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.
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.
There have been conflicting results regarding the differences between men and women on the function and structure of the brain. To address this question, we propose a novel method to distinguish the effects of sex on brain structure and function based on identifying subgroups that maximize the between-sex differences. In a large sample of 19975 women and 17568 men, we demonstrate that our method can identify individuals at the extremities of the "maleness-femaleness" continuum and is able to quantify the maleness/femaleness of their features. These findings have widespread implications for studies assessing sex and its impact on the brain.
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.
The organization of the Alzheimer's disease (AD) connectome has been studied using single neuroimaging modalities. However, different neuroimaging modalities are not independent and often interact with each other in the course of AD.
Here, we integrate the networks obtained from T1-weighted and 18F-Florbetapir PET to build a multiplex connectome using BRAPH 2.0 and assess how it changes across different AD stages. We assessed the overlapping strength, multilayer communities, overlapping connections, and the multiplex participation and clustering coefficients.
There was a reorganization of the communities across the four groups and we found significant changes reflecting a loss of multiplex hubs and overlapping connections in medial frontal and occipital areas in the patients’ groups.
These findings indicate that multiplex network changes can be useful to understand the relationship between amyloid pathology and gray matter atrophy occurring over the course of AD.
Recent advances in network neuroscience have provided new insights into brain organization in health and disease. In particular, graph theory analyses of brain networks have shown that the human brain is characterized by a high level of integration between distant brain regions and good local communication between neighboring areas. However, these brain networks are normally analyzed using single neuroimaging modalities such as functional magnetic resonance or diffusion tensor imaging. Machine learning techniques for graph structures, such as Graph Neural Networks (GNN), are used to infer and predict from the graph data.
Here we present BRAPH 2.0 (BRain Analysis using graPH theory version 2.0 ), which is a major update of the first version. BRAPH 2..0 Genesis utilizes the capability of an object-oriented programming paradigm and a new engine to provide clear, robust, clean, modular, maintainable, testable, and machine learning ready code.
The brain is a complex network that relies on the interaction between its various regions, known as the connectome. The human connectome undergoes complex changes with aging and shows differences in many functional network measures between men and women; however, the effects of aging and sex on the brain connectome are not well characterized. In this study, we assess functional connectivity changes in a large cohort of men and women between 45 and 79 years of age using conventional methods as well as a novel approach based on multilayer brain connectivity. Our findings provide a deeper insight into the sex differences that occur in functional connectivity over the course of aging. Moreover, they indicate that multilayer networks provide a natural way to integrate the information from positive and negative functional connections, providing important information on the effects of sex and age on network topology.
The brain connectome can be modeled as a large-scale complex network characterized by high clustering and short network paths. Most studies assess these properties by comparing them to a null network model, generated by randomly rewiring the edges between the nodes of the original network, known as edge swapping. However, this method is computationally expensive and time consuming, mainly in networks with a high number of connections. In this study, we developed an alternative method to create null network models, the allin method. We show that both methods compute null networks with comparable topology, however, the allin method performed the randomization procedure in noticeably less time. The allin method is particularly more effective in the case of high-resolution networks and relatively higher densities. As such, these results suggest that the allin method is a more time efficient alternative to compute null model networks compared to the traditionally used edgeswap method.
The brain is a complex network that relies on the interaction between its various regions, known as the connectome. The organization of the human connectome has been studied on different imaging modalities using a single network approach.
Here, we integrate the networks obtained from amyloid positron emission tomography (PET) and structural magnetic resonance imaging (MRI) data into multilayer networks using BRAPH 2.0 (BRain Analysis using graPH theory, http://braph.org/) and compare these networks between patients with Alzheimer’s Disease and controls. Multilayer modularity, multi-participation coefficient, and multilayer motifs are calculated, and group comparisons are carried out using permutation testing. The study of multilayer brain networks is a promising new field that can potentially provide new insights into the interaction between anatomical, functional and metabolic brain connectivity.
There is increasing evidence showing that graph theory is a promising tool to study the human brain connectome. By representing brain regions and their connections as nodes and edges, it allows assessing properties that reflect how well brain networks are organized and how they become disrupted in neurological diseases such as Alzheimer’s disease, Parkinson’s disease, epilepsy, schizophrenia, multiple sclerosis and autism.
Here, we present BRAPH 2.0 (BRain Analysis using graPH theory version 2.0), which is a major update of the first object-oriented open source software written in Matlab for graph-theoretical analysis that also implements a graphical interface (GUI). BRAPH utilizes the capability of object-oriented programming paradigm to provide clear, robust, clean, modular, maintainable, and testable code.
Parkinson's disease (PD) is a complex neurodegenerative disorder characterized by motor and non-motor deficits. Several studies found changes in the topological organization of functional networks in PD built by methods that assume a simultaneous and undirected activation between brain areas. However, changes associated with PD may result in a specific alteration of the directed activity patterns between brain areas. In this study, we propose a new method to build directed functional networks in patients with PD. We show that the directed network analyses can identify widespread functional brain changes in PD characterized by higher efficiency, clustering and transitivity as well as lower modularity. Some of these network measures were associated with motor, executive and memory deficits, suggesting they are sensitive to clinical impairment in PD. Altogether our findings suggest that the directional flow in brain activation could be used as an indicator of PD-related neuronal changes.
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