Cognitive performance in functional neurological disorder: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Cognitive complaints are common in functional neurological disorder (FND), but it is unclear whether objective neurocognitive deficits are present. This systematic review summarised validated/standardised cognitive test performance in FND samples across cognitive domains. Methods: Embase, PsycInfo and MEDLINE were searched from inception to 15th May 2023, combining terms for FND and cognitive domains (e.g., attention, memory, executive functioning). Studies included a range of FND phenotypes (seizures, motor, cognitive disorder, mixed), compared to healthy or clinical controls. Risk of bias was assessed with the modified Newcastle-Ottawa Scale and a qualitative synthesis/narrative review of cognitive performance in FND was conducted. Test performance scores were extracted, and random-effects meta-analyses were run where appropriate. This review was registered on PROSPERO, CRD42023423139.Results: Fifty-six studies including2260 individuals with FND were eligible. Although evidence for some impairments emerged across domains of executive functioning, attention, memory, and psychomotor/processing speed, this was inconsistent across studies and FND phenotypes. Common confounds included group differences in demographics, medication, and intellectual functioning. Only 24% of studies objectively assessed performance validity. Meta-analyses revealed higher scores on tests of naming (g=.67, 95%CI [.50, .84]) and long-term memory (g=.43, 95%CI [.13, .74]) in functional seizures versus epilepsy, but no significant differences in working (g=-.08, 95%CI [-.44, .29]) or immediate (g=.25, 95%CI [-.02, .53]) memory and cognitive flexibility (g=-.01, 95%CI [-.29, .28]). Conclusions: There is mixed evidence for objective cognitive deficits in FND. Future research should control for confounds, include tests of performance validity, and assess relationships between objective and subjective neurocognitive functioning.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".