14 Differentiating functional cognitive disorder from early neurodegeneration: a clinic-based study
Bibliographic record
Abstract
Objectives/Aims Functional Cognitive Disorder (FCD) describes distressing or disabling cognitive symptoms that can be positively identified as internally inconsistent with recognised brain or systemic disease processes. FCD is common amongst attendees to cognitive or memory clinics. We aimed to improve the clinical characterisation of such patients, and identify means to differentiate them from patients with early neurodegeneration. Methods We identified two samples of patients recruited from a specialist cognitive clinic, classified on the basis of consensus expert clinical opinion following relevant investigations: FCD, (n=21), and neurodegenerative Mild Cognitive Impairment ‘MCI’, (n=17). We also recruited healthy control participants (n=25). All participants completed a cognitive battery: Montreal Cognitive Assessment (MoCA), Hopkins Verbal Learning Test-Revised (HVLT-R), Trail Making Test part B (TMT-B); and the Minnesota Multiphasic Personality Inventory (MMPI-2RF). Analyses included regression models controlling for age and gender. Analysis of the personality data focused on specific hypotheses generated from previous work on functional disorders. Results As expected, the FCD participants were younger than the MCI participants (mean age 58 vs 72), and were more likely to be occupationally active (35% vs 6%). As described previously in this sample*, subjective cognitive symptoms were equally elevated in FCD and MCI compared to controls. Both the FCD and MCI groups were impaired in comparison to controls on MoCA, TMT-B and the initial recall component of HVLT-R. However, FCD participants demonstrated a dip in scores from free recall to recognition on HVLT-R, which was not seen in MCI (p<0.05). FCD and MCI groups scored equally high relative to controls on anxiety and depression, and on four personality indices: emotional or internalising dysfunction, somatic complaints (cognitive and non-cognitive analysed separately), and negative emotional experiences. There were no group differences in ‘introversion/low positive emotionality’. Conclusions Cognitive symptoms, basic bedside cognitive testing, personality analysis, and mood symptoms are all similar across both early neurodegenerative and FCD groups, making them hard to disentangle clinically. We hope that by highlighting certain testing modalities that can illustrate internal inconsistency (such as delayed recall spared relative to recognition, as opposed to consistently poor delayed recall and recognition that is more typical of Alzheimer’s neurodegeneration), we can improve diagnosis and thereby management strategies. It is unclear why both mood and non-cognitive somatic symptoms are elevated in both FCD and MCI; this could reflect an epiphenomenon of distress surrounding the cognitive symptoms, despite diverse origins of the cognitive symptoms. Reference Pennington C, Ball HA, Swirski M.Functional cognitive disorder: diagnostic challenges and future directions.Diagnostics2019;9: 131.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".