The influence of construction methodology on structural brain network measures: A review

Qi, Shouliang; Meesters, Stephan; Nicolay, Klaas; Romeny, Bart M. ter Haar; Ossenblok, Pauly

doi:10.1016/j.jneumeth.2015.06.016

Cited by 87 publications

(74 citation statements)

References 96 publications

(175 reference statements)

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“…Connections were then combined into 76 × 76 undirected and weighted matrices. As there is no consensus in the literature regarding the optimal graph thresholding strategy (51) and results can vary widely based on the chosen approach (52), SIFT2 was our preferred method of bias correction. Indeed, the creators of SIFT2 argue against the use of graph theory thresholding, as it introduces an arbitrary threshold value (53).…”

Section: Methodsmentioning

confidence: 99%

Topological length of white matter connections predicts their rate of atrophy in premanifest Huntington’s disease

McColgan

Seunarine²,

Gregory

et al. 2017

JCI Insight

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We lack a mechanistic explanation for the stereotyped pattern of white matter loss seen in Huntington’s disease (HD). While the earliest white matter changes are seen around the striatum, within the corpus callosum, and in the posterior white matter tracts, the order in which these changes occur and why these white matter connections are specifically vulnerable is unclear. Here, we use diffusion tractography in a longitudinal cohort of individuals yet to develop clinical symptoms of HD to identify a hierarchy of vulnerability, where the topological length of white matter connections between a brain area and its neighbors predicts the rate of atrophy over 24 months. This demonstrates a new principle underlying neurodegeneration in HD, whereby brain connections with the greatest topological length are the first to suffer damage that can account for the stereotyped pattern of white matter loss observed in premanifest HD.

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Section: Methodsmentioning

confidence: 99%

Topological length of white matter connections predicts their rate of atrophy in premanifest Huntington’s disease

McColgan

Seunarine²,

Gregory

et al. 2017

JCI Insight

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“…Material analysis is also an important application domain of the CBMIA methods, including food microstructure analysis (Ding and Gunasekaran 1998), collagen fiber analysis (Elbischger et al 2004), metallography image analysis (Grzegorzek 2010), membranes porosity evaluation (Chwojnowski et al 2012), cement quality analysis (Wang et al 2014) and so on. Furthermore, because medial image analysis needs to solve many similar technical problems as the CBMIA tasks , it can also share a lot of research strategies from the CBMIA field and extend them to related application tasks, like cerebral cortical analysis (Suri et al 2002), MRI image analysis (Balafar et al 2010a, b), retinal vessel analysis ), brain network analysis (Qi et al 2015(Qi et al , 2016 and so on. Besides the image analysis in the above scientific fields, we can even extend the CBMIA methods into the daily life image analysis domain, like text detection , video and film analysis (Shirahama et al 2013;Xu 2016), and so on.…”

Section: Potential Application Fields Of Cbmia Methodologymentioning

confidence: 99%

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

Wang

2017

Artif Intell Rev

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Microorganisms such as protozoa and bacteria play very important roles in many practical domains, like agriculture, industry and medicine. To explore functions of different categories of microorganisms is a fundamental work in biological studies, which can assist biologists and related scientists to get to know more properties, habits and characteristics of these tiny but obbligato living beings. However, taxonomy of microorganisms (microorganism classification) is traditionally investigated through morphological, chemical or physical analysis, which is time and money consuming. In order to overcome this, since the 1970s innovative content-based microscopic image analysis (CBMIA) approaches are introduced to microbiological fields. CBMIA methods classify microorganisms into different categories using multiple artificial intelligence approaches, such as machine vision, pattern recognition and machine learning algorithms. Furthermore, because CBMIA approaches are semior full-automatic computer-based methods, they are very efficient and labour cost saving, supporting a technical feasibility for microorganism classification in our current big data age. In this article, we review the development history of microorganism classification using CBMIA approaches with two crossed pipelines. In the first pipeline, all related works are grouped by their corresponding microorganism application domains. By this pipeline, it is easy for microbiologists to have an insight into each special application domain and find their interested applied CBMIA techniques. In the second pipeline, the related works in each application domain are reviewed by time periods. Using this pipeline, computer scientists can see the dynamic of technological development clearly and keep up with the future devel-

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“…The effect of different parameters on network construction is gaining attention ([8,9,10,11,12,13], see [14] for review) because there is currently no universally-accepted standard set of parameters. As a result, most published studies use different methods to construct networks from diffusion MRI data.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, most published studies use different methods to construct networks from diffusion MRI data. One important choice in constructing a whole-brain structural network is how to define the nodes of the network (see [14,15] for review). The number of nodes, how they are defined (e.g., which atlas, if any, is used), and how they are used in the construction of the network have been shown to affect network properties such as small-worldness [8] and hub identity [9].…”

Section: Introductionmentioning

confidence: 99%

Seed Location Impacts Whole-Brain Structural Network Comparisons between Healthy Elderly and Individuals with Alzheimer’s Disease

et al. 2017

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Whole-brain networks derived from diffusion tensor imaging (DTI) data require the identification of seed and target regions of interest (ROIs) to assess connectivity patterns. This study investigated how initiating tracts from gray matter (GM) or white matter (WM) seed ROIs impacts (1) structural networks constructed from DTI data from healthy elderly (control) and individuals with Alzheimer’s disease (AD) and (2) between-group comparisons using these networks. DTI datasets were obtained from the Alzheimer’s disease Neuroimaging Initiative database. Deterministic tractography was used to build two whole-brain networks for each subject; one in which tracts were initiated from WM ROIs and another in which they were initiated from GM ROIs. With respect to the first goal, in both groups, WM-seeded networks had approximately 400 more connections and stronger connections (as measured by number of streamlines per connection) than GM-seeded networks, but shared 94% of the connections found in the GM-seed networks. With respect to the second goal, between-group comparisons revealed a stronger subnetwork (as measured by number of streamlines per connection) in controls compared to AD using both WM-seeded and GM-seeded networks. The comparison using WM-seeded networks produced a larger (i.e., a greater number of connections) and more significant subnetwork in controls versus AD. Global, local, and nodal efficiency were greater in controls compared to AD, and between-group comparisons of these measures using WM-seeded networks had larger effect sizes than those using GM-seeded networks. These findings affirm that seed location significantly affects the ability to detect between-group differences in structural networks.

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The influence of construction methodology on structural brain network measures: A review

Cited by 87 publications

References 96 publications

Topological length of white matter connections predicts their rate of atrophy in premanifest Huntington’s disease

Topological length of white matter connections predicts their rate of atrophy in premanifest Huntington’s disease

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

Seed Location Impacts Whole-Brain Structural Network Comparisons between Healthy Elderly and Individuals with Alzheimer’s Disease

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