Dataset Information

Identifying motor functional neurological disorder using resting-state functional connectivity.

ABSTRACT:

Background

Motor functional neurological disorder (mFND) is a clinical diagnosis with reliable features; however, patients are reluctant to accept the diagnosis and physicians themselves bear doubts on potential misdiagnoses. The identification of a positive biomarker could help limiting unnecessary costs of multiple referrals and investigations, thus promoting early diagnosis and allowing early engagement in appropriate therapy.

Objectives

To test whether resting-state (RS) functional magnetic resonance imaging could discriminate patients suffering from mFND from healthy controls.

Methods

We classified 23 mFND patients and 25 age- and gender-matched healthy controls based on whole-brain RS functional connectivity (FC) data, using a support vector machine classifier and the standard Automated Anatomic Labeling (AAL) atlas, as well as two additional atlases for validation.

Results

Accuracy, specificity and sensitivity were over 68% (p = 0.004) to discriminate between mFND patients and controls, with consistent findings between the three tested atlases. The most discriminative connections comprised the right caudate, amygdala, prefrontal and sensorimotor regions. Post-hoc seed connectivity analyses showed that these regions were hyperconnected in patients compared to controls.

Conclusions

The good accuracy to discriminate patients from controls suggests that RS FC could be used as a biomarker with high diagnostic value in future clinical practice to identify mFND patients at the individual level.

SUBMITTER: Wegrzyk J

PROVIDER: S-EPMC5651543 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Identifying motor functional neurological disorder using resting-state functional connectivity.

Wegrzyk Jennifer J Kebets Valeria V Richiardi Jonas J Galli Silvio S de Ville Dimitri Van DV Aybek Selma S

NeuroImage. Clinical 20171012

<h4>Background</h4>Motor functional neurological disorder (mFND) is a clinical diagnosis with reliable features; however, patients are reluctant to accept the diagnosis and physicians themselves bear doubts on potential misdiagnoses. The identification of a positive biomarker could help limiting unnecessary costs of multiple referrals and investigations, thus promoting early diagnosis and allowing early engagement in appropriate therapy.<h4>Objectives</h4>To test whether resting-state (RS) funct ...[more]

PMID: 29071210

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Project description:BackgroundPatients 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.MethodsThis 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).ResultsFND 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%).ConclusionsThe 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.

Project description:BackgroundPrefrontal subregions, including the ventromedial prefrontal cortex (PFC), dorsomedial PFC, and dorsolateral PFC (DLPFC), are differentially implicated in the pathophysiology of posttraumatic stress disorder (PTSD), though few existing studies have examined subregional differences in resting-state functional connectivity (rsFC). We hypothesized that PTSD would involve weaker positive rsFC between ventromedial PFC, dorsomedial PFC, and other default mode network regions and increased negative rsFC between DLPFC and posterior default mode network regions. Additionally, we hypothesized that prefrontal regions exhibiting group differences in rsFC would be characterized by alterations in cortical thickness.MethodsParticipants included 36 healthy control subjects, 30 trauma-exposed control subjects, and 21 individuals with current DSM-IV PTSD resulting from community-acquired trauma. Participants completed the Clinician Administered PTSD Scale, questionnaires (Childhood Trauma Questionnaire, Adverse Childhood Events, Life Events Checklist, Beck Depression Inventory), structural neuroimaging, and resting-state functional magnetic resonance imaging. rsFC of DLPFC, ventromedial PFC, and dorsomedial PFC seeds was evaluated in SPM12 and CONN. Cortical thickness for regions with significant rsFC findings was assessed using FreeSurfer.ResultsRelative to both healthy control and trauma-exposed control subjects, individuals with PTSD showed increased negative rsFC between the DLPFC and a region of precuneus. This finding was associated with increased overall symptom severity but not with trauma load or childhood trauma exposure. Greater negative DLPFC-precuneus connectivity was associated with greater bilateral precuneus thickness.ConclusionsGiven participation of precuneus subregions in the central executive network, increased anticorrelation between right DLPFC and precuneus in this sample may reflect increased opposition between anterior and posterior central executive network hubs in PTSD.

Dataset Information

Identifying motor functional neurological disorder using resting-state functional connectivity.

Background

Objectives

Methods

Results

Conclusions

Publications

Identifying motor functional neurological disorder using resting-state functional connectivity.

Similar Datasets

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