36 Study of the prevalence of autistic traits and alexithymia, with associated psychiatric comorbidity, in an outpatient program of patients with functional neurological symptom disorder (FNSD)
Bibliographic record
Abstract
Introduction Whilst higher rates of alexithymia have previously been reported in FNSD, little is known about the prevalence of autistic traits in adults with FNSD. We aim to: Report on the prevalence of autistic traits in an outpatient group of adults diagnosed with FNSD using the Autism Spectrum Quotient (AQ-10) Report on the prevalence of alexithymia using the Toronto Alexithymia Scale (TAS-20) Report on differences in symptom severity of psychiatric comorbidity between those scoring <6 or ≥6 on the AQ-10, and those with or without alexithymia Method Out of 105 consecutive patients reviewed in an outpatient FNSD program, 91 completed self-report assessments for autistic traits, alexithymia, generalised anxiety, depression, social phobia, somatic symptom severity, attention deficit hyperactivity disorder (ADHD) and dyslexia. Patients were grouped by AQ-10 scores of <6 or ≥6 and compared for significant differences in tested variables using a Mann-Whitney U test. Kruskal-Wallis H tested differences in alexithymia status. Simple effects were tested using pairwise comparisons. Results 40% screened positive on the AQ-10 (scoring ≥6), and 40% screen positive for alexithymia. When comparing those scoring < or ≥6 on the AQ-10, those with the higher number of autistic traits scored significantly higher on scales of alexithymia, depression, generalised anxiety, social phobia,ADHD, and dyslexia. Positive alexithymia status was significantly associated with a higher number of autistic traits as well symptoms of generalised anxiety, depression, somatic symptoms severity, social phobia and dyslexia. Conclusion Whilst higher rates of neurodevelopmental disorders have previously been reported in FNSD, we report new evidence for a high proportion of autistic traits and further evidence of a high prevalence of alexithymia in a group of adults with FNSD.1–10Mechanistic insights are limited however autistic traits may be associated with FNSD due to altered sensitivity to sensory data, as well as cognitive or affective biases, or increased susceptibility to panic. There may be an additional contribution from psychosocial stressors. Clinically, the AQ-10 and TAS-20 may be important tools in the management of FNSD, and a higher prevalence of autistic traits may highlight a need for specialised communication styles in the MDT.11 This builds on research exploring the relationship between autistic traits, alexithymia and FNSD. Previous research suggests that alexithymia and altered interoceptive awareness may be modifying factors in the relationship between autistic traits and FNSD,12and further research is required to clarify the nature of these relationships. References Demartini B, Nisticò V, Goeta D, Tedesco R, Giordano B, Faggioli R,et al. Clinical overlap between functional neurological disorders and autism spectrum disorders: A preliminary study.Journal of the Neurological Sciences2021 Oct;429:117648. Freedmanet al. Psychogenic nonepileptic events in pediatric patients with autism. Hatta K, Hosozawa M, Tanaka K, Shimizu T. Exploring traits of autism and their impact on functional disability in children with somatic symptom disorder.Journal of Autism and Developmental Disorders2019 Feb 15;49(2):729–37. Jester KA, Londino DL, Hayman J. 2.68 examining the occurrence of conversion disorder diagnoses and asd among adolescents and young adults in the emergency department.Journal of the American Academy of Child & Adolescent Psychiatry [Internet]. 2019 Oct 1 [cited 2022 Feb 7];58(10):S193. Available from: http://www.jaacap.org/article/S089085671931617X/fulltext McWilliams A, Reilly C, Gupta J, Hadji-Michael M, Srinivasan R, Heyman I. Autism spectrum disorder in children and young people with non-epileptic seizures.Seizure2019 Dec 1;73:51–5. Mierschet al. A retrospective study of 131 patients with psychogenic non-epileptic seizures (PNES)- Comorbid diagnoses and outcome after inpatient treatment. Nimmo-Smith V, Heuvelman H, Dalman C, Lundberg M, Idring S, Carpenter P,etal. Anxiety disorders in adults with autism spectrum disorder: a population-based study.Journal of Autism and Developmental Disorders. 2020 Jan 1;50(1):308–18. Pun P, Frater J, Broughton M, Dob R, Lehn A. Psychological profiles and clinical clusters of patients diagnosed with functional neurological disorder.Frontiers in Neurology2020 Oct 15;11. Zdankiewicz-Scigała E, Scigała D, Sikora J, Kwaterniak W, Longobardi C. Relationship between interoceptive sensibility and somatoform disorders in adults with autism spectrum traits. The mediating role of alexithymia and emotional dysregulation.PLoS ONE. 2021 Aug 1;16(8 August). Demartini B, Petrochilos P, Ricciardi L, Price G, Edwards MJ, Joyce E. The role of alexithymia in the development of functional motor symptoms (conversion disorder).Journal of Neurology, Neurosurgery & Psychiatry [Internet]. 2014 Oct 1 [cited 2022 Feb 8];85(10):1132–7. Available from: https://jnnp.bmj.com/content/85/10/1132 Cooper M, Gale K, Langley K, Broughton T, Massey TH, Hall NJ,et al. Neurological consultation with an autistic patient.Practical Neurology [Internet]. 2021 Oct 8 [cited 2022 Jan 14];practneurol-2020-002856. Available from: https://pn.bmj.com/content/early/2021/10/07/practneurol-2020-002856 Shah P, Hall R, Catmur C, Bird G. Alexithymia, not autism, is associated with impaired interoception.Cortex2016 Aug 1;81:215–20.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".