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.

Standardized statistical framework for comparison of biomarkers: Techniques from ADNI.

INTRODUCTION: Well-chosen biomarkers have the potential to increase the efficiency of clinical trials and drug discovery and should show good precision as well as clinical validity. METHODS: We suggest measures that operationalize these criteria and describe a general approach that can be used for inference-based comparisons of biomarker performance. The methods are applied to measures obtained from structural magnetic resonance imaging (MRI) from individuals with mild dementia (n = 70) or mild cognitive impairment (MCI; n = 303) enrolled in the Alzheimer's Disease Neuroimaging Initiative. RESULTS: Ventricular volume and hippocampal volume showed the best precision in detecting change over time in both individuals with MCI and with dementia. Differences in clinical validity varied by group. DISCUSSION: The methodology presented provides a standardized framework for comparison of biomarkers across modalities and across different methods used to generate similar measures and will help in the search for the most promising biomarkers. HIGHLIGHTS: A framework for comparison of biomarkers on pre-defined criteria is presented. Criteria for comparison include precision in capturing change and clinical validity. Ventricular volume has high precision in change for both dementia and mild cognitive impairment (MCI) trials. Imaging measures' performance in clinical validity varies more for dementia than for MCI.

The ADNI PET Core at 20.

 : The Alzheimer's Disease Neuroimaging Initiative (ADNI) PET Core has evolved over time, beginning with positron emission tomography (PET) imaging of a subsample of participants with [18F]fluorodeoxyglucose (FDG)-PET, adding tracers for measurement of ß-amyloid, followed by tau tracers. This review examines the evolution of the ADNI PET Core, the novel aspects of PET imaging in each stage of ADNI, and gives an accounting of PET images available in the ADNI database. The ADNI PET Core has been and continues to be a rich resource that provides quantitative PET data and preprocessed PET images to the scientific community, allowing interrogation of both basic and clinically relevant questions. By standardizing methods across different PET scanners and multiple PET tracers, the Core has demonstrated the feasibility of large-scale, multi-center PET studies. Data managed and disseminated by the PET Core has been critical to defining pathophysiological models of Alzheimer's disease (AD) and helped to drive methods used in modern therapeutic trials. HIGHLIGHTS: The ADNI PET Core began with FDG-PET and now includes three amyloid and three tau PET ligands. The PET Core has standardized acquisition and analysis of multitracer PET images. The ADNI PET Core helped to develop methods that have facilitated clinical trials in AD.

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.

Overview of ADNI MRI.

The magnetic resonance imaging (MRI) Core has been operating since Alzheimer's Disease Neuroimaging Initiative's (ADNI) inception, providing 20 years of data including reliable, multi-platform standardized protocols, carefully curated image data, and quantitative measures provided by expert investigators. The overarching purposes of the MRI Core include: (1) optimizing and standardizing MRI acquisition methods, which have been adopted by many multicenter studies and trials worldwide and (2) providing curated images and numeric summary values from relevant MRI sequences/contrasts to the scientific community. Over time, ADNI MRI has become increasingly complex. To remain technically current, the ADNI MRI protocol has changed substantially over the past two decades. The ADNI 4 protocol contains nine different imaging types (e.g., three dimensional [3D] T1-weighted and fluid-attenuated inversion recovery [FLAIR]). Our view is that the ADNI MRI data are a greatly underutilized resource. The purpose of this paper is to educate the scientific community on ADNI MRI methods and content to promote greater awareness, accessibility, and use. HIGHLIGHTS: The MRI Core provides multi-platform standardized protocols, carefully curated image data, and quantitative analysis by expert groups. The ADNI MRI protocol has undergone major changes over the past two decades to remain technically current. As of April 25, 2024, the following numbers of image series are available: 17,141 3D T1w; 6877 FLAIR; 3140 T2/PD; 6623 GRE; 3237 dMRI; 2846 ASL; 2968 TF-fMRI; and 2861 HighResHippo (see Table 1 for abbreviations). As of April 25, 2024, the following numbers of quantitative analyses are available: FreeSurfer 10,997; BSI 6120; tensor based morphometry (TBM) and TBM-SYN 12,019; WMH 9944; dMRI 1913; ASL 925; TF-fMRI NFQ 2992; and medial temporal subregion volumes 2726 (see Table 4 for abbreviations). ADNI MRI is an underutilized resource that could be more useful to the research community.

The origins of ADNI.

A brief history of events surrounding the conceptualization and original implementation of the Alzheimer's Disease Neuroimaging Initiative (ADNI) as a public-private partnership (PPP) is provided from the perspective of three individuals directly involved from the outset. Potential barriers and how they were addressed are summarized, especially the decision to make all data freely accessible in real-time. Decisions made at the beginning of ADNI are revisited in light of what has been learned over the past 20 years, especially the importance of the investment in cerebrospinal fluid (CSF) and blood measures and the commitment to data sharing. The key elements of ADNI's success from the authors' perspective are also summarized. HIGHLIGHTS: Informal interactions among colleagues were the beginning of something big. An NIH Director's personal decision on open data sharing has had perhaps the greatest impact of any single decision in the past several decades in terms of advancing clinical biomarker research. After 20 years, blood-based biomarkers of brain disease may soon take the place of brain imaging for purposes of diagnosis and drug development.

Utility of cerebrovascular imaging biomarkers to detect cerebral amyloidosis.

INTRODUCTION: The relationship between cerebrovascular disease (CVD) and amyloid beta (Aß) in Alzheimer's disease (AD) is understudied. We hypothesized that magnetic resonance imaging (MRI)-based CVD biomarkers-including cerebral microbleeds (CMBs), lacunar infarction, and white matter hyperintensities (WMHs)-would correlate with Aß positivity on positron emission tomography (Aß-PET). METHODS: We cross-sectionally analyzed data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 1352). Logistic regression was used to calculate odds ratios (ORs), with Aß-PET positivity as the standard-of-truth. RESULTS: Following adjustment, WMHs (OR = 1.25) and superficial CMBs (OR = 1.45) remained positively associated with Aß-PET positivity (p < 0.001). Deep CMBs and lacunes exhibited a varied relationship with Aß-PET in cognitive subgroups. The combined diagnostic model, which included CVD biomarkers and other accessible measures, significantly predicted Aß-PET (pseudo-R2 = 0.41). DISCUSSION: The study highlights the translational value of CVD biomarkers in diagnosing AD, and underscores the need for more research on their inclusion in diagnostic criteria. CLINICALTRIALS: gov: ADNI-2 (NCT01231971), ADNI-3 (NCT02854033). HIGHLIGHTS: Cerebrovascular biomarkers linked to amyloid beta (Aß) in Alzheimer's disease (AD). White matter hyperintensities and cerebral microbleeds reliably predict Aß-PET positivity. Relationships with Aß-PET vary by cognitive stage. Novel accessible model predicts Aß-PET status. Study supports multimodal diagnostic approaches.

The ADNI4 Digital Study: A novel approach to recruitment, screening, and assessment of participants for AD clinical research.

INTRODUCTION: We evaluated preliminary feasibility of a digital, culturally-informed approach to recruit and screen participants for the Alzheimer's Disease Neuroimaging Initiative (ADNI4). METHODS: Participants were recruited using digital advertising and completed digital surveys (e.g., demographics, medical exclusion criteria, 12-item Everyday Cognition Scale [ECog-12]), Novoic Storyteller speech-based cognitive test). Completion rates and assessment performance were compared between underrepresented populations (URPs: individuals from ethnoculturally minoritized or low education backgrounds) and non-URPs. RESULTS: Of 3099 participants who provided contact information, 654 enrolled in the cohort, and 595 completed at least one assessment. Two hundred forty-seven participants were from URPs. Of those enrolled, 465 met ADNI4 inclusion criteria and 237 evidenced possible cognitive impairment from ECog-12 or Storyteller performance. URPs had lower ECog and Storyteller completion rates. Scores varied by ethnocultural group and educational level. DISCUSSION: Preliminary results demonstrate digital recruitment and screening assessment of an older diverse cohort, including those with possible cognitive impairment, are feasible. Improving engagement and achieving educational diversity are key challenges. HIGHLIGHTS: A total of 654 participants enrolled in a digital cohort to facilitate ADNI4 recruitment. Culturally-informed digital ads aided enrollment of underrepresented populations. From those enrolled, 42% were from underrepresented ethnocultural and educational groups. Digital screening tools indicate > 50% of participants likely cognitively impaired. Completion rates and assessment performance vary by ethnocultural group and education.

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.