NIRSTORM: a Brainstorm extension dedicated to functional near-infrared spectroscopy data analysis, advanced 3D reconstructions, and optimal probe design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Significance: Understanding the brain's complex functions requires multimodal approaches that combine data from various neuroimaging techniques. Functional near-infrared spectroscopy (fNIRS) offers valuable insights into hemodynamic responses, complementing other modalities such as electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance imaging. However, there is a lack of comprehensive and accessible toolboxes able to integrate fNIRS advanced analyses with other modalities. NIRSTORM addresses this gap by offering a unified platform for multimodal neuroimaging analysis. Aim: NIRSTORM aims to provide a user-friendly and comprehensive environment for multimodal analysis while supporting the entire fNIRS analysis pipeline, from experiment planning to the reconstruction of hemodynamic fluctuations on the cortex. Approach: , NIRSTORM operates as a Brainstorm plugin, enhancing Brainstorm's capabilities for analyzing fNIRS data. Brainstorm is a widely used, GUI-based software originally designed for statistical analysis and source imaging of EEG and MEG data. Results: NIRSTORM supports conventional fNIRS preprocessing and statistical analyses while introducing new advanced features such as optimal montage for planning optode placement and maximum entropy on the mean (MEM) for reconstructing hemodynamic fluctuations on the cortical surface. Conclusion: As an open-access and user-friendly plugin, NIRSTORM extends Brainstorm's functionality to fNIRS, bridging the gap between EEG/MEG and hemodynamic analyses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it