Positive Mild Cognitive Impairment Research Articles

The spatial distribution of coupling between tau and neurodegeneration in amyloid-β positive mild cognitive impairment

Synergies between amyloid-β (Aβ), tau, and neurodegeneration persist along the Alzheimer’s disease (AD) continuum. This study aimed to evaluate the extent of spatial coupling between tau and neurodegeneration (atrophy) and its relation to Aβ positivity in mild cognitive impairment (MCI). Data from 409 participants were included (95 cognitively normal controls, 158 Aβ positive (Aβ+) MCI, and 156 Aβ negative (Aβ-) MCI). Florbetapir PET, Flortaucipir PET, and structural MRI were used as biomarkers for Aβ, tau and atrophy, respectively. Individual correlation matrices for tau load and atrophy were used to layer a multilayer network, with separate layers for tau and atrophy. A measure of coupling between corresponding regions of interest (ROIs) in the tau and atrophy layers was computed, as a function of Aβ positivity. Fewer than 25% of the ROIs across the brain showed heightened coupling between tau and atrophy in Aβ+ , relative to Aβ- MCI. Coupling strengths in the right rostral middle frontal and right paracentral gyri, in particular, mediated the association between Aβ burden and cognition in this sample.

Plasma oligomer βeta‐amyloid associated with disease severity and cerebral amyloid deposition in Alzheimer’s disease spectrum

AbstractBackgroundMultimer detection system‐oligomeric amyloid‐ß (MDS‐OAß) measures plasma OAß level and is associated pathology of Alzheimer’s disease (AD). However, no study investigated MDS‐OAß and cerebral Aß deposition among difference stages within AD spectrum.MethodWe studied 40 normal control (CN), 30 amyloid‐PET (A‐PET) negative mild cognitive impairment (MCI), 31 A‐PET positive MCI, and 22 A‐PET positive dementia.ResultThe one‐way analysis of variance showed group difference in MDS‐OAß. Post‐hoc analysis showed that MDS‐OAß was lowest in CN group and highest in A‐PET positive MCI group. Both A‐PET negative MCI and A‐PET positive dementia groups showed lower MDS‐OAß than A‐PET positive group dementia group (MDS‐OAß: CN < A‐PET negative MCI = A‐PET positive dementia < A‐PET positive MCI) (Figure 1). MDS‐OAß showed a positive correlation with global and regional cerebral Aß deposition measure using A‐PET.ConclusionOur results suggest that MDS‐OAβ shows a reverse U‐shape: increase with disease onset or when clinical symptoms appear, even before Aß pathology is evident in A‐PET, and then elevate as disease progresses but lower down as the disease becomes severe.

Predicting Transitions from Mild Cognitive Impairment to Dementia within 5‐years

AbstractBackgroundEarly detection of individuals at high risk of future progression toward dementia is in great need, particularly in the era of emerging treatments for Alzheimer’s disease. Given the easier administration of neuropsychological (NP) tests over invasive and expensive biomarker acquisition, we seek to predict the future cognitive status of an individual based on digital voice recordings of NP exams.MethodWe utilized automatic speech recognition and natural language processing techniques to create an automated tool that predicts if an individual with mild cognitive impairment (MCI) will develop dementia in a fixed follow‐up window (5 years). We characterize the predictive accuracy of this approach using data from the Framingham Heart Study. First, the automated transcription of the digital voice recordings were classified into 8 main NP sub‐tests including memory assessment, naming and language skill, verbal fluency, general questions, etc. Using a large language model, only the participants’ sentences were encoded into quantitative data. These data and the participants’ characteristics, such as age, sex, ApoE4 (+/‐) genotype, and education were employed to train and test a binary classifier, predicting dementia withing 5 years after a positive MCI diagnosis. We also provide a comparison between our approach and alternative models using demographics and ApoE genotype or the Mini‐Mental State Examination (MMSE) score.ResultWe evaluated the performance of the classification task on the data containing 111 stable MCI and 103 progressive MCI subjects. Our method achieved an average accuracy of 75.5% on the held‐out test data while a baseline model using age, sex, education, and ApoE has an average accuracy of 71.8%. The sensitivity and specificity of the proposed method versus the baseline model are 78.2% vs. 81.8% and 72.7% vs. 61.8%, respectively. Our approach also significantly outperforms the model based on the MMSE score by a margin of 14% in accuracy.ConclusionWe have analyzed digital voice recordings of NP exams for participants with MCI to predict their cognitive status in 5 years. The proposed approach offers a fully automated procedure with higher accuracy, providing an opportunity to develop an accessible and easy‐to‐administer screening tool that could be easily adapted to any language.

BrainChart cortical thickness centile scores: a generalizable tool to detect age‐inappropriate cortical neurodegeneration in Alzheimer’s disease

AbstractBackgroundIdentifying age‐inappropriate cortical neurodegeneration on structural MRI is a key component of Alzheimer’s disease (AD) assessment. However, widely available reference standards that enable objective interpretation are lacking. BrainChart (www.BrainChart.io; https://github.com/BrainChart/Lifespan) calculates age and sex adjusted centile scores for structural MRI metrics using normative data from 101,457 participants across the lifecourse and can be applied across different datasets. We applied this approach to regional cortical thickness estimates and explored its utility in identifying patients with AD associated neurodegeneration.MethodsCross‐sectional BrainChart cortical thickness centile scores were calculated for 241 individuals with pathologically confirmed AD (median scan time prior to death 5.8 years), as well as 1432 cognitively normal participants (CN) from the NACC dataset. Area under the receiver operating characteristic curve (AUC) analyses were performed to identify the cortical regions that best identified patients with pathologically confirmed AD. This approach was then applied to 305 participants from the ADNI‐3 cohort, all of whom underwent clinical amyloid (florbetapir) PET and tau (flortaucipir) PET within 6 months of structural MRI (CN = 185; Mild Cognitive Impairment (MCI) = 95); AD = 26).ResultsThe top ten performing regions for differentiating pathologically confirmed AD from CN in the NACC dataset were entorhinal, middle temporal, inferior parietal, isthmus cingulate, fusiform, supramarginal, inferior temporal, superior temporal, precuneus and temporal pole. AUCs ranged from 0.72 – 0.77 (figure 1 and table 1). Combining these ten regions into a single model significantly improved diagnostic separability (AUC = 0.81, figure 1). Optimal cut‐points for individual regions ranged from the 25th centile to the 35th centile, with sensitivities ranging from 57.7% to 68.9%, and specificities ranging from 72.6% to 80.9%. Using progressively lower centile scores increased specificity but decreased sensitivity (table 1).In ADNI‐3 participants, a combined model of the same ten regions derived from the NACC dataset was able to discriminate amyloid and tau pet positive AD patients from amyloid and tau negative CN (AUC = 0.98), as well as amyloid and tau positive MCI patients from amyloid and tau negative CN (AUC = 0.85) (figure 2).ConclusionBrainChart age and sex adjusted cortical thickness centile scores, derived from an extensive normative dataset, represent a generalizable method for objectively identifying cortical neurodegeneration in AD.

Clinical and neuroimaging predictors of Alzheimer’s Dementia Conversion in patients with mild cognitive impairment using 18F‐Florapronol amyloid PET by quantitative analysis over 2 years

AbstractBackgroundPatients with mild cognitive impairment(MCI) are a clinically important group because of the increased risk for the progression to Alzheimer’s dementia(AD). Amyloid positron emission tomography(PET) was useful as a predictor for identifying AD pathology in the pre‐dementia stage. We determined the rates of conversion to AD in MCI patients based on amyloid positivity by visual and quantitative analysis over 2 years. We also investigated the clinical and neuroimaging predictors for conversion to AD.MethodIndividuals aged 50 and above with MCI according to Petersen’s criteria were eligible. Subjects underwent laboratory tests including APOE genotyping, neuropsychological tests, brain magnetic resonance imaging(MRI), and amyloid PET assessed by 18F‐FC119S. Amyloid positivity was determined in each lobe of the brain and quantitatively analyzed. The follow‐up period was at least 2 years. Cox regression analysis was performed to identify prognostic factors which are independently related to time to AD conversion.ResultWe recruited 50 patients and 39 subjects were enrolled. 5 patients dropped out at 2 years, so 34 subjects were finally included. 13 of 34(38.2%) by visual analysis and 12 of 34(35.3%) by quantitative analysis of 18F‐FC119S PET showed amyloid positive. The basic demographics were not significantly different between the groups except for Korea‐Instrumental Activities of Daily Living(K‐IADL) at baseline(p = 0.004) and 1 year(p = 0.001), Mini‐mental State Examination(MMSE) at 2 years(p = 0.047) and Global Deterioration Scale(GDS) at 2 years(p = 0.029). 4 of 13(30.8%) by visual analysis, 4 of 12(66.7%) by quantitative analysis amyloid‐positive MCI patients converted to AD. Amyloid positive in posterior cingulate was marginally significant(p = 0.066). AD conversion group had a higher Clinical Dementia Rating‐Sum of box(CDR‐SOB) at baseline(p = 0.002) compared with remained MCI group. In multivariate cox regression, CDR‐SOB at baseline, MMSE at 1 year and K‐IADL at 2 years were included.ConclusionAmyloid‐positive MCI group was more likely to progress to AD. But long‐term follow‐up was required. By quantitative analysis of 18F‐FC119S PET, amyloid deposition in posterior cingulate was highly associated with the AD conversion group. CDR‐SOB at baseline, MMSE at 1 year and K‐IADL at 2 years were considered as clinically valuable prognostic predictors of disease progression. Especially, CDR‐SOB is important as a predictive biomarker in the pre‐dementia stage.

BrainChart cortical thickness centile scores: a generalizable tool to detect age‐inappropriate cortical neurodegeneration in Alzheimer’s disease

AbstractBackgroundIdentifying age‐inappropriate cortical neurodegeneration on structural MRI is a key component of Alzheimer’s disease (AD) assessment. However, widely available reference standards that enable objective interpretation are lacking. BrainChart (www.BrainChart.io; https://github.com/BrainChart/Lifespan) calculates age and sex adjusted centile scores for structural MRI metrics using normative data from 101,457 participants across the lifecourse and can be applied across different datasets. We applied this approach to regional cortical thickness estimates and explored its utility in identifying patients with AD associated neurodegeneration.MethodCross‐sectional BrainChart cortical thickness centile scores were calculated for 241 individuals with pathologically confirmed AD (median scan time prior to death 5.8 years), as well as 1432 cognitively normal participants (CN) from the NACC dataset. Area under the receiver operating characteristic curve (AUC) analyses were performed to identify the cortical regions that best identified patients with pathologically confirmed AD. This approach was then applied to 305 participants from the ADNI‐3 cohort, all of whom underwent clinical amyloid (florbetapir) PET and tau (flortaucipir) PET within 6 months of structural MRI (CN = 185; Mild Cognitive Impairment (MCI) = 95); AD = 26).ResultThe top ten performing regions for differentiating pathologically confirmed AD from CN in the NACC dataset were entorhinal, middle temporal, inferior parietal, isthmus cingulate, fusiform, supramarginal, inferior temporal, superior temporal, precuneus and temporal pole. AUCs ranged from 0.72 – 0.77 (figure 1 and table 1). Combining these ten regions into a single model significantly improved diagnostic separability (AUC = 0.81, figure 1). Optimal cut‐points for individual regions ranged from the 25th centile to the 35th centile, with sensitivities ranging from 57.7% to 68.9%, and specificities ranging from 72.6% to 80.9%. Using progressively lower centile scores increased specificity but decreased sensitivity (table 1). In ADNI‐3 participants, a combined model of the same ten regions derived from the NACC dataset was able to discriminate amyloid and tau pet positive AD patients from amyloid and tau negative CN (AUC = 0.98), as well as amyloid and tau positive MCI patients from amyloid and tau negative CN (AUC = 0.85) (figure 2).ConclusionBrainChart age and sex adjusted cortical thickness centile scores, derived from an extensive normative dataset, represent a generalizable method for objectively identifying cortical neurodegeneration in AD.

Cognitive Trajectories in Preclinical and Prodromal Alzheimer's Disease Related to Amyloid Status and Brain Atrophy: A Bayesian Approach.

Cognitive decline is a key outcome of clinical studies in Alzheimer's disease (AD). To determine effects of global amyloid load as well as hippocampus and basal forebrain volumes on longitudinal rates and practice effects from repeated testing of domain specific cognitive change in the AD spectrum, considering non-linear effects and heterogeneity across cohorts. We included 1,514 cases from three cohorts, ADNI, AIBL, and DELCODE, spanning the range from cognitively normal people to people with subjective cognitive decline and mild cognitive impairment (MCI). We used generalized Bayesian mixed effects analysis of linear and polynomial models of amyloid and volume effects in time. Robustness of effects across cohorts was determined using Bayesian random effects meta-analysis. We found a consistent effect of amyloid and hippocampus volume, but not of basal forebrain volume, on rates of memory change across the three cohorts in the meta-analysis. Effects for amyloid and volumetric markers on executive function were more heterogeneous. We found practice effects in memory and executive performance in amyloid negative cognitively normal controls and MCI cases, but only to a smaller degree in amyloid positive controls and not at all in amyloid positive MCI cases. We found heterogeneity between cohorts, particularly in effects on executive functions. Initial increases in cognitive performance in amyloid negative, but not in amyloid positive MCI cases and controls may reflect practice effects from repeated testing that are lost with higher levels of cerebral amyloid.

T cell exhaustion is associated with cognitive status and amyloid accumulation in Alzheimer’s disease

Studies over the last 100 years have suggested a link between inflammation, infectious disease, and Alzheimer’s Disease (AD). Understanding how the immune system changes during the development of AD may facilitate new treatments. Here, we studied an aging cohort who had been assessed for AD pathology with amyloid positron emission tomography and cognitive testing, and conducted high dimensional flow cytometry on peripheral blood mononuclear and cerebrospinal fluid cells. Participants were assigned a classification of being amyloid negative cognitively normal, amyloid positive cognitively normal (APCN), or amyloid positive mild cognitive impairment (APMCI), an early stage of AD. We observed major alterations in the peripheral innate immune system including increased myeloid and plasmacytoid dendritic cells in the blood of APMCI participants. When the adaptive immune system was examined, amyloid positive participants, regardless of cognitive status, had increased CD3+ T cells. Further analyses of CD4+ and CD8+ T cells revealed that APMCI participants had an increase in more differentiated phenotype T cells, such as effector memory and effector memory CD45RA expressing (TEMRA), compared to those with normal cognition. When T cell function was measured, we observed that T cells from APCN participants had increased IFNγ+GzB- producing cells compared to the other participants. In contrast, we demonstrate that APMCI participants had a major increase in T cells that lacked cytokine production following restimulation and expressed increased levels of PD-1 and Tox, suggesting these are exhausted cells. Rejuvenation of these cells may provide a potential treatment for AD.

Exploratory Assessment of Proteomic Network Changes in Cerebrospinal Fluid of Mild Cognitive Impairment Patients: A Pilot Study.

(1) Background: Despite the existence of well-established, CSF-based biomarkers such as amyloid-β and phosphorylated-tau, the pathways involved in the pathophysiology of Alzheimer's disease (AD) remain an active area of research. (2) Methods: We measured 3072 proteins in CSF samples of AD-biomarker positive mild cognitive impairment (MCI) participants (n = 38) and controls (n = 48), using the Explore panel of the Olink proximity extension assay (PEA). We performed group comparisons, association studies with diagnosis, age, and APOE ε4 status, overrepresentation analysis (ORA), and gene set enrichment analysis (GSEA) to determine differentially expressed proteins and dysregulated pathways. (3) Results: GSEA results demonstrated an enrichment of granulocyte-related and chemotactic pathways (core enrichment proteins: ITGB2, ITGAM, ICAM1, SELL, SELP, C5, IL1A). Moreover, some of the well-replicated, differentially expressed proteins in CSF included: ITGAM, ITGB2, C1QA, TREM2, GFAP, NEFL, MMP-10, and a novel tau-related marker, SCRN1. (4) Conclusion: Our results highlight the upregulation of neuroinflammatory pathways, especially chemotactic and granulocyte recruitment in CSF of early AD patients.

Amyloid beta plaque accumulation with longitudinal [18F]AZD4694 PET.

[18F]AZD4694 is an amyloid beta (Aβ) imaging agent used in several observational studies and clinical trials. However, no studies have yet published data on longitudinal Aβ accumulation measured with [18F]AZD4694. We assessed 146 individuals who were evaluated with [18F]AZD4694 at baseline and 2-year follow-up. We calculated annual rates of [18F]AZD4694 change for clinically defined and biomarker-defined groups. Cognitively unimpaired (CU) older adults displayed subtle [18F]AZD4694 standardized uptake value ratio (SUVR) accumulation over the follow-up period. In contrast, Aβ positive CU older adults displayed higher annual [18F]AZD4694 SUVR increases. [18F]AZD4694 SUVR accumulation in Aβ positive mild cognitive impairment (MCI) and dementia was modest across the neocortex. Larger increases in [18F]AZD4694 SUVR were observed in CU individuals who had abnormal amyloid positron emission tomography levels at baseline. [18F]AZD4694 can be used to monitor Aβ levels in therapeutic trials as well as clinical settings, particularly prior to initiating anti-amyloid therapies.

The effect of the tumour necrosis factor-alpha-inhibitor etanercept on microglial activation patients with mild cognitive impairment—a PET study

Aim: Microglial activation is increasingly recognised as a factor in the progression of Alzheimer’s disease (AD) and may be modified by systemic inflammatory signals including serum tumour necrosis factor (TNF)-α. The aim was to investigate whether blockade of peripheral TNF-α with peripheral inhibitors such as etanercept reduces microglial activation in prodromal AD. Methods: A one-year, multi-centre, phase 2, double-blind randomised placebo-controlled trial (RPCT) was performed, to assess the effect of weekly 50 mg s.c. etanercept in amyloid positive mild cognitive impaired participants on the change in microglial activation as measured by [11C](R)-PK11195 positron emission tomography (PET). Secondary objectives were to ascertain the change in cortical amyloid load on PET and the change in the Montreal Cognitive Assessment (MoCA). Results: Forty-four subjects consented to the study. Twenty-eight subjects failed screening including six subjects who were amyloid negative on visual read of the AmyvidTM PET scans. Thirteen of sixteen subjects with mild cognitive impairment (MCI) due to AD completed the baseline [11C](R)-PK11195 PET scan and were randomised to either placebo or etanercept. Three patients who consented were not able to complete screening due to early termination of the study following delays in study commencement. [11C](R)-PK11195 binding potential (BP) at baseline showed an almost global increase in MCI patients as compared to age-matched controls. Compliance to medication was high over the twelve-month trial period with etanercept being well tolerated. The study did not achieve statistical power to show a significant effect of etanercept over 52 weeks in the limited number of patients with MCI on microglial activation as measured by [11C](R)-PK11195 PET. Overall uptake of florbetapir in the follow up (FU) scans remained stable. The study was not powered to show statistical differences in psychometric ratings between groups. Conclusions: This study did not show evidence that treatment with etanercept over one year would modulate microglial activation in amyloid positive MCI patients (EudraCT identifier: 2015-002145-63, https://www.clinicaltrialsregister.eu; International Standard Randomised Controlled Trial Number identifier: ISRCTN12472821, https://www.isrctn.com).

Development of prediction models for distinguishable cognitive trajectories in patients with amyloid positive mild cognitive impairment

The clinical outcomes of patients with amyloid beta-positive (Aβ+) mild cognitive impairment (MCI) are heterogeneous. We therefore developed prediction models for distinguishable cognitive trajectories in Aβ+ participants with MCI. We included 238 Aβ+ participants with MCI from the Alzheimer's Disease Neuroimaging Initiative to develop a group-based trajectory model and 63 Aβ+ participants with MCI from the Samsung Medical Center for external validation. Three distinguishable classes, slow decliners (18.5%), intermediate decliners (42.9%), and fast decliners (38.7%), were identified. Intermediate decliners were associated with older age, higher AV45 standardized uptake value ratios (SUVR) and lower fluorodeoxyglucose (FDG) SUVR than slow decliners. Fast decliners were associated with older age, presence of APOE ε4, higher AV45 SUVR and lower FDG SUVR than slow decliners. Prediction models of cognitive decline showed good discrimination and calibration capabilities in the development and validation data sets. Our analysis yields novel insights into the cognitive trajectories of Aβ+ patients with MCI, which will facilitate their effective stratification in Aβ-targeted clinical trials.

Differential levels of plasma biomarkers of neurodegeneration in Lewy body dementia, Alzheimer’s disease, frontotemporal dementia and progressive supranuclear palsy

ObjectivesThis longitudinal study compared emerging plasma biomarkers for neurodegenerative disease between controls, patients with Alzheimer’s disease (AD), Lewy body dementia (LBD), frontotemporal dementia (FTD) and progressive supranuclear palsy (PSP).MethodsPlasma phosphorylated...

Comparing the clinical utility and diagnostic performance of cerebrospinal fluid P‐tau181, P‐tau217 and P‐tau231 assays

AbstractBackgroundSeveral studies indicate that increases in cerebrospinal fluid (CSF) phosphorylated tau (P‐tau) in Alzheimer’s disease (AD) occur in response to very early amyloid‐β (Aβ) pathology and precede widespread tau aggregation. Thus far, however, there are no studies comparing different P‐tau isoforms—i.e., tau phosphorylated at threonine‐181 (P‐tau181), 217 (P‐tau217) and 213 (P‐tau231) levels—in relation to Aβ and Tau PET across the symptomatic stages of AD, nor data directly comparing their diagnostic performance for separating AD dementia from non‐AD neurodegenerative disorders and for identifying abnormal Aβ and Tau PET status. We herein aimed to address these questions using cross‐sectional data from the Swedish BioFINDER‐2 study.MethodWe included cross‐sectional data for 629 participants (cognitively unimpaired, amyloid‐β (Aβ)‐positive mild cognitive impairment, Alzheimer’s disease (AD) dementia and non‐AD disorders). In addition to comparing P‐tau181 and P‐tau217 measured using assays with the same capture antibodies from Eli Lilly (P‐tau181Lilly and P‐tau217Lilly; Meso Scale Discovery platform) with P‐tau231 from ADx NeuroSciences (P‐tau231ADx; sandwich ELISA), we also compared P‐tau181Lilly with P‐tau181 measurements from two commonly used assays (Innotest [P‐tau181Innotest; sandwich ELISA] and Elecsys [P‐tau181Elecsys; fully automated electrochemiluminescence immunoassay]). Aβ and Tau PET were performed using [18F]flutemetamol and [18F]RO948.The discriminative performance of CSF P‐tau measures was assessed using the area under the receiver operating characteristic curve (AUC).ResultThough all P‐tau variants increased across the AD continuum, P‐tau217Lilly showed the greatest dynamic range (13‐fold‐increase vs 1.9‐5.4‐fold‐increase for other P‐tau variants for AD dementia vs non‐AD) (Figure 1). P‐tau217Lillyshowed stronger correlations with Aβ‐ and tau‐PET (P<0.0001) (Figure 2). P‐tau217Lilly exhibited higher accuracy than other P‐tau variants for separating AD dementia from non‐AD (AUC, 0.991vs 0.906‐0.982, P<0.0001) and for identifying Aβ‐PET positivity (AUC, 0.951 vs 0.816‐0.924, P<0.0001) and tau‐PET positivity (AUC, 0.957 vs 0.836‐0.938, P<0.0001) (Figure 3). Finally, P‐tau181Lilly generally performed better than the other P‐tau181 assays, (e.g., AD dementia vs non‐AD, AUC, 0.976 vs 0.923, P<0.0001).ConclusionCSF P‐tau217Lilly seem to be more useful than other included P‐tau assays in the diagnostic work‐up of AD and for tracking disease progression. Varied results across P‐tau181 assays also highlights the importance of anti‐tau antibodies for biomarker performance.

Increased intra‐network functional connectivity in MCI patients progressing towards dementia compared to stable MCI

AbstractBackgroundFew resting‐state functional magnetic resonance imaging (rs‐fMRI) studies were carried out in patients with mild cognitive impairment (MCI). Most were cross‐sectional and brought conflicting results with both decrease and increase of rs‐fMRI connectivity reported. We used a recently developed atlas of resting‐state networks specific for late adulthood to investigate longitudinal changes in rs‐fMRI in MCI patients, both within and between networks.MethodSixty older adults underwent rs‐fMRI using the Atlas55+[1] to assess intra and inter‐network functional connectivity and a baseline [18F]‐Flutemetamol‐PET to estimate their Amyloid‐β (Aβ) load. Sixteen stable Aβ‐negative clinically normal volunteers performed a baseline rs‐fMRI scan while 44 MCI patients performed 2 to 4 acquisitions with an average follow‐up duration of 14,5 months. Aβ was expressed in Centiloids and a threshold of 27 was used to define Aβ positivity based on previous work[2]. Intra and inter‐network functional connectivity analyses were performed using both progression to dementia and Aβ status as grouping variables. Demographics and follow‐up durations are provided in Table 1. Linear mixed‐models with random intercept and slope predicted changes in rs‐fMRI over time, adjusting for age, sex, education and e4 status.ResultWe observed that MCI patients who progressed to dementia during follow‐up had increased intra‐network connectivity over time, compared to stable MCI. Specifically, the time‐by‐outcome (progression vs stable) interaction for the intra‐network functional connectivity in the default‐mode, the executive control and the visual networks were significant (Fig. 1‐3). Similar results were found when limiting analyses to Aβ positive MCI and adjusting for volume effects, except for the visual network. Baseline intra‐network functional connectivity did not differ between groups (all p‐values>0,05). Inter‐network functional connectivity did not change between groups over time (all p>0,05).ConclusionIn Alzheimer's Disease, intra‐network functional connectivity increases during the progression of MCI to dementia compared to stable MCI.

Remote identification of MCI using self‐ and study partner‐report subjective cognitive decline in the Brain Health Registry

AbstractBackgroundEfficient identification of older adults with Mild Cognitive Impairment (MCI), including MCI due to AD, is a pressing challenge. Remote methods to accurately identify older adults with MCI are a promising approach. Subjective cognitive decline (SCD), reported by individuals and their study partners, can be efficiently collected remotely.MethodsIn 1286 older adults in the Brain Health Registry (BHR, Table 1), using logistic regression, we determined the accuracy of online self‐report mood (Geriatric Depression Scale Short Form, GDS), family history of AD, Everyday Cognition Scale (ECog) as a measure of SCD, and Cogstate Brief Battery one card learning (OCL) accuray to distinguish (1) Cognitively‐unimpaired (CU) from MCI, and (2) CU from b‐amyloid (Ab)+ MCI. In a subset of 289 participants with enrolled study partners, we measured the contribution of study partner‐report SCD to predicting diagnostic category, and the correlation between self‐ and study partner‐report SCD. Models covaried for age, gender, and education level.ResultsSelf‐ and study partner‐report SCD scores were moderately correlated (r=0.44); associations between the two measures were significantly lower in participants with higher GDS scores, indicating more depressive symptoms (p=0.03). Models including self‐ and study partner‐report SCD and Cogstate OCL scores had the highest accuracy for distinguishing CU from all MCI, as well as CU from Ab+ MCI (AUC=0.90 for CU vs MCI; AUC=0.93 for CU vs Ab+ MCI). Models excluding study partner‐report SCD had moderate accuracy at diagnostic classification (AUC=0.81 for CU vs MCI; AUC=0.83 for CU vs Ab+ MCI). Models excluding all SCD measures, and instead relying on OCL accuracy scores, had lower accuracy (AUC=0.73 for CU vs MCI; AUC=0.76 for CU vs Ab+ MCI). Model outputs are summarized in Table 2.ConclusionsRemotely‐collected self‐ and study partner‐report SCD accurately distinguish CU from MCI, as well as CU from biomarker positive MCI. SCD measures perform well in identifying MCI, even in the absence of objective cognitive measures. Results support utilization of remote SCD measures to identify older adults for prodromal AD clinical trials, and who are suitable candidates to receive future anti‐amyloid therapeutics.

Clinical evaluation of SPARE‐TauAD, an AV‐1451‐derived tau index

AbstractBackgroundThe deposition of abnormal proteins defines neurodegenerative diseases. Among these proteins, tau deposits define frontotemporal lobar degeneration (FTLD) tau, and together with Aβ, it defines the more prevalent Alzheimer’s disease (AD). PET scans with ligands binding AD tau neurofibrillary tangles (NFT) topographically quantify the amount of pathology. However, for clinical purposes, this information needs to be summarized into easily applicable and meaningful indices. Here we develop a machine learning‐derived tau PET index, the Spatial Pattern of Abnormality for Recognition of Tau in AD (SPARE‐TauAD).Method603 subjects were included in the study. Their clinical diagnoses were: 366 cognitively normal (CN), 177 mild cognitive impairment (MCI), and 60 dementia subjects. These subjects were classified as Aβ positive and negative based on CSF Aβ1‐42 levels or florbetapir PET summary composite. SPARE‐TauAD was derived using a support vector machine classifier comparing AV‐1451 PET scans in 141 Aβ negative CN subjects against 108 Aβ positive MCI and dementia subjects. We then evaluated its association with CSF t‐tau, global cognition (measured using ADAS‐Cog 13), clinical diagnosis, and clinical progression.ResultSPARE‐TauAD and CSF t‐tau showed a moderate correlation (rho=0.51, p<0.0001). SPARE‐TauAD explained a larger variance of ADAS‐Cog13 scores (R2=0.60) compared to CSF t‐tau (R2=0.21) and the florbetapir PET summary composite (R2=0.23). Using 5‐fold cross‐validation in an independent sample of Aβ negative CN subjects (n=77) against Aβ positive MCI and dementia subjects (n=35 for CSF), SPARE‐TauAD showed higher balanced accuracy (79.6%) compared to CSF t‐tau (61.6%). SPARE‐TauAD was also associated with faster progression from CN to MCI/dementia (H.R.=6.83, p‐value<0.0001) and MCI to dementia (H.R.=3.43, p‐value<0.0001). Whereas CSF t‐tau was no associated with clinical progression.ConclusionSPARE‐TauAD showed, as expected, a moderate association with CSF t‐tau. However, it offered better cross‐sectional classification accuracy and correlation with cognitive scores. It also outperformed CSF t‐tau to predict longitudinal progression in this small with limited longitudinal follow‐up. One limitation is that the SPARE‐TauAD index has been developed in an AD MCI and dementia cohort. The used tau ligand, AV‐1451, has a greater affinity for NFT present in AD than tau pathology present in FTLD‐tau.

Determination of diagnostic cutoffs for tau‐PET: A preliminary study based on tau and beta‐amyloid PET measurements

AbstractBackgroundAlzheimer’s Disease (AD) is characterized by abnormal concentration of beta‐amyloid (AB) and tau proteins which can be assessed using positron emission tomography (PET). Currently, no relative standardized uptake value (rSUV) cutoff defines an AD diagnostic tau‐PET, and the literature is mainly focused on defining a cutoff per Braak stage which correlates with disease progression.MethodIn this study, we used tau‐rSUV values of cortical and subcortical regions to define three diagnostic cutoffs for tau‐PET using age‐ and gender‐matched data (average age = 72.9 ± 7.36 y.o.; gender = 56.1% male) of AD and mild cognitive impairment (MCI) patients, and cognitively normal (CN) subjects from the Alzheimer's Disease Neuroimaging Initiative. The diagnostic groups were: 1) CN (n = 54) vs MCI and AD (nMCI = 46, nAD = 14); 2) AB negative CN (n = 35) vs AB positive MCI and AD (nMCI = 26, nAD = 12); and 3) AB negative (n = 87: 65 CN, 20 MCI, 2 AD) vs AB positive (n=73: 36 CN, 26 MCI, 12 AD).Tau‐rSUV values were normalized for each region volume using the inferior cerebellum as a reference. Regions that provided significant differences in rSUV values between the diagnostic groups were considered to compute a composite tau‐rSUV value. Then, for each case, a cutoff value was defined using the composite tau‐rSUV value that maximizes the chi‐square statistic value with the highest significance.ResultFor the case CN vs (MCI+AD) the diagnostic cutoff tau‐rSUV value was 1.10 (Qui‐square = 19.9; p‐value < 0.001); for AB negative CN vs AB positive MCI and AD the cutoff tau‐rSUV was 1.16 (Qui‐square = 30.3; p‐value < 0.001); while in AB negative vs AB positive the cutoff tau‐rSUV was 1.21 (Qui‐square = 31.0; p‐value < 0.001).ConclusionOur diagnostic cutoff tau‐rSUV values are within the interval of values [1.19 to 1.34] previously reported [1]–[3]. The results highlight that the cutoff values are related to AB status: if the latter is unknown, the cutoff may be more restricted; while the third cutoff indicates that AB negative MCI and AD subjects already show the presence of tau protein.

Alpha power is associated with hippocampal volume in Alzheimer’s disease: A combined MEG and MRI study

AbstractBackground New Therapeutics in Alzheimer’s Disease (NTAD) is a multi‐centre, longitudinal study to develop reliable and sensitive biomarkers for patients in the early stage of Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). The hallmark of E/MEG abnormalities in AD patients is changes to the power and frequency of the alpha rhythm. Here, we explore spontaneous alpha oscillations in the NTAD cohort and how they relate to diagnostic condition (patient vs control), resting task (eyes open vs eyes closed) and hippocampal atrophy.MethodAt present, there has been MRI and E/MEG data collected on neurologically healthy controls (N=15; MMSE M=29.4±0.74) and amyloid positive MCI and AD patients (N=46; MMSE M=24.8±3.37). Resting‐state E/MEG recordings were obtained for two 5‐minute sessions; eyes opened and eyes closed. Average power and peak frequency were computed for each participant. A T1‐weighted MR scan was used to calculate hippocampal volume, whilst accounting for individual brain volume. Firstly, we compared the average power and peak frequency for patients and controls in both the eyes opened and eyes closed conditions. Secondly, we explored how alpha varies across these conditions and diagnoses and how these differences relate to hippocampal volume.ResultWe found that patients had lower peak frequency and power compared to controls. Specifically, there was a decreased power in eyes closed, and decreased frequency in eyes opened. These differences were also present in hippocampal volume as patients had significantly smaller hippocampi than controls, and this was correlated with decreased alpha peak frequency specifically in the eyes opened session (R2 = 0.172). These individual differences were further associated with MMSE scores.ConclusionThere are individual differences between alpha frequency and power between patients and controls. Alpha decreases with increasing cognitive impairment and is related to hippocampal volume. Whilst this is specific to eyes opened or eyes closed, results suggest that progressive atrophy of the hippocampus is associated with decreased alpha frequency. This confirms the clinical benefit of E/MEG measurements in dementia research and improve our understanding of the integration between structural and electrophysiological measures of synaptic dysfunction. Ongoing data analysis will be conducted to standardise these measures for future clinical trials.

Decreased Global EEG Synchronization in Amyloid Positive Mild Cognitive Impairment and Alzheimer's Disease Patients-Relationship to APOE ε4.

The apolipoprotein E (APOE) ε4 allele is a risk factor for Alzheimer’s disease (AD) that has been linked to changes in brain structure and function as well as to different biological subtypes of the disease. The present study aimed to investigate the association of APOE ε4 genotypes with brain functional impairment, as assessed by quantitative EEG (qEEG) in patients on the AD continuum. The study population included 101 amyloid positive patients diagnosed with mild cognitive impairment (MCI) (n = 50) and AD (n = 51) that underwent resting-state EEG recording and CSF Aβ42 analysis. In total, 31 patients were APOE ε4 non-carriers, 42 were carriers of one, and 28 were carriers of two APOE ε4 alleles. Quantitative EEG analysis included computation of the global field power (GFP) and global field synchronization (GFS) in conventional frequency bands. Amyloid positive patients who were carriers of APOE ε4 allele(s) had significantly higher GFP beta and significantly lower GFS in theta and beta bands compared to APOE ε4 non-carriers. Increased global EEG power in beta band in APOE ε4 carriers may represent a brain functional compensatory mechanism that offsets global EEG slowing in AD patients. Our findings suggest that decreased EEG measures of global synchronization in theta and beta bands reflect brain functional deficits related to the APOE ε4 genotype in patients that are on a biomarker-verified AD continuum.