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Recent work suggests that indentations of the cerebral cortex, or sulci, may be uniquely vulnerable to atrophy in aging and Alzheimer's disease (AD) and that posteromedial cortex (PMC) is particularly vulnerable to atrophy and pathology accumulation. However, these studies did not consider small, shallow, and variable tertiary sulci that are located in association cortices and are often associated with human-specific aspects of cognition. Here, we manually defined 4,362 PMC sulci in 432 hemispheres in 216 human participants (50.5% female) and found that these smaller putative tertiary sulci showed more age- and AD-related thinning than larger, more consistent sulci, with the strongest effects for two newly uncovered sulci. A model-based approach relating sulcal morphology to cognition identified that a subset of these sulci was most associated with memory and executive function scores in older adults. These findings lend support to the retrogenesis hypothesis linking brain development and aging, and provide new neuroanatomical targets for future studies of aging and AD.

Significance StatementLarge-scale changes in cortical structure in aging suggest sulci are particularly vulnerable to atrophy. However, tertiary sulci, the smallest and most individually variable cortical folds associated with cognitive development, have not been studied in aging. Here, we investigate tertiary sulci for the first time in aging and Alzheimer's disease (AD). We find that these smaller and shallower sulci show more age- and AD-related thinning than larger sulci in posteromedial cortex (PMC), and that the atrophy of a subset of PMC sulci is most associated with cognition in older adults. These findings support classical theories linking developmental and aging trajectories at a novel anatomical resolution and provide insight into relationships between individual differences in structural brain changes and cognitive decline.

 

Motivated by a multimodal neuroimaging study for Alzheimer's disease, in this article, we study the inference problem, that is, hypothesis testing, of sequential mediation analysis. The existing sequential mediation solutions mostly focus on sparse estimation, while hypothesis testing is an utterly different and more challenging problem. Meanwhile, the few mediation testing solutions often ignore the potential dependency among the mediators or cannot be applied to the sequential problem directly. We propose a statistical inference procedure to test mediation pathways when there are sequentially ordered multiple data modalities and each modality involves multiple mediators. We allow the mediators to be conditionally dependent and the number of mediators within each modality to diverge with the sample size. We produce the explicit significance quantification and establish theoretical guarantees in terms of asymptotic size, power, and false discovery control. We demonstrate the efficacy of the method through both simulations and an application to a multimodal neuroimaging pathway analysis of Alzheimer's disease.

 

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk. 

In the Alzheimer’s disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management. Our goal was to develop generalizable multivariate models that integrate high-dimensional data (multimodal neuroimaging and cerebrospinal fluid biomarkers, genetic factors, and measures of cognitive resilience) for identification of MCI individuals who progress to AD within 3 years. Our main findings were i) we were able to build generalizable models with clinically relevant accuracy (~93%) for identifying MCI individuals who progress to AD within 3 years; ii) markers of AD pathophysiology (amyloid, tau, neuronal injury) accounted for large shares of the variance in predicting progression; iii) our methodology allowed us to discover that expression ofCR1(complement receptor 1), an AD susceptibility gene involved in immune pathways, uniquely added independent predictive value. This work highlights the value of optimized machine learning approaches for analyzing multimodal patient information for making predictive assessments.