Alzheimer Disease: Kinkingnéhun S

Magnin B, Mesrob L, Kinkingnéhun S, Pélégrini-Issac M, Colliot O, Sarazin M, Dubois B, Lehéricy S, Benali H. · UMR-S 678, Inserm, Paris, France. · Neuroradiology. · Pubmed #18846369 No free full text.

Abstract: PURPOSE: We present and evaluate a new automated method based on support vector machine (SVM) classification of whole-brain anatomical magnetic resonance imaging to discriminate between patients with Alzheimer's disease (AD) and elderly control subjects. MATERIALS AND METHODS: We studied 16 patients with AD [mean age +/- standard deviation (SD) = 74.1 +/- 5.2 years, mini-mental score examination (MMSE) = 23.1 +/- 2.9] and 22 elderly controls (72.3 +/- 5.0 years, MMSE = 28.5 +/- 1.3). Three-dimensional T1-weighted MR images of each subject were automatically parcellated into regions of interest (ROIs). Based upon the characteristics of gray matter extracted from each ROI, we used an SVM algorithm to classify the subjects and statistical procedures based on bootstrap resampling to ensure the robustness of the results. RESULTS: We obtained 94.5% mean correct classification for AD and control subjects (mean specificity, 96.6%; mean sensitivity, 91.5%). CONCLUSIONS: Our method has the potential in distinguishing patients with AD from elderly controls and therefore may help in the early diagnosis of AD.

Kinkingnéhun S, Sarazin M, Lehéricy S, Guichart-Gomez E, Hergueta T, Dubois B. · INSERM U610, Paris, France. · Neurology. · Pubmed #18448872 No free full text.

Abstract: OBJECTIVE: To determine whether regional atrophy or neuropsychological factors can predict the rate of decline in patients with mild Alzheimer disease (AD). BACKGROUND: Despite important implications for planning the care and treatment strategy, few prognostic factors of severe AD progression are known. METHODS: Twenty-three patients with mild AD were followed up every 6 months over the course of 3 years. At baseline, patients with AD and 18 controls underwent a neuropsychological battery and a brain MRI. At the end of the 3 years, patients with AD were dichotomized into slow decliners (SLD) or fast decliners (FD) groups on the basis of their decline in Mini-Mental State Examination score over time. We compared baseline cognitive performance and imaging data using voxel-based morphometry (VBM). RESULTS: SLD and FD groups did not differ in age, gender, level of education, mean estimated duration of illness, and standard neuropsychological data at inclusion, except for the Attentional Battery of the Cambridge Neuropsychological Tests Automated Battery (speed processing in shifting condition). VBM comparison between SLD and FD groups demonstrated more gray matter tissue loss in the FD group in the medial occipitoparietal areas, especially in the precuneus, the lingual gyrus, the cuneus, and the surrounding cortex of the parieto-occipital sulcus bilaterally. CONCLUSION: Voxel-based morphometry analysis demonstrated that patients who will have a faster decline at 3 years already had a more extensive cortical atrophy than SLD patients, especially in the medial occipitoparietal areas, which was not yet detected by clinical and neuropsychological assessment.

Chupin M, Mukuna-Bantumbakulu AR, Hasboun D, Bardinet E, Baillet S, Kinkingnéhun S, Lemieux L, Dubois B, Garnero L. · Department of Clinical and Experimental Epilepsy, Institute of Neurology, UCL, UK. · Neuroimage. · Pubmed #17178234 No free full text.

Abstract: We describe a new algorithm for the automated segmentation of the hippocampus (Hc) and the amygdala (Am) in clinical Magnetic Resonance Imaging (MRI) scans. Based on homotopically deforming regions, our iterative approach allows the simultaneous extraction of both structures, by means of dual competitive growth. One of the most original features of our approach is the deformation constraint based on prior knowledge of anatomical features that are automatically retrieved from the MRI data. The only manual intervention consists of the definition of a bounding box and positioning of two seeds; total execution time for the two structures is between 5 and 7 min including initialisation. The method is evaluated on 16 young healthy subjects and 8 patients with Alzheimer's disease (AD) for whom the atrophy ranged from limited to severe. Three aspects of the performances are characterised for validating the method: accuracy (automated vs. manual segmentations), reproducibility of the automated segmentation and reproducibility of the manual segmentation. For 16 young healthy subjects, accuracy is characterised by mean relative volume error/overlap/maximal boundary distance of 7%/84%/4.5 mm for Hc and 12%/81%/3.9 mm for Am; for 8 Alzheimer's disease patients, it is 9%/84%/6.5 mm for Hc and 15%/76%/4.5 mm for Am. We conclude that the performance of this new approach in data from healthy and diseased subjects in terms of segmentation quality, reproducibility and time efficiency compares favourably with that of previously published manual and automated segmentation methods. The proposed approach provides a new framework for further developments in quantitative analyses of the pathological hippocampus and amygdala in MRI scans.