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Accounting for movement increases sensitivity in detecting brain activity in Parkinson's disease

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Holiga,  Štefan
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Möller,  Harald E.
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Schroeter,  Matthias L.
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Clinic for Cognitive Neurology and Leipzig Research Center for Civilization Diseases, University of Leipzig, and FTLD Consortium, Leipzig, Germany;

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Mueller,  Karsten
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Holiga_2012_Accounting.pdf
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Citation

Holiga, Š., Möller, H. E., Sieger, T., Schroeter, M. L., Jech, R., & Mueller, K. (2012). Accounting for movement increases sensitivity in detecting brain activity in Parkinson's disease. PLoS One, 7(5): e36271. doi:10.1371/journal.pone.0036271.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-A012-F
Abstract
Parkinson's disease (PD) is manifested by motor impairment, which may impede the ability to accurately perform motor tasks during functional magnetic resonance imaging (fMRI). Both temporal and amplitude deviations of movement performance affect the blood oxygenation level-dependent (BOLD) response. We present a general approach for assessing PD patients' movement control employing simultaneously recorded fMRI time series and behavioral data of the patients' kinematics using MR-compatible gloves. Twelve male patients with advanced PD were examined with fMRI at 1.5T during epoch-based visually paced finger tapping. MR-compatible gloves were utilized online to quantify motor outcome in two conditions with or without dopaminergic medication. Modeling of individual-level brain activity included (i) a predictor consisting of a condition-specific, constant-amplitude boxcar function convolved with the canonical hemodynamic response function (HRF) as commonly used in fMRI statistics (standard model), or (ii) a custom-made predictor computed from glove time series convolved with the HRF (kinematic model). Factorial statistics yielded a parametric map for each modeling technique, showing the medication effect on the group level. Patients showed bilateral response to levodopa in putamen and globus pallidus during the motor experiment. Interestingly, kinematic modeling produced significantly higher activation in terms of both the extent and amplitude of activity. Our results appear to account for movement performance in fMRI motor experiments with PD and increase sensitivity in detecting brain response to levodopa. We strongly advocate quantitatively controlling for motor performance to reach more reliable and robust analyses in fMRI with PD patients.