Multi-template approaches for segmenting the hippocampus: the case of the SACHA software

International audienceThe hippocampus has been shown to play a crucial role in memory and learning. Its volumetry is a well-established biomarker of Alzheimer’s Disease (AD) and hippocampal sclerosis in temporal lobe epilepsy (TLE). Manual segmentation being time consuming and suffering from low reproducibility,robust automatic segmentation from routine T1 images is of high interest for studying large datasets. We previously proposed such an approach (SACHA, Chupin et al, 2007, 2009), based on competitive region deformation constrained by both anatomical landmarks and a single probabilistic template of 16 young healthy subjects registered using SPM5. The atlas being introduced as a soft constraint, robust results have been obtained in large series of patients with various pathologies. In recent years, multitemplate approaches have proven to be a powerful mean to increase segmentation robustness (Barnes et al, 2008) (Aljabar et al, 2009) (Heckemann et al 2006), more specifically for subjects with very large atrophy or atypical shapes (such as malrotations (Bernasconi et al, 2005) (Kim et al, 2012)).We propose here to evaluate the introduction of multiple-template constraints in SACHA

Alzheimers Dement

Introduction: The free and cued selective reminding test is used to identify memory deficits in mild cognitive impairment and demented patients. It allows assessing three processes: encoding, storage, and recollection of verbal episodic memory. Methods: We investigated the neural correlates of these three memory processes in a large cohort study. The Memento cohort enrolled 2323 outpatients presenting either with subjective cognitive decline or mild cognitive impairment who underwent cognitive, structural MRI and, for a subset, fluorodeoxyglucose-positron emission tomography evaluations. Results: Encoding was associated with a network including parietal and temporal cortices; storage was mainly associated with entorhinal and parahippocampal regions, bilaterally; retrieval was associated with a widespread network encompassing frontal regions. Discussion: The neural correlates of episodic memory processes can be assessed in large and standardized cohorts of patients at risk for Alzheimer's disease. Their relation to pathophysiological markers of Alzheimer's disease remains to be studied

Segmentation of white matter hyperintensities: a comparison of automatic methods on multicentre data

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Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation

International audienceWhite matter hyperintensities (WMH) on T2 or FLAIR sequences have been commonly observed on MR images of elderly people. They have been associated with various disorders and have been shown to be a strong risk factor for stroke and dementia. WMH studies usually required visual evaluation of WMH load or time-consuming manual delineation. This paper introduced WHASA (White matter Hyperintensities Automated Segmentation Algorithm), a new method for automatically segmenting WMH from FLAIR and T1 images in multicentre studies. Contrary to previous approaches that were based on intensities, this method relied on contrast: non linear diffusion filtering alternated with watershed segmentation to obtain piecewise constant images with increased contrast between WMH and surroundings tissues. WMH were then selected based on subject dependant automatically computed threshold and anatomical information. WHASA was evaluated on 67 patients from two studies, acquired on six different MRI scanners and displaying a wide range of lesion load. Accuracy of the segmentation was assessed through volume and spatial agreement measures with respect to manual segmentation; an intraclass correlation coefficient (ICC) of 0.96 and a mean similarity index (SI) of 0.72 were obtained. WHASA was compared to four other approaches: Freesurfer and a thresholding approach as unsupervised methods; k-nearest neighbours (kNN) and support vector machines (SVM) as supervised ones. For these latter, influence of the training set was also investigated. WHASA clearly outperformed both unsupervised methods, while performing at least as good as supervised approaches (ICC range: 0.87-0.91 for kNN; 0.89-0.94 for SVM. Mean SI: 0.63-0.71 for kNN, 0.67-0.72 for SVM), and did not need any training set

Neural basis of interindividual variability in social perception in typically developing children and adolescents using diffusion tensor imaging

International audienceHumans show great interindividual variability in the degree they engage in social relationship. The neural basis of this variability is still poorly understood, particularly in children. In this study, we aimed to investigate the neural basis of interindividual variability in the first step of social behavior, that is social perception, in typically developing children. For that purpose, we first used eye-tracking to objectively measure eye-gaze processing during passive visualization of social movie clips in 24 children and adolescents (10.5 ± 2.9 y). Secondly, we correlated eye-tracking data with measures of fractional anisotropy, an index of white matter microstructure, obtained using diffusion tensor imaging MRI. The results showed a large interindividual variability in the number of fixations to the eyes of characters during visualization of social scenes. In addition, whole-brain analysis showed a significant positive correlation between FA and number of fixations to the eyes,mainly in the temporal part of the superior longitudinal fasciculi bilaterally, adjacent to the posterior superior temporal cortex. Our results indicate the existence of a neural signature associated with the interindividual variability in social perception in children, contributing for better understanding the neural basis of typical and atypical development of a broader social expertise

The INSIGHT cohort: baseline analysis of structural MR imaging in asymptomatic subjects at risk for Alzheimer's disease

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Quantitative evaluation of all methods on different test sets.

<p>Quantitative evaluation of all methods on different test sets.</p

Computational time of the different methods.

<p>Computational time of the different methods.</p

SI distribution for all methods.

<p>*: significantly lower than WHASA methods (one-tailed t-test, p<0.05).</p

Representative slice showing segmentation results for all methods.

<p>Reference volume: 31.5 mL; Freesurfer (Volume = 10.5 mL, SI = 0.40) ; Thresholding (Vol = 17.9 mL, SI = 0.70) ; WHASA(Vol = 26.6 mL, SI = 0.79) ; kNN test set 1 (Volume = 24.6 mL, SI = 0.81) ; kNN test set 2 (Volume = 10.7 mL, SI = 0.48) ; kNN test set 3 (Volume = 18.7 mL, SI = 0.71) ; SVM test set 1 (Volume = 26.3 mL , SI = 0.82) ; SVM test set 2 (Volume = 16.7 mL , SI = 0.67) ; SVM test set 3 (Volume = 25.1 mL , SI = 0.80).</p