Differential Patterns of Domain-Specific Cognitive Complaints and Awareness Across the Alzheimer's Disease Spectrum

Background: Characterizing self- and informant-reported cognitive complaints, as well as awareness of cognitive decline (ACD), is useful for an early diagnosis of Alzheimer's disease (AD). However, complaints and ACD related to cognitive functions other than memory are poorly studied. Furthermore, it remains unclear which source of information is the most useful to distinguish various groups on the AD spectrum. Methods: Self- and informant-reported complaints were measured with the Everyday Cognition questionnaire (ECog-Subject and ECog-StudyPartner) in four domains (memory, language, visuospatial, and executive). ACD was measured as the subject-informant discrepancy in the four ECog scores. We compared the ECog and ACD scores across cognitive domains between four groups: 71 amyloid-positive individuals with amnestic AD, 191 amnestic mild cognitive impairment (MCI), or 118 cognitively normal (CN), and 211 amyloid-negative CN controls, selected from the ADNI database. Receiver operating characteristic curves analysis was performed to evaluate the accuracy of the ECog and ACD scores in discriminating clinical groups. Results: Self- and informant-reported complaints were generally distributed as follows: memory, language, executive, and visuospatial (from the most severe to the least severe). Both groups of CN participants presented on average more memory and language complaints than their informant. MCI participants showed good agreement with their informants. AD participants presented anosognosia in all domains, but especially for the executive domain. The four ECog-StudyPartner sub-scores allowed excellent discrimination between groups in almost all classifications and performed significantly better than the other two classifiers considered. The ACD was excellent in distinguishing the participants with AD from the two groups of CN participants. The ECog-Subject was the least accurate in discriminating groups in four of the six classifications performed. Conclusion: In research, the study of complaint and anosognosia should not be reduced solely to the memory domain. In clinical practice, non-amnestic complaints could also be linked to Alzheimer's disease. The presence of an informant also seems necessary given its accuracy as a source of information

Three simple ideas for predicting progression to Alzheimer's disease

International audienceIn spite of the amount of research done in the prediction of the progression of mild cognitive impaired (MCI) subjects to Alzheimer's disease (AD), there is still room for further improvement. Sophisticated methods have been proposed, some reaching classification accuracies of up to 85%. In the present paper, we propose a combination of simple ideas to determine if they allow to obtain similar accuracies when predicting MCI to AD conversion. We present three approaches making use of ADNI database. We set a performance baseline using only demographic and clinical data (gender, education level, APOE4, MMSE, CDR sum of boxes, ADASCog) that provides a balanced accuracy of 76% (AUC of 0.84). When using imaging data, an important finding is that when an SVM is trained for discriminating between cognitive normal (CN) subjects and AD patients, and the resulting classifier is applied to MCI subjects to predict conversion, performance using FDG PET data improves to 76% of balanced accuracy and an AUC of 0.82. The third approach, consisting of multimodal data, namely the combination of the scores obtained from SVM for T1w and FDG PET data, and the demographic and clinical data, provided the best prediction results (80% balanced accuracy, AUC of 0.88). These prediction accuracies, resulting from the combination simple ideas, are in line with state-of-the-art results, and provide a new baseline to compare more sophisticated methods against. All the code of the framework and the experiments will be publicly available at https://gitlab.icm-institute.org/aramislab/AD-ML

Predicting progression to Alzheimer’s disease from clinical and imaging data: a reproducible study

International audienceVarious machine learning approaches have been developed for predicting progression to Alzheimer’s disease (AD) in patients with mild cognitive impairment (MCI) from MRI and PET data. Objective comparison of these approaches is nearly impossible because of differences at all steps, from data management to image processing and evaluation procedures. Moreover, with a few exceptions, these papers rarely compare their results to that obtained with clinical/cognitive data only, a critical point to demonstrate the practical utility of neuroimaging in this context. We previously proposed a framework for the reproducible evaluation of ML algorithms for AD classification. This framework was applied to AD classification using unimodal neuroimaging data (T1 MRI and FDG PET). Here, we extend our previous work to the combination of multimodal clinical and neuroimaging data for predicting progression to AD among MCI patients. All the code is publicly available at: https://github.com/aramis-lab/AD-ML

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

The meta-memory ratio: a new cohort- independent way to measure cognitive awareness in asymptomatic individuals at risk for Alzheimer's disease

International audienceBackground: Lack of awareness of cognitive decline (ACD) has been described at the preclinical and prodromal stages of Alzheimer's disease (AD). In this study, we introduced a meta-memory ratio (MMR) and explored how it is associated with neuroimaging AD biomarkers in asymptomatic individuals at risk for AD. Method: Four hundred forty-eight cognitively healthy participants from two cohorts of subjective memory complainers (INSIGHT-PreAD and ADNI) were included. Regression models were used to assess the impact of AD biomarkers on the MMR. Result: In both cohorts, there was a significant quadratic effect of cerebral amyloidosis on the MMR value. In particular, participants had a high ACD up to the amyloid positivity threshold, above which a decrease of ACD was eventually observed as the amyloid load increased. Conclusion: This nonlinear evolution of ACD in very early AD must be taken into account in clinical care and for trial enrollment as well

Awareness of cognitive decline trajectories in asymptomatic individuals at risk for AD

Abstract BackgroundLack of awareness of cognitive decline (ACD) is common in late-stage Alzheimer's disease (AD). Recent studies showed that ACD can also be reduced in the early stages.MethodsWe performed a Latent Class Analysis to identify longitudinal changes of ACD over 3 years in 314 elderly memory-complainers and its association to amyloid burden and brain metabolism. We also analysed the impact of ACD at baseline on the cognitive scores’ evolution.Results76.8% of subjects constantly had an accurate ACD (reference class). 18.95% showed a persistent heightened ACD (“worried-well” individuals). 4.25% constantly showed low ACD. They had higher amyloid burden than the reference class, and were mostly men. We found no overall effect of baseline ACD on cognitive scores’ evolution.ConclusionsACD begins to decrease during the preclinical phase in a certain group of individuals, who are of great interest because more at risk of being affected by AD.Trial registrationThe present study was conducted as part of the INSIGHT-PreAD study. The identification number of INSIGHT-PreAD study (ID-RCB) is 2012-A01731-42</jats:p