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Background: Recently, we showed that Metacognitive Interpersonal Therapy (MIT) is effective in improving clinical symptoms in borderline personality disorder (BPD). Here, we investigated whether the effect of MIT on clinical features is associated to microstructural changes in brain circuits supporting core BPD symptoms., Methods: Forty-seven BPD were randomized to MIT or structured clinical management, and underwent a clinical assessment and diffusion-weighted imaging before and after the intervention. Fractional anisotropy (FA), mean, radial, and axial diffusivities maps were computed using FSL toolbox. Microstructural changes were assessed (i) voxel-wise, with tract based spatial statistics (TBSS) and (ii) ROI-wise, in the triple network system (default mode, salience, and executive control networks). The effect of MIT on brain microstructure was assessed with paired tests using FSL PALM (voxel-wise), Linear Mixed-Effect Models or Generalized Linear Mixed Models (ROI-wise). Associations between microstructural and clinical changes were explored with linear regression (voxel-wise) and correlations (ROI-wise)., Results: The voxel-wise analysis showed that MIT was associated with increased FA in the bilateral thalamic radiation and left associative tracts (p < .050, family-wise error rate corrected). At network system level, MIT increased FA and both interventions reduced AD in the executive control network (p = .05, uncorrected)., Limitations: The DTI metrics can't clarify the nature of axonal changes., Conclusions: Our results indicate that MIT modulates brain structural connectivity in circuits related to associative and executive control functions. These microstructural changes may denote activity-dependent plasticity, possibly representing a neurobiological mechanism underlying MIT effects., Trial Registration: ClinicalTrials.govNCT02370316 (https://clinicaltrials.gov/study/NCT02370316)., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024. Published by Elsevier B.V.)

Introduction: We investigated in vivo the microstructural integrity of the pathway connecting the locus coeruleus to the transentorhinal cortex (LC-TEC) in patients with Alzheimer's disease (AD) and frontotemporal dementia (FTD)., Methods: Diffusion-weighted MRI scans were collected for 21 AD, 20 behavioral variants of FTD (bvFTD), and 20 controls. Fractional anisotropy (FA), mean, axial, and radial diffusivities (MD, AxD, RD) were computed in the LC-TEC pathway using a normative atlas. Atrophy was assessed using cortical thickness and correlated with microstructural measures., Results: We found (i) higher RD in AD than controls; (ii) higher MD, RD, and AxD, and lower FA in bvFTD than controls and AD; and (iii) a negative association between LC-TEC MD, RD, and AxD, and entorhinal cortex (EC) thickness in bvFTD (all p  < 0.050)., Discussion: LC-TEC microstructural alterations are more pronounced in bvFTD than AD, possibly reflecting neurodegeneration secondary to EC atrophy., Highlights: Microstructural integrity of LC-TEC pathway is understudied in AD and bvFTD.LC-TEC microstructural alterations are present in both AD and bvFTD.Greater LC-TEC microstructural alterations in bvFTD than AD.LC-TEC microstructural alterations in bvFTD are associated to EC neurodegeneration., Competing Interests: The authors declare that they have no competing interests. Author disclosures are available in the supporting information., (© 2024 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Purpose: Several [ 18 F]Flortaucipir cutoffs have been proposed for tau PET positivity (T + ) in Alzheimer's disease (AD), but none were data-driven. The aim of this study was to establish and validate unsupervised T + cutoffs by applying Gaussian mixture models (GMM)., Methods: Amyloid negative (A - ) cognitively normal (CN) and amyloid positive (A + ) AD-related dementia (ADRD) subjects from ADNI (n=269) were included. ADNI (n=475) and Geneva Memory Clinic (GMC) cohorts (n=98) were used for validation. GMM-based cutoffs were extracted for the temporal meta-ROI, and validated against previously published cutoffs and visual rating., Results: GMM-based cutoffs classified less subjects as T + , mainly in the A - CN (<3.4% vs >28.5%) and A + CN (<14.5% vs >42.9%) groups and showed higher agreement with visual rating (ICC=0.91 vs ICC<0.62) than published cutoffs., Conclusion: We provided reliable data-driven [ 18 F]Flortaucipir cutoffs for in vivo T + detection in AD. These cutoffs might be useful to select participants in clinical and research studies., (© 2023. The Author(s).)

The current conceptualization of Alzheimer disease (AD) is driven by the amyloid hypothesis, in which a deterministic chain of events leads from amyloid deposition and then tau deposition to neurodegeneration and progressive cognitive impairment. This model fits autosomal dominant AD but is less applicable to sporadic AD. Owing to emerging information regarding the complex biology of AD and the challenges of developing amyloid-targeting drugs, the amyloid hypothesis needs to be reconsidered. Here we propose a probabilistic model of AD in which three variants of AD (autosomal dominant AD, APOE ε4-related sporadic AD and APOE ε4-unrelated sporadic AD) feature decreasing penetrance and decreasing weight of the amyloid pathophysiological cascade, and increasing weight of stochastic factors (environmental exposures and lower-risk genes). Together, these variants account for a large share of the neuropathological and clinical variability observed in people with AD. The implementation of this model in research might lead to a better understanding of disease pathophysiology, a revision of the current clinical taxonomy and accelerated development of strategies to prevent and treat AD., (© 2021. Springer Nature Limited.)

Brain Health Services are a novel approach to the personalized prevention of dementia. In this paper, we consider how such services can best reflect their social, cultural, and economic context and, in doing so, deliver fair and equitable access to risk reduction. We present specific areas of challenge associated with the social context for dementia prevention. The first concentrates on how Brain Health Services engage with the "at-risk" individual, recognizing the range of factors that shape an individual's risk of dementia and the efficacy of risk reduction measures. The second emphasizes the social context of Brain Health Services themselves and their ability to provide equitable access to risk reduction. We then elaborate proposals for meeting or mitigating these challenges. We suggest that considering these challenges will enable Brain Health Services to address two fundamental questions: the balance between an individualized "high-risk" and population focus for public health prevention and the ability of services to meet ethical standards of justice and health equity., (© 2021. The Author(s).)

Cognitive complaints in the absence of objective cognitive impairment, observed in patients with subjective cognitive decline (SCD), are common in old age. The first step to postpone cognitive decline is to use techniques known to improve cognition, i.e., cognitive enhancement techniques.We aimed to provide clinical recommendations to improve cognitive performance in cognitively unimpaired individuals, by using cognitive, mental, or physical training (CMPT), non-invasive brain stimulations (NIBS), drugs, or nutrients. We made a systematic review of CMPT studies based on the GRADE method rating the strength of evidence.CMPT have clinically relevant effects on cognitive and non-cognitive outcomes. The quality of evidence supporting the improvement of outcomes following a CMPT was high for metamemory; moderate for executive functions, attention, global cognition, and generalization in daily life; and low for objective memory, subjective memory, motivation, mood, and quality of life, as well as a transfer to other cognitive functions. Regarding specific interventions, CMPT based on repeated practice (e.g., video games or mindfulness, but not physical training) improved attention and executive functions significantly, while CMPT based on strategic learning significantly improved objective memory.We found encouraging evidence supporting the potential effect of NIBS in improving memory performance, and reducing the perception of self-perceived memory decline in SCD. Yet, the high heterogeneity of stimulation protocols in the different studies prevent the issuing of clear-cut recommendations for implementation in a clinical setting. No conclusive argument was found to recommend any of the main pharmacological cognitive enhancement drugs ("smart drugs", acetylcholinesterase inhibitors, memantine, antidepressant) or herbal extracts (Panax ginseng, Gingko biloba, and Bacopa monnieri) in people without cognitive impairment.Altogether, this systematic review provides evidence for CMPT to improve cognition, encouraging results for NIBS although more studies are needed, while it does not support the use of drugs or nutrients., (© 2021. The Author(s).)

Although prevention of dementia and late-life cognitive decline is a major public health priority, there are currently no generally established prevention strategies or operational models for implementing such strategies into practice. This article is a narrative review of available evidence from multidomain dementia prevention trials targeting several risk factors and disease mechanisms simultaneously, in individuals without dementia at baseline. Based on the findings, we formulate recommendations for implementing precision risk reduction strategies into new services called Brain Health Services. A literature search was conducted using medical databases (MEDLINE via PubMed and SCOPUS) to select relevant studies: non-pharmacological multidomain interventions (i.e., combining two or more intervention domains), target population including individuals without dementia, and primary outcomes including cognitive/functional performance changes and/or incident cognitive impairment or dementia. Further literature searches covered the following topics: sub-group analyses assessing potential modifiers for the intervention effect on cognition in the multidomain prevention trials, dementia risk scores used as surrogate outcomes in multidomain prevention trials, dementia risk scores in relation to brain pathology markers, and cardiovascular risk scores in relation to dementia. Multidomain intervention studies conducted so far appear to have mixed results and substantial variability in target populations, format and intensity of interventions, choice of control conditions, and outcome measures. Most trials were conducted in high-income countries. The differences in design between the larger, longer-term trials that met vs. did not meet their primary outcomes suggest that multidomain intervention effectiveness may be dependent on a precision prevention approach, i.e., successfully identifying the at-risk groups who are most likely to benefit. One such successful trial has already developed an operational model for implementing the intervention into practice. Evidence on the efficacy of risk reduction interventions is promising, but not yet conclusive. More long-term multidomain randomized controlled trials are needed to fill the current evidence gaps, especially concerning low- and middle-income countries and integration of dementia prevention with existing cerebrovascular prevention programs. A precision risk reduction approach may be most effective for dementia prevention. Such an approach could be implemented in Brain Health Services., (© 2021. The Author(s).)

Growing evidence suggests dementia incidence can be reduced through prevention programs targeting risk factors. To accelerate the implementation of such prevention programs, a new generation of brain health services (BHS) is envisioned, involving risk profiling, risk communication, risk reduction, and cognitive enhancement. The purpose of risk communication is to enable individuals at risk to make informed decisions and take action to protect themselves and is thus a crucial step in tailored prevention strategies of the dementia incidence. However, communicating about dementia risk is complex and challenging.In this paper, we provide an overview of (i) perspectives on communicating dementia risk from an ethical, clinical, and societal viewpoint; (ii) insights gained from memory clinical practice; (iii) available evidence on the impact of disclosing APOE and Alzheimer's disease biomarker test results gathered from clinical trials and observational studies; (iv) the value of established registries in light of BHS; and (v) practical recommendations regarding effective strategies for communicating about dementia risk.In addition, we identify challenges, i.e., the current lack of evidence on what to tell on an individual level-the actual risk-and on how to optimally communicate about dementia risk, especially concerning worried yet cognitively unimpaired individuals. Ideally, dementia risk communication strategies should maximize the desired impact of risk information on individuals' understanding of their health/disease status and risk perception and minimize potential harms. More research is thus warranted on the impact of dementia risk communication, to (1) evaluate the merits of different approaches to risk communication on outcomes in the cognitive, affective and behavioral domains, (2) develop an evidence-based, harmonized dementia risk communication protocol, and (3) develop e-tools to support and promote adherence to this protocol in BHSs.Based on the research reviewed, we recommend that dementia risk communication should be precise; include the use of absolute risks, visual displays, and time frames; based on a process of shared decision-making; and address the inherent uncertainty that comes with any probability., (© 2021. The Author(s).)

We envisage the development of new Brain Health Services to achieve primary and secondary dementia prevention. These services will complement existing memory clinics by targeting cognitively unimpaired individuals, where the focus is on risk profiling and personalized risk reduction interventions rather than diagnosing and treating late-stage disease. In this article, we review key potentially modifiable risk factors and genetic risk factors and discuss assessment of risk factors as well as additional fluid and imaging biomarkers that may enhance risk profiling. We then outline multidomain measures and risk profiling and provide practical guidelines for Brain Health Services, with consideration of outstanding uncertainties and challenges. Users of Brain Health Services should undergo risk profiling tailored to their age, level of risk, and availability of local resources. Initial risk assessment should incorporate a multidomain risk profiling measure. For users aged 39-64, we recommend the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) Dementia Risk Score, whereas for users aged 65 and older, we recommend the Brief Dementia Screening Indicator (BDSI) and the Australian National University Alzheimer's Disease Risk Index (ANU-ADRI). The initial assessment should also include potentially modifiable risk factors including sociodemographic, lifestyle, and health factors. If resources allow, apolipoprotein E ɛ4 status testing and structural magnetic resonance imaging should be conducted. If this initial assessment indicates a low dementia risk, then low intensity interventions can be implemented. If the user has a high dementia risk, additional investigations should be considered if local resources allow. Common variant polygenic risk of late-onset AD can be tested in middle-aged or older adults. Rare variants should only be investigated in users with a family history of early-onset dementia in a first degree relative. Advanced imaging with 18-fluorodeoxyglucose positron emission tomography (FDG-PET) or amyloid PET may be informative in high risk users to clarify the nature and burden of their underlying pathologies. Cerebrospinal fluid biomarkers are not recommended for this setting, and blood-based biomarkers need further validation before clinical use. As new technologies become available, advances in artificial intelligence are likely to improve our ability to combine diverse data to further enhance risk profiling. Ultimately, Brain Health Services have the potential to reduce the future burden of dementia through risk profiling, risk communication, personalized risk reduction, and cognitive enhancement interventions., (© 2021. The Author(s).)

Dementia has a devastating impact on the quality of life of patients and families and comes with a huge cost to society. Dementia prevention is considered a public health priority by the World Health Organization. Delaying the onset of dementia by treating associated risk factors will bring huge individual and societal benefit. Empirical evidence suggests that, in higher-income countries, dementia incidence is decreasing as a result of healthier lifestyles. This observation supports the notion that preventing dementia is possible and that a certain degree of prevention is already in action. Further reduction of dementia incidence through deliberate prevention plans is needed to counteract its growing prevalence due to increasing life expectancy.An increasing number of individuals with normal cognitive performance seek help in the current memory clinics asking an evaluation of their dementia risk, preventive interventions, or interventions to ameliorate their cognitive performance. Consistent evidence suggests that some of these individuals are indeed at increased risk of dementia. This new health demand asks for a shift of target population, from patients with cognitive impairment to worried but cognitively unimpaired individuals. However, current memory clinics do not have the programs and protocols in place to deal with this new population.We envision the development of new services, henceforth called Brain Health Services, devoted to respond to demands from cognitively unimpaired individuals concerned about their risk of dementia. The missions of Brain Health Services will be (i) dementia risk profiling, (ii) dementia risk communication, (iii) dementia risk reduction, and (iv) cognitive enhancement. In this paper, we present the organizational and structural challenges associated with the set-up of Brain Health Services., (© 2021. The Author(s).)

Purpose: Assess the individual and combined diagnostic value of amyloid-PET and tau-PET in a memory clinic population., Methods: Clinical reports of 136 patients were randomly assigned to two diagnostic pathways: AMY-TAU, amyloid-PET is presented before tau-PET; and TAU-AMY, tau-PET is presented before amyloid-PET. Two neurologists independently assessed all reports with a balanced randomized design, and expressed etiological diagnosis and diagnostic confidence (50-100%) three times: (i) at baseline based on the routine diagnostic workup, (ii) after the first exam (amyloid-PET for the AMY-TAU pathway, and tau-PET for the TAU-AMY pathway), and (iii) after the remaining exam. The main outcomes were changes in diagnosis (from AD to non-AD or vice versa) and in diagnostic confidence., Results: Amyloid-PET and tau-PET, when presented as the first exam, resulted in a change of etiological diagnosis in 28% (p = 0.006) and 28% (p < 0.001) of cases, and diagnostic confidence increased by 18% (p < 0.001) and 19% (p < 0.001) respectively, with no differences between exams (p > 0.05). We observed a stronger impact of a negative amyloid-PET versus a negative tau-PET (p = 0.014). When added as the second exam, amyloid-PET and tau-PET resulted in a further change in etiological diagnosis in 6% (p = 0.077) and 9% (p = 0.149) of cases, and diagnostic confidence increased by 4% (p < 0.001) and 5% (p < 0.001) respectively, with no differences between exams (p > 0.05)., Conclusion: Amyloid-PET and tau-PET significantly impacted diagnosis and diagnostic confidence in a similar way, although a negative amyloid-PET has a stronger impact on diagnosis than a negative tau-PET. Adding either of the two as second exam further improved diagnostic confidence., Trial Number: PB 2016-01346.

Introduction: Hippocampal volume is one of the main biomarkers of Alzheimer's Dementia (AD). Over the years, advanced tools that performed automatic segmentation of Magnetic Resonance Imaging (MRI) T13D scans have been developed, such as FreeSurfer (FS) and ACM-Adaboost (AA). Hippocampal volume is considered abnormal when it is below the 5th percentile of the normative population. The aim of this study was to set norms, established from the Alzheimer's Disease Neuroimaging Initiative (ADNI) population, for hippocampal volume measured with FS v.6.0 and AA tools in the neuGRID platform (www.neugrid2.eu) and demonstrate their applicability for the Italian population., Methods: Norms were set from a large group of 545 healthy controls belonging to ADNI. For each pipeline, subjects with segmentation errors were discarded, resulting in 532 valid segmentations for FS and 421 for AA (age range 56-90 years). The comparability of ADNI and the Italian Brain Normative Archive (IBNA), representative of the Italian general population, was assessed testing clinical variables, neuropsychological scores and normalized hippocampal volumes. Finally, percentiles were validated using the Italian Alzheimer's disease Repository Without Borders (ARWiBo) as external independent data set to evaluate FS and AA generalizability., Results: Hippocampal percentiles were checked with the chi-square goodness of fit test. P -values were not significant, showing that FS and AA algorithm distributions fitted the data well. Clinical, neuropsychological and volumetric features were similar in ADNI and IBNA ( p > 0.01). Hippocampal volumes measured with both FS and AA were associated with age ( p < 0.001). The 5th percentile thresholds, indicating left/right hippocampal atrophy were respectively: (i) below 3,223/3,456 mm 3 at 56 years and 2,506/2,415 mm 3 at 90 years for FS; (ii) below 4,583/4,873 mm 3 at 56 years and 3,831/3,870 mm 3 at 90 years for AA. The average volumes computed on 100 cognitively intact healthy controls (CN) selected from ARWiBo were close to the 50th percentiles, while those for 100 AD patients were close to the abnormal percentiles., Discussion: Norms generated from ADNI through the automatic FS and AA segmentation tools may be used as normative references for Italian patients with suspected AD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 De Francesco, Galluzzi, Vanacore, Festari, Rossini, Cappa, Frisoni and Redolfi.)

In 2018, the US National Institute on Aging and the Alzheimer's Association proposed a purely biological definition of Alzheimer's disease that relies on biomarkers. Although the intended use of this framework was for research purposes, it has engendered debate and challenges regarding its use in everyday clinical practice. For instance, cognitively unimpaired individuals can have biomarker evidence of both amyloid β and tau pathology but will often not develop clinical manifestations in their lifetime. Furthermore, a positive Alzheimer's disease pattern of biomarkers can be observed in other brain diseases in which Alzheimer's disease pathology is present as a comorbidity. In this Personal View, the International Working Group presents what we consider to be the current limitations of biomarkers in the diagnosis of Alzheimer's disease and, on the basis of this evidence, we propose recommendations for how biomarkers should and should not be used for diagnosing Alzheimer's disease in a clinical setting. We recommend that Alzheimer's disease diagnosis be restricted to people who have positive biomarkers together with specific Alzheimer's disease phenotypes, whereas biomarker-positive cognitively unimpaired individuals should be considered only at-risk for progression to Alzheimer's disease., Competing Interests: Declaration of interests BD reports personal fees from Biogen and grants paid to his institution from Roche, Merck-Avenir Foundation, and Fondation Recherche sur Alzheimer, outside the submitted work. NV reports grants from Fondation Recherche Alzheimer, Département Médical Universitaire APHP–Sorbonne Université, and non-financial support from GE Healthcare, Merz Pharma, UCB Pharma, Medtronic, and Laboratoire Aguettant, outside the submitted work. GDR reports grants from US National Institutes of Health (NIH)–National Institute on Aging (NIA), Alzheimer's Association, and Rainwater Charitable Foundation during the study; grants from Avid Radiopharmaceuticals, Eli Lilly, GE Healthcare, Life Molecular Imaging, American College of Radiology, Genentech, and Roche; and personal fees from Eisai, Merck, Johnson & Johnson, Genentech, Roche, Avid Radiopharmaceuticals, GE Healthcare, and Axon Neurosciences, outside the submitted work; and is an Associate Editor of JAMA Neurology. MS reports personal fees from Allergan, Biogen, Grifols, vTv therapeutics, Sanofi, Neurotrope, Cortexyme; stock options from uMethod Health, Brain Health, Versanum, Optimal Cognitive Health Company; and stocks from Athira, outside the submitted work. SC reports personal fees from Biogen, Roche, and Nutricia, outside the submitted work. SE reports personal fees from Biogen, Roche, and GE Healthcare, outside the submitted work. M-OH reports personal fees from Blue Earth, outside the submitted work. AN reports personal fees from Roche, outside the submitted work. KB reports personal fees from Abcam, Axon Neuroscience, Biogen, Lilly, MagQu, Novartis, Roche Diagnostics, JOMDD/Shimadzu, Julius Clinical, and Siemens Healthineers, outside the submitted work, and is co-founder of Brain Biomarker Solutions in Gothenburg, a GU Ventures platform company at the University of Gothenburg, Sweden. DG reports personal fees from Biogen, vTv Pharmaceuticals, Fujirebio, and Springer; and grants from NIA and the Alzheimer's Drug Discovery Foundation, outside the submitted work. YS reports grants from NIA, and personal fees from Eisai and Takeda, outside the submitted work. CCR reports grants from Cerveau Technologies, Eisai, NHMRC Australia, and US Department of Defense; and personal fees from Biogen and Cerveau Technologies, outside the submitted work. SS reports grants from Biogen, Eisai, and Avid; personal fees from Biogen, Eisai, Avid, Novartis, Lilly, Genentech, and Roche; and non-financial support from Biogen, Avid, Novartis, Lilly, and Roche, outside the submitted work. LSS reports grants from Eli Lilly, Merck, Roche/Genentech, Biogen, Novartis, Biohaven, Washington University–NIA for the Dominantly Inherited Alzheimer Network Trial, and personal fees from Samus, Eli Lilly, Avraham, Boehringer Ingelheim, Merck, Neurim, Neuronix, Cognition, Eisai, Takeda, vTv, Roche/Genentech, Abbott, and Samus, outside the submitted work. JLC reports grants from NIH–US National Institute of General Medical Sciences, NIH, and NIH–US National Institute of Neurological Disorders and Stroke, during the study; personal fees from Acadia, Actinogen, AgeneBio, Alkahest, Alzheon, Annovis, Avanir, Axsome, Biogen, Cassava, Cerecin, Cerevel, Cognoptix, Cortexyme, EIP Pharma, Eisai, Foresight, Green Valley, Grifols, Karuna, Nutricia, Orion, Otsuka, Probiodrug, ReMYND, Resverlogix, Roche, Samumed, Samus Therapeutics, Third Rock, Signant Health, Sunovion, Suven, and United Neuroscience pharmaceutical and assessment companies; personal fees from Alzheimer Drug Discovery Foundation; and stock ownership from ADAMAS, BioAsis, MedAvante, QR Pharma, and United Neuroscience, outside the submitted work; JLC has a patent Neuropsychiatric Inventory with royalties paid, and is Chief Scientific Advisor of CNS Innovations. HHF reports grants to University of California San Diego (UCSD) from Toyama Pharmaceuticals, Biohaven Pharmaceuticals, Annovis (QR Pharma), AC Immune, Vivoryon Therapeutics (Probiodrug), and LuMind; service agreements via UCSD for consulting with Novo Nordisk, Eisai Pharmaceuticals, Merck Pharmaceuticals, Samus Therapeutics, Arkuda Therapeutics, Samumed, and Axon Neurosciences; institutional service agreements to serve on a data monitoring committee and data and safety monitoring board for Roche/Genentech Pharmaceuticals and Janssen Research & Development; serves on the scientific advisory board for the Tau Consortium; and reports travel expenses paid to his institution from World Events Forum (ADDF), Samus, Samumed, Axon, and Novo Nordisk, outside the submitted work. All other authors declare no competing interests., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: Subjective cognitive decline (SCD) is the subjective perception of a decline in memory and/or other cognitive functions in the absence of objective evidence. Some SCD individuals however may suffer from very early stages of neurodegenerative diseases (such as Alzheimer's disease, AD), minor psychiatric conditions, neurological, and/or somatic comorbidities. Even if a theoretical framework has been established, the etiology of SCD remains far from elucidated. Clinical observations recently lead to the hypothesis that individuals with incipient AD may have overestimated metacognitive judgements of their own cognitive performance, while those with psychiatric disorders typically present underestimated metacognitive judgements. Moreover, brain connectivity changes are known correlates of AD and psychiatric conditions and might be used as biomarkers to discriminate SCD individuals of different etiologies. The aim of the COSCODE study is to identify metacognition, connectivity, behavioral, and biomarker profiles associated with different etiologies of SCD. Here we present its rationale and study design., Methods: COSCODE is an observational, longitudinal (4 years), prospective clinical cohort study involving 120 SCD, and 80 control study participants (40 individuals with no cognitive impairment, and 40 living with mild cognitive impairment - MCI, or dementia due to AD), all of which will undergo diffusion magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) as well as behavioral and biomarker assessments at baseline and after 1 and 2 years. Both hypothesis-driven and data-driven cluster analysis approaches will be used to identify SCD sub-types based on metacognition, connectivity, behavioral, and biomarker features., Conclusion: COSCODE will allow defining and interpreting the constellation of signs and symptoms associated with different etiologies of SCD, paving the way to the development of cost-effective risk assessment and prevention protocols.

Alzheimer's disease (AD) is a neurodegenerative disorder which spans several years from preclinical manifestations to dementia. In recent years, interest in the application of machine learning (ML) algorithms to personalized medicine has grown considerably, and a major challenge that such models face is the transferability from the research settings to clinical practice. The objective of this work was to demonstrate the transferability of the Subtype and Stage Inference (SuStaIn) model from well-characterized research data set, employed as training set, to independent less-structured and heterogeneous test sets representative of the clinical setting. The training set was composed of MRI data of 1043 subjects from the Alzheimer's disease Neuroimaging Initiative (ADNI), and the test set was composed of data from 767 subjects from OASIS, Pharma-Cog, and ViTA clinical datasets. Both sets included subjects covering the entire spectrum of AD, and for both sets volumes of relevant brain regions were derived from T1-3D MRI scans processed with Freesurfer v5.3 cross-sectional stream. In order to assess the predictive value of the model, subpopulations of subjects with stable mild cognitive impairment (MCI) and MCIs that progressed to AD dementia (pMCI) were identified in both sets. SuStaIn identified three disease subtypes, of which the most prevalent corresponded to the typical atrophy pattern of AD. The other SuStaIn subtypes exhibited similarities with the previously defined hippocampal sparing and limbic predominant atrophy patterns of AD. Subject subtyping proved to be consistent in time for all cohorts and the staging provided by the model was correlated with cognitive performance. Classification of subjects on the basis of a combination of SuStaIn subtype and stage, mini mental state examination and amyloid-β 1-42 cerebrospinal fluid concentration was proven to predict conversion from MCI to AD dementia on par with other novel statistical algorithms, with ROC curves that were not statistically different for the training and test sets and with area under curve respectively equal to 0.77 and 0.76. This study proves the transferability of a SuStaIn model for AD from research data to less-structured clinical cohorts, and indicates transferability to the clinical setting., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Archetti, Young, Oxtoby, Ferreira, Mårtensson, Westman, Alexander, Frisoni and Redolfi.)

Background: Previous studies reported default mode network (DMN) and limbic network (LIN) brain perfusion deficits in patients with amnestic mild cognitive impairment (aMCI), frequently a prodromal stage of Alzheimer's disease (AD). However, the validity of these measures as AD markers has not yet been tested using MRI arterial spin labeling (ASL)., Objective: To investigate the convergent and discriminant validity of DMN and LIN perfusion in aMCI., Methods: We collected core AD markers (amyloid-β 42 [Aβ42], phosphorylated tau 181 levels in cerebrospinal fluid [CSF]), neurodegenerative (hippocampal volumes and CSF total tau), vascular (white matter hyperintensities), genetic (apolipoprotein E [APOE] status), and cognitive features (memory functioning on Paired Associate Learning test [PAL]) in 14 aMCI patients. Cerebral blood flow (CBF) was extracted from DMN and LIN using ASL and correlated with AD features to assess convergent validity. Discriminant validity was assessed carrying out the same analysis with AD-unrelated features, i.e., somatomotor and visual networks' perfusion, cerebellar volume, and processing speed., Results: Perfusion was reduced in the DMN (F = 5.486, p = 0.039) and LIN (F = 12.678, p = 0.004) in APOE ɛ4 carriers compared to non-carriers. LIN perfusion correlated with CSF Aβ42 levels (r = 0.678, p = 0.022) and memory impairment (PAL, number of errors, r = -0.779, p = 0.002). No significant correlation was detected with tau, neurodegeneration, and vascular features, nor with AD-unrelated features., Conclusion: Our results support the validity of DMN and LIN ASL perfusion as AD markers in aMCI, indicating a significant correlation between CBF and amyloidosis, APOE ɛ4, and memory impairment.

Early diagnosis of Alzheimer's disease (AD) is a crucial starting point in disease management. Blood-based biomarkers could represent a considerable advantage in providing AD-risk information in primary care settings. Here, we report new data for a relatively unknown blood-based biomarker that holds promise for AD diagnosis. We evaluate a p53-misfolding conformation recognized by the antibody 2D3A8, also named Unfolded p53 (U-p53 2D3A8+ ), in 375 plasma samples derived from InveCe.Ab and PharmaCog/E-ADNI longitudinal studies. A machine learning approach is used to combine U-p53 2D3A8+ plasma levels with Mini-Mental State Examination (MMSE) and apolipoprotein E epsilon-4 (APOEε4) and is able to predict AD likelihood risk in InveCe.Ab with an overall 86.67% agreement with clinical diagnosis. These algorithms also accurately classify (AUC = 0.92) Aβ + -amnestic Mild Cognitive Impairment (aMCI) patients who will develop AD in PharmaCog/E-ADNI, where subjects were stratified according to Cerebrospinal fluid (CSF) AD markers (Aβ42 and p-Tau). Results support U-p53 2D3A8+ plasma level as a promising additional candidate blood-based biomarker for AD.

Background: Three different amyloid tracers labeled with 18-flourine have been introduced into clinical use. The leaflets of tracers indicate different visual criteria for PET reporting. In clinical practice, it is not yet ascertained whether these criteria are equivalent in terms of diagnostic accuracy or if anyone is better than another. We aimed to evaluate the inter and intra-rater variability of visual assessment of 18 F-Florbetapir PET/CT images among six independent readers with different clinical experience., Methods: We analyzed 252 PET/CT scans, visually assessed by each reader three times, applying independently the three different reading criteria proposed. Each reader evaluated the regional uptake specifying for each cortical region a numeric value of grading of positivity in order to assign a final score. At the end of each reading a level of confidence was determined by assigning a score from 0 (negative) to 4 (positive). After first reading, those cases in which the evaluations by two experienced readers did not match (discordant cases) were independently reevaluated merging all the three different visual interpretation criteria., Results: Good agreement was observed for visual interpretation among the six readers' confidence-level using independently the three visual reading criteria: ICC=0.83 (0.80-0.86) for 18 F-florbetapir, ICC=0.84 (0.81-0.87) for 18 F-florbetaben, and ICC=0.86 (0.83-0.88) for 18 F-flutemetamol reading. A good inter-rater agreement was observed for final-score too: ICC=0.74 (0.70-0.78) for 18 F-florbetapir; ICC=0.82 (0.79-0.85) for 18 F-florbetaben; ICC=0.84 (0.81-0.87) for 18 F-flutemetamol. Intra-rater agreement was good for final-score (from 0.76 to 0.90; P<0.001) and confidence-level (Spearman's rho from 0.89 to 1.00; P<0.001). Disagreement between the two experienced readers was observed in 22 of 252 cases (9%). The agreement converged over a second round of independent reading in 12 of 22 cases (54%), by merging all the criteria., Conclusions: All the criteria proposed are useful to determine the grading of positivity or negativity of amyloid deposition and their merging improves the diagnostic confidence and provides a better agreement.

Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify-CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD ( n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from "slight" to "significant" in 80% of the cases. Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology., (Copyright © 2020 Redolfi, De Francesco, Palesi, Galluzzi, Muscio, Castellazzi, Tiraboschi, Savini, Nigri, Bottini, Bruzzone, Ramusino, Ferraro, Gandini Wheeler-Kingshott, Tagliavini, Frisoni, Ryvlin, Demonet, Kherif, Cappa and D'Angelo.)

Background: The amygdala and the hippocampus are two limbic structures that play a critical role in cognition and behavior, however their manual segmentation and that of their smaller nuclei/subfields in multicenter datasets is time consuming and difficult due to the low contrast of standard MRI. Here, we assessed the reliability of the automated segmentation of amygdalar nuclei and hippocampal subfields across sites and vendors using FreeSurfer in two independent cohorts of older and younger healthy adults., Methods: Sixty-five healthy older (cohort 1) and 68 younger subjects (cohort 2), from the PharmaCog and CoRR consortia, underwent repeated 3D-T1 MRI (interval 1-90 days). Segmentation was performed using FreeSurfer v6.0. Reliability was assessed using volume reproducibility error (ε) and spatial overlapping coefficient (DICE) between test and retest session., Results: Significant MRI site and vendor effects (p ​< ​.05) were found in a few subfields/nuclei for the ε, while extensive effects were found for the DICE score of most subfields/nuclei. Reliability was strongly influenced by volume, as ε correlated negatively and DICE correlated positively with volume size of structures (absolute value of Spearman's r correlations >0.43, p ​< ​1.39E-36). In particular, volumes larger than 200 ​mm 3 (for amygdalar nuclei) and 300 ​mm 3 (for hippocampal subfields, except for molecular layer) had the best test-retest reproducibility (ε ​< ​5% and DICE ​> ​0.80)., Conclusion: Our results support the use of volumetric measures of larger amygdalar nuclei and hippocampal subfields in multisite MRI studies. These measures could be useful for disease tracking and assessment of efficacy in drug trials., Competing Interests: Declaration of competing interest None., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: To apply the ATN scheme to memory clinic patients, to assess whether it discriminates patient populations with specific features., Methods: We included 305 memory clinic patients (33% subjective cognitive decline [SCD]: 60 ± 9 years, 61% M; 19% mild cognitive impairment [MCI]: 68 ± 9 years, 68% M; 48% dementia: 66 ± 10 years, 58% M) classified for positivity (±) of amyloid (A) ([ 18 F]Florbetaben PET), tau (T) (CSF p-tau), and neurodegeneration (N) (medial temporal lobe atrophy). We assessed ATN profiles' demographic, clinical, and cognitive features at baseline, and cognitive decline over time., Results: The proportion of A+T+N+ patients increased with syndrome severity (from 1% in SCD to 14% in MCI and 35% in dementia), while the opposite was true for A-T-N- (from 48% to 19% and 6%). Compared to A-T-N-, patients with the Alzheimer disease profiles (A+T+N- and A+T+N+) were older (both p < 0.05) and had a higher prevalence of APOE ε4 (both p < 0.05) and lower Mini-Mental State Examination (MMSE) (both p < 0.05), memory (both p < 0.05), and visuospatial abilities (both p < 0.05) at baseline. Non-Alzheimer profiles A-T-N+ and A-T+N+ showed more severe white matter hyperintensities (both p < 0.05) and worse language performance (both p < 0.05) than A-T-N-. A linear mixed model showed faster decline on MMSE over time in A+T+N- and A+T+N+ ( p = 0.059 and p < 0.001 vs A-T-N-), attributable mainly to patients without dementia., Conclusions: The ATN scheme identified different biomarker profiles with overlapping baseline features and patterns of cognitive decline. The large number of profiles, which may have different implications in patients with vs without dementia, poses a challenge to the application of the ATN scheme., (© 2019 American Academy of Neurology.)

Objective: The aim of this study is to assess the impact of age at onset on the prognostic value of Alzheimer's biomarkers in a large sample of patients with mild cognitive impairment (MCI)., Methods: We measured Aβ42, t-tau, hippocampal volume on magnetic resonance imaging (MRI) and cortical metabolism on fluorodeoxyglucose-positron emission tomography (FDG-PET) in 188 MCI patients followed for at least 1 year. We categorised patients into earlier and later onset (EO/LO). Receiver operating characteristic curves and corresponding areas under the curve (AUCs) were performed to assess and compar the biomarker prognostic performances in EO and LO groups. Linear Model was adopted for estimating the time-to-progression in relation with earlier/later onset MCI groups and biomarkers., Results: In earlier onset patients, all the assessed biomarkers were able to predict cognitive decline (p < 0.05), with FDG-PET showing the best performance. In later onset patients, all biomarkers but t-tau predicted cognitive decline (p < 0.05). Moreover, FDG-PET alone in earlier onset patients showed a higher prognostic value than the one resulting from the combination of all the biomarkers in later onset patients (earlier onset AUC 0.935 vs later onset AUC 0.753, p < 0.001). Finally, FDG-PET showed a different prognostic value between earlier and later onset patients (p = 0.040) in time-to-progression allowing an estimate of the time free from disease., Discussion: FDG-PET may represent the most universal tool for the establishment of a prognosis in MCI patients and may be used for obtaining an onset-related estimate of the time free from disease.

Background: Borderline personality disorder (BPD) is a psychiatric condition associated with the impairment of the frontolimbic network. However, a growing body of studies suggests that brain dysfunction underling BPD could involve other brain areas. We explored the whole-brain white matter (WM) organization in BPD patients to clarify the structural pattern underlying the disease and its relationship with clinical features., Methods: Fourteen BPD patients and 14 healthy controls underwent a multidimensional clinical assessment and diffusion tensor imaging acquisition. Measures of fractional anisotropy (FA) and mean, axial and radial (RD) diffusivity were collected, and alterations in the WM were assessed using the voxelwise approach, including substance and alcohol abuse as covariates. Voxelwise regression analysis was performed to identify associations between microstructural changes and clinical feature in BPD., Results: Group comparisons showed alterations only for FA and RD: FA decreased in the right posterior hemisphere, while RD increased bilaterally and widespread in anterior and posterior areas (p < 0.05, threshold-free cluster enhancement corrected). Moreover, WM alterations of the corpus callosum were related to anxiety in BPD group., Discussion: Our data support the idea that structural alterations underling BPD also involve cortico-cortical pathways, corticothalamic and corticostriatal tracts, suggesting that the frontolimbic model should be reinterpreted. © 2019 John Wiley & Sons, Ltd., (© 2019 John Wiley & Sons, Ltd.)

Introduction: Reimbursement of amyloid-positron emission tomography (PET) is lagging due to the lack of definitive evidence on its clinical utility and cost-effectiveness. The Amyloid Imaging to Prevent Alzheimer's Disease-Diagnostic and Patient Management Study (AMYPAD-DPMS) is designed to fill this gap., Methods: AMYPAD-DPMS is a phase 4, multicenter, prospective, randomized controlled study. Nine hundred patients with subjective cognitive decline plus, mild cognitive impairment, and dementia possibly due to Alzheimer's disease will be randomized to ARM1, amyloid-PET performed early in the diagnostic workup; ARM2, amyloid-PET performed after 8 months; and ARM3, amyloid-PET performed whenever the physician chooses to do so., Endpoints: The primary endpoint is the difference between ARM1 and ARM2 in the proportion of patients receiving a very-high-confidence etiologic diagnosis after 3 months. Secondary endpoints address diagnosis and diagnostic confidence, diagnostic/therapeutic management, health economics and patient-related outcomes, and methods for image quantitation., Expected Impacts: AMYPAD-DPMS will supply physicians and health care payers with real-world data to plan management decisions., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

A few studies reported functional abnormalities at rest in borderline personality disorder (BPD), but their relationship with clinical aspect is unclear. We aimed to assess functional connectivity (FC) in BPD patients and its association with BPD clinical features. Twenty-one BPD patients and 14 healthy controls (HC) underwent a multidimensional assessment and resting-state fMRI. Independent component analysis was performed to identify three resting-state networks: default mode network (DMN), salience network (SN), and executive control network (ECN). FC differences between BPD and HC were assessed with voxel-wise two-sample t-tests. Additionally, we investigated the mean FC within each network and the relationship between connectivity measures and BPD clinical features. Patients showed significant lower mean FC in the DMN and SN, while, at the local level, a cluster of lower functional connectivity emerged in the posterior cingulate cortex of the DMN. The DMN connectivity was positively correlated with the anger-state intensity and expression, while the SN connectivity was positively correlated with metacognitive abilities and a negative correlation emerged with the interpersonal aggression. The dysfunctional connectivity within these networks might explain clinical features of BPD patients., (Copyright © 2018. Published by Elsevier B.V.)

NIA-AA diagnostic criteria include volumetric or visual rating measures of hippocampal atrophy (HA) as a diagnostic biomarker of Alzheimer's disease (AD). We aimed to determine its utility as a diagnostic biomarker for early onset Alzheimer's disease (EOAD) by assessing Medial Temporal Atrophy (MTA) and hippocampal volume (HV) determination. MTA score and HV quantified by FreeSurfer were assessed in 140 (aged ≤65) subjects with biomarker supported diagnosis: 38 amnesic (A-EOAD), 20 non-amnesic (NA-EOAD), 30 late onset AD (LOAD), 20 fronto-temporal dementia (FTD) and 32 healthy controls (HC). The results showed that the proportion of MTA ≥ 1.5 was higher on LOAD and FTD than EOAD and HC but none of the MTA thresholds (≥1, ≥1.5 and ≥ 2) showed acceptable diagnostic accuracy. LOAD had lower HV than the other groups. A-EOAD HV was lower than NA-EOAD and HC but equal to FTD. The 6258 mm3 cut-off showed good diagnostic accuracy between A-EOAD and HC. Both tools showed a moderate inverse correlation. In conclusion, MTA has a limited diagnostic utility as an EOAD biomarker as it does not discriminate AD from FTD or HC in initial symptomatic stages. HV may discriminate A-EOAD from HC but not from FTD., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

We assessed first-year hippocampal atrophy in stroke patients and healthy controls using manual and automated segmentations: AdaBoost, FIRST (fsl/v5.0.8), FreeSurfer/v5.3 and v6.0, and Subfields (in FreeSurfer/v6.0). We estimated hippocampal volumes in 39 healthy controls and 124 stroke participants at three months, and 38 controls and 113 stroke participants at one year. We used intra-class correlation, concordance, and reduced major axis regression to assess agreement between automated and 'Manual' estimations. A linear mixed-effect model was used to characterize hippocampal atrophy. Overall, hippocampal volumes were reduced by 3.9% in first-ever stroke and 9.2% in recurrent stroke at three months post-stroke, with comparable ipsi-and contra-lesional reductions in first-ever stroke. Mean atrophy rates between time points were 0.5% for controls and 1.0% for stroke patients (0.6% contra-lesionally, 1.4% ipsi-lesionally). Atrophy rates in left and right-hemisphere strokes were comparable. All methods revealed significant volume change in first-ever and ipsi-lesional stroke (p < 0.001). Hippocampal volume estimation was not impacted by hemisphere, study group, or scan time point, but rather, by the interaction between the automated segmentation method and hippocampal size. Compared to Manual, Subfields and FIRST recorded the lowest bias. FreeSurfer/v5.3 overestimated volumes the most for large hippocampi, while FIRST was the most accurate in estimating small volumes. AdaBoost performance was average. Our findings suggest that first-year ipsi-lesional hippocampal atrophy rate especially in first-ever stroke, is greater than atrophy rates in healthy controls and contra-lesional stroke. Subfields and FIRST can complementarily be effective in characterizing the hippocampal atrophy in healthy and stroke cohorts., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Introduction: We test the hypothesis that amyloid-positron emission tomography prescriptions, considered appropriate based on the Amyloid Imaging Taskforce (AIT) criteria, lead to greater clinical utility than AIT-inappropriate prescriptions., Methods: We compared the clinical utility between patients who underwent amyloid-positron emission tomography appropriately or inappropriately and among the subgroups of patients defined by the AIT criteria. Finally, we performed logistic regressions to identify variables associated with clinical utility., Results: We identified 171 AIT-appropriate and 67 AIT-inappropriate patients. AIT-appropriate and AIT-inappropriate cases did not differ in any outcomes of clinical utility (P > .05). Subgroup analysis denoted both expected and unexpected results. The logistic regressions outlined the primary role of clinical picture and clinical or neuropsychological profile in identifying patients benefitting from amyloid-positron emission tomography., Discussion: Contrary to our hypothesis, also AIT-inappropriate prescriptions were associated with clinical utility. Clinical or neuropsychological variables, not taken into account by the AIT criteria, may help further refine criteria for appropriateness., (Copyright © 2018 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.)

Research criteria for Alzheimer's disease recommend the use of biomarkers for diagnosis, but whether biomarkers improve the diagnosis in clinical routine has not been systematically assessed. The aim is to evaluate the evidence for use of medial temporal lobe atrophy (MTA) as a biomarker for Alzheimer's disease at the mild cognitive impairment stage in routine clinical practice, with an adapted version of the 5-phase oncology framework for biomarker development. A literature review on visual assessment of MTA and hippocampal volumetry was conducted with other biomarkers addressed in parallel reviews. Ample evidence is available for phase 1 (rationale for use) and phase 2 (discriminative ability between diseased and control subjects). Phase 3 (early detection ability) is partly achieved: most evidence is derived from research cohorts or clinical populations with short follow-up, but validation in clinical mild cognitive impairment cohorts is required. In phase 4, only the practical feasibility has been addressed for visual rating of MTA. The rest of phase 4 and phase 5 have not yet been addressed., (Copyright © 2016 Elsevier Inc. All rights reserved.)

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., (Copyright © 2017 Elsevier Inc. All rights reserved.)