Functional near-infrared spectroscopy in psychiatry
Bibliographic record
Abstract
Summary Functional near-infrared spectroscopy (fNIRS) has been used in healthcare and medical research for the past two decades. In particular, the use of fNIRS in academic and clinical psychiatry has increased rapidly owing to its advantages over other neuroimaging modalities. fNIRS is a tool that can potentially supplement clinical interviews and mental state examinations to establish a psychiatric diagnosis and monitor treatment progress. This article provides a review of the theoretical background of fNIRS, key principles of its applications in psychiatry and its limitations, and shares a vision of its future applicability in psychiatric research and clinical practice. Learning Objectives • Understand the theoretical background, mechanism of action and clinical applications of fNIRS and compare it to other neuroimaging modalities • Understand the use of fNIRS in academic and clinical psychiatry through current research findings • Be able to evaluate the future potential of fNIRS and formulate new ideas for using fNIRS in academic and clinical psychiatry
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".