Atherosclerotic cardiovascular disease remains the leading cause of death worldwide. While many cell types contribute to the growing atherosclerotic plaque, the vascular smooth muscle cell (SMC) is a major contributor due in part to its remarkable plasticity and ability to undergo phenotype switching in response to injury. SMCs can migrate into the fibrous cap, presumably stabilizing the plaque, or accumulate within the lesional core, possibly accelerating vascular inflammation. How SMCs expand and react to disease stimuli has been a controversial topic for many decades. While early studies relying on X-chromosome inactivation were inconclusive due to low resolution and sensitivity, recent advances in multi-color lineage tracing models have revitalized the concept that SMCs likely expand in an oligoclonal fashion during atherogenesis. Current efforts are focused on determining whether all SMCs have equal capacity for clonal expansion or if a “stem-like” progenitor cell may exist, and to understand how constituents of the clone decide which phenotype they will ultimately adopt as the disease progresses. Mechanistic studies are also beginning to dissect the processes which confer cells with their overall survival advantage, test whether these properties are attributable to intrinsic features of the expanding clone, and define the role of cross-talk between proliferating SMCs and other plaque constituents such as neighboring macrophages. In this review, we aim to summarize the historical perspectives on SMC clonality, highlight unanswered questions, and identify translational issues which may need to be considered as therapeutics directed against SMC clonality are developed as a novel approach to targeting atherosclerosis. The mosuspected –16. For uspected , and thauspected , 15 but uspected . Phenotyuspected . The pheuspected , 18, andIn the context of atherosclerosis, SMC plasticity is considered to be a major determinant of plaque stability , 20. PlaAmongst all of the cell-fate decisions the vascular SMC is capable of making, one particular form of phenotype switching has recently (re)captured the attention of the field: clonal expansion. Clonal expansion is the process by which a specific subpopulation of cells proliferates and accumulates as the progeny of a single parent cell. This concept was originally advanced to describe how the adaptive immune system worked, wherein certain lymphocyte precursor cells  recognizing specific antigen(s) proliferate into expanded clones , 30. Lat+ and G6PD− cell types  which were interspersed with cells of the other color  randomly and permanently inactivated in a process known as lyonization. Pioneering work conducted by Benditt & Benditt performed zymograms to measure the enzymatic activity of the X-linked gene, glucose-6-phosphate dehydrogenase (G6PD), on vascular samples. These studies revealed that normal aortic and iliac arterial walls are composed of both G6PDll types . This sull types  samples ll types , 39. Thill types . Using cer color . With thGFP transgenic mouse line where they observed a mixture of GFP+ and GFP− in both the embryonic and adult descending aorta  in response to Cre-induced recombination. The labeled cell and its progeny will continue to express the same color, regardless of phenotype switching or de-differentiation, permitting high-fidelity tracing  . Using ang aorta . These rng aorta , Table 13HTdR) into swine and fed the animals a hypercholesterolemic diet for 30–60 days to trace the proliferation of vascular cells during early lesion development in that species  demonstrated a single X-linked pattern, suggesting those cells were potentially clonal . HoweverApoe−/− mice carrying both ROSA26R-confetti multicolor Cre reporter and SM22α-driven tamoxifen-inducible Cre recombinase, CreERT2, to trace individual SMCs , SMCs in the monochromatic regions of plaques appeared to derive from a single progenitor  in bone marrow-derived cells stimulated medial SMC transdifferentiation and shifted the monoclonal pattern of plaque SMCs towards a polyclonal status. This was the first evidence that disturbance of one gene could regulate the clonality of SMCs during plaque development. This study also led the investigators to hypothesize that medial SMCs first expand into the fibrous cap and from there invade the core region of the plaque, rather than migrate into the two regions separately.In one of the first studies pursuing the molecular mechanisms that might promote the observed clonal expansion of SMCs in atherosclerosis, Misra et al. conducted a series of elegant experiments in mice containing the ROSA26R-Rainbow system driven by ckground . These mckground . IntriguTet2, have epigenetically silenced Itgb3, and have increased TNF signaling—stimulates polyclonal SMC expansion in atherosclerotic lesions, contrary to the monoclonality of plaque SMCs in young mice  led to the removal of the dominant clone, a restoration of polyclonality, and smaller lesions.Using a similar ROSA26R-Rainbow system driven by  plaques , and des plaques . We founIn summary, the totality of these increasingly sophisticated lineage tracing studies has firmly consolidated the consensus that there is mono- or oligo-clonal expansion of SMCs during atherogenesis ; Table 13HTdR) after carotid balloon surgery and found a mixture of proliferative and non-dividing cells derived from the media  in healthy vessels which may mark SMCs undergoing phenotypic modulation . It is possible that in youth, the phagocytic clearance of a dominant SMC clone with a pro-inflammatory phenotype can ameliorate disease progression, while in aged mice, TNFα secreted by TET2-down-regulated macrophages may stimulate the expansion of secondary clones which promote plaque enlargement. These and other considerations hint at the complexity and potential pitfalls associated with targeting the clonally expanding SMC and highlight the fact their physiology is likely context-dependent.For example, excessively proliferating SMCs can undergo replicative senescence, as has been observed in the fibrous caps of human atherosclerotic lesions , 83. In  complex , interve complex , 67, may complex . SMC apo complex . Previouclerosis . It willclerosis , 50. We l status . We founl status  but shifl status . The disVascular SMCs are fundamental players in atherogenesis due to their competing roles in plaque formation, cap stabilization, and lesional inflammation. SMCs demonstrate remarkable heterogeneity and capacity for plasticity both in healthy arteries and during vascular remodeling. A major focus of current research relates to how SMCs expand in the vessel wall. Historically, there has been a long-standing debate regarding their capacity for clonal expansion in humans due to a lack of high-resolution experimental techniques. Recent advances in multi-color lineage tracing mouse models have provided robust evidence supporting the concept that medial SMCs exist in a polyclonal manner, while plaque SMCs expand in a monoclonal or oligoclonal pattern, at least in mice. However, our mechanistic knowledge of how clonal expansion is initiated is still in its infancy. Recent single-cell transcriptomic studies suggested the existence of SMC subpopulations with markedly varying phenotypic profiles. It has therefore been hypothesized that a collection of pre-existing SMC progenitors which are primed to respond to pathological stimuli may exist and form the basis for the emergence of a dominant clone. Alternatively, it is possible that all medial SMCs have the potential to expand, but only a few are ultimately selected due to a survival advantage conferred by somatic mutation, epigenetic alteration, resistance to phagocytic clearance, lateral inhibition, or some other unknown mechanism. Though the molecular drivers underlying SMC clonal expansion remain to be fully elucidated, recent studies have begun to point to some initial clues , 43, 44.


There is a higher risk for HIV acquisition during pregnancy and postpartum. Pre-exposure prophylaxis (PrEP) is recommended during this period for those at high risk of infection; integrated delivery in maternal and child health (MCH) clinics is feasible and acceptable but requires implementation optimization.The PrEP in Pregnancy, Accelerating Reach and Efficiency study  engaged stakeholders to prioritize determinants of PrEP delivery (using Likert scores) and prioritize PrEP delivery implementation strategies. Using a sequential explanatory mixed methods design, we conducted quantitative surveys with healthcare workers at 55 facilities in Western Kenya and a stakeholder workshop , yielding visual plots of stakeholders’ perceived feasibility and effectiveness of the strategies. A stepwise elimination process was used to identify seven strategies for empirical testing. Facilitator debriefing reports from the workshop were used to qualitatively assess the decision-making process.Among 146 healthcare workers, the strongest reported barriers to PrEP delivery were insufficient providers and inadequate training, insufficient space, and high volume of patients. Sixteen strategies were assessed, 14 of which were included in the final analysis. Among rankings from 182 healthcare workers and 44 PrEP policymakers and implementers, seven strategies were eliminated based on low post-workshop ranking scores (bottom 50th percentile) or being perceived as low feasibility or low effectiveness for at least 50% of the workshop groups. The top seven strategies included delivering PrEP within MCH clinics instead of pharmacies, fast-tracking PrEP clients to reduce waiting time, delivering PrEP-related health talks in waiting bays, task shifting PrEP counseling, task shifting PrEP risk assessments, training different providers to deliver PrEP, and retraining providers on PrEP delivery. All top seven ranked strategies were grouped into bundles for subsequent testing. Facilitator debriefing reports generally aligned with rankings but noted how stakeholders’ decision-making changed when considering the impact of strategies on facility staff and non-PrEP clients.The most impactful barriers to integrated PrEP delivery in MCH clinics were insufficient staffing and space. Implementation strategies prioritized through multiple methods of stakeholder input focused on co-location of services and increasing clinic efficiency. Future testing of these stakeholder-prioritized strategy bundles will be conducted to assess the effectiveness and implementation outcomes.The online version contains supplementary material available at 10.1186/s43058-023-00481-9. Contributions to the literaturePre-exposure prophylaxis (PrEP) is a recommended intervention for pregnant women at risk for HIV, but implementation is sub-optimalStakeholders involved in the delivery and receipt of PrEP can provide valuable insights on implementation barriers and strategies that clinics can test to better meet the needs of PrEP clients.The most impactful barriers were insufficient staffing and space. Using three strategy prioritization methods, stakeholders prioritized co-location, task shifting, and flow modifications as implementation strategies to address these barriers.These findings set the foundation for testing the impact of stakeholder-prioritized PrEP delivery strategies on implementation outcomes.Women are at elevated risk for HIV acquisition during pregnancy and the postpartum period , 2. Wome. In one of these studies, qualitative focus group discussions were conducted among 50 healthcare workers (HCWs) offering PrEP in MCH and family planning (FP) clinics [Several projects have been implemented in Kenya to provide PrEP for pregnant and postpartum women –16. Qual clinics . Using t clinics . HCWs fe clinics . However clinics . HCWs id clinics . While sUsing the RE-AIM evaluative framework, a systematic review identified planned, ongoing, or existing studies that address determinants and test strategies for delivering integrated PrEP into MCH clinics, categorizing determinants in the social-ecological model . IncludePrEP in Pregnancy, Accelerating Reach and Efficiency  study gathered qualitative and quantitative data from stakeholders to identify determinants of PrEP implementation and PrEP implementation strategies. This analysis of data from the PrEPARE study is a sequential explanatory mixed methods evaluation of three strategy prioritization methods and one method for strategy grouping [The grouping . A mixedThis study was conducted in three counties in Kenya which rank among the top 5 Kenyan counties for high HIV prevalence (9% or higher): Kisumu, Homa Bay, and Siaya Counties . For thePrevious qualitative work using CFIR helped identify barriers to PrEP implementation and potential strategies to address these barriers , 29. To x-axis) and effectiveness (y-axis) scores were plotted; strategies whose mean feasibility and effectiveness scores each averaged 2.5 or higher fell within the “go-zone” in the top-right quadrant of the go-zone. Afterwards, participants were asked to respond to an online REDCap survey and individually rank a set of 16 PrEP delivery strategies on their perceived effectiveness. In these post-workshop rankings, strategies were sequentially ranked from 1 (most effective) to 16 (least effective).The stakeholder workshop utilized the nominal group technique (NGT), a group prioritization method that democratizes decision-making , 31. TheThe Kenyan study staff with experience delivering PrEP in MCH clinics were trained as small-group facilitators for the workshop activities. Facilitators compiled notes during the small group discussions to track the pros and cons of each strategy’s feasibility and effectiveness and took notes on the small groups’ overall reflections about each strategy after viewing the go-zone plots, including additional group suggestions for implementation considerations. We utilized debriefing reports, completed by discussion facilitators, to evaluate the small group decision-making process. The debriefing reports followed a structured format to collect key takeaway messages from the small group discussions; in a previous evaluation of structured debriefing reports, they were found to accurately reflect the key thematic content from full in-depth interview transcripts . During n) and percentages. To determine the top three PrEP delivery strategy bundles, all 16 strategies were first ranked using the past experience surveys from PrEP-experienced HCWs. Rankings were determined by the percentage of HCWs who indicated that the strategy had been tested at their facility and improved PrEP delivery. Strategies were then excluded if they ranked in the bottom 50% of post-workshop strategy rankings or fell outside the go-zone for the majority of stakeholders. The 50% cutoff point was subjectively selected to determine the strategies that fell above and below the median ranking and to limit the number of strategies for testing. In the post-workshop rankings, strategies’ scores were averaged across participants. Strategies were also excluded if they fell outside of the go-zone for more than 50% of the go-zone plots in which it was evaluated to identify the strategies that fell above the median ranking. All data were analyzed using R version 4.2.2.Descriptive statistics were calculated by summarizing continuous variables using medians and interquartile ranges. Categorical variables were summarized using sample sizes  guidelines for cross-sectional studies can be found in Additional file A total of 185 HCWs were invited to complete the past experience survey, of which 183 completed the survey. There were 48 PrEP stakeholders invited to participate in the workshop, of which 46 attended; 44 participants (91.7%) completed the pre-small-group and go-zone plot rankings while 40 (83.3%) completed the post-small-group rankings. Participant demographics are described in Table In the past experience surveys, HCWs identified determinants that had a high impact on the ability to deliver PrEP: insufficient provider-patient ratios, inadequate provider training, lack of physical space for PrEP services, and documentation burden. Barriers that had little to no impact on PrEP delivery included PrEP and document stockouts, HIV testing burden, competing partner priorities, and language barriers Fig. .Fig. 1PrTo prioritize strategies for implementation and testing as part of the PrEPARE study, 16 strategies identified through prior qualitative work were evaluated using the three different prioritization approaches . HoweverSeven PrEP delivery strategies were selected utilizing the three prioritization methods Fig. . Seven sThe highest scoring strategies using the past experience surveys were “delivering PrEP commodities within MCH clinics instead of pharmacies,” “fast tracking PrEP clients to reduce waiting time within MCH,” and “delivering PrEP-related health talks in waiting bays.” These three strategies were also in the top 50th percentile for the go-zone plots and the post-workshop surveys. The remaining strategies had heterogeneous rankings across the three ranking approaches, without clear patterns. The “retraining providers” strategy was the second lowest ranked strategy in the past experience surveys but highly ranked in both the go-zone and post-workshop survey and therefore included in the final list of strategies to be tested; importantly, 48% of PrEP-experienced healthcare workers indicated that this strategy had not been tested at their facilities Fig. .Fig. 3DiThe summary of facilitator notes is provided . A total of seven strategies were selected and grouped for three rounds of future testing within MCH clinics. The first set of strategies includes “fast tracking PrEP clients” and “retraining providers on PrEP”; the second and third sets respectively include “task shifting PrEP counseling to HIV testing services (HTS) providers” and “training different providers” as well as “delivering PrEP-related health talks in waiting bays” and “providing communication aids.” Delivering PrEP in MCH clinics will be tested in all three rounds as this strategy was the most highly ranked. Audit and feedback of data systems will also be provided per the request of clinic staff. Each round of testing involves an additional two strategies which were grouped together after discussion with the study staff. Furthermore, a recent large cluster randomized trial by our team revealed that risk scores provide no additional benefit in appropriate allocation of PrEP, suggesting that risk score-guided PrEP should not be prioritized for continuation in Kenya . As a reThe three strategy prioritization methods used in this study enabled the identification of seven unique strategies that may be feasible and effective for delivering PrEP into MCH clinics. Overall, PrEP-experienced HCWs identified several substantial barriers to PrEP implementation that fall under the Implementation Process Domain of CFIR including insufficient provider-patient ratios, lack of provider training, and documentation burden ; the higIn a recent systematic review on PrEP in pregnancy and implementation science, the Reach, Efficacy, Adoption, Implementation, and Maintenance (RE-AIM) Framework was used to evaluate the current scientific evidence in PrEP implementation through determinants, outcomes, and strategies at the individual, provider, facility, and program levels , 40. Of The facilitator notes from the debriefing reports aligned with the strategies’ feasibility and effectiveness score ratings as well as the overall go-zone rankings. However, it is important to note that there are different cultural interpretations of the words “feasibility” and “effectiveness.” In this study, feasibility was described as, “How easy would it be to do strategy X?”, and effectiveness was described as, “How much of an impact would strategy X have?”. These descriptions were chosen instead of using the Feasibility of Intervention Measure after consultation with teams who had experience adapting descriptions of implementation outcomes in a Kenyan setting , 49. ImpOur study has several strengths. First, we evaluated the perspectives of a diverse group of stakeholders. Collecting strategy rankings from PrEP-experienced healthcare workers in addition to from policymakers and individuals living with HIV enabled us to incorporate diverse perspectives regarding what will be feasible and effective to implement. The past experience rankings survey is, to date, the largest data collection effort in the world that focuses on healthcare workers’ experiences in PrEP in pregnancy. Obtaining feedback from a variety of stakeholders allowed us to take a more holistic approach in evaluating which strategies should be implemented and evaluated as part of the PrEPARE study. The use of the democratic process in achieving stakeholder consensus is a unique contribution of this study.Our study has several limitations. First, the past experience rankings from healthcare workers may have had recall bias. There were power differences between the stakeholder workshop participants who were national- and county-level officials, healthcare workers, and PrEP users. These power differences may have stifled discussion and potentially introduced social desirability bias, but the NGT was used to democratize decision-making by giving all group members an equal voice in go-zone plot creation. Additionally, cadre groups were assigned a set of strategies that best aligned with their experiences and expertise to ensure that all participants could make meaningful contributions to the small group discussions. Each of the three strategy ranking methods used during the stakeholder workshop was also conducted individually with participants to minimize social desirability bias. The main limitation of this analysis is that we did not formally match strategies and barriers, nor did we seek to prioritize strategies based on previously identified barriers. Some natural groupings of strategies and barriers emerged post hoc, but this was not considered in the analysis a priori. While it is not possible to know how our results would have been different if we had utilized barrier-strategy matching methods—stemming from a lack of empiric comparison data in the literature—we theorize that we might have selected strategies that more closely aligned with commonly experienced barriers or those that had a high critical impact on service delivery. During the workshop, participants were invited to suggest any additional strategies during the pre-workshop surveys; suggestions primarily included increased adherence counseling and community sensitization and delivery of PrEP. We did not incorporate these two suggestions into the go-zone plot surveys or discussions due to (1) the technical challenge of incorporating new strategies real-time into the go-zone plots production and (2) the suggested strategies were either a specific topic (adherence counseling) covered under other included strategies or a demand generation rather than facility-based service provision strategy (community sensitization). This approach differed from the first step of a traditional NGT, limiting direct methodologic comparison. Purposive sampling was used to recruit participants for the stakeholder workshop. While purposive sampling is not statistically representative, the goal of this study was to gain knowledge from content experts and understand stakeholder perspectives on PrEP implementation in MCH clinics. Finally, our team has a companion manuscript that includes a comparison of methodologic performance and pragmatism between the prioritization methods utilized in this study .This study sought to identify three bundles of PrEP delivery strategies to implement and test in Kenyan MCH clinics. Through engagement with PrEP-experienced healthcare workers, we identified several high-priority barriers to integrating PrEP delivery into MCH clinics including insufficient staff and space, inadequate provider knowledge of PrEP, and increased volumes of patients. Through the past experience surveys and the workshop activities, we identified a set of seven strategies that might address these barriers. Strategies included task shifting and fast tracking to increase clinic efficiency and reduce provider workload as well as retraining current providers and training new providers to bolster knowledge of PrEP and quality of care across services. The use of health talks in waiting bays and communication aids also seek to improve the efficiency of patient-provider communication regarding PrEP. Future testing of these strategies will allow for the assessment of effectiveness and implementation outcomes at the individual, provider, and facility levels.Additional file 1. Summary of study designs, settings, subjects, and data collection across data sources.Additional file 2. STROBE Statement—Checklist of items that should be included in reports of cross-sectional studies.Additional file 3. Facilitator notes from the PrEPARE stakeholder workshop.


Much of the world’s population lives close to coastlines and this proximity is becoming increasingly impactful because of sea-level rise (SLR). Barrier islands and backbarrier saltmarshes, which comprise >10% of these coasts, are particularly susceptible. To better understand this risk, we model backbarrier morphologic and hydrodynamic evolution over a 200-year period of SLR, incorporating an erodible bed and a range of grain sizes. Here, we show that reduction in intertidal area creates negative feedback, shifting transport of coarse sediment (silt and sand) through the inlet from net export to net import. Imposing a modest marsh vertical accretion rate decreases the period of silt and sand import to 40 years (years 90 to 130) before being exported again. Clay is continuously exported thereby decreasing inorganic deposition on marshes and threatening their sustainability. Simulated marsh loss increases tidal prism and the volume of sand contained in ebb deltas, depleting coastal sand resources. Barrier islands and backbarrier saltmarshes are particularly threatened by sea level rise. Here, the authors show how reduction in intertidal areas create negative feedback, shifting transport of coarse sediment through the inlet from net export to net import. SLR increases the risk of coastal flooding and storm impacts, particularly for the ~10% of the global population who live in coastal areas less than 10 m above sea level2. Continued coastal population growth3, accelerating sea-level rise4, and increased storminess5 exacerbate this threat to coastal inhabitants, infrastructure, and economic activities. Ten percent of the world’s shorelines are fronted by barrier chains6 and half of these coasts consist of mixed-energy barriers backed by marsh and tidal channels and separated by numerous tidal inlets (Hayes 1979). The inlets provide a means of the tidal exchange between the coastal ocean and the backbarrier basin. Future coastal impacts to barrier coasts will largely be driven by SLR, but the extent of change will also depend on how barrier-related geomorphic features evolve in response to these forcings7. Although anecdotal examples and conceptual models have been put forward to predict the future of these systems, a rigorous quantification is needed to lower uncertainty and demonstrate theorized feedbacks. To date, research using modeling to predict the effects of SLR on basin geometry and inlet sediment transport trends is divided. Some studies predict that tidal inlets will import sediment13, which is supported by field evidence14. Alternatively, other studies suggest that sediment will be exported as sea level rises16, retarding the ability of marshes to build vertically15. Here, we examine how gradual submergence of the backbarrier system by SLR will change the backbarrier morphology and hydrodynamics, which alters sediment transport trends for different grain sizes.Quantifying the physical response of coastal systems to SLR remains one of the most important tasks within the fields of coastal geology and engineering. During the 20th century, global (eustatic) Mean Sea Level (GMSL) rose at a rate of ~2 mm/yr, but this rate has steadily increased, and by 2100, projections suggest a cumulative rise of between 0.79 and 1.46 m (RCP 8.5)18. This occurs when the mean water depth is small, such that the geometry of tidal channels and resulting flow patterns are significantly different between high and low tide19. Imbalance in the flood and ebb periods impacts the associated current velocities; a shorter period transporting the same volume of water generates a higher velocity. The nonlinear dependence of the transport of coarse sediments on velocity means that even a slight asymmetry in velocity produces net bedload (sand) transport, facilitating sediment import if tides are flood-dominant or export if tides are ebb-dominant19. However, for suspended load (clay/fine silt), slack water duration dictates residual flux rather than maximum current asymmetries, due to the slower rate of settling21. Residual flood-directed transport of fine-grained sediment is enhanced when channel depth decreases in a landward direction, or if the velocity variation is slower near the pre-ebb slack than the pre-flood slack. This trend in net sediment transport can be counteracted by wind waves, which, due to the shallower water depths, maintain more sediment in suspension near pre-flood slack than pre-ebb slack, when tidal flats are flooded21. Under these conditions, variations in the wetted area impact slack water durations and therefore, the net transport of fine sediment. The direction of the net transport is controlled by the elevation of intertidal regions, with high intertidal regions compared to the channel depth being characterized by longer pre-ebb slack, promoting fine-grained import, while a greater area of lower intertidal regions has the opposite effect, with longer low water slacks and net fine-grained export. A large intertidal area also leads to slower drainage from flats to the channels during the ebb; this water surface gradient leads to a peak in ebb-velocities that can result in residual ebb-directed transport of coarse-grained sediment22.How sediment is re-distributed during the evolution of coastal systems depends greatly on the underlying hydrodynamics of the basin, which in turn are strongly influenced by the distribution of saltmarsh and tidal channels. Tidal asymmetry, the difference in magnitude or duration between ebb and flood currents, is produced by distortion of the tidal wave as it propagates through tidal basins22. Relatively small deepening of the channels due to SLR will have little impact on the friction of ebbing flows within the channels. Finally, increasing tidal prism will also enlarge the channel cross-section, thus, with faster propagation of the flooding tide, there will be a smaller discrepancy between flooding and ebbing durations such that ebb-dominance is reduced. Thus, the gradual loss of intertidal storage area due to marsh submergence coupled with marsh edge erosion will serve to re-establish the natural flood-dominance of a progressive wave (due to nonlinear effects) or co-oscillating wave in moderate-length basins 22. Because sediment transport is related to a power function of velocity22, a slight shift to stronger flood versus ebb velocities will lead to net landward sediment movement through the inlet and within backbarrier channels22. For example, field evidence of landward sand transport in flood-dominated backbarrier by channels has been documented in Willipa Bay, Washington23 and in Essex Bay, Massachusetts24.If changes in basin morphology are neglected, then rising sea level will gradually increase water depths in the backbarrier throughout the tidal cycle. While the change in depth will be small relative to deep channels, water depths over the marsh platform could increase substantially. The increased depth over intertidal areas at high tide decreases frictional effects and allows faster propagation of the flooding tidal wave crest within the basin25. Several researchers have applied a sediment equilibrium model, aggregating morphologic response into volume changes of tidal flats, inlet channel, ebb-delta, and adjacent coast elements26. At two inlets along the Dutch Wadden Sea, the model predicts that increasing SLR rates induces a disequilibrium in element volumes that is compensated by expanding the inlet cross-section, as well as erosion of interior flats and the ebb-tidal delta8. Recent studies have also suggested that the demand of sediment in tidal basins is a product of increasing accommodation related to SLR13. Additional research has focused on process-based, morphologic models of conceptual tidal basins with varying geometries28. Using a schematized model of the Ameland Inlet9, one study found that initially flood-dominant hydrodynamics are enhanced with SLR, resulting in increased import of sand that is partially supplied from the eroding ebb-delta. Despite consistent sediment import, the tidal channel network and tidal flats are only maintained under low rates of SLR (0.2 m increase from 1990 water level by 2100). Resulting morphologies show the development of relatively deep, narrow backbarrier tidal channels with multiple ebb-dominant channels dissecting the ebb-tidal delta29. This work was expanded to incorporate the development of a laterally unconstrained inlet-basin system with different SLR rates and varying tidal ranges31. Results show that, for a SLR rate of 5.6 mm/y, tidal prism increases due to a lateral expansion of inundated area and consistent growth of ebb-delta shoals. However, trends in residual sediment transport direction (import or export) were found to vary with tidal range31. Long-term  modeling of a conceptual, large, elongated estuary  focused on tidal asymmetries responding to the loss of intertidal areas due to an imposed SLR of up to 6.7 mm/y10. In all SLR scenarios, the basin shifted from exporting to importing sediment, consistent with developing overtides due to tidal asymmetry32, although at rates insufficient to prevent the intertidal area loss10. Recent work modeled SLR effects on sediment budgets in Plum Island Sound, Massachusetts, finding that SLR enhanced marsh sedimentation, while at the same time increasing the ebb-dominance of the system and export of sediment15. Another study tested the impacts of imposed marsh loss on six tidal basins along the U.S. East Coast, finding positive feedback whereby marsh loss reduced the sediment trapping efficiency of remaining marshes and enhanced ebb-dominance16. In a later investigation of Jamaica Bay, New York, the same authors33 suggest that sedimentation inside the basin is derived from marsh edge erosion, as well as from marine sources. Finally, studies focusing solely on sedimentation of the marsh surface indicate conflicting projections. For example, a meta-analysis of existing data suggests that most marshes will be stable34 which contrasts with ecogeomorphic modeling35 indicating that marshes are highly vulnerable to SLR. A recent global meta-analysis shows that while marshes appear to be presently resilient, they are increasingly vulnerable with accelerating SLR due to auto-compaction and subsidence36.The response of tidal inlet and basin systems to SLR has previously been investigated with both semi-empirical or rule-based, and process-based models37 that are common throughout the world  generated by the Type I geometry are applied over an expanding inlet area50.In Type I basins, the tidal wave propagates faster in the deep channels than on the shallow flats, leading to a strong current during the last stage of the ebb-cycle when slower intertidal drainage produces higher water surface gradientssee Fig. . Peak eb19. For a Type I basin , currents accelerate and decelerate more quickly at high tide than at low tide. This condition favors an extended pre-flood slack water and fine sediment export19. While the increasing inundation depth over the extensive tidal flats with SLR shifts the basin away from the idealized Type I (favoring import of fine sediments), a counteracting effect reduces this effect. The tendency of sediment import increases as the period of high slack water increases, which allows more fine sediment to settle out of the water column over tidal flats20. However, as SLR increases, the water over the tidal flats is continually deepening, despite marsh accretion, which increases the required settling time for particles to reach the substrate, thereby decreasing the amount of sediment deposited on the flats.For moderate-length tidal basins without significant frictional effects, the acceleration around slack tide is proportional to the basin area, and inversely proportional to the rate of change of channel cross-sectional area57. The presence of wind-generated waves within the basin (a process not included in this model formulation) would naturally serve to further increase fine sediment export by reducing deposition over shallow tidal flats during the slack water period before ebb19, and offshore waves would create a more-realistic ebb-delta morphology characterized by an arc-shaped bar and single main channel.The duration of slack tide before flood for both the control and the SRL case Fig.  remains 58  releasing a fixed quantity of sediment in the backbarrier and evaluating where it was transported and deposited after a period of 30 days without allowing the bed to erode or aggrade. Moreover, the study16 accommodates increasing tidal prism by widening the channel without deepening the inlet, a process shown to reduce ebb-dominance (in our study). Additionally, our study shows that SLR deepens the inlet including the backbarrier channels, which enhances flood dominance causing an influx of coarse sediment. Our model demonstrates the importance of co-evolution of the inlet-basin system forced by SLR and increasing tidal prism. Furthermore, our study is consistent with previous findings showing that with increasing tidal prism: (1) fine-grained sediment is exported15 (2) inlet cross-sectional area increases46, (3) ebb-tidal delta expands17, (4) backbarrier tidal creeks enlarge33, and (5) sand is imported31.Overall, our results show a shift from sediment export to a period of import with SLR. This is counter to recent findings37. These systems are characterized by moderate-length basins  at the inlet and within the major backbarrier channels, whereas the growth of the ebb-tidal delta is caused by the enlarging tidal prism. Sediment transported into the basin and onto the ebb-tidal delta will come, in part, from inlet deepening, but a primary source will come from the littoral system via longshore sediment transport73.Our conceptual backbarrier and tidal inlet system is modeled after mixed-energy barrier island chains that occur throughout the worldns Table  with rel9, results from this study infer that the ebb-tidal delta will expand with SLR due to increasing tidal prism17, which may reflect the use of a moderate-sized tidal inlet-basin system for the modeling. Sediment losses to the backbarrier system and trapped on the ebb-delta will lessen inlet sediment bypassing7 and gradually deplete proximal sediment reservoirs, most likely from the adjacent barriers. This will make some barriers more susceptible to breaching and transgression.Contrary to recent studies of mixed-energy tidal systems, particularly in the Netherlands59. Further research is needed to evaluate the eventual disposition of the coarse sediment fraction entering through the inlet. For example, an observation that peat in Plum Island Sound marsh is composed chiefly of silt and fine sand74 suggests that imported coarse sediment could contribute to marsh mineral accretion. Likewise, incorporating the effects of vegetation, and waves and major storms would aid in making the modeling predictions more robust.We show that different sediment classes respond differently to SLR despite a hydrodynamic shift from ebb-to flood-dominant for periods of the simulation. The transport of coarse sediment switches from being exported to being imported for varying time periods, whereas fine sediment is continually exported. The long-term loss of fine-grained sediment to the coastal ocean further threatens backbarrier marshes whose sustainability relies on both increased biomass production and mineral sedimentation75. Physics-based numerical models allow for increasingly sophisticated representation of coastal hydrodynamics and morphology over timescales relevant to barrier island transgression and tidal basin submergence. This research provides opportunities to link process causation to long-term development10. Here we employ a broadly used numerical modeling system (Delft3D)76 to simulate the evolution of a backbarrier/tidal basin system and study the processes driving sediment exchange and morphologic evolution therein.Hydrodynamics and sediment transport in coastal systems are becoming increasingly quantified, however, practical limitations on the spatial and temporal resolutions of field-surveyed data limit the degree to which process links can be established at longer time scalesWe used an idealized basin so that relationships can be easily quantified and compared with theory. Both hydrodynamics and morphology are evolved over decadal to centennial timescales, complementing spatially- and temporally limited field studies of natural systems. The model is forced at the open-ocean boundary with a tidal signal superimposed by varying SLR scenarios. The evolving inlet and basin hydrodynamics are tracked using time-series outputs of backbarrier water level and inlet throat tidal currents. Sediment volume change for the morphological elements is calculated using bathymetric surface differencing, and sediment exchange between the basin and open coast is tracked using integrated fluxes through the inlet.45. Using a symmetrical, undistorted tidal signal at the offshore open boundaries ensures that any emerging asymmetries are entirely due to variations in the inlet and basin geometry with water level increases19 . The final bathymetry following the 2-year simulation was used as the initial bathymetry for the experiments we performed here. While a 200-y morphologic timescale simulation is inadequate to achieve full dynamic equilibrium with no further bathymetry changes47 and tidal range (3 m) that are representative of backbarrier systems along mixed-energy barrier island coasts (sensu: Davis and Hayes 1984) in Fig. . As seen5 km and 79 and a higher SLR case (8 mm/y) with marsh platform accretion of 3 mm/y manually imposed every 5 years of simulation. With the same rate of SLR relative to marsh accretion , the 8 mm/y SLR run investigates the influence of basin elevations and hypsometry interacting with higher SLR rates expected after 20501 and the influence of any organic accretion that is not included in other simulations. A 5-yr marsh platform accretion update interval was chosen to maximize the simulation time between manual bathymetry updates while limiting the cumulative accretion between updates to a reasonably small value that would not impact model stability when instantaneously imposed . Additional simulations with varying rates of SLR and marsh accretion were conducted, with the three cases presented here being representative, and respective elevations of the Mean Low Water (MLW), Mean Sea Level (MSL), and Mean High Water (MHW) tidal datums during the three simulations shown , 64 μm (noncohesive coarse silt), and cohesive clay with a fall velocity of 0.25 mm/s . The use of multiple sediment classes provides the potential to capture differences in net behavior of fine and coarse sediment under dynamically evolving basin geometry and changing tidal asymmetry. We employed a homogeneous and uniform stratigraphy throughout the basin and modeled area based on equal volumes of the three grain sizes. The sediment transport module used a critical shear stress for the erosion of cohesive sediments of 0.25 Pa, vertically uniform mixed stratigraphy initially composed of approximately 43% fine sand, 43% coarse silt, and 14% clay, and horizontally uniform sediment properties. The erosion and deposition of cohesive sediment are calculated using the Partheniades‐Krone method , and transport of noncohesive sediment is calculated using the Van Rijn formula80. We specified a sediment thickness of approximately 7 m to avoid limiting erosion, beyond which we used a non-erosional depth of 7 m to represent reasonably consolidated material or bedrock that would not easily erode for the timescales simulated. The dry cell erosion factor was set to one to allow tidal channel lateral migration , a 6-second period, and 0.25-m wave height, we see that the seaward portion of the ebb-tidal delta undergoes uniform erosion while the region inside the inlet experiences areas of slight erosion and deposition (see SI for more detailed results and discussion). It is noted that events triggering wave-induced sediment transport inside the basin are highly infrequent and exceedingly diminutive when compared to tidal transport. We acknowledge the importance of waves in modifying the ebb-tidal deltas and resuspending fine-grained sediment in certain nearshore settings82, using an accretion rate based on average values from the literature, estimated the conversion of a New England marsh from high to low marsh by 2055 , in the same marsh system, but using a fully parameterized MEM marsh accretion model83 found the conversion to occur between 2045 and 2070, and most likely around 2058.Representative accretion rates were chosen based on measured values in both vegetated and unvegetated regions, according to elevation. Applying an elevation-related accretion rate, using empirical data, has previously been shown to provide very similar results to the implementation of a vegetation-based morphological model, such as the Marsh Elevation Model (MEM) that computes accretion rates based on biomass production and inorganic sedimentation, but with lower computational expense. For example, previous analysisBaptist 2 model84) that increases bed roughness over the marsh platform84, defined as the basin areas with elevations between the time-varying MSL and MHW elevations. For the 5 mm/y SLR case, inlet sediment fluxes were negligibly impacted despite minor increases in marsh deposition (Supplementary Fig. The impacts of vegetation on the flow across the marsh surface and resulting influence on sediment transport patterns are neglected in the model experiments presented herein. Additional sensitivity analyses were performed that utilize the Delft3D trachytope roughness schematization (as a function of vegetation stem height and density according to the The analysis of net sediment transport trends was made possible by use of a cross-section across the inlet of the system, to correctly account for all sediment fluxes for each of the sediment classes used. Sediment fluxes at this location were integrated to determine cumulative transport. Similarly, inlet geometry information such as inlet area, tidal prism volume, instantaneous and cumulative water flux, water velocity, and water level information were also evaluated at this location to establish correlations and facilitate analysis used in the paper, including comparison to theory.Supplementary InformationPeer Review File


This cross-sectional study examines the characteristics and geographic distribution of eskatamine prescribers among Medicare beneficiaries in the US from 2019 to 2020. Ensuring equitable access to new drugs like esketamine is of interest given well-documented rural-urban disparities in access to mental health care and differences in the prevalence of major depressive disorder and other mood disorders between rural and urban populations. Further intersectional disparities may exist, as psychiatric clinicians who only accept self-pay or private insurances may adopt therapies at different rates than those who accept lower-reimbursing public insurances.This cross-sectional study examines early adoption and prescription patterns of esketamine among Medicare prescribers and beneficiaries from 2019 to 2020. The study aims to assess which clinicians have been early adopters of prescribing esketamine to their patients and how these clinicians are geographically distributed.STROBE) reporting guideline. This study did not require institutional review board approval as it was based on publicly available information, in accordance with 45 CFR §46. Informed consent was not needed because patient data were deidentified and anonymous.This study followed Strengthening the Reporting of Observational Studies in Epidemiology .Information about prescribers, beneficiaries, costs, and counties of esketamine prescriptions were obtained from publicly available Medicare Part D Provider Utilization and Payment Data. The 2013 Rural-Urban Continuum Codes (RUCC) were obtained for counties of prescription from the National Center for Health Statistics. Counties were categorized into metropolitan (RUCC 1-3) or nonmetropolitan (RUCC 4-9). Shapiro-Wilk testing was used to assess normality, with Aggregate cost and claims information from all prescribers submitting claims to Medicare for esketamine are presented in P < .001). Prescriber type, gender, and practice location (metropolitan vs nonmetropolitan) were not significantly associated with total claims.Prescribers in metropolitan areas submitted a similar quantity of claims to those in nonmetropolitan areas in 2020 , and total claims per clinician increased in 2020 (44.1 vs 27.2 in 2019, This cross-sectional study suggests that both the number of prescribers and the frequency of prescriptions for esketamine have increased rapidly, while costs per drug supply day have increased negligibly. This analysis demonstrates similar prescribing behavior regardless of clinician type and practice setting.While psychiatrists are the primary prescribers of esketamine across metropolitan areas, APCs represent the highest percentage of prescribers in nonmetropolitan areas and are growing as a percentage of prescribers in both metropolitan and nonmetropolitan areas. Few if any comparative analyses exist of new therapy adoption in physicians vs nonphysician prescribers. This study suggests independent APCs are critical early adopters of novel therapies, particularly in rural areas.This study has limitations. This study only includes prescribing patterns for Medicare-eligible recipients, which excludes clinics that exclusively accept self-pay or private insurance and are administering R,S-ketamine and esketamine for depression management. Furthermore, geographic data are only available for prescribers with more than 10 esketamine claims submitted to Medicare. Supply chain disruptions could have also played a role in prescribing patterns. Nonetheless, this analysis is the first to report adoption patterns of a novel-mechanism antidepressant in this country. These findings may provide a useful benchmark for evaluating the adoption and impact of future therapies for psychiatric conditions.


How do attention and consciousness interact in the human brain? Rival theories of consciousness disagree on the role of fronto-parietal attentional networks in conscious perception. We recorded neural activity from 727 intracerebral contacts in 13 epileptic patients, while they detected near-threshold targets preceded by attentional cues. Clustering revealed three neural patterns: first, attention-enhanced conscious report accompanied sustained right-hemisphere fronto-temporal activity in networks connected by the superior longitudinal fasciculus (SLF) II-III, and late accumulation of activity (>300 ms post-target) in bilateral dorso-prefrontal and right-hemisphere orbitofrontal cortex (SLF I-III). Second, attentional reorienting affected conscious report through early, sustained activity in a right-hemisphere network (SLF III). Third, conscious report accompanied left-hemisphere dorsolateral-prefrontal activity. Task modeling with recurrent neural networks revealed multiple clusters matching the identified brain clusters, elucidating the causal relationship between clusters in conscious perception of near-threshold targets. Thus, distinct, hemisphere-asymmetric fronto-parietal networks support attentional gain and reorienting in shaping human conscious experience. Analysis of intracranial EEG from epilepsy patients suggests three neural patterns associated with spatial attention and consciousness, and implicate high-level visual areas and lateralized fronto-parietal networks in shaping human conscious experience. However, how these processes interact with consciousness remain unclear6. For example, the available evidence suggests that endogenous, or top-down, attention plays little role in supporting conscious perception8, while exogenous (bottom-up or stimulus-driven) attention is a necessary, although not sufficient, condition for conscious perception10.How does attention impact consciousness? Decades of research have revealed that attention is composed of a family of neurocognitive processes, including endogenous and exogenous spatial attention, featuring partially distinct behavioral and neural dynamics11, which are strongly associated with attention processing13. Consequently, the proposed relationship between attention and consciousness is one of the key distinctions between consciousness theories14. Some theories explicitly include attention as a modulating factor for consciousness, without taking into account the full complexity of attentional processes16. According to the global neuronal workspace hypothesis, to attain conscious processing near-threshold stimuli must be attended and consequently receive top-down amplification15. In conscious perception, neural information is sustained and globally broadcasted across the brain, with an important role for dorsolateral prefrontal cortex (PFC) and inferior parietal cortex17. This idea about the relationship between attention and consciousness is also consistent with the gateway hypothesis16. In other theories, consciousness depends partly or not at all on attention-associated regions in the frontal or parietal cortex, without an explicit conceptual role for attention. For example, the integrated information theory18 postulates that conscious information is integrated in a temporo-parietal-occipital hot zone19, and the recurrent processing theory20 holds that conscious experience emerges from reverberating activity in sensory areas. Both theories postulate that FP networks contribute to post-conscious cognitive processing and task relevance of targets, such as motor planning or verbal report22, and claim that attention and consciousness are distinct both conceptually and neurally. Alternatively, attention and consciousness could be implemented by distinct neural mechanisms but have cumulative influence on the behavioral report. The cumulative influence hypothesis postulates the existence of an interaction between attention and consciousness solely at the behavioral level, but not in neural activity6.Rival theories of consciousness vary in the role ascribed to attention in conscious perception, both conceptually and neurally, and especially in the role of fronto-parietal (FP) networks23. This increase is accompanied by a higher activation of the dorsal FP attentional network24 for seen compared to unseen targets at attended locations25. Moreover, neurological patients with signs of spatial neglect26 display a systematic pattern of association between right-biased exogenous attention and unawareness of left-sided events27. This clinical evidence strongly suggests a specific role for right hemisphere FP attention networks in conscious processing.One reason for this theoretical divergence might be that the exogenous/endogenous distinction is not always taken into account. In neurotypical participants, exogenous cues near the spatial location of an upcoming near-threshold target increase the target’s conscious detectionDespite this converging evidence, which points to the modulation of consciousness by exogenous attention in both behavior and neural activity, it is still unclear where this interaction occurs in the brain and how different brain networks interact to achieve this effect. Further, the spatiotemporal resolution of neuroimaging techniques like fMRI and EEG used so far to study these questions is too rough for establishing the neural basis of the rapid and dynamic exogenous attention modulation of conscious perception. Facing the divergence of theoretical predictions, and the resolution limitations of the evidence collected hitherto, we decided to use a data-driven approach to try to establish the dynamics of the neural interactions between attention and consciousness on a fine scale, by taking advantage of the excellent spatiotemporal resolution of human intracerebral EEG (iEEG) recordings. Using unsupervised clustering, we tested empirically the division of functional clusters of iEEG contact based on the neural temporal patterns of the interaction between exogenous attention and conscious report , which was experimentally manipulated in a subjective conscious report task. This allowed us to reveal the brain areas supporting different patterns of interactions between attention and consciousness. We further employed white-matter tractography to collect connectomic evidence on the network architecture of the functional clusters. Finally, we used recurrent neural network models to causally examine the computations in the neural clusters and elucidate inter-cluster interactions, which critically contribute to behavior.28 . Patients performed a near-threshold target detection task28 Fig. , in whicp < 0.001, η² = 0.315). Although the main validity effect did not reach significance , validity interacted with conscious detection, because validly cued targets were more likely to be reported than invalidly cued ones . Signal detection theory (SDT) analysis showed that on target-present trials valid cues induced a higher detection rate , and a more liberal response criterion  than invalid cues did . However, no significant difference emerged in sensitivity . No significant interaction effects were found in response times.To see whether exogenous attention modulates the conscious perception of targets in the discrimination task, we performed a two-way ANOVA with the factors of cue validity and conscious report on the percentage of accurate responses. As expected, participants were more accurate in discriminating target direction when the targets were reported to be perceived than when they went unseen . Neural activity components of all contacts were visualized in a two-dimensional t-distributed stochastic neighbor embedding (t-SNE) . This procedure allowed us to group intracerebral contacts based on their temporal profile of neural activities across the experimental conditions. For each contact, we computed the temporal trajectory in the eight-dimensional condition space, i.e., the path of each contact’s HFBB power over time across all conditions . Using k-means clustering, each trajectory was then assigned to the cluster with the nearest trajectory-centroid, by iteratively minimizing within-cluster Manhattan distances. A ten clusters solution reflected the highest average silhouette score serving as the evaluation criterion of the clustering results . The number of contacts in these clusters was stable . Additionally, the Visual cluster showed an interaction between target side and cue validity in the cue-target period . This cluster mainly consisted of contacts in the right posterior temporoparietal areas (there was no available electrode in the homolog areas of the left hemisphere). To test whether the cue-dependent potentiation of baseline in the cueing period leads to subsequent increased target perception, we compared the activity associated with seen and unseen targets in the hemisphere contralateral to the cue, before target occurrence. We observed that the cues which preceded conscious target reports elicited higher neural activation in the Visual cluster . The contacts of the Sustained cluster were mainly located in the bilateral temporal cortex, the right angular gyrus, and the right PFC, around the central portion of the right superior frontal gyrus; the activity of the third cluster (67 contacts) slowly increased with over time build-up in the post-target period for seen targets , but there was no significant conscious report effect in the cue-target period. Most of the contacts in the Late accumulation cluster were located in the bilateral PFC, around the left inferior frontal gyrus (IFG), the right orbitofrontal cortex and the caudal portion of the right superior frontal gyrus. Similar to the attentional enhancement on conscious report in behavior, there was an attention-related enhancement interaction with conscious report in the three neural clusters . The amplitude of this interaction did not significantly differ across the three clusters by direct comparison , possibly due to faint target contrasts. A further explorative analysis compared this enhancement, by using Cohen’s d values which were derived from time-resolved t-test for the interaction contrast  − , around the time points where the interaction was significant. The result showed an increasing effect size gradient from the Visual cluster to the Sustained and Late accumulation clusters , suggesting an increasing attention modulation on conscious report along these clusters.First, the first interaction pattern encompassed three out of the five clusters and showed enhanced conscious report neural effect for validly cued targets compared to invalidly cued targets: the first cluster (42 contacts) showed an early transient post-target effect, with stronger activation for seen targets compared to unseen ones . This cluster presented fast activity in the cue-target period , as well as early in the post-target period , that resulted in seen reports for uncued targets. This cluster was localized in the right hemisphere temporoparietal junction (TPJ)/IFG. Activity in this cluster was sustained after target onset, then showed a transient peak around 180 ms only for invalid seen targets; however, for seen targets after valid cues, activity decreased once targets appeared. This cluster thus was likely to reflect reorienting of attention from the invalid cue to the opposite target24.Second, another neural cluster (19 contacts) showed a reversed pattern when attention was reoriented from invalidly cued locations to the target, with higher activity for seen targets after invalid cues than for seen targets after valid cues, early after target onset , independent of cue validity . This cluster contains contacts from the left posterior portion of dorsolateral PFC, around the left frontal eye field, and the bilateral posterior temporal area.Third, the last neural cluster (38 contacts) showed a late sustained neural activity selective for reported targets  into 20 quantiles according to their response time (RT) in the discrimination task . We tested the relation of the neural activities in RT-bins using time-resolved one-way repeated measures ANOVA, in a time window from target onset to 1000 ms, to avoid the influence of neural activity in the subsequent trial. The Late accumulation cluster showed target-locked sustained neural activity in trials evoking slower RTs . Specifically, the Sustained cluster was mainly connected by the right superior longitudinal fasciculus (SLF) II (43.4%) and III (36.8%). The Late accumulation cluster was mainly linked by the right SLF I (39.4%) and III (25.1%). The Reorienting cluster was connected by right SLF III (84.2%). No statistically significant tracts emerged from the analysis of the Visual and the Conscious report cluster.To specify the anatomical connections between contacts within each cluster, we performed white matter tractography analysis paired with probability maps in 176 healthy individuals from the Human Connectome databaseTo better understand the relation between activity dynamics and behavior, we simulated the task with a recurrent neural network (RNN) model Fig. . We sepa37. This curve differed from that characterizing an untrained model with random Gaussian connectivity weights , there was sustained unit activity in a Sustained cluster , and late activity in a Late accumulation cluster . Mirroring the model’s task performance, the higher the target contrast, the greater the activity in these clusters. Importantly, these clusters showed significantly enhanced activities for validly cued targets at sufficiently high stimulus contrast . A t-SNE visualization of unit activity of these clusters showed distinct unit component patterns for seen targets preceded by valid vs. invalid cues . We examined how these model clusters are interconnected, and the nature of their computation. In the trained model, we extracted input weights (sensory enhancement gain), connection weights between units, and output weights (report gain) . The stimuli input was mainly connected to the excitatory units in the Sustained cluster, which was associated with the conscious processing-Sustained intracerebral cluster. The Late accumulation excitatory units were connected to the decision output, confirming its role in decision making. Notably, the Reorienting excitatory units also received a branch of stimuli input and showed strong excitatory connection to the inhibitory units in the Sustained and in the Late accumulation clusters, reflecting the Reorienting cluster’s role in early target monitoring and in executing inhibitory control over stimuli processing and decision making units.To examine potential similarities between RNN clusters and human neural clusters, we used a temporal trajectory correlation analysis. RNN unit activity in the Sustained, Late accumulation, and Reorienting clusters in the trained model were significantly more similar to the equivalent human neural clusters than to the clusters obtained from the untrained model  a Sustained cluster showed attention-enhanced sustained activity for validly cued targets; (2) a Late accumulation cluster with progressively showed increasing activity until report; (3) a Reorienting cluster showed an early, sustained response to invalidly cued targets. Using RNN modeling, we discovered multiple clusters matching the identified neural clusters, clarifying the nature of inter-cluster interactions and uncovered the causal contribution of these clusters to behavior. Altogether, our behavioral, neural, and modeling findings consistently demonstrate that exogenous attention, the process that makes external salient stimuli stand out in a visual scene, modulates conscious report.We combined human intracerebral EEG, white matter tractography, and computational modeling to elucidate the fine-scale spatiotemporal dynamics of brain networks underlying exogenous attention modulation of conscious report. Unsupervised temporal clustering revealed three patterns of neural activity in fronto-parietal networks, partially diverging from the classical model of dorsal and ventral attentional networksWe demonstrated distinct neural dynamic patterns that implemented signal enhancement and attentional reorienting interacting with conscious report in three right-hemisphere FP networks connected by branches of SLF, as well as inter-network interactions.38. Importantly, the discrimination task, which served as an objective measure of performance, was performed first, closer to the cue and target presentation, and thus the attentional benefits (increased discrimination) were expected to be more readily observable in it. The subjective conscious report task was performed later, after an additional delay, and therefore the cueing benefits (increased a’ sensitivity) might have been masked by cognitive and motor processes related to the preceding discrimination task . Therefore, attentional benefits are expected to be observed more in the discrimination task than in the subjective reporting (detection) task. Our results are consistent with this prediction: an attention/consciousness interaction benefit was observed in the discrimination task , demonstrating the effectiveness of cues in attracting participants' attention.At the behavioral level, despite no significant difference in sensitivity between valid and invalid conditions, valid cues increased both discrimination accuracy and detection rate of near-threshold peripheral target and shifted the criterion toward the liberal side, in line with previous findingsDovetailing this attentional enhancement of conscious report effect in behavior, we identified three neural clusters in which validly cued seen targets elicited stronger neural activity than invalidly cued ones. This enhancement occurred first in high-level visual areas as a fast transient target-related activity, then in FP and temporal regions showing a sustained activity, and finally in bilateral PFC, presenting late accumulation activity, which lasted until the motor response.40. The present findings add to this evidence by indicating the neural location of the effects of attention on conscious report in the visual cortex. Our results clarified that spatial cuing potentiated report-related activity in high-level visual areas , but not in the early visual areas.Previous evidence showed that attention modulates neural responses across the visual cortical hierarchy, with an increasing magnitude from early to higher-level visual areas15. The involvement of the SLF II in the attentional modulation of conscious perception is in line with previous clinical evidence showing that damage to the right SLF II is the best anatomical predictor of the occurrence of neglect signs in stroke patients41. Importantly, transitory electrical inactivation of the SLF II in the human right hemisphere in a patient undergoing brain surgery provoked severe, if transient, rightward shifts in line bisection, akin to signs of left spatial neglect42. The present evidence specifies the temporal dynamics of the right hemisphere SLF II network, by demonstrating its role in attentional amplification of future targets.The Sustained cluster included hotspots around the right superior frontal gyrus and inferior parietal lobule, which were connected by the SLF II network. The attentional enhancement of conscious perception in this cluster may reflect recurrent neural activity which provides a specific neural substrate for neural amplification as suggested by the global neuronal workspace hypothesis45.Importantly, our findings suggest that the SLF II network, together with high-level visual cortex, support conscious reporting by modeling early cue-related baseline activity. Such activity is likely to improve perception of the subsequent target, in line with a wealth of previous research46. Alternatively, the activity of this cluster might reflect temporal expectancy for conscious report of targets48. In line with early EEG studies49, our results demonstrate that attention could enhance conscious expectancy through this FP network.The Late accumulation cluster included focal contacts around the right mesio-frontal and supplementary motor areas connected by SLF I-III network. This cluster showed a late accumulation activity until report, which was enhanced by spatial cueing. This interaction is consistent with its known role of SLF I network in evidence integration and decision making50, connected by the SLF III. The activity of this Reorienting cluster supports the role of the SLF III network in the conscious perception of targets preceded by an invalid cue at the wrong location. Previous neuroimaging evidence has shown the implication of this network in reorienting attention from the invalidly cued location to the targets occurring on the opposite side24. Importantly, our time-resolved results specify that this activity happens earlier than previously thought, before target presentation. This early activity (pre-target until around 150 ms post-target) suggests an anticipatory (lookout) activity for unexpected events such as invalidly cued targets and if necessary, it reorients attention before the attentional enhancement implemented by the SLF II network. Consistent with this hypothesis, early SLF III network activity decreased when the reported targets appeared at the validly cued location.A reversed pattern of interaction between attention and conscious report occurred when targets appeared at the uncued location: when reported, these targets elicited early, sustained activity in the right hemisphere TPJ and IFG (reorienting network)51, this neural activity might reflect the integration of sensory evidence and the formation of decision variables in post-orienting processes that are closely associated with conscious access mechanisms. Another possibility is that the left dorsolateral PFC biases perceptual decisions in conditions of uncertainty52, such as near-threshold detection. It remains to be seen how our intracerebral data relate to putative markers of consciousness derived from surface EEG21.Finally, independent of attention, seen, but not unseen, targets elicited late sustained activity in the Conscious report cluster located around the posterior portion of left dorsolateral PFC. We ascribed the function of this cluster as likely related to the set of processes associated with conscious access and the subsequent report. In line with previous neuroimaging evidence53 and inhibitory control54. Additionally, a previous simulation study of physiological leftward bias (pseudoneglect) found a similar excitatory influence of the right ventral attentional network linked by the SLF III on the dorsal attentional network55. Our results extended this prediction to an excitatory-inhibitory neuronal interaction with conscious report, at least for detecting near-threshold stimuli. Strong inter-regional excitation, balanced by local inhibition, can enable reliable sensory signal propagation to the PFC, which in turn can lead to global ignition56 and pave the path to conscious visual processing, consistent with the global neuronal workspace hypothesis57. More direct causal evidence of the interplay of these FP attentional networks for conscious perception comes from the finding that damage to the right dorsal PFC and decreased microstructural integrity of the SLF III impaired the conscious perception of near-threshold information58.Our RNN model of the task displayed striking similarities with the human intracerebral data, and allowed us to make causal inferences on the inter-network interactions of the neural clusters we observed. Two distinct components of the attention/consciousness interactions, attentional enhancement and attentional reorienting, clearly emerged in the trained RNN models. The Sustained excitatory units receive the majority of stimuli input and selectively enhance target-related information for conscious report. The Reorienting excitatory units directly receive a smaller branch of visual input. The inhibitory units in the Sustained and Late accumulation RNN clusters receive excitatory input from the Reorienting units. Critically, these results suggest that the right Reorienting neural cluster reorients spatial attention by monitoring the unexpected events in the environment and stopping the ongoing stimulus processing by inhibiting inappropriate activities of other clusters. RNN lesion data further support specific causal contributions of the identified neural clusters and their associated SLF networks in modulating conscious report of near-threshold stimuli. Our model predictions are consistent with neuroimaging evidence showing the involvement of right-hemisphere FP networks in attentional enhancement16, and the global neuronal workspace hypothesis17. Both theories postulate that the dorsal FP attentional network gates the way to conscious access, which our findings support and extend also to the ventral attention network. However, our current study was not designed to directly disentangle current theories of consciousness, and other results may not be in total disagreement with the remaining theories . For instance, we observed an early activation of the high-level visual cortex for the consciously perceived targets, a finding potentially in line with the Recurrent processing theory. Our findings specify the temporal dynamics and computational mechanisms underlying attention and consciousness interactions. More generally, our results shed light on the role of dorsolateral, ventrolateral and orbital PFC as well as high-level visual cortex, in human conscious report of near-threshold targets. Previous work on non-human primates showed that reported stimuli were associated with strong sustained PFC activity59. However, this late extensive PFC activity might reflect decision-making instead of conscious experience22. Our findings reconcile this debate by demonstrating, on the one hand, the late accumulation activity in right FP areas connected by SLF I–III, which lasted until target report; on the other hand, our results also show the sustained activity for consciously reported target in left PFC and right FP areas connected by SLF II–III, independent of decision time.Taken together, the observed interactions between exogenous attention and conscious report support current models of consciousness, which explicitly conceptualize the role of attention in consciousness, such as the gateway hypothesis60. However, a recent fMRI study using no-report paradigms to dissociate awareness-related neural activation from potential decision-making effects found results consistent with the present ones: activation of FP attention networks was independent of task report and eye movement activity61. Second, patients were instructed to maintain central fixation, but the clinical setting did not allow us to use eye-tracking recording. Third, our subjects had chronic epilepsy, although contacts with epileptic activity were excluded from analysis. Fourth, cortical coverage was necessarily limited , because it was obviously only determined by clinical needs. The limited and uneven brain coverage across patients also restricted our ability to analyze between-patient variability. However, assessing between-patient variability would not be informative regarding the generalizability of the findings to the neurotypical brain, as the patients serve only as a model for studying neurotypical cognition due to the inevitable lack of a neurotypical control group62. Last, we didn’t have implanted electrodes in thalamus in our patients, thus we have no empirical data regarding, for example, the potential involvement of recurrent thalamocortical loops, which may well be major contributors to conscious processing66.The present study has some limitations. First, the use of a subjective conscious report task to probe conscious perception, in which participants had to choose between three alternatives indicating the perceived location of the target (left or right) or its absence. This measure provides information concerning whether participants consciously perceived the target, but it does not provide more subtle information regarding the participants’ awareness of their perception, unlike confidence ratings14. Our findings establish specific roles for the right-hemisphere SLF II network in the attentional enhancement of near-threshold targets, for the right-hemisphere SLF III network in perceiving previously unattended targets, and confirm the hypothesized role of left-hemisphere dorsolateral PFC in perceptual decision. This attention/consciousness interaction relies on specific excitatory and inhibitory inter-network interaction mechanisms that causally contribute to conscious perception of near-threshold targets. Thus, distinct, hemisphere-asymmetric fronto-parietal networks support attentional gain and reorienting in shaping human conscious experience.Despite these considerations, the present evidence establishes the neural dynamics of distinct FP networks and high-level visual areas in the attentional modulation of conscious report of near-threshold stimuli, which is one of the hallmark concepts distinguishing different consciousness theoriesWe recruited sixteen patients who underwent presurgical evaluation of pharmaco-resistant focal epilepsy with iEEG implantation, at the Department of Neurosurgery of the Hôpital Pitié-Salpêtrière, Paris, France. All participants had normal or corrected-to-normal vision  and provided their written informed consent. The implantation of electrodes and the participation of patients to cognitive tasks received approval from local ethic committee ; CPP INSERM C19–55). Patients differed in their prescribed medical treatment, which was reduced or stopped after implantation depending on each patient’s medical needs and the delay between the surgery and the experimental recording (range 3–14 days). Three patients were excluded from the data analysis due to poor data quality , leaving a total of thirteen patients in the final sample . Patients were implanted with 4–13 multilead stereotactic depth electrodes  endowing 4–12 platinum contacts with a diameter of 1.12 mm and length of 2.41 mm, with nickel-chromium wiring. The distance between the centers of two contiguous contacts was 5 mm. The location of electrode implantation was based exclusively on clinical criteria. In five patients the neural activity was recorded with a 128 channels clinical video-EEG recording system , sampling at 1024 Hz with a band-pass filter of 0.15–250 Hz. In the other eight patients, the recording was implemented with a Neuralynx system  which allowed recording up to 160 depth-EEG channels sampled at 4 kHz with 0.1 Hz to 1000 Hz band-pass. The patient-dependent least active contact, preferably in the white matter, was selected as the reference electrode.The stimuli presentation was controlled by E-Prime 2.0 software (SCR_009567) running on a laptop with a 60 Hz refresh rate. Three black boxes (4.9° long and 3.6° large) were arranged around a central fixation point, persisted for the whole duration of the trial, with 6° horizontally separating the central box center from the peripheral boxes’ center. The two peripheral boxes were located in the lower visual field, 4° of visual angle under the central box. Figurehttps://jasp-stats.org/). We performed a two-way ANOVA with the factors of cue validity and conscious report (in the subjective conscious report task) on the discrimination accuracy. RTs analysis was not reported since participants had to wait for the response screen to give their responses.For each participant, we first excluded trials with RT faster than 150 ms or exceeding three standard deviations. Statistical tests in behavioral analysis were performed using JASP software  and when the Gabor was absent . The criterion C summarizes the distance of the threshold relative to the noise distribution from the threshold of an ideal observer. A smaller value of C represents a more liberal threshold in target detection.Using a nonparametric measuret-test.We compared detection rate, false alarm rate, sensitivity, and criterion between valid and invalid trials with paired sample 69 developed by the STIM engineering platform in the Paris Brain Institute (https://icm-institute.org/en/cenir-stim) with co-registered pre-implantation 1.5 T or 3 T MRI scans and post-implantation CT and MRI scans. After the normalization of MRI-pre, MRI-post and CT-post into the MNI space, contact localization was automatically labeled referring to Desikan-Killiany-Tourville atlas parcellation70 in the patient’s native space, using Freesurfer image analysis suite (https://surfer.nmr.mgh.harvard.edu/) embedded in the Epiloc toolbox, followed by a manual verification and correction, if necessary. Figure 70 29 contacts (4%) from occipital, 332 (46%) from temporal, 78 (11%) from parietal, 202 (28%) from frontal, 24 (3%) subcortical and 62 (9%) in white matter. There were 288 contacts in the left hemisphere and 439 contacts in the right hemisphere.Spatial localization of each electrode was recovered using the Epiloc toolbox71. First, all signals were down sampled to 512 Hz and all contacts were re-referenced to their adjacent neighbor contact on the same electrode, yielding a bipolar montage, in order to ensure that iEEG signals could be considered as originating from a cortical volume centered within the two contacts. Coordinates of bipolar contacts were computed as the mean of the MNI coordinates of two adjacent contacts composing the bipole. An initial visual inspection of continuous signals was performed to remove time segments showing transient epileptic or interictal activity. Contacts with excessive epileptic spikes or near suspected epileptic focus were also rejected. We extracted time courses from −1300 to 1200 ms around target onset for trial epochs. A second artifact inspection was performed on the epoched data, where trials and contacts with excessive maximal signal, z value, variance or kurtosis of the signal distribution were rejected. After signal preprocessing, 727 of the bipolar contacts out of 887 contacts were retained for further analysis.Signal preprocessing was conducted using Matlab  and FieldTrip toolbox (fieldtrip-20210529)30, but also see31, was extracted from each bipolar contact by wavelet time frequency transformation using the Morlet wavelets implemented in Fieldtrip , in fourteen equally spaced center frequency bands. We kept high-frequency band power time courses from −800 to 1000 ms to target onset to discard the 1/f signal drop off at the edges. Baseline normalization was applied on each trial by means of a z-score relative to the period in the 200 ms prior to cue onset. Finally, HFBB powers were down-sampled to 100 Hz for further analysis.We then adopted a pseudo-whole-brain analysis approach, by pooling contacts across all thirteen patients on a standardized brain in MNI space. High-frequency broadband (HFBB) power (70–140 Hz), a proxy of spiking activity of the local neuronal ensembleWe visualized neural activities and unit activities in a two-component space by a machine learning visualization approach, t-distributed stochastic neighbor embedding (t-SNE). In neural data, we averaged neural activities across contacts by conditions and time points, of which the resulting matrix served as input to compute temporal embedding. t-SNE was computed by an implementation in Scikit learn 1.0.2 in Python 3. We adopted a perplexity of 30 and a learning rate of 100. The embedding was initiated with the PCA option and optimized upon 1000 iterations, by default. The neural data in each condition was projected to a two-dimensional t-SNE embedding. Thus, the ensemble of time points formed a temporal trajectory of neural activities in a reduced manifold. A larger difference between two trajectory components represented a more distinct neural activity pattern between the two conditions. In RNN simulation, we averaged unit activity across units by condition and time point, and kept the above parameters in computing its t-SNE temporal embedding.k-means clustering to classify contacts by their temporal profiles32, implemented through Matlab . This data-driven approach was able to capture the prototypical patterns of neural dynamics that might be sensitive to cue validity and seen/unseen reports. We conducted clustering on all bipolar contacts. In each contact, we took the trajectories of the mean target-locked activity across an 8-dimensional condition space . Activity across conditions was z-scored relative to the distribution of the trials’ entire duration. Contacts were then iteratively partitioned (10000 iterations) into 2–12 clusters, in which each contact was assigned to the cluster with the nearest centroid trajectory. This was achieved by minimizing the sum of time-point-by-time-point Manhattan distances across conditions, to quantify trajectories similarity while preserving temporal order. Based on the silhouette evaluation (silhouette in Matlab), we adopted a ten clusters solution, which reflected the highest average silhouette score , cue validity , and conscious report (seen/unseen). We tested on HFBB power in both cue-target period (from −300 ms to target) and post-target period (from target onset to 500 ms), across contacts on each cluster. For each contact, we averaged HFBB power across trials by conditions. Holm–Bonferroni correction was applied over all the time points for multiple comparisons. For clusters showing significant consciousness effect, post-hoc comparisons were performed on time points where the Cue validity × Conscious report interaction was significant, with Holm-Bonferroni correction. Further, in order to compare the degree of attentional enhancement across clusters that showed higher neural activity for validly cued seen targets, we performed a linear contrast testing around time points where the clusters showed significant effects.To understand the functional roles of neural clusters, we related the neural activity to the response time. In each cluster, we pooled trials from all conditions and contacts. We then sorted the trials and binned them into 20 quantiles according to their response time in the discrimination task. We compared RT-bins neural activity using time-resolved one-way ANOVA in a short time window . To specifically test whether higher sustained neural activity was related to the slower response time, we then compared the neural activity of the ten RT bins with slowest response to the neural activity of the 10 RT bins with fastest response. Holm-Bonferroni correction was applied over all the time points. We also visually compared the RT bins sorted neural activity in a longer time window (target onset to 5000 ms) in order to identify the neural activity patterns associated with visual modulation and task report in each cluster.34. We performed tracts filtering in TrackVis toolbox72 to obtain tracts connecting frontal contacts and parietal contacts in frontal association tracts35 . For each cluster, the tractography maps of the 176 healthy individuals were subsequently binarised and then smoothed with a three-dimensional Gaussian filter . To test the presence of tracts across individuals, we used a threshold-free cluster enhancement-based non-parametric test, with 1000 permutations (randomize function in FSL) and a height threshold of 0.95 to control significance level at p < 0.05. We computed the volumetric ratio of the labeled tracts in each cluster with those of the standard atlas in BCBtoolkit73 (http://toolkit.bcblab.com/), where we filtered the atlas probabilistic maps with a threshold of 80% to reduce their overlapping.We dissected white matter tracts connecting parietal contacts with frontal contacts. We modeled each intracerebral contact by a 3 mm-sphere. In each cluster, we created two region-of-interests, respectively consisting of frontal contacts and parietal contacts. The parcellation of contacts was referred to Desikan–Killiany–Tourville atlas  are networks in which neurons (units) can send and receive feedback to and from each other. Therefore, the activity of the units is affected not only by the current external stimulus, but also by the current state of the network2 × N matrix), the recurrent layer (N × N) and the output layer (N × 2). Our RNN model contained a single layer trained with mini-batch gradient descent learned by backpropagation. Before time discretization, the network activity 79, we abstracted the relevant visual stimuli properties from the patient task , weight initializer and the loss function . The noise in each recurrent unit was set to 0.05. The input, output and recurrent weight was randomly initiated by a glorot gauss distribution from −1 to 1. The transfer function was rectified linear unit (ReLU). The loss function was defined as the mean squared error. The connections between hidden units were constrained according to Dale’s principle: neurotransmitters tend to be either excitatory or inhibitory such that the post-synaptic weights of each recurrent unit are all of the same sign78. There were 80% of units fixed to be excitatory and the remaining 20% of units were inhibitory. The strong inhibitory signaling in the recurrent neural network enables stable temporal dynamics77. The cue contrast was set as 0.30 SD and we trained RNNs, one at a time, with various target stimulus levels from zero to 0.13 SD, which mimics the near-threshold targets setting in the patient task. The RNNs were trained with 150000 iterations and the model accuracy was tested on 50 batches of 50 sample trials for each target contrast, respectively. Finally, we fitted a psychometric detection curve by logistic function in the subjective conscious report task and in the discrimination task, respectively. To verify the validity of the model, we compared the performance of trained models with an untrained model that was initialized with random Gaussian connectivity weights without feeding stimuli inputs for learning. We also tested RNNs with different hidden units’ size  and found out that those RNNs could not achieve human-level performance when the units were less than 16. To keep sufficient units for further clustering analysis, we adopted the number of hidden units N as 50 to balance the model’s complexity and variability.We implemented the model training with PsychRNNt-test in the post-target period . However, only a late cluster showed a significant consciousness effect. Other clusters may not have reached the significance level because of the low target contrast.We applied the above-mentioned trajectory clustering method to classify dynamic patterns of the 50 hidden units for intermediate target contrast (0.10 SD). We generated 20 batches of 50 trials  and averaged unit activity by condition . Unit trajectories were iteratively partitioned into 2–10 clusters across the three-dimensional condition space. The highest averaged silhouette value was obtained while the number of clusters equals five , we averaged neural activity in seen and in unseen trials from cue onsets to 500 ms post-targets. We then generated 500 batches of 50 trials with the RNN model simulation for both trained and untrained models. In each batch, we averaged RNN clusters’ unit activity in seen and in unseen trials. The similarity was quantified by Pearson correlation coefficient between the RNN clusters’ temporal trajectory to the neural cluster ones, with the averaged coefficient of conditions. Therefore, we obtained a distribution of correlation coefficients with 500 samples for the trained and for the untrained models. Finally, we conducted a one-sided permutation test with 1000 permutations to compare the two distributions, in order to identify the RNN clusters being significantly more similar to the neural clusters in the trained model than in the untrained model.t-test was performed with significance level controlled at p < 0.05, Holm-Sidak corrected for multi-comparison among groups of units.The iEEG contacts of the neural clusters were pooled from different patients, which limits the possibility to analyze their inter-cluster connections. However, our modeling approach provides the possibility to examine how unit clusters are inter-connected as an integrated model. To this end, we extracted unit input weights, directed unit-to-unit connection weights and output weights of the trained model . Intracranial data were processed using Matlab  and FieldTrip toolbox (fieldtrip-20210529). Tractography data were processed using TrackVis toolbox (https://trackvis.org/) and BCBtoolkit (http://toolkit.bcblab.com/). The RNN simulation was performed using PsychRNN .We analyzed data from Further information on research design is available in the Supplementary informationDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary