Multi-centre classification of functional neurological disorders based on resting-state functional connectivity

Samantha Weber, Salome Heim, Jonas Richiardi, Dimitri Van De Ville, Tereza Serranova, Robert Jech, Ramesh S. Marapin, Marina A. J. Tijssen, Selma Aybek*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

9 Citations (Scopus)
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Abstract

Background: Patients suffering from functional neurological disorder (FND) experience disabling neurological symptoms not caused by an underlying classical neurological disease (such as stroke or multiple sclerosis). The diagnosis is made based on reliable positive clinical signs, but clinicians often require additional time-and cost consuming medical tests and examinations. Resting-state functional connectivity (RS FC) showed its potential as an imaging-based adjunctive biomarker to help distinguish patients from healthy controls and could represent a "rule-in" procedure to assist in the diagnostic process. However, the use of RS FC depends on its applicability in a multi-centre setting, which is particularly susceptible to inter-scanner variability. The aim of this study was to test the robustness of a classification approach based on RS FC in a multi-centre setting.Methods: This study aimed to distinguish 86 FND patients from 86 healthy controls acquired in four different centres using a multivariate machine learning approach based on whole-brain resting-state functional connectivity. First, previously published results were replicated in each centre individually (intra-centre cross validation) and its robustness across inter-scanner variability was assessed by pooling all the data (pooled cross-validation). Second, we evaluated the generalizability of the method by using data from each centre once as a test set, and the data from the remaining centres as a training set (inter-centre cross-validation).Results: FND patients were successfully distinguished from healthy controls in the replication step (accuracy of 74%) as well as in each individual additional centre (accuracies of 73%, 71% and 70%). The pooled cross validation confirmed that the classifier was robust with an accuracy of 72%. The results survived post-hoc adjustment for anxiety, depression, psychotropic medication intake, and symptom severity. The most discriminant features involved the angular-and supramarginal gyri, sensorimotor cortex, cingular-and insular cortex, and hippocampal regions. The inter-centre validation step did not exceed chance level (accuracy below 50%).Conclusions: The results demonstrate the applicability of RS FC to correctly distinguish FND patients from healthy controls in different centres and its robustness against inter-scanner variability. In order to generalize its use across different centres and aim for clinical application, future studies should work towards optimization of acquisition parameters and include neurological and psychiatric control groups presenting with similar symptoms.

Original languageEnglish
Article number103090
Number of pages13
JournalNeuroImage. Clinical
Volume35
DOIs
Publication statusPublished - 2022

Keywords

  • Functional connectivity
  • Biomarker
  • Multi-site
  • Conversion disorder
  • Inter-scanner variability
  • PSYCHOGENIC MOVEMENT-DISORDERS
  • CONVERSION DISORDER
  • ALZHEIMERS-DISEASE
  • MOTOR
  • FMRI
  • EMOTION
  • MACHINE
  • NETWORK
  • PARCELLATION
  • BIOMARKERS

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