Kolla, S.; Falakshahi, H.; Abrol, A.; Fu, Z.; Calhoun, V.D. Intra-Atlas Node Size Effects on Graph Metrics in fMRI Data: Implications for Alzheimer’s Disease and Cognitive Impairment. Sensors2024, 24, 814.
Kolla, S.; Falakshahi, H.; Abrol, A.; Fu, Z.; Calhoun, V.D. Intra-Atlas Node Size Effects on Graph Metrics in fMRI Data: Implications for Alzheimer’s Disease and Cognitive Impairment. Sensors 2024, 24, 814.
Kolla, S.; Falakshahi, H.; Abrol, A.; Fu, Z.; Calhoun, V.D. Intra-Atlas Node Size Effects on Graph Metrics in fMRI Data: Implications for Alzheimer’s Disease and Cognitive Impairment. Sensors2024, 24, 814.
Kolla, S.; Falakshahi, H.; Abrol, A.; Fu, Z.; Calhoun, V.D. Intra-Atlas Node Size Effects on Graph Metrics in fMRI Data: Implications for Alzheimer’s Disease and Cognitive Impairment. Sensors 2024, 24, 814.
Abstract
Network neuroscience delves into comprehending the intricate arrangement and interactions within the neural networks of the brain. By merging insights from both neuroscience and network theory, it investigates the brain's structure and function through the lens of interconnected nodes (symbolizing brain regions) and edges (symbolizing the connections among these regions) forming a dynamic network. Graph metrics play a pivotal role in neuroscience by providing quantifiable insights into the intricate connectivity patterns within the brain's neural networks. Frequently, nodes are extracted from functional or structural atlases, which can lead to diverse shapes and sizes. Nevertheless, our understanding of how these differing node characteristics and definitions impact the computed graph metrics in neuroimaging data remains limited. This study adopts a data-driven methodology to delineate functional nodes, subsequently examining the influence of their sizes on resultant graph metrics. By employing the Neuromark framework, an entirely automated independent component analysis (ICA) is applied to resting state fMRI data. Through this, functional network connectivity (FNC) matrices are computed—capturing Pearson correlations between component-time courses—while employing a proportional threshold to streamline node connections based on these correlations. Subsequently, various global and local graph metrics are computed, offering insights into network characteristics. Global metrics provide an overarching summary of the network structure, while local measures delve into structural intricacies at the individual component level. Node sizes are computed based on voxel counts surpassing a designated threshold. Next, we calculated the Pearson correlation between the obtained node sizes and the graph metrics, which we term 'node-metric coupling' (NMC). Our findings revealed consistent and noteworthy correlations between the values of graph metrics and the dimensions of brain nodes. To delve deeper into the implications of this correlation, we examined the node-metric coupling within a dataset comprising Alzheimer's disease, mild cognitive impairment, and control subjects. The disparities observed in node-metric coupling among these groups underscore the necessity of accounting for this factor during analysis. The two principal outcomes of this study are as follows: Firstly, the substantial interplay between varying node sizes within a given atlas and the resultant graph metrics; and secondly, the potential utility of node-metric coupling as a viable biomarker for brain disorders. These discoveries hold significant ramifications. While comparing studies employing diverse atlases is already a challenge, our work highlights an additional, critical source of variability: accounting for node sizes. Consequently, the association between intra-atlas node size and graph metric should be thoughtfully acknowledged in future neuroimaging investigations.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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