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Published on: October 22, 2015
Can meaningful effective connectivities be obtained between auditory cortical regions?
M S Gonçalves1, D A Hall, I S Johnsrude
1MRC Institute of Hearing Research, University Park, Nottingham NG7 2RD, United Kingdom. miguel@ihr.mrc.ac.uk
Neuroimage
|November 15, 2001
Summary
Structural Equation Modeling (SEM) of auditory fMRI data is challenging due to temporal sampling and anatomical constraints. Connectivity patterns derived from SEM are sensitive to data processing, limiting generalizability.
Area of Science:
- Neuroimaging
- Cognitive Neuroscience
- Auditory Neuroscience
Background:
- Structural Equation Modeling (SEM) is used to analyze neuroimaging data, assessing model fit and effective connectivity.
- Auditory fMRI presents challenges due to sparse temporal sampling and limited anatomical constraints for functional models.
Purpose of the Study:
- To investigate the efficacy of SEM in describing fMRI data within an auditory network across adjacent cortical fields.
- To evaluate the goodness of fit and generalizability of connectivity patterns derived from SEM.
Main Methods:
- Applied SEM with latent variables to auditory fMRI data from four adjacent cortical fields.
- Tested 14 models across two hemispheres, including individual subjects and a group analysis.
- Assessed model fit and path coefficients to infer effective connectivity.
Main Results:
- Seven out of 14 SEM models provided a plausible description of the auditory fMRI data.
- Good-fitting models revealed significant subject-to-subject variability in connectivity patterns.
- Connectivity patterns were not consistently replicable across different stimulus conditions.
Conclusions:
- SEM analysis of central auditory function is highly sensitive to voxel selection and time-series sampling methods.
- The generalizability of SEM-derived connectivity patterns requires robust validation due to variability and lack of anatomical confirmation.
- Further refinement of SEM methodologies is needed for reliable analysis of auditory network connectivity using fMRI.
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