Julien Milles

Whole brain resting-state analysis reveals decreased functional connectivity in major depression.

Recently, both increases and decreases in resting-state functional connectivity have been found in major depression. However, these studies only assessed functional connectivity within a specific network or between a few regions of interest, while comorbidity and use of medication was not always controlled for. Therefore, the aim of the current study was to investigate whole-brain functional connectivity, unbiased by a priori definition of regions or networks of interest, in medication-free depressive patients without comorbidity. We analyzed resting-state fMRI data of 19 medication-free patients with a recent diagnosis of major depression (within 6 months before inclusion) and no comorbidity, and 19 age- and gender-matched controls. Independent component analysis was employed on the concatenated data sets of all participants. Thirteen functionally relevant networks were identified, describing the entire study sample. Next, individual representations of the networks were created using a dual regression method. Statistical inference was subsequently done on these spatial maps using voxel-wise permutation tests. Abnormal functional connectivity was found within three resting-state networks in depression: (1) decreased bilateral amygdala and left anterior insula connectivity in an affective network, (2) reduced connectivity of the left frontal pole in a network associated with attention and working memory, and (3) decreased bilateral lingual gyrus connectivity within ventromedial visual regions. None of these effects were associated with symptom severity or gray matter density. We found abnormal resting-state functional connectivity not previously associated with major depression, which might relate to abnormal affect regulation and mild cognitive deficits, both associated with the symptomatology of the disorder.

Intensity non-uniformity correction in MRI: Existing methods and their validation

Magnetic resonance imaging is a popular and powerful non-invasive imaging technique. Automated analysis has become mandatory to efficiently cope with the large amount of data generated using this modality. However, several artifacts, such as intensity non-uniformity, can degrade the quality of acquired data. Intensity non-uniformity consists in anatomically irrelevant intensity variation throughout data. It can be induced by the choice of the radio-frequency coil, the acquisition pulse sequence and by the nature and geometry of the sample itself. Numerous methods have been proposed to correct this artifact. In this paper, we propose an overview of existing methods. We first sort them according to their location in the acquisition/processing pipeline. Sorting is then refined based on the assumptions those methods rely on. Next, we present the validation protocols used to evaluate these different correction schemes both from a qualitative and a quantitative point of view. Finally, availability and usability of the presented methods is discussed.

Hierarchical functional modularity in the resting‐state human brain

Functional magnetic resonance imaging (fMRI) studies have shown that anatomically distinct brain regions are functionally connected during the resting state. Basic topological properties in the brain functional connectivity (BFC) map have highlighted the BFCs small‐world topology. Modularity, a more advanced topological property, has been hypothesized to be evolutionary advantageous, contributing to adaptive aspects of anatomical and functional brain connectivity. However, current definitions of modularity for complex networks focus on nonoverlapping clusters, and are seriously limited by disregarding inclusive relationships. Therefore, BFCs modularity has been mainly qualitatively investigated. Here, we introduce a new definition of modularity, based on a recently improved clustering measurement, which overcomes limitations of previous definitions, and apply it to the study of BFC in resting state fMRI of 53 healthy subjects. Results show hierarchical functional modularity in the brain. Hum Brain Mapp, 2009.

Mapping Displacement and Deformation of the Heart With Local Sine-Wave Modeling

The new SinMod method extracts motion from magnetic resonance imaging (MRI)-tagged (MRIT) image sequences. Image intensity in the environment of each pixel is modeled as a moving sine wavefront. Displacement is estimated at subpixel accuracy. Performance is compared with the harmonic-phase analysis (HARP) method, which is currently the most common method used to detect motion in MRIT images. SinMod can handle line tags, as well as speckle patterns. In artificial images (tag distance six pixels), SinMod detects displacements accurately (error < pixels). Effects of noise are suppressed effectively. Sharp transitions in motion at the boundary of an object are smeared out over a width of 0.6 tag distance. For MRIT images of the heart, SinMod appears less sensitive to artifacts, especially later in the cardiac cycle when image quality deteriorates. For each pixel, the quality of the sine-wave model in describing local image intensity is quantified objectively. If local quality is low, artifacts are avoided by averaging motion over a larger environment. Summarizing, SinMod is just as fast as HARP, but it performs better with respect to accuracy of displacement detection, noise reduction, and avoidance of artifacts.

Fully Automated Motion Correction in First-Pass Myocardial Perfusion MR Image Sequences

This paper presents a novel method for registration of cardiac perfusion magnetic resonance imaging (MRI). The presented method is capable of automatically registering perfusion data, using independent component analysis (ICA) to extract physiologically relevant features together with their time-intensity behavior. A time-varying reference image mimicking intensity changes in the data of interest is computed based on the results of that ICA. This reference image is used in a two-pass registration framework. Qualitative and quantitative validation of the method is carried out using 46 clinical quality, short-axis, perfusion MR datasets comprising 100 images each. Despite varying image quality and motion patterns in the evaluation set, validation of the method showed a reduction of the average right ventricle (LV) motion from 1.26plusmn0.87 to 0.64plusmn0.46 pixels. Time-intensity curves are also improved after registration with an average error reduced from 2.65plusmn7.89% to 0.87plusmn3.88% between registered data and manual gold standard. Comparison of clinically relevant parameters computed using registered data and the manual gold standard show a good agreement. Additional tests with a simulated free-breathing protocol showed robustness against considerable deviations from a standard breathing protocol. We conclude that this fully automatic ICA-based method shows an accuracy, a robustness and a computation speed adequate for use in a clinical environment.

Atlas-based whole-body segmentation of mice from low-contrast Micro-CT data

This paper presents a fully automated method for atlas-based whole-body segmentation in non-contrast-enhanced Micro-CT data of mice. The position and posture of mice in such studies may vary to a large extent, complicating data comparison in cross-sectional and follow-up studies. Moreover, Micro-CT typically yields only poor soft-tissue contrast for abdominal organs. To overcome these challenges, we propose a method that divides the problem into an atlas constrained registration based on high-contrast organs in Micro-CT (skeleton, lungs and skin), and a soft tissue approximation step for low-contrast organs. We first present a modification of the MOBY mouse atlas (Segars et al., 2004) by partitioning the skeleton into individual bones, by adding anatomically realistic joint types and by defining a hierarchical atlas tree description. The individual bones as well as the lungs of this adapted MOBY atlas are then registered one by one traversing the model tree hierarchy. To this end, we employ the Iterative Closest Point method and constrain the Degrees of Freedom of the local registration, dependent on the joint type and motion range. This atlas-based strategy renders the method highly robust to exceptionally large postural differences among scans and to moderate pathological bone deformations. The skin of the torso is registered by employing a novel method for matching distributions of geodesic distances locally, constrained by the registered skeleton. Because of the absence of image contrast between abdominal organs, they are interpolated from the atlas to the subject domain using Thin-Plate-Spline approximation, defined by correspondences on the already established registration of high-contrast structures (bones, lungs and skin). We extensively evaluate the proposed registration method, using 26 non-contrast-enhanced Micro-CT datasets of mice, and the skin registration and organ interpolation, using contrast-enhanced Micro-CT datasets of 15 mice. The posture and shape varied significantly among the animals and the data was acquired in vivo. After registration, the mean Euclidean distance was less than two voxel dimensions for the skeleton and the lungs respectively and less than one voxel dimension for the skin. Dice coefficients of volume overlap between manually segmented and interpolated skeleton and organs vary between 0.47+/-0.08 for the kidneys and 0.73+/-0.04 for the brain. These experiments demonstrate the methods effectiveness for overcoming exceptionally large variations in posture, yielding acceptable approximation accuracy even in the absence of soft-tissue contrast in in vivo Micro-CT data without requiring user initialization.

Automated segmentation of myocardial scar in late enhancement MRI using combined intensity and spatial information

Accurate assessment of the size and distribution of a myocardial infarction (MI) from late gadolinium enhancement (LGE) MRI is of significant prognostic value for postinfarction patients. In this paper, an automatic MI identification method combining both intensity and spatial information is presented in a clear framework of (i) initialization, (ii) false acceptance removal, and (iii) false rejection removal. The method was validated on LGE MR images of 20 chronic postinfarction patients, using manually traced MI contours from two independent observers as reference. Good agreement was observed between automatic and manual MI identification. Validation results showed that the average Dice indices, which describe the percentage of overlap between two regions, were 0.83 ± 0.07 and 0.79 ± 0.08 between the automatic identification and the manual tracing from observer 1 and observer 2, and the errors in estimated infarct percentage were 0.0 ± 1.9% and 3.8 ± 4.7% compared with observer 1 and observer 2. The difference between the automatic method and manual tracing is in the order of interobserver variation. In conclusion, the developed automatic method is accurate and robust in MI delineation, providing an objective tool for quantitative assessment of MI in LGE MR imaging. Magn Reson Med, 2010.

Morphological Hippocampal Markers for Automated Detection of Alzheimer's Disease and Mild Cognitive Impairment Converters in Magnetic Resonance Images

In this study, we investigated the use of hippocampal shape-based markers for automatic detection of Alzheimers disease (AD) and mild cognitive impairment converters (MCI-c). Three-dimensional T1-weighted magnetic resonance images of 50 AD subjects, 50 age-matched controls, 15 MCI-c, and 15 MCI-non-converters (MCI-nc) were taken. Manual delineations of both hippocampi were obtained from normalized images. Fully automatic shape modeling was used to generate comparable meshes for both structures. Repeated permutation tests, run over a randomly sub-sampled training set (25 controls and 25 ADs), highlighted shape-based markers, mostly located in the CA1 sector, which consistently discriminated ADs and controls. Support vector machines (SVMs) were trained, using markers from either one or both hippocampi, to automatically classify control and AD subjects. Leave-1-out cross-validations over the remaining 25 ADs and 25 controls resulted in an optimal accuracy of 90% (sensitivity 92%), for markers in the left hippocampus. The same morphological markers were used to train SVMs for MCI-c versus MCI-nc classification: markers in the right hippocampus reached an accuracy (and sensitivity) of 80%. Due to the pattern recognition framework, our results statistically represent the expected performances of clinical set-ups, and compare favorably to analyses based on hippocampal volumes.

Shape analysis of subcortical nuclei in Huntington's disease, global versus local atrophy — Results from the TRACK-HD study

Huntingtons disease (HD) is characterized by brain atrophy. Localized atrophy of a specific structure could potentially be a more sensitive biomarker reflecting neuropathologic changes rather than global volume variation. We examined 90 TRACK-HD participants of which 30 were premanifest HD, 30 were manifest HD and 30 were controls. Using FMRIBs Integrated Registration and Segmentation Tool, segmentations were obtained for the pallidum, caudate nucleus, putamen, thalamus, accumbens nucleus, amygdala, and hippocampus and overall volumes were calculated. A point distribution model of each structure was obtained using Growing and Adaptive Meshes. Permutation testing between groups was performed to detect local displacement in shape between groups. In premanifest HD overall volume loss occurred in the putamen, accumbens and caudate nucleus. Overall volume reductions in manifest HD were found in all subcortical structures, except the amygdala, as compared to controls. In premanifest HD shape analysis showed small areas of displacement in the putamen, pallidum, accumbens and caudate nucleus. When the premanifest group was split into two groups according to predicted disease onset, the premanifest HD group close to expected disease onset showed more pronounced displacements in caudate nucleus and putamen compared to premanifest HD far from disease onset or the total premanifest group. Analysis of shape in manifest HD showed widespread shape differences, most prominently in the caudal part of the accumbens nucleus, body of the caudate nucleus, putamen and dorsal part of the pallidum. We conclude that shape analysis provides new insights in localized intrastructural atrophy patterns in HD, but can also potentially serve as specific target areas for disease tracking.

Shape Abnormalities of the Striatum in Alzheimer's Disease

Postmortem studies show pathological changes in the striatum in Alzheimers disease (AD). Here, we examine the surface of the striatum in AD and assess whether changes of the surface are associated with impaired cognitive functioning. The shape of the striatum (n. accumbens, caudate nucleus, and putamen) was compared between 35 AD patients and 35 individuals without cognitive impairment. The striatum was automatically segmented from 3D T1 magnetic resonance images and automatic shape modeling tools (Growing Adaptive Meshes) were applied for morphometrical analysis. Repeated permutation tests were used to identify locations of consistent shape deformities of the striatal surface in AD. Linear regression models, corrected for age, gender, educational level, head size, and total brain parenchymal volume were used to assess the relation between cognitive performance and local surface deformities. In AD patients, differences of shape were observed on the medial head of the caudate nucleus and on the ventral lateral putamen, but not on the accumbens. The head of the caudate nucleus and ventral lateral putamen are characterized by extensive connections with the orbitofrontal and medial temporal cortices. Severity of cognitive impairment was associated with the degree of deformity of the surfaces of the accumbens, rostral medial caudate nucleus, and ventral lateral putamen. These findings provide evidence for the hypothesis that in AD primarily associative and limbic cerebral networks are affected.