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Abstract:
Introduction:
Patients with functional (psychogenic) movement disorders (FMD) can have various motor and non-motor symptoms including anxiety and depression. An aberrant integration of the sensorimotor and affective processing, and a dysfunctional sense of agency have been proposed as the key mechanisms involved in FMD development suggesting that FMD have features of a multiple network disorder. Therefore, exploration of brain connectivity mapping could provide important insights in the pathophysiology of FMD. The aim of this study was to analyze resting state fMRI to avoid variability related to aberrant movements or somatosensory misperception while taking into account anxiety and depression as confounding factors. To avoid selection bias, we used the eigen-vector centrality (EC) mapping to allow data-driven detection of connectivity hubs with rich connections to other regions of the brain that are highly connected.
Methods:
Forty-four patients with FMD (34F, age 45±(SD)9 years, disease duration 8.4±5,6 years) with heterogeneous motor phenotypes were compared to 44 matched control subjects (33F, 44±10y). The anxiety state (STAIX-1) and trait (STAIX-2) and Beck depression inventory (BDI-II) were recorded. All subjects were instructed to watch a cross for 10 minutes during rs-fMRI acquisition (3T, Siemens, Skyra: T2*-weighted gradient echo echo-planar imaging, TR=2s, TE=30ms, FA=90, 300 scans). The preprocessing and statistics were performed with the SPM12 software (London, UK). General connectivity was derived from EC mapping (Lohmann et al., 2010) with a new correlation metric called ReLU correlation (Lohmann et al., 2018). For comparison, we also computed the EC with three other correlation metrics. Group analysis was based on a GLM implementing the contrast between both groups of subjects including 'STAIX-2', 'age', 'gender', and 'antidepressant drugs' as additional covariates. For selective connectivity, voxel-wise correlations using seeds derived from the results of the group EC analysis were computed in each subject. Group results were obtained with non-parametric statistics based on threshold-free cluster enhancement (TFCE toolbox, Jena, Germany) using the family-wise error correction at p<0.05.
Results:
FMD patients in comparison with controls showed decreased general connectivity in the SMA and midcingulate bilaterally and in the right superior frontal gyrus and right insula. This result became significant if the STAIX-2 was used as the nuisance covariate in the model (Figure 1). Selective connectivity from the seeds in the SMA and midcingulate showed disconnections in FMD patients in comparison with controls in multiple regions involving Rolandic areas, superior temporal gyri, posterior cingulate, insula, inferolateral prefrontal cortex, visual cortex and cerebellum (Figure 2). We obtained similar results with all correlation metrics, however, the RLC approach appeared to be most sensitive for the detection of EC differences between both groups.
Conclusions:
Despites motor symptoms variability, the brain of FMD patients exhibited a uniform connectivity pattern involving the SMA and midcingulate, which were generally less connected with all other cortical and subcortical regions than in controls. The results are in line with previous reports (Roelofs et al., 2019) using different approaches to rs-fMRI or brain morphometry analyses and fit well to our knowledge of the functional role of these regions and their possible malfunctions. Disconnected SMA is potentially associated with dysfunctional planning of voluntary movements or with insufficient suppression of unwanted actions (Nachev et al., 2008; Ruan et al., 2018). Disconnection of the midcingulate may affect multisensory orientation, nociceptive processing or nocifensive behavior (Vogt, 2016; Ospina et al., 2019). Our findings thus support FMD as a network disorder affecting hubs connecting motor and non-motor networks. Supported by the grant AZV 16-29651A.
