K-Surfer: a KNIME extension for the management and analysis of Human brain MRI FreeSurfer/FSL data

Human brain imaging techniques, such as Magnetic Resonance Imaging (MRI) or Diffusion Tensor Imaging (DTI), have been established as scientific and diagnostic tools and their adoption is growing in popularity. Statistical methods, machine learning and data mining algorithms have successfully been adopted to extract predictive and descriptive models from neuroimage data. However, the knowledge discovery process typically requires also the adoption of pre-processing, post-processing and visualisation techniques in complex data workflows. Currently, a main problem for the integrated preprocessing and mining of MRI data is the lack of comprehensive platforms able to avoid the manual invocation of preprocessing and mining tools, that yields to an error-prone and inefficient process. In this work we present K-Surfer, a novel plug-in of the Konstanz Information Miner (KNIME) workbench, that automatizes the preprocessing of brain images and leverages the mining capabilities of KNIME in an integrated way. K-Surfer supports the importing, filtering, merging and pre-processing of neuroimage data from FreeSurfer, a tool for human brain MRI feature extraction and interpretation. K-Surfer automatizes the steps for importing FreeSurfer data, reducing time costs, eliminating human errors and enabling the design of complex analytics workflow for neuroimage data by leveraging the rich functionalities available in the KNIME workbench.

Cerebral Cortex Involvement In Machado-joseph Disease.

Machado-Joseph disease (MJD/SCA3) is the most frequent spinocerebellar ataxia, characterized by brainstem, basal ganglia and cerebellar damage. Few magnetic resonance imaging based studies have investigated damage in the cerebral cortex. The objective was to determine whether patients with MJD/SCA3 have cerebral cortex atrophy, to identify regions more susceptible to damage and to look for the clinical and neuropsychological correlates of such lesions. Forty-nine patients with MJD/SCA3 (mean age 47.7 ± 13.0 years, 27 men) and 49 matched healthy controls were enrolled. All subjects underwent magnetic resonance imaging scans in a 3 T device, and three-dimensional T1 images were used for volumetric analyses. Measurement of cortical thickness and volume was performed using the FreeSurfer software. Groups were compared using ancova with age, gender and estimated intracranial volume as covariates, and a general linear model was used to assess correlations between atrophy and clinical variables. Mean CAG expansion, Scale for Assessment and Rating of Ataxia (SARA) score and age at onset were 72.1 ± 4.2, 14.7 ± 7.3 and 37.5 ± 12.5 years, respectively. The main findings were (i) bilateral paracentral cortex atrophy, as well as the caudal middle frontal gyrus, superior and transverse temporal gyri, and lateral occipital cortex in the left hemisphere and supramarginal gyrus in the right hemisphere; (ii) volumetric reduction of basal ganglia and hippocampi; (iii) a significant correlation between SARA and brainstem and precentral gyrus atrophy. Furthermore, some of the affected cortical regions showed significant correlations with neuropsychological data. Patients with MJD/SCA3 have widespread cortical and subcortical atrophy. These structural findings correlate with clinical manifestations of the disease, which support the concept that cognitive/motor impairment and cerebral damage are related in disease.

Cortical Thickness Reduction of Normal Appearing Cortex in Patients with Polymicrogyria

OBJECTIVE To examine cortical thickness and volumetric changes in the cortex of patients with polymicrogyria, using an automated image analysis algorithm. METHODS Cortical thickness of patients with polymicrogyria was measured using magnetic resonance imaging (MRI) cortical surface-based analysis and compared with age-and sex-matched healthy subjects. We studied 3 patients with disorder of cortical development (DCD), classified as polymicrogyria, and 15 controls. Two experienced neuroradiologists performed a conventional visual assessment of the MRIs. The same data were analyzed using an automated algorithm for tissue segmentation and classification. Group and individual average maps of cortical thickness differences were produced by cortical surface-based statistical analysis. RESULTS Patients with polymicrogyria showed increased thickness of the cortex in the same areas identified as abnormal by radiologists. We also identified a reduction in the volume and thickness of cortex within additional areas of apparently normal cortex relative to controls. CONCLUSIONS Our findings indicate that there may be regions of reduced cortical thickness, which appear normal from radiological analysis, in the cortex of patients with polymicrogyria. This finding suggests that alterations in neuronal migration may have an impact in the cortical formation of the cortical areas that are visually normal. These areas are associated or occur concurrently with polymicrogyria.

Use of SVM Methods with Surface-Based Cortical and Volumetric Subcortical Measurements to Detect Alzheimer`s Disease

Here, we examine morphological changes in cortical thickness of patients with Alzheimer`s disease (AD) using image analysis algorithms for brain structure segmentation and study automatic classification of AD patients using cortical and volumetric data. Cortical thickness of AD patients (n = 14) was measured using MRI cortical surface-based analysis and compared with healthy subjects (n = 20). Data was analyzed using an automated algorithm for tissue segmentation and classification. A Support Vector Machine (SVM) was applied over the volumetric measurements of subcortical and cortical structures to separate AD patients from controls. The group analysis showed cortical thickness reduction in the superior temporal lobe, parahippocampal gyrus, and enthorhinal cortex in both hemispheres. We also found cortical thinning in the isthmus of cingulate gyrus and middle temporal gyrus at the right hemisphere, as well as a reduction of the cortical mantle in areas previously shown to be associated with AD. We also confirmed that automatic classification algorithms (SVM) could be helpful to distinguish AD patients from healthy controls. Moreover, the same areas implicated in the pathogenesis of AD were the main parameters driving the classification algorithm. While the patient sample used in this study was relatively small, we expect that using a database of regional volumes derived from MRI scans of a large number of subjects will increase the SVM power of AD patient identification.

Automatização da medição do volume do hipocampo em doentes de Alzheimer

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do Grau de Mestre em Engenharia Biomédica.

Estudo do volume cerebeloso de doentes com síndrome de Down com diferentes graus de demência

O principal objectivo deste trabalho é determinar as diferenças existentes entre os volumes cerebelosos de pacientes com síndrome de Down e de uma população controlo. Adicionalmente, pretende-se verificar se os resultados obtidos a partir de uma segmentação automática coincidem com os resultados obtidos da segmentação manual. A população com síndrome de Down é composta por 7 sujeitos, 3 do sexo masculino e 4 do sexo feminino, e idades compreendidas entre os 25 e os 36 anos. A população controlo é constituída por 7 sujeitos, 6 do sexo masculino e 1 do sexo feminino, com idades compreendidas entre os 22 e os 46 anos. Os volumes do cerebelo e do volume total intracraniano foram calculados a partir de imagens de ressonância magnética, de forma manual e automática. Os softwares MRIcron e Freesurfer/FSL foram utilizados para a segmentação manual e automática dos volumes, respectivamente. Após a obtenção dos dados foi realizada uma análise correlativa de forma a identificar a semelhança existente entre os dados obtidos de forna manual e automática, e também, verificar a relação existente entre o volume cerebeloso e a idade dos sujeitos. Com o objectivo de avaliar a concordância entre resultados, foi ainda aplicado o método de Bland-Altman, de forma a visualizar graficamente a dispersão existente entre os valores obtidos. Os resultados obtidos neste estudo permitem afirmar que existe uma maior correlação entre os volumes obtidos do Freesurfer, e os volumes calculados manualmente. Os resultados ainda mostram que os pacientes com síndrome de Down têm volumes cerebelosos reduzidos em comparação com os resultados da população controlo. Da análise da relação do volume cerebeloso com a idade não foi possível obter resultados satisfatórios.

Central and Cortical Gray Mater Segmentation of Magnetic Resonance Images of the Fetal Brain

Motivation. The study of human brain development in itsearly stage is today possible thanks to in vivo fetalmagnetic resonance imaging (MRI) techniques. Aquantitative analysis of fetal cortical surfacerepresents a new approach which can be used as a markerof the cerebral maturation (as gyration) and also forstudying central nervous system pathologies [1]. However,this quantitative approach is a major challenge forseveral reasons. First, movement of the fetus inside theamniotic cavity requires very fast MRI sequences tominimize motion artifacts, resulting in a poor spatialresolution and/or lower SNR. Second, due to the ongoingmyelination and cortical maturation, the appearance ofthe developing brain differs very much from thehomogenous tissue types found in adults. Third, due tolow resolution, fetal MR images considerably suffer ofpartial volume (PV) effect, sometimes in large areas.Today extensive efforts are made to deal with thereconstruction of high resolution 3D fetal volumes[2,3,4] to cope with intra-volume motion and low SNR.However, few studies exist related to the automatedsegmentation of MR fetal imaging. [5] and [6] work on thesegmentation of specific areas of the fetal brain such asposterior fossa, brainstem or germinal matrix. Firstattempt for automated brain tissue segmentation has beenpresented in [7] and in our previous work [8]. Bothmethods apply the Expectation-Maximization Markov RandomField (EM-MRF) framework but contrary to [7] we do notneed from any anatomical atlas prior. Data set &Methods. Prenatal MR imaging was performed with a 1-Tsystem (GE Medical Systems, Milwaukee) using single shotfast spin echo (ssFSE) sequences (TR 7000 ms, TE 180 ms,FOV 40 x 40 cm, slice thickness 5.4mm, in plane spatialresolution 1.09mm). Each fetus has 6 axial volumes(around 15 slices per volume), each of them acquired inabout 1 min. Each volume is shifted by 1 mm with respectto the previous one. Gestational age (GA) ranges from 29to 32 weeks. Mother is under sedation. Each volume ismanually segmented to extract fetal brain fromsurrounding maternal tissues. Then, in-homogeneityintensity correction is performed using [9] and linearintensity normalization is performed to have intensityvalues that range from 0 to 255. Note that due tointra-tissue variability of developing brain someintensity variability still remains. For each fetus, ahigh spatial resolution image of isotropic voxel size of1.09 mm is created applying [2] and using B-splines forthe scattered data interpolation [10] (see Fig. 1). Then,basal ganglia (BS) segmentation is performed on thissuper reconstructed volume. Active contour framework witha Level Set (LS) implementation is used. Our LS follows aslightly different formulation from well-known Chan-Vese[11] formulation. In our case, the LS evolves forcing themean of the inside of the curve to be the mean intensityof basal ganglia. Moreover, we add local spatial priorthrough a probabilistic map created by fitting anellipsoid onto the basal ganglia region. Some userinteraction is needed to set the mean intensity of BG(green dots in Fig. 2) and the initial fitting points forthe probabilistic prior map (blue points in Fig. 2). Oncebasal ganglia are removed from the image, brain tissuesegmentation is performed as described in [8]. Results.The case study presented here has 29 weeks of GA. Thehigh resolution reconstructed volume is presented in Fig.1. The steps of BG segmentation are shown in Fig. 2.Overlap in comparison with manual segmentation isquantified by the Dice similarity index (DSI) equal to0.829 (values above 0.7 are considered a very goodagreement). Such BG segmentation has been applied on 3other subjects ranging for 29 to 32 GA and the DSI hasbeen of 0.856, 0.794 and 0.785. Our segmentation of theinner (red and blue contours) and outer cortical surface(green contour) is presented in Fig. 3. Finally, torefine the results we include our WM segmentation in theFreesurfer software [12] and some manual corrections toobtain Fig.4. Discussion. Precise cortical surfaceextraction of fetal brain is needed for quantitativestudies of early human brain development. Our workcombines the well known statistical classificationframework with the active contour segmentation forcentral gray mater extraction. A main advantage of thepresented procedure for fetal brain surface extraction isthat we do not include any spatial prior coming fromanatomical atlases. The results presented here arepreliminary but promising. Our efforts are now in testingsuch approach on a wider range of gestational ages thatwe will include in the final version of this work andstudying as well its generalization to different scannersand different type of MRI sequences. References. [1]Guibaud, Prenatal Diagnosis 29(4) (2009). [2] Rousseau,Acad. Rad. 13(9), 2006, [3] Jiang, IEEE TMI 2007. [4]Warfield IADB, MICCAI 2009. [5] Claude, IEEE Trans. Bio.Eng. 51(4) (2004). [6] Habas, MICCAI (Pt. 1) 2008. [7]Bertelsen, ISMRM 2009 [8] Bach Cuadra, IADB, MICCAI 2009.[9] Styner, IEEE TMI 19(39 (2000). [10] Lee, IEEE Trans.Visual. And Comp. Graph. 3(3), 1997, [11] Chan, IEEETrans. Img. Proc, 10(2), 2001 [12] Freesurfer,http://surfer.nmr.mgh.harvard.edu.

Early white matter alterations in men predisposed to FXTAS revealed by MT imaging.

Introduction: The Fragile X - associated Tremor Ataxia Syndrome (FXTAS) is a recently described, and under-diagnosed, late onset (≈ 60y) neurodegenerative disorder affecting male carriers of a premutation in the Fragile X Mental Retardation 1 (FMR1) gene. The premutation is an CGG (Cytosine-Guanine-Guanine) expansion (55 to 200 CGG repeats) in the proximal region of the FMR1 gene. Patients with FXTAS primarily present with cerebellar ataxia and intention tremor. Neuroradiological features of FXTAS include prominent white matter disease in the periventricular, subcortical, middle cerebellar peduncles and deep white matter of the cerebellum on T2-weighted or FLAIR MR imaging (Jacquemmont 2007, Loesch 2007, Brunberg 2002, Cohen 2006). We hypothesize that a significant white matter alteration is present in younger individuals many years prior to clinical symptoms and/or the presence of visible lesions on conventional MR sequences and might be detectable by magnetization transfer (MT) imaging. Methods: Eleven asymptomatic premutation carriers (mean age = 55 years) and seven intra-familial controls participated to the study. A standardized neurological examination was performed on all participants and a neuropsychological evaluation was carried out before MR scanning performed on a 3T Siemens Trio. The protocol included a sagittal T1-weighted 3D gradient-echo sequence (MPRAGE, 160 slices, 1 mm^3 isotropic voxels) and a gradient-echo MTI (FA 30, TE 15, matrix size 256*256, pixel size 1*1 mm, 36 slices (thickness 2mm), MT pulse duration 7.68 ms, FA 500, frequency offset 1.5 kHz). MTI was performed by acquiring consecutively two set of images; first with and then without the MT saturation pulse. MT images were coregistered to the T1 acquisition. The MTR for every intracranial voxel was calculated as follows: MTR = (M0 - MS)/M0*100%, creating a MTR map for each subject. As first analysis, the whole white matter (WM) was used to mask the MTR image in order to create an histogram of the MTR distribution in the whole tissue class over the two groups examined. Then, for each subject, we performed a segmentation and parcellation of the brain by means of Freesurfer software, starting from the high resolution T1-weighted anatomical acquisition. Cortical parcellations was used to assign a label to the underlying white matter by the construction of a Voronoi diagram in the WM voxels of the MR volume based on distance to the nearest cortical parcellation label. This procedure allowed us to subdivide the cerebral WM in 78 ROIs according to the cortical parcellation (see example in Fig 1). The cerebellum, by the same procedure, was subdivided in 5 ROIs (2 per each hemisphere and one corresponding to the brainstem). For each subject, we calculated the mean value of MTR within each ROI and averaged over controls and patients. Significant differences between the two groups were tested using a two sample T-test (p<0.01). Results: Neurological examination showed that no patient met the clinical criteria of Fragile X Tremor and Ataxia Syndrome yet. Nonetheless, premutation carriers showed some subtle neurological signs of the disorder. In fact, premutation carriers showed a significant increase of tremor (CRST, T-test p=0.007) and increase of ataxia (ICARS, p=0.004) when compared to controls. The neuropsychological evaluation was normal in both groups. To obtain general characterizations of myelination for each subject and premutation carriers, we first computed the distribution of MTR values across the total white matter volume and averaged for each group. We tested the equality of the two distributions with the non parametric Kolmogorov-Smirnov test and we rejected the null-hypothesis at a p=0.03 (fig. 2). As expected, when comparing the asymptomatic permutation carriers with control subjects, the peak value and peak position of the MTR values within the whole WM were decreased and the width of the distribution curve was increased (p<0.01). These three changes point to an alteration of the global myelin status of the premutation carriers. Subsequently, to analyze the regional myelination and white matter integrity of the same group, we performed a ROI analysis of MTR data. The ROI-based analysis showed a decrease of mean MTR value in premutation carriers compared to controls in bilateral orbito-frontal and inferior frontal WM, entorhinal and cingulum regions and cerebellum (Fig 3). The detection of these differences in these regions failed with other conventional MR techniques. Conclusions: These preliminary data confirm that in premutation carriers, there are indeed alterations in "normal appearing white matter" (NAWM) and these alterations are visible with the MT technique. These results indicate that MT imaging may be a relevant approach to detect both global and local alterations within NAWM in "asymptomatic" carriers of premutations in the Fragile X Mental Retardation 1 (FMR1) gene. The sensitivity of MT in the detection of these alterations might point towards a specific physiopathological mechanism linked to an underlying myelin disorder. ROI-based analyses show that the frontal, parahippocampal and cerebellar regions are already significantly affected before the onset of symptoms. A larger sample will allow us to determine the minimum CGG expansion and age associated with these subclinical white matter alterations.

A post-processing pipeline to reconstruct the developing fetal brain using low-resolution MRI

Introduction. Development of the fetal brain surfacewith concomitant gyrification is one of the majormaturational processes of the human brain. Firstdelineated by postmortem studies or by ultrasound, MRIhas recently become a powerful tool for studying in vivothe structural correlates of brain maturation. However,the quantitative measurement of fetal brain developmentis a major challenge because of the movement of the fetusinside the amniotic cavity, the poor spatial resolution,the partial volume effect and the changing appearance ofthe developing brain. Today extensive efforts are made todeal with the âeurooepost-acquisitionâeuro reconstruction ofhigh-resolution 3D fetal volumes based on severalacquisitions with lower resolution (Rousseau, F., 2006;Jiang, S., 2007). We here propose a framework devoted tothe segmentation of the basal ganglia, the gray-whitetissue segmentation, and in turn the 3D corticalreconstruction of the fetal brain. Method. Prenatal MRimaging was performed with a 1-T system (GE MedicalSystems, Milwaukee) using single shot fast spin echo(ssFSE) sequences in fetuses aged from 29 to 32gestational weeks (slice thickness 5.4mm, in planespatial resolution 1.09mm). For each fetus, 6 axialvolumes shifted by 1 mm were acquired (about 1 min pervolume). First, each volume is manually segmented toextract fetal brain from surrounding fetal and maternaltissues. Inhomogeneity intensity correction and linearintensity normalization are then performed. A highspatial resolution image of isotropic voxel size of 1.09mm is created for each fetus as previously published byothers (Rousseau, F., 2006). B-splines are used for thescattered data interpolation (Lee, 1997). Then, basalganglia segmentation is performed on this superreconstructed volume using active contour framework witha Level Set implementation (Bach Cuadra, M., 2010). Oncebasal ganglia are removed from the image, brain tissuesegmentation is performed (Bach Cuadra, M., 2009). Theresulting white matter image is then binarized andfurther given as an input in the Freesurfer software(http://surfer.nmr.mgh.harvard.edu/) to provide accuratethree-dimensional reconstructions of the fetal brain.Results. High-resolution images of the cerebral fetalbrain, as obtained from the low-resolution acquired MRI,are presented for 4 subjects of age ranging from 29 to 32GA. An example is depicted in Figure 1. Accuracy in theautomated basal ganglia segmentation is compared withmanual segmentation using measurement of Dice similarity(DSI), with values above 0.7 considering to be a verygood agreement. In our sample we observed DSI valuesbetween 0.785 and 0.856. We further show the results ofgray-white matter segmentation overlaid on thehigh-resolution gray-scale images. The results arevisually checked for accuracy using the same principlesas commonly accepted in adult neuroimaging. Preliminary3D cortical reconstructions of the fetal brain are shownin Figure 2. Conclusion. We hereby present a completepipeline for the automated extraction of accuratethree-dimensional cortical surface of the fetal brain.These results are preliminary but promising, with theultimate goal to provide âeurooemovieâeuro of the normal gyraldevelopment. In turn, a precise knowledge of the normalfetal brain development will allow the quantification ofsubtle and early but clinically relevant deviations.Moreover, a precise understanding of the gyraldevelopment process may help to build hypotheses tounderstand the pathogenesis of several neurodevelopmentalconditions in which gyrification have been shown to bealtered (e.g. schizophrenia, autismâeuro¦). References.Rousseau, F. (2006), 'Registration-Based Approach forReconstruction of High-Resolution In Utero Fetal MR Brainimages', IEEE Transactions on Medical Imaging, vol. 13,no. 9, pp. 1072-1081. Jiang, S. (2007), 'MRI of MovingSubjects Using Multislice Snapshot Images With VolumeReconstruction (SVR): Application to Fetal, Neonatal, andAdult Brain Studies', IEEE Transactions on MedicalImaging, vol. 26, no. 7, pp. 967-980. Lee, S. (1997),'Scattered data interpolation with multilevel B-splines',IEEE Transactions on Visualization and Computer Graphics,vol. 3, no. 3, pp. 228-244. Bach Cuadra, M. (2010),'Central and Cortical Gray Mater Segmentation of MagneticResonance Images of the Fetal Brain', ISMRM Conference.Bach Cuadra, M. (2009), 'Brain tissue segmentation offetal MR images', MICCAI.

How to measure cortical folding from MR images: a step-by-step tutorial to compute local gyrification index.

Cortical folding (gyrification) is determined during the first months of life, so that adverse events occurring during this period leave traces that will be identifiable at any age. As recently reviewed by Mangin and colleagues(2), several methods exist to quantify different characteristics of gyrification. For instance, sulcal morphometry can be used to measure shape descriptors such as the depth, length or indices of inter-hemispheric asymmetry(3). These geometrical properties have the advantage of being easy to interpret. However, sulcal morphometry tightly relies on the accurate identification of a given set of sulci and hence provides a fragmented description of gyrification. A more fine-grained quantification of gyrification can be achieved with curvature-based measurements, where smoothed absolute mean curvature is typically computed at thousands of points over the cortical surface(4). The curvature is however not straightforward to comprehend, as it remains unclear if there is any direct relationship between the curvedness and a biologically meaningful correlate such as cortical volume or surface. To address the diverse issues raised by the measurement of cortical folding, we previously developed an algorithm to quantify local gyrification with an exquisite spatial resolution and of simple interpretation. Our method is inspired of the Gyrification Index(5), a method originally used in comparative neuroanatomy to evaluate the cortical folding differences across species. In our implementation, which we name local Gyrification Index (lGI(1)), we measure the amount of cortex buried within the sulcal folds as compared with the amount of visible cortex in circular regions of interest. Given that the cortex grows primarily through radial expansion(6), our method was specifically designed to identify early defects of cortical development. In this article, we detail the computation of local Gyrification Index, which is now freely distributed as a part of the FreeSurfer Software (http://surfer.nmr.mgh.harvard.edu/, Martinos Center for Biomedical Imaging, Massachusetts General Hospital). FreeSurfer provides a set of automated reconstruction tools of the brain's cortical surface from structural MRI data. The cortical surface extracted in the native space of the images with sub-millimeter accuracy is then further used for the creation of an outer surface, which will serve as a basis for the lGI calculation. A circular region of interest is then delineated on the outer surface, and its corresponding region of interest on the cortical surface is identified using a matching algorithm as described in our validation study(1). This process is repeatedly iterated with largely overlapping regions of interest, resulting in cortical maps of gyrification for subsequent statistical comparisons (Fig. 1). Of note, another measurement of local gyrification with a similar inspiration was proposed by Toro and colleagues(7), where the folding index at each point is computed as the ratio of the cortical area contained in a sphere divided by the area of a disc with the same radius. The two implementations differ in that the one by Toro et al. is based on Euclidian distances and thus considers discontinuous patches of cortical area, whereas ours uses a strict geodesic algorithm and include only the continuous patch of cortical area opening at the brain surface in a circular region of interest.

Brain Connectivity Analysis in Children. Effects of Prematurity. and Prenatal Growth Restriction

Introduction: Survival of children born prematurely or with very low birth weight has increased dramatically, but the long term developmental outcome remains unknown. Many children have deficits in cognitive capacities, in particular involving executive domains and those disabilities are likely to involve a central nervous system deficit. To understand their neurostructural origin, we use DTI. Structurally segregated and functionally regions of the cerebral cortex are interconnected by a dense network of axonal pathways. We noninvasively map these pathways across cortical hemispheres and construct normalized structural connection matrices derived from DTI MR tractography. Group comparisons of brain connectivity reveal significant changes in fiber density in case of children with poor intrauterine grown and extremely premature children (gestational age<28 weeks at birth) compared to control subjects. This changes suggest a link between cortico-axonal pathways and the central nervous system deficit. Methods: Sixty premature born infants (5-6 years old) were scanned on clinical 3T scanner (Magnetom Trio, Siemens Medical Solutions, Erlangen, Germany) at two hospitals (HUG, Geneva and CHUV, Lausanne). For each subject, T1-weighted MPRAGE images (TR/TE=2500/2.91,TI=1100, resolution=1x1x1mm, matrix=256x154) and DTI images (30 directions, TR/TE=10200/107, in-plane resolution=1.8x1.8x2mm, 64 axial, matrix=112x112) were acquired. Parent(s) provided written consent on prior ethical board approval. The extraction of the Whole Brain Structural Connectivity Matrix was performed following (Cammoun, 2009 and Hagmann, 2008). The MPARGE images were registered using an affine registration to the non-weighted-DTI and WM-GM segmentation performed on it. In order to have equal anatomical localization among subjects, 66 cortical regions with anatomical landmarks were created using the curvature information, i.e. sulcus and gyrus (Cammoun et al, 2007; Fischl et al, 2004; Desikan et al, 2006) with freesurfer software (http://surfer.nmr.mgh.harvard.edu/). Tractography was performed in WM using an algorithm especially designed for DTI/DSI data (Hagmann et al., 2007) and both information were then combined in a matrix. Each row and column of the matrix corresponds to a particular ROI. Each cell of index (i,j) represents the fiber density of the bundle connecting the ROIs i and j. Subdividing each cortical region, we obtained 4 Connectivity Matrices of different resolution (33, 66, 125 and 250 ROI/hemisphere) for each subject . Subjects were sorted in 3 different groups, namely (1) control, (2) Intrauterine Growth Restriction (IUGR), (3) Extreme Prematurity (EP), depending on their gestational age, weight and percentile-weight score at birth. Group-to-group comparisons were performed between groups (1)-(2) and (1)-(3). The mean age at examination of the three groups were similar. Results: Quantitative analysis were performed between groups to determine fibers density differences. For each group, a mean connectivity matrix with 33ROI/hemisphere resolution was computed. On the other hand, for all matrix resolutions (33,66,125,250 ROI/hemisphere), the number of bundles were computed and averaged. As seen in figure 1, EP and IUGR subjects present an overall reduction of fibers density in both interhemispherical and intrahemispherical connections. This is given quantitatively in table 1. IUGR subjects presents a higher percentage of missing fiber bundles than EP when compared to control subjects (~16% against 11%). When comparing both groups to control subjects, for the EP subjects, the occipito-parietal regions seem less interhemispherically connected whilst the intrahemispherical networks present lack of fiber density in the lymbic system. Children born with IUGR, have similar reductions in interhemispherical connections than the EP. However, the cuneus and precuneus connections with the precentral and paracentral lobe are even lower than in the case of the EP. For the intrahemispherical connections the IUGR group preset a loss of fiber density between the deep gray matter structures (striatum) and the frontal and middlefrontal poles, connections typically involved in the control of executive functions. For the qualitative analysis, a t-test comparing number of bundles (p-value<0.05) gave some preliminary significant results (figure 2). Again, even if both IUGR and EP appear to have significantly less connections comparing to the control subjects, the IUGR cohort seems to present a higher lack of fiber density specially relying the cuneus, precuneus and parietal areas. In terms of fiber density, preliminary Wilcoxon tests seem to validate the hypothesis set by the previous analysis. Conclusions: The goal of this study was to determine the effect of extreme prematurity and poor intrauterine growth on neurostructural development at the age of 6 years-old. This data indicates that differences in connectivity may well be the basis for the neurostructural and neuropsychological deficit described in these populations in the absence of overt brain lesions (Inder TE, 2005; Borradori-Tolsa, 2004; Dubois, 2008). Indeed, we suggest that IUGR and prematurity leads to alteration of connectivity between brain structures, especially in occipito-parietal and frontal lobes for EP and frontal and middletemporal poles for IUGR. Overall, IUGR children have a higher loss of connectivity in the overall connectivity matrix than EP children. In both cases, the localized alteration of connectivity suggests a direct link between cortico-axonal pathways and the central nervous system deficit. Our next step is to link these connectivity alterations to the performance in executive function tests.

An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease.

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease.

A structural MRI study of motor conversion disorder: evidence of reduction in thalamic volume.

OBJECTIVE: To investigate potential abnormalities in subcortical brain structures in conversion disorder (CD) compared with controls using a region of interest (ROI) approach. METHODS: Fourteen patients with motor CD were compared with 31 healthy controls using high-resolution MRI scans with an ROI approach focusing on the basal ganglia, thalamus and amygdala. Brain volumes were measured using Freesurfer, a validated segmentation algorithm. RESULTS: Significantly smaller left thalamic volumes were found in patients compared with controls when corrected for intracranial volume. These reductions did not vary with handedness, laterality, duration or severity of symptoms. CONCLUSIONS: These differences may reflect a primary disease process in this area or be secondary effects of the disorder, for example, resulting from limb disuse. Larger, longitudinal structural imaging studies will be required to confirm the findings and explore whether they are primary or secondary to CD.

Quantitative morphometry analysis of the fetal brain using clinical MR imaging

In vivo fetal magnetic resonance imaging provides aunique approach for the study of early human braindevelopment [1]. In utero cerebral morphometry couldpotentially be used as a marker of the cerebralmaturation and help to distinguish between normal andabnormal development in ambiguous situations. However,this quantitative approach is a major challenge becauseof the movement of the fetus inside the amniotic cavity,the poor spatial resolution provided by very fast MRIsequences and the partial volume effect. Extensiveefforts are made to deal with the reconstruction ofhigh-resolution 3D fetal volumes based on severalacquisitions with lower resolution [2,3,4]. Frameworkswere developed for the segmentation of specific regionsof the fetal brain such as posterior fossa, brainstem orgerminal matrix [5,6], or for the entire brain tissue[7,8], applying the Expectation-Maximization MarkovRandom Field (EM-MRF) framework. However, many of theseprevious works focused on the young fetus (i.e. before 24weeks) and use anatomical atlas priors to segment thedifferent tissue or regions. As most of the gyraldevelopment takes place after the 24th week, acomprehensive and clinically meaningful study of thefetal brain should not dismiss the third trimester ofgestation. To cope with the rapidly changing appearanceof the developing brain, some authors proposed a dynamicatlas [8]. To our opinion, this approach however faces arisk of circularity: each brain will be analyzed /deformed using the template of its biological age,potentially biasing the effective developmental delay.Here, we expand our previous work [9] to proposepost-processing pipeline without prior that allow acomprehensive set of morphometric measurement devoted toclinical application. Data set & Methods: Prenatal MRimaging was performed with a 1-T system (GE MedicalSystems, Milwaukee) using single shot fast spin echo(ssFSE) sequences (TR 7000 ms, TE 180 ms, FOV 40 x 40 cm,slice thickness 5.4mm, in plane spatial resolution1.09mm). For each fetus, 6 axial volumes shifted by 1 mmwere acquired under motherâeuro?s sedation (about 1min pervolume). First, each volume is segmentedsemi-automatically using region-growing algorithms toextract fetal brain from surrounding maternal tissues.Inhomogeneity intensity correction [10] and linearintensity normalization are then performed. Brain tissues(CSF, GM and WM) are then segmented based on thelow-resolution volumes as presented in [9]. Ahigh-resolution image with isotropic voxel size of 1.09mm is created as proposed in [2] and using B-splines forthe scattered data interpolation [11]. Basal gangliasegmentation is performed using a levet setimplementation on the high-resolution volume [12]. Theresulting white matter image is then binarized and givenas an input in FreeSurfer software(http://surfer.nmr.mgh.harvard.edu) to providetopologically accurate three-dimensional reconstructionsof the fetal brain according to the local intensitygradient. References: [1] Guibaud, Prenatal Diagnosis29(4) (2009). [2] Rousseau, Acad. Rad. 13(9), 2006. [3]Jiang, IEEE TMI 2007. [4] Warfield IADB, MICCAI 2009. [5]Claude, IEEE Trans. Bio. Eng. 51(4) 2004. [6] Habas,MICCAI 2008. [7] Bertelsen, ISMRM 2009. [8] Habas,Neuroimage 53(2) 2010. [9] Bach Cuadra, IADB, MICCAI2009. [10] Styner, IEEE TMI 19(39 (2000). [11] Lee, IEEETrans. Visual. And Comp. Graph. 3(3), 1997. [12] BachCuadra, ISMRM 2010.

Correlação entre espessura cortical frontal e desempenho de funções executivas em pacientes com infecção pelo vírus da imunodeficiência humana

OBJETIVO: Investigar a relação entre a espessura cortical medida pela ressonância magnética em regiões frontais e o desempenho em instrumentos que avaliam funções executivas em pacientes com HIV positivo. MATERIAIS E MÉTODOS: Participaram deste estudo 22 pacientes HIV-positivos, com déficits em funções executivas, sob terapia antirretroviral, idades entre 45 e 65 anos e escolaridade entre 3 e 20 anos. Foi realizada ressonância magnética com sequências convencionais, T1 3D, processado pelo Freesurfer para verificar espessura cortical. Instrumentos de avaliação das funções executivas: Teste de Trilhas, Wisconsin, Hayling, Dígitos (WAIS-III), fluência verbal ortográfica e Stroop. Para análise da relação espessura versus cognição, utilizou-se coeficiente de correlação de Pearson. RESULTADOS: Correlações significativas foram encontradas entre escores de: Wisconsin e espessura das regiões pré-central e orbitofrontal lateral à direita e pré-central esquerda; Teste de Trilhas e espessura da área pré-central direita e cíngulo anterior caudal esquerdo; e Teste Hayling e espessura da área lateral orbitofrontal esquerda. CONCLUSÃO: As correlações existentes entre medidas de espessura cortical pela ressonância magnética e desempenho cognitivo sugerem que os déficits executivos em pacientes HIV-positivos relacionam-se a uma redução da espessura cortical das regiões frontais.