Apathy in depression: An arterial spin labeling perfusion MRI study.

INTRODUCTION: Apathy, as defined as a deficit in goal-directed behaviors, is a critical clinical dimension in depression associated with chronic impairment. Little is known about its cerebral perfusion specificities in depression. To explore neurovascular mechanisms underpinning apathy in depression by pseudo-continuous arterial spin labeling (pCASL) magnetic resonance imaging (MRI). METHODS: Perfusion imaging analysis was performed on 90 depressed patients included in a prospective study between November 2014 and February 2017. Imaging data included anatomical 3D T1-weighted and perfusion pCASL sequences. A multiple regression analysis relating the quantified cerebral blood flow (CBF) in different regions of interest defined from the FreeSurfer atlas, to the Apathy Evaluation Scale (AES) total score was conducted. RESULTS: After confound adjustment (demographics, disease and clinical characteristics) and correction for multiple comparisons, we observed a strong negative relationship between the CBF in the left anterior cingulate cortex (ACC) and the AES score (standardized beta = -0.74, corrected p value = 0.0008). CONCLUSION: Our results emphasized the left ACC as a key region involved in apathy severity in a population of depressed participants. Perfusion correlates of apathy in depression evidenced in this study may contribute to characterize different phenotypes of depression.

Structural abnormalities associated with poor outcome of a major depressive episode: The role of thalamus.

An identification of precise biomarkers contributing to poor outcome of a major depressive episode (MDE) has the potential to improve therapeutic strategies by reducing time to symptomatic relief. In a cross-sectional volumetric study with a 6 month clinical follow-up, we performed baseline brain grey matter volume analysis between 2 groups based on illness improvement: 27 MDD patients in the "responder" (R) group (Clinical Global Impression- Improvement (CGI-I) score ≤ 2) and 30 in the "non-responder" (NR) group (CGI-I > 2), using a Voxel Based-Morphometry analysis. NR had significantly smaller Grey Matter (GM) volume in the bilateral thalami, in precentral gyrus, middle temporal gyrus, precuneus and middle cingulum compared to R at baseline. Additionally, they exhibited significant greater GM volume increase in the left anterior lobe of cerebellum and posterior cingulate cortex. The latter result was not significant when participants with bipolar disorder were excluded from the analysis. NR group had higher baseline anxiety scores. Our study has pointed out the role of thalamus in prognosis of MDE. These findings highlight the involvement of emotion regulation in the outcome of MDE. The present study provides a step towards the understanding of neurobiological processes of treatment resistant depression.

Exposure of pregnant women to organophosphate insecticides and child motor inhibition at the age of 10-12 years evaluated by fMRI.

BACKGROUND: Organophosphate pesticides (OP) are widely used for both agricultural and domestic purposes. Epidemiological studies suggest neurotoxicity in children after exposure to organophosphates pesticides (OP) at low levels but possible mechanism is still unclear. OBJECTIVES: We aimed at investigating the effects of prenatal exposure to OPs on inhibitory control of 10-12 year-old-children assessed by a motor inhibition task during functional magnetic resonance imaging (fMRI). METHODS: Ninety-five children from the PELAGIE cohort (Brittany-France, from 2002) underwent a fMRI examination during which inhibition was assessed by a Go/No-Go task. Task performance was assessed by average response latency, commission rate and composite performance score (PS). Whole brain activation was estimated by modeling the hemodynamic response related to inhibition demand and successful inhibition. OP exposure was assessed by measuring six dialkylphosphate (DAP) metabolites in the urine of women in early pregnancy (<19 WG). Concentrations were summed to obtain overall levels of diethylphosphate (DE), dimethylphosphate (DM) and total non-specific metabolites (DAP), standardized to homogenize sampling conditions and categorized into levels of exposure: low (reference), moderate or high. Regression models were adjusted for potential cofounders considered by restriction and statistical criteria. RESULTS: Moderate levels of DAP were associated with a decreased commission rate (ß = -6.65%, p = 0.04), indicating improved performance. Increasing levels of DM and DE were associated with decreased brain activity in the left inferior and bilateral superior frontal regions during successful inhibition. We did not observe any differential activation related to inhibitory demands. DISCUSSION: These results suggest that prenatal OPs may be associated with altered pattern of brain activity in regions related to inhibition among children and need to be confirmed by additional studies.

[Innovations in functional MR imaging of the brain: arterial spin labeling and diffusion]. / Innovations en IRM fonctionnelle cérébrale : marquage de spins artériels et diffusion.

The standard technique for brain activation functional MRI (fMRI) is the BOLD sequence. Two new techniques have emerged: arterial spin labeling (ASL) MRI and diffusion MRI. Both have the theoretical advantage of more accurately directly demonstrating neuronal activation compared to BOLD imaging, resulting in improved spatial and temporal resolution. ASL is a perfusion sequence using labeled arterial protons as an endogenous perfusion agent. In spite of methodological difficulties, quantitative CBF measurements are possible. ASL is less susceptible to venous contamination than BOLD and more reproducible. Diffusion MRI evaluates neuronal activation at the cellular level with the prospect of excellent spatial resolution. The main limitations for both techniques are the technical difficulties in the acquisition and the low SNR. AS such, ASL is not widely used clinically and diffusion remains in the field of research. However, the increasing availability of 3T MR systems coupled with multi-channel surface coils and improved postprocessing techniques should improve the detection of the brain activation signal. It is thus possible that these techniques could become clinically available either in complement to or as a replacement for BOLD imaging.

An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images.

A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. Denoising is a crucial step to increase image quality and to improve the performance of all the tasks needed for quantitative imaging analysis. The method proposed in this paper is based on a 3-D optimized blockwise version of the nonlocal (NL)-means filter (Buades, et al., 2005). The NL-means filter uses the redundancy of information in the image under study to remove the noise. The performance of the NL-means filter has been already demonstrated for 2-D images, but reducing the computational burden is a critical aspect to extend the method to 3-D images. To overcome this problem, we propose improvements to reduce the computational complexity. These different improvements allow to drastically divide the computational time while preserving the performances of the NL-means filter. A fully automated and optimized version of the NL-means filter is then presented. Our contributions to the NL-means filter are: 1) an automatic tuning of the smoothing parameter; 2) a selection of the most relevant voxels; 3) a blockwise implementation; and 4) a parallelized computation. Quantitative validation was carried out on synthetic datasets generated with BrainWeb (Collins, et al., 1998). The results show that our optimized NL-means filter outperforms the classical implementation of the NL-means filter, as well as two other classical denoising methods [anisotropic diffusion (Perona and Malik, 1990)] and total variation minimization process (Rudin, et al., 1992) in terms of accuracy (measured by the peak signal-to-noise ratio) with low computation time. Finally, qualitative results on real data are presented .

MRI-based automated computer classification of probable AD versus normal controls.

Automated computer classification (ACC) techniques are needed to facilitate physician's diagnosis of complex diseases in individual patients. We provide an example of ACC using computational techniques within the context of cross-sectional analysis of magnetic resonance images (MRI) in neurodegenerative diseases, namely Alzheimer's dementia (AD). In this paper, the accuracy of our ACC methodology is assessed when presented with real life, imperfect data, i.e., cohorts of MRI with varying acquisition parameters and imaging quality. The comparative methodology uses the Jacobian determinants derived from dense deformation fields and scaled grey-level intensity from a selected volume of interest centered on the medial temporal lobe. The ACC performance is assessed in a series of leave-one-out experiments aimed at separating 75 probable AD and 75 age-matched normal controls. The resulting accuracy is 92% using a support vector machine classifier based on least squares optimization. Finally, it is shown in the Appendix that determinants and scaled grey-level intensity are appreciably more robust to varying parameters in validation studies using simulated data, when compared to raw intensities or grey/white matter volumes. The ability of cross-sectional MRI at detecting probable AD with high accuracy could have profound implications in the management of suspected AD candidates.

Federating distributed and heterogeneous information sources in neuroimaging: the NeuroBase Project.

The NeuroBase project aims at studying the requirements for federating, through the Internet, information sources in neuroimaging. These sources are distributed in different experimental sites, hospitals or research centers in cognitive neurosciences, and contain heterogeneous data and image processing programs. More precisely, this project consists in creating of a shared ontology, suitable for supporting various neuroimaging applications, and a computer architecture for accessing and sharing relevant distributed information. We briefly describe the semantic model and report in more details the architecture we chose, based on a media-tor/wrapper approach. To give a flavor of the future deployment of our architecture, we de-scribe a demonstrator that implements the comparison of distributed image processing tools applied to distributed neuroimaging data.

STREM: a robust multidimensional parametric method to segment MS lesions in MRI.

We propose to segment Multiple Sclerosis (MS) lesions overtime in multidimensional Magnetic Resonance (MR) sequences. We use a robust algorithm that allows the segmentation of the abnormalities using the whole time series simultaneously and we propose an original rejection scheme for outliers. We validate our method using the BrainWeb simulator. To conclude, promising preliminary results on longitudinal multi-sequences of clinical data are shown.

Retrospective evaluation of intersubject brain registration.

Although numerous methods to register brains of different individuals have been proposed, no work has been done, as far as we know, to evaluate and objectively compare the performances of different nonrigid (or elastic) registration methods on the same database of subjects. In this paper, we propose an evaluation framework, based on global and local measures of the relevance of the registration. We have chosen to focus more particularly on the matching of cortical areas, since intersubject registration methods are dedicated to anatomical and functional normalization, and also because other groups have shown the relevance of such registration methods for deep brain structures. Experiments were conducted using 6 methods on a database of 18 subjects. The global measures used show that the quality of the registration is directly related to the transformation's degrees of freedom. More surprisingly, local measures based on the matching of cortical sulci did not show significant differences between rigid and non rigid methods.

Interindividual functional mapping: a nonlinear local approach.

Within the scope of three-dimensional brain imaging, we propose an interindividual fusion scheme to register functional activations according to anatomical cortical structures, the sulci. This paper is based on the assumption that an important part of functional intersubject variability is encoded in anatomical variability. The aim of this paper is therefore to propose a generic framework to register functional activations according to the relevant anatomical landmarks. Compared to "classical" interindividual fusion schemes, this approach is local. It relies on a statistical sulci shape model accounting for the interindividual variability of a population of subjects and providing deformation modes relative to a reference shape (a mean sulcus). The deformation field obtained between a given sulcus and the reference sulcus is extended to a neighborhood of the given sulcus by using the thin-plate spline interpolation. It is then applied to functional activations located in the vicinity of this sulcus. This approach is compared with rigid and nonrigid registration methods. In this paper, we present results on MEG somatosensory data acquired on 18 subjects. We show that the nonlinear local fusion scheme significantly reduces the observed functional variability.

Segmentation of brain 3D MR images using level sets and dense registration.

This paper presents a strategy for the segmentation of brain from volumetric MR images which integrates 3D segmentation and 3D registration processes. The segmentation process is based on the level set formalism. A closed 3D surface propagates towards the desired boundaries through the iterative evolution of a 4D implicit function. In this work, the propagation relies on a robust evolution model including adaptive parameters. These depend on the input data and on statistical distribution models. The main contribution of this paper is the use of an automatic registration method to initialize the surface, as an alternative solution to manual initialization. The registration is achieved through a robust multiresolution and multigrid minimization scheme. This coupling significantly improves the quality of the method, since the segmentation is faster, more reliable and fully automatic. Quantitative and qualitative results on both synthetic and real volumetric brain MR images are presented and discussed.

Hierarchical estimation of a dense deformation field for 3-D robust registration.

A new method for medical image registration is formulated as a minimization problem involving robust estimators. We propose an efficient hierarchical optimization framework which is both multiresolution and multigrid. An anatomical segmentation of the cortex is introduced in the adaptive partitioning of the volume on which the multigrid minimization is based. This allows to limit the estimation to the areas of interest, to accelerate the algorithm, and to refine the estimation in specified areas. At each stage of the hierarchical estimation, we refine current estimate by seeking a piecewise affine model for the incremental deformation field. The performance of this method is numerically evaluated on simulated data and its benefits and robustness are shown on a database of 18 magnetic resonance imaging scans of the head.

Statistical sulcal shape comparisons: application to the detection of genetic encoding of the central sulcus shape.

Principal Component Analysis allows a quantitative description of shape variability with a restricted number of parameters (or modes) which can be used to quantify the difference between two shapes through the computation of a modal distance. A statistical test can then be applied to this set of measurements in order to detect a statistically significant difference between two groups. We have applied this methodology to highlight evidence of genetic encoding of the shape of neuroanatomical structures. To investigate genetic constraint, we studied if shapes were more similar within 10 pairs of monozygotic twins than within interpairs and compared the results with those obtained from 10 pairs of dizygotic twins. The statistical analysis was performed using a Mantel permutation test. We show, using simulations, that this statistical test applied on modal distances can detect a possible genetic encoding. When applied to real data, this study highlighted genetic constraints on the shape of the central sulcus. We found from 10 pairs of monozygotic twins that the intrapair modal distance of the central sulcus was significantly smaller than the interpair modal distance, for both the left central sulcus (Z = -2.66; P < 0.005) and the right central sulcus (Z = -2.26; P < 0.05). Genetic constraints on the definition of the central sulcus shape were confirmed by applying the same experiment to 10 pairs of normal young individuals (Z = -1.39; Z = -0.63, i.e., values not significant at the P < 0.05 level) and 10 pairs of dizygotic twins (Z = 0.47; Z = 0.03, i.e., values not significant at the P < 0.05 level).

Automated extraction and variability analysis of sulcal neuroanatomy.

Systematic mapping of the variability in cortical sulcal anatomy is an area of increasing interest which presents numerous methodological challenges. To address these issues, we have implemented sulcal extraction and assisted labeling (SEAL) to automatically extract the two-dimensional (2-D) surface ribbons that represent the median axis of cerebral sulci and to neuroanatomically label these entities. To encode the extracted three-dimensional (3-D) cortical sulcal schematic topography (CSST) we define a relational graph structure composed of two main features: vertices (representing sulci) and arcs (representing the relationships between sulci). Vertices contain a parametric representation of the surface ribbon buried within the sulcus. Points on this surface are expressed in stereotaxic coordinates (i.e., with respect to a standardized brain coordinate system). For each of these vertices, we store length, depth, and orientation as well as anatomical attributes (e.g., hemisphere, lobe, sulcus type, etc.). Each arc stores the 3-D location of the junction between sulci as well as a list of its connecting sulci. Sulcal labeling is performed semiautomatically by selecting a sulcal entity in the CSST and selecting from a menu of candidate sulcus names. In order to help the user in the labeling task, the menu is restricted to the most likely candidates by using priors for the expected sulcal spatial distribution. These priors, i.e., sulcal probabilistic maps, were created from the spatial distribution of 34 sulci traced manually on 36 different subjects. Given these spatial probability maps, the user is provided with the likelihood that the selected entity belongs to a particular sulcus. The cortical structure representation obtained by SEAL is suitable to extract statistical information about both the spatial and the structural composition of the cerebral cortical topography. This methodology allows for the iterative construction of a successively more complete statistical models of the cerebral topography containing spatial distributions of the most important structures, their morphometrics, and their structural components.

Computerized brain atlases as decision support systems: a methodological approach.

This paper deals with the development of computerized brain atlases addressing both research and clinical needs. The authors analyze in detail the potential of these systems and discuss the capabilities and limitations of the digital atlases currently being developed around the world. The authors propose to reconsider the concept of a brain atlas, regarding both its content, and the way it has to be used and managed in order to set up more effective cooperation between the user and the system. Particular emphasis is placed on extensibility and reuse issues. which are critical in this rapidly evolving field. These orientations result from both the authors' experience and the analysis of current trends in the field of neuroimaging. The general methodology is illustrated with examples related to computer aided surgical planning.

Comparison and evaluation of retrospective intermodality brain image registration techniques.

PURPOSE: The primary objective of this study is to perform a blinded evaluation of a group of retrospective image registration techniques using as a gold standard a prospective, marker-based registration method. To ensure blindedness, all retrospective registrations were performed by participants who had no knowledge of the gold standard results until after their results had been submitted. A secondary goal of the project is to evaluate the importance of correcting geometrical distortion in MR images by comparing the retrospective registration error in the rectified images, i.e., those that have had the distortion correction applied, with that of the same images before rectification. METHOD: Image volumes of three modalities (CT, MR, and PET) were obtained from patients undergoing neurosurgery at Vanderbilt University Medical Center on whom bone-implanted fiducial markers were mounted. These volumes had all traces of the markers removed and were provided via the Internet to project collaborators outside Vanderbilt, who then performed retrospective registrations on the volumes, calculating transformations from CT to MR and/ or from PET to MR. These investigators communicated their transformations again via the Internet to Vanderbilt, where the accuracy of each registration was evaluated. In this evaluation, the accuracy is measured at multiple volumes of interest (VOIs), i.e., areas in the brain that would commonly be areas of neurological interest. A VOI is defined in the MR image and its centroid c is determined. Then, the prospective registration is used to obtain the corresponding point c' in CT or PET. To this point, the retrospective registration is then applied, producing c" in MR. Statistics are gathered on the target registration error (TRE), which is the distance between the original point c and its corresponding point c". RESULTS: This article presents statistics on the TRE calculated for each registration technique in this study and provides a brief description of each technique and an estimate of both preparation and execution time needed to perform the registration. CONCLUSION: Our results indicate that retrospective techniques have the potential to produce satisfactory results much of the time, but that visual inspection is necessary to guard against large errors.

Data fusion in medical imaging: merging multimodal and multipatient images, identification of structures and 3D display aspects.

Data fusion in medical imaging can be seen into two ways (i) multisensors fusion of anatomical and functional information and (ii) interpatient data fusion by means of warping models. These two aspects set the methodological framework necessary to perform anatomical modelling especially when concerning the modelling of brain structures. The major relevance of the work presented here concerns the interpretation of multimodal 3D neuro-anatomical data bases. Three types of data fusion problems are considered in this paper. The first one concerns the problem of data combination which includes multimodal registration (multisensor fusion applied to CT, MRI, DSA, PET, SPECT, or MEG). In particular, the problem of warping patient data to an anatomical atlas is reviewed and a solution is proposed. The second problem of data fusion addressed in this paper is the identification of anatomical structures by means of image analysis methods. Two techniques have been developed. The first one deals with the analysis of image geometrical features to end up with the determination of a fuzzy mask to label the structure of interest. The second technique consists of labelling major cerebral structures by means of statistical image features associated with relaxation techniques. Finally, the paper presents a review of up to date 3D display techniques with a special emphasis on volume rendering and 3D display of combined data.

[Fusion of data. Multimodality in neurological imaging]. / Fusion de données multi-modalité en neuro-imagerie.

The development of tomographic imaging methods, which provide anatomical and functional information in a digital form, has transformed the approach to the central nervous system. Patient management is now frequently based on fusion of data from the same or different modalities, which potentiates the performance of each technique. These fusions were initially performed manually on film supports, but are now increasingly performed by consultation stations which process digital data bases. In view of the increasing demands for data fusion, it has become necessary to develop, in parallel with these new techniques, image transmission networks and visualisation stations on which reconstructions and fusions are performed. Improvements in software should facilitate acquisition techniques which should increasingly resemble standard techniques. The logistic applied must also be as simple as possible in order to be widely implanted and easy to use.

Interactive display and analysis of 3-D medical images.

The ANALYZE software system, which permits detailed investigation and evaluation of 3-D biomedical images, is discussed. ANALYZE can be used with 3-D imaging modalities based on X-ray computed tomography, radionuclide emission tomography, ultrasound tomography, and magnetic resonance imaging. The package is unique in its synergistic integration of fully interactive modules for direct display, manipulation, and measurement of multidimensional image data. One of the most versatile and powerful capabilities in ANALYZE is image volume rendering for 3-D display. An important advantage of this technique is that it can be used to display 3-D images directly from the original data set and to provide on-the-fly combinations of selected image transformations, such as surface segmentation, cutting planes, transparency, and/or volume set operations (union, intersection, difference, etc.). The module has been optimized to be fast (interactive) without compromising image quality. The software is written entirely in C and runs on standard UNIX workstations.