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Amyloid-β (Aβ) and tau are important biomarkers to predict the progression of cognitively unimpaired (CU) to dementia due to Alzheimer's disease (AD), according to the diagnosis framework from the US National Institute on Aging and the Alzheimer's Association (NIA-AA). However, it is clinically difficult to predict those subjects who were already with Aβ positive (A +) or tau positive (T +). As a typical characteristic of neurodegeneration in the diagnosis framework, the hypometabolism of the posterior cingulate cortex (PCC) has significant clinical value in the early prediction and prevention of AD. In this paper, we proposed the glucose metabolism in the PCC as a biomarker supplement to Aβ and tau biomarkers. First, we calculated the standard uptake value ratio (SUVR) of PCC based on fluorodeoxyglucose positron emission computed tomography (FDG PET) imaging. Secondly, we performed Kaplan–Meier (KM) survival analyses to explore the predictive performance of PCC SUVR, and the hazard ratio (HR) was calculated. Finally, we performed Pearson correlation analyses to explore the physiological significance of PCC SUVR. As a result, the PCC SUVR showed a consistent downward trend along the AD continuum. KM analyses showed better predictive performance when we combined PCC SUVR with cerebro-spinal fluid (CSF) Aβ42 (from HR = 2.56 to 3.00 within 5 years; from HR = 2.76 to 4.20 within 10 years) and ptau-181 (from 2.83 to 3.91 within 5 years; from HR = 2.32 to 4.17 within 10 years). There was a slight correlation between Aβ42/Aβ40 and PCC SUVR (r = 0.14, p = 0.02). In addition, several cognition scales were also correlated to PCC SUVR (from r = –0.407 to 0.383, p < 0.05). Our results showed that glucose metabolism in PCC may be a potential biomarker supplement to the Aβ and tau biomarkers to predict the progression of CU to AD. [ABSTRACT FROM AUTHOR]

We aimed to compare longitudinal brain atrophy in patients with neuromyelitis optica spectrum disorder (NMOSD) with healthy controls (HCs). The atrophy rate in patients with anti-aquaporin-4 antibody-positive NMOSD (AQP4 + NMOSD) was compared with age-sex-matched HCs recruited from the Japanese Alzheimer's Disease Neuroimaging Initiative study and another study performed at Chiba University. Twenty-nine patients with AQP4 + NMOSD and 29 HCs were enrolled in the study. The time between magnetic resonance imaging (MRI) scans was longer in the AQP4 + NMOSD group compared with the HCs (median; 3.2 vs. 2.9 years, P = 0.009). The annualized normalized white matter volume (NWV) atrophy rate was higher in the AQP4 + NMOSD group compared with the HCs (median; 0.37 vs. − 0.14, P = 0.018). The maximum spinal cord lesion length negatively correlated with NWV at baseline MRI in patients with AQP4 + NMOSD (Spearman's rho = − 0.41, P = 0.027). The annualized NWV atrophy rate negatively correlated with the time between initiation of persistent prednisolone usage and baseline MRI in patients with AQP4 + NMOSD (Spearman's rho = − 0.43, P = 0.019). Patients with AQP4 + NMOSD had a greater annualized NWV atrophy rate than HCs. Suppressing disease activity may prevent brain atrophy in patients with AQP4 + NMOSD. [ABSTRACT FROM AUTHOR]

For optimal design of anti-amyloid-β (Aβ) and anti-tau clinical trials, we need to better understand the pathophysiological cascade of Aβ- and tau-related processes. Therefore, we set out to investigate how Aβ and soluble phosphorylated tau (p-tau) relate to the accumulation of tau aggregates assessed with PET and subsequent cognitive decline across the Alzheimer's disease (AD) continuum. Using human cross-sectional and longitudinal neuroimaging and cognitive assessment data, we show that in early stages of AD, increased concentration of soluble CSF p-tau is strongly associated with accumulation of insoluble tau aggregates across the brain, and CSF p-tau levels mediate the effect of Aβ on tau aggregation. Further, higher soluble p-tau concentrations are mainly related to faster accumulation of tau aggregates in the regions with strong functional connectivity to individual tau epicenters. In this early stage, higher soluble p-tau concentrations is associated with cognitive decline, which is mediated by faster increase of tau aggregates. In contrast, in AD dementia, when Aβ fibrils and soluble p-tau levels have plateaued, cognitive decline is related to the accumulation rate of insoluble tau aggregates. Our data suggest that therapeutic approaches reducing soluble p-tau levels might be most favorable in early AD, before widespread insoluble tau aggregates. The interplay between amyloid and tau pathology in Alzheimer's disease is still not well understood. Here, the authors show that amyloid-related increased in soluble p-tau is related to subsequent accumulation of tau aggregates and cognitive decline in early stage of the disease. [ABSTRACT FROM AUTHOR]

With the explosive growth of biomarker data in Alzheimer's disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model's parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer's Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status. [ABSTRACT FROM AUTHOR]

The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research. The Polygenic Risk Score Knowledge Base (PRSKB) is a web-based interface that stores data from >2,300 distinct genome-wide association studies, and can estimate polygenic risk scores for general use. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain's functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer's disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process. [ABSTRACT FROM AUTHOR]

Understanding Alzheimer's disease (AD) heterogeneity is important for understanding the underlying pathophysiological mechanisms of AD. However, AD atrophy subtypes may reflect different disease stages or biologically distinct subtypes. Here we use longitudinal magnetic resonance imaging data (891 participants with AD dementia, 305 healthy control participants) from four international cohorts, and longitudinal clustering to estimate differential atrophy trajectories from the age of clinical disease onset. Our findings (in amyloid-β positive AD patients) show five distinct longitudinal patterns of atrophy with different demographical and cognitive characteristics. Some previously reported atrophy subtypes may reflect disease stages rather than distinct subtypes. The heterogeneity in atrophy rates and cognitive decline within the five longitudinal atrophy patterns, potentially expresses a complex combination of protective/risk factors and concomitant non-AD pathologies. By alternating between the cross-sectional and longitudinal understanding of AD subtypes these analyses may allow better understanding of disease heterogeneity. Different types of atrophy in Alzheimer's disease may reflect different disease stages or biologically distinct subtypes. Here the authors use longitudinal neuroimaging data to demonstrate five distinct patterns of atrophy with different demographical and cognitive characteristics. [ABSTRACT FROM AUTHOR]

Exploring individual hallmarks of brain ageing is important. Here, we propose the age-related glucose metabolism pattern (ARGMP) as a potential index to characterize brain ageing in cognitively normal (CN) elderly people. We collected 18F-fluorodeoxyglucose (18F-FDG) PET brain images from two independent cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 127) and the Xuanwu Hospital of Capital Medical University, Beijing, China (N = 84). During follow-up (mean 80.60 months), 23 participants in the ADNI cohort converted to cognitive impairment. ARGMPs were identified using the scaled subprofile model/principal component analysis method, and cross-validations were conducted in both independent cohorts. A survival analysis was further conducted to calculate the predictive effect of conversion risk by using ARGMPs. The results showed that ARGMPs were characterized by hypometabolism with increasing age primarily in the bilateral medial superior frontal gyrus, anterior cingulate and paracingulate gyri, caudate nucleus, and left supplementary motor area and hypermetabolism in part of the left inferior cerebellum. The expression network scores of ARGMPs were significantly associated with chronological age (R = 0.808, p < 0.001), which was validated in both the ADNI and Xuanwu cohorts. Individuals with higher network scores exhibited a better predictive effect (HR: 0.30, 95% CI: 0.1340 ~ 0.6904, p = 0.0068). These findings indicate that ARGMPs derived from CN participants may represent a novel index for characterizing brain ageing and predicting high conversion risk into cognitive impairment. [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]

Alzheimer's disease is a neurodegenerative disease that imposes a substantial financial burden on society. A number of machine learning studies have been conducted to predict the speed of its progression, which varies widely among different individuals, for recruiting fast progressors in future clinical trials. However, because the data in this field are very limited, two problems have yet to be solved: the first is that models built on limited data tend to induce overfitting and have low generalizability, and the second is that no cross-cohort evaluations have been done. Here, to suppress the overfitting caused by limited data, we propose a hybrid machine learning framework consisting of multiple convolutional neural networks that automatically extract image features from the point of view of brain segments, which are relevant to cognitive decline according to clinical findings, and a linear support vector classifier that uses extracted image features together with non-image information to make robust final predictions. The experimental results indicate that our model achieves superior performance (accuracy: 0.88, area under the curve [AUC]: 0.95) compared with other state-of-the-art methods. Moreover, our framework demonstrates high generalizability as a result of evaluations using a completely different cohort dataset (accuracy: 0.84, AUC: 0.91) collected from a different population than that used for training. [ABSTRACT FROM AUTHOR]

The early stages of Alzheimer's disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future pathological tau accumulation. In particular, we use machine learning to quantify interactions between key pathological markers (β-amyloid, medial temporal lobe atrophy, tau and APOE 4) at mildly impaired and asymptomatic stages of AD. Using baseline non-tau markers we derive a prognostic index that: (a) stratifies patients based on future pathological tau accumulation, (b) predicts individualised regional future rate of tau accumulation, and (c) translates predictions from deep phenotyping patient cohorts to cognitively normal individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design targeting the earliest stages of AD. The authors present a machine learning approach that combines baseline multimodal data to accurately predict individualised trajectories of future pathological tau accumulation at asymptomatic and mildly impaired stages of Alzheimer's disease. [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]

Objectives: Subjective cognitive decline (SCD) may be a preclinical stage of Alzheimer's disease (AD). Neuroimaging studies suggest that abnormal brain connectivity plays an important role in the pathophysiology of SCD. However, most previous studies focused on single modalities only. Multimodal combinations can more effectively utilize various information and little is known about their diagnostic value in SCD.Methods: One hundred ten SCD individuals and well-matched healthy controls (HCs) were recruited in this study (the primary sample: 35 SCD and 36 HC; the validation sample: 21 SCD and 18 HC). Multimodal imaging data were used to construct functional, anatomical, and morphological networks, respectively. These networks were used in combination with a multiple kernel learning-support vector machine to predict SCD individuals. We validated our model on another independent sample. Multiple linear regression (MLR) analyses were conducted to investigate the relationships among network metrics, cognition, and pathological biomarkers.Results: We found that the characteristics identified from the multimodal network were primarily located in the default mode network (DMN) and salience network (SN), achieving an accuracy of 88.73% (an accuracy of 79.49% for an independent sample) based on the integration of the three modalities. MLR analyses showed that increased AV45 SUVRs were significantly associated with impaired memory function, the enhanced functional connectivity, and the decreased morphological connectivity.Conclusion: This study suggests that abnormal multimodal connections within DMN and SN can be used as effective biomarkers to identify SCD and provide insight into understanding the pathophysiological mechanisms underlying SCD.Key Points: • Multimodal brain networks improve the detection accuracy of SCD. • Abnormal connections within DMN and SN can be used as effective biomarkers for the identification of SCD. [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]

Alzheimer's disease (AD) is a complex and heterogeneous disease that can be affected by various genetic factors. Although the cause of AD is not yet known and there is no treatment to cure this disease, its progression can be delayed. AD has recently been recognized as a brain-specific type of diabetes called type 3 diabetes. Several studies have shown that people with type 2 diabetes (T2D) have a higher risk of developing AD. Therefore, it is important to identify subgroups of patients with AD that may be more likely to be associated with T2D. We here describe a new approach to identify the correlation between AD and T2D at the genetic level. Subgroups of AD and T2D were each generated using a non-negative matrix factorization (NMF) approach, which generated clusters containing subsets of genes and samples. In the gene cluster that was generated by conventional gene clustering method from NMF, we selected genes with significant differences in the corresponding sample cluster by Kruskal–Wallis and Dunn-test. Subsequently, we extracted differentially expressed gene (DEG) subgroups, and candidate genes with the same regulation direction can be extracted at the intersection of two disease DEG subgroups. Finally, we identified 241 candidate genes that represent common features related to both AD and T2D, and based on pathway analysis we propose that these genes play a role in the common pathological features of AD and T2D. Moreover, in the prediction of AD using logistic regression analysis with an independent AD dataset, the candidate genes obtained better prediction performance than DEGs. In conclusion, our study revealed a subgroup of patients with AD that are associated with T2D and candidate genes associated between AD and T2D, which can help in providing personalized and suitable treatments. [ABSTRACT FROM AUTHOR]

Neuroinflammation is associated with Alzheimer's disease, but the application of cerebrospinal fluid measures of inflammatory proteins may be limited by overlapping pathways and relationships between them. In this work, we measure 15 cerebrospinal proteins related to microglial and T-cell functions, and show them to reproducibly form functionally-related groups within and across diagnostic categories in 382 participants from the Alzheimer's Disease Neuro-imaging Initiative as well participants from two independent cohorts. We further show higher levels of proteins related to soluble tumor necrosis factor receptor 1 are associated with reduced risk of conversion to dementia in the multi-centered (p = 0.027) and independent (p = 0.038) cohorts of people with mild cognitive impairment due to predicted Alzheimer's disease, while higher soluble TREM2 levels associated with slower decline in the dementia stage of Alzheimer's disease. These inflammatory proteins thus provide prognostic information independent of established Alzheimer's markers. Neuroinflammation is observed in Alzheimer's disease. Here the authors show that 15 proteins related to inflammation found in CSF can potentially be used as a prognostic biomarker. [ABSTRACT FROM AUTHOR]

Klotho-VS heterozygosity (KL-VShet) is associated with reduced risk of Alzheimer's disease (AD). However, whether KL-VShet is associated with lower levels of pathologic tau, i.e., the key AD pathology driving neurodegeneration and cognitive decline, is unknown. Here, we assessed the interaction between KL-VShet and levels of beta-amyloid, a key driver of tau pathology, on the levels of PET-assessed neurofibrillary tau in 551 controls and patients across the AD continuum. KL-VShet showed lower cross-sectional and longitudinal increase in tau-PET per unit increase in amyloid-PET when compared to that of non-carriers. This association of KL-VShet on tau-PET was stronger in Klotho mRNA-expressing brain regions mapped onto a gene expression atlas. KL-VShet was related to better memory functions in amyloid-positive participants and this association was mediated by lower tau-PET. Amyloid-PET levels did not differ between KL-VShet carriers versus non-carriers. Together, our findings provide evidence to suggest a protective role of KL-VShet against amyloid-related tau pathology and tau-related memory impairments in elderly humans at risk of AD dementia. The KL-VS haplotype of the Klotho gene has been associated with reduced risk of Alzheimer's disease and dementia. Here the authors show an association between the KL-VS haplotype and amyloid-dependent tau accumulation using PET data. [ABSTRACT FROM AUTHOR]

Introduction: Amyloid beta (Aβ) is partially cleared from the CSF via skull base perivascular and perineural lymphatic pathways, particularly at the nasal cavity. In vivo differences in Aβ level at the nasal cavity between patients with Alzheimer's disease (AD), subjects with mild cognitive impairment (MCI) and cognitively normal (CN) individuals have not been previously assessed.Methods: This is a retrospective evaluation of subject level data from the ADNI-1/GO database. Standardized uptake value ratio (SUVR) maximum on 11C-Pittsburgh compound-B (PiB)-PET was assessed at the nasal cavity on 223 scans. Exploratory ROI analysis was also performed at other skull base sites. SUVR maximum values and their differences between groups (CN, MCI, AD) were assessed. CSF Aβ levels and CSF Aβ 42/40 ratios were correlated with SUVR maximum values.Results: 103 subjects with 223 PiB-PET scans (47 CN, 32 AD and 144 MCI) were included in the study. The SUVR maxima at the nasal cavity were significantly lower in subjects with AD [1.35 (± 0.31)] compared to CN [1.54 (± 0.30); p = 0.024] and MCI [1.49 (± 0.33); p = 0.049]. At very low CSF Aβ, less than 132 pg/ml, there was significant correlation with nasal cavity SUVR maximum. The summed averaged SUVR maximum values were significantly lower in subjects with AD [1.35 (± 0.16)] compared to CN [1.49 (± 0.17); p = 0.003] and MCI [1.40 (± 0.17); p = 0.017].Conclusion: Patients with AD demonstrate reduced nasal cavity PiB-PET radiotracer uptake compared to MCI and CN, possibly representing reduced Aβ clearance via perineural/perivascular lymphatic pathway. Further work is necessary to elucidate the true nature of this finding. [ABSTRACT FROM AUTHOR]

The neural mechanisms underlying the polygenic effects of the endocytosis pathway on the brain function of Alzheimer's Disease (AD) remain unclear, especially in the prodromal stages of AD from early mild cognitive impairment (EMCI) to late mild cognitive impairment (LMCI). We used an imaging genetic approach to investigate the polygenic effects of the endocytosis pathway on the hippocampal network across the prodromal stages of AD. The subjects' data were selected from the Alzheimer's Disease Neuroimaging Initiative. Hippocampal volumes were examined in subjects of cognitive normal (CN), EMCI and LMCI groups. Multivariate linear regression analysis was employed to measure the effects of disease and endocytosis-based multilocus genetic risk scores (MGRS) on the hippocampal network which was constructed using the bilateral hippocampal regions. We identified hippocampal volumes in LMCI group were smaller than those in CN and EMCI groups. Endocytosis-based MGRS was widely influenced the neural structures within the hippocampal network, especially in the prefrontal-occipital regions and striatum. Compared to low endocytosis-based MGRS carriers, high MGRS carriers showed the opposite trajectory of hippocampal network functional connectivity (FC) across the prodromal stages of AD. Further, a model composed of selected hippocampal FCs and hippocampal volume yielded strong classification powers of EMCI and LMCI. These findings expand our understanding of the pathophysiology of polygenic effects underlying brain network in the prodromal stages of AD. [ABSTRACT FROM AUTHOR]

The apolipoprotein E (APOE) ε4 allele is a genetic risk factor for Alzheimer's disease, whereas educational attainments have protective effects against cognitive decline in aging and patients with Alzheimer's disease. We examined the possible effects of years of education and APOE genotype on the topological properties of the functional network in normal aging, mild cognitive impairment and Alzheimer's disease. The years of education showed a significant, negative association with the local efficiency, clustering coefficient and small-worldness of functional networks in APOE ε4 noncarriers but not in ε4 carriers. These associations were mainly observed in normal aging and were reduced in mild cognitive impairment and Alzheimer's disease. Moreover, regions of the inferior frontal gyrus, temporal pole, and cuneus also showed correlations between education and nodal degree. Our findings demonstrated that the protective effects of education persist in APOE ε4 noncarriers but diminish in ε4 carriers. In addition, the protective effects of education were attenuated or reduced in the progression of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

The present work aims at providing a methodological approach for the investigation of resilience factors and mechanisms in normal aging, Alzheimer's disease (AD) and other neurodegenerative disorders. By expanding and re-conceptualizing traditional regression approaches, we propose an approach that not only aims at identifying potential resilience factors but also allows for a differentiation between general and dynamic resilience factors in terms of their association with pathology. Dynamic resilience factors are characterized by an increasing relevance with increasing levels of pathology, while the relevance of general resilience factors is independent of the amount of pathology. Utility of the approach is demonstrated in age and AD-related brain pathology by investigating widely accepted resilience factors, including education and brain volume. Moreover, the approach is used to test hippocampal volume as potential resilience factor. Education and brain volume could be identified as general resilience factors against age and AD-related pathology. Beyond that, analyses highlighted that hippocampal volume may not only be disease target but also serve as a potential resilience factor in age and AD-related pathology, particularly at higher levels of tau-pathology (i.e. dynamic resilience factor). Given its unspecific and superordinate nature the approach is suitable for the investigation of a wide range of potential resilience factors in normal aging, AD and other neurodegenerative disorders. Consequently, it may find a wide application and thereby promote the comparability between studies. [ABSTRACT FROM AUTHOR]

The APOE ε4 allele is associated with impaired intrinsic functional connectivity in neural networks, especially in the default mode network (DMN). However, effective connectivity (EC) reflects the direct causal effects of one brain region to another, which has rarely been investigated. Recently, Granger causality analysis (GCA) proved suitable for the study of directionality in neuronal interactions. Using GCA, we examined the differences in the EC between the anterior medial prefrontal cortex/posterior cingulate cortex (aMPFC/PCC) and the whole brain in 17 ε4 carrying and 32 non-carrying cognitively intact elderly individuals. Furthermore, correlation analyses were performed between the abnormal EC and cognition/neuropathological indices. Compared with the non-carriers, the results showed that the ε4 carriers exhibited decreased EC from the PCC to the whole brain in the middle temporal gyrus (MTG), the anterior cingulate cortex (ACC), and the precuneus (PCu). Meanwhile, the ε4 carriers demonstrated increased EC from the whole brain to the aMPFC in the inferior parietal lobe (IPL) and the postcentral gyrus (PCG). The correlation analyses suggested that the EC from the IPL/PCG to the aMPFC was related to episodic memory in non-carriers, while the decreased EC from the PCC to the ACC was associated with increased levels of t-tau in the ε4 carriers. In ε4 carriers, a negative influence can be traced from the PCC to both the anterior and posterior DMN subsystems; meanwhile, the anterior DMN subsystem receives compensatory effects from the parietal cortex. Early increases in AD-related pathologies in the PCC may act as first factors during this pathological process. [ABSTRACT FROM AUTHOR]

Transfer learning has been successfully used in the early diagnosis of Alzheimer's disease (AD). In these methods, data from one single or multiple related source domain(s) are employed to aid the learning task in the target domain. However, most of the existing methods utilize data from all source domains, ignoring the fact that unrelated source domains may degrade the learning performance. Also, previous studies assume that class labels for all subjects are reliable, without considering the ambiguity of class labels caused by slight differences between early AD patients and normal control subjects. To address these issues, we propose to transform the original binary class label of a particular subject into a multi-bit label coding vector with the aid of multiple source domains. We further develop a robust multi-label transfer feature learning (rMLTFL) model to simultaneously capture a common set of features from different domains (including the target domain and all source domains) and to identify the unrelated source domains. We evaluate our method on 406 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data. The experimental results show that the proposed rMLTFL method can effectively improve the performance of AD diagnosis, compared with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Neuropsychiatric symptoms (NPS) are common in Alzheimer's disease (AD)-associated neurodegeneration. However, NPS lack a consistent relationship with AD pathology. It is unknown whether any common neural circuits can link these clinically disparate while mechanistically similar features with AD pathology. Here, we explored the neural circuits of NPS in AD-associated neurodegeneration using multivariate pattern analysis (MVPA) of resting-state functional MRI data. Data from 98 subjects (70 amnestic mild cognitive impairment and 28 AD subjects) were obtained. The top 10 regions differentiating symptom presence across NPS were identified, which were mostly the fronto-limbic regions (medial prefrontal cortex, caudate, etc.). These 10 regions' functional connectivity classified symptomatic subjects across individual NPS at 69.46-81.27%, and predicted multiple NPS (indexed by Neuropsychiatric Symptom Questionnaire-Inventory) and AD pathology (indexed by baseline and change of beta-amyloid/pTau ratio) all above 70%. Our findings suggest a fronto-limbic dominated neural circuit that links multiple NPS and AD pathology. With further examination of the structural and pathological changes within the circuit, the circuit may shed light on linking behavioral disturbances with AD-associated neurodegeneration. [ABSTRACT FROM AUTHOR]

Postmortem studies on patients with Alzheimer's disease (AD) have confirmed that the dorsal raphe nucleus (DRN) in the brainstem is the first brain structure affected in the earliest stage of AD. The present study examined the brainstem in the early stage of AD using magnetic resonance (MR) imaging. T1-weighted MR images of the brains of 81 subjects were obtained from the publicly available Open Access Series of Imaging Studies (OASIS) database, including 27 normal control (NC) subjects, 27 patients with very mild AD (AD-VM) and 27 patients with mild AD (AD-M). The brainstem was interactively segmented from the MR images using ITK-SNAP. The present voxel-based morphometry (VBM) study was designed to investigate the brainstem differences between the AD-VM/AD-M groups and the NC group. The results showed bilateral loss in the pons and the left part of the midbrain in the AD-M group compared to the NC group. The AD-M group showed greater loss in the left midbrain than the AD-VM group (PFWEcorrected < 0.05). The results revealed that brainstem atrophy occurs in the early stages of AD (Clinical Dementia Rating = 0.5 and 1.0). Most of these findings were also investigated in a multicenter dataset. This is the first VBM study that provides evidence of brainstem alterations in the early stage of AD. [ABSTRACT FROM AUTHOR]

While strong cross-sectional evidence supported the use of fornix microstructure as a marker for detecting Alzheimer's disease (AD), longitudinal data remains inconclusive on the sequential nature of fornix microstructure abnormalities and AD progression. An unequivocal longitudinal relationship between fornix microstructure and markers of AD progression -memory impairment and hippocampal atrophy, must be established to validate fornix microstructure as a marker of AD progression. We included 115 participants from the Alzheimer's Disease Neuroimaging Initiative across the non-demented AD spectrum- defined as those who had at least one AD risk marker at baseline (e.g., mild cognitive impairment (MCI) due to AD diagnosis, amyloid or ApoE4 positivity) and/or 'cognitively normal individuals who converted to MCI due to AD or AD, with structural and diffusion tensor imaging scans at baseline and two years follow-up. Hippocampal volumes (HV), fractional anisotropy (FA) and mean diffusivity (MD) in the fornix were extracted. Memory was indexed via composite scores of verbal memory tests. Structural equation models tested the bidirectional cross-lagged effects of fornix microstructure, memory, and HV. Impaired memory and smaller HV at baseline significantly predicted worse fornix microstructure (decreased FA and increased MD) two years later. Baseline fornix microstructure was not associated with subsequent changes in memory and HV. Fornix microstructure is compromised likely at a later stage, where significant decline in memory and hippocampal atrophy have occurred. This limits the utility of fornix microstructure in the early detection of AD. Our findings inform the possible pathophysiology and refined the use of AD neural markers. [ABSTRACT FROM AUTHOR]

Objective: To examine the patterns of longitudinal tau accumulation and cortical atrophy and their association in subjects with mild cognitive impairment (MCI).Methods: We collected 23 participants (60-89 years old, 11 males/12 females) with MCI from the Alzheimer's Disease Neuroimaging Initiative database. All participants underwent 18F flortaucipir (FTP) positron emission tomography (PET) and structural magnetic resonance imaging (MRI) scans at the baseline and follow-up visits (12-36 months). General linear models with covariates (baseline age, sex) were used to detect brain areas of significant tau accumulation and atrophy over time. Mediation analysis was employed to explore the potential reason for sequential biomarker changes in MCI progression, adjusting for baseline age, sex, and education level.Results: Voxel-wise tau accumulation in MCI subjects was predominantly located in the inferior temporal cortex, middle temporal cortex, parietal cortex, posterior cingulate, precuneus, and temporoparietal regions (P < 0.001), and MRI atrophy included the inferior-middle temporal lobe, parietal lobe, and precuneus (P < 0.001). Longitudinal FTP accumulation was moderately associated with annualized MRI cortical atrophy (r = 0.409, 95% CI: 0.405-0.414, P < 0.01). Regional analyses indicated significant bivariate associations between annualized MRI cortical atrophy and FTP accumulation (baseline FTP cortical uptake and longitudinal FTP change). The results of the mediation analysis showed that the relationship between baseline FTP uptake and longitudinal cortical atrophy was partly mediated by the longitudinal FTP cortical change (indirect effect: 0.0107, P = 0.04).Conclusions: Our findings provide a preliminary description of the patterns of longitudinal FTP accumulation and annualized cortical atrophy in MCI progression, and MCI subjects with high tau binding levels show an increase risk of longitudinal tau accumulation, atrophy, and cognitive decline. Trial registration NCT00106899. Registered 1 April 2005, https://clinicaltrials.gov/ct2/show/study/NCT00106899. [ABSTRACT FROM AUTHOR]

Genome–wide association studies (GWAS) have revealed a plethora of putative susceptibility genes for Alzheimer's disease (AD), with the sole exception of APOE gene unequivocally validated in independent study. Considering that the etiology of complex diseases like AD could depend on functional multiple genes interaction network, here we proposed an alternative GWAS analysis strategy based on (i) multivariate methods and on a (ii) telescope approach, in order to guarantee the identification of correlated variables, and reveal their connections at three biological connected levels. Specifically as multivariate methods, we employed two machine learning algorithms and a genetic association test and we considered SNPs, Genes and Pathways features in the analysis of two public GWAS dataset (ADNI-1 and ADNI-2). For each dataset and for each feature we addressed two binary classifications tasks: cases vs. controls and the low vs. high risk of developing AD considering the allelic status of APOEe4. This complex strategy allowed the identification of SNPs, genes and pathways lists statistically robust and meaningful from the biological viewpoint. Among the results, we confirm the involvement of TOMM40 gene in AD and we propose GRM7 as a novel gene significantly associated with AD. [ABSTRACT FROM AUTHOR]

Mild cognitive impairment (MCI) is a heterogeneous condition with variable outcomes. Improving diagnosis to increase the likelihood that MCI reliably reflects prodromal Alzheimer's Disease (AD) would be of great benefit for clinical practice and intervention trials. In 230 cognitively normal (CN) and 394 MCI individuals from the Alzheimer's Disease Neuroimaging Initiative, we studied whether an MCI diagnostic requirement of impairment on at least two episodic memory tests improves 3-year prediction of medial temporal lobe atrophy and progression to AD. Based on external age-adjusted norms for delayed free recall on the Rey Auditory Verbal Learning Test (AVLT), MCI participants were further classified as having normal (AVLT+, above -1 SD, n = 121) or impaired (AVLT -, -1 SD or below, n = 273) AVLT performance. CN, AVLT+, and AVLT- groups differed significantly on baseline brain (hippocampus, entorhinal cortex) and cerebrospinal fluid (amyloid, tau, p-tau) biomarkers, with the AVLT- group being most abnormal. The AVLT- group had significantly more medial temporal atrophy and a substantially higher AD progression rate than the AVLT+ group (51% vs. 16%, p < 0.001). The AVLT+ group had similar medial temporal trajectories compared to CN individuals. Results were similar even when restricted to individuals with above average (based on the CN group mean) baseline medial temporal volume/thickness. Requiring impairment on at least two memory tests for MCI diagnosis can markedly improve prediction of medial temporal atrophy and conversion to AD, even in the absence of baseline medial temporal atrophy. This modification constitutes a practical and cost-effective approach for clinical and research settings. [ABSTRACT FROM AUTHOR]

Tau is a hallmark pathology of Alzheimer's disease, and animal models have suggested that tau spreads from cell to cell through neuronal connections, facilitated by β-amyloid (Aβ). We test this hypothesis in humans using an epidemic spreading model (ESM) to simulate tau spread, and compare these simulations to observed patterns measured using tau-PET in 312 individuals along Alzheimer's disease continuum. Up to 70% of the variance in the overall spatial pattern of tau can be explained by our model. Surprisingly, the ESM predicts the spatial patterns of tau irrespective of whether brain Aβ is present, but regions with greater Aβ burden show greater tau than predicted by connectivity patterns, suggesting a role of Aβ in accelerating tau spread. Altogether, our results provide evidence in humans that tau spreads through neuronal communication pathways even in normal aging, and that this process is accelerated by the presence of brain Aβ. The tau protein is theorized to spread transneuronally in Alzheimers disease, though this theory remains unproven in humans. Our simulations of epidemic-like protein spreading across human brain networks support this theory, and suggest the spreading dynamics are modified by β-amyloid [ABSTRACT FROM AUTHOR]

Objective: The objectives of the present study were to investigate (1) whether trinary visual interpretation of amyloid positron emission tomography (PET) imaging (negative/equivocal/positive) reflects quantitative amyloid measurements and the time course of 11C-Pittsburgh compound B (PiB) amyloid accumulation, and (2) whether visually equivocal scans represent an early stage of the Alzheimer's disease (AD) continuum in terms of an intermediate state of quantitative amyloid measurements and the changes in amyloid accumulation over time.Methods: From the National Bioscience Database Center Human Database of the Japanese Alzheimer's Disease Neuroimaging Initiative, we selected 133 individuals for this study including 33 with Alzheimer's disease dementia (ADD), 52 with late mild cognitive impairment (LMCI), and 48 cognitively normal (CN) subjects who underwent clinical assessment, PiB PET, and structural magnetic resonance imaging (MRI) with 2 or 3-years of follow-up. Sixty-eight of the 133 individuals underwent cerebrospinal fluid amyloid-β1-42 (CSF-Ab42) analysis at baseline. The standard uptake value ratio (SUVR) of PiB PET was calculated with a method using MRI at each visit. The cross-sectional values, longitudinal changes in SUVR, and baseline CSF-Ab42 were compared among groups, which were categorized based on trinary visual reads of amyloid PET (negative/equivocal/positive).Results: From the trinary visual interpretation of the PiB PET images, 55 subjects were negative, 8 were equivocal, and 70 were positive. Negative interpretation was most frequent in the CN group (70.8/10.4/18.8%: negative/equivocal/positive), and positive was most frequent in the LMCI group (34.6/1.9/63.5%) and in the ADD group (9.1/6.1/84.8%). The baseline SUVRs were 1.08 ± 0.06 in the negative group, 1.23 ± 0.15 in the equivocal group, and 1.86 ± 0.31 in the positive group (F = 174.9, p < 0.001). The baseline CSF-Ab42 level was 463 ± 112 pg/mL in the negative group, 383 ± 125 pg/mL in the equivocal group, and 264 ± 69 pg/mL in the positive group (F = 37, p < 0.001). Over the 3-year follow-up, annual changes in SUVR were - 0.00 ± 0.02 in the negative group, 0.02 ± 0.02 in the equivocal group, and 0.04 ± 0.07 in the positive group (F = 8.4, p < 0.001).Conclusions: Trinary visual interpretation (negative/equivocal/positive) of amyloid PET imaging reflects quantitative amyloid measurements evaluated with PET and the CSF amyloid test as well as the amyloid accumulation over time evaluated with PET over 3 years. Subjects in the early stage of the AD continuum could be identified with an equivocal scan, because they showed intermediate quantitative amyloid PET, CSF measurements, and the amyloid accumulation over time. [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]

There is well-documented evidence of brain network differences between individuals with Alzheimer's disease (AD) and healthy controls (HC). To date, imaging studies investigating brain networks in these populations have typically been cross-sectional, and the reproducibility of such findings is somewhat unclear. In a novel study, we use the longitudinal ADNI data on the whole brain to jointly compute the brain network at baseline and one-year using a state of the art approach that pools information across both time points to yield distinct visit-specific networks for the AD and HC cohorts, resulting in more accurate inferences. We perform a multiscale comparison of the AD and HC networks in terms of global network metrics as well as at the more granular level of resting state networks defined under a whole brain parcellation. Our analysis illustrates a decrease in small-worldedness in the AD group at both the time points and also identifies more local network features and hub nodes that are disrupted due to the progression of AD. We also obtain high reproducibility of the HC network across visits. On the other hand, a separate estimation of the networks at each visit using standard graphical approaches reveals fewer meaningful differences and lower reproducibility. [ABSTRACT FROM AUTHOR]

The physiological basis of resilience to age-associated and AD-typical neurodegenerative pathology is still not well understood. So far, the established resilience factor intelligence has been shown to be associated with white matter network global efficiency, a key constituent of which are highly connected hubs. However, hub properties have also been shown to be impaired in AD. Individual predisposition or vulnerability of hub properties may thus modulate the impact of pathology on cognitive outcome and form part of the physiological basis of resilience. 85 cognitively normal elderly subjects and patients with MCI with DWI, MRI and AV45-PET scans were included from ADNI. We reconstructed the global WM networks in each subject and characterized hub-properties of GM regions using graph theory by calculating regional betweenness centrality. Subsequently, we investigated whether regional GM volume (GMV) and structural WM connectivity (WMC) of more hub-like regions was more associated with resilience, quantified as cognitive performance independent of amyloid burden, tau and WM lesions. Subjects with higher resilience showed higher increased regional GMV and WMC in more hub-like compared to less hub-like GM-regions. Additionally, this association was in some instances further increased at elevated amounts of brain pathology. Higher GMV and WMC of more hub-like regions may contribute more to resilience compared to less hub-like regions, reflecting their increased importance to brain network efficiency, and may thus form part of the neurophysiological basis of resilience. Future studies should investigate the factors leading to higher GMV and WMC of hubs in non-demented elderly with higher resilience. [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]

Purpose: To describe structural network differences in individuals with mild cognitive impairment (MCI) with high versus low executive abilities, as reflected by measures of white matter connectivity using diffusion tensor imaging (DTI).Materials and Methods: This was a retrospective, cross-sectional study. Of the 128 participants from the Alzheimer's Disease Neuroimaging Initiative database who had both a DTI scan as well as a diagnosis of MCI, we used an executive function score to classify the top 15 scoring patients as high executive ability, and the bottom-scoring 16 patients as low executive ability. Using a regions-of-interest-based analysis, we constructed networks and calculated graph theory measures on the constructed networks. We used automated tractography in order to compare differences in major white matter tracts.Results: The high executive ability group yielded greater network size, density and clustering coefficient. The high executive ability group reflected greater fractional anisotropy bilaterally in the inferior and superior longitudinal fasciculi.Conclusions: The network measures of the high executive ability group demonstrated greater white matter integrity. This suggests that white matter reserve may confer greater protection of executive abilities. Loss of this reserve may lead to greater impairment in the progression to Alzheimer's disease dementia.Key Points: • The MCI high executive ability group yielded a larger network. • The MCI high executive ability group had greater FA in numerous tracts. • White matter reserve may confer greater protection of executive abilities. • Loss of executive reserve may lead to greater impairment in AD dementia. [ABSTRACT FROM AUTHOR]

Objectives: The aims of this study were to examine whether visual MRI rating scales used in diagnostics of cognitive disorders can be estimated computationally and to compare the visual rating scales with their computed counterparts in differential diagnostics.Methods: A set of volumetry and voxel-based morphometry imaging biomarkers was extracted from T1-weighted and FLAIR images. A regression model was developed for estimating visual rating scale values from a combination of imaging biomarkers. We studied three visual rating scales: medial temporal lobe atrophy (MTA), global cortical atrophy (GCA), and white matter hyperintensities (WMHs) measured by the Fazekas scale. Images and visual ratings from the Amsterdam Dementia Cohort (ADC) (N = 513) were used to develop the models and cross-validate them. The PredictND (N = 672) and ADNI (N = 752) cohorts were used for independent validation to test generalizability.Results: The correlation coefficients between visual and computed rating scale values were 0.83/0.78 (MTA-left), 0.83/0.79 (MTA-right), 0.64/0.64 (GCA), and 0.76/0.75 (Fazekas) in ADC/PredictND cohorts. When performance in differential diagnostics was studied for the main types of dementia, the highest balanced accuracy, 0.75-0.86, was observed for separating different dementias from cognitively normal subjects using computed GCA. The lowest accuracy of about 0.5 for all the visual and computed scales was observed for the differentiation between Alzheimer's disease and frontotemporal lobar degeneration. Computed scales produced higher balanced accuracies than visual scales for MTA and GCA (statistically significant).Conclusions: MTA, GCA, and WMHs can be reliably estimated automatically helping to provide consistent imaging biomarkers for diagnosing cognitive disorders, even among less experienced readers.Key Points: • Visual rating scales used in diagnostics of cognitive disorders can be estimated computationally from MRI images with intraclass correlations ranging from 0.64 (GCA) to 0.84 (MTA). • Computed scales provided high diagnostic accuracy with single-subject data (area under the receiver operating curve range, 0.84-0.94). [ABSTRACT FROM AUTHOR]

The neuroimaging-based computer-aided diagnosis (CAD) for Alzheimer's disease (AD) has shown its effectiveness in recent years. In general, the multimodal neuroimaging-based CAD always outperforms the approaches based on a single modality. However, single-modal neuroimaging is more favored in clinical practice for diagnosis due to the limitations of imaging devices, especially in rural hospitals. Learning using privileged information (LUPI) is a new learning paradigm that adopts additional privileged information (PI) modality to help to train a more effective learning model during the training stage, but PI itself is not available in the testing stage. Since PI is generally related to the training samples, it is then transferred to the learned model. In this work, a LUPI-based CAD framework for AD is proposed. It can flexibly perform a classifier- or feature-level LUPI, in which the information is transferred from the additional PI modality to the diagnosis modality. A thorough comparison has been made among three classifier-level algorithms and five feature-level LUPI algorithms. The experimental results on the ADNI dataset show that all classifier-level and deep learning based feature-level LUPI algorithms can improve the performance of a single-modal neuroimaging-based CAD for AD by transferring PI. Graphical abstract Graphical abstract for the framework of the LUPI-based CAD for AD. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to verify the reliability and generalizability of an automatic tool for the detection of Alzheimer-related hypometabolic pattern based on a Support-Vector-Machine (SVM) model analyzing 18F-fluorodeoxyglucose (FDG) PET data.Methods: The SVM model processed metabolic data from anatomical volumes of interest also considering interhemispheric asymmetries. It was trained on a homogeneous dataset from a memory clinic center and tested on an independent multicentric dataset drawn from the Alzheimer's Disease Neuroimaging Initiative. Subjects were included in the study and classified based on a diagnosis confirmed after an adequate follow-up time.Results: The accuracy of the discrimination between patients with Alzheimer Disease (AD), in either prodromal or dementia stage, and normal aging subjects was 95.8%, after cross-validation, in the training set. The accuracy of the same model in the testing set was 86.5%. The role of the two datasets was then reversed, and the accuracy was 89.8% in the multicentric training set and 88.0% in the monocentric testing set. The classification rate was also evaluated in different subgroups, including non-converter mild cognitive impairment (MCI) patients, subjects with MCI reverted to normal conditions and subjects with non-confirmed memory concern. The percent of pattern detections increased from 77% in early prodromal AD to 91% in AD dementia, while it was about 10% for healthy controls and non-AD patients.Conclusions: The present findings show a good level of reproducibility and generalizability of a model for detecting the hypometabolic pattern in AD and confirm the accuracy of FDG-PET in Alzheimer disease. [ABSTRACT FROM AUTHOR]

Cerebrospinal fluid (CSF) Neurogranin has recently been proposed as a potential biomarker for cognitive decline and brain injury in Alzheimer's disease (AD). To test whether CSF Neurogranin levels are increased in AD and its association with cognitive decline, we examined 99 cognitively normal (CN) subjects, 171 patients with mild cognitive impairment (MCI), and 81 patients with AD in the cross-sectional study from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The results showed that CSF Neurogranin was increased in both AD and MCI compared with controls. CSF Neurogranin was particularly high in patients with MCI and AD dementia with Aβ pathologic features. Neurogranin levels were significantly higher in females compared to males with MCI. Levels of Neurogranin between the males and females with AD and CN did not differ. Neurogranin levels were significantly higher in APOE ε4 carriers compared to APOE ε4 non-carriers with MCI. Levels of Neurogranin between the APOE ε4 carriers and APOE ε4 non-carriers with AD and CN did not differ. Elevated CSF Neurogranin levels were positively correlated with levels of total tau and P-tau in AD. The results indicated that CSF Neurogranin was increased at the prodromal stage of AD and might reflect synaptic injury as cognitive decline in AD. [ABSTRACT FROM AUTHOR]

α-Synuclein is a 140-amino acid protein produced predominantly by neurons in the brain which plays a role in the regulation of neurotransmitter release, synaptic function, and plasticity, thus making it the focus in understanding the etiology of a group of neurodegenerative diseases. We conducted genome-wide association studies (GWAS) of α-synuclein levels in cerebrospinal fluid (CSF) with 209 non-Hispanic white participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI-1) cohort using a linear regression model to identify novel variants associated with α-synuclein concentration. The minor allele (T) of rs7072338 in the long intergenic non-protein coding RNA 1515 (LINC01515) and the minor allele (T) of rs17794023 in clusterin-associated protein 1 (CLUAP1) were associated with higher CSF α-synuclein levels at genome-wide significance (P = 4.167 × 10-9 and 9.56 × 10-9, respectively). In addition, single nucleotide polymorphisms (SNPs) near amyloid beta precursor protein (APP) (rs1394839) (P = 2.31 × 10-7), Rap guanine nucleotide exchange factor 1 (RAPGEF1) (rs10901091) (P = 8.07 × 10-7), and two intergenic loci on chromosome 2 and 14 (rs11687064 P = 2.50 × 10-7and rs7147386 P = 4.05 × 10-7) were identified as suggestive loci associated with CSF α-synuclein levels. We have identified significantly associated SNPs for CSF α-synuclein. These associations have important implications for a better understanding of α-synuclein regulation and allow researchers to further explore the relationships between these SNPs and α-synuclein-related neurodegenerative disorders. [ABSTRACT FROM AUTHOR]

Purpose: To assess disease-related patterns of in vivo pathology in 11 patients with Corticobasal Syndrome (CBS) compared to 20 healthy controls and 33 mild cognitive impairment (MCI) patients due to Alzheimer’s disease.Methods: We assessed tau aggregates with [18F]AV1451 PET, amyloid-β depositions with [18F]AV45 PET, and volumetric microstructural changes with MRI. We validated for [18F]AV1451 standardised uptake value ratio (SUVRs) against input functions from arterial metabolites and found that SUVRs and arterial-derived distribution volume ratio (DVRs) provide equally robust measures of [18F]AV1451 binding.Results: CBS patients showed increases in [18F]AV1451 SUVRs in parietal (P < 0.05) and frontal (P < 0.05) cortices in the affected hemisphere compared to healthy controls and in precentral (P = 0.008) and postcentral (P = 0.034) gyrus in the affected hemisphere compared to MCI patients. Our data were confirmed at the histopathological level in one CBS patient who underwent brain biopsy and showed sparse tau pathology in the parietal cortex co-localizing with increased [18F]AV1451 signal. Cortical and subcortical [18F]AV45 uptake was within normal levels in CBS patients. In parietal and frontal cortices of the most affected hemisphere we found also grey matter loss (P < 0.05), increased mean diffusivity (P < 0.05) and decreased fractional anisotropy (P < 0.05) in CBS patients compared to healthy controls and MCI patients. Grey matter loss and white matter changes in the precentral gyrus of CBS patients were associated with worse motor symptoms.Conclusions: Our findings demonstrate disease-related patterns of in vivo tau and microstructural pathology in the absence of amyloid-β, which distinguish CBS from non-affected individuals and MCI patients. [ABSTRACT FROM AUTHOR]

Purpose: We estimated whether amyloid involvement in subcortical regions may predict cognitive impairment, and established an amyloid staging scheme based on degree of subcortical amyloid involvement.Methods: Data from 240 cognitively normal older individuals, 393 participants with mild cognitive impairment, and 126 participants with Alzheimer disease were acquired at Alzheimer’s Disease Neuroimaging Initiative sites. To assess subcortical involvement, we analyzed amyloid deposition in amygdala, putamen, and caudate nucleus. We staged participants into a 3-stage model based on cortical and subcortical amyloid involvement: 382 with no cortical or subcortical involvement as stage 0, 165 with cortical but no subcortical involvement as stage 1, and 203 with both cortical and subcortical involvement as stage 2.Results: Amyloid accumulation was first observed in cortical regions and spread down to the putamen, caudate nucleus, and amygdala. In longitudinal analysis, changes in MMSE, ADAS-cog 13, FDG PET SUVR, and hippocampal volumes were steepest in stage 2 followed by stage 1 then stage 0 (p value <0.001). Stage 2 showed steeper changes in MMSE score (β [SE] = −0.02 [0.004], p < 0.001), ADAS-cog 13 (0.05 [0.01], p < 0.001), FDG PET SUVR (−0.0008 [0.0003], p = 0.004), and hippocampal volumes (−4.46 [0.65], p < 0.001) compared to stage 1.Conclusions: We demonstrated a downward spreading pattern of amyloid, suggesting that amyloid accumulates first in neocortex followed by subcortical structures. Furthermore, our new finding suggested that an amyloid staging scheme based on subcortical involvement might reveal how differential regional accumulation of amyloid affects cognitive decline through functional and structural changes of the brain. [ABSTRACT FROM AUTHOR]

Neuroimaging genomics is an emerging field that provides exciting opportunities to understand the genetic basis of brain structure and function. The unprecedented scale and complexity of the imaging and genomics data, however, have presented critical computational bottlenecks. In this work we present our initial efforts towards building an interactive visual exploratory system for mining big data in neuroimaging genomics. A GPU accelerated browsing tool for neuroimaging genomics is created that implements the ANOVA algorithm for single nucleotide polymorphism (SNP) based analysis and the VEGAS algorithm for gene-based analysis, and executes them at interactive rates. The ANOVA algorithm is 110 times faster than the 4-core OpenMP version, while the VEGAS algorithm is 375 times faster than its 4-core OpenMP counter part. This approach lays a solid foundation for researchers to address the challenges of mining large-scale imaging genomics datasets via interactive visual exploration. [ABSTRACT FROM AUTHOR]