The identification of toe-off and initial foot contact events.

<p>A: Angular velocity of the heel around the medio-lateral axis. The angular velocity increased when the heel left the ground, and it decreased at the swing phase after the toe left the ground. Toe-off event was defined as the positive peak after the heel left the ground. B: Linear acceleration of the ankle in the anterio-posterior direction. The acceleration increased at the swing phase, and initial foot contact event was defined as the negative peak when the foot landed the ground after the swing phase.</p

Gait parameters during straight walking.

<p>FOG–P: Patients with freezing of gait (FOG) with little to no parkinsonism.</p><p>PD–FOG: Patients with Parkinson’s disease without FOG.</p><p>Group: The main effects of group (FOG–P and PD–FOG).</p><p>Condition: The main effects of walking condition (‘Go’ and ‘Back’).</p><p>Interaction: The interaction between group and walking condition.</p

Parameters quantified by the model of coupled phase-oscillators.

<p>Mesh figures show how each <i>φCV</i>, <i>φ_deviation</i>, the slope of the regression line between the relative step phase and the phase change, and <i>stride time CV</i> varying according to the strength of the phase reset (<i>amp</i>) and the magnitude of noise in the phase reset (<i>σ</i>). Strong phase reset (large values of <i>amp</i>) decreased <i>φCV</i> and <i>φ_deviation</i>, and increased <i>stride time CV</i>. All three of these parameters increased as noise (<i>σ</i>) increased. The slope of regression line between the relative step phase and the phase change decreased with increases in the strength of phase reset. The model mimicking the gait patterns in FOG−P (red dots) showed stronger (larger <i>amp</i>) and noisier (larger <i>σ</i>) phase reset than the model mimicking the gait patterns in PD−FOG (blue dots). The mesh figures show results from one typical set of model parameters (<i>ω₁</i> = <i>ω₂</i> = 1/2π, <i>α</i> = <i>β</i> = 0.05), therefore the red and blue dots are not consistent with the values shown by the mesh figures. However, the results obtained from the other set of model parameters also exhibited the same tendency.</p

Step phase regulation.

<p>Linear regression analyses between the relative step phase of each step (<i>φ_i</i>) and the phase change from each step to the following step () in a PD−FOG patient (left) and a FOG−P patient (right) during the ‘Go’ (upper) and ‘Back’ (lower) portions of the walking task. The analyses were performed separately for the left-to-right phase changes (blue) and for the right-to-left phase changes (red). The slope of the relation was smaller and the noise in the step phase regulation was larger in FOG−P than in PD−FOG.</p

Patients’ clinical features.

<p>FOG–P: Patients with freezing of gait (FOG) with little to no parkinsonism.</p><p>PD–FOG: Patients with Parkinson’s disease without FOG.</p><p>FOG onset: The time from symptom onset to FOG onset.</p><p>UPDRS: Unified Parkinson’s disease rating scale.</p><p>Axial: The total of standing, posture, gait and postural instability items.</p><p>Upper limb movement: The total of upper limb repetitive movement items.</p><p>Lower limb movement: The total of lower limb repetitive movement items.</p><p>Rigidity: The total rigidity score for the neck and limbs.</p><p>Tremor: The total tremor score for the neck and limbs.</p><p>NFOG-Q: New freezing of gait questionnaire.</p

Noisy Interlimb Coordination Can Be a Main Cause of Freezing of Gait in Patients with Little to No Parkinsonism

<div><p>Freezing of gait in patients with Parkinson’s disease is associated with several factors, including interlimb incoordination and impaired gait cycle regulation. Gait analysis in patients with Parkinson’s disease is confounded by parkinsonian symptoms such as rigidity. To understand the mechanisms underlying freezing of gait, we compared gait patterns during straight walking between 9 patients with freezing of gait but little to no parkinsonism (freezing patients) and 11 patients with Parkinson’s disease (non-freezing patients). Wireless sensors were used to detect foot contact and toe-off events, and the step phase of each foot contact was calculated by defining one stride cycle of the other leg as 360°. Phase-resetting analysis was performed, whereby the relation between the step phase of one leg and the subsequent phase change in the following step of the other leg was quantified using regression analysis. A small slope of the regression line indicates a forceful correction (phase reset) at every step of the deviation of step phase from the equilibrium phase, usually at around 180°. The slope of this relation was smaller in freezing patients than in non-freezing patients, but the slope exhibited larger step-to-step variability. This indicates that freezing patients executed a forceful but noisy correction of the deviation of step phase, whereas non-freezing patients made a gradual correction of the deviation. Moreover, freezing patients tended to show more variable step phase and stride time than non-freezing patients. Dynamics of a model of two coupled oscillators interacting through a phase resetting mechanism were examined, and indicated that the deterioration of phase reset by noise provoked variability in step phase and stride time. That is, interlimb coordination can affect regulation of the gait cycle. These results suggest that noisy interlimb coordination, which probably caused forceful corrections of step phase deviation, can be a cause of freezing of gait.</p></div

Representative sequences of gait parameters.

<p>Stride time, step time, step phase, swing time and stance time during a single walking trial (20 m straight walk, 180° clockwise turn, and 20 m straight walk) in a PD−FOG patient (left) and a FOG−P patient (right). Blue and red circles represent the data from the left and the right leg, respectively. Crosses marks indicate the data recorded during the turn. Grey shading represents FOG. The PD−FOG patient exhibited deviation of step phase from 180° but stable stride time and step phase during straight walking, and slightly increased stride time and asymmetric step phase during the turn. In contrast, the FOG−P patient exhibited large variability in stride time and step phase with step phase close to 180° during straight walking, and reduced stride time and increased step phase deviation during the turn, which preceded FOG.</p

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Impact of Aspiration Pneumonia on the Clinical Course of Progressive Supranuclear Palsy: A Retrospective Cohort Study

<div><p>Introduction</p><p>Although aspiration pneumonia is the most common complication of progressive supranuclear palsy (PSP), the clinical impact of aspiration pneumonia on disease course and survival has not been fully estimated. Thus, we retrospectively analyzed the prognostic factors and clinical consequences of pneumonia in PSP.</p><p>Methods</p><p>The clinical course of patients with aspiration pneumonia was surveyed. The association between baseline clinical features (2 years from disease onset) and latency to the initial development of pneumonia was investigated using survival time and Cox regression analyses.</p><p>Results</p><p>Ninety patients with a clinical diagnosis of PSP were observed for 5.1±3.8 years (mean±SD), and 22 had aspiration pneumonia. Subsequently, 20 patients (91%) had to discontinue oral feeding entirely and 13 (59%) died, whereas, of 68 patients without pneumonia, only three patients (4%) died. Time to initial development of pneumonia was strongly correlated with survival time (Spearman R = 0.92, <i>P</i><0.001), with a mean latency of 2.3 years to death. Among baseline clinical features, early fall episodes and cognitive decline were significant predictors of pneumonia (<i>P</i> = 0.001 and <i>P</i><0.001, respectively, log rank test). Cox regression analysis demonstrated that early fall episodes (adjusted hazard ratio: 3.9, 95% confidence interval: 1.2–12.5, <i>P</i> = 0.03) and cognitive decline (adjusted hazard ratio: 5.2, 95% confidence interval: 1.4–19.3, <i>P</i> = 0.02) independently predicted pneumonia. By contrast, dysphagia was not associated with pneumonia (<i>P</i> = 0.2, log rank test).</p><p>Conclusion</p><p>Initial development of pneumonia indicates an unfavorable clinical course and predicts survival time (mean survival time 2.3 years). Patients with early falls and cognitive decline were at high risk of early development of pneumonia.</p></div

Xanthine Oxidase Mediates Axonal and Myelin Loss in a Murine Model of Multiple Sclerosis

<div><p>Objectives</p><p>Oxidative stress plays an important role in the pathogenesis of multiple sclerosis (MS). Though reactive oxygen species (ROS) are produced by various mechanisms, xanthine oxidase (XO) is a major enzyme generating ROS in the context of inflammation. The objectives of this study were to investigate the involvement of XO in the pathogenesis of MS and to develop a potent new therapy for MS based on the inhibition of ROS.</p><p>Methods</p><p>XO were assessed in a model of MS: experimental autoimmune encephalomyelitis (EAE). The contribution of XO-generated ROS to the pathogenesis of EAE was assessed by treating EAE mice with a novel XO inhibitor, febuxostat. The efficacy of febuxostat was also examined in <i>in vitro</i> studies.</p><p>Results</p><p>We showed for the first time that the expression and the activity of XO were increased dramatically within the central nervous system of EAE mice as compared to naïve mice. Furthermore, prophylactic administration of febuxostat, a XO inhibitor, markedly reduced the clinical signs of EAE. Both <i>in vivo</i> and <i>in vitro</i> studies showed infiltrating macrophages and microglia as the major sources of excess XO production, and febuxostat significantly suppressed ROS generation from these cells. Inflammatory cellular infiltration and glial activation in the spinal cord of EAE mice were inhibited by the treatment with febuxostat. Importantly, therapeutic efficacy was observed not only in mice with relapsing-remitting EAE but also in mice with secondary progressive EAE by preventing axonal loss and demyelination.</p><p>Conclusion</p><p>These results highlight the implication of XO in EAE pathogenesis and suggest XO as a target for MS treatment and febuxostat as a promising therapeutic option for MS neuropathology.</p></div