Construction of brain structural connectivity network using a novel integrated algorithm based on ensemble average propagator

Wu, Zhanxiong; Peng, Yun; Xu, Dong; Hong, Ming; Zhang, Yingchun

doi:10.1016/j.compbiomed.2019.103384

Cited by 3 publications

(4 citation statements)

References 46 publications

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“…This gives a formula for the distribution of degree in terms of thermodynamic temperature β. From Equation (20). The exponential term is controlled by temperature and depends on the total number of nodes and edges in the network.…”

Section: Microscopic Quantities In Nodesmentioning

confidence: 99%

“…Instead of describing the network using macroscopic thermal quantities, here we attempt to explore the microscopic characterisations for nodes. Equation (20) gives the relationship between the average degree and the inverse temperature as…”

Section: Microscopic Quantities In Nodesmentioning

confidence: 99%

“…Here we propose an alternative definition of particles in the statistical ensembles that the network nodes are analogous to particles and the energy for each node is the degree [19]. We study two kinds of statistical ensemble, namely, the microcanonical ensemble and the canonical ensemble, and use these to describe the corresponding generated fMRI networks [20,21]. In physics, the microcanonical ensemble is used to describe a group of thermal systems each with the same fixed energy [22].…”

Section: Introductionmentioning

confidence: 99%

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Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease

Wang

2021

Entropy

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This paper seeks to advance the state-of-the-art in analysing fMRI data to detect onset of Alzheimer’s disease and identify stages in the disease progression. We employ methods of network neuroscience to represent correlation across fMRI data arrays, and introduce novel techniques for network construction and analysis. In network construction, we vary thresholds in establishing BOLD time series correlation between nodes, yielding variations in topological and other network characteristics. For network analysis, we employ methods developed for modelling statistical ensembles of virtual particles in thermal systems. The microcanonical ensemble and the canonical ensemble are analogous to two different fMRI network representations. In the former case, there is zero variance in the number of edges in each network, while in the latter case the set of networks have a variance in the number of edges. Ensemble methods describe the macroscopic properties of a network by considering the underlying microscopic characterisations which are in turn closely related to the degree configuration and network entropy. When applied to fMRI data in populations of Alzheimer’s patients and controls, our methods demonstrated levels of sensitivity adequate for clinical purposes in both identifying brain regions undergoing pathological changes and in revealing the dynamics of such changes.

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Section: Microscopic Quantities In Nodesmentioning

confidence: 99%

Section: Microscopic Quantities In Nodesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease

Wang

2021

Entropy

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“…The connection strength of each pair of WM voxels was then assigned as the connection strength with the largest connection possibility. Finally, the connection strength between each ROI pair was calculated as the sum of the connection probabilities for each pair of WM voxels within the ROIs (Wu et al, 2019a). The resulting link strength depended on the alignment of the EAP along connecting pathways, with higher strengths indicating better alignment.…”

Section: Reconstruction Of the Structural Connectivity Networkmentioning

confidence: 99%

Development of Brain Structural Networks Over Age 8: A Preliminary Study Based on Diffusion Weighted Imaging

Peng

Selvaraj

et al. 2020

Front. Aging Neurosci.

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Brain structural network changes provide key information about the aging process of the brain. Unfortunately, there has yet to be a detailed characterization of these structural networks across different age groups. Efforts to classify these networks have also been hampered by their reliance on technically limited traditional methods, which are unable to track multiple fiber orientations within a voxel and consequently are prone to false detection and artifacts. In this study, a newly developed Ensemble Average Propagator (EAP) based probabilistic tractography method was applied to construct a structural network, with the strength of the link between any two brain functional regions estimated according to the alignment of the EAP along connecting pathways. Age-related changes in the topological organization of human brain structural networks were thereby characterized across a broad age range (ages 8-75 years). The data from 48 healthy participants were divided into four age groups (Group 1 aged 8-15 years; Group 2 aged 25-35 years; Group 3 aged 45-55 years; and, Group 4 aged 65-75 years; N = 12 per group). We found that the brain structural network continues to strengthen during later adolescence and adulthood, through the first 20-30 years of life. Older adults, aged 65-75, had a significantly less optimized topological organization in their structural network, with decreased global efficiency and increased path lengths versus subjects in other groups. This study suggests that probabilistic tractography based on EAP provides a reliable method to construct macroscale structural connectivity networks to capture the age-associated changes of brain structures.

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Effective Connectivity Extracted from Resting-State fMRI Images Using Transfer Entropy

Chen

Gao

et al. 2021

IRBM

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Construction of brain structural connectivity network using a novel integrated algorithm based on ensemble average propagator

Cited by 3 publications

References 46 publications

Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease

Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease

Development of Brain Structural Networks Over Age 8: A Preliminary Study Based on Diffusion Weighted Imaging

Effective Connectivity Extracted from Resting-State fMRI Images Using Transfer Entropy

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