For patients with diabetes, suboptimal medication adherence contributes to disease progression, complications, and increased healthcare costs. Identification of, and intervention for patient-identified reasons for nonadherence are essential to improving medication adherence. This prospective, quality improvement study was conducted at an independent community pharmacy in the Mid-West United States. Patients with a proportion of days covered (PDC) for their oral antidiabetic medications of less than 80% were contacted by telephone and interviewed by a clinical pharmacist. The interviews and corresponding adherence interventions were guided by an abbreviated version of the Drug Adherence Work-Up (DRAW©) tool that focused on oral medications for diabetes. The change in PDC 120-days post-interview was assessed to determine the change in adherence rates. Patients receiving the pharmacist-delivered adherence intervention had significantly higher 120 day PDC values which are likely to indicate more regular medication-taking at home. Almost half of study patients signed up for medication synchronization and these patients trended toward higher PDC values, although the relative difference was not statistically significant from those receiving the intervention and not opting to have their medications synchronized. As of 2015, over 30.3 million Americans have a diabetes diagnosis, and another 84.1 million have prediabetes. In 2012, total direct and indirect costs of healthcare for patients with diabetes was estimated at $245 billion with average annual medical expenses of $13,700 per patient. In 2014 alone, 14.2 million emergency room visits and 7.2 million hospitalizations were for patients with diabetes [Medication non-adherence is common among patients with type 2 diabetes who are taking oral antidiabetic medications. Studies have reported rates that range anywhere from 36% to 93% medication adherence for oral antidiabetic medications ,5. The sThere are a variety of interventions that have been used to improve adherence . SuccessThe objective of this this study was to pilot a telephone intervention for providing targeted adherence interventions to patients taking oral medicines for diabetes. The justification for focusing on diabetes is that non-adherence is common for this condition and medication adherence is important to avoiding or delaying diabetes disease progression.This study was a prospective, pharmacist-initiated, quality improvement initiative that used a pre-post comparison where patients served as their own comparator. The study was conducted at a single site of a small independent pharmacy chain in the Mid-West, United States and occurred over a year-long PGY1 Community Pharmacy Residency. Interventions occurred from 30 September 2016, to 7 February 2017. The University of Iowa Institutional Review Board approved the project as a quality-improvement study.At the initiation of the study, a query using a third party software platform for facilitating pharmacy quality improvement identified 96 patients taking at least one oral antidiabetic medication and who also had a proportion of days covered (PDC) for their oral antidiabetic medications of less than 80%. Proportion of days covered is a standard measure for medication adherence that makes important adjustments to avoid over-estimation . The thrEligible patients were contacted via their listed pharmacy profile telephone number up to 3 times. Interviews were performed by the pharmacy resident and guided by an abbreviated version of the DRAW© tool  specificThe primary outcome of this study was the change in PDC for oral antidiabetic medications following the telephone intervention. Patients served as their own control. Pre-interview PDCs were collected from the third-party software connected to the dispensing system for the pharmacy on 30 September 2016. These baseline PDCs were calculated based on refill history for the past 270 days, or when the medication was initiated, if sooner. Post-interview PDCs were assessed 120 days following the interaction between pharmacist and patient using the third-party software. In some instances, manual PDCs had to be calculated such as when patients had cash transactions that did not go through insurance. For manual PDC calculations, patient profiles were analyzed looking at fill dates for all oral antidiabetic medications for the needed period. The number of days in the period was adjusted if patients began an oral antidiabetic regimen after the standard period length. For patients taking multiple oral antidiabetic medications, the PDC was calculated based on if at least one medication provided coverage during the designated period as defined by the Pharmacy Quality Alliance . For eacThe secondary outcome was to characterize the reasons for non-adherence and pharmacist interventions made during the telephone interviews. The reasons and interventions were abstracted from the paper forms completed by the pharmacist and assigned to the categories contained in the DRAW tool . The corData were entered and managed using Microsoft Excel . Demographics for study variables were calculated. A paired t-test (2-sided) was used to test for a difference in PDCs from the 270-day before interview period and the 120-day post interview period using an alpha of 0.05. Statistical analyses were performed in SPSS v 24 . Frequencies were calculated for the various categories of reasons for non-adherence and interventions contained in the completed DRAW tools. Text in addition to the DRAW categories was examined to identify additional detail about the identified reasons for non-adherence and corresponding interventions for the purpose of adding context to the quality improvement effort.An investigation of patient medication profiles and refill histories resulted in the exclusion of 36 patients from the initial pool of 96patients. This included patients who were no longer taking oral antidiabetic medications, used a different pharmacy, or had documented barriers to participating in a telephone interview. The remaining 58 patients were called via telephone, 33 did not answer after three attempts and 25 and were interviewed . Demogran = 25) was 65.2%. The average 120-day post interview PDC rose to 78.7%, a 13.5% increase (p < 0.002), see The average baseline PDC (n = 13) of patients self-identified at least one reason for nonadherence in response to the prompts from the DRAW© tool interview, and 12 reported no barriers. Of those reporting a reason, most (69%) stated forgetfulness. Other recognized reasons for non-adherence include: feeling as though the medication is not helping with their disease state, experiencing side effects from their medication(s), and refill complications either at the pharmacy or from their provider, see A simple majority, 52%  agreed to an intervention during the interview; including medication box counseling to all 6 patients with four of these patients enrolling in the pharmacy’s medication synchronization program.Following the identification of reasons for non-adherence, the pharmacist offered targeted interventions at their discretion to 18 patients (72%) with some receiving more than one intervention. There was an average of 1.6 interventions per patients performed. Recommended interventions were detailed on the abbreviated DRAWn = 12) were enrolled in medication synchronization. Other interventions are detailed in Patients were eligible for medication synchronization if they filled more than three prescriptions on more than two separate days in a 30-day period as determined by third-party software. Seventeen patients were eligible and offered medication synchronization during interview. Of those receiving any intervention, two-thirds (Patients undergoing medication synchronization experienced an average increase change in PDC of 18.8%; resulting from a starting average PDC of 63.2% and an ending PDC of 82%. Patients who were not eligible for medication synchronization or who chose not to participate experience an average increase change in PDC of 8.5%; resulting from a starting average PDC of 67.1% and an ending PDC of 75.5%, see The study supports the role of community pharmacists in improving the medication adherence of patients with diabetes. This particular interview approach was associated with an average increase in PDC of 13.5%, 72% of patients having an increase in PDC, and 48% achieving a PDC of >80–threshold associated with acceptable adherence .The most common intervention in this telephone based interview program was to initiate medication synchronization. This process aligns a patient’s chronic medication refills so they all can be picked up at the same time. This option is beneficial for some patients, but may not work for everyone’s needs, as evident by a small number of patients not being interested in the service when offered. The analysis suggested that medication synchronization may be associated with a greater increase in PDC over a 120-day post-intervention period compared to patients who did not participate in medication synchronization, although statistical significance of the difference was not established for this small sample. Such a comparison may be problematic due to selection bias that can occur with voluntary enrollment into this service. Patients that sign up to have their medications synchronized may have different medication beliefs, practices, and other characteristics than those that decline the service. For example, the medication synchronization patients in our study started with a lower average PDCs, so they had more room to improve.About half of patients participating in the pharmacist interview did not identify a reason for non-adherence despite their being targeted by the software as having evidence of non-adherence. This inconsistency may be due to social desirability bias or the patient not wanting to discuss adherence with a pharmacist or over the telephone. Alternatively, the patient may have had an adherence barrier that was not prompted by the interview guide. Alternatively, a logistical reason related to the days supply of medication, the doctor having given new instructions without a new prescription, or some other situation may have resulted in an inaccurate adherence alert . RegardlThis pilot and feasibility study had several limitations. First, this study lacked a control group and therefore it cannot be determined if the adherence changes were solely due to the intervention or other influences. Also, a study of 1 pharmacy location with a small sample limits generalizability. More research in different settings with different patients is needed. Future research may include comparing the abbreviated DRAW© with the original DRAW© tool or other iterations like the M-DRAW . CompariThis pharmacy-based, telephonic adherence intervention focused on patients with diabetes who were non-adherent to their diabetes medications. The clinical pharmacist used a newly developed abbreviated version of the DRAW© tool to identify patient reasons for non-adherence and recommend interventions such as medication synchronization and targeted counseling. Patients receiving the intervention had a significant improvement in adherence over the study period as measured by PDCs.


We report detection of Seoul virus in 3 patients in France over a 2-year period. These patients accounted for 3 of the 4 Seoul virus infections among 434 hantavirus infections (1.7%) reported during this time. More attention should be given to this virus in Europe where surveillance has been focused mostly on Puumala and Dobrava-Belgrade hantaviruses. Rattus norvegicus), a commensal rodent that is found in all human-inhabited locations (––Seoul virus (SEOV), a hantavirus and the etiologic agent of a mild-to-moderate hemorrhagic fever with a renal syndrome, is associated worldwide with brown rats  search. Exposure to the virus was suspected to have occurred during building restoration work  of case-patient 2, bought ≈1 month before onset of symptoms, was considered the presumptive source of the virus. The animal was euthanized after consent of the patient was obtained. An identical partial SEOV small RNA sequence was obtained from the liver of the animal.The pet rat  from specimens from case-patients 1 and 2 (samples were not available for case-patient 1); specimens from the pet brown rat and raised brown rats suspected to be sources of infection for case-patients 2 and 3; and specimens from 2 wild brown rats suspected to sources of infection for a serologically confirmed human infection with SEOV detected in 2014 .–Four hantaviruses , have been detected in France; the first 3 viruses were associated with humans, and most human infections were with PUUV  (10–13).Detection of SEOV in 3 case-patients in our study and a recent report of a human infected with SEOV . Four other hospitals used IgM and IgG ELISA kits with mixtures of recombinant antigens, including those from SEOV or Hantaan virus strains.Most (83.4%) diagnostic tests in 2014 were requested for patients in the area of France to which PUUV is endemic. Consequently, some SEOV infections might have been be missed because hantavirus infection is rarely suspected outside this area. Samples with positive results for SEOV are sent to NRC for diagnostic confirmation and surveillance purposes. The 3 case-patients we report initially showed positive results by ELISA at local laboratories but then showed negative results by a ReaScan Puumala IgM test (Reagena) at NRC . TherefoThis negative result could have also occurred in other countries in Europe that used the same test. Consequently, use of a pan hantavirus serologic assay is preferred. Furthermore, PUUV was detected by molecular techniques for most PUUV-infected patients during the acute phase of the disease (Furthermore, epidemiologic investigations showed that several animal traders in Europe sold pet rats or food rats to case-patients 2 and 3. These investigations also identified weak traceability of rat batches, absence of health-monitoring data for rats, and no information for the 3 case-patients on zoonotic risks for infection. Awareness of rat owners, traceability, and health monitoring of these animals, as performed for laboratory rats (


Brain stimulation modulates the excitability of neural circuits and drives neuroplasticity. While the local effects of stimulation have been an active area of investigation, the effects on large-scale networks remain largely unexplored. We studied stimulation-induced changes in network dynamics in two macaques. A large-scale optogenetic interface enabled simultaneous stimulation of excitatory neurons and electrocorticographic recording across primary somatosensory (S1) and motor (M1) cortex . We tracked two measures of network connectivity, the network response to focal stimulation and the baseline coherence between pairs of electrodes; these were strongly correlated before stimulation. Within minutes, stimulation in S1 or M1 significantly strengthened the gross functional connectivity between these areas. At a finer scale, stimulation led to heterogeneous connectivity changes across the network. These changes reflected the correlations introduced by stimulation-evoked activity, consistent with Hebbian plasticity models. This work extends Hebbian plasticity models to large-scale circuits, with significant implications for stimulation-based neurorehabilitation. From riding a bike to reaching for a cup of coffee, all skilled actions rely on precise connections between the sensory and motor areas of the brain. While sensory areas receive and analyse input from the senses, motor areas plan and trigger muscle contractions. Precisely adjusting the connections between these and other areas enables us to learn new skills, and it also helps us to relearn skills lost as a result of brain injury or stroke.About 70 years ago, a psychologist named Donald Hebb came up with an idea for how this process might occur. He proposed that whenever two neurons are active at the same time, the connection between them becomes stronger. This idea, that ‘cells that fire together, wire together’, became known as Hebb’s rule. Many studies have since shown that Hebb’s rule can explain changes in the strength of connections between pairs of neurons. But can it also explain how connections between entire brain regions become stronger or weaker?New results show that it can. The data were obtained using a technique called optogenetics, in which viruses are used to introduce genes for light-sensitive proteins into neurons. Shining light onto the brain will then activate any cells within that area that contain the resulting proteins. Yazdan-Shahmorad, Silversmith et al. used this technique to activate small regions of either sensory or motor brain tissue in live macaque monkeys. Doing so strengthened the overall connectivity between the two areas. The effects were more variable at the level of smaller brain regions, with some connections becoming weaker rather than stronger. However, Yazdan-Shahmorad, Silversmith et al. show that Hebb’s rule explains most of the observed changes.Many neurological and psychiatric disorders stem from abnormal brain connectivity. Simple forms of brain stimulation are already used to treat certain neurological disorders, such as Parkinson’s disease. Stimulating the brain to induce specific changes in connectivity may ultimately enable us to leverage the brain’s natural learning mechanisms to cure, instead of just treat, these conditions. Many neurological and psychiatric disorders arise from dysfunctional neural dynamics at the network level, which in turn stem from aberrant neural connectivity . The braThe simple Hebbian model of plasticity  and spikHere, we measure connectivity across sensorimotor cortex and track changes in network connectivity in response to open-loop stimulation. This work takes advantage of a large-scale optogenetic interface  that ena2 of primary somatosensory (S1) and motor (M1) cortices  from about 1.5 cmcortices . The vircortices . These ncortices . Activatcortices . In two We quantified inter-area functional connectivity between S1 and M1 using two different measures—one based on the network response to optogenetic stimulation and the other based on spontaneous neural activity.The first measure focused on how the response to optical stimulation propagates through the network. Optogenetic stimulation in S1 and M1 evoked responses across both cortical areas, and we characterized these responses according to their amplitudes and delays . The delThe second measure evaluates functional connectivity during spontaneous activity. We focused on the coherence in field potential recordings between electrodes, a widely used measure of connectivity that captures the degree of phase-locking between two signals . We calcBecause SERR and coherence are derived independently from recordings with and without simultaneous stimulation, they might reflect fundamentally different aspects of network connectivity. Therefore, in each experiment we compared these two measures prior to conditioning stimulation. We used a simple stimulation protocol—we delivered 5 ms laser light pulses at a frequency of 5 or 7 Hz at either one or two cortical sites . For our−11) in mean SERR. Similar increases in SERR were observed for a majority of experiments (14 out of 18 experiments) across both monkeys  coherence following conditioning, as shown in We showed in To further address this, we replicated this analysis for our control (no stimulation) sessions. In this case, we did not see a significant decrease in the correlation between measures after control sessions  and baseline recording (5 min in most experiments) blocks, one might expect that stimulation-induced changes would revert back to baseline. We did observe a significant decrease  in the SERR between the last 100 pulses of the conditioning block and the following test block, indicating some unlearning of the stimulation-induced connectivity changes.We next evaluated the fine-scale effects of conditioning across the entire network. Since SERR is restricted to connectivity from the stimulation site, this analysis was conducted using only changes in pairwise coherence. We next asked whether such heterogeneous changes are to be expected. A Hebbian plasticity model suggests that the fine-scale changes in connectivity should reflect the statistics of plasticity-inducing activity. In this case, we predicted that correlated stimulation-evoked activity between two cortical sites would strengthen the functional connectivity between them, while uncorrelated or anti-correlated stimulation-evoked activity would weaken their functional connectivity. We quantified stimulation-induced correlations by measuring the pairwise coherence during the conditioning block and subtracting out the coherence calculated during baseline recording . We refer to this difference as the stimulus-evoked coherence. Indeed, in the example session of To quantify this similarity, we used linear regression to predict changes in baseline coherence across sites based on the stimulus-evoked coherence. Importantly, both of these quantities use the initial baseline coherence as a reference. So to avoid spurious correlations introduced by subtracting the same data from the two regression variables, we split the baseline recording blocks into two intervals and used the data from only one interval for each regressor . Data from the example session in To further test this model, we conducted some experimental sessions using a more complex spatio-temporal pattern of optical stimulation. Specifically, by reducing the latency between the two light sources to either 10 or 30 ms , we intrThese results demonstrate a robust relationship between stimulus-evoked correlations and changes in baseline connectivity across the network. These findings are well explained by a Hebbian model of large-scale stimulation-induced plasticity.In this study we investigated how the functional connectivity between and across sensory and motor areas changes in response to stimulation. With optogenetics, we selectively manipulated local populations of excitatory neurons within this sensorimotor network. We compared two different methods to measure functional connectivity at both gross- and fine-scales, and demonstrated how these measures change in response to conditioning stimulation. We showed that these changes are consistent with Hebbian synaptic plasticity rules, extending Hebbian models of stimulation-driven plasticity to large-scale networks. This work demonstrates the feasibility of driving targeted plasticity with optogenetic stimulation. This framework is a starting point for designing principled approaches to large-scale neuroplasticity and stimulation-based therapies for neurological and neuropsychiatric disorders.Functional connectivity between M1 and S1 has been demonstrated in fMRI , electroIn principle, these two measures reflect different aspects of functional connectivity. SERR is a more direct measure of the projections from the stimulation site. Importantly, stimulation likely evokes activity from both terminals and cell bodies located at the stimulation site, though C1V1 does not express well down at the axon . SecondaNetwork effects). These coupled mechanisms lead the network to a stable, steady state  result in changes that are stable over timescales comparable to the length of conditioning . FurtherGiven that Hebbian rules have been used extensively to explain synaptic plasticity, we wanted to explore the idea that large-scale stimulation-induced changes are consistent with Hebbian plasticity. The strongest test of the Hebbian model is to compare the correlations directly induced by stimulation with the changes in network correlations observed after conditioning. Our ability to record artifact-free signals during stimulation allowed us to perform this test. We did find a predictive linear relationship between stimulus-evoked coherence and the changes in baseline coherence after conditioning. Furthermore, the model held for both simple and complex spatiotemporal patterns of stimulation-evoked activity.There is a large body of research on sensory cortical plasticity . This stThe large-scale interface used in this study enabled robust estimation of functional connectivity and stimulation-evoked correlations. However, there are several limitations in our experimental setup. First, surface stimulation combined with high blue light absorption and scattering in tissue limits the depth of light penetration . The abiOur results offer a proof-of-concept that optogenetic stimulation can drive predictable changes in network-scale connectivity. The success of optogenetics in NHP represents an important step forward for translational use . The conTwo adult male rhesus monkeys  were used in this study. We used the same animals and interface published in . All pro12 virus molecules/ml; Penn Vector Core, University of Pennsylvania, PA, USA, Addgene number: 35499) into primary somatosensory (S1) and motor (M1) cortices based on convection-enhanced delivery (CED). We infused 200 μL of virus in four sites (two in M1 and two in S1) in monkey G and 250 μL in five sites (two in M1 and three in S1) of monkey J. Infusion rate started at 1 µl/min and was increased to 5 µl/min. Following infusion we used an artificial dura to protect the brain while maintaining optical access. We verified expression with epifluorescent imaging. To record the evoked responses in both M1 and S1, we used either one or two 96 channel micro-electrocorticography (μ-ECoG) arrays that were designed to allow minimally-attenuated  that was connected to a 488 nm laser  and was positioned above the array . We usedWe chose the locations of stimulation based on the results of our epifluorescent imaging (see ) and wheThe monkeys were awake sitting in primate chairs for the duration of experiments. To ensure that the monkey remained awake, we provided random reward as well as audio and visual stimuli (displaying a cartoon). In addition, we visually monitored the animal throughout the experiment.Our stimulation and recording paradigms consisted of baseline recording, testing and conditioning blocks . During All processing and statistical analyses were performed using custom MATLAB  code. After signal acquisition, broadband surface potentials (sampled at 24 kHz) were visually inspected, and faulty recording sites were removed from further analysis. Next, stimulation-triggered responses were visually inspected for photoelectric artifacts , and reDuring test blocks, we delivered 100 stimulus pulses at 5 Hz through each laser. To capture high-fidelity timing of the signal and avoid significant phase distortions we acausally band-pass filtered [0.1–500 Hz] the broadband signal. We first calculated the delay between the onset of the stimulus pulse and the response measured at each electrode. We calculated the delay from an average waveform across the distribution of evoked responses (bootstrapped 1000 times). This bootstrapped averaging was less susceptible to artifacts and noise that could dominate the signal on individual trials. The average evoked response lasted no longer than 30 ms with the trough of the response occurring in the first 5–15 ms. We defined the delay of the evoked response as the time of the trough within 20 ms of stimulation onset . The disNext, we calculated the amplitude of each evoked response. We downsampled the raw surface field potentials to 1 kHz  and applied an acausal band-pass filter to capture high gamma activity (60–200 Hz)), which is known to be representative of neural activity of local cortical columns . The peaChanges in SERR were calculated by taking the difference in SERR between the initial and final testing blocks for each secondary site. A paired, two-sided t-test was used to determine whether SERR changes were significant across secondary recording sites. Similarly, a paired, two-sided t-test was applied to determine significance across sessions separately for controls (in which no stimulation was applied during conditioning), stimulation in M1, and stimulation in S1. Unpaired, two-sided t-tests were used to directly compare the change in SERR between all stimulation sessions and all control sessions. P-values less than. 05 were considered significant.xx and Gyy refer to power spectral density of channels x and y respectively, and Gxy refers to their cross-spectral density. For simplicity, in future equations we refer to the coherence between channels x and y as C. Coherence in the theta band (4–8 Hz) was used in all analyses, unless otherwise specified.First, broadband surface potentials were downsampled to 1 kHz . Then pairwise coherences were calculated in 10 s Hamming windows in 4 Hz frequency bands. The coherence between channels x and y is defined as:Changes in inter-area coherence were calculated by taking the difference in coherence between the initial and final recording blocks. Significant changes after individual sessions were detected with a paired, two-sided t-test across secondary sites. Across sessions, changes in inter-area coherence in each frequency band were compared between stimulation sessions and control sessions with an unpaired, two-sided t-test, applying the Bonferroni correction for multiple comparisons.Both the SERR and inter-area theta coherence were measured for each baseline recording and each test block during the experiment. To measure connectivity during the conditioning block, the block was sectioned into 100-pulse segments, and the SERR was calculated for each of these segments. To understand the connectivity trends, we pooled experiments together and calculated the average connectivity measures. To account for initial differences in connectivity across sessions and monkeys, we calculated the average changes in each connectivity measure with respect to its initial value and computed their Pearson correlation across sessions and blocks.c) is different than the coherence calculated during baseline recording blocks (Cr). This difference, or ‘stimulus-evoked coherence’ captures the correlations introduced through stimulation. We propose that these correlations drive plasticity in a Hebbian manner, so that the change in baseline coherence between pre- and post-conditioning reflects the stimulus-evoked coherence. Therefore, we assessed how well the change in recording coherence is predicted, across blocks and electrode pairs, by the stimulus-evoked coherence using linear regression,r,α and Cr,β) and used these as independent measures of baseline coherence for the regression,Since stimulation evokes a network-wide pattern of activity, the coherence during conditioning blocks (CAB_303395) was diluted 1:15,000 in PBS containing 0.01% Triton X-100 and was applied to the tissue for 48 hr at 4°C. Sections were then rinsed in PBS, incubated in biotinylated donkey anti-rabbit antibody,  for 12 hr at 4°C, rinsed and incubated in ExtrAvidin  for 5–6 hr in room temperature. Peroxidase was detected using a diaminobenzidine (DAB) chromogenic reagent (Sigma-Aldrich). Sections were rinsed in PBS, mounted on gelatin-coated slides, air dried, dehydrated in graded alcohols, cleared in xylene and coverslipped with D.P.X. mounting media (Sigma-Aldrich). Additional adjacent sections were stained with cresyl violet (Nissl) using standard techniques, to reveal cortical cytoarchitecture.Monkeys were deeply sedated  and perfused transcardially with heparinized phosphate buffered saline (PBS) followed by cold 4% paraformaldehyde in phosphate buffer. The brain was extracted and post-fixed in the same fixative for 24 hr at 4°C and then dissected into twelve 6 mm-thick coronal blocks using a custom matrix. After 7–10 days incubation in 30% sucrose, blocks were frozen and cut on a cryostat  into 50 μm thick sections. Representative sections were selected from each block and processed for EYFP immunocytochemistry using a free-floating technique. Sections were initially washed in PBS, incubated in 3% hydrogen peroxide in PBS for 10 min to quench endogenous peroxidase activity, then rinsed in two changes of 50% ethanol followed by three changes of PBS for 5 min each. Next we incubated the sections in 5% normal donkey serum in PBS for 1 hr to block non-specific binding. Primary rabbit polyclonal anti-GFP antibody ; goat polyclonal anti-GFP antibody . These were paired with one of the following primary antibodies: mouse monoclonal antibody for parvalbumin, ; mouse monoclonal antibody for calbindin (1:800 Sigma-Aldrich CB-955); rat monoclonal antibody for somatostatin ; rabbit polyclonal anti-GAD65/67 ; a cocktail of mouse monoclonal antibody for GAD67  and mouse monoclonal antibody for GAD65 . Sections were blocked using normal donkey serum and processed for 4–6 hr in a mix of matching secondary antibodies; all of which were raised in donkey : anti-rabbit Alexa Fluor 488, anti-goat Alexa Fluor 488 (both for GFP). Interneuron markers were visualized using anti-mouse Alexa Fluor 594, anti-rat Alexa Fluor 594; anti-rabbit Alexa Fluor 594. Sections were mounted using Vectashield (Vector Labs) and imaged using Zeiss LSM510 Meta confocal microscope  and 90i imaging system equipped with a CCD camera . In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been favorably evaluated by Sabine Kastner (Senior Editor) and three reviewers, one of whom, Charles Schroeder (Reviewer #1), is a member of our Board of Reviewing Editors. The following individuals involved in review of your submission have agreed to reveal their identity: Luke Sjulson (Reviewer #2); Michael Michaelides (Reviewer #3).Thank you for submitting your article "Targeted cortical reorganization using optogenetics in non-human primates" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:This paper reports on investigation of stimulation-induced changes in intracortical functional connectivity in 2 monkeys. S1 and M1 were studied with dense arrays of micro-ECoG electrodes positioned at the Pial surfaces of the areas. Stimulation-related changes in both "high-gamma" band activity  and changes in resting theta band coherence at rest were examined. Stimulation was provided by optogenetic methods that were designed to be selective to excitatory  neurons. The experiments are a significant technical achievement, and the idea of evoking and measuring plasticity between two brain regions is interesting. The main effect of periods of conditioning stimulation was a general increase in connectivity by all measures, though it had heterogeneous characteristics whose underpinnings were unclear. The authors conclude that their findings generalize the concept of Hebbian plasticity to the network scale and that this has clinical implications. Despite the paper's strengths, numerous problems prevent the main scientific result of optogenetic Hebbian plasticity from being established clearly. Additionally, parts of the manuscript are sufficiently confusing that it was difficult to understand what the experimental conditions were in order to evaluate them.Essential revisions:1) "Traditional" somatosensory plasticity work in monkeys  relates to re-organization after input change due to injury or some other perturbation. How does the present study interface with that extensive body of findings? Notably the somatosensory reorganization work focuses on the somatotopic representation in S1, but that is not dealt with at all here (see next point) noted, the somatosensory and motor system are already interconnected, but within area there is a somatotopic organization, and across areas, connectivity tends to be between ~ corresponding somatotopic representations. It would add greatly to the clarity of this paper if this point were addressed.2) No mention of the monkeys' state of anesthesia is made here. Presumably they were anesthetized. Regardless, that issue point be clearly discussed, as one would reasonably expect that the monkeys' state would have a large impact on the plasticity of the system.3) SERR is a critical measure for this paper, and it needs to be described and illustrated in much more detail – see below Figure 2 also. In particular, why is the peak to trough the measure of interest, rather than, e.g., power in the 60-200 Hz band over a defined post-stimulation epoch. Additionally, if the lag of the response peak in the secondary response  is important in that case, why not here as well? How exactly were the peaks and troughs scored and how is peak to trough amplitude measured?4) The manuscript tacitly promotes a description of the experiment that may not be correct, in which excitatory cells in S1 and M1 are stimulated independently. In reality, there are substantial projections from S1 to M1 and vice versa, and illumination of either structure would be expected to evoke action potentials in both the illuminated somata and the axon terminals of neurons from the other structure. Xiong et al. (https://www.ncbi.nlm.nih.gov/pubmed/25731173) and others have measured this effect in vivo using techniques similar to those used in this manuscript. One feature of C1V1 that may work in the authors' favor is that it traffics poorly to long-range projections (https://www.ncbi.nlm.nih.gov/pubmed/26436451), but nevertheless, the authors must address this point. One suggestion would be to break down the temporal components of the LFP responses as in Xiong et al.5) In Figure 4, the authors show comparisons with a "No Stim" condition. However, the no stim condition is not plotted in 4G or any of the subsequent figures in the paper. Additionally, the various statistical tests for increased connectivity are not compared against the no stim condition, as they should be.6) I think the main claim being made by this paper is that correlated stimulation causes strengthening of functional connectivity, and uncorrelated stimulation weakens it. However, the paper is sufficiently unclear that it is difficult to determine whether that is in fact the claim the authors intend to make. Figure 1B shows only one protocol, but the Materials and methods section lists multiple protocols in incomprehensible fashion. This manuscript needs a transparent explanation of what the experimental conditions were, and Figures 6-7 need to contain direct statistical comparisons of these conditions against each other (including the "no stim" condition). Ideally all of the experimental conditions should be illustrated as in Figure 1B.7) In their 2016 publication, the authors report the use of an AAV5 C1V1 virus. Here they report a pseudotyped AAV2/5. Are these the same virus? If not why did the authors switch. Neither show up on the UPENN vector core website catalog. Were they custom made? This is important information that should be shared so that other research groups are able to perform similar experiments if they so wish to. Also in the 2016 publication, the authors reported that the artificial dura led to anatomical changes in the targeted area. The authors used the same approach in these experiments but any such effects or measures were not reported. Did they observe such changes here?8) The authors show convincing and high quality immunohistochemical data however they did not report whether they attempted to quantify the number of cells that expressed the opsin in each monkey, especially at the site of stimulation and whether this was related to their network connectivity measures. It would have been useful (but not necessary at this point if they do not have this data) if they had chosen to stimulate in different regions and then examined their downstream measures as a function of cell transduction. In relation to the above point, how did the authors decide on the specific areas within each region to stimulate? Was this based on where the injections were made? Essential revisions:1) "Traditional" somatosensory plasticity work in monkeys  relates to re-organization after input change due to injury or some other perturbation. How does the present study interface with that extensive body of findings? Notably the somatosensory reorganization work focuses on the somatotopic representation in S1, but that is not dealt with at all here (see next point) noted, the somatosensory and motor system are already interconnected, but within area there is a somatotopic organization, and across areas, connectivity tends to be between ~ corresponding somatotopic representations. It would add greatly to the clarity of this paper if this point were addressed.Unfortunately due to time constraints in running these experiments, we did not obtain somatotopic maps for the cortical regions under study. These animals have subsequently been sacrificed to obtain histological data.As the reviewers note, there is a great body of research that focuses on sensory cortical plasticity; we see this paper as complementary. While previous work has largely focused on how changes in the statistics of sensory input and/or motor output affect the cortical representation in sensory cortex, we have directly manipulated cortical circuits, irrespective of their somatotopic map. And where previous work studied plasticity on the time scale of days to months, we are studying changes across minutes. Despite the substantial differences between, however, both appear to support a key role for Hebbian learning in sensorimotor cortical plasticity. Still, our ability to link the present work to previous work is clearly limited by the lack of cortical mapping and the inability to study changes on the intermediate times scales. These are clearly important questions for future work. We have added these ideas to the Discussion.2) No mention of the monkeys' state of anesthesia is made here. Presumably they were anesthetized. Regardless, that issue point be clearly discussed, as one would reasonably expect that the monkeys' state would have a large impact on the plasticity of the system.We thank the reviewers for pointing out this omission. In fact, the monkeys were awake, sitting in primate chairs, and watching enrichment videos (cartoons) during these experiments. To ensure that the monkey stayed awake for the duration of the experiment we provided random reward as well as audio and visual stimuli. In addition we monitored the monkeys via a camera. We have added this information to Materials and methods.3) SERR is a critical measure for this paper, and it needs to be described and illustrated in much more detail – see below Figure 2 also. In particular, why is the peak to trough the measure of interest, rather than, e.g., power in the 60-200 Hz band over a defined post-stimulation epoch. Additionally, if the lag of the response peak in the secondary response  is important in that case, why not here as well? How exactly were the peaks and troughs scored and how is peak to trough amplitude measured?A priori, we chose to measure SERR with the high gamma amplitude  as a response metric, since high gamma potentials are thought to represent the population activity of local cortical columns. Per the reviewers’ suggestion, we re-evaluated our results based on several different measures extracted from the evoked responses. These measures include energy in the 60-200 Hz (high-gamma) band, energy of the broadband signal, amplitude of the broadband signal and the slope of the broadband signal. All of these measures showed similar changes after stimulation, though only the original measure exhibited a significant difference between stimulation and control sessions. More generally, the high gamma amplitude yielded the most robust results and best correlated with coherence. These data and a discussion of them were added to Table 2. We have also added more detailed explanation of how the peaks and troughs are measured. Furthermore, we modified Figure 2 and the text to explain the SERR better.4) The manuscript tacitly promotes a description of the experiment that may not be correct, in which excitatory cells in S1 and M1 are stimulated independently. In reality, there are substantial projections from S1 to M1 and vice versa, and illumination of either structure would be expected to evoke action potentials in both the illuminated somata and the axon terminals of neurons from the other structure. Xiong et al. (https://www.ncbi.nlm.nih.gov/pubmed/25731173) and others have measured this effect in vivo using techniques similar to those used in this manuscript. One feature of C1V1 that may work in the authors' favor is that it traffics poorly to long-range projections (https://www.ncbi.nlm.nih.gov/pubmed/26436451), but nevertheless, the authors must address this point. One suggestion would be to break down the temporal components of the LFP responses as in Xiong et al.The Xiong et al. paper was able to parse the timing of the evoked response at a given site in order to evaluate synaptic efficacy because they were stimulating axon terminals and recording cells at the stimulation site. This technique cannot be applied to our paradigm, where we are stimulating at the  upstream site and recording at the  downstream site, since the temporal effects will be washed out by a variety of factors, including transduction and spike generation at the stimulation site and axonal conduction to the recording site.In fact, we are likely evoking activity from both terminals and cell bodies at the stimulation site, though as the referee notes, C1V1 does not express well down the axon. Therefore, our secondary responses arise from a combination of synaptic, antridromic, and indirect (network) effects. That said, we do not expect the antidromic effects to contribute to stimulation-induced plastic changes in the response. And the effect of larger-scale network contributions are discussed in the last section of the Results. We have added a section on this topic to the Discussion.5) In Figure 4, the authors show comparisons with a "No Stim" condition. However, the no stim condition is not plotted in 4G or any of the subsequent figures in the paper. Additionally, the various statistical tests for increased connectivity are not compared against the no stim condition, as they should be.We have added figures to the supplementary material that compares no-stim to the results presented for Figures 4 and 5. Also we have added statistical tests and results to the text to compare the changes following stimulation with the no-stim condition. As expected, the control sessions do not show the plasticity effects that are the main result of the paper.6) I think the main claim being made by this paper is that correlated stimulation causes strengthening of functional connectivity, and uncorrelated stimulation weakens it. However, the paper is sufficiently unclear that it is difficult to determine whether that is in fact the claim the authors intend to make. Figure 1B shows only one protocol, but the Materials and methods section lists multiple protocols in incomprehensible fashion. This manuscript needs a transparent explanation of what the experimental conditions were, and Figures 6-7 need to contain direct statistical comparisons of these conditions against each other (including the "no stim" condition). Ideally all of the experimental conditions should be illustrated as in Figure 1B.The main claims of the paper are: 1) Stimulating at one site drives correlated activity that strengthens functional connectivity between brain areas. 2) Given stimulus-evoked correlated activity, a Hebbian model predicts changes in functional connectivity. 3) This model holds even when complex patterns of stimulation drive a combination of strengthening and weakening of connectivity. We have tried to make these claims clearer by changing our section and figure titles and by making clarifying edits throughout the paper.In addition, we have modified Figure 1 and added schematics of the stimulation protocols to Figures 4 and 7 to better explain the stimulation protocols. In addition we have done direct statistical analysis between “no stim” and stim data for Figures 6 and 7. The results of these comparisons are added to the text and Supplementary file 1.7) In their 2016 publication, the authors report the use of an AAV5 C1V1 virus. Here they report a pseudotyped AAV2/5. Are these the same virus? If not why did the authors switch. Neither show up on the UPENN vector core website catalog. Were they custom made? This is important information that should be shared so that other research groups are able to perform similar experiments if they so wish to. Also in the 2016 publication, the authors reported that the artificial dura led to anatomical changes in the targeted area. The authors used the same approach in these experiments but any such effects or measures were not reported. Did they observe such changes here?We thank the referee for pointing out this omission. In fact, the data presented here are from the same animals as the 2016 Neuron paper, using the same interfaces and the same cortical areas. The virus (AAV5.CamKIIa.C1V1(E122T/E162T).TS.eYFP.WPRE.hGH) was custom made by UPENN vector core and the Addgene number is 35499. We have added this information to the text.As the reviewer notes, we did see some cortical thinning in monkey G post-mortem (as reported in our 2016 Neuron paper). In contrast, the histology for monkey J, presented here does not show change in the cortical thickness . This difference has been added to Materials and methods and discussed in the supplement.8) The authors show convincing and high quality immunohistochemical data however they did not report whether they attempted to quantify the number of cells that expressed the opsin in each monkey, especially at the site of stimulation and whether this was related to their network connectivity measures. It would have been useful (but not necessary at this point if they do not have this data) if they had chosen to stimulate in different regions and then examined their downstream measures as a function of cell transduction. In relation to the above point, how did the authors decide on the specific areas within each region to stimulate? Was this based on where the injections were made?Please see our 2016 Neuron paper  for more detailed report on the optogenetic expression level for Monkey G. We chose the locations of stimulation to be within regions of high expression, as assessed with epifluorescent imaging . Based on our histological and electrophysiological analyses , we concluded that there was a fairly uniform distribution of expression around our infusion sites, including the locations that we chose to stimulate for this paper. We have added this information to the paper.


European guidelines recommend offering an HIV test to individuals who display HIV indicator conditions (ICs). We aimed to investigate the incidence of ICs in primary care reported in medical records prior to HIV diagnosis.We did a cross-sectional search in an electronic general practice database using a matched case-control design to identify which predefined ICs registered by Dutch GPs were most associated with an HIV-positive status prior to the time of diagnosis.n = 136, 60.7%) of cases were diagnosed with one or more ICs in the period up to five years prior to the index date compared to 18.7% (n = 411) of controls. Cases were more likely to have an IC than controls: in the one year prior to the index date, the odds ratio (OR) for at least one condition was 11.7 (95% CI: 8.3 to 16.4). No significant differences were seen in the strength of the association between HIV diagnosis and ICs when comparing genders, age groups or urbanisation levels. There is no indication that subgroups require a different testing strategy.We included 224 HIV cases diagnosed from 2009 to 2013, which were matched with 2,193 controls. Almost two thirds (Our study shows that there are opportunities for IC-guided testing in primary care. We recommend that IC-guided testing be more integrated in GPs’ future guidelines and that education strategies be used to facilitate its implementation in daily practice.The online version of this article (doi:10.1186/s12875-016-0556-2) contains supplementary material, which is available to authorized users. In 2014, UNAIDS set the challenging goal to end the AIDS epidemic by 2030 [3 or with an AIDS-defining event regardless of CD4 count) [By the end of 2014, 19,382 persons in the Netherlands, or 88% of the estimated total number of persons living with HIV, had been diagnosed with the infection. In total, 16,821 had started treatment, and of these 15,463 had achieved viral suppression [The insight of starting treatment as prevention, demonstrated public health benefit of early treatment . RecentlPeople who are unaware of their infection play an important role in maintaining the HIV epidemic . Dutch gDutch GP guidelines already promote provider-initiated HIV testing among high-risk populations, but this strategy alone shows limits in the implementation to detect HIV-infected people . For exaA UK case-control study in primary care found multiple ICs that were associated with a subsequent HIV diagnosis .A Dutch case-control pilot study conducted in a higher HIV-prevalence area showed that one or more ICs were observed prior to diagnosis among cases compared to controls . The resThe aim of the present study was to investigate the incidence of ICs reported in medical records up to five years prior to HIV diagnosis among HIV cases compared to matched controls using a primary care database representative of the general population.We carried out a retrospective population-based nested case-control study; data were retrieved from electronic patient medical records (EMRs).The Integrated Primary Care Information (IPCI) database is a longitudinal primary care research database managed at the Erasmus University Medical Center in Rotterdam, the Netherlands , 23. ThiThe study population from which cases and controls were extracted consisted of all persons in the database who were 18 years or older and registered at one of the GP practices during the period from 1 January 2009 up to 31 December 2013 .Cases were defined as HIV-positive individuals. If no HIV diagnosis was recorded, an individual could act as control.Potential cases were selected by extracting the International Classification of Primary Care (ICPC) B90 code ‘HIV infection’ from the EMRs . In addiPotential cases were manually validated. The extraction of potential cases was performed in duplicate by two of the project’s researchers (IKJ and DT). Discrepancies were discussed until consensus was reached. Uncertain potential cases were presented to a third researcher (IvdB). Cases were matched to controls (up to a maximum of 10) for age, gender and registration at the same general practice in the same calendar year and at least one year of registration time (follow-up time) prior to the index date (date of HIV diagnosis in cases and matched date in the control group). Controls were selected from persons without an ICPC B90 code in their EMRs. Cases could be selected as controls until the year before they became a case. The number of selected controls per case depended on the availability. Selecting as many controls as possible per case ensures maximum statistical power in matched case-control studies to detect differences in the occurrence of rare events .For the confirmed cases within the study period (2009–13) and their matched controls, a history of five years of patient EMRs of validated registration time before the index date was included if available, but at least one year was required . The inclusion of the cases was limited by the start date of the GP participation in IPCI or by the date the patient registered with the GP prior to the index date (date of HIV diagnosis).A total of 26 predefined ICs were selected from the list of ICs with an associated (undiagnosed) HIV prevalence of >0.1%  yes recorded (at least once); 2) absent; and 3) uncertain. Incident ICs and their date of diagnosis were manually transferred into a validation tool. If a person had multiple consultations for the same IC, we used the most recent one before the index date that was recorded by the GP. The search for incidence ICs was carried out in duplicate by the same two researchers . They were not blinded for HIV diagnosis in the EMRs as this was not possible due to technical limitations. A third researcher (IvdB) was consulted if consensus could not be reached or if ICs were classified as uncertain.According to the Dutch GP guidelines, persons who are diagnosed with chlamydia, gonorrhoea, syphilis or hepatitis B should be advised to have an HIV test.  We did aWe performed conditional logistic regression to investigate whether having at least one STI or IC prior to the index date was associated with testing HIV positive. We performed exact conditional logistic regression to investigate whether specific ICs prior to the index date were associated with testing HIV positive. For very small or zero incidence rates among the cases or controls, odds ratios (ORs) were estimated with ‘median unbiased estimate’. Sensitivity analyses were performed, comparing the associations between ICs and HIV diagnosis when including versus not including unconfirmed (classified as ‘uncertain’) ICs. Furthermore, the interaction of presence of any IC with gender, age group and urbanisation level was tested in a conditional logistic regression model.Confidence intervals for the percentages of HIV testing in STI diagnosed patients were obtained by Wilson score method .Analyses were performed with SPSS  except for the exact logistic regression, for which STATA  was used.n = 452). Of the remaining 1,616 potential cases, 897 patients were excluded because no HIV diagnosis was reported in the consultation. For the 719 potential cases a more in-depth evaluation was conducted using the complete EMR to confirm HIV diagnosis; the diagnosis was confirmed in 613 cases. Patients with an HIV diagnosis outside the study period (n = 319) or outside the validated IPCI time (n = 70) were excluded.The eligible study population (18 years and older) consisted of the EMRs of 1,255,440 persons . The majority (88.4%) of cases were male, the median age was 40 (IQR 31 to 47) and 55.8% lived in areas with a high level of urbanisation  of cases had been diagnosed with one or more ICs up to five years prior to the index date, compared to 18.7% (n = 411) of controls  . The proportions of the number of STIs and ICs per person prior to index date are reported in Additional file The ICs most strongly associated with the occurrence of HIV were gonorrhoea  and syphilis  Table . SeveralWhen uncertain diagnoses were included in the reported ICs, lymphogranuloma venereum  was also associated with the occurrence of HIV diagnosis.The OR for the occurrence of ICs was higher for men than for women, but the difference was not significant (see Table In 32.1% (95% CI: 17.9 to 50.7) of persons diagnosed with syphilis (9/28 of the cases and controls) no HIV test was reported in the EMRs in the same or follow-up consultation(s) within a time window of three months; for gonorrhoea this was 44.7% (95% CI: 30.1 to 60.3), (17/38), for hepatitis B 61.5% (95% CI: 35.5 to 82.3), (8/13) and for chlamydia 54.2% (95% CI: 40.3 to 67.4), (26/48).Since 2012, European guidelines recommend IC-guided testing in primary care. This new strategy is also mentioned in the updated Dutch national STI guideline for GPs which was published at the end of 2013. Our findings strengthen the evidence for implementation of this new strategy as we showed that ICs were more commonly diagnosed in HIV-positive persons than in matched controls, in a period up to five years prior to HIV diagnosis. HIV testing after detection of other STIs has already been recommended for years. However, our results showed that an HIV test is not always performed according to the medical files. Our data showed no indication that the IC-guided testing strategy would have a different effect in sex- or age-based subgroups or in high-prevalence areas.A Spanish cross-sectional study showed that offering HIV testing to patients diagnosed with an IC could be cost-effective in primary care since the prevalence of HIV in these patients was higher than 0.1% for most of the suggested ICs . Also, tIn a qualitative study, Dutch GPs suggested that implementing the IC-guided testing might be more beneficial in a higher HIV-prevalence area than in areas with a lower HIV-prevalence . In our Dutch national GP guidelines recommend that persons in high-risk groups should be tested for all five major STIs, namely chlamydia, gonorrhoea, hepatitis B, syphilis and HIV . In our Acute HIV infection is a syndrome that can present as a mononucleosis-like illness, and therefore its detection can lead to the detection of HIV as early as possible. Mononucleosis-like illness includes such symptoms as fever, malaise, skin rash and generalised lymphadenopathy , 32. MonThis study was carried out in a GP network covering a representative sample of the Dutch population. Cases and ICs were searched for in the EMRs with the help of open source information extraction algorithms, which are known to significantly improve case detection when combined with codes .The controls were matched with the cases to limit confounding. The selection of 10 controls per case ensured more statistical power to detect differences in rare events of ICs .Reporting bias was minimised because the EMRs were based on observations that were routinely recorded before the HIV diagnosis was made. Therefore, there is no reason for differences between cases and controls in the accuracy of the information collected.During data extraction we searched for ICs defined as ‘symptoms’ (e.g. fever) in the evaluation line of the consultation form , to avoid the risk of overestimating this IC (e.g. fever often mentioned as complaint). As a result, an underestimation is likely and associations between symptoms and HIV diagnosis may be even stronger.Not all ICs are likely to have been recorded in the EMRs . STIs maIf a person had multiple consultations for an IC, we used only the most recent one before the index date that was recorded by the GP. A limitation is that the information of repeated episodes was disregarded; a patient could have been diagnosed multiple times with the same IC prior to diagnosis, for example, multiple chlamydia diagnoses. We chose for this method because it was difficult to determine from the records whether a condition was diagnosed twice or if the same condition reappeared. This may have led to a slight underestimation of the associations between IC conditions and HIV diagnosis; however, we still found quite strong and significant associations.Potential confounders or effect modifiers, such as ethnicity, sexual preference and socioeconomic status, could not be included in the analyses, because the standard information recorded in patients’ medical files does not include these characteristics . In otheOur study showed that there are opportunities for IC-guided testing in primary care in the Netherlands. Unlike risk-based testing, this provider-initiated strategy is also applicable for all patients.The majority of the ICs in the European guideline are not often seen by GPs, although all ICs are listed as relevant for HIV testing in primary care . We recoThe context of pre-test information and how the HIV test is offered when an IC is diagnosed is important if patients are to agree to be tested , 37. GPs


Textural measures have been widely explored as imaging biomarkers in cancer. However, their robustness under dynamic range and spatial resolution changes in brain 3D magnetic resonance images (MRI) has not been assessed. The aim of this work was to study potential variations of textural measures due to changes in MRI protocols.Twenty patients harboring glioblastoma with pretreatment 3D T1-weighted MRIs were included in the study. Four different spatial resolution combinations and three dynamic ranges were studied for each patient. Sixteen three-dimensional textural heterogeneity measures were computed for each patient and configuration including co-occurrence matrices (CM) features and run-length matrices (RLM) features. The coefficient of variation was used to assess the robustness of the measures in two series of experiments corresponding to (i) changing the dynamic range and (ii) changing the matrix size.No textural measures were robust under dynamic range changes. Entropy was the only textural feature robust under spatial resolution changes .Textural measures of three-dimensional brain tumor images are not robust neither under dynamic range nor under matrix size changes. Standards should be harmonized to use textural features as imaging biomarkers in radiomic-based studies. The implications of this work go beyond the specific tumor type studied here and pose the need for standardization in textural feature calculation of oncological images. Textural analysis refers to a variety of mathematical methods used to quantify the spatial variations in grey levels within an image to derive the so-called ‘textural features’. These techniques have attracted much attention recently because of their potential use as imaging biomarkers , in partMany methods have been proposed to quantify tumor texture and heterogeneity from imaging data. First-order features quantify the grey level distribution accounting for the frequency of appearance of each grey level within the tumor . Second-On the other hand, run-length matrix (RLM) based features quantify the heterogeneity by measuring the distributions and area sizes (groups of connected voxels) within the tumor having similar grey level values, providing information on regional heterogeneity .Texture characterization using CM and RLM-based methods has been extensively used in oncology , 10, 11.The influence of the acquisition protocol on textural measures has been controversial in the literature and may depend on the specific parameter, tumors studied, etc , 16, 17.To our knowledge most previous work has been focused on two-dimensional 2D) descriptors. However, it is already acknowledged that three-dimensional (3D) features are more representative of tumor properties and should be used instead D descrip–20.3D textural features have been less used in brain tumor imaging. Two studies have depicted their discrimination properties among VOIs containing metastases, gliomas and meningiomas . Only a The aim of this study was to analyze the robustness of the most common second order textural features used to characterize brain tumors in three-dimensional (3D) scenarios under changes of spatial resolution and dynamic range.3 .67 patients with biopsy-proven glioblastoma were first considered for the study. The inclusion criterion was the availability of 3D pretreatment T1-weighted images. After that, in order to make sound analyses, we selected the most common image configuration and exclude the remaining patients. So, 20 patients (64.80 ± 9.12 years-old) were finally included in the study, that is, those with matrix of 432x432 pixels and voxel size of 0.5x0.5x1 mmThe study was approved by the Institutional Review Board (IRB) of Hospital Universitario Marqués de Valdecilla. Informed consent was waived by the IRB because we included only patients who had previously provided authorization for use of their medical records for research.All examinations were performed with a three-dimensional (3D) spoiled gradient recalled echo (SPGR). T1-weighted images of the whole brain without magnetization transfer were recorded after intravenous administration of a single-dose of gadobenate dimeglumine  with a 6-min delay.3).Images were obtained using a 3-T magnet machine  with 22 cm field of view. Imaging parameters were: repetition time/echo time (TR/TE) of 20/11 ms; flip angle of 25°; matrix 432x432 pixels, voxel size  and processed using an in-house semi-automatic 3D image segmentation procedure . AfterwaA set of textural quantities derived from co-occurrence (CM) and run-length matrices (RLM) were chosen for this study and computed for tumors after segmentation.i is a neighbor of another voxel with intensity j in 3D. Most common features derived from the CM were the Entropy, Homogeneity, Contrast, Dissimilarity and Uniformity [The CM is a square matrix with dimension equal to the number of grey levels present in the image (dynamic range). It measures the relations between pairs of voxels within an image (8). It is usually characterized by a distance between pixels  and one angle/direction . Changing the distance or the direction leads to different matrices. Many previous studies have constructed different co-occurrence matrices by combining these parameters. However, in oncological imaging a more straightforward workflow is to obtain an averaged CM matrix considering every possible direction, i.e. the relations between each voxel and its 26 neighbors in 3D , 24. In iformity , 25. ForThe RLM matrix constructed in this work follows the same principles used for the CM. Thus, each cell RLM was constructed as the number of runs of length j formed by voxels of intensity in box i considering all the 13 possible 3D directions , 27. It Further details on the measures definition can be found in The construction of the CM and RLM depends on the spatial resolution and dynamic range considered. Two different matrix sizes were used in this work: 432x432 (raw matrix) and 256x256 (standard MRI matrix size). The latter was obtained by interpolation using the Matlab software (v. R2016b) on the raw images. Also, we considered two different resolutions along z-axis: 1 mm (raw) and 2 mm . For these four combinations of spatial resolutions, we discretized the dynamic range in 16, 32 and 64 different grey levels, as these are the most used and recommended dynamic ranges for these measures . This prFinally, a set of 5 CM and 11 RLM heterogeneity textural features were computed for each image configuration using the Matlab software. Once the textural features were computed, robustness was assessed by means of the coefficient of variation (CV) . The CV The mean of the CVs was obtained for all patients included. Textural features with a mean CV smaller than 10% were considered to be robust.We computed the features listed in For each textural feature, patient and spatial resolution, we computed the CV using data for dynamic ranges of 64, 32 and 16 grey levels. This parameter provided a measure of the dependence of the textural features under changes of the grey levels. Then, the mean value and the standard deviation for the set of 20 patients included in the study were computed.The same result was found when patients were considered individually instead of the means. For example, in Next, for each textural feature, patient and dynamic range, we computed the CV for the four spatial resolutions considered. The CM entropy was the outstanding feature and the only robust feature for every dynamic range observed . Only a Textural features dependence of different image reconstruction parameters is shown in The analysis of texture parameters is a useful way of increasing the information obtainable from medical images.This is a promising field of research, with applications ranging from the detection of lesions to differentiation between pathological and healthy tissue in different organs and the segmentation of anatomical structures. Texture analysis uses radiological images obtained in routine diagnostic practice, but involves an ensemble of mathematical computations performed with the data contained within the images. In oncology, there is an increasing evidence that texture analysis has the potential to augment diagnosis and characterization as well as improve tumor staging and therapy response assessment in oncological practice .Different textural features, tumors and imaging modalities have been considered in the literature for cancer detection , 6, respSeveral works have employed CM and RLM based 2D features, using varied distances and directions between pixels, leading to large sets of ‘different’ measures , 29, 30.Previous findings imply, as long textural features should be used in clinical practice, that dynamic range has to be fixed in order to compare different studies. Its optimal threshold depends on the requisites of imaging analysis . A dynamIn any case, the very concept of identification of spatial structures by their closeness in an arbitrary ‘uniform’ grey level scale is one of the major weaknesses of RLM-based features, whatever is the number of grey levels taken.As to the spatial resolution, we used only high resolution brain tumor scans in line with recent recommendations . Thus, iOnly the CM entropy was robust under spatial variations in our analysis. This feature is one of the most relevant textural measures and has been reported to have a predictive value in GBM patients , to distOne would expect properly defined measures to be only weakly dependent–or no dependent at all- on the spatial discretization parameters. At least, they should converge to certain limit values when both the dynamic range and/or the spatial resolutions are sufficiently high. This is not the case for the measures studied here. We choose the RLM and CM measures because of their widespread use in oncological image-based studies either as individual predictors or as ‘imaging genes’ in the context of radiomics.3 [On the basis of our results there is a need for standardization of the spatial resolution if these measures are to be used in clinical practice. Most current images can be registered at a matrix size of 256x256. As to the slice thickness it should be fixed to 1 mm with 0 mm gap. This provides typically voxels of volume around 1 mm3 .This study has some advantages over previous works. First, to the best of our knowledge, robustness analysis under spatial resolution and grey-level changes has not been performed in brain tumor MR imaging. Secondly, our study used standard MR contrast-enhanced T1-weighted sequences (CE-T1WI). Also, we used normalized textural measures definitions .As to the potential limitations of our study, we have to point out the synthetic nature of our methodology: we decided to use one T1 sequence and use the Matlab software to generate three additional sequences from it. Another option could have been to submit patients to four different MRIs with different image parameters. However, that procedure would be susceptible to patient’s movement artifacts, contrast-enhancement decay along time or other external variations. On the other hand, interpolation methods have shown their effectiveness, thus not changing substantially image information and reducing such possible artifacts . Second,In this work, the variations of textural features due to different imaging protocols using postcontrast pretreatment 3D CE-T1WI were assessed. The displayed lack of robustness lies in the nature of the measures considered and not on the specific sequence, however it could be interesting to analyze the variability of the measures using other imaging sequences such as 3D FLAIR.The results found here go beyond of neuro-oncology setting and suggest that textural feature analysis of oncological images should be done carefully if results from different scanners/resolutions/dynamic ranges are to be compared. Also, while there are other types of textural features, such as those of spectral type , 10, mosWe have performed a wide analysis of the robustness of usual 3D textural features regarding different matrix sizes and dynamic range configurations. Results achieved show that 3D textural feature values depend substantially on the dynamic range, thus it is mandatory to fix it in order to obtain reliable and comparable results. After fixing the dynamic range, the CM Entropy was the only robust textural measure under spatial discretization changes. Due to the lack of robustness of the other measures, their use to assess heterogeneity in multi-center studies has to be standardized. As practical recommendations for brain tumor images we recommend using 16 levels of gray as dynamic range and resampling higher resolution images to 256x256 as matrix size corresponding typically to 1 mm voxels.