Cognitively defined Alzheimer's dementia subgroups have distinct atrophy patterns

INTRODUCTION

We sought to determine structural magnetic resonance imaging (MRI) characteristics across subgroups defined based on relative cognitive domain impairments using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and to compare cognitively defined to imaging-defined subgroups.

METHODS

We used data from 584 people with Alzheimer's disease (AD) (461 amyloid positive, 123 unknown amyloid status) and 118 amyloid-negative controls. We used voxel-based morphometry to compare gray matter volume (GMV) for each group compared to controls and to AD-Memory.

RESULTS

There was pronounced bilateral lower medial temporal lobe atrophy with relative cortical sparing for AD-Memory, lower left hemisphere GMV for AD-Language, anterior lower GMV for AD-Executive, and posterior lower GMV for AD-Visuospatial. Formal asymmetry comparisons showed substantially more asymmetry in the AD-Language group than any other group (p = 1.15 × 10-10 ). For overlap between imaging-defined and cognitively defined subgroups, AD-Memory matched up with an imaging-defined limbic predominant group.

DISCUSSION

MRI findings differ across cognitively defined AD subgroups.

Biomarkers for dementia in Latin American countries: Gaps and opportunities

Limited knowledge on dementia biomarkers in Latin American and Caribbean (LAC) countries remains a serious barrier. Here, we reported a survey to explore the ongoing work, needs, interests, potential barriers, and opportunities for future studies related to biomarkers. The results show that neuroimaging is the most used biomarker (73%), followed by genetic studies (40%), peripheral fluids biomarkers (31%), and cerebrospinal fluid biomarkers (29%). Regarding barriers in LAC, lack of funding appears to undermine the implementation of biomarkers in clinical or research settings, followed by insufficient infrastructure and training. The survey revealed that despite the above barriers, the region holds a great potential to advance dementia biomarkers research. Considering the unique contributions that LAC could make to this growing field, we highlight the urgent need to expand biomarker research. These insights allowed us to propose an action plan that addresses the recommendations for a biomarker framework recently proposed by regional experts.

UNRAVELING THE AMBIGUITY BETWEEN COGNITION AND DEPRESSION: A LONGITUDINAL ANALYSIS OF OLDER ADULTS

Research on directionality of the relationship between cognitive functioning and depression is ambiguous, especially when considering implications for patients with mild cognitive impairment or Alzheimer disease (AD). Previous research suggests that depression in late life could be a pre-clinical manifestation of AD before other cognitive symptoms are detectable. Some research supports the hypothesis that level of depression can independently predict level of cognition. Other research suggests that depression is a risk factor for developing dementia or AD late in life. Further research on the impact of subjective cognitive decline versus objective cognitive performance in association with depressive symptoms is a critical area to explore. Using data from the Social Networks in Alzheimer Disease (SNAD) study and the Indiana Alzheimer Disease Research Center (IADRC), we conducted a preliminary longitudinal analysis of 196 focal subjects (Mage = 71.6 years, Pfemale = 63%). Interviews conducted one year apart were leveraged to elucidate the bidirectional relationship between depressive symptomology and cognition. Using a lagged dependent variable approach controlling for age, sex, race, and education, the results indicate that only executive function predicts depression at timepoint 2 (-0.31 SD, p< 0.001). However, depression predicts focal Cognitive Change Index (CCI) (0.06 SD, p< 0.05), processing speed (-0.04 SD, p< 0.05), and episodic memory (-0.04 SD, p< 0.05). Other cognitive domains examined, including attention, language, visual/spatial skills, MoCA score, and informant-rated CCI, were not significant as predictors or as outcomes. These results suggest that depression may be a more robust predictor of cognition than cognition is of depression.

NEUROLOGICAL CORRELATES OF SOCIAL BONDING AND BRIDGING

Social connectedness has been linked to decreased rates of cognitive decline in later life. However, recent work suggests that particular social network characteristics (i.e., bonding and bridging) may buffer against age-related degeneration. The present study analyzes social network and structural MRI data of 176 older adults from the Social Networks and Alzheimer’s Disease (SNAD) study. Results indicate that increased social bridging is associated with greater grey matter (GM) volume in several limbic structures. Increased social bonding is associated with greater GM volumes in several cerebral cortex structures as well as greater volumes in some components of the limbic system. Most notably, the effects of bridging are primarily lateralized in the left hemisphere while the effects of bonding are observed mostly in the right hemisphere. These results suggest that the neurocognitive benefits of social connectedness depend on the preponderance of bridging and/or bonding ties in older adults’ social networks.

Development and validation of language and visuospatial composite scores in ADNI

INTRODUCTION

Composite scores may be useful to summarize overall language or visuospatial functioning in studies of older adults.

METHODS

We used item response theory to derive composite measures for language (ADNI-Lan) and visuospatial functioning (ADNI-VS) from the cognitive battery administered in the Alzheimer's Disease Neuroimaging Initiative (ADNI). We evaluated the scores among groups of people with normal cognition, mild cognitive impairment (MCI), and Alzheimer's disease (AD) in terms of responsiveness to change, association with imaging findings, and ability to differentiate between MCI participants who progressed to AD dementia and those who did not progress.

RESULTS

ADNI-Lan and ADNI-VS were able to detect change over time and predict conversion from MCI to AD. They were associated with most of the pre-specified magnetic resonance imaging measures. ADNI-Lan had strong associations with a cerebrospinal fluid biomarker pattern.

DISCUSSION

ADNI-Lan and ADNI-VS may be useful composites for language and visuospatial functioning in ADNI.

Joint High-Order Multi-Task Feature Learning to Predict the Progression of Alzheimer's Disease

Alzheimer's disease (AD) is a degenerative brain disease that affects millions of people around the world. As populations in the United States and worldwide age, the prevalence of Alzheimer's disease will only increase. In turn, the social and financial costs of AD will create a difficult environment for many families and caregivers across the globe. By combining genetic information, brain scans, and clinical data, gathered over time through the Alzheimer's Disease Neuroimaging Initiative (ADNI), we propose a new Joint High-Order Multi-Modal Multi-Task Feature Learning method to predict the cognitive performance and diagnosis of patients with and without AD.

NETWORK-BASED GENOME WIDE STUDY OF HIPPOCAMPAL IMAGING PHENOTYPE IN ALZHEIMER'S DISEASE TO IDENTIFY FUNCTIONAL INTERACTION MODULES

Identification of functional modules from biological network is a promising approach to enhance the statistical power of genome-wide association study (GWAS) and improve biological interpretation for complex diseases. The precise functions of genes are highly relevant to tissue context, while a majority of module identification studies are based on tissue-free biological networks that lacks phenotypic specificity. In this study, we propose a module identification method that maps the GWAS results of an imaging phenotype onto the corresponding tissue-specific functional interaction network by applying a machine learning framework. Ridge regression and support vector machine (SVM) models are constructed to re-prioritize GWAS results, followed by exploring hippocampus-relevant modules based on top predictions using GWAS top findings. We also propose a GWAS top-neighbor-based module identification approach and compare it with Ridge and SVM based approaches. Modules conserving both tissue specificity and GWAS discoveries are identified, showing the promise of the proposal method for providing insight into the mechanism of complex diseases.

Transcriptome-Guided Imaging Genetic Analysis via a Novel Sparse CCA Algorithm

Imaging genetics is an emerging field that studies the influence of genetic variation on brain structure and function. The major task is to examine the association between genetic markers such as single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) extracted from neuroimaging data. Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP-multi-QT associations. In imaging genetics, genes associated with a phenotype should at least expressed in the phenotypical region. We study the association between the genotype and amyloid imaging data and propose a transcriptome-guided SCCA framework that incorporates the gene expression information into the SCCA criterion. An alternating optimization method is used to solve the formulated problem. Although the problem is not biconcave, a closed-form solution has been found for each subproblem. The results on real data show that using the gene expression data to guide the feature selection facilities the detection of genetic markers that are not only associated with the identified QTs, but also highly expressed there.

Structured sparse CCA for brain imaging genetics via graph OSCAR

BACKGROUND

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.

RESULTS

We propose a new structured SCCA model, which employs the graph OSCAR (GOSCAR) regularizer to encourage those highly correlated features to have similar or equal canonical weights. Our GOSCAR based SCCA has two advantages: 1) It does not require to pre-define the sign of the sample correlation, and thus could reduce the estimation bias. 2) It could pull those highly correlated features together no matter whether they are positively or negatively correlated. We evaluate our method using both synthetic data and real data. Using the 191 ROI measurements of amyloid imaging data, and 58 genetic markers within the APOE gene, our method identifies a strong association between APOE SNP rs429358 and the amyloid burden measure in the frontal region. In addition, the estimated canonical weights present a clear pattern which is preferable for further investigation.

CONCLUSIONS

Our proposed method shows better or comparable performance on the synthetic data in terms of the estimated correlations and canonical loadings. It has successfully identified an important association between an Alzheimer's disease risk SNP rs429358 and the amyloid burden measure in the frontal region.

Surface-Based Morphometric Analysis of Hippocampal Subfields in Mild Cognitive Impairment and Alzheimer's Disease

The hippocampus is widely studied with neuroimaging techniques given its importance in learning and memory and its potential as a biomarker for Alzheimer's disease (AD). Its complex folding anatomy often presents analytical challenges. In particular, the critical subfield information is typically not addressed by the existing hippocampal shape studies. To bridge this gap, we present a computational framework for surface-based morphometric analysis of hippocampal subfields. The major strengths of this framework are as follows: (a) it performs detailed hippocampal shape analysis, (b) it embraces, rather than ignores, the important hippocampal subfield information, and (c) it analyzes regular magnetic resonance imaging scans and is applicable to large scale studies. We demonstrate its effectiveness by applying it to the identification of regional hippocampal subfield atrophy patterns associated with mild cognitive impairment and AD.

Building a Surface Atlas of Hippocampal Subfields from MRI Scans using FreeSurfer, FIRST and SPHARM

The hippocampus is widely studied with neuroimaging techniques given its importance in learning and memory and its potential as a biomarker for brain disorders such as Alzheimer's disease and epilepsy. However, its complex folding anatomy often presents analytical challenges. In particular, the critical hippocampal subfield information is usually ignored by hippocampal registration in detailed morphometric studies. Such an approach is thus inadequate to accurately characterize hippocampal morphometry and effectively identify hippocampal structural changes related to different conditions. To bridge this gap, we present our initial effort towards building a computational framework for subfield-guided hippocampal morphometry. This initial effort is focused on surface-based morphometry and aims to build a surface atlas of hippocampal subfields. Using the FreeSurfer software package, we obtain valuable hippocampal subfield information. Using the FIRST software package, we extract reliable hippocampal surface information. Using SPHARM, we develop an approach to create an atlas by mapping interpolated subfield information onto an average surface. The empirical result using ADNI data demonstrates the promise and good reproducibility of the proposed method.

Sparse Multi-Task Regression and Feature Selection to Identify Brain Imaging Predictors for Memory Performance

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions, which makes regression analysis a suitable model to study whether neuroimaging measures can help predict memory performance and track the progression of AD. Existing memory performance prediction methods via regression, however, do not take into account either the interconnected structures within imaging data or those among memory scores, which inevitably restricts their predictive capabilities. To bridge this gap, we propose a novel Sparse Multi-tAsk Regression and feaTure selection (SMART) method to jointly analyze all the imaging and clinical data under a single regression framework and with shared underlying sparse representations. Two convex regularizations are combined and used in the model to enable sparsity as well as facilitate multi-task learning. The effectiveness of the proposed method is demonstrated by both clearly improved prediction performances in all empirical test cases and a compact set of selected RAVLT-relevant MRI predictors that accord with prior studies.

Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression

Traditional neuroimaging studies in Alzheimer's disease (AD) typically employ independent and pairwise analyses between multimodal data, which treat imaging biomarkers, cognitive measures, and disease status as isolated units. To enhance mechanistic understanding of AD, in this paper, we conduct a new study for identifying imaging biomarkers that are associated with both cognitive measures and AD. To achieve this goal, we propose a new sparse joint classification and regression method. The imaging biomarkers identified by our method are AD-sensitive and cognition-relevant and can help reveal complex relationships among brain structure, cognition and disease status. Using the imaging and cognition data from Alzheimer's Disease Neuroimaging Initiative database, the effectiveness of the proposed method is demonstrated by clearly improved performance on predicting both cognitive scores and disease status.