• Christiana Westlin, Andrew J Guthrie, Sara Paredes-Echeverri, Julie Maggio, Sara Finkelstein, Ellen Godena, Daniel Millstein, Julie MacLean, Jessica Ranford, Jennifer Freeburn, Caitlin Adams, Christopher Stephen, Ibai Diez, and David L Perez.
• Functional Neurological Disorder Research Group, Division of Behavioral Neurology & Integrated Brain Medicine, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA dlperez@nmr.mgh.harvard.edu cwestlin@mgh.harvard.edu.
• J. Neurol. Neurosurg. Psychiatr. 2024 Jul 20.

BackgroundBrain imaging studies investigating grey matter in functional neurological disorder (FND) have used univariate approaches to report group-level differences compared with healthy controls (HCs). However, these findings have limited translatability because they do not differentiate patients from controls at the individual-level.Methods183 participants were prospectively recruited across three groups: 61 patients with mixed FND (FND-mixed), 61 age-matched and sex-matched HCs and 61 age, sex, depression and anxiety-matched psychiatric controls (PCs). Radial basis function support vector machine classifiers with cross-validation were used to distinguish individuals with FND from HCs and PCs using 134 FreeSurfer-derived grey matter MRI features.ResultsPatients with FND-mixed were differentiated from HCs with an accuracy of 0.66 (p=0.005; area under the receiving operating characteristic (AUROC)=0.74); this sample was also distinguished from PCs with an accuracy of 0.60 (p=0.038; AUROC=0.56). When focusing on the functional motor disorder subtype (FND-motor, n=46), a classifier robustly differentiated these patients from HCs (accuracy=0.72; p=0.002; AUROC=0.80). FND-motor could not be distinguished from PCs, and the functional seizures subtype (n=23) could not be classified against either control group. Important regions contributing to statistically significant multivariate classifications included the cingulate gyrus, hippocampal subfields and amygdalar nuclei. Correctly versus incorrectly classified participants did not differ across a range of tested psychometric variables.ConclusionsThese findings underscore the interconnection of brain structure and function in the pathophysiology of FND and demonstrate the feasibility of using structural MRI to classify the disorder. Out-of-sample replication and larger-scale classifier efforts incorporating psychiatric and neurological controls are needed.© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.

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