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Estimating individual contribution from group-based structural correlation networks.

Manish Saggar1, S M Hadi Hosseini1, Jennifer L Bruno1

  • 1Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA USA.

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Summary
This summary is machine-generated.

New methods reveal individual brain network differences in fragile X syndrome (FXS). These structural correlation networks (SCNs) link to intelligence and genetics, offering potential biomarkers for neurodevelopmental disorders.

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Network Analysis

Background:

  • Structural correlation networks (SCNs) derived from brain morphology infer population-level brain networks and synchronized maturational changes.
  • SCNs reflect anatomical and functional connectivity, complementing diffusion and resting-state functional networks.
  • Current SCN methods focus on group differences, lacking insights into individual variations linked to behavior, cognition, or disorder states.

Purpose of the Study:

  • To introduce novel distance-based approaches for extracting individual differences from group-level SCNs.
  • To apply and validate these methods in individuals with fragile X syndrome (FXS) and typically developing (TD) controls.
  • To assess the association of extracted individual contributions with intelligence and genetic data.

Main Methods:

  • Developed two novel distance-based methods to derive individual SCN contributions from group-level data.
  • Applied methods to a dataset of 100 participants (50 FXS, 50 TD).
  • Utilized permutation analysis for stability testing and examined associations with intelligence scores and genetic data.

Main Results:

  • The novel methods demonstrated stable extraction of individual contributions from group-level SCNs.
  • Individual contributions were significantly associated with intelligence estimates in both FXS and TD groups.
  • Significant relationships were also found between individual contributions and genetic data in both groups.

Conclusions:

  • The developed approaches effectively capture individual differences in SCNs, linking them to behavioral and genetic factors.
  • These methods offer a promising avenue for identifying putative biomarkers of altered brain connectivity in neurodevelopmental disorders.
  • This work advances the understanding of individual variability within brain network structures.