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Spatially informed voxelwise modeling for naturalistic fMRI experiments.

Emin Çelik1, Salman Ul Hassan Dar2, Özgür Yılmaz2

  • 1Neuroscience Program, Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey; National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey.

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Summary

Spatially-informed voxelwise modeling (SPIN-VM) improves brain activity prediction by using neighboring voxel data. This method enhances sensitivity and captures more coherent information representations compared to standard voxelwise modeling (VM).

Keywords:
Coherent representationComputational neuroscienceResponse correlationsSpatial regularizationVoxelwise modelingfMRI

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Voxelwise modeling (VM) predicts brain activity from stimuli but ignores spatial correlations between voxels.
  • This limitation reduces sensitivity, especially with noisy measurement data, hindering the detection of functional selectivity.
  • Existing methods do not fully leverage the spatially correlated nature of neural responses.

Purpose of the Study:

  • To introduce a novel method, spatially-informed voxelwise modeling (SPIN-VM), that incorporates spatial neighborhood information.
  • To enhance the prediction accuracy and information representation recovery in neuroimaging data.
  • To improve the detection of functional selectivity in the presence of measurement noise.

Main Methods:

  • Developed SPIN-VM, which performs regularization across spatial neighborhoods in addition to model features.
  • SPIN-VM generates single-voxel response predictions while leveraging response correlations in voxel neighborhoods.
  • Evaluated SPIN-VM performance using a rich dataset from a natural vision experiment.

Main Results:

  • SPIN-VM achieved higher prediction accuracies compared to standard VM.
  • SPIN-VM demonstrated improved capture of locally congruent information representations across the cortex.
  • The method effectively utilizes shared information within spatial neighborhoods.

Conclusions:

  • SPIN-VM offers superior performance in predicting single-voxel responses.
  • The approach enhances the recovery of coherent information representations in the brain.
  • SPIN-VM provides a more sensitive and robust framework for analyzing neuroimaging data.