• 1st Neuroimaging Workgroup Meeting in Functional Neurological Disorder (FND). • Underscores the importance of FND cohort characterization in brain imaging research. • Details methodological approaches taken in FND neuroimaging research to date. • Research agenda proposed to definitely elucidate the neural circuitry of FND. • Discussions underway regarding having FND researchers join the ENIGMA consortium.

Hyperkinetic movement disorders (HMD) manifest as abnormal and uncontrollable movements. Despite reported involvement of several neural circuits, exact connectivity profiles remain elusive. Providing a comprehensive literature review of resting-state brain connectivity alterations using resting-state fMRI (rs-fMRI). We additionally discuss alterations from the perspective of brain networks, as well as correlations between connectivity and clinical measures. A systematic review was performed according to PRISMA guidelines and searching PubMed until October 2022. Rs-fMRI studies addressing ataxia, chorea, dystonia, myoclonus, tics, tremor, and functional movement disorders (FMD) were included. The standardized mean difference was used to summarize findings per region in the Automated Anatomical Labeling atlas for each phenotype. Furthermore, the activation likelihood estimation meta-analytic method was used to analyze convergence of significant between-group differences per phenotype. Finally, we conducted hierarchical cluster analysis to provide additional insights into commonalities and differences across HMD phenotypes. Most articles concerned tremor (51), followed by dystonia (46), tics (19), chorea (12), myoclonus (11), FMD (11), and ataxia (8). Altered resting-state connectivity was found in several brain regions: in ataxia mainly cerebellar areas; for chorea, the caudate nucleus; for dystonia, sensorimotor and basal ganglia regions; for myoclonus, the thalamus and cingulate cortex; in tics, the basal ganglia, cerebellum, insula, and frontal cortex; for tremor, the cerebello-thalamo-cortical circuit; finally, in FMD, frontal, parietal, and cerebellar regions. Both decreased and increased connectivity were found for all HMD. Significant spatial convergence was found for dystonia, FMD, myoclonus, and tremor. Correlations between clinical measures and resting-state connectivity were frequently described. Key brain regions contributing to functional connectivity changes across HMD often overlap. Possible increases and decreases of functional connections of a specific region emphasize that HMD should be viewed as a network disorder. Despite the complex interplay of physiological and methodological factors, this review serves to gain insight in brain connectivity profiles across HMD phenotypes.

Treatment for essential tremor (ET) and cortical myoclonus (CM) differs. As their clinical distinction can be difficult, with large inter- and intra-observer variability, there is a need for additional diagnostic tools. We aim to develop a machine learning based approach using accelerometry data to support clinicians in their diagnosis of ET versus CM. We recorded the upper body movements of 19 ET and 19 CM patients performing 21 tasks using eight accelerometry sensors. The phenotype was classified by applying an explainable machine learning method called generalized matrix learning vector quantization (GMLVQ) to the power spectrum derived from the accelerometry recordings. In addition to the general classification performance, GMLVQ provides context for the decisions made. This includes the relevance of frequencies and thus the identification of distinct patterns in the phenotypes. For all postures and dynamic tasks, excellent classification results were reached with AUROC approaching 1,0. For the classification, GMLVQ considers the overall shape of the power spectrum, whereby the frequencies 5-7 Hz as well as 3-4 and 9-10 Hz are of particular importance. These frequencies correspond to the tremor peak and local minima that are also observed as characteristics in the literature. The excellent classification results provide a proof of concept for the discrimination of ET and CM by applying a GMLVQ machine learning analysis to power spectra derived from accelerometry recordings. Such a system could support clinicians to improve the diagnostic accuracy.

• We found altered functional brain state dynamics in FMD. • Midline brain areas seem to play an important role in this process. • Our findings may support theories about attention and sense of agency. • Applying chronnectome framework in the future may discover novel biomarkers in FMD.

Objective: To explore changes in resting-state networks in patients with jerky and tremulous functional movement disorders (JT-FMD). Methods: Resting-state fMRI data from seventeen patients with JT-FMD and seventeen age-, sex-, and education matched healthy controls was investigated. Independent component analysis was used to examine the frontoparietal network (FPN), dorsal attention network (DAN), salience network (SN), and default mode network (DMN). Frequency distribution of network signal fluctuations, intra- and internetwork functional connectivity were investigated. Symptom severity was measured using the Clinical Global Impression-Severity scale. Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI) scores were collected to measure depression and anxiety in FMD, respectively. Results: Compared with healthy controls, patients with JT-FMD had significantly decreased power of lower-range (0.01-0.10 Hz) frequency fluctuations in a precuneus and posterior cingulate cortex (PCC) component of the DMN, and in the DAN component (FDR-corrected P<0.05). No significant group differences were found for intra- and internetwork functional connectivity. In patients with JT-FMD, symptom severity was not significantly correlated with network measures. Depression scores were weakly correlated with intranetwork functional connectivity in the medial prefrontal cortex, while anxiety was not found to be related to network connectivity. Conclusions: Given the changes in the postero-dorsal components of the DMN and DAN, we postulate that the JT-FMD-related functional alterations found in these regions could provide support for the concept that particularly impaired attentional control is a fundamental disturbance in these patients.

Background: Currently, the pathophysiology of myoclonus‐dystonia (M‐D) remains insufficiently understood. This study addresses this gap by adding innovative multivariate pattern analysis (MVPA) to traditional univariate analysis of functional magnetic resonance imaging (fMRI) data.Methods: Data from 18 M‐D patients and 18 age‐matched healthy volunteers who performed a finger tapping fMRI task were analyzed. Whole‐brain univariate and searchlight (MVPA) analysis with varying hemodynamic response function (HRF) delays were employed to examine brain responses associated with the visually guided motor task.Results: Distinguishing response patterns between M‐D patients and healthy volunteers revealed significant response reductions in the putamen, insula, and visual cortex. Compared to univariate analysis, searchlight analysis was more sensitive for brain activity patterns associated with finger tapping in both M‐D patients and healthy volunteers. At short HRF delays, increased (pre)motor cortical responses were evident in M‐D patients, whereas such responses emerged at a later HRF delay in healthy volunteers.Conclusion: The task‐related effects observed in M‐D patients support the involvement of the basal ganglia‐thalamo‐cortical network. Notably, cerebellar involvement was not strongly implicated in our study. We postulate that inherent deficits in the putamen trigger either premature or downstream compensatory (motor) cortical effects. The potential involvement of the visual cortex in the M‐D pathophysiology is new, but its role has been suggested by a previous study investigating visual sensory processing in SGCE gene‐positive M‐D patients. Our findings, including the innovative searchlight method, pave the way for further studies investigating the complex interplay between brain regions and networks and their role in M‐D pathogenesis.