The association between posterior resting-state EEG alpha rhythms and functional MRI connectivity in older adults with subjective memory complaint.

Resting-state eyes-closed electroencephalographic (rsEEG) alpha rhythms are dominant in posterior cortical areas in healthy adults and are abnormal in subjective memory complaint (SMC) persons with Alzheimer's disease amyloidosis. This exploratory study in 161 SMC participants tested the relationships between those rhythms and seed-based resting-state functional magnetic resonance imaging (rs-fMRI) connectivity between thalamus and visual cortical networks as a function of brain amyloid burden, revealed by positron emission tomography and cognitive reserve, measured by educational attainment. The SMC participants were divided into 4 groups according to 2 factors: Education (Edu+ and Edu-) and Amyloid burden (Amy+ and Amy-). There was a statistical interaction (p < 0.05) between the two factors, and the subgroup analysis using estimated marginal means showed a positive association between the mentioned rs-fMRI connectivity and the posterior rsEEG alpha rhythms in the SMC participants with low brain amyloidosis and high CR (Amy-/Edu+). These results suggest that in SMC persons, early Alzheimer's disease amyloidosis may contrast the beneficial effects of cognitive reserve on neurophysiological oscillatory mechanisms at alpha frequencies and connectivity between the thalamus and visual cortical networks.

Theta Band-Power Shapes Amyloid-Driven Longitudinal EEG Changes in Elderly Subjective Memory Complainers At-Risk for Alzheimer's Disease.

BACKGROUND: Alzheimer's disease (AD) includes progressive symptoms spread along a continuum of preclinical and clinical stages. Although numerous studies uncovered the neuro-cognitive changes of AD, very little is known on the natural history of brain lesions and modifications of brain networks in elderly cognitively-healthy memory complainers at risk of AD for carrying pathophysiological biomarkers (amyloidopathy and tauopathy). OBJECTIVE: We analyzed resting-state electroencephalography (EEG) of 318 cognitively-healthy subjective memory complainers from the INSIGHT-preAD cohort at the time of their first visit (M0) and two-years later (M24). METHODS: Using 18F-florbetapir PET-scanner, subjects were stratified between amyloid negative (A-; n = 230) and positive (A+; n = 88) groups. Differences between A+ and A- were estimated at source-level in each band-power of the EEG spectrum. RESULTS: At M0, we found an increase of theta power in the mid-frontal cortex in A+ compared to A-. No significant association was found between mid-frontal theta and the individuals' cognitive performance. At M24, theta power increased in A+ relative to A- individuals in the posterior cingulate cortex and the pre-cuneus. Alpha band revealed a peculiar decremental trend in posterior brain regions in the A+ relative to the A- group only at M24. Theta power increase over the mid-frontal and mid-posterior cortices suggests an hypoactivation of the default-mode network in the A+ individuals and a non-linear longitudinal progression at M24. CONCLUSION: We provide the first source-level longitudinal evidence on the impact of brain amyloidosis on the EEG dynamics of a large-scale, monocentric cohort of elderly individuals at-risk for AD.

Subtle postoperative cognitive disorder in preclinical Alzheimer's disease.

BACKGROUND: We examined the association between preclinical Alzheimer's disease (AD) and undergoing anesthesia and surgery ("surgery" henceforth) in a cohort of elderly individuals with a subjective cognitive decline (SCD). METHODS: Individuals with SCD (N = 268) were enrolled in a longitudinal follow-up study. Participants underwent comprehensive yearly cognitive evaluation for a period of 4 years. Brain amyloid load and glucose metabolism were studied by 18F-Florbetapir and Fluorodeoxyglucose positron emission tomography (PET) at baseline and after two years of follow-up. Exposure to surgery was systematically assessed during the first two years of follow-up. The association between surgery, cognition and AD markers was evaluated using generalized linear mixed models for cognition and linear models for neuroimaging markers. RESULTS: Sixty-five participants (24.25%) underwent surgery during the first year of follow-up, and 43 (16.04%) during the second year. Undergoing surgery had no overall impact on cognition over 4 years of follow-up nor on amyloid load and brain metabolism at two years of follow-up. However, a second step analysis revealed a small but significant association between undergoing surgery and a subtle decline in executive functions such as mental flexibility and divided attention (TMT B-A), in participants with greater amyloid load at baseline (Cohen's f2 = 0.01, multiple comparison corrected p < 0.001). Highly educated participants with surgery had significantly decreased metabolism over two years, when compared to low educated participants (Cohen's f2 = 0.04, p = 0.031). CONCLUSIONS: Our results suggest that surgery is associated with an increased risk of subtle cognitive decline after surgery, in the cognitively healthy elderly at risk for AD.

Prevalence Estimates of Amyloid Abnormality Across the Alzheimer Disease Clinical Spectrum.

IMPORTANCE: One characteristic histopathological event in Alzheimer disease (AD) is cerebral amyloid aggregation, which can be detected by biomarkers in cerebrospinal fluid (CSF) and on positron emission tomography (PET) scans. Prevalence estimates of amyloid pathology are important for health care planning and clinical trial design. OBJECTIVE: To estimate the prevalence of amyloid abnormality in persons with normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia and to examine the potential implications of cutoff methods, biomarker modality (CSF or PET), age, sex, APOE genotype, educational level, geographical region, and dementia severity for these estimates. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional, individual-participant pooled study included participants from 85 Amyloid Biomarker Study cohorts. Data collection was performed from January 1, 2013, to December 31, 2020. Participants had normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia. Normal cognition and subjective cognitive decline were defined by normal scores on cognitive tests, with the presence of cognitive complaints defining subjective cognitive decline. Mild cognitive impairment and clinical AD dementia were diagnosed according to published criteria. EXPOSURES: Alzheimer disease biomarkers detected on PET or in CSF. MAIN OUTCOMES AND MEASURES: Amyloid measurements were dichotomized as normal or abnormal using cohort-provided cutoffs for CSF or PET or by visual reading for PET. Adjusted data-driven cutoffs for abnormal amyloid were calculated using gaussian mixture modeling. Prevalence of amyloid abnormality was estimated according to age, sex, cognitive status, biomarker modality, APOE carrier status, educational level, geographical location, and dementia severity using generalized estimating equations. RESULTS: Among the 19 097 participants (mean [SD] age, 69.1 [9.8] years; 10 148 women [53.1%]) included, 10 139 (53.1%) underwent an amyloid PET scan and 8958 (46.9%) had an amyloid CSF measurement. Using cohort-provided cutoffs, amyloid abnormality prevalences were similar to 2015 estimates for individuals without dementia and were similar across PET- and CSF-based estimates (24%; 95% CI, 21%-28%) in participants with normal cognition, 27% (95% CI, 21%-33%) in participants with subjective cognitive decline, and 51% (95% CI, 46%-56%) in participants with mild cognitive impairment, whereas for clinical AD dementia the estimates were higher for PET than CSF (87% vs 79%; mean difference, 8%; 95% CI, 0%-16%; P = .04). Gaussian mixture modeling-based cutoffs for amyloid measures on PET scans were similar to cohort-provided cutoffs and were not adjusted. Adjusted CSF cutoffs resulted in a 10% higher amyloid abnormality prevalence than PET-based estimates in persons with normal cognition (mean difference, 9%; 95% CI, 3%-15%; P = .004), subjective cognitive decline (9%; 95% CI, 3%-15%; P = .005), and mild cognitive impairment (10%; 95% CI, 3%-17%; P = .004), whereas the estimates were comparable in persons with clinical AD dementia (mean difference, 4%; 95% CI, -2% to 9%; P = .18). CONCLUSIONS AND RELEVANCE: This study found that CSF-based estimates using adjusted data-driven cutoffs were up to 10% higher than PET-based estimates in people without dementia, whereas the results were similar among people with dementia. This finding suggests that preclinical and prodromal AD may be more prevalent than previously estimated, which has important implications for clinical trial recruitment strategies and health care planning policies.

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.

A machine learning approach to screen for preclinical Alzheimer's disease.

Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on 18F-florbetapir and 18F-fluorodeoxyglucose PET, respectively. We used a nested cross-validation approach with non-invasive features (electroencephalography [EEG], APOE4 genotype, demographic, neuropsychological and MRI data) to predict: 1/ amyloid status; 2/ neurodegeneration status; 3/ decline to prodromal AD at 5-year follow-up. Importantly, EEG was most strongly predictive of neurodegeneration, even when reducing the number of channels from 224 down to 4, as 4-channel EEG best predicted neurodegeneration (negative predictive value [NPV] = 82%, positive predictive value [PPV] = 38%, 77% specificity, 45% sensitivity). The combination of demographic, neuropsychological data, APOE4 and hippocampal volumetry most strongly predicted amyloid (80% NPV, 41% PPV, 70% specificity, 58% sensitivity) and most strongly predicted decline to prodromal AD at 5 years (97% NPV, 14% PPV, 83% specificity, 50% sensitivity). Thus, machine learning can help to screen patients at high risk of preclinical AD using non-invasive and affordable biomarkers.

Investigating the association between cancer and the risk of dementia: Results from the Memento cohort.

INTRODUCTION: Studies on the association of cancer and risk of dementia are inconclusive due to result heterogeneity and concerns of survivor bias and unmeasured confounding. METHODS: This study uses data from the Memento cohort, a French multicenter cohort following persons with either mild or isolated cognitive complaints for a median of 5 years. Illness-death models (IDMs) were used to estimate transition-specific hazard ratios (HRs) and 95% confidence intervals (CIs) for incident cancer in relation to dementia from time since study entry. RESULTS: The analytical sample (N = 2258) excluded 65 individuals without follow-up information. At the end of follow-up, 286 individuals were diagnosed with dementia, 166 with incident cancer, and 95 died. Incident cancer was associated with a reduced risk of dementia (HR = 0.58, 95% CI = 0.35-0.97), with a corresponding E-value of 2.84 (lower CI = 1.21). DISCUSSION: This study supports a protective relationship between incident cancer and dementia, encouraging further investigations to understand potential underlying mechanisms.

Partial Volume Correction Increases the Sensitivity of 18F-Florbetapir-Positron Emission Tomography for the Detection of Early Stage Amyloidosis.

Purpose: To test whether correcting for unspecific signal from the cerebral white matter increases the sensitivity of amyloid-PET for early stages of cerebral amyloidosis. Methods: We analyzed 18F-Florbetapir-PET and cerebrospinal fluid (CSF) Aß42 data from 600 older individuals enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI), including people with normal cognition, mild cognitive impairment (MCI), and Alzheimer's disease (AD) dementia. We determined whether three compartmental partial volume correction (PVC-3), explicitly modeling signal spill-in from white matter, significantly improved the association of CSF Aß42 levels with global 18F-Florbetapir-PET values compared with standard processing without PVC (non-PVC) and a widely used two-compartmental PVC method (PVC-2). In additional voxel-wise analyses, we determined the sensitivity of PVC-3 compared with non-PVC and PVC-2 for detecting early regional amyloid build-up as modeled by decreasing CSF Aß42 levels. For replication, we included an independent sample of 43 older individuals with subjective memory complaints from the INveStIGation of AlzHeimer's PredicTors cohort (INSIGHT-preAD study). Results: In the ADNI sample, PVC-3 18F-Florbetapir-PET values normalized to whole cerebellum signal showed significantly stronger associations with CSF Aß42 levels than non-PVC or PVC-2, particularly in the lower range of amyloid levels. These effects were replicated in the INSIGHT-preAD sample. PVC-3 18F-Florbetapir-PET data detected regional amyloid build-up already at higher (less abnormal) CSF Aß42 levels than non-PVC or PVC-2 data. Conclusion: A PVC approach that explicitly models unspecific white matter binding improves the sensitivity of amyloid-PET for identifying the earliest stages of cerebral amyloid pathology which has implications for future primary prevention trials.

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

BACKGROUND: 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.

What electrophysiology tells us about Alzheimer's disease: a window into the synchronization and connectivity of brain neurons.

Electrophysiology provides a real-time readout of neural functions and network capability in different brain states, on temporal (fractions of milliseconds) and spatial (micro, meso, and macro) scales unmet by other methodologies. However, current international guidelines do not endorse the use of electroencephalographic (EEG)/magnetoencephalographic (MEG) biomarkers in clinical trials performed in patients with Alzheimer's disease (AD), despite a surge in recent validated evidence. This position paper of the ISTAART Electrophysiology Professional Interest Area endorses consolidated and translational electrophysiological techniques applied to both experimental animal models of AD and patients, to probe the effects of AD neuropathology (i.e., brain amyloidosis, tauopathy, and neurodegeneration) on neurophysiological mechanisms underpinning neural excitation/inhibition and neurotransmission as well as brain network dynamics, synchronization, and functional connectivity, reflecting thalamocortical and corticocortical residual capacity. Converging evidence shows relationships between abnormalities in EEG/MEG markers and cognitive deficits in groups of AD patients at different disease stages. The supporting evidence for the application of electrophysiology in AD clinical research as well as drug discovery pathways warrants an international initiative to include the use of EEG/MEG biomarkers in the main multicentric projects planned in AD patients, to produce conclusive findings challenging the present regulatory requirements and guidelines for AD studies.

Reduction of recruitment costs in preclinical AD trials: validation of automatic pre-screening algorithm for brain amyloidosis.

We propose a method for recruiting asymptomatic Amyloid positive individuals in clinical trials, using a two-step process. We first select during a pre-screening phase a subset of individuals which are more likely to be amyloid positive based on the automatic analysis of data acquired during routine clinical practice, before doing a confirmatory PET-scan to these selected individuals only. This method leads to an increased number of recruitments and to a reduced number of PET-scans, resulting in a decrease in overall recruitment costs. We validate our method on three different cohorts, and consider five different classification algorithms for the pre-screening phase. We show that the best results are obtained using solely cognitive, genetic and socio-demographic features, as the slight increased performance when using MRI or longitudinal data is balanced by the cost increase they induce. We show that the proposed method generalizes well when tested on an independent cohort, and that the characteristics of the selected set of individuals are identical to the characteristics of a population selected in a standard way. The proposed approach shows how Machine Learning can be used effectively in practice to optimize recruitment costs in clinical trials.

Relationships between objectives sleep parameters and brain amyloid load in subjects at risk for Alzheimer's disease: the INSIGHT-preAD Study.

STUDY OBJECTIVES: Sleep changes have been associated with increased risks of developing cognitive disturbances and Alzheimer's disease (AD). A bidirectional relation is underlined between amyloid-beta (Aß) and sleep disruptions. The sleep profile in participants at risk to develop AD is not fully deciphered. We aim to investigate sleep-wake changes with objective sleep measurements in elderly participants without cognitive impairment depending on their brain amyloid status, positive (Aß+) or negative (Aß-) based on standard absorption ratios (SUVr) positron emission tomography-florbetapir imaging. METHODS: Sixty-eight participants without cognitive impairment who have accepted to be involved in the sleep ancillary study from the InveStIGation of Alzheimer's Predictors in Subjective Memory Complainers (INSIGHT-pre AD) cohort, aiming to record sleep profile based on the analyses of an ambulatory accelerometer-based assessment (seven consecutive 24-hour periods). Neuropsychological tests were performed and sleep parameters have been individualized by actigraph. Participants also underwent a magnetic resonance imaging scan to assess their hippocampal volume. Based on SUVr PET-florbetapir imaging, two groups Aß+ and Aß- were compared. RESULTS: Participants were divided into two groups: Aß+ (n = 24) and Aß- (n = 44). Except for the SUVr, the two subgroups were comparable. When looking to sleep parameters, increased sleep latency, sleep fragmentation (wake after sleep onset [WASO] score and awakenings) and worst sleep efficiency were associated with cortical brain amyloid load. CONCLUSION: Actigraphic sleep parameters were associated with cortical brain amyloid load in participants at risk to develop AD. The detection of sleep abnormalities in those participants may be of interest to propose some preventive strategies.

Plasma amyloid ß 40/42 ratio predicts cerebral amyloidosis in cognitively normal individuals at risk for Alzheimer's disease.

INTRODUCTION: Blood-based biomarkers of pathophysiological brain amyloid ß (Aß) accumulation, particularly for preclinical target and large-scale interventions, are warranted to effectively enrich Alzheimer's disease clinical trials and management. METHODS: We investigated whether plasma concentrations of the Aß1-40/Aß1-42 ratio, assessed using the single-molecule array (Simoa) immunoassay, may predict brain Aß positron emission tomography status in a large-scale longitudinal monocentric cohort (N = 276) of older individuals with subjective memory complaints. We performed a hypothesis-driven investigation followed by a no-a-priori hypothesis study using machine learning. RESULTS: The receiver operating characteristic curve and machine learning showed a balanced accuracy of 76.5% and 81%, respectively, for the plasma Aß1-40/Aß1-42 ratio. The accuracy is not affected by the apolipoprotein E (APOE) ε4 allele, sex, or age. DISCUSSION: Our results encourage an independent validation cohort study to confirm the indication that the plasma Aß1-40/Aß1-42 ratio, assessed via Simoa, may improve future standard of care and clinical trial design.

Cognitive and neuroimaging features and brain ß-amyloidosis in individuals at risk of Alzheimer's disease (INSIGHT-preAD): a longitudinal observational study.

BACKGROUND: Improved understanding is needed of risk factors and markers of disease progression in preclinical Alzheimer's disease. We assessed associations between brain ß-amyloidosis and various cognitive and neuroimaging parameters with progression of cognitive decline in individuals with preclinical Alzheimer's disease. METHODS: The INSIGHT-preAD is an ongoing single-centre observational study at the Salpêtrière Hospital, Paris, France. Eligible participants were age 70-85 years with subjective memory complaints but unimpaired cognition and memory (Mini-Mental State Examination [MMSE] score ≥27, Clinical Dementia Rating score 0, and Free and Cued Selective Reminding Test [FCSRT] total recall score ≥41). We stratified participants by brain amyloid ß deposition on 18F-florbetapir PET (positive or negative) at baseline. All patients underwent baseline assessments of demographic, cognitive, and psychobehavioural, characteristics, APOE ε4 allele carrier status, brain structure and function on MRI, brain glucose-metabolism on 18F-fluorodeoxyglucose (18F-FDG) PET, and event-related potentials on electroencephalograms (EEGs). Actigraphy and CSF investigations were optional. Participants were followed up with clinical, cognitive, and psychobehavioural assessments every 6 months, neuropsychological assessments, EEG, and actigraphy every 12 months, and MRI, and 18F-FDG and 18F-florbetapir PET every 24 months. We assessed associations of amyloid ß deposition status with test outcomes at baseline and 24 months, and with clinical status at 30 months. Progression to prodromal Alzheimer's disease was defined as an amnestic syndrome of the hippocampal type. FINDINGS: From May 25, 2013, to Jan 20, 2015, we enrolled 318 participants with a mean age of 76·0 years (SD 3·5). The mean baseline MMSE score was 28·67 (SD 0·96), and the mean level of education was high (score >6 [SD 2] on a scale of 1-8, where 1=infant school and 8=higher education). 88 (28%) of 318 participants showed amyloid ß deposition and the remainder did not. The amyloid ß subgroups did not differ for any psychobehavioural, cognitive, actigraphy, and structural and functional neuroimaging results after adjustment for age, sex, and level of education More participants positive for amyloid ß deposition had the APOE ε4 allele (33 [38%] vs 29 [13%], p<0·0001). Amyloid ß1-42 concentration in CSF significantly correlated with mean 18F-florbetapir uptake at baseline (r=-0·62, p<0·0001) and the ratio of amyloid ß1-42 to amyloid ß1-40 (r=-0·61, p<0·0001), and identified amyloid ß deposition status with high accuracy (mean area under the curve values 0·89, 95% CI 0·80-0·98 and 0·84, 0·72-0·96, respectively). No difference was seen in MMSE (28·3 [SD 2·0] vs 28·9 [1·2], p=0·16) and Clinical Dementia Rating scores (0·06 [0·2] vs 0·05 [0·3]; p=0·79) at 30 months (n=274) between participants positive or negative for amyloid ß. Four participants (all positive for amyloid ß deposition at baseline) progressed to prodromal Alzheimer's disease. They were older than other participants positive for amyloid ß deposition at baseline (mean 80·2 years [SD 4·1] vs 76·8 years [SD 3·4]) and had greater 18F-florbetapir uptake at baseline (mean standard uptake value ratio 1·46 [SD 0·16] vs 1·02 [SD 0·20]), and more were carriers of the APOE ε4 allele (three [75%] of four vs 33 [39%] of 83). They also had mild executive dysfunction at baseline (mean FCSRT free recall score 21·25 [SD 2·75] vs 29·08 [5·44] and Frontal Assessment Battery total score 13·25 [1·50] vs 16·05 [1·68]). INTERPRETATION: Brain ß-amyloidosis alone did not predict progression to prodromal Alzheimer's disease within 30 months. Longer follow-up is needed to establish whether this finding remains consistent. FUNDING: Institut Hospitalo-Universitaire and Institut du Cerveau et de la Moelle Epinière (IHU-A-ICM), Ministry of Research, Fondation Plan Alzheimer, Pfizer, and Avid.

The Emergence of a New Conceptual Framework for Alzheimer's Disease.

The New Criteria for the diagnosis of Alzheimer's disease (AD), published by a group of experts in 2007, have resulted in a revolution in the comprehension of the disease. Before 2007, the diagnosis of AD dementia was done through a process of exclusion: it was considered in the case of patients with a dementia syndrome without identified etiologies. This traditional algorithm had three major limitations that penalize the disease: 1) a low accuracy of the performance which may share responsibility for negative results in clinical trials; 2) a late identification of the patients only when they reach the threshold of dementia which may delay the activation of optimal care; and last but not least, 3) an absence of clear recognition of AD as a disease because of the lack of specific arguments for its identification. Since 2007, the disease has gained a clear definition based on positive evidence: a specific clinical phenotype (the amnestic syndrome of the hippocampal type) and the presence of biomarkers, considered as a biological signature of the disease. Thanks to these positive arguments, AD is a clinically and biologically well-delineated disease, no longer defined as "probable". It is now possible to certify that a given patient has or does not have the disease. Like diabetes, cancer, hyperthyroidism or any other disorder, AD has now a clear definition with well-defined borders. The disease has entered the world of medicine with identified diseases with a biological fingerprint. This is the story of this adventure that we will present now.

Strategic roadmap for an early diagnosis of Alzheimer's disease based on biomarkers.

The diagnosis of Alzheimer's disease can be improved by the use of biological measures. Biomarkers of functional impairment, neuronal loss, and protein deposition that can be assessed by neuroimaging (ie, MRI and PET) or CSF analysis are increasingly being used to diagnose Alzheimer's disease in research studies and specialist clinical settings. However, the validation of the clinical usefulness of these biomarkers is incomplete, and that is hampering reimbursement for these tests by health insurance providers, their widespread clinical implementation, and improvements in quality of health care. We have developed a strategic five-phase roadmap to foster the clinical validation of biomarkers in Alzheimer's disease, adapted from the approach for cancer biomarkers. Sufficient evidence of analytical validity (phase 1 of a structured framework adapted from oncology) is available for all biomarkers, but their clinical validity (phases 2 and 3) and clinical utility (phases 4 and 5) are incomplete. To complete these phases, research priorities include the standardisation of the readout of these assays and thresholds for normality, the evaluation of their performance in detecting early disease, the development of diagnostic algorithms comprising combinations of biomarkers, and the development of clinical guidelines for the use of biomarkers in qualified memory clinics.

Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment.

Alzheimer's disease (AD) is increasingly considered as a disconnection syndrome. Previous studies of the structural connectome in early AD stages have focused on mild cognitive impaired subjects (MCI), considering them as a homogeneous group. We studied 168 subjects from the Alzheimer's Disease Neuroimaging Initiative database (116 MCI and 52 cognitively normal subjects). Biomarker-based stratification using amyloid biomarkers (AV45 PET) and neurodegeneration biomarkers (MRI and FDG PET) led to 4 subgroups based on amyloid positivity (A+/-) and neurodegeneration positivity (N+/-): A-N-, A+N-, A-N+, and A+N+. Using diffusion MRI, we showed that both MCI A-N+ and MCI A+N+ subjects displayed an alteration of the white matter in the fornix and a significant bihemispheric network of decreased connections. These network alterations in MCI A+N+ are stronger and more focal than those of MCI A-N+. Only MCI A+N+ subjects exhibited specific changes in hippocampal connectivity and an AD-like alteration pattern. Our results indicate that the connectome disintegration pattern of MCI subgroups differ with respect to brain amyloid and neurodegeneration. Each of these 2 AD biomarkers induces a connectome alteration that is maximal when they coexist.

The biomarker-based diagnosis of Alzheimer's disease. 2-lessons from oncology.

Biomarkers for the diagnosis of Alzheimer's disease (AD) are not yet validated for use in clinical settings. We aim to provide a methodological framework for their systematic validation, by reference to that developed for oncology biomarkers. As for this discipline, the steps for the systematic validation of AD biomarkers need to target analytical validity, clinical validity, and clinical utility. However, the premises are different from oncology: the nature of disease (neurodegeneration vs. cancer), the purpose (improve diagnosis in clinically affected vs. screening preclinical individuals), and the target population (mild cognitive impairment patients referring to memory clinics vs. general population) lead to important differences, influencing both the design of validation studies and the use of selected biomarkers. This framework is applied within a wider initiative to assess the current available evidence on the clinical validity of biomarkers for AD, for the final aim to identify gaps and research priorities, and to inform coordinated research efforts boosting AD biomarkers research.

Clinical validity of delayed recall tests as a gateway biomarker for Alzheimer's disease in the context of a structured 5-phase development framework.

Although Alzheimer's disease criteria promote the use of biomarkers, their maturity in clinical routine still needs to be assessed. In the light of the oncology framework, we conducted a literature review on measures used to assess delayed recall impairment due to medial temporal lobe dysfunction (i.e., free and cued word list recall tests). Ample evidence is available for phases 1 (rationale for use), 2 (discriminative ability), and 3 (early detection ability) for many of the tests in routine use. Evidence about phase 4 (performance in real world) and phase 5 (quantify impact and costs) is yet to come. Administration procedures have been standardized and cutoff scores are well validated in large Alzheimer's disease and mild cognitive impaired series. Some aspects (e.g., different task formats), however, hamper the comparability of results among different populations and the reproducibility between laboratories. No definite guideline for their use can thus be proposed at the moment. Accordingly, the maturity of such markers is not yet sufficient and requires future investigation to promote the proper use of memory measures in clinical settings.

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.