Network Modeling in Biology: Statistical Methods for Gene and Brain Networks

Y. X. Rachel Wang; Lexin Li; Jingyi Jessica Li; Haiyan Huang

doi:10.1214/20-sts792

A Connectomic Atlas of the Human Cerebrum—Chapter 18: The Connectional Anatomy of Human Brain Networks

Operative Neurosurgery ◽

10.1093/ons/opy272 ◽

2018 ◽

Vol 15 (suppl_1) ◽

pp. S470-S480 ◽

Cited By ~ 4

Author(s):

Robert G Briggs ◽

Andrew K Conner ◽

Cordell M Baker ◽

Joshua D Burks ◽

Chad A Glenn ◽

...

Keyword(s):

Network Modeling ◽

Brain Networks ◽

Network Models ◽

Brain Surgery ◽

Functional Magnetic Resonance ◽

Novel Technologies ◽

Fiber Tracts ◽

Cortical Parcellation ◽

Cortical Regions ◽

And Task

ABSTRACT BACKGROUND It is widely understood that cortical functions are mediated by complex, interdependent brain networks. These networks have been identified and studied using novel technologies such as functional magnetic resonance imaging under both resting-state and task-based conditions. However, no one has attempted to describe these networks in terms of their cortical parcellations. OBJECTIVE To describe our approach to network modeling and discuss its significance for the future of neuronavigation in brain surgery using the cortical parcellation scheme detailed within this supplement. METHODS Using network models previously elucidated by our group using coordinate-based meta-analytic techniques, we show the anatomic position and underlying white matter tracts of the cortical regions comprising 8 functional networks of the human cerebrum. These network models are displayed using Synaptive's clinically available BrightMatter tractography software (Synaptive Medical, Toronto, Canada). RESULTS The relevant cortical parcellations of 8 different cerebral networks have been identified. The fiber tracts between these regions were used to construct anatomically precise models of the networks. Models are described for the dorsal attention, ventral attention, semantic, auditory, supplementary motor, ventral premotor, default mode, and salience networks. CONCLUSION Our goal is to move towards more precise, anatomically specific models of brain networks that can be constructed for individual patients and utilized in navigational platforms during brain surgery. We believe network modeling and future advances in navigation technology can provide a foundation for improving neurosurgical outcomes by allowing us to preserve complex brain networks.

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Approximation Techniques for Neuromimetic Calculus

International Journal of Neural Systems ◽

10.1142/s0129065799000228 ◽

1999 ◽

Vol 09 (03) ◽

pp. 227-234

Author(s):

VINCENT VIGNERON ◽

CLAUDE BARRET

Keyword(s):

Neural Network ◽

Least Squares ◽

Statistical Methods ◽

Data Structures ◽

Approximation Theory ◽

Entire Range ◽

Network Modeling ◽

Ordinary Least Squares ◽

Neural Network Modeling ◽

Approximation Techniques

Approximation Theory plays a central part in modern statistical methods, in particular in Neural Network modeling. These models are able to approximate a large amount of metric data structures in their entire range of definition or at least piecewise. We survey most of the known results for networks of neurone-like units. The connections to classical statistical ideas such as ordinary least squares (LS) are emphasized.

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Statistics and parallaxes

International Astronomical Union Colloquium ◽

10.1017/s0252921100073875 ◽

1978 ◽

Vol 48 ◽

pp. 7-29

Author(s):

T. E. Lutz

Keyword(s):

Experimental Design ◽

Statistical Methods ◽

Review Paper ◽

Systematic Errors ◽

Past Experience ◽

Random Errors ◽

Trigonometric Parallaxes ◽

Systematic And Random Errors

This review paper deals with the use of statistical methods to evaluate systematic and random errors associated with trigonometric parallaxes. First, systematic errors which arise when using trigonometric parallaxes to calibrate luminosity systems are discussed. Next, determination of the external errors of parallax measurement are reviewed. Observatory corrections are discussed. Schilt’s point, that as the causes of these systematic differences between observatories are not known the computed corrections can not be applied appropriately, is emphasized. However, modern parallax work is sufficiently accurate that it is necessary to determine observatory corrections if full use is to be made of the potential precision of the data. To this end, it is suggested that a prior experimental design is required. Past experience has shown that accidental overlap of observing programs will not suffice to determine observatory corrections which are meaningful.

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Lessons From the Neural Bases of Speech and Voice

Perspectives on Speech Science and Orofacial Disorders ◽

10.1044/ssod21.1.5 ◽

2011 ◽

Vol 21 (1) ◽

pp. 5-14

Author(s):

Christy L. Ludlow

Keyword(s):

Brain Networks ◽

Neural Substrates ◽

Voice Behavior ◽

Therapeutic Approaches ◽

Neural Bases ◽

Sound Foundation ◽

The Brain ◽

Normal Speech

The premise of this article is that increased understanding of the brain bases for normal speech and voice behavior will provide a sound foundation for developing therapeutic approaches to establish or re-establish these functions. The neural substrates involved in speech/voice behaviors, the types of muscle patterning for speech and voice, the brain networks involved and their regulation, and how they can be externally modulated for improving function will be addressed.

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