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Abstract Background In Alzheimer’s disease (AD), fibrillar tau initially occurs locally and progresses preferentially between closely connected regions. However, the underlying sources of regional vulnerability to tau pathology remain unclear. Previous brain-autopsy findings suggest that the myelin levels—which differ substantially between white matter tracts in the brain—are a key modulating factor of region-specific susceptibility to tau deposition. Here, we investigated whether myelination differences between fiber tracts of the human connectome are predictive of the interregional spreading of tau pathology in AD. Methods We included two independently recruited samples consisting of amyloid-PET-positive asymptomatic and symptomatic elderly individuals, in whom tau-PET was obtained at baseline (ADNI: n = 275; BioFINDER-1: n = 102) and longitudinally in a subset (ADNI: n = 123, mean FU = 1.53 [0.69–3.95] years; BioFINDER-1: n = 39, mean FU = 1.87 [1.21–2.78] years). We constructed MRI templates of the myelin water fraction (MWF) in 200 gray matter ROIs and connecting fiber tracts obtained from adult cognitively normal participants. Using the same 200 ROI brain-parcellation atlas, we obtained tau-PET ROI values from each individual in ADNI and BioFINDER-1. In a spatial regression analysis, we first tested the association between cortical myelin and group-average tau-PET signal in the amyloid-positive and control groups. Secondly, employing a previously established approach of modeling tau-PET spreading based on functional connectivity between ROIs, we estimated in a linear regression analysis, whether the level of fiber-tract myelin modulates the association between functional connectivity and longitudinal tau-PET spreading (i.e., covariance) between ROIs. Results We found that higher myelinated cortical regions show lower tau-PET uptake (ADNI: rho = − 0.267, p

Abstract Background FDG-PET hypermetabolism can be observed in mild cognitive impairment (MCI), but the link to primary pathologies of Alzheimer’s diseases (AD) including amyloid and tau is unclear. Methods Using voxel-based regression, we assessed local interactions between amyloid- and tau-PET on spatially matched FDG-PET in 72 MCI patients. Control groups included cerebrospinal fluid biomarker characterized cognitively normal (CN, n = 70) and AD dementia subjects (n = 95). Results In MCI, significant amyloid-PET by tau-PET interactions were found in frontal, lateral temporal, and posterior parietal regions, where higher local tau-PET was associated with higher spatially corresponding FDG-PET at low levels of local amyloid-PET. FDG-PET in brain regions with a significant local amyloid- by tau-PET interaction was higher compared to that in CN and AD dementia and associated with lower episodic memory. Conclusion Higher tau-PET in the presence of low amyloid-PET is associated with abnormally increased glucose metabolism that is accompanied by episodic memory impairment.

Abstract Microglia activation is the brain's major immune response to amyloid plaques in Alzheimer's disease (AD). Both cerebrospinal fluid (CSF) levels of soluble TREM2 (sTREM2), a biomarker of microglia activation, and microglia PET are increased in AD; however, whether an increase in these biomarkers is associated with reduced amyloid‐beta (Aβ) accumulation remains unclear. To address this question, we pursued a two‐pronged translational approach. Firstly, in non‐demented and demented individuals, we tested CSF sTREM2 at baseline to predict (i) amyloid PET changes over ∼2 years and (ii) tau PET cross‐sectionally assessed in a subset of patients. We found higher CSF sTREM2 associated with attenuated amyloid PET increase and lower tau PET. Secondly, in the AppNL‐G-F mouse model of amyloidosis, we studied baseline 18F‐GE180 microglia PET and longitudinal amyloid PET to test the microglia vs. Aβ association, without any confounding co‐pathologies often present in AD patients. Higher microglia PET at age 5 months was associated with a slower amyloid PET increase between ages 5‐to‐10 months. In conclusion, higher microglia activation as determined by CSF sTREM2 or microglia PET shows protective effects on subsequent amyloid accumulation.

PurposeTo assess the pathological aging effect on caudate functional connectivity among mild cognitive impairment (MCI) participants and examine whether and how sex and amyloid contribute to this process.Materials and MethodsTwo hundred and seventy-seven functional magnetic resonance imaging (fMRI) sessions from 163 cognitive normal (CN) older adults and 309 sessions from 139 participants with MCI were included as the main sample in our analysis. Pearson's correlation was used to characterize the functional connectivity (FC) between caudate nuclei and each brain region, then caudate nodal strength was computed to quantify the overall caudate FC strength. Association analysis between caudate nodal strength and age was carried out in MCI and CN separately using linear mixed effect (LME) model with covariates (education, handedness, sex, Apolipoprotein E4, and intra-subject effect). Analysis of covariance was conducted to investigate sex, amyloid status, and their interaction effects on aging with the fMRI data subset having amyloid status available. LME model was applied to women and men separately within MCI group to evaluate aging effects on caudate nodal strength and each region's connectivity with caudate nuclei. We then evaluated the roles of sex and amyloid status in the associations of neuropsychological scores with age or caudate nodal strength. An independent cohort was used to validate the sex-dependent aging effects in MCI.ResultsThe MCI group had significantly stronger age-related increase of caudate nodal strength compared to the CN group. Analyzing women and men separately revealed that the aging effect on caudate nodal strength among MCI participants was significant only for women (left: P = 6.23 × 10−7, right: P = 3.37 × 10−8), but not for men (P > 0.3 for bilateral caudate nuclei). The aging effects on caudate nodal strength were not significantly mediated by brain amyloid burden. Caudate connectivity with ventral prefrontal cortex substantially contributed to the aging effect on caudate nodal strength in women with MCI. Higher caudate nodal strength is significantly related to worse cognitive performance in women but not in men with MCI.ConclusionSex modulates the pathological aging effects on caudate nodal strength in MCI regardless of amyloid status. Caudate nodal strength may be a sensitive biomarker of pathological aging in women with MCI.

Multiple epidemiological studies have revealed an association between presbycusis and Alzheimer’s Disease (AD). Unfortunately, the neurobiological underpinnings of this relationship are not clear. It is possible that the two disorders share a common, as yet unidentified, risk factor, or that hearing loss may independently accelerate AD pathology. Here, we examined the relationship between reported hearing loss and brain volumes in normal, mild cognitive impairment (MCI) and AD subjects using a publicly available database. We found that among subjects with AD, individuals that reported hearing loss had smaller brainstem and cerebellar volumes in both hemispheres than individuals without hearing loss. In addition, we found that these brain volumes diminish in size more rapidly among normal subjects with reported hearing loss and that there was a significant interaction between cognitive diagnosis and the relationship between reported hearing loss and these brain volumes. These data suggest that hearing loss is linked to brainstem and cerebellar pathology, but only in the context of the pathological state of AD. We hypothesize that the presence of AD-related pathology in both the brainstem and cerebellum creates vulnerabilities in these brain regions to auditory deafferentation-related atrophy. These data have implications for our understanding of the potential neural substrates for interactions between hearing loss and AD.

Purpose: To quantitatively investigate the microstructural properties of the optic nerve (ON) in vivo using diffusion magnetic resonance imaging (dMRI) tractography in an elderly population and to determine the differences between the ON diffusion properties stratified by basic demographics.Methods: We measured fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) of the intraorbital ON in cognitively normal controls selected from the Alzheimer's Disease Neuroimaging Initiative 3 database (n =104; mean age = 73. 8 ± 8.1 years) using dMRI probabilistic tractography and evaluated the correlation between diffusion parameters and demographic factors. Diffusion parameters were measured in 20 equidistant nodes along the tract, and the data from proximal 70% (14 nodes) of the intraorbital ON were averaged.Results: The mean FA of the intraorbital ON was 0.392 ± 0.063, and the mean MD was 1.163 ± 0.165 μm2/s. The mean RD was 0.882 ± 0.152 μm2/s, and the mean AD was 1.693 ± 0.183 μm2/s. The multiple linear regression model showed a negative correlation between FA and age. FA in females was significantly higher than males, whereas RD in female was significantly lower.Conclusions: We measured the diffusion properties of the intraorbital ON using dMRI tractography in an elderly cognitively normal population. The diffusion properties detected by dMRI tractography may substantially reflect the microstructure of the ON.

The BIN1 SNP rs744373 is associated with higher CSF tau and phosphorylated tau levels. Here the authors show, using PET imaging, that this SNP is associated with tau accumulation in the brain as well as impaired memory in older individuals without dementia.

Amyloid-β and hyperphosphorylated tau protein are known drivers of neuropathology in Alzheimer's disease. Tau in particular spreads in the brains of patients following a spatiotemporal pattern that is highly sterotypical and correlated with subsequent neurodegeneration. Novel medical imaging techniques can now visualize the distribution of tau in the brain in vivo, allowing for new insights to the dynamics of this biomarker. Here we personalize a network diffusion model with global spreading and local production terms to longitudinal tau positron emission tomography data of 76 subjects from the Alzheimer's Disease Neuroimaging Initiative. We use Bayesian inference with a hierarchical prior structure to infer means and credible intervals for our model parameters on group and subject levels. Our results show that the group average protein production rate for amyloid positive subjects is significantly higher with 0.019±0.27/yr, than that for amyloid negative subjects with −0.143±0.21/yr (p = 0.0075). These results support the hypothesis that amyloid pathology drives tau pathology. The calibrated model could serve as a valuable clinical tool to identify optimal time points for follow-up scans and predict the timeline of disease progression.

BackgroundSmoking is a modifiable risk factor for Alzheimer’s disease (AD). However, smoking-related effects on intrinsic brain activity in high-risk AD population are still unclear.ObjectiveWe aimed to explore differences in smoking effects on brain function between healthy elderly and amnestic mild cognitive impairment (aMCI) patients using ReHo mapping.MethodsWe identified 64 healthy elderly controls and 116 aMCI patients, including 98 non-smoking and 18 smoking aMCI. Each subject underwent structural and resting-state functional MRI scanning and neuropsychological evaluations. Regional homogeneity (ReHo) mapping was used to assess regional brain synchronization. After correction for age, gender, education, and gray matter volume, we explored the difference of ReHo among groups in a voxel-wise way based on analysis of covariance (ANCOVA), followed by post hoc two-sample analyses (p < 0.05, corrected). Further, we correlated the mean ReHo with neuropsychological scales.ResultsThree groups were well-matched in age, gender, and education. Significant ReHo differences were found among three groups, located in the left supramarginal gyrus (SMG) and left angular gyrus (AG). Specifically, non-smoking aMCI had lower ReHo in SMG and AG than smoking aMCI and controls. By contrast, smoking aMCI had greater AG ReHo than healthy controls (p < 0.05). Across groups, correlation analyses showed that left AG ReHo correlated with MMSE (r = 0.18, p = 0.015), clock drawing test (r = 0.20, p = 0.007), immediate recall (r = 0.36, p < 0.001), delayed recall (r = 0.34, p < 0.001), and auditory verbal learning test (r = 0.20, p = 0.007).ConclusionSmoking might pose compensatory or protective effects on intrinsic brain activity in aMCI patients.

Progressive diseases tend to be heterogeneous in their underlying aetiology mechanism, disease manifestation, and disease time course. Here, Young and colleagues devise a computational method to account for both phenotypic heterogeneity and temporal heterogeneity, and demonstrate it using two neurodegenerative disease cohorts.

There is an urgent need to identify biomarkers for Alzheimer’s disease (AD), but the identification of reliable blood-based biomarkers has proven to be much more difficult than initially expected. The current availability of high-throughput multi-omics data opens new possibilities in this titanic task. Candidate Single Nucleotide Polymorphisms (SNPs) from large, genome-wide association studies (GWAS), meta-analyses exploring AD (case-control design), and quantitative measures for cortical structure and general cognitive performance were selected. The Genotype-Tissue Expression (GTEx) database was used for identifying expression quantitative trait loci (eQTls) among candidate SNPs. Genes significantly regulated by candidate SNPs were investigated for differential expression in AD cases versus controls in the brain and plasma, both at the mRNA and protein level. This approach allowed us to identify candidate susceptibility factors and biomarkers of AD, facing experimental validation with more evidence than with genetics alone.

Abstract Background The relative performance of semi-quantitative amyloid positron emission tomography (PET) and cerebrospinal fluid (CSF) markers in diagnosing Alzheimer’s disease (AD) and predicting the cognitive evolution of patients with mild cognitive impairment (MCI) is still debated. Methods Subjects from the Alzheimer’s Disease Neuroimaging Initiative 2 with complete baseline cognitive assessment (Mini Mental State Examination, Clinical Dementia Rating [CDR] and Alzheimer’s Disease Assessment Scale–Cognitive Subscale [ADAS-cog] scores), CSF collection (amyloid-β1–42 [Aβ], tau and phosphorylated tau) and 18F-florbetapir scans were included in our cross-sectional cohort. Among these, patients with MCI or substantial memory complaints constituted our longitudinal cohort and were followed for 30 ± 16 months. PET amyloid deposition was quantified using relative retention indices (standardised uptake value ratio [SUVr]) with respect to pontine, cerebellar and composite reference regions. Diagnostic and prognostic performance based on PET and CSF was evaluated using ROC analysis, multivariate linear regression and survival analysis with the Cox proportional hazards model. Results The cross-sectional study included 677 participants and revealed that pontine and composite SUVr values were better classifiers (AUC 0.88, diagnostic accuracy 85%) than CSF markers (AUC 0.83 and 0.85, accuracy 80% and 75%, for Aβ and tau, respectively). SUVr was a strong independent determinant of cognition in multivariate regression, whereas Aβ was not; tau was also a determinant, but to a lesser degree. Among the 396 patients from the longitudinal study, 82 (21%) converted to AD within 22 ± 13 months. Optimal SUVr thresholds to differentiate AD converters were quite similar to those of the cross-sectional study. Composite SUVr was the best AD classifier (AUC 0.86, sensitivity 88%, specificity 81%). In multivariate regression, baseline cognition (CDR and ADAS-cog) was the main predictor of subsequent cognitive decline. Pontine and composite SUVr were moderate but independent predictors of final status and CDR/ADAS-cog progression rate, whereas baseline CSF markers had a marginal influence. The adjusted HRs for AD conversion were 3.8 (p = 0.01) for PET profile, 1.2 (p = ns) for Aβ profile and 1.8 (p = 0.03) for tau profile. Conclusions Semi-quantitative amyloid PET appears more powerful than CSF markers for AD grading and MCI prognosis in terms of cognitive decline and AD conversion.

Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases. In the last decade, studies on AD diagnosis has attached great significance to artificial intelligence-based diagnostic algorithms. Among the diverse modalities of imaging data, T1-weighted MR and FDG-PET are widely used for this task. In this paper, we propose a convolutional neural network (CNN) to integrate all the multi-modality information included in both T1-MR and FDG-PET images of the hippocampal area, for the diagnosis of AD. Different from the traditional machine learning algorithms, this method does not require manually extracted features, instead, it utilizes 3D image-processing CNNs to learn features for the diagnosis or prognosis of AD. To test the performance of the proposed network, we trained the classifier with paired T1-MR and FDG-PET images in the ADNI datasets, including 731 cognitively unimpaired (labeled as CN) subjects, 647 subjects with AD, 441 subjects with stable mild cognitive impairment (sMCI) and 326 subjects with progressive mild cognitive impairment (pMCI). We obtained higher accuracies of 90.10% for CN vs. AD task, 87.46% for CN vs. pMCI task, and 76.90% for sMCI vs. pMCI task. The proposed framework yields a state-of-the-art performance. Finally, the results have demonstrated that (1) segmentation is not a prerequisite when using a CNN for the classification, (2) the combination of two modality imaging data generates better results.

Background: With the rapid development of neurobiology and neuroimaging technologies, mounting evidence shows that Alzheimer's disease (AD) is caused by the build-up of two abnormal proteins, amyloid-β plaques (A) and neurofibrillary tangles (T). Over time, these AD-related neuropathological burdens begin to spread throughout the brain, which results in the characteristic progression of symptoms in AD.Objective: Although tremendous efforts have been made to link biological indicators to the progression of AD, limited attention has been paid to investigate the multi-factorial role of socioeconomic status (SES) in the prevalence or incidence of AD. There is high demand to explore the synergetic effect of sex and SES factors in moderating the neurodegeneration process caused by the accumulation of A and T biomarkers.Methods: We carry out a meta-data analysis on the longitudinal neuroimaging data, clinical outcomes, genotypes, and demographic data in Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu).Results: Our major findings include 1) education and occupation show resilience effects at the angular gyrus, superior parietal lobule, lateral occipital-temporal sulcus, and posterior transverse collateral sulcus where we found significant slowdown of neurodegeneration due to higher education level or more advanced occupation rank; 2) A and T biomarkers manifest different spatial patterns of brain resilience; 3) BDNF (brain-derived neurotrophic factor) single nucleotide polymorphism (SNP) rs10835211 shows strong association to the identified resilience effect; 4) the identified resilience effect is associated with the clinical manifestation in memory, learning, and organization performance.Conclusion: Several brain regions manifest resilience from SES to A and T biomarkers. BDNF SNPs have a potential association with the resilience effect from SES. In addition, cognitive measures of learning and memory demonstrate the resilience effect. [ABSTRACT FROM AUTHOR]

Background: Accurate, cheap, and easy to promote methods for dementia prediction and early diagnosis are urgently needed in low- and middle-income countries. Integrating various cognitive tests using machine learning provides promising solutions. However, most effective machine learning models are black-box models that are hard to understand for doctors and could hide potential biases and risks.Objective: To apply cognitive-test-based machine learning models in practical dementia prediction and diagnosis by ensuring both interpretability and accuracy.Methods: We design a framework adopting Rule-based Representation Learner (RRL) to build interpretable diagnostic rules based on the cognitive tests selected by doctors. According to the visualization and test results, doctors can easily select the final rules after analysis and trade-off. Our framework is verified on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 606) and Peking Union Medical College Hospital (PUMCH) dataset (n = 375).Results: The predictive or diagnostic rules learned by RRL offer a better trade-off between accuracy and model interpretability than other representative machine learning models. For mild cognitive impairment (MCI) conversion prediction, the cognitive-test-based rules achieve an average area under the curve (AUC) of 0.904 on ADNI. For dementia diagnosis on subjects with a normal Mini-Mental State Exam (MMSE) score, the learned rules achieve an AUC of 0.863 on PUMCH. The visualization analyses also verify the good interpretability of the learned rules.Conclusion: With the help of doctors and RRL, we can obtain predictive and diagnostic rules for dementia with high accuracy and good interpretability even if only cognitive tests are used. [ABSTRACT FROM AUTHOR]

The concept of age acceleration, the difference between biological age and chronological age, is of growing interest, particularly with respect to age-related disorders, such as Alzheimer's Disease (AD). Whilst studies have reported associations with AD risk and related phenotypes, there remains a lack of consensus on these associations. Here we aimed to comprehensively investigate the relationship between five recognised measures of age acceleration, based on DNA methylation patterns (DNAm age), and cross-sectional and longitudinal cognition and AD-related neuroimaging phenotypes (volumetric MRI and Amyloid-β PET) in the Australian Imaging, Biomarkers and Lifestyle (AIBL) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). Significant associations were observed between age acceleration using the Hannum epigenetic clock and cross-sectional hippocampal volume in AIBL and replicated in ADNI. In AIBL, several other findings were observed cross-sectionally, including a significant association between hippocampal volume and the Hannum and Phenoage epigenetic clocks. Further, significant associations were also observed between hippocampal volume and the Zhang and Phenoage epigenetic clocks within Amyloid-β positive individuals. However, these were not validated within the ADNI cohort. No associations between age acceleration and other Alzheimer's disease-related phenotypes, including measures of cognition or brain Amyloid-β burden, were observed, and there was no association with longitudinal change in any phenotype. This study presents a link between age acceleration, as determined using DNA methylation, and hippocampal volume that was statistically significant across two highly characterised cohorts. The results presented in this study contribute to a growing literature that supports the role of epigenetic modifications in ageing and AD-related phenotypes. [ABSTRACT FROM AUTHOR]

Previous studies report hippocampal volume loss can help predict conversion from normative aging to mild cognitive impairment to dementia. Additionally, a growing literature indicates that stress-related allostatic load may increase disease vulnerability. The current study examined the relationship between stress-related cytokines (ie, interleukin-6 [IL-6]), cognition as measured by Mini-Mental State Examination (MMSE) scores, and hippocampal volume. Mixed models were employed to examine both within- (across time) and between-subject effects of IL-6 and hippocampal volume on MMSE score among 566 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The within-subject analysis found left hippocampal volume significantly (p = .009) predicted MMSE score. Between-subject analysis found the effect of IL-6 on MMSE was moderated by right hippocampal volume (p = .001). These results replicate previous findings and also extend prior work demonstrating stress-related cytokines may play a role in Alzheimer's disease progression. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is defined by amyloid (A) and tau (T) pathologies, with T better correlated to neurodegeneration (N). However, T and N have complex regional relationships in part related to non-AD factors that influence N. With machine learning, we assessed heterogeneity in 18F-flortaucipir vs. 18F-fluorodeoxyglucose positron emission tomography as markers of T and neuronal hypometabolism (NM) in 289 symptomatic patients from the Alzheimer's Disease Neuroimaging Initiative. We identified six T/NM clusters with differing limbic and cortical patterns. The canonical group was defined as the T/NM pattern with lowest regression residuals. Groups resilient to T had less hypometabolism than expected relative to T and displayed better cognition than the canonical group. Groups susceptible to T had more hypometabolism than expected given T and exhibited worse cognitive decline, with imaging and clinical measures concordant with non-AD copathologies. Together, T/NM mismatch reveals distinct imaging signatures with pathobiological and prognostic implications for AD. In Alzheimer's disease (AD) tau and neurodegeneration have complex regional relationships. Here, the authors show neuronal hypometabolism discordant with tau burden defines functional resilience or susceptibility to Alzheimer's pathology via limbic/cortical axes. Susceptible groups have faster cognitive decline and evidence of non-Alzheimer's pathologies. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD.Objective: To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Alzheimer's Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion.Methods: We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables.Results: The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects.Conclusion: Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex. [ABSTRACT FROM AUTHOR]

Objective: To explore if changes over time of plasma phosphorylated tau (p-tau)181 and neurofilament light chain (NfL) predict future tau and amyloid β (Aβ) PET load and cognitive performance, we studied a subsample of the Alzheimer's disease (AD) neuroimaging cohort with longitudinal blood peptide assessments.Methods: Eight hundred and sixty-five AD Neuroimaging Initiative participants were included. Using established AD cut-points for the cerebrospinal fluid concentrations of Aβ42, total-tau and p-tau181, subjects were classified according to the National Institute on Aging-Alzheimer's Association research framework, grouping markers into those of Aβ deposition (A), tau pathology (T) and neurodegeneration (N). Analysis of variance was used to compare the plasma biomarker data between the ATN groups. The rate of change over time of p-tau181 and NfL was obtained from linear mixed effects models and compared between the ATN groups. Linear regression analysis was used to investigate the association of baseline plasma biomarker concentrations and rates of change with future PET tau and Aβ load and cognitive performance.Results: P-tau181 and NfL plasma concentrations increased along the AD spectrum, but only NfL showed greater rates of change in AD patients versus controls. Cognitive performance was associated cross-sectionally with NfL in all subgroups, and with p-tau181 only in AD spectrum individuals. The baseline concentrations of both plasma markers predicted PET Aβ and tau load and cognitive performance. The rate of change of NfL predicted future PET tau and cognitive performance.Conclusions: P-tau and NfL behave differently within the same individuals over time and may therefore offer complementary diagnostic information.Trial Registration Number: NCT02854033, NCT01231971. [ABSTRACT FROM AUTHOR]

Resting state functional connectivity (rs-fMRI) is impaired early in persons who subsequently develop Alzheimer's disease (AD) dementia. This impairment may be leveraged to aid investigation of the pre-clinical phase of AD. We developed a model that predicts brain age from resting state (rs)-fMRI data, and assessed whether genetic determinants of AD, as well as beta-amyloid (Aβ) pathology, can accelerate brain aging. Using data from 1340 cognitively unimpaired participants between 18–94 years of age from multiple sites, we showed that topological properties of graphs constructed from rs-fMRI can predict chronological age across the lifespan. Application of our predictive model to the context of pre-clinical AD revealed that the pre-symptomatic phase of autosomal dominant AD includes acceleration of functional brain aging. This association was stronger in individuals having significant Aβ pathology. Alzheimer's disease has been associated with increased structural brain aging. Here the authors describe a model that predicts brain aging from resting state functional connectivity data, and demonstrate this is accelerated in individuals with pre-clinical familial Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Body mass index (BMI) is a risk factor for Alzheimer's disease (AD) although the relationship is complex. Obesity in midlife is associated with increased risk for AD, whereas evidence supports both higher and lower BMI increasing risk for AD in late life. This study examined the influence of individual differences in genetic risk for AD to further clarify the relationship between late-life BMI and conversion to AD. Participants included 52 individuals diagnosed as having mild cognitive impairment (MCI) at baseline who converted to AD within 24 months and 52 matched MCI participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. BMI was measured at baseline. Genetic risk for AD was assessed via genome-wide polygenic risk scores. Conditional logistic regression models were run to determine if BMI and polygenic risk predicted conversion to AD. Results showed an interaction between BMI and genetic risk, such that individuals with lower BMI and higher polygenic risk were more likely to convert to AD relative to individuals with higher BMI. These results remained significant after adjusting for cerebrospinal fluid biomarkers of AD. Exploratory sex-stratified analyses revealed this relationship only remained significant in males. These results show that higher genetic risk in the context of lower BMI predicts conversion to AD in the next 24 months, particularly among males. These findings suggest that genetic risk for AD in the context of lower BMI may serve as a prodromal risk factor for future conversion to AD. [ABSTRACT FROM AUTHOR]

Background: Amnestic mild cognitive impairment (MCI) is a transitional stage between normal aging and Alzheimer's disease (AD). However, the clinical conversion from MCI to AD is unpredictable. Hence, identification of noninvasive biomarkers able to detect early changes induced by dementia is a pressing need.Purpose: To explore the added value of histogram analysis applied to measures derived from diffusion tensor imaging (DTI) for detecting brain tissue differences between AD, MCI, and healthy subjects (HS).Study Type: Prospective.Population/subjects: A local cohort (57 AD, 28 MCI, 23 HS), and an Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (41 AD, 58 MCI, 41 HS).Field Strength: 3T. Dual-echo turbo spin echo (TSE); fluid-attenuated inversion recovery (FLAIR); modified-driven-equilibrium-Fourier-transform (MDEFT); inversion-recovery spoiled gradient recalled (IR-SPGR); diffusion tensor imaging (DTI).Assessment: Normal-appearing white matter (NAWM) masks were obtained using the T1 -weighted volumes for tissue segmentation and T2 -weighted images for removal of hyperintensities/lesions. From DTI images, fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AXD), and radial diffusivity (RD) were obtained. NAWM histograms of FA, MD, AXD, and RD were derived and characterized estimating: peak height, peak location, mean value (MV), and quartiles (C25, C50, C75), which were compared between groups. Receiver operating characteristic (ROC) and area under ROC curves (AUC) were calculated. To confirm our results, the same analysis was repeated on the ADNI dataset.Statistical Tests: One-way analysis of variance (ANOVA), post-hoc Student's t-test, multiclass ROC analysis.Results: For the local cohort, C25 of AXD had the maximum capability of group discrimination with AUC of 0.80 for "HS vs. patients" comparison and 0.74 for "AD vs. others" comparison. For the ADNI cohort, MV of AXD revealed the maximum group discrimination capability with AUC of 0.75 for "HS vs. patients" comparison and 0.75 for "AD vs. others" comparison.Data Conclusion: AXD of NAWM might be an early marker of microstructural brain tissue changes occurring during the AD course and might be useful for assessing disease progression.Level Of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017. [ABSTRACT FROM AUTHOR]

Joint analysis of amyloid and metabolic PET patterns across healthy, mild cognitive impairment (MCI), and Alzheimer's disease (AD) subjects was performed using baseline 18F-florbetapir and 18F-FDG PET of 684 subjects from the ADNI (251 normal, 204 stable MCI, 85 AD converters, and 144 AD). Correlation between regional amyloid and metabolic uptake was measured and predictive value of PET profile regarding AD conversion in cognitively impaired subjects was assessed using survival analysis and support vector machine classification (SVM). The highest correlations were found in the temporal cortex, precuneus, and posterior cingulum. With respect to normal controls, amyloid load increase was diffuse and early in MCI subjects, whereas metabolism decrease occurred later and predominated in temporo-parietal, precuneus, and cingulate cortices. Five-year AD conversion rates in cognitively impaired subjects were 5%, 22%, 42%, and 78% in amyloid-/FDG-, amyloid-/FDG+, amyloid+/FDG-, and amyloid+/FDG+ subjects respectively (mean follow-up 37±14 months). Using SVM, the combination of ADAS-cog score, amyloid PET, and FDG PET yielded better performance in predicting AD conversion (77% accuracy; 58% positive predictive value; 88% negative predictive value) than ADAS-cog (72%; 52%; 86%), amyloid PET (72%; 52%; 87%), and FDG PET (67%; 47%; 84%). This study attests the complementary value of amyloid and FDG PET in MCI assessment and the efficiency of combined cognitive, amyloid, and metabolic scores to predict AD conversion. [ABSTRACT FROM AUTHOR]

Amnestic mild cognitive impairment can be further classified as single-domain aMCI (SD-aMCI) with isolated memory deficit, or multi-domain aMCI (MD-aMCI) if memory deficit is combined with impairment in other cognitive domains. Prior studies reported these clinical subtypes presumably differ in etiology. Thus, we aimed to explore the possible mechanisms between different aMCI subtypes by assessing alteration in brain activity and brain vasculature, and their relations with CSF AD biomarkers. 49 healthy controls, 32 SD-aMCI, and 32 MD-aMCI, who had undergone structural scans, resting-state functional MRI (rsfMRI) scans and neuropsychological evaluations, were identified. Regional homogeneity (ReHo) was employed to analyze regional synchronization. Periventricular white matter hyperintensities (PWMH) and deep WMH (DWMH) volume of each participant was quantitatively assessed. AD biomarkers from CSF were also measured. SD-aMCI showed decreased ReHo in medial temporal gyrus (MTG), and increased ReHo in lingual gyrus (LG) and superior temporal gyrus (STG) relative to controls. MD-aMCI showed decreased ReHo, mostly located in precuneus (PCu), LG and postcentral gyrus (PCG), relative to SD-aMCI and controls. As for microvascular disease, MD-aMCI patients had more PWMH burden than SD-aMCI and controls. Correlation analyses indicated mean ReHo in differenced regions were related with memory, language, and executive function in aMCI patients. However, no significant associations between PWMH and behavioral data were found. The Aβ level was related with the ReHo value of STG in SD-aMCI. MD-aMCI displayed different patterns of abnormal regional synchronization and more severe PWMH burden compared with SD-aMCI. Therefore aMCI is not a uniform disease entity, and MD-aMCI group may show more complicated pathologies than SD-aMCI group. [ABSTRACT FROM AUTHOR]

In Alzheimer's diseases (AD), tau pathology is strongly associated with cognitive decline. Preclinical evidence suggests that tau spreads across connected neurons in an activity-dependent manner. Supporting this, cross-sectional AD studies show that tau deposition patterns resemble functional brain networks. However, whether higher functional connectivity is associated with higher rates of tau accumulation is unclear. Here, we combine resting-state fMRI with longitudinal tau-PET in two independent samples including 53 (ADNI) and 41 (BioFINDER) amyloid-biomarker defined AD subjects and 28 (ADNI) vs. 16 (BioFINDER) amyloid-negative healthy controls. In both samples, AD subjects show faster tau accumulation than controls. Second, in AD, higher fMRI-assessed connectivity between 400 regions of interest (ROIs) is associated with correlated tau-PET accumulation in corresponding ROIs. Third, we show that a model including baseline connectivity and tau-PET is associated with future tau-PET accumulation. Together, connectivity is associated with tau spread in AD, supporting the view of transneuronal tau propagation. Tau accumulation is associated with disease progression in Alzheimer's disease. Here the authors use resting state fMRI and tau-PET to demonstrate that baseline connectivity in Alzheimer's disease is associated with tau spreading. [ABSTRACT FROM AUTHOR]

Background: Previous work has suggested that the brain and cerebrospinal fluid (CSF) levels of a neural protein involved in synaptic transmission, VGF (a noninitialism), may be altered in mild cognitive impairment (MCI) and Alzheimer Disease (AD). The objective of the current work is to examine the potential of CSF levels of a peptide derived from VGF to predict conversion from MCI to AD.Materials and Methods: Using multivariate analytical approaches, the performance of the conventional biomarkers (CSF Aβ1-42 and phosphorylated tau +/- hippocampal volume) was compared with the same biomarkers combined with CSF VGF peptide levels in a large publicly available data set from human subjects.Results: It was observed that VGF peptides are lowered in CSF of patients with AD compared with controls and that combinations of CSF Aβ1-42 and phosphorylated tau, hippocampal volume, and VGF peptide levels outperformed conventional biomarkers alone (hazard ratio=2.2 vs. 3.9), for predicting MCI to AD conversion.Conclusions: CSF VGF enhances the ability of conventional biomarkers to predict MCI to AD conversion. Future work will be needed to determine the specificity of VGF for AD versus other neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Abstract: There is great value to use of structural neuroimaging in the assessment of Alzheimer's disease (AD). However, to date, predictive value of structural imaging tend to range between 80% and 90% in accuracy and it is unclear why this is the case given that structural imaging should parallel the pathologic processes of AD. There is a possibility that clinical misdiagnosis relative to the gold standard pathologic diagnosis and/or additional brain pathologies are confounding factors contributing to reduced structural imaging classification accuracy. We examined potential factors contributing to misclassification of individuals with clinically diagnosed AD purely from cortical thickness measures. Correctly classified and incorrectly classified groups were compared across a range of demographic, biological, and neuropsychological data including cerebrospinal fluid biomarkers, amyloid imaging, white matter hyperintensity (WMH) volume, cognitive, and genetic factors. Individual subject analyses suggested that at least a portion of the control individuals misclassified as AD from structural imaging additionally harbor substantial AD biomarker pathology and risk, yet are relatively resistant to cognitive symptoms, likely due to “cognitive reserve,” and therefore clinically unimpaired. In contrast, certain clinical control individuals misclassified as AD from cortical thickness had increased WMH volume relative to other controls in the sample, suggesting that vascular conditions may contribute to classification accuracy from cortical thickness measures. These results provide examples of factors that contribute to the accuracy of structural imaging in predicting a clinical diagnosis of AD, and provide important information about considerations for future work aimed at optimizing structural based diagnostic classifiers for AD. [ABSTRACT FROM AUTHOR]