LIONirs

LIONirs performs preprocessing, artifact correction, decomposition, visualization, multimodal integration, task-based analysis, and functional connectivity assessment of functional near-infrared spectroscopy (fNIRS) data to support investigation of brain function, including in pediatric and other challenging populations.

Key Features:

• Flexible processing pipelines: Support for exploring multiple analytical methods within a defined processing pipeline.
• 3D topographical visualization: Projection of fNIRS data onto a 3D head model for topographical inspection.
• Artifact detection and correction: Detection and rejection of noisy intervals and artifact correction using decomposition approaches.
• Data decomposition methods: Inclusion of decomposition techniques to isolate brain activity signatures and potential artifacts.
• Multimodal data integration: Visualization and integration of multimodal recordings including physiological signals, electroencephalography (EEG), and audio–video.
• Task-based and functional connectivity analysis: Implementation of task-based analysis workflows and assessment of functional connectivity measures.
• Automated and standardized processing: Code structure supports automated and standardized analyses across large or longitudinal datasets.
• Compatibility with other tools: Support for common data formats to enable integration with existing neuroinformatics workflows.

Scientific Applications:

• fNIRS studies of brain function: Analysis of hemodynamic responses and related signatures using fNIRS.
• Pediatric and challenging populations: Application to studies where traditional neuroimaging is less feasible, including pediatric and special-needs groups.
• Basic neuroscience research: Use in experimental paradigms probing cognitive and neural processes.
• Clinical and high-throughput studies: Support for clinical investigations and large-scale or longitudinal datasets requiring standardized pipelines.

Methodology:

Implemented in MATLAB, LIONirs provides preprocessing with noisy-interval rejection and artifact correction, data decomposition methods, 3D topographical projection, multimodal data visualization and integration (physiological signals, EEG, audio–video), task-based analysis and functional connectivity assessment, and support for automated standardized processing and common data formats.