Original research•

Development of a diagnostic checklist to identify functional cognitive disorder versus other neurocognitive disorders

Abstract

Background Functional cognitive disorder (FCD) poses a diagnostic challenge due to its resemblance to other neurocognitive disorders and limited biomarker accuracy. We aimed to develop a new diagnostic checklist to identify FCD versus other neurocognitive disorders.

Methods The clinical checklist was developed through mixed methods: (1) a literature review, (2) a three-round Delphi study with 45 clinicians from 12 countries and (3) a pilot discriminative accuracy study in consecutive patients attending seven memory services across the UK. Items gathering consensus were incorporated into a pilot checklist. Item redundancy was evaluated with phi coefficients. A briefer checklist was produced by removing items with >10% missing data. Internal validity was tested using Cronbach’s alpha. Optimal cut-off scores were determined using receiver operating characteristic curve analysis.

Results A full 11-item checklist and a 7-item briefer checklist were produced. Overall, 239 patients (143 FCD, 96 non-FCD diagnoses) were included. The checklist scores were significantly different across subgroups (FCD and other neurocognitive disorders) (F(2, 236)=313.3, p<0.001). The area under the curve was excellent for both the full checklist (0.97, 95% CI 0.95 to 0.99) and its brief version (0.96, 95% CI 0.93 to 0.98). Optimal cut-off scores corresponded to a specificity of 97% and positive predictive value of 91% for identifying FCD. Both versions showed good internal validity (>0.80).

Conclusions This pilot study shows that a brief clinical checklist may serve as a quick complementary tool to differentiate patients with neurodegeneration from those with FCD. Prospective blind large-scale validation in diverse populations is warranted.Cite Now

What is already known on this topic

  • Functional cognitive disorder diagnosis is based on positive diagnostic features suggesting internal inconsistency. However, the diagnosis lacks standardisation and an operationalised clinical approach is needed.

What this study adds

  • We developed a clinical checklist based on literature evidence and clinical expert consensus, with excellent discriminative power to distinguish between functional cognitive disorder and other neurocognitive disorder subgroups in this pilot evaluation.

How this study might affect research, practice or policy

  • The diagnostic checklist may serve as an accessible way to improve identification of functional cognitive disorder versus other neurocognitive disorders in clinical and research grounds. Future prospective blind studies will clarify whether it supports the stratification of diagnostic workflow and reduces the need for costly and harmful investigations and therapies. The checklist also influences inclusion/exclusion criteria for future trials in cognitive disorders and supports history-taking.

Introduction

Functional cognitive disorder (FCD) is the cognitive subtype of functional neurological disorder (FND) and accounts for around one-third of patients seen in specialised memory services.1 Diagnosis is challenging as FCD can superficially resemble a neurodegenerative illness.1 A recent consensus panel defined FCD as cognitive symptoms with clinical evidence of internal inconsistency (ie, ability to perform a task at certain times with impairment at other times, particularly if the task is the focus of attention), but the application of current diagnostic criteria to distinguish FCD from neurodegeneration has not been evaluated.2

FCD is likely underdiagnosed in clinical practice and often grouped together with subjective cognitive impairment or mild cognitive impairment (MCI), diagnostic labels that encompass a variety of underlying conditions.1 3 There is also a real chance of misdiagnosis with early-stage neurodegeneration, triggering life-changing decisions and exposure to potentially harmful medications including anticholinesterase inhibitors, particularly in less specialised settings.4 Anecdotal evidence from clinical practice also shows that patients with a neurodegenerative condition can be wrongly diagnosed with FCD. In both circumstances, patients are deprived of appropriate care for their needs, postdiagnostic support and accurate prognosis information, further impacting on individual, healthcare and wider societal costs, with reduced productivity, unemployment and quality-of-life impairment.5

Over the last decade, our understanding of the biological basis of neurocognitive disorders, in particular Alzheimer’s disease (AD), led to the emergence of novel fluid and brain imaging biomarkers. However, currently available investigations have significant limitations in accurately distinguishing between FCD and other neurocognitive disorders, are not widely available, and there is no agreement on consensus approach.6 Patients with amyloid positive/tau negative cerebrospinal fluid (CSF) biomarkers and positron emission tomography (PET) scans have an increased risk of false positive AD diagnosis at autopsy7 8 and may never progress to MCI and dementia in comparison with controls.9 10 More recently, blood-based biomarkers did not demonstrate superiority over clinical reference models in predicting underlying AD,11 suggesting they are not suitable for indiscriminate testing. Additionally, structural neuroimaging findings1 12 and psychometric profiles of FCD1 13–16 may be indistinguishable from those with early neurodegeneration, especially in individuals with a higher cognitive reserve.17 Hence, a demonstration of normal or borderline test results is insufficient and inappropriate to make a correct diagnosis, particularly in patients who continue to experience debilitating cognitive symptoms.

In a recent systematic review, we found several bedside clinical signs discriminating between FCD and neurodegeneration.16 We then conducted a Delphi study with expert clinicians, using fictional cognitive vignettes of FCD and neurodegeneration and observed high inter-rater agreement on the separation of FCD from neurodegeneration but lower consensus regarding the conceptualisation and management in FCD versus other neurodegenerative conditions, emphasising the need for diagnostic operationalisation and the importance of careful clinical characterisation.6

We hypothesised that a brief bedside checklist could constitute a practical clinical tool to differentiate FCD from neurodegeneration, in patients with cognitive symptoms. The checklist was developed based on key clinical features identified in the literature and consensus expert opinion. We then conducted a pilot diagnostic accuracy study in a retrospective sample of patients attending outpatient memory services across seven centres in the UK.

Methods

The checklist was developed iteratively using a mixed‐methods approach in three related studies (figure 1).

Figure 1
Request permission

Schematic representation of checklist development. In light grey, published studies from our group that informed the checklist development stage.6 16 In orange, studies covered in this article. FCD, functional cognitive disorder.

Development of the checklist prototype: Delphi study

A modified Delphi methodology was followed. Experts in cognitive disorders and FND were invited to participate, as we previously described.6

A systematic literature review16 informed a survey with clinical characteristics that were found to be typical of FCD.1 Experts were asked to rank each statement according to how much they thought the items favour a diagnosis of FCD, using a Likert scale ranging from ‘extremely unimportant (1)’ to ‘extremely important (7)’, through three consecutive rounds.18 The survey was opened in March 2022, and the third round was concluded by August 2022, when the prototype of the future diagnostic checklist was achieved.

Statistical analysis

The overall rating assigned to each statement was quantified using median and interquartile range (IQR). Strong or moderate consensus to include or exclude a statement was achieved when a statement was rated in the top or bottom 2 scores of the 7-point Likert scale by at least 80% and 66% of the participants, respectively (table 1).19

Table 1
•
Consensus criteria for inclusion or exclusion of the statements in the checklist

Diagnostic accuracy study

Data collection and patient population

The checklist was retrospectively piloted in a sample of consecutive patients with cognitive symptoms (memory, attention and concentration difficulties, and/or problems with thinking) attending seven independent specialist memory services across the UK (National Health Service (NHS) Lothian, NHS Fife, NHS Forth Valley, North Bristol NHS Trust, Ashford and St. Peter’s Hospitals, St George’s University Hospitals and Oxford University Hospitals. All patients had been evaluated by eleven consultant-level clinicians (nine consultant neurologists and two consultant neuropsychiatrists). ‘Reference’ expert diagnoses (FCD and other neurocognitive disorders) were made independently of the checklist items according to local pragmatic practices based on clinical assessment, multidisciplinary discussion, neuropsychological testing, brain imaging and fluid diagnostic tests when deemed appropriate (eg, AD was diagnosed based on multidomain cognitive decline, abnormal CSF/genetics and/or neuroimaging). A multifactorial dementia label was used for patients with more than one cause accounting for their cognitive impairment. Patients with an uncertain diagnosis, no aetiology for their cognitive symptoms, advanced dementia (ie, significant cognitive and/or physical disability and reliance on others for their own care), any toxic, metabolic or systemic disease, a major psychiatric disorder or psychoactive medication likely to account for the cognitive difficulties were excluded. Comorbidities, including mild mood disorders, were allowed in all disease groups if they did not account for the whole symptomatology. Three of the clinicians contributing with patient data had not been involved in the development of the checklist during the initial Delphi study. None of the 11 clinicians were involved in the statistical analyses. All were familiar with the proposed criteria for FCD2 and identified FCD cases based on the presence of internal inconsistency (eg, variability in task performance, significant discrepancy between the self-reported symptoms and everyday functioning or cognitive test performance), in the absence of another medical or psychiatric condition better explaining the symptoms.2

Clinicians were invited to fill in the diagnostic checklist using information from the clinical letters and medical charts from at least 10 consecutive FCD patients attending their clinics and 10 others with other neurocognitive diagnoses to create 2 groups of a balanced number of patients with FCD and non-FCD diagnoses. Extraction was done between September 2023 and February 2024. Besides the checklist items, coded as present or absent, attending clinicians also extracted: sex, age of symptom onset, age at diagnosis and final diagnosis at last follow-up. Patients were followed longitudinally for at least 12 months. During the extraction, items not available in the records were left blank, to reduce any potential bias and diagnostic imprecision. Missing data were registered for analysis. For the full-checklist analysis, it was assumed that if certain features were not mentioned in the medical records, they were likely not present because clinicians are less likely to mention absent findings even if they were asked as part of the interview, as in previous studies.20 21

Statistical analyses

Statistical analysis on deidentified data was performed by VC. The study follows the Standards for Reporting of Diagnostic Accuracy (STARD) guidelines.22

Checklist reduction

The per cent of positive responses for each item in the FCD and non-FCD diagnostic groups was compared with verify if all items were more frequent in the FCD group. Phi coefficients were calculated for each pair of questions to assess for redundant items (ie, correlation higher than 0.9). As all items were more frequent in the FCD group, and there were no near-perfect correlations, no item was eliminated based on the two previous steps. A briefer checklist was produced by removing items with more than 10% missing values.23 The internal validity of the checklist was tested with Cronbach’s alpha: 0.8–0.9 good, >0.9 excellent.24

Checklist performance

Scores on the total and brief diagnostic checklist were obtained for all patients, with higher scores suggesting FCD. The normal distribution was confirmed using the Shapiro-Wilk and the Kolmogorov-Smirnov tests. Independent t-tests were calculated for continuous variables and χ2 analyses for categorical variables. One-way analyses of variance were performed to compare mean total scores across diagnostic groups (FCD and other neurocognitive disorders including AD). Statistical significance was established at two-sided p≤0.05.

Optimal cut-off scores for discrimination between FCD and other neurocognitive disorders were determined using receiver operating characteristic (ROC) curve analysis. The associated area under the curve (AUC) statistic indicates the discriminative accuracy and was classified as follows: ≤0.7 poor, 0.7–0.8 fair, 0.8–0.9 good and≥0.9 excellent.25 Sensitivity, specificity, positive predictive value (+PV) and negative predictive value (−PV) were calculated for each cut-off value. Given that the objective of this checklist is to identify FCD versus other neurocognitive disorders rather than screening all at-risk patients when determining the optimal checklist score cut-off point, a threshold with higher specificity was selected. Youden indices (optimal cut-off points) were also calculated.26 All statistical analyses were performed using R V.3.3 and MedCalc27 for ROC curves.

The protocol was registered in OSF (https://osf.io/rpqc6). The current study is part of the innovative training network (Encompassing Training in fUnctional Disorders across Europe; https://etude-itn.eu/).28

Results

Delphi study

45 experts participated in the first round, and 39 (87%) in subsequent rounds. Most of the experts worked in cognitive clinics in tertiary hospitals.6 29 pilot items were initially generated based on an initial review.16 In round 1, strong (≥80%) or moderate (≥66%) consensus was obtained for three items: ‘discrepancies between self-reported and observed cognitive functioning’, ‘patients giving a detailed history of their memory complaints with specific examples’ and ‘ability to detail their list of prescribed drugs and/or recall previous interactions with other doctors’. Two items were merged due to redundancy after discussion: ‘symptoms of longer duration without progression’ and ‘stability or improvement over time’. Participants were anonymously sent their own rating and received feedback regarding the level of group consensus achieved for each statement. Five additional items were suggested, which were reviewed in round 2 together with those for which no consensus had been achieved in the previous round, and moderate to strong consensus (≥66%) was obtained for ‘marked variability of the symptoms in different situations’ and ‘comorbid non-cognitive functional disorder’. Items with low/borderline low (≥50%) consensus were discussed openly by the group in the final round, which culminated in the inclusion of six additional items(online supplemental file 1).

The preliminary 11-item version of the ‘FCD diagnostic checklist’ is presented in table 2, with further guidance presented in box 1. The score (maximum 11) is obtained by adding positive answers.

Table 2
•
FCD versus other neurocognitive disorders diagnostic checklist
Box 1

Item explanation/scoring instructions

Discrepancy between objective/subjective reporting

Subjectively reported marked cognitive difficulties and/or low standardised cognitive test scores, directly in contrast with, for example: ability to keep a cognitively demanding job without any difficulties, conversational abilities observed during interview, ability to perform certain activities such as reading a book, managing finances and driving without difficulties.

Ability to detail memory complaints

During the interview, the patient provides specific examples of memory failures, with detailed and beyond solicited information. In contrast with patients with neurodegeneration, patients with functional cognitive disorder (FCD) might be able to speak for longer periods, if not interrupted.

Distractibility/fluctuation of symptoms

Difficulties only occurring in particular situations, for example, patients who report detailed episodes of memory loss and are able to sustain attention during the interview but show disproportional impairment of the same functions in other situations (eg, during cognitive testing and when the attention is diverted to the symptoms). This is distinct from simple fluctuation over time, which can be observed in many other processes (such as delirium and Lewy body dementia).

Ability to recall a list of drugs and/or previous interactions with other doctors

Ability to recall previous interactions with other doctors, including particular aspects of prior diagnoses and investigations. Similarly, an ability to detail a list of drugs from memory, and their indications, is often a sign of well-functioning memory, which is incongruent with the symptoms reported.

History of a non-cognitive functional neurological disorder and/or functional somatic disorders

The presence of other symptoms, such as pain, fatigue, dissociation and other functional diagnoses, might be a helpful (although not necessary) lead pointing to FCD.

Patient more aware of the cognitive changes than others

In FCD, collateral history often suggests that the patient’s concern is significantly higher than in the accompanying person/relatives (a sign of increased introspection and awareness of memory difficulties). Supportive demonstrations are attending the clinic alone and/or being self-referred (patient taking the initiative to see a doctor due to his/her/their concerns).

Normal and/or incongruent cognitive performance

Patients with FCD might show a normal cognitive performance, overperform in relation to neurodegenerative conditions or underperform. More important than a normal cognitive performance is the demonstration of inconsistent patterns of performance, especially in the same cognitive domain (eg, worse on immediate recall than delayed recall, stronger performance repeating digits backward compared with digits forward, approximate answers). This indicates cognitive processes performing better when accessed automatically, rather than explicitly. Some patients may show poor persistence across tasks or give vague responses that improve with encouragement. Other mitigating factors that intervene (eg, fluctuations in consciousness, psychiatric state or a significant headache) should be taken into consideration, as this can explain attention deficits.

Symptoms stable or improving over time

Patients might present abruptly with symptoms that start severely and remain stable over time. Other times, patients will have a long history of symptoms that did not progress and/or improve over time. Care should be taken in patients with vascular cognitive impairment or post-traumatic brain injury (TBI) who show stability of symptoms or improvement over time with management of comorbidities.

Ability to detail symptom onset with precision/abrupt onset

Some patients will be able to describe symptom onset with precision, sometimes establishing a connection with a previous injury or specific event like a migraine attack, an episode of dissociation or a mild head injury.

Psychological stressor

Psychological stressors can act as predisposing, precipitant or perpetuating factors in FCD. For a subset of patients, the pathophysiology of FCD is linked with depressive symptoms and anxiety, and other stressor life events. For some patients, memory symptoms might be linked to significant life events like a recent bereavement or a physical illness, although this may also occur in neurodegeneration and should never be interpreted in isolation.

Ability to answer compound/double-barrelled questions

Ability to address parts of a compound question was more frequently reported in FCD versus neurodegeneration. Generally, although patients with mild cognitive impairment (MCI) due to neurodegeneration might be able to address these questions (especially if highly educated), in FCD, this will be incongruent with severe symptoms, while in MCI due to neurodegeneration it may just be in keeping with low level of impairment in early stages.

Diagnostic accuracy study

Patient population

A total of 239 patients (143 FCD and 96 neurocognitive disorders), including 129 female and 110 male, were included (table 3). Compared with other neurocognitive disorders, FCD patients had a younger onset of symptoms (mean age 50 years vs 65 years) and younger age at diagnosis (p<0.001). The mean duration of symptom prior to diagnosis (3 years) and sex did not differ between groups.

Table 3
•
Patient demographics and per cent of positive responses on single items of the checklist

Checklist reduction and internal validity

All items on the checklist were significantly more frequent in patients in the FCD group (table 3). As expected, many items were correlated, but no pair had a perfect correlation (online supplemental figure 2). Four items had more than 10% missing data (3, 4, 7 and 11) and a briefer checklist without these four items was produced. The full checklist had a good internal validity (Cronbach’s alpha=0.85), and so did the brief version (Cronbach’s alpha=0.80).

Checklist performance

There was a significant difference in the full and brief checklist scores between FCD and other neurocognitive disorders (figure 2A,B). Across diagnostic subgroups, the FCD group obtained a mean full checklist score of 7.9, followed by the group with probable AD (mean score 1.9) and then other non-AD cognitive disorders including multifactorial dementia, frontotemporal dementias, Lewy body dementias, MCI, vascular dementia, normal pressure hydrocephalus and autoimmune encephalitis (overall with a mean full checklist score of 2.4) (F(10, 228)=73.89, p<0.001 for the full checklist and F(10, 228)=70.4, p<0.001 for the brief version without items 3, 4, 7 and 11) (figure 2C and table 4).

Table 4
•
Mean scores on the ‘FCD diagnostic checklist’ (full and brief versions), across diagnostic subgroups
Figure 2
Request permission

(A, B) Bar plots with distribution of scores across FCD and non-FCD groups, for the full (A) and brief (B) versions of the checklist; (C) distribution of the full checklist scores across diagnostic groups (n≥5 only). AD, Alzheimer’s disease; bvFTD, behavioural variant of frontotemporal dementia; FCD, functional cognitive disorder; MCI, mild cognitive impairment; PPA, primary progressive aphasia.

The AUC was excellent both for the full (AUC=0.97, 95% CI 0.95 to 0.99, p<0.001) and brief (AUC=0.96, 95% CI 0.93 to 0.98, p<0.001) versions of the checklist to discriminate between FCD and other neurocognitive disorders. The best-computed cut-off points for higher diagnostic specificity were 5.5 for the full checklist and 3.5 for the brief checklist. A cut-off score of 6 for the full checklist and 4 for the brief version corresponded to a specificity of 97% and a +PV of 91% (figure 3 with FCD as positive actual state).

Figure 3
Request permission

ROC curves for FCD versus other neurocognitive diagnoses (n=239). Full checklist (11 items) on the left, and brief version (7 items) on the right, with FCD as positive state. Coordinates of the ROC curve used to determine cut-off values under the respective curve. A cut-off point maximising specificity and +PV, while keeping a reasonable sensitivity was chosen (grey row). AUC, area under the curve; FCD, functional cognitive disorder; +PV, positive predictive value; −PV, negative predictive value; ROC, receiver operating characteristic.

Discussion

Cognitive symptoms pose a significant diagnostic challenge to clinicians due to similarities in clinical presentation and diagnostic investigations between FCD and early stages of neurodegeneration, particularly AD. While positive discriminative features have been previously identified,1 2 16 the usefulness of these features had not been standardised nor examined in a systematic way. This pilot diagnostic checklist represents an initial attempt to group clinical features from the history and examination alone to support an accurate discrimination between FCD and other neurocognitive disorders.6 16 In this initial retrospective study, this novel tool obtained robust internal validity and excellent accuracy to differentiate between FCD and non-FCD neurocognitive diagnoses. A brief seven-item version showed comparable accuracy and may simplify the assessment in busy or less specialised settings. A score of 6 or above in the full checklist, and 4 or more in the brief checklist, yield a diagnostic specificity of 97% and a +PV of 91% for FCD.

During our multistep Delphi, the panel voting was extended to include discussions regarding the key challenges in identifying FCD among the wider group of neurocognitive disorders. The items for which higher consensus was achieved (at least two-thirds of the panel) were further supported by the existing literature. Specifically, the inclusion of symptom reporting items aimed to capture observed discrepancies between reported symptoms and actual daily functioning, the ability to provide detailed symptom descriptions, and the presence of cognitive fluctuations or distractibility, all of which support an FCD diagnosis.2 29 30 Further, increased introspection and awareness of memory problems and a discordance between an informant’s rapport and patient’s concern were found to be helpful as a screening method,31 supporting the inclusion of item 6. Comorbidities, such as non-cognitive FNDs, somatic symptoms (pain, fatigue) and psychological stressors, are common in FCD,15 and potential predisposing and precipitating factors.5 14 FCD patients often show stability or improvement over time2 14 and may exhibit a sudden onset,16 32 in contrast to the progressive and insidious onset seen in neurodegenerative disorders.1 16 Demographic factors such as younger age, female sex, educational level and family history of dementia did not reach sufficient consensus for inclusion in this checklist, as their added value for individual risk of progression is limited.33 Similarly, while personality traits may point to increased vulnerability for FCD, these are unlikely to be diagnostically helpful and may emphasise subjective judgments. Performance validity tests did not reach sufficient consensus for inclusion and were found to be abnormal in only a minority of patients;34 further FCD patients do not necessarily fail these tests more often than other groups.35 The subsequent retrospective analysis of checklist performance confirmed the relevance of the items included, reinforcing our confidence in the selected set of symptoms and signs to aid in the early identification of FCD versus other neurodegenerative disorders. Yet, it is critical to emphasise that the checklist was designed so that no individual items are taken in isolation, and these findings require further blinded prospective evaluation.

As potential advantages, the diagnostic checklist can be completed in approximately 5 min based on the information obtained from a standard clinical interview and does not rely on subjective severity ratings or invasive diagnostic assays.

Our findings add to the literature on diagnostic screening tools for FCD. Previously, a patient-facing tool identified typical symptoms of FCD but was found to overdiagnose FCD in healthy individuals.36 Other machine learning approaches based on conversational analysis, language and interactional features have shown excellent accuracy but were tested in much smaller sample sizes and require sophisticated equipment and analysis that are not widely available.29 37

Implications of the checklist and future research

The application of the checklist by different clinicians across multiple centres supports the diagnosis based on positive signs (‘ruling in’) instead of exclusionary features (ie, negative investigations).2 Yet, it is a decision-making aid and is not intended to replace a comprehensive clinical assessment.38

Future blinded prospective validations in larger samples and diverse settings, including primary care, low-income countries and in populations with lower education profiles,4 should be pursued before clinical use is recommended. We plan to explore the checklist’s ability to discriminate between FCD and other mimics including atypical presentations of neurodegeneration, psychiatric diagnoses and cognitive effects of medications and sleep disorders,15 and whether this instrument adds value to existing tools and biomarkers.38 Future studies may also clarify whether the checklist can contribute to stratifying diagnostic workflow and referral, as well as reducing the need for costly, invasive and potentially harmful diagnostic tests with insufficient specificity. For instance, patients needing an assessment for neurodegeneration, who may be eligible for disease modifying therapies, might take the highest priority for specialised cognitive clinics, while FCD could be better managed in neuropsychiatry services with access to more appropriate therapies like psychotherapy and speech and language therapy.39 Early identification of FCD also has implications for inclusion/exclusion criteria of disease-modifying therapies for neurodegenerative disorders and future FCD trials.39 40 Last but not least, dissemination of the checklist serves an educational purpose by supporting history-taking and increasing the confidence of non-expert clinicians in diagnosing FCD and communicating this diagnosis with higher assurance.

Limitations

This pilot study has important limitations. The checklist was filled retrospectively and clinicians were not blinded, so diagnostic suspicion bias (ie, clinicians eliciting and accurately documenting the features included in the checklist in FCD patients only) cannot be excluded. This approach was chosen to obtain a larger sample size with at least 12 months follow-up to increase confidence in individual diagnoses. While problematic, this is somewhat mitigated by (1) multistep Delphi methodology with multidisciplinary expertise supported by literature; (2) information recorded prior to checklist development and (3) three clinicians involved in the diagnostic accuracy study had not contributed to the development of the checklist. Although the scale could not be filled in real time, this principally affected four items, which motivated the development of a briefer checklist that obtained similar preliminary accuracy. UK-based memory services focus on individuals under 65 years old, potentially limiting the generalisability of these findings, as seen by the modest sample size of other neurodegenerative diagnoses including vascular dementia and Lewy body dementia which predominantly affect older populations. Experienced clinicians may assimilate the information in a different way to those with lower expertise. A further potential criticism relates to the use of a clinical expert diagnosis as ‘reference standard’. While from a scientific standpoint pathological support in all cases is desirable, our approach aligns with current pragmatic practices, multidisciplinary assessment and diagnostic criteria of FCD.2 Yet, we cannot exclude that a small number of FCD patients may be in prodromal stages of AD, even if 94% of our FCD sample was stable or improving at follow-up. In a previous position statement, this panel argued that conducting invasive tests for every patient would be rather impractical and economically unjustifiable when the diagnosis made by an expert is secure, risks false positive diagnoses and is dependent on local availability.6 Practically, a small degree of overlap in the scores for the FCD and non-FCD groups is expected, but this made less than 6% and 8% of the patients with non-FCD in our sample, using the full and brief checklist, respectively. Borderline cut-offs should call for further investigations, and follow-up is advised. It is unknown whether the checklist can identify FCD patients with shorter symptom duration for which stability of symptoms or improvement over time may be difficult to judge. Because of these limitations, the checklist including cut-off scores will have to be validated prospectively in larger more diverse cohorts before clinical use is recommended.

Conclusions

A novel pilot checklist to identify FCD versus other neurodegenerative disorders may be a complementary clinical tool to facilitate an earlier and more accurate FCD diagnosis. Future prospective validation in diverse settings, by clinicians blinded for the diagnosis and with lower expertise, is needed to validate this checklist and assess for potential improvements in consistency and cost-effectiveness of FCD diagnosis and management.

Supplementary PDF Icon
Supplementary PDF

  • X: @Veronicabreira, @Dr_Di_Lorenzo, @JMassano, @_Emmi_Scott

  • Contributors: VC, LM, JSch and AC: conception and design; VC, JA, SAnt, RA, SAyb, HAB, GB, RB, JC, BD, ME, SRF, KSF, TH, BH, RH, JH, JDI, CL, AL, FDL, JMai, EM, CM, JMas, ERM, IPM, FN, SPal, CMP, MT-P, DP, SPop, DR, MRos, MRus, IS, JSto, EPS, RT, TT, MT, STom, STon, CWT, TW, MZ, LM: acquisition of data; VC, AC: analysis and interpretation of data; VC: writing the first draft; JA, SAnt, RA, SAyb, HAB, GB, RB, JC, BD, ME, SRF, KSF, TH, BH, RH, JH, JDI, CL, AL, FDL, JMai, EM, CM, JMas, ERM, IPM, FN, SPal, CMP, MT-P, DP, SPop, DR, MRos, MRus, IS, JMS, EPS, RT, TT, MT, STom, STon, CWT, TW, MZ, LF, LM, JSch, AC: editing and revising. VC, LF, JSto, AC: Funding acquisition. VC and AC are the guarantors of the study.

  • Funding: VC receives funding from the EU H2020 Marie Skłodowska-Curie Innovative Training Network, grant agreement 956673.

  • Disclaimer: This article reflects only the author's view, the Agency is not responsible for any use that may be made of the information it contains.

  • Competing interests: LM receives funding from the Scottish Government Chief Scientist Office. LM provides independent medical testimony in court cases regarding patients with functional disorders, is secretary of the British Neuropsychiatry Association, and receives research funding from the Scottish Government Chief Scientist Office. JSch reports personal fees from UptoDate, outside the submitted work, runs a self-help website for patients with functional neurological symptoms (www. neurosymptoms.org) which is free and has no advertising, provides independent medical testimony in personal injury and negligence cases regarding patients with functional disorders and is secretary of the International Functional Neurological Disorder Society. He is a Chief Scientists Office NHS Research Scotland Career Researcher. AC is a director of a limited personal services company that provides independent medical testimony in court cases on a range of neuropsychiatric topics on a 50% pursuer 50% defender basis, a paid associate editor of the Journal of Neurology Neurosurgery and Psychiatry, and unpaid president elect of the International Functional Neurological Disorder Society. DP has received honoraria for continuing medical education lectures in FND; royalties from Springer for a functional movement disorder textbook and honoraria from Elsevier for a functional neurological disorder textbook; is on the editorial boards of Brain and Behavior (paid), Epilepsy.

  • Provenance and peer review: Not commissioned; externally peer reviewed.

  • Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Data availability statement

Data are available on reasonable request. VC and AC have full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Data are available from the authors on reasonable request.

Ethics statements

Patient consent for publication:
Ethics approval:

Ethics approval was obtained by the University of Edinburgh (23-EMREC-026) and reviewed by local research and development (R&D) departments in each recruiting centre. Patient informed consent was waived on the basis that all clinical information was initially obtained as a standard of routine medical care, the data were deidentified, and the checklist would be filled by the attending physician.

  1. close McWhirter L, Ritchie C, Stone J, et al. Functional cognitive disorders: a systematic review. Lancet Psychiatry 2020; 7:191–207.
    doi:10.1016/S2215-0366(19)30405-5•Google Scholar
  2. close Ball HA, McWhirter L, Ballard C, et al. Functional cognitive disorder: dementia’s blind spot. Brain (Bacau) 2020; 143:2895–903.
    doi:10.1093/brain/awaa224•Google Scholar
  3. close Bailey C, Bell SM, Blackburn DM, et al. How the UK describes functional memory symptoms. Psychogeriatrics 2017; 17:336–7.
    doi:10.1111/psyg.12232•Google Scholar
  4. close Borelli WV, de Senna PN, Brum WS, et al. Functional Cognitive Disorder Presents High Frequency and Distinct Clinical Profile in Patients With Low Education. Front Aging Neurosci 2022; 14.
    doi:10.3389/fnagi.2022.789190•Google Scholar
  5. close Bhome R, Huntley JD, Price G, et al. Clinical presentation and neuropsychological profiles of Functional Cognitive Disorder patients with and without co-morbid depression. Cogn Neuropsychiatry 2019; 24:152–64.
    doi:10.1080/13546805.2019.1590190•Google Scholar
  6. close Cabreira V, Alty J, Antic S, et al. Perspectives on the diagnosis and management of functional cognitive disorder: An international Delphi study. Eur J Neurol 2025; 32.
    doi:10.1111/ene.16318•Google Scholar
  7. close Hansson O. Biomarkers for neurodegenerative diseases. Nat Med 2021; 27:954–63.
    doi:10.1038/s41591-021-01382-x•Google Scholar
  8. close Vromen EM, de Boer SCM, Teunissen CE, et al. Biomarker A+T-: is this Alzheimer’s disease or not? A combined CSF and pathology study. Brain (Bacau) 2023; 146:1166–74.
    doi:10.1093/brain/awac158•Google Scholar
  9. close Visser PJ, Verhey F, Knol DL, et al. Prevalence and prognostic value of CSF markers of Alzheimer’s disease pathology in patients with subjective cognitive impairment or mild cognitive impairment in the DESCRIPA study: a prospective cohort study. Lancet Neurol 2009; 8:619–27.
    doi:10.1016/S1474-4422(09)70139-5•Google Scholar
  10. close Ossenkoppele R, Pichet Binette A, Groot C, et al. Amyloid and tau PET-positive cognitively unimpaired individuals are at high risk for future cognitive decline. Nat Med 2022; 28:2381–7.
    doi:10.1038/s41591-022-02049-x•Google Scholar
  11. close Planche V, Bouteloup V, Pellegrin I, et al. Validity and Performance of Blood Biomarkers for Alzheimer Disease to Predict Dementia Risk in a Large Clinic-Based Cohort. Neurology (ECronicon) 2023; 100:e473–84.
    doi:10.1212/WNL.0000000000201479•Google Scholar
  12. close van Tol MJ, van der Wee NJA, van den Heuvel OA, et al. Regional brain volume in depression and anxiety disorders. Arch Gen Psychiatry 2010; 67:1002–11.
    doi:10.1001/archgenpsychiatry.2010.121•Google Scholar
  13. close Bharambe V, Larner AJ. Functional cognitive disorders: demographic and clinical features contribute to a positive diagnosis. Neurodegener Dis Manag 2018; 8:377–83.
    doi:10.2217/nmt-2018-0025•Google Scholar
  14. close Ball HA, Swirski M, Newson M, et al. Differentiating Functional Cognitive Disorder from Early Neurodegeneration: A Clinic-Based Study. Brain Sci 2021; 11.
    doi:10.3390/brainsci11060800•Google Scholar
  15. close Teodoro T, Edwards MJ, Isaacs JD, et al. A unifying theory for cognitive abnormalities in functional neurological disorders, fibromyalgia and chronic fatigue syndrome: systematic review. J Neurol Neurosurg Psychiatry 2018; 89:1308–19.
    doi:10.1136/jnnp-2017-317823•Google Scholar
  16. close Cabreira V, Frostholm L, McWhirter L, et al. Clinical signs in functional cognitive disorders: A systematic review and diagnostic meta-analysis. J Psychosom Res 2023; 173:111447.
    doi:10.1016/j.jpsychores.2023.111447•Google Scholar
  17. close Jones RN, Manly J, Glymour MM, et al. Conceptual and measurement challenges in research on cognitive reserve. J Int Neuropsychol Soc 2011; 17:593–601.
    doi:10.1017/S1355617710001748•Google Scholar
  18. close McKenna HP. The Delphi technique: a worthwhile research approach for nursing? J Adv Nurs 1994; 19:1221–5.
    doi:10.1111/j.1365-2648.1994.tb01207.x•Google Scholar
  19. close Green B, Jones M, Hughes D, et al. Applying the Delphi technique in a study of GPs’ information requirements. Health Soc Care Community 1999; 7:198–205.
    doi:10.1046/j.1365-2524.1999.00176.x•Google Scholar
  20. close Lagrand T, Tuitert I, Klamer M, et al. Functional or not functional; that’s the question: Can we predict the diagnosis functional movement disorder based on associated features? Eur J Neurol 2021; 28:33–9.
    doi:10.1111/ene.14488•Google Scholar
  21. close Lenio S, Kerr WT, Watson M, et al. Validation of a predictive calculator to distinguish between patients presenting with dissociative versus epileptic seizures. Epilepsy Behav 2021; 116:107767.
    doi:10.1016/j.yebeh.2021.107767•Google Scholar
  22. close Cohen JF, Korevaar DA, Altman DG, et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open 2016; 6.
    doi:10.1136/bmjopen-2016-012799•Google Scholar
  23. close Fournier CN, Bedlack R, Quinn C, et al. Development and Validation of the Rasch-Built Overall Amyotrophic Lateral Sclerosis Disability Scale (ROADS). JAMA Neurol 2020; 77:480–8.
    doi:10.1001/jamaneurol.2019.4490•Google Scholar
  24. close Taber KS. The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Res Sci Educ 2018; 48:1273–96.
    doi:10.1007/s11165-016-9602-2•Google Scholar
  25. close Nahm FS. Receiver operating characteristic curve: overview and practical use for clinicians. Korean J Anesthesiol 2022; 75:25–36.
    doi:10.4097/kja.21209•Google Scholar
  26. close Ruopp MD, Perkins NJ, Whitcomb BW, et al. Youden Index and optimal cut-point estimated from observations affected by a lower limit of detection. Biom J 2008; 50:419–30.
    doi:10.1002/bimj.200710415•Google Scholar
  27. close MedCalc statistical software version 19.2.6. Belgium:MedCalc Software 2020;
    Google Scholar
  28. close Rosmalen JGM, Burton C, Carson A, et al. The European Training Network ETUDE (Encompassing Training in fUnctional Disorders across Europe): a new research and training program of the EURONET-SOMA network recruiting 15 early stage researchers. J Psychosom Res 2021; 141:110345.
    doi:10.1016/j.jpsychores.2020.110345•Google Scholar
  29. close Reuber M, Blackburn DJ, Elsey C, et al. An Interactional Profile to Assist the Differential Diagnosis of Neurodegenerative and Functional Memory Disorders. Alzheimer Dis Assoc Disord 2018; 32:197–206.
    doi:10.1097/WAD.0000000000000231•Google Scholar
  30. close Elsey C, Drew P, Jones D, et al. Towards diagnostic conversational profiles of patients presenting with dementia or functional memory disorders to memory clinics. Patient Educ Couns 2015; 98:1071–7.
    doi:10.1016/j.pec.2015.05.021•Google Scholar
  31. close Numbers K, Crawford JD, Kochan NA, et al. Participant and informant memory-specific cognitive complaints predict future decline and incident dementia: Findings from the Sydney Memory and Ageing Study. PLoS One 2020; 15.
    doi:10.1371/journal.pone.0232961•Google Scholar
  32. close Picon EL, Todorova EV, Palombo DJ, et al. Memory Perfectionism is Associated with Persistent Memory Complaints after Concussion. Arch Clin Neuropsychol 2022; 37:1177–84.
    doi:10.1093/arclin/acac021•Google Scholar
  33. close Cullen NC, Janelidze S, Stomrud E, et al. Plasma amyloid-β42/40 and apolipoprotein E for amyloid PET pre-screening in secondary prevention trials of Alzheimer’s disease. Brain Commun 2023; 5.
    doi:10.1093/braincomms/fcad015•Google Scholar
  34. close Silverberg ND, Rush BK. Neuropsychological evaluation of functional cognitive disorder: A narrative review. Clin Neuropsychol 2024; 38:302–25.
    doi:10.1080/13854046.2023.2228527•Google Scholar
  35. close McWhirter L, Ritchie CW, Stone J, et al. Performance validity test failure in clinical populations-a systematic review. J Neurol Neurosurg Psychiatry 2020; 91:945–52.
    doi:10.1136/jnnp-2020-323776•Google Scholar
  36. close Schmidtke K, Metternich B. Validation of two inventories for the diagnosis and monitoring of functional memory disorder. J Psychosom Res 2009; 67:245–51.
    doi:10.1016/j.jpsychores.2009.04.005•Google Scholar
  37. close O’Malley RPD, Mirheidari B, Harkness K, et al. Fully automated cognitive screening tool based on assessment of speech and language. J Neurol Neurosurg Psychiatry 2020;
    doi:10.1136/jnnp-2019-322517•Google Scholar
  38. close Frisoni GB, Festari C, Massa F, et al. European intersocietal recommendations for the biomarker-based diagnosis of neurocognitive disorders. Lancet Neurol 2024; 23:302–12.
    doi:10.1016/S1474-4422(23)00447-7•Google Scholar
  39. close Godena EJ, Freeburn JL, Silverberg ND, et al. A Case of Functional Cognitive Disorder: Psychotherapy and Speech and Language Therapy Insights. Harv Rev Psychiatry 2023; 31:248–56.
    doi:10.1097/HRP.0000000000000379•Google Scholar
  40. close Poole N, Cope S, Vanzan S, et al. Feasibility randomised controlled trial of online group Acceptance and Commitment Therapy for Functional Cognitive Disorder (ACT4FCD). BMJ Open 2023; 13.
    doi:10.1136/bmjopen-2023-072366•Google Scholar

  • Received: 23 September 2024
  • Accepted: 31 January 2025
  • First Published: 27 February 2025

Article metrics
Altmetricopen-url
Dimensionsopen-url