Detection of Alzheimer's disease signature in MR images seven years before conversion to dementia: Toward an early individual prognosis

Abstract

Finding very early biomarkers of Alzheimer's Disease (AD) to aid in individual prognosis is of major interest to accelerate the development of new therapies. Among the potential biomarkers, neurodegeneration measurements from MRI are considered as good candidates but have so far not been effective at the early stages of the pathology. Our objective is to investigate the efficiency of a new MR-based hippocampal grading score to detect incident dementia in cognitively intact patients. This new score is based on a pattern recognition strategy, providing a grading measure that reflects the similarity of the anatomical patterns of the subject under study with dataset composed of healthy subjects and patients with AD. Hippocampal grading was evaluated on subjects from the Three-City cohort, with a followup period of 12 years. Experiments demonstrate that hippocampal grading yields prediction accuracy up to 72.5% (P < 0.0001) 7 years before conversion to AD, better than both hippocampal volume (58.1%, P = 0.04) and MMSE score (56.9%, P = 0.08). The area under the ROC curve (AUC) supports the efficiency of imaging biomarkers with a gain of 8.4 percentage points for hippocampal grade (73.0%) over hippocampal volume (64.6%). Adaptation of the proposed framework to clinical score estimation is also presented. Compared with previous studies investigating new biomarkers for AD prediction over much shorter periods, the very long followup of the Three-City cohort demonstrates the important clinical potential of the proposed imaging biomarker. The high accuracy obtained with this new imaging biomarker paves the way for computer-based prognostic aides to help the clinician identify cognitively intact subjects that are at high risk to develop AD.

MRI and cognitive scores complement each other to accurately predict Alzheimer’s dementia 2 to 7 years before clinical onset

“…We previously showed that using only SNIPE scores for hippocampus, plus age and sex, one can reach an overall accuracy of 71% for prediction of progression from MCI to dementia over a 3y follow-up period [11]. In a more recent work looking at a cohort of cognitively healthy older individuals, we showed that our MR-driven SNIPE biomarker was sensitive to AD-related changes in a cognitively normal cohort on average seven years before clinical diagnosis of AD dementia [13].…”

“…Hippocampal and entorhinal SNIPE grading scores were used as the only MRI biomarker features in the predictive classifier. The SNIPE score is described fully in [13,20]. In short, SNIPE assigns a similarity metric to each voxel, which shows how much that voxel's neighbourhood resembles the anatomy of either a group of patients with Alzheimer's dementia or a group of normal controls.…”

“…In short, SNIPE assigns a similarity metric to each voxel, which shows how much that voxel's neighbourhood resembles the anatomy of either a group of patients with Alzheimer's dementia or a group of normal controls. The final SNIPE score is an average of all the voxels in the desired anatomical structure in each hemisphere [12,13]. For this study the SNIPE scores are corrected for age and sex using the method presented in [21] based only on the cognitively normal population to correct for the effect of normal aging and preserve the effect of diseaserelated changes.…”

Your algorithm might think the hippocampus grows in Alzheimer's disease: Caveats of longitudinal automated hippocampal volumetry

“…While this could be an important undertaking in the context of the hippocampal volume segmentation, such analyses are a topic of hot debate in the literature (Coupe et al, 2011a; Coupe et al, 2015; Eskildsen et al, 2013; Davatzikos et al, 2011; Wang et al, 2010). Such a problem would require several design choices, which themselves will be controversial, each of which could easily account for a source of detailed investigation.…”

Rey's Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer's disease

“…The disease processes leading to AD are known to start while individuals are still cognitively normal and may precede clinical symptoms by years or decades (Jack et al, 2010, Adaszewski et al, 2013). Reflecting this and the call for the biological evidence for AD diagnosis, several AD specific biomarkers have been identified, including multivariate patterns of structural brain atrophy measured by magnetic resonance imaging (MRI) (Moradi et al, 2015, Bron et al, 2015, Salvatore et al, 2015, Coupé et al, 2015, Eskildsen et al, 2013, Wee et al, 2013). MRI-based biomarkers have the advantages of being non-invasive and widely available.…”