Background: Previous studies have attempted to infer the category of objects in a stimulus image from functional magnetic resonance imaging (fMRI) data recoded during image-viewing. Most studies focus on extracting activity patterns within a given region or across multiple voxels, and utilize the relationships among voxels to decipher the category of a stimulus image. Yet, the functional connectivity (FC) patterns across regions of interest in response to image categories, and their potential contributions to category classification are largely unknown.
New method: We investigated whole-brain FC patterns in response to 4 image category stimuli (cats, faces, houses, and vehicles) using fMRI in healthy adult volunteers, and classified FC patterns using machine learning framework (Support Vector Machine [SVM] and Random Forest). We further examined the FC robustness and the influence of the window length on FC patterns for neural decoding.
Results: The average one-vs.-one classification accuracy of the two classification models were 74% within subjects and 80% between subjects, which are higher than the chance level (50%). The Random Forest results were better than SVM results, and the 48-s FC results were better than the 24-s FC results.
Comparison with existing method(s): We compared the classification performance of our FC patterns with two other existing methods, inter-block and intra-block, without overlapping temporal information.
Conclusions: Whole-brain FC patterns for different window lengths (24 and 48 s) can predict images categories with high accuracy. These results reveal novel mechanisms underlying the representation of categorical information in large-scale FC patterns in the human brain.
Keywords: Functional connectivity; Image categorization; Machine learning; Random Forest; Support Vector Machine.
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