Several lines of evidence suggest the involvement of neuroinflammatory changes in Alzheimer's disease (AD) pathophysiology such as amyloidosis and neurodegeneration. In fact, genome-wide association studies (GWAS) have shown a link between genes involved in neuroinflammation and AD. In order to further investigate whether interactions between candidate genetic variances coding for neuroinflammatory molecules are associated with brain amyloid β (Aβ) fibrillary accumulation, we conducted an epistasis analysis on a pool of genes associated with molecular mediators of inflammation.

To evaluate the potential clinical value of quantitative functional FDG PET and pathological amyloid-β PET with cerebrospinal fluid (CSF) biomarkers and clinical assessments in the prediction of Alzheimer's disease (AD) progression.

Chronic neuroinflammation is thought to potentiate medial temporal lobe (MTL) atrophy and memory decline in Alzheimer's disease (AD). It has become increasingly important to find novel immunological biomarkers of neuroinflammation or other processes that can track AD development and progression. Our study explored which pro- or anti-inflammatory cerebrospinal fluid (CSF) biomarkers best predicted AD neuropathology over 24months. Using Alzheimer's Disease Neuroimaging Initiative data (N=285), CSF inflammatory biomarkers from mass spectrometry and multiplex panels were screened using stepwise regression, followed up with 50%/50% model retests for validation. Neuronal Pentraxin 2 (NPTX2) and Chitinase-3-like-protein-1 (C3LP1), biomarkers of glutamatergic synaptic plasticity and microglial activation respectively, were the only consistently significant biomarkers selected. Once these biomarkers were selected, linear mixed models were used to analyze their baseline and longitudinal associations with bilateral MTL volume, memory decline, global cognition, and established AD biomarkers including CSF amyloid and tau. Higher baseline NPTX2 levels corresponded to less MTL atrophy [R2=0.287, p<0.001] and substantially less memory decline [R2=0.560, p<0.001] by month 24. Conversely, higher C3LP1 modestly predicted more MTL atrophy [R2=0.083, p<0.001], yet did not significantly track memory decline over time. In conclusion, NPTX2 is a novel pro-inflammatory cytokine that predicts AD-related outcomes better than any immunological biomarker to date, substantially accounting for brain atrophy and especially memory decline. C3LP1 as the microglial biomarker, by contrast, performed modestly and did not predict longitudinal memory decline. This research may advance the current understanding of AD etiopathogenesis, while expanding early diagnostic techniques through the use of novel pro-inflammatory biomarkers, such as NPTX2. Future studies should also see if NPTX2 causally affects MTL morphometry and memory performance.

Genome-wide association studies (GWAS) have effectively identified genetic factors for many diseases. Many diseases, including Alzheimer's disease (AD), have epistatic causes, requiring more sophisticated analyses to identify groups of variants which together affect phenotype.

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<10-3), and are biologically meaningful on several fronts. For example, close genetic relationships are seen in cortical regions within the same lobes and, the frontal lobe, a region showing great evolutionary expansion and functional complexity, has the most distant genetic relationship with other lobes. The frontal lobe also exhibits the most distinct expression pattern relative to the other regions, implicating a number of genes with known functions mediating immune and related processes. Our analyses reflect one of the first attempts to provide an assessment of the biological consistency of a genetic phenomenon involving the brain that leverages very different types of data, and therefore is not just statistical replication which purposefully use very similar data sets.

Automatic and reliable segmentation of hippocampus from MR brain images is of great importance in studies of neurological diseases, such as epilepsy and Alzheimer's disease. In this paper, we proposed a novel metric learning method to fuse segmentation labels in multi-atlas based image segmentation. Different from current label fusion methods that typically adopt a predefined distance metric model to compute a similarity measure between image patches of atlas images and the image to be segmented, we learn a distance metric model from the atlases to keep image patches of the same structure close to each other while those of different structures are separated. The learned distance metric model is then used to compute the similarity measure between image patches in the label fusion. The proposed method has been validated for segmenting hippocampus based on the EADC-ADNI dataset with manually labelled hippocampus of 100 subjects. The experiment results demonstrated that our method achieved statistically significant improvement in segmentation accuracy, compared with state-of-the-art multi-atlas image segmentation methods.

Recently, structured sparse canonical correlation analysis (SCCA) has received increased attention in brain imaging genetics studies. It can identify bi-multivariate imaging genetic associations as well as select relevant features with desired structure information. These SCCA methods either use the fused lasso regularizer to induce the smoothness between ordered features, or use the signed pairwise difference which is dependent on the estimated sign of sample correlation. Besides, several other structured SCCA models use the group lasso or graph fused lasso to encourage group structure, but they require the structure/group information provided in advance which sometimes is not available.

The apolipoprotein E (APOE) e4 genotype is a powerful risk factor for late-onset Alzheimer's disease (AD). In the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, we previously reported significant baseline structural differences in APOE e4 carriers relative to non-carriers, involving the left hippocampus more than the right--a difference more pronounced in e4 homozygotes than heterozygotes. We now examine the longitudinal effects of APOE genotype on hippocampal morphometry at 6-, 12- and 24-months, in the ADNI cohort. We employed a new automated surface registration system based on conformal geometry and tensor-based morphometry. Among different hippocampal surfaces, we computed high-order correspondences, using a novel inverse-consistent surface-based fluid registration method and multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance. At each time point, using Hotelling's T(2) test, we found significant morphological deformation in APOE e4 carriers relative to non-carriers in the full cohort as well as in the non-demented (pooled MCI and control) subjects at each follow-up interval. In the complete ADNI cohort, we found greater atrophy of the left hippocampus than the right, and this asymmetry was more pronounced in e4 homozygotes than heterozygotes. These findings, combined with our earlier investigations, demonstrate an e4 dose effect on accelerated hippocampal atrophy, and support the enrichment of prevention trial cohorts with e4 carriers.

The definition of "objective cognitive impairment" in current criteria for mild cognitive impairment (MCI) varies considerably between research groups and clinics. This study aims to compare different methods of defining memory impairment to improve prediction models for the development of Alzheimer's disease (AD) from baseline to 24 months.

Alzheimer's disease (AD) has become a considerable public health issue. The mechanisms underlying AD onset and progression remain largely unclear. 3-Hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) is a strong functional AD candidate gene because it encodes part of the statin-binding domain of the enzyme, which serves as the rate-limiting step in cholesterol synthesis in all mammalian cells. Here, we evaluated the potential role of HMGCR (rs3846662) in AD-related pathology by assessing neuroimaging biomarkers. We enrolled in 812 subjects from the Alzheimer's disease Neuroimaging Initiative dataset. In general, it is possible that HMGCR (rs3846662) could be involved in preventing the atrophy of right entorhinal (P=0.03385) and left hippocampus (P=0.01839) in the follow-up research of two years. What's more, it lowered the drop rate of glucose metabolism in right temporal. We then further validated them in the AD, mild cognitive impairment (MCI), normal control (NC) sub-groups. All the results in the MCI groups confirmed the association. The results of our study indicated that HMGCR (rs3846662) plays a vital role in AD pathology mainly by influencing brain structure and glucose metabolism during AD progression.

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.

Predicting progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell's C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.

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.

Objective: To investigate whether APOE ε4 affects the association of verbal memory with neurodegeneration presented by the hippocampal volume/intracranial volume ratio (HpVR). Methods: The study sample included 371 individuals with normal cognition (NC), 725 subjects with amnestic mild cognitive impairment (aMCI), and 251 patients with mild Alzheimer's disease (AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who underwent the rey auditory verbal learning test (RAVLT). Multiple linear regression models were conducted to assess the effect of the APOE ε4∗HpVR interaction on RAVLT in all subjects and in each diagnostic group adjusting for age, gender and educational attainment, and global cognition. Results: In all subjects, there was no significant APOE ε4 × HpVR interaction for immediate recall or delayed recall (p > 0.05). However, in aMCI subjects, there was a significant APOE ε4 × HpVR interaction for delayed recall (p = 0.008), but not immediate recall (p = 0.15). More specifically, the detrimental effect of APOE ε4 on delayed recall altered by HpVR such that this effect was most evident among subjects with small to moderate HpVR, but this disadvantage was absent or even reversed among subjects with larger HpVR. No significant interaction was observed in the NC or AD group. Conclusion: These findings highlight a potential role of APOE ε4 status in affecting the association of hippocampus size with delayed recall memory in the early stage of AD.

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.

Optimized abnormalities of individual brain network may allow earlier detection of mild cognitive impairment (MCI) and accurate prediction of its conversion to Alzheimer's disease (AD). Currently, most studies constructed individual networks based on region-to-region correlation without employing multi-region information. In order to develop the potential discriminative power of network and provide supportive evidence for feasibility of individual metabolic network study, we propose a new approach to extract features from network with indirect relation based on 18F-FDG PET data.

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.

The current criteria for diagnosing Alzheimer's disease (AD) require the presence of relevant cognitive deficits, so the underlying neuropathological damage is important by the time the diagnosis is made. Therefore, the evaluation of new biomarkers to detect AD in its early stages has become one of the main research focuses. The purpose of the present study was to evaluate a set of texture parameters as potential biomarkers of the disease. To this end, the ALTEA (ALzheimer TExture Analyzer) software tool was created to perform 2D and 3D texture analysis on magnetic resonance images. This intuitive tool was used to analyze textures of circular and spherical regions situated in the right and left hippocampi of a cohort of 105 patients: 35 AD patients, 35 patients with early mild cognitive impairment (EMCI) and 35 cognitively normal (CN) subjects. A total of 25 statistical texture parameters derived from the histogram, the Gray-Level Co-occurrence Matrix and the Gray-Level Run-Length Matrix, were extracted from each region and analyzed statistically to study their predictive capacity. Several textural parameters were statistically significant (p < 0.05) when differentiating AD subjects from CN and EMCI patients, which indicates that texture analysis could help to identify the presence of AD.

A relationship quantitative trait locus exists when the correlation between multiple traits varies by genotype for that locus. Relationship quantitative trait loci (rQTL) are often involved in gene-by-gene (G×G) interactions or gene-by-environmental interactions, making them a powerful tool for detecting G×G.

Often derived from partial correlations or many pairwise analyses, covariance networks represent the inter-relationships among regions and can reveal important topological structures in brain measures from healthy and pathological subjects. However both approaches are not consistent network estimators and are sensitive to the value of the tuning parameters. Here, we propose a consistent covariance network estimator by maximising the network likelihood (MNL) which is robust to the tuning parameter. We validate the consistency of our algorithm theoretically and via a simulation study, and contrast these results against two well-known approaches: the graphical LASSO (gLASSO) and Pearson pairwise correlations (PPC) over a range of tuning parameters. The MNL algorithm had a specificity equal to and greater than 0.94 for all sample sizes in the simulation study, and the sensitivity was shown to increase as the sample size increased. The gLASSO and PPC demonstrated a specificity-sensitivity trade-off over a range of values of tuning parameters highlighting the discrepancy in the results for misspecified values. Application of the MNL algorithm to the case study data showed a loss of connections between healthy and impaired groups, and improved ability to identify between lobe connectivity in contrast to gLASSO networks. In this work, we propose the MNL algorithm as an effective approach to find covariance brain networks, which can inform the organisational features in brain-wide analyses, particularly for large sample sizes.