Aging impacts brain connectivity and can lead to cognitive decline in healthy individuals, but how this occurs is not well understood. In a study of 640 cognitively normal individuals ranging in age from 18 to 88, we used deep learning and DTI MRI scans to construct individual graphs representing connectivity matrices of white matter fiber bundles between brain regions. They we explored these connections with a graph neural network and found that age-related changes in connectivity were strongly located in frontal regions, indicating that the prefrontal cortex may be particularly affected by aging. Additionally, we observed significant age-related changes in the connections of regions corresponding to the default mode network (DMN), suggesting that alterations in DMN connectivity may contribute to cognitive decline in healthy aging. These findings offer new insights into the neural mechanisms underlying cognitive aging in healthy individuals and demonstrate the potential of graph neural networks for investigating complex brain connectivity patterns.
The study focuses on the complex relationship between aging and functional brain connectivity, and the need for advanced artificial intelligence approaches to understanding them. To identify the underlying mechanisms that drive cognitive decline in aging, we present a novel graph attention network model to detect nonlinear changes in functional brain connections across the aging process. The results have the potential to improve our understanding of the complexities of aging-related diseases, such as Alzheimer's disease, and aid in the development of effective diagnostic tools and treatments.
We used reservoir computing to explore the changes in the connectivity patterns of whole-brain anatomical networks derived by diffusion-weighted imaging, and their impact on cognition during aging. The networks showed optimal performance at small densities. This performance decreased with increasing density, with the rate of decrease being strongly associated with age and performance on behavioural tasks measuring cognitive function. This suggests that a network core of anatomical hubs is crucial for optimal functioning, while weaker connections are more susceptible to aging effects. This study highlights the potential utility of reservoir computing in understanding age-related changes in cognitive function.
Functional heterogeneity in the posterior medial parietal cortex is associated with cognition was recorded at SPIE Optics + Photonics held in San Diego, California, United States 2022.
Implementation of graph theory for novel approaches to analyze the brain in an easy, accurate, and reproducible manner requires a modern solution tool. Here, we present BRAPH 2.0 (BRain Analysis using graPH theory version 2.0, www.braph.org), a comprehensive extension of the first version of this software that includes these novel approaches.
The MatLab-based BRAPH 2.0 uses object-oriented programming and a completely new software engine to provide clear, robust, clean, modular, maintainable, and testable code. The core of BRAPH 2.0 consists of a set of functions that can automatically transform a user-provided script into an object that is intertwined with the rest of the code. In this way, BRAPH 2.0 provides a scaffold on which users can define custom analysis pipelines with alternative network measures, additional statistical tests, or different options for network visualization.
In this study we assess the dynamic modular organization in patients at different stages of Alzheimer’s Disease (AD) using resting-state functional MRI data. We built a temporal multiplex network using the time-series of 200 regions from different non-overlapping time windows. The organization of these networks was evaluated using the temporal multilayer modularity and node flexibility. We found changes in the temporal dynamics of functional brain networks in AD such a loss of flexibility as well as rearrangement and loss of brain modules across time windows. These alterations were more prominent in cognitively impaired groups, suggesting they might be useful in characterizing clinical progression in later disease stages.
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