Differential atrophy along the longitudinal hippocampal axis in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects the hippocampus. Since hippocampal studies have highlighted a differential subregional regulation along its longitudinal axis, a more detailed analysis addressing subregional changes along the longitudinal hippocampal axis has the potential to provide new relevant biomarkers. This study included structural brain MRI data of 583 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Cognitively normal (CN) subjects, mild cognitively impaired (MCI) subjects and AD patients were conveniently selected considering the age and sex match between clinical groups. Structural MRI acquisitions were pre-processed and analysed with a new longitudinal axis segmentation method, dividing the hippocampus in three subdivisions (anterior, intermediate, and posterior). When normalizing the volume of hippocampal sub-divisions to total hippocampus, the posterior hippocampus negatively correlates with age only in CN subjects (r = -.31). The longitudinal ratio of hippocampal atrophy (anterior sub-division divided by the posterior one) shows a significant increase with age only in CN (r = .25). Overall, in AD, the posterior hippocampus is predominantly atrophied early on. Consequently, the anterior/posterior hippocampal ratio is an AD differentiating metric at early disease stages with potential for diagnostic and prognostic applications.

Associations of tau, Aß, and brain volume of the Papez circuit with cognition in Alzheimer's disease.

This study aimed to investigate the cross-sectional associations between regional Alzheimer's disease (AD) biomarkers, including tau, ß-amyloid (Aß), and brain volume, within the Papez circuit, and neuropsychological functioning across the preclinical and clinical spectrum of AD. We utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 251 Aß-positive participants. Participants were categorized into three groups based on the Clinical Dementia Rating (CDR): 73 individuals with preclinical AD (CDR = 0), 114 with prodromal AD (CDR = 0.5), and 64 with clinical AD dementia (CDR ≥ 1). Linear regression analyses, adjusted for age, gender, and education years, were employed to evaluate the associations between five regions of interest (the hippocampus, para-hippocampus, entorhinal cortex, posterior cingulate cortex, and thalamus) and five neuropsychological tests across the three imaging modalities. In the preclinical stage of AD, flortaucipir PET was associated with impaired global cognition and episodic memory (range standardized ß = 0.255-0.498, p < 0.05 corrected for multiple comparisons), while florbetapir PET and brain volume were marginally related to global cognition (range standardized ß = 0.221-0.231, p < 0.05). In the clinical stages of AD (prodromal and dementia), both increased flortaucipir uptake and decreased brain volume were significantly associated with poorer global neuropsychological and episodic memory performance (range standardized ß = 0.222-0.621, p < 0.05, most regions of interest survived correction for multiple comparisions). However, a slight relationship was observed between florbetapir uptake and poorer global cognitive function. The regions most affected by flortaucipir PET were the hippocampus, para-hippocampus, and posterior cingulate cortex. During the clinical stages, the hippocampus and entorhinal cortex exhibited the most significant volumetric changes. Tau PET and brain volume measurements within the Papez circuit are more sensitive indicators of early cognitive deficits in AD than Aß PET. Furthermore, during the clinical stages of AD, both flortaucipir PET and brain volume of the Papez circuit are closely correlated with cognitive decline. These findings underscore the importance of integrating multiple biomarkers for the comprehensive evaluation of AD pathology and its impact on cognition.

Data sharing as the foundation of discovery: ADNI and breakthroughs in Alzheimer's disease.

The COVID pandemic has shown that when the research community comes together, we can conquer the most complex biomedical challenges. Collaboration and teamwork among federal agencies, private organizations, and researchers have been crucial in the development of vaccines and therapeutics against COVID. Possibly the first example of such cross-functional collaboration is the Alzheimer's Disease Neuroimaging Initiative (ADNI), the largest and longest continually monitored Alzheimer's study. ADNI was designed and operated as a public-private partnership, managed by the Foundation for the National Institutes of Health. This article shows how recent successes in the Alzheimer's field are directly a result of ADNI's open and transparent sharing of knowledge, expertise, and resources, which have allowed researchers to advance their understanding of Alzheimer's and tackle challenges in a relatively short period of time. ADNI's approach to open-source innovation also served as a model for addressing other complex diseases and led to numerous collaborative research initiatives. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) was designed, structured, and operated as a public-private partnership, managed by the Foundation for the National Institutes of Health. The recent successes in the Alzheimer's field are directly a result of ADNI's efforts. Open and transparent sharing of knowledge, expertise, and resources allowed researchers to advance their understanding of Alzheimer's and tackle challenges in a relatively short period of time.

Brain morphometry in older adults with and without dementia using extremely rapid structural scans.

T1-weighted structural MRI is widely used to measure brain morphometry (e.g., cortical thickness and subcortical volumes). Accelerated scans as fast as one minute or less are now available but it is unclear if they are adequate for quantitative morphometry. Here we compared the measurement properties of a widely adopted 1.0 mm resolution scan from the Alzheimer's Disease Neuroimaging Initiative (ADNI = 5'12'') with two variants of highly accelerated 1.0 mm scans (compressed-sensing, CSx6 = 1'12''; and wave-controlled aliasing in parallel imaging, WAVEx9 = 1'09'') in a test-retest study of 37 older adults aged 54 to 86 (including 19 individuals diagnosed with a neurodegenerative dementia). Rapid scans produced highly reliable morphometric measures that largely matched the quality of morphometrics derived from the ADNI scan. Regions of lower reliability and relative divergence between ADNI and rapid scan alternatives tended to occur in midline regions and regions with susceptibility-induced artifacts. Critically, the rapid scans yielded morphometric measures similar to the ADNI scan in regions of high atrophy. The results converge to suggest that, for many current uses, extremely rapid scans can replace longer scans. As a final test, we explored the possibility of a 0'49'' 1.2 mm CSx6 structural scan, which also showed promise. Rapid structural scans may benefit MRI studies by shortening the scan session and reducing cost, minimizing opportunity for movement, creating room for additional scan sequences, and allowing for the repetition of structural scans to increase precision of the estimates.

Bayesian sparse heritability analysis with high-dimensional neuroimaging phenotypes.

Heritability analysis plays a central role in quantitative genetics to describe genetic contribution to human complex traits and prioritize downstream analyses under large-scale phenotypes. Existing works largely focus on modeling single phenotype and currently available multivariate phenotypic methods often suffer from scaling and interpretation. In this article, motivated by understanding how genetic underpinning impacts human brain variation, we develop an integrative Bayesian heritability analysis to jointly estimate heritabilities for high-dimensional neuroimaging traits. To induce sparsity and incorporate brain anatomical configuration, we impose hierarchical selection among both regional and local measurements based on brain structural network and voxel dependence. We also use a nonparametric Dirichlet process mixture model to realize grouping among single nucleotide polymorphism-associated phenotypic variations, providing biological plausibility. Through extensive simulations, we show the proposed method outperforms existing ones in heritability estimation and heritable traits selection under various scenarios. We finally apply the method to two large-scale imaging genetics datasets: the Alzheimer's Disease Neuroimaging Initiative and United Kingdom Biobank and show biologically meaningful results.

Neuropsychiatric symptoms and their neural correlates in individuals with mild cognitive impairment.

OBJECTIVES: Neuropsychiatric symptoms are common in subjects with MCI and associated with higher risk of progression to AD. The cognitive and neuroanatomical correlates of neuropsychiatric symptoms in MCI have not been fully elucidated. In this study, we sought to evaluate the association between neuropsychiatric symptoms, cognitive function, regional tau deposition, and brain volumes in MCI subjects. METHODS: A total of 233 MCI and 305 healthy comparisons were selected from the ADNI-3 cohort. All the subjects underwent a comprehensive neuropsychological assessment, volumetric MR brain scan, and Flortaucipir PET for in vivo assessment of regional tau deposition. Prevalence of neuropsychiatric symptoms was evaluated by means of the NPI questionnaire. Multivariate analyses of variance were used to detect differences in cognitive and imaging markers in MCI subjects with and without neuropsychiatric symptoms. RESULTS: 61.4% MCI subjects showed at least one neuropsychiatric symptom, with the most prevalent ones being depression (26.1%), irritability (23.6%), and sleep disturbances (23.6%). There was a significant effect of neuropsychiatric symptoms on cognitive tests of frontal and executive functions. MCI subjects with neuropsychiatric symptoms showed reduced brain volumes in the orbitofrontal and posterior cingulate cortices, while no effects were detected on regional tau deposition. Posterior cingulate cortex volume was the only predictor of global neuropsychiatric burden in this MCI population. CONCLUSIONS: Neuropsychiatric symptoms occur early in the AD trajectory and are mainly related to defects of control executive abilities and to the reduction of gray matter volume in the orbitofrontal and posterior cingulate cortices. A better understanding of the cognitive and neuroanatomical mechanisms of neuropsychiatric symptoms in MCI could help develop more targeted and efficacious treatment alternatives.

Transfer learning for cognitive reserve quantification.

Cognitive reserve (CR) has been introduced to explain individual differences in susceptibility to cognitive or functional impairment in the presence of age or pathology. We developed a deep learning model to quantify the CR as residual variance in memory performance using the Structural Magnetic Resonance Imaging (sMRI) data from a lifespan healthy cohort. The generalizability of the sMRI-based deep learning model was tested in two independent healthy and Alzheimer's cohorts using transfer learning framework. Structural MRIs were collected from three cohorts: 495 healthy adults (age: 20-80) from RANN, 620 healthy adults (age: 36-100) from lifespan Human Connectome Project Aging (HCPA), and 941 adults (age: 55-92) from Alzheimer's Disease Neuroimaging Initiative (ADNI). Region of interest (ROI)-specific cortical thickness and volume measures were extracted using the Desikan-Killiany Atlas. CR was quantified by residuals which subtract the predicted memory from the true memory. Cascade neural network (CNN) models were used to train RANN dataset for memory prediction. Transfer learning was applied to transfer the T1 imaging-based model from source domain (RANN) to the target domains (HCPA or ADNI). The CNN model trained on the RANN dataset exhibited strong linear correlation between true and predicted memory based on the T1 cortical thickness and volume predictors. In addition, the model generated from healthy lifespan data (RANN) was able to generalize to an independent healthy lifespan data (HCPA) and older demented participants (ADNI) across different scanner types. The estimated CR was correlated with CR proxies such education and IQ across all three datasets. The current findings suggest that the transfer learning approach is an effective way to generalize the residual-based CR estimation. It is applicable to various diseases and may flexibly incorporate different imaging modalities such as fMRI and PET, making it a promising tool for scientific and clinical purposes.

Applications of Bayesian shrinkage prior models in clinical research with categorical responses.

BACKGROUND: Prediction and classification algorithms are commonly used in clinical research for identifying patients susceptible to clinical conditions such as diabetes, colon cancer, and Alzheimer's disease. Developing accurate prediction and classification methods benefits personalized medicine. Building an excellent predictive model involves selecting the features that are most significantly associated with the outcome. These features can include several biological and demographic characteristics, such as genomic biomarkers and health history. Such variable selection becomes challenging when the number of potential predictors is large. Bayesian shrinkage models have emerged as popular and flexible methods of variable selection in regression settings. This work discusses variable selection with three shrinkage priors and illustrates its application to clinical data such as Pima Indians Diabetes, Colon cancer, ADNI, and OASIS Alzheimer's real-world data. METHODS: A unified Bayesian hierarchical framework that implements and compares shrinkage priors in binary and multinomial logistic regression models is presented. The key feature is the representation of the likelihood by a Polya-Gamma data augmentation, which admits a natural integration with a family of shrinkage priors, specifically focusing on Horseshoe, Dirichlet Laplace, and Double Pareto priors. Extensive simulation studies are conducted to assess the performances under different data dimensions and parameter settings. Measures of accuracy, AUC, brier score, L1 error, cross-entropy, and ROC surface plots are used as evaluation criteria comparing the priors with frequentist methods as Lasso, Elastic-Net, and Ridge regression. RESULTS: All three priors can be used for robust prediction on significant metrics, irrespective of their categorical response model choices. Simulation studies could achieve the mean prediction accuracy of 91.6% (95% CI: 88.5, 94.7) and 76.5% (95% CI: 69.3, 83.8) for logistic regression and multinomial logistic models, respectively. The model can identify significant variables for disease risk prediction and is computationally efficient. CONCLUSIONS: The models are robust enough to conduct both variable selection and prediction because of their high shrinkage properties and applicability to a broad range of classification problems.

Caspase-1 variant influencing CSF tau and FDG PET levels in non-demented elders from the ADNI cohort.

BACKGROUND: Genetic variations in the inflammatory Caspase-1 gene have been shown associated with cognitive function in elderly individuals and in predisposition to Alzheimer's disease (AD), but its detailed mechanism before the typical AD onset was still unclear. Our current study evaluated the impact of Caspase-1 common variant rs554344 on the pathological processes of brain amyloidosis, tauopathy, and neurodegeneration. METHODS: Data used in our study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. We examined the relationship between Caspase-1 rs554344 allele carrier status with AD-related cerebrospinal fluid (CSF), PET, and MRI measures at baseline by using a multiple linear regression model. We also analyzed the longitudinal effects of this variant on the change rates of CSF biomarkers and imaging data using a mixed effect model. RESULTS: We found that Caspase-1 variant was significantly associated with FDG PET levels and CSF t-tau levels at baseline in total non-demented elderly group, and especially in mild cognitive impairment (MCI) subgroup. In addition, this variant was also detected associated with CSF p-tau levels in MCI subgroup. The mediation analysis showed that CSF p-tau partially mediated the association between Caspase-1 variant and CSF t-tau levels, accounting for 80% of the total effect. CONCLUSIONS: Our study indicated a potential role of Caspase-1 variant in influencing cognitive function might through changing tau related-neurodegeneration process.

Right entorhinal cortical thickness is associated with Mini-Mental State Examination scores from multi-country datasets using MRI.

PURPOSE: To discover common biomarkers correlating with the Mini-Mental State Examination (MMSE) scores from multi-country MRI datasets. METHODS: The first dataset comprised 112 subjects (49 men, 63 women; range, 46-94 years) at the National Hospital Organization Kyushu Medical Center. A second dataset comprised 300 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (177 men, 123 women; range, 57-91 years). Three-dimensional T1-weighted MR images were collected from both datasets. In total, 14 deep gray matter volumes and 70 cortical thicknesses were obtained from MR images using FreeSurfer software. Total hippocampal volume and the ratio of hippocampus to cerebral volume were also calculated. Correlations between each variable and MMSE scores were assessed using Pearson's correlation coefficient. Parameters with moderate correlation coefficients (r > 0.3) from each dataset were determined as independent variables and evaluated using general linear model (GLM) analyses. RESULTS: In Pearson's correlation coefficient, total and bilateral hippocampal volumes, right amygdala volume, and right entorhinal cortex (ERC) thickness showed moderate correlation coefficients (r > 0.3) with MMSE scores from the first dataset. The ADNI dataset showed moderate correlations with MMSE scores in more variables, including bilateral ERC thickness and hippocampal volume. GLM analysis revealed that right ERC thickness correlated significantly with MMSE score in both datasets. Cortical thicknesses of the left parahippocampal gyrus, left inferior parietal lobe, and right fusiform gyrus also significantly correlated with MMSE score in the ADNI dataset (p < 0.05). CONCLUSION: A positive correlation between right ERC thickness and MMSE score was identified from multi-country datasets.

Structural Brain Differences Between Cognitively Impaired Patients With and Without Apathy.

OBJECTIVE: Since apathy increases in prevalence with severity of dementia pathology, we sought to distinguish concomitant neurodegenerative processes from brain differences associated with apathy in persons with mild cognitive impairment (MCI) and Alzheimer's Disease (AD). We examined relative structural brain differences between case-control matched cognitively impaired patients with and without apathy. DESIGN: Cross-sectional case-control study. SETTING: Fifty-eight clinical sites in phase 2 of the AD Neuroimaging Initiative across the United States and Canada. PARTICIPANTS: The ≥ 55 years of age with MCI or AD dementia and no major neurological disorders aside from suspected incipient AD dementia. Participants with apathy (n=69) were age-, sex-, apolipoprotein E ε4 allele carrier status-, Mini-Mental State Exam score-, and MCI or AD dementia diagnosis-matched to participants without apathy (n=149). INTERVENTIONS: The 3-tesla T1-weighted MRI scan and neurocognitive assessments. Using the Neuropsychiatric Inventory apathy domain scores, participants were dichotomized into a with-apathy group (score ≥ 1) and a without-apathy group (score = 0). MEASUREMENTS: Cortical thicknesses from 24 a priori regions of interest involved in frontostriatal circuits and frontotemporal association areas. RESULTS: False-discovery rate adjusted within-group comparisons between participants with apathy and participants without apathy showed thinner right medial orbitofrontal (mOFC; meandifference(MD)±standarderrorofMD(SE)=-0.0879±0.0257mm; standardizedMD(d)=-0.4456) and left rostral anterior cingulate (rACC; MD±SE=-0.0905±0.0325mm; d=-0.3574) cortices and thicker left middle temporal cortices (MTC; MD±SE=0.0688±0.0239mm; d=0.3311) in those with apathy. CONCLUSION: Atrophy of the right mOFC and left rACC and sparing of atrophy in the left MTC are associated with apathy in cognitively impaired persons.

Bayesian inference and dynamic prediction of multivariate joint model with functional data: An application to Alzheimer's disease.

Alzheimer's disease (AD) is a severe neurodegenerative disorder impairing multiple domains, for example, cognition and behavior. Assessing the risk of AD progression and initiating timely interventions at early stages are critical to improve the quality of life for AD patients. Due to the heterogeneous nature and complex mechanisms of AD, one single longitudinal outcome is insufficient to assess AD severity and disease progression. Therefore, AD studies collect multiple longitudinal outcomes, including cognitive and behavioral measurements, as well as structural brain images such as magnetic resonance imaging (MRI). How to utilize the multivariate longitudinal outcomes and MRI data to make efficient statistical inference and prediction is an open question. In this article, we propose a multivariate joint model with functional data (MJM-FD) framework that relates multiple correlated longitudinal outcomes to a survival outcome, and use the scalar-on-function regression method to include voxel-based whole-brain MRI data as functional predictors in both longitudinal and survival models. We adopt a Bayesian paradigm to make statistical inference and develop a dynamic prediction framework to predict an individual's future longitudinal outcomes and risk of a survival event. We validate the MJM-FD framework through extensive simulation studies and apply it to the motivating Alzheimer's Disease Neuroimaging Initiative (ADNI) study.

Harnessing forgetfulness: can episodic-memory tests predict early Alzheimer's disease?

A rapid increase in the number of patients with Alzheimer's disease (AD) is expected over the next decades. Accordingly, there is a critical need for early-stage AD detection methods that can enable effective treatment strategies. In this study, we consider the ability of episodic-memory measures to predict mild cognitive impairment (MCI) to AD conversion and thus, detect early-stage AD. For our analysis, we studied 307 participants with MCI across four years using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Using a binary logistic regression, we compared episodic-memory tests to each other and to prominent neuroimaging methods in MCI converter (MCI participants who developed AD) and MCI non-converter groups (MCI participants who did not develop AD). We also combined variables to test the accuracy of mixed-predictor models. Our results indicated that the best predictors of MCI to AD conversion were the following: a combined episodic-memory and neuroimaging model in year one (59.8%), the Rey Auditory Verbal Learning Test in year two (71.7%), a mixed episodic-memory predictor model in year three (77.7%) and the Logical Memory Test in year four (77.2%) of ADNI. Overall, we found that individual episodic-memory measure and mixed models performed similarly when predicting MCI to AD conversion. Comparatively, individual neuroimaging measures predicted MCI conversion worse than chance. Accordingly, our results indicate that episodic-memory tests could be instrumental in detecting early-stage AD and enabling effective treatment.

The topology of ventricle surfaces and its application in the analysis of hydrocephalic ventricles: a proof-of-concept study.

PURPOSE: The cerebral ventricles deform in a non-uniform fashion in response to increased CSF volume and/or pressure in hydrocephalic syndromes. Current research is focused on volumetric analyses, while topological analysis of ventricular surfaces remains understudied. We developed a method of quantitatively modeling the curvature of ventricular surfaces to analyze changes in ventricular surfaces in normal pressure hydrocephalus (NPH) and Alzheimer's disease (AD), using the left frontal horn as an example. METHODS: Twenty-one patients with NPH were recruited from our institution, and 21 healthy controls (HC) and patients with Alzheimer's disease (AD) were identified from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. On T1-weighted fine-cut magnetic resonance sequences, 3D Slicer was used to segment the left frontal horn. Next, the mean curvatures at a set of points on the ventricular surface were determined. The frontal horns were scaled and centered into normalized volumes, allowing for pooling across the study subjects. The frontal horn was divided into superolateral, superomedial, inferolateral, and inferomedial surfaces, and locoregional mean curvatures were analyzed. Statistical comparisons were made between NPH, AD, and HC groups. RESULTS: Significant differences in the mean curvature of lateral surfaces of the ventricles distinguished patterns of distortion between all three cohorts. Significant flattening of the superomedial surface discriminated NPH from HC and AD. However, significant rounding of the inferomedial surface compared to controls was a distinguishing feature of NPH alone. CONCLUSION: NPH ventricles deform non-uniformly. The pattern of surface distortion may be used as an additional tool to differentiate between these hydrocephalic conditions.

Major depression disorder diagnosis and analysis based on structural magnetic resonance imaging and deep learning.

Major depression disorder is one of the diseases with the highest rate of disability and morbidity and is associated with numerous structural and functional differences in neural systems. However, it is difficult to analyze digital medical imaging data without computational intervention. A voxel-wise densely connected convolutional neural network, Three-dimensional Densenet (3D-DenseNet), is proposed to mine the feature differences. In addition, a novel transfer learning method, called Alzheimer's Disease Neuroimaging Initiative Transfer (ADNI-Transfer), is designed and combined with the proposed 3D-DenseNet. The experimental results on a database that contains 174 subjects, including 99 patients with major depression disorder and 75 healthy controls, show that large changes in brain structures between major depressive disorder patients and healthy controls mainly are located in the regions including superior frontal gyrus, dorsolateral, middle temporal gyrus, middle frontal gyrus, postcentral gyrus, inferior temporal gyrus. In addition, the proposed deep learning network can better extract different features of brain structures between major depressive disorder patients and healthy controls and achieve excellent classification results of major depressive disorder. At the same time, the designed transfer learning method can further improve classification performance. These results verify that our proposed method is feasible and valid for diagnosing and analyzing major depression disorder.

Combined Atlas and Convolutional Neural Network-Based Segmentation of the Hippocampus from MRI According to the ADNI Harmonized Protocol.

Hippocampus atrophy is an early structural feature that can be measured from magnetic resonance imaging (MRI) to improve the diagnosis of neurological diseases. An accurate and robust standardized hippocampus segmentation method is required for reliable atrophy assessment. The aim of this work was to develop and evaluate an automatic segmentation tool (DeepHarp) for hippocampus delineation according to the ADNI harmonized hippocampal protocol (HarP). DeepHarp utilizes a two-step process. First, the approximate location of the hippocampus is identified in T1-weighted MRI datasets using an atlas-based approach, which is used to crop the images to a region-of-interest (ROI) containing the hippocampus. In the second step, a convolutional neural network trained using datasets with corresponding manual hippocampus annotations is used to segment the hippocampus from the cropped ROI. The proposed method was developed and validated using 107 datasets with manually segmented hippocampi according to the ADNI-HarP standard as well as 114 multi-center datasets of patients with Alzheimer's disease, mild cognitive impairment, cerebrovascular disease, and healthy controls. Twenty-three independent datasets manually segmented according to the ADNI-HarP protocol were used for testing to assess the accuracy, while an independent test-retest dataset was used to assess precision. The proposed DeepHarp method achieved a mean Dice similarity score of 0.88, which was significantly better than four other established hippocampus segmentation methods used for comparison. At the same time, the proposed method also achieved a high test-retest precision (mean Dice score: 0.95). In conclusion, DeepHarp can automatically segment the hippocampus from T1-weighted MRI datasets according to the ADNI-HarP protocol with high accuracy and robustness, which can aid atrophy measurements in a variety of pathologies.

Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data.

While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce noise and bias into estimation of biological variability of interest. We propose a method for harmonizing longitudinal multi-scanner imaging data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional neuroimaging data. Using longitudinal cortical thickness measurements from 663 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, we demonstrate the presence of additive and multiplicative scanner effects in various brain regions. We compare estimates of the association between diagnosis and change in cortical thickness over time using three versions of the ADNI data: unharmonized data, data harmonized using cross-sectional ComBat, and data harmonized using longitudinal ComBat. In simulation studies, we show that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate. The proposed method would be useful for other types of longitudinal data requiring harmonization, such as genomic data, or neuroimaging studies of neurodevelopment, psychiatric disorders, or other neurological diseases.

Quantitative phenotype scan statistic (QPSS) reveals rare variant associations with Alzheimer's disease endophenotypes.

BACKGROUND: Current sequencing technologies have provided for a more comprehensive genome-wide assessment and have increased genotyping accuracy of rare variants. Scan statistic approaches have previously been adapted to genetic sequencing data. Unlike currently-employed association tests, scan-statistic-based approaches can both localize clusters of disease-related variants and, subsequently, examine the phenotype association within the resulting cluster. In this study, we present a novel Quantitative Phenotype Scan Statistic (QPSS) that extends an approach for dichotomous phenotypes to continuous outcomes in order to identify genomic regions where rare quantitative-phenotype-associated variants cluster. RESULTS: We demonstrate the performance and practicality of QPSS with extensive simulations and an application to a whole-genome sequencing (WGS) study of cerebrospinal fluid (CSF) biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Using QPSS, we identify regions of rare variant enrichment associated with levels of AD-related proteins, CSF Aß1-42 and p-tau181P. CONCLUSIONS: QPSS is implemented under the assumption that causal variants within a window have the same direction of effect. Typical self-contained tests employ a null hypothesis of no association between the target variant set and the phenotype. Therefore, an advantage of the proposed competitive test is that it is possible to refine a known region of interest to localize disease-associated clusters. The definition of clusters can be easily adapted based on variant function or annotation.

Impacts of CR1 genetic variants on cerebrospinal fluid and neuroimaging biomarkers in alzheimer's disease.

BACKGROUND: The complement component (3b/4b) receptor 1 gene (CR1) gene has been proved to affect the susceptibility of Alzheimer's disease (AD) in different ethnic and districts groups. However, the effect of CR1 genetic variants on amyloid ß (Aß) metabolism of AD human is still unclear. Hence, the aim of this study was to investigate genetic influences of CR1 gene on Aß metabolism. METHODS: All data of AD patients and normal controls (NC) were obtained from alzheimer's disease neuroimaging initiative database (ADNI) database. In order to assess the effect of each single nucleotide polymorphism (SNP) of CR1 on Aß metabolism, the PLINK software was used to conduct the quality control procedures to enroll appropriate SNPs. Moreover, the correlation between CR1 genotypes and Aß metabolism in all participants were estimated with multiple linear regression models. RESULTS: After quality control procedures, a total of 329 samples and 83 SNPs were enrolled in our study. Moreover, our results identified five SNPs (rs10494884, rs11118322, rs1323721, rs17259045 and rs41308433), which were linked to Aß accumulation in brain. In further analyses, rs17259045 was found to decrease Aß accumulation among AD patients. Additionally, our study revealed the genetic variants in rs12567945 could increase CSF Aß42 in NC population. CONCLUSIONS: Our study had revealed several novel SNPs in CR1 genes which might be involved in the progression of AD via regulating Aß accumulation. These findings will provide a new basis for the diagnosis and treatment AD.

Statin Use and Risk of Cognitive Decline in the ADNI Cohort.

OBJECTIVE: To investigate associations between statin use and cognitive change, as well as diagnostic conversion, in individuals with cognitively normal (CN) status, mild cognitive impairment (MCI), and dementia due to Alzheimer disease (AD-dementia). METHODS: A multicenter cohort study with 1629 adults 48 to 91 years old with CN status, early MCI (EMCI), late MCI (LMCI), or AD-dementia at baseline followed prospectively for 24 months. Statin use was assessed at baseline, and cognition was measured over time with a composite memory score, a composite executive function score, and a global cognition score (Alzheimer's Disease Assessment Scale). Conversion to a more impaired diagnostic category was determined by clinician assessment. Repeated measures linear mixed-effects models were used to evaluate associations between statin use and change in cognition over time. Cox proportional hazards models were used to evaluate associations between statin use and time to diagnostic conversion. All models were stratified by baseline diagnostic group. RESULTS: Statin use was not associated with change in cognitive measures for CN, LMCI, or AD-dementia participants. Among EMCI participants, statin use was associated with a significantly slower rate of decline on the memory composite, but no other cognitive measure. Statin use was not associated with time to conversion for any diagnostic group. CONCLUSIONS: This study did not support an association between statin use and diagnostic conversion but suggested a possible association between statin use and cognitive change in EMCI. Additional randomized clinical trials of statins may be warranted in the prodromal EMCI stage of AD.