Linking brain activation to standing balance performance: A systematic review and meta analysis of functional near-infrared spectroscopy literature
Bibliographic record
Abstract
BACKGROUND: Functional Near-Infrared Spectroscopy (fNIRS) holds promise for clinical applications in the field of balance impairment amelioration; however, the relationship between fNIRS metrics and balance performance remains uncertain. We aimed to quantify the correlations between fNIRS-derived brain activation and standing balance performance, and determine variables that influence these associations. METHODS: We systematically reviewed English-language studies, published across PuBMed, PsycINFO, Embase, CINAHL, Ovid Medline, and Web of Science from inception up until July 1, 2024, that assessed standing balance tasks in adults > 18 years old with or without medical diagnosis measured with fNIRS. Pooled correlation coefficients were synthesized using a random effects restricted maximum likelihood model. RESULTS: Overall, 17 studies were included with 420 participants. Key factors influencing the identified relationships were brain region and participant diagnosis. We identified moderate correlations between balance performance and cortical activation recorded by fNIRS in the supplementary motor area (SMA) (r = 0.52, 95 % CI = 0.39 0.64), and the prefrontal cortex (PFC) (r = 0.47, 95 % CI=0.32 - 0.60). In the PFC, increased oxygenated haemoglobin (HbO) was negatively associated with balance measures. The reverse relationship was reported in the PFC for individuals with physical and cognitive impairment. In the SMA, HbO was positively associated with balance. Few studies found associations between deoxygenated haemoglobin (HbR) and total hemoglobin (HbT) with balance performance. SIGNIFICANCE: Current evidence supports a relationship between fNIRS measures, specifically HbO, with standing balance performance. This relationship depends on the brain region measured, age, and the diagnosis of the participants. To better understand this relationship, there is a need to report standardized balance performance metrics alongside other metrics of interest to better synthesize data across publications. Improved understanding the neural basis of standing balance with fNIRS will lead to more informed interventions for balance rehabilitation.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.019 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".