Jamie Anderson results 29
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Interpretable deep learning reveals spatiotemporal MRI features of brain aging that align with neurodegenerationNARA Subscribed
Cortical thinning and atrophy are hallmarks of brain aging that have been characterized using magnetic resonance imaging (MRI). Brain aging involves many neuroanatomic features whose effects on brain structure remain unexplored. To address this challenge, we trained interpretable deep neural networks (DNNs) to estimate brain age (BA) from T 1 -weighted ( T 1 w) MRI. By identifying MRI features unapparent to humans, DNNs can fi...
Alzheimer’s disease (AD) disproportionately affects women and carriers of the apolipoprotein E ε4 allele (APOE4), yet little is known about how sex and APOE interact to influence white matter (WM) integrity during disease progression. We integrated diffusion MRI and matched blood transcriptomic data to investigate these interactions and their underlying biological mechanisms. WM microstructure was quantified using diffusion te...
A review of image processing and analysis of computed tomography images using deep learning methodsNARA Subscribed
The use of machine learning has seen extraordinary growth since the development of deep learning techniques, notably the deep artificial neural network. Deep learning methodology excels in addressing complicated problems such as image classification, object detection, and natural language processing. A key feature of these networks is the capability to extract useful patterns from vast quantities of complex data, including ima...
Predicting the progression of MCI and Alzheimer’s disease on structural brain integrity and other features with machine learningNARA Subscribed
Machine learning (ML) on structural MRI data shows high potential for classifying Alzheimer’s disease (AD) progression, but the specific contribution of brain regions, demographics, and proteinopathy remains unclear. Using Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we applied an extreme gradient-boosting algorithm and SHAP (SHapley Additive exPlanations) values to classify cognitively normal (CN) older adults, th...
Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohortNARA Subscribed
Machine learning models are increasingly being used to estimate “brain age” from neuroimaging data. The gap between chronological age and the estimated brain age gap (BAG) is potentially a measure of accelerated and resilient brain aging. Brain age calculated in this fashion has been shown to be associated with mortality, measures of physical function, health, and disease. Here, we estimate the BAG using a voxel-based elastic...
Equitable implementation of lung cancer screening: avoiding its potential to mirror existing inequities among people who use tobaccoNARA Subscribed
Purpose Lung cancer is the leading cause of cancer death, but the advent of lung cancer screening using low-dose computed tomography offers a tremendous opportunity to improve lung cancer outcomes. Unfortunately, implementation of lung cancer screening has been hampered by substantial barriers and remains suboptimal. Specifically, the commentary emphasizes the intersectionality of smoking history and several important sociodem...
Integrating knowledge on green infrastructure, health and well-being in ageing populations: Principles for research and practiceNARA Subscribed
Ageing and urbanisation pose significant challenges for public health and urban planning. Ageing populations are at particular risk from hazards arising from urbanisation processes, some of which are in turn exacerbated by climate change. One approach for mitigating the negative effects of urbanisation on ageing populations is the leveraging of the beneficial effects of urban green infrastructure as a public health interventio...
Environmental variation within a species’ range can create contrasting selective pressures, leading to divergent selection and novel adaptations. The conservation value of populations inhabiting environmentally marginal areas remains in debate and is closely related to the adaptive potential in changing environments. Strong selection caused by stressful conditions may generate novel adaptations, conferring these populations di...
A new null model approach to quantify performance and significance for ecological niche models of species distributionsNARA Subscribed
Aim Ecological niche modelling requires robust estimation of model performance and significance, but common evaluation approaches often yield biased estimates. Null models provide a solution but are rarely used in this field. We implemented an important modification to existing null model tests, evaluating null models with the same withheld records that were used to evaluate the real model. We built and evaluated models across...
Sustainability indices are proliferating, both to help synthesize scientific understanding and inform policy. However, it remains poorly understood how such indices are affected by underlying assumptions of the data and modelling approaches used to compute indicator values. Here, we focus on one such indicator, the fisheries goal within the Ocean Health Index ( OHI ), which evaluates the sustainable provision of food from wild...
Trade‐offs for data‐limited fisheries when using harvest strategies based on catch‐only modelsNARA Subscribed
Many of the world's fisheries are unassessed, with little information about population status or risk of overfishing. Unassessed fisheries are particularly predominant in developing countries and in small‐scale fisheries, where they are important for food security. Several catch‐only methods based on time series of fishery catch and commonly available life‐history traits have been developed to estimate stock status (defined as...
Toward ecologically realistic predictions of species distributions: A cross‐time example from tropical montane cloud forestsNARA Subscribed
There is an urgent need for more ecologically realistic models for better predicting the effects of climate change on species’ potential geographic distributions. Here we build ecological niche models using MAXENT and test whether selecting predictor variables based on biological knowledge and selecting ecologically realistic response curves can improve cross‐time distributional predictions. We also evaluate how the method cho...
Fishery managers must often reconcile conflicting estimates of population status and trend. Superensemble models, commonly used in climate and weather forecasting, may provide an effective solution. This approach uses predictions from multiple models as covariates in an additional “superensemble” model fitted to known data. We evaluated the potential for ensemble averages and superensemble models (ensemble methods) to improve...