Resting-State Functional MRI Analysis for Local Brain Activity Characterisation: A Narrative Review of Features and Methods
Bibliographic record
Abstract
Resting-state fMRI (rsfMRI) is a widely used neuroimaging technique that measures spontaneous fluctuations in brain activity in the absence of specific external cognitive, motor, emotional or other stimuli, based on the blood-oxygen-level-dependent (BOLD) signal. Functional connectivity (FC) is a popular rsfMRI analysis examining BOLD signal correlations between brain regions. Nevertheless, there are alternative analyses that provide different but collectively informative characteristics of the BOLD signal and, thus, local brain activity. This narrative review aimed to provide a comprehensive conceptual, mathematical, and significance investigation of common rsfMRI analyses in addition to FC. To achieve this, a narrative review was conducted on studies using the most common rsfMRI analysis to investigate global, regional, and local brain activity in healthy and diseased populations. Five rsfMRI analyses were described, summarizing the common initial steps in data processing and explaining the main characteristics and how each metric is calculated. The rsfMRI analyses described are (1) FC, reflecting BOLD global connectivity; (2) the amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF), representing the intensity of the BOLD signal; (3) regional homogeneity (ReHo), which reflects BOLD local connectivity; (4) entropy, depicting the BOLD predictability and (5) Hurst exponent (H), depicting autocorrelation of the BOLD signal. As rsfMRI is a vital tool for exploring brain function, selecting an analysis that aligns with the research question is essential. This review offers an initial catalog of standard rsfMRI analyses, highlighting their key features, concepts, and considerations to support informed decisions by researchers and clinicians.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".