ASSOCIATION BETWEEN HYPERTENSION and CIRCULATING VASCULAR-RELATED microRNAs

microRNAs (miRNAs) play a key role in regulating inflammation, vascular health and in-turn, cardiovascular disease. Specifically, altered circulating expression of miR-17, miR-21, miR-34a, miR-92a, miR-126, miR-145, miR-146a and miR-150 has been linked with the pathogenesis and progression of cardiovascular disease. The aim of this study was to determine whether the circulating profile of these vascular-related miRNAs is disrupted with hypertension. Thirty sedentary, middle-aged adults were studied: 15 normotensive (10M/5F; age: 56±1 yr; BP: 114/71±2/1 mmHg) and 15 hypertensive (10M/5F; 56±2 yr; 140/87±2/2 mmHg). All subjects were non-obese and free of other cardiometabolic disorders. Circulating miRNAs were determined in plasma using standard RT-PCR techniques with miRNA primers of interest. Expression was normalized to exogenous C. elegans miR-39 and reported as relative expression in arbitrary units (AU). Circulating expression of miR-34a (9.18±0.94 vs 5.33±0.91 AU) was higher (~170%; P<0.01) whereas the expression of miR-21 (1.32±0.25 vs 2.50±0.29 AU), miR-126 (0.85±0.10 vs 1.74±0.27 AU) and miR-146a (1.50±0.20 vs 3.10±0.50 AU) were markedly lower (~50%, ~55% and ~55% respectively; P<0.05) in the hypertensive vs normotensive groups. Moreover, circulating levels of miR-34a, miR-21 and miR-126 were significantly related to systolic blood pressure (r=0.48, r=−0.38; r=−0.48); whereas, miR-146a was significantly related to both systolic (r=−0.58) and diastolic (r=−0.55) blood pressure. There were no significant group differences in circulating miR-17, miR-92a, miR-145 and miR-150. In summary, these results suggest that hypertension, independent of other cardiometabolic risk factors, adversely affects the circulating profile of a subset of vascular-related miRNAs that have been link to CVD risk and development.

INTRODUCTION
Hypertension, defined as a systolic blood pressure (SBP) greater than 130 mmHg and/or diastolic blood pressure (DBP) greater than 80 mmHg, is an independent risk factor for atherosclerotic cardiovascular disease (CVD). Adults with hypertension are two to four times more prone to develop vascular disease and suffer a myocardial infarction or stroke compared with their normotensive counterparts of similar age 1 . It is estimated that ~40% of adults in the United States have blood pressure in the hypertensive range and this number is expected to increase as the population ages 2 . The mechanisms responsible the increased risk and prevalence of CVD risk with elevated blood pressure are diverse and involve a myriad of vascular abnormalities such as, impaired vasomotor and fibrinolytic function as well as elevated vascular inflammation 3,4 .
It is now recognized that microRNAs (miRNAs) play a central role in regulating vascular health and function 5 . miRNAs are short (18-22 nt), single stranded, noncoding RNAs that are involved in the regulation of a number of physiological and pathological processes 5 . Circulating concentrations of a subset of miRNAs, specifically miR-17, miR-21, miR-34a, miR-92a, miR-126, miR-145, miR-146a and miR-150, have garnered significant clinical interest due to their regulatory influence on inflammatory burden, vascular function and, in turn, cardiovascular risk and disease [5][6][7][8] . Indeed, levels of these specific vascular-related miRs have been shown to be dysregulated with CVD 5 and, more importantly, may be involved in the development and progression of vascular disease and its clinical consequences 6,8 . The influence of hypertension on the circulating profile of vascular-related miRNAs is not well understood. miRNA's may reflect not only a biomarker of vascular risk but also a therapeutic target to improve vascular health in adults with elevated blood pressure.
Accordingly, the aim of the present study was to determine whether hypertension, independent of other cardiometabolic risk factors, is associated with a disrupted circulating miRNA signature profile. Specifically, we tested the hypothesis that circulating levels of miR-17, miR-21, miR-34a and miR-92a would be higher and miR-126, miR-145, miR-146a and miR-150 lower in hypertensive compared with normotensive adults. The rationale for the postulated blood-pressure related differences in circulating miRNA profile is based on the directional-association of each circulating miRNA with CVD risk and outcome 5,7 .

Subjects
Thirty sedentary middle-aged adults (51-65 years) were studied: 15 normotensive (10M/5F; blood pressure: 97-119/ 58-79 mmHg) and 15 hypertensive (10M/5F; 130-151/80-95 mmHg). All subjects were sedentary, non-obese, non-smokers, normolipidemic, nonmedicated and free of overt cardiovascular, metabolic, renal, and hematologic disease, as assessed by medical history, resting and exercise electrocardiograms, and fasting blood chemistries. Female subjects were at least 1 year postmenopausal and had never taken or had discontinued use of hormone replacement therapy at least 1 year before the start of the study. Prior to participation in the study, all subjects had the research study and its potential risk and benefits explained before providing written informed consent according to the guidelines of the University of Colorado Boulder. This study was approved by the University of Colorado Institutional Review Board.

Blood Pressure Measurement
Resting blood pressure measurements were performed in the sitting position on at least two separate days at least one week apart. Subjects were instructed not to ingest caffeinecontaining beverages prior to all BP measurements. The recordings were made under quiet, comfortable ambient (~24°C) laboratory conditions. To avoid the possibility of investigator bias, measurements were made with a semi-automated device (Dinamap, Crtikon, FL) that uses an oscillometric technique over the brachial artery. Recordings were made in triplicate in the upright sitting position. All measurements conformed to American Heart Association guidelines as established by the Council for High Blood Pressure Research.

Body Composition and Metabolic Measures
Body mass was measured to the nearest 0.1 kg using a medical beam balance. Percent body fat was determined by dual energy X-ray absorptiometry (Lunar Corp., Madison, WI, USA). Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. Fasting plasma lipid, lipoprotein, glucose, and insulin concentrations were determined using standard techniques.

MicroRNA isolation and Reverse Transcription Quantitative Polymerase Chain Reaction Analysis (RT-qPCR)
Blood samples were collected from the antecubital vein after an overnight fast into EDTA containing tubes (BD Bioscience, Franklin Lakes, NJ, USA). Blood was centrifuged at 600 x g for 20 minutes at 4°C. Plasma was removed and centrifuged at 1500 x g for 15 minutes at 4°C to remove any additional cellular debris. Platelet poor plasma was aliquoted and stored at −80°C for batch analysis.

Statistical Analysis
Group differences in subject characteristics and circulating miRNA levels were determined by ANOVA. There were no significant group x sex interactions, therefore the data were pooled and presented together. Pearson correlations were determined between variables of interest. All values are expressed as mean±SE. Statistical significance was set a priori at P<0.05.

DISCUSSION
Over the past decade, interest in circulating miRNAs has increased due to the role of miRNAs as important regulators of vascular function and their potential use as biomarkers and mediators of CVD 5,7 . The key finding of the present study is that circulating levels of miR-21, miR-34a, miR-126 and miR-146a are significantly altered in hypertensive compared with normotensive adults. Circulating concentrations of miR-21, miR-126 and miR-146a were significantly lower, whereas miR34a was significantly higher in the hypertensive adults. Disruption in the circulating profile of these vascular-related microRNAs may be a contributing factor to the increased CVD risk and prevalence associated with hypertension 1,7 . To our knowledge, this is the first study to determine the influence of hypertension per se, on circulating miRNAs.
Previous studies have clearly demonstrated independent links between circulating miRNAs (including: miR-21, miR-34a, miR-126 and miR-146a) and both the prevention and development of vascular dysfunction and disease as well as its clinical consequences 11,12 . From a cardioprotective perspective, miR-126 is recognized to be critical for the maintenance of vascular homeostasis 11,13,14 . Predominately expressed in endothelial cells, miR-126 reduces atherosclerotic lesion development by lowering the expression of proteins involved in endothelial cell activation and inflammation (e.g.: HMGB1, vascular cell adhesion molecule (VCAM)-1, CXCL-12 and SPREAD-1) thereby reducing endothelial inflammation and arresting lesion development and progression 11,14 . Thus, lower cellular expression of miR-126 promotes inflammation, endothelial cell dysfunction and reduced capacity for vascular repair 11,13 . In addition, lower circulating levels of miR-126 has been shown to be predictive of the development of CVD 15 as well as diabetes 16 . Herein, we demonstrate that the circulating level of miR-126 is markedly lower (~55%) in adults with hypertension. Given the primary source of miR-126 expression, hypertension-related inflammation and endothelial dysfunction may be a consequence of miR-126 dysregulation 14 .
Similar to miR-126, miR-146a plays a central role in vascular inflammation and reduced expression has been linked with atherogenesis 17 . Indeed, lower expression of miR-146a promotes vascular inflammation and the development of CVD in animal models 17,18 . Moreover, in a seminal study, Ma et al. 18 , demonstrated that exogenously increasing circulating miR-146a levels blunts the development and progression of atherosclerotic lesions in a rat model of CVD, indicating that higher circulating levels of miR-146a is cardioprotective. miR-146a is a major regulator of the inflammatory mediator, nuclear factor-kB (NF-kB). NF-kB activates the transcription of proinflammatory genes including interluekin-6 and tumor necrosis factor-α, increasing inflammatory burden 17 . In addition to inflammation, NF-κB activity contributes to the pathogenesis of atherosclerosis and has been shown to be elevated with hypertension 19 . miR-146a directly suppresses the NF-kB activation cascade 17 thereby reducing inflammatory mediators. Reduction in miR-146a may underlie the escalation in systemic inflammation associated with hypertension 18 .
In addition to miR-126 and miR-146a, circulating miR-21 levels were also significantly lower (~50%) in the hypertensive compared with normotensive adults. miR-21 has been linked with both favorable and unfavorable vascular effects. For example, through the regulation of PTEN, a phosphatase involved in the suppression of cell cycling and activation of the Akt pathway 20 , miR-21 has been shown to promote cardiovascular fibrosis and myointimal hyperplasia accelerating atherosclerotic lesion formation and progression 20,21 . Conversely, miR-21 neutralizes inhibitors of endothelial nitric oxide synthase (eNOS) protecting eNOS activity and, in turn, nitric oxide production 22 . Lower miR-21 levels in our hypertensive adults are certainly consistent with previous findings from our laboratory 4 and others 23 demonstrating that nitric oxide-mediated endothelium-dependent vasodilation is severely diminished in adults with elevated blood pressure. Interestingly, sheer stress has been shown to down-regulate miR-21 expression 22 . Thus, it is possible that lower miR-21 levels associated with elevated blood pressure may be due to a shear stress related phenomenon.
Contrary to miR-21, miR-126 and miR-146a, circulating levels of miR-34a were significantly higher in the hypertensive than normotensive adults. Higher expression of miR-34a is associated with endothelial cell senescence and dysfunction and has been linked with the development of CVD 24,25 . miR-34a promotes cellular senescence and dysfunction through the regulation of sirtuin-1 (SIRT-1) and the apoptotic protein BCL-2 24 . Importantly, in addition to tissue levels, circulating concentrations of miR-34a have been shown to be elevated with disease 25 . For example, circulating miR-34a has been reported to be two-fold higher in adults with coronary artery disease compared with healthy controls 25 and is thought to play a contributing role in the development of heart failure and cardiac death 24,25 . In fact, reduction in circulating levels of miR-34a in animal models has been shown to limit ventricular remodeling and cell death and improve cardiac function after myocardial infarction 24 . In the present study, circulating miR-34a was markedly higher (~170%) in hypertensive adults. Considering that elevations in blood pressure induce a prosenescent endothelial phenotype, miR-34a dysregulation may play a part in this pathology and the deleterious cardiac effects of hypertension [24][25][26] .
In stark contrast to miR-21, miR-126, miR-146 and miR-34a, we observed no blood pressure-related differences in circulating concentrations of miR-17, miR-92a, miR-145 and miR-150. Elevations in miR-17 and miR-92a and reductions in miR-145 and miR-150 have been linked to CVD events and outcome 7 . For example, miR-92a targets Kruppel like factor (KLF)-2 and -4 resulting in diminished endothelial repair capacity, eNOS expression and endothelial function 6,8,27 . Increased cellular expression of miR-92a is etiologically involved in the formation of atherosclerotic lesions 27 and elevations in circulating levels of miR-92 have been shown to be associated with CVD 6 . Whereas, miR-145 and miR-150 confer cardioprotective effects. Indeed, miR-145 mediated reductions in vascular smooth muscle cell proliferation has been shown to blunt atherosclerotic lesion enlargement 8,28 and miR-150 limits atherogenesis by moderating immune cell activation and secretion of cytokines as well as enhancing endothelial progenitor cell function and vascular repair 29 . Lower circulating levels of both miR-145 and miR-150 are associated with a myriad of CVD risk factors as well as vascular events 5 . Given the putative roles of these miRNAs in the etiology of CVD, it is somewhat surprising that their circulating levels were not altered in the hypertensive adults. It is possible that the level of hypertension (stage 1 hypertension) may not have been severe enough or that other cardiometabolic risk factors (such as dyslipidemia and diabetes) or disease may have to accompany hypertension in order to induce changes in circulating miR-17, miR-92, miR-145 and miR-150 7,15,16,30,31 . There are a few experimental considerations regarding the present study that deserve mention. Firstly, inherent with all cross-sectional human studies we cannot dismiss the possibility that genetic and/or lifestyle behaviors may have influenced the results of our group comparisons. However, to minimize the effects of other lifestyle behaviors, all subjects were similar in age, sedentary, nonsmokers, and not taking medication. Moreover, all subjects were carefully screened to eliminate the confounding effects of clinically overt metabolic or cardiovascular disease. The presence of other cardiometabolic risk factors, or disease, would undoubtedly influence circulating miRNAs making it difficult, if not impossible, to tease out the primary influence of hypertension 15,16,30,31 . This distinction highlights the novelty of the present study. Previous studies addressing elevated blood pressure and circulating miRNAs have involved patients varying in adiposity, on various blood pressure medications, and presenting with numerous comorbidities making it difficult to discern the impact of hypertension, per se, on circulating miRNA signature patterns 30,[32][33][34] . Secondly, we are unable to identify which cells or tissues are involved in the production and release of miRNAs into the circulation. However, circulating miRNA levels, regardless of cell origin or mechanism of release, are highly correlated with disease 35 and disruption of the circulating miRNA milieu has been shown to provide mechanistic insight into the pathogenesis and progression of disease [5][6][7] . Thirdly, although we observed no significant main effect or interaction of sex on the blood pressure-related differences in circulating miRNAs, the small number of women in the present study precludes us from definitively dismissing possible sex differences with respect to the effect of hypertension on circulating miRNAs. Finally, although our study design does not allow elucidation of mechanisms, the strict inclusion/exclusion criteria employed to isolate the potential independent influence of blood pressure as well as the observed univariate correlations does suggest a primary effect of elevated blood pressure on circulating miRNAs. Future studies are needed to identify the mechanistic links between blood pressure and miRNA expression.
In conclusion, the results of the present study indicate that hypertension, independent of other cardiometabolic risk factors, adversely affects the circulating profile of a subset of vascular-related miRNAs. Blood pressure-related changes in circulating concentrations of miR-21, miR-34a, miR-126 and miR-146a may play a role in the increased risk of vascular disease and associated events in adults with hypertension. To be sure, the observed disruption in these miRNAs is consistent with the aberrant circulating miRNA signature noted to be associated with CVD pathogenesis, progression and prognosis 5 .

What is known about topic •
Hypertension is an independent risk factor for atherosclerotic cardiovascular disease (CVD). The mechanisms responsible the increased risk and prevalence of CVD risk with elevated blood pressure are diverse and involve a myriad of vascular abnormalities as well as elevated vascular inflammation.

•
It is now recognized that microRNAs (miRNAs) play a central role in regulating vascular health and function as well as disease pathogenesis.

What this study adds •
The key finding of the present study is that hypertension, independent of other cardiometabolic risk factors, adversely affects the circulating profile of a subset of vascular-related miRNAs. Specifically, circulating levels of miR-21, miR-34a, miR-126 and miR-146a are significantly altered in hypertensive compared with normotensive adults.

•
The hypertension-related dysregulation in miR-21, miR-34a, miR-126 and miR-146a is consistent with the aberrant circulating miRNA signature noted to be associated with CVD pathogenesis, progression and prognosis.


extfieldsofstudy: Psychology Medicine

s2fieldsofstudy: Biology Medicine

text:
Partial correlation analysis reveals abnormal retinotopically organized functional connectivity of visual areas in amblyopia

Amblyopia is a prevalent developmental visual disorder of childhood that typically persists in adults. Due to altered visual experience during critical periods of youth, the structure and function of adult visual cortex is abnormal. In addition to substantial deficits shown with task-based fMRI, previous studies have used resting state measures to demonstrate altered long-range connectivity in amblyopia. This is the first study in amblyopia to analyze connectivity between regions of interest that are smaller than a single cortical area and to apply partial correlation analysis to reduce network effects. We specifically assess short-range connectivity between retinotopically defined regions of interest within the occipital lobe of 8 subjects with amblyopia and 7 subjects with normal vision (aged 19–45). The representations of visual areas V1, V2, and V3 within each of the four quadrants of visual space were further subdivided into three regions based on maps of visual field eccentricity. Connectivity between pairs of all nine regions of interest in each quadrant was tested via correlation and partial correlation for both groups. Only the tests of partial correlation, i.e., correlation between time courses of two regions following the regression of time courses from all other regions, yielded significant differences between resting state functional connectivity in amblyopic and normal subjects. Subjects with amblyopia showed significantly higher partial correlation between para-foveal and more eccentric representations within V1, and this effect associated with poor acuity of the worse eye. In addition, we observed reduced correlation in amblyopic subjects between isoeccentricity regions in V1 and V2, and separately, between such regions in V2 and V3. We conclude that partial correlation-based connectivity is altered in an eccentricity-dependent pattern in visual field maps of amblyopic patients. Moreover, results are consistent with known clinical and psychophysical vision loss. More broadly, this provides evidence that abnormal cortical adaptations to disease may be better isolated with tests of partial correlation connectivity than with the regular correlation techniques that are currently widely used.

Introduction
Amblyopia is the most common visual disorder in children, with a prevalence of 2-4% in the general population. Amblyopia is clinically defined as decreased acuity (typically 20/30-20/80) in an otherwise healthy eye. Impairments result not from ocular lesion but from abnormal neural development associated with atypical early visual experience. The most common etiological types of monocular amblyopia are those associated with strabismus, anisometropia, or both (von Noorden and Campos, 2001;McKee et al., 2003;Joly and Frankò, 2014). Strabismus is the uncorrected misalignment of the visual axis, while anisometropia is a large refractive imbalance of the two eyes.
Based on numerous psychophysical, physiological (e.g., Levi et al., T 1984;Movshon et al., 1987;Sengpiel and Blakemore, 1996;Kiorpes et al., 1998) as well as neuroimaging (Mendola et al., 2005;Conner et al., 2007b;Ding et al., 2013;Lin et al., 2012;Joly and Frankó, 2014;Brown et al., 2016) studies, the physiological basis for amblyopia is believed to begin at the level of primary visual cortex where binocular processing commences. This premise is consistent with widespread observations that abnormal suppressive interocular interactions characterize amblyopia (e.g., Sireteanu, 1982;Sengpiel and Blakemore, 1996;Agrawal et al., 2006;Conner et al., 2007b;Farivar et al., 2011;Adams et al., 2013;Hess et al., 2014;Birch et al., 2016), and the traditional clinical premise that pre-cortical sites are essentially normal. Nevertheless, some studies have reported altered morphology and function in the lateral geniculate nucleus (LGN) of amblyopic animal models and of amblyopic human subjects (e.g., Barnes et al., 2010;Ding et al., 2013;Joly and Frankó, 2014;Crewther and Crewther, 2015). In addition, fMRI studies have reported reduced LGN activity (Hess et al., 2009b), and reduced Granger effective connectivity from LGN to V1 for amblyopic eye viewing conditions in six subjects of mixed etiology (Li et al., 2011). Although LGN-V1 feedforward and feedback connectivities were similarly affected, only the forward effect showed an ipsilateral specificity. Therefore, a modest feedforward deficit is possible (e.g., Brown et al., 2013), although these LGN losses are likely to also be influenced by altered modulatory feedback connections from V1. Overall, evidence suggests that deafferentation of cortical binocular cells in V1 is a primary site of abnormality that is possibly reinforced at the level of the LGN (Joly and Frankó, 2014). Both strabismic and anisometropic amblyopia tend to show a selective perceptual loss of the central visual field that relies on the fovea (Levi et al., 1984;Sireteanu and Fronius, 1990;Levi et al., 2002;Hess and Pointer 1985;Movshon et al., 1987;Kiorpes et al., 1998;Asper et al., 2000;Babu et al., 2017). In addition, more recent neuroimaging studies have generally come to consistent conclusions (Muckli et al., 2006;Conner et al., 2007b;Clavagnier et al., 2015;Huang et al., 2017). Conner et al. (2007a) noted the recruitment of parafoveal representations in cortical positions in the occipital pole that represented foveal mapping in control eyes. They suggested that the susceptibility of foveal representations to interocular mismatch, and to compensatory interocular inhibition, is higher compared to other localized representations, due to the small receptive field sizes of central vision (e.g., Sireteanu, 1982). This concept is supported by past psychophysical studies that noted qualitative similarities between the amblyopia fovea and the normal periphery (Levi et al., 1984).
In recent years, the value of resting state functional magnetic resonance imaging (rs-fMRI) has been demonstrated in the study of amblyopia (Lin et al., 2012;Ding et al., 2013;Wang et al., 2014;Liang et al., 2016). Resting state, unlike task-based fMRI, requires neither stimulation nor response and may serve as an indicator of intrinsic brain function. These signals represent the spontaneous neuronal activity and connectivity of the human brain Shmuel and Leopold, 2008;Smith et al., 2009;Wang et al., 2013). At rest, distant cortical areas that are functionally related often show coherent spontaneous, slow (< 0.1 Hz) fluctuations in fMRI signal (Biswal et al., 1995;Nir et al., 2006). It has been suggested that the underlying anatomical connections shape the spatial pattern of resting-state functional connectivity (De Luca et al., 2006;Fox and Raichle, 2007;van den Heuvel et al., 2009;Wang et al., 2013), although functional connectivity is not identical to anatomical connectivity (Honey et al., 2010). The differences between structure and function can be attributed in part to the contribution of polysynaptic interactions to resting-state functional connectivity (Vincent et al., 2007;Shmuel and Leopold, 2008;Honey et al., 2009;Adachi et al., 2012).
A few studies have used rs-fMRI to evaluate whole-brain long-and shorter-range functional connections in children or adults with anisometropic amblyopia (Lin et al., 2012;Ding et al., 2013;Wang et al., 2014;Liang et al., 2016). Overall, abnormal patterns were reported for many brain regions including calcarine, middle occipital, precuneous, posterior parietal cortex, posterior frontal cortex, and cerebellum in amblyopic subjects. Presumably, these latter regions are involved in visuomotor action and visuospatial attention, among other functions, which appear to be impaired in amblyopes (Niechwiej-Szwedo et al., 2012. Two similar studies have now examined subjects with strabismic amblyopia and also find multiple sites of abnormality including lingual and inferior temporal cortex, as well as cerebellum, angular and cingulate and medial frontal cortex Tan et al., 2016). While these wide-ranging cortical effects are likely to result from the chronically impaired visual input experienced in visual cortex, none of these studies have actually defined the functional boundaries of individual visual areas or evaluated the connectivity between them.
Recent evidence suggests that correlations in rs-fMRI do in fact reflect functional organization at a scale finer than that of individual cortical areas (Dawson et al., 2016). For example, the synchronous fluctuations between topographically organized visual areas might be related to their shared intrinsic retinotopy, similar to the retinotopically-targeted anatomical connections known from monkey visual cortex (e.g., Amir et al., 1993;Angelucci et al., 2002;Shmuel et al., 2005). Indeed, such eccentricity-specific correlations have been demonstrated between visual areas in normal macaques and humans (Vincent et al., 2007;Heinzle et al., 2011;Arcaro et al., 2015;Dawson et al., 2016).
In the current study, we assess how resting state functional connectivity in visual areas depends on the level of visual hierarchy and eccentricity in amblyopic as well as visually normal control subjects. We use methods that have been previously validated and applied to normal visual cortex (Dawson et al., 2013;Dawson et al., 2016). By specifically studying rs-fMRI signals in the first three visual areas, V1, V2, V3, our goal is to determine how hierarchical connectivity between retinotopic visual areas differs in amblyopia. Furthermore, we ask how the connectivity between sub-regions within V1-V3, representing different eccentricities of the visual field, might differ in amblyopia. Based on the evidence reviewed above, we hypothesize that cortical regions representing the central visual field have a greater degree of abnormality in organization compared to regions representing the peripheral visual field in amblyopic patients. Our aim is to specifically test the representations within the occipital lobe with the most relevance to visual acuity loss in central vision.

Subjects
We studied 15 adult volunteers aged 19-45 years (13 female, 2 male). Seven were control subjects with normal or corrected-to-normal vision, six had previously been diagnosed with strabismic amblyopia, and two had previously been diagnosed with anisometropic amblyopia. Any subject with other known or suspected neurological or psychiatric conditions was excluded. These subjects were recruited through public advertisement in Montreal. Informed consent was obtained from all subjects, in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki).
Our subject groups were matched for mean age (control, 28.6 years, SD = 7.6; amblyopic, 27.9 years, SD = 8.9). All amblyopic subjects had a history of patch occlusion treatment during childhood, but the presence of visual impairment at the time of testing demonstrates that the deficit was never completely reversed. One of the six strabismic subjects also reported surgical correction of their deviation in childhood.
All subjects completed a screening to confirm their diagnosis. Diagnosis of anisometropic amblyopia was assigned on the basis of: (1) interocular refractive difference of hyperopia ≥ +1.0 diopter, astigmatism ≥ +1.0 diopter, or myopia ≥ −2.5 diopters; or (2) history of anisometropia but no history of strabismus or strabismus surgery. Diagnosis of strabismus was made on the basis of a history of strabismus or strabismus surgery, but no anisometropia (as defined above). In clinical practice it is common to find that some subjects with amblyopia present with a mixed anisometropic-strabismic diagnosis, although little consensus exists regarding additional subtypes. The direction and magnitude of strabismic deviation in our subjects was determined with cover-uncover, alternate cover and prism testing. Three of our strabismic subjects showed inward eye deviations (esotropia) and three of our strabismic subjects showed outward deviations (exotropia). Manifest deviations for our subjects ranged from 0 to 14 prism diopters. The ophthalmological tests also included examination of the fundus with dilation, documentation of any ductions and versions, autorefraction, and a sensory exam including Snellen visual acuity (Lombart Instrument, Norfolk, VA, USA), and stereoacuity (Titmus Optical, Inc., Petersburg, VA, USA). The degree of stereoacuity was measured with the Titmus stereoacuity test, scored according to highest level of detectable horizontal disparity for the Wirt rings or for the Titmus fly. The crudest stereoacuity measurable with this test is 3000 arcsec, assigned for patients able to perceive disparity only in the Titmus fly illustration. In addition to diagnosis validation, these results provide a general indication of the residual amount of binocular integration in each subject. See Table 1 for more information on each subject. All subjects completed 2 scan sessions: a scan session to obtain high-resolution anatomical images and resting state functional data, and a scan session for functional retinotopy data (2nd scan session).

Resting state fMRI
Resting state data was collected during the 1st scan session. During the resting state functional scans, the subjects kept their eyes closed. Subjects were scanned in a Siemens 3 T Magnetom TrioTim scanner, using a 32channel phased array head coil. Echo-planar imaging (EPI) was used to measure blood oxygenation level-dependent (BOLD) changes in image intensity. Each run consisted of 270 contiguous EPI whole-brain functional volumes [repetition time (TR) = 2000 ms; echo time (TE) = 30 ms; flip angle = 90°; 38 slices; matrix = 64 × 64; field of view (FOV) = 230 mm; acquisition voxel size = 3.6 × 3.6 × 3.6 mm].
In the same session as the resting state data acquisition, 3 highresolution anatomical volumes were acquired and averaged together to improve the signal-to-noise ratio. For each of the 3 volumes, three-dimensional (3D) anatomical images were acquired via averaging together 3 consecutive T1-weighted MPRAGE (magnetization-prepared rapid gradient echo) sequences (312 s each), containing 176 interleaved 1.0 mm slices with 1.0 mm × 1.0 mm in-plane resolution, oriented along true AC-PC (TR = 2300 ms, TE = 3.4 ms, flip angle = 90°, FOV = 256 mm, voxel resolution = 1.0 mm × 1.0 mm × 1.0 mm).
Resting state runs were preprocessed using the FMRIB Software Library's FEAT software package (Jenkinson et al., 2012). Using the prestats option, we discarded the first three volumes to avoid non steady-state effects, performed motion correction, high-pass filtering (0.01 Hz), slice timing correction, and registration to the T1-weighted (MPRAGE) anatomical volume from the retinotopy session. Spatial smoothing and co-registration between subjects were not applied to avoid unnecessary signal spread over space.
Following the FSL FEAT pre-stats step, the resting state functional runs underwent denoising. A cerebrospinal fluid (CSF) and white matter (WM) masks were created by segmenting the resting state anatomical volume using FSL's FAST software package (Zhang et al., 2001). The masks were then eroded by one voxel, thresholded (80% tissue type probability), and registered to each preprocessed functional volume. The average time courses from the CSF and WM masks were obtained and the two time courses were regressed out of the functional volume. Since we analyzed resting state fMRI data from a relatively small part of the brain, neither global signal regression (Carbonell et al., 2011) nor correction for the impact of the global signal on functional connectivity (Carbonell et al., 2014) were performed.

Retinotopy
Retinotopy data was collected during the 2nd scan session. Subjects were scanned in a Siemens 3 T Magnetom TrioTim scanner, using a 20channel phased array head coil. After a localizing scan was obtained, a high-resolution anatomical volume was pursued. For this volume, three-dimensional (3D) anatomical images were acquired using a T1weighted MPRAGE (magnetization-prepared rapid gradient echo) sequence, containing 176 1.0 mm thick slices with 1.0 mm × 1.0 mm inplane resolution (TR = 2300 ms, TE = 3.0 ms, flip angle = 9°., FOV = 256 mm, voxel resolution = 1.0 mm × 1.0 mm × 1.0 mm). This anatomical volume was later utilized to register functional retinotopy data from the 2nd session to the cortical surface model obtained from the high-resolution anatomical data in the 1st session. Retinotopy data was acquired with blood oxygenation level-dependent (BOLD) functional imaging, using 28 interleaved 3.0 mm thick slices with 2.1 mm × 2.1 mm in-plane resolution, oriented obliquely parallel to the calcarine sulcus (TR = 2000 ms, TE = 30 ms, flip angle = 90°., FOV = 263 mm, voxel resolution = 2.1 mm × 2.1 mm × 3.0 mm).
Retinotopy stimuli were projected onto a rear-projection screen visible from within the MRI scanner via an angled mirror. All subjects viewed the stimulus with both eyes open under natural conditions. The visual stimuli were projected from a liquid crystal display (LCD) projector at 1024 × 768 resolution and 60 Hz refresh rate onto a translucent screen at the end of the scanner bore. The subjects viewed the screen at the total viewing distance of 138 cm through a mirror mounted to the coil, which yielded 32°× 24°(40°in diagonal) of viewing angle. A central fixation target was presented at all times during retinotopy functional scans. Subjects were instructed to maintain fixation on this target throughout the scan. The target was a small red arrowhead (0.5°) pointing in one of four directions (i.e., up, down, left or right), which randomly changed direction every 4 s. In order to aid fixation stability and maintenance of attention, subjects were given the task of reporting the direction change of the fixation point using a fibre-optic button box. The cortical representation of retinotopic space was determined using a phase-encoded design in which the cardinal axes of visual space ('polar angle' and 'eccentricity') were mapped separately (Engel et al., 1997). The stimuli consisted of two different high-contrast, multi-colored checkerboard patterns, on a grey background, subtending a maximum visual angle of 24°× 24°(33.94°in diagonal). The polar rotating wedge stimulus swept through the polar angle dimension clockwise or counter-clockwise, while the "eccentricity" ring stimulus mapped eccentricity by starting from the centre of the visual field and expanding outward, or starting from the outer edge of the visual field and contracting inward. The ring and the wedge stimuli consisted of checkerboard patterns where brightness and colors changed at a rate of 8 Hz to maximize the neural responses of the visual areas of interest. The eccentricity stimulus traversed space with a logarithmically increasing rate as has been used previously (Sereno et al., 1995;Conner et al., 2004), whereas the arc angle of wedge stimulus was constant at 10°. These phase-encoded stimuli used a 64 s cycle, completing 8 cycles per scan. Each scan was 512 s long, with 256 time points per functional scan volume. Four scans of this type were administered, two for eccentricity (1 expanding scan and 1 contracting scan) and two for polar angle (1 clockwise scan and 1 counter-clockwise scan). Paired clockwise/counter-clockwise and expansion/contraction scans were used in order to cancel the effects of residual hemodynamic phase delays.
The functional analysis of retinotopic data was completed using the FS-FAST software tools available in FreeSurfer software at (http:// www.nmr.mgh.harvard.edu/freesurfer) Fischl et al., 1999;Fischl et al., 2001). Before statistical analysis, raw MR images were first motion-corrected to the midpoint volume (i.e. the 128th volume of 256 volumes for 'polar' and 'eccentricity' scans) of the first run using an iterated linearized weighted least squares method through the FS-FAST implementation of the AFNI 3dvolreg algorithm (Cox and Jesmanowicz, 1999). This algorithm provides a sum-of-squares estimate of average head motion throughout the fMRI scanning session, which was similar for both subject groups. The MR volumes were subsequently intensity normalized using the average in-brain voxel intensity.
Fast Fourier transform analysis was conducted on the time series of each voxel to statistically correlate retinotopic stimulus location with visual cortical anatomy. This analysis rejected low frequencies due to head motion or baseline drift and extracted both a magnitude and phase relative to the stimulus cycle frequency. Signal magnitude reflects retinotopic specificity, which can be low due to either lack of visually induced response or equivalent response to all retinotopic locations. The phase component of the signal codes specific retinotopic location.

Region of interest definition
Surface reconstructions of each subject's cerebral cortex were generated from three separate acquisitions of high-resolution anatomical images using the FreeSurfer version 5.3 software package Fischl et al., 1999;Fischl et al., 2001). The computed average cortical surface representation was inflated and then flattened by introducing a series of cuts to the 3-D surface to isolate the occipital pole (Sereno et al., 1995). The resulting occipital patch was used to display the phases of the eccentricity and, separately, polar angle obtained from the retinotopy analysis of FS-FAST.
Visual areas V1, V2, and V3 were retinotopically identified on the occipital patch according to the criteria of Larsson and Heeger (2006). We drew the borders at the horizontal meridians to delineate V1-V2 and the outer edge of V3 and at the vertical meridian to delineate V2-V3. The visual areas were defined as labels on the surface. The labels of visual area, eccentricity, and polar angle, which were all defined on the surface, were projected into the anatomical voxels using FreeSurfer's 'mri_surf2vol' command. We divided the visual areas into subregions, or regions of interest (ROIs), according to a set of criteria outlined in detail in Dawson et al. (2013). First, we included only voxels that were within ± 42.5̊in polar angle of the oblique meridian in each quadrant of the visual field. This leaves a minimum 5̊gap between ROIs in adjacent visual areas in order to minimize mixing of signal between different visual areas. Second, we included voxels that were significantly activated (p < 0.05) in both eccentricity and polar angle runs. Third, we divided each visual area of each quadrant into three eccentricity bins: 0.25-3.2̊(central, e1), 3.2-7.27̊(mid, e2), 7.27-14̊(peripheral, e3). For each ROI, the BOLD fMRI resting state time courses of all voxels within that ROI that passed the inclusion criteria were averaged together and normalized. For our network analyses, we divided the visual cortex into four quadrants based on hemisphere and dorsal/ventral position. Overall, we created 9 ROIs (3 visual areas × 3 eccentricity bins) in each of our 4 quadrants for a total of 36 ROIs per subject.

Statistics
Functional connectivity was assessed by calculating the Pearson correlation (Corr) and partial correlation (Pcorr) coefficients between all pairs of ROI resting state time courses within a particular quadrant, resulting in 36 Corr or Pcorr coefficients per quadrant per run per subject. Partial correlation refers to the correlation between two ROIs' time-courses, from which the time courses of all other 7 ROIs in the network were regressed out.
Fisher's z-transformation was applied on the Corr and Pcorr coefficients using the following: Fisher's z-transformation is a variance-stabilizing transformation that converts Pearson's r to the normally distributed z variable for use in statistical testing. A two-tailed one-sample t-test was conducted to assess whether Fisher transformed Corr/Pcorr coefficients were significantly different from zero in controls and separately in amblyopes. Next, a two-tailed two-sample t-test was conducted to compare Fisher transformed Corr/Pcorr coefficients between controls and amblyopes. Each subject had 8 Corr and 8 Pcorr values (4 quadrants * 2 runs) for each pair of ROIs. Using an alpha of 0.05, p-values were assessed for significance following False Discovery Rate (FDR) correction (Benjamini and Hochberg, 1995).

Results
We present functional connectivity data (Pcorr and Corr) separately for Amblyopes (Fig. 1A-C) and Controls (Fig. 1D-F). In Fig. 1A and D we show Pcorr and Corr coefficients for each pair of ROIs, averaged across each quadrant, run, and subject for Amblyopes and Controls, respectively. The Fisher transformed values are presented in Fig. 1B and E. Note that the results of Pcorr are presented in the bottom left portion of each matrix below the diagonal line, while the results of Corr are presented in the upper right portion.
In agreement with our previous findings in Dawson et al. (2016), we observe retinotopic organization of functional connectivity within a quadrant in Controls as well as Amblyopes. There is an evident eccentricity effect: connections between adjacent eccentricity regions (e1-e2 and e2-e3) within an area [shown with open circles in Fig. 1C, F] had higher Pcorr and Corr than distant eccentricity regions (e1-e3). This was consistently seen in all visual areas in both subject groups. In addition, Pcorr revealed that connections between adjacent visual areas with same eccentricity (e.g., V1e1 & V2e1) [shown with dots in Fig. 1C, F] had higher connectivity measures than connections between distant visual areas and/or different eccentricity. However, this effect was not obvious in Corr and only manifested in Pcorr. Again, this was consistent in both subject groups.
In order to determine which connections were significant in Amblyopes and Controls, we conducted two-tailed one-sample t-tests on Fisher transformed Pcorr and Corr coefficients against zero. Fig. 1C and F show the results of the t-test for Pcorr and Corr for Amblyopes and for Controls, respectively, with an alpha of 0.05. For both Amblyopes and Controls, all Corr coefficients were significantly greater than zero (FDR corrected alpha of 1.06 × 10 −15 and 4.619 × 10 −17 , respectively). Pcorr was more selective, however. Specifically, within-area, adjacenteccentricity connections were significant, as were between-area, sameeccentricity connections in Amblyopes and Controls (FDR corrected alpha of 0.0139 and 0.0155, respectively). Comparisons coded in white did not meet statistical significance.
To test whether the functional connectivity was different between amblyopic patients and control subjects, in Fig. 2A we present the difference in the mean Pcorr/Corr between Amblyopes and Controls (Amblyopes minus Controls). Fig. 2B shows the difference between the Fisher transformed Pcorr/Corr values. We next conducted a two-tailed two-sample t-test comparing Fisher transformed Pcorr/Corr values between Amblyopes and Controls with alpha of 0.05. In each group, each subject contributed 8 Pcorr/Corr values per comparison (4 quadrants × 2 runs), which were pooled together and submitted to the twosample t-test. Fig. 2C shows the t-statistic of the t-test and Fig. 2D shows the pooled standard deviation. The uncorrected p-values of the t-test are displayed in Fig. 2E. We found no significant difference in Fisher transformed Corr values between Amblyopes and Controls. Pcorr, on the other hand, revealed a few differences. A number of comparisons, all involving V1, yielded significantly different Fisher transformed Pcorr values. However, after correction for multiple comparisons, only the V1e2-V1e3 comparison remained statistically significant (Amblyopes > Controls, FDR corrected alpha of 0.0004) (Fig. 3).
In order to determine possible functional relevance of the above pair-wise Pcorr values we then tested for any association between this V1e2-V1e3 Pcorr values and our behavioral scores for worse eye acuity and for residual depth perception (stereoacuity). For the V1e2-V1e3 connection, low/high partial correlation was associated with less/more impairment of acuity. Inspection of this association indicated two potential subgroups rather than a continuum (Fig. 4A). To assess this, we performed K-means clustering with 2-7 clusters and plotted the within group sum of squares as a function of the number of clusters (Fig. 4B). Based on the point where this plot had its maximal curvature (the 'elbow' criterion), the cluster analysis identified two clusters: one with poor acuity scores and high partial correlation and another with better acuity scores and low partial correlation. In the case of stereo-acuity, however, no significant relationship was found. We also computed correlations with behaviour for the four connections that did not survive FDR correction (shown in light blue in Fig. 2F), but none were significant.
Two final analyses were conducted in response to observations we made regarding the pattern of differences between the amblyopic and control subjects seen in Fig. 2C. Recall that the bottom left portion of the matrix shows Pcorr, while the results of Corr are presented in the upper right portion. As can be seen in Fig. 2C, the six partial correlation connectivity measures between isoeccentricity regions of adjacent areas (V1 and V2, and separately, V2 and V3) were reduced for amblyopic subjects [marked with white dots]. Testing with repeated measures the partial correlation across the six identified connections and subjects showed that the mean partial correlation was lower in Amblyopes versus Controls, t(88) = −2.6950, p = 0.0084.
Finally, it is also notable that for most pair-wise connections Corr is smaller in amblyopic than control subjects (upper right of matrix). We again applied repeated measures (with 36 connections/subject) statistics, and found that there was indeed a significant difference in correlation between Amblyopes and Controls, t(538) = −2.2866, p = 0.0226. This indicates that across the entire network, Amblyopes show decreased correlation (Corr) compared to Controls.

Discussion
We report a localized increase in partial correlation between regions within V1 as well as an overall reduction in correlation between isoeccentric regions of V1 to V2 and V2 to V3 for subjects with amblyopia. This is the first study to analyze connectivity within and between ROIs that are smaller than an entire visual area in amblyopia. Our results first suggest cortical adaptions that cause a loss of effective resolution of visual representations in V1. Secondly, we find a general loss of normal drive from V1 to V2, and again from V2 to V3 that might build upon any reduced subcortical gain. Prior psychophysical evidence already exists in support of the idea that amblyopia results in these local and global perturbations of normal cortical connections. Our findings provide possible mechanisms for such psychophysical findings. Moreover, the significant association between more or less impairment of amblyopic eye acuity and increased partial correlation within V1, argues E.  V2 V3 Fig. 3. A schematic diagram for visualization of the pattern of differences between Amblyopes and Controls (AMB minus CTRL). The one partial correlation that survived correction for multiple comparisons is shown in red between the e3 and e2 eccentricity ROIs of V1. This correlation was stronger in amblyopes, and it correlated with worse eye acuity. It was also noted that similar trends existed (for both partial correlation and correlation) for enhanced within-area connectivity between e2 and e3 in V2 and V3 (shown in light red). Finally, the pervasive trend (both partial and regular correlation) for isoeccentricity hierarchical connections between visual areas to be reduced in amblyopes compared to control is indicated in light blue. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) for the functional relevance of these findings.

Visual cortex organization in amblyopia
In the current study, we found one connection with a significantly enhanced partial correlation between adjacent regions of interest in peripheral V1 in amblyopic subjects. The spatial layout of this increased connection is interesting considering that non-foveal representations (3-14°) were affected. A possible explanation could be decreased resolution in the vision of amblyopes. A shift toward a cruder retinotopy (i.e., larger receptive fields) could lead to an increased connectivity between these cross-eccentricity representations. One detailed study recently found that while cortical magnification is normal in the foveal field of strabismic amblyopes, the population-level receptive field sizes are enlarged for the amblyopic eye (Clavagnier et al., 2015). Consistently, we found this increased Pcorr within V1 is associated with poor acuity in strabismics. Potentially similar associations have already been reported in normal subjects, although in this case between Vernier acuity and cortical magnification factor (Duncan and Boynton, 2003). Finally, McKee, Levi, and Movshon (2003) have found that strabismic subjects, especially those with very poor stereoacuity, tend to show poor monocular acuity yet relatively good monocular contrast sensitivity. These authors suggest that this pattern of psychophysical performance could result if V1 in strabismic subjects contains a greater number of monocular neurons (possibly with larger receptive fields). The trend we found for poor stereo-acuity in subjects with poor acuity is consistent with this model.
In addition to the shorter-range connectivity differences within V1, our results also include longer-range connectivity differences within the V1-V3 visual cluster. As might be expected from the generally reduced fMRI responses during task-based fMRI, overall connectivity was reduced in amblyopia compared to controls. These reduced longer-range connections seen in amblyopia may be representative of reduced feedforward connections within this hierarchical visual system (Li et al., 2011). Thus, deficits may increase as information goes up the hierarchy. Consistently, Muckli et al. (2006) showed that responses to amblyopic eye stimulation were progressively reduced in higher-tier cortical areas that emphasize central vision. This concept can serve as a plausible explanation for altered temporal and parieto-occipital cortical activity observed in amblyopes, as well as other behavioral deficits (Levi, 2006), such as impaired visuomotor behavior (Niechwiej-Szwedo et al., 2012 or depth perception (Joly and Frankó, 2014). One possible source of reduced feedforward connections is interocular suppression. Behaviorally, this has been found to encompass the central 20°of the visual field, although the greatest impact is in the central-most regions (Babu et al., 2013;Hess et al., 2014;Babu et al., 2017). Visual evoked potentials (VEP) and fMRI and have demonstrated that amblyopic eye stimulation generally results in lower activation in V1, compared to the fellow eye or compared to control subjects (e.g., Barnes, et al., 2001;Lee et al., 2001;Lerner et al., 2006;Muckli et al., 2006;Conner et al., 2007b;Hess et al., 2009a;Wang et al., 2012). Whether such physiological attenuation or suppression grows in strength or simply "passes-through" subsequent higher-tier areas is not known (Levi, 2006;Jurcoane et al., 2009;Chen and Tarczy-Hornoch, 2011). Suggestive evidence for active suppression of the foveal representations, including those beyond the occipital pole in the occipitoparietal cortex (Wandell et al., 2005), has also been reported when amblyopic eye stimulation with fellow eye open was contrasted with fellow eye closed (Conner et al., 2007b). Additional studies may be beneficial in resolving the debate between reduced input gain and active suppression. This can be done by identifying decreased connectivity in amblyopia of "higher-tier" cortical regions, comparing the strength of signal reduction to that of earlier cortical regions, and determining the functional deficits associated with abnormalities in these higher-order regions.
For comparison, we mention here recent results from resting state studies in subjects with complete loss of vision in childhood (Bock et al., 2015;Butt et al., 2015). Results suggest that while the gross retinotopic structure of correlations from V1 to V2, and from V1 to V3 is maintained in early-blind subjects, enhanced correlations between cortical areas may exist. Therefore our finding of reduced correlations between adjacent visual areas would suggest a different mechanism in amblyopia. It seems likely that complete blindness makes reallocation of function in visual cortex possible, and V1 likely participates in other whole-brain networks (e.g., Amedi, et al., 2003). For example, there is evidence from blind subjects that connectivity with regions as distant as Broca's area in frontal cortex increases substantially (Sabbah et al., 2016). This would contrast with amblyopia where a disconnection of V1, at least locally, might result from the discordant binocular signals and resultant suppression to avoid diplopia (e.g., Conner et al., 2007b).
In the future, it would be ideal to include pediatric subjects and potentially acquire data longitudinally. It has been reported recently that in a large sample of children with both types of amblyopia, perceptual suppression of the weak eye's stimulus is common and testable as young as age 3 years (Birch et al., 2016). We know these children have highly asymmetrical interocular suppression that can vary in severity, but the specific neural mechanisms are still unidentified. Resting state studies are also feasible for very young children and offer the real potential to track etiological mechanisms and distinguish them from normal developmental changes. Ultimately, lagged-based measures of effective connectivity might allow us to further distinguish feedback connections from feedforward input. Finally, functional MRI at 7 T enables smaller regions of interest that distinguish cortical columns or layers (Chaimow et  Acuity of Amblyopic Eye A.

Fig. 4. (A)
Plot of V1e2-V1e3 partial correlation score versus acuity score of worse eye for all eight amblyopic subjects. The Pcorr assigned to the connection between V1e2 and V1e3 showed the only significant difference between amblyopic patients and control subjects (Fig. 2). The individual acuity scores are also given in Table 1. More impaired acuity is associated with higher Pcorr scores. The distribution of our sample suggests two subgroups, rather than a continuum or a spurious correlation driven by one outlier. (B) Estimation of the number of clusters within the scatter of data points presented in panel A. The estimation is based on the K-means clustering sum of squares of distances to the mean within clusters versus cluster number. The cluster analysis identified two clusters.
The "elbow" is the point of maximal curvature from two to seven clusters, indicating that two clusters account optimally for the variability of the data. One cluster has worse acuity scores (higher logMAR) and high partial correlation scores while the other has better acuity scores and lower partial correlation. Huber et al., 2017;Yacoub et al., 2007). This can potentially support the differentiation of activity in input and output layers of areas in the visual cortex.

Previous resting state studies
The complex retinotopic organization of the human visual system is relatively well-known (e.g., Wang et al., 2015), and the degree to which this information is evident in the spontaneous fluctuations in fMRI data has been an active topic of investigation. Others have also extracted BOLD signals from functionally defined areas (V1, V2, or V3) in normal subjects, and found that the resting state correlation pattern between areas reflects the widespread eccentricity organization of visual cortex, in which the highest correlations are observed for cortical sites with isoeccentricity representations (Buckner and Yeo, 2014;Arcaro et al., 2015). Previous rs-MRI of visual cortex at 7 T also revealed retinotopically precise correlations between V1, V2, and V3 over the same and opposite hemisphere (Gravel et al., 2014;Raemaekers et al., 2014;Genç et al., 2016). However, such functional connectivity between regions smaller than a cortical area was observed only after the global contributions from a large-scale bilateral network were removed (Raemaekers et al., 2014). Another study that specifically included measures from subcortical nuclei also emphasized the mixture of independent signal sources in the correlation structure measured in visual cortex (de Zwart et al., 2013). Moreover, in our previous study (Dawson et al., 2016), we have explicitly shown that correlation-based functional connectivity is non-selectively high across lower visual areas V1-V3, even between regions within these areas that do not share direct anatomical connections. We therefore suggest that the mechanisms underlying correlation based functional connectivity likely involve network effects caused by the dense anatomical connectivity within this cortex and feedback projections from higher visual areas. In contrast, partial correlation, which minimizes network effects, follows expectations based on the direct anatomical connections verified in non-human primate visual cortex better than correlation. Our findings with partial correlation discussed above are thus based on well-validated methods.
As mentioned in the introduction, a few studies of larger scale rs-fMRI in subjects with amblyopia have been reported recently (Lin et al., 2012;Ding et al., 2013;Wang et al., 2014;Liang et al., 2016). Both increased and decreased correlation compared to normal subjects were observed. However, none of these studies compared correlation to partial correlation. We suspect that many of these findings may not be replicated with a more selective partial correlation test. Moreover, for these whole-brain measures, the interpretation of how the findings relate to functional consequence can be challenging. In only two instances, modest but suggestive correlations were found with some behavioral measures. Tan et al. (2016) found an association between their ALFF measure (amplitude of low frequency fluctuation) in angular gyrus and duration of strabismus, while Liang et al. (2016) correlated the standardized ALFF of the precuneus to the amount of optical anisometropia (but not to acuity). Finally, the effective (lag-based) connectivity (Corr) loss from LGN to V1 reported by Li et al. (2011) during visual stimulation of the worse eye correlated with the worse eye acuity (degree of amblyopia). Thus, the suggestive relationship of partial correlation with acuity that we found for short-range connections within V1 (Fig. 4) may derive in part from precortical alterations. That this local effect within resting V1 was only detectable with partial correlation highlights the emerging understanding of amblyopic deficits as manifestations of abnormal interactions between distinct neural populations that may or may not be physically adjacent. Finally, we point out that even the application of partial correlation analysis to previously published resting state data from amblyopic subjects could reduce the network effects and the effect of common input. We expect that this could provide a much more selective picture of amblyopic cortical adaptations than the current literature suggests.

Conclusions
Retinotopically guided functional connectivity analysis of areas V1, V2, and V3 shows reduced interaction between V1 and V2 and separately between V2 and V3 in adult amblyopia. In addition, only partial correlation selectively isolated abnormally strong functional connectivity across eccentricities within V1. This effect was strongest in strabismic subjects with the worst acuity. Future studies including more adults with anisometropia and children with both types of amblyopia would be very useful. It remains unknown when during visual cortex development resting state connectivity becomes abnormal in amblyopic subjects. Measurement of rs-fMRI connectivity, with a special emphasis on partial correlation, requires minimal assumptions about the adaptations that underlie amblyopia, and seems suitable to reveal additional neural substrates beyond those currently recognized. Finally, we stress that the use of partial correlation analysis is applicable to all resting state connectivity studies and may offer additional specificity and sensitivity for the characterization of a wide array of brain disorders.


extfieldsofstudy: Medicine Biology

s2fieldsofstudy:
Biology
Environmental Science

text:
The role of parasitism in the energy management of a free-ranging bird

ABSTRACT Parasites often prompt sub-lethal costs to their hosts by eliciting immune responses. These costs can be hard to quantify but are crucial to our understanding of the host's ecology. Energy is a fundamental currency to quantify these costs, as energetic trade-offs often exist between key fitness-related processes. Daily energy expenditure (DEE) comprises of resting metabolic rate (RMR) and energy available for activity, which are linked via the energy management strategy of an organism. Parasitism may play a role in the balance between self-maintenance and activity, as immune costs can be expressed in elevated RMR. Therefore, understanding energy use in the presence of parasitism enables mechanistic elucidation of potential parasite costs. Using a gradient of natural parasite load and proxies for RMR and DEE in a wild population of breeding European shags (Phalacrocorax aristotelis), we tested the effect of parasitism on maintenance costs as well as the relationship between proxies for RMR and DEE. We found a positive relationship between parasite load and our RMR proxy in females but not males, and no relationship between proxies for RMR and DEE. This provides evidence for increased maintenance costs in individuals with higher parasite loads and suggests the use of an allocation energy management strategy, whereby an increase to RMR creates restrictions on energy allocation to other activities. This is likely to have fitness consequences as energy allocated to immunity is traded off against reproduction. Our findings demonstrate that understanding energy management strategies alongside fitness drivers is central to understanding the mechanisms by which these drivers influence individual fitness. Summary: Demonstration of the sub-lethal costs of endo-parasitism for a wild population of seabirds via increased maintenance metabolism.

INTRODUCTION
Parasites cause major fitness consequences to a huge array of taxa (Booth et al., 1993;Gooderham and Schulte-Hostedde, 2011;Reed et al., 2008). Often, parasites prompt sub-lethal effects to the host by eliciting immune or stress responses (Lettini and Sukhdeo, 2010;Martin et al., 2003;Sheldon and Verhulst, 1996;Smyth and Drea, 2016). However, the costs of these sub-lethal effects can be hard to quantify and consequently their impact on individuals and populations is often neglected in ecological research (Binning et al., 2017). Additionally, the mechanisms underlying the fitness effects of parasitism and individual variation in these effects are not well understood. Ignoring these effects of parasites reduces our understanding of their host's ecology, as we know that parasites can drive a large array of fitness-related traits (Boulinier et al., 2016;Hamilton and Zuk, 1982;Norris and Evans, 2000;Reed et al., 2008;Sheldon and Verhulst, 1996).
Energy can be a limited resource, and individuals are required to allocate energy to the demands of competing life-history traits to maximize fitness (Stearns, 1992). Therefore, trade-offs may exist in energetic terms between key fitness-related behaviours and processes, and so energy is a central mechanism by which fitness responses to parasitism are manifested. A fundamental potential trade-off exists between basal or resting metabolic rate (RMR) and the amount of energy apportioned to activity, because ceilings can exist on the sum of these two components: the daily rate of energy expenditure (DEE) (Careau and Garland, 2012;Elliott et al., 2014;Mathot and Dingemanse, 2015;Welcker et al., 2010). For endotherms, RMR is usually defined as the minimum energetic cost of living during thermo-neutral rest in a free-ranging animal (Mathot and Dingemanse, 2015;Welcker et al., 2015) and therefore largely represents the cost of self-maintenance, including immune activity (Burton et al., 2011). The remaining energy that is available to allocate to activity is referred to as either metabolic scope or activity metabolism (Careau and Garland, 2012;Mathot and Dingemanse, 2015). DEE, RMR and activity metabolism are linked in different ways depending on which energy management strategy the animal is operating under; namely, the performance, independent or allocation strategies (see Fig. 1 and Appendix for details). A recent review of energy management in birds and mammals suggests that no single energy management strategy operates across all species (Portugal et al., 2016).
Parasitism may play a central role in the relationship between selfmaintenance and activity because a primary predicted cost of parasitism is increasing RMR, through immune-associated costs (Svensson et al., 1998). Hosts should invest in immunity to reduce the impacts of parasitism, particularly in long-lived species, in which, in contrast to short-lived species, survival contributes more to fitness, and individuals should be selected to allocate more resources to a rapid, strong immune response to combat cumulative damage from parasites and to protect future reproductive success (Lochmiller and Deerenberg, 2000;Moe et al., 2007). Under the allocation model of energy management, this investment would create a trade-off between immune activity and other essential fitness-related organismal functions (Råberg et al., 2000;Sheldon and Verhulst, 1996). Under the performance model, there would be no trade-off with increased RMR, but instead activity metabolism would also increase with a resultant relatively large increase in DEE. Likewise, under the independent model, despite no relationship between RMR and activity metabolism, an increase in RMR results in an equivalent increase in DEE, since this is the sum of activity metabolism and RMR (Mathot and Dingemanse, 2015;Welcker et al., 2015). The lack of a trade-off would itself likely have implications for fitness, and animals might ultimately reach maximal or optimal limits of DEE (Careau and Garland, 2012;Mathot and Dingemanse, 2015).
Understanding the impacts of natural parasite burdens on individual metabolic rate is crucial, with experimental studies suggesting that endo-parasites and elevated immune function incur significant costs to hosts (Albon et al., 2002;Newborn and Foster, 2002;Sheldon and Verhulst, 1996). However, there has been little work on the role of parasitism in mediating the energy management of individuals, despite this being fundamental to the understanding of the fitness effects of parasitism. This is in large part due to the difficulties in measuring natural parasite loads, particularly endoparasites, in wild populations. Energetic rates are also hard to measure in free-ranging animals and so various proxies are used to evaluate energetic costs (Butler et al., 2004;Chastel et al., 2003;Halsey et al., 2011).
Here, we measure proxies of both DEE and RMR in individuals with known natural endo-parasite loads in a free-living population of breeding European shags [Phalacrocorax aristotelis (Linnaeus 1761)] to understand the effect of endo-parasites on maintenance costs as well as the relationship between maintenance and total energy expenditure under high energetic demands during the breeding period. Specifically, we investigate two questions: (1) Does parasite load relate to RMR? We expect that individuals with higher parasite loads will have higher immune costs and that this will be reflected in elevated RMR. (2) Does DEE relate to RMR and what can this tell us about the allocation between energy to reproduction and self-maintenance of this species? We predict that individuals will be energetically constrained by rapidly growing chicks during the chick-rearing period under the allocation model and therefore any increase in RMR will not result in an increase in DEE.

MATERIALS AND METHODS Study site and species
The study was carried out at the Isle of May National Nature Reserve, south-east Scotland (56°11′N, 2°33′W) during the breeding seasons of 2015-2017. All individuals were part of a long-term population study and are individually marked with a unique metal ring to allow accurate aging of individuals that were first ringed as chicks and a coded plastic ring to facilitate identification from a distance.
Previous sampling of this population has shown a high prevalence of the nematode parasite Contracaecum rudolphii Hartwich 1964 (Burthe et al., 2013;Granroth-Wilding et al., 2014;Reed et al., 2008) in European shags, although parasite loads vary markedly between individuals (Burthe et al., 2013;Granroth-Wilding et al., 2014;Reed et al., 2012). Shags become infected with third-stage larvae via their fish diet. Larval worms moult to become sexually mature adults, which attach to the lining of the proventriculus and lower oesophagus in the final seabird host (Abollo et al., 2001;Burthe et al., 2013).

Estimating resting metabolic rate
Measuring RMR in the field is difficult (Wilson et al., 2006). Our previous work using calibrated estimates of RMR from accelerometry (Hicks et al., 2017) suggested that RMR of European shags is linked to parasite burden (Hicks et al., 2018). However, despite evidence that movement costs from accelerometry can indeed be used to evaluate costs during relative inactivity (Green et al., 2009), we wanted to use a more sensitive proxy for RMR to investigate this effect further. Thyroid hormone plasma concentrations are increasingly being used in this context in free-ranging animals. The role of thyroid hormones in energy metabolism in birds in particular is now well established (Chastel et al., 2003;Elliott et al., 2013;Welcker et al., 2013). Triiodothyronine hormone (T3; 3,3′,5-triiodo-L-thyronine) especially is considered one of the major controllers for the regulation of tissue oxygen consumption and metabolic activity in endotherms (McNab, 1997). Studies carried out both in the laboratory and the field show close relationships for a range of species and life-history strategies (e.g. Chastel et al., 2003; Mathot and Dingemanse (2015) and Careau and Garland (2012) showing the relationship between RMR and DEE and activity metabolism under the three scenarios. The dashed 1-to-1 line is presented to aid interpretation of the slope of the lines. It represents a theoretical situation under which animals are completely inactive and thus DEE is equal to RMR. 2013; Zheng et al., 2014). While a direct calibration of T3 hormone and RMR has yet to be conducted for our study species, we are unaware of any calibration studies during breeding which have failed to establish such a relationship. Thus, we feel confident that T3 hormone can be used as a proxy for RMR at the individual level, without the confounding effect of stress associated with respirometry (Welcker et al., 2015).
Adult European shags were captured on the nest during chick rearing (when the chicks were between 5 and 36 days old) using a crook on the end of a long pole. At capture, a 1 ml blood sample was collected from the brachial vein using a heparinized syringe and a 25gauge needle. To avoid an effect of handling stress on hormone concentrations, sampling took place within 3 min of capture. Blood samples were taken between 03:30 and 07:30 h, before shags left for their first foraging trip of the day, and therefore birds were assumed to be post-absorptive, which was later confirmed by endoscopy. Throughout the fieldwork period, local air temperature was comfortably above the estimated lower critical temperature of 6°C for European shags (Enstipp et al., 2005); thus, birds were in thermoneutrality at all times. Blood samples were stored on ice in the field. Whole blood was centrifuged and plasma and red blood cells were kept frozen at −20°C until subsequent analyses.
Total T3 hormone assays were performed at the Centre d'Etudes Biologiques de Chizé (CEBC), France. T3 analyses were performed using a single radioimmunoassay (RIA) and total thyroid hormone levels were assessed in duplicate without extraction as in Chastel et al. (2003) and Welcker et al. (2013). A total of 25 µl of plasma was incubated with 10,000 cpm of 125I-hormone (Perkin Elmer) and polyclonal rabbit antiserum (Sigma-Aldrich). The bound fraction was separated from the free fraction by addition of a sheep anti-rabbit antibody. After overnight incubation and centrifugation, the bound fraction activity was then counted on a Wizard 2 gamma counter (Perkin Elmer). For quality control, reference materials were used and were within the acceptable range determined by the laboratory. Furthermore, every plasma was run in duplicate, and samples with a coefficient of variance (CV) higher than 12% were re-assayed. Inter-and intra-assay variations were, respectively, 14.34 and 7.75%. Cross-reactions of T3 antiserum were as follows: triiodo-D-thyroacetic acid 6%, L-thyroxine 0.2%, diiodo-L-thyrosine <0.01%, monoiodo-L-thyrosine <0.01%. Cross-reactions of T4 antiserum were as follows: triiodothyronine 4%, diiodo-L-thyrosine <0.01%, monoiodo-L-thyrosine <0.01%.

Quantifying parasite load
Worms were counted visually using the endoscope video screen (for detailed endoscopy methods, see Burthe et al., 2013). Burdens higher than 40 worms were hard to quantify in the field accurately and these were counted retrospectively via endoscopy video footage. Quantification of parasite burdens was found to be repeatable within an individual across a season (Burthe et al., 2013). All endoscopy and blood sampling was performed by trained personnel (S.J.B.) holding a personal licence, and under a project licence issued by the UK Home Office.

Estimating daily energy expenditure
Tri-axial accelerometers (little Leonardo D3GT, AXY3 and Gulf Coast Data Concepts X8) were used to estimate DEE in a subset of the birds used for the hormone assay. Accelerometers were set to record at 25 or 50 Hz and attached on the midline of the mid back of individuals (as close to the centre of gravity as possible) using Tesa tape. All birds were successfully recaptured and accelerometers were retrieved after 3 days of deployment. Energy expenditure was estimated for diving, flying and resting [the main activities of European shags (Sakamoto et al., 2009)] using behaviour-specific calibrations derived from respirometry calibrations with heart rate and accelerometry (see Hicks et al., 2017 for behaviour-specific calibrations and detailed methods). These data were then used to estimate total DEE, the sum of the energetic costs of all behavioural bouts within 24-h periods of activity in terms of the rate of oxygen consumption (l day −1 ). This included both activity and RMR and any costs of thermoregulation in water (see Hicks et al., 2018 for full details). Adult RMR and DEE have been found to vary with mass, reproductive stage and age in many seabirds (Elliott et al., 2014;Green et al., 2013;Grémillet, 1997;Weimerskirch et al., 1995). Thus, adult mass and age were recorded at endoscopy and chick age at time of sampling was later back-calculated from wing length at ringing (Granroth-Wilding et al., 2014).

Statistical analysis
All models were fitted separately for males and females due to nonindependence of nest pairs. To answer the first question of whether parasite load is related to elevated RMR, we modelled T3 concentration using linear mixed effects models (LMMs). Parasite load, brood size (number of chicks), brood age (age of the oldest chick in the brood at the time of sampling), year, and adult age and body mass were explanatory variables and we controlled for variation between birds and repeated sampling by including individual as a random factor. We fitted models containing all combinations of the fixed effects, and interactions between parasite load and brood age and parasite load and brood size (see Table 1 for explanations of model terms). Year Year in which the data was collected; may have effects on thyroid hormone levels.
Year is included as a factor to account for this variation.
Secondly, to understand how variation in RMR is related to DEE, we modelled estimated DEE as a function of T3 concentration (a proxy for RMR) using LMMs. Adult age, adult body mass, brood age (at the time of sampling), brood size and year were also included as explanatory variables (Table 1). We controlled for variation between birds and repeated sampling by including individual as a random factor.
All models were fitted using the lme4 package in R (Bates et al., 2014; http://www.R-project.org/) and model selection for both sets of models was based on Akaike's information criterion (AIC) (Burnham and Anderson, 2001).

Relationship between parasite load and RMR
A total of 87 (48 males and 39 females; 2015: 8, 2016: 42, 2017: 37) individuals were blood sampled and endoscoped over 3 years (of which 27 were sampled in more than one year). Whilst the sample size in 2015 is clearly smaller than the other years, removal of these data had no impact on the conclusions of the analyses and were therefore included. The best-supported model for females showed a positive relationship between parasite load and T3 concentration, an effect of year, and an interaction between brood age and parasite load in that there was a more positive relationship between parasite load and T3 when broods were younger (see Fig. 2 and Table 2). When year and brood age are accounted for, T3 concentration increased by 150% across the range of natural parasite load. The best-supported model for males showed no effect of parasite load on T3 concentration. There was a negative effect of age as well as an effect of year on T3 concentration in males (see Fig. 3 and Table 2).

Relationship between proxies for RMR and DEE
DEE was estimated in a subset of the birds (71 individuals; 2015: 3, 2016: 40, 2017: 28) to gain insight into the relationship between RMR and DEE. The best-supported model for females was the null model with no relationship found between T3 and estimated DEE (see Fig. 4 and Table 3). For males, the best-supported model indicated a negative effect of body mass on estimated DEE and no effect of T3 (see Fig. 4 and Table 3). However, the null model is also equally well supported (within Δ2 AIC of the top model); thus, there is very little support for an effect of mass.

DISCUSSION
Measuring both natural parasite burdens and metabolic rates in freeranging animals is challenging. In particular, estimating metabolic rate requires the use of proxies, the potential limitations of which have been extensively documented (e.g. Butler et al., 2004;Halsey et al., 2011). Our study builds on previous work (Hicks et al., 2018) to further explore links between RMR and parasite loads in European shags and how these might link to DEE. Despite the potential limitations, we found a positive relationship between natural parasite load and our hormonal proxy for RMR across multiple years in female shags, strongly suggesting increased maintenance costs in individuals with higher parasite loads. Host maintenance costs are likely to be increased in response to parasitism due to increased cell repair and cost of dealing with the parasite infection as well as the immune response itself (Lochmiller and Deerenberg, 2000). The limitations of the approaches used would have made it difficult to distinguish between the independent and performance strategies, since our proxies for RMR and DEE use different units of measurement. However, the lack of relationship between these two proxies provides strong evidence that female European shags operate under an allocation energy management strategy. Therefore, a heightened immune response to parasitism creates restrictions on the allocation of energy to other activities and is likely associated with subsequent fitness consequences.
We found no relationship between our proxy for RMR and parasitism in males despite the strong relationship found in females. Males are hypothesized to experience the immunosuppressive qualities of testosterone (Grossman, 1985); thus, the lack of relationship may be due to suppression of their parasite-induced immune response. Additionally, trade-offs between reproduction and self-maintenance are especially strong in female shags (Hicks The interaction between parasite load and brood age in relation to T3 concentration, the explanatory terms in the best-supported model. Solid lines represent predicted lines from the best-supported model under different brood age scenarios −1 s.d. of the mean brood age (light blue), mean brood age (mid-blue) and +1 s.d. of the mean (dark blue). Reed et al., 2008), who have higher estimated DEE than males on average (see Fig. 3 and Hicks et al., 2018) and can be more adversely affected by environmental conditions than males (Lewis et al., 2015). Females across many taxa often invest more in reproduction than males (Clutton-Brock, 1991), meaning that females may be closer to their energy expenditure limits than males. Thus, the greater energetic constraint of females to extrinsic and intrinsic conditions could explain these sex differences in response to parasitism (Hicks et al., 2018;Lewis et al., 2015).
This study is the first to use T3 concentration as a proxy for RMR in the context of parasitism effects. This enables us to gain greater insight into the energy use of free-ranging animals and complements our previous findings and other approaches. Hicks et al. (2018) show that, for female European shags, flight costs are positively associated with parasite burden but that the proportion of time spent in flight per day reduced with higher parasite loads. However, this decrease in flight duration was more than expected based on the increased cost of flight alone. The results from this   study suggest that the reduction in flight duration observed in heavily parasitized birds may be due to compensation for increased maintenance costs as well as the increased cost of flight (Hicks et al., 2018). For European shags, it is not possible to quantify the extent of this compensation as we have yet to calibrate T3 concentration against RMR using respirometry. However, T3 plasma concentration is increasingly being used as a proxy for RMR due to strong correlations found in multiple species and the reduction in the confounding effect of stress associated with respirometry (Blackmer et al., 2005;Bouwhuis et al., 2011;Chastel et al., 2003;Elliott et al., 2013;Moe et al., 2007;Welcker et al., 2013Welcker et al., , 2015. In an experimental study, a 50% increase in free T3 corresponded to a 50% increase in RMR in kittiwakes (Welcker et al., 2015). In this study, we found a 150% change in T3 concentration across the natural range of parasite load, meaning that the corresponding change in RMR to parasitism could be even greater than in previous experimental work. It is likely that energy management is not fixed across time but varies according to particular conditions, as seen in Hicks et al., (2018) where estimated DEE increased with brood age but not in relation to parasite load. An adult's cumulative energetic investment in its brood increases with brood age (Drent and Daan, 1980). Thus, the trade-off between allocation to reproduction and selfmaintenance may also shift with brood age. In a young brood, when cumulative investment is lower than in an older brood, energy allocation to immune response may be beneficial to ensure that the next reproductive event is reached , but may reduce parental effort (Råberg et al., 2000) and negatively affect offspring success. However, when cumulative investment in a brood is higher, investment to current reproduction may be prioritised. Consistent with this concept, we found that the positive relationship between RMR and parasite load is greater when the adult's chicks are younger, suggesting that the shags are investing more in parasite resistance at this time.
At the scale of a lifetime, life-history theory predicts that tradeoffs between reproduction and self-maintenance change with age.
As the probability of future reproduction declines with age, it is predicted that resources will be increasingly allocated to current reproduction rather than maintenance (Herborn et al., 2016;van Noordwijk and de Jong, 1986). There is good evidence for this in short-lived species such as great tits and zebra finch (Bouwhuis et al., 2011;Moe et al., 2009); however, in longer-lived birds, evidence is equivocal (e.g. Blackmer et al., 2005;Elliott et al., 2015;Moe et al., 2007). In this study, we found a negative relationship between age and RMR in males, suggesting that older individuals invest less energy in maintenance, as in thick-billed murres . However, we found no effect of age on RMR in females. Sex-specific senescence occurs in a number of wild vertebrate populations, possibly due to different trade-offs existing with age in males and females (Clay et al., 2018;Froy et al., 2013Froy et al., , 2017Murgatroyd et al., 2018).
We found no evidence for a relationship between proxies for RMR and DEE, indicating that DEE does not vary with maintenance costs. Despite parasite-induced behavioural costs, European shag estimated DEE does not correlate with parasite load (Hicks et al., 2018), suggesting that there is a level of energy expenditure for this population that they are unwilling or unable to exceed. Drent and Daan (1980) first introduced the concept of an 'optimal working capacity' as a rate beyond which parents would increasingly suffer from any kind of risks or dangers with the consequence that their lifetime reproductive success would be reduced (Green et al., 2009). Evidence for such an optimum, occasionally described as an 'energetic ceiling', have been described previously (e.g. Welcker et al., 2010) and provide evidence for the existence of allocation management strategies. Our results also provide evidence for both an optimum to energy expenditure and the allocation management strategy in European shags (Drent and Daan, 1980;Mathot and Dingemanse, 2015). As such, an increase in RMR will cause a reduction in activity metabolism, which may have consequences for fitness-related behaviours such as chick provisioning or foraging. Our findings are in agreement with studies providing evidence for the allocation management strategy in three seabird species (Blackmer et al., 2005;Elliott et al., 2014;Welcker et al., 2015) but are contrary to other studies (including those of other seabird species) providing evidence for both performance and independent strategies (Chastel et al., 2003;Portugal et al., 2016). It is unclear why species vary in their energy management strategy, although, during energetically demanding periods, the capacity to increase expenditure can impose energy limits, in terms of foraging constraints, making an allocation strategy more likely (Elliott et al., 2014). Under limited energy it can be difficult to buffer the additional costs of parasites. Therefore, considering the role of parasitism in mediating energy allocation between reproduction and selfmaintenance is crucial in understanding the mechanism of its fitness effects. The allocation strategy creates energetic trade-offs under an overarching constraint yet, before now, we had little knowledge about what drives these trade-offs. Changes in energy allocation can influence reproduction in the short term but in the long term there may be negative consequences of reducing allocation to self-maintenance (Blackmer et al., 2005). Therefore, quantifying energy management strategies alongside potential fitness drivers is crucial to understand the mechanisms by which they act and influence energy allocation to optimize individual success.

APPENDIX Energy management strategies Performance model
Variation in RMR reflects variation in the size of organs that mobilize energy (e.g. digestive organs, muscle). Therefore, individuals with higher RMR are able to maintain higher levels of energy output (i.e. have higher DEE). This strategy predicts higher RMR to be associated with both greater activity metabolism and therefore, when summed, a higher DEE (Careau et al., 2008).

Allocation model
Variation in RMR does not reflect variation in the size of organs that mobilize energy; differences in RMR are therefore not associated with differences in DEE. Individuals with higher RMR have less energy available to allocate to energetically costly behaviours and so are predicted to have lower expression of such traits (Mathot and Dingemanse, 2015).

Independent model
Regulation of RMR and DEE are independent but an increase in RMR causes an increase in total energy expenditure as activity metabolism is also independent of RMR. Therefore, higher RMR is associated with higher DEE because, although activity metabolism is unaffected, it sums with an increasing RMR to drive an increased DEE.