Neurodis Foundation

Sainte-Foy-lès-Lyon, France

Neurodis Foundation

Sainte-Foy-lès-Lyon, France
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Robinson E.C.,Imperial College London | Hammers A.,Clinical science Center | Hammers A.,Neurodis Foundation | Ericsson A.,Imperial College London | And 2 more authors.
NeuroImage | Year: 2010

Models of whole-brain connectivity are valuable for understanding neurological function, development and disease. This paper presents a machine learning based approach to classify subjects according to their approximated structural connectivity patterns and to identify features which represent the key differences between groups. Brain networks are extracted from diffusion magnetic resonance images obtained by a clinically viable acquisition protocol. Connections are tracked between 83 regions of interest automatically extracted by label propagation from multiple brain atlases followed by classifier fusion. Tracts between these regions are propagated by probabilistic tracking, and mean anisotropy measurements along these connections provide the feature vectors for combined principal component analysis and maximum uncertainty linear discriminant analysis. The approach is tested on two populations with different age distributions: 20-30 and 60-90 years. We show that subjects can be classified successfully (with 87.46% accuracy) and that the features extracted from the discriminant analysis agree with current consensus on the neurological impact of ageing. © 2009 Elsevier Inc. All rights reserved.


Faillenot I.,Central Integration of Pain Unit Lyon Center for Neuroscience | Heckemann R.A.,Neurodis Foundation | Heckemann R.A.,Sahlgrenska University Hospital | Heckemann R.A.,Imperial College London | And 4 more authors.
NeuroImage | Year: 2017

The human insula is implicated in numerous functions. More and more neuroimaging studies focus on this region, however no atlas offers a complete subdivision of the insula in a reference space. The aims of this study were to define a protocol to subdivide insula, to create probability maps in the MNI152 stereotaxic space, and to provide normative reference volume measurements for these subdivisions. Six regions were manually delineated bilaterally on 3D T1 MR images of 30 healthy subjects: the three short gyri, the anterior inferior cortex, and the two long gyri. The volume of the insular grey matter was 7.7 ± 0.9 cm3 in native space and 9.9 ± 0.6 cm3 in MNI152 space. These volumes expressed as a percentage of the ipsilateral grey matter volume were minimally larger in women (2.7±0.2%) than in men (2.6±0.2%). After spatial normalization, a stereotactic probabilistic atlas of each subregion was produced, as well as a maximum-probability atlas taking into account surrounding structures. Automatically labelling insular subregions via a multi-atlas propagation and label fusion strategy (MAPER) in a leave-one-out experiment showed high spatial overlaps of such automatically defined insular subregions with the manually derived ones (mean Jaccard index 0.65, corresponding to a mean Dice index of 0.79), with an average mean volume error of 2.6%. Probabilistic and maximum probability atlases and the original delineations are available on the web under free academic licences. © 2017


Keihaninejad S.,Imperial College London | Heckemann R.A.,Imperial College London | Heckemann R.A.,Neurodis Foundation | Fagiolo G.,Imperial College London | And 4 more authors.
NeuroImage | Year: 2010

As population-based studies may obtain images from scanners with different field strengths, a method to normalize regional brain volumes according to intracranial volume (ICV) independent of field strength is needed. We found systematic differences in ICV estimation, tested in a cohort of healthy subjects (n = 5) that had been imaged using 1.5T and 3T scanners, and confirmed in two independent cohorts. This was related to systematic differences in the intensity of cerebrospinal fluid (CSF), with higher intensities for CSF located in the ventricles compared with CSF in the cisterns, at 3T versus 1.5T, which could not be removed with three different applied bias correction algorithms. We developed a method based on tissue probability maps in MNI (Montreal Neurological Institute) space and reverse normalization (reverse brain mask, RBM) and validated it against manual ICV measurements. We also compared it with alternative automated ICV estimation methods based on Statistical Parametric Mapping (SPM5) and Brain Extraction Tool (FSL). The proposed RBM method was equivalent to manual ICV normalization with a high intraclass correlation coefficient (ICC = 0.99) and reliable across different field strengths. RBM achieved the best combination of precision and reliability in a group of healthy subjects, a group of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) and can be used as a common normalization framework. © 2010 Elsevier Inc. All rights reserved.


Yakushev I.,University of Mainz | Muller M.J.,University of Mainz | Lorscheider M.,University of Mainz | Lorscheider M.,The Interdisciplinary Center | And 7 more authors.
Neuropsychologia | Year: 2010

Recent neuroanatomical and functional neuroimaging studies indicate that the anterior part of the hippocampus, rather than the whole structure, may be specifically involved in episodic memory. In the present work, we examined whether anterior structural measurements are superior to other regional or global measurements in mapping functionally relevant degenerative alterations of the hippocampus in Alzheimer's disease (AD).Twenty patients with early AD (MMSE 25.7 ± 1.7) and 18 healthy controls were studied using magnetic resonance and diffusion-tensor imaging. Using a regions-of-interest analysis, we obtained volumetric and diffusivity measures of the hippocampal head and body-tail-section as well as of the whole hippocampus. Detailed cognitive evaluation was based on the CERAD battery.All volumetric measures as well as diffusivity of the hippocampus head were significantly (p<0.01) altered in patients as compared to controls. In patients, increased left head diffusivity significantly (p<0.01) correlated with performance on free delayed verbal recall test (DVR) (r=-0.74, p=0.0002) and with the CERAD global score. Reduced volume of the left body-tail was also associated with performance on DVR (r=0.62, p=0.004). Stepwise regression analyses revealed that increased left head diffusivity was the only predictor for performance on DVR (R2=52%, p<0.0005).These findings suggest that anterior hippocampus diffusivity is more closely related to verbal episodic memory impairment than other regional or global structural measures. Our data support the hypothesis of functional differentiation in general and the specific role of the anterior hippocampus in episodic memory in particular. Diffusivity measurements might be highly sensitive to functionally relevant degenerative alterations of the hippocampus. © 2010 Elsevier Ltd.


Tomasi G.,Imperial College London | Bertoldo A.,University of Padua | Cobelli C.,University of Padua | Pavese N.,Imperial College London | And 4 more authors.
NeuroImage | Year: 2011

Introduction: In Positron Emission Tomography (PET) quantification of physiological parameters at the voxel level may result in unreliable estimates due to the high noise of voxel time activity curves. Global-Two-Stage (GTS), an estimation technique belonging to the group of "population approaches", can be used to tackle this problem. GTS was previously tested on simulated PET data and yielded substantial improvements when compared to standard estimation approaches such as Weighted NonLinear Least Squares (WNLLS) and Basis Function Method (BFM). In this work GTS performance is assessed in a clinical context using the neuroinflammation marker [11C]-(R)-PK11195 applied to a cohort of Huntington's disease (HD) patients with and without symptoms. Materials and methods: Parametric maps of binding potential (BPND) of 12 normal controls (NC), 9 symptomatic and 9 presymptomatic HD patients were generated by applying a modified reference tissue model that accounts for tracer vascular activity in both reference and target tissues (SRTMV). GTS was then applied to SRTMV maps and its performance compared with that of SRTMV. Three smoothed versions of SRTMV, obtained by filtering the original SRTMV maps with Gaussian filters of 3mm, 5mm and 7mm Full Width Half Maximum (FWHM), were also included in the comparison. Since striatal degeneration is the hallmark of HD, sensitivity was assessed for all methods by computing the mean of z-scores in caudate, putamen and globus pallidus in the voxel-by-voxel statistical comparison of BPND between HD and NC. Results: Application of GTS to parametric maps brought a substantial qualitative improvement to SRTMV maps to the extent that anatomical structures often became visible. In addition, most parameter estimates that were outside the physiological range with SRTMV were corrected by GTS. GTS yielded a 2.3-fold increase in sensitivity with respect to SRTMV for the symptomatic cohort (mean of striatal z-scores of 0.76 for SRTMV and 1.79 for GTS) and an even more substantial increase for the presymptomatic cohort (mean of striatal z-scores of 0.34 for SRTMV and 0.96 for GTS). The sensitivity of GTS was similar to the one obtained with a filter of 7. mm FWHM applied to the initial SRTMV maps but GTS images were not characterized by the notable loss of resolution typical of smoothed maps. GTS, additionally, does not require to change/define settings according to the tracer and level of noise, whereas the choice of the FWHM value of the Gaussian filter normally employed in the smoothing procedure is typically arbitrary. Conclusions: GTS is a powerful and robust tool for improving the quality of parametric maps in PET. The method is particularly appealing in that it can be applied to any tracer and estimation method, provided that initial estimates of the parameter vector and of its covariance are available. Although the benefits of GTS are far from being exhaustively assessed, the significant improvements obtained both on real and simulated data suggest that it could become an important tool for dynamic PET in the future. © 2010 Elsevier Inc.


Rizzo G.,University of Padua | Veronese M.,University of Padua | Veronese M.,King's College London | Heckemann R.A.,Gothenburg University | And 5 more authors.
Journal of Cerebral Blood Flow and Metabolism | Year: 2014

Substantial efforts are being spent on postmortem mRNA transcription mapping on the assumption that in vivo protein distribution can be predicted from such data. We tested this assumption by comparing mRNA transcription maps from the Allen Human Brain Atlas with reference protein concentration maps acquired with positron emission tomography (PET) in two representative systems of neurotransmission (opioid and serotoninergic). We found a tight correlation between mRNA expression and specific binding with 5-HT1A receptors measured with PET, but for opioid receptors, the correlation was weak. The discrepancy can be explained by differences in expression regulation between the two systems: transcriptional mechanisms dominate the regulation in the serotoninergic system, whereas in the opioid system proteins are further modulated after transcription. We conclude that mRNA information can be exploited for systems where translational mechanisms predominantly regulate expression. Where posttranscriptional mechanisms are important, mRNA data have to be interpreted with caution. The methodology developed here can be used for probing assumptions about the relationship of mRNA and protein in multiple neurotransmission systems. © 2014 ISCBFM.


Yakushev I.,University Medical Center Mainz | Gerhard A.,University Medical Center Mainz | Gerhard A.,University of Manchester | Mu ller M.J.,University Medical Center Mainz | And 9 more authors.
Brain Structure and Function | Year: 2011

Abnormal microstructural integrity and glucose metabolism of the hippocampus are common in subjects with Alzheimer's disease (AD) that typically manifest as episodic memory impairment. The above-tissue alterations can be captured in vivo using diffusion tensor imaging (DTI) and positron emission tomography with [18F] fluorodeoxyglucose (FDG-PET). Here, we explored relationships between the above neuroimaging and cognitive markers of early AD-specific hippocampal damage. Twenty patients with early AD (MMSE 25.7 ± 1.7) were studied using DTI and FDG-PET. Episodic memory performance was assessed using the free delayed verbal recall task (DVR). In the between-modality correlation analysis, FDG uptake was strongly associated with diffusivity in the left anterior hippocampus only (r = -0.81, p<0.05 Bonferroni's corrected for multiple tests). Performance on DVR significantly correlated with left anterior (r = -0.80, p<0.05) and left mean (r = -0.72, p<0.05) hippocampal diffusivity, while the correlation with left anterior FDG uptake did not reach statistical significance (r = 0.52, n.s.). DTI-derived diffusivity of the anterior hippocampus might be a sensitive early marker of hippocampal dysfunction as reflected at the synaptic and cognitive levels. This neurobiological distinction of the anterior hippocampus might be related to the disruption of the perforant pathway that is known to occur early in the course of AD. © Springer-Verlag 2011.


Keihaninejad S.,Imperial College London | Heckemann R.A.,Imperial College London | Heckemann R.A.,Neurodis Foundation | Gousias I.S.,Imperial College London | And 7 more authors.
PLoS ONE | Year: 2012

Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hippocampal atrophy (TLE-HA and TLE-N, respectively), and for determining the presumed side of seizure onset. The framework employs multi-atlas segmentation to estimate the volumes of 83 brain structures. A kernel-based separability criterion was then used to identify structures whose volumes discriminate between the groups. Next, we applied support vector machines (SVM) to the selected set for classification on the basis of volumes. We also computed pairwise similarities between all subjects and used spectral analysis to convert these into persubject features. SVM was again applied to these feature data. After training on a subgroup, all TLE-HA patients were correctly distinguished from controls, achieving an accuracy of 96 ± 2% in both classification schemes. For TLE-N patients, the accuracy was 86 ± 2% based on structural volumes and 91 ± 3% using spectral analysis. Structures discriminating between patients and controls were mainly localized ipsilaterally to the presumed seizure focus. For the TLE-HA group, they were mainly in the temporal lobe; for the TLE-N group they included orbitofrontal regions, as well as the ipsilateral substantia nigra. Correct lateralization of the presumed seizure onset zone was achieved using hippocampi and parahippocampal gyri in all TLE-HA patients using either classification scheme; in the TLE-N patients, lateralization was accurate based on structural volumes in 86 ± 4%, and in 94 ± 4% with the spectral analysis approach. Unilateral TLE has imaging features that can be identified automatically, even when they are invisible to human experts. Such morphometric image features may serve as classification and lateralization criteria. The technique also detects unsuspected distinguishing features like the substantia nigra, warranting further study. © 2012 Keihaninejad et al.


Malik S.J.,Imperial College London | Keihaninejad S.,Imperial College London | Hammers A.,Neurodis Foundation | Hajnal J.V.,Imperial College London
Magnetic Resonance in Medicine | Year: 2012

Brain images acquired at 3T often display central brightening with spatially varying tissue contrast, caused by inhomogeneity in the transmit radiofrequency fields used for excitation. Tailored radiofrequency pulses can provide mitigation of radiofrequency field inhomogeneity, but previous designs have been unsuitable for 3D imaging in rapid pulse sequences. This article presents a nonselective pulse design based on a short (1 ms) 3D spiral k-space trajectory that covers low spatial frequencies. The resulting excitations are optimized to produce a uniform excitation within a specified volume of interest covering the whole brain. B1 mapping and pulse calculation times were reduced by optimizing in only five slices within the brain. The method has been tested with both single and parallel transmission: in phantom experiments, normalized root-mean-square error in excitation was 0.022 for single and 0.020 for parallel transmission. The corresponding results in vivo were 0.066 and 0.055 respectively. A pilot brain imaging study using the proposed pulses for excitation within the Alzheimer's disease neuroimaging initiative magnetization prepared rapid gradient echo (MP-RAGE) protocol, yielded excellent image quality with improved signal to noise ratio in peripheral brain regions and enhanced uniformity of contrast compared with standard excitation. Greatest performance enhancement was achieved using parallel transmission, but single channel transmission offers significant improvement over standard excitation pulses. Copyright © 2011 Wiley Periodicals, Inc.


Ledig C.,Imperial College London | Wolz R.,Imperial College London | Aljabar P.,Imperial College London | Lotjonen J.,VTT Technical Research Center of Finland | And 3 more authors.
Proceedings - International Symposium on Biomedical Imaging | Year: 2012

In recent years, multi-atlas segmentation has emerged as one of the most accurate techniques for the segmentation of brain magnetic resonance (MR) images, especially when combined with intensity-based refinement techniques such as graph-cut or expectation-maximization (EM) optimization. However, most of the work so far has focused on intensity-based refinement strategies for individual anatomical structures such as the hippocampus. In this work we extend a previously proposed framework for labeling whole brain scans by incorporating a global and stationary Markov random field that ensures the consistency of the neighbourhood relations between structures with an a-priori defined model. In particular we improve the segmentation result of a locally weighted multi-atlas fusion method for 41 different structures simultaneously by applying a subsequent EM optimization step. We evaluate the proposed approach on 30 manually annotated brain MR images and observe an improvement of label overlaps to a manual reference by up to 6%. We also achieved a considerably improved group separation when the proposed segmentation framework is applied to a volumetric analysis of 404 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. © 2012 IEEE.

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