Postural instability via a loss of intermittent control in elderly and patients with Parkinson's disease: a model-based and data-driven approach

Postural instability is one of the major symptoms of Parkinson's disease. Here, we assimilated a model of intermittent delay feedback control during quiet standing into postural sway data from healthy young and elderly individuals as well as patients with Parkinson's disease to elucidate the possible mechanisms of instability. Specifically, we estimated the joint probability distribution of a set of parameters in the model using the Bayesian parameter inference such that the model with the inferred parameters can best-fit sway data for each individual. It was expected that the parameter values for three populations would distribute differently in the parameter space depending on their balance capability. Because the intermittent control model is parameterized by a parameter associated with the degree of intermittency in the control, it can represent not only the intermittent model but also the traditional continuous control model with no intermittency. We showed that the inferred parameter values for the three groups of individuals are classified into two major groups in the parameter space: one represents the intermittent control mostly for healthy people and patients with mild postural symptoms and the other the continuous control mostly for some elderly and patients with severe postural symptoms. The results of this study may be interpreted by postulating that increased postural instability in most Parkinson's patients and some elderly persons might be characterized as a dynamical disease

High-capacitance supercapacitors using nitrogen-decorated porous carbon derived from novolac resin containing peptide linkage

We fabricated nitrogen-decorated porous carbon exhibiting high capacitance per unit volume and unit weight via chemical activation of novolac resin containing peptide linkage. The porosity and the amount of nitrogen atoms were controlled by changing the molecular weight of novolac resin, the added amount of potassium hydroxide, or both. After chemical activation, positively charged nitrogen atoms (i.e., pyridine/pyrrole) at 400.3 eV in photoemission spectra contributed to both a shift in the point of zero charge toward negative potential and the generation of pseudocapacitance. Suitably developed pores and the positively charged nitrogen atoms make nitrogen-decorated novolac resin-derived porous carbon a promising material for electrodes in high-performance supercapacitors.ArticleELECTROCHIMICA ACTA. 55(20):5624-5628 (2010)journal articl

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

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

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

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

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