The Experts below are selected from a list of 249 Experts worldwide ranked by ideXlab platform
Helen Dawes - One of the best experts on this subject based on the ideXlab platform.
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Insights into gait disorders: Walking variability using Phase Plot analysis, Huntington's disease
Gait & posture, 2014Co-Authors: Johnny Collett, Patrick Esser, Hanan Khalil, Monica Busse, Lori Quinn, Katy Debono, Anne Elizabeth Rosser, Andrea H Nemeth, Helen DawesAbstract:Huntington's Disease (HD) is a progressive inherited neurodegenerative disorder. Identifying sensitive methodologies to quantitatively measure early motor changes have been difficult to develop. This exploratory observational study investigated gait variability and symmetry in HD using Phase Plot analysis. We measured the walking of 22 controls and 35 HD gene carriers (7 premanifest (PreHD)), 16 early/mid (HD1) and 12 late stage (HD2) in XXXXX and XXXXX, UK. The Unified Huntington's Disease Rating Scale-Total Motor Scores (UHDRS-TMS) and Disease Burden Scores (DBS) were used to quantify disease severity. Data was collected during a clinical walk test (8.8 or 10 m) using an inertial measurement unit attached to the trunk. The 6 middle strides were used to calculate gait variability determined by spatiotemporal parameters (co-efficient of variation (CoV)) and Phase Plot analysis. Phase Plots considered the variability in consecutive wave forms from vertical movement and were quantified by SDA (spatiotemporal variability), SDB (temporal variability), Ratio∀ (ratio SDA:SDB) and Δangleβ (symmetry). Step time CoV was greater in manifest HD (p 0.05). Phase Plot analysis identified differences between manifest HD and controls for SDB, Ratio∀and Δangle (all p < 0.01, both manifest groups). Furthermore Ratio∀ was smaller in PreHD compared with controls (p < 0.01). Ratio ∀also produced the strongest correlation with UHDRS-TMS (r = -0.61, p =
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insights into gait disorders walking variability using Phase Plot analysis huntington s disease
Gait & Posture, 2013Co-Authors: Johnny Collett, Patrick Esser, Hanan Khalil, Monica Busse, Lori Quinn, Katy Debono, Anne Elizabeth Rosser, Andrea H Nemeth, Helen DawesAbstract:Huntington's Disease (HD) is a progressive inherited neurodegenerative disorder. Identifying sensitive methodologies to quantitatively measure early motor changes have been difficult to develop. This exploratory observational study investigated gait variability and symmetry in HD using Phase Plot analysis. We measured the walking of 22 controls and 35 HD gene carriers (7 premanifest (PreHD)), 16 early/mid (HD1) and 12 late stage (HD2) in XXXXX and XXXXX, UK. The Unified Huntington's Disease Rating Scale-Total Motor Scores (UHDRS-TMS) and Disease Burden Scores (DBS) were used to quantify disease severity. Data was collected during a clinical walk test (8.8 or 10 m) using an inertial measurement unit attached to the trunk. The 6 middle strides were used to calculate gait variability determined by spatiotemporal parameters (co-efficient of variation (CoV)) and Phase Plot analysis. Phase Plots considered the variability in consecutive wave forms from vertical movement and were quantified by SDA (spatiotemporal variability), SDB (temporal variability), Ratio∀ (ratio SDA:SDB) and Δangleβ (symmetry). Step time CoV was greater in manifest HD (p 0.05). Phase Plot analysis identified differences between manifest HD and controls for SDB, Ratio∀and Δangle (all p < 0.01, both manifest groups). Furthermore Ratio∀ was smaller in PreHD compared with controls (p < 0.01). Ratio ∀also produced the strongest correlation with UHDRS-TMS (r = -0.61, p = <0.01) and was correlated with DBS (r = -0.42, p = 0.02). Phase Plot analysis may be a sensitive method of detecting gait changes in HD and can be performed quickly during clinical walking tests.
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Insights into gait disorders: walking variability using Phase Plot analysis, Parkinson's disease.
Gait & posture, 2013Co-Authors: Patrick Esser, Johnny Collett, Helen Dawes, Ken HowellsAbstract:Gait variability may have greater utility than spatio-temporal parameters and can, be an indication for risk of falling in people with Parkinson's disease (PD). Current methods rely on prolonged data collection in order to obtain large datasets which may be demanding to obtain. We set out to explore a Phase Plot variability analysis to differentiate typically developed adults (TDAs) from PD obtained from two 10 m walks. Fourteen people with PD and good mobility (Rivermead Mobility Index≥8) and ten aged matched TDA were recruited and walked over 10-m at self-selected walking speed. An inertial measurement unit was placed over the projected centre of mass (CoM) sampling at 100 Hz. Vertical CoM excursion was derived to determine modelled spatiotemporal data after which the Phase Plot analysis was applied producing a cloud of datapoints. SDA described the spread and SDB the width of the cloud with β the angular vector of the data points. The ratio (∀) was defined as SDA: SDB. Cadence (p=.342) and stride length (p=.615) did not show a significance between TDA and PD. A difference was found for walking speed (p=.041). Furthermore a significant difference was found for β (p=.010), SDA (p=.004) other than SDB (p=.385) or ratio ∀ (p=.830). Two sequential 10-m walks showed no difference in PD for cadence (p=.193), stride length (p=.683), walking speed (p=.684) and β (p=.194), SDA (p=.051), SDB (p=.145) or ∀ (p=.226). The proposed Phase Plot analysis, performed on CoM motion could be used to reliably differentiate PD from TDA over a 10-m walk.
Johnny Collett - One of the best experts on this subject based on the ideXlab platform.
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Insights into gait disorders: Walking variability using Phase Plot analysis, Huntington's disease
Gait & posture, 2014Co-Authors: Johnny Collett, Patrick Esser, Hanan Khalil, Monica Busse, Lori Quinn, Katy Debono, Anne Elizabeth Rosser, Andrea H Nemeth, Helen DawesAbstract:Huntington's Disease (HD) is a progressive inherited neurodegenerative disorder. Identifying sensitive methodologies to quantitatively measure early motor changes have been difficult to develop. This exploratory observational study investigated gait variability and symmetry in HD using Phase Plot analysis. We measured the walking of 22 controls and 35 HD gene carriers (7 premanifest (PreHD)), 16 early/mid (HD1) and 12 late stage (HD2) in XXXXX and XXXXX, UK. The Unified Huntington's Disease Rating Scale-Total Motor Scores (UHDRS-TMS) and Disease Burden Scores (DBS) were used to quantify disease severity. Data was collected during a clinical walk test (8.8 or 10 m) using an inertial measurement unit attached to the trunk. The 6 middle strides were used to calculate gait variability determined by spatiotemporal parameters (co-efficient of variation (CoV)) and Phase Plot analysis. Phase Plots considered the variability in consecutive wave forms from vertical movement and were quantified by SDA (spatiotemporal variability), SDB (temporal variability), Ratio∀ (ratio SDA:SDB) and Δangleβ (symmetry). Step time CoV was greater in manifest HD (p 0.05). Phase Plot analysis identified differences between manifest HD and controls for SDB, Ratio∀and Δangle (all p < 0.01, both manifest groups). Furthermore Ratio∀ was smaller in PreHD compared with controls (p < 0.01). Ratio ∀also produced the strongest correlation with UHDRS-TMS (r = -0.61, p =
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insights into gait disorders walking variability using Phase Plot analysis huntington s disease
Gait & Posture, 2013Co-Authors: Johnny Collett, Patrick Esser, Hanan Khalil, Monica Busse, Lori Quinn, Katy Debono, Anne Elizabeth Rosser, Andrea H Nemeth, Helen DawesAbstract:Huntington's Disease (HD) is a progressive inherited neurodegenerative disorder. Identifying sensitive methodologies to quantitatively measure early motor changes have been difficult to develop. This exploratory observational study investigated gait variability and symmetry in HD using Phase Plot analysis. We measured the walking of 22 controls and 35 HD gene carriers (7 premanifest (PreHD)), 16 early/mid (HD1) and 12 late stage (HD2) in XXXXX and XXXXX, UK. The Unified Huntington's Disease Rating Scale-Total Motor Scores (UHDRS-TMS) and Disease Burden Scores (DBS) were used to quantify disease severity. Data was collected during a clinical walk test (8.8 or 10 m) using an inertial measurement unit attached to the trunk. The 6 middle strides were used to calculate gait variability determined by spatiotemporal parameters (co-efficient of variation (CoV)) and Phase Plot analysis. Phase Plots considered the variability in consecutive wave forms from vertical movement and were quantified by SDA (spatiotemporal variability), SDB (temporal variability), Ratio∀ (ratio SDA:SDB) and Δangleβ (symmetry). Step time CoV was greater in manifest HD (p 0.05). Phase Plot analysis identified differences between manifest HD and controls for SDB, Ratio∀and Δangle (all p < 0.01, both manifest groups). Furthermore Ratio∀ was smaller in PreHD compared with controls (p < 0.01). Ratio ∀also produced the strongest correlation with UHDRS-TMS (r = -0.61, p = <0.01) and was correlated with DBS (r = -0.42, p = 0.02). Phase Plot analysis may be a sensitive method of detecting gait changes in HD and can be performed quickly during clinical walking tests.
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Insights into gait disorders: walking variability using Phase Plot analysis, Parkinson's disease.
Gait & posture, 2013Co-Authors: Patrick Esser, Johnny Collett, Helen Dawes, Ken HowellsAbstract:Gait variability may have greater utility than spatio-temporal parameters and can, be an indication for risk of falling in people with Parkinson's disease (PD). Current methods rely on prolonged data collection in order to obtain large datasets which may be demanding to obtain. We set out to explore a Phase Plot variability analysis to differentiate typically developed adults (TDAs) from PD obtained from two 10 m walks. Fourteen people with PD and good mobility (Rivermead Mobility Index≥8) and ten aged matched TDA were recruited and walked over 10-m at self-selected walking speed. An inertial measurement unit was placed over the projected centre of mass (CoM) sampling at 100 Hz. Vertical CoM excursion was derived to determine modelled spatiotemporal data after which the Phase Plot analysis was applied producing a cloud of datapoints. SDA described the spread and SDB the width of the cloud with β the angular vector of the data points. The ratio (∀) was defined as SDA: SDB. Cadence (p=.342) and stride length (p=.615) did not show a significance between TDA and PD. A difference was found for walking speed (p=.041). Furthermore a significant difference was found for β (p=.010), SDA (p=.004) other than SDB (p=.385) or ratio ∀ (p=.830). Two sequential 10-m walks showed no difference in PD for cadence (p=.193), stride length (p=.683), walking speed (p=.684) and β (p=.194), SDA (p=.051), SDB (p=.145) or ∀ (p=.226). The proposed Phase Plot analysis, performed on CoM motion could be used to reliably differentiate PD from TDA over a 10-m walk.
Patrick Esser - One of the best experts on this subject based on the ideXlab platform.
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Insights into gait disorders: Walking variability using Phase Plot analysis, Huntington's disease
Gait & posture, 2014Co-Authors: Johnny Collett, Patrick Esser, Hanan Khalil, Monica Busse, Lori Quinn, Katy Debono, Anne Elizabeth Rosser, Andrea H Nemeth, Helen DawesAbstract:Huntington's Disease (HD) is a progressive inherited neurodegenerative disorder. Identifying sensitive methodologies to quantitatively measure early motor changes have been difficult to develop. This exploratory observational study investigated gait variability and symmetry in HD using Phase Plot analysis. We measured the walking of 22 controls and 35 HD gene carriers (7 premanifest (PreHD)), 16 early/mid (HD1) and 12 late stage (HD2) in XXXXX and XXXXX, UK. The Unified Huntington's Disease Rating Scale-Total Motor Scores (UHDRS-TMS) and Disease Burden Scores (DBS) were used to quantify disease severity. Data was collected during a clinical walk test (8.8 or 10 m) using an inertial measurement unit attached to the trunk. The 6 middle strides were used to calculate gait variability determined by spatiotemporal parameters (co-efficient of variation (CoV)) and Phase Plot analysis. Phase Plots considered the variability in consecutive wave forms from vertical movement and were quantified by SDA (spatiotemporal variability), SDB (temporal variability), Ratio∀ (ratio SDA:SDB) and Δangleβ (symmetry). Step time CoV was greater in manifest HD (p 0.05). Phase Plot analysis identified differences between manifest HD and controls for SDB, Ratio∀and Δangle (all p < 0.01, both manifest groups). Furthermore Ratio∀ was smaller in PreHD compared with controls (p < 0.01). Ratio ∀also produced the strongest correlation with UHDRS-TMS (r = -0.61, p =
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insights into gait disorders walking variability using Phase Plot analysis huntington s disease
Gait & Posture, 2013Co-Authors: Johnny Collett, Patrick Esser, Hanan Khalil, Monica Busse, Lori Quinn, Katy Debono, Anne Elizabeth Rosser, Andrea H Nemeth, Helen DawesAbstract:Huntington's Disease (HD) is a progressive inherited neurodegenerative disorder. Identifying sensitive methodologies to quantitatively measure early motor changes have been difficult to develop. This exploratory observational study investigated gait variability and symmetry in HD using Phase Plot analysis. We measured the walking of 22 controls and 35 HD gene carriers (7 premanifest (PreHD)), 16 early/mid (HD1) and 12 late stage (HD2) in XXXXX and XXXXX, UK. The Unified Huntington's Disease Rating Scale-Total Motor Scores (UHDRS-TMS) and Disease Burden Scores (DBS) were used to quantify disease severity. Data was collected during a clinical walk test (8.8 or 10 m) using an inertial measurement unit attached to the trunk. The 6 middle strides were used to calculate gait variability determined by spatiotemporal parameters (co-efficient of variation (CoV)) and Phase Plot analysis. Phase Plots considered the variability in consecutive wave forms from vertical movement and were quantified by SDA (spatiotemporal variability), SDB (temporal variability), Ratio∀ (ratio SDA:SDB) and Δangleβ (symmetry). Step time CoV was greater in manifest HD (p 0.05). Phase Plot analysis identified differences between manifest HD and controls for SDB, Ratio∀and Δangle (all p < 0.01, both manifest groups). Furthermore Ratio∀ was smaller in PreHD compared with controls (p < 0.01). Ratio ∀also produced the strongest correlation with UHDRS-TMS (r = -0.61, p = <0.01) and was correlated with DBS (r = -0.42, p = 0.02). Phase Plot analysis may be a sensitive method of detecting gait changes in HD and can be performed quickly during clinical walking tests.
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Insights into gait disorders: walking variability using Phase Plot analysis, Parkinson's disease.
Gait & posture, 2013Co-Authors: Patrick Esser, Johnny Collett, Helen Dawes, Ken HowellsAbstract:Gait variability may have greater utility than spatio-temporal parameters and can, be an indication for risk of falling in people with Parkinson's disease (PD). Current methods rely on prolonged data collection in order to obtain large datasets which may be demanding to obtain. We set out to explore a Phase Plot variability analysis to differentiate typically developed adults (TDAs) from PD obtained from two 10 m walks. Fourteen people with PD and good mobility (Rivermead Mobility Index≥8) and ten aged matched TDA were recruited and walked over 10-m at self-selected walking speed. An inertial measurement unit was placed over the projected centre of mass (CoM) sampling at 100 Hz. Vertical CoM excursion was derived to determine modelled spatiotemporal data after which the Phase Plot analysis was applied producing a cloud of datapoints. SDA described the spread and SDB the width of the cloud with β the angular vector of the data points. The ratio (∀) was defined as SDA: SDB. Cadence (p=.342) and stride length (p=.615) did not show a significance between TDA and PD. A difference was found for walking speed (p=.041). Furthermore a significant difference was found for β (p=.010), SDA (p=.004) other than SDB (p=.385) or ratio ∀ (p=.830). Two sequential 10-m walks showed no difference in PD for cadence (p=.193), stride length (p=.683), walking speed (p=.684) and β (p=.194), SDA (p=.051), SDB (p=.145) or ∀ (p=.226). The proposed Phase Plot analysis, performed on CoM motion could be used to reliably differentiate PD from TDA over a 10-m walk.
Eric A Newman - One of the best experts on this subject based on the ideXlab platform.
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mechanisms and distribution of ion channels in retinal ganglion cells using temperature as an independent variable
Journal of Neurophysiology, 2010Co-Authors: Jurgen F Fohlmeister, Ethan D Cohen, Eric A NewmanAbstract:Trains of action potentials of rat and cat retinal ganglion cells (RGCs) were recorded intracellularly across a temperature range of 7–37°C. Phase Plots of the experimental impulse trains were precision fit using multicompartment simulations of anatomically reconstructed rat and cat RGCs. Action potential excitation was simulated with a “Five-channel model” [Na, K(delayed rectifier), Ca, K(A), and K(Ca-activated) channels] and the nonspace-clamped condition of the whole cell recording was exploited to determine the channels' distribution on the dendrites, soma, and proximal axon. At each temperature, optimal Phase-Plot fits for RGCs occurred with the same unique channel distribution. The “waveform” of the electrotonic current was found to be temperature dependent, which reflected the shape changes in the experimental action potentials and confirmed the channel distributions. The distributions are cell-type specific and adequate for soma and dendritic excitation with a safety margin. The highest Na-channel density was found on an axonal segment some 50–130 μm distal to the soma, as determined from the temperature-dependent “initial segment–somadendritic (IS-SD) break.” The voltage dependence of the gating rate constants remains invariant between 7 and 23°C and between 30 and 37°C, but undergoes a transition between 23 and 30°C. Both gating-kinetic and ion-permeability Q10s remain virtually constant between 23 and 37°C (kinetic Q10s = 1.9–1.95; permeability Q10s = 1.49–1.64). The Q10s systematically increase for T <23°C (kinetic Q10 = 8 at T = 8°C). The Na channels were consistently “sleepy” (non-Arrhenius) for T <8°C, with a loss of spiking for T <7°C.
David B Grayden - One of the best experts on this subject based on the ideXlab platform.
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predicting the location of the axon initial segment using spike waveform analysis simulations of retinal ganglion cell physiology
BMC Neuroscience, 2013Co-Authors: Matias I Maturana, Raymond C S Wong, Tania Kameneva, Shaun L Cloherty, Michael R Ibbotson, Alex E Hadjinicolaou, David B GraydenAbstract:There are 16 morphologically defined classes of rat retinal ganglion cells (RGCs). Most commonly, they are classified on the basis of several criteria including: soma size, dendritic field diameter, the dendritic branching pattern and the depth of stratification in the inner plexiform layer. Recently, it has also been shown that the intrinsic physiological properties of each rat RGC type vary enormously. Using multicompartment models of RGC types we investigated whether the location of the axon initial segment (AIS), the site of greatest sodium channel density and lowest voltage threshold, can be predicted by measurements of spike waveform made at the soma. The action potential waveform in many neurons consists of several components, which can be determined by examining the first and second derivatives of the membrane potential. In this study, we focus on this technique as an objective method to analyze the action potential waveform for different morphological RGC types. In addition, we analyze the features of the Phase Plot, which shows the rate of change of the membrane potential against the membrane potential itself. Phase Plot analysis allows the measurement of subtle differences in the action potential waveform such as the initial segment-soma/dendritic break (ISSD), which corresponds to the early rising Phase of the action potential. When the recording is made at the soma, the presence of the ISSD in the Phase Plot indicates that a low threshold region (i.e. the AIS) is further away from the soma. Rat RGCs were characterized electrophysiologically using standard whole cell patch clamp recording techniques. Data were acquired at 20 kHz using custom software developed in LabView (National Instruments). Spontaneous spikes and spikes evoked by just-threshold current were used for analysis. For each of the recordings, the amplitude and time of the trough between the peaks in the second-order derivatives were analyzed. After three dimensional confocal reconstruction of each recorded cell (Zeiss PASCAL) it was classified morphologically into one of the 16 predefined types. Multicompartment models of real retinal ganglion cells were constructed from 3D rendering confocal reconstructions and their physiology was simulated using the Hodgkin-Huxley formalism in the NEURON environment. Sodium channel density in the AIS and its distance from the soma were systematically varied and the effects on the Phase Plot analyzed. Simulations showed that the further the AIS was from the soma, the more pronounced the ISSD break, resulting in a larger break with a deeper trough between the two peaks in the Phase Plot. This result allows us to predict the location of the AIS based on recordings of the impulse waveform. In addition, we found that the density of sodium channels in the AIS affects spike propagation into the soma. We observed that decreasing sodium conductance in the AIS, required two spikes to occur in the AIS in order to evoke a somatic spike. This was also observed experimentally, in particular in C4 cells. Further analysis of individual RGC spike waveforms demonstrated that certain RGC types could be reliably identified using their spike waveforms.