Parkinson's disease rigidity: relation to brain connectivity and motor performance.

OBJECTIVE: (1) To determine the brain connectivity pattern associated with clinical rigidity scores in Parkinson's disease (PD) and (2) to determine the relation between clinically assessed rigidity and quantitative metrics of motor performance. BACKGROUND: Rigidity, the resistance to passive movement, is exacerbated in PD by asking the subject to move the contralateral limb, implying that rigidity involves a distributed brain network. Rigidity mainly affects subjects when they attempt to move; yet the relation between clinical rigidity scores and quantitative aspects of motor performance are unknown. METHODS: Ten clinically diagnosed PD patients (off-medication) and 10 controls were recruited to perform an fMRI squeeze-bulb tracking task that included both visually guided and internally guided features. The direct functional connectivity between anatomically defined regions of interest was assessed with Dynamic Bayesian Networks (DBNs). Tracking performance was assessed by fitting Linear Dynamical System (LDS) models to the motor performance, and was compared to the clinical rigidity scores. A cross-validated Least Absolute Shrinkage and Selection Operator (LASSO) regression method was used to determine the brain connectivity network that best predicted clinical rigidity scores. RESULTS: The damping ratio of the LDS models significantly correlated with clinical rigidity scores (p = 0.014). An fMRI connectivity network in subcortical and primary and premotor cortical regions accurately predicted clinical rigidity scores (p < 10(-5)). CONCLUSION: A widely distributed cortical/subcortical network is associated with rigidity observed in PD patients, which reinforces the importance of altered functional connectivity in the pathophysiology of PD. PD subjects with higher rigidity scores tend to have less overshoot in their tracking performance, and damping ratio may represent a robust, quantitative marker of the motoric effects of increasing rigidity.

theta, beta But not alpha-band EEG connectivity has implications for dual task performance in Parkinson's disease.

People with Parkinson's disease (PD) have difficulty performing dual tasks or simultaneous movements, even if the same movements can be easily performed individually. This has particular significance clinically, as for example falling injuries may occur if care is not taken to perform tasks one at a time. We investigated whether this difficultyx results from impaired dopamine-modulated connectivity. We recorded the EEG in PD subjects off and on l-dopa medication performing simultaneous and unimanual tracking tasks. To deal with the inherent non-stationarity of the EEG during motor tasks, we segmented the data into task-related sections based on transient synchronisation between independent components of the data, before assessing the mutual information (MI) between each EEG channel pair. In both tasks, PD subjects off-medication demonstrated enhanced fronto-central and decreased occipital synchronisation within theta and alpha bands, and widespread increased beta-band synchronisation, compared to controls. Synchronisation changes in theta and beta bands were partially normalised by l-dopa, but l-dopa had relatively little effect on alpha band synchronisation. When comparing simultaneous movements to unimanual tracking, PD subjects off-medication demonstrated synchronisation changes within theta and beta bands, however alpha connectivity was largely unchanged. These results suggest that downstream influences of impaired basal ganglia function on cortico-cortical connectivity may result in difficulties with dual task performance in PD.

Motor reserve and novel area recruitment: amplitude and spatial characteristics of compensation in Parkinson's disease.

Motor symptoms of Parkinson's disease (PD) do not appear until the majority of dopaminergic cells in the substantia nigra pars compacta are lost, suggesting significant redundancy or compensation in the motor systems affected by PD. Using functional magnetic resonance imaging, we examined whether compensation in PD is manifested by changes in amplitude and/or spatial extent of activity within normal networks (active motor reserve) and/or newly recruited regions [novel area recruitment (NAR)]. Ten PD subjects off and on medication and 10 age-matched controls performed a visually guided sinusoidal force task at 0.25, 0.5 and 0.75 Hz. Regression was used to determine the combination of regions where activation amplitude scaled linearly with movement speed in controls. We then determined the activation of PD subjects in this network, as well as the corresponding PD network. To measure the spatial variance of activation, we used an invariant spatial feature approach. Control subjects monotonically increased activity within striato-thalamo-cortical and cerebello-thalamo-cortical regions with increasing movement speed. In PD subjects, the activity of this network at low speeds was similar to that in controls at higher speeds. Additionally, PD subjects off medication demonstrated NARs of the bilateral cerebellum and primary motor cortex, which were incompletely normalized by levodopa. Our results suggest that PD subjects tap into motor reserve, increase the spatial extent of activation and demonstrate NAR to maintain near-normal motor output.

Dynamic Bayesian network modeling of fMRI: a comparison of group-analysis methods.

Bayesian network (BN) modeling has recently been introduced as a tool for determining the dependencies between brain regions from functional-magnetic-resonance-imaging (fMRI) data. However, studies to date have yet to explore the optimum way for meaningfully combining individually determined BN models to make group inferences. We contrasted the results from three broad approaches: the "virtual-typical- subject" (VTS) approach which pools or averages group data as if they are sampled from a single, hypothetical virtual typical subject; the "individual-structure" (IS) approach that learns a separate BN for each subject, and then finds commonality across the individual structures, and the "common-structure" (CS) approach that imposes the same network structure on the BN of every subject, but allows the parameters to differ across subjects. To explore the effects of these three approaches, we applied them to an fMRI study exploring the motor effect of L-dopa medication on ten subjects with Parkinson's disease (PD), as the profound clinical effects of this medication suggest that fMRI activation in PD subjects after medication should start approaching that of age-matched controls. We found that none of these approaches is generally superior over the others, according to Bayesian-information-criterion (BIC) scores, and that they led to considerably different group-level results. The IS approach was more sensitive to the normalization effect of the L-dopa medication on brain connectivity. However, for the more homogeneous control population, the VTS approach was superior. Group-analysis approaches should be selected carefully with consideration of both statistical and biomedical evidence.

Characterizing task-related temporal dynamics of spatial activation distributions in fMRI BOLD signals.

We present a new functional magnetic resonance imaging (fMRI) analysis method that incorporates both spatial and temporal dynamics of blood-oxygen-level dependent (BOLD) signals within a region of interest (ROI). 3D moment descriptors are used to characterize the spatial changes in BOLD signals over time. The method is tested on fMRI data collected from eight healthy subjects performing a bulb-squeezing motor task with their right-hand at various frequencies. Multiple brain regions including the left cerebellum, both primary motor cortices (MI), both supplementary motor areas (SMA), left prefrontal cortex (PFC), and left anterior cingulate cortex (ACC) demonstrate significant task-related changes. Furthermore, our method is able to discriminate differences in activation patterns at the various task frequencies, whereas using a traditional intensity based method, no significant activation difference is detected. This suggests that temporal dynamics of the spatial distribution of BOLD signal provide additional information regarding task-related activation thus complementing conventional intensity-based approaches.

Spectral clustering of fMRI data within regions of interest: clarification of L-dopa effects in Parkinson's disease.

Identifying active regions of the brain that are task-related is important in fMRI study. Current methods of determining functional Regions of Interest (ROIs) are unsatisfactory because they either reduce the effect size or bias the statistical results. We propose a spectral clustering method for assessing those voxels within an ROI that are suitable for further task-activation analysis. Different similarity functions are studied and the correlation index is chosen based on the simulation study. In real fMRI study, further group analysis employing regression is investigated to identify different brain activation patterns between groups in order to reveal the effects of disease and medicine. A real fMRI case study in Parkinson's disease suggests that the technique is promising, warranting further study.

Joint spatial denoising and active region of interest delineation in functional magnetic resonance imaging.

In region of interest (ROI) based functional magnetic resonance imaging (fMRI) group analysis, errors in delineation of an ROI or inclusion of non-active voxels within an ROI can bias the statistical results. Addressing these concerns, this paper presents a new fMRI processing method that simultaneously refines ROI delineation and spatially denoises fMRI activation statistics within the ROI. The underlying assumption is that activation statistics within a small neighborhood are spatially correlated, thereby exhibit similar levels of influence on the overall ROI's response. Based on this assumption, we first identify outlier voxels as those having undue influence on an ROI's feature. Isolated outlier voxels at region boundaries are then removed, thereby refining the ROI delineation. The remaining outlier voxels are de-weighted based on their influence relative to their neighbors to reduce the effects of voxels deemed falsely active in later analysis. The proposed method was tested on real fMRI data collected from 8 healthy subjects performing a bulb-squeezing motor task at various frequencies. Using the proposed method, enhanced capability for detection of frequency-related activation map feature differences (AMFD) was demonstrated when compared to Gaussian spatial smoothing of ROI activation statistics. The validity of the proposed method is suggested by the fact that using one feature for denoising (e.g. spatial variance) results in greater effect size in another feature (e.g. average activation statistics magnitude). Our results demonstrate the importance of accurate ROI delineation in ROI-based fMRI analysis.