Brain networks can be naturally divided into clusters or communities where the cluster’s nodes dynamics have similar trajectories in phase space. This process is known as synchronization, and represents characteristics of intragroup features and not between groups. Fractional calculus represents a generalization of ordinary differentiation and integration to arbitrary non-integer order, and can be thought of as a smooth interpolation between different orders of differentiation/integration, providing the ability to probe the system from many different viewpoints of the dynamics. Fractional calculus has been explored as an excellent tool for the description of memory in many processes and may be more accurate for modeling brain processes than traditional integer-order ones. We apply the concept of cluster synchronization in fractional-order structural brain networks ranging from healthy controls to Alzheimer’s disease subjects and determine whether cluster synchronization can be achieved in these networks. We observe the existence of a hypersynchronization only in AD structural networks and consider that this could represent an excellent non-invasive biomarker for tracking the disease evolution and decide upon therapeutic interventions.
Brain connectivity is usually analyzed based on graph theory and pinning control theory. Previous studies suggested that the topological properties of structural and functional networks for brain networks may be altered in association with neurodegnerative diseases. To better understand and characterize these alterations, we introduce a new approach - robustness of network controllability to evaluate network robustness, and identify the critical nodes, whose removals maximally destroys the network’s functionality. These alterations are due to external or internal changes in the network. Understanding and describing these interactions at the level of large-scale brain circuitry may be a significant step towards unraveling dementia disease evolution. In this study, we analyze structural and functional brain networks for healthy controls, MCI and AD patients such that we reveal the connection between network robustness and architecture and the differences between patients’ groups. We determine the critical and driver nodes of these networks as the key components for robustness of network controllability. Our results suggest that healthy controls for both functional and structural connectivity have more critical nodes than AD and MCI networks, and that these critical nodes appear clustered in almost all networks. Our findings provide useful information for determining disease evolution in dementia under the aspects of controllability and robustness.
A variety of deep learning approaches have been proposed to automatically classify Alzheimer’s disease (AD) from medical images. However, common approaches such as traditional convolutional neural networks (CNNs), lack interpretability and are prone to overfitting when trained on small datasets. As an alternative, significantly less work has explored applying deep learning approaches to region-based features that are commonly attained from atlas partitions of known regions of interest (ROI). In this work, we combine CNNs with graph neural networks (GNNs) to jointly learn an adjacency matrix of connectivity’s between ROIs as a prior for learning meaningful features for AD prediction. We apply our method to the ADNI dataset and systematically inspect the different intermediate layers of our network using t-SNE projections that show strong separation on out-of-sample data. Finally, we show that the edge probabilities alone are sufficient to reach high classification accuracy by training a secondary random forest classifier on the adjacency matrices outputted from our network and illustrate the interpretability properties of the graphs by visualizing the feature importance for all edges.
Normal and aberrant cognitive functions are the result of the dynamic interplay between large-scale neural circuits. Describing the nature of these interactions has been a challenging task yet important for neurodegenerative disease evolution. Graph theory has been the standard tool to provide biomarkers in imaging connectomics showing the Alzheimer’s disease (AD). We propose a novel concept - graph signal processing - to analyze the evolution of disease graphs leading from mild cognitive impairment (MCI) to AD and derive frequency-based biomarkers representative for this disease. We show that high oscillations derived from the graph Fourier decomposition can provide important discriminatory information. To quantify the qualitative intuition of high oscillations, we use two concepts from signal theory: (1) zero crossings and (2) total variations. We apply these concepts on functional and structural brain connectivity networks for control (CN), mild cognitive impairment (MCI) and Alzheimer’s disease (AD) subjects. Our results applied to functional brain networks suggest that graph signal processing can accurately describe the frequencies of brain networks, and explain how AD is associated with low frequency and localized averaging confirmed by clinical results.
Numerous deep learning approaches have been proposed to automatically classify Alzheimer’s disease (AD) from medical images. However, common approaches, such as convolutional neural networks (CNNs), lack interpretability and are prone to over-fitting when trained on small datasets. As an alternative, significantly less work has explored applying deep learning approaches to region-based features commonly obtained from atlas partitions of known regions of interest (ROI). In this paper, we propose a self-attention mechanism to jointly learn a graph of ROI connectivity as a prior for learning meaningful features for AD prediction. We apply our method to both the classification of AD subjects from healthy controls and to predict whether mild cognitive impaired (MCI) subjects will progress to AD (pMCI) or not (sMCI). We systematically show that our model’s performance compares well with other common ML approaches for ROI-based methods, such as neural networks and support vector machines. Finally, we perform exploratory graph analysis to illustrate the interpretability properties of the attention graphs and how they can provide insight for scientific discovery.
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