Baseline Functional Connectivity Features of Neural Network Nodes Can Predict Improvement After Sound Therapy Through Adjusted Narrow Band Noise in Tinnitus Patients
Bibliographic record
Abstract
Previous resting-state functional magnetic resonance imaging (fMRI) studies have shown neural connectivity alterations after the treatment of tinnitus. We aim to study the value of the baseline functional connectivity features of neural network nodes to predict outcomes of sound therapy through adjusted narrow band noise. The fMRI data of 27 untreated tinnitus patients and 27 matched healthy controls were analyzed. We calculated the graph-theoretical metric degree centrality (DC) to characterize the functional connectivity of the neural network nodes. Therapeutic outcomes are determined by the changes in the Tinnitus Handicap Inventory (THI) score after a 12-week intervention. The connectivity of 10 brain nodes in tinnitus patients was significantly increased at baseline. The functional connectivity of right insula, inferior parietal lobule (IPL), bilateral thalami, and left middle temporal gyrus was significantly modified with the sound therapy, and such changes correlated with THI changes in tinnitus patients. Receiver operating characteristic curve analyses revealed that the measurements from the five brain regions were effective at classifying improvement after therapy. After age, gender, and education correction, the adjusted area under the curve (AUC) values for the bilateral thalami were the highest (left, 0.745; right, 0.708). Our study further supported the involvement of the fronto-parietal-cingulate network in tinnitus and found that the connectivity of the thalamus at baseline is an object neuroimaging-based indicator to predict clinical outcome of sound therapy through adjusted narrow band noise.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".