DiCER

DiCER identifies and removes spatially widespread signal deflections in resting-state functional magnetic resonance imaging (rsfMRI) data to reduce artifactual influences on functional connectivity and preserve neural activation patterns.

Key Features:

• Identification and Removal of Widespread Signal Deflections (WSDs): Identifies and removes diverse spatially widespread signal deflections visible as large, periodic bands in carpet plots where rows denote voxels and columns represent time.
• Iterative Correction Method: Employs an iterative approach that identifies representative signals associated with large clusters of coherent voxels instead of applying global signal regression (GSR) of the mean brain signal.
• Enhanced Functional Connectivity Analysis: Reduces correlations between functional connectivity and head-motion estimates, decreases inter-individual variability in global correlation structures, and increases anatomical specificity of functional-connectivity patterns.
• Preservation of Task-Related Activation Patterns: Preserves the spatial structure of expected task-related activations across multiple contrasts and tasks, as demonstrated using Human Connectome Project data.
• Improved Quality-Control Metrics: Improves quality-control metrics used to assess the reliability and validity of rsfMRI preprocessing.

Scientific Applications:

• Resting-State fMRI Data Analysis: Mitigates artifacts in rsfMRI preprocessing to improve downstream functional connectivity analyses while preserving neural signal integrity.
• Task-Based fMRI Studies: Protects true task-evoked activations during preprocessing of task fMRI data to support accurate task-contrast mapping.

Methodology:

Reorders carpet plots to emphasize cluster structures, detects a greater diversity of WSDs, and iteratively identifies and removes representative signals associated with large voxel clusters rather than applying global signal regression.