Characteristics of subjective cognitive decline associated with amyloid positivity.

INTRODUCTION: The evidence for characteristics of persons with subjective cognitive decline (SCD) associated with amyloid positivity is limited. METHODS: In 1640 persons with SCD from 20 Amyloid Biomarker Study cohort, we investigated the associations of SCD-specific characteristics (informant confirmation, domain-specific complaints, concerns, feelings of worse performance) demographics, setting, apolipoprotein E gene (APOE) ε4 carriership, and neuropsychiatric symptoms with amyloid positivity. RESULTS: Between cohorts, amyloid positivity in 70-year-olds varied from 10% to 76%. Only older age, clinical setting, and APOE ε4 carriership showed univariate associations with increased amyloid positivity. After adjusting for these, lower education was also associated with increased amyloid positivity. Only within a research setting, informant-confirmed complaints, memory complaints, attention/concentration complaints, and no depressive symptoms were associated with increased amyloid positivity. Feelings of worse performance were associated with less amyloid positivity at younger ages and more at older ages. DISCUSSION: Next to age, setting, and APOE ε4 carriership, SCD-specific characteristics may facilitate the identification of amyloid-positive individuals.

Clinical and neuropathological diversity of tauopathy in MAPT duplication carriers.

Microduplications of the 17q21.31 chromosomal region encompassing the MAPT gene, which encodes the Tau protein, were identified in patients with a progressive disorder initially characterized by severe memory impairment with or without behavioral changes that can clinically mimic Alzheimer disease. The unique neuropathological report showed a primary tauopathy, which could not be unanimously classified in a given known subtype, showing both 4R- and 3R-tau inclusions, mainly within temporal cortical subregions and basal ganglia, without amyloid deposits. Recently, two subjects harboring the same duplication were reported with an atypical extrapyramidal syndrome and gait disorder. To decipher the phenotypic spectrum associated with MAPT duplications, we studied ten carriers from nine families, including two novel unrelated probands, gathering clinical (n = 10), cerebrospinal fluid (n = 6), MRI (n = 8), dopamine transporter scan (n = 4), functional (n = 5), amyloid (n = 3) and Tau-tracer (n = 2) PET imaging data as well as neuropathological examination (n = 4). Ages at onset ranged from 37 to 57 years, with prominent episodic memory impairment in 8/10 patients, associated with behavioral changes in four, while two patients showed atypical extrapyramidal syndrome with gait disorder at presentation, including one with associated cognitive deficits. Amyloid imaging was negative but Tau imaging showed significant deposits mainly in both mesiotemporal cortex. Dopaminergic denervation was found in 4/4 patients, including three without extrapyramidal symptoms. Neuropathological examination exclusively showed Tau-immunoreactive lesions. Distribution, aspect and 4R/3R tau aggregates composition suggested a spectrum from predominantly 3R, mainly cortical deposits well correlating with cognitive and behavioral changes, to predominantly 4R deposits, mainly in the basal ganglia and midbrain, in patients with prominent extrapyramidal syndrome. Finally, we performed in vitro seeding experiments in HEK-biosensor cells. Morphological features of aggregates induced by homogenates of three MAPT duplication carriers showed dense/granular ratios graduating between those induced by homogenates of a Pick disease and a progressive supranuclear palsy cases. These results suggest that MAPT duplication causes a primary tauopathy associated with diverse clinical and neuropathological features.

Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel.

The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12 Hz) and widespread delta (< 4 Hz) and theta (4-8 Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.

Radiological classification of dementia from anatomical MRI assisted by machine learning-derived maps.

BACKGROUND AND PURPOSE: Many artificial intelligence tools are currently being developed to assist diagnosis of dementia from magnetic resonance imaging (MRI). However, these tools have so far been difficult to integrate in the clinical routine workflow. In this work, we propose a new simple way to use them and assess their utility for improving diagnostic accuracy. MATERIALS AND METHODS: We studied 34 patients with early-onset Alzheimer's disease (EOAD), 49 with late-onset AD (LOAD), 39 with frontotemporal dementia (FTD) and 24 with depression from the pre-existing cohort CLIN-AD. Support vector machine (SVM) automatic classifiers using 3D T1 MRI were trained to distinguish: LOAD vs. Depression, FTD vs. LOAD, EOAD vs. Depression, EOAD vs. FTD. We extracted SVM weight maps, which are tridimensional representations of discriminant atrophy patterns used by the classifier to take its decisions and we printed posters of these maps. Four radiologists (2 senior neuroradiologists and 2 unspecialized junior radiologists) performed a visual classification of the 4 diagnostic pairs using 3D T1 MRI. Classifications were performed twice: first with standard radiological reading and then using SVM weight maps as a guide. RESULTS: Diagnostic performance was significantly improved by the use of the weight maps for the two junior radiologists in the case of FTD vs. EOAD. Improvement was over 10 points of diagnostic accuracy. CONCLUSION: This tool can improve the diagnostic accuracy of junior radiologists and could be integrated in the clinical routine workflow.

Understanding the Variability in Graph Data Sets through Statistical Modeling on the Stiefel Manifold.

Network analysis provides a rich framework to model complex phenomena, such as human brain connectivity. It has proven efficient to understand their natural properties and design predictive models. In this paper, we study the variability within groups of networks, i.e., the structure of connection similarities and differences across a set of networks. We propose a statistical framework to model these variations based on manifold-valued latent factors. Each network adjacency matrix is decomposed as a weighted sum of matrix patterns with rank one. Each pattern is described as a random perturbation of a dictionary element. As a hierarchical statistical model, it enables the analysis of heterogeneous populations of adjacency matrices using mixtures. Our framework can also be used to infer the weight of missing edges. We estimate the parameters of the model using an Expectation-Maximization-based algorithm. Experimenting on synthetic data, we show that the algorithm is able to accurately estimate the latent structure in both low and high dimensions. We apply our model on a large data set of functional brain connectivity matrices from the UK Biobank. Our results suggest that the proposed model accurately describes the complex variability in the data set with a small number of degrees of freedom.

EEG evidence of compensatory mechanisms in preclinical Alzheimer's disease.

Early biomarkers are needed to identify individuals at high risk of preclinical Alzheimer's disease and to better understand the pathophysiological processes of disease progression. Preclinical Alzheimer's disease EEG changes would be non-invasive and cheap screening tools and could also help to predict future progression to clinical Alzheimer's disease. However, the impact of amyloid-ß deposition and neurodegeneration on EEG biomarkers needs to be elucidated. We included participants from the INSIGHT-preAD cohort, which is an ongoing single-centre multimodal observational study that was designed to identify risk factors and markers of progression to clinical Alzheimer's disease in 318 cognitively normal individuals aged 70-85 years with a subjective memory complaint. We divided the subjects into four groups, according to their amyloid status (based on 18F-florbetapir PET) and neurodegeneration status (evidenced by 18F-fluorodeoxyglucose PET brain metabolism in Alzheimer's disease signature regions). The first group was amyloid-positive and neurodegeneration-positive, which corresponds to stage 2 of preclinical Alzheimer's disease. The second group was amyloid-positive and neurodegeneration-negative, which corresponds to stage 1 of preclinical Alzheimer's disease. The third group was amyloid-negative and neurodegeneration-positive, which corresponds to 'suspected non-Alzheimer's pathophysiology'. The last group was the control group, defined by amyloid-negative and neurodegeneration-negative subjects. We analysed 314 baseline 256-channel high-density eyes closed 1-min resting state EEG recordings. EEG biomarkers included spectral measures, algorithmic complexity and functional connectivity assessed with a novel information-theoretic measure, weighted symbolic mutual information. The most prominent effects of neurodegeneration on EEG metrics were localized in frontocentral regions with an increase in high frequency oscillations (higher beta and gamma power) and a decrease in low frequency oscillations (lower delta power), higher spectral entropy, higher complexity and increased functional connectivity measured by weighted symbolic mutual information in theta band. Neurodegeneration was associated with a widespread increase of median spectral frequency. We found a non-linear relationship between amyloid burden and EEG metrics in neurodegeneration-positive subjects, either following a U-shape curve for delta power or an inverted U-shape curve for the other metrics, meaning that EEG patterns are modulated differently depending on the degree of amyloid burden. This finding suggests initial compensatory mechanisms that are overwhelmed for the highest amyloid load. Together, these results indicate that EEG metrics are useful biomarkers for the preclinical stage of Alzheimer's disease.

Simultaneous quantification of tau and α-synuclein in cerebrospinal fluid by high-resolution mass spectrometry for differentiation of Lewy Body Dementia from Alzheimer's Disease and controls.

Tau and α-synuclein are central in several neurodegenerative diseases, including Alzheimer Disease (AD), Dementia with Lewy Bodies (DLB) and Parkinson Disease (PD). New analytical methods for precise quantification of cerebrospinal fluid (CSF) levels of both tau and α-synuclein are required to differentiate between dementias or monitor therapeutic responses. Notably, levels of total α-synuclein reported by ELISA are inconsistent among studies, impacted by antibody specificity or lack of standardization. Here, we report on the development and validation of a sensitive and robust mass spectrometry-based assay for the simultaneous quantification of tau and α-synuclein in CSF. The optimized workflow avoided any affinity reagents, and involved the combination of two enzymes, Glu-C and trypsin for optimal sequence coverage of α-synuclein acidic C-terminus. Up to 7 α-synuclein peptides were quantified, including the C-terminal peptide (132-140), resulting in a sequence coverage of 54% in CSF. The lower limits of quantification (LLOQ) ranged from 0.1 ng mL-1 to 1 ng mL-1 depending on the peptide. Regarding CSF tau, 4 peptides common to all isoforms were monitored, and LLOQ ranged from 0.5 ng mL-1 to 0.75 ng mL-1. The multiplex method was successfully applied to CSF samples from AD and DLB patients, two clinically overlapping neurodegenerative diseases. CSF α-synuclein levels were significantly lower in DLB patients compared to AD and controls. Moreover, tau and α-synuclein concentrations showed opposite trends in AD and DLB patients, suggesting the benefit of combining the two biomarkers for differentiation of DLB from AD and controls.

Multimorbidity Is Associated with Preclinical Alzheimer's Disease Neuroimaging Biomarkers.

BACKGROUND: Identifying comorbidities that influence preclinical Alzheimer's disease (AD) can give some insight into the AD early stages trajectories to allow new treatment venues and to guide public health systems to prevent subsequent dementia. OBJECTIVE: To examine the association of multimorbidity with AD neuroimaging markers in cognitively normal older adults. METHODS: This study had a cross-sectional design. Data regarding 14 comorbidities were obtained for all 318 adults aged 70-85 years, recruited from the community to an ongoing prospective monocentric cohort. They underwent standardized neuropsychological and neuroimaging assessment with automated methods that measured hippocampal volumes, white matter hyperintensity volumes, fluorodeoxyglucose positron emission tomography (FDG-PET) standardized uptake values (SUV) in AD signature regions, and amyloid positron emission tomography (amyloid-PET) SUV ratios. Linear regression was used to assess the association of multimorbidity with AD neuroimaging biomarkers. RESULTS: Multimorbidity is signif icantly associated with lower hippocampal volumes (-0.03 ± 0.01; p = 0.012; R2 = 0.017) and lower FDG-PET SUV (-0.027 ± 0.009; p = 0.005; R2 = 0.022), with no association with amyloid deposition (0.001 ± 0.007; p = 0.884; R2 = 0.0001). Taken individually, obesity and excessive alcohol use are associated with lower FDG-PET values, whereas obstructive sleep apnea and mood disorders are related to lower amyloid-PET SUV ratios. CONCLUSION: Multimorbidity is associated with preclinical AD imaging markers of neurodegeneration, but not with amyloid.

Alzheimer's disease biomarker-guided diagnostic workflow using the added value of six combined cerebrospinal fluid candidates: Aß1-42, total-tau, phosphorylated-tau, NFL, neurogranin, and YKL-40.

INTRODUCTION: The diagnostic and classificatory performances of all combinations of three core (amyloid ß peptide [i.e., Aß1-42], total tau [t-tau], and phosphorylated tau) and three novel (neurofilament light chain protein, neurogranin, and YKL-40) cerebrospinal fluid biomarkers of neurodegeneration were compared among individuals with mild cognitive impairment (n = 41), Alzheimer's disease dementia (ADD; n = 35), frontotemporal dementia (FTD; n = 9), and cognitively healthy controls (HC; n = 21), using 10-fold cross-validation. METHODS: The combinations ranking in the top 10 according to diagnostic accuracy in differentiating between distinct diagnostic categories were identified. RESULTS: The single biomarkers or biomarker combinations generating the best area under the receiver operating characteristics (AUROCs) were the following: the combination [amyloid ß peptide + phosphorylated tau + neurofilament light chain] for distinguishing between ADD patients and HC (AUROC = 0.86), t-tau for distinguishing between ADD and FTD patients (AUROC = 0.82), and t-tau for distinguishing between FTD patients and HC (AUROC = 0.78). CONCLUSIONS: Novel and established cerebrospinal fluid markers perform with at least fair accuracy in the discrimination between ADD and FTD. The classification of mild cognitive impairment individuals was poor.

Effect of Alzheimer's disease risk and protective factors on cognitive trajectories in subjective memory complainers: An INSIGHT-preAD study.

INTRODUCTION: Cognitive change in people at risk of Alzheimer's disease (AD) such as subjective memory complainers is highly variable across individuals. METHODS: We used latent class growth modeling to identify distinct classes of nonlinear trajectories of cognitive change over 2 years follow-up from 265 subjective memory complainers individuals (age 70 years and older) of the INSIGHT-preAD cohort. We determined the effect of cortical amyloid load, hippocampus and basal forebrain volumes, and education on the cognitive trajectory classes. RESULTS: Latent class growth modeling identified distinct nonlinear cognitive trajectories. Education was associated with higher performing trajectories, whereas global amyloid load and basal forebrain atrophy were associated with lower performing trajectories. DISCUSSION: Distinct classes of cognitive trajectories were associated with risk and protective factors of AD. These associations support the notion that the identified cognitive trajectories reflect different risk for AD that may be useful for selecting high-risk individuals for intervention trials.

APP, PSEN1, and PSEN2 mutations in early-onset Alzheimer disease: A genetic screening study of familial and sporadic cases.

BACKGROUND: Amyloid protein precursor (APP), presenilin-1 (PSEN1), and presenilin-2 (PSEN2) mutations cause autosomal dominant forms of early-onset Alzheimer disease (AD-EOAD). Although these genes were identified in the 1990s, variant classification remains a challenge, highlighting the need to colligate mutations from large series. METHODS AND FINDINGS: We report here a novel update (2012-2016) of the genetic screening of the large AD-EOAD series ascertained across 28 French hospitals from 1993 onwards, bringing the total number of families with identified mutations to n = 170. Families were included when at least two first-degree relatives suffered from early-onset Alzheimer disease (EOAD) with an age of onset (AOO) ≤65 y in two generations. Furthermore, we also screened 129 sporadic cases of Alzheimer disease with an AOO below age 51 (44% males, mean AOO = 45 ± 2 y). APP, PSEN1, or PSEN2 mutations were identified in 53 novel AD-EOAD families. Of the 129 sporadic cases screened, 17 carried a PSEN1 mutation and 1 carried an APP duplication (13%). Parental DNA was available for 10 sporadic mutation carriers, allowing us to show that the mutation had occurred de novo in each case. Thirteen mutations (12 in PSEN1 and 1 in PSEN2) identified either in familial or in sporadic cases were previously unreported. Of the 53 mutation carriers with available cerebrospinal fluid (CSF) biomarkers, 46 (87%) had all three CSF biomarkers-total tau protein (Tau), phospho-tau protein (P-Tau), and amyloid ß (Aß)42-in abnormal ranges. No mutation carrier had the three biomarkers in normal ranges. One limitation of this study is the absence of functional assessment of the possibly and probably pathogenic variants, which should help their classification. CONCLUSIONS: Our findings suggest that a nonnegligible fraction of PSEN1 mutations occurs de novo, which is of high importance for genetic counseling, as PSEN1 mutational screening is currently performed in familial cases only. Among the 90 distinct mutations found in the whole sample of families and isolated cases, definite pathogenicity is currently established for only 77%, emphasizing the need to pursue the effort to classify variants.

Free and Cued Selective Reminding Test - accuracy for the differential diagnosis of Alzheimer's and neurodegenerative diseases: A large-scale biomarker-characterized monocenter cohort study (ClinAD).

INTRODUCTION: The International Working Group recommended the Free and Cued Selective Reminding Test (FCSRT) as a sensitive detector of the amnesic syndrome of the hippocampal type in typical Alzheimer's disease (AD). But does it differentiate AD from other neurodegenerative diseases? METHODS: We assessed the FCSRT and cerebrospinal fluid (CSF) AD biomarkers in 992 cases. Experts, blinded to biomarker data, attributed in 650 cases a diagnosis of typical AD, frontotemporal dementia, posterior cortical atrophy, Lewy body disease, progressive supranuclear palsy, corticobasal syndrome, primary progressive aphasias, "subjective cognitive decline," or depression. RESULTS: The FCSRT distinguished typical AD from all other conditions with a sensitivity of 100% and a specificity of 75%. Non-AD neurodegenerative diseases with positive AD CSF biomarkers ("atypical AD") did not have lower FCSRT scores than those with negative biomarkers. DISCUSSION: The FCSRT is a reliable tool for diagnosing typical AD among various neurodegenerative diseases. At an individual level, however, its specificity is not absolute. Our findings also widen the spectrum of atypical AD to multiple neurodegenerative conditions.

Preclinical Alzheimer's disease: A systematic review of the cohorts underlying the concept.

Preclinical Alzheimer's disease (AD) is a relatively recent concept describing an entity characterized by the presence of a pathophysiological biomarker signature characteristic for AD in the absence of specific clinical symptoms. There is rising interest in the scientific community to define such an early target population mainly because of failures of all recent clinical trials despite evidence of biological effects on brain amyloidosis for some compounds. A conceptual framework has recently been proposed for this preclinical phase of AD. However, few data exist on this silent stage of AD. We performed a systematic review to investigate how the concept is defined across studies. The review highlights the substantial heterogeneity concerning the three main determinants of preclinical AD: "normal cognition," "cognitive decline," and "AD pathophysiological signature." We emphasize the need for a harmonized nomenclature of the preclinical AD concept and standardized population-based and case-control studies using unified operationalized criteria.

Two-level diagnostic classification using cerebrospinal fluid YKL-40 in Alzheimer's disease.

INTRODUCTION: We assessed the diagnostic accuracy of cerebrospinal fluid (CSF) YKL-40 in discriminating (1) clinical Alzheimer's disease (AD) from cognitively healthy controls (HCs) and frontotemporal dementia (FTD) (level I) and (2) patients stratified by different pathophysiological profiles from HCs and FTD following a novel unbiased/descriptive categorization based on CSF biomarkers, independent of cognitive impairment severity (level II). METHODS: YKL-40 was compared among HCs (n = 21), mild cognitive impairment (n = 41), AD (n = 35), and FTD (n = 9) (level I) and among HCs (n = 21), AD pathophysiology (tau and amyloid ß) negative (n = 15), tau positive (n = 15), amyloid ß positive (n = 13), AD pathophysiology positive (n = 33), and FTD (n = 9) (level II). RESULTS: Level I: YKL-40 discriminated AD from HC and FTD (area under the receiver operating characteristic curves [AUROCs] = 0.69, 0.71). Level II: YKL-40 discriminated tau-positive individuals and AD pathophysiology-positive individuals from HC, AD pathophysiology-positive patients from FTD (AUROCs = 0.76, 0.72, 0.73). DISCUSSION: YKL-40 demonstrates fair performance in distinguishing tau-positive patients from HCs, suggesting it may aid clinical diagnosis and support a biomarker-guided pathophysiological stratification.

Mechanical stress models of Alzheimer's disease pathology.

INTRODUCTION: Extracellular accumulation of amyloid-ß protein and intracellular accumulation of tau in brain tissues have been described in animal models of Alzheimer's disease (AD) and mechanical stress-based diseases of different mechanisms, such as traumatic brain injury (TBI), arterial hypertension (HTN), and normal pressure hydrocephalus (NPH). METHODS: We provide a brief overview of experimental models of TBI, HTN, and NPH showing features of tau-amyloid pathology, neuroinflammation, and neuronal loss. RESULTS: "Alzheimer-like" hallmarks found in these mechanical stress-based models were compared with AD features found in transgenic models. DISCUSSION: The goal of this review is, therefore, to build on current concepts of onset and progression of AD lesions. We point to the importance of accumulated mechanical stress in brain as an environmental and endogenous factor that pushes protein deposition and neuronal injury over the disease threshold. We further encourage the development of preventing strategies and drug screening based on mechanical stress models.

Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria.

During the past decade, a conceptual shift occurred in the field of Alzheimer's disease (AD) considering the disease as a continuum. Thanks to evolving biomarker research and substantial discoveries, it is now possible to identify the disease even at the preclinical stage before the occurrence of the first clinical symptoms. This preclinical stage of AD has become a major research focus as the field postulates that early intervention may offer the best chance of therapeutic success. To date, very little evidence is established on this "silent" stage of the disease. A clarification is needed about the definitions and lexicon, the limits, the natural history, the markers of progression, and the ethical consequence of detecting the disease at this asymptomatic stage. This article is aimed at addressing all the different issues by providing for each of them an updated review of the literature and evidence, with practical recommendations.

[Subjective cognitive complaint. From anxiety to prodromal Alzheimer's disease?]. / Face à une plainte mnésique: phénomène banal ou prodrome de la maladie d'Alzheimer?

Subjective cognitive impairment or SCl is frequent (50% of the population after 50 years of age) and corresponds to numerous etiologies. To diagnose the etiology of a SCI, one must refer to the specific cognitive stage impaired (registration, storage, and retrieval) and use specific tests that may resolve the issue. The possibility of a neurodegenerative disorder as a cause of SCI must lead to caution. Paraclinical and complete neuropsychological testing is required as soon as this etiology s possible after an initial neurological consult.

Forecasting individual progression trajectories in Alzheimer's disease.

The anticipation of progression of Alzheimer's disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of more than 4,600 patients from four continents. We measured the accuracy of the method for selecting participants displaying a progression of clinical endpoints during a hypothetical trial. We show that enriching the population with the predicted progressors decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease and is therefore highly suitable for use in prevention trials.

Validation study of "Santé-Cerveau", a digital tool for early cognitive changes identification.

BACKGROUND: There is a need for a reliable, easy-to-use, widely available, and validated tool for timely cognitive impairment identification. We created a computerized cognitive screening tool (Santé-Cerveau digital tool (SCD-T)) including validated questionnaires and the following neuropsychological tests: 5 Word Test (5-WT) for episodic memory, Trail Making Test (TMT) for executive functions, and a number coding test (NCT) adapted from the Digit Symbol Substitution Test for global intellectual efficiency. This study aimed to evaluate the performance of SCD-T to identify cognitive deficit and to determine its usability. METHODS: Three groups were constituted including 65 elderly Controls, 64 patients with neurodegenerative diseases (NDG): 50 AD and 14 non-AD, and 20 post-COVID-19 patients. The minimum MMSE score for inclusion was 20. Association between computerized SCD-T cognitive tests and their standard equivalent was assessed using Pearson's correlation coefficients. Two algorithms (a simple clinician-guided algorithm involving the 5-WT and the NCT; and a machine learning classifier based on 8 scores from the SCD-T tests extracted from a multiple logistic regression model, and data from the SCD-T questionnaires) were evaluated. The acceptability of SCD-T was investigated through a questionnaire and scale. RESULTS: AD and non-AD participants were older (mean ± standard deviation (SD): 72.61 ± 6.79 vs 69.91 ± 4.86 years old, p = 0.011) and had a lower MMSE score (Mean difference estimate ± standard error: 1.74 ± 0.14, p < 0.001) than Controls; post-COVID-19 patients were younger than Controls (mean ± SD: 45.07 ± 11.36 years old, p < 0.001). All the computerized SCD-T cognitive tests were significantly associated with their reference version. In the pooled Controls and NDG group, the correlation coefficient was 0.84 for verbal memory, -0.60 for executive functions, and 0.72 for global intellectual efficiency. The clinician-guided algorithm demonstrated 94.4% ± 3.8% sensitivity and 80.5% ± 8.7% specificity, and the machine learning classifier 96.8% ± 3.9% sensitivity and 90.7% ± 5.8% specificity. The acceptability of SCD-T was good to excellent. CONCLUSIONS: We demonstrate the high accuracy of SCD-T in screening cognitive disorders and its good acceptance even in individuals with prodromal and mild dementia stages. SCD-T would be useful in primary care to faster refer subjects with significant cognitive impairment (and limit unnecessary referrals) to specialized consultation, improve the AD care pathway and the pre-screening in clinical trials.

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.