Lower arterial cerebral blood flow is associated with worse neuroinflammation and immunomodulation composite proteomic scores.

Background: Brain hypoperfusion is linked with worse physical, cognitive and MRI outcomes in multiple sclerosis (MS). Understanding the proteomic signatures related to hypoperfusion could provide insights into the pathophysiological mechanism.

Methods: 140 people with MS (pwMS; 86 clinically isolated syndrome (CIS)/relapsing-remitting (RRMS) and 54 progressive (PMS)) were included.

Glial cell injury and atrophied lesion volume as measures of chronic multiple sclerosis inflammation.

Background: Atrophied lesion volume (aLV), a proposed biomarker of disability progression in multiple sclerosis (MS) and transition into progressive MS (PMS), depicts chronic periventricular white matter (WM) pathology. Meningeal infiltrates, imaged as leptomeningeal contrast enhancement (LMCE), are linked with greater cortical pathology.

Objectives: To determine the relationship between serum-derived proteomic data with the development of aLV and LMCE in a heterogeneous group of people with MS (pwMS).

A US payer perspective health economic model assessing value of monitoring disease activity to inform discontinuation and re-initiation of DMT in multiple sclerosis.

Objectives: We evaluate the potential clinical and cost impacts of discontinuing disease-modifying therapy (DMT) in people with multiple sclerosis (PwMS) when age-related immunosenescence can reduce DMT efficacy while increasing associated risks.

Methods: A Markov model simulated clinical and cost impacts to the patient and payers when a proportion of eligible patients with relapsing remitting multiple sclerosis (RRMS) discontinue DMT. Eligibility was defined as age >55 years, an RRMS diagnosis of >5 years, and no history of relapses for 5 years.

Proteomic signatures of physical, cognitive, and imaging outcomes in multiple sclerosis.

Background: A quantitative measurement of serum proteome biomarkers that would associate with disease progression endpoints can provide risk stratification for persons with multiple sclerosis (PwMS) and supplement the clinical decision-making process.

Materials And Methods: In total, 202 PwMS were enrolled in a longitudinal study with measurements at two time points with an average follow-up time of 5.4 years.

Clinical validation of a multi-protein, serum-based assay for disease activity assessments in multiple sclerosis.

An 18-protein multiple sclerosis (MS) disease activity (DA) test was validated based on associations between algorithm scores and clinical/radiographic assessments (N = 614 serum samples; Train [n = 426; algorithm development] and Test [n = 188; evaluation] subsets). The multi-protein model was trained based on presence/absence of gadolinium-positive (Gd+) lesions and was also strongly associated with new/enlarging T2 lesions, and active versus stable disease (composite of radiographic and clinical evidence of DA) with improved performance (p < 0.05) compared to the neurofilament light single protein model.

A Review of the use of Transcranial Doppler Waveform Morphology for Acute Stroke Assessment.

Acute ischemic stroke is a source of long-term disability in the United States, of which a large portion of cases are a result of large vessel occlusion (LVO). LVO strokes have high rates of morbidity and mortality due to difficulty of treatments in achieving recanalization. Recently, however, results of randomized clinical trials have shown that treatment options are expanding in both availability and efficacy.

Review: pathophysiology of intracranial hypertension and noninvasive intracranial pressure monitoring.

Measurement of intracranial pressure (ICP) is crucial in the management of many neurological conditions. However, due to the invasiveness, high cost, and required expertise of available ICP monitoring techniques, many patients who could benefit from ICP monitoring do not receive it. As a result, there has been a substantial effort to explore and develop novel noninvasive ICP monitoring techniques to improve the overall clinical care of patients who may be suffering from ICP disorders.

Toward automated classification of pathological transcranial Doppler waveform morphology via spectral clustering.

Cerebral Blood Flow Velocity waveforms acquired via Transcranial Doppler (TCD) can provide evidence for cerebrovascular occlusion and stenosis. Thrombolysis in Brain Ischemia (TIBI) flow grades are widely used for this purpose, but require subjective assessment by expert evaluators to be reliable. In this work we seek to determine whether TCD morphology can be objectively assessed using an unsupervised machine learning approach to waveform categorization.

Algorithm for Reliable Detection of Pulse Onsets in Cerebral Blood Flow Velocity Signals.

Transcranial Doppler (TCD) ultrasound has been demonstrated to be a valuable tool for assessing cerebral hemodynamics via measurement of cerebral blood flow velocity (CBFV), with a number of established clinical indications. However, CBFV waveform analysis depends on reliable pulse onset detection, an inherently difficult task for CBFV signals acquired via TCD. We study the application of a new algorithm for CBFV pulse segmentation, which locates pulse onsets in a sequential manner using a moving difference filter and adaptive thresholding.

Objective Assessment of Beat Quality in Transcranial Doppler Measurement of Blood Flow Velocity in Cerebral Arteries.

Objective: Transcranial Doppler (TCD) ultrasonography measures pulsatile cerebral blood flow velocity in the arteries and veins of the head and neck. Similar to other real-time measurement modalities, especially in healthcare, the identification of high-quality signals is essential for clinical interpretation. Our goal is to identify poor quality beats and remove them prior to further analysis of the TCD signal.

EMG-Torque Dynamics Change With Contraction Bandwidth.

An accurate model for ElectroMyoGram (EMG)-torque dynamics has many uses. One of its applications which has gained high attention among researchers is its use, in estimating the muscle contraction level for the efficient control of prosthesis. In this paper, the dynamic relationship between the surface EMG and torque during isometric contractions at the human ankle was studied using system identification techniques.

A Physical Model Suggests That Hip-Localized Balance Sense in Birds Improves State Estimation in Perching: Implications for Bipedal Robots.

In addition to a vestibular system, birds uniquely have a balance-sensing organ within the pelvis, called the lumbosacral organ (LSO). The LSO is well developed in terrestrial birds, possibly to facilitate balance control in perching and terrestrial locomotion. No previous studies have quantified the functional benefits of the LSO for balance.

Physiological tremor increases when skeletal muscle is shortened: implications for fusimotor control.

Key Points: In tonic, isometric, plantarflexion contractions, physiological tremor increases as the ankle joint becomes plantarflexed. Modulation of physiological tremor as a function of muscle stretch differs from that of the stretch reflex amplitude. Amplitude of physiological tremor may be altered as a function of reflex pathway gains.

Forearm Flexor Muscles in Children with Cerebral Palsy Are Weak, Thin and Stiff.

Children with cerebral palsy (CP) often develop reduced passive range of motion with age. The determining factor underlying this process is believed to be progressive development of contracture in skeletal muscle that likely changes the biomechanics of the joints. Consequently, to identify the underlying mechanisms, we modeled the mechanical characteristics of the forearm flexors acting across the wrist joint.

Ankle Joint Intrinsic Dynamics is More Complex than a Mass-Spring-Damper Model.

This paper describes a new small signal parametric model of ankle joint intrinsic mechanics in normal subjects. We found that intrinsic ankle mechanics is a third-order system and the second-order mass-spring-damper model, referred to as IBK, used by many researchers in the literature cannot adequately represent ankle dynamics at all frequencies in a number of important tasks. This was demonstrated using experimental data from five healthy subjects with no voluntary muscle contraction and at seven ankle positions covering the range of motion.

Measurement of Dynamic Joint Stiffness from Multiple Short Data Segments.

This paper presents our new method, Short Segment-Structural Decomposition SubSpace (SS-SDSS), for the estimation of dynamic joint stiffness from short data segments. The main application is for data sets that are only piecewise stationary. Our approach is to: 1) derive a data-driven, mathematical model for dynamic stiffness for short data segments; 2) bin the non-stationary data into a number of short, stationary data segments; and 3) estimate the model parameters from subsets of segments with the same properties.

A Subspace Approach to the Structural Decomposition and Identification of Ankle Joint Dynamic Stiffness.

Objective: The purpose of this paper is to present a structural decomposition subspace (SDSS) method for decomposition of the joint torque to intrinsic, reflexive, and voluntary torques and identification of joint dynamic stiffness.

Methods: First, it formulates a novel state-space representation for the joint dynamic stiffness modeled by a parallel-cascade structure with a concise parameter set that provides a direct link between the state-space representation matrices and the parallel-cascade parameters. Second, it presents a subspace method for the identification of the new state-space model that involves two steps: 1) the decomposition of the intrinsic and reflex pathways and 2) the identification of an impulse response model of the intrinsic pathway and a Hammerstein model of the reflex pathway.

Neuromorphic meets neuromechanics, part II: the role of fusimotor drive.

Objective: We studied the fundamentals of muscle afferentation by building a Neuro-mechano-morphic system actuating a cadaveric finger. This system is a faithful implementation of the stretch reflex circuitry. It allowed the systematic exploration of the effects of different fusimotor drives to the muscle spindle on the closed-loop stretch reflex response.

Neuromorphic meets neuromechanics, part I: the methodology and implementation.

Objective: One goal of neuromorphic engineering is to create 'realistic' robotic systems that interact with the physical world by adopting neuromechanical principles from biology. Critical to this is the methodology to implement the spinal circuitry responsible for the behavior of afferented muscles. At its core, muscle afferentation is the closed-loop behavior arising from the interactions among populations of muscle spindle afferents, alpha and gamma motoneurons, and muscle fibers to enable useful behaviors.

Identification of time-varying dynamics of reflex EMG in the ankle plantarflexors during time-varying, isometric contractions.

The dynamic relationship between the joint position and reflex EMG in ankle muscles of healthy human subjects was studied for time-varying (TV) contractions. A linear parameter varying (LPV) identification algorithm was used to estimate the Hammerstein system relating ankle position to the reflex EMG response. The estimated Hammerstein system comprised a time-invariant (TI) linear element and a TV static nonlinearity that resembled a half-wave rectifier with a threshold and linear gain.

Identification of ankle joint stiffness from short segments of data: application to passive dynamics during movement.

This paper presents a state-space (subspace) method for identification of parallel-cascade joint stiffness from short segments of data. It provides unbiased estimates of stiffness by accounting for the contributions of initial conditions of each segment. The method is important in situations where it is not possible to acquire a long stationary data due to switching or time-varying behavior.

Identification of ankle joint stiffness during passive movements--a subspace linear parameter varying approach.

This paper describes a novel method for the identification of time-varying ankle joint dynamic stiffness during large passive movements. The method estimates a linear parameter varying parallel-cascade (LPV-PC) model of joint stiffness consisting of two pathways: (a) an LPV impulse response function (IRF) for intrinsic mechanics and (b) an LPV Hammerstein cascade with time-varying static nonlinearity and a time-invariant linear dynamics for the reflex pathway. A subspace identification technique is used to estimate a statespace representation of the reflex stiffness dynamics.

Subspace method decomposition and identification of the parallel-cascade model of ankle joint stiffness: theory and simulation.

This paper describes a state-space representation of the parallel-cascade model of ankle joint stiffness whose parameters are directly related to the underlying dynamics of the system. It then proposes a two step subspace method to identify this model. In the first step, the intrinsic stiffness is estimated using proper orthogonal projections.

Identification of a parametric, discrete-time model of ankle stiffness.

Dynamic ankle joint stiffness defines the relationship between the position of the ankle and the torque acting about it and can be separated into intrinsic and reflex components. Under stationary conditions, intrinsic stiffness can described by a linear second order system while reflex stiffness is described by Hammerstein system whose input is delayed velocity. Given that reflex and intrinsic torque cannot be measured separately, there has been much interest in the development of system identification techniques to separate them analytically.

A novel algorithm for linear parameter varying identification of Hammerstein systems with time-varying nonlinearities.

This paper describes a novel method for the identification of Hammerstein systems with time-varying (TV) static nonlinearities and time invariant (TI) linear elements. This paper develops a linear parameter varying (LPV) state-space representation for such systems and presents a subspace identification technique that gives individual estimates of the Hammerstein components. The identification method is validated using simulated data of a TV model of ankle joint reflex stiffness where the threshold and gain of the model change as nonlinear functions of an exogenous signal.