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

Agreement and disagreement in pediatric functional neurological symptom disorders: Comparing patient reported outcome measures (PROMs) and clinician assessments

Youth with functional neurological symptom disorder (FNSD) often perceive themselves as having limited capabilities, which may not align with clinical evaluations. This study assessed the disparities between clinician evaluations and patient-reported outcome measures (PROMs) regarding pain, motor function, and learning difficulties in youth with FNSD. Sixty-two youths with FNSD participated in this study, all of whom reported experiencing pain, motor problems, and/or learning difficulties. Clinicians also assessed these domains, resulting in a two-by-two categorization matrix: (1) agreement: child and clinician report "problems"; (2) agreement: child and clinician report "no problems"; (3) disagreement: child reports "problems" while the clinician does not; and (4) disagreement: clinician reports "problems" while the child does not. Agreement/disagreement differences were analyzed. No significant differences in prevalence were observed between the evaluators regarding pain (clinician-85%, child-88%), motor (clinician-98%, child-95%), or learning problems (clinician-69%, child-61%). More than 80% of the children and clinicians report pain and motor disorders. Instances in which children and clinicians reported learning problems (40.3%) exceeded cases in which both reported no problems (9.6%) or only the child reported problems (20.9%). Overall, the agreement between pain and motor function assessments was high (>90%), whereas that concerning learning difficulties was moderate (49.9%). Disagreement in pain/motor assessments was minimal (<5%), whereas for learning difficulties, disagreement rates were high (>20%). In conclusion, a significant concordance exists between PROMs and clinician assessments of pain and motor problems. However, the higher frequency of disagreements regarding learning difficulties emphasizes the importance of incorporating patient and clinician evaluations in pediatric FNSD treatment.

Machine learning classification of functional neurological disorder using structural brain MRI features

BACKGROUND

Brain 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.

METHODS

183 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.

RESULTS

Patients 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.

CONCLUSIONS

These 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.

Heartbeat evoked potentials and autonomic arousal during dissociative seizures: insights from electrophysiology and neuroimaging

Introduction

Dissociative seizures often occur in the context of dysregulated affective arousal and entail dissociative symptoms such as a disintegration of bodily awareness. However, the interplay between affective arousal and changes in interoceptive processing at the onset of dissociative seizures is not well understood.

Methods

Using retrospective routine data obtained from video-electroencephalography telemetry in a university hospital epilepsy monitoring unit, we investigate ictal changes in cardiac indices of autonomic arousal and heartbeat evoked potentials (HEPs) in 24 patients with dissociative seizures.

Results

Results show autonomic arousal during seizures with increased heart rate and a shift towards sympathetic activity. Compared with baseline, ictal HEP amplitudes over central and right prefrontal electrodes (F8, Fz) were significantly less pronounced during seizures, suggesting diminished cortical representation of interoceptive information. Significant correlations between heart rate variability measures and HEPs were observed at baseline, with more sympathetic and less parasympathetic activity related to less pronounced HEPs. Interestingly, these relationships weakened during seizures, suggesting a disintegration of autonomic arousal and interoceptive processing during dissociative seizures. In a subgroup of 16 patients, MRI-based cortical thickness analysis found a correlation with HEP amplitudes in the left somatosensory association cortex.

Conclusions

These findings possibly represent an electrophysiological hint of how autonomic arousal could negatively impact bodily awareness in dissociative seizures, and how these processes might be related to underlying brain structure.

Predictive Processing and the Pathophysiology of Functional Neurological Disorder

The contemporary neuroscience understanding of the brain as an active inference organ supports that our conscious experiences, including sensorimotor perceptions, depend on the integration of probabilistic predictions with incoming sensory input across hierarchically organized levels. As in other systems, these complex processes are prone to error under certain circumstances, which may lead to alterations in their outcomes (i.e., variations in sensations and movements). Such variations are an important aspect of functional neurological disorder, a complex disorder at the interface of brain-mind-body interactions. Thus, predictive processing frameworks offer fundamental mechanistic insights into the pathophysiology of functional neurological disorder. In recent years, many of the aspects relevant to the neurobiology of functional neurological disorder - e.g., aberrant motor and sensory processes, symptom expectation, self-agency, and illness beliefs, as well as interoception, allostasis, and emotion - have been investigated through the lens of predictive processing frameworks. Here, we provide an overview of the current state of research on predictive processing and the pathophysiology of functional neurological disorder.

Non-invasive neuromodulation of the right temporoparietal junction using theta-burst stimulation in functional neurological disorder

Background

Disrupted sense of agency (SoA)-the sense of being the agent of one's own actions-has been demonstrated in patients with functional neurological disorder (FND), and a key area of the corresponding neuronal network is the right temporoparietal junction (rTPJ). Several functional MRI (fMRI) studies have found hypoactivation as well as hyperactivation of the rTPJ in FND. In a proof-of-concept study, we tested whether repetitive transcranial magnetic stimulation (rTMS) over the rTPJ could restore this aberrant activity.

Methods

In a randomised, crossover, single-blinded, sham-controlled study design, theta-burst stimulation (tb-rTMS) was applied over the rTPJ in 23 patients with FND and 19 healthy controls (HC), with each participant undergoing three stimulatory visits (inhibitory continuous TBS (cTBS), excitatory intermittent TBS (iTBS) and sham). During fMRI, participants played a visuomotor task artificially reducing their SoA (manipulated agency, MA), repeated after each neurostimulation. We compared brain activity and behavioural SoA as primary outcomes before and after tb-rTMS and investigated the feasibility of tb-rTMS over the rTPJ in FND as secondary outcome.

Results

At baseline, patients showed decreased accuracy in detecting reduced agency compared with controls (p<0.001), paralleled by lower brain activation in the rTPJ during MA (p=0.037, volume of interest). A region of interest analysis on the rTPJ showed no effect of the sham condition in FND or HC (p=0.917; p=0.375) but revealed a significant effect of stimulation protocol (cTBS/iTBS, p=0.037) in patients with FND, with the excitatory protocol increasing the blood-oxygen-level-dependent (BOLD) signal, whereas this effect was not found in HC. In neither group, a behavioural effect of tb-rTMS was observed.

Conclusion

Aberrant processing of agency in FND was confirmed at baseline, reflected in behavioural outcome and reduced activity in the rTPJ. Tb-rTMS over this key region elicited neuronal changes in patients, paving ways for future studies exploring TMS as neurobiologically informed intervention to restore SoA in FND. We critically discuss methodological intricacies and outline further steps in this research line.

Functional connectivity reveals different brain networks underlying the idiopathic foreign accent syndrome

Foreign accent syndrome (FAS) is characterized by new onset speech that is perceived as foreign. Available data from acquired cases suggests focal brain damage in language and sensorimotor brain networks, but little remains known about abnormal functional connectivity in idiopathic cases of FAS without structural damage. Here, connectomic analyses were completed on three patients with idiopathic FAS to investigate unique functional connectivity abnormalities underlying accent change for the first time. Machine learning (ML)-based algorithms generated personalized brain connectomes based on a validated parcellation scheme from the Human Connectome Project (HCP). Diffusion tractography was performed on each patient to rule out structural fiber damage to the language system. Resting-state-fMRI was assessed with ML-based software to examine functional connectivity between individual parcellations within language and sensorimotor networks and subcortical structures. Functional connectivity matrices were created and compared against a dataset of 200 healthy subjects to identify abnormally connected parcellations. Three female patients (28-42 years) who presented with accent changes from Australian English to Irish (n = 2) or American English to British English (n = 1) demonstrated fully intact language system structural connectivity. All patients demonstrated functional connectivity anomalies within language and sensorimotor networks in numerous left frontal regions and between subcortical structures in one patient. Few commonalities in functional connectivity anomalies were identified between all three patients, specifically 3 internal-network parcellation pairs. No common inter-network functional connectivity anomalies were identified between all patients. The current study demonstrates specific language, and sensorimotor functional connectivity abnormalities can exist and be quantitatively shown in the absence of structural damage for future study.