Nasreddine, Ziad

Abstract Background Individuals with preclinical AD are difficult to detect using traditional clinical tools. Yet, many individuals classified as cognitively unimpaired (CU) can exhibit a heterogeneous pattern of subtle clinical abnormalities, potentially linked to early Alzheimer's disease (AD). Primary care providers often have access only to clinical tests to study at‐risk community‐dwelling older adults who visit them without cognitive complaints. Since early intervention is crucial in AD, identifying clinical tools to detect preclinical AD is important in areas with limited access to blood or imaging biomarkers. In this study, we evaluate the link between Montreal Cognitive Assessment (MoCA) total scores and AD pathology in CU individuals. Methods We studied 204 cognitively unimpaired (CU) older adults who underwent both 18 F‐FTP and 18 F‐MK6240 scans. These individuals were stratified based on their amyloid PET status, determined by visual reading, into CU A+ ( n = 35) and CU A‐ ( n = 137), as part of the ongoing HEAD study. CU adults had Clinical Dementia Rating (CDR) of 0 and were clinically identified as non‐MCI and non‐demented. Voxel‐wise linear regression models tested the association between MoCA total scores and tau pathology. Results CU A+T+ had significantly lower total MoCA scores than A‐T‐ individuals (Figure 1). Lesser MoCA total score was significantly associated with greater mediobasal temporal tau deposition, using both 18 F‐MK6240 and 18 F‐FTP [Figure 2]. When we separated the population by Aβ status, we found that these results were driven by the CU A+ group (Figure 3A) and were not present in CU A‐ individuals (Figure 3B). Discussion These findings indicate that the MoCA, a simple routine in‐office clinical test, can detect subtle cognitive dysfunction associated with preclinical AD. This suggests that simple cognitive testing can play a role in the early detection of AD and therefore an option for prescreening older adults in non‐specialized clinical settings, the initial interface for most older adults without cognitive complaints. Conclusion MoCA total, the common dementia screening test also plays a valuable role in detecting preclinical AD.

AbstractBackgroundAs the only optically accessible part of the central nervous system, the retina represents an intriguing opportunity for the detection of biomarkers for Alzheimer’s disease (AD). This study evaluated the performance of the Retinal Deep PhenotypingTM platform, a digital biomarker platform comprising a hyperspectral retinal camera and image analysis algorithms, for the detection of likely positron‐emission tomography (PET) amyloid status (negative or positive) in older adults. A set of phenotypic features that correlates with the cerebral amyloid status as determined by amyloid PET scan were identified and used to train a classifying algorithm.MethodHyperspectral retinal images acquired with a Mydriatic Hyperspectral Retinal Camera from 194 participants (age ≥ 50 years), including cognitively normal and cognitively impaired (mild cognitive impairment and dementia) across 5 imaging sites were processed in order to train the model. Of these 194 participants, 73 individuals (38%) were amyloid‐positive, as confirmed by unanimous readings of PET scans by a panel of 3 expert reviewers. The pre‐processed hyperspectral images were segmented into various anatomical sites, and a texture‐based approach was used to extract several thousands of spatial‐spectral features. The most relevant features for the classification task were selected using a minimum redundancy maximum relevance (MRMR) algorithm and used to train a linear support vector machine (SVM) classifier. A nested, cross‐validation technique was used to evaluate the performance of the classifier.ResultThe resulting model based on the 17 most significant features showed high performance to discriminate between amyloid positive and negative subjects with an area under the receiver operating curve (AUCROC) of 0.87 (95% CI: 0.83 – 0.92).ConclusionThe Retinal Deep PhenotypingTM platform shows promise for detecting the likely cerebral amyloid PET status in adults 50 years and older from a simple, non‐invasive retinal scan and could provide an accessible means to identify individuals with abnormal cerebral amyloid in a clinical or drug development context. This phenotyping platform provides a flexible approach that could also be used for the detection of multiple biomarkers involved in cognitive decline from the same hyperspectral images of the retina.

AbstractBackgroundTranslating recent advances in the diagnosis of Alzheimer’s disease (AD) and other dementias to clinical practice using biomarker‐based approaches requires accessible, affordable biomarkers. This protocol tested the performance of a Retinal Deep Phenotyping platform for the detection of likely positron‐emission tomography (PET) amyloid status (negative or positive) in human subjects. This platform generates data rich retinal reflectance images captured non‐invasively with a Metabolic Hyperspectral Retinal Camera (MHRC) yielding specific spatial‐spectral features which are analyzed by a machine learning algorithm relatively to a recognized gold standard biomarker (in this case PET amyloid imaging).Methods134 subjects (50 years and older) from 3 clinical sites were included, 94 with normal, 40 with abnormal cognition, imaged with the MHRC and amyloid PET (unanimous visual reads from a panel of 3 expert reviewers; 42 amyloid positive, 92 negative). Over 600 spatial‐spectral features were extracted from hyperspectral retinal images obtained at 450‐900 nm. After a feature selection based on F‐score ranking, a classifier was trained on the datasets from 87 patients (2/3 of the cohort) using the most discriminating features. The classification performance was then evaluated with the datasets from the remaining 47 patients.ResultsResubstituting the training set of 87 patients into the classifier yielded sensitivity and specificity values of 88% and 91%, respectively. The training misclassification rate was predicted using the k‐fold loss metric, with a predicted error rate of 19%. To verify this performance, the test set of 47 patients was classified by the model resulting in sensitivity and specificity values of 80% and 81%, respectively.ConclusionsThe Retinal Deep Phenotyping platform shows promise for detecting the likely cerebral amyloid PET status in human subjects and could serve as a screening tool to identify subjects in the AD continuum, for instance in a clinical or drug development context. The platform could also be used for the detection of other biomarkers involved in cognitive decline.