Posts Tagged biomarker

[ARTICLE] Brain oscillatory activity as a biomarker of motor recovery in chronic stroke – Full Text

Abstract

In the present work, we investigated the relationship of oscillatory sensorimotor brain activity to motor recovery. The neurophysiological data of 30 chronic stroke patients with severe upper‐limb paralysis are the basis of the observational study presented here. These patients underwent an intervention including movement training based on combined brain–machine interfaces and physiotherapy of several weeks recorded in a double‐blinded randomized clinical trial. We analyzed the alpha oscillations over the motor cortex of 22 of these patients employing multilevel linear predictive modeling. We identified a significant correlation between the evolution of the alpha desynchronization during rehabilitative intervention and clinical improvement. Moreover, we observed that the initial alpha desynchronization conditions its modulation during intervention: Patients showing a strong alpha desynchronization at the beginning of the training improved if they increased their alpha desynchronization. Patients showing a small alpha desynchronization at initial training stages improved if they decreased it further on both hemispheres. In all patients, a progressive shift of desynchronization toward the ipsilesional hemisphere correlates significantly with clinical improvement regardless of lesion location. The results indicate that initial alpha desynchronization might be key for stratification of patients undergoing BMI interventions and that its interhemispheric balance plays an important role in motor recovery.

1 INTRODUCTION

Stroke is a major global health problem. The number of stroke victims has been rising in the past years all around the world. Millions of stroke survivors are left with very limited motor function or complete paralysis and depend on assistance (Feigin et al., 2016). Therapeutic approaches such as constraint‐induced movement therapy are not applicable to the group of patients with severe limb weakness (Birbaumer, Ramos‐Murguialday, & Cohen, 2008). However, brain–machine interface (BMI) training has demonstrated efficacy in promoting motor recovery in chronic paralyzed stroke patients (Ramos‐Murguialday et al., 2013), and long term effects (Ramos‐Murguialday et al., 2019). Subsequent work has replicated and confirmed BMI efficacy. Arm and hand movements are trained using a body actuator (e.g., orthotic robots) that is controlled by oscillatory activity of the brain (Ang et al., 2014; Frolov et al., 2017; Kim, Kim, & Lee, 2016; Leeb et al., 2016; Mokienko et al., 2016; Ono et al., 2014). Brain signals can thus travel to the limb muscles along an alternative pathway. Contingently linking movement‐related patterns of brain activity and visuo‐proprioceptive feedback of the movement supports associative learning (Ramos‐Murguialday et al., 2012; Sirigu et al., 1995).

Changes in sensorimotor brain oscillations involved in planning and execution of movements were used as control signals for the BMI in the aforementioned studies. The sensorimotor rhythm (SMR) is an oscillation within the alpha frequency range of the EEG during a motionless resting state over the central‐parietal brain regions. Movement planning, imagination and execution lead to its suppression. In the present work, we investigate EEG brain oscillations of the alpha frequency, ranging from 8 to 12 Hz, over the motor cortex, and we term them “alpha oscillations.”

Biomarkers could be defined as indicators “of disease state that can be used as a measure of underlying molecular/cellular processes that may be difficult to measure directly in humans” (Boyd et al., 2017). When dealing with a condition as heterogeneous as stroke validated biomarkers of recovery could help plan treatments and support efficient allocation of resource while maximizing outcome for the patients. Alpha brain oscillations have been evaluated as markers of ischaemia and predictors of clinical outcome in acute patients (Finnigan & van Putten, 2013; Rabiller, He, Nishijima, Wong, & Liu, 2015). Desynchronization in the alpha frequency range has also been investigated as a marker of stroke and a predictor of recovery in the same patient group. Tangwiriyasakul, Verhagen, Rutten, and Putten (2014) showed that the recovery of motor function was accompanied by an increase of alpha desynchronization on the ipsilesional side. In subacute patients presenting mild to moderate motor deficits recovery lead to a similar increase of alpha desynchronization on the affected hemisphere (Platz, Kim, Engel, Kieselbach, & Mauritz, 2002). Furthermore, first attempts investigated correlations of alpha desynchronization with motor improvements in chronically impaired patients (Kaiser et al., 2012). In a controlled study, a group of subacute patients with severe deficits used motor imagery, guided by a brain–computer interface, in addition to their regular physiotherapeutic rehabilitation protocol. They showed a higher probability for motor improvements with increased alpha desynchronization (Pichiorri et al., 2015).

In the present work, we investigated what changes in the oscillatory activity of the brain a proprioceptive BMI coupled with physiotherapy produces over the course of a training intervention and if these correlate with recovery in severely paralyzed chronic stroke patients. We hypothesized that functional motor improvements are accompanied by an ipsilesional increase and a contralesional decrease in alpha desynchronization indicating reorganization of compensatory brain activity from the contralesional to the ipsilesional hemisphere. We intend to establish alpha oscillatory activity as a biomarker of motor impairment and as a building block of statistical models of stroke neurorehabilitation.[…]

 

Continue —->  Brain oscillatory activity as a biomarker of motor recovery in chronic stroke – Ray – – Human Brain Mapping – Wiley Online Library

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Figure 1
Schematics of the data acquisition phase and the offline analysis for EEG and EMG. Neurophysiological data was acquired using a 16 channel EEG cap and 4 bipolar EMG electrodes on each arm. EEG data were cleaned from eye movement artifacts and trials containing other artifacts (e.g., cranial EMG, head movements, and so on). EMG data were analyzed to detect compensatory muscle contractions on the healthy upper limb and on the paretic side during resting intervals to identify these trials as contaminated because the muscle activity is a sign of undesired EEG activity. Only data free of artifacts were used for the final analysis of EEG oscillatory activity

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[ARTICLE] Diffusion Tensor Imaging Biomarkers to Predict Motor Outcomes in Stroke: A Narrative Review – Full Text

Stroke is a leading cause of disability worldwide. Motor impairments occur in most of the patients with stroke in the acute phase and contribute substantially to disability. Diffusion tensor imaging (DTI) biomarkers such as fractional anisotropy (FA) measured at an early phase after stroke have emerged as potential predictors of motor recovery. In this narrative review, we: (1) review key concepts of diffusion MRI (dMRI); (2) present an overview of state-of-art methodological aspects of data collection, analysis and reporting; and (3) critically review challenges of DTI in stroke as well as results of studies that investigated the correlation between DTI metrics within the corticospinal tract and motor outcomes at different stages after stroke. We reviewed studies published between January, 2008 and December, 2018, that reported correlations between DTI metrics collected within the first 24 h (hyperacute), 2–7 days (acute), and >7–90 days (early subacute) after stroke. Nineteen studies were included. Our review shows that there is no consensus about gold standards for DTI data collection or processing. We found great methodological differences across studies that evaluated DTI metrics within the corticospinal tract. Despite heterogeneity in stroke lesions and analysis approaches, the majority of studies reported significant correlations between DTI biomarkers and motor impairments. It remains to be determined whether DTI results could enhance the predictive value of motor disability models based on clinical and neurophysiological variables.

Introduction

Stroke is the second cause of death and the third leading cause of loss of DALYs (Disability-Adjusted Life Years) worldwide. Despite substantial advances in prevention and treatment, the global burden of this condition remains massive (1). In ischemic stroke (IS; 80–85% of the cases), hypoperfusion leads to cell death and tissue loss while in hemorrhagic stroke (HS), primary injury derives from hematoma formation and secondary injury, from a cascade of events resulting in edema and cellular death (2). In IS, cytotoxic edema is a result of glucose and oxygen deprivation, leading to a failure of ion pumps in the cell membranes and consequently to collapse of osmotic regulation, when water shifts from the extracellular to the intracellular compartment (3). In HS, heme degradation products are the primary cytotoxic event and secondarily, an inflammatory process based on degradation of the hematoma takes place (4).

Diffusion MRI (dMRI) is a powerful diagnostic tool in acute IS (5) and is widely used in clinical practice (6). dMRI sequences are sensitive to water displacement. Acute infarcts appear hyperintense on diffusion-weighted imaging (DWI) reflecting the decrease in the apparent diffusion coefficient of water molecules. DWI can be acquired and interpreted over a few minutes. It provides key information for eligibility to reperfusion therapies from 6 to 24 h after onset of symptoms (DAWN study) (7) and in wake-up strokes (8). A search on MEDLINE using the terms “stroke” and “diffusion MRI” yielded 1 article in 1991 and 279, in 2018. Diffusion tensor imaging (DTI) involves more complex post-processing, mathematical modeling of the DW signal (9) and provides measures associated with white matter (WM) microstructural properties (10).

Stroke can directly injure WM tracts and also lead to Wallerian degeneration, the anterograde distal degeneration of injured axons accompanied by demyelination (11). DTI metrics have been studied as biomarkers of recovery or responsiveness to rehabilitation interventions (1214). The bulk of DTI studies addressed specifically the corticospinal tract (CST), crucial for motor performance or recovery (1215), and frequently affected by stroke lesions. Paresis occurs in the majority of the subjects in the acute phase and contributes substantially to disability (16). It is thus understandable that the CST is in the spotlight of research in the field.

Two meta-analyses included from six to eight studies and reported strong correlations between DTI metrics and upper-limb motor recovery in IS and HS (1718). In both meta-analyses, heterogeneity between the studies was moderate. In addition, the quality of the evidence of DTI as a predictor of motor recovery was considered only moderate by a systematic review of potential biomarkers (19). The main limitations of the reviewed studies were the lack of cross-validation and evaluation of minimal clinically important differences for motor outcomes as well as the small sample sizes. Heterogeneity in DTI data collection and analysis strategies may also contribute to inconsistencies and hinder comparisons between studies.

In this narrative review, first we review the key concepts of dMRI. Second, we present an overview of state-of-art methodological practices in DTI processing. Third, we critically review challenges of DTI in stroke and results of studies that investigated the correlation between DTI metrics in the CST and motor outcomes at different stages after stroke, according to recommendations of the Stroke Recovery and Rehabilitation Roundtable taskforce (20).

Concepts of Diffusion MRI

Different MRI paradigms address WM qualitatively and quantitatively (i.e., volume, contrast as signal hyperintensities), but only dMRI allows indirect inferences about WM microstructure by providing information about the underlying organization of the tissue. In regions of little restriction of water displacement (such as the ventricles), water molecules tend to move almost freely (randomly). On the other hand, within tracts, the environment tends to be organized within sets of axons aligned in parallel orientation. Water movement usually follows a specific orientation near axons compactly organized and constrained by the myelin packing (21).

The diffusion tensor is the most commonly used mathematical modeling of the diffusion signal and can be decomposed into its eigenvalues (λ) and eigenvectors (ε), required to characterize the signal of water displacement within a voxel. Each eigenvector represents an axis of dominant diffusion with the magnitude of diffusion determined by the corresponding eigenvalues. If the diffusion is isotropic (the same along each orientation), then the eigenvalues have approximately the same magnitude (λ1 ≈ λ2 ≈ λ3), which can be depicted by a sphere. By contrast, if there is a preferential orientation of the diffusion, then the first eigenvalue is bigger than the other two, which can be visualized typically by an ellipsoid (λ1 >> λ2, λ3) (Figure 1).

FIGURE 1

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Figure 1. Model of the tensor showing the eingenvalues. Diffusivities are depicted in FA representation (λll—parallel or axial diffusivity—AD, λperpendicular or radial diffusivity—RD).

Hence, the tensor calculation is typically based on a 3 × 3 symmetric matrix, in which the eigenvalues derived from each combination of directions provide different metrics. At least one b0 (non-diffusion-weighted) and 6 non-collinear directions of diffusion-weighted acquisitions are required to minimally describe water displacement with DTI (10). Generally, the more directions, the better.

The most widely used DTI metrics are: fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD). FA describes the degree of anisotropy (represented as an ellipsoid), a value between 0 (isotropic) and 1 (the most anisotropic). Anisotropy tends to increase in the presence of highly oriented fibers (Figure 1). The biggest value is supposed to be found in the center of the tracts. In particular, for CST analysis in stroke or other focal brain lesions, FA results can be reported as ratios between FA extracted from the ipsilesional and the contralesional hemispheres (rFA = FA ipsilesional/FA contralesional). Alternatively, asymmetry in FA can be described (aFA = (FA ipsilesional – FA contralesional)/(FA ipsilesional + FA contralesional).

MD describes the magnitude of diffusion and the biggest value is supposed to be found in the ventricles. RD represents the average diffusivity perpendicular to the first eigenvector and AD is the first eigenvalue (λ1) representing the diffusivity along the dominant diffusion direction.

Many studies have focused exclusively on FA. The proper interpretation of FA often demands knowledge about results of the other three DTI metrics (22). Changes in anisotropy may reflect several biological underpinnings, such as axonal packing density, axonal diameter, myelinization, neurite density, and orientation distribution (2123). FA can be decreased in conditions that injure the WM but also when multiple crossing fibers are present in the voxel. In case of partial volume effects, both FA and MD may be altered (2425).[…]

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https://www.frontiersin.org/articles/10.3389/fneur.2019.00445/full

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[REVIEW ARTICLE] Blood Biomarkers for Traumatic Brain Injury: A Quantitative Assessment of Diagnostic and Prognostic Accuracy – Full Text

Blood biomarkers have been explored for their potential to provide objective measures in the assessment of traumatic brain injury (TBI). However, it is not clear which biomarkers are best for diagnosis and prognosis in different severities of TBI. Here, we compare existing studies on the discriminative abilities of serum biomarkers for four commonly studied clinical situations: detecting concussion, predicting intracranial damage after mild TBI (mTBI), predicting delayed recovery after mTBI, and predicting adverse outcome after severe TBI (sTBI). We conducted a literature search of publications on biomarkers in TBI published up until July 2018. Operating characteristics were pooled for each biomarker for comparison. For detecting concussion, 4 biomarker panels and creatine kinase B type had excellent discriminative ability. For detecting intracranial injury and the need for a head CT scan after mTBI, 2 biomarker panels, and hyperphosphorylated tau had excellent operating characteristics. For predicting delayed recovery after mTBI, top candidates included calpain-derived αII-spectrin N-terminal fragment, tau A, neurofilament light, and ghrelin. For predicting adverse outcome following sTBI, no biomarker had excellent performance, but several had good performance, including markers of coagulation and inflammation, structural proteins in the brain, and proteins involved in homeostasis. The highest-performing biomarkers in each of these categories may provide insight into the pathophysiologies underlying mild and severe TBI. With further study, these biomarkers have the potential to be used alongside clinical and radiological data to improve TBI diagnostics, prognostics, and evidence-based medical management.

Introduction

Traumatic brain injury (TBI) is a common cause of disability and mortality in the US (1) and worldwide (2). Pathological responses to TBI in the CNS include structural and metabolic changes, as well as excitotoxicity, neuroinflammation, and cell death (34). Fluid biomarkers that may track these injury and inflammatory processes have been explored for their potential to provide objective measures in TBI assessment. However, at present there are limited clinical guidelines available regarding the use of biomarkers in both the diagnosis of TBI and outcome prediction following TBI. To inform future guideline formulation, it is critical to distinguish between different clinical situations for biomarker use in TBI, such as detection of concussion, prediction of positive and negative head computed tomography (CT) findings, and prediction of outcome for different TBI severities. This allows for comparisons to determine which biomarkers may be used most appropriately to characterize different aspects of TBI.

The identification of TBI severity has become a contentious issue. Currently, inclusion in TBI clinical trials is primarily based on the Glasgow Coma Scale (GCS), which stratifies patients into categories of mild, moderate, and severe TBI. The GCS assesses consciousness and provides prognostic information, but it does not inform the underlying pathologies that may be targeted for therapy (56). Furthermore, brain damage and persistent neurological symptoms can occur across the spectrum of TBI severity, limiting the use of GCS-determined injury severity to inform clinical management. Biomarkers in TBI have the potential to provide objective and quantitative information regarding the pathophysiologic mechanisms underlying observed neurological deficits. Such information may be more appropriate for guiding management than initial assessments of severity alone. Since the existing literature primarily focuses on applications of biomarkers in either suspected concussion, mild TBI (mTBI), or severe TBI (sTBI), we will discuss biomarker usage in these contexts.

Concussion is a clinical syndrome involving alteration in mental function induced by head rotational acceleration. This may be due to direct impact or unrestrained rapid head movements, such as in automotive crashes. Although there are over 30 official definitions of concussion, none include the underlying pathology. Missing from the literature have been objective measures to not only identify the underlying pathology associated with the given clinical symptoms, but also to indicate prognosis in long-term survival. Indeed, current practices in forming an opinion of concussion involve symptom reports, neurocognitive testing, and balance testing, all of which have elements of subjectivity and questionable reliability (7). While such information generally reflects functional status, it does not identify any underlying processes that may have prognostic or therapeutic consequences. Furthermore, because patients with concussion typically present with negative head CT findings, there is a potential role for blood-based biomarkers to provide objective information regarding the presence of concussion, based on an underlying pathology. This information could inform management decisions regarding resumption of activities for both athletes and non-athletes alike.

Blood-based biomarkers have utility far beyond a simple detection of concussion by elucidating specific aspects of the injury that could drive individual patient management. For example, biomarkers may aid in determining whether a mTBI patient presenting to the emergency department requires a CT scan to identify intracranial pathology. The clinical outcome for a missed epidural hematoma in which the patient is either discharged or admitted for routine observation is catastrophic; 25% are left severely impaired or dead (8). The Canadian CT Head Rule (9) and related clinical decision instruments achieve high sensitivities in predicting the need for CT scans in mild TBI cases. However, they do this at specificities of only 30–50% (10). Adding a blood biomarker to clinical evaluation may be useful to improve specificity without sacrificing sensitivity, as recently suggested (11). In addition, given concern about radiation exposure from head CT scans in concussion cases, particularly in pediatric populations, identification of patients who would be best assessed with neuroimaging is crucial. Thus, the use of both sensitive and specific biomarkers may serve as cost-effective tools to aid in acute assessment, especially in the absence of risk factors for intracranial injury (12). S-100B, an astroglial protein, has been the most extensively studied biomarker for TBI thus far and has been incorporated into some clinical guidelines for CT scans (1314). However, S-100B is not CNS-specific (1516) and has shown inconsistent predictive capacity in the outcome of mild TBI (1718). Given that several other promising biomarkers have also been investigated in this context, it is important to evaluate and compare the discriminative abilities of S-100B with other candidate blood-based biomarkers for future use.

Blood biomarkers also have the potential to help predict unfavorable outcomes across the spectrum of TBI severity. Outcome predication is difficult; in mTBI, existing prognostic models performed poorly in an external validation study (19). Identifying biomarkers that best predict delayed recovery or persistent neurological symptoms following mTBI would help with the direction of resources toward patients who may benefit most from additional rehabilitation or prolonged observation. In sTBI, poorer outcome has often been associated with a low GCS score (20). However, factors such as intoxication or endotracheal intubation may make it difficult to assess GCS reliably in the acute setting (2122). The addition of laboratory parameters to head CT and admission characteristics have improved prognostic models (23). Thus, prognostic biomarkers in sTBI could help determine whether patients are likely to benefit from intensive treatment. Several candidate biomarkers that correlate with various pathologies of mild and severe TBI have been studied (24), but their relative prognostic abilities remain unclear.

Existing reviews on biomarkers in TBI have provided valuable insight into the pathologic correlates of biomarkers, as well as how biomarkers may be used for diagnosis and prognosis (2531). However, there has been no previous quantitative comparison of the literature regarding biomarkers’ discriminative abilities in specific clinical situations. Here, we compare existing studies on the discriminative abilities of serum biomarkers for four commonly studied clinical situations: detecting concussion, predicting intracranial damage after mTBI, predicting delayed recovery after mTBI, and predicting adverse outcome after sTBI.[…]

 

Continue —-> Frontiers | Blood Biomarkers for Traumatic Brain Injury: A Quantitative Assessment of Diagnostic and Prognostic Accuracy | Neurology

Figure 2. Anatomical locations of potential TBI biomarkers. The biomarkers included in this schematic all rated as “good” (AUC=0.800.89) or better for any of the four clinical situations studied (detecting concussion, predicting intracranial damage after concussion, predicting delayed recovery after concussion, and predicting adverse outcome after severe TBI). Biomarkers with a pooled AUC <0.8 are not shown. 1Also found in adipose tissue; 2synthesized in cells of stomach and pancreas; may regulate HPA axis; 3found mostly in pons; 4also found extracellularly; 5lectin pathway of the complement system; 6also found in endothelial cells. BBB, blood brain barrier. ECM, Extracellular matrix. Image licensed under Creative Commons Attribution-ShareAlike 4.0 International license. https://creativecommons.org/licenses/by-sa/4.0/deed.en. See Supplementary Material for image credits and licensing.

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[Abstract] Pharmacological Optimization for Successful Traumatic Brain Injury Drug Development

The purpose of this review is to highlight the pharmacological barrier to drug development for traumatic brain injury (TBI) and to discuss best practice strategies to overcome such barriers. Specifically, this article will review the pharmacological considerations of moving from the disease target “hit” to the “lead” compound with drug-like and central nervous system (CNS) penetrant properties. In vitro assessment of drug-like properties will be detailed, followed by pre-clinical studies to ensure adequate pharmacokinetic and pharmacodynamic characteristics of response. The importance of biomarker development and utilization in both pre-clinical and clinical studies will be detailed, along with the importance of identifying diagnostic, pharmacodynamic/response, and prognostic biomarkers of injury type or severity, drug target engagement, and disease progression. This review will detail the important considerations in determining in vivo pre-clinical dose selection, as well as cross-species and human equivalent dose selection. Specific use of allometric scaling, pharmacokinetic and pharmacodynamic criteria, as well as incorporation of biomarker assessments in human dose selection for clinical trial design will also be discussed. The overarching goal of this review is to detail the pharmacological considerations in the drug development process as a method to improve both pre-clinical and clinical study design as we evaluate novel therapies to improve outcomes in patients with TBI.

 

via Pharmacological Optimization for Successful Traumatic Brain Injury Drug Development | Journal of Neurotrauma

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[ARTICLE] Advances in closed-loop deep brain stimulation devices – Full Text

Abstract

Background

Millions of patients around the world are affected by neurological and psychiatric disorders. Deep brain stimulation (DBS) is a device-based therapy that could have fewer side-effects and higher efficiencies in drug-resistant patients compared to other therapeutic options such as pharmacological approaches. Thus far, several efforts have been made to incorporate a feedback loop into DBS devices to make them operate in a closed-loop manner.

Methods

This paper presents a comprehensive investigation into the existing research-based and commercial closed-loop DBS devices. It describes a brief history of closed-loop DBS techniques, biomarkers and algorithms used for closing the feedback loop, components of the current research-based and commercial closed-loop DBS devices, and advancements and challenges in this field of research. This review also includes a comparison of the closed-loop DBS devices and provides the future directions of this area of research.

Results

Although we are in the early stages of the closed-loop DBS approach, there have been fruitful efforts in design and development of closed-loop DBS devices. To date, only one commercial closed-loop DBS device has been manufactured. However, this system does not have an intelligent and patient dependent control algorithm. A closed-loop DBS device requires a control algorithm to learn and optimize the stimulation parameters according to the brain clinical state.

Conclusions

The promising clinical effects of open-loop DBS have been demonstrated, indicating DBS as a pioneer technology and treatment option to serve neurological patients. However, like other commercial devices, DBS needs to be automated and modernized.

Background

Deep Brain Stimulation (DBS) can be classified into open-loop (also known as conventional) and closed-loop (also known as adaptive) paradigms. Closed-loop DBS employs a sensor to record a signal linked to symptoms while open-loop DBS does not use a sensor for recording the brain condition; therefore, stimulation parameters including duration, amplitude, and frequency of the pulse train remain constant in open-loop DBS regardless of fluctuations in the disease state. The recorded signal is known as a biomarker and can have varying nature, e.g. bioelectric, physiologic, biochemical, etc. In the open-loop DBS, a specialist tracks the patient’s clinical state and manually programs the device in a trial-and-error based manner. Adjustments of stimulation parameters are not conducted in real-time based on the ongoing neurophysiological variations in the brain; therefore, adverse effects on the patient may be induced due to the brain overstimulation. On the other hand, in the closed-loop DBS, the stimulation pulses are delivered when the brain is in an abnormal state, or they are automatically and dynamically adjusted based on the variations in the recorded signal over the time. Figure 1 compares open-loop and closed-loop DBS and illustrates how they act in different brain states.

Fig. 1 Overview of open-loop DBS (a) versus closed-loop DBS (b). In open-loop DBS, a neurologist manually adjusts the stimulation parameters every 3–12 months after DBS implantation. On the other hand, in closed-loop DBS, programming of the stimulation parameters is performed automatically based on the measured biomarker. c Demonstration of two different brain states and the action of open-loop and closed-loop DBS. When the brain enters a specific state, it remains in that state for a short or long time. Closed-loop DBS gets deactivated when the brain enters the normal state. Open-loop DBS continues the stimulation regardless of the brain state

Although the conventional DBS is a successful therapy, the closed-loop DBS is potentially capable of further and more efficient improvements in neurological diseases. A systematic review of the clinical literature by Hamani et al. [1] stated that adjusting the stimulation parameters of DBS devices could reduce or abolish adverse effects reported in 142 (19%) of 737 Parkinson’s disease (PD) patients treated with subthalamic nucleus (STN) DBS. In addition, Rosin et al. [2] demonstrated the superior function of closed-loop DBS, which automatically adjusts the stimulation parameters, to alleviate PD symptoms. Moreover, Little et al. [3] indicated that motor scores in eight PD patients improved by 50% (blinded) and 66% (unblinded) during closed-loop DBS, which were 27% (p = 0.005) to 29% (p = 0.03) higher than that of open-loop DBS. Besides these therapeutic benefits, they reported 56% reduction in stimulation time, as well as a decrease in the energy requirement of the closed-loop DBS compared to open-loop DBS. Therefore, patients may also benefit from fewer surgeries for replacement of the neurostimulator battery as a result of less power consumption in non-continuous stimulations [3]. Little et al. [3] and Wu et al. [4] reported that in order to obtain similar results from open-loop and closed-loop DBS, 44% less electrical stimulation is required using closed-loop DBS, which means higher efficiency, fewer surgery numbers, lower power consumption, and longer battery lifespan.

Although DBS is a successful therapy, its operation mechanism is mainly uncertain. Hess et al. [5] explained how the temporal pattern of stimulations might have key information for clarification of the DBS mechanism. A recent short review [6] on the physiological mechanism of DBS suggests the “disruption hypothesis” in which abnormal information is prevented from flowing into the stimulation site as a result of DBS dissociation effect on input and output signals. However, it is still under debate and remains to be confirmed by more pre-clinical research. Another review by Herrington et al. [7] accounts several non-exclusive mechanisms for DBS that depend on the condition being treated and the stimulation target. Despite the existence of different theories on the DBS mechanism, there are still questions in regard to the closed-loop DBS. Does adaptive control of DBS alter the DBS mechanism? If yes, how does it alter the DBS mechanism? These questions deserve consideration in the future experimental studies.

This paper presents a comprehensive review of portable closed-loop DBS devices. While there exists a number of excellent reviews on closed-loop DBS systems [8, 9, 10, 11, 12, 13, 14, 15, 16], this work differs from the existing works as described in the following. Among the published reviews, ref. [8] mainly highlights the applications of closed-loop DBS in the rehabilitation of movement disorders. Ref. [12] mainly describes the benefits of closed-loop DBS which using local field potentials (LFPs) as the feedback biomarker. Ref. [13] mainly reviews DBS (both open-loop and closed-loop) in terms of neurological aspects and clinical benefits. Ref. [9] indicates the available biomarkers for closing the feedback loop, and gives control strategies for manipulating measured signals relating to PD patient clinical state. Ref. [10] concentrates on emerging techniques in DBS including new electrode design, new stimulation patterns, and novel targeting techniques. Ref [16] has mainly focused on selection of biomarker and its benefits and problems. Ref. [14] introduces adaptive DBS, and outlines some technological advances in DBS including stimulation type and patterns, energy harvesting, and methods for increasing life quality of patients. Similarly, ref. [15] reviews some technological advancement such as surgical targeting, DBS parameters programming, and electrode design. On the other hand, ref. [11] highlights a range of issues associated with closed-loop DBS including biomarker sensing and processing, DBS parameters programming, control algorithm, wireless telemetry, and device size and power consumption.

This paper, on the other hand, provides a comprehensive review of closed-loop DBS devices, and covers a wider range of issues and advancements associated with such devices including: (i) biomarker selection, (ii) DBS parameters programming, (iii) stimulation type and pattern, (iv) control algorithms, (v) concurrent stimulation and recording, (vi) portability, (vii) battery-less technique, (viii) user-friendly interface, and (x) remote monitoring and wireless telemetry. The paper combines the key features of the current reviews going beyond devices that are used for specific disorders or biomarkers. It covers closed-loop DBS devices reported in the latest research publications not included in the existing reviews. The paper gives a brief history of closed-loop DBS. Next, it discusses different biomarkers for closing the feedback loop. Then, it reviews the algorithms developed for controlling stimulation parameters. After that, it highlights the current challenges and limitations for implementing closed-loop DBS. Also, it reviews the technological developments in closed-loop DBS. Then, it describes commercial closed-loop DBS systems. After that, it compares research-based closed-loop DBS devices highlighting future design expectations, and giving future directions and recommendations on closed-loop DBS devices. […]

Continue —> Advances in closed-loop deep brain stimulation devices | Journal of NeuroEngineering and Rehabilitation | Full Text

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[ARTICLE] Biomarkers of stroke recovery: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation Roundtable – Full Text

In practical terms, biomarkers should improve our ability to predict long-term outcomes after stroke across multiple domains. This is beneficial for: (a) patients, caregivers and clinicians; (b) planning subsequent clinical pathways and goal setting; and (c) identifying whom and when to target, and in some instances at which dose, with interventions for promoting stroke recovery.2 This last point is particularly important as methods for accurate prediction of long-term outcome would allow clinical trials of restorative and rehabilitation interventions to be stratified based on the potential for neurobiological recovery in a way that is currently not possible when trials are performed in the absence of valid biomarkers. Unpredictable outcomes after stroke, particularly in those who present with the most severe impairment3 mean that clinical trials of rehabilitation interventions need hundreds of patients to be appropriately powered. Use of biomarkers would allow incorporation of accurate information about the underlying impairment, and thus the size of these intervention trials could be considerably reduced,4 with obvious benefits. These principles are no different in the context of stroke recovery as compared to general medical research.5

Interventions fall into two broad mechanistic categories: (1) behavioural interventions that take advantage of experience and learning-dependent plasticity (e.g. motor, sensory, cognitive, and speech and language therapy), and (2) treatments that enhance the potential for experience and learning-dependent plasticity to maximise the effects of behavioural interventions (e.g. pharmacotherapy or non-invasive brain stimulation).6 To identify in whom and when to intervene, we need biomarkers that reflect the underlying biological mechanisms being targeted therapeutically.

Our goal is to provide a consensus statement regarding the evidence for SRBs that are helpful in outcome prediction and therefore identifying subgroups for stratification to be used in trials.7 We focused on SRBs that can investigate the structure or function of the brain (Table 1). Four functional domains (motor, somatosensation, cognition, and language (Table 2)) were considered according to recovery phase post stroke (hyperacute: <24 h; acute: 1 to 7 days; early subacute: 1 week to 3 months; late subacute: 3 months to 6 months; chronic: > 6 months8). For each functional domain, we provide recommendations for biomarkers that either are: (1) ready to guide stratification of subgroups of patients for clinical trials and/or to predict outcome, or (2) are a developmental priority (Table 3). Finally, we provide an example of how inclusion of a clinical trial-ready biomarker might have benefitted a recent phase III trial. As there is generally limited evidence at this time for blood or genetic biomarkers, we do not discuss these, but recommend they are a developmental priority.912 We also recognize that many other functional domains exist, but focus here on the four that have the most developed science. […]

Continue —> Biomarkers of stroke recovery: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation RoundtableInternational Journal of Stroke – Lara A Boyd, Kathryn S Hayward, Nick S Ward, Cathy M Stinear, Charlotte Rosso, Rebecca J Fisher, Alexandre R Carter, Alex P Leff, David A Copland, Leeanne M Carey, Leonardo G Cohen, D Michele Basso, Jane M Maguire, Steven C Cramer, 2017

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[ARTICLE] Using Brain Oscillations and Corticospinal Excitability to Understand and Predict Post-Stroke Motor Function – Full Text

What determines motor recovery in stroke is still unknown and finding markers that could predict and improve stroke recovery is a challenge. In this study, we aimed at understanding the neural mechanisms of motor function recovery after stroke using neurophysiological markers by means of cortical excitability (Transcranial Magnetic Stimulation – TMS) and brain oscillations (electroencephalography – EEG). In this cross-sectional study, fifty-five subjects with chronic stroke (62±14 yo, 17 women, 32±42 months post-stroke) were recruited in two sites. We analyzed TMS measures (i.e. motor threshold – MT – of the affected and unaffected sides) and EEG variables (i.e. power spectrum in different frequency bands and different brain regions of the affected and unaffected hemispheres) and their correlation with motor impairment as measured by Fugl-Meyer. Multiple univariate and multivariate linear regression analyses were performed to identify the predictors of good motor function. A significant interaction effect of MT in the affected hemisphere and power in beta bandwidth over the central region for both affected and unaffected hemispheres was found. We identified that motor function positively correlates with beta rhythm over the central region of the unaffected hemisphere, while it negatively correlates with beta rhythm in the affected hemisphere. Our results suggest that cortical activity in the affected and unaffected hemisphere measured by EEG provides new insights on the association between high frequency rhythms and motor impairment, highlighting the role of excess of beta in the affected central cortical region in poor motor function in stroke recovery.

Introduction

Stroke is a leading cause of morbidity, mortality, and disability worldwide (12). Among the sequels of stroke, motor impairment is one of the most relevant, since it conditions the quality of life of patients, it reduces their capability to perform their daily activities and it impairs their autonomy (3). Despite the advancements of the acute stroke therapy, patients require an intensive rehabilitation program that will partially determine the extent of their recovery (4). These rehabilitation programs aim at stimulating cortical plasticity to improve motor performance and functional recovery (5). However, what determines motor improvement is still unknown. Indeed, finding markers that could predict and enhance stroke recovery is still a challenge (6). Different types of biomarkers exist: diagnostic, prognostic, surrogate outcome, and predictive biomarkers (7). The identification of these biomarkers is critical in the management of stroke patients. In the field of stroke research, great attention has been put to biomarkers found in the serum, especially in acute care. However, research on biomarkers of stroke recovery is still limited, especially using neurophysiological tools.

A critical research area in stroke is to understand the neural mechanisms underlying motor recovery. In this context, neurophysiological techniques such as transcranial magnetic stimulation (TMS) and electroencephalography (EEG) are useful tools that could be used to identify potential biomarkers of stroke recovery. However, there is still limited data to draw further conclusions on neural reorganization in human trials using these techniques. A few studies have shown that, in acute and sub-acute stage, stroke patients present increased power in low frequency bands (i.e., delta and theta bandwidths) in both affected and unaffected sides, as well as increased delta/alpha ratio in the affected brain area; these patterns being also correlated to functional outcome (811). Recently, we have identified that, besides TMS-indexed motor threshold (MT), an increased excitability in the unaffected hemisphere, coupled with a decreased excitability in the affected hemisphere, was associated with poor motor function (12), as measured by Fugl-Meyer (FM) [assessing symptoms severity and motor recovery in post-stroke patients with hemiplegia—Fugl-Meyer et al. (13); Gladstone et al. (14)]. However, MT measurement is associated with a poor resolution as it indexes global corticospinal excitability. Therefore, combining this information with direct cortical measures such as cortical oscillations, as measured by EEG, can help us to understand further neural mechanisms of stroke recovery.

To date, there are very few studies looking into EEG and motor recovery. For that reason, we aimed, in the present study, to investigate the relationship between motor impairment, EEG, and TMS variables. To do so, we conducted a prospective multicenter study of patients who had suffered from a stroke, in which we measured functional outcome using FM and performed TMS and EEG recordings. Based on our preliminary work, we expected to identify changes in interhemispheric imbalances on EEG power, especially in frequency bands associated with learning, such as alpha and beta bandwidths. […]

Continue —> Frontiers | Using Brain Oscillations and Corticospinal Excitability to Understand and Predict Post-Stroke Motor Function | Neurology

Figure 1. Topoplots showing the topographic distribution of high-beta bandwidth (25 Hz) for every individual. Red areas represent higher high-beta activity, while blue areas represent lower high-beta activity. Central region (C3 or C4) in red stands for the affected side. For patients with poor motor function, a higher beta activity of the affected central region as compared to the affected side is observed in 16 out of 28 individuals. For patients with good motor function, a similar activity over central regions bilaterally, or higher activity over the unaffected central area can be identified in 21 out of 27 individuals. FM = Fugl-Meyer.

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