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

PloS One
Štefan HoligaKarsten Mueller

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...Continue Reading

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Methods Mentioned

BETA
PCA

Software Mentioned

UnSA
Matlab®
SPSS Statistics
SPSS
Marsbar SPM
SPM
SPM8
EVSENG

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