19 research outputs found

    Microstructural MRI basis of the cognitive functions in patients with Spinocerebellar ataxia type 2

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    Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disease involving the cerebellum. The particular atrophy pattern results in some typical clinical features mainly including motor deficits. In addition, the presence of cognitive impairments, involving language, visuospatial and executive functions, has been also shown in SCA2 patients and it is now widely accepted as a feature of the disease. The aim of the study is to investigate the microstructural patterns and the anatomo-functional substrate that could account for the cognitive symptomatology observed in SCA2 patients. In the present study, diffusion tensor imaging (DTI) based-tractography was performed to map the main cerebellar white matter (WM) bundles, such as Middle and Superior Cerebellar Peduncles, connecting cerebellum with higher order cerebral regions. Damage-related diffusivity measures were used to determine the pattern of pathological changes of cerebellar WM microstructure in patients affected by SCA2 and correlated with the patients' cognitive scores. Our results provide the first evidence that WM diffusivity is altered in the presence of the cerebellar cortical degeneration associated with SCA2 thus resulting in a cerebello-cerebral dysregulation that may account for the specificity of cognitive symptomatology observed in patients

    Bayesian segmentation of brainstem structures in MRI

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    VK: Lampinen, J.In this paper we present a method to segment four brainstem structures (midbrain, pons, medulla oblongata and superior cerebellar peduncle) from 3D brain MRI scans. The segmentation method relies on a probabilistic atlas of the brainstem and its neighboring brain structures. To build the atlas, we combined a dataset of 39 scans with already existing manual delineations of the whole brainstem and a dataset of 10 scans in which the brainstem structures were manually labeled with a protocol that was specifically designed for this study. The resulting atlas can be used in a Bayesian framework to segment the brainstem structures in novel scans. Thanks to the generative nature of the scheme, the segmentation method is robust to changes in MRI contrast or acquisition hardware. Using cross validation, we show that the algorithm can segment the structures in previously unseen T1 and FLAIR scans with great accuracy (mean error under 1 mm) and robustness (no failures in 383 scans including 168 AD cases). We also indirectly evaluate the algorithm with a experiment in which we study the atrophy of the brainstem in aging. The results show that, when used simultaneously, the volumes of the midbrain, pons and medulla are significantly more predictive of age than the volume of the entire brainstem, estimated as their sum. The results also demonstrate that the method can detect atrophy patterns in the brainstem structures that have been previously described in the literature. Finally, we demonstrate that the proposed algorithm is able to detect differential effects of AD on the brainstem structures. The method will be implemented as part of the popular neuroimaging package FreeSurfer.Peer reviewe

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

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    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

    A Comparative Evaluation of Voxel-based Spatial Mapping in Diffusion Tensor Imaging

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    This paper presents a comparative evaluation of methods for automated voxel-based spatial mapping in diffusion tensor imaging studies. Such methods are an essential step in computational pipelines and provide anatomically comparable measurements across a population in atlas-based studies. To better understand their strengths and weaknesses, we tested a total of eight methods for voxel-based spatial mapping in two types of diffusion tensor templates. The methods were evaluated with respect to scan-rescan reliability and an application to normal aging. The methods included voxel-based analysis with and without smoothing, two types of region-based analysis, and combinations thereof with skeletonization. The templates included a study-specific template created with DTI-TK and the IIT template serving as a standard template. To control for other factors in the pipeline, the experiments used a common dataset, acquired at 1.5T with a single shell high angular resolution diffusion MR imaging protocol, and tensor-based spatial normalization with DTI-TK. Scan-rescan reliability was assessed using the coefficient of variation (CV) and intraclass correlation (ICC) in eight subjects with three scans each. Sensitivity to normal aging was assessed in a population of 80 subjects aged 25 to 65 years old, and methods were compared with respect to the anatomical agreement of significant findings and the R(2) of the associated models of fractional anisotropy. The results show that reliability depended greatly on the method used for spatial mapping. The largest differences in reliability were found when adding smoothing and comparing voxel-based and region-based analyses. Skeletonization and template type were found to have either a small or negligible effect on reliability. The aging results showed agreement among the methods in nine brain areas, with some methods showing more sensitivity than others. Skeletonization and smoothing were not major factors affecting sensitivity to aging, but the standard template showed higher R(2) in several conditions. A structural comparison of the templates showed that large deformations between them may be related to observed differences in patterns of significant voxels. Most areas showed significantly higher R(2) with voxel-based analysis, particularly when clusters were smaller than the available regions-of-interest. Looking forward, these results can potentially help to interpret results from existing white matter imaging studies, as well as provide a resource to help in planning future studies to maximize reliability and sensitivity with regard to the scientific goals at hand

    Development of an Atlas-Based Segmentation of Cranial Nerves Using Shape-Aware Discrete Deformable Models for Neurosurgical Planning and Simulation

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    Twelve pairs of cranial nerves arise from the brain or brainstem and control our sensory functions such as vision, hearing, smell and taste as well as several motor functions to the head and neck including facial expressions and eye movement. Often, these cranial nerves are difficult to detect in MRI data, and thus represent problems in neurosurgery planning and simulation, due to their thin anatomical structure, in the face of low imaging resolution as well as image artifacts. As a result, they may be at risk in neurosurgical procedures around the skull base, which might have dire consequences such as the loss of eyesight or hearing and facial paralysis. Consequently, it is of great importance to clearly delineate cranial nerves in medical images for avoidance in the planning of neurosurgical procedures and for targeting in the treatment of cranial nerve disorders. In this research, we propose to develop a digital atlas methodology that will be used to segment the cranial nerves from patient image data. The atlas will be created from high-resolution MRI data based on a discrete deformable contour model called 1-Simplex mesh. Each of the cranial nerves will be modeled using its centerline and radius information where the centerline is estimated in a semi-automatic approach by finding a shortest path between two user-defined end points. The cranial nerve atlas is then made more robust by integrating a Statistical Shape Model so that the atlas can identify and segment nerves from images characterized by artifacts or low resolution. To the best of our knowledge, no such digital atlas methodology exists for segmenting nerves cranial nerves from MRI data. Therefore, our proposed system has important benefits to the neurosurgical community

    Development and evaluation of biomarkers in Huntington’s Disease: furthering our understanding of the disease and preparing for clinical trials

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    Huntington’s Disease (HD) is a devastating hereditary neurodegenerative disease for which there are currently only symptomatic treatments. Several potentially curative pharmaceutical and genetic therapies are however in varying stages of development and therefore an increasing number of large-scale clinical trials of disease-modifying therapies are imminent. There is consequently a need for biomarkers which are sensitive to beneficial attenuation of disease-related changes. Functional, neuroimaging and biochemical biomarkers have been developed in HD (Andre et al. 2014;Weir et al. 2011). Neuroimaging biomarkers are strong candidates based on their clear relevance to the neuropathology of disease, proven precision and superior sensitivity compared with some standard functional measures (Tabrizi et al. 2011;Tabrizi et al. 2012). Their use in early-stage clinical trials, as surrogate end-points providing initial evidence of biological effect, is becoming increasingly common. Comparison of biomarkers in HD will help to clarify which measures, over varying time intervals, are most sensitive to disease progression. Additionally, the identification of robust fully-automated methods, comparable to manual and semi-automated gold-standards, would facilitate large-scale volumetric analysis. These methods however require validation in observational studies of neurodegenerative disease before they can be applied to sensitive clinical trial data. This thesis will develop and evaluate biomarkers for use in HD; both furthering our understanding of the disease and in preparation for use as end-points in clinical trials. A direct comparison of the sensitivity of diffusion and volumetric imaging biomarkers to HD-related change will be reported for the first time. Several exploratory imaging investigations are also described which enhance current knowledge of the relationship between neuroimaging metrics, brain functioning and behaviour, additionally strengthening the argument for the clinical relevance of neuroimaging measures as surrogate end-points in HD. The thesis will conclude with a comprehensive biomarker evaluation in early-stage HD, along with suggested strategies for selection of primary and secondary trial end-points based on effect sizes and corresponding sample size requirements

    Improving the clinico-radiological association in neurological diseases

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    Despite the key role of magnetic resonance imaging (MRI) in the diagnosis and monitoring of multiple sclerosis (MS) and cerebral small vessel disease (SVD), the association between clinical and radiological disease manifestations is often only moderate, limiting the use of MRI-derived markers in the clinical routine or as endpoints in clinical trials. In the projects conducted as part of this thesis, we addressed this clinico-radiological gap using two different approaches. Lesion-symptom association: In two voxel-based lesion-symptom mapping studies, we aimed at strengthening lesion-symptom associations by identifying strategic lesion locations. Lesion mapping was performed in two large cohorts: a dataset of 2348 relapsing-remitting MS patients, and a population-based cohort of 1017 elderly subjects. T2-weighted lesion masks were anatomically aligned and a voxel-based statistical approach to relate lesion location to different clinical rating scales was implemented. In the MS lesion mapping, significant associations between white matter (WM) lesion location and several clinical scores were found in periventricular areas. Such lesion clusters appear to be associated with impairment of different physical and cognitive abilities, probably because they affect commissural and long projection fibers. In the SVD lesion mapping, the same WM fibers and the caudate nucleus were identified to significantly relate to the subjects’ cerebrovascular risk profiles, while no other locations were found to be associated with cognitive impairment. Atrophy-symptom association: With the construction of an anatomical physical phantom, we aimed at addressing reliability and robustness of atrophy-symptom associations through the provision of a “ground truth” for atrophy quantification. The built phantom prototype is composed of agar gels doped with MRI and computed tomography (CT) contrast agents, which realistically mimic T1 relaxation times of WM and grey matter (GM) and showing distinguishable attenuation coefficients using CT. Moreover, due to the design of anatomically simulated molds, both WM and GM are characterized by shapes comparable to the human counterpart. In a proof-of-principle study, the designed phantom was used to validate automatic brain tissue quantification by two popular software tools, where “ground truth” volumes were derived from high-resolution CT scans. In general, results from the same software yielded reliable and robust results across scans, while results across software were highly variable reaching volume differences of up to 8%

    Diffusion tensor imaging and resting state functional connectivity as advanced imaging biomarkers of outcome in infants with hypoxic-ischaemic encephalopathy treated with hypothermia

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    Therapeutic hypothermia confers significant benefit in term neonates with hypoxic-ischaemic encephalopathy (HIE). However, despite the treatment nearly half of the infants develop an unfavourable outcome. Intensive bench-based and early phase clinical research is focused on identifying treatments that augment hypothermic neuroprotection. Qualified biomarkers are required to test these promising therapies efficiently. This thesis aims to assess advanced magnetic resonance imaging (MRI) techniques, including diffusion tensor imaging (DTI) and resting state functional MRI (fMRI) as imaging biomarkers of outcome in infants with HIE who underwent hypothermic neuroprotection. FA values in the white matter (WM), obtained in the neonatal period and assessed by tract-based spatial statistics (TBSS), correlated with subsequent developmental quotient (DQ). However, TBSS is not suitable to study grey matter (GM), which is the primary site of injury following an acute hypoxic-ischaemic event. Therefore, a neonatal atlas-based automated tissue labelling approach was applied to segment central and cortical grey and whole brain WM. Mean diffusivity (MD) in GM structures, obtained in the neonatal period correlated with subsequent DQ. Although the central GM is the primary site of injury on conventional MRI following HIE; FA within WM tissue labels also correlated to neurodevelopmental performance scores. As DTI does not provide information on functional consequences of brain injury functional sequel of HIE was studied with resting state fMRI. Diminished functional connectivity was demonstrated in infants who suffered HIE, which associated with an unfavourable outcome. The results of this thesis suggest that MD in GM tissue labels and FA either determined within WM tissue labels or analysed with TBSS correlate to subsequent neurodevelopmental performance scores in infants who suffered HIE treated with hypothermia and may be applied as imaging biomarkers of outcome in this population. Although functional connectivity was diminished in infants with HIE, resting state fMRI needs further study to assess its utility as an imaging biomarker following a hypoxic-ischaemic brain injury.Open Acces

    Moving beyond DTI: non-gaussian diffusion in the brain and skeletal muscle

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    Diffusion Magnetic Resonance Imaging (dMRI) is a diagnostic technique able to provide in- vivo measures that are related to the microstructure of tissues. Thanks to the sensitivity to microstructural tissue changes, Diffusion Weighted Imaging (DWI) and derived metrics, as the Apparent Diffusion Coefficient (ADC) , became the gold standard for the detection of strokes and ischemia since the early 90‟s. In 1994 Basser and colleagues introduced Diffusion Tensor Imaging (DTI), the first quantification approach able to capture the anisotropy of the diffusion process in in-vivo biological tissues. Chapter II shows the results we obtained applying DTI to investigate white matter alterations of a population affected by Friedreich‟s Ataxia. After more than 20 years from its introduction, DTI is still widely applied. However, concerns about the limitations of the technique have been increasingly risen over-time, with particular reference to the lack of specificity of the model and the coexistence of tissues with multiple architectures. Additionally, the tensor model can be applied only to a range of “moderate” diffusion sensitizations, after which the presence of biological membranes becomes non-negligible and gives origin to phenomena of “non-Gaussian diffusion”, that violate the assumptions of the model. Chapter III and Chapter IV deal specifically with these limitations, addressing the problem with two different approaches and applications. Another popular technique to investigate the dMRI signal is Spherical Deconvolution (SD), that in Chapter IV is presented in a tissue specific formulation and applied to derive diffusivity metrics specific to white matter, gray matter and cerebrospinal fluid, both in healthy controls and in a patient affected by MS. Since the early days of dMRI, experiments have been performed not only in the brain but in several body districts, including the skeletal muscle. Back in 1986 Le Bihan et al. observed that the water flowing in the micro vascular network and in the vessels was contributing to the acquisition of data at very low diffusion sensitization, and proposed the “Intra- Voxel Incoherent Motion” (IVIM) model. IVIM can be seen either as a model to obtain measures of pseudo-diffusion, or as a technique to obtain perfusion free ADC measures, thus recognizing it as an artifact. Although dMRI and DTI were applied to the skeletal muscle since its early days, later evolutions as Diffusion Kurtosis Imaging have only recently been applied to the skeletal muscle to fit dMRI data acquired at strong diffusion sensitization. The concepts of IVIM and DKI are developed in Chapter V, where the effects of the first on DTI and DKI, as well as the relation between DTI and DKI metrics are investigated through simulations and MRI data of the calf. In line with the current dMRI literature, the first 5 chapters of this thesis depict the diffusion signal as a complex measure arising from multiple tissue components. Chapter VI investigates a multi-compartment pseudo-continuous deconvolution approach, a technique that does not require explicit modeling of the tissues. Finally, Chapter VII presents an overview of other research topics I have work on during the PhD

    Segmentation and skeletonization techniques for cardiovascular image analysis

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