Arduino Plant Watering System

A biomarker based on gene expression indicates plant water status

In Controlled And Natural Environments

Gwenaëlle Marchand 1,2, Baptiste Mayjonade 1,2, Didier Varès 1,2, Nicolas Blanchet 1,2, Marie-Claude Boniface 1,2, Pierre Maury 3,5, Fety Andrianasolo Nambinina 4,5, Philippe Burger 5,6, Philippe Debaeke 5,6, Pierre Casadebaig 5,6, Patrick Vincourt 1,2,7, Nicolas B. Langlade 1,2,7

Addresses :

1 INRA, Laboratoire des Interactions Plantes-Microorganismes (LIPM), UMR441, F-31326 Castanet-

Tolosan, France

2 CNRS, Laboratoire des Interactions Plantes-Microorganismes (LIPM), UMR2594, F-31326 Castanet-

Tolosan, France

3 Université Toulouse, INPT ENSAT, UMR1248 AGIR, F-31320 Castanet-Tolosan, France 4 CETIOM, Centre INRA de Toulouse, F-31320 Castanet-Tolosan, France 5 INRA, UMR1248 AGIR, F-31320 Castanet-Tolosan, France 6 Université Toulouse, INPT, UMR AGIR, F-31029 Toulouse, France 7 These authors contributed equally to this work.

Abstract

Plant or soil water status are required in many scientific fields to understand plant responses to drought. Because the transcriptomic response to abiotic conditions, such as water deficit, reflects plant water status, genomic tools could be used to develop a new type of molecular biomarker.

Using the sunflower (Helianthus annuus L.) as a model species to study the transcriptomic response to water deficit both in greenhouse and field conditions, we specifically identified three genes that showed an expression pattern highly correlated to plant water status as estimated by the pre-dawn leaf water potential, fraction of transpirable soil water, soil water content or fraction of total soil water in controlled conditions. We developed a generalized linear model to estimate these classical water status indicators from the expression levels of the three selected genes under controlled conditions. This estimation was independent of the four tested genotypes and the stage (pre- or post-flowering) of the plant. We further validated this gene expression biomarker under field conditions for four genotypes in three different trials, over a large range of water status, and we were able to correct their expression values for a large diurnal sampling period.

Keyword Index

Soil water deficit, sunflower, Helianthus annuus L., transcriptomic, drought, biomarker, leaf water

Introduction

Water deficit in plants can be defined as the imbalance between the actual evaporative demand resulting from climatic conditions and the available water in the soil (Tardieu, Granier & Muller, 2011) This major environmental stress affects the growth and physiology of the entire plant and can therefore dramatically reduce crop yield and quality (Bhatnagar-Mathur, Vadez & Sharma, 2008). Recent intensification of drought events in Europe, Australia and North America, together with climatic model forecasts, suggest that drought will continue to dramatically affect crop productivity in the 21st century (Moriondo, Bindi, Kundzewicz, Szwed, Chorynski, Matczak, Radziejewski, McEvoy & Wreford, 2010). At the same time, the reduction of arable land area, the scarcity of water resources and the development of the human population all amplify the need to develop agrosystems that are more tolerant to water deficit or less water-consuming.

In this context, plant molecular physiologists, eco-physiologists and agronomists conduct experiments to estimate the plant response to water stress. One major requirement of these experiments is the estimation of soil water available to crops, at least at the most critical points of the developmental process. The direct estimation of water accessible to individual plants can be very difficult or even impossible in natural environments such as the field. Water status indicators can be of two types: soil- or plant-based measurements.

Soil-based measurements use either thermogravimetry, which requires samples for over-drying, or physical measurements of soil properties varying with its water content (Dobriyal, Qureshi, Badola & Hussain, 2012). Neutron probes have been widely used since the 1960s (reviewed by Gardner, Bell, Cooper, Dean, Gardner & Hodnett, 1991; Klenke & Flint, 1991), and dielectric methods, such as time- domain reflectometry (Topp & Davis, 1985) or capacitance sensors (Whalley, Dean & Izzard, 1992), have been used since the 1980s. Generally, these techniques can only describe limited soil regions that may not

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correctly represent the plant rhizosphere (Ferreira, Valancogne, Daudet, Ameglio, Pacheco & Michaelsen, 1996). The access tube used for the probe measurement is difficult to install and often modifies the water circulation and root dynamics surrounding the tube. Furthermore, these tools are time-consuming and labor-intensive and cannot be scaled up for high-throughput evaluation, which is needed in genetic analyses.

Therefore, plant-based measurements are often preferred. These measurements are based on the fact that the plant status reflects soil water availability. Morphological indicators can be used to evaluate drought stress. For example, breeders often score leaf rolling to estimate plant water status in monocots (O'Toole, Cruz & Singh, 1979). For perennial species, trunk diameter reflects water fluxes in the plant and can be used to manage irrigation (Goldhamer & Fereres, 2001). In eco-physiological studies, different indicators of plant status are commonly measured at different organizational levels, such as whole-plant transpiration, leaf water potential or stomatal conductance. For example, water transpiration cools the leaves and can therefore be monitored through thermal infrared measurements such as in wheat (Blum, Mayer & Gozlan, 1982). The measurement of pre-dawn leaf water potential (ΨPD) has been largely used for decades and is considered to be a standard. It is based on the fact that at dawn, water equilibrates between the rhizosphere and leaves, reflecting the water available to the plant. Therefore, it is subject to some limitations for heterogeneous soils (Ameglio, Archer, Cohen, Valancogne, Daudet, Dayau & Cruiziat, 1999) and its determination in numerous plants is restricted by the need of operating at pre- dawn.

Another method that is successfully used in controlled environments is based on the measurement of the daily transpiration of water-stressed plants relative to well-watered controls. This method defines a scale for water available for plant transpiration between an upper and lower limit of soil water content (SWC).

The upper limit matches the SWC at field capacity and the lower limit is the SWC where relative transpiration decreases to less than 0.1 (Sinclair, 1986). According to Sinclair (2005), plants respond to the progressive drying of soil in a similar manner across a wide range of environmental conditions when

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this scale is used, where water stress is expressed as the fraction of transpirable soil water (FTSW). Variability in the leaf expansion and plant transpiration rate in response to water stress has been previously reported in different sunflower genotypes by using this method (Casadebaig, Debaeke & Lecoeur, 2008). When dealing with field conditions, several main drawbacks limit the applicability of this method. First the need for a control plot (in order to measure transpiration in well-watered conditions) doubles the experimental space required, which thus precludes the use of this indicator for large-scale genetic programs in crops. More importantly, to estimate FTSW in field environment, measurements of soil depth (soil profile, endoscopy), portion of soil explored by roots and soil water content (probes, soil cores) are required but are often unrealistic for genetic studies at microplot scale or poorly estimated.

In the context of the development of high-throughput phenotyping platforms, the need to develop a tool that would allow the early quantification of water deficit in a dose- and time-dependent manner is even more acute. Following the definition of Ernst & Peterson (1994) a soil water content biomarker could be a biochemical, physiological or morphological change in plants that measures their exposure to the environment (i.e., water deficit). This biomarker could therefore be used to reveal the status and trends in environmental assessment and also to predict crop responses to other biotic and abiotic stresses that interact with drought.

The transcriptomic response to water deficit is a widely described molecular process that allows plants to adapt to the water imbalance between supply and demand, and to develop a large range of morpho- physiological changes (Shinozaki & Yamaguchi-Shinozaki, 2007). The gene regulation cascade begins from the composite molecular perception of the environmental signal (the biophysical water imbalance) and moves via signal transduction down to the level of enzymes and structural proteins to produce biochemical compounds, fluxes and developmental adaptations. In accordance, some transcript expression levels are correlated only to FTSW but not to other major plant responses (Rengel, Arribat, Maury, Martin-Magniette, Hourlier, Laporte, Vares, Carrere, Grieu, Balzergue, Gouzy, Vincourt & Langlade, 2012). Such genes correspond to the definition of biomarkers that reflect soil water status.

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Assembling several genes robustly correlated to soil water content appears to now be an attainable goal given recent progress in the description of the transcriptomic response to water deficit at the interface of molecular biology and eco-physiology (Ingram & Bartels, 1996); Ramanjulu & Bartels, 2002; Bartels & Sunkar, 2005; Harb, Krishnan, Ambavaram & Pereira, 2010; Aasamaa & Sober, 2011).

In fact, gene expression biomarker search and development is a long-standing goal of the reference meta- analysis platform Genevestigator (Zimmermann, Laule, Schmitz, Hruz, Bleuler & Gruissem, 2008). Recently, a successful meta-analysis of a large transcriptomic data set in maize allowed the development of a composite gene expression scoring system to quantitatively assess the response of maize to nitrogen conditions (Yang, Wu, Ziegler, Yang, Zayed, Rajani, Zhou, Basra, Schachtman, Peng, Armstrong, Caldo, Morrell, Lacy & Staub, 2011). Importantly, this first gene expression biomarker for in planta nitrogen status is independent of genotype, does not vary throughout plant development and was validated in field and greenhouse conditions.

The development of such tools has certainly been hampered by the rapid variation of plant transcriptome in response to many external factors, such as illumination and handling/wounding, as well as internal factors, such as the circadian clock. However, whole transcriptomic studies now show that part of the transcriptome robustly reacts to the plant environment in a dose- and time-dependent manner, which allows statistical models to be built, notably for the sunflower (Rengel et al., 2012).

In this context, we used the sunflower as a model to develop a composite gene expression biomarker that is independent of genotype, developmental stage and time of day and that allows the estimation of soil water constraint in greenhouse and field experiments. This biomarker was standardized using the pre- dawn leaf water potential, FTSW, soil water content and fraction of total soil water when available.

Plant Material And Growing Conditions

Four experiments, i.e., one in greenhouse conditions and three in field conditions, using the four sunflower (Helianthus annuus L.) inbred lines XRQ, PSC8, their F1 named Inedi and another cultivated hybrid Melody were conducted in 2012 near Toulouse (Haute-Garonne, France).

For the greenhouse experiment conducted from May to June 2012, bleach-sterilized seeds were germinated on Petri dishes with Apron XL and Celeste solutions (Syngenta, Basel, Switzerland) for 3 days at 28°C. Plantlets were transplanted in 236 individual pots, and each pot contained one single plant.

Pots were filled with 15 L of a substrate composed of 10% sand, 40% P.A.M.2 potting soil (Proveen distributed by Soprimex, Chateaurenard, Bouches-du-Rhône, France) and 50% clay loam from the INRA site in Auzeville-Tolosane (Haute-Garonne, France).

Plantlets were sown on two different dates to obtain plants at two different stages (before and after flowering) respectively, 10 weeks and 4 weeks before the beginning of the stress treatment. For each phenological (pre-flowering or post-flowering) stage with, respectively, 144 and 92 plants, the pots were arranged in a split-split-plot design with three blocks. The stress intensity (i.e., FTSW values of 0.8, 0.7, 0.5, 0.35, 0.20 and 0.12) was the main factor within the block, the genotype was the second factor, and finally the treatment (control plants were well-watered and treated plants were water-deprived) was the third factor. After an assessment of Alternaria blight evolution, plants of genotype PSC8 were not considered in the experiment dedicated to the post-flowering stage. Each pot was fertilized and irrigated as in Rengel et al. (2012) before the beginning of the water stress application.

In field conditions, the same genotypes were sown at three different locations: Samatan (Gers, France),

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Fleurance (Gers, France) and Auzeville-Tolosane. In Samatan, the four genotypes were sown on April 20, 2012 and grown without irrigation. In Fleurance, PSC8, Melody and Inedi were sown on April 6, 2012 and grown without irrigation. In Auzeville-Tolosane, Inedi and Melody were sown on May 25, 2012 and grown in both irrigated (163 mm) and non-irrigated conditions.

The field experiments were designed in six randomized blocks for each location or location*condition combination. Each plot consisted of 12 rows with a length of 10 m, 12 rows with a length of 6 m and 9 rows with a length of 5.2 m for each genotype in Samatan, Fleurance and Auzeville-Tolosane, respectively, at the same plant population density (6.5 plants.m-2).

Soil Analysis

An 800-g soil sample was taken at depths of 60 cm and 30 cm in each trial and sent to the INRA LAS laboratory (Arras, Pas-de-Calais, France) for physical and chemical analyses.

Water Stress Treatment

In the greenhouse experiment, the pots were saturated with water 31 and 73 days after germination, respectively, for pre-flowering and post-flowering plants. The following morning, excessive water was drained for two hours and pots were weighed to obtain the saturation mass. From this point, irrigation was stopped for water-deprived (WD) plants. Both control and WD plants were weighed every day between 16:00 and 17:00 to determine the daily evapotranspiration. The water lost was added back to the control plants. To prevent soil evaporation, pots were covered with a 3-mm-thick polystyrene sheet. However, soil evaporation could not be neglected.

In the field experiments, 53, 70 and 40 mm of water were provided, respectively, on June 29, July 11 and August 13 2012 in the irrigated condition in Auzeville-Tolosane.

Soil Evaporation Estimation In The Greenhouse

Six pots without plants that represented the different water content were also weighed every day for two weeks during the experiment. Climate conditions, such as relative humidity and average temperature in the greenhouse, were monitored daily. The soil evaporation could, therefore, be estimated by performing a linear regression with pot water content, relative humidity and average temperature using the function regress (MATLAB version 7.13.0.564, Statistics Toolbox 7.6).

This model is detailed in Supporting Table S1 and was used to estimate the soil evaporation during the greenhouse experiment. Plant leaf area and transpiration in the greenhouse For all plants, the length and width of odd leaves were measured every other day. The total leaf area was calculated from these measurements as described in Casadebaig et al. (2008) The plant transpiration (E in g.mm-2) for each pot was calculated every day as the difference between the water lost by the pot and the water lost by soil evaporation divided by the total plant leaf area.

The normalized transpiration (EN) for each WD plant was calculated every day as the ratio between its transpiration and the average transpiration of control plants of the same genotype in the same block. Estimation of the fraction of transpirable soil water (FTSW) in the greenhouse

Experiment

The total transpirable soil water (TTSW) is the maximum amount of soil water available to the plant. In our experiment, 8 treated plants reached EN values less than or equal to 10% and were used to estimate

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the TTSW. This weight (W10%) corresponded to the dry soil plus 26% (w/w) of the water contained in the saturated pot. The fraction of transpirable soil water (FTSW) was finally calculated as follows: FTSW = (Wd-W10%)/TTSW, where Wd is the weight of the pot at day d.

The FTSW value was used to determine whether a plant had reached the target stress intensity. Estimation of the soil water content (SWC) in the greenhouse experiment At the end of the greenhouse experiment, a soil core was collected from each pot. The soil samples were weighed to obtain the fresh weight and then dried for 48 h at 120°C before a second weighing to obtain the dry weight. The soil water content (SWC in percentage w/w) was calculated daily as follows: SWCj = (Wfi-Wd)/(Wd-Wd’), where Wfi is the weight of the fresh soil and plant at day i, Wd is the weight of the dry soil and plant and Wd’ is the weight of the dry plant. The weight of the fresh plant at day i is estimated using the dry weight of the plant and based on the assumption that the plant water content was, on average, 81% for post-flowering plants and 87% for pre-flowering plants and was the same for the different plant tissues.

Estimation of the fraction of total soil water (FtotSW) in the greenhouse

Experiment

Another soil water content indicator is the fraction of total soil water (FtotSW), which was estimated as follows: FtotSW=(Wfi-Wd)/(Wsat -Wd), where Wsat is the weight of the water-saturated pot. Measurement of leaf water potential (Ψ) in greenhouse and field experiments In the greenhouse experiment, the harvested plants for transcriptomic analysis (WD and control plants) were placed in a dark room until the next morning. The water status at the time of harvest (between 11:00

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and 12:30) was noted ΨPD’ and estimated as the leaf water potential, after equilibrium with the soil was reached. ΨPD’ was measured on the nth leaf for each harvested plant using a Scholander’s pressure chamber (Soil Moisture Equipment Corp., California, U.S.A.), where n was 2/3 of the total leaf number Ntot.

In the field experiments, the water status at dawn was estimated as the classical pre-dawn leaf water potential ΨPD and was measured for one plant per plot between 4:00 and 5:30 once a week for three weeks. Measurements began when plants were at the F1 stage (CETIOM nomenclature). In the Fleurance and Samatan trials, the first measurement occurred, respectively, on July 18 and 19, 2012. In Auzeville- Tolosane, the measurements began on July 31, 2012. The water potential was measured for the 5th leaf from the head (Ntot -5) using a Scholander’s pressure chamber.

It is important to note that contrary to the transcriptomic harvests that were always performed at noon (except for the diurnal variation study), ΨPD and ΨPD’ are slightly different measurements of plant water status. ΨPD was measured at dawn (between 4:00 and 5:30) after a normal night, thus representing soil- plant water status the day before leaf harvest (Fig S2 A). In contrast, ΨPD’ was measured after the soil- plant water equilibrium had been reached during artificial night in dark room representing the exact plant water status at leaf harvest time (between 11:00 and 12:30) (Fig S2 B).

In the Auzeville-Tolosane trial, to study the influence of the diurnal variations on leaf water potential and gene expression, leaves were harvested in each of 3 blocks for each genotype, both in irrigated and non- irrigated conditions, in the same order, between 4:00 and 5:30, 7:00 and 8:30, 10:00 and 11:30, 11:30 and 13:00, 13:00 and 14:30, 16:00 and 17:30, 19:00 and 20:30, 22:00 and 23:30, and 1:00 and 2:30 (Fig S2 C). Separate leaves were used from the same plant for the leaf water potential measurement and for transcriptomic analysis. This study occurred on August 9 and 10, 2012 under high evaporative demand and constant sunny conditions.

Gene Indicators Of Water Status

From the study of Rengel et al. (2012), we selected genes that were found to (1) be correlated to the integrated transpired water (ITW) in fixed duration stress and in fixed intensity stress (R2>0.65) and (2) show a treatment or a treatment*genotype interaction effect in the ANOVA for either type of stress. From this list of 143 genes, we chose to focus on sunflower homologues of Arabidopsis genes that have been described in the literature to be involved in abiotic stress responses. We finally kept 28 genes. Detailed descriptions of these genes are presented together with all the genes in this study in Supporting Table S3.

Circadian Clock-Related Genes

Numerous genes have been identified to vary according to the circadian clock. We chose the four Arabidopsis circadian clock regulators TIMING OF CAB EXPRESSION 1 (TOC1), LATE ELONGATED HYPOCOTYL (LHY), CONSTANS (CO) and ZEITLUPPE (ZTL) (Alabadi, Oyama, Yanovsky, Harmon, Mas & Kay, 2001; Wilkins, Brautigam & Campbell, 2010) and identified the best BLAST hits in the sunflower transcriptome (https://www.heliagene.org/HaT13l). HaDHN1 and HaDHN2 of the sunflower were first described by Cellier, Conejero & Casse (2000) to vary during the circadian cycle and were re- examined in this study. All correspondences are summarized in Supporting Table S3.

Genes showing a genotype*treatment interaction effect in ANCOVA In addition to the 28 genes correlated to water stress intensity, we studied the gene expression levels of

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four transcripts: HaT13l002164, HaT13l009999, HaT13l009995 and HaT13l020030. The expression of these transcripts was found to be correlated to three other morpho-physiological variables in Rengel et al. (2012): carbon isotopic discrimination (CID), evapotranspiration (ET) and osmotic potential (OP). The identification of Arabidopsis homologs was performed according to the best BLAST hits. These four genes and a fifth gene originally correlated to ITW in Rengel et al., (2012) study, were used to illustrate a genotype*WSB interaction effect in ANCOVA analysis explained below. Detailed descriptions of the corresponding genes are presented in Supporting Table S3.

Tool

(http://probes.pw.usda.gov/batchprimer3/index.html) using the default parameters with an optimal product size of 60bp (min=50bp, max=80bp). All primers are summarized in Supporting Table S4.

Tissue Harvest And Rna Extraction

One non-senescent and non-growing leaf by plant was harvested between 11:00 and 13:00, except during the diurnal variation study. Each leaf was sampled and treated separately. After freezing and grinding the samples, RNA was extracted and checked for quality and quantity. Detailed protocol of these steps and the cDNA synthesis is provided in Supporting Material and Method.

Estimation Of Gene Expression By Qrt-Pcr

Gene expression was estimated by qRT-PCR. and was normalized according to the amplification efficiency and the expression levels of seven reference genes identified in Rengel et al. (2012). Detailed protocol of these steps is provided in Supporting Material and Method and reference gene information is summarized in Supporting Table S3.

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Gene expression correction following the time of the day The linear regression of gene expression as a function of the hour of the day was performed on the diurnal variation study’s data between 10:00 and 20:00 for genes chosen for the biomarker model using the robustfit function in MATLAB. The linear regression was set to pass by means of the expression levels of samples harvested between 11:00 and 12:00, to match with the harvest time observed in the field and greenhouse experiments that were used to calibrate and validate the biomarker models. We corrected the gene expression for samples harvested at different times of the day to obtain an estimated gene expression at 11:30 using linear regression parameters.

Test Of Genotypic Effect On The Models

For each selected gene correlated with water stress intensity, we performed a covariance analysis (aoctool function in MATLAB) by testing genotype-dependent (1) and non-genotype-dependent (2) models for the gene expression level as a function of water stress status as follows: (1) Yi,t=ai+biXi,t+Zit : genotype-dependent model and (2) Yi,t=a+bXi,t+Z’it : genotype-independent model, where Yi,t is the expression level of the gene for genotype i and for the actual water stress intensity t (with different values in each of the three blocks), Xi,t is the value of the stress intensity, and Zi,t and Z’i,t are the residues.

The gene expression was considered to not have no genotypic effect if the F-test performed as follows

F = (Sse2-Sse1/(2*(G-1))/(Sse1/Df1),

where SSE1 and SSE2 are, respectively, the sum of the squared errors for model (1) and model (2), G is the number of genotypes and df1 is the number of degrees of freedom attached to the error in the model (1).

Statistical calibration and validation of the water status biomarker (WSB) All combinations of 3, 4, 5 or 6 genes that were correlated to stress intensity and presented no genotypic effect were tested to construct a model to estimate the pre-dawn leaf water potential. The model fitting was performed by the GeneralizedLinearModel.fit function in MATLAB using the greenhouse data as the calibration set. For each of the four types of models, we selected the 50 best models according to the AIC criterion.

Selected models were then tested using the predict function in MATLAB, and field data served as the validation set. For each of the four types of models, we selected the best model according to the R of the correlation between WSBΨPD predictions and the corrected values of observed ΨPD.using the regress function in MATLAB (corrections are described in Supporting Material and Methods and Fig S5). We compared the four types of models and chose the best one according to the R of the correlation.

Test Of Trial Effect

We considered samples harvested only in non-irrigated conditions for the three field trials. For the four genes correlated to morpho-physiological traits and and one gene to ITW, we performed an ANOVA using the anovan function in MATLAB to test the genotypic, trial and genotypic*trial interaction effects.

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Genes found to have no trial effect were tested for the genotype*WSB interaction. We performed a covariance analysis using the aoctool function in MATLAB with the following model:

Yi,B = Ai +Bixi,B+Zi,B,

where Yi,b is the expression level of the gene for genotype i and biomarker level b, Xi,b is the value of the WSB level and Zi,b is the residue.

Selection Of Candidate Genes

Based on our previous results (Rengel et al., 2012), we selected 28 genes that were found to be correlated to the integrated transpired water (ITW) in fixed duration stress and in fixed intensity stress (R2>0.65). As the expression of these genes was independent of the tested genotypes, they were strong candidates to build a biomarker for plant water status. To assess a particular level of gene expression that reflects stress intensity, we needed to study these genes through a larger range and at a finer scale of drought stress.

Establishment Of A Fine Scale Of Drought Stress

To study changes in gene expression at different stress levels, we established a large range of drought stress with a fine scale. For the treated plants, the water status indicators ranged from 0.97 to -0.087 for the FTSW, from -0.2 to -2.4MPa for the pre-dawn leaf water potential (ΨPD’), from 54.3% to 5.98% for the soil water content (SWC) and from 0.13 to 1 for the FtotSW. The four genotypes and the two stages were represented through the entire range.

The four water status indicators measured during the greenhouse experiment were highly correlated with the R2 values, ranging from 0.65 to 0.96 (Fig. 1). Interestingly, the ΨPD’ was only correlated with FTSW values below 0.4, SWC values below 25% and FtotSW values below 0.5. This selective correlation reflects that, in our data, ΨPD’ did not discriminate high water status levels.

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Figure 1: The distributions and correlations between the four water status indicators measured in the greenhouse experiment: ΨPD’, FTSW, SWC and FtotSW. Correlation between gene expression and water status indicators As a confirmation of our previous results using the FTSW (Rengel et al., 2012), we estimated the correlations of 28 selected genes to the four water status indicators over the new finer and larger scales of water status, considering together the treated plants of all genotypes and growth stages. Raw data of the gene expression level compared to FTSW level for the 28 candidate genes are shown in figure S6. We identified 18 genes whose expression was correlated (p<0.01) to the FTSW, 20 for ΨPD’, 21 for the SWC

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and 18 for FtotSW (Table 1 and Fig S7). Figure 2. A schematic description of the water status biomarker construction. WSBΨPD was developed in greenhouse conditions and validated in the field. WSBFtotSW, WSBFTSW and WSBSWC were built in the greenhouse environment using field-robust genes used for WSBΨPD.

A covariance analysis was used to test genotype-dependent correlations (p<0.01). Among the correlated genes, we found two genes (according to the water status indicator) whose correlations were genotype- dependent (summarized in Table 1 and Fig S8).

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Finally, among the 28 initial genes, we retained 14 genes that showed neither genotype nor stage effects in the greenhouse experiment and that were technically robust in both greenhouse and field experiments. These first steps of gene selection for biomarker construction are summarized in Figure 2.

0.18

Table 1. The number of genes correlated (p<0.01) to each water deprivation indicator and genotype effects to be

Used In Gene Combinations For Biomarker Fitting

Construction of Generalized Linear Models to estimate plant water status

Model For Ψpd’ In Glasshouse

According to the AIC criterion, we selected the 50 best generalized linear models with 3, 4, 5 and 6 genes to fit the ΨPD’ from the greenhouse data (Fig 2). The adjusted R and RMSEc for the different types of models are presented in Table 2. Considering only these glasshouse data, the adjusted R increased with the number of genes introduced in the model, and the RMSEc values for the four types of models were similar.

0.65-0.68

Table 2. The range of adjusted R and RMSEc for the 50 best linear models with 3, 4, 5 and 6 genes fitting the ΨPD' in the greenhouse experiment.

Field Experiment Validation With Ψpd

We used the results of three field trials to select the best predictive model for ΨPD’ (Fig 2). The field trial experiments were implemented in environments with deep (Auzeville-Tolosane) or shallow (Fleurance and Samatan) soils. The Fleurance and Samatan trials had clay soils (respectively in average 52.5% and 52.8% of clay) with a low water-holding capacity. The Auzeville-Tolosane trial had soil with an equilibrate texture between the silt loam and sand (composed in average of 24.3% of clay, 29.8% of silt and 45.9% of sand), and therefore, with a high water holding capacity and therefore with a high field capacity (Supporting Table S9). Trials were chosen with different soil characteristics to ensure a wide range of plant water statuses. For the same reason, we harvested samples and measured ΨPD over 3 weeks for six repetitions per genotype. Finally, the Samatan data over the last week was disturbed by an important rain event and was discarded. Overall, we obtained a good range of water stress across the entire experiment: ΨPD ranged from -0.5 to -2.2 MPa in the Fleurance trial and from -0.7 to -2.3 MPa in the Samatan trial, whereas in Auzeville-Tolosane, where we set up irrigated and non-irrigated conditions, ΨPD ranged from -0.3 to -1.5 MPa.

Using field experiment data, we compared the models’ prediction (WSBΨPD) and ΨPD’ estimated from the measured ΨPD. We observed that models with 6 genes that were better in the greenhouse environment introduced errors in field predictions. We selected the three-gene model that showed the best correlation between the observed and predicted ΨPD’ with an R equal to 0.61 (Fig 3) and an RMSEp of 0.67. With

This Model, The Wsbψpd Was Estimated As Follows:

WSBΨPD=ln(-ΨPD’) = 1.53 + 0.35*ln(dCtHaT13l002207) – 0.39*ln(dCtHaT13l002636) +0.16*ln(dCtHaT13l5199). where ψPD’ is expressed in 0.1MPa. In the greenhouse, this model had an adjusted R of 0.78 and an RMSEc of 0.64 and therefore offered better prediction in both controlled and field environments.

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Models for water stress indicators not accessible in field conditions The three genes used in the model to predict ΨPD’ appeared to be robust enough in predicting the stress intensity in both the greenhouse and field environments. We used these same genes in the construction of models for water stress indicators that are not accessible in field conditions. FTSW, SWC and FtotSW were estimated by generalized linear modeling using gene expression levels of HaT13l002722,

Hat13L002636 And Hat13L005199 As Follows:

WSBFTSW = 0.42 - 0.0618*ln(dCtHaT13l2207) +0.21*ln(dCtHaT13l002636) - 0.04*ln(dCtHaT13l005199), WSBSWC = 27.70 - 3.83*ln(dCtHaT13l002207) + 8.51*ln(dCtHaT13l002636) - 1.79*ln(dCtHaT13l005199), and WSBFtotSW = 0.54 - 0.06*ln(dCtHaT13l002207) + 0.17*ln(dCtHaT13l002636) - 0.03*lnd(CtHaT13l005199).

Models had adjusted R values of, respectively, 0.69, 0.72 and 0.74 for FTSW, SWC and FtotSW, and their RMSEc values were, respectively, 0.20, 9.31 and 0.18.

Authors:

Peder EZ Larson 1, 2,* , Jenna ML Bernard1, James A Bankson 3, Nikolaj Bøgh 4, Robert A Bok1, Albert P. Chen 5, Charles H Cunningham 6,7, Jeremy Gordon1, Jan-Bernd Hövener 8, Christoffer Laustsen 4, Dirk Mayer 9,10, Mary A McLean11 12, Franz Schilling13, James Slater1, Jean-Luc Vanderheyden5, 14, Cornelius von Morze 15, Daniel B Vigneron1, 2, Duan Xu1, 2, and the HP 13C

94143, Usa.

Denmark. 5 GE Healthcare, Menlo Park, California, USA. 6 Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.

8 Section Biomedical Imaging, Molecular Imaging North Competence Center (MOIN CC), Medicine, Baltimore, MD, USA. Cambridge, United Kingdom.

14Jlvmi Consulting Llc, Dousman, Wi, Usa

#See Acknowledgements for a list of all HP 13C MRI Consensus Group Members This work was supported by the ISMRM Hyperpolarized Media MR Study Group, the ISMRM Hyperpolarization Methods & Equipment Study Group, and the Hyperpolarized MRI Technology Resource Center (NIH/NIBIB grant P41EB013598).

Abstract

MRI with hyperpolarized (HP) 13C agents, also known as HP 13C MRI, can measure processes such as localized metabolism that is altered in numerous cancers, liver, heart, kidney diseases, and more. It has been translated into human studies during the past 10 years, with recent rapid growth in studies largely based on increasing availability of hyperpolarized agent preparation methods suitable for use in humans. This paper aims to capture the current successful practices for HP MRI human studies with [1-13C]pyruvate - by far the most commonly used agent, which sits at a key metabolic junction in glycolysis. The paper is divided into four major topic areas: (1) HP 13C-pyruvate preparation, (2) MRI system setup and calibrations, (3) data acquisition and image reconstruction, and (4) data analysis and quantification. In each area, we identified the key components for a successful study, summarized both published studies and current practices, and discuss evidence gaps, strengths, and limitations. This paper is the output of the “HP 13C MRI Consensus Group” as well as the ISMRM Hyperpolarized Media MR and Hyperpolarized Methods & Equipment study groups. It further aims to provide a comprehensive reference for future consensus building as the field continues to advance human studies with this metabolic imaging modality.

Keywords: Hyperpolarized MRI, metabolic imaging, carbon-13, pyruvate, dissolution dynamic

Introduction

MRI with hyperpolarized 13C agents, also known as hyperpolarized (HP) 13C MRI, has shown great potential as a novel imaging modality, particularly for its ability to probe metabolic processes in real time. The first human studies with HP [1-13C]pyruvate were performed in 2011 in prostate cancer patients (1).

Since then, there have been over 60 papers published with imaging results of human subjects from 13 different sites, with applications including prostate cancer, brain tumors, breast cancer, kidney cancer, pancreatic cancer, metastatic disease, liver disease, ischemic heart disease, diabetes and cardiomyopathies. The vast majority of these studies used [1-13C]pyruvate (1–63), where [2-13C]pyruvate (64) and 13C-urea (56) have been demonstrated too.

As clinical HP 13C MRI advances, there is a growing need to build consensus for best practices, which are critical for comparing data across sites, performing multi-site trials,deploying methods to new sites, partnering with vendors, and potentially for obtaining broader regulatory approvals.

In March 2022, we initiated an effort to build consensus within the HP 13C MRI community with this opportunity in mind, and it was greeted with strong enthusiasm. The “HP 13C MRI Consensus Group”, containing over 55 members from 27 sites, identified the area of greatest need and opportunity for consensus building to be HP [1-13C]pyruvate human

●

Pyruvate is the most mature and widely used HP agent and has the most significant translational evidence emphasizing the potential clinical impact.

●

Clinical trials, particularly multi-site trials, have the strongest need for consensus methods to ensure that data can be combined across sites. This work is a Position Paper for which the goal is to describe current successful practices and study methods for HP [1-13C]pyruvate human studies along with justification to support those practices. This is divided into four major topic areas: (1) HP 13C-pyruvate preparation, (2) MRI system setup and calibrations, (3) data acquisition and image reconstruction, and (4) data analysis and quantification (Fig. 1). The current successful practices and study methods include a literature review of published peer-reviewed journal papers showing human HP [1-13C]pyruvate study data, up to September 2022 (1–63), as well as new unpublished information from surveys of HP 13C study sites. Based on this information, we also highlight the evidence gaps, strengths, and limitations of current practices which are summarized at the end of each section.

Figure 1: Illustration of the HP 13C MRI human study process, including the 4 major areas covered in this paper: Hyperpolarized 13C-pyruvate preparation, MRI system setup and calibration, Acquisition and Reconstruction, and Data Analysis and Quantification.

Figure 2: Anatomical targets of HP [1-13C]pyruvate MRI human studies published up to September 2022.

Hyperpolarized 13C-Pyruvate Preparation

This section covers the processes for creating the HP agent, 13C pyruvate, and will include many aspects and considerations that are needed to safely and effectively prepare doses for metabolic imaging studies in human subjects. These include material, personnel, equipment and facility, fluid path preparation, quality control, and release.

It is helpful to understand that the specifications of a dose of 13C pyruvate suitable for in vivo MR HP metabolic imaging were shaped in part by early preclinical studies performed by GE HealthCare summarized in Ref. (65). In short, the safety of the two novel drug components, 13C pyruvate and the electron paramagnetic agent (EPA) AH111501, were demonstrated in those studies. The more precise formulation of the dose suitable for human use was then determined from clinical studies (66) that included two Phase 1 clinical trials in young and elderly healthy volunteers without hyperpolarization of the 13C nuclei and another Phase 1/2a dose escalation and imaging feasibility study with HP 13C pyruvate in 31 prostate cancer patients at the With the exception of the first HP 13C imaging clinical trial, which utilized a prototype device in a cleanroom (1), all HP 13C studies performed in humans to date have utilized the SPINlab polarizer (manufactured by GE HealthCare). Consequently all doses of the HP 13C pyruvate delivered by SPINlab have been produced using the “SPINlab Pharmacy Kit” that serves as the container-closure system for the various drug components (13C pyruvic acid and EPA mixture, dissolution medium, and neutralization and dilution medium) during sample polarization, dissolution and quality control (QC) processes. Thus many aspects of the HP sample preparation considerations discussed below are related to the SPINlab instrument and the consumables designed to be used with it (67).

General Considerations

While more than 860 patients or healthy subjects having been injected with HP 13C pyruvate as of January 2022 without reports of any serious adverse events (68), HP 13C pyruvate injection remains an investigational MR contrast agent and can only be administered by those with Investigational New Drug (IND) exemption from the Food and Drug Administration (FDA) in the USA, a Clinical Trial Application (CTA) in Canada, approval from National Research Ethics Committee Services in the UK, or approval from the relevant local regulatory body. Thus, methods and processes involved to produce a dose should have patient safety as the first priority. Since utilizing dissolution dynamic nuclear polarization (dissolution-DNP) for human use is still a relatively new development, there are no existing published regulatory guidelines specifically for this method.

There are two major production styles that determine how various sites approach the agent preparation. In the US, the most common approach is to rely on a sterilizing filter (“Terminal Sterilization”) to ensure sterility of the final product, akin to PET tracer production, where a starting molecule with a radioisotope is processed using various other ingredients to make the final, desired and injectable contrast agent within a necessarily short amount of time (69). For these sites, sterilization of the components and accessories upstream of this filter are not required, although many of them were manufactured and tested following Good Manufacturing Practice (GMP) or Good Laboratory Practice (GLP) requirements. The filling process is usually performed under an ISO 5 laminar flow hood, but a clean room or an isolator is not required.

This approach is typically accompanied by testing the integrity of the sterilizing filter prior to release of the dose for injection. Typically, post release endotoxin and sterility tests are performed using an aliquot reserved from each released dose.

In the UK and EU, the most common approach is to more-closely follow sterile pharmaceutical compounding guidelines (70), where all components and ingredients are required to be sterile or manufactured under GMP guidelines and are assembled and filled within a clean room environment or an isolator system (“Sterile Preparation”). Typically a batch of Pharmacy Kits for HP 13C pyruvate injection are prepared together. The sterility of the final dose is also ensured by batch validation testing, in addition to the sterility of the ingredients and the sterile compounding process. The endotoxin and sterility testing are performed for the process validation but are not performed for each injected dose.

Some institutions fill and assemble the Pharmacy Kit required for a specific study on the same day or the day prior to polarization, dissolution, and patient administration, but others have also demonstrated the feasibility of preparing a batch of kits, keeping them in a -20ºC freezer and using them over a period of a few months.

Beyond the obvious requirements that the process and the facility has to ultimately produce a dose that is safe to inject into a human, regulatory authorities will also focus on the question “Are you in control of your processes?”. To be in control of your process requires an in-depth and broad understanding of all processes involved in pre, post, and during the production process.

Personnel

It is typical and may be required to have licensed personnel involved in the production process depending on local regulations.Typically a pharmacist, radiopharmacist or other similarly qualified person (QP), in charge of the facility where the Pharmacy Kit filling and preparation is taking place, is responsible for the overall process and the release of the injectable dose.

Qualified cleanroom technicians are often involved in the Pharmacy Kit filling under the supervision of the pharmacist or QP. As is required for pharmaceutical compounding or PET tracer production, training requirements and training records for all personnel need to be maintained and available for audit by the FDA or equivalent.

Equipment And Facility

The facility and all equipment need to have standard operating procedures (SOPs) that describe how equipment is used, maintained, and calibrated to comply with relevant legislation. Currently, almost all the filling of the Pharmacy Kit takes place within a compounding laminar flow hood or isolator (typically ISO 5). At some sites, the filling is conducted within a cleanroom, while at others, it is conducted in a dedicated non-cleanroom space, reflecting differences in cleanroom approach and specifications between regulators worldwide (71). Some equipment or facilities, such as the compounding hood or cleanroom, may require external certified laboratories for testing.

Material Handling

Material handling guidelines (69,70) require SOPs detailing a system to track all of the materials involved in the HP production process for a particular patient dose, similar to current good manufacturing practice (cGMP) requirements for material handling for drug compounding. This includes acceptance standards, storage conditions, amount used in the patient dose for each ingredient and materials used in the assembly of the fluid path and Pharmacy Kit. Currently some users choose to open and inspect and sometimes modify the Pharmacy Kits upon arrival, but some users keep them in the sealed packaging until they are required for dose preparation.

Pharmacy Kit Filling And Assembling

As required by an IND or its equivalent, the preparation of the doses of HP 13C agent are detailed in the Chemistry, Manufacturing, and Control (CMC) section of an applicable regulatory submission; an example of this has been made available (72). It describes the processes of filling the Pharmacy Kit with the different components that make up the final drug product, and of assembling the final kit for either storage or immediate use in the polarizer. Special attention should be given to the laser welding process in order to satisfy installation qualification (IQ) and operational qualification (OQ). Typically, the final developed process is validated by process qualification (PQ) runs, during which 3 or more Pharmacy Kits are filled and used and the final HP 13C products are tested for endotoxin and sterility and to confirm that they meet the dose specifications for injections (usually including pyruvate concentration, residual EPA concentration, pH, liquid state polarization level and dose temperature). The data from 3 consecutive PQ runs are submitted as part of the IND submission (or its equivalent), and are often also reviewed by the Institutional Review Board (IRB) where the studies are conducted.

Quality Control And Dose Release

The quality control (QC) and dose release can be separated into two aspects: one is the QC and release of the filled Pharmacy Kit, and second is the QC and release of the HP 13C agent for injection, after polarization and dissolution. For institutions filling a batch of kits and storing them to use over a period of time, typically the batch can be released based on initial validation, environmental monitoring data from the day of kit production, and if filters are used during preparation of any of the components, filter integrity testing. But in some cases one or more kits are used for validation before the batch of kits are released for future use. For institutions that fill only the kits required for specific studies shortly before the experiment, the filled kits often do not go through separate release tests before they are used.

The quality control of the HP 13C pyruvate solution post dissolution is primarily performed to ensure that the agent meets the dose specifications (Table 1) before it is administered to the subject. These specifications target both safety (pH, residual EPA, temperature) and efficacy (pyruvate concentration, polarization, volume). Typically, the pyruvate concentration, residual EPA concentration, pH, dose temperature, dose volume, and liquid state polarization are measured by the QC accessory associated with the SPINlab polarizer. Some users perform a secondary measurement for one of the parameters, such as pH, using a different instrument or pH paper. For sites that do not go through a separate release testing process for batch filled kits, the integrity of the sterilization assurance filter, a part of the Pharmacy Kit, is typically tested as a part of the dose release. It is also common for these users to preserve an aliquot of the final HP 13C pyruvate solution for post-release endotoxin and sterility testing. This testing cannot be completed fast enough to test an individual dose prior to injection, but this is why other processes such as PQ runs and validation testing are done to minimize the chance a subject could be injected with a contaminated dose.

The Final Dose Release And Injection

should be done under the supervision of a licensed professional, based on local regulations.

Some Key Challenges

Many of the challenges associated with HP 13C pyruvate preparation can be attributed to the conditions required for the dissolution-DNP method of high magnetic field (~3-7 T) and very low temperature (~1 K) during polarization, with pressurized and superheated water necessary for the rapid dissolution event. These extreme conditions are quite challenging for the design of the container-closure and fluid path system. In particular, the cryogenic temperature in the polarizer requires special attention to any moisture or ambient (moist) air introduced into that portion of the fluid path, which can form an ice block at ~1 K. This ice can lead to flow restriction during the dissolution event and reduce the strength of the laser welded bond between the cryovial and its cap. This can ultimately produce failures in the dissolution step, including variations in final pyruvate concentration and pH that may fail to meet QC release criteria as well as fluid path ruptures that provide no available dose and result in polarizer down-time.

The polarization of the HP 13C pyruvate sample decays quickly over the span of a few minutes after dissolution, and thus the process of dissolution, QC for release, and injection should be completed as fast as possible to preserve the high polarization level achieved. Any delays in the preparation process, such as transportation time or equipment malfunction, can significantly reduce the final polarization and result in lower quality imaging data.

Current Practices

A summary of data collected from all sites performing clinical trials with HP 13C-pyruvate is shown in Fig. 3 and Table 1, including the specification of the final dose and how the quality control and release of the final dose are performed. There is a split in the Production Style, described in the General Considerations section above, with 8/13 sites using Sterile Preparation versus 5/13 using Terminal Sterilization. While many of the dose specifications show notable differences in acceptable ranges, all of these variations listed in tables have been successfully and safely been used to perform HP 13C pyruvate studies in humans. Their differences depend on the institutions’ preferences, resources and their particular regulatory situation. There is high similarity in pyruvate ranges, temperature ranges, EPA limits, and volume limits. There is modest variability in pH ranges and large variability in the endotoxin test limit. There is a 3-fold difference in acceptable polarization levels, which are measured to ensure a futile dose is not injected since the polarization is directly proportional to SNR. This reflects the decision by several sites to believe that useful data can be still be obtained with suboptimal polarizations.

Figure 3: Hyperpolarized agent preparation methods reported by sites currently performing HP

In House

Table 1: HP 13C-pyruvate preparation parameters, methods, and dose specifications used for quality control testing and release as well as validation. These were obtained from a survey of all sites performing clinical trials with HP [1-13C]pyruvate. The parameters used for product release are noted in bold text, otherwise these parameters are measured for batch validation or other QC measurements. The endotoxin and sterility testing are performed during process validation of the batch and/or post-injection, and largely depends on the agent production approach.

Summary

The overall safety record of HP 13C-pyruvate has been very strong, and the SPINlab hyperpolarizer has proven to provide high polarizations at human sized doses while meeting numerous QC and release criteria. A weakness remains the failure modes of the SPINlab Phamacy Kits (e.g. ice blocks, path ruptures), which are placed under extreme requirements particularly during dissolution. The preparation process still requires a high degree of expertise.

Therefore, there is a significant need to improve the reliability, robustness, and ease of operation for generating HP 13C-pyruvate doses for human studies. Furthermore, there is a divide between manufacturing and sterile compounding style preparation as well as other site-specific practices, resulting in variations in SOPs and justification required to relevant regulatory bodies. There have also been no comparisons between these approaches. It is also unclear what release criteria and QC parameters are truly required to ensure patient safety.

However, all of the reported methods are acceptable and approved by the appropriate regulatory authorities, and have led to the rapid expansion of successful human studies in recent years.

Mri System Setup And Calibrations

This section covers the MRI system setup, including the imaging system, RF coils, phantoms, and prescan calibration methods.

Imaging System

The main prerequisite for a given MRI scanner to be capable of supporting studies with HP 13C is its “broadband” capability to transmit and receive radiofrequency (RF) signal at the frequency of 13C, which is around 4 times lower than 1H. This does not come as a default on clinical MR devices. The transmit power of the broadband amplifier should also be sufficient to support the intended flip angle and RF pulse shape with the employed transmission RF coil(s) for 13C. Most studies to date use relatively low flip angles (< 90 degrees) for HP 13C in order to preserve polarization for time-resolved imaging. The capability to receive 13C signal on multiple channels is also desirable to increase SNR, as discussed further in the “RF coils” section.

The choice of magnetic field strength is primarily dependent on the metabolites’ frequency separation due to chemical shift dispersion and 1H imaging. High field strengths do not enhance hyperpolarized 13C signal as they do for 1H because the signal strength in a HP experiment relies on manipulating the population of quantum energy states outside of the MRI scanner.

However, the injected HP 13C-pyruvate and its metabolic products have greater frequency separation at higher fields, and it may thus be easier to separate and quantify these resonances at higher fields. This comes at the cost of a reduction in the achievable T2* and often reduced T1. As the initial polarization is independent of the imaging field strength it has been proposed that the increased T2* at 1.5T can potentially be exploited to increase SNR by adapting the acquisition bandwidth or reduce off-resonance imaging effects in cases when the decay of the transverse magnetization is dominated by T2* (73). In practice, 3T has been used in all published human 13C-pyruvate studies surveyed (Supporting Table S1), and comprises the majority of scanners currently in use for human studies (Table 3). A field strength of 3T is well-suited for 1H MRI anatomical reference and correlative imaging.

Stronger and more rapidly slewing magnetic field gradients support more rapid spatial encoding, particularly for metabolite-specific single-shot imaging using echo-planar imaging (EPI) or spiral imaging (See “Acquisition and Reconstruction”). Although the spatial resolution acquired for HP 13C imaging is typically much coarser than for 1H MRI, the factor of ~4 in gyromagnetic ratio leads to the same reduction factor in performance of the gradient system, so 13C experiments are potentially more limited by gradient hardware performance. To date, all human studies have used the commercially-available integrated gradient systems provided in clinical MRI scanners.

Optimization of scanner design has understandably focused on minimization of artifacts in 1H MRI, where devices such as room lights, the gradient amplifiers, and the motors driving the patient bed are checked to ensure that they do not produce RF interference at the 1H frequency, but artifacts may arise at other frequencies. Eddy current compensation is also not always appropriately adjusted for nuclei at other frequencies (74). In order to optimize for 13C, many sites have performed checks on phantoms for RF interference, gradient artifacts, and eddy currents (74), including the use of post-hoc gradient impulse response function characterisation and correction, and some vendors have fixed these issues as well.

Rf Coils

For HP 13C imaging studies in humans, RF coils for both 1H and 13C nuclei are needed, with 1H MRI providing an anatomical reference for registration and optional additional multiparametric MRI readouts. At the Larmor frequency of 13C nuclei, the relative contributions from coil noise compared to sample noise increase compared to 1H (73,75), although sample noise still is likely the dominant contributor for human-sized coils at 32.1MHz - the resonance frequency of 13C nuclei at 3T.

The key requirement for human 13C-pyruvate RF coils are that the coil geometry and sensitive volume must cover the volume of interest in the subject. Table 2 and Figure 4 shows coil configurations that have been used and optimized for applications in different anatomic regions.

Volume resonators are most commonly used for transmit, as they surround the subject to

Provide B1 Transmit Across The Fov (B1

+). While 1H relies on a large birdcage (“body”) coil built into the scanner, 13C transmit coils must be placed inside the bore. This takes up valuable space within the magnet, and also has led to the use of designs with relatively inhomogeneous

B1

+. Many human studies have used Helmholz pair resonators for transmit, including the “clamshell coil”, which has a notably inhomogeneous B1

+ Profile But Has Been Used Because Of

relatively easy integration into the scanner bore. B1

+ Variation Results In Variations In The Flip

angles that control the use of the hyperpolarized magnetization and creates errors in common HP metrics (9,76). The exception are head coils, where birdcage designs with highly

Homogeneous B1

+ can be placed around the head while easily fitting inside the bore. As with 1H MRI, higher SNR can typically be achieved by smaller receive coil elements, such as surface coils or phased arrays, and the majority of 13C receive coils used have layouts similar to 1H phased arrays.

RF coil quality control is important to ensure proper functioning of the coils to provide consistent imaging quality, especially with limited natural abundance 13C signal in vivo. It typically involves 1) a physical integrity check of the coil cables and connectors and 2) phantom SNR tests to check the coil’s performance and to monitor it over time (see Phantoms below). An useful reference for RF coil quality control is outlined in the MRI accreditation program of the American College of Radiology (77) and can be adapted for 13C coils.

Notably, configurations for brain and prostate studies used dual-tuned 1H/13C coil designs, which greatly simplify workflow and registration of 1H and 13C images, as no switching of coils is needed.

(1)

Table 2: RF coil configurations reported for human HP [1-13C]pyruvate studies.

Tx = Transmit

coil, RX = receive coil. The commonly used “clamshell” TX coil is a Helmholz pair design. For 1H RF configurations, all used the Body coil for TX unless otherwise noted, and “repositioned” indicates the 13C coil was removed for 1H imaging. One representative reference is listed for each configuration. The RF coil configurations reported in the reviewed papers are shown in Supporting Table S1.

Figure 4: Examples of RF coil configurations used for human HP [1-13C]pyruvate brain studies. (A,B) 13C Clamshell TX (Helmholz pair) and 2× 4-channel paddle RX arrays. (C) 13C Birdcage volume TX and 32-channel RX array (RX array slides into TX coil). (D) 13C Birdcage volume TX and 24-channel RX array, combined with a 1H 8-channel RX array. Image reproduced with permission from Ref (16).

Phantoms

Since hyperpolarized magnetization is non-renewable, phantoms containing 13C nuclei are important to: 1) test the multi-nuclear capabilities of the imaging system, including all parts of the signal excitation and receive chain; 2) perform calibration measurements before a scan with hyperpolarized nuclei; and 3) perform necessary pre-scan adjustments (see “Prescan Calibration” section). The phantoms currently in use are listed in Table 3. Their composition must provide sufficient 13C signal, with additional considerations of conductivity, stability, chemical shift(s) present, potential for dynamic imaging, and cost. The phantom geometries are typically either compact, in order to be used alongside the subject during a HP scan, or large enough to mimic the inner volume of a RF coil for system testing.

One popular compact design contains enriched 13C-urea at high concentration, typically 8 M, which provides a single resonance, placed inside a small container ~1 mL. The most common recipe mixes 13C-urea in a 90% water/10% glycerol solution, with glycerol used to increase the urea solubility and doping with a Gd-based contrast agent to shorten T1 which increases the potential SNR per unit time. For example, when Dotarem is added at a 3:1000 volume ratio the 13C-urea T1 is around 500 ms and T2 is around 100 ms. However, when testing pulse sequences influenced by T1 and T2, doping should be used carefully. This phantom is suitable for frequency calibration, transmit gain calibration, sequence testing, and as a fiducial marker when placed next to a patient. However, enriched 13C-urea has a relatively high cost compared to natural abundance compounds.

For larger volumes (>100 ml), the phantoms most often used contain undiluted ethylene glycol, glycerol, or dimethyl silicone. These compounds have sufficiently high carbon concentrations to provide sufficient 13C signal even with the 1.1% natural abundance of 13C. These larger phantoms matching the inner volume of an RF coil are useful for coil testing, including transmit

+) And Receive (B1

-) coil profile mapping, as well as to mimic acquisitions using in vivo FOV requirements. In this case, size and conductivity should match the expected subject size in order to mimic coil loading and get a realistic estimation of B1+. Large-volume natural abundance urea phantoms have also been used by some sites, but suffer from higher conductivity compared to biological tissues. Typically, it is easier to increase the conductivity and hence coil loading of the non-conductive phantom by adding NaCl to match physiological loading (16,78).

Dynamic phantoms that aim to mimic metabolite kinetics have also been developed (79–81), and have the potential to more closely mimic the HP experiment, but so far these are not widely used.

Prescan Calibration

Prior to performing an MRI acquisition, the so-called prescan procedure is used to set the shim parameters to maximize B0 homogeneity over the field of view (FOV) or a specific region of interest (ROI), the scanner center frequency (CF), the RF transmit gain, and the receiver gain.

While this calibration procedure is usually automated for 1H, the lack of sufficient natural abundance 13C signal prevents use of automated methods. (Although natural abundance 13C lipid signal has been detected, there are so far no reports on using this signal for prescan.) Table 3 shows current practices across sites.

Maximizing B0 homogeneity is independent of the nucleus and is therefore performed prior to 13C imaging using the 1H water signal and existing shimming tools, such as by a standard automated process (“Auto Shimming”) or using high order shimming routines. Similarly, the 13C CF can be calculated from the 1H CF using a predetermined scaling factor that depends on the target chemical shift (82). Another common approach used is to have a small, high-concentration 13C phantom, e.g. 8M 13C-urea, integrated in the RF coil or placed next to the scan subject (1). The reference frequency can also be based on real-time measurements after the HP injection but prior to imaging (83). Both the CF and B0 shimming are critical when using spectrally-selective RF pulses, as inmetabolite-specific imaging methods, where the desired excitation bandwidths are typically very narrow and frequency offsets can lead to a failure mode that is only apparent after injection.

The calibration of the RF transmit power is typically performed on a small, high-concentration 13C phantom placed near the region of interest during the scan or on a large 13C phantom of similar size and coil loading as the subject, prior to the subject scan. Reference power is often done by sweeping the power in a pulse-acquire sequence (53,62), or the Bloch-Siegert method (52,84). When using a small phantom, the location of the phantom, B1

+ Inhomogeneity As Well

as any shielding effects, e.g., when the phantom is integrated into a coil (1), may degrade the accuracy. Other methods include real-time Bloch-Siegert method measurements after the HP injection (83), and using the stronger natural abundance 23Na signal that is close enough to the 13C resonance frequency to be detected by 13C coils (82).

The receiver gain is predetermined, either systematically based on independent phantom measurements and assuming the dose and polarization of the HP compound is known prior to injection, or based on past HP imaging studies.

Power [Kw]

Phantom(s) - during study Phantom(s) - before study 13C Frequency

8

13C-bicarbonate doped with dimethyl silicone, various

Maximum Values

Table 3: Summary of the imaging systems, phantoms, and prescan procedures used at sites currently performing HP 13C-pyruvate human studies. These were obtained from a survey of all sites performing clinical trials with HP [1-13C]pyruvate. *Previously performed studies with a Siemens 3T Tim Trio. The imaging systems, phantoms, and prescan procedures reported in the reviewed papers are shown in Supporting Table S1.

Summary

Commercially available 3T MRI systems are by far the most commonly used for human HP 13C-pyruvate studies, although a systematic investigation of the impact of B0 has only recently been investigated (73). The multi-nuclear RF transmit and receive chain has proven sufficient for current acquisition strategies, although many sites have observed artifacts due to RF interference, gradient interference, and residual eddy currents when operating at the 13C frequency. A variety of 13C RF coils, tailored for numerous anatomical targets, have been successfully demonstrated, with the main limitation that most transmit coils take up a lot of additional space inside the bore and provide relatively inhomogeneous B1

+ Profiles. The

phantoms used have converged into generally 2 categories - small phantoms containing 13C-enriched compounds that can be used during the study and human-sized phantoms containing compounds with high carbon concentrations but without 13C enrichment that are used to test and calibrate the coils. There are no standardized compositions or geometry, and dynamic phantoms that recapitulate in vivo kinetics would be desirable but are still an emerging area. Prescan calibration procedures were not well defined in most publications, so we surveyed individual sites to determine current practices. Calibration procedures for the B0 field (13C CF and shimming) for most sites take advantage of 1H signal and methods, while methods

For Calibration Of B1

+ is more variable across sites, likely a reflection of remaining challenges in how to perform this calibration. Standardization of both phantoms and calibration procedures would synergistically improve the robustness and reproducibility of HP 13C studies.

Acquisition And Reconstruction

Data acquisition strategies in human HP [1-13C]pyruvate MRI studies must account for multiple chemical shifts, efficiently utilize the non-renewable HP magnetization, and acquire data quickly relative to metabolism and relaxation decay processes. These studies require spectral encoding to separate metabolites, necessitating pulse sequences that efficiently encode up to 5D data (3 spatial + 1 spectral + 1 temporal dimension). RF pulses must efficiently sample without immediately saturating the non-renewable HP magnetization, and sequences must acquire data quickly and be robust to both experimental and physiologic variation (e.g. B1

+ Inhomogeneity,

variation in perfusion) to ensure reproducibility and minimize scan-to-scan variability. This section covers current successful practices for data acquisition in human [1-13C]pyruvate studies, and accompanying 1H imaging, from different anatomic regions, including scan parameters and image reconstruction.

Acquisition And Reconstruction Methods

The acquisition methods used in human [1-13C]pyruvate studies can be classified into 3 categories: 1) MR spectroscopy or MR spectroscopic imaging (“MRS/I”), 2) chemical shift encoding methods, and 3) metabolite-specific imaging (Fig. 5).

Mrs/I Methods Specifically

resolve a spectrum that can be analyzed to extract expected as well as unexpected resonances, making this approach very robust. It was used in many initial studies (1).

Chemical Shift

encoding methods, most commonly the Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL) method, use imaging sequences acquired with multiple TEs and rely on a model-based separation of expected chemical shifts (85).

Metabolite-specific imaging methods use specialized RF pulses that are spatially and spectrally selective to excite individual metabolites which are then typically imaged with fast k-space trajectories such as echo planar imaging (EPI) or spirals (86).

Their Application To Different

organ systems is described below. The image reconstruction methods used in human [1-13C]pyruvate studies have typically been conventional methods (e.g. FFT, non-uniform FFT, or equivalent). The incorporation of accelerated imaging and advanced reconstruction methods including parallel imaging (4,57,87) and compressed sensing (7) has also been applied in human studies for improved spatial resolution, temporal resolution and coverage, but have the potential for additional artifacts as well as SNR losses due to ill-conditioning of the reconstruction (e.g. g-factor).

The Majority Of

published studies do not use accelerated imaging indicating the resolution and coverage achievable without acceleration is currently adequate for successful data collection. Performing coil combination, even with fully sampled data has also been shown to have specific challenges for HP human images: using naive sum-of-squares methods suffer from high noise amplification in the relatively low SNR regime of HP [1-13C]pyruvate (compared to 1H), motivating several HP 13C-specific methods that include data-driven coil sensitivity estimation which have shown obvious improvements over sum-of-squares (11).

More recently denoising techniques have been applied as post-processing of human HP data(41,42,44). The techniques applied are based on spatial-temporal singular value decomposition for unsupervised estimation of signal and noise components. They have shown improvements in apparent SNR in the brain and liver, while care must be taken to choose parameters such as the rank threshold to avoid oversmoothing and overfitting to the estimated signal components.

Prostate Studies

Prostate cancer was the first human application of HP [1-13C]pyruvate (1), and data was acquired with MRS/I methods: 1D dynamic MRS, single-slice 2D dynamic echo-planar spectroscopic imaging (EPSI), and single time point 3D EPSI. Advances in imaging strategies led to the development and application of new acquisition schemes, including undersampled 3D EPSI with compressed-sensing (7), model-based chemical shift encoding methods that use a priori information (47,59), and metabolite-specific EPI (10), all of which can provide volumetric whole-organ coverage and dynamic acquisitions.

The pyruvate bolus arrival in the prostate can vary by ± 10 s between patients, necessitating dynamic imaging to reliably and consistently capture the pyruvate bolus (18). For this reason, all currently ongoing studies acquire dynamic data. While MRS/I, chemical shift encoding, and metabolite-specific imaging can all achieve dynamic imaging, chemical shift encoding and metabolite-specific imaging provide greater dynamic and volumetric coverage (85). For scan prescriptions, the FOV is designed to provide full prostate coverage and typically to match the orientation of the anatomic imaging used for registration. Flip angles used in current studies are constant through time, as quantification with a variable-through-time flip scheme is highly sensitive to bolus timing (8) and errors in the RF transmit (B1 +) field (76).

Heart Studies

Data acquisition methods for 13C imaging in the heart must be designed to meet the demands of significant cardiac motion and blood flow. To cope with the periodic cardiac motion, most human heart studies to date used gating to the diastolic window, the longest cardiac cycle interval, which has reduced motion (2,22,28,30,35,36,38,45,52). The duration of the diastolic window limits the available data sampling time, making cardiac acquisitions the most time-constrained of the HP 13C MRI applications. The most common acquisition approach is metabolite-specific imaging with spiral k-space trajectories (2). Their single-shot imaging capability makes these methods particularly robust to motion effects. Furthermore, spiral k-space trajectories provide rapid k-space coverage and relatively benign flow and motion artifacts. The majority of studies have used 2D multi-slice acquisitions, but 3D encoding has also been used successfully (35).

Brain Studies

For HP 13C MRI of the human brain, the majority of studies have also used 2D (slice selective) acquisitions (10–12,14,16,28,33,40,41,44,51,53,60), with a trend toward volumetric coverage using 2D multi-slice metabolite-specific imaging. 3D metabolite-specific imaging of the whole brain, with phase encoding of the slice direction (34,57), has been shown to provide similar SNR efficiency (88) compared with multislice imaging. A number of studies have employed MRS/I (5,6,29,31–33,50,55) resulting in a spectrum from each voxel, which has the advantage of not requiring a priori information about which peaks to encode. This was important in early brain studies when it was not known which peaks would be detectable. Chemical shift encoding, using a set of images with different echo times and an iterative reconstruction of the individual resonances (i.e. the IDEAL approach (85)), has also been used (12,49,54), with the drawback that coverage in the slice direction was limited due to the time required to acquire multiple echo time images.

Abdomen And Breast Studies

The fundamental approaches to data acquisition and reconstruction in the abdomen and breast are largely similar to the aforementioned applications, but demand attention to particular challenges associated with these anatomic regions, especially relating to respiratory motion.

Although it has been shown that a basic 2D MRSI approach based on phase encoding and FID readout can be successfully applied for HP 13C imaging in breast (15) and kidney (13), major advantages in terms of spatiotemporal resolution and coverage have been realized using tailored approaches based on metabolite-specific imaging (43,62) and chemical shift encoding (43), which have facilitated multi-slice or 3D dynamic acquisitions over large FOVs in the abdomen (4,37,46).

The significant respiratory motion encountered in these regions can directly blur 13C images, and has further favored these rapid acquisition strategies. Motion also degrades B0 homogeneity, which can shift frequency-selective excitation profiles and introduce artifacts into rapid imaging readouts. This makes accurate determination of the acquisition center frequency and shimming essential in these regions which often cover large FOVs. (See “Prescan Calibration” section for more information). In some studies, breath-holding was used to minimize motion effects and enforce frame-to-frame data consistency (42). A pragmatic and reasonably effective approach for dealing with respiratory motion during 13C data acquisition is an initial breath-hold (as long as can be tolerated), followed by free-breathing (46,62).

1H Imaging

Collection of 1H imaging data is essential both for prescribing the 13C acquisition and for interpretation of the resulting 13C data. Multi-planar 1H scouts are acquired prior to 13C acquisition to enable graphical prescription of the 13C imaging region. All human HP 13C-pyruvate imaging studies acquire conventional MRI scans (e.g. T1- and T2-weighted volumes) for anatomic reference, aiming to cover at least the full 13C FOV. Acquiring these anatomic scans as close as possible to the time of 13C imaging (immediately before or after) minimizes potential misregistration between the data sets. Depending on the application, other advanced 1H sequences are also acquired (e.g. diffusion-weighted imaging for cancer imaging).

When contrast-enhanced data is acquired, it is done after 13C imaging, as paramagnetic contrast agents will accelerate 13C relaxation.

Reported Study Parameters

Figures 5 and 6, and Supporting Table S2 shows the reported acquisition study parameters for human HP [1-13C]pyruvate studies published as of September 2022. Figure 5 shows a mixture of MRS/I, metabolite-specific imaging, and chemical shift encoding methods have been successfully used, where spectroscopy-based methods have become less prevalent in recent studies. Figure 6 shows the acquisition timing, including the important start time and interval/temporal resolution, is quite variable across studies.

Figure 5: Acquisition methods used in published HP [1-13C]pyruvate human studies published up to September 2022, classified into: MR spectroscopy and spectroscopy imaging (MRS/I); chemical shift encoding methods, such as IDEAL, that use multiple TEs and model-based reconstructions; and metabolite-specific imaging methods that use spectrally-selective excitation to image a single resonance at a time.

Figure 6: Temporal acquisition characteristics reported in HP [1-13C]pyruvate human studies published up to September 2022. (a) Reported referencing of acquisition start times.

(B)

Acquisition start times reported when using dynamic imaging and when timing was reported relative to the end of the injection. (c) Temporal resolutions. “Not Applicable” indicates dynamic imaging was not used.

Summary

Three general categories of acquisition strategies have been used successfully for human HP 13C-pyruvate studies: MRS/I, model-based chemical shift encoding (e.g. IDEAL) methods, and metabolite-specific imaging methods. These have enabled successful studies in the prostate, heart, brain, abdomen, and breast. Recent studies increasingly have used the imaging-based strategies of metabolite-specific imaging and chemical shift encoding which are the fastest methods, although a heads-to–head comparison between techniques has not been performed.

Metabolite-specific imaging is quite popular because of its speed and compatibility with single-shot imaging, but is sensitive to B0 field variations and thus requires careful calibrations. Nearly all studies surveyed acquired data dynamically, allowing measurement of the bolus and metabolite kinetics. The exact timings and associated flip angles vary quite widely across reported studies, with no consensus yet as to how to choose these parameters. Image reconstruction is typically done directly using Fourier Transform methods, and accelerated imaging strategies are uncommon.

Data Analysis And Quantification

This section covers the analysis of data from human HP [1-13C]pyruvate studies, including modeling and metrics, visualization, as well as considerations for how to store data and metadata. Depending on study design, the analysis may need to give quantitative or semi-quantitative output reflecting a biological process or may just reflect a contrast between different regions of interest for quantitative evaluation.

Metrics

Figure 7: HP [1-13C]pyruvate raw data (A) have typically been quantified using four categories of metrics depending on the acquisition. Data acquired as a single time point are often quantified using normalized metabolite images or metabolite ratios (B). Dynamic data can be quantified using normalized metabolite images or metabolite ratios (B), or with metabolite timings such as time-to-peak (TTP) or pharmacokinetic (PK) models (C). The latter two require the data to be time-resolved. [1-13C]alanine and 13C-bicarbonate are analyzed similarly to [1-13C]lactate but omitted here for display.

Metabolite images are commonly used as summary metrics for HP MRI data, often including some form of normalization as well as summed over time as an area under the time curve (AUC) (17). These are analogous to the visual evaluation that is most used for routine clinical work (89,90). In these metabolite images, we expect that the [1-13C]pyruvate AUC signal is predominantly weighted towards perfusion and uptake, while [1-13C]lactate, [1-13C]alanine and 13C-bicarbonate AUCs represent metabolic conversion. The strength of this approach lies in its simplicity and relatively few underlying assumptions. Limitations to the use of single-metabolite images or AUCs include sensitivity to inhomogeneous coil profiles (57,87,91), the acquisition strategy and acquisition parameters, pyruvate polarization and concentration level, and signal relaxation rates (92). Further, the reader must be careful to interpret all the images in conjunction to better understand the underlying biology; for example, increased [1-13C]lactate in the presence of decreased [1-13C]pyruvate delivery can have a very different meaning compared to increased [1-13C]lactate with increased [1-13C]pyruvate delivery.

In an attempt to address variations in coil sensitivity, polarization level, and pyruvate delivery, AUC images are often computed by normalizing to a specified parameter, such as the maximum pyruvate or average lactate signals, or presented as a ratio such as lactate/pyruvate or divided by “total Carbon” - the sum total of HP 13C signal observed across all metabolites. The AUC ratios between metabolites and pyruvate are proportional to the corresponding forward kinetic rates (81,93), but are not directly comparable to rate constants when magnetization loss rates (e.g. relaxation and losses due to signal excitation) differ between studies. Similarly, the ratios between the produced metabolites (e.g. bicarbonate/lactate) can reflect the balance between downstream metabolic pathways (12,55). Care must be taken to consider how AUC images are calculated and normalized before comparing values between studies.

To further quantify the interpretation, pharmacokinetic (PK) modeling approaches were developed to compute the apparent kinetics of pyruvate-to-metabolite exchange (92,94–99). These yield semi-quantitative to quantitative apparent rate constants, given in s-1. Some models require a vascular input function, while others avoid this requirement (95). PK models can explicitly account for acquisition-specific details such as excitation angle and repetition time, and thus may reduce the effects of these details on quantification. An input-less model, provided in the Hyperpolarized-MRI-Toolbox (https://github.com/LarsonLab/hyperpolarized-mri-toolbox) (100) and thus frequently employed for human data, has been shown to fit well and robustly to prostate and brain data (8,20). PK models are quantitative in nature, arguably provide more relevant biological information (8,20), and appear to be reproducible across sites (51). However, rate constants derived from PK models are still apparent rates, and likely do not reflect a single biological characteristic.

Some additional considerations include whether complex or magnitude data is used, as the noise behaviors will impact the analysis differently. Additionally, cut-off thresholds or other criteria may be used to identify and avoid voxels with insufficient SNR before analysis to improve robustness (20,41).

Regardless of the analysis approach, the underlying biology is not always clearly represented by the data; instead, the metrics may be influenced by perfusion, barrier permeability, intercellular shuttles, enzyme activities, co-substrate concentrations, or combinations thereof, depending on the organ and disease of interest (19,43,94,101–103). This may be addressed by incorporating complementary information. As an example, HP 13C pyruvate data is influenced by perfusion, and thus addition of perfusion MRI could be important for interpretation (98,104,105).

All the methods outlined above have been explored in clinical studies, described in Supporting Table 3 and summarized in Figure 8. As of September 2022, approximately 52% of studies involving human subjects report rate constants derived from a PK model with a few different models reported. A nearly equal fraction (51%) of the studies report AUC ratio values.

Approximately 66% of these studies report metabolite-specific images or AUC values. About 40% report SNR values; this metric is particularly frequent in manuscripts that describe technical developments for clinical HP MRI. Approximately 16% of these studies summarize model-free metrics, and 10% report measurements from a single timepoint. Most studies report a combination of quantities.

Figure 8: Reported metrics used for analysis in HP [1-13C]pyruvate human studies published up to September 2022.

Visualization

A wide variety of approaches have been used for visualizing data from human HP 13C-MRI studies. The challenges and practical considerations are: 1) choosing the appropriate metrics to display, 2) how to encode the parameters (e.g. the colormap), and 3) choosing how to provide anatomical context and other multi-parametric data. The choice of visualization also depends on the goal which could be for diagnostic interpretation, but also quality control, reproducibility among readers and publication.

Metrics

The choice of HP 13C metrics is described in detail above. At this stage in HP 13C development where there is no standardized metric, often a combination of metabolite images and ratios or PK model parameters are shown.

Parameter Encoding

The mapping function chosen should provide an adequate, often quantitative, impression of the parameter mapped. There is a consensus in the visualization field that perceptually uniform maps are best suited to visualize continuous parameters, like the greyscale typically used by radiologists as well as other monochrome (black to blue) and color ranges (fire-type, rainbow-type) (106,107). Multi-color heatmaps have been the most frequently employed method for HP 13C data, while greyscale has infrequently been used but it ensures there is no coloring-based bias as well as facilitating later reuse (Fig. 9a). Among the color schemes employed in the clinical HP 13C literature, fire-type scheme seems to be the most common [similar to “Plasma” or “Inferno” in matplotlib.org]. Next most commonly employed is the rainbow-type scheme [similar to “Rainbow” in matplotlib.org].

Anatomical Context

HP MRI faces the challenge that it does not necessarily depict the anatomical features, similar to PET, and thus requires an anatomical reference. Most often, a grayscale anatomical image is overlaid with a HP colormap (Fig. 9c,d). This approach is very intuitive, but can skew perception as the grey-scale anatomical reference may affect the brightness of the HP data (e.g. signal in the skull). This bias does not occur when showing adjacent maps (Fig. 9a, b). Here, anatomical outlines may help to provide reference (Fig. 9b).

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