FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using Large Deformation Diffeomorphic Metric Mapping.

Fully-automated brain segmentation methods have not been widely adopted for clinical use because of issues related to reliability, accuracy, and limitations of delineation protocol. By combining the probabilistic-based FreeSurfer (FS) method with the Large Deformation Diffeomorphic Metric Mapping (LDDMM)-based label-propagation method, we are able to increase reliability and accuracy, and allow for flexibility in template choice. Our method uses the automated FreeSurfer subcortical labeling to provide a coarse-to-fine introduction of information in the LDDMM template-based segmentation resulting in a fully-automated subcortical brain segmentation method (FS+LDDMM). One major advantage of the FS+LDDMM-based approach is that the automatically generated segmentations generated are inherently smooth, thus subsequent steps in shape analysis can directly follow without manual post-processing or loss of detail. We have evaluated our new FS+LDDMM method on several databases containing a total of 50 subjects with different pathologies, scan sequences and manual delineation protocols for labeling the basal ganglia, thalamus, and hippocampus. In healthy controls we report Dice overlap measures of 0.81, 0.83, 0.74, 0.86 and 0.75 for the right caudate nucleus, putamen, pallidum, thalamus and hippocampus respectively. We also find statistically significant improvement of accuracy in FS+LDDMM over FreeSurfer for the caudate nucleus and putamen of Huntington\u27s disease and Tourette\u27s syndrome subjects, and the right hippocampus of Schizophrenia subjects

Reduced Caudate and Nucleus Accumbens Response to Rewards in Unmedicated Subjects with Major Depressive Disorder

Objective: Major depressive disorder is characterized by impaired reward processing, possibly due to dysfunction in the basal ganglia. However, few neuroimaging studies of depression have distinguished between anticipatory and consummatory phases of reward processing. Using functional MRI (fMRI) and a task that dissociates anticipatory and consummatory phases of reward processing, the authors tested the hypothesis that individuals with major depression would show reduced reward-related responses in basal ganglia structures. Method: A monetary incentive delay task was presented to 30 unmedicated individuals with major depressive disorder and 31 healthy comparison subjects during fMRI scanning. Whole-brain analyses focused on neural responses to reward-predicting cues and rewarding outcomes (i.e., monetary gains). Secondary analyses focused on the relationship between anhedonic symptoms and basal ganglia volumes. Results: Relative to comparison subjects, participants with major depression showed significantly weaker responses to gains in the left nucleus accumbens and the caudate bilaterally. Group differences in these regions were specific to rewarding outcomes and did not generalize to neutral or negative outcomes, although relatively reduced responses to monetary penalties in the major depression group emerged in other caudate regions. By contrast, evidence for group differences during reward anticipation was weaker, although participants with major depression showed reduced activation to reward cues in a small sector of the left posterior putamen. In the major depression group, anhedonic symptoms and depression severity were associated with reduced caudate volume bilaterally. Conclusions: These results suggest that basal ganglia dysfunction in major depression may affect the consummatory phase of reward processing. Additionally, morphometric results suggest that anhedonia in major depression is related to caudate volume.Psycholog

Reduced Caudate and Nucleus Accumbens Response to Rewards in Unmedicated Subjects with Major Depressive Disorder

Objective: Major depressive disorder is characterized by impaired reward processing, possibly due to dysfunction in the basal ganglia. However, few neuroimaging studies of depression have distinguished between anticipatory and consummatory phases of reward processing. Using functional MRI (fMRI) and a task that dissociates anticipatory and consummatory phases of reward processing, the authors tested the hypothesis that individuals with major depression would show reduced reward-related responses in basal ganglia structures. Method: A monetary incentive delay task was presented to 30 unmedicated individuals with major depressive disorder and 31 healthy comparison subjects during fMRI scanning. Whole-brain analyses focused on neural responses to reward-predicting cues and rewarding outcomes (i.e., monetary gains). Secondary analyses focused on the relationship between anhedonic symptoms and basal ganglia volumes. Results: Relative to comparison subjects, participants with major depression showed significantly weaker responses to gains in the left nucleus accumbens and the caudate bilaterally. Group differences in these regions were specific to rewarding outcomes and did not generalize to neutral or negative outcomes, although relatively reduced responses to monetary penalties in the major depression group emerged in other caudate regions. By contrast, evidence for group differences during reward anticipation was weaker, although participants with major depression showed reduced activation to reward cues in a small sector of the left posterior putamen. In the major depression group, anhedonic symptoms and depression severity were associated with reduced caudate volume bilaterally. Conclusions: These results suggest that basal ganglia dysfunction in major depression may affect the consummatory phase of reward processing. Additionally, morphometric results suggest that anhedonia in major depression is related to caudate volume.Psycholog

A fully-automatic caudate nucleus segmentation of brain MRI: Application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder

Background Accurate automatic segmentation of the caudate nucleus in magnetic resonance images (MRI) of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations. Method We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure. Results We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis. Conclusion CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD

Baseline MRI Predictors of Conversion from MCI to Probable AD in the ADNI Cohort

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a multi-center study assessing neuroimaging in diagnosis and longitudinal monitoring. Amnestic Mild Cognitive Impairment (MCI) often represents a prodromal form of dementia, conferring a 10-15% annual risk of converting to probable AD. We analyzed baseline 1.5T MRI scans in 693 participants from the ADNI cohort divided into four groups by baseline diagnosis and one year MCI to probable AD conversion status to identify neuroimaging phenotypes associated with MCI and AD and potential predictive markers of imminent conversion. MP-RAGE scans were analyzed using publicly available voxel-based morphometry (VBM) and automated parcellation methods. Measures included global and hippocampal grey matter (GM) density, hippocampal and amygdalar volumes, and cortical thickness values from entorhinal cortex and other temporal and parietal lobe regions. The overall pattern of structural MRI changes in MCI (n=339) and AD (n=148) compared to healthy controls (HC, n=206) was similar to prior findings in smaller samples. MCI-Converters (n=62) demonstrated a very similar pattern of atrophic changes to the AD group up to a year before meeting clinical criteria for AD. Finally, a comparison of effect sizes for contrasts between the MCI-Converters and MCI-Stable (n=277) groups on MRI metrics indicated that degree of neurodegeneration of medial temporal structures was the best antecedent MRI marker of imminent conversion, with decreased hippocampal volume (left > right) being the most robust. Validation of imaging biomarkers is important as they can help enrich clinical trials of disease modifying agents by identifying individuals at highest risk for progression to AD

Investigating microstructural variation in the human hippocampus using non-negative matrix factorization

In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses

A comparison of FreeSurfer-generated data with and without manual intervention

This paper examined whether FreeSurfer - generated data differed between a fully – automated, unedited pipeline and an edited pipeline that included the application of control points to correct errors in white matter segmentation. In a sample of 30 individuals, we compared the summary statistics of surface area, white matter volumes, and cortical thickness derived from edited and unedited datasets for the 34 regions of interest (ROIs) that FreeSurfer (FS) generates. To determine whether applying control points would alter the detection of significant differences between patient and typical groups, effect sizes between edited and unedited conditions in individuals with the genetic disorder, 22q11.2 deletion syndrome (22q11DS) were compared to neurotypical controls. Analyses were conducted with data that were generated from both a 1.5 tesla and a 3 tesla scanner. For 1.5 tesla data, mean area, volume, and thickness measures did not differ significantly between edited and unedited regions, with the exception of rostral anterior cingulate thickness, lateral orbitofrontal white matter, superior parietal white matter, and precentral gyral thickness. Results were similar for surface area and white matter volumes generated from the 3 tesla scanner. For cortical thickness measures however, seven edited ROI measures, primarily in frontal and temporal regions, differed significantly from their unedited counterparts, and three additional ROI measures approached significance. Mean effect sizes for edited ROIs did not differ from most unedited ROIs for either 1.5 or 3 tesla data. Taken together, these results suggest that although the application of control points may increase the validity of intensity normalization and, ultimately, segmentation, it may not affect the final, extracted metrics that FS generates. Potential exceptions to and limitations of these conclusions are discussed

Improved Quantification of Connectivity in Human Brain Mapping

Diffusion magnetic resonance imaging (dMRI) is an advanced MRI methodology that can be used to probe the microstructure of biological tissue. dMRI can provide orientation information by modeling the process of water diffusion in white matter. This thesis presents contributions in three areas of diffusion imaging technology: diffusion reconstruction, quantification, and validation of derived metrics. It presents a novel reconstruction method by combining generalized q-sampling imaging, spherical harmonic basis functions and constrained spherical deconvolution methods to estimate the fiber orientation distribution function (ODF). This method provides improved spatial localization of brain nuclei and fiber tract separation. A novel diffusion anisotropy metric is presented that provides anatomically interpretable measurements of tracts that are robust in crossing areas of the brain. The metric, directional Axonal Volume (dAV) provides an estimate of directional water content of the tract based on the (ODF) and proton density map. dAV is a directionally sensitive metric and can separate anisotropic water content for each fiber population, providing a quantification in milliliters of water. A method is provided to map voxel-based dAV onto tracts that is not confounded by crossing areas and follows the tract morphology. This work introduces a novel textile based hollow fiber anisotropic phantom (TABIP) for validation of reconstruction and quantification methods. This provides a ground truth reference for axonal scale water tubular structures arranged in various anatomical configurations, crossing and mixing patterns. Analysis shows that: 1) the textile tracts are identifiable with scans used in human imaging and produced tracts and voxel metrics in the range of human tissue; 2) the current methods could resolve crossing at 90o and 45o but not 30o; 3) dAV/NODDI model closely matches (r=0.95) the number of fibers whereas conventional metrics poorly match (i.e., FA r=0.32). This work represents a new accurate quantification of axonal water content through diffusion imaging. dAV shows promise as a new anatomically interpretable metric of axonal connectivity that is not confounded by factors such as axon dispersion, crossing and local isotropic water content. This will provide better anatomical mapping of white matter and potentially improve the detection of axonal tract pathology

Apport de l'imagerie par résonance magnétique dans la détermination de l'âge chez le sujet vivant

La détermination de l'âge est une problématique importante en anthropologie médico-légale. Chez le sujet vivant, elle peut être demandée dans certaines démarches juridiques ou administratives. L'objectif de ce travail était d'étudier l'apport de l'Imagerie par Résonance Magnétique (IRM) dans la détermination de l'âge chez l'individu vivant. Dans la première partie, les principales méthodes d'estimation de l'âge osseux sont rappelées. Puis, nous avons étudié des IRM de cheville et de pied à partir d'une classification en trois stades de la fusion métaphyso-épiphysaire de l'extrémité distale du tibia et du calcanéum. Enfin, nous avons développé une méthode automatisée de lecture des images d'IRM de l'extrémité distale du tibia. Cette méthode était basée sur les variations de niveaux de gris au sein de la jonction métaphyso-épiphysaire, après une standardisation des images pour corriger les inhomogénéités d'intensité. Les résultats de notre étude suggèrent que le développement de méthodes automatisées de lecture des images IRM peut représenter une nouvelle thématique de recherche dans le domaine de la détermination de l'âge chez le sujet vivant.Age estimation of living individuals has become an integral part of forensic practice. It is proceeded for people involved in criminal or asylum proceedings. The aim of this work was to study the contribution of Magnetic Resonance Imaging (MRI) in determining age of living subjects. In the first part of our work, the main age estimation methods are exposed. Then, we developed a MRI staging system for epiphyseal fusion of growth plate maturation of the distal tibial epiphysis and the calcaneum, and evaluated its reliability. Lastly, we proposed an automatic method based on the analysis of variations of grey levels within the epiphyseal-metaphyseal junction, after homogenisation of MR scans to correct artifactual intensity variation. Our results suggest that automated classification of MR scans could represent a new area of research in the fied of age estimation in living individuals