Prolapse affects 30–40% of women. Those using a pessary for prolapse usually receive care as an outpatient. This trial determined effectiveness and cost-effectiveness of pessary self-management (SM) vs clinic-based care (CBC) in relation to condition-specific quality of life (QoL).https://doi.org/10.1186/ISRCTN62510577.Parallel-group, superiority randomised controlled trial, recruiting from 16 May 2018 to 7 February 2020, with follow-up to 17 September 2021. Women attending pessary clinics, ≥18 years, using a pessary , with pessary retained ≥2 weeks were eligible. Limited manual dexterity; cognitive deficit; pregnancy; or requirement for non-English teaching were exclusions. SM group received a 30-min teaching session; information leaflet; 2-week follow-up call; and telephone support. CBC group received usual routine appointments. The primary clinical outcome was pelvic floor-specific QoL (PFIQ-7), and incremental net monetary benefit for cost-effectiveness, 18 months post-randomisation. Group allocation was by remote web-based application, minimised on age, user type (new/existing) and centre. Participants, intervention deliverers, researchers and the statistician were not blinded. The primary analysis was intention-to-treat based. Trial registration: The requisite 340 women were randomised  across 21 centres. There was not a statistically significant difference between groups in PFIQ-7 at 18 months . SM was less costly than CBC. The incremental net benefit of SM was £564 . A lower percentage of pessary complications was reported in the SM group . There was no meaningful difference in general self-efficacy. Self-managing women were more confident in self-management activities. There were no reported suspected unexpected serious adverse reactions, and 31 unrelated serious adverse events .Pessary self-management is cost-effective, does not improve or worsen QoL compared to CBC, and has a lower complication rate.10.13039/501100000272National Institute for Health and Care Research, Health Technology Assessment Programme (16/82/01). There have been no previous randomised trials evaluating pessary self-management, as confirmed in the 2023 International Consultation on Incontinence evidence review on conservative management of prolapse undertaken by the lead author (search dates: 9th September 2015 to 31st December 2020). Non-randomised studies have indicated self-management is associated with: higher levels of convenience, ability to access help, support and comfort; lower adverse event rates; and greater likelihood of continuing pessary use. These studies suggested that self-management was a viable treatment option, but effectiveness had not been evaluated.This is the only trial to date comparing pessary self-management with clinic-based care for management of prolapse. As such, it provides the first randomised evidence on effectiveness and cost-effectiveness to guide practice and service provision.This trial demonstrated comparable quality of life for women who self-managed and those who received clinic-based pessary care, with fewer complications and less health care resource use and cost. These findings support routinely offering women who can self-manage the option to do so. This is currently not widespread practice and if adopted would potentially decrease health care resource use and reduce unpleasant pessary complications for women.,Pelvic organ prolapse is the descent of one or more of the anterior vaginal wall, posterior vaginal wall, uterus or vaginal vault, with associated symptoms.,Prolapse can be treated conservatively or surgically. Up to 18.5% of women who have prolapse surgery may need a further prolapse repair procedureFor women who use a pessary as treatment for prolapse it is usually fitted at a gynaecological clinic, or occasionally at a GP surgery, and the woman returns approximately six-monthly for follow-up. However, having to return to clinic every six months may be inconvenient for women, and uninterrupted placement of a pessary may increase the risk of complications .We undertook a parallel group multicentre randomised controlled trial, with individual randomisation , which assessed the superiority of pessary self-management compared to clinic-based care for women who used a pessary for prolapse. Full details of the trial protocol have been published previously.Recruitment took place in healthcare settings in the UK National Health Service. Participants were recruited from 21 centres where pessary care was routinely provided. Women who were new pessary users (had used a pessary for 3 months or less) and existing users  were identified by centre staff via patient notes, clinic lists, caseloads and referral letters. Potentially eligible women were approached by their centre, either by letter or in clinic, and their details entered on a screening log. Women were eligible for inclusion if they were 18 years or older, were using a pessary of any material or type  and they had successfully retained the pessary for at least two weeks. Women were excluded if they had limited manual dexterity that would impede their ability to remove and replace their own pessary; were judged by their healthcare team to have a cognitive deficit such that it was not possible for them to give informed consent or to self-manage; were pregnant; or had insufficient understanding of English language (the self-management teaching was only available in English).Allocation was carried out remotely via a web-based computerised system developed and maintained by the Centre for Healthcare Randomised Trials, University of Aberdeen, with minimisation on age (<65/≥65 years), pessary user type (new/existing user) and centre. Centre staff accessed the system to allocate eligible, consenting women to their group. Participants, intervention deliverers, researchers and the statistician were not masked to group allocation.•a 30-min self-management teaching appointment with a trained healthcare professional where they were taught and given the opportunity to remove, clean and reinsert their own pessary;•an information leaflet which provided written and diagrammatic information on pessary self-management;•a 2-week follow-up telephone call to establish whether they had been able to remove, clean and reinsert the pessary since the teaching appointment, and any difficulties they experienced;•a telephone support number for their local clinical centre.Trial centres received a training visit during which the research team explained the principles of self-management and trained the intervention delivery staff  in the components of the intervention.Women in the clinic-based care group received usual care comprising appointments where their pessary was removed, cleaned and re-inserted or renewed by a healthcare professional. The frequency of appointments was determined by the usual practice of the centre, with most centres seeing women every 6 months. Median pessary change interval in the clinic-based care group was 186 days (IQR 165–201) (excluding Covid-19 lockdown periods).Recruitment was complete prior to the start of the Covid-19 pandemic, however the associated lockdown necessitated some adjustments to intervention delivery and data collection methods that are detailed in the Statistical Analysis Plan and the forthcoming final report.Levels of adherence to study protocol in each group were assessed. Women in the self-management group were defined as being ‘on treatment’ if at 18 months they were using a pessary, had received trial-specific self-management teaching and reported inserting their pessary at any follow-up time-point. In the clinic-based care group ‘on treatment’ was defined as using a pessary at 18 months and not having reported inserting their pessary at any follow-up time-point.The primary clinical outcome measure for effectiveness was pelvic floor-specific quality of life at 18 months, measured using the Pelvic Floor Impact Questionnaire-7 (PFIQ-7), a validated, participant-completed measure with three subscales (Urinary Impact Questionnaire (UIQ-7), Colorectal-Anal Impact Questionnaire (CRAIQ-7), Pelvic Organ Prolapse Impact Questionnaire (POPIQ-7)), each ranging from 0 to 100, total score ranging from 0 to 300 .Secondary outcome measures were: the EuroQol EQ-5D-5L questionnaire (range −0.594 to 1) and EQ-5D visual analogue scale (range 0–100), higher scores reflect better generic health-related quality of lifeThe secondary outcomes detailed in the trial registry were updated in the protocol to include newer versions of the EQ-5D and the PISQ, and to add telephone support uptake and vaginal tissue health outcomes.Participants in the clinic-based care group were asked about the occurrence of adverse and serious adverse events at every pessary follow-up appointment, whilst those in the self-management group were asked during the teaching appointment, and advised in the self-management information leaflet to call the telephone support number if they experienced symptoms indicative of an Adverse Event (AE) or Serious Adverse Event (SAE). All participants were asked about pessary complications in questionnaires at each time-point. Six, 12 and 18-month questionnaires asked participants about admissions to hospital, any accidents and new or changed medication regimens. The following events arising from pessary treatment are common and were not considered adverse events: granulation of vaginal tissue, involuntary expulsion of pessary, vaginal smell, vaginal discharge and bleeding during pessary change.A sample size of 330 women (165 per group) was required to provide 90% power to detect a difference of 20 points in the PFIQ−7 score at 18 months after randomisation, assuming a standard deviation of 50, two-sided alpha of 0.05, and 20% loss to follow-up. Although there was no published minimal clinically important difference for the PFIQ-7 in this population, wide consultation with PPI representatives and clinical collaborators led to consensus on a 20-point difference being meaningful. Analysis was carried out in accordance with the Statistical Analysis Plan (SAP).The main analysis was an intention-to-treat (ITT) analysis, i.e., all randomised participants were included and analysed by treatment as allocated. Missing PFIQ-7 responses were assumed to be Missing At Random. Difference between groups was tested by longitudinal analysis of covarianceTwo further pre-specified analyses were conducted for the primary clinical outcome, the first reflecting crossovers to the other trial group using a Complier Average Causal Effect model , and the second including only participants defined as ‘on treatment’ at the 18-month follow-up. In addition, the impact of missing primary clinical outcome data was assessed in two analyses: a complete case analysis, using only cases where 18-month PFIQ-7 was available (Missing Completely At Random); a pattern mixture model, increasing and decreasing the imputed PFIQ-7 values by the minimal clinically important difference of 20 points (Missing Not At Random), for participants in both groups and then in each group separately.Further planned sensitivity analyses of the primary clinical outcome measure were conducted: the inclusion of previous hysterectomy as a covariate; applying a repeated measures model with the baseline value in the outcome vector; and an alternative model to fit the distribution of the primary outcome data . An analysis excluding participants whose care was disrupted by the Covid-19 pandemic was added post hoc.,Pre-specified subgroup analyses of the primary clinical outcome were carried out for age (<65/65+ years), pessary user type (new/existing) and previous hysterectomy (yes/no), as studies have indicated these factors may influence pessary management.A within-trial cost utility analysis was conducted according to a pre-specified Health Economics Analysis Plan. A health sector perspective was taken, with costs attached to resource use for the delivery of the self-management and clinic-based care interventions as well as all health care-related resource use for each participant during the follow-up period. Unit costs from the Personal Social Services Research Unit were attached to each item of resource. Outcomes were measured using the EQ-5D-5L and quality adjusted life years (QALYs) calculated from these data. The EQ-5D-5L utility score values were calculated following the approach recommended by the National Institute for Health and Care Excellence (NICE) using the cross walk from the UK EQ-5D-3L tariff.Analyses were conducted in Stata v16 . An independent Data Monitoring and Ethics Committee reviewed accumulating data and ethical issues. No interim analyses were planned or conducted. An independent Trial Steering Committee had oversight of the trial conduct. The trial was registered with ISRCTN (ISRCTN62510577) on 6 October 2017, prior to any participant recruitment.The trial received ethical approval from the West of Scotland Research Ethics Service, West of Scotland REC 3 (17/WS/0267) on 17th February 2018. Participants were provided with a Participant Information Leaflet and gave written informed consent to take part.The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript.Between 16 May 2018 and 7 February 2020, 2514 women were screened for inclusion in the trial at 21 participating centres  Supplem. The mosThe recruitment target was met, with 340 women randomised (169 to self-management and 171 to clinic-based care) and primary outcome data available for 82.2% and 88.9%, respectively . The maiThere were 291 out of a possible 340 (85.6%) 18-month questionnaires with valid primary clinical outcome data available for analysis: 139 (82.2%) in the self-management group and 152 (88.9%) in the clinic-based care group.The ITT analysis indicated there was not a statistically significant difference (self-management minus clinic-based care) between groups in PFIQ-7 at 18 months (adjusted mean difference −0.03 (−9.32 to 9.25)), or at 6 or 12 months . The conSimilarly, planned sensitivity analyses found no statistically significant group difference in PFIQ-7 at 18 months with the following model adjustments: inclusion of previous hysterectomy as a covariate (−0.03 (95% CI −9.32 to 9.25)); applying the constrained longitudinal model with the baseline value in the outcome vector (95% CI −0.60 (−10.42 to 9.22)); zero inflated Poisson regression model for the 18-month PFIQ-7 (0.01 (95% CI −0.03 to 0.05)). Neither did a post-hoc analysis excluding 26 clinic-based care participants who had appointments cancelled due to the Covid-19 pandemic .Subgroup analysis of the primary outcome showed no significant effect of treatment group by subgroup interactions  .Fig. 3SuParticipants reported in the 18-month questionnaire whether they had experienced each of 15 categories of pessary-related complication, if applicable to them . A lowerAt 18 months there was no significant difference between the groups in PFDI-20, PISQ-IR or General Self-Efficacy but women in the self-management group were more confident in their ability to manage pessary-related problems and to insert and remove their pessary . AnalysiOf participants who answered questions about sexual activity at both baseline and 18 months in the self-management group, 51.1% (72/141) were not sexually active at baseline and of these 9 reported being sexually active at 18-month follow-up. In the clinic-based care group, 51.0% (77/151) reported not being sexually active at baseline and 4 of these women changed to reporting being sexually active at 18 months. At 18 months a higher percentage of the self-management group compared to clinic-based group participants intended to continue pessary use , and found pessary changes convenient , whilst similar percentages found pessary changes to be comfortable  and acceptable . Ulceration, granulation or other clinical concerns about the vaginal tissues  on examination at 18 months were less common in the self-management group . Rates of serious adverse events were similar  and these were all evaluated as unrelated to participation in the trial. There were 323 reports of uptake of other prolapse-related healthcare  from 143 women in the self-management group over the 18-month follow-up (mean 2.3 per participant), compared to 428 reports from 154 women in the clinic-based care group (mean 2.8 per participant).Health care resource use over the 18 months was summarised for each group . ResourcEQ-5D-5L responses were available for 264 participants at the 18-month time-point. No significant differences were found between the self-management and clinic-based care groups at any time-point .Table 6EThe incremental cost and incremental effectiveness (QALYs) of self-management compared to clinic-based care were calculated along with the incremental cost-effectiveness ratio (ICER) and incremental net benefit . The incThere was no evidence that pessary self-management was better or worse than clinic-based care in terms of the primary clinical outcome, women’s pelvic floor-specific quality of life. Self-management was less costly than clinic-based care, and this was driven by less resource use and health-seeking behaviour in the self-management group. Women who self-managed reported less pessary-related complications and greater confidence in managing pessary problems than women who received clinic-based care, and they were more likely to perceive an improvement in their pessary care.Further analyses of the primary clinical outcome under differing assumptions relating to data distribution, non-compliance and missing data all led to the same conclusion as the intention-to-treat analysis, as did an analysis excluding data for women who had clinic-based care disrupted due to COVID-19. Pessary user type, age, hysterectomy and local oestrogen use status were investigated in subgroup analyses of the primary clinical outcome measure, however there were no significant differences in effect for any subgroup.,A key strength of this trial is its uniqueness in the field. There is no other randomised trial comparing self-management to clinic-based care for vaginal pessary.Women who were randomised were slightly younger (64 vs 67 years) and less likely to be an existing user (68% vs 80%) than those who were eligible but did not participate. This may mean the trial findings are less generalisable to older, existing pessary users. Although there were no significant subgroup effects relating to age or pessary user type, the direction of the effect favoured self-management for older and existing users which might suggest a differential effect of self-management on pelvic floor-specific quality of life in these groups. Thirty-nine percent declined randomisation because they had a treatment preference . Low participation rates and strong treatment preferences are seen in other conditions such as cancer.Follow-up response rates were greater than 87% at all time-points. Although the 18-month response rate was higher for the clinic-based care group (88.9% vs 82.2%), it is reassuring that the investigation of data missing not at random concluded that this did not impact the primary outcome analysis conclusions. The core finding of no difference between groups was consistent across time and sensitivity analyses. The sample was drawn from geographically spread and diverse locations across the UK, however there was minimal ethnic diversity.Thirty-four women (20.1%) crossed over from self-management to clinic-based care, including 11 women who had been unable to remove their pessary, and nearly 40% of those randomised to clinic-based care inserted their pessary themselves at least once over the 18-month follow-up. Both actions could potentially dilute the pelvic floor-specific quality of life effect of self-management in an intention-to-treat analysis, consequently leading to a loss of statistical power. However, this was a pragmatic trial, and cross-overs between treatments therefore occurred as they would in routine pessary management, and as such, this is an important part of the assessment of effectiveness. In addition, the on treatment analysis findings agreed with those of the ITT analysis.There have been no previous randomised trials evaluating pessary self-management.https://www.nihr.ac.uk/documents/21583-patient-reported-outcome-measures-proms-for-pelvic-floor-disorders-commissioning-brief/29590) and will be useful in future pessary research. Generic quality of life as measured by the EQ-5D-5L also did not differ between groups, adding further evidence of no quality of life impact, positive or negative, of self-management in this population.One possible explanation for the finding of no difference is that the trial compared two models of delivering the same intervention . Both groups of women used a pessary which is likely to have improved symptoms and quality of life, irrespective of the delivery model. More sensitive measures of quality of life factors that are important to women with pelvic floor dysfunction, other than symptoms, are being developed  and bowel (24.3% vs 36.4%) were more prevalent in the clinic-based care group, as were issues with vaginal tissues on examination at 18-month follow-up (17.8% vs 26.9%). This was unlikely to be due to differences in group characteristics, for example in pessary material or use of vaginal oestrogen, as groups were comparable at baseline. One small observational study of 100 women with prolapse identified self-management as a means of reducing adverse events, with 16% of self-managing women experiencing adverse events compared to 62% of non-self-managing womenFewer complications in the self-management group may be explained by these women having greater confidence in their abilities to remove and insert their pessary, to change it more frequently and use it for less time. One small randomised study identified that the rate of vaginal pessary complications was lower when pessary changes were more frequent, although not statistically so.Previous observational, longitudinal studies have found that 86.1% of women continue pessary use at 5 yearsThe UK Clinical Guideline for best practice in vaginal pessary use for prolapse recommends that women who are assessed to be willing and suitable should be offered self-management.Further research is needed to understand the mechanisms through which self-management reduces complication rates, and whether it leads to a reduction in pessary discontinuation. Research on models of pessary care in ethnic minority populations is also needed. A validated pessary complications questionnaire needs to be developed to enhance the rigor and consistency of future trials of pessaries. Future research is needed to focus on models of pessary self-management follow-up, for example, should a pessary review be woman-initiated or does it require to be planned at specific intervals? A 4-year follow-up is underway in this trial, and an implementation study planned, which will contribute data to address some of these gaps.All authors were involved in reading, commenting upon, and taking the decision to submit the manuscript for publication. All authors had access to the data analysis output and SH, CB and AE directly accessed and verified the underlying data reported in the manuscript. Additional contributions were as follows:SH: Had responsibility overall for the manuscript, and for delivery of the trial. She was co-Chief investigator and was involved in early conception of the trial and made intellectual input to the study design.RK: Had responsibility overall for the clinical aspects of the trial delivery. She was co-Chief investigator and was involved in early conception of the trial and made intellectual input to the study design.KG: Was Trial Manager, responsible for the day-to-day operationalisation and management of the trial.CB: Was responsible for preparing the statistical analysis plan, delivery and reporting of the statistical analysis.AE: Was responsible for supervising the conduct of the trial data analysis.LM: Was responsible for trial data management.LD: Was involved in early conception of the trial, contributed to developing the trial interventions, delivered training and on-going clinical support to clinical staff at recruiting centres.MD: Was responsible for delivery and reporting of the process evaluation component of the trial.MG: Provided advice from the perspective of a service user, preparing the protocol and contributing to the Project Management Group.WA: Contributed expertise in clinical trials and pelvic floor dysfunction and was a local Principal Investigator.SB: Provided senior trial management expertise and support for the trial.JC: Provided advice from the perspective of a service user, preparing the protocol and contributing to the Project Management Group.AF: Provided advice from the perspective of a service user, preparing the protocol and contributing to the Project Management Group.MF: Developed and provided on-going support for the trial database.KG: Contributed expertise in clinical trials and pelvic floor dysfunction and was a local Principal Investigator.CH: Contributed expertise in clinical trials and pelvic floor dysfunction and was a local Principal Investigator.AK: Contributed expertise in clinical trials and pelvic floor dysfunction and was a local Principal Investigator.SM: Conducted the health economics analysis.HM: Was responsible for delivery, supervision and reporting of the health economics analysis.DM: Was involved in early conception of the trial, made intellectual input to the study design.JN: Contributed expertise in complex intervention trials methodology.RT: Contributed expertise in clinical trials and pelvic floor dysfunction and was a local Principal Investigator.CBu: Was Chief Investigator for the entire research, led the conceptualisation of the trial and parallel process evaluation, and was responsible for delivery and reporting of the qualitative components of the study.Individual participant data collected for this trial, and a data dictionary defining each field in the dataset, will be made available to others; all available data will be de-identified participant data. To access data, a request should be submitted to the corresponding author with a scientific proposal including objectives. The Chief Investigator, in collaboration with the sponsor, will assess all requests for data sharing. Consent from participants has been obtained to share their anonymised data with other researchers to support future research.Lucy Dwyer reports being a member of the NICE guideline committee for non-surgical management and prevention of pelvic floor dysfunction and a member of the UK Clinical Guideline for best practice in the use of vaginal pessaries for pelvic organ prolapse committee; Aethele Khunda reports receiving an educational grant from Olympus Medical Systems to cover travel and accommodation costs to attend a laparoscopic urogynaecology workshop; Wael Agur reports grants from NIHR, consulting fees and payment for testimony from Oaklaw Consultancy Ltd., for Medico-legal Consultancy and financial/non-financial interest associated with Medical Innovation Systems; Karen Guerrero reports payment for expert testimony for the Medical Advisor NHS Scotland Central Legal Office and for NHS-funded study leave; Christine Hemming reports grants from NIHR HTA for two RCTs (11/129/183 and 07/60/18); John Norrie reports being a member of the following committees: NIHR CTU Standing Advisory Committee (2018-23), NIHR HTA & EME Editorial Board (2015–2019), EME Funding Committee Member (2019–2022), HTA General Committee (2016–2019), HTA Post-Funding Committee (2016–2019), HTA Funding Committee Policy Group (2016–2019), COVID-19 Reviewing (2020); Ranee Thakar reports a voluntary role at Royal College of Obstetricians and Gynaecologists; Rohna Kearney reports being topic lead for prolapse on NICE guideline NG123 published 2019; Angela Forrest reports payment for attending project meetings relating to the research; Suzanne Breeman reports grants from NIHR HTA for the VUE trial (11/129/183) and the PROSPECT trial (07/60/18 and NIHR133665). Suzanne Hagen, Carol Bugge, Andrew Elders, Helen Mason, Kirsteen Goodman, Doreen McClurg, Melanie Dembinsky, Lynn Melone, Catherine Best, Sarkis Manoukian, Margaret Graham, Jane Culverhouse and Mark Forrest have no additional disclosures to report relating to the manuscript.All authors declare a grant from National Institute for Health and Care Research (NIHR) Health Technology Assessment (HTA) programme relating to the research described in the manuscript. In addition


Classifications of single neurons at brain-wide scale is a powerful way to characterize the structural and functional organization of a brain. We acquired and standardized a large morphology database of 20,158 mouse neurons, and generated a whole-brain scale potential connectivity map of single neurons based on their dendritic and axonal arbors. With such an anatomy-morphology-connectivity mapping, we defined neuron connectivity types and subtypes  for neurons in 31 brain regions. We found that neuronal subtypes defined by connectivity in the same regions may share statistically higher correlation in their dendritic and axonal features than neurons having contrary connectivity patterns. Subtypes defined by connectivity show distinct separation with each other, which cannot be recapitulated by morphology features, population projections, transcriptomic, and electrophysiological data produced to date. Within this paradigm, we were able to characterize the diversity in secondary motor cortical neurons, and subtype connectivity patterns in thalamocortical pathways. Our finding underscores the importance of connectivity in characterizing the modularity of brain anatomy, as well as the cell types and their subtypes. These results highlight that c-types supplement conventionally recognized transcriptional cell types (t-types), electrophysiological cell types (e-types), and morphological cell types (m-types) as an important determinant of cell classes and their identities. A mammalian brain is a complex network of tens of millions or more neurons and supporting cells that work together to carry out its functions . These nLarge-scale data acquisition and analyses at single-neuron resolution have succeeded in identifying neurons based on their 3-D registered soma-locations to the standard brain atlases. For mouse brains, the Allen Common Coordinate Framework  and imagThe description of the morphology of neurons has been a crucial force to advance neuroscience since the time of Cajal . While hDrosophila hemibrain that also have a limited use of the cell connectivity to define cell types  neurons were smoothly clustered into four connection groups , and ventral posterolateral nucleus (VPL) neurons that have heavily overlapping m-features, as seen in both the overlap scores and the feature scatter plot  top. HowTo investigate whether c-features would classify cell types better than conventionally used m-features  . Except n>60) . Remarkan>60) . In anotWe further directly compared corresponding m-scores and c-scores to quantify the improvement of cell typing performance of connectivity features over morphological features. Here, 76% of entries in the ratio matrix of c-scores and m-scores , and 3E After establishing that connectivity is a powerful attribute for classifying neurons types, we investigated whether c-features would also help identify sub-types of neurons that share their soma locations in the same anatomical area. To do so, we generated a distance map (d-map) to measure the spatial separation of two neurons based on their soma locations . BecauseIn the example of CP neurons, we calculated the pairwise m-score and c-score matrices  sorted iWe computed the corresponding d-maps, m-score, and c-score matrices for all 31 s-types of neurons. Overall, we found that for any pair of neurons, their c-scores are only slightly greater than the m-scores . There iAnatomical sub-grouping of neurons within a specific brain region reflects the spatial coherence of these cells. As c-features correlate more strongly with the spatial adjacency of neurons, for each s-type we combined connectivity profiles and spatial adjacency to cluster neurons and identify potential anatomical subtypes. We called this approach Spatially-Tuned c-Features, with which we produced clear subtyping of neurons  that we In particular, for cortical neurons ~F, we foWe also attempted to identify subregions in the thalamic gateway related to sensory and motor input, particularly VPL , VPM Fi, and LGdMOs neurons have long axonal projections that subserve animal decisions . In addiWe also examined both the axonal and dendritic morphologies of MOs5 subtypes. While the most dendritic features of the two genuine subtypes, MOs5_2 and MOs5_3, are similar to each other, their axonal features  are cleaWe also produced UMAP analyses to compare the transcriptomic subtypes of single MOs neurons , connectMoreover, we performed a joint analysis of the m-type, c-type, t-type and e-type data based on retrieving the publicly available electrophysiological and transcriptomic recordings of single neurons that also fall into the brain regions used in this study. For the primary visual area (VISp), we analyzed 48 fully reconstructed neuron morphologies and their regional connectivity patterns , along wBy subtyping single cell reconstructions of 390 VPM and 83 VPL neurons, we were able to document the broad regional connections of VP neurons in a comprehensive manner. First, we clustered individual neurons’ detailed projections onto cortical areas and layers into 8 subtypes as a matrix . These 8ch as CP .Furthermore, we found that a single VP neuron could target multiple sensory areas. For example, a VPM neuron can simultaneously projects to SSs and sub-areas of SSp, such as SSp-m, SSp-n, SSp-ll or SSp-ul  and 7E. 2) is twice larger than that of a barrel  receives thalamocortical projection from the sensory-motor relay nuclei VAL and the modulatory or high order nuclei like VM or PO . The goal of this study, however, is to factorize the analysis in an understandable way. In this sense, our paradigm can contribute to a more organized and clear communication in this field.We performed a detailed analysis of 1741 fully reconstructed single neuron morphologies  called BWe generated 10860 dendrite reconstructions from fMOST imaging with the following protocol. First, we collected image samples following the same protocol in our previous study on generating the full reconstructions . Next, whttp://neuromorpho.org/KeywordSearch.jsp). When possible, we only kept the neuron reconstructions tagged as complete or, in case those were not available, moderately complete. We searched neurons in 4 different brain regions   at 25μm  (CCFv3) ). We sel (CCFv3) . BIC is We defined 3-D dendritic domains by using the pooled, clustered SWC coordinate dataset. We found the minimal volume enclosing all nodes belonging to each single cluster by obtaining the 3D n 1.3.1) . The 3D gulation  with a pThe definition of dendritic domains based on full tracings was obtained both for raw data distributed in both brain hemispheres and for flipped neurons, ensuring that all of them had somas in the same hemisphere. To further analyze connectivity, in that case, dendritic domains were flipped to also recapitulate homologous contra-lateral regions.In addition to the arbor domains obtained from fully traced neurons, we also generated single dendritic domains from using all node coordinates for dendritic tracings with somas inside each of the 19 brain regions with most neurons. 3D To define outgoing connections from single fully traced neurons to dendritic arbor domains, we measured the spatial overlap between single neurons and arbor domains. To do so, we obtained all voxels enclosed by each domain 3D ion 1.35 . We thenTo assess the relevance of arbor domain connectivity for defining cell types and subtypes, we used a Support Vector Machine  to classify neurons with somas located in MOp, SUB and VPL regions . For eacThe m/c-score can be used to quantify the dissimilarity of morphological  in Seaborn Python package (version 0.11.2). We used umap-learn Python package (version 0.5.1) to implement UMAP decomposition with default parameters and plotted results as scatterplots with Matplotlib (version 3.3.4).The distance metric follows the Mahalanobis definition . Let si=Distance-weighted connectivity-based clustering was used to cluster s-type cells based on both their connectivity feature similarity and physical distance of somas. Two matrices were generated to represent these components: a connectivity similarity matrix (c-similarity matrix denoted by Harabasz  score (mWe used a transcriptomic dataset of 34,331 neurons in MOs and FRP brain regions, which is collected from a newly released dataset . The anaFor electrophysiological modality, we selected 919 cells in VISp layers from a Path-seq dataset . The selSupplement 1


Prolonged physical inactivity with bed rest or spaceflight is associated with cardiovascular and neuromuscular deconditioning; however, its impact on neural integrity of cardio-postural reflexes and possible mitigation with exercise has not been examined. We assessed the association between the physiological deconditioning of bed rest immobilization with neural injury markers and the effects of 60–75 min of daily exercise.clinicaltrials.gov identifier: NCT04964999) at the McGill University Medical Centre. Twenty-two 55- to 65-year-old healthy volunteers gave informed consent and took part. Within sex, participants were randomly assigned to exercise (60- to 75-min daily) or control (inactive) groups and spent 14 days in continuous 6° head-down tilt. Neural injury , as well as interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α), and insulin-like growth factor 1 (IGF-1) biomarkers were measured before, during, and after bed rest. The false discovery rate with Huber M-estimation was used to correlate changes in biomarkers with cardiovascular and muscular function changes over bed rest.Data were collected as part of a randomized clinical trial (Bed rest elevated NfL, GFAP, TNF-α, and IL-6 in all participants and reduced IGF-1 in females only. With standing, changes in heart rate, blood pressure, and lower limb muscle motoneuron activity correlated with changes in TNF-α and BDNF. Baroreflex control, leg muscle maximal voluntary contraction, and postural sway are correlated with GFAP and NfL. Exercise participants had fewer interactions than control participants, but significant correlations still existed, with both groups exhibiting similar reductions in orthostatic tolerance.An hour of daily exercise in older persons otherwise immobilized for 2 weeks did not abate bed rest-induced increases in serum signatures of neural injury or pro-inflammatory markers. Exercise reduced the number of physiological interactions of biomarkers, but significant cardio-postural correlations remained with no protection against post-bed rest orthostatic intolerance. The identification of associations of inflammatory and neural injury biomarkers with changes in cardio-postural physiology and exercise points to biotherapeutic opportunities and improved exercise interventions for astronauts and individuals in bed rest.https://www.clinicaltrials.gov/search?cond=NCT04964999, identifier: NCT04964999. Spaceflight produces observable physiological changes in humans  and activates neurodegenerative processes. Since exercise training has been shown to improve myelin outcomes, including related elevations in spinal cord IGF-1 (insulin-like growth factor 1) in adult mice , and simulates the fluid shift seen in spaceflight. Our team was one of eight chosen to participate in this first-of-its-kind project that combined older adults in a space analog with a space-based exercise intervention , the Canadian Institutes of Health Research (CIHR), and the Canadian Frailty Network (CFN) as part of the Canadian Aging and Inactivity Study (CAIS) involving older adults placed in 14 days of 6° head-down tilt bed rest (HDBR). HDBR is often used as an analog of spaceflight because it induces inactivity, unloads the musculoskeletal system in the head-to-feet axis , the Canadian Frailty Network (CFN), and the Canadian Space Agency (CSA). The overall aim was to investigate the effects of bed rest inactivity/immobilization on older persons (55–65 years old), with specific emphasis on exercise interventions and applications to spaceflight. The Center for Innovative Medicine (CIM) of the McGill University Health Centre Research Institute (RI-MUHC) conducted the trial, which comprised four 26-day bed rest campaigns during which 5–6 participants per campaign were placed in HDBR as a ground-based analog to spaceflight.Recruitment provided a balance between biological sex across test groups, with half of the participants receiving bed rest-specific exercise counter measures (CMs) . Specifically, we quantified changes in the well-studied marker of astrocyte reactivity, glial fibrillary acidic protein (GFAP)  HD-kit analysis was performed for NfL, GFAP, UCH-L1, IL-6, TNF-α, and t-Tau. IGF-1, myelin basic protein (MBP), and BDNF were analyzed from aliquots of the same serum samples with multiplex Luminex ELISA (Thermo Fisher Scientific) in the Brockman and Brumme Laboratory at Simon Fraser University.Standard venipuncture from the antecubital vein of the arm was performed during baseline data collection 2 days before bed rest (BDC-2), the ninth day of bed rest (HDBR9), and the day they came out of bed rest (R+0) . One allThe cardio-postural control system was activated and evaluated using a supine-to-stand (StS) test  was used. The device was used to examine both isometric and isotonic maximum voluntary contractions (MVC). Isometric contractions involve holding a muscle contraction without joint movement, while isotonic contractions involve moving a joint through a specific range of motion while maintaining a constant force output. Simultaneous EMGs from the S, MG, and LG were collected using the same procedure described for supine-to-stand. During the isometric test, the participants sat and pushed against the device using their ankle muscles , aiming to produce the strongest possible isometric contractions. The dynamometer was attached to the feet to measure the strength and power of the ankle plantar flexor muscles. During the isotonic assessments, each participant moved their ankle joint back and forth while applying force against the device, which generated a resistance load. The test consisted of three repetitions of either isometric or isotonic movements, and the MVC for each muscle was determined based on the highest muscle contraction recorded across all three tests. Throughout the examination, participants received both verbal and visual feedback. Prior to the test, the subjects received instruction on appropriate exercise techniques and equipment orientation.The soleus, medial gastrocnemius, and lateral gastrocnemius muscles make up most of the plantar flexor muscles of the ankle joint .post-hoc tests were performed using Tukey's HSD. If the data were determined to be not normally distributed, a Friedman test was used.Data are presented as mean (95% lower and upper confidence limits). Data within groups  were tested for normality using the Shapiro–Wilk goodness-of-fit test. If the variables were normally distributed, a repeated measures ANOVA was used to assess time-dependent changes of the biomarkers as a function of sex  and intervention . If the RM-ANOVA revealed significant differences, To investigate the possible relationship between changes in participant physiological characteristics and the serum biomarkers, data were converted from absolute pre- and post-values to the change (Δ) from pre- to post-HDBR. Negative values represented a decrease in biomarker or physiological value, and positive values represented an increase after bed rest.Given the small number of participants in the subject groups and the possible non-linear relationship between biomarkers and physiological measurements, Spearman's rank correlation was used to assess the strength of correlations between changes in biomarker concentration and changes in physical assessments made by the participants before and after bed rest. Data are presented using the correlation coefficient (ρ).With multiple comparisons, the false discovery rate (FDR) . We also performed discriminant analysis on our data to investigate whether biomarkers and physiological variables could be used to differentiate between the categorical groups of sex  and intervention , as well as the four subgroups . A stepwise procedure was used to enter and exit variables until there was no overlap between the groups.−1·kg−1). Two participants withdrew from the study on R+2 for medical reasons following the completion of the post-bed rest physical measurements; however, three participants  were missing muscle and/or baroreflex data because of poor signal quality. Therefore, correlational analysis of the biomarkers with physical measurements contains the results from 19 participants.One participant dropped out of the study on day 3 of HDBR, leaving 22 participants equally divided between exercise  and control . Control and exercise participants were not significantly different in aerobic fitness as measured by p = 0.854) with an overall value of 135  g/L. Male participants had consistently higher hemoglobin values than female participants .Aliquots for serum analysis were taken from the same sample used for the blood biomarkers. Hemoglobin was used as a systemic control protein for all analyses using an identical statistical approach. No significant change in hemoglobin was found over the time course of BDC-2 through R+0 (p ≥ 0.05) but UCH-L1 (p = 0.027) were found to be normally distributed (Shapiro–Wilk) within sex and intervention. NfL increased from the beginning to the end of bed rest (p < 0.0001), mostly by day 9 (p = 0.34) or intervention (p = 0.80) effects. GFAP increased from the beginning to the end of bed rest (p < 0.0001) (p = 0.53) or intervention (p = 0.56) effects. Bed rest elevated TNF-α (p = 0.006) (p = 0.006). IL-6 increased significantly from the start of bed rest to HDBR9, when it plateaued (p = 0.0006) (p = 0.97) or intervention (p = 0.90). No group effects were observed for t-Tau  (p = 0.018)  with intervention  or by sex . MBP was not detected with the assay used .We analyzed sera to investigate whether bed rest, intervention, or sex significantly altered the concentrations of our selected biomarkers. All  ; Table 1= 0.006) ; Table 1 = 0.41] ; Table 1= 0.018) . No consWhen all participants were grouped together, several measured variables were found to be significantly altered from baseline to the end of bed rest . SimilarHere we report for the first time, in older persons, the effects of HDBR on changes in postural sway, maximal voluntary contraction of calf muscles, calf muscle EMG during maximal isometric and isotonic contraction, and the maximal jump test . There wWhen compared across sex , left orWhen comparing across sex, there were significant declines in more muscle-pump indices for the male participants  compared to the females (FTA). In female participants, there was a decline in total EMG output, while in males, the decline was observed with EMGimp. Control participants had reduced muscle-pump causality, while the exercise group had reduced FTA. There was an observed decrease in both total muscle EMG and EMGimp in the control group, with the exercise group only showing a decrease in EMGimp. Finally, only the exercise group had changes in EMG output during isotonic contractions or muscle MVC. The exercise group had lower MVC in the medial gastrocnemius and lower EMG during isotonic contractions .We used Spearman's rank correlation analysis to determine possible relationships between an individual's change in a biomarker and changes in their main cardiovascular and muscular system physiological measurements  for all p ≤ 0.05) revealed some trends in biomarker distribution between the cardiovascular and muscular systems across all participants  control. UCH-L1 was also significantly implicated in motoneuron activation during isotonic contraction. More interactions were seen for MVC of the soleus muscle than any other measure, with positive relationships with both pro-inflammatory  and neural injury  markers.Although most interactions were positively correlated (Spearman's), negative relationships were observed for TNF-α with systolic blood pressure and NfL with the causality of muscle contractions on blood pressure. Changes in the jump test results  were negatively associated with IL-6.p ≤ 0.05)  and intervention  using FDR with M-Huber estimation ( ≤ 0.05) .In females, with respect to the cardiovascular system, standing heart rate was positively correlated with t-Tau, and vascular changes were positively correlated with IGF-1 and BDNF. Changes in baroreflex function were negatively related to changes in UCH-L1 and positively associated with IL-6 and GFAP. An increase in NfL was correlated with a decrease in muscle-pump coupling and changes in blood pressure. In relation to muscle contraction changes, GFAP was positively associated with MVC of the soleus and EMG generation during maximal isotonic contraction, while t-Tau, TNF-α, and IGF-1 were inversely associated with MVC of the medial gastrocnemius. Total tau was also directly correlated with sway velocity (COPrv).In the male cardiovascular system, TNF-α was positively correlated with changes in HR, while IGF-1 was positively correlated and IL-6 was negatively correlated with blood pressure. Muscle activity was also related to biomarker changes. Total tau, BDNF, and GFAP changed in the opposite direction from changes in MVC. In addition, t-Tau was negatively correlated with EMG with maximal isometric contraction, and TNF-α changed directly with changes in EMG from maximal isotonic contractions. The metrics from the jump test were predominantly associated with the pro-inflammatory proteins IFG-1 and IL-6. Decreases in peak velocity, flight time, and flight height were correlated with biomarker increases. Changes in peak power were positively associated with t-Tau. Finally, increases in sway parameters were correlated with neurodegenerative markers. COPr increased with t-Tau, and COPrv increased with UCH-L1.In the control group, 11 physiological measures were significantly correlated with one of the seven biomarkers, whereas in the exercise group, only five physiological measures were associated with three of the biomarkers. TNF-α was positively correlated with HR and COPr and negatively correlated with cardiac baroreflex gain. Inflammatory proteins, IGF-1 and IL-6, were positively correlated with HR and muscle-pump baroreflex FTA . SimilarDiscriminant analysis for sex , using tWhen applied to the four distinct groups , a combiBased on inclusion into the multiple regression model for cardiac baroreflex, IGF-1 had a negative association, and GFAP had a positive association . We alsoIn this first-of-its-kind study at the intersection of spaceflight with aging and inactivity, we show that in older persons, (i) 14 days of simulated spaceflight using 6° head-down tilt bed rest promotes signs of neurodegenerative processes independent of 60- to 75-min daily rigorous exercise, and (ii) many of these biomarkers correlate with observed changes in cardiovascular and muscular function.The global increase in neural injury markers and inflammatory cytokines in our participants is a powerful indicator of the severity of imposed inactivity in the 6° head-down position. This environment not only results in the physical deconditioning of these participants   in a serum marker? With intra-individual variability associated with differences in participant physiology, it was expected that responses to bed rest may not have been similar in magnitude or direction and that this would mask biomarker–physiological function relationships when investigated as group effects. However, if the underlying physiological processes were similar, then any changes observed in physiological function would be reflected in consistent changes in their associated biomarkers and could be tested with correlation analyses.Spearman's ranked test was first applied to find the directionality and strength of correlation. Given the large number of multiple comparisons, false discovery rate analysis (FDR) was used. The robust Huber M-estimation was used due to non-normally distributed data and outlier effects. Because of the complexity of conducting a bed rest project involving older people, only a small number of participants in each subgroup was feasible. Therefore, we have limited in-depth discussion of the effects associated with bed rest, with links to possible sex or intervention differences. Finally, Spearman's rank test, FDR, discriminant, and multiple regression analyses are inferential, not causal, and are used to identify areas of interest for future targeted research.Two serum markers, namely TNF-α and BDNF, were associated with the cardiovascular system when all participants were grouped together. Changes in TNF-α were correlated with elevated cardiovascular stress during standing, while BDNF may have had a repairing role in the presence of cardiovascular stressors.TNF-α is secreted by several cardiovascular cells  and has wide-ranging effects on these systems through cardiac muscle function and vasodilation  and peripheral nervous system (PNS), and its production is triggered by neuronal damage or dysfunction  and blood following CNS injuries or neurodegenerative conditions  . BDNF was significantly correlated with EMG during standing and soleus MVC. paired with in relation to We also observed significant decreases in these same physical and physiological responses with bed rest. This may indicate a strong influence of inflammation (TNF-α) and the recruitment of neural restorative measures (BDNF), as well as MVC of the soleus.The neural injury marker, t-Tau, correlated negatively with the MVC of the medial gastrocnemius. Tau is a protein that stabilizes the structure of neurons in the brain. Total tau is a biomarker that measures the total amount of tau protein in the blood or cerebrospinal fluid. Although there is limited prior research on how t-Tau levels may change after bed rest, studies have shown that t-Tau increases with age and may be a marker of age-related cognitive decline and neurodegeneration  is a protein that is primarily expressed in neurons and plays an important role in development. UCH-L1 has been identified as a potential biomarker for traumatic brain injury and other neurological conditions  were in each category. This limited our ability to investigate the sex/intervention interactions for the biomarkers from the ANOVA analysis; however, we have presented the means (95% CI) as pilot data for future comparisons.For the first time, we had the opportunity, as part of a dedicated head-down tilt bed rest study with older persons, to investigate cardio-postural control and biomarkers of neural injury in males and females with an exercise intervention. This provided unique data in relation to post-HDBR and possible insights into spaceflight neural deconditioning. We examined the correlation between the changes in these biomarkers with a robust false discovery rate analysis correcting for multiple comparisons. This revealed relationships between neurodegenerative biomarkers and blood pressure and posture control, providing evidence in support of cardio-postural impairment through HDBR-associated neural injury. The association of neural injury , growth factors (IGF-1), and pro-inflammatory (TNF-α) markers was also supported through multiple regression analysis. Both correlational and multiple regressions do not indicate causal relationships and are only used here to provide support to hypothesize and form the base for future studies.As with the analysis of biomarkers by ANOVA, we were limited in our ability to test relationships between biomarkers and physiological measurements at the sex/intervention level. However, the use of discriminant analysis, which used the full data set, provided important information regarding the influence of serum markers and physiological outcomes in relation to both sex and exercise and their respective combinations.This study was one of eight research groups embedded in the HDBR project. Our hypotheses focused on cardio-postural control declines with bed rest, making many measurements beyond the scope of our study. For example, we were unable to relate the changes observed with the neurodegenerative biomarkers to associated cognitive and psychological outcomes. Furthermore, the multi-modal exercise intervention was designed by an external expert committee  of HDBR in older persons can elevate major protein markers of neural injury, which were correlated with changes in the cardiovascular and muscular systems, and in particular those systems involving the baroreflex and postural sway. Additionally, discriminant and multiple regression analyses point to neurodegenerative components associated with baroreflex impairment following HDBR that exercise was unable to halt. This raises significant concerns regarding post-bed rest recovery and its long-lasting effects, particularly in the elderly. Finally, these results highlight the need for neurological monitoring of astronauts as space missions become longer.andrew_blaber@sfu.ca.The datasets presented in this article are not readily available because data may only be shared for the use under which it was ethically approved. Requests to access the datasets should be directed to The studies involving humans were approved by McGill University Health Centre Research Ethics Board, Office of Research Ethics at Simon Fraser University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.AB had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. AB and IS contributed to the concept and design and after obtaining funding, AB coordinated and supervised data acquisition and statistical analysis. FS and DN conducted all sit-to-stand tests and collected and analyzed muscle activity, jump test, and baroreflex function data. All contributed to interpreting the data and drafting the manuscript. All authors contributed to the article and approved the submitted version.


COVID-19 affects brain function, as deduced by the “brain fog” that is often encountered in COVID-19 patients and some cognitive impairment that is observed in many a patient in the post-COVID-19 period. Approximately one-third of patients, even when they have recovered from the acute somatic disease, continue to show posttraumatic stress disorder (PTSD) symptoms. We hypothesized that the persistent changes induced by COVID-19 on brain structure would overlap with those associated with PTSD. We performed a thorough PubMed search on 25 April 2023 using the following strategy: ((posttraumatic OR PTSD) AND COVID-19 AND ) OR (COVID-19 AND brain AND (voxel[ti] OR VBM[ti] OR magnetic[ti] OR resonance[ti] OR imaging[ti] OR neuroimaging[ti] OR neuroimage[ti] OR positron[ti] OR photon*[ti] OR PET[ti] OR SPET[ti] OR SPECT[ti] OR spectroscop*[ti] OR MRS[ti])), which produced 486 records and two additional records from other sources, of which 36 were found to be eligible. Alterations were identified and described and plotted against the ordinary PTSD imaging findings. Common elements were hypometabolism in the insula and caudate nucleus, reduced hippocampal volumes, and subarachnoid hemorrhages, while white matter hyperintensities were widespread in both PTSD and post-COVID-19 brain infection. The comparison partly supported our initial hypothesis. These data may contribute to further investigation of the effects of long COVID on brain structure and function. The Coronavirus Disease-2019 (COVID-19) pandemic, first declared on 11 March 2020  to 45% , but figMultiple factors have concurred with the determination of the traumatic impact of contracting COVID-19 , and socOther nuclei that have been involved with the development of PTSD are the amygdala, hippocampus, rostral anterior cingulate cortex (ACC), and ventromedial prefrontal cortex (PFC). Results generally indicate that smaller hippocampal volumes are associated with PTSD , but it We especially focused on the neuroimaging findings of patients with neurological symptoms of any origin. Neurological symptoms may not be the result of a viral invasion of the brain or viral replication in the brain , but theHaving hypothesized that the brain alterations that are associated with PTSD would overlap with COVID-19–induced brain alterations, we were prompted to seek literature reporting on the neuroimaging findings of patients who had recently suffered from COVID-19. We here systematically review this evidence.post-mortem studies ; studies containing no neuroimaging data; and reviews, but the latter were downloaded and hand-searched for possible additional eligible references that could have eluded our search.To review systematically the neuroimaging findings of the brain alterations in patients who either currently or previously had COVID-19, we carried out a PubMed search using the following search strategy: ((posttraumatic OR PTSD) AND COVID-19 AND ) OR (COVID-19 AND brain AND (voxel[ti] OR VBM[ti] OR magnetic[ti] OR resonance[ti] OR imaging[ti] OR neuroimaging[ti] OR neuroimage[ti] OR positron[ti] OR photon*[ti] OR PET[ti] OR SPET[ti] OR SPECT[ti] OR spectroscop*[ti] OR MRS[ti])). The choice of the above search strategy was based on the need to be omnicomprehensive; all authors added their expertise to refine the search. Inclusion criteria were having performed structural neuroimaging and reporting data. MRI (either voxel-based morphometry (VBM) or regions-of-interest (ROI) approaches), positron emission tomography (PET), single-photon emission computerized tomography (SPECT), and computerized tomography (CT) were considered appropriate. Functional MRI (fMRI) or FC studies were considered when the aforementioned techniques were involved, but they were excluded if they included only examination. Excluded were also case reports or case series; opinion papers, such as editorials or letters to the editor; comments on other articles; duplicates; article corrections referring to an article that was already present in the search; articles unrelated to what we were searching for; unfocused or inadequate designs (inadequate for our purposes); animal or in vitro studies; papers not reporting on COVID-19 patients; articles of side effects of COVID-19 vaccination (labeled as unfocused); protocols, which usually do not report data but pave the way for future studies ; exclusively Post-mortem.After completing the search, we labeled all resulting records according to whether they were to be included or excluded . The priThree authors independently conducted the agreed search and compared their results. Eligibility was based on being an original study on patients who had contracted COVID-19 or who had a current COVID-19 infection, including humans, that provided data on patients’ neuroimaging. To establish eligibility for each study, all authors ran Delphi rounds until a full consensus was reached. All studies were downloaded with the exception of manifestly unrelated studies. Thereafter, all titles were introduced into our database where their characteristics were defined.To assess the quality of eligible studies, we used the appraisal tool for cross-sectional studies (AXIS)  on each There were no ethical concerns to address or ethical committee approval to obtain. The authors adhered to the principles of the WMA Helsinki Declaration of Human Rights and its subsequent amendments in their handling of information related to the patients involved in the eligible studies.18F-fluorodeoxyglucose-PET/computerized tomography (8 studies), hemorrhages  (7 studies), hypoxic changes (7 studies), and supratentorial, middle cerebellar peduncular subcortical, periventricular, and deep white matter lesions (7 studies). Details are shown in On 29 April 2023, our above PubMed search yielded 486 articles; 2 more were obtained from the references of the obtained literature. Eligible were 36 studies ; Table 1Of the 36 eligible studies, the majority (N = 23) were single-site, while 11 were multicenter ; one used BioBank data across the entire United Kingdom (UK) and another was an international call on behalf of the American Society of Pediatric Neuroradiology (ASPN), which recruited from 10 countries. As is currently fashionable, no multicenter study reported on intersite differences. The majority of studies were conducted in the United States (USA) , 7 were conducted in France, 2 were conducted in Italy (plus 1 in the aforementioned 5-center/4-country study), 2 were conducted in Sweden, 2 were conducted in Brazil (plus 1 in the 5-center/4-country study), 2 were conducted in the UK, 2 were conducted in Turkey, 2 were conducted in China, 1 was conducted in Spain (plus 1 in the 5-center/4-country study), 1 was conducted in the Netherlands, 1 was conducted in Switzerland, and the international call of the ASPN resulted in recruitment from France, the UK, the USA, Brazil, Argentina, India, Peru, and Saudi Arabia. Location did not affect study quality  scan, identifying patients with putative abnormalities, and progressing thereafter to more sophisticated and in-depth investigation methods like MRI and PET. MRI used more commonly 3Tesla instrumentation (6 studies) and less frequently 1.5Tesla apparatuses, indicating a transition in instrumentation use towards upgraded tools. We did not exclude articles including 1.5Tesla apparatuses as the studies using them still provide valid results. Seven studies used PET with 2-deoxy-2-[fluorine-18]fluoro-D-glucose, four studies combined it with MRI, and three used it alone. The studies included here ,63,64,65In this review, we included studies reporting brain alterations in patients with recent or current COVID-19 infections; these included patients with long COVID, whom we expected to manifest PTSD symptoms. However, just one study reported PTSD symptoms , probablBy performing specific PubMed searches, we identified one study identifying frontal cortex hypometabolism in torture victims with PTSD  and two Subarachnoid hemorrhages are related to PTSD. People with a subarachnoid hemorrhage are more likely to develop PTSD in a Chinese population  and the Hypoxia is a factor in PTSD allowing memory loss of a traumatic event in PTSD patients . Post-avMany studies investigated white matter integrity and identified diffuse hyperintensities in patients with PTSD. These studies employed more sophisticated techniques, like diffusion tensor imaging, while, among our eligible studies, few of them used it ,60. One This review has not been registered to PROSPERO. Furthermore, due to design heterogeneity, we could not perform meta-analyses or assess the risk of bias of each included study. However, we used the AXIS instrument to assess the quality of the cross-sectional studies we included  (althougThe lack of prospective longitudinal studies does not allow strong conclusions to be drawn about predictors and risk factors, but, in COVID-19 times, cross-sectional studies on the particular issue we examined were all that we could obtain. Despite these limitations, the included studies allowed us to draw some conclusions that could constitute the basis of future studies.Summarizing, of the brain alterations we identified in COVID-19 patients, some match the alterations encountered in PTSD and for some others, which are the most frequently observed changes in this review, the evidence is weaker.Taking together all the evidence of brain alterations in COVID-19 and in PTSD, they overlap only partially, thus partially backing our hypothesis. One-third of patients with COVID-19 who developed PTSD could likely find a neurobiological basis of COVID-19-induced brain alterations. Since neuroinflammatory and immune reactivity mechanisms were advocated for the neurological symptoms of COVID-19, studies focusing on neuroinflammation and immunity are warranted. Such studies, despite appearing in the late nineties, are still scarce. It is important to continue to study this issue since it could open-up new ways of treating both PTSD cases and people who have suffered COVID-19 by enforcing treatment programs shared by both populations.


Background: This study aimed to predict pathologic complete response (pCR) in neoadjuvant chemotherapy for ER+HER2- locally advanced breast cancer (LABC), a subtype with limited treatment response. Methods: We included 265 ER+HER2- LABC patients (2010–2020) with pre-treatment MRI, neoadjuvant chemotherapy, and confirmed pathology. Using data from January 2016, we divided them into training and validation cohorts. Volumes of interest (VOI) for the tumoral and peritumoral regions were segmented on preoperative MRI from three sequences: T1-weighted early and delayed contrast-enhanced sequences and T2-weighted fat-suppressed sequence (T2FS). We constructed seven machine learning models using tumoral, peritumoral, and combined texture features within and across the sequences, and evaluated their pCR prediction performance using AUC values. Results: The best single sequence model was SVM using a 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase (AUC = 0.9447). Among the combinations, the top-performing model was K-Nearest Neighbor, using 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase and 3 mm peritumoral VOI in T2FS (AUC = 0.9631). Conclusions: We suggest that a combined machine learning model that integrates tumoral and peritumoral radiomic features across different MRI sequences can provide a more accurate pretreatment pCR prediction for neoadjuvant chemotherapy in ER+HER2- LABC. Breast cancer is the most common female cancer worldwide ,2. AmongThe treatment of ER+HER2- breast cancer can be broadly divided into early-stage breast cancer with no lymph node (LN) involvement and advanced-stage breast cancer with LN involvement. Currently, for early-stage breast cancer without LN involvement, the oncotype DX breast recurrence score is used as a quantitative measurement, using real-time PCR to predict the response to chemotherapy in patients requiring adjuvant chemotherapy . For locNonetheless, when the effect of NAC on ER+HER2- breast cancer is compared to that of other molecular subtypes of breast cancer, it is shown to have a poor NAC response ,5. AccorWe aimed to use magnetic resonance imaging (MRI), a representative modality for evaluating treatment response, to classify patients and potentially provide clinical assistance ,7,10. InMost previous studies that used MRI to predict pCR rates among patients with breast cancer can be broadly divided into two general aspects based on the region of interest in the imaging. First, many studies have attempted to explain treatment responses based on temporal changes in lesions between the initial MRI and the early or mid-term MRI after NAC ,16. HoweTherefore, this study aimed to develop and validate a reproducible practical machine learning model with texture features incorporating both tumoral and peritumoral regions across initial pretreatment MRI sequences in patients with ER+HER2- LABC, whose NAC response was notably low. Through this study, we hope to provide practical help to clinicians in establishing tailored therapeutic strategies by stratifying this patient population prior to treatment.This retrospective study was approved by the institutional review board of our hospital, which waived the requirement for informed consent.Between January 2010 and December 2020, 2349 patients with advanced breast cancer received NAC at our institution. Among these, 818 were diagnosed with ER+HER2- LABC subtype. First, 403 patients were excluded because of a lack of raw data for the dynamic study in the Picture Archiving and Communication System (PACS). Patients with a history of previous treatment, no cytology report on the initial axillary LN metastasis, no report on the final pathological results, no pretreatment MRI, no verifiable information on the four MRI sequences essential to this study , and insufficient image quality for lesion segmentation were excluded. Finally, the inclusion criteria were patients who (1) had a pretreatment MRI performed at our center, (2) completed all cycles of NAC and had surgery with a final pathologic report on achievement of pCR or not, and (3) had all four sequences with sufficient quality for segmentation, resulting in a total of 265 enrolled patients (stage IIB through IIIC according to the 8th edition of the AJCC cancer staging system).Based on the date of the pretreatment MRI scans, patients were divided into training and validation cohorts. A total of 195 patients who underwent MRI between 2010 and 2015 were included in the training cohort. Another 70 patients who underwent MRI between 2016 and 2020 were included in the temporal validation cohort. The patient selection process is illustrated in Breast MRI examinations were performed with the patients in the prone position using a 3.0 T scanner . The following images have been commonly obtained after the localizer images from one of the two types of scanners: T2-weighted fast spin echo axial images , T2-weighted short-time inversion recovery (STIR) axial images , and T1-weighted fat-suppressed pre-contrast and 3D dynamic post-contrast enhanced (DCE) axial images  with one pre-contrast and six post-contrast dynamic series obtained before and after a bolus injection of 0.1 mmol/kg body weight of gadolinium-based contrast agent  at a rate of 2 mL/s, followed by 20 mL saline flush. Post-processing, image subtraction was performed by subtracting pre-contrast images from the post-contrast images. The field of view was 32–34 cm for all MRI sequences.The VOI segmentation of tumors was first semi-automatically performed along the margin of the tumor in the axial scan of T1-weighted fat-suppressed early post-contrast subtraction sequences (Ph2) by a radiologist  using 3D-Slicer (version 5.0.2) software, and the accuracy of the image up to the 3D margin on the coronal and sagittal planes was checked with necessary modifications. For peritumoral VOI segmentation, the existing tumor mask was subtracted after 3D dilation by 1 mm and 3 mm units .The same process was applied to T1-weighted fat-suppressed delayed post-contrast subtraction sequences (Ph6) and T2-weighted fat-suppressed sequences (T2FS). Thus, 15 VOIs of tumoral, peritumoral , and tumoral + peritumoral  were obtained for Ph2, Ph6, and T2FS in each patient’s pretreatment MRI. The process was evaluated by another senior radiologist  to assess and revise the tumoral and peritumoral VOI segmentations to reconfirm the entire procedure.For the segmented VOIs, N4ITK MRI bias correction was applied to improve the non-uniformity of MR images between different patients , and then = 12), which are information on the entire image, not VOI, and shape features among the original features (n = 14), which were information related to tumor size or volume measurable on conventional MRI, were excluded. The final feature set incorporated 2511 features for each sequence, and 7533 features were extracted from each patient.Here, x is the amplitude of the image, p < 0.05). Second, using the random forest (RF) algorithm, the top 30 features were selected for radiomic feature importance in pCR prediction. Prior to data training, a standard scaler was applied to adjust the deviating scales of the radiomic features and reduce the influence of outliers. Additionally, the synthetic minority over-sampling technique (SMOTE) was performed to reduce the problem of overfitting toward non-pCR due to the numerical imbalance between the pCR and non-pCR groups, even if the number reflected the actual clinical pCR rate of ER+HER2- LABC.Python 3.8 was used for data handling in the machine learning steps, and the key feature selection on the radiomic features extracted from each VOI was performed in two steps. First, the Mann–Whitney U test was used with statistical significance related to pCR or non-pCR prediction  in the MRI sequences of the training cohort: binary classification model, K-Nearest Neighbor model, Support Vector Machine (SVM), Decision Tree classifier, AdaBoost classifier, Random Forest (RF) classifier, and Light Gradient-Boosting Machine (LightGBM). A grid search approach was used to find the best hyperparameters for each of these seven models. This method systematically explores various combinations of hyperparameters to identify the optimal configuration for each model. To evaluate the performance of each model and its hyperparameter combination, we employed a five-fold cross-validation. The dataset was divided into five subsets. During each iteration, four subsets were designated for training, while the remaining subset was allocated for validation. This cycle was repeated five times, ensuring that each subset served as the validation set once. The optimal model and its hyperparameters for each VOI were selected based on the area under the curve (AUC) value, representing the true positive rate (sensitivity) plotted against the false positive rate (1-specificity) , which mNext, to construct a more sophisticated pCR prediction model, seven machine learning models were created with sets of selected key radiomic features in combination for tumoral, peritumoral, and tumoral + peritumoral VOIs across sequences from the training cohort. The training and testing processes were identical to those described as above, and AUC values were used to select the optimal model.Finally, a model incorporating clinical factors instead of radiomic features was created as a comparison group, and its performance was evaluated. Excluding the molecular subtype and axillary LN metastasis of breast cancer, which were fixed in this study, patient age, tumor size, and estrogen receptor (ER) and progesterone receptor (PR) expression levels were selected as clinical factors potentially associated with disease prognosis.We validated the predictive performance of the optimal models developed using radiomic features extracted from the VOIs of each sequence, radiomic features combined from the VOIs across different sequences, and clinical factors in the validation cohort. After calculating the AUC, precision , recall , and F1 scores , the predictive performance of the model was evaluated using the AUC of the receiver operating characteristic (ROC) curve .PrecisiThe process of this study is summarized in 2 test. Statistical significance was accepted when p values were <0.05.Statistical analyses of clinical factors, including patient age, tumor size, and ER and PR expression levels, were performed as follows: continuous variables were expressed as means and standard deviations, while categorical variables were expressed as frequencies and percentages. Continuous variables were tested using the independent samples t-test or the Mann–Whitney U test based on the results of the Shapiro–Wilk test for normality, and categorical variables were compared using the χThis study included pretreatment MRIs scans of 265 patients with ER+HER2- LABC with axillary LN metastasis. The clinical and histological factors of the pCR and non-pCR groups, considering pCR as the endpoint in this study, are shown in A comparison of the training and validation cohorts is shown in As previously mentioned, excluding diagnostic and shape features, 837 radiomic texture features per VOI were extracted from each patient’s pretreatment MRI. The 837 radiomic texture features included 93 original , and 744 wavelet features . The ManWe confirmed the predictive performance of pCR for the optimal machine learning model developed from 75 VOIs, combining the tumoral and peritumoral regions in two different sequences in the validation cohort. The KNN model with key radiomic features derived from a combination of VOIs ranging from the tumor-to-peritumor 1 mm in Ph2 and peritumor 3 mm VOI in T2FS exhibited the best pCR prediction performance, with an AUC of 0.96. The pCR prediction performance based on the combination of tumoral and peritumoral regions of different sequences is shown in p < 0.001).Cochran’s Q test verified that there was a significant difference between these five models  has a poor pathologic complete response (pCR) rate of approximately 10% compared with the 3–40% pCR rates of other molecular subtypes after surgical intervention following neoadjuvant chemotherapy (NAC) . TherefoTo construct a sophisticated model for pCR prediction after NAC in patients with ER+HER2- LABC, the radiomic texture features of MRI were extracted from the tumor, peritumor 1 mm, peritumor 3 mm, area from tumor-to-peritumor 1 mm, and area from tumor-to-peritumor 3 mm for early post-contrast sequences, delayed post-contrast sequences, and T2-weighted fat-saturated sequences. In line with previous studies, it was further established that early post-contrast images predominantly contain the most useful texture features in machine learning models as a single sequence model evaluation ,30,31. TAnother strength of this study is that not only is the tumoral region the basis for determining the VOI for radiomic feature extraction in MRI, but the peritumoral region, which is also reported to form a microenvironment that affects the NAC response ,22, was This study had several limitations. First, there was a possibility of selection bias because the study was a retrospective study conducted at a single tertiary referral center, and due to the unavailability of raw data for MRI dynamic studies from patients accumulated over a substantial ten-year period, certain patients had to be excluded. Second, this study intentionally focused on ER+HER2- LABC, which is a molecular subtype with relatively poor NAC response. Because the results were based on a single molecular subtype, it may be difficult to generalize the findings to all patients with breast cancer. We hope that follow-up studies will be conducted in future. Third, regarding the potential clinical utility, more time seems necessary for the immediate clinical application of the findings through rapid and reliable automatic segmentation. An accurate VOI segmentation process for a tumor is a prerequisite for providing accurate key texture features to constitute machine learning models. Although this study used a 3D slicer to produce images in a semi-automatic manner, the reliability of the VOI produced by the program decreased as the irregularity of the tumor margin increased, which required modification by a radiologist and reconfirmation by a senior radiologist to refine the VOI segmentation. Lastly, the most fundamental limitation was found in the revision of treatment plans for patients with ER+HER2- LABC. Despite a mere 10% pCR rate after NAC, NAC is still administered to patients with ER+HER2- LABC, mainly because more effective and specific treatments for this patient group are still in progress. However, if we accumulate evidence supporting the accurate classification of patients who exhibited a favorable response to NAC before treatment initiation, we can significantly influence practical treatment decisions made by oncologists and surgeons by instilling confidence grounded in sound reasoning. This approach could potentially shift the focus for ER+HER2- LABC patients expected to exhibit a poor response to NAC towards earlier surgical interventions and the determination of the scope of post-surgery treatment, leveraging tools such as Oncotype Dx for adjuvant chemotherapy decisions. Consequently, we anticipate that these efforts will assist in the establishment of tailored therapy plans that prioritize benefits over risks, ultimately improving patient quality of life.To assess the NAC response of patients with ER+HER2- LABC on pretreatment MRI, this study applied radiomic texture features to the tumoral and peritumoral regions across MRI sequences. We suggest that a combination of machine learning models incorporating tumoral and peritumoral texture features across different MRI sequences can provide a more accurate prediction of pCR for NAC response in these patients. These results are also expected to make a potential contribution to the development of novel clinical therapeutic strategies.