The Neurodis Foundation Fondation Neurodis

Sainte-Foy-lès-Lyon, France

The Neurodis Foundation Fondation Neurodis

Sainte-Foy-lès-Lyon, France
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Gousias I.S.,Imperial College London | Edwards A.D.,Imperial College London | Rutherford M.A.,Imperial College London | Counsell S.J.,Imperial College London | And 4 more authors.
NeuroImage | Year: 2012

Premature birth is a major and growing problem. Investigations into neuroanatomical correlates and consequences of preterm birth are hampered by complex neonatal brain anatomy and unavailability of atlases and protocols covering the whole brain. We developed delineation protocols for the manual segmentation of cerebral magnetic resonance (MR) images from newborn infants into 50 regions with comprehensive coverage of the brain. We then segmented MR scans from 15 infants born preterm at median 29, range 26-35, weeks postmenstrual age and scanned at term-corrected age, and five term-born infants born at median 41, range 39-45, weeks postmenstrual age. Total and regional brain volumes were estimated in each infant, and regional volumes expressed as a fraction of total brain volume. Total brain volumes were higher with greater age at birth and at time of scan, but once corrected for age at scan there was no difference between preterm and term infants. Fractional age-corrected regional volumes were bigger unilaterally in terms in middle and inferior temporal gyri, anterior temporal lobe, fusiform gyrus and posterior cingulate gyrus. Fractional age-corrected regional volumes were larger in preterms bilaterally in hippocampus, amygdala, thalamus and lateral ventricles, left superior temporal gyrus and right caudate nucleus. These differences were not significant after correcting for multiple hypothesis testing, but suggest subtle differences between preterms and term-borns accessible to regional analysis. Detailed illustrated protocols are made available in the Appendix. © 2012 Elsevier Inc.


Rizzo G.,University of Padua | Turkheimer F.E.,Imperial College London | Keihaninejad S.,Imperial College London | Bose S.K.,Imperial College London | And 4 more authors.
NeuroImage | Year: 2012

We propose a general approach to generate parametric maps. It consists in a multi-stage hierarchical scheme where, starting from the kinetic analysis of the whole brain, we then cascade the kinetic information to anatomical systems that are akin in terms of receptor densities, and then down to the voxel level. A-priori classes of voxels are generated either by anatomical atlas segmentation or by functional segmentation using unsupervised clustering. Kinetic properties are transmitted to the voxels in each class using maximum a posteriori (MAP) estimation method. We validate the novel method on a [ 11C]diprenorphine (DPN) test-retest data-set that represents a challenge to estimation given [ 11C]DPN's slow equilibration in tissue. The estimated parametric maps of volume of distribution (V T) reflect the opioid receptor distributions known from previous [ 11C]DPN studies. When priors are derived from the anatomical atlas, there is an excellent agreement and strong correlation among voxel MAP and ROI results and excellent test-retest reliability for all subjects but one. Voxel level results did not change when priors were defined through unsupervised clustering. This new method is fast (i.e. 15min per subject) and applied to [ 11C]DPN data achieves accurate quantification of V T as well as high quality V T images. Moreover, the way the priors are defined (i.e. using an anatomical atlas or unsupervised clustering) does not affect the estimates. © 2011 Elsevier Inc.


Van der Lijn F.,Erasmus University Rotterdam | Verhaaren B.F.J.,Erasmus University Rotterdam | Ikram M.A.,Erasmus University Rotterdam | Klein S.,Erasmus University Rotterdam | And 12 more authors.
NeuroImage | Year: 2012

It has been hypothesized that white matter lesions at different locations may have different etiology and clinical consequences. Several approaches for the quantification of local white matter lesion load have been proposed in the literature, most of which rely on a distinction between lesions in a periventricular region close to the ventricles and a subcortical zone further away. In this work we present a novel automated method for local white matter lesion volume quantification in magnetic resonance images. The method segments and measures the white matter lesion volume in 43 regions defined by orientation and distance to the ventricles, which allows a more spatially detailed study of lesion load. The potential of the method was demonstrated by analyzing the effect of blood pressure on the regional white matter lesion volume in 490 elderly subjects taken from a longitudinal population study. The method was also compared to two commonly used techniques to assess the periventricular and subcortical lesion load. The main finding was that high blood pressure was primarily associated with lesion load in the vascular watershed area that forms the border between the periventricular and subcortical regions. It explains the associations found for both the periventricular and subcortical load computed for the same data, and that were reported in the literature. But the proposed method can localize the region of association with greater precision than techniques that distinguish between periventricular and subcortical lesions only. © 2011 Elsevier Inc.


Heckemann R.A.,Imperial College London | Heckemann R.A.,The Neurodis Foundation Fondation Neurodis | Keihaninejad S.,Imperial College London | Aljabar P.,Imperial College London | And 4 more authors.
NeuroImage | Year: 2010

Automatic anatomical segmentation of magnetic resonance human brain images has been shown to be accurate and robust when based on multiple atlases that encompass the anatomical variability of the cohort of subjects. We observed that the method tends to fail when the segmentation target shows ventricular enlargement that is not captured by the atlas database. By incorporating tissue classification information into the image registration process, we aimed to increase the robustness of the method. For testing, subjects who participated in the Oxford Project to Investigate Memory and Aging (OPTIMA) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) were selected for ventriculomegaly. Segmentation quality was substantially improved in the ventricles and surrounding structures (9/9 successes on visual rating versus 4/9 successes using the baseline method). In addition, the modification resulted in a significant increase of segmentation accuracy in healthy subjects' brain images. Hippocampal segmentation results in a group of patients with temporal lobe epilepsy were near identical with both approaches. The modified approach (MAPER, multi-atlas propagation with enhanced registration) extends the applicability of multi-atlas based automatic whole-brain segmentation to subjects with ventriculomegaly, as seen in normal aging as well as in numerous neurodegenerative diseases. © 2010 Elsevier Inc.


Heckemann R.A.,The Neurodis Foundation Fondation Neurodis | Heckemann R.A.,Imperial College London | Keihaninejad S.,Imperial College London | Keihaninejad S.,University College London | And 7 more authors.
NeuroImage | Year: 2011

This paper presents a novel, publicly available repository of anatomically segmented brain images of healthy subjects as well as patients with mild cognitive impairment and Alzheimer's disease. The underlying magnetic resonance images have been obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. T1-weighted screening and baseline images (1.5. T and 3. T) have been processed with the multi-atlas based MAPER procedure, resulting in labels for 83 regions covering the whole brain in 816 subjects. Selected segmentations were subjected to visual assessment. The segmentations are self-consistent, as evidenced by strong agreement between segmentations of paired images acquired at different field strengths (Jaccard coefficient: 0.802 ± 0.0146). Morphometric comparisons between diagnostic groups (normal; stable mild cognitive impairment; mild cognitive impairment with progression to Alzheimer's disease; Alzheimer's disease) showed highly significant group differences for individual regions, the majority of which were located in the temporal lobe. Additionally, significant effects were seen in the parietal lobe. Increased left/right asymmetry was found in posterior cortical regions. An automatically derived white-matter hypointensities index was found to be a suitable means of quantifying white-matter disease. This repository of segmentations is a potentially valuable resource to researchers working with ADNI data. © 2011 Elsevier Inc.


Butler C.,University of Oxford | van Erp W.,Nijmegen Medical Center | Bhaduri A.,Highgate Mental Health Center | Hammers A.,The Neurodis Foundation Fondation Neurodis | And 4 more authors.
Epilepsy and Behavior | Year: 2013

Transient epileptic amnesia (TEA) is a recently described epilepsy syndrome characterized by recurrent episodes of isolated memory loss. It is associated with two unusual forms of interictal memory impairment: accelerated long-term forgetting (ALF) and autobiographical amnesia. We investigated the neural basis of TEA using manual volumetry and automated multi-atlas-based segmentation of whole-brain magnetic resonance imaging data from 40 patients with TEA and 20 healthy controls. Both methods confirmed the presence of subtle, bilateral hippocampal atrophy. Additional atrophy was revealed in perirhinal and orbitofrontal cortices. The volumes of these regions correlated with anterograde memory performance. No structural correlates were found for ALF or autobiographical amnesia. The results support the hypothesis that TEA is a focal medial temporal lobe epilepsy syndrome but reveal additional pathology in connected brain regions. The unusual interictal memory deficits of TEA remain unexplained by structural pathology and may reflect physiological disruption of memory networks by subclinical epileptiform activity. © 2013 Elsevier Inc.


Traynor C.,King's College London | Heckemann R.A.,Imperial College London | Heckemann R.A.,The Neurodis Foundation Fondation Neurodis | Hammers A.,Imperial College London | And 5 more authors.
NeuroImage | Year: 2010

Reliable identification of thalamic nuclei is required to improve targeting of electrodes used in Deep Brain Stimulation (DBS), and for exploring the role of thalamus in health and disease. A previously described method using probabilistic tractography to segment the thalamus based on connections to cortical target regions was implemented. Both within- and between-subject reproducibility were quantitatively assessed by the overlap of the resulting segmentations; the effect of two different numbers of target regions (6 and 31) on reproducibility of the segmentation results was also investigated. Very high reproducibility was observed when a single dataset was processed multiple times using different starting conditions. Thalamic segmentation was also very reproducible when multiple datasets from the same subject were processed using six cortical target regions. Within-subject reproducibility was reduced when the number of target regions was increased, particularly in medial and posterior regions of the thalamus. A large degree of overlap in segmentation results from different subjects was obtained, particularly in thalamic regions classified as connecting to frontal, parietal, temporal and pre-central cortical target regions. © 2010 Elsevier Inc.


Gousias I.S.,Imperial College London | Hammers A.,Imperial College London | Hammers A.,The Neurodis Foundation Fondation Neurodis | Counsell S.J.,Imperial College London | And 11 more authors.
PLoS ONE | Year: 2013

We studied methods for the automatic segmentation of neonatal and developing brain images into 50 anatomical regions, utilizing a new set of manually segmented magnetic resonance (MR) images from 5 term-born and 15 preterm infants imaged at term corrected age called ALBERTs. Two methods were compared: individual registrations with label propagation and fusion; and template based registration with propagation of a maximum probability neonatal ALBERT (MPNA). In both cases we evaluated the performance of different neonatal atlases and MPNA, and the approaches were compared with the manual segmentations by means of the Dice overlap coefficient. Dice values, averaged across regions, were 0.81±0.02 using label propagation and fusion for the preterm population, and 0.81±0.02 using the single registration of a MPNA for the term population. Segmentations of 36 further unsegmented target images of developing brains yielded visibly high-quality results. This registration approach allows the rapid construction of automatically labeled age-specific brain atlases for neonates and the developing brain. © 2013 Gousias et al.


Klein-Koerkamp Y.,French National Center for Scientific Research | Heckemann R.A.,Sahlgrenska University Hospital | Heckemann R.A.,The Neurodis Foundation Fondation Neurodis | Heckemann R.A.,Imperial College London | And 11 more authors.
Current Alzheimer Research | Year: 2014

Current research suggests that amygdalar volumes in patients with Alzheimer's disease (AD) may be a relevant measure for its early diagnosis. However, findings are still inconclusive and controversial, partly because studies did not focus on the earliest stage of the disease. In this study, we measured amygdalar atrophy in 48 AD patients and 82 healthy controls (HC) by using a multi-atlas procedure, MAPER. Both hippocampal and amygdalar volumes, normalized by intracranial volume, were significantly reduced in AD compared with HC. The volume loss in the two structures was of similar magnitude (~24%). Amygdalar volume loss in AD predicted memory impairment after we controlled for age, gender, education, and, more important, hippocampal volume, indicating that memory decline correlates with amygdalar atrophy over and above hippocampal atrophy. Amygdalar volume may thus be as useful as hippocampal volume for the diagnosis of early AD. In addition, it could be an independent marker of cognitive decline. The role of the amygdala in AD should be reconsidered to guide further research and clinical practice. © 2014 Bentham Science Publishers.


PubMed | The Neurodis Foundation Fondation Neurodis
Type: Journal Article | Journal: NeuroImage | Year: 2011

This paper presents a novel, publicly available repository of anatomically segmented brain images of healthy subjects as well as patients with mild cognitive impairment and Alzheimers disease. The underlying magnetic resonance images have been obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) database. T1-weighted screening and baseline images (1.5T and 3T) have been processed with the multi-atlas based MAPER procedure, resulting in labels for 83 regions covering the whole brain in 816 subjects. Selected segmentations were subjected to visual assessment. The segmentations are self-consistent, as evidenced by strong agreement between segmentations of paired images acquired at different field strengths (Jaccard coefficient: 0.8020.0146). Morphometric comparisons between diagnostic groups (normal; stable mild cognitive impairment; mild cognitive impairment with progression to Alzheimers disease; Alzheimers disease) showed highly significant group differences for individual regions, the majority of which were located in the temporal lobe. Additionally, significant effects were seen in the parietal lobe. Increased left/right asymmetry was found in posterior cortical regions. An automatically derived white-matter hypointensities index was found to be a suitable means of quantifying white-matter disease. This repository of segmentations is a potentially valuable resource to researchers working with ADNI data.

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