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Related Experiment Video

Updated: May 19, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
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PREDICTING TEMPORAL LOBE VOLUME ON MRI FROM GENOTYPES USING L(1)-L(2) REGULARIZED REGRESSION.

Omid Kohannim1, Derrek P Hibar, Neda Jahanshad

  • 1Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|August 21, 2012
PubMed
Summary
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Sparse regression methods identified genetic variants linked to temporal lobe volume in Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. This imaging genomics approach highlights RBFOX1 and GRIN2B genes

Area of Science:

  • Neuroimaging
  • Genomics
  • Biostatistics

Background:

  • Sparse regression methods are increasingly used in imaging genomics for feature selection.
  • These methods identify relevant predictors from large datasets, even with small individual effects.

Purpose of the Study:

  • To apply a multivariate L(1)-L(2)-regularized regression (elastic net) approach.
  • To predict temporal lobe volume using magnetic resonance imaging (MRI) tensor-based morphometry.
  • To analyze genome-wide data from Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects.

Main Methods:

  • Elastic net regression was employed for prediction.
  • Model parameters were optimized using internal cross-validation.
  • Model performance was assessed on independent test sets.

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  • Statistical significance was determined through comparison with 100,000 permutations.

    • Main Results:

    • The elastic net model achieved statistically significant predictions (p ~ 0.001).
    • The rs9933137 variant in the RBFOX1 gene was identified as a highly contributory genotype.
    • Genetic variants rs10845840 (GRIN2B) and rs2456930 were also found to be significant predictors.

    Conclusions:

    • Multivariate sparse regression is effective in imaging genomics.
    • Specific genetic variants (RBFOX1, GRIN2B) are associated with temporal lobe volume in ADNI.
    • This study advances the understanding of genetic contributions to brain structure in Alzheimer's disease.