Breakdown of the affective‐cognitive network in functional dystonia

Abstract

Previous studies suggested that brain regions subtending affective‐cognitive processes can be implicated in the pathophysiology of functional dystonia (FD). In this study, the role of the affective‐cognitive network was explored in two phenotypes of FD: fixed (FixFD) and mobile dystonia (MobFD). We hypothesized that each of these phenotypes would show peculiar functional connectivity (FC) alterations in line with their divergent disease clinical expressions. Resting state fMRI (RS‐fMRI) was obtained in 40 FD patients (12 FixFD; 28 MobFD) and 43 controls (14 young FixFD‐age‐matched [yHC]; 29 old MobFD‐age‐matched [oHC]). FC of brain regions of interest, known to be involved in affective‐cognitive processes, and independent component analysis of RS‐fMRI data to explore brain networks were employed. Compared to HC, all FD patients showed reduced FC between the majority of affective‐cognitive seeds of interest and the fronto‐subcortical and limbic circuits; enhanced FC between the right affective‐cognitive part of the cerebellum and the bilateral associative parietal cortex; enhanced FC of the bilateral amygdala with the subcortical and posterior cortical brain regions; and altered FC between the left medial dorsal nucleus and the sensorimotor and associative brain regions (enhanced in MobFD and reduced in FixFD). Compared with yHC and MobFD patients, FixFD patients had an extensive pattern of reduced FC within the cerebellar network, and between the majority of affective‐cognitive seeds of interest and the sensorimotor and high‐order function (“cognitive”) areas with a unique involvement of dorsal anterior cingulate cortex connectivity. Brain FC within the affective‐cognitive network is altered in FD and presented specific features associated with each FD phenotype, suggesting an interaction between brain connectivity and clinical expression of the disease.

Progression of brain functional connectivity and frontal cognitive dysfunction in ALS

“…Rs-fMRI data processing was carried out using the FMRIB software library (FSLv5.0) as described previously. ( Canu et al, 2020 ) The first four volumes of the rs-fMRI data were removed to reach complete magnet signal stabilization. The following FSL-standard preprocessing pipeline was applied: (1) motion correction using MCFLIRT; (2) high-pass temporal filtering (lower frequency: 0.01 Hz); (3) spatial smoothing (Gaussian Kernel of FWHM 6 mm); (4) single-session independent component analysis-based automatic removal of motion artifacts (ICA_AROMA) ( Pruim et al, 2015 ) in order to identify those independent components (ICs) representing motion-related artifacts.…”

“…Based on findings observed from the independent component analyses (i.e., an increased rs-FC of the MFG was present in ALS patients after six months within the frontostriatal and the left frontoparietal networks, and was related with the patients cognitive dysfunction at baseline; see Results for details), we performed a seed-based analysis, as previously described. ( Canu et al, 2020 ) Two regions of interest were selected: left and right MFG. These regions were defined in the MNI space using the automated anatomical labelling atlas (AAL) in WFU PickAtlas (toolbox of SPM12), moved to each subject native T1-weighted space trough non-linear and affine registrations, and visually inspected in the individual brains by neuroimaging expert researchers.…”

Neuroimaging in Functional Neurological Disorder: State of the Field and Research Agenda

“…As detailed in a recent systematic review ( Bègue et al, 2019 ), grey matter alterations in sensorimotor, cingulo-insular and amygdala brain areas have been identified, although findings have been inconsistent ( Aybek et al, 2014a , Espay et al, 2018c , Labate et al, 2012 , Maurer et al, 2018 , Nicholson et al, 2014 , Tomic et al, 2018 ). The importance of individual differences and possible subgroup specific effects in understanding the pathophysiology of FND have also been demonstrated (e.g., Perez et al, 2017b showed that reduced left anterior insula volume was only present in those patients reporting the most severe physical health impairments compared to healthy controls) ( Aybek et al, 2014a , Canu et al, 2020 , Labate et al, 2012 , Maurer et al, 2016 , Maurer et al, 2018 , Perez et al, 2017b , Perez et al, 2018a , Tomic et al, 2018 ). White matter characterization in FND is in its early stages, with initial findings pointing towards altered limbic and associative fiber bundles compared to healthy ( Diez et al, 2021 , Hernando et al, 2015 , Lee et al, 2015 , Sojka et al, 2021 , Tomic et al, 2018 ) and traumatic brain injury controls ( Goodman et al, 2020 ).…”

“…), while the parallel use of healthy controls contextualize findings as outside or inside the range of normal. While a transdiagnostic approach embracing the recruitment of mixed FND cohorts is increasingly being adopted to aid the investigation of shared neural mechanisms across subpopulations ( Perez et al, 2015 ), including two or more isolated FND subtypes (e.g., functional limb weakness vs. functional dystonia) may aid the identification of subtype-specific findings ( Canu et al, 2020 , Sojka et al, 2021 , Tomic et al, 2018 ). Relatedly, initial machine learning neuroimaging studies investigating the utility of such approaches as adjunctive diagnostic tools used healthy controls ( Vasta et al, 2018 , Wegrzyk et al, 2018 ), but including conditions on the differential diagnosis for FND (e.g., epilepsy, primary dystonia) will further test the specificity of such methods.…”

“…A common connectivity measure used in FND research is seed-based functional connectivity that characterizes relationships between a seed (region-of-interest (ROI)) and the rest of the brain. In FND studies, seed selection has been based on task fMRI activations within the same cohort ( Allendorfer et al, 2019 , Baek et al, 2017 , Szaflarski et al, 2018 , van der Kruijs et al, 2012 ), neuroanatomical atlases (e.g., automatic anatomic labeling) ( Canu et al, 2020 , Lee et al, 2018 , Li et al, 2015b , Morris et al, 2017 ), voxel-based coordinates informed by meta -analyses ( Canu et al, 2020 , Maurer et al, 2016 ), or a priori hypotheses ( Spagnolo et al, 2020 ). While seed-based connectivity is readily interpretable, this approach does not necessarily capture a complete picture of the global brain architecture.…”