Journal article
Identifying motor functional neurological disorder using resting-state functional connectivity.
- Wegrzyk J Department of Clinical Neuroscience, Geneva University Hospital, Rue Gabrielle-Perret-Gentil 4, 1211 Geneva, Switzerland.
- Kebets V Department of Neuroscience, University of Geneva, Campus Biotech, Chemin des Mines 9, 1202 Geneva, Switzerland.
- Richiardi J Department of Neuroscience, University of Geneva, Campus Biotech, Chemin des Mines 9, 1202 Geneva, Switzerland.
- Galli S Department of Clinical Neuroscience, Geneva University Hospital, Rue Gabrielle-Perret-Gentil 4, 1211 Geneva, Switzerland.
- de Ville DV Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Campus Biotech, Chemin des Mines 9, 1202 Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Campus Biotech, Chemin des Mines 9, 1202 Geneva, Switzerland.
- Aybek S Department of Clinical Neuroscience, Geneva University Hospital, Rue Gabrielle-Perret-Gentil 4, 1211 Geneva, Switzerland; Department of Neuroscience, University of Geneva, Campus Biotech, Chemin des Mines 9, 1202 Geneva, Switzerland; Neurology University Clinic, InselSpital, Department of Clinical Neuroscience, 3010 Bern, Switzerland. Electronic address: selma.aybek@insel.ch.
- 2017-10-27
Published in:
- NeuroImage. Clinical. - 2018
Biomarker
Classification
Functional connectivity
Functional neurological disorder
Resting state functional magnetic resonance imaging
Adult
Brain
Brain Mapping
Case-Control Studies
Female
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Male
Middle Aged
Motor Disorders
Nervous System Diseases
Oxygen
Psychiatric Status Rating Scales
Rest
Sensitivity and Specificity
Support Vector Machine
English
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.
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.
- Language
-
- English
- Open access status
- gold
- Identifiers
-
- DOI 10.1016/j.nicl.2017.10.012
- PMID 29071210
- Persistent URL
- https://folia.unifr.ch/global/documents/142682
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