Principles of Haemodynamic Coupling for fMRI

Talk from the 23 & 24 January 2012 "GlaxoSmithKline - Neurophysics Workshop on Pharmacological MRI", an activity hosted at Warwick University and coordinated with the Neurophysics Marie Curie Initial Training Network of which GSK is a participant

Data_Sheet_1_Remote Monitoring in the Home Validates Clinical Gait Measures for Multiple Sclerosis.docx

<p>Background: The timed 25-foot walk (T25FW) is widely used as a clinic performance measure, but has yet to be directly validated against gait speed in the home environment.</p><p>Objectives: To develop an accurate method for remote assessment of walking speed and to test how predictive the clinic T25FW is for real-life walking.</p><p>Methods: An AX3-Axivity tri-axial accelerometer was positioned on 32 MS patients (Expanded Disability Status Scale [EDSS] 0–6) in the clinic, who subsequently wore it at home for up to 7 days. Gait speed was calculated from these data using both a model developed with healthy volunteers and individually personalized models generated from a machine learning algorithm.</p><p>Results: The healthy volunteer model predicted gait speed poorly for more disabled people with MS. However, the accuracy of individually personalized models was high regardless of disability (R-value = 0.98, p-value = 1.85 × 10⁻²²). With the latter, we confirmed that the clinic T25FW is strongly predictive of the maximum sustained gait speed in the home environment (R-value = 0.89, p-value = 4.34 × 10⁻⁸).</p><p>Conclusion: Remote gait monitoring with individually personalized models is accurate for patients with MS. Using these models, we have directly validated the clinical meaningfulness (i.e., predictiveness) of the clinic T25FW for the first time.</p

Image_1_Remote Monitoring in the Home Validates Clinical Gait Measures for Multiple Sclerosis.JPEG

<p>Background: The timed 25-foot walk (T25FW) is widely used as a clinic performance measure, but has yet to be directly validated against gait speed in the home environment.</p><p>Objectives: To develop an accurate method for remote assessment of walking speed and to test how predictive the clinic T25FW is for real-life walking.</p><p>Methods: An AX3-Axivity tri-axial accelerometer was positioned on 32 MS patients (Expanded Disability Status Scale [EDSS] 0–6) in the clinic, who subsequently wore it at home for up to 7 days. Gait speed was calculated from these data using both a model developed with healthy volunteers and individually personalized models generated from a machine learning algorithm.</p><p>Results: The healthy volunteer model predicted gait speed poorly for more disabled people with MS. However, the accuracy of individually personalized models was high regardless of disability (R-value = 0.98, p-value = 1.85 × 10⁻²²). With the latter, we confirmed that the clinic T25FW is strongly predictive of the maximum sustained gait speed in the home environment (R-value = 0.89, p-value = 4.34 × 10⁻⁸).</p><p>Conclusion: Remote gait monitoring with individually personalized models is accurate for patients with MS. Using these models, we have directly validated the clinical meaningfulness (i.e., predictiveness) of the clinic T25FW for the first time.</p

Image_2_Remote Monitoring in the Home Validates Clinical Gait Measures for Multiple Sclerosis.JPEG

<p>Background: The timed 25-foot walk (T25FW) is widely used as a clinic performance measure, but has yet to be directly validated against gait speed in the home environment.</p><p>Objectives: To develop an accurate method for remote assessment of walking speed and to test how predictive the clinic T25FW is for real-life walking.</p><p>Methods: An AX3-Axivity tri-axial accelerometer was positioned on 32 MS patients (Expanded Disability Status Scale [EDSS] 0–6) in the clinic, who subsequently wore it at home for up to 7 days. Gait speed was calculated from these data using both a model developed with healthy volunteers and individually personalized models generated from a machine learning algorithm.</p><p>Results: The healthy volunteer model predicted gait speed poorly for more disabled people with MS. However, the accuracy of individually personalized models was high regardless of disability (R-value = 0.98, p-value = 1.85 × 10⁻²²). With the latter, we confirmed that the clinic T25FW is strongly predictive of the maximum sustained gait speed in the home environment (R-value = 0.89, p-value = 4.34 × 10⁻⁸).</p><p>Conclusion: Remote gait monitoring with individually personalized models is accurate for patients with MS. Using these models, we have directly validated the clinical meaningfulness (i.e., predictiveness) of the clinic T25FW for the first time.</p

Image_3_Remote Monitoring in the Home Validates Clinical Gait Measures for Multiple Sclerosis.JPEG

<p>Background: The timed 25-foot walk (T25FW) is widely used as a clinic performance measure, but has yet to be directly validated against gait speed in the home environment.</p><p>Objectives: To develop an accurate method for remote assessment of walking speed and to test how predictive the clinic T25FW is for real-life walking.</p><p>Methods: An AX3-Axivity tri-axial accelerometer was positioned on 32 MS patients (Expanded Disability Status Scale [EDSS] 0–6) in the clinic, who subsequently wore it at home for up to 7 days. Gait speed was calculated from these data using both a model developed with healthy volunteers and individually personalized models generated from a machine learning algorithm.</p><p>Results: The healthy volunteer model predicted gait speed poorly for more disabled people with MS. However, the accuracy of individually personalized models was high regardless of disability (R-value = 0.98, p-value = 1.85 × 10⁻²²). With the latter, we confirmed that the clinic T25FW is strongly predictive of the maximum sustained gait speed in the home environment (R-value = 0.89, p-value = 4.34 × 10⁻⁸).</p><p>Conclusion: Remote gait monitoring with individually personalized models is accurate for patients with MS. Using these models, we have directly validated the clinical meaningfulness (i.e., predictiveness) of the clinic T25FW for the first time.</p

White matter microstructure measures in unmedicated and medicated participants with hypertension after propensity score matching.

<p>White matter microstructure measures in unmedicated and medicated participants with hypertension after propensity score matching.</p

Illustrative representation of the contrast between microstructure measures of each 27 white matter tracts in the pre-hypertensive and normotensive groups.

<p>Probabilistic tractographies colored by <i>p</i> values adjusted for Bonferroni correction were shown from top and down. White-colored tracts indicate adjusted <i>p</i>>0.05. Abbreviations: FA, fractional anisotropy; ICVF, intracellular volume fraction; ISOVF, isotropic volume fraction; MD, mean diffusivity.</p

Illustrative representation of the contrast between microstructure measures of each 27 white matter tracts in the hypertensive and non-hypertensive groups.

<p>Probabilistic tractographies colored by <i>p</i> values adjusted for Bonferroni correction were shown from top and down. White-colored tracts indicate adjusted <i>p</i>>0.05. Abbreviations: FA, fractional anisotropy; ICVF, intracellular volume fraction; ISOVF, isotropic volume fraction; MD, mean diffusivity.</p

Illustrative representation of the contrast between microstructure measures of each 27 white matter tracts in the medicated and unmedicated groups.

<p>Probabilistic tractographies colored by <i>p</i> values adjusted for Bonferroni correction were shown from top and down. White-colored tracts indicate adjusted <i>p</i>>0.05. Abbreviations: FA, fractional anisotropy; ICVF, intracellular volume fraction; ISOVF, isotropic volume fraction; MD, mean diffusivity.</p

Participant characteristics in non-hypertensive and hypertensive groups after propensity score matching.

<p>Participant characteristics in non-hypertensive and hypertensive groups after propensity score matching.</p