Diffusion and functional MRI findings and their relationship to behaviour in postconcussion syndrome: a scoping review
Bibliographic record
Abstract
Postconcussion syndrome (PCS) is a term attributed to the constellation of symptoms that fail to recover after a concussion. PCS is associated with a variety of symptoms such as headaches, concentration deficits, fatigue, depression and anxiety that have an enormous impact on patients' lives. There is currently no diagnostic biomarker for PCS. There have been attempts at identifying structural and functional brain changes in patients with PCS, using diffusion tensor imaging (DTI) and functional MRI (fMRI), respectively, and relate them to specific PCS symptoms. In this scoping review, we appraised, synthesised and summarised all empirical studies that (1) investigated structural or functional brain changes in PCS using DTI or fMRI, respectively, and (2) assessed behavioural alterations in patients with PCS. We performed a literature search in MEDLINE (Ovid), Embase (Ovid) and PsycINFO (Ovid) for primary research articles published up to February 2020. We identified 8306 articles and included 45 articles that investigated the relationship between DTI and fMRI parameters and behavioural changes in patients with PCS: 20 diffusion, 20 fMRI studies and 5 papers with both modalities. Most frequently studied structures were the corpus callosum, superior longitudinal fasciculus in diffusion and the dorsolateral prefrontal cortex and default mode network in the fMRI literature. Although some white matter and fMRI changes were correlated with cognitive or neuropsychiatric symptoms, there were no consistent, converging findings on the relationship between neuroimaging abnormalities and behavioural changes which could be largely due to the complex and heterogeneous presentation of PCS. Furthermore, the heterogeneity of symptoms in PCS may preclude discovery of one biomarker for all patients. Further research should take advantage of multimodal neuroimaging to better understand the brain-behaviour relationship, with a focus on individual differences rather than on group comparisons.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".