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A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT)-a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike, providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams-illustrated on an example dataset-allow for voxel-based, surface-based, and region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT while offering a citable standard for the neuroscience community., (© The Author(s) 2024. Published by Oxford University Press on behalf of GigaScience.)

For patients with cognitive disorders and dementia, accurate prognosis of cognitive worsening is critical to their ability to prepare for the future, in collaboration with health-care providers. Despite multiple efforts to apply computational brain magnetic resonance image (MRI) analysis in predicting cognitive worsening, with several successes, brain MRI is not routinely quantified in clinical settings to guide prognosis and clinical decision-making. To encourage the clinical use of a cutting-edge image segmentation method, we developed a prediction model as part of an established web-based cloud platform, MRICloud. The model was built in a training dataset from Alzheimer's Disease Neuroimaging Initiative (ADNI) where baseline MRI scans were combined with clinical data over time. Each MRI was parcellated into 265 anatomical units based on the MRICloud fully automated image segmentation function, to measure the volume of each parcel. The Mini Mental State Examination (MMSE) was used as a measure of cognitive function. The normalized volume of 265 parcels, combined with baseline MMSE score, age, and sex were input variables for a Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, with MMSE change in the subsequent two years as the target for prediction. A leave-one-out analysis performed on the training dataset estimated a correlation coefficient of 0.64 between true and predicted MMSE change. A receiver operating characteristic (ROC) analysis estimated a sensitivity of 0.88 and a specificity of 0.76 in predicting substantial cognitive worsening after two years, defined as MMSE decline of ≥4 points. This MRICloud prediction model was then applied to a test dataset of clinically acquired MRIs from the Johns Hopkins Memory and Alzheimer's Treatment Center (MATC), a clinical care setting. In the latter setting, the model had both sensitivity and specificity of 1.0 in predicting substantial cognitive worsening. While the MRICloud prediction model demonstrated promise as a platform on which computational MRI findings can easily be extended to clinical use, further study with a larger number of patients is needed for validation.

Background: Multi-site neuroimaging offer several benefits and poses tough challenges in the drug development process. Although MRI protocol and clinical guidelines developed to address these challenges recommend the use of good quality images, reliable assessment of image quality is hampered by the several shortcomings of existing techniques., Methods: Given a test image two feature images are extracted. They are grayscale and contrast feature images. Four binary images are generated by setting four different global thresholds on the feature images. Image quality is predicted by measuring the structural similarity between appropriate pairs of binary images. The lower and upper limits of the quality index are 0 and 1. Quality prediction is based on four quality attributes; luminance contrast, texture, texture contrast and lightness., Results: Performance evaluation on test data from three multi-site clinical trials show good objective quality evaluation across MRI sequences, levels of distortion and quality attributes. Correlation with subjective evaluation by human observers is ≥ 0.6., Conclusion: The results are promising for the evaluation of MRI protocols, specifically the standardization of quality index, designed to overcome the challenges encountered in multi-site clinical trials.

Alzheimer's disease (AD) is a chronic neurodegenerative disease of the central nervous system that has no cure and leads to death. One of the most prevalent tools for AD diagnosis is magnetic resonance imaging (MRI), because of its capability to visualize brain anatomical structures. There is a variety of classification methods for automatic diagnosis of AD, such as support vector machines, genetic algorithms, Bayes classifiers, neural networks, random forests, etc., but none of them provides robust information about the stage of the AD, they can just reveal the presence of disease. In this paper, a new approach for classification of MRI images using a fuzzy inference system is proposed. Two statistical moments (mean and standard deviation) of 116 anatomical regions of interests (ROIs) are used as input features for the classification system. A t-test feature selection method is used to identify the most discriminative ROIs. In order to evaluate the proposed system, MRI images from a database consisting of 818 subjects (229 normal, 401 mild cognitive impairment and 188 AD subjects) collected from the Alzheimer's disease neuroimaging initiative (ADNI) is analyzed. The receiver operating characteristics (ROC) curve and the area under the curve (AUC) of the proposed fuzzy inference system fed by statistical input features are employed as the evaluation criteria with k-fold cross validation. The proposed system yields promising results in normal vs. AD classification with AUC of 0.99 on the training set and 0.8622±0.0033 on the testing set.

Magnetic Resonance Imaging (MRI) is of fundamental importance in neuroscience, providing good contrast and resolution, as well as not being considered invasive. Despite the development of newer techniques involving radiopharmaceuticals, it is still a recommended tool in Alzheimer's Disease (AD) neurological practice to assess neurodegeneration, and recent research suggests that it could reveal changes in the brain even before the symptomatology appears. In this paper we propose a method that performs a Spherical Brain Mapping, using different measures to project the three-dimensional MR brain images onto two-dimensional maps revealing statistical characteristics of the tissue. The resulting maps could be assessed visually, but also perform a significant feature reduction that will allow further supervised or unsupervised processing, reducing the computational load while maintaining a large amount of the original information. We have tested our methodology against a MRI database comprising 180 AD affected patients and 180 normal controls, where some of the mappings have revealed as an optimum strategy for the automatic processing and characterization of AD patterns, achieving up to a 90.9% of accuracy, as well as significantly reducing the computational load. Additionally, our maps allow the visual analysis and interpretation of the images, which can be of great help in the diagnosis of this and other types of dementia.

ObjectivesWe proposed a novel deep learning radiomics (DLR) method to distinguish cognitively normal adults at risk of Alzheimer’s disease (AD) from normal control based on T1-weighted structural MRI images.MethodsIn this study, we selected MRI data from the Alzheimer’s Disease Neuroimaging Initiative Database (ADNI), which included 417 cognitively normal adults. These subjects were divided into 181 individuals at risk of Alzheimer’s disease (preAD group) and 236 normal control individuals (NC group) according to standard uptake ratio >1.18 calculated by amyloid Positron Emission Tomography (PET). We further divided the preaAD group into APOE+ and APOE− subgroups according to whether APOE ε4 was positive or not. All data sets were divided into one training/validation group and one independent test group. The proposed DLR method included three steps: (1) the pre-training of basic deep learning (DL) models, (2) the extraction, selection and fusion of DLR features, and (3) classification. The support vector machine (SVM) was used as the classifier. In the comparative experiments, we compared our proposed DLR method with three existing models: hippocampal model, clinical model, and traditional radiomics model. Ten-fold cross-validation was performed with 100 time repetitions.ResultsThe DLR method achieved the best classification performance between preAD and NC than other models with an accuracy of 89.85% ± 1.12%. In comparison, the accuracies of the other three models were 72.44% ± 1.37%, 82.00% ± 4.09% and 79.65% ± 2.21%. In addition, the DLR model also showed the best classification performance (85.45% ± 9.04% and 92.80% ± 2.61%) in the subgroup experiment.ConclusionThe results showed that the DLR method provided a potentially clinical value to distinguish preAD from NC.

Methods: Alzheimer's disease and Frontotemporal dementia are the first and third most common forms of dementia. Due to their similar clinical symptoms, they are easily misdiagnosed as each other even with sophisticated clinical guidelines. For disease-specific intervention and treatment, it is essential to develop a computer-aided system to improve the accuracy of their differential diagnosis. Recent advances in deep learning have delivered some of the best performance for medical image recognition tasks. However, its application to the differential diagnosis of AD and FTD pathology has not been explored.Approach: In this study, we proposed a novel deep learning based framework to distinguish between brain images of normal aging individuals and subjects with AD and FTD. Specifically, we combined the multi-scale and multi-type MRI-base image features with Generative Adversarial Network data augmentation technique to improve the differential diagnosis accuracy.Results: Each of the multi-scale, multitype, and data augmentation methods improved the ability for differential diagnosis for both AD and FTD. A 10-fold cross validation experiment performed on a large sample of 1,954 images using the proposed framework achieved a high overall accuracy of 88.28%.Conclusions: The salient contributions of this study are three-fold: (1) our experiments demonstrate that the combination of multiple structural features extracted at different scales with our proposed deep neural network yields superior performance than individual features; (2) we show that the use of Generative Adversarial Network for data augmentation could further improve the discriminant ability of the network regarding challenging tasks such as differentiating dementia sub-types; (3) and finally, we show that ensemble classifier strategy could make the network more robust and stable.

Abstract Aims/hypothesis Midlife obesity is a risk factor for dementia. We investigated the impact of obesity on brain structure, metabolism, and cerebrospinal fluid (CSF) core Alzheimer's disease (AD) biomarkers in healthy elderly. Methods We selected controls from ADNI2 with CSF AD biomarkers and/or fluorodeoxyglucose positron emission tomography (FDG‐PET) and 3T‐MRI. We measured cortical thickness, FDG uptake, and CSF amyloid beta (Aβ)1‐42, p‐tau, and t‐tau levels. We performed regression analyses between these biomarkers and body mass index (BMI). Results We included 201 individuals (mean age 73.5 years, mean BMI 27.4 kg/m2). Higher BMI was related to less cortical thickness and higher metabolism in brain areas typically not involved in AD (family‐wise error [FWE]

Introduction: Alzheimer’s disease (AD) is a neurodegenerative disorder with a clinical presentation characterized by memory impairment and executive dysfunction. Our group previously demonstrated significant alterations in white matter microstructural metrics in AD compared to healthy older adults. We aimed to further investigate the relationship between white matter microstructure in AD and cognitive function, including memory and executive function.Methods: Diffusion tensor imaging (DTI) and neuropsychological data were downloaded from the AD Neuroimaging Initiative database for 49 individuals with AD and 48 matched healthy older adults. The relationship between whole-brain fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AxD), radial diffusivity (RD), and composite scores of memory and executive function was examined. We also considered voxel-wise relationships using Tract-Based Spatial Statistics.Results: As expected, individuals with AD had lower composite scores on tests of memory and executive function, as well as disrupted white matter integrity (low FA, high MD, AxD, and RD) relative to healthy older adults in widespread regions, including the hippocampus. When the AD and healthy older adult groups were combined, we found significant relationships between DTI metrics (FA/MD/AxD/RD) and memory scores across widespread regions of the brain, including the medial temporal regions. We also found significant relationships between DTI metrics (FA/MD/AxD/RD) and executive function in widespread regions, including the frontal areas in the combined group. However, when the groups were examined separately, no significant relationships were found between DTI metrics (FA/MD/AxD/RD) and memory performance for either group. Further, we did not find any significant relationships between DTI metrics (FA/MD/AxD/RD) and executive function in the AD group, but we did observe significant relationships between FA/RD, and executive function in healthy older adults.Conclusion: White matter integrity is disrupted in AD. In a mixed sample of AD and healthy elderly persons, associations between measures of white matter microstructure and memory and executive cognitive test performance were evident. However, no significant linear relationship between the degree of white matter disruption and level of cognitive functioning (memory and executive abilities) was found in those with AD. Future longitudinal studies of the relations between DTI metrics and cognitive function in AD are required to determine whether DTI has potential to measure progression of AD and/or treatment efficacy.

Early detection of neurodegeneration, and prediction of when neurodegenerative diseases will lead to symptoms, are critical for developing and initiating disease modifying treatments for these disorders. While each neurodegenerative disease has a typical pattern of early changes in the brain, these disorders are heterogeneous, and early manifestations can vary greatly across people. Methods for detecting emerging neurodegeneration in any part of the brain are therefore needed. Prior publications have described the use of Bayesian linear mixed-effects (BLME) modeling for characterizing the trajectory of change across the brain in healthy controls and patients with neurodegenerative disease. Here, we use an extension of such a model to detect emerging neurodegeneration in cognitively healthy individuals at risk for dementia. We use BLME to quantify individualized rates of volume loss across the cerebral cortex from the first two MRIs in each person and then extend the BLME model to predict future values for each voxel. We then compare observed values at subsequent time points with the values that were expected from the initial rates of change and identify voxels that are lower than the expected values, indicating accelerated volume loss and neurodegeneration. We apply the model to longitudinal imaging data from cognitively normal participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI), some of whom subsequently developed dementia, and two cognitively normal cases who developed pathology-proven frontotemporal lobar degeneration (FTLD). These analyses identified regions of accelerated volume loss prior to or accompanying the earliest symptoms, and expanding across the brain over time, in all cases. The changes were detected in regions that are typical for the likely diseases affecting each patient, including medial temporal regions in patients at risk for Alzheimer's disease, and insular, frontal, and/or anterior/inferior temporal regions in patients with likely or proven FTLD. In the cases where detailed histories were available, the first regions identified were consistent with early symptoms. Furthermore, survival analysis in the ADNI cases demonstrated that the rate of spread of accelerated volume loss across the brain was a statistically significant predictor of time to conversion to dementia. This method for detection of neurodegeneration is a potentially promising approach for identifying early changes due to a variety of diseases, without prior assumptions about what regions are most likely to be affected first in an individual.

Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical heterogeneity contrasted against normal brain structure, to identify disease subtypes through neuroimaging signatures. When applied to regional volumes derived from T1-weighted MRI (two studies; 2,832 participants; 8,146 scans) including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified four patterns or axes of neurodegeneration. Applying this framework to longitudinal data revealed two distinct progression pathways. Measures of expression of these patterns predicted the pathway and rate of future neurodegeneration. Pattern expression offered complementary performance to amyloid/tau in predicting clinical progression. These deep-learning derived biomarkers offer potential for precision diagnostics and targeted clinical trial recruitment.

IntroductionAmyloid beta (Aβ), tau, and neurodegeneration jointly with the Alzheimer's disease (AD) risk factors affect the severity of clinical symptoms and disease progression.MethodsWithin 248 Aβ-positive elderly with and without cognitive impairment and dementia, partial least squares structural equation pathway modeling was used to assess the direct and indirect effects of imaging biomarkers (global Aβ-positron emission tomography [PET] uptake, regional tau-PET uptake, and regional magnetic resonance imaging-based atrophy) and risk-factors (age, sex, education, apolipoprotein E [APOE], and white-matter lesions) on cross-sectional cognitive impairment and longitudinal cognitive decline.ResultsSixteen percent of variance in cross-sectional cognitive impairment was accounted for by Aβ, 46% to 47% by tau, and 25% to 29% by atrophy, although 53% to 58% of total variance in cognitive impairment was explained by incorporating mediated and direct effects of AD risk factors. The Aβ-tau-atrophy pathway accounted for 50% to 56% of variance in longitudinal cognitive decline while Aβ, tau, and atrophy independently explained 16%, 46% to 47%, and 25% to 29% of the variance, respectively.DiscussionThese findings emphasize that treatments that remove Aβ and completely stop downstream effects on tau and neurodegeneration would only be partially effective in slowing of cognitive decline or reversing cognitive impairment.

Neuropathological research suggests the tau pathology of Alzheimer's disease may originate in brainstem nuclei, yet it remains unknown whether tau-mediated degeneration of brainstem nuclei influences cognitive impairment in prodromal Alzheimer's disease. The present study examined cognitive domains impacted in prodromal Alzheimer's disease and brainstem substructure volume in cognitively normal older adults (n = 814) and those with mild cognitive impairment (n = 542). Subsamples of cognitively normal (n = 112) and mild cognitive impairment (n = 202) also had cerebrospinal fluid Alzheimer's disease biomarker characterization. Region-of-interest and voxel-level analyses related whole brainstem, midbrain, pons, and locus coeruleus volumes to cognition with multiple linear regression models corrected for age, sex, education, apolipoprotein-ε4 carrier status, and MRI magnet strength. Within mild cognitive impairment participants, smaller midbrain and locus coeruleus volumes were significantly related to poorer performance on tests of attention and executive function, and the relationship between locus coeruleus volume and executive abilities remained significant in the mild cognitive impairment subsample with biomarker-confirmed Alzheimer's disease. A brainstem-masked voxel-wise regression further demonstrated an association between locus coeruleus volume and executive abilities. Brainstem volumes were not significantly related to memory processes. Study findings implicate midbrain and locus coeruleus volume in attention and executive deficits in mild cognitive impairment. Together with prior neuropathological studies, our data suggest a link between Alzheimer's disease-related degeneration of brainstem nuclei and cognitive deficits in prodromal Alzheimer's disease.

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.

Causal Structure Discovery (CSD) is the problem of identifying causal relationships from large quantities of data through computational methods. With the limited ability of traditional association-based computational methods to discover causal relationships, CSD methodologies are gaining popularity. The goal of the study was to systematically examine whether (i) CSD methods can discover the known causal relationships from observational clinical data and (ii) to offer guidance to accurately discover known causal relationships. We used Alzheimer's disease (AD), a complex progressive disease, as a model because the well-established evidence provides a "gold-standard" causal graph for evaluation. We evaluated two CSD methods, Fast Causal Inference (FCI) and Fast Greedy Equivalence Search (FGES) in their ability to discover this structure from data collected by the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used structural equation models (which is not designed for CSD) as control. We applied these methods under three scenarios defined by increasing amounts of background knowledge provided to the methods. The methods were evaluated by comparing the resulting causal relationships with the "gold standard" graph that was constructed from literature. Dedicated CSD methods managed to discover graphs that nearly coincided with the gold standard. For best results, CSD algorithms should be used with longitudinal data providing as much prior knowledge as possible.

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.

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.

BackgroundLate-onset Alzheimer's disease (AD) is characterized by primary memory impairment, which then progresses towards severe deficits across cognitive domains. Here, we report how performance in cognitive domains relates to patterns of tau deposition and cortical thickness.MethodsWe analyzed data from 131 amyloid-β positive participants (55 cognitively normal, 46 mild cognitive impairment, 30 AD) of the Alzheimer's Disease Neuroimaging Initiative who underwent magnetic resonance imaging (MRI), flortaucipir (FTP) positron emission tomography, and neuropsychological testing. Surface-based vertex-wise and region-of-interest analyses were conducted between FTP and cognitive test scores, and between cortical thickness and cognitive test scores.ResultsFTP and thickness were differentially related to cognitive performance in several domains. FTP-cognition associations were more widespread than thickness-cognition associations. Further, FTP-cognition patterns reflected cortical systems that underlie different aspects of cognition.ConclusionsOur findings indicate that AD-related decline in domain-specific cognitive performance reflects underlying progression of tau and atrophy into associated brain circuits. They also suggest that tau-PET may have better sensitivity to this decline than MRI-derived measures of cortical thickness.

The single nucleotide polymorphism (SNP) rs744373 in the bridging integrator-1 gene (BIN1) is a risk factor for Alzheimer's disease (AD). In the brain, BIN1 is involved in endocytosis and sustaining cytoskeleton integrity. Post-mortem and in vitro studies suggest that BIN1-associated AD risk is mediated by increased tau pathology but whether rs744373 is associated with increased tau pathology in vivo is unknown. Here we find in 89 older individuals without dementia, that BIN1 rs744373 risk-allele carriers show higher AV1451 tau-PET across brain regions corresponding to Braak stages II-VI. In contrast, the BIN1 rs744373 SNP was not associated with AV45 amyloid-PET uptake. Furthermore, the rs744373 risk-allele was associated with worse memory performance, mediated by increased global tau levels. Together, our findings suggest that the BIN1 rs744373 SNP is associated with increased tau but not beta-amyloid pathology, suggesting that alterations in BIN1 may contribute to memory deficits via increased tau pathology.

ObjectiveTo study longitudinal relationships between type 2 diabetes mellitus (T2DM), cortical thickness, and cognitive function in older people with normal cognition, mild cognitive impairment, and Alzheimer disease (AD).MethodsThe sample was derived from the Alzheimer's Disease Neuroimaging Initiative cohort who underwent brain MRI and cognitive tests annually for 5 years. Presence of T2DM was based on fasting blood glucose ≥7.0mml/L or the use of glucose-lowering agents. We used latent growth curve modeling to explore longitudinal relationships between T2DM, cortical thickness, and cognitive function, adjusting for relevant covariates and testing for interactions.ResultsThere were 124 people with T2DM (mean age 75.5 years, SD 6.2) and 693 without T2DM (mean age 75.1 years, SD 6.9) with at least 1 MRI available. AD and lower cortical thickness at study entry was associated with a lower chance of having a MRI available at each follow-up phase (all p < 0.001). T2DM was associated with lower baseline cortical thickness (p = 0.01). We found no direct effect of T2DM on decline in cortical thickness or cognitive function, but there was an indirect pathway linking T2DM and cognitive decline via baseline cortical thickness (β = -0.17, p = 0.022). There was an interaction between T2DM and education whereby the negative effect of T2DM on baseline cortical thickness was reduced in those with greater education (β = 0.34, p = 0.037). These associations changed minimally when adjusted for baseline cognitive diagnosis.ConclusionsIn an older cohort with low cerebrovascular disease burden, T2DM contributes to cognitive decline via neurodegeneration. Prior brain and cognitive reserve may protect against this effect.

IntroductionBile acids (BAs) are the end products of cholesterol metabolism produced by human and gut microbiome co-metabolism. Recent evidence suggests gut microbiota influence pathological features of Alzheimer's disease (AD) including neuroinflammation and amyloid-β deposition.MethodSerum levels of 20 primary and secondary BA metabolites from the AD Neuroimaging Initiative (n = 1562) were measured using targeted metabolomic profiling. We assessed the association of BAs with the "A/T/N" (amyloid, tau, and neurodegeneration) biomarkers for AD: cerebrospinal fluid (CSF) biomarkers, atrophy (magnetic resonance imaging), and brain glucose metabolism ([18F]FDG PET).ResultsOf 23 BAs and relevant calculated ratios after quality control procedures, three BA signatures were associated with CSF Aβ1-42 ("A") and three with CSF p-tau181 ("T") (corrected P

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.

BackgroundA clinical diagnosis of cognitive impairment is traditionally based on a single cognitive exam, but serial cognitive testing can be sensitive to subtle cognitive changes in asymptomatic individuals and inform cognitive trajectory.ObjectiveWe evaluated the prognostic utility of identifying longitudinal neuropsychological decline along with single cognitive exam and Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers in predicting dementia. We also examined brain volumetric differences based on decline trajectories.MethodRegression models quantified 12-month neuropsychological decline relative to normative expectations among non-demented older adults (N = 1,074). Progression to dementia over follow-up (18-120 months) was diagnosed using independent modes of assessment.ResultsIn Cox regression models controlling for age, sex, education, apolipoprotein E4, and baseline cognitive diagnosis, neuropsychological decline predicted increased dementia risk, χ2 = 69.861, p

Time-dependent data collected in studies of Alzheimer's disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing step. In this paper we use a random forest to learn the relationship between pairs of data points at different time separations. The input vector is a summary of the time series history and it includes both demographic and non-time varying variables such as genetic data. To test the method we use data from the TADPOLE grand challenge, an initiative which aims to predict the evolution of subjects at risk of Alzheimer's disease using demographic, physical and cognitive input data. The task is to predict diagnosis, ADAS-13 score and normalised ventricles volume. While the competition proceeds, forecasting methods may be compared using a leaderboard dataset selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and with standard metrics for measuring accuracy. For diagnosis, we find an mAUC of 0.82, and a classification accuracy of 0.73 compared with a benchmark SVM predictor which gives mAUC = 0.62 and BCA = 0.52. The results show that the method is effective and comparable with other methods.

This study examines the atrophy rates of subjects with mild cognitive impairment (MCI) compared to controls in four regions within the medial temporal lobe: the transentorhinal cortex (TEC), entorhinal cortex (ERC), hippocampus, and amygdala. These regions were manually segmented and then corrected for undesirable longitudinal variability via Large Deformation Diffeomorphic Metric Mapping (LDDMM) based longitudinal diffeomorphometry. Diffeomorphometry techniques were used to compare thickness measurements in the TEC with the ERC. There were more significant changes in thickness atrophy rate in the TEC than medial regions of the entorhinal cortex. Volume measures were also calculated for all four regions. Classifiers were constructed using linear discriminant analysis to demonstrate that average thickness and atrophy rate of TEC together was the most discriminating measure compared to the thickness and volume measures in the areas examined, in differentiating MCI from controls. These findings are consistent with autopsy findings demonstrating that initial neuronal changes are found in TEC before spreading more medially in the ERC and to other regions in the medial temporal lobe. These findings suggest that the TEC thickness could serve as a biomarker for Alzheimer's disease in the prodromal phase of the disease.

PurposeWe estimated whether amyloid involvement in subcortical regions may predict cognitive impairment, and established an amyloid staging scheme based on degree of subcortical amyloid involvement.MethodsData 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.ResultsAmyloid 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

The change in hypometabolism affects the regional links in the brain network. Here, to understand the underlying brain metabolic network deficits during the early stage and disease evolution of AD (Alzheimer disease), we applied correlation analysis to identify the metabolic connectivity patterns using 18F-FDG PET data for NC (normal control), sMCI (stable MCI), pMCI (progressive MCI) and AD, and explore the inter- and intra-hemispheric connectivity between anatomically-defined brain regions. Regions extracted from 90 anatomical structures were used to construct the matrix for measuring the inter- and intra-hemispheric connectivity. The brain connectivity patterns from the metabolic network show a decreasing trend of inter- and intra-hemispheric connections for NC, sMCI, pMCI and AD. Connection of temporal to the frontal or occipital regions is a characteristic pattern for conversion of NC to MCI, and the density of links in the parietal-occipital network is a differential pattern between sMCI and pMCI. The reduction pattern of inter and intra-hemispheric brain connectivity in the metabolic network depends on the disease stages, and is with a decreasing trend with respect to disease severity. Both frontal-occipital and parietal-occipital connectivity patterns in the metabolic network using 18F-FDG PET are the key feature for differentiating disease groups in AD.

Despite great interest in using magnetic resonance imaging (MRI) for studying the effects of genes on brain structure in humans, current approaches have focused almost entirely on predefined regions of interest and had limited success. Here, we used multivariate methods to define a single neuroanatomical score of how William's Syndrome (WS) brains deviate structurally from controls. The score is trained and validated on measures of T1 structural brain imaging in two WS cohorts (training, n = 38; validating, n = 60). We then associated this score with single nucleotide polymorphisms (SNPs) in the WS hemi-deleted region in five cohorts of neurologically and psychiatrically typical individuals (healthy European descendants, n = 1863). Among 110 SNPs within the 7q11.23 WS chromosomal region, we found one associated locus (p = 5e-5) located at GTF2IRD1, which has been implicated in animal models of WS. Furthermore, the genetic signals of neuroanatomical scores are highly enriched locally in the 7q11.23 compared with summary statistics based on regions of interest, such as hippocampal volumes (n = 12,596), and also globally (SNP-heritability = 0.82, se = 0.25, p = 5e-4). The role of genetic variability in GTF2IRD1 during neurodevelopment extends to healthy subjects. Our approach of learning MRI-derived phenotypes from clinical populations with well-established brain abnormalities characterized by known genetic lesions may be a powerful alternative to traditional region of interest-based studies for identifying genetic variants regulating typical brain development.

Alzheimer's Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1-3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature.

Accurate diagnosis of mild cognitive impairment (MCI) before conversion to Alzheimer's disease (AD) is invaluable for patient treatment. Many works showed that MCI and AD affect functional and structural connections between brain regions as well as the shape of cortical regions. However, 'shape connections' between brain regions are rarely investigated -e.g., how morphological attributes such as cortical thickness and sulcal depth of a specific brain region change in relation to morphological attributes in other regions. To fill this gap, we unprecedentedly design morphological brain multiplexes for late MCI/AD classification. Specifically, we use structural T1-w MRI to define morphological brain networks, each quantifying similarity in morphology between different cortical regions for a specific cortical attribute. Then, we define a brain multiplex where each intra-layer represents the morphological connectivity network of a specific cortical attribute, and each inter-layer encodes the similarity between two consecutive intra-layers. A significant performance gain is achieved when using the multiplex architecture in comparison to other conventional network analysis architectures. We also leverage this architecture to discover morphological connectional biomarkers fingerprinting the difference between late MCI and AD stages, which included the right entorhinal cortex and right caudal middle frontal gyrus.

Models of Alzheimer's disease (AD) hypothesize stereotyped progression via white matter (WM) fiber connections, most likely via trans-synaptic transmission of toxic proteins along neuronal pathways. An important question in the field is whether and how organization of fiber pathways is affected by disease. It remains unknown whether fibers act as conduits of degenerative pathologies, or if they also degenerate with the gray matter network. This work uses graph theoretic modeling in a longitudinal design to investigate the impact of WM network organization on AD pathology spread. We hypothesize if altered WM network organization mediates disease progression, then a previously published network diffusion model will yield higher prediction accuracy using subject-specific connectomes in place of a healthy template connectome. Neuroimaging data in 124 subjects from ADNI were assessed. Graph topology metrics show preserved network organization in patients compared to controls. Using a published diffusion model, we further probe the effect of network alterations on degeneration spread in AD. We show that choice of connectome does not significantly impact the model's predictive ability. These results suggest that, despite measurable changes in integrity of specific fiber tracts, WM network organization in AD is preserved. Further, there is no difference in the mediation of putative pathology spread between healthy and AD-impaired networks. This conclusion is somewhat at variance with previous results, which report global topological disturbances in AD. Our data indicates the combined effect of edge thresholding, binarization, and inclusion of subcortical regions to network graphs may be responsible for previously reported effects.

ObjectivesTo investigate the association between cognitive decline and cortical atrophy in individuals with mild cognitive impairment (MCI) and chronic subsyndromal symptoms of depression (SSD) over a 4-year period.DesignProspective cohort study.SettingMulticenter, clinic-based.ParticipantsWithin the Alzheimer's Disease Neuroimaging Initiative repository, the Neuropsychiatric Inventory was used to identify individuals with MCI and stable endorsement (SSD group N = 32) or no endorsement (non-SSD group N = 69) of depressive symptoms across time points.MeasurementsRepeated measures of cognitive outcomes, cortical atrophy, and their associations were evaluated with mixed effects models adjusting for age, education, sex, and APOE genotype.ResultsThe SSD group demonstrated accelerated decline on measures of global cognition (Alzheimer Disease Assessment Scale; df = 421, t = 2.242, p = 0.025), memory (Wechsler Memory Scale-Revised Logical Memory II; df = 244, t = -2.525, p = 0.011), information processing speed (Trail Making Test Parts A [df = 421, t = 2.376, p = 0.018] and B [df = 421, t = 2.533, p = 0.012]), and semantic fluency (Category Fluency; df = 424, t = -2.418, p = 0.016), as well as accelerated frontal lobe (df = 341, t = -2.648, p = 0.008) and anterior cingulate (df = 341, t = -3.786, p

ImportanceThere are conflicting results claiming that Alzheimer disease signature neurodegeneration may be more, less, or similarly advanced in individuals with β-amyloid peptide (Aβ)-negative (Aβ-) suspected non-Alzheimer disease pathophysiology (SNAP) than in Aβ-positive (Aβ+) counterparts.ObjectiveTo examine patterns of neurodegeneration in individuals with SNAP compared with their Aβ+ counterparts.Design, setting, and participantsA longitudinal cohort study was conducted among individuals with mild cognitive impairment (MCI) and cognitively normal individuals receiving care at Alzheimer's Disease Neuroimaging Initiative sites in the United States and Canada for a mean follow-up period of 30.5 months from August 1, 2005, to June 30, 2015. Several neurodegeneration biomarkers and longitudinal cognitive function were compared between patients with distinct SNAP (Aβ- and neurodegeneration-positive [Aβ-N+]) subtypes and their Aβ+N+ counterparts.Main outcomes and measuresParticipants were classified according to the results of their florbetapir F-18 (Aβ) positron emission tomography and their Alzheimer disease-associated neurodegeneration status (temporoparietal glucose metabolism determined by fluorodeoxyglucose F 18 [FDG]-labeled positron emission tomography and/or hippocampal volume [HV] determined by magnetic resonance imaging: participants with subthreshold HV values were regarded as exhibiting hippocampal volume atrophy [HV+], while subthreshold mean FDG values were considered as FDG hypometabolism [FDG+]).ResultsThe study comprised 265 cognitively normal individuals (135 women and 130 men; mean [SD] age, 75.5 [6.7] years) and 522 patients with MCI (225 women and 297 men; mean [SD] age, 72.6 [7.8] years). A total of 469 individuals with MCI had data on neurodegeneration biomarkers; of these patients, 107 were Aβ-N+ (22.8%; 63 FDG+, 82 HV+, and 38 FDG+HV+) and 187 were Aβ+N+ (39.9%; 135 FDG+, 147 HV+, and 95 FDG+HV+ cases). A total of 209 cognitively normal participants had data on neurodegeneration biomarkers; of these, 52 were Aβ-N+ (24.9%; 30 FDG+, 33 HV+, and 11 FDG+HV+) and 37 were Aβ+N+ (17.7%; 22 FDG+, 26 HV+, and 11 FDG+HV+). Compared with their Aβ+ counterparts, all patients with MCI SNAP subtypes displayed better preservation of temporoparietal FDG metabolism (mean [SD] FDG: Aβ-N+, 1.25 [0.11] vs Aβ+N+, 1.19 [0.11]), less severe atrophy of the lateral temporal lobe, and lower mean (SD) cerebrospinal fluid levels of tau (59.2 [32.8] vs 111.3 [56.4]). In MCI with SNAP, sustained glucose metabolism and gray matter volume were associated with disproportionately low APOE ε4 (Aβ-N+, 18.7% vs Aβ+N+, 70.6%) and disproportionately high APOE ε2 (18.7% vs 4.8%) carrier prevalence. Slower cognitive decline and lower rates of progression to Alzheimer disease (Aβ-N+, 6.5% vs Aβ+N+, 32.6%) were also seen in patients with MCI with SNAP subtypes compared with their Aβ+ counterparts. In cognitively normal individuals, neurodegeneration biomarkers did not differ between Aβ-N+ and Aβ+N+ cases.Conclusions and relevanceIn MCI with SNAP, low APOE ε4 and high APOE ε2 carrier prevalence may account for differences in neurodegeneration patterns between Aβ-N+ and Aβ+N+ cases independent from the neuroimaging biomarker modality used to define neurodegeneration associated with Alzheimer disease.

See Vandenberghe and Schaeverbeke (doi:10.1093/awx065) for a scientific commentary on this article. A long-term goal of our field is to determine the sequence of pathological events, which ultimately lead to cognitive decline and dementia. In this study, we first assessed the patterns of brain tau tangle accumulation (measured with the positron emission tomography tracer 18F-AV-1451) associated with well-established Alzheimer's disease factors in a cohort including cognitively healthy elderly individuals and individuals at early symptomatic stages of Alzheimer's disease. We then explored highly associated patterns of greater 18F-AV-1451 binding and increased annualized change in cortical amyloid-β plaques measured as florbetapir positron emission tomography binding antecedent to 18F-AV-1451 positron emission tomography scans, and to what extent these multimodal pattern associations explained the variance in cognitive performance and clinical outcome measures, independently and jointly. We found that: (i) 18F-AV-1451 positron emission tomography retention was differentially associated with age, and cross-sectional florbetapir positron emission tomography retention, but not with years of education, gender, or APOE genotype; (ii) increased annualized change in florbetapir retention, antecedent to 18F-AV-1451 positron emission tomography scans, in the parieto-temporal and precuneus brain regions was associated with greater 18F-AV-1451 PET retention most prominently in the inferior temporal and inferior parietal regions in the full cohort, with florbetapir positive/negative-associated variability; and (iii) this 18F-AV-1451 positron emission tomography retention pattern significantly explained the variance in cognitive performance and clinical outcome measures, independent of the associated antecedent increased annualized change in florbetapir positron emission tomography retention. These findings are in agreement with the pathology literature, which suggests that tau tangles but not amyloid-β plaques correlate with cognition and clinical symptoms. Furthermore, non-local associations linking increased amyloid-β accumulation rates with increased tau deposition are of great interest and support the idea that the amyloid-β pathology might have remote effects in disease pathology spread potentially via the brain's intrinsic connectivity networks.

IntroductionThe overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI-3, which began on August 1, 2016, is a 5-year renewal of the current ADNI-2 study.MethodsADNI-3 will follow current and additional subjects with normal cognition, mild cognitive impairment, and AD using innovative technologies such as tau imaging, magnetic resonance imaging sequences for connectivity analyses, and a highly automated immunoassay platform and mass spectroscopy approach for cerebrospinal fluid biomarker analysis. A Systems Biology/pathway approach will be used to identify genetic factors for subject selection/enrichment. Amyloid positron emission tomography scanning will be standardized using the Centiloid method. The Brain Health Registry will help recruit subjects and monitor subject cognition.ResultsMultimodal analyses will provide insight into AD pathophysiology and disease progression.DiscussionADNI-3 will aim to inform AD treatment trials and facilitate development of AD disease-modifying treatments.

Accurate prediction of Alzheimer's disease (AD) is important for the early diagnosis and treatment of this condition. Mild cognitive impairment (MCI) is an early stage of AD. Therefore, patients with MCI who are at high risk of fully developing AD should be identified to accurately predict AD. However, the relationship between brain images and AD is difficult to construct because of the complex characteristics of neuroimaging data. To address this problem, we present a longitudinal measurement of MCI brain images and a hierarchical classification method for AD prediction. Longitudinal images obtained from individuals with MCI were investigated to acquire important information on the longitudinal changes, which can be used to classify MCI subjects as either MCI conversion (MCIc) or MCI non-conversion (MCInc) individuals. Moreover, a hierarchical framework was introduced to the classifier to manage high feature dimensionality issues and incorporate spatial information for improving the prediction accuracy. The proposed method was evaluated using 131 patients with MCI (70 MCIc and 61 MCInc) based on MRI scans taken at different time points. Results showed that the proposed method achieved 79.4% accuracy for the classification of MCIc versus MCInc, thereby demonstrating very promising performance for AD prediction.

There is considerable debate whether Alzheimer's disease (AD) originates in basal forebrain or entorhinal cortex. Here we examined whether longitudinal decreases in basal forebrain and entorhinal cortex grey matter volume were interdependent and sequential. In a large cohort of age-matched older adults ranging from cognitively normal to AD, we demonstrate that basal forebrain volume predicts longitudinal entorhinal degeneration. Models of parallel degeneration or entorhinal origin received negligible support. We then integrated volumetric measures with an amyloid biomarker sensitive to pre-symptomatic AD pathology. Comparison between cognitively matched normal adult subgroups, delineated according to the amyloid biomarker, revealed abnormal degeneration in basal forebrain, but not entorhinal cortex. Abnormal degeneration in both basal forebrain and entorhinal cortex was only observed among prodromal (mildly amnestic) individuals. We provide evidence that basal forebrain pathology precedes and predicts both entorhinal pathology and memory impairment, challenging the widely held belief that AD has a cortical origin.

We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer's disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid-positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.

Little is known about how genetic variation contributes to neuroanatomical variability, and whether particular genomic regions comprising genes or evolutionarily conserved elements are enriched for effects that influence brain morphology. Here, we examine brain imaging and single-nucleotide polymorphisms (SNPs) data from ∼2,700 individuals. We show that a substantial proportion of variation in cortical surface area is explained by additive effects of SNPs dispersed throughout the genome, with a larger heritable effect for visual and auditory sensory and insular cortices (h(2)∼0.45). Genome-wide SNPs collectively account for, on average, about half of twin heritability across cortical regions (N=466 twins). We find enriched genetic effects in or near genes. We also observe that SNPs in evolutionarily more conserved regions contributed significantly to the heritability of cortical surface area, particularly, for medial and temporal cortical regions. SNPs in less conserved regions contributed more to occipital and dorsolateral prefrontal cortices.

The many subcomponents of the human cortex are known to follow an anatomical pattern and functional relationship that appears to be highly conserved between individuals. This suggests that this pattern and the relationship among cortical regions are important for cortical function and likely shaped by genetic factors, although the degree to which genetic factors contribute to this pattern is unknown. We assessed the genetic relationships among 12 cortical surface areas using brain images and genotype information on 2,364 unrelated individuals, brain images on 466 twin pairs, and transcriptome data on 6 postmortem brains in order to determine whether a consistent and biologically meaningful pattern could be identified from these very different data sets. We find that the patterns revealed by each data set are highly consistent (p

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD-abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions.

Mild cognitive impairment (MCI) is a precursor phase of Alzheimer's disease (AD). As current treatments may be effective only at the early stages of AD, it is important to track MCI patients who will convert to AD. The aim of this study is to develop a high performance semi-mechanism based approach to predict the conversion from MCI to AD and improve our understanding of MCI-to-AD conversion mechanism. First, analysis of variance (ANOVA) test and lasso regression are employed to identify the markers related to the conversion. Then the Bayesian network based on selected markers is established to predict MCI-to-AD conversion. The structure of Bayesian network suggests that the conversion may start with fibrin clot formation, verbal memory impairment, eating pattern changing and hyperinsulinemia. The Bayesian network achieves a high 10-fold cross-validated prediction performance with 96% accuracy, 95% sensitivity, 65% specificity, area under the receiver operating characteristic curve of 0.82 on data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The semi-mechanism based approach provides not only high prediction performance but also clues of mechanism for MCI-to-AD conversion.

ImportanceThe use of anticholinergic (AC) medication is linked to cognitive impairment and an increased risk of dementia. To our knowledge, this is the first study to investigate the association between AC medication use and neuroimaging biomarkers of brain metabolism and atrophy as a proxy for understanding the underlying biology of the clinical effects of AC medications.ObjectiveTo assess the association between AC medication use and cognition, glucose metabolism, and brain atrophy in cognitively normal older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Indiana Memory and Aging Study (IMAS).Design, setting, and participantsThe ADNI and IMAS are longitudinal studies with cognitive, neuroimaging, and other data collected at regular intervals in clinical and academic research settings. For the participants in the ADNI, visits are repeated 3, 6, and 12 months after the baseline visit and then annually. For the participants in the IMAS, visits are repeated every 18 months after the baseline visit (402 cognitively normal older adults in the ADNI and 49 cognitively normal older adults in the IMAS were included in the present analysis). Participants were either taking (hereafter referred to as the AC+ participants [52 from the ADNI and 8 from the IMAS]) or not taking (hereafter referred to as the AC- participants [350 from the ADNI and 41 from the IMAS]) at least 1 medication with medium or high AC activity. Data analysis for this study was performed in November 2015.Main outcomes and measuresCognitive scores, mean fludeoxyglucose F 18 standardized uptake value ratio (participants from the ADNI only), and brain atrophy measures from structural magnetic resonance imaging were compared between AC+ participants and AC- participants after adjusting for potential confounders. The total AC burden score was calculated and was related to target measures. The association of AC use and longitudinal clinical decline (mean [SD] follow-up period, 32.1 [24.7] months [range, 6-108 months]) was examined using Cox regression.ResultsThe 52 AC+ participants (mean [SD] age, 73.3 [6.6] years) from the ADNI showed lower mean scores on Weschler Memory Scale-Revised Logical Memory Immediate Recall (raw mean scores: 13.27 for AC+ participants and 14.16 for AC- participants; P = .04) and the Trail Making Test Part B (raw mean scores: 97.85 seconds for AC+ participants and 82.61 seconds for AC- participants; P = .04) and a lower executive function composite score (raw mean scores: 0.58 for AC+ participants and 0.78 for AC- participants; P = .04) than the 350 AC- participants (mean [SD] age, 73.3 [5.8] years) from the ADNI. Reduced total cortical volume and temporal lobe cortical thickness and greater lateral ventricle and inferior lateral ventricle volumes were seen in the AC+ participants relative to the AC- participants.Conclusions and relevanceThe use of AC medication was associated with increased brain atrophy and dysfunction and clinical decline. Thus, use of AC medication among older adults should likely be discouraged if alternative therapies are available.

ObjectiveTo estimate points along the spectrum of β-amyloid pathology at which rates of change of several measures of neuronal injury and cognitive decline begin to accelerate.MethodsIn 460 patients with mild cognitive impairment (MCI), we estimated the points at which rates of florbetapir PET, fluorodeoxyglucose (FDG) PET, MRI, and cognitive and functional decline begin to accelerate with respect to baseline CSF Aβ42. Points of initial acceleration in rates of decline were estimated using mixed-effects regression.ResultsRates of neuronal injury and cognitive and even functional decline accelerate substantially before the conventional threshold for amyloid positivity, with rates of florbetapir PET and FDG PET accelerating early. Temporal lobe atrophy rates also accelerate prior to the threshold, but not before the acceleration of cognitive and functional decline.ConclusionsA considerable proportion of patients with MCI would not meet inclusion criteria for a trial using the current threshold for amyloid positivity, even though on average, they are experiencing cognitive/functional decline associated with prethreshold levels of CSF Aβ42. Future trials in early Alzheimer disease might consider revising the criteria regarding β-amyloid thresholds to include the range of amyloid associated with the first signs of accelerating rates of decline.

ObjectiveTo examine the clinical and biomarker characteristics of patients with amyloid-negative Alzheimer disease (AD) and mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a prospective cohort study.MethodsWe first investigated the reliability of florbetapir- PET in patients with AD and patients with MCI using CSF-Aβ1-42 as a comparison amyloid measurement. We then compared florbetapir- vs florbetapir+ patients with respect to several AD-specific biomarkers, baseline and longitudinal cognitive measurements, and demographic and clinician report data.ResultsFlorbetapir and CSF-Aβ1-42 +/- status agreed for 98% of ADs (89% of MCIs), indicating that most florbetapir- scans were a reliable representation of amyloid status. Florbetapir- AD (n = 27/177; 15%) and MCI (n = 74/217, 34%) were more likely to be APOE4-negative (MCI 83%, AD 96%) than their florbetapir+ counterparts (MCI 30%, AD 24%). Florbetapir- patients also had less AD-specific hypometabolism, lower CSF p-tau and t-tau, and better longitudinal cognitive performance, and were more likely to be taking medication for depression. In MCI only, florbetapir- participants had less hippocampal atrophy and hypometabolism and lower functional activity questionnaire scores compared to florbetapir+ participants.ConclusionsOverall, image analysis problems do not appear to be a primary explanation of amyloid negativity. Florbetapir- ADNI patients have a variety of clinical and biomarker features that differ from their florbetapir+ counterparts, suggesting that one or more non-AD etiologies (which may include vascular disease and depression) account for their AD-like phenotype.

The human hippocampal formation can be divided into a set of cytoarchitecturally and functionally distinct subregions, involved in different aspects of memory formation. Neuroanatomical disruptions within these subregions are associated with several debilitating brain disorders including Alzheimer's disease, major depression, schizophrenia, and bipolar disorder. Multi-center brain imaging consortia, such as the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium, are interested in studying disease effects on these subregions, and in the genetic factors that affect them. For large-scale studies, automated extraction and subsequent genomic association studies of these hippocampal subregion measures may provide additional insight. Here, we evaluated the test-retest reliability and transplatform reliability (1.5T versus 3T) of the subregion segmentation module in the FreeSurfer software package using three independent cohorts of healthy adults, one young (Queensland Twins Imaging Study, N=39), another elderly (Alzheimer's Disease Neuroimaging Initiative, ADNI-2, N=163) and another mixed cohort of healthy and depressed participants (Max Planck Institute, MPIP, N=598). We also investigated agreement between the most recent version of this algorithm (v6.0) and an older version (v5.3), again using the ADNI-2 and MPIP cohorts in addition to a sample from the Netherlands Study for Depression and Anxiety (NESDA) (N=221). Finally, we estimated the heritability (h(2)) of the segmented subregion volumes using the full sample of young, healthy QTIM twins (N=728). Test-retest reliability was high for all twelve subregions in the 3T ADNI-2 sample (intraclass correlation coefficient (ICC)=0.70-0.97) and moderate-to-high in the 4T QTIM sample (ICC=0.5-0.89). Transplatform reliability was strong for eleven of the twelve subregions (ICC=0.66-0.96); however, the hippocampal fissure was not consistently reconstructed across 1.5T and 3T field strengths (ICC=0.47-0.57). Between-version agreement was moderate for the hippocampal tail, subiculum and presubiculum (ICC=0.78-0.84; Dice Similarity Coefficient (DSC)=0.55-0.70), and poor for all other subregions (ICC=0.34-0.81; DSC=0.28-0.51). All hippocampal subregion volumes were highly heritable (h(2)=0.67-0.91). Our findings indicate that eleven of the twelve human hippocampal subregions segmented using FreeSurfer version 6.0 may serve as reliable and informative quantitative phenotypes for future multi-site imaging genetics initiatives such as those of the ENIGMA consortium.

ObjectiveTo investigate the association between periventricular white mater hyperintensities (PVWMH) and biomarkers of elevated cerebral β-amyloid (Aβ) in the Alzheimer's Disease Neuroimaging Initiative, a large prospective multicenter observational study.MethodsThe burden of frontal, parietal, and occipital PVWMH on 3T fluid-attenuated inversion recovery MRI was evaluated in 698 cognitively normal participants and participants with mild cognitive impairment (MCI) using a novel semiquantitative visual rating scale. Results were correlated with CSF-Aβ, florbetapir-PET, and fluorodeoxyglucose (FDG)-PET.ResultsIncreased burden of parietal, occipital, and frontal PVWMH was associated with elevated cerebral amyloid evidenced by high florbetapir-PET signal (p < 0.01) and low CSF-Aβ (p < 0.01). In logistic regression models, including PVWMH, age, sex, APOE status, vascular risk factors, pulse pressure, vascular secondary prevention medications, education, ethnicity, and race, parietal, occipital, and frontal PVWMH burden was independently associated with high florbetapir-PET uptake (p < 0.05). In a similar logistic regression model, parietal and occipital (p < 0.05) but not frontal (p = 0.05) PVWMH were independently associated with CSF-Aβ. Weaker associations were found between parieto-occipital PVWMH and elevated CSF-tau (p < 0.05) and occipital PVWMH and elevated CSF-phospho-tau (p < 0.05). PVWMH were associated with cerebral hypometabolism on FDG-PET independent of CSF-Aβ levels (p < 0.05). Absolute and consistency of agreement intraclass correlation coefficients were, respectively, 0.83 and 0.83 for frontal, 0.78 and 0.8 for parietal, and 0.45 and 0.75 for occipital PVWMH measurements.ConclusionsIncreased PVWMH were associated with elevated cerebral amyloid independent of potential confounders such as age, APOE genotype, and vascular risk factors. The mechanisms underlying the association between PVWMH and cerebral amyloid remain to be clarified.

As the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has high chance to convert to AD. Effective prediction of such conversion from MCI to AD is of great importance for early diagnosis of AD and also for evaluating AD risk pre-symptomatically. Unlike most previous methods that used only the samples from a target domain to train a classifier, in this paper, we propose a novel multimodal manifold-regularized transfer learning (M2TL) method that jointly utilizes samples from another domain (e.g., AD vs. normal controls (NC)) as well as unlabeled samples to boost the performance of the MCI conversion prediction. Specifically, the proposed M2TL method includes two key components. The first one is a kernel-based maximum mean discrepancy criterion, which helps eliminate the potential negative effect induced by the distributional difference between the auxiliary domain (i.e., AD and NC) and the target domain (i.e., MCI converters (MCI-C) and MCI non-converters (MCI-NC)). The second one is a semi-supervised multimodal manifold-regularized least squares classification method, where the target-domain samples, the auxiliary-domain samples, and the unlabeled samples can be jointly used for training our classifier. Furthermore, with the integration of a group sparsity constraint into our objective function, the proposed M2TL has a capability of selecting the informative samples to build a robust classifier. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database validate the effectiveness of the proposed method by significantly improving the classification accuracy of 80.1 % for MCI conversion prediction, and also outperforming the state-of-the-art methods.