Microstructural Changes in Motor Functional Conversion Disorder: Multimodal Imaging Approach on a Case

Changes in effective connectivity during the visual-motor integration tasks: a preliminary f-NIRS study

BACKGROUND

Visual-motor integration (VMI) is an essential skill in daily life. The present study aimed to use functional near-infrared spectroscopy (fNIRS) technology to explore the effective connectivity (EC) changes among brain regions during VMI activities of varying difficulty levels.

METHODS

A total of 17 healthy participants were recruited for the study. Continuous Performance Test (CPT), Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A), and Beery VMI test were used to evaluate attention performance, executive function, and VMI performance. Granger causality analysis was performed for the VMI task data to obtain the EC matrix for all participants. One-way ANOVA analysis was used to identify VMI load-dependent EC values among different task difficulty levels from brain network and channel perspectives, and partial correlation analysis was used to explore the relationship between VMI load-dependent EC values and behavioral performance.

RESULTS

We found that the EC values of dorsal attention network (DAN) → default mode network (DMN), DAN → ventral attention network (VAN), DAN → frontoparietal network (FPN), and DAN → somatomotor network (SMN) in the complex condition were higher than those in the simple and moderate conditions. Further channel analyses indicated that the EC values of the right superior parietal lobule (SPL) → right superior frontal gyrus (SFG), right middle occipital gyrus (MOG) → left SFG, and right MOG → right postcentral gyrus (PCG) in the complex condition were higher than those in the simple and moderate conditions. Subsequent partial correlation analysis revealed that the EC values from DAN to DMN, VAN, and SMN were positively correlated with executive function and VMI performance. Furthermore, the EC values of right MOG → left SFG and right MOG → right PCG were positively correlated with attention performance.

CONCLUSIONS

The DAN is actively involved during the VMI task and thus may play a critical role in VMI processes, in which two key brain regions (right SPL, right MOG) may contribute to the EC changes in response to increasing VMI load. Meanwhile, bilateral SFG and right PCG may also be closely related to the VMI performance.

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

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Using machine learning on multi-centre data, FND patients were successfully classified with an accuracy of 72%.

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The angular- and supramarginal gyri, cingular- and insular cortex, and the hippocampus were the most discriminant regions.

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To provide diagnostic utility, future studies must include patients with similar symptoms but different diagnoses.

Background

Patients suffering from functional neurological disorder (FND) experience disabling neurological symptoms not caused by an underlying classical neurological disease (such as stroke or multiple sclerosis). The diagnosis is made based on reliable positive clinical signs, but clinicians often require additional time- and cost consuming medical tests and examinations. Resting-state functional connectivity (RS FC) showed its potential as an imaging-based adjunctive biomarker to help distinguish patients from healthy controls and could represent a “rule-in” procedure to assist in the diagnostic process. However, the use of RS FC depends on its applicability in a multi-centre setting, which is particularly susceptible to inter-scanner variability. The aim of this study was to test the robustness of a classification approach based on RS FC in a multi-centre setting.

Methods

This study aimed to distinguish 86 FND patients from 86 healthy controls acquired in four different centres using a multivariate machine learning approach based on whole-brain resting-state functional connectivity. First, previously published results were replicated in each centre individually (intra-centre cross-validation) and its robustness across inter-scanner variability was assessed by pooling all the data (pooled cross-validation). Second, we evaluated the generalizability of the method by using data from each centre once as a test set, and the data from the remaining centres as a training set (inter-centre cross-validation).

Results

FND patients were successfully distinguished from healthy controls in the replication step (accuracy of 74%) as well as in each individual additional centre (accuracies of 73%, 71% and 70%). The pooled cross validation confirmed that the classifier was robust with an accuracy of 72%. The results survived post-hoc adjustment for anxiety, depression, psychotropic medication intake, and symptom severity. The most discriminant features involved the angular- and supramarginal gyri, sensorimotor cortex, cingular- and insular cortex, and hippocampal regions. The inter-centre validation step did not exceed chance level (accuracy below 50%).

Conclusions

The results demonstrate the applicability of RS FC to correctly distinguish FND patients from healthy controls in different centres and its robustness against inter-scanner variability. In order to generalize its use across different centres and aim for clinical application, future studies should work towards optimization of acquisition parameters and include neurological and psychiatric control groups presenting with similar symptoms.

Acute non-traumatic tetraparesis - Differential diagnosis

INTRODUCTION

Potentially life-threatening disorders may present in the emergency department with acute tetraparesis, and their recognition is crucial for an appropriate management and timely treatment. Our review aims to systematize the differential diagnosis of acute non-traumatic tetraparesis.

RESULTS

Causes of tetraparesis can be classified based on the site of defect: upper motor neuron (UMN), peripheral nerve, neuromuscular junction or muscle. History of present illness should include the distribution of weakness (symmetric/asymmetric or distal/proximal/diffuse) and associated clinical features (pain, sensory findings, dysautonomia, and cranial nerve abnormalities such as diplopia and dysphagia). Neurological examination, particularly tendon reflexes, helps further in the localization of nerve lesions and distinction between UMN and lower motor neuron. Ancillary studies include blood and cerebral spinal fluid analysis, neuroaxis imaging, electromyography, muscle magnetic resonance and muscle biopsy.

CONCLUSIONS

Acute tetraparesis is still a debilitating and potentially serious neurological condition. Despite all the supplementary ancillary tests, the neurological examination is the key to achieve a correct diagnosis. The identification of life-threatening neurologic disorders is pivotal, since failing to identify patients at risk of complications, such as acute respiratory failure, may have catastrophic results.