Communicability disruption in Alzheimer’s disease connectivity networks

Lella, Eufemia; Amoroso, Nicola; Lombardi, Angela; Maggipinto, T.; Tangaro, Sabina; Bellotti, R.; Initiative, Alzheimer’s Disease Neuroimaging

doi:10.1093/comnet/cny009

Cited by 30 publications

(37 citation statements)

References 47 publications

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“…This procedure resulted in ten sets of adjacency matrices, one set for each threshold value. We use the same thresholding procedure described in [30], which is also similar to the procedure used for example in [67]. In Fig.…”

Section: Effects Of Threshold Selection and Normalizationmentioning

confidence: 99%

“…Here we start by adopting the SI-model for the propagation of a disease factor in AD. However, we use this model to connect with the theory of network communicability, which has been widely used in network neurosciences (for some applications of communicability in pathoconnectomics see [30,31,32,33,34,35,36,37,38,39,40]). That is, we will provide a theoretical connection between the network communicability and the probability of a disease factor of propagating from one node to another in a network.…”

Section: Introductionmentioning

confidence: 99%

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Communicability distance reveals hidden patterns of Alzheimer disease

Lella

Estrada

2020

Preprint

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AbstractThe communicability distance between pairs of regions in human brain is used as a quantitative proxy for studying Alzheimer disease. Using this distance we obtain the shortest communicability path lengths between different regions of brain networks from Alzheimer diseased (AD) patients and healthy cohorts (HC). We show that the shortest communicability path length is significantly better than the shortest topological path length in distinguishing AD patients from HC. Based on this approach we identify 399 pairs of brain regions for which there are very significant changes in the shortest communicability path length after AD appears. We find that 42% of these regions interconnect both brain hemispheres, 28% connect regions inside the left hemisphere only and 20% affects vermis connection with brain hemispheres. These findings clearly agree with the disconnection syndrome hypothesis of Alzheimer disease. Finally, we show that in 76.9% damaged brain regions the shortest communicability path length drops in AD in relation to HC. This counterintuitive finding indicates that AD transforms the brain network into a more efficient system from the perspective of the transmission of the disease, because it drops the circulability of the disease factor around the brain regions in relation to its transmissibility to other regions.

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Section: Effects Of Threshold Selection and Normalizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Communicability distance reveals hidden patterns of Alzheimer disease

Lella

Estrada

2020

Preprint

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“…5) since here the degree reflects all the number of co-authors who do not necessarily author the same paper. The highest degree is 444 corresponding to Paul M. Thompson neuroscientist, who is also an author of the paper with the highest number of coauthors [27]. The network has a high assortativity coefficient of 0.57 that suggests that nodes tend to be connected to other nodes with similar degrees.…”

Section: Analysis Of the Co-authorship Networkmentioning

confidence: 99%

Two decades of network science

Molontay

Nagy

2019

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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“…Communicability quantifies the ease of communications between node pairs in a network by considering not only the shortest path connecting them, but all possible available routes [14]. For this reason, this metric revealed to be particularly sensitive to the disruption of communication between brain regions due to AD [15,16]. In [15], communicability was able to outperform more classic graph measures on a mixed cohort of healthy control (HC) subjects and AD patients from the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/).…”

mentioning

confidence: 99%

“…For this reason, this metric revealed to be particularly sensitive to the disruption of communication between brain regions due to AD [15,16]. In [15], communicability was able to outperform more classic graph measures on a mixed cohort of healthy control (HC) subjects and AD patients from the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). Since the main goal was to compare communicability to other classic measures, we fixed the classification model to be used, i.e., support vector machines.…”

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confidence: 99%

Machine Learning and DWI Brain Communicability Networks for Alzheimer’s Disease Detection

et al. 2020

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Signal processing and machine learning techniques are changing the clinical practice based on medical imaging from many perspectives. A major topic is related to (i) the development of computer aided diagnosis systems to provide clinicians with novel, non-invasive and low-cost support-tools, and (ii) to the development of new methodologies for the analysis of biomedical data for finding new disease biomarkers. Advancements have been recently achieved in the context of Alzheimer’s disease (AD) diagnosis through the use of diffusion weighted imaging (DWI) data. When combined with tractography algorithms, this imaging modality enables the reconstruction of the physical connections of the brain that can be subsequently investigated through a complex network-based approach. A graph metric particularly suited to describe the disruption of the brain connectivity due to AD is communicability. In this work, we develop a machine learning framework for the classification and feature importance analysis of AD based on communicability at the whole brain level. We fairly compare the performance of three state-of-the-art classification models, namely support vector machines, random forests and artificial neural networks, on the connectivity networks of a balanced cohort of healthy control subjects and AD patients from the ADNI database. Moreover, we clinically validate the information content of the communicability metric by performing a feature importance analysis. Both performance comparison and feature importance analysis provide evidence of the robustness of the method. The results obtained confirm that the whole brain structural communicability alterations due to AD are a valuable biomarker for the characterization and investigation of pathological conditions.

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Communicability disruption in Alzheimer’s disease connectivity networks

Cited by 30 publications

References 47 publications

Communicability distance reveals hidden patterns of Alzheimer disease

Communicability distance reveals hidden patterns of Alzheimer disease

Two decades of network science

Machine Learning and DWI Brain Communicability Networks for Alzheimer’s Disease Detection

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