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James D. Wilson

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YNIMG Journal 2025 Journal Article

NetBat: A network-driven harmonization method for structural connectivity

  • Gustav R. Sjobeck
  • Mahbaneh Eshaghzadeh Torbati
  • Davneet S. Minhas
  • Charles S. DeCarli
  • James D. Wilson
  • Dana L. Tudorascu

As the practice of aggregating multi-site neuroimaging data has become more common, the field of neuroscience has increasingly recognized the importance of harmonization , or the removal of scanner effects from brain imaging data. While many harmonization methods exist, like ComBat and CovBat, few explicitly incorporate the network structure of the brain. Researchers studying structural connectivity are therefore not guaranteed to model the true underlying brain network. This study offers a new harmonization method, called NetBat, which was designed to incorporate network parameters from the weighted stochastic block model (WSBM) as covariates in the popular ComBat harmonization method. NetBat is demonstrated through analysis of eighteen neurotypical individuals each scanned on four MRI scanners. Results suggest that under tested circumstances NetBat provides more accurate overall harmonization and better retention of network structure than competing methods.

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YNIMG Journal 2019 Journal Article

A consistent organizational structure across multiple functional subnetworks of the human brain

  • Paul E. Stillman
  • James D. Wilson
  • Matthew J. Denny
  • Bruce A. Desmarais
  • Skyler J. Cranmer
  • Zhong-Lin Lu

A recurrent theme of both cognitive and network neuroscience is that the brain has a consistent subnetwork structure that maps onto functional specialization for different cognitive tasks, such as vision, motor skills, and attention. Understanding how regions in these subnetworks relate is thus crucial to understanding the emergence of cognitive processes. However, the organizing principles that guide how regions within subnetworks communicate, and whether there is a common set of principles across subnetworks, remains unclear. This is partly due to available tools not being suited to precisely quantify the role that different organizational principles play in the organization of a subnetwork. Here, we apply a joint modeling technique – the correlation generalized exponential random graph model (cGERGM) – to more completely quantify subnetwork structure. The cGERGM models a correlation network, such as those given in functional connectivity, as a function of activation motifs – consistent patterns of coactivation (i. e. , connectivity) between collections of nodes that describe how the regions within a network are organized (e. g. , clustering) – and anatomical properties – relationships between the regions that are dictated by anatomy (e. g. , Euclidean distance). By jointly modeling all features simultaneously, the cGERGM models the unique variance accounted for by each feature, as well as a point estimate and standard error for each, allowing for significance tests against a random graph and between graphs. Across eight functional subnetworks, we find remarkably consistent organizational properties guiding subnetwork architecture, suggesting a fundamental organizational basis for subnetwork communication. Specifically, all subnetworks displayed greater clustering than would be expected by chance, but lower preferential attachment (i. e. , hub use). These findings suggest that human functional subnetworks follow a segregated highway structure, in which tightly clustered subcommunities develop their own channels of communication rather than relying on hubs.

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JBHI Journal 2018 Journal Article

Tracking Fetal Movement Through Source Localization From Multisensor Magnetocardiographic Recordings

  • Recep Avci
  • James D. Wilson
  • Diana Escalona-Vargas
  • Hari Eswaran

Due to its high spatial and temporal resolution, fetal magnetocardiography (fMCG) measurements have been used for fetal movement (FM) detection in several studies, which considered the changes in the amplitude and/or morphology of measured fMCG signals. Using source localization for fMCG measurements, we propose a novel method to fit a magnetic dipole moment to fetal heart signals and investigate the positional changes of magnetic dipole in order to detect FMs. We first split each fMCG recording into 6-s time windows. Then, the magnetic dipole location and orientation for each time window are estimated using our inverse solution model. Finally, the distance between magnetic dipole positions in adjacent time windows is computed. Also, we calculate the dot products of the normalized magnetic dipoles to monitor the orientational changes. We analyzed 28 fMCG measurements from 23 subjects to investigate accuracy of the dipole fitting results. For each dipole fit, our model described the measured data with a goodness-of-fit value over 97% and with a fitting error of less than 2%. We observed that magnetic dipole positions significantly moved for some time windows. The time points at which the significant movement was observed were correlated with the heart rate acceleration as well. In addition to identifying the time points of the movement, our method is capable of observing rotational movement checking orientation of the dipoles.

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JMLR Journal 2017 Journal Article

Community Extraction in Multilayer Networks with Heterogeneous Community Structure

  • James D. Wilson
  • John Palowitch
  • Shankar Bhamidi
  • Andrew B. Nobel

Multilayer networks are a useful way to capture and model multiple, binary or weighted relationships among a fixed group of objects. While community detection has proven to be a useful exploratory technique for the analysis of single-layer networks, the development of community detection methods for multilayer networks is still in its infancy. We propose and investigate a procedure, called Multilayer Extraction, that identifies densely connected vertex-layer sets in multilayer networks. Multilayer Extraction makes use of a significance based score that quantifies the connectivity of an observed vertex-layer set through comparison with a fixed degree random graph model. Multilayer Extraction directly handles networks with heterogeneous layers where community structure may be different from layer to layer. The procedure can capture overlapping communities, as well as background vertex-layer pairs that do not belong to any community. We establish consistency of the vertex-layer set optimizer of our proposed multilayer score under the multilayer stochastic block model. We investigate the performance of Multilayer Extraction on three applications and a test bed of simulations. Our theoretical and numerical evaluations suggest that Multilayer Extraction is an effective exploratory tool for analyzing complex multilayer networks. Publicly available code is available at github.com/jdwilson4/Multila yerExtraction. [abs] [ pdf ][ bib ] &copy JMLR 2017. ( edit, beta )

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