Samantha J. Palmer

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.

LEVODOPA-SENSITIVE, DYNAMIC CHANGES IN EFFECTIVE CONNECTIVITY DURING SIMULTANEOUS MOVEMENTS IN PARKINSON'S DISEASE

Changes in effective connectivity during the performance of a motor task appear important for the pathogenesis of motor symptoms in Parkinsons disease (PD). One type of task that is typically difficult for individuals with PD is simultaneous or bimanual movement, and here we investigate the changes in effective connectivity as a potential mechanism. Eight PD subjects off and on l-DOPA medication and 10 age-matched healthy control subjects performed both simultaneous and unimanual motor tasks in an fMRI scanner. Changes in effective connectivity between regions of interest (ROIs) during simultaneous and unimanual task performance were determined with structural equation modeling (SEM), and changes in the temporal dynamics of task performance were determined with multivariate autoregressive modeling (MAR). PD subjects demonstrated alterations in both effective connectivity and temporal dynamics compared with control subjects during the performance of a simultaneous task. l-DOPA treatment was able to partially normalize effective connectivity and temporal patterns of activity in PD, although some connections remained altered in PD even after medication. Our results suggest that difficulty performing simultaneous movements in PD is at least in part mediated by a disruption of effective communication between widespread cortical and subcortical areas, and l-DOPA assists in normalizing this disruption. These results suggest that even when the site of neurodegeneration is relatively localized, study of how disruption in a single region affects connectivity throughout the brain can lead to important advances in the understanding of the functional deficits caused by neurodegenerative disease.

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.

θ, β But not α-band EEG connectivity has implications for dual task performance in Parkinson’s disease

People with Parkinsons 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.

Cortical muscle coupling in Parkinson’s disease (PD) bradykinesia

OBJECTIVES To determine if novel methods establishing patterns in EEG-EMG coupling can infer subcortical influences on the motor cortex, and the relationship between these subcortical rhythms and bradykinesia. BACKGROUND Previous work has suggested that bradykinesia may be a result of inappropriate oscillatory drive to the muscles. Typically, the signal processing method of coherence is used to infer coupling between a single channel of EEG and a single channel of rectified EMG, which demonstrates 2 peaks during sustained contraction: one, approximately 10 Hz, which is pathologically increased in PD, and a approximately 30 Hz peak which is decreased in PD, and influenced by pharmacological manipulation of GABAA receptors in normal subjects. MATERIALS AND METHODS We employed a novel multiperiodic squeezing paradigm which also required simultaneous movements. Seven PD subjects (on and off L-Dopa) and five normal subjects were recruited. Extent of bradykinesia was inferred by reduced relative performance of the higher frequencies of the squeezing paradigm and UPDRS scores. We employed Independent Component Analysis (ICA) and Empirical Mode Decomposition (EMD) to determine EEG/EMG coupling. RESULTS Corticomuscular coupling was detected during the continually changing force levels. Different components included those over the primary motor cortex (ipsilaterally and contralaterally) and over the midline. Subjects with greater bradykinesia had a tendency towards increased approximately 10 Hz coupling and reduced approximately 30 Hz coupling that was erratically reversed with L-dopa. CONCLUSIONS These results suggest that lower approximately 10 Hz peak may represent pathological oscillations within the basal ganglia which may be a contributing factor to bradykinesia in PD.

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.

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 ROIs response. Based on this assumption, we first identify outlier voxels as those having undue influence on an ROIs 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.

Freesurfer-initialized large deformation diffeomorphic metric mapping with application to Parkinson's disease

We apply a recently developed automated brain segmentation method, FS+LDDMM, to brain MRI scans from Parkinsons Disease (PD) subjects, and normal age-matched controls and compare the results to manual segmentation done by trained neuroscientists. The data set consisted of 14 PD subjects and 12 age-matched control subjects without neurologic disease and comparison was done on six subcortical brain structures (left and right caudate, putamen and thalamus). Comparison between automatic and manual segmentation was based on Dice Similarity Coefficient (Overlap Percentage), L1 Error, Symmetrized Hausdorff Distance and Symmetrized Mean Surface Distance. Results suggest that FS+LDDMM is well-suited for subcortical structure segmentation and further shape analysis in Parkinsons Disease. The asymmetry of the Dice Similarity Coefficient over shape change is also discussed based on the observation and measurement of FS+LDDMM segmentation results.

SPHARM-Based Spatial fMRI Characterization With Intersubject Anatomical Variability Reduction

It has been recently shown that spatial patterns of activation within regions of interest (ROIs) in functional magnetic resonance imaging (fMRI) data can be used as sensitive markers of brain activation differences. In this paper, we propose novel invariant features for characterizing such spatial activation patterns based on spherical harmonic (SPHARM) data representations. The proposed three dimensional (3-D) spatial features are novel in that; first, they provide a unique representation of any ROIs functional data; second, they simultaneously account for inherent inter-subject anatomical variability that may influence any spatial characterization; third, they are invariant to similarity transformations and hence allow for direct comparisons between ROIs without any requirement for normalization to an atlas. We present quantitative validation demonstrating our methods improved sensitivity in performing group analysis when compared to traditional spatial normalization using synthetic data at the ROI level. We also use the proposed technique along with traditional normalization approach on real fMRI data collected from PD patients and normal subjects. The proposed features provide a powerful means to sensitively detect group-wise changes in ROI-based fMRI activation patterns even in the presence of anatomical variability.

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 Parkinsons disease suggests that the technique is promising, warranting further study.