cortical language network. However, pioneering post-mortem human studies and animal models suggest that dyslexia might also be associated with alterations in subcortical sensory thalami and early sensory pathways. The largely cortico-centric view of dyslexia is due in part to considerable technical challenges in assessing the human sensory thalami non-invasively using conventional magnetic resonance imaging (MRI). As a result, the role that sensory thalami may play in dyslexia has been largely unaddressed. In this dissertation, I leveraged recent advances in high-field MRI to investigate the role of the human lateral geniculate nucleus (LGN) of the visual thalamus in adults with dyslexia in-vivo. In three multi-modal high-field MRI studies, I show that (i) dyslexia is associated with structural alterations in the direct V1-bypassing white matter pathway connecting the LGN with cortical motion-sensitive area V5/MT in the left hemisphere; (ii) the connectivity strength of which predicts a core symptom of the disorder, i.e., rapid naming ability. I further demonstrate that (iii) the two major functional subdivisions of the LGN can be distinguished non-invasively based on differences in tissue microstructure; and that (iv) adults with dyslexia show functional response alterations specifically in the magnocellular subdivision of the LGN. I also demonstrate that this subdivision deficit (v) is more pronounced in male than female dyslexics; and (vi) predicts rapid naming ability in male dyslexics only. The results of this doctoral thesis are the first to confirm previous post-mortem evidence of LGN alterations in dyslexia in-vivo and point to their relevance to key symptoms of the disorder. In synergy, our research findings offer new perspectives on explanatory models of dyslexia and bear potential implications also for prospective treatment strategies.:Contribution Statement i Acknowledgments iii Abstract v Table of Contents vii 1 General Introduction 1 1.1 Developmental Dyslexia 1 1.1.1 Diagnostic Criteria 1 1.1.2 Prevalence and Etiology 2 1.1.3 Cognitive and Behavioral Symptoms 3 1.1.4 Explanatory Models in Cognitive Neuroscience 4 1.2 Lateral Geniculate Nucleus 7 1.2.1 Anatomy and Function 7 1.2.2 Technical Challenges in Conventional MRI 8 1.2.3 High-Field MRI 9 1.3 Research Aim and Chapter Outline 10 2 Altered Structural Connectivity of the Left Visual Thalamus in Developmental Dyslexia 13 2.1 Summary 14 2.2 Results and Discussion 15 2.3 Conclusions 22 2.4 Materials and Methods 23 2.4.1 Subject Details 23 2.4.2 High-Resolution MRI Acquisition and Preprocessing 23 2.4.3 Lateral Geniculate Nucleus Definition 24 2.4.4 Cortical Region of Interest Definition 26 2.4.5 Probabilistic Tractography 27 2.4.6 Quantification and Statistical Analysis 29 2.5 Supplementary Information 30 3 Mapping the Human Lateral Geniculate Nucleus and its Cytoarchitectonic Subdivisions Using Quantitative MRI 33 3.1 Abstract 34 3.2 Introduction 35 3.3 Materials and Methods 37 3.3.1 In-Vivo MRI 37 3.3.2 Post-Mortem MRI and Histology 41 3.4 Results 44 3.4.1 Lateral Geniculate Nucleus Subdivisions in In-Vivo MRI 44 3.4.2 Lateral Geniculate Nucleus Subdivisions in Post-Mortem MRI 46 3.5 Discussion 50 3.6 Supplementary Information 54 3.6.1 In-Vivo MRI 54 3.6.2 Post-Mortem MRI and Histology 58 3.6.3 Data and Code Availability 60 4 Dysfunction of the Visual Sensory Thalamus in Developmental Dyslexia 61 4.1 Abstract 62 4.2 Introduction 63 4.3 Materials and Methods 66 4.3.1 Subject Details 66 4.3.2 High-Resolution MRI Experiments 66 4.3.3 High-Resolution MRI Acquisition and Preprocessing 67 4.3.4 Lateral Geniculate Nucleus Definition 68 4.3.5 Quantification and Statistical Analysis 69 4.4 Results 70 4.5 Discussion 75 4.6 Supplementary Information 77 4.6.1 Supporting Methods 77 4.6.2 Supporting Results 81 4.6.3 Data and Code Availability 82 5 General Conclusion 83 5.1 Summary of Research Findings 83 5.2 Implications for Dyslexia Models 84 5.2.1 Phonological Deficit Hypothesis 84 5.2.2 Magnocellular Theory 84 5.2.3 Model According to Ramus 85 5.2.4 Need for Revised Model 86 5.3 Implications for Remediation 87 5.4 Research Prospects 88 5.5 Brief Concluding Remarks 90 6 Bibliography 91 7 List of Tables 113 8 List of Figures 115 9 Selbstständigkeitserklärung 11