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Related Concept Videos

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Motion-invariant variational autoencoding of brain structural connectomes.

Yizi Zhang1, Meimei Liu2, Zhengwu Zhang3

  • 1Department of Statistics, Columbia University, New York, NY, United States.

Imaging Neuroscience (Cambridge, Mass.)
|August 13, 2025
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This study introduces a new method to create accurate brain maps by removing head motion effects from diffusion MRI scans. This allows for a better understanding of how brain networks connect to cognitive abilities.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Human Connectomics

Background:

  • Diffusion magnetic resonance imaging (dMRI) enables mapping of human brain structural connectomes.
  • Head motion during dMRI acquisition introduces artifacts, compromising connectome accuracy and hindering the study of brain-trait relationships.
  • Accurate connectome reconstruction is crucial for understanding cognition.

Purpose of the Study:

  • To develop a generative model for low-dimensional structural connectome representations invariant to motion artifacts.
  • To generate motion-adjusted connectomes for more accurate links between brain networks and human traits.
  • To evaluate the performance of the proposed motion-invariant model.

Main Methods:

  • Development of a motion-invariant variational autoencoder (inv-VAE) model.
  • Application of the inv-VAE to dMRI data from the Adolescent Brain Cognitive Development (ABCD) study and the Human Connectome Project (HCP).
  • Comparison of motion-adjusted connectomes with conventional approaches in relation to cognitive traits.

Main Results:

  • The proposed inv-VAE model effectively learns motion-invariant representations of structural connectomes.
  • Motion-adjusted connectomes derived from the inv-VAE show stronger associations with various cognition-related traits.
  • Empirical results demonstrate superior performance of the inv-VAE over existing methods.

Conclusions:

  • The developed motion-invariant generative model enhances the accuracy of structural connectome analysis.
  • Motion-adjusted connectomes provide more reliable insights into the relationship between brain structure and cognition.
  • This approach facilitates more precise investigations into brain-behavior associations.