Dataset Information

The "Whack-a-Mole" Sign in Functional Movement Disorders.

ABSTRACT: Making the diagnosis of functional movement disorders can be challenging. Identifying positive physical signs and diagnostic maneuvers is critical to this process. Distractibility, entrainability, and variability are examples of classic physical findings in these patients. In this case series, we identify and characterize another phenomenon observed in some of these patients. In this phenomenon, movement suppression of one body part is followed by immediate reemergence of movement in another. We propose that this phenomenon be referred to as the "whack-a-mole" sign. This name is derived from the arcade game whack-a-mole, in which a mole, when hit into its original hole, re-emerges elsewhere. We present a case series of 4 patients with functional movement disorders who exhibit this sign.

SUBMITTER: Park JE

PROVIDER: S-EPMC6353526 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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The "Whack-a-Mole" Sign in Functional Movement Disorders.

Park Jung E JE Maurer Carine W CW Hallett Mark M

Movement disorders clinical practice 20150525 3

Making the diagnosis of functional movement disorders can be challenging. Identifying positive physical signs and diagnostic maneuvers is critical to this process. Distractibility, entrainability, and variability are examples of classic physical findings in these patients. In this case series, we identify and characterize another phenomenon observed in some of these patients. In this phenomenon, movement suppression of one body part is followed by immediate reemergence of movement in another. We ...[more]

PMID: 30838230

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Project description:Introduction: Functional movement disorder (FMD) is a type of functional neurological disorder characterized by abnormal movements that patients do not perceive as self-generated. Prior imaging studies show a complex pattern of altered activity, linking regions of the brain involved in emotional responses, motor control, and agency. This study aimed to better characterize these relationships by building a classifier using a support vector machine to accurately distinguish between 61 FMD patients and 59 healthy controls using features derived from resting-state functional magnetic resonance imaging. Materials and Methods: First, we selected 66 seed regions based on prior related studies, then we calculated the full correlation matrix between them before performing recursive feature elimination to winnow the feature set to the most predictive features and building the classifier. Results: We identified 29 features of interest that were highly predictive of the FMD condition, classifying patients and controls with 80% accuracy. Several key features included regions in the right sensorimotor cortex, left dorsolateral prefrontal cortex, left cerebellum, and left posterior insula. Conclusions: The features selected by the model highlight the importance of the interconnected relationship between areas associated with emotion, reward, and sensorimotor integration, potentially mediating communication between regions associated with motor function, attention, and executive function. Exploratory machine learning was able to identify this distinctive abnormal pattern, suggesting that alterations in functional linkages between these regions may be a consistent feature of the condition in many FMD patients. Clinical-Trials.gov ID: NCT00500994 Impact statement Our research presents novel results that further elucidate the pathophysiology of functional movement disorder (FMD) with a machine learning model that classifies FMD and healthy controls correctly 80% of the time. Herein, we demonstrate how known differences in resting-state functional magnetic resonance imaging connectivity in FMD patients can be leveraged to better understand the complex pattern of neural changes in these patients. Knowing that there are measurable predictable differences in brain activity in patients with FMD may help both clinicians and patients conceptualize and better understand the illness at the point of diagnosis and during treatment. Our methods demonstrate how an effective combination of machine learning and qualitative approaches to analyzing functional brain connectivity can enhance our understanding of abnormal patterns of brain activity in FMD patients.

Dataset Information

The "Whack-a-Mole" Sign in Functional Movement Disorders.

Publications

The "Whack-a-Mole" Sign in Functional Movement Disorders.

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