Rubus idaeus L., using a range of yield metrics—fruit set, marketable fruit set, fruit weight, and marketable fruit weight—over 3 years (2019–2021), to quantify the effects of yield metric, interannual variation, and cultivar on pollinator dependence ratio estimates. We found a difference in the pollinator dependence ratio for fruit set of 0.71 between 2019 and 2020, showing the importance of carrying out exclusion studies over multiple years. Averaged over multiple years and two cultivars, the dependence ratio was 0.68 measured using marketable fruit set and 0.64 using marketable fruit weight. Imposing a quality threshold (size and shape) below which fruits would not be of commercial value (marketable fruit set/weight) dramatically increased both the pollinator dependence ratio and subsequent economic valuations of pollination service derived from it. Our study shows that, for raspberry, estimates of the pollinator dependence ratio, and therefore, the economic value of insect pollinators, are highly sensitive to the choice of yield metric and can change between years and cultivars. Many economic decisions about pollinator management, at farm, regional and national scales rely on estimates of pollinator dependence. We, therefore, recommend that for estimating pollinator dependence ratios, pollinator exclusion studies are conducted over three or more years and use yield metrics that incorporate quality criteria linked to actual market values and commercial thresholds.The benefit of pollinators to crop production is normally calculated using “pollinator dependence ratios,” which reflect the proportion of yield lost  in the absence of pollinators; these ratios are quantified experimentally using pollinator exclusion experiments. Pollinator dependence ratio estimates can vary considerably for a single crop, creating large, frequently overlooked, uncertainty in economic valuations of pollinators. The source of this variation is usually unclear. We experimentally measured the pollinator dependence ratio of two UK commercial cultivars of raspberry  Pollinator dependence ratios are important for quantifying the benefit of pollinators to crops. Pollinator dependence ratios often are not calculated using commercially relevant yield metrics and are frequently informed by single‐year studies. We show that the pollinator dependence on raspberry varied greatly between years and with differing yield metrics. Imposing a threshold fruit quality (size and shape) below which fruits would not be of commercial value (marketable fruit set) dramatically increased both the pollinator dependence ratio and subsequent economic valuations of pollination service derived from it. Our results show the influence of yield metrics on pollinator dependence estimates and the economic value of pollinators, and we recommend that pollinator exclusion studies are conducted over multiple years (three or more) and use yield metrics that incorporate quality criteria linked to actual market values and commercial thresholds of the product in question. An estimated 79% of angiosperms have improved seed sets in the presence of pollinators  can be quantified by providing additional pollen to flowers that have access to pollinators  and comparing the resulting yield to flowers that were not pollen supplemented. Pollination deficits can be used to determine whether and how much pollination service management needs to be improved to maximize yields. Pollinator exclusion and pollen supplementation experiments have been used to quantify the pollinator dependence and pollination deficits of over 80 different crops  production was valued at £133.3 million, 12.8% of total UK fruit production value for that year and the third largest income for a single type of fruit behind strawberries and dessert apples , with and without accounting for marketability. We calculated the different pollinator dependence ratios and determined whether there was a pollination deficit despite highly managed pollinator input. We asked whether and how much the pollinator dependence ratio differed between years, cultivars, and yield metrics to determine whether these are likely causes of the variation in pollinator dependence found between raspberry pollination studies. We asked whether the mean pollinator dependence taken from Klein et al.  and used22.1Rubus idaeus); “Diamond Jubilee” and “Sapphire” were included in the study, both developed by BerryWorld and made available for growers in 2013 . Both cultivars were grown throughout each study period. Each experimental site was made up of one commercial field of >1.5 ha surrounded by uncropped field margins. There were small areas of semi‐natural grassland and patchy woodland on the farm and within the immediate surrounding area. Both raspberry cultivars were grown under Spanish polytunnels in drip‐fertilized and irrigated pots, each with two canes per pot. Raspberry canes are only harvested for one growing season, and 153 and 149 rented honeybee hives were in place at the farm during 2019 and 2020, respectively, equating to ~2 hives/ha of farmland, throughout the raspberry flowering season, for the purpose of crop pollination of both raspberries and strawberries. This dropped to 81 colonies in 2021, due to colony losses and relocation to other sites reducing the stocking rate to 1 hive/ha. No managed bumblebee colonies (Bombus terrestris) were active on the farm during the study periods in 2019 and 2020. A few colonies were still active in an adjacent field to Diamond Jubilee in 2021; however, they were at the end of their 10th week in situ when the first study flowers opened. It is therefore likely that the bees leaving these colonies were gynes and males.The study was carried out on an 81‐ha commercial soft fruit farm near Reading, south England  throughout the period from June to September 2019, 2020, and 2021. Two self‐compatible cultivars of red raspberry , 30 in 2020 , and 36 in 2021 . Canes were only used for one growing season at the study site and so new canes were selected each year for the exclusion study. Cultivars of raspberry and strawberry were also rotated between fields and so repeated sampling from the same field over multiple years was not possible. Sample size was increased in later years to provide contingency because some inflorescences were lost from the experiment in 2019 and 2020, due to accidental picking or disease. For each plant, three lateral branches with ≥7 flower buds were selected and randomly assigned to one of three treatments; insect pollination (IP), insect exclusion (IE), and insect exclusion with pollen supplementation (IES) or “hand pollination,” for 2020 and 2021 this was exactly 10 buds per treatment on each plant but for 2019 this varied between 7 and 18. Any open flowers were removed at the start of the study. The flowers assigned to the insect exclusion and insect exclusion with pollen supplementation treatments were covered by 27 × 27 cm bags made of 1 mm mesh, tied at the bottom with string to prevent pollination by insects , without excessive bubbled drupelets  were harvested when bright red and the fruit could be detached easily from the receptacle and counted. In 2020 and 2021, fruits were also weighed and measured (length and width) at the widest points using calipers. Fruits that were visibly infected with molds such as D represents the pollinator dependence ratio, x¯IP is the mean yield metric  for insect‐pollinated flowers, and x¯IE is the mean yield metric  for insect‐excluded flowers.This was calculated using the following formula:Fruit set was calculated as the percentage of flowers that resulted in fruits, so buds that did not result in a flower were excluded from this analysis.2.4D), along with the quantity of commercial crop (in tonnes) produced (Q) and the price per tonne received at market (P):The economic value of insect pollination to raspberry production was calculated following the bioeconomic approach of Gallai et al. . This eqQ×P) for 2020 (2021 data not confirmed at time of submission) multiplied by our dependence ratios for fruit set and marketable fruit set for the two cultivars and 3 years combined. Due to the differing prices per tonne for each retailer, cultivar‐specific economic valuations of pollination services to the crop were not calculated. For illustration purposes only, we used the pollinator dependence ratios calculated in this study to represent all UK‐grown commercial raspberry crops in this equation.For this study, we used Defra's horticultural statistics Defra,  which ga2.5p < .0001, and Corr.coeff = .855, p < .0001 respectively). Full details of all variables and maximal models are shown in Table Analyses were conducted using general(ized) linear mixed models (GLMMs) in R statistical software , and only 6% was due to being underweight without the presence of bubbles or crumbliness.In total, 2456 ripe fruit were harvested from 2733 study raspberry crop flowers across both varieties, all years, and pollination treatments. About 110 of these were harvested by commercial pickers so we could not weigh or measure them, though they were included as marketable fruit in the fruit set analyses, and 385 fruits were excluded from all analyses as they were infected with 3.1n = 58 branches, 92.53% ± 2.58) did not significantly increase the percentage of marketable fruit set compared with insect‐pollinated bagged branches , in fact, marketable fruit set was significantly lower for pollen supplemented flowers, perhaps due to interference between pollen tubes. These results show that there was no pollination deficit for raspberry crop flowers in this system when producing marketable fruit. Insect‐excluded branches  yielded significantly less marketable fruit than insect pollinated , showing that pollinators are important for producing highly marketable yields of these cultivars of raspberry. Hand‐pollinated bagged branches  yielded significantly less marketable fruit than insect‐pollinated branches , indicating a negative effect of hand pollination, perhaps due to physical damage or incomplete pollination. Cultivar was a significant predictor of marketable fruit set, with Sapphire  yielding a significantly higher % marketable fruit set from all study branches across all treatments compared to Diamond Jubilee . The total marketable fruit set differed between all 3 years  and 2021  than in 2019  and was also significantly higher in 2021 than in 2020  did not significantly increase the percentage of fruit set compared with insect‐pollinated branches . Insect‐excluded branches  yielded significantly less fruit than insect‐pollinated branches . Hand‐pollinated bagged branches  yielded significantly less fruit than insect‐pollinated branches . Cultivar was a significant predictor of fruit set, with Sapphire  yielding a significantly higher % fruit set from all study branches across all treatments compared with Diamond Jubilee . Fruit set was significantly higher in 2020  and 2021  than in 2019  and was also significantly higher in 2021 than in 2020  or Sapphire  suggesting that marketable fruit weight was not pollen limited in either variety. The pollinator dependence ratio was calculated using measurements in this study. We have also shown that the pollinator dependence ratio using fruit weight marketable fruit weight for both cultivars is shown in Table F = 3.313, p = .08, Sapphire: F = 1.828, p = .187; Figure Our results for the effects of pollination treatment and year on marketable fruit weight are shown in Figure 3.3The economic value of insect pollination to raspberry production was calculated using Equation 4D = 0.68) across our two varieties and three study years (Table D = 0.16), as expected due to the self‐compatibility of cultivated R. idaeus. This yield metric included low‐quality fruit that would not reach the market as fresh raspberries, and therefore have no economic value for commercial producers in our system, negating its usefulness in pollinator dependence ratios for commercial crops.The importance of insect pollination to UK raspberry production is evidenced by the average reduction in marketable fruit set of 68.2% . Marketable fruit weight and marketable fruit set together provide a more complete picture of the benefit of insect pollinators to crop yield where yield is paid for by weight rather than units. In this study, bubbles or crumbliness Figure  were theHand pollination was included as a treatment in the first year 2019) as this was thought to represent the maximum fruit set as in other crops  for this study was similar to the 20% mean yield reduction reported by Prodorutti and Frilli  was greater than the between‐study variation for previous raspberry pollination studies (10%–70% yield reduction in the absence of pollinators). This suggests that between‐study variation is likely to be at least in part, explained by differences in yield metric, study year, and cultivar. Using a multi‐year study and a more appropriate yield metric, we have validated the h et al.  and Potth et al. .5We have shown that two varieties of commercially produced raspberry in the UK (Diamond Jubilee and Sapphire) are pollinator dependent using four different yield metrics over three study years. However, the strength of this dependence is highly sensitive to the cultivar, year, yield metric used, and the environmental conditions of the study, as well as the criteria used to decide which fruit are included in “yield” measurements. Where exclusion studies are used to calculate the economic value of pollination services using dependence ratios, we strongly recommend that studies are conducted over multiple years (three or more) to generate a range of uncertainty, and that commercial quality criterion linked to actual market value are incorporated into the calculations of dependence.Imogen C. Ryan: Conceptualization ; data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); project administration ; visualization (lead); writing – original draft (lead). Lynn V. Dicks: Conceptualization ; funding acquisition (lead); methodology (supporting); project administration ; supervision (lead); writing – review and editing (lead). Jack D. Shutt: formal analysis (supporting); supervision (supporting); writing – review and editing (supporting)The authors declare that they have no conflict of interest.Data S1.Click here for additional data file.


Arbacia lixula is often associated with communities dominated by encrusting corallines, devoid of fleshy algae. While it is commonly known as a diurnal herbivore, this species also migrates at night from hidden to more exposed habitats. Here, we provide the first experimental evidence of an adjustment to a predominant nocturnal behaviour in a population of A. lixula. Sea urchin densities changed from nearly zero during daytime to more than 16 urchins m−2 at night in treatment plots where the sea urchins were removed. We suggest that the observed behaviour was triggered by our experimental manipulations and was a response to the presence of dead conspecifics and small predatory fishes attracted by the urchin culling. Further research is needed to assess whether our findings can be generalised to the behaviour of A. lixula in areas where sea urchins are under strong pressure from diurnal predators. In these cases, it is important to perform sea urchin density counts at night to avoid misleading assessments about the herbivore pressure in a littoral area.In Mediterranean rocky shores, the black sea urchin  Arbacia lixula. We suggest that the observed behaviour was triggered by our experimental manipulations and was a response to the presence of dead conspecifics and small predator fishes attracted by the urchin culling. It is important to perform sea urchin density counts at night to avoid misleading assessments about the herbivore pressure in a littoral area.Here, we provide the first experimental evidence of an adjustment to a predominant nocturnal behaviour in a population of  In an attempt to achieve the main objective of our study, we eventually witnessed the adjustment to an almost complete nocturnal grazing in the population of A. lixula studied.In 2015, our research group had a chance to test if the removal of sea urchins could trigger the growth of FA in sublittoral habitats where both these algae and EC co‐occurred Figure . Both evA. lixula triggered by our experimental manipulations.Here, we describe the results of an experiment carried out to ascertain why, after 4 months of sea urchin removal and in spite of the apparent lack of sea urchins, grazing continued at the treatment plots. We provide evidence for a possible adjustment in the behaviour of 2Coris julis and Thalassoma pavo, the sea breams Diplodus sargus and D. vulgaris together with several species of blennies, fed on the injured sea urchins. Because of their reduced size, these diurnal predators were never observed feeding on uninjured sea urchins (from 2 to 7 cm diameter). As in many breakwaters from the same area where spear and rod fishing are widespread, no large fish predators were ever observed during the study period.At the end of July 2015, we haphazardly selected 20 boulders at around 1–3 m depth along the dock of the Blanes harbour (NW Mediterranean), which were devoid of FA. We selected 10 concrete boulders as replicates for the sea urchin exclusion treatment and the relative controls  cover—at the centre of the upper sides of the boulders—and herbivore feeding trails were noticed Figure .To assess a possible adjustment to a predominant nocturnal grazing, we decided to conduct a day–night experiment on the same plots. On 11 November 2015, a SCUBA diver conducted five dives, at 10:30 am, 4:30 pm, 10:30 pm, 1:30 am and 8:00 am, recorded the numbers of sea urchins present over the upper sides of the boulders and took photographs of each of them.2 boulder upper surfaces) repeatedly sampled in time (sampling days) for both the exclusion and control treatments. We had the same experimental design for the day–night experiment but, in this case, we wanted to test the effects of exclusion treatment on the abundance of sea urchins. The data from the day–night experiment did not meet some of the basic assumptions for general univariate inference or linear regressions analyses (e.g. a repeated‐measure ANOVA). Most importantly, there was an expected lack of independence between the observations in time (about 6‐h lapse), and the numbers of observations for the day versus night comparisons were unequal . This species, which is regularly harvested in the study area by commercial fishermen, was playing a marginal role in grazing on FA at the study site and it was not further investigated in this study.At the start of the general experiment, in July 2015, the boulder‐exposed surfaces had 6.75 ± 1.75 (mean and SE) individuals of Laurencia sp.  to the upper surfaces of the boulders and they also show a predominant nocturnal foraging behaviour for A. lixula.Our results support the idea of a night movement of It is well known that sea urchins may show different foraging behaviours depending on the presence of predators, dead conspecifics and shelters  and in the absence of finfish large enough to prey on adult sea urchins at the study site, we suggest that the presence of dead conspecifics and small fish predators enhanced by our experimental manipulations may have triggered the behavioural adjustment in the grazing patterns in the population of A. lixula in areas where sea urchins are under strong pressure from diurnal predators. In all these cases, it would be recommended to perform sea urchin density counts at night to avoid misleading assessments of herbivore abundance and grazing pressure.Further research is needed to assess whether our findings can be generalised to the behaviour of Simone Mariani: Conceptualization ; data curation ; formal analysis ; methodology ; software ; supervision ; validation ; visualization ; writing – original draft ; writing – review and editing . Susana Pinedo: Conceptualization ; methodology ; validation ; writing – review and editing . Esther Jordana: Conceptualization ; methodology ; validation ; writing – review and editing . Maria Elena Cefalì: Conceptualization ; methodology ; validation ; writing – review and editing . Xavier Torras: Conceptualization ; methodology ; validation ; writing – review and editing . Marina Bagur Bendito: Methodology ; writing – original draft ; writing – review and editing . Jana Verdura: Methodology ; writing – review and editing . Enric Ballesteros: Conceptualization ; funding acquisition ; methodology ; project administration ; supervision ; validation ; writing – review and editing .The authors declare no conflict of interest.


Faxonius virilis) that are indicative of a potential trade‐off between energy allocation for body size versus chelae size growth. Northern crayfish undergo cyclic dimorphism, a process characterized by seasonal morphological changes associated with reproductive status. We measured carapace length and chelae length before and after molting and compared these growth increments between the four morphological transitions of the northern crayfish. Consistent with our predictions, reproductive crayfish molting to the non‐reproductive form and non‐reproductive crayfish molting within the non‐reproductive form experienced a larger carapace length growth increment. Reproductive crayfish molting within the reproductive form and non‐reproductive crayfish molting to the reproductive form, on the other hand, experienced a larger growth increment in chelae length. The results of this study support that cyclic dimorphism evolved as a strategy for optimizing energy allocation for body and chelae size growth during discrete periods of reproduction in crayfish with complex life histories.Complex life histories are frequently associated with biological trade‐offs, as the use of one trait can decrease the performance of a second trait due to the need to balance competing demands to maximize fitness. Here, we examine growth patterns in invasive adult male northern crayfish ( Complex life histories are frequently associated with biological trade‐offs, as the use of one trait can decrease the performance of a second trait due to the need to balance competing demands to maximize fitness. Here, we examine growth patterns that are indicative of a potential trade‐off between energy allocation for body size versus chelae size growth in a crayfish species that undergoes cyclic dimorphism. The results of this study suggest that cyclic dimorphism evolved as a strategy for optimizing energy allocation for body and chelae size growth during discrete periods of reproductive activity and inactivity in form alternating crayfish. For exaWhen the available energy is subjected to a trade‐off between growing in body size and other traits useful during reproduction, individual differences in growth rate may arise from differences in reproductive status , a crayfish in the Astacidae family, has recently been reported to display morphological patterns consistent with cambarid form alternation  and a reproductive morphotype (commonly referred to as form I or FI). The short period of reproductive inactivity occurs in the summer during the intervals between the breeding seasons Faxon, . Mating Crustacean molting is the process of casting off the exoskeleton to permit the expansion of soft tissues and an increase in body size via dimensional increases occurring over intervals , reproductive males tend to have larger  by following individuals across their molting activity. Native to the north‐central United States and south‐central Canada east of the Continental Divide, northern crayfish have been introduced throughout much of the western United States and across the Appalachian region of the eastern United States  and aggression (n = 84). Depending on the nature of the experiment, crayfish were held in the laboratory for various lengths of time (3–6 weeks). As a result, long‐term housing allowed us to capture morphological measurements before and after molting events as they occurred. Therefore, measurements for the present study were opportunistically collected from 33 of these crayfish that happened to molt while being held in the lab, as follows: 12 in 2019 and 21 in 2020. All crayfish originated from a non‐native population located at Strawberry Reservoir, Wasatch County, Utah . Introductions of northern crayfish in this region are presumed to have been initiated by management agencies as a source of forage for non‐native sportfish  and with bait (n = 6), crayfish were transported to a laboratory at Brigham Young University. Animals were housed individually in plastic aquaria  filled with dechlorinated tap water and were held under a 16 h:8 h light : dark cycle. Temperatures in individual plastic aquaria ranged from 17.8 to 20.7°C. Each container was continually aerated and supplied with a small piece of PVC shelter. Crayfish were fed twice a week ad libitum with commercial sinking wafers (Hikari).Morphometric data for the present study were derived opportunistically from adult male northern crayfish collected as part of separate experiments that took place over a two‐year period. Crayfish in these separate experiments were held in the laboratory for behavioral measurements that included measuring activity  upon collection and post‐molting by visual inspection of the swimmerets  and so was excluded from the models. Following each ANCOVA, we performed a Tukey's Test for multiple comparisons using the multcomp package , but was not influenced by pre‐molt carapace length . The carapace length growth increment for reproductive crayfish molting to the non‐reproductive form  was larger than for reproductive crayfish molting within the reproductive form , though this difference was only moderately significant  and was influenced by pre‐molt chelae length . The chelae length growth increment for non‐reproductive crayfish molting within the non‐reproductive form  was smaller than for reproductive crayfish molting within the reproductive form ; data curation (lead); methodology (lead); formal analysis ; writing – original draft (lead); writing – review and editing . Blaine D. Griffen: Conceptualization ; formal analysis ; supervision (lead); writing – review and editing .The authors declare no competing interests.


Graph theory has been used in cognitive neuroscience to understand how organisational properties of structural and functional brain networks relate to cognitive function. Graph theory may bridge the gap in integration of structural and functional connectivity by introducing common measures of network characteristics. However, the explanatory and predictive value of combined structural and functional graph theory have not been investigated in modelling of cognitive performance of healthy adults. In this work, a Principal Component Regression approach with embedded Step‐Wise Regression was used to fit multiple regression models of Executive Function, Self‐regulation, Language, Encoding and Sequence Processing with a collection of 20 different graph theoretic measures of structural and functional network organisation used as regressors. The predictive ability of graph theory‐based models was compared to that of connectivity‐based models. The present work shows that using combinations of graph theory metrics to predict cognition in healthy populations does not produce a consistent benefit relative to making predictions based on structural and functional connectivity values directly. Schematic presenting the principal component regression with step‐wise regression pipeline. A graph is a set of elements, referred to as nodes, and connections between them, referred to as edges. An adjacency matrix is a square matrix representing the graph. This approach to characterising complex systems has become increasingly popular in neuroscience as human brains naturally form networks that can be represented by graphs .To expand on the research from previous predictive modelling studies, the present work aims to investigate if graph theory measures of network organisation  can be used to construct predictive models of healthy adult cognition. Furthermore, our work aims to investigate if these predictive models are better at predicting cognition than predictive models constructed with raw connectivity . To achieve this, 19 graph theory measures were obtained. These measures were then used to fit predictive models of cognition. Then, we compared the effectiveness of graph theory‐based predictive models in predicting cognitive performance with effectiveness of connectivity‐based predictive models obtained in our previous work . For consistent treatment of behavioural and neuroimaging subjects' data selection, one subject was excluded from the neuroimaging analysis due to incomplete behavioural data. The sample consisted of 138 females and 111 males in the age range between of 22 and 36 years.2.2http://www.nihtoolbox.org) and tasks from the Penn computerised neurocognitive battery . rs‐fMRI data were collected using the gradient‐echo EPI . Scans were collected in two sessions, each lasting approximately 15 min. The rs‐fMRI data were collected both in left‐to‐right and right‐to‐left directions. In addition, in the original data, spin echo phase reversed images were acquired for registration with T1 images and the spin echo field maps were acquired for bias field correction. Diffusion weighted MR images were acquired with spin‐echo EPI sequence . Each gradient consisted of 90 diffusion weighting directions plus 6 2.4Neuroimaging data were processed following the same processing pipeline as in our previous work . These counts are asymmetric since the count of streamlines from region i to j is not necessarily equal to the count of streamlines from region j to i (Mij≠Mji), but they are highly correlated for all subjects . Based on these counts, the weight Wij (entries of the SC matrix) between any two pairs of regions i and j was defined as the ratio of the total streamline counts in both directions (Mij+Mji), to the maximum possible number of streamlines that can be shared between the two regions, which is Ni+Nj*5000 :As additional steps to the minimal processing pipeline, the diffusion data were further analysed using the BEDPOSTX procedure in FSL, which runs Markov Chain Monte Carlo sampling to estimate probability distributions on diffusion parameters at each voxel. This information was used in the FDT module of FSL to run ROI‐to‐ROI probabilistic tractography with ProbtrackX. Tractography was run between parcels obtained with a high‐resolution functionally defined brain parcellation with 278 parcels  between nodes i and j, which accounts for possible bias due to different sizes of the seed regions . Grey matter, white matter, cerebrospinal fluid, and non‐brain tissues were then segmented. Images were normalized and smoothed with a 6 mm Full Width at Half Maximum Gaussian kernel. Next, the data were denoised with default Conn denoising options using the anatomical component‐based noise correction procedure . Measures of node, edge and global network organisation are described in Table Graph theoretic measures were calculated based on the weighted, undirected SC and FC matrices of every subject, using The Brain Connectivity Toolbox   using various combinations of graph theory measures of structural and functional networks as predictors. To fit each model we used the Principal Component Regression (PCR) approach, where the predictor's matrix of graph theory measures is first orthogonalised by using PCA to obtain the associated orthogonal principal components' scores. The extracted component scores are then used as candidate predictors in a step‐wise regression (SWR) analysis. SWR was implemented with Bayesian Information Criterion (BIC) as a criterion for including or excluding components (ΔBIC = 0). BIC balances the goodness‐of‐fit of the model (model accuracy) against its complexity (the number of parameters included in the model). Consequently, components were only added to or removed from the regression model if they improved model quality. Finally, the resulting regression coefficients in PCA space were projected back to the original space of graph theoretic measures.Each graph theoretic measure describes one network property, such as the strength of the node or its degree. Due to their complementary nature, individual graph theory measures can therefore provide a limited explanation of cognition Appendix . The remp‐value for testing models' performance was determined by computing the proportion of resampled statistics at least as high or greater than the observed statistics. As a complementary analysis, we used the non‐parametric Wilcoxon rank sum tests for equal medians to assess the significance of the difference in performance between different connectivity models and graph theory models. These comparisons were only done for models which performed better than chance, and the results were based on coefficient of determination.The Bootstrap Bias Corrected Cross‐Validation (BBC‐CV) was implemented to validate the PCR as a learning method and to evaluate the (out‐of‐sample) predictive performance of the models  is interpreted as weak (barely worth a mention) (1–3 units), positive (3–20 units) or strong (20–150 units) evidence in favour of M2 , local  , and local and global   graph theory models. Models combining structural and functional information explained significantly more variation using raw connectivity  than global and local graph theory measures  .In structural networks, raw connectivity models  explained more variation in the Executive Function of the validation sample than the edge  .Z = 14.45, p < .001), edge  , node  , local  , and local and global   models. In functional modality, only raw connectivity models explained more variation in Language than chance . When combined structural‐functional models were considered, raw connectivity  explained significantly more variation in Language than the edge  , node  , local  , local and global   graph theory measures but not the global graph theory measures  .In structural modality, raw connectivity models  explained significantly more variation in the Language of the validation sample than global  . However, functional edge graph theory measures explained more variation than raw connectivity  .Finally, when Sequence Processing was considered, only the functional network could produce generalizable models of cognition. Raw connectivity models  explained more variation in Sequence Processing than global graph theory measures  . Data were provided by the Human Connectome Project, WU‐Minn Consortium  funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.The authors declare no conflicts of interest.Appendix S1: Supporting information.Click here for additional data file.


Depression has been characterized by lowered mood and unfavorable changes in neural emotional reactivity . Physical exercise is a well‐established strategy to improve the mood of healthy and depressed individuals. Increasing evidence suggests that exercise might also improve emotional reactivity in healthy adults by increasing or decreasing brain responses to positive or negative stimuli, respectively. It is unknown, however, if exercise could also benefit emotional reactivity in depressed individuals. We investigated the effects of a single aerobic exercise session on mood and emotional reactivity in 24 depressed and 24 matched healthy young adults. Self‐reported mood and neural reactivity to emotional pictures  were assessed before and after two experimental protocols: exercise  and seated rest condition (36 min). In the healthy control group, exercise improved self‐reported mood and neural emotional reactivity (increasing LPP to positive pictures). In the depressed group, exercise improved self‐reported mood; however, it did not affect neural emotional reactivity. Additional analyses performed on both groups revealed that exercise‐induced changes in emotional reactivity are associated with the severity of depressive symptoms: the effectiveness of exercise in improving emotional reactivity decreases with the severity of depressive symptoms. Overall, the study further strengthens the claim of a beneficial role of exercise on mood and emotional reactivity. It also suggests that a single aerobic exercise session might have a limited influence on neural emotional reactivity in depressed individuals. Acute exercise positively influences neuronal emotional reactivity in healthy adults by increasing or decreasing brain responses to positive or negative stimuli, respectively; however, it is still unclear whether this applies to individuals who are depressed. Our study provides new insights into the effectiveness of exercise in influencing neural emotional responsiveness in depressed individuals. Even one session of exercise (hereafter exercise) benefits mood. Recent evidence has shown that exercise may also improve neural emotional reactivity by increasing the brain's response to positive stimuli or inhibiting responses to negative stimuli in healthy adults. As emotional reactivity is altered in depressed individuals, exercise might be a promising intervention to counteract these adverse changes. The study's main aim is to shed more light on the effects of exercise on neuronal emotional responsiveness in depressed individuals.1.1Depression is one of the most prevalent, debilitating, and highly recurrent forms of psychopathology and is manifested by feelings of sadness or loss of interest in once‐enjoyed activities   and emotional reactivity  may serve to improve the health of depressed individuals. Current depression treatments focus primarily on the use of psychopharmacology. Antidepressant treatment can ameliorate some adverse changes in emotional reactivity, specifically in severely disordered patients, thus promoting reactivity to positive stimuli  alleviates negative . Although Thom et al.  found thStudies testing emotional reactivity after exercise using measures of brain responses that are complementary to EEG provided additional support for the LPP data. By utilizing source reconstructions of brain activity measured by magnetoencephalography (MEG), the recent study of Ligeza et al.  tested t1.4The positive effects of exercise on emotional reactivity in healthy adults seem to be particularly interesting in the context of interventions aimed at counteracting adverse changes in emotional reactivity in depressed individuals. So far, however, only two studies have tested this possibility.Both studies focused on the influence of exercise on LPP in response to emotional pictures. Brush et al.  tested tTo summarize, while the first study provided evidence that exercise increases responses to positive stimuli regardless of depressive symptoms, the second study suggested that exercise might positively influence only the emotional reactivity of depressed individuals with intact mood reactivity. These two studies provide essential insights into the potential role of exercise in affecting emotional reactivity in depressed individuals. However, as both studies showed different results, more research is needed to understand the role of exercise in affecting the emotional responsiveness of depressed individuals.Most importantly, as suggested by the study of Brush et al. , conside1.5In the current study, we aimed to investigate the impact of a single exercise session on mood and emotional reactivity in depressed and healthy young adults. Our primary goal was to test the influence of exercise on neural responsiveness in a depressive group. Nevertheless, our study also aimed to provide further evidence to support the claim that a session of exercise improves mood in both groups, and it improves neural emotional responsiveness in the healthy group.Taking into account the potentially limiting factors of the previous studies outlined above, in this study, we assess neural emotional reactivity in response to negative and positive pictures . Based on previous studies on the influence of exercise on emotional reactivity in healthy adults, we expected that emotional reactivity to pictures (as measured by LPP) would change after exercise (compared with before exercise) ; Beck's Depression Inventory  . Participants meeting these criteria were invited for a qualifying meeting.Based on the survey output, participants were selected for the further recruitment process according to the following criteria: 1) at least mild depressive disorders or no depressive disorder  (version 7.0.2 of MINI), whereas the severity of depressive symptoms was assessed using a structured interview   group or healthy control (HC) group. The inclusion criterion for the DEP group was the confirmed presence of depressive disorders with at least mild severity (at least 12 points in HRSD); for the HC group, the inclusion criterion was confirmation of no depressive disorders and less than 7 points in HRSD. After qualifying for the study, each participant was given a brief introduction and was asked to provide informed written consent. The experimental procedure complied with the directives of the Helsinki Declaration and was approved by the ethical board of Jagiellonian University in Krakow (Poland).After several participants had completed the experimental procedure, subsequent participants were recruited based on additional criteria to match participants in the DEP group and participants in the HC group in terms of gender, age, and (as closely as possible) reported physical activity levels (SDPARQ). Based on our previous study on the influence of exercise on emotional reactivity , participants who qualified for the study completed an additional set of questionnaires and an IQ test. Additional questionnaires included the Spielberger State–Trait Anxiety Inventory  and 40 unpleasant scenes .www.gimp.org). The grayscale scenes were presented at 800 × 600 resolution using PsychoPy v2021.2.1 software  = 17.59, p < .01]; the two sets did not differ with regard to emotional arousal .After completing all the experimental procedures, participants rated the hedonic valence and emotional arousal of all pictures presented during the procedure using a computerized version of the Self‐Assessment Manikin , developed by Grove and Prapavessis , and theCronbach's αCronbach's α = 0.92). Five of the scales measure negative mood: (1) Tension ; (2) Anger ; (3) Fatigue ; (4) Depression ; (5) Confusion . The Global Total Mood Disturbance (TMD) scale is calculated by summing the totals of the negative subscales (TEN + ANG + FAT + DEP + CON) and then subtracting the totals of the positive subscales .The used version of POMS consists of 40 items (adjectives referring to feelings) that are rated on a 5‐point Likert scale ranging from “Not at all” to “Extremely.” Individual items form seven subscales. Two of the scales measure positive mood: (1) Esteem‐related Affect ; (2) Psychological Distress ; and (3) Fatigue .SEES measures global psychological responses to exercise. It is a 12‐item questionnaire scored on a 7‐point Likert‐like scale, which is anchored with “Not at all” and “Very much so.” The SEES provides three subscales: (1) Psychological Well‐Being . During both experimental protocols , participants' heart rate (HR) was measured using an HR belt chest strap . The study overview is presented in Figure The present study consisted of two experimental protocols in two separate experimental sessions: Exercise (EX) and Seated Rest Condition (REST). Additionally, each participant underwent the VO2.4.12peak) was indirectly estimated using the Ǻstrand‐Rhyming cycle test to assess the physical fitness of participants. This test assumes a linear relationship between HR and VO2peak . The average HR during the protocol was 111.79 bpm ± 12.93. The average HR during the protocol did not differ across the DEP and HC groups . Given the physiological parameters of this test, it should be considered a standard warm‐up before the main effort.Peak oxygen uptake (VO2.4.2Intensity and duration for the exercise protocol (EX) were chosen based on previous studies that have shown that moderate‐intensity aerobic exercise lasting 20–40 min improves mood most effectively , 27 min of moderate‐intensity interval exercise, and a 3‐min cool‐down phase . The workload in the warm‐up phase was set according to the VO2peak protocol, whereas the workload for the exercise phase was adjusted individually for each participant according to their maximal heart rate (HRmax). HRmax was calculated using the formula HRmax = 208 – (0.7 × age in years)  consisted of a 6‐min warm‐up , participants were asked to sit in the exercise room and were instructed to relax or read the provided sport‐related magazines for 36 min.2.5After qualification for the study, each participant was asked to complete additional questionnaires and an IQ test. Afterward, they were familiarized with the experimental task by performing practice trials of the picture perception task . This training session was designed to minimize novelty effects during the experimental sessions and exclude participants from the experiment for whom viewing arousing pictures could cause psychological discomfort (participants were asked to close their eyes and interrupt the session if they felt discomfort). Next, the dates of the two experimental sessions were set. Participants were instructed to avoid any physical activity for 24 h before each laboratory visit, abstain from alcohol consumption 12 h before each visit, and abstain from food for 2 h before each visit. The two experimental sessions were conducted at the same time of day on separate days, 2–7 days apart. The order of protocols during the experimental sessions (EX/REST) was randomized and fully counterbalanced across the final 48 participants.At the beginning of each experimental session, participants were seated in a softly lit, sound‐attenuated, air‐conditioned EEG chamber; EEG equipment was attached, and they were asked to complete the POMS and SEES questionnaires. Next, participants were presented with the picture perception task, during which EEG was recorded (pre‐test). Then, participants performed one of the experimental protocols in an adjacent exercise room. During the experimental protocol, the participants were already wearing the EEG cap so that the acquisition could start without delay after the protocol. In the last 30 seconds of the EX or REST protocols, participants were asked to rate their perceived exertion during the protocol using the Borg scale  Borg,  and prov2.6The EEG signal was recorded at 256 Hz from 64 Ag/AgCl scalp electrodes, positioned at the standard 10–20 locations, mounted in an elastic cap, using the Biosemi Active Two recording system. The hardware low‐pass filtering was set to 410 Hz with a 5th‐order sinc response filter. To minimize low‐frequency (sweat‐related) artifacts, we used a gel with high saline content. Electrodes were initially referenced online to the Common Mode Sense electrode located at the C1 electrode. The electrode offset was kept within the range recommended by the BioSemi EEG User Guide (±25 μV). A horizontal and vertical electrooculogram (EOG) was recorded bipolarly using electrodes placed below and above the right eye and at the outer canthus of each eye.p = .006, η2 = 0.15]. The number of remaining (artifact‐free) trials was lower for POST‐TEST than for PRE‐TEST. The number of remaining trials did not differ between groups , between positive and negative pictures , or between the Rest and Exercise protocols . Importantly, the analysis also did not reveal any significant interaction of the tested factors . The mean number of remaining trails by conditions (max. 240) after EEG artifact rejection is presented in Table EEG data were analyzed using EMEGS software  was used as a measure of emotional reactivity to the presented pictures. Based on previous studies on the effects of exercise on emotional reactivity in depressed participants  and JASP version 0.14.1 (JASP team 2020) software.2.8.1To test the effectiveness of the experimental manipulation, we used 2 × 2 repeated analyses of variance (ANOVAs). Each ANOVA was calculated separately for the mean heart rate (HR) and the rate of perceived exertion (RPE) measured during both experimental protocols. Each ANOVA consisted of a 2‐level within‐subject factor PROTOCOL  and a 2‐level between‐subject factor GROUP . We were particularly interested in the PROTOCOL effect  and the PROTOCOL by GROUP interaction . All of the pairwise comparisons were corrected using the Bonferroni method.2.8.2To test the influence of the exercise on self‐reported mood, 2 × 2 × 2 ANOVAs were performed separately for each scale of the POMS  and SEES  questionnaires. Each ANOVA consisted of a 2‐level within‐subject factor PROTOCOL , a 2‐level within‐subject factor TIME , and a 2‐level between‐subject factor GROUP . We were particularly interested in the effects of GROUP ; PROTOCOL*TIME (which reflects the effect of the exercise protocol on mood), and GROUP*PROTOCOL*TIME . Additionally, pairwise planned comparisons were utilized to specifically test the hypothesis regarding the change of mood after vs. before the experimental protocol. All the pairwise comparisons were corrected using the Bonferroni method.2.8.3To test the influence of the exercise on LPP amplitude, a 2 × 2 × 2 × 2 ANOVA was performed with a 2‐level within‐subject factor PROTOCOL , a 2‐level within‐subject factor TIME , a 2‐level within‐subject factor VALENCE , and a 2‐level between‐subject factor GROUP . We were particularly interested in the following effects: VALENCE ; GROUP ; VALENCE*GROUP ; PROTOCOL*TIME ; PROTOCOL*TIME*GROUP (to test if the effects of exercise differed across the two tested groups); PROTOCOL*TIME*VALENCE*GROUP (to test if the effects of exercise differently affected positive and negative picture processing between the DEP and HC groups). Additionally, pairwise planned comparisons were utilized to specifically test the hypothesis regarding the change of LPP in response to positive and negative pictures after vs. before exercise. All the pairwise comparisons were corrected using the Bonferroni method.LPP post‐experimental protocol minus the LPP pre‐experimental protocol (POSTTEST – PRETEST), separately for each group , experimental protocol , and valence of pictures .Moreover, to simplify the visualization of the results and enable further exploratory analyses, we reduced the TIME factor by computing the LPP subtraction‐based difference scores. Specifically, the difference scores were computed as the The measures calculated above are referred to as LPP_diff for the rest of the manuscript. LPP_diff represents the effect of the experimental session on LPP such that negative values of LPP_diff indicate a decrease of the magnitude of LPP due to the experimental protocol. In contrast, positive values indicate an increase in the magnitude of LPP due to the experimental protocol. Similarly, mood reactivity to exercise was calculated as difference scores for each scale of the POMS and SESS questionnaires . The difference scores for each scale were calculated by subtracting each scale's score after the experimental protocols from the score before the experimental protocols.2max, HRSD, SDPARQ, sRAMP, STAI, ERQ, and PSS); the measures of mood reactivity collected during the study ; and ratings of the pictures used in the study . Due to the explanatory nature of these analyses and the multiple testing that was performed, the relevant p values of all correlation tests were subjected to the false discovery rate correction (FDR) procedure   = 2226.75, p < .001, η2 = 0.98] and rates of perceived exertion (RPE) . The RPE and HR measures were lower for the REST condition as compared with the EX condition (p < .001).The PROTOCOL showed a significant effect on mean heart rates (HR)  or RPE . The RPE and HR measures for the REST and EX conditions were not significantly different across the two tested groups (p = .57 and p = .88 respectively). The characteristics of the workloads during the experimental protocols are presented in Table The analysis showed no significant PROTOCOL*GROUP interaction effect on mean HR . The average score on the TMD scale was significantly higher  for the DEP group  than for the HC group  group. There was a significant PROTOCOL*TIME interaction . As compared with the pre‐test, the average score on the TMD scale decreased significantly  after the EX protocol , whereas it did not significantly change after the REST protocol  . The GROUP*PROTOCOL*TIME interaction was non‐significant.The analyses revealed a significant effect of GROUP . The average score on the TEN scale was significantly higher  for the DEP group  than for the HC group . The PROTOCOL*TIME and GROUP*PROTOCOL*TIME interactions were non‐significant.The analyses revealed a significant effect of GROUP, . The average score on the ANG scale was significantly higher  for the DEP group  than for the HC group . The PROTOCOL*TIME and GROUP*PROTOCOL*TIME interactions were non‐significant.The analyses revealed a significant effect of GROUP . The average score on the FAT scale was significantly higher  for the DEP group  than for the HC group  group. The PROTOCOL*TIME and GROUP*PROTOCOL*TIME interactions were non‐significant.The analyses revealed a significant effect of GROUP, . The average score on the DEP scale was significantly higher  for the DEP group  than for the HC group . There was a significant PROTOCOL*TIME interaction . As compared with the pre‐test, the average score on the DEP scale decreased significantly  after the EX protocol , whereas it did not significantly change after the REST protocol  . The GROUP*PROTOCOL*TIME interaction was non‐significant.The analyses revealed a significant effect of GROUP . The average score on the ERA scale was significantly lower  for the DEP group  than for the HC group  group. There was a significant PROTOCOL*TIME interaction . As compared with the pre‐test, the average score on the ERA scale decreased significantly  after the EX protocol , whereas it did not significantly change after the REST protocol  . The GROUP*PROTOCOL*TIME interaction was non‐significant.The analyses revealed a significant effect of GROUP . The average score on the VIG scale was significantly  lower for the DEP group  than for the HC group  group. There was a significant PROTOCOL*TIME interaction . As compared with the pre‐test, the average score on the VIG scale increased significantly  after the EX protocol , whereas it decreased significantly after the REST protocol  . The GROUP*PROTOCOL*TIME interaction was non‐significant.The analyses revealed a significant effect of GROUP . The average score on the CON scale was significantly higher  for the DEP group  than for the HC group  group. The PROTOCOL*TIME and GROUP*PROTOCOL*TIME interactions were non‐significant.The analyses revealed a significant effect of GROUP, . The average score on the PWB scale was significantly lower  for the DEP group  than for the HC group  group. There was a significant PROTOCOL*TIME interaction . As compared with the pre‐test, the average score on the PWB scale increased significantly  after the EX protocol , whereas it decreased significantly after the REST protocol  . The GROUP*PROTOCOL*TIME interaction was non‐significant.The analyses revealed a significant effect of GROUP . The average score on the PD scale was significantly higher  for the DEP group  than for the HC group  group. There was a significant PROTOCOL*TIME interaction . As compared with the pre‐test, the average score on the PD scale decreased significantly  after the EX protocol , whereas it did not significantly change after the REST protocol  . The GROUP*PROTOCOL*TIME interaction was non‐significant.The analyses revealed a significant effect of GROUP . The average score on the F scale was significantly higher  for the DEP group  than for the HC group . The PROTOCOL*TIME and GROUP*PROTOCOL*TIME interactions were non‐significant.The analyses revealed a significant effect of GROUP, . The average amplitude of LPP in response to pictures was significantly lower  for the DEP group  than for the HC group . The VALENCE*GROUP, PROTOCOL*TIME, and PROTOCOL*TIME*GROUP interactions were non‐significant.Analysis revealed a significant effect of GROUP . As compared with the pre‐test, the average amplitude of LPP increased significantly  after the EX protocol, but only for the HC group, and only in response to pictures of positive VALENCE . The post‐test vs pre‐test differences in the mean amplitude of LPP were not significant for any other combinations of factor levels [p > .05]. The difference scores of the LPP  for each GROUP, PROTOCOL, and VALENCE are presented in Table There was a significant PROTOCOL*TIME*VALENCE*GROUP interaction . None of the remaining correlations were statistically significant. The correlations between LPP_diff in response to negative pictures and the remaining variables were not significant.The correlation of LPP_diff in response to positive pictures with the remaining variables was significant only for the score on the Hamilton Rating Scale for Depression (HRSD) [4This study investigated the effects of acute aerobic exercise on mood and emotional reactivity in depressed (DEP) and healthy young adults (HC). Self‐reported mood and neural reactivity to emotional pictures  were assessed before and after two experimental protocols: (1) exercise protocol ; (2) seated rest condition . In line with our predictions, exercise improved self‐reported mood in both groups. The exercise was also related to positive changes in neural emotional reactivity. However (contrary to our expectations), this effect was limited to the HC group. Additional analyses revealed that exercise‐related changes in emotional reactivity are associated with the severity of depressive symptoms across both groups: the effectiveness of exercise in benefiting emotional reactivity decreases as the severity of depressive symptoms increases. Below, we discuss the obtained findings.4.1The effects of exercise on mood were assessed using POMS and SEES questionnaires, which were administered before and after the two experimental protocols. Analyses included testing for differences in mood between the two groups and testing for the effects of the experimental protocols on mood. In the first case, the DEP group reported generally lowered mood, regardless of the measurement time and experimental protocols. Significant differences concerned all of the eight POMS subscales and all of the three SEES subscales: the DEP group scored higher on negatively valenced scales , Tension (TEN), Anger (ANG), Fatigue (FAT), Depression (DEP), Confusion (CON), and Psychological Distress (PD)); the DEP group scored lower on positively valenced scales (Vigor (VIG), Esteem‐related Affect (ERA), and Psychological Well‐Being (PWB)). In this regard, the results confirm the presence of mood disturbances in the DEP group.Regarding the impact of the experimental protocols on mood, exercise positively influenced most of the POMS and SEES scales across both tested groups, which was in line with the expectations. Specifically, the exercise protocol decreased scores on the Total mood disturbances (TMD), Depression (DEP), and Psychological Distress (PD) scales; it increased scores on the Vigor (VIG), Esteem‐Related Affect (ERA), and Positive Well‐Being (PWB) scales. At the same, the seated rest condition (REST) either did not influence mood  or negatively influenced it . The positive effects on mood of the experimental protocols were not different across the two tested groups. However, the pairwise comparisons that were performed regardless of the insignificant effect of the PROTOCOL*TIME interaction showed some differences in how exercise‐influenced mood across the two groups. The exercise protocol decreased scores on the Depression (DEP) and Total Mood Disturbances (TMD) scales only in the DEP group. In contrast, exercise did not change the DEP and TMD scales scores in the HC group. This effect might suggest that exercise had a more profound effect on mood for the DEP group. It has to be remembered, though, that the HC group scored relatively low on the DEP and TMD scales, so an exercise‐induced decrease in negative feelings would barely have been possible for this group (due to the floor effect) . We expected that exercise would positively influence emotional reactivity in both groups by increasing LPP in response to positive pictures and/or decreasing LPP in response to negative pictures.In line with the expectations, exercise was associated with improved emotional reactivity in the healthy controls (HC). When comparing post‐test with pre‐test measurements, the exercise protocol increased LPP in response to positive pictures but did not change LPP in response to negative pictures. At the same time, the REST protocol did not change LPP in response to either positive or negative pictures. As such, the results for the HC group support increasing evidence on the beneficial effect of exercise on emotional neural reactivity  was associated with the other measured variables, including selected background characteristics of participants , and changes in mood as a result of exercise . Among all the variables, only the severity of depressive symptoms showed significant correlations with LPP_diff in response to positive pictures. The more severe the depressive symptoms, the smaller the exercise‐related benefits in emotional reactivity. In this regard, our results are not entirely consistent with the study of Brush et al. , in whicAdditionally, Brush et al.  suggesteemotion context insensitivity model (ECI) of depression, which states that depressed individuals display reduced emotional reactivity to both positive and negative stimuli  was characterized by overall reduced LPP amplitudes in response to both positive and negative pictures. This observation aligns with the Notably, this study provides evidence on the relationship between exercise and emotional responsiveness in depressed individuals that is more robust than the previously available evidence. To our knowledge, this is the first EEG study that has utilized both pre‐test and post‐test measurements  in healthy and depressed groups. Since mood and emotional reactivity both reflect affective experience, it was expected that the beneficial effect of exercise on mood would be associated with corresponding changes in emotional reactivity. Such a mood‐congruent effect of emotional reactivity after exercise was previously reported by Brush et al. , but we It is important to emphasize that we tested the acute effects of only a single exercise session. Future research should also investigate the effect of a longer intervention  during which depressed participants are regularly active. Longer‐lasting interventions, which are usually associated with changes in brain plasticity . This was motivated by concern for the reliability of the correlation coefficient in small participant samples , as depressive symptoms were the only variable that showed statistically significant associations with the LPP effect. Therefore, we believe that the consistency of the conclusions of these two analyses strengthens the main message of our study.5This study confirms the well‐grounded positive effects of acute aerobic exercise on mood; more importantly, it sheds more light on the associations between exercise and neural emotional reactivity in healthy and depressed individuals. Regarding healthy controls, the study indicates that exercise might be associated with positive changes in brain responses to positive but not negative stimuli, supporting emotional reactivity. In depressed individuals, the study indicates that the positive effects of exercise may be limited. Although exercise benefits the mood of depressed participants, it is not associated with their neural emotional responsiveness. It also seems that the association between exercise and emotional responsiveness is related to the severity of depressive symptoms across healthy and depressed populations: the more severe depressive symptoms are, the less likely it is that exercise can improve neural emotional reactiveness.Tomasz S. Ligeza: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; software; visualization; writing – original draft; writing – review and editing. Marcin Maciejczyk: Methodology; resources; writing – review and editing. Miroslaw Wyczesany: Supervision; writing – review and editing. Markus Junghöfer: Conceptualization; methodology; software; supervision; writing – review and editing.


Clean Cut is a multimodal, adaptive, checklist‐based infection prevention programme designed to improve compliance with six critical perioperative infection prevention practices. After introducing the programme at five hospitals in Ethiopia, compliance with critical infection prevention standards significantly improved and the relative risk of infection fell by 35 per cent.keep it simple Clean Cut is an adaptive, multimodal programme to identify improvement opportunities and safety changes in surgery by enhancing outcomes surveillance, closing gaps in surgical infection prevention standards, and strengthening underlying processes of care. Surgical‐site infections (SSIs) are common in low‐income countries, so this study assessed a simple intervention to improve perioperative infection prevention practices in one.Clean Cut was implemented in five hospitals in Ethiopia from August 2016 to October 2018. Compliance data were collected from the operating room focused on six key perioperative infection prevention standards. Process‐mapping exercises were employed to understand barriers to compliance and identify locally driven improvement opportunities. Thirty‐day outcomes were recorded on patients for whom intraoperative compliance information had been collected.P < 0·001). The relative risk of surgical infections after Clean Cut implementation was 0·65 . Improved compliance with standards reduced the risk of postoperative infection by 46 per cent .Compliance data were collected from 2213 operations (374 at baseline and 1839 following process improvements) in 2202 patients. Follow‐up was completed in 2159 patients (98·0 per cent). At baseline, perioperative teams complied with a mean of only 2·9 of the six critical perioperative infection prevention standards; following process improvement changes, compliance rose to a mean of 4·5 (The Clean Cut programme improved infection prevention standards to reduce SSI without infrastructure expenses or resource investments. However, implementation of these guidelines is challenging, especially where resources, staff, and infrastructure are limited,4. Such programmes are more difficult to implement in low‐income countries owing to human resource constraints, weak management practices and inadequate compliance measures; knowledge and training gaps are a further impediment to adopting best practices. As a result, infection rates are persistently higher in low‐resource settings, even when risk adjusting for differences in urgency and presentation; the GlobalSurg Collaborative5 reported that infection rates following gastrointestinal surgery were 23 per cent in low‐income countries (nearly twice that of high‐income countries)5, and the African Surgical Outcomes Study6 found that infections occur in 10 per cent of all patients undergoing surgery, of whom nearly one in ten die.Postoperative infections are a leading cause of morbidity and mortality in surgical patients, particularly in low‐ and middle‐income countries. The WHO7 – a non‐profit organization focused on improving surgical safety worldwide – established an infection reduction programme called Clean Cut to improve adherence to guidelines and reduce postoperative infection rates. Clean Cut is an adaptive, multimodal checklist‐based programme aimed at improving compliance with six critical perioperative infection prevention standards: appropriate skin and hand antisepsis, maintenance of a sterile field, instrument sterilization, appropriate prophylactic antibiotic administration, routine gauze counting, and routine use of the Surgical Safety Checklist (SSC)8. It is implemented by a locally led, multidisciplinary perioperative team that establishes a surveillance system for compliance with perioperative standards and surgical outcomes, conducts a process‐mapping exercise to determine gaps in perioperative processes and opportunities for improvement, and then facilitates the identification and implementation of locally driven solutions to improve upstream and supportive care routines. It leverages current concepts in implementation science, such as discrete implementation strategies, while also ensuring that specific implementation outcomes, such as acceptability and sustainability, are considered as the programme is adaptedTo address the combined challenges of high surgical infection rates and known gaps in perioperative practices, Lifebox11, the timing was opportune for such work. The programme was implemented between August 2016 and October 2018.Clean Cut was introduced in five surgical referral hospitals in Ethiopia: Jimma University Specialized Hospital (JUSH) in Jimma; Tikur Anbessa Specialized Hospital (TASH), Menelik II Specialized Hospital (MII) – both affiliated with Addis Ababa University – and St Peter's Hospital in Addis Ababa; and Fitche Hospital in Oromia, Ethiopia. The first three are high‐volume tertiary teaching facilities, whereas St Peter's is a regional referral hospital and Fitche is a district hospital under the auspices of the Oromia Regional Health Bureau. Together they have a combined catchment population of over 25 million people. As the Ethiopian Federal Ministry of Health was preparing to launch a nationwide programme to improve access to safe surgical care12.Clean Cut was designed to improve adherence to infection prevention standards through multidisciplinary team building, data collection and surveillance, and process mapping coupled with root cause analysis and identification of intervention opportunities. The aims of the programme were to assess process breakdowns, identify opportunities, and implement facility‐specific interventions to improve compliance with six surgical infection prevention standards, thereby reducing surgical infections, and was designed to be adaptive based on facility needs and circumstances. The standards targeted for improvement include: hand and surgical‐site skin antisepsis; maintenance of the sterile field by ensuring integrity and sterility of gowns, drapes and gloves; appropriate instrument decontamination and sterilization; appropriate timing and selection of prophylactic antibiotics; routine surgical gauze counting; and routine use of the SSC. Three of these standards are embedded within the checklist itself: appropriate timing of antibiotics within 60 min of skin incision, confirmation of instrument sterility and routine swab counting. Two others – appropriate hand and surgical‐site preparation, and use of sterile gowns, drapes and gloves – are fundamental tenets of surgical antisepsis. As use of the SSC is now itself a standard of care, it was included as a means of promoting communication among the perioperative team and reinforcing the other five practices. The definitions and rationale for these six specific standards have been described previously13 and feedback cycle that included stakeholder meetings to review compliance to infection prevention standards and patient outcomes, brainstorming of solutions and prioritization of interventions. A surgical resident supported implementation by organizing team meetings once or twice per month at each of the hospitals, guiding initial modification of the checklist, conducting training for staff to ensure accurate data collection, leading process‐mapping exercises, and facilitating the review of surveillance and process‐mapping information to identify opportunities for improvement.The programme follows a typical plan–do–study–act cycle by which locally driven interventions were identified and implemented, and adherence to process reassessed continually for improvements. To achieve these goals, Clean Cut used three sequential steps: team building through the introduction and local modification of the SSC with clinical stakeholders ; ongoing assessment of compliance with perioperative infection prevention standards and patient outcomes using trained data collectors; and process improvement through a locally led process‐mapping exerciseAppendix S1 and Fig. S1 , who were trained in its use and the definitions and mechanism for collection15. To operationalize the SSI definition, standardize capture and decrease the potential for misclassification, SSI was defined by pus draining from the wound, previously closed wound now opened  and wound with foul smell.Postoperative surveillance was carried out on all patients who underwent observation of perioperative adherence to the six standards. The primary outcome was surgical‐site infection (SSI); secondary outcomes included duration of hospital stay and mortality. SSIs were classified as superficial, deep or organ space according to Centers for Disease Control and Prevention (CDC) definitions12, the surveillance methods were modified for the other four sites, where inpatient surveillance was conducted by direct observation of the surgical wounds and brief review of the written medical chart by a group of ward nurses from postoperative day 1 until discharge. As the majority of surgical infectious complications typically occur after discharge16, telephone follow‐up was undertaken 30 days after surgery in the local language for outpatient surveillance using the wound definitions described above. For all patients with suspicion of a complication , the written medical charts were reviewed manually by the visiting surgical fellows to assess accuracy and evaluate discrepancies.For the initial piloting of the programme at Jimma, surveillance data were collected by review of the written medical record at discharge by a visiting surgical fellow and trained data collectors. Given the gross underestimation of infectious complications based on chart review alone17. This specific technique has been described previously for the Clean Cut programme13. The frequency with which certain process failures occurred was also recorded on the process maps.Breakdowns in the perioperative processes of care frequently lead to failure in compliance with one or more of the infection prevention standards. Process breakdowns and potential opportunities for improvement were identified using process mapping, a technique adapted from manufacturing involving preparation of diagrams of activities, tasks and decisions within a work flow in order to understand and subsequently improve the overall process13. Up‐to‐date patient outcome data were included in these meetings to provide context. After development and prioritization of facility‐specific interventions, two subsequent stakeholder meetings were held during the course of the 6‐month programme to assess interventions and outcomes. Identified interventions included: improving the use of alcohol hand rub following surgical hand scrubbing; establishing a procedure to mend holes in gowns and drapes; training operating room cleaning staff on appropriate decontamination and packaging of instruments, including use of sterility indicator tape in instrument trays; establishing a standard for gauze counting; and verbal use of the checklist at critical moments before induction of anaesthesia and skin incision.Quantitative adherence to the Clean Cut perioperative infection prevention standards was compiled over a 4–6‐week baseline period. The degree of adherence and visual process map for each infection prevention standard were delivered to the local stakeholders through individual and focus group meetings, as described previouslyAs this was a quality improvement initiative to improve perioperative practices and did not introduce new clinical methods or involve any direct risk to patients, there were no exclusion criteria; all patients undergoing surgery in the operating theatres selected for evaluation were eligible and patient consent was not obtained. Institutional review boards at Stanford University, the College of Health Sciences at Jimma University and the College of Health Sciences at Addis Ababa University approved the study; approval was also obtained from appropriate administrative bodies at each hospital. Neither patients nor the public were involved in designing this study.2 tests for categorical variables and Student's t test for age as a continuous variable. Adherence to the six standards at baseline compared with after intervention was evaluated in univariable analyses using χ2 tests. The relative risk of infection after implementation of the programme was calculated using modified Poisson regression analysis with a robust error variance18, controlling for age, sex, urgency, wound class, type of operation and hospital modelled as fixed effects. The relative risk of infection was also assessed based on the degree of adherence to the standards of interest using a similar model adjusted for the same co‐variables. Sensitivity analyses were undertaken using multivariable logistic regression models, controlling for the same factors, to assess the relative risk of infection during the postintervention phase as well as with increased adherence to infection prevention standards; these regression models were also computed using log‐binomial regression, as well as mixed‐effects hierarchical regression with a random effect by hospital . Demographics of the patients in the baseline and postintervention cohorts are shown in Adherence to each of the infection prevention standards significantly improved after process mapping and quality improvement interventions (P < 0·001) (P = 0·246) on univariable analysis. In multivariable analysis controlling for sex, age, urgency, wound class, type of operation and facility, the relative risk of infection following Clean Cut implementation was 0·65  (versus those with high compliance (3 or more), the relative risk of infection dropped by 46 per cent  . Furthermore, although oversight was provided by the senior author, data analysis was undertaken by a separate team not involved in programme implementation. Data collection was challenging, and obtaining 30‐day follow‐up information was limited by the ability to reach patients and families by telephone; despite this, it was possible to follow up 98·0 per cent of patients undergoing surgery, a much higher proportion than originally anticipated. An additional unexpected finding was the satisfaction the nursing staff expressed at being able to contact patients after discharge23. The observed infection rates were lower than has been reported previously in these settings, although still fairly high overall. The diagnosis of surgical infection was modified from the CDC definition, and antibiotics are commonly used empirically after surgery without regard to the actual presence of infection. Imaging modalities and other ancillary testing such as laboratory blood work was not readily available; definitions were therefore based on clearly defined, easily observable objective signs of surgical infection . This may have limited the ability to capture more subtle wound infections, but as these definitions were applied consistently during the study the findings are still valid. Improvements in the ability to detect infections were noted as the study progressed, but this would be expected to bias the study against findings of improvement. Furthermore, this study was not powered to detect significant improvements at individual facilities and, in agreement with each facility, all results are reported in aggregate owing to reputation considerations. However, each site was able to improve compliance with standards to some degree, indicating an ability to enact positive changes to the perioperative routine (Appendix S1, A limitation of this study is its pre–post design, and lack of involvement with patient advocates and the general public during planning and delivery. Similar to many surgical safety checklist and other quality improvement studies27.Implementation of Clean Cut required the concerted efforts of a number of practitioners and administrators not typically used to working together. It also required the ongoing support of a programme manager and surgical research fellow to help coordinate the work across facilities, and leverage support within them and with the health ministry. As much of the data did not exist separately from the programme, the hiring of data collectors was seen as critical. For all these reasons, long‐term sustainability is not yet proven. Furthermore, the actual implementation costs are unclear. Start‐up costs to develop, adapt and assess the programme were substantial, but subsequent work has required sequentially fewer resources; new‐site onboarding and ongoing maintenance costs are under investigation. However, the programme now has the support of the Federal Ministry of Health, and Clean Cut is being rolled out in a number of new facilities using staff familiar with the programme to encourage the new teams and to drive training, organization of workflow and high‐quality data collection. The authors continue to support the efforts in Ethiopia and anticipate bringing Clean Cut to new settings in the coming years. A robust, large‐scale experimental study, such as a pragmatic stepped‐wedge cluster‐randomized trial, would be ideally suited to test the intervention more rigorously, and allow closer observation of the features and strategies that lead to successful implementationznaa187_Supplementary_DataClick here for additional data file.