Gait disturbances represent a therapeutic challenge in Parkinson's disease (PD). To further investigate their underlying pathophysiological mechanisms, we compared brain activation related to mental imagery of gait between 15 PD patients and 15 age-matched controls using a block-design functional MRI experiment. On average, patients showed altered locomotion relatively to controls, as assessed with a standardized gait test that evaluated the severity of PD-related gait disturbances on a 25-m path. The experiment was conducted in the subjects as they rehearsed themselves walking on the same path with a gait pattern similar as that during locomotor evaluation. Imagined walking times were measured on a trial-by-trial basis as a control of behavioral performance. In both groups, mean imagined walking time was not significantly different from that measured during real gait on the path used for evaluation. The between-group comparison of the mental gait activation pattern with reference to mental imagery of standing showed hypoactivations within parieto-occipital regions, along with the left hippocampus, midline/lateral cerebellum, and presumed pedunculopontine nucleus/mesencephalic locomotor area, in patients. More specifically, the activation level of the right posterior parietal cortex located within the impaired gait-related cognitive network decreased proportionally with the severity of gait disturbances scored on the path used for gait evaluation and mental imagery. These novel findings suggest that the right posterior parietal cortex dysfunction is strongly related to the severity of gait disturbances in PD. This region may represent a target for the development of therapeutic interventions for PD-related gait disturbances.

The motor clinical hallmarks of Parkinson's disease (PD) are usually quantified by physicians using validated clinimetric scales such as the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). However, clinical ratings are prone to subjectivity and inter-rater variability. The PD medical community is therefore looking for a simple, inexpensive, and objective rating method. As a first step towards this goal, a triaxial accelerometer-based system was used in a sample of 36 PD patients and 10 age-matched controls as they performed the MDS-UPDRS finger tapping (FT) task. First, raw signals were epoched to isolate the successive single FT movements. Next, eighteen FT task movement features were extracted, depicting MDS-UPDRS features and accelerometer specific features. An ordinal logistic regression model and a greedy backward algorithm were used to identify the most relevant features in the prediction of MDS-UPDRS FT scores, given by 3 specialists in movement disorders (SMDs). The Goodman-Kruskal Gamma index obtained (0.961), depicting the predictive performance of the model, is similar to those obtained between the individual scores given by the SMD (0.870 to 0.970). The automatic prediction of MDS-UPDRS scores using the proposed system may be valuable in clinical trials designed to evaluate and modify motor disability in PD patients.

Abstract-Recently, machine learning models have been applied to neuroimaging data, allowing to make predictions about a variable of interest based on the pattern of activation or anatomy over a set of voxels. These pattern recognition based methods present undeniable assets over classical (univariate) techniques, by providing predictions for unseen data, as well as the weights of each voxel in the model. However, the obtained weight map cannot be thresholded to perform regionally specific inference, leading to a difficult localization of the variable of interest. In this work, we provide local averages of the weights according to regions defined by anatomical or functional atlases (e.g. Brodmann atlas). These averages can then be ranked, thereby providing a sorted list of regions that can be (to a certain extent) compared with univariate results. Furthermore, we defined a "ranking distance", allowing for the quantitative comparison between localized patterns. These concepts are illustrated with two datasets.

BackgroundThe mechanisms underlying the interictal habituation deficit of cortical visual evoked potentials (VEP) in migraine are not well understood. Abnormal long-term functional plasticity of the visual cortex may play a role and it can be assessed experimentally by light deprivation (LD).MethodsWe have compared the effects of LD on VEP in migraine patients without aura between attacks (MO, n = 17) and in healthy volunteers (HV, n = 17). Six sequential blocks of 100 averaged VEP at 3.1 Hz were recorded before and after 1 hour of LD. We measured VEP P100 amplitude of the 1st block of 100 sweeps and its change over 5 sequential blocks of 100 responses.ResultsIn HV, the consequence of LD was a reduction of 1st block VEP amplitude and of the normal habituation pattern. By contrast, in MO patients, the interictal habituation deficit was not significantly modified, although 1st block VEP amplitude, already lower than in HV before LD, further decreased after LD.ConclusionsLight deprivation is thought to decrease both excitatory and subsequent inhibitory processes in visual cortex, which is in line with our findings in healthy volunteers. The VEP results in migraine patients suggest that early excitation was adequately suppressed, but not the inhibitory mechanisms occurring during long term stimulation and habituation. Accordingly, deficient intracortical inhibition is unlikely to be a primary factor in migraine pathophysiology and the habituation deficit.

A new finding in this study is that MI of brisk walking in young healthy individuals strongly involves processes lateralized in right fronto-parietal regions along with left cerebellum. These results show that brisk walking might be a non automatic locomotor activity requiring a high-level supraspinal control.