Recent decades have seen a move toward evidence-based medicine to inform the clinical decision-making process with reproducible findings from high-quality research studies. There is a need for objective, quantitative measurement tools to increase the reliability and reproducibility of studies evaluating the efficacy of healthcare interventions, particularly in the field of physical and rehabilitative medicine. Surface electromyography (sEMG) is a non-invasive measure of muscle activity that is widely used in research but is under-utilized as a clinical tool in rehabilitative medicine. Other types of electrophysiological signals  are commonly recorded by healthcare practitioners, however, sEMG has yet to successfully transition to clinical practice. Surface EMG has clear clinical potential as an indicator of muscle activation, however reliable extraction of information requires knowledge of the appropriate methods for recording and analyzing sEMG and an understanding of the underlying biophysics. These concepts are generally not covered in sufficient depth in the standard curriculum for physiotherapists and kinesiologists to encourage a confident use of sEMG in clinical practice. In addition, the common perception of sEMG as a specialized topic means that the clinical potential of sEMG and the pathways to application in practice are often not apparent. The aim of this paper is to address barriers to the translation of sEMG by emphasizing its benefits as an objective clinical tool and by overcoming its perceived complexity. The many useful clinical applications of sEMG are highlighted and examples provided to illustrate how it can be implemented in practice. The paper outlines how fundamental biophysics and EMG signal processing concepts could be presented to a non-technical audience. An accompanying tutorial with sample data and code is provided which could be used as a tool for teaching or self-guided learning. The importance of observing sEMG in routine use in clinic is identified as an essential part of the effective communication of sEMG recording and signal analysis methods. Highlighting the advantages of sEMG as a clinical tool and reducing its perceived complexity could bridge the gap between theoretical knowledge and practical application and provide the impetus for the widespread use of sEMG in clinic. Surface EMG is currently an under-utilized clinical tool in rehabilitative medicine, despite its clear potential as a non-invasive measure of muscle activity. It is often considered more complex to analyze than intramuscular EMG, a technique commonly applied in clinical neurology, as parameters of direct clinical relevance cannot be readily extracted  from the recorded signal. However, with relatively basic signal processing, important information on muscle activation patterns and muscle properties can be obtained from surface electromyographic (sEMG) signals. This information can potentially provide an objective, quantitative method of assessing muscle function, movement patterns, and local muscle fatigue to inform the clinical decision-making process. Surface EMG features may also provide a more effective means of objectively capturing differences in motor control following surgical or therapeutic interventions, or training and rehabilitation protocols, when compared with more subjective measures based on visual observation, manual palpation, mechanical manipulation, or standard clinical tests. Application of sEMG in terms of real-time feedback can also be used as a tool to help patients gain greater awareness of their own muscle activity and support re-training of movement patterns. Recent developments in wearable sensing technologies enable movement and EMG data to be recorded in more natural environments outside the laboratory setting, during the activities of daily life. These technologies present a range of new opportunities for quantitative assessment and medium to long-term monitoring of movement. However, a basic understanding of signal processing is required to extract or interpret information from EMG or accelerometry signals recorded by the sensors. With smartphones and tablets placing high-powered data sensing and processing capability into the hands of all, and technology becoming an integral part of all professions, it is responsibility of educators and professional bodies to ensure that the next generation of therapists have the technical competency and know-how needed to make the most of this capability within their clinical practice and harness the opportunities it offers.The speed at which technological innovations are adopted and disseminated is governed by several factors, including the perceived complexity and benefit of the innovation , 2. The The perception of sEMG as a specialized subject with limited relevance to clinical practice is likely to be first formed during the education and training of practitioners. Physiotherapy and kinesiology education varies considerably across different countries, ranging from an apprenticeship involving clinical or hospital-based training to professional masters or doctoral degree programs  from he, Kamen aThe aim of this paper is to address technical barriers to the widespread adoption of sEMG in the clinic, specifically those related to the perceived complexity and benefit of sEMG. Examples are described to illustrate the wide range of clinical sEMG applications, from simple biofeedback  to more advanced sEMG signal analysis that can provide additional detail on neuromuscular function . We then identify key information that is needed to successfully record, process and interpret sEMG signals in a clinical setting, and aim to present it in an accessible way to a non-technical audience. The paper begins with an overview of the physics and physiology underlying the generation of sEMG signals and examples of clinical applications (section 2: Background and Applications). Basic signal concepts including time and frequency domain analyses are introduced in section 3: Basic Signal Concepts. The main factors to consider when choosing equipment and recording EMG signals are then outlined  and key topics in signal processing relevant to sEMG analysis explained, i.e., sampling, filtering, and frequency domain analysis . The topics covered could be incorporated into the curricula for physiotherapists to provide the foundational knowledge needed to reliably record and interpret sEMG signals to extract clinically meaningful information. Although the material covers topics that may be unfamiliar to readers coming from a non-engineering background, the only pre-requisite to understanding the material is familiarity with basic mathematical concepts, e.g., concept of an equation, sine, logarithm. This allows the material to be more easily understood by readers from a non-technical background when compared to other introductory signal processing texts, which often require a relatively strong mathematical knowledge.https://doi.org/10.5281/zenodo.4001609). These data and software codes could form the basis of a practical tutorial or workshop on sEMG. The code provided can be used as a template to be adapted and used by readers in the analysis of their own signals. Although many sEMG recording systems supply software to provide estimates of signal features, such as sEMG amplitude or frequency content, it is often not clear to the user how these features are calculated. To the non-expert user, the appropriate choice of parameters to extract the signal features of interest may not be apparent. However, enabling users to develop and alter their own code allows them to explore how changing the analysis parameters can affect the features extracted from the sEMG. This empowers the user, providing transparency and the awareness of the processing methods rather than a “black box” type approach, and enables them to tailor their analysis to specific applications. Even when using standard software packages, it is important that users understand how the parameters chosen for signal analysis influence the results obtained.This paper is designed as a tutorial to enable readers to bridge the gap between theory and how it is applied in practice though EMG signal analysis. To promote the practical application of the key concepts covered in this paper, EMG data is available in the Finally, by introducing these topics in a way that is accessible and clinically useful, we demonstrate a pathway for incorporating technical and scientific aspects of sEMG recording and analysis into the educational curriculum for physiotherapists. In this way, we aim to reduce technical barriers to the incorporation of surface EMG in clinical applications and provide a bridge between theoretical concepts and practical applications by both reducing the perceived complexity of sEMG and highlighting its benefits as a clinical tool.The EMG signal is the electrical activity generated by a contracting muscle which can be detected by placing an electrodeSignals from the brainstem/spinal cord are transmitted to the muscle by motoneurons. When a motoneuron is activated , synaptic transmission at the neuromuscular junction results in a transient change in electrical potential, known as an action potential, across the muscle fiber membrane of each muscle fiber innervated by the motoneuron. The motor unitIn the assessment and diagnosis of neuromuscular disorders, EMG is typically invasively recorded within the muscle using needle electrodes (either concentric or monopolar). Intramuscular EMG recordings can isolate single motor unit activity and are used to detect abnormalities in motor unit firing patterns or in action potential shape, and pathological spontaneous activity in relaxed muscles. However, due to the small detection volume of needle electrodes, intramuscular EMG recordings reflect the activity of a small number of motor units whose muscle fibers are located close to the detection site. Moreover, this technique is usually limited to low levels of isometric muscle contraction with a relatively small number of active motor units in order to reliably discriminate or extract the activity of individual motor units. Physiotherapists are often more interested in extracting information on temporal patterns of activity from the muscle as a whole or from groups of muscles, often during functional movement. Surface EMG provides a non-invasive, global measurement of muscle activity, which may be more suitable for applications in movement analysis that require frequent assessments or information on the patterns of activation of multiple muscles . Surface EMG can be a valuable tool in both the assessment and treatment of patients and can be used to objectively, and quantitatively, measure progress, and evaluate treatment outcomes.Examples of applications of sEMG signals in the assessment and treatment of patients are summarized in Surface EMG can also offer insights into diseases of the central nervous system which can affect the regulation and coordination of movement across the body. This can manifest as a reduction in (paresis/weakness), or loss of , the desired motor output. Other diseases of the central nervous system can cause involuntary movements of the body . Surface EMG offers a non-invasive alternative to intramuscular EMG in the detection of involuntary muscle twitches arising from spontaneous motor unit activity  , 38. SurIn addition to providing information on muscle activation, in certain conditions sEMG can also be used to indirectly monitor changes in muscle force and in the underlying muscle fiber conduction velocity (MFCV). During isometricMore recently, non-linear methods, such as recurrence quantification analysis and entropy, have been used to characterize the degree of similarity and repeating structure within sEMG signals , 46, seeIn recent years, sEMG electrode arrays or grids have become more widely used, offering several advantages over traditional/conventional sEMG recordings. Electrode arrays can be used to locate the innervation zone of a muscle, accurately estimate muscle fiber conduction velocity, map motor unit action potential propagation across a muscle, and inform on the length and orientation of the muscle fibers. Using high density sEMG arrays the sEMG signal can be sampled across different points above the muscle, so that the spatial distribution of the sEMG signal can also be analyzed. Surface EMG recordings from electrode arrays can be decomposed using specialized algorithms to provide information on single/individual motor unit activities. Though sEMG arrays currently have limited clinical use, they offer significant potential for applications in neurology to monitor changes in the characteristics of the motor unit action potential waveform and motoneuron firing patterns in different neuromuscular disorders. However, a main drawback of sEMG array recordings is that they are more complex to analyze and interpret, requiring specialized algorithms to extract individual motor unit activities, and to assess the accuracy of the detected motor unit firing trains. For many applications in sport and rehabilitative medicine, much of the required information can be obtained from traditional bipolar sEMG . The placement of conventional sEMG electrodes is relatively straightforward and recorded sEMG signals can be successfully processed and analyzed with some basic knowledge of signal processing (with the key topics outlined in this paper). The varied clinical applications of sEMG are discussed in detail in Kamen and Gabriel , CriswelA), frequency (f), and phase (ϕ), Any physical quantity  that varies over time can be described as a signal and can be represented visually on a graph depicting its waveform or pattern over time, T seconds (s), with a frequency of 1/T “cycles per second” or Hertz (Hz), https://doi.org/10.5281/zenodo.4001609).Sine waves are periodic signals, which means that they repeat in time after a period of A) or power of each sinusoidal component can be examined as a function of frequency, providing the amplitude and power spectrum of the signal, respectively, V2)Signals can be represented in the time domain, For a perfect sine wave, all the energy in the signal is contained at one frequency , Tr) determines the “frequency resolution” of the Fourier-transformed signal and the lowest detectable frequency component (1/Tr). For example, when 0.25 s of the sEMG recording in The length of the signal segment or epoch . A conductive gel can be rubbed on the skin . However, the procedure for recording and analyzing sEMG data from electrode grids is more complex, and specialized decomposition algorithms are required to extract the firing times of individual motor units. Considerations for the choice of electrode (both surface and intramuscular) are outlined in detail in Soderberg and Knutson  and BesoThe selectivity of sEMG electrodes is determined by the distance between electrode contacts , and the area of the detection surface (contact area between electrode and skin surface), see Example (xi) in Tutorial Code. The sEMG recorded by the electrode is the average of the voltage at the skin surface underneath the electrode contact . Large ehttp://seniam.org/sensor_location.htm). Typically, the preferred location of the electrode is on the midline of the muscle, midway between the nearest innervation zoneInformation on recommended electrode placement procedures for different muscles and muscle areas can also be found in the SENIAM guidelines . The use of active electrodes reduces the amount of signal noise being amplified, which is important in experiments where there is a high likelihood of EMG signal contamination from motion artifact . Motion artifact can be further reduced with wireless electrodes.EMG signals must be amplified  and filtered before they are sampled and stored for processing. In order to reach the amplitude required for signal sampling and conversion from analog to digital format , surface EMG signals must be amplified . In active electrodes, the pre-amplifier is located on or within the electrode itself, Vd = V+ - V−) between two points (electrodes) by a factor of Ad  to obtain the output voltage, Vout, Vout = AdVd). Signals appearing identically at both the V+ and V− inputs at the same time will not be amplified. An example of an unwanted signal that could appear simultaneously at both amplifier inputs  would be electrical interference from power lines, which occurs at a frequency of 50 or 60 Hz, e.g., Figure 10C. Amplifiers also suppress EMG signals from distant muscles, which appear at the amplifier as common-mode signals. Ideally noise or interference signals would be completely rejected by the amplifier, however, in practice these unwanted common-mode voltages will receive some small amplification and the output voltage of the amplifier, Vout, will contain some unwanted common-mode signals. A difference in the electrode impedance at the V+ and V− inputs (due to inadequate skin preparation) can also increase the level of unwanted common-mode signals . ChoosiThe choice of electrode and amplifier will determine the level of noise in the EMG signal. Random fluctuations in voltage can be observed in the voltage output from the amplifier, even when an electrode is placed on a fully relaxed muscle. This baseline noise arises from voltage fluctuations generated at the electrode-skin interface (see section Skin Preparation) and within the internal stages of the amplifier and its circuit components . If the amplifier and analog to digital conversion meet recommended standards, the signal-to-noise ratio of an EMG signal will be primarily determined by the properties of the electrode-skin interface. Careful preparation of the skin surface prior to electrode placement is therefore essential to minimize baseline noise and optimize the signal-to-noise ratio in the sEMG signal . SourceOne of the most common sources of unwanted fluctuations in voltage that contaminate or interfere with the detected sEMG signal is power line interference. Alternating currents (AC) in power lines and electrical wiring/equipment produce electromagnetic fields that fluctuate at the same frequency as the AC power supply (50 Hz in Europe and 60 Hz in the USA) and its harmonics . These electromagnetic fields can induce currents in the electrode leads and the subject's body through parasitic capacitive couplingZe1and Ze2, respectively) are unequal, a portion of the unwanted common-mode interference signal will differ between V+ and V−. This difference will be amplified by the differential gain Ad of the amplifierThe magnitude of this power line artifact can be reduced by minimizing any difference in the electrode-skin impedance between the two amplifier input terminals through adequate skin preparation, and by choosing an amplifier with a high input impedance. If the electrode-skin impedances at the two terminals , turning off fluorescent or LED lighting, and using the driven-right leg techniquehttps://isek.org/resources/).The sEMG signal can be used to infer information about the behavior of the underlying motor unit population. Useful information can be obtained from the time-domain EMG signal, and by examining the power spectrum of the sEMG signal in the frequency domain, L, which can overlap in time. The power spectral density can then be estimated for each signal epoch, and these local estimates averaged to obtain the power spectral density of the entire signal length, see L or increasing the overlap between successive signal segments, The frequency content of an EMG signal can be examined by applying the Fourier Transform, as described in section Frequency Domain Analysis. EMG signals are typically analyzed over short time intervals or epochs (0.5–1 s) in the time and frequency domains, as they are generated by non-stationary processesThe quality of a recorded EMG signal is influenced by the signal-to-noise ratio, which describes the relative power of the “true” EMG signal to that of unwanted or artifactual signal components  in the overall signal. Methods for reducing noise contamination in the EMG signal are outlined in Section(s) Choice of Electrode, Choice of Amplifier, and Noise in EMG Recordings, and in Clancy et al. . HoweverSurface EMG signals are typically band-pass filtered between 20 and 500 Hz  of the EMG signal. The EMG window under analysis is then shifted forward in time, incrementally, to obtain an estimate for each sequential section of the signal. The moving average is a simple method to smooth sEMG data, acting as a low pass filter and reducing random fluctuations, An increase in the amplitude of the sEMG signal can indicate an increase in muscle activation , with additional motor units recruited to increase force production and/or a change in motor unit firing rates. In Example (xv) in Tutorial Code, the sEMG signal is shown at three different levels of voluntary muscle contraction force, exhibiting an increase in the RMS/ARV of the EMG signal amplitude as the level of muscle force is increased. Both the amplitude and frequency characteristics of the raw EMG signal are sensitive to many factors, some of which can be experimentally controlled , and others which typically cannot be controlled (intrinsic factors) and depend on the physiological, anatomical, and biochemical characteristics of the muscle under investigation . de Luca  providesA moving average of the normalized, rectified EMG signal amplitude can be used to determine whether a particular muscle is activated during a task. One method for establishing whether a muscle is “active” or “inactive” involves setting a threshold for muscle activation, e.g., Examining sEMG signals in the frequency domain can provide information on changes in the frequency content of the signal, manifesting as alterations in the shape of the EMG amplitude/power spectrum, The primary advantage of sEMG over intramuscular EMG, along with its non-invasive nature, is that the signals are relatively straightforward to record and analyze . It also provides an estimate of the overall activity of a muscle or group of muscles in contrast to the more selective nature of intramuscular recordings. Surface EMG, however, is only suitable for recording from superficial muscles and is not appropriate for recording deep muscle activity. Recording from small muscles without contamination from surrounding muscles can be difficult and signals can be prone to cross-talk, particularly over regions where there is substantial subcutaneous fat , 92. FurSensor technology has developed rapidly over the past 30 years, but physiotherapy education has lagged behind in training new therapists to use the newest sEMG innovations in clinical practice. For sEMG to be widely adopted as a quantitative assessment tool, therapists need to see how this technology can directly benefit everyday practice, with guidance from a trusted source such as an educator or clinical mentor. Therapists also need to be empowered with the technical knowledge to enable them to record and analyze their own data. Through education this can be provided in a succinct and accessible manner that removes the perceived complexity of the technology. Modern curricula for physiotherapists may provide a basic introduction to recording and analyzing sEMG signals. However, without practical experience working with sEMG signals, and a comprehensive overview of the technical aspects involved in recording and analyzing the signal, it is difficult to bridge the gap between knowledge of the theory and the ability to apply this in practice. This paper provides a concise guide that simplifies and condenses the relevant information for recording and processing sEMG. Although this material and accompanying tutorial may help to remove some of the educational barriers , access to information alone is not enough to motivate therapists to adopt sEMG in their own practice. To effectively promote and encourage the use of sEMG in clinic, practical experience working with sEMG needs to be embedded into education in the form of workshops, tutorials, or course placements. A deeper understanding of signal processing concepts, and the confidence to apply these methods to sEMG signals is hard to achieve without first-hand experience of working with sEMG signals in a guided setting. Placements in clinics or research labs that use sEMG can also expose students to the utility of sEMG and how it is used by experienced practitioners demonstrating the practical benefits. This can be facilitated by establishing links between local clinics and physiotherapy and biomedical engineering departments within universities. Tutorials that form part of the biomedical engineering course could be adapted for a clinical audience and offered to physiotherapy students. Prior experience either using sEMG themselves or observing first-hand how it can be successfully implemented in clinic is likely to be a deciding factor in a practitioner's decision to adopt sEMG in their own practice.A lack of prior exposure to sEMG signal analysis may thus present the greatest barrier to physiotherapists wishing to incorporate sEMG as a measurement tool in their clinical practice. However, therapists may also be discouraged by the fact that many resources and scientific papers currently available for sEMG analysis are targeted at a technical audience. These resources often assume the reader has prior coding experience and the resources to develop customized code for signal analysis. Clinicians and therapists may alternatively opt to use software packages to extract relevant features from the sEMG signal where the underlying calculations may not be evident. Even in these cases, a basic understanding of signal processing is still important so that the user can select appropriate analysis parameters for different conditions and justify this choice when interpreting and reporting their results. To encourage the uptake of sEMG in clinic, this paper presents an overview of key topics that could be used to guide the content of lectures/tutorials on sEMG in the curricula for physiotherapists or used to form the base of an elective module on EMG applications. It is important to note that some of the simplest applications of sEMG  may be the most useful in practice . The basics of sEMG could be relayed in a single workshop/practical (~ 3 h), which could be incorporated into the undergraduate curriculum for physiotherapists. More advanced signal analysis could be then be covered in postgraduate or elective modules. Combining sEMG theory with practical classes in basic computer programming  is an effective way to teach sEMG concepts to physiotherapists, break down its perceived complexity, and encourage them to incorporate sEMG as a measurement tool in their practice .https://doi.org/10.5281/zenodo.4001609) accompanying this paper that could be used in sEMG tutorials in cases where it is not feasible to record sEMG signals. Sample codes for signal analysis are also provided, illustrating how to extract commonly used features such as the RMS or ARV amplitude and the mean or median frequency of sEMG (see Key Functions Code). The background material and the signal analysis code are intended to be used in parallel, often practical examples can help to simplify more difficult concepts. The examples also illustrate the importance of understanding these signal processing concepts, by illustrating how they can influence outcome measures. The parameters used to analyze the sEMG signals in these examples can also be altered in the code to directly examine the effect on signal output, which can provide a greater insight into the function and relevance of each signal parameter. Though the material is designed to be accessible, it will require a time investment to read, run the accompanying code and understand the output. However, for those interested in incorporating sEMG into their practice, time dedicated to developing a deeper understanding of the technical aspects of sEMG will be rewarded as it will enable them to optimize and tailor their recording and analysis to address the problems they are most interested in. While some may wish to leave sEMG recording and processing to clinical engineers or technicians, with an understanding of the topics outlined in this paper and some investment of time, there is no reason that recording and processing cannot be performed within the clinic by physiotherapists themselves. Therapists themselves are the ones best placed to know where sEMG could be most useful and practical in clinic, and what is practical to implement during the time allotted for a patient appointment. Even where rehabilitation engineering support and resources are available, a common language and understanding enhances the collaboration between engineers and therapists to ensure the most appropriate application of sEMG to address each research question.Sample sEMG signals are provided in the More widespread use of sEMG in clinical practice should contribute to increasing the reliability and reproducibility of studies evaluating the efficacy of healthcare interventions in physical and rehabilitative medicine, however, the full potential of sEMG in clinical assessment and neurorehabilitation has yet to be realized. Through the material presented here we aim to facilitate this by addressing some of the educational and technical barriers that limit the clinical translation of sEMG.https://doi.org/10.5281/zenodo.4001609.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in LM and ML conceived and designed research and drafted manuscript. LM prepared figures. LM, GD, and ML edited and revised manuscript and approved final version of manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The study of brain clearance mechanisms is an active area of research. While we know that the cerebrospinal fluid (CSF) plays a central role in one of the main existing clearance pathways, the exact processes for the secretion of CSF and the removal of waste products from tissue are under debate. CSF is thought to be created by the exchange of water and ions from the blood, which is believed to mainly occur in the choroid plexus. This exchange has not been thoroughly studied in vivo.2 CSF signal. ASL signal was observed at long TEs (793 ms and higher), indicating presence of labeled water transported from blood to CSF. This signal appeared both in the CSF proximal to the choroid plexus and in the subarachnoid space surrounding the cortex. ASL signal was separated into its blood, gray matter and CSF components by fitting a triexponential function with T2s taken from literature. A two-compartment dynamic model was introduced to describe the exchange of water through time and TE. From this, a water exchange time from the blood to the CSF (Tbl->CSF) was mapped, with an order of magnitude of approximately 60 s.We propose a modified arterial spin labeling (ASL) MRI sequence and image analysis to track blood water as it is transported to the CSF, and to characterize its exchange from blood to CSF. We acquired six pseudo-continuous ASL sequences with varying labeling duration (LD) and post-labeling delay (PLD) and a segmented 3D-GRASE readout with a long echo train (8 echo times (TE)) which allowed separation of the very long-T The interaction of cerebrospinal fluid (CSF) and interstitial fluid (ISF) is thought to form a key aspect of clearance; however, the exact circulating pathways of these fluids, and the driving forces for motion in and out of tissue, are still under debate. In fact, this field of study, also sometimes referred to as “glymphatics” after one of the hypothesized pathways for these processes , has gar1 decay of CSF (T1CSF ~4300 ms), thereby allowing a build-up of labeled signal over time  can be exploited to measure the delivery of labeled blood water across the BCSFB, as it allows monitoring of water entering the brain after it has been magnetically labeled inside the large arteries of the neck. In traditional ASL sequences, there is a short wait  between labeling and imaging to allow the bolus of label to reach the tissue compartment. The passage of water across the BCSFB is thought to happen on a slower and smaller scale than gray matter perfusion, and therefore there is typically little to no signal detected in CSF with ASL. However, by prolonging the labeling duration and using a longer PLD, ASL signal should cross the BCSFB and accumulate in the CSF at a measurable level, which is supported by the very slow Tver time . Additioms at 3T ) of CSF ms at 3T . ImagingThis study aims to use a multi-time-point, multi-echo ASL protocol to measure the amount and location of water transport across the BCSFB in the human brain, and to model and characterize the dynamics of blood-CSF water exchange.22.1All data were acquired on a Philips Achieva XT 3T system  with a 32-channel head coil. Two protocols were used in this study: the main protocol (henceforth referred to as the “multi-PLD” protocol) aimed to dynamically follow labeled blood water from its arrival in the imaging region and subsequently through to its transport across the blood-CSF barrier. The second protocol was for validation purposes, acquired on a separate day, to assess reproducibility of the method, and included a scan with labeling above the imaging region to confirm that the observed signal did indeed originate from labeled arterial blood. 12 healthy subjects  underwent the multi-PLD protocol and, of those, 4  were also scanned with the validation protocol on a separate day. All subjects provided written informed consent in accordance with our center’s IRB regulations.0 scan without labeling. For all of these, a segmented 3D-gradient and spin echo (3D-GRASE) readout was employed with multiple echo times, acquired by extending and dividing the turbo-spin echo (TSE) train into 8 sections, corresponding to 8 echo times . The readout was segmented into 4 separate acquisitions in the EPI phase encoding direction (anterior-posterior in this case). The acquisition matrix was 64 ×60 (RLxAP) and the imaging volume was reconstructed to a matrix of 80 × 80 voxels and 28 slices (resolution 3 × 3 × 6 mm3). This was accomplished with a TSE factor of 200 , where the echo train is divided into 8 echoes , a slice oversampling factor 1.8, and an EPI echo train length of 15. In this implementation, the acquisition is repeated four times and averaged before reconstruction. The pCASL scans had variable TR in order to accommodate the total length of the LD/PLD  and the M0 scan had a TR of 10 s to ensure full recovery of magnetization of the blood and tissue, and almost full recovery of CSF. The PCASL scans also employed two background suppression pulses with timings optimized to reduce signal from both static tissue and CSF. The total time of this protocol including all scans was approximately one hour.The multi-PLD protocol ). This session also included additional background suppression experiments (not discussed here) and the total acquisition time was just under one hour.The validation protocol was acquired in a separate session on a different day and included a repeat of the PCASL scan with LD/PLD = 3/4 s, the same scan with the labeling plane placed symmetrically above the brain, and a high resolution 3D-FFE T2 quantification (or separation of compartments based on T2 in this study). Due to the high number of refocusing pulses, in combination with the lower flip angle (160°) of all but the first refocusing pulse of our sequence, it was important to investigate this effect on our measurements. In one subject (#9), we performed an additional scan session in which we repeated the multi-echo scan with LD/PLD 3/2.5 s, as well as acquiring two scans employing a T2-prep module  to reproduce the third and fourth echo times , in order to compare the observed signals. For this experiment, background suppression pulse timings had to be changed to null the signal at the beginning of the T2-prep module rather than at the beginning of readout, so as to not interfere with the T2-prep module.Finally, multi-echo spin-echo sequences are known to create stimulated echoes which can affect T2.20 image. Similarly, a manual threshold was applied to the first TE image of the  ASL signal to obtain a gray matter mask , and to the M0 scan at TE = 498 ms to create a CSF mask. To avoid overlap, all voxels that were assigned as being in both the CSF and GM masks were removed from the GM mask. To assess the reproducibility of the method and presence of artefactual signal, the signal from the  scan acquired with (i) the multi-PLD protocol; (ii) the validation protocol; and (iii) the labeling plane above the brain (which was acquired with the same parameters except for the label position) were compared. The methods for reproducibility analysis in ASL traditionally use the different signal averages acquired in one sequence to compare variability within and between sequences.  and compared this to the signal obtained when labeling above the head. This allows us to observe the amount by which signal varies between scans and the spatial distribution of these variations.ASL control and label images were pair-wise subtracted to extract the ASL signal at all PLDs and TEs. A brain mask was created to remove any signal from the background by thresholding the first-echo Mquences.  However,σ = 2 voxels).Prior to performing voxel-wise analysis, ASL images were smoothed with a 3 ×3 gaussian kernel △ = ATT τ = LD (labeling duration)w = PLD (post-labeling delay)2-decay to a tri-exponential function: The ASL signal was first separated into its blood, gray matter (GM) and CSF components by fitting the Tbl, SGM and SCSF being the signal at TE = 0 coming from the blood, GM and CSF compartments respectively, and T2bl, T2GM and T2CSF the T2 values associated with these compartments, which were taken from the literature , in addition, T2 values of white and gray matter are similar and therefore difficult to distinguish with this approach , respectively. The blood and gray matter were combined because their short T2 values compared to the echo times used in this experiment made them difficult to separate. The model assumes instantaneous mixing within compartments , no outflow, unidirectional exchange from the blood to the CSF compartment, and instantaneous exchange of water between the blood and CSF . This is summarized schematically in 1 or T2 of the compartment, before or after the excitation pulse, respectively. The integral form of the model before excitation is: This model was inspired by the one proposed by Gregori et al. , but mod0 represents the equilibrium magnetization of blood, α is the labeling efficiency, Tbl->CSF is a time constant that describes the exchange of labeled water between the blood and CSF compartment, and T1 are the longitudinal relaxation times associated with the two compartments, set at 1650 and 4300 ms respectively. We use the  notation for equations that have the same form for both compartments, to make the formulation more compact. In this part of the model, the time t is equal to the total time from beginning of labeling to excitation, i.e. t = τ + w.Here MAfter excitation, the equations are: t = τ + w + TE) and δDirac denotes the Dirac delta function. Note that the signal that is measured equals Stotal = S2bl(TE) + S2CSF(TE). For a complete solution to these equations, refer to the 1 as well as the T2 values fixed, there remain 3 parameters which can be fitted in this model: CBF, ATT, and the exchange time from the blood/GM to the CSF Tbl->CSF. By inserting data from all time points and all TEs (6 LD/PLD combinations x 8 TEs =48 data points per voxel), maps of the three fitted parameters can be generated. From our model and these maps, the signal coming from each compartment could be recalculated at arbitrary time points (any LD and PLD combination) to create videos which dynamically show the signal evolution and distribution in the brain in a more intuitive manner (combined changes in LD and PLD used in this study make interpretation difficult).With TE being the echo time , as well as the white matter as a control region where no CSF exchange is expected. For this, the choroid plexus was manually delineated on the CSF mask by isolating the posterior part of the lateral ventricles and the subarachnoid space ROI was defined by removing the ventricles from the CSF mask. The white matter mask was obtained by removing the gray matter and CSF from a brain mask.The data and code used in this project are confidential but may be provided upon request when signing a data use agreement.3.12 allows signal to persist at those TEs. Starting at PLD =1.5 s, this long-T2 signal begins to appear, increasing and peaking around PLD =2.5 s. Interestingly, this signal is not confined to the choroid plexus  for each echo with the multi-echo and the T2-prep methods. Since background suppression had to be modified in the T2-prep sequence to optimize static tissue nulling at the beginning of the module instead of the beginning of readout, the apparent reduction in SNR for the T2-prep method is expected. We also performed a simulation of the effect of the imperfect, 160° refocusing pulses on the signal evolution through the echo train  informed our decision to combine them into a single compartment for the dynamic model, using an intermediate T2 value.The results of the triexponential fit of the ASL signal for one subject are presented in 3.3bl-> CSF, shown in bl->CSF image is noisier but it is possible to distinguish areas containing CSF (choroid plexus (arrowheads), around the cortex, and in higher slices) by their shorter values.The dynamic compartmental model (outlined in bl->CSF maps are masked (including the gray matter and CSF masks) to better appreciate the values without the noise originating from the white matter region.bl->CSF in the white matter ROI, consistent with a lower amount of blood to CSF exchange, while these values are very similar in the choroid plexus and subarachnoid space. The WM ROI is included as a control region where we do not expect to observe blood to CSF exchange to compare with the values of other ROIs . This is the case both because there is little to no overlap of white matter and CSF, and because the ASL signal in white matter is very low  based on the maps of the fitted parameters see . This si42 and that a simple dynamic compartment model can provide a reasonable fit to the observed signal evolutions.In this study, we used a modified multi-PLD ASL protocol to measure the exchange of water from the blood to the CSF. Our main conclusions were as follows: firstly, imaging CSF-ASL signal in the human brain is feasible and shows sites of exchange that extend beyond the choroid plexus; secondly, our results indicate that CSF signal can be isolated from other compartments based on its distinctly long T2s of arterial blood , gray m0–250 ms ) and CSF0–250 ms ), can on0–250 ms , here it0–250 ms  and the 0–250 ms  bring fu0–250 ms ) in the 0–250 ms . These v0–250 ms  did not 0–250 ms ), this d0–250 ms .2 and our proposed dynamic compartmental model adequately describes the signal evolution and therefore exchange between compartments. The triexponential fit , with TEs chosen specifically to be tailored to this compartment. The dynamic compartment analysis uses more parameters to fully describe the exchange between compartments, but the separation of signal from these compartments is fundamentally dependent on the assumed T2s. Including the compartmental T2 values as unknown fitted parameters resulted in unstable fits. Notably, the triexponential fit  in comparison to labeling parameters (LD + PLD <10 s) and therefore should be interpreted as an order of magnitude rather than a precise measurement. Other model assumptions could be affecting the value of this parameter, such as the assumption that all labelled water eventually is transported into the CSF , whereas other considerations such as bulk flow of blood and mixing of CSF could provide a more complete, but also more complex, picture. Because the T2s of blood and gray matter are so short and close to each other in comparison to the TEs of the imaging sequence, we have opted for a two-compartment model, which combines the blood and gray matter into one compartment. A three-compartment model, which includes a Tbl->GM parameter, could be fitted, provided that a range of shorter TEs be also acquired with the protocol, for example by combining our approach with a T2-prep module. (bl->CSF) around the large arteries , as shown in σ= 6 mm). Another source of error for our model could come from the assumption that there is no spatial mixing of labeled water in the CSF between voxels. In reality, CSF flow is pulsatile and can reach velocities up to 8 cm/s in areas such as the cerebral aqueduct  that can be acquired within one scan session, especially later time points which require increasingly long sequences to perform. Here we wanted to sample the entire passage of the bolus from its arrival in the voxel to its transition into CSF. This meant that a compromise had to be achieved, and only the initial phase of water influx into the CSF compartment could be sampled. In this proof of concept study, it was important to measure the passage of the labeled bolus over long labeling duration/PLD times in order to distinguish CSF from blood signal with greater certainty. Now that the signal from labeled blood water that has been delivered to the CSF has been shown to exist and be readily measurable, and preliminary measurements of Tequences  could be module.  Moreoveraqueduct . HoweverThis proof of concept study leaves several questions to be answered. For one, it is not possible from our results to determine whether there is net inflow of water from the blood to the CSF, i.e. what we could characterize as CSF formation. As discussed above, there is some indication of outflow from the CSF compartment which could be attributed to exchange back into the blood. Further studies with a better sampling of the CSF signal in time are needed to confirm this. Also, we currently speculate that the observed ASL-CSF signal in the subarachnoid space is coming from the leptomeningeal and pial vasculature because of the findings of a recent study  observinbl->CSF) in the range 50–70 s. This work supports the emerging view that the “third circulation” theory, in which CSF is secreted exclusively at the choroid plexus and later reabsorbed in arachnoid granulations, is an insufficient explanation for CSF physiology and brain clearance. Indeed, we add to a mounting body of evidence that CSF exchange occurs outside of these key areas and is widespread throughout the brain.In conclusion, we have shown that imaging of CSF-ASL signal is feasible in the human brain, and that isolating this signal and describing its exchange with surrounding compartments can be achieved with the appropriate imaging parameters (LD/PLD and TE). We found that ASL signal is present in both ventricular CSF proximal to the choroid plexus and the subarachnoid space, it is approximately 5 times lower than the traditional perfusion signal measured in the gray matter, and we estimate a blood-CSF water exchange time (T10.1016/j.neuroimage.2021.118755.Supplementary material associated with this article can be found, in the online version, at doi: Supplementary MaterialSupplementary MovieAppendix


The research was aimed at checking the effect of monoperoxy derivative of epoxy resin (PO) on the possibility of rubber crosslinking and a subsequent adhesion of the modified rubber to silver wires. Three of the commonly industrially used rubbers were selected for the study: styrene-butadiene rubber (SBR), acrylonitrile-butadiene rubber (NBR) and carboxylated acrylonitrile-butadiene rubber (XNBR), together with the popular, commercially available Epidian 6 epoxy resin, subjected to the functionalization. An improvement in the adhesion of rubbers to silver wires was observed when using the modified resin. In some cases, an improvement in the mechanical properties of the rubber was observed, especially when the resin was used for crosslinking together with dicumyl peroxide (DCP). Crosslinking synergy between dicumyl peroxide and the modified resin could be observed especially in the case of PO applied for peroxide curing of SBR and NBR. Another method involved the thermal evaporation of PPS to form a buffer layer on Cu, followed by plasma treatment with reactive gases such as O2, H2, and N2 on the PPS buffer layer. However, such modifications pose some technological difficulties: they are usually time and cost consuming and often have a negative impact on the environment. The natural alternative seems to be a proadhesive modification of the polymer matrix, which can be not only technologically less complicated but also proecological. The subject literature describes the use of various modifiers: first of all including functional silanes, resorcinol or resorcinol novolacs [Nowadays, it has become necessary to use more and more advanced polymer composites in order to meet increasingly demanding applications. For composites containing metallic phases, it becomes crucial to ensure proper adhesion between the continuous and dispersed phases, which can be helped by adding proadhesive systems in the form of modified resins to polymer matrix . The aimnovolacs ,17,18,19novolacs , and nonnovolacs , such asnovolacs . A proadnovolacs , can alsnovolacs ,24 and pnovolacs . The lonnovolacs . The queMonoperoxy derivative of Epidian 6 epoxy resin (PO) of the formula presented in was synthesized in a three-necked reactor equipped with a mechanical stirrer, back condenser, thermometer and dropping funnel. 100 g of Epidian 6 epoxy resin and 100 mL of toluene were placed in the reactor. The mixture was heated with stirring to 50 °C and next, gradually adding the mixture containing 84 g of 70% aqueous tert-butyl hydroperoxide solution, 19.1 g of benzyl triethylammonium chloride (BTEACh) and 3.9 g of potassium hydroxide (KOH), dissolved in 100 mL of toluene. The whole reactor content was kept at 50 °C with stirring for 6 h, then cooled down to room temperature and transferred to the separatory funnel. The aqueous layer was separated, and the organic residue was washed with water until it became neutral. The product was transferred to a vacuum distillation unit, where its distillation was carried out at 50–60 °C and a residual pressure of 133–266 Pa until distilling 2/3 of the flask content was finished. Then the residue was poured into petroleum ether. After separation of the oligomeric product, petroleum ether was drained, and the residue dried under vacuum at 50–55 °C to constant weight. In this way, 120.8 g of PO with 420 g/mol molecular weight, 2.5% of active oxygen and 8.4% of epoxy content was obtained. Physical properties of the resin are presented in our previous paper [Composition of the rubber compounds studied is presented in The rubber compounds studied were prepared in a Brabender Plasticorder laboratory micromixer (Germany), and then plate-calibrated to 2 mm thickness. The following mixing conditions were applied: rotational speed of 30 rpm; time of 7 min, room temperature. First, the rubber was plasticized for 3 min, then the appropriate amount of dicumyl peroxide  was added and mixed together for another 2 min, and then the PO resin was added to the micromixer chamber. The whole content was then mixed for another 2 min. The samples were crosslinked in a steel mold under pressure, at 160 °C and during the optimum time of curing—t90, determined rheometrically according to ISO 3417.The curing process of rubber mixes was investigated using an Alpha MDR 2000 (USA) rheometer, operating with an oscillation frequency of 1.70 Hz and an oscillation angle of 3°, at 160 °C, according to ISO 3417. Based on experimental data, the curing parameters were determined, namely: optimum vulcanization time (t90), scorch time (t05), max. (MH), and min. (ML) torque. The increase of torque ΔM = MH − ML−1 range  and the characteristic absorption peaks assigned. Experiments were performed with a Thermo Fisher Scientific (USA) Nicolet 6700 FTIR spectrometer equipped with a Harrick Scientific (USA) SplitPea micro ATR accessory with a diamond crystal.The mechanisms of rubber crosslinking was studied with Fourier Transformed Infrared Spectroscopy (FTIR). Absorbance spectra were collected within the 400–4000 cmCrosslink density of the rubber samples (ν) was determined by equilibrium swelling method in toluene, according to PN-ISO 1817: 2001. Samples cut from 2 mm thick plates, weighing 30–50 mg, were exposed to the solvent for 72 h, subjected to decanting and replenishing every 24 h. After drying on a filter paper, the samples were weighed again and then dried to constant weight at 60 °C in a laboratory oven. The results obtained were used to calculate volumetric equilibrium swelling values of the rubber samples, according to the standard procedure described in the literature , and theTensile Strength (TS) of crosslinked rubber samples was determined with an universal mechanical testing machine Zwick 1345 , according to ISO 37. Tear Strength (TES) of the samples was determined with the same instrument—Zwick 1345 , according to ISO 34. Hardness of the samples was determined with a Zwick Shore A durometer 3130 , according to EN ISO 868.The pull-out tests of silver wires (ø = 0.5 mm) from crosslinked rubber samples were performed according to PN-81/C-04267 . It is particularly visible in the case of the XNBR samples, which clearly exceed the crosslink density of the SBR and NBR rubbers. Only XNBR could be cured with the resin, even without any addition of DCP. The curing of SBR and NBR is possible with PO resin alone, but the crosslink density values obtained are very low. Compared to SBR, both nitrile rubbers achieve higher crosslinking densities using DCP, because, unlike the former, they are additionally capable of thermal crosslinking (NBR) , or specThe suggested mechanisms of rubber crosslinking with DCP and the modified resin are presented below:Crosslinking of SBR—Crosslinking of SBR using PO:−1, vibrations of free OH groups3648 cm−1, symmetric and asymmetric vibrations of CH2 and CH3 groups2925 and 2853 cm−1, related to C = O vibrations, probably originated from aldehyde groups,1740 cm−1, vibrations of CH2 and CH3 groups1470 and 1375 cm−1, vibrations in alcohol and/or ether fragments,1325, 1215, 1140 and 1050 cm−1 vibrations characteristic for epoxide groups.949 and 912 cmFTIR spectrum of PO crosslinked at 160 °C A contain3)3CO• radical is able to attack the SBR molecule according to Equation (7) and turns into tert-butyl alcohol or may form an acetone molecule and CH3• radical via the β-schedule. The CH3• radical can further attack the rubber molecule, which is described in Equation (9). The oligomer radical (III), (Equation (6)) undergoes a recombination reaction (Equation (10)) with radicals formed according to Equations (7) and (9), and thus becomes attached to SBR molecule. As a result, the SBR molecule contains free epoxy and secondary hydroxy groups. IR spectroscopic studies of SBR_PO after curing were conducted in order to confirm the abovementioned reactions. The absorption band at 949 cm−1, corresponding to the stretching vibrations of the epoxy ring, is observed in the FTIR spectrum (Monoperoxy derivative of Epidian 6 epoxy resin (PO) contains free epoxy, secondary hydroxy and labile peroxy groups in its structure. Due to the peroxide curing of SBR, the decomposition of –O–O– bond in PO occurs, and free radicals are formed as is described in Equation (6). The formed –(8)). The absorption peaks at 2830 and 2745 cm−1, and the lack of peaks from aldehyde, which confirms the proposed mechanism of crosslinking SBR with PO presented by Equations (10) and (11). In the FTIR spectrum of the crosslinked rubber B, apart The crosslinking of SBR using DCP + PO proceeds according to Equations (1)–(12).Crosslinking of NBR—The radical formed by Equation (2) has the form:Whereas the radical formed by Equation (4) has the form:During curing by DCP, crosslinking occurs as a result of the interaction:Crosslinking of NBR using PO:In this case, there are reactions that should occur by the proposed Equations (6)–(11). Radicals formed according to Equations (7) and (9) look like the radical (IX) and (X), respectively. When the radical (III) is reacted with (IX), a compound of the structure shown below is the most likely formed:When CH3· radical interacts with (XI), a new radical is formed (XII):Then the NBR vulcanization involving the PO occurs as a result of the interaction:−1 proves the presence of secondary hydroxy groups. The peaks at 1100 and 1239 cm−1 corresponding to –C–O–C– asymmetric stretching vibrations indicate the attachment of the PO fragment to the SBR molecule. The introduction of the PO fragment into the SBR structure is also confirmed by an absorption peak at 758 cm−1, corresponding to –C–H deformation stretching vibrations in 1,4-disubstituted benzene derivatives. Absorption peaks at 698, 963, 2849 and 2916 cm−1 corresponding to the deformation stretching vibrations of –C–H in styrene, nonplanar deformation vibrations of –C–H in–C=C–H, symmetric and asymmetric stretching vibrations of CH2 in saturated hydrocarbons, respectively, prove the presence of SBR fragments. The occurrence of curing (in accordance with Equations (10) and (11)) and the preservation o thef double bond is confirmed by absorption peaks at 963, 1449 and 1639 cm−1, corresponding to the –C=C–deformation stretching vibrations.The absorption peak at 1040 cm−1) and secondary hydroxy groups . The structure XII is formed in the same manner as described above and is confirmed by –C–O–C–asymmetric stretching vibrations at 1247 cm−1. The presence of –C≡N group in NBR is proved by stretching vibrations at 2230 cm−1 and the rubber fragment presence —by absorption peaks at 964, 1435, 1640, 2848 and 2916 cm−1. Similar to the crosslinking of SBR, in the FTIR spectrum of NBR cured with PO –(8)) disappear from the spectrum of the crosslinked rubber. The characteristic absorption peaks originating from C-H bending vibrations in the double pendant bonds at 970 cm−1 and 915 cm−1 arise, confirming the proposed mechanism of crosslinking NBR with PO resin.The analysis of the IR-spectrum of the NBR_2PO  shows th with PO  absorptiThe crosslinking of NBR using DCP + PO proceeds via the reactions proposed by Equations (13) and (14).Crosslinking of XNBR—The reaction occurs similarly to Equations (1)–(4). Two possible macroradicals can be formed. Similarly to Equation (2) of the appearance:as well as similarly to Equation (4) in the form:Then the crosslinked structure is formed by the interaction:Crosslinking of XNBR using PO:The reaction occurs similarly to the course described by Equations (6)–(11). Only Equation (7) doubles the radical (XIII), and radical (XIV) is produced according to Equation (9).A compound of the form below is produced according to Equation (10):And when CH3· radical interacts with (XV), a radical (XVI) is formed according to Equation (11):The XNBR fragment presented (XV) and the radical formed (XVI) can react with PO due to the interaction of the free carboxyl group in the above compounds and the epoxy group radical (16):This process can lead to a denser crosslinking. The reaction in this case occurs according to the schemes below Equation (17):−1, indicating the thermal decomposition product of the resin (6–8), does not disappear for rubber crosslinked with PO, but shifts to lower wavelength 1680–1700 cm−1, indicating their XNBR origin and probably representing the rubber crosslinking process. Characteristic absorption peaks, originating from hydroxyl groups at 3200–3400 cm−1, C-H bending vibrations in pendant double bonds at 970 cm−1 and 915 cm−1 arise in both cases, confirming the proposed mechanism of crosslinking XNBR with PO resin.Contrary to the crosslinking of SBR and NBR with the resin, in the FTIR spectrum of crosslinked XNBR  the absoThe mechanical properties of the crosslinked rubber samples are demonstrated in The influence of the crosslinking system on the mechanical strength of the crosslinked samples tested depends on the type of rubber. For SBR, there is a general trend that systems containing both an organic peroxide and a modified resin exhibit higher tensile strength than those based solely on DCP. The difference is most apparent for samples crosslinked with 0.2 DCP and 0.2 PO. Elongation at the break values indicates that rubbers crosslinked with the mixed systems (DCP/resin) demonstrate a slightly higher strength but less flexibility than samples crosslinked only with DCP. Rubbers crosslinked with PO only exhibit indeed much lower strength but associated with higher elongation at break. Increasing DCP content results in an increase of the mixed system crosslinked samples stiffness, but their tensile strength does not improve, due to significant reduction in elongation at break.In the NBR-based samples, there are no significant differences between stress at break for mixed systems and systems based only on DCP. However, the former have the highest TS value. Contrary to the case of samples based on SBR, the use of PO resin alone as a crosslinking agent reduces the mechanical strength of the rubber with an increase in its elongation at break. In general, however, the mechanical parameters of the NBR vulcanizates tested are inferior to the corresponding composition of SBR-based systems.In the group of samples based on XNBR, those based on PO resin alone showed the highest flexibility. Using a system with conventional crosslinking agents in the amount of 0.2 phr together with 2 phr of PO made the samples’ elongation at break decrease and the accompanying stress increase.None of the samples crosslinked with a resin alone has been torn, similar to the XNBR compounds crosslinked with 0.2 phr of DCP and the resin. For all the rubbers tested, it is observed that the addition of PO resin generally reduces the force needed to tear the sample. The exception are rubbers containing 0.4 phr of DCP and 2 phr of PO as a crosslinking system, for which the tear force increases. Therefore, it can be concluded that the introduction of the modified resins into the DCP crosslinking system improves the tear resistance of the crosslinked rubbers tested.The impact of the modification applied to the peroxide crosslinking system on the hardness of the rubber samples tested is presented in The addition of the modified resin to the rubbers tested, in combination with organic peroxide, causes a slight hardening of the samples, most evident in the case of those ones based on SBR. In turn, the use of PO resin alone reduces rubber hardness, especially SBR and NBR, indicating a plasticizing effect. In the systems based on XNBR, the observed changes to the rubber hardness as a result of the modification of the crosslinking system composition are the lowest among the rubber samples tested.Due to the inability to measure the adhesion at the interface between the rubber matrix and the silver nanowires, a standard, the so-called “H” shape specimen method, often used for macroscopic wire/cord–rubber adhesion tests, was applied. Thus, information on the proadhesive potential of the resin additive in relation to selected synthetic rubbers crosslinked with dicumyl peroxide was expected. The results obtained are summarized in The addition of resin to peroxide rubber compounds increases adhesion between the SBR-based vulcanizates and the silver wires. In each case, taking into account the mechanical strength of rubbers , a cohesl4⋯5H20 or FeCl3⋯6H20 and an acid catalyst can be used, even without peroxide’s presence, for the crosslinking of unsaturated rubbers such as NR, SBR, EPDM, IIR and NBR. However, it is worth mentioning that the scorch time of acrylonitrile-butadiene rubber is relatively short (low processing safety), subsequent to the very fast curing observed in this case. Crosslinking with resins may also be applicable to active hydrogen-containing rubbers such as Hypalon, acrylic rubber or XNBR [A synergistic effect of PO resin addition can be observed for the peroxide crosslinking of selected synthetic rubbers of technical importance: SBR, NBR and XNBR. It has been known since 1940s that phenol-formaldehyde resins (resoles) or halomethyl phenols in the presence of SnC or XNBR . NeverthFor mixed systems containing DCP and PO resin, it is often observed that the crosslink density of rubbers is higher than would result from the addition of DCP or resin alone. This affects the mechanical properties of the cured rubbers. The introduction of the resin into the peroxide curing system does not cause drastic changes to the mechanical strength of the rubbers studied; however, it can reduce their elongation at break. It was also observed that the addition of PO resin reduces the tear strength of the samples. The exceptions are the systems containing 0.4 phr of DCP and 2 phr of PO, for which TES increases. With a few exceptions, the addition of the modified resin also has no significant effect on the peroxide curing parameters of the rubbers tested. Moreover, the addition of the modified resin also results in a significant increase in adhesion between the SBR rubber and the silver wire, which can be used to help the research devoted to nanocomposites filled with silver nanowires, which can be used as mechanical stress sensors . A slighRather surprisingly there is not much information about silver–polymer interface in the subject literature. Some information can be find for fluorine polymers protecting silver mirrors, applied in concentrators for solar thermal systems ,36. DespTo sum up, among the rubbers studied, only SBR crosslinked with DCP and the addition of PO can be recommended as a potential matrix for nanocomposites filled with silver nanowires, serving e.g., as a potential sensor matrix for stress testing.The above results clearly demonstrate the potential of using a monoperoxy derivative of epoxy resin (PO) as a multifunctional additive to synthetic rubbers in order to improve their adhesion to metal, promoting cross-linking and modifying crosslinks’ structures. Despite general lack of information about silver–polymer interface in the subject literature, the application of PO, together with dicumy peroxide (DCP) allows for the modification of a polymer matrix to be “tailored” for silver. The modification was shown to be especially effective for styrene-butadiene rubber (SBR), despite SBR having the lowest polarity among the rubbers studied. The research performed concerned silver; nevertheless, there are many indications that the modification could also work for other metals. Recent studies  have sho


Further, we systematically evaluated the utility of several machine learning techniques for the diagnosis of PD. 1H NMR and mass spectrometry-based metabolomics, in combination with bioinformatic analyses, provided useful information highlighting previously unreported biochemical pathways and CSF-based biomarkers associated with both sporadic PD (sPD) and LRRK2 PD. Results of this metabolomics study further support our group’s previous findings identifying bile acid metabolism as one of the major aberrant biochemical pathways in PD patients. This study demonstrates that a combination of two complimentary techniques can provide a much more holistic view of the CSF metabolome, and by association, the brain metabolome. Future studies for the prediction of those at risk of developing PD should investigate the clinical utility of these CSF-based biomarkers in more accessible biomatrices. Further, it is essential that we determine whether the biochemical pathways highlighted here are recapitulated in the brains of PD patients with the aim of identifying potential therapeutic targets.CSF from unique groups of Parkinson’s disease (PD) patients was biochemically profiled to identify previously unreported metabolic pathways linked to PD pathogenesis, and novel biochemical biomarkers of the disease were characterized. Utilizing both  Parkinson’s disease (PD) is a progressive, adult-onset neurodegenerative disorder associated with the degeneration of dopaminergic (DAergic) neurons and the presence of proteinaceous inclusions such as α-synuclein . PD is tClinically, the diagnostic criteria for PD are overwhelmingly based on impaired motor and non-motor functions and confirmed following improvements with dopamine treatment . HoweverThe development of molecular biomarkers, specific to PD, in body fluids such as urine, saliva, blood, and CSF, would prove invaluable, allowing detection of the disease at the preclinical stage, monitoring the progression of the disease, and evaluating the therapeutic impacts of disease modifiers. Metabolomics, or metabolic profiling, is the quantitative measurement of metabolites in cells, biofluids, tissues, or organisms, and accurately represents the biochemical phenotype of the organism in diseased and healthy states . In recen = 20), those genetically predisposed to the disease (LRRK2 acquired PD) (n = 20), and age and gender-matched controls for each group to include patients with the G2019S mutation in the LRRK2 gene (n = 20) and those without (n = 20). Samples were immediately processed according to the Parkinson’s Progression Markers Initiative (PPMI) recommendations [The study was approved by the Institutional Review Board at the Beaumont Research Institute and at St. Olav’s Hospital . Written informed consent was obtained from all participants who presented in the morning following an overnight fast with a stable neurological condition. All participants were sequenced for the main pathogenic Leucine-Rich Repeat Kinase 2 (LRRK2) mutations prior to sample collection. A total of 80 subjects with no known history of liver dysfunction were enrolled in this study; CSF specimens were obtained via lumbar puncture from participants suffering from sPD , 233mM imidazole, and 0.47% NaN3 in H2O . The CSF sample (200 μL) was then transferred to a standard 3 mm NMR tube. In total, 80 CSF samples were prepared in this manner, each containing 0.16 mM DSS, 10 mM imidazole, and 0.02% NaN3 at a pH of 7.3 to 7.7. All 1H NMR spectral data were acquired at 300 (± 0.05) K using a Bruker Ascend III HD NMR spectrometer  operating at 600 MHz equipped with a 5 mm TCI cryo-probe and a z-gradient system; a Bruker SampleJet sample changer was used to transfer samples; samples were kept at 4 °C while queued for analysis. Prior to analysis, samples were heated to room temperature over 3 min before being transferred to the magnet. 1H NMR spectra were acquired at 300 K using the first transient of a standard NOESY-presaturation pulse sequence, chosen for its high degree of high quantitative accuracy [1H NMR spectra were processed and analyzed using the Chenomx NMR Suite Professional software package version 8.1 . Prior to statistical analysis, all NMR spectra were manually inspected for technical faults.Samples were thawed at room temperature. CSF samples were filtered through 3-kDa cut-off centrifuge filter units  to remove any protein. Centrifugal filters were washed 7 times prior to filtering CSF specimens to remove excess glycerol  An aliquUsing the commercially available AbsoluteIDQ p180 Kit (Biocrates Life Sciences AG)  we biochBile acids were analyzed using the commercially available Biocrates Bile Acids Kit  as described by Marksteiner et al. . This ast-test and ANOVA to determine statistical significance  and false discovery rates . To assess the distribution of our data and to check for any spurious/inflated results, quantile-quantile plots (Q–Q-plots) were generated. For non-normal distributions, p-values were calculated using a Mann–Whitney U test. Bonferroni corrected p-values (p = 0.05/number of metabolites) were used to correct for multiple comparisons.Using MetaboAnalyst (v. 4.0) ,18,19, tp < 0.05). This was further investigated by plotting T2-values (distance to model) against residual Q2 (the variance not covered by the model) for each group. Following outlier detection, orthogonal projections to latent structure discriminant analysis (O-PLS-DA) was employed to determine if it would be possible to differentiate LRRK2 PD from the LRRK2 control, sPD from the sPD control, and LRRK2 PD from sporadic PD, and to identify features responsible for the observed separation. For each model, the optimum number of components was assessed by a single 10-fold venetian blind cross-validation. The validity of the group separation for each model was evaluated through permutation testing with 2000 repeats. The raw metabolomics data were also subjected to unsupervised hierarchical clustering modelling to generate heat map representation between each group.To ensure no violation of the normality assumption, data were standardized by sum normalization, followed by scaling to mean zero and unit variance, and log transformed. Before performing pattern recognition, metabolomics data from each individual group were subjected to explorative multivariate statistical data analysis by PCA to check for potential outliers or systematic variation  method using logistic regression was applied to each pair-wise comparison  to find the most discriminative variables ,21,22. FThe synthetic minority over-sampling technique (SMOTE) method from the imbalanced-learn library in python and KNN were used to account for oversampling. Different number of neighbors and different number of data points for oversampling were tested to determine the best combination. Using the k-fold approach, only the training portion was oversampled, and the test set was kept original. Model performance was assessed by classification accuracy rate, using the area under the receiver operating characteristic (AUROC) curve, and by the identification of true discriminating features.p value and a q value < 0.1 were considered altered due to parkinsonism.Metabolite pathway enrichment analysis (MSEA) was completed using MetaboAnalyst (v4.0) . Metabol1H NMR  reveal that the majority of data points fell within the expected range (λ ≈ 1). Further, our analyses identified some non-normally distributed metabolites which require their statistical significance to be calculated using non-parametric testing . We also identified six biochemical pathways to be significantly perturbed in LRRK2 PD CSF as compared with LRRK2 controls . Following the univariate analysis, pathway enrichment analysis highlighted five metabolic pathways to be significantly disturbed in sporadic PD as compared to their controls. These include fatty acid biosynthesis, ethanol degradation, ketone body metabolism, bile acid synthesis, and propionate metabolism .The utility of different machine learning approaches for diagnosing parkinsonism was intensively evaluated. Performance metrics, model specific tuning parameters, and the panel of metabolites used for each approach are listed in 1H NMR spectroscopy and mass spectrometry. Using complementary techniques allowed us to detect and quantify many more metabolites, and as such we required much more robust and accurate analytical data tools to help us discriminate between sample types. Further, we compared our values recorded herein with existing metabolite CSF concentration values in the literature [To the authors’ knowledge, this is the first reported study to identify sPD and LRRK2 PD-specific metabolic signatures in CSF using a targeted metabolomics approach employing both terature . Wishartterature .The primary objective of this study was to identify a panel of biomarkers which could accurately diagnose PD using CSF and to gain an insight into the biochemistry behind PD. To ensure we got the most accurate snapshot of the disease’s metabolome, only samples from unmedicated participants were utilized to minimize any confounding factors.When diagnosing LRRK2 PD, logistic regression (AUC = 0.89), linear support vector machine (SVM) (AUC = 0.92), and SVM kernel (AUC = 0.92) performed similarly and outscored the other ML-based algorithms . For disp < 0.05; q < 0.1). MSEA also highlighted several metabolic pathways perturbed in the CSF harvested from LRRK2 PD patients . Mutations in ADH genes could play a role in the etiology of Parkinson’s disease (PD) because of the important function they undertake, particularly in retinoid and dopamine metabolism and/or aldehyde detoxification . In suppInterestingly, propionate metabolism was found to be significantly disturbed between sPD and corresponding controls. . PropionWe also report inositol-related metabolic pathways to be perturbed when LRRK2 PD CSF was compared to sPD . InositoOur study is not without its limitations. Firstly, our small sample size limits what we can deduce from the results. Secondly, the available clinical information is lacking, such as UPDSR. This precluded us from considering key clinical variables in the models to optimize performance and to determine whether there are other confounding factors that could affect the metabolic profile.This study introduces a novel approach to identify potential metabolic pathways and unique biochemical profiles associated with PD pathogenesis using CSF samples. Whereas conventional approaches utilize only one multivariate statistical method, here, the utility and predictive performances of several machine learning methods were investigated. The machine learning predictive model provided highly discriminating models for distinguishing LRRK2 PD from LRRK2 controls, and sPD from sPD controls, respectively. Results suggest, for the first time, that inositol-related metabolic pathways might be the key for better understanding the LRRK2 PD. Future studies for the prediction and diagnosis of those at risk of developing PD should investigate whether the biochemical pathways highlighted here are recapitulated in the brain of PD patients and whether the CSF panel of biomarkers identified are also as effective for diagnosing or predicting those at greatest risk of developing PD in less invasively harvested biomatrices.


The progressive phase of multiple sclerosis (MS) is characterized by an intrathecal (IT) compartmentalization of inflammation, involving B-cells within meningeal follicles, and resisting all the available immunosuppressive treatments. A new therapeutic paradigm may be to target this inflammation by injecting immunosuppressive drugs inside the central nervous system compartment. NCT02545959). Patients were randomized into three arms (1 : 1 : 1): control group, IT rituximab  group, and intravenous+IT (IV+IT) group. The main outcome was a change in levels of CSF biomarkers of inflammation (osteopontin). Secondary outcomes were changes in levels of CSF biomarkers of axonal loss (neurofilament light chain) and clinical and MRI changes.  We designed a single-center, open-label, randomized, controlled, phase II study designed to evaluate the safety and efficacy of IT rituximab in progressive MS . Clinical parameters remained stable and leptomeningeal enhancements remained unchanged.  Ten patients were included (2 : 4 : 4). No adverse event occurred. OPN level remained stable in CSF at each time point, whereas NFL had slightly decreased (-8.7%) at day 21 ( Clinical outcome and biomarkers of inflammation were not dramatically modified after IT injection of rituximab, probably due to its limited efficiency in CSF. Drug issues for future studies are discussed. All the available treatments of multiple sclerosis (MS) target the relapsing phase of the disease, but no treatment is available to halt the secondary progressive (SP) phase, which remains the main cause of the evolving impairment associated with this disorder. The progressive phase is associated with persistent low-grade inflammation of the central nervous system (CNS), compartmentalized behind the blood-brain-barrier (BBB), and involving several processes including persistent oligoclonal bands and the presence of autoreactive T-cells and B-cells undergoing affinity maturation and somatic hypermutations (review in –3). ExteDrugs targeting CD20+ lymphocytes are highly efficient in preventing MS relapses but do not prevent SP MS impairment. The rationale of the study was to target the compartmentalized CNS inflammation, especially CD20+ lymphocytes of meningeal TLOs, by IT injection of rituximab in SP MS patients. Since rituximab is quickly cleared from the CNS due to the high level of CSF turnover, we focused our exploratory study on its short-term biological and clinical effects. Unfortunately, we obtained negative results as recently observed –11.This single-center, open-label, randomized, controlled, phase II study called EFFRITE  was designed to evaluate the safety and efficacy of IT rituximab in progressive MS. It was approved by the regional ethics board and the institutional research committee. All patients gave their written informed consent. Briefly, key eligibility criteria were primary or secondary progressive (PP or SP) MS for more than 2 years without clinical and radiological relapse, EDSS≥6.0, absence of immunosuppressive in the last 6 months, feasibility and tolerance of MRI and lumbar puncture (detailed criteria in Supplementary Table Patients were randomized (1 : 1 : 1) into three arms: control (Ctrl) group, IT rituximab (IT) group, and intravenous+IT (IV+IT) group. To avoid loss of opportunity, patients randomized to the control arm were allowed to rerandomize to the active arms after the completion of the study.Rituximab infused into CSF is cleared into the blood flow within days and may also deplete extra-CNS B-cells, but the systemic effect of low-dose rituximab (20 mg) was unknown at the time of the study design. Therefore, we planned to offer IV rituximab to half of the IT-treated patients to ensure the complete depletion of blood B-cells and to prevent early reentrance in the CNS. This exploratory study was planned to enroll 12 patients.2 IV given once. IT rituximab was gently infused for 1 min under strict asepsis following a depletive sampling (10 mL) lumbar puncture (LP) obtained by gentle suction with a 5 mL syringe. The patient was in the Trendelenburg position (15°) for one hour to maximize diffusion of lumbar CSF to the brain. All patients including controls received pretreatment drugs  given to prevent clinical manifestations of allergy and cytokine release syndrome. IV rituximab was started one hour after IT injection.Rituximab dose was 20 mg IT (diluted to 10 mL in isotonic solution) and 375 mg/mA CSF aliquot (2 mL) was used for biochemistry and cell count, the other was put on ice and centrifuged, and the supernatant was frozen (-80°C) until biomarker tests. Osteopontin (OPN) and neurofilament light chain (NFL) were measured using ELISA kits . Clinical examination and blood sampling were scheduled at day 0, day 4, day 21, and months 6 and 12. CSF was sampled at each visit up to month 6 .Loc), and radiological changes.The primary objective was the evolution of OPN in CSF, which is a marker of CNS inflammation. Secondary outcome was the evolution of NFL level in CSF, which is a marker of neurodegeneration. Other endpoints were safety, clinical benefits, IgG synthesis , Timed 25-foot walk (T25FW), Symbol Digit Modalities Test (SDMT), Nine Holes Peg Test (NHPT), Modified Fatigue Impact Scale (MFIS), analogic fatigue scale (AFS), and clinical examination. Maximal score (300 sec) was assigned to aborted assays of NHPT. Subjective impressions of patients were recorded. Clinical ratings were done by unblinded investigators .2, FOV 256 × 256 mm2) acquired before and after gadolinium injection, and late acquired (≥20 min) contrast-enhanced 3D-FLAIR sequences.Brain MRI scans  were performed before treatment/LP at day 0, month 6, and month 12. MRI protocols included conventional sequences (FLAIR), volumetric T1 sequences  rank-sum test and the Kruskal-Wallis (KW) test by rank were used to compare biological values between two and three groups, respectively. To avoid biases due to the limited size of the control group, we analyzed changes from baseline biological values by two different approaches. The distribution of the difference of each time point value with mean basal values was compared by the MWW test (between two groups) and by the KW test (between three groups). In a second approach avoiding the control group and pooling treated groups, the distribution of values at each time point was compared with baseline values using the Wilcoxon signed rank test (WSR).http://www.graphpad.com).Clinical data were recorded as improved (+1), stable (0), or worsened (-1) by comparison with baseline values, according to predefined cut-offs of meaningful changes from baseline exceeding: ≥0.5 point in EDSS; ≥20% in T25FW and NHPT; ≥10% in SDMT, MFIS, AFS. Results from the pooled treated group were compared with the control group by the Fisher bilateral test (FBT), and changes from baseline over time among the pooled treated group with the z-test for proportions. The SAS package was used for statistical analysis, and figures were plotted with GraphPad Prism 8.0.0 for OsX  . Due to 3), and rituximab infusion induced dramatic B-cell depletion as soon as day 4, followed by differential recovery among the groups (3 [0-16]), but recovery was still incomplete at month 12 (46/mm3 [7 to 138]). In the IT group on low-dose rituximab, complete B-cell depletion was achieved in 3 of 4 patients (group mean 4/mm3 [0 to 17]) at day 4, but recovery started as soon as day 21 (24/mm3 [0 to 58]) and progressed over a year to incomplete values (128/mm3 [43 to 248]).Basal mean CD19+ B-cell lymphocyte count was normal in each group (198-223/mme groups . CompletAlb (BBB permeability) was observed in the pooled treated group at day 4  and at day 21 , as expected after steroid infusion. IgG index and OCB remained stable in all cases. IgGLoc was slightly increased in the pooled treated group at day 4 , suggesting a direct but very transient consequence of restricted BBB permeability. However, values at day 21 and month 6 remained close to baseline. No statistical difference was observed between the treated and control groups for QAlb and IgGLoc values.Median CSF volume sampled was 10 mL (7 to 12 mL). Cell count, protein level, and glucose were in the normal range, and red cell count remained low (<5 red cells/mL). A minimal decrease in QNo significant differences in median CSF level of NFL and OPN were observed between the three groups (KW) or using pooled treated groups (MWW). Changes in CSF level of NFL and OPN over time were not different between controls and pooled (MWW) or nonpooled (KW) treated groups. Unfortunately, basal CSF levels of OPN (68.6 ± 13.1 ng/mL vs. 100.5 ± 56.7 ng/mL) and NFL (377.6 ± 167.4 pg/mL vs. 581.5 ± 295.7 pg/mL) were lower in control than in treated patients, so a definitive group comparison was not possible. Furthermore, the control group was undersampled.p = 0.38) and had decreased slightly at day 21  and at day 180 (p = 0.09), although the decrease was not significant.To avoid this bias, we analyzed changes over time by intragroup comparison in the pooled treated group at each time point versus baseline (WSR). OPN level remained stable at each point. NFL level was stable at day 4 (p = 0.07) and day 21 , but it had disappeared at later time points.None of the patients experienced MS relapse during the follow-up period. EDSS remained unchanged. Fatigue scores (MFIS and AFS) and SDMT remained roughly stable. Ambulation was already lost at baseline in 3 patients , so they did not undergo T25FW. No improvement was observed in pooled results from the active groups. Severe limitation of hand movements was observed in 3 patients, and assays were aborted in 11 examinations (4.5%). The median time to perform 9HPT remained unchanged in each group. Results remained unchanged even when data from the paretic arm were discarded. No difference in clinical scores was observed between control and treated patients (FBT). Considering pooled treated patients, a trend to improvement (z-test) was observed at day 4 .Lesion load remained unchanged on follow-up imaging, without any new white matter lesion or enhancement on T1w sequences. At least one LME was observed in 5 of the treated patients on baseline MRI . No new Since no treatment is available for progressive MS, drugs crossing the BBB to safely target persisting IT low-grade inflammation are eagerly awaited. Rituximab was initially a promising candidate for three reasons: its efficacy on extra-CNS inflammation, its exclusion from the CNS by BBB after blood injection preventing action within the CNS, and its safety profile, which has already been demonstrated for IT use in CNS lymphomas. Our study and others confirm that the safety profile of the IT injection of rituximab makes it a candidate for use in MS patients. We observed none of the side-effects  classically observed in association with injection of higher dosages, since they were probably related to tumor lysis syndrome in the context of high lesion load in IT lymphomas but not in MS, and they were prevented by systematic pretreatment (reviewed in ).The positive effect of a treatment halting progressive MS would be difficult to capture by clinical examination only. Indeed, and as already observed, no meaningful change was observed on clinical parameters. The pathophysiology of fatigue, which is one of the major causes of clinical impairment in MS, is poorly understood but may involve bathing the CNS with high levels of CSF cytokines. Since one of the objectives of the study was to strongly reduce CNS and CSF inflammation, we expected to detect an early transient effect on fatigue. Unfortunately, we did not observe any such effect. All clinical parameters remained stable throughout the follow-up and failed to capture any beneficial effect, as observed previously with other treatments . These dWe focused on early changes potentially induced by IT injection based on known pharmacokinetic data. Unlike blood-infused rituximab, the drug is cleared from the CSF within a few days , 13, 14,Ideally, the objective would be to suppress the CNS inflammation underlying progressive MS. Since progressive MS is slowly degenerative, CSF biomarkers are able to predict late evolution and could be early markers of drug effects. To investigate persistent inflammation, we focused on OPN, which is a pleiotropic sensitive biomarker of CNS inflammation that is synthesized by most immune cells and is involved in the differentiation of Th-17 T-cells. Levels of OPN are higher in the CSF of MS patients than in healthy controls or other inflammatory disorders –19 and aChanges in degenerative profile were assessed by CSF NFL levels, which are predictive of 10-year outcome, mirror the rate of CNS atrophy, and could be a future predictive biomarker of outcome . They arAt least part of the pathophysiology of progressive MS is due to cortical and meningeal inflammation (TLO) , 22. Thi2 brain area. In EAE models, careful examination of whole brain revealed up to 60 TLO per rat brain [The spatial relationship between TLO and subpial lesions may be more complex than the initially supposed local bystander toxic effect. The fate of these tiny structures remains unknown, and only some of them tend to vanish over time   injected in CSF (but rapidly cleared to blood) were able to completely deplete B-cells from blood , 11, 26.Soluble CD21 (sCD21), which is a marker of B-cell pool, was decreased in serum after rituximab infusion in relation with systemic B-cell depletion, whereas CSF sCD21 was less involved , 11. Thedim NK-cells, which are involved in ADCC, are far lower in CSF (<0.1%) than in blood [This study has several limitations. First, since the safety of the procedure had not yet been assessed when this phase 2 trial was initiated, we planned to include a limited number of patients. However, we were unable to include all the expected patients due to the invasiveness of the procedure and because other treatments were being offered elsewhere at this time. Second, there was an inclusion bias towards severe and older patients. The lack of clinical and radiological activity during the study period was not considered to be a limitation, since the main goal of the study was dedicated to CSF biomarkers. Third, while we focused exploratory biological tests on highly sensitive markers of inflammation and neurodegeneration, we cannot rule out that a transient biological effect may have been missed and that it might have been revealed by other markers. Investigations were limited to the main biomarkers available in our center, and CSF B-cell count was unavailable. We cannot exclude that methods with improved sensibility to test OPN and NFL  may have allowed the detection of small differences induced by treatment. Lastly, drugs were administered by lumbar puncture so we cannot be sure that they diffused homogeneously to higher levels, although diffusion is widely thought to be optimal. Moreover, it was recently demonstrated that rituximab activity was unexpectedly and dramatically decreased in the CSF bath, in relation with the unavailability of effectors. Concentrations of CD56in blood –29. The in blood , 31, whein blood . Therefoin blood , 11, 29.Finally, this work and others failed to efficiently target CNS B-cells –11, and Although biological or clinical changes obtained after IT treatment were minor, its rationale remains well-founded although the methodology should be completely redesigned. The key factor prompting the choice of rituximab was its widespread safe IT used to treat lymphomas, without precluding the fact that similar conditions of efficiency could be reached in MS patients. Since B-cells are poorly targeted by IT rituximab, the absence of impact on the biological markers of neurodegeneration does not rule out IT B-cells being valuable targets in the future, but care must be taken to choose the appropriate drugs that are active in CSF.