Wolfgang Bermel

Denis Courtier-Murias

Ifsttar Nantes

Keywords: NMR, MRI, 1H, 13C, in-vivo, metabolomics, sub-lethal toxicity, environmental, superparamagnetic, iron oxide nanoparticles NMR, MRI, 1 H, 13 C, in-vivo, metabolomics, sub-lethal toxicity, environmental, superparamagnetic, iron oxide nanoparticles Wiley -UK Magnetic Resonance

Superparamagnetic Iron Oxide Nanoparticles (SPIONs) are a contaminant of emerging interest, often used in the medical field as an imaging contrast agent, with additional uses in wastewater treatment and as food additives. While the use of SPIONs is increasing, little research has been conducted on the toxic impacts to living organisms beyond traditional lethal concentration endpoints. Daphnia magna are model organisms for aquatic toxicity testing with a well understood metabolome and high sensitivity to SPIONs. Thus, as environmental concentrations continue to increase, it is becoming critical to understand their sub-lethal toxicity. Due to the paramagnetic nature of SPIONs, a range of potential Nuclear Magnetic Resonance Spectroscopy (NMR) experiments are possible, offering the potential to probe the physical location (via imaging), binding (via relaxation weighted spectroscopy), as well as the biochemical pathways impacted (via in-vivo metabolomics). Results indicate binding to carbohydrates, likely chitin in Page 1 of 17 Wiley -UK Magnetic Resonance in Chemistry F o r P e e r R e v i e w 2 the exoskeleton, along with a decrease in energy metabolites and specific biomarkers of oxidative stress. The holistic NMR framework used here helps provide a more comprehensive understanding of SPIONs impacts on Daphnia magna, and showcases NMR's versatility in providing physical, chemical and biochemical insights.

A holistic NMR framework to understand environmental impact: Examining the impacts of superparamagnetic iron oxide nanoparticles (SPIONs) in Daphnia magna via imaging, spectroscopy, and metabolomics Amy Jenne, 1 Ronald Soong, 1 Oliver Gruschke, 2 Monica Bastawrous, 1 Patricia Monks, 3 Cara Moloney, 4 Dermot F. Brougham, [START_REF] Amirshaghaghi | Molecular Imaging: Principles and Practice[END_REF] Falko Busse, 2 Wolfgang Bermel, 2 Denis Courtier-Murias [START_REF] Matuszak | [END_REF]7 Bing Wu, 8 * and Andre Simpson 1 *

Introduction

Nanomaterials have become an increasingly prevalent substance used in numerous applications from food additives to preservatives, as well as in medicine and wastewater treatment, and production only continues to expand. [1][2][3][4] In the last decade there has been a 25-fold increase in products which used nanoparticles during their production. [3] However, despite their increased use, little research has been performed as to their environmental impact. Due to their small size, nanoparticles can move through the environment with ease and as many nanoparticles are metal-based, the potential ramifications to the biotic environment can be severe. [2] Superparamagnetic Iron Oxide Nanoparticles (SPIONs) are one such potential contaminant where impacts to the environment are not fully understood. The most common use of SPIONs is in the medical field as clinical Magnetic Resonance Imaging (MRI) contrast agents. The presence of SPIONs alters the surrounding water molecules spin-spin relaxation in particular, causing them to relax faster and resulting in local suppression of image intensity (darkening) under T 2 -weighted imaging conditions. [START_REF] Amirshaghaghi | Molecular Imaging: Principles and Practice[END_REF] This is particularly useful in disease monitoring as the suppression is proportional to the concentration of particles. [START_REF] Matuszak | [END_REF] When surface functionalized with appropriate ligands, SPIONs can be selectively consumed by cancer cells, helping identify tumorous regions, [7] passive targeting is also well documented.

For human consumption, SPIONs are coated in a stable ligand, thus considered non-toxic. As a result, SPIONs move through the body based on their half-life (proportional to their size) and then are passed through the liver and are eventually eliminated as waste. [START_REF] Amirshaghaghi | Molecular Imaging: Principles and Practice[END_REF]7,8] However, there has been limited research examining the toxicity of released particles to aquatic life, particularly from sub-lethal or mechanistic perspectives. Nanoparticles released by the body end up in wastewater, and as they enter our freshwater systems, they can be consumed by primary consumers such as Daphnia magna. D. magna are one of the most utilized model organisms in environmental toxicity testing and multiple studies have measured the impacts of other types of nanoparticles such as silver, titanium, and silicon oxide particles. [9][10][11] However, to date, metabolomic studies with iron oxide nanoparticles are lacking. The limited research that has been conducted on the toxic impacts of SPIONs have focused on apical endpoints with respect to lethal concentrations, rather than a mechanistic understanding. Daphnia magna have been shown to be incredibly susceptible to SPIONs, with an LC 50 of 2.3 x 10 -4 mg/mL. [12] However, information targeting the "where, what, how, and why" of SPION toxicity is still lacking.

Magnetic resonance imaging and spectroscopy when used in combination have a unique ability to provide comprehensive insights into the issue. As the contaminant is a contrast agent MRI has 3 potential to show within Daphnia magna physically "where" SPIONs accumulate, while also providing average or localized changes in relaxation. Hence relaxation measurements are useful when evaluating how effectively different size SPIONs are consumed. This is especially important as Daphnia are filter feeders, and little is known about the minimum size of particles they can consume or how specific ligand coatings may impact uptake. Another consideration is determining whether the nanoparticles are consumed at all, or if the toxic response is only as a result of the interactions with the outside of the organism. [9][10][11] If they are ingested, then "what" the nanoparticles bind with inside the organism at the molecular level must be examined. Here, relaxation filtered 2D NMR spectroscopy could be useful to study the categories of molecules or even structures that bind to SPIONs, aiding in understanding their excretion or bioaccumulation. [13,14] Finally, the question as to "how" the SPIONs are toxic can be tackled using in-vivo NMR metabolomics. This is the measurement of changes to an organism's metabolome as a result of an external stressor. [15] Sub-lethal toxicity in Daphnia magna has been assessed in this way, however utilizing in-vivo NMR spectroscopy is particularly desirable, as changes to the organism's metabolome can be measured in close to real time. [16][17][START_REF] Anaraki | NMR-Based Metabolomics[END_REF][START_REF] Soong | [END_REF][20][21][22] To the authors knowledge, to date there have been no studies of nanoparticles in-vivo. Perturbations to specific metabolic pathways could provide important information on the toxicity of SPIONs and help explain the toxic-mode-of-action inside Daphnia magna, as an exemplar aquatic organism, which are essential for setting updated environmental policies.

In this study, a-proof-of-concept study is presented that combines imaging, relaxation filtered 2D NMR, and in-vivo metabolomics to provide a more comprehensive overview addressing the "why" of SPION fate and toxicity. Given magnetic resonance's ability to provide detailed molecular information in a non-invasive fashion, such unified approaches drawing from different branches of NMR/MRI are shown to provide holistic understanding of the complex physiochemical processes in SPION toxicity, that may prove generally translatable.

Materials and Methods

Unless otherwise stated, all chemicals were purchased from Sigma Aldrich (St. Louis, Missouri, U.S).

SPION Synthesis and Validation Measurements

Preparation of Primary Nanoparticles 4 nm (core size) primary SPIONs were prepared with slight modifications to the Pinna method. [23] In brief, 20 mL of 141.6 mM Fe(acac) 3 in benzyl alcohol was purged with N 2 for 20 minutes and refluxed at 205°C for 7 hours. The resulting black solution contained 10 mg/mL γ-Fe 2 O 3 (90% yield) of 8.6 nm SPIONs as determined by transmission electron microscopy (TEM). To prepare the 12 nm SPIONs used in this study, 20 mL of 8.6 nm SPIONs were washed twice with benzyl alcohol and resuspended in 20 mL of 141.6 mM Fe(acac) 3 in benzyl alcohol. The solution was purged with N 2 for 20 minutes and refluxed at 205°C for 7 hours. The resulting black solution contained 20 mg/mL γ-Fe 2 O 3 (90% yield) of 12 nm SPIONs as determined by TEM. SPIONs were stored under N 2 until use to prevent oxidation. 5 mL of nanoparticles were washed twice with acetone using a magnetic separator. 37 μL of GLYMO in 2 mL THF was added to the wet nanoparticles and tumbled for 24 h. 10 μL of hydrochloric acid (1 M) and ethanol were also added to catalyze the hydrolysis and condensation of the alkoxysilane groups. This process yields a stable dispersion of GLYMO-functionalized Fe 3 O 4 nanoparticles. PEG-NH 2 (of different M n ) in THF was added to the SPIONs in a 10:1 polymer:GLYMO ratio. Following the addition of 50 μL KOH (1 M), the PEG-grafted SPIONs precipitated out, were washed with THF and redispersed in water. The procedure has previously been reported. [24] TEM Transmission electron microscopy was performed on a FEI Tecai G2 TWIN 200 kV microscope. Aqueous suspensions of SPIONs were prepared (concentrations of 5 mM Fe) and 5 μL was pipetted onto a carbon TEM grid (Formvar and carbon films on 400 μm copper grid, Agra Scientific). Size analysis was performed using ImageJ software.

Stabilization of Primary Nanoparticles

DLS

Dynamic light scattering (DLS) measurements were performed using a Malvern Nano ZetaSizer (Malvern Instruments, Malvern UK). A 3 mW He-Ne laser operating at a wavelength of 633 nm was used and back scattered light is detected at an angle of 173° to the incident beam. Data was processed using Dispersion technology software (v 7.13, Malvern instruments, Malvern UK) using the multiple narrow modes algorithm based upon a non-negative least square fit to calculate the hydrodynamic diameter (d hyd ) and the polydispersity Index (PDI). Samples were diluted to 5 mM Fe and analysis was performed at 25 °C, with a thermal equilibration time of 30 seconds prior to measurement.

Daphnia magna Culturing D. magna were cultured from a colony originally purchased from Ward's Scientific but have been maintained in the lab since 2017. Organisms were cultured at 20°C, a water hardness of 124 mg/L CaCO 3 , and pH 7.5-8.5, consistent with local freshwater conditions. Cultures were kept under a 16:8 light/dark cycle and a 40% water media exchange occurred three times per week. For in/exvivo experiments, species were cultured on a diet of 99% 13 C enriched Chlamydomonas reinhardtii (purchased from Silantes, GmbH) [25] as their sole food source from birth. Prior to experiments, organisms were placed in fresh dechlorinated water to clear off residual algae and fed 12 C algae to clear their gut, meaning only signals from the Daphnia would be measured. During in-vivo experiments, organisms were continuously fed unenriched Chlamydomonas reinhardtii cultured in the lab. Adult Daphnia magna (~two weeks old) born from 13 C enriched parents, are used in the study which have been shown to be ~99% 13 C enriched. [26,27] For imaging experiments, organisms were not isotopically enriched, but other culturing parameters remained the same.

Nanoparticle Exposure

In Daphnia samples used for ex-vivo exposure, 250 mL beakers were filled with 100 mL of culturing media (as above), 10 adult Daphnia magna were added to each beaker and nanoparticles were dosed in at a volume creating a total concentration of 10% of the LC 50 (2.3 x F o r P e e r R e v i e w 5 10 -5 mg/mL). [12] Organisms were exposed for 48 hours and fed halfway through exposure to ensure a lack of stress response from starvation. Once completed, organisms were removed from exposure vessels and placed in beakers with clean media to allow any aggregated nanoparticles on the outside of their bodies to be washed. These were then prepared for the imaging and T 1 weighted spectroscopy. Note for the in-vivo metabolomics, the organisms were exposed inside the NMR as described below.

Imaging Daphnia magna Experiments Immediately prior to experiments, Daphnia magna, one from control cultures and one from exposed, were placed in a 2.5 mm glass NMR tube, cut to the size of the imaging probe. Organisms were alive when they were placed in the tube and suspended in Fomblin (Solvay Fomblin® Y, LVAC 06/6, average mol wt. 1800), but died once immersed in the oil. Note, Fomblin is a perfluorinated oil and contributes no 1 H signal. In each tube there was a single control and exposed Daphnia to measure the differences in the contrast through proton imaging. Experiments were run in triplicate for each ligand size (3 SPION samples, 9 samples total). Ex-vivo Daphnia magna -T 1 Weighted Spectroscopy 360 fully grown Daphnia magna were removed from the culture (180 from the control culture, 180 from the nanoparticles exposure), immediately frozen with liquid nitrogen to halt metabolite response, and lyophilized. 30 minutes prior to experimentation 60 freeze-dried organisms (from either control or exposed samples) were placed in a 5 mm NMR tube and re-swollen with 600 µL of 90% H 2 0/10% D 2 O with 0.5% W/W sodium azide to prevent bacterial growth. Organisms were kept intact and analyzed at 5°C inside the NMR to prevent degradation. Experiments were conducted in triplicate.

In-vivo Daphnia magna -Metabolomics

Immediately prior to experiments, 30 Daphnia magna from standard cultures (not yet exposed to nanoparticles) were transferred into a specially designed low-flow in-vivo flow system (including an external lock capillary isolated from the organisms), as previously described. [22] The water reservoir contained the same media as the culture, and algae concentrations were maintained for the duration of the exposure to ensure no starvation responses. The sample was kept at 5°C for the experiment. [START_REF] Soong | [END_REF] After 6 hours, SPIONS were added to the flow system to create a total concentration the same as that used above (10% of the LC 50 ). Organisms were monitored for a total of 24 hours. Control experiments were also conducted for a full 24 hours maintaining the flow to show differences between exposed and non-exposed organisms.

Experimental Parameters

Unless otherwise stated, all experiments were performed at 278 K using a Bruker Avance III HD NMR 500 MHz spectrometer, fitted with a 5 mm 13 C- 15 N TCI Prodigy cryoprobe fitted with an actively shielded Z-gradient. a Bruker DIFF50 probe with a Z-gradient of 28.5 T m -1 maximum (at Bruker, Rheinstetten/Germany). For 3D microimaging acquisition with T 2 -weighting, a multi-slice spinecho sequence (MSME) was used with a repetition time (TR) of 0.5 s, an echo time (TE) of 4 ms and 16 average (NA). For T 2 analyses, a MSME-based protocol (Bruker MSME-T 2 -map) was used with a resolution of 0.1 mm × 0.1 mm. All images were collected with Bruker ParaVision 6.0.1. T 1 Weighted Spectroscopy A pseudo 3D 1 H- 13 C HSQC correlation via double inept transfer using an Echo/Antiecho-TPPI gradient, for selection and sensitivity improvement. Data were collected with 96 increments, 24 scans, at 12 different T 1 's (from 0 -2.0 s). The T 1 delay was applied with 13 C magnetization along Z, while a train of 180 o 1 H pulses was applied to refocus H-C couplings. Experiments were collected with 2048-time domain points, a recycle delay of 5.0 s, 1 J CH of 145 Hz, an adiabatic shape pulse for inversion (500 s, Crp60,0.5,20.1), and an adiabatic shape pulse for refocusing (2 ms, Crp60comp.4). The 90° pulses were determined for each sample. Data were processed with a sine-squared function phase shifted by 90° in both dimensions and a zero-filling factor of 2. Fitting was performed in Bruker Protein Dynamic Center to extract the T 1 relaxation times. The 3D T 1 heat map (Figure 5) was generated using Python 3.0 via Thonny based on the percent changes of the T 1 relative to the controls. The code can be found in the supplementary materials. Briefly, a 3D gaussian shape was used to generate the peak shape for each T 1 value where the width of the gaussian was adjusted to match that of the 1 H- 13 C HSQC.

In-vivo NMR Experiments

A phase sensitive 1 H- 13 C HSQC using Echo/Antiecho-TPPI gradient selection was run using a total of 128 increments, each with 52 scans, 2048-time domain points, a recycle delay of 1.0 s, 1 J CH of 145 Hz, and an adiabatic shape pulse for inversion (500 s, Crp60,0.5,20.1). 90° pulses were determined for each sample. Data were processed with a sine-squared function phase shifted by 90° in both dimensions and a zero-filling factor of 2.

Spectral Assignments

Compound identification and assignment was completed using AMIX (Analysis of MIXtures software package, version 3.9.15, Bruker BioSpin, in combination with the Bruker Bio-reference NMR databases version 2-0-0 through 2-0-5). Spectra were calibrated against the Bruker Bioreference NMR databases using tyrosine and D-glucose resonances as reference peaks. Assignments were performed using a procedure previously described. [28,29] Relative changes in metabolites during in-vivo exposure were found using MestReNova 14.1.0 using spectral stacks and the peaks graphs resulting from global spectral deconvolution.

Results and Discussion

Nanoparticle Size Daphnia magna are filter feeding organisms. As they move through water, food (or particulate matter) is taken in and must pass through a filter before being ingested. [30,31] Only if the particles are the correct size, will they proceed into the gut. [31] If a particle is too large they will be removed by the Daphnia furcal claws, while particles that are too small pass through without interacting with the organism. [30] Previous studies have shown 0.5 m particles result in the highest uptake by Daphnia magna, but particles down to 0.1 m have been measured. [30] To our knowledge, no studies have examined uptake and accumulation in the lower nanometer range. However, the increased prevalence of nano-sized contaminants (micro/nano plastics and nanoparticles) support the need for more research in this area. In addition, acute toxicity tests have shown that SPIONs are toxic to Daphnia, [12] yet the wide-spread implications and toxic mechanisms are not well understood. [32,33] To address this gap, nanoparticles of c.12 nm core size were synthesized with three different ligand shell sizes of polyethylene glycol (PEG): 1, 5, and 10 kDa. PEG has low cytotoxicity, is clinically approved for some applications, and it is one of the most used ligands with SPIONs. The highly hydrated and dynamic grafted ethyleneglycol chains can provide colloidal stabilization and dispersion of the particles in isotonic media at different pH. [32] The chains also reduce protein binding in-vivo, which reduces uptake by macrophages in the human body. [START_REF] Amirshaghaghi | Molecular Imaging: Principles and Practice[END_REF]32] TEM and DLS measurements were undertaken, see Figure 1. The average core size, from TEM, is 12.4 nm and the average hydrodynamic size, from DLS, progressively increasing from ~16.5, to ~18.0 to ~19.1 nm, for 1, 5 and 10 kDa grafts respectively, and the polydispersity indices were low. Hence the difference in d hyd arise from the chain length, which confirms full particle dispersion (absence of aggregates in suspension). This is important as we have shown that for fully dispersed particles the MRI contrast effect is unchanged for different graft types and lengths. [34] Daphnia magna were then exposed to determine which size was most impactful in terms of consumption.

Imaging

In 1 H MRI, water inside the subject is imaged allowing for the visualization of different internal structures. One of the primary uses of SPIONs is in the medical field as an MRI contrast agent, resulting in localized darkening. To our knowledge, MR imaging of Daphnia magna has not yet Of more interest to this specific study is the impact of SPIONs on Daphnia magna images, which provide may insight as to "where" the SPIONs accumulate. Figure 3 shows an example of SPIONs inside Daphnia magna. On the right side is a control organism, while the left is one that has been exposed to 10% of the LC 50 of SPIONs for 48 hours prior to imaging. [12] Note, the organisms were washed to remove any nanoparticles interacting with the outside of the organism, allowing internal uptake by the Daphnia to be better evaluated. SPIONs here are T 2 contrast agents, meaning they reduce the relaxation time of the surrounding water, darkening the image where they are localized. Thus, imaging can theoretically show where SPIONs accumulate internally in the organism. Firstly, it can be confirmed that the washing was successful, and nanoparticles are not simply stuck to the outside of the organism, as the image would have been completely dark around all the edges (Figure 3). Additionally, considerable darkening is seen within the organism interior, confirming nanoparticle uptake. Nanoparticles are clearly present in the stomach and other internal compartments of the organism, but not the carapace (the outer exoskeleton of the organism). There is also a lack of darkening in the brood pouch on top of the organism, indicating the SPIONs had not yet accumulated in the eggs. Previous work examined the results of feeding 10 m microbeads to Daphnia, in which they were contained strictly in the digestive track. [35] However, the widespread darkening here, especially the region around the heart, suggests the SPIONs can access many of the organs following ingestion. In particular, the heart is not visible at all. This suggests the SPIONs may be able to enter the hemolymph and thus may be transported within the organism's circulatory system.

While localization of the nanoparticles within the organism is difficult to assess given the size of the animal, the average change in the T 2 relaxation caused by the SPIONs provide useful insights Darkening can be seen in the internal organs, stomach, and especially the heart. The regions least darkened are the carapace (the outer exoskeleton) and the brood pouch. In past studies nanoparticles have been seen to bind to the outside of the carapace, this has been reduced here via washing so that internal uptake is easier to discern. 4. The smallest size (1 kDa ligand) appears to have little impact on the T 2 relaxation indicating it was either too small for Daphnia to ingest, quickly passed through the digestive system, or sedimented from the suspension (due to the shorter chain length) and was not available for uptake. All suspensions were sonicated prior to of dosing the Daphnia and the DLS analysis suggests no difference in the colloidal stability, so it is not clear what the cause is. In any case, of the two larger sizes, the 5 kDa ligand reduced the average relaxation time the most, consistent with it accumulating to the highest extent within the organisms. Ultimately, imaging provides an excellent way to quickly gauge nanoparticle uptake following exposure and confirms consumption. Based on the changes in average T 2 relaxation time, the 12 nm:5 kDa nanoparticles were the most impactful and were therefore selected for use in the remainder of this study. In the 1 kDa ligand there was no obvious decrease in the relaxation time (orange), indicating no uptake of nanoparticles, whereas in both 5 and 10 kDa there was a decrease. 12 nm:5 kDa nanoparticles were determined to be most impactful due to the highest average decrease in relaxation time (~30 ms) based on uptake and were chosen for further study. Each experiment was conducted in triplicate and error bars represent standard error in the measured changes.

T 1 Weighted Spectroscopy While imaging can provide a physical overview of SPION locations, the impact of relaxation weighted spectroscopy on SPIONs can be useful in unravelling the chemistry, or the "what" with respect to binding, in the system. Due to their paramagnetic nature, SPIONs can be studied by relaxation weighted spectroscopy, which holds potential to elucidate the types of chemical structures within Daphnia magna they interact with. Having exploited the T 2 effect [36] of SPIONs 13 C HSQC is used to provide additional spectral dispersion to allow identification of chemical structures. To provide T 1 information, a 3 rd dimension is acquired where a T 1 storage period is ramped via a delay list. After the processing the 3D cube contains a series of HSQC spectra which are progressively dampened via T 1 relaxation. To visualize the important differences in the data, a heat map in Figure 5 is used to show the changes in the relaxation times of each metabolite inside Daphnia magna relative to control organisms. Chemical shifts that are red have the largest changes in T 1 , as compared to control (SPION free), where beige shows little to no interaction.

In in-vivo 1 H NMR spectra there are three general  areas, namely: aliphatics, carbohydrates, and aromatics, which can be seen in coloured boxes in Figure 5. Within the aliphatics and aromatics, the largest changes were present in the amino acid region from alanine, leucine, histidine, asparagine, proline, and tyrosine. Not all amino acids can be assigned due to spectral overlap but reduction in the T 1 time of a wide range of amino acids is generally consistent with protein binding. It is well documented that despite their great hydration and rapid dynamics PEG chains can interact with proteins in aqueous solution. [37] Protein -PEG interactions could play an important role in SPION retention and could help explain the preferential uptake of the 12 5kDa/10kDa coated particles observed with imaging. As imaging supports the high concentrations of SPIONs around the heart, it is possible that the SPIONs in the circulatory system are interacting with hemolymph proteins. Such interactions are well documented when high molecular weight PEG grafts are present. [38] However, the regions with the largest percent T 1 changes were the carbohydrates. These regions most likely arise from chitin, which is the predominant structure in the carapace. [39][40][41] Interactions were not seen with the outside of the shell in imaging due to the washing step. However, here the intact but dead organisms are re-swollen and unwashed, thus the reduction in the T 1 times of chitin-like signals are consistent with the carapace playing an important role in SPION binding in nature, as seen with other nanoparticles in the environment. [9][10][11] Unfortunately, it is not possible to determine the binding mechanism, but the fact that washing removes the nanoparticles suggests the interaction is relatively weak, with one possibility being that nanoparticles are able to be partially trapped in the relatively porous chitin structure. [42] Determining where nanoparticles bind is important to their potential uptake, retention, and impact in the organisms. However, it provides little information as to the sub-lethal toxicity. To determine how the organisms respond to the SPIONs presence, in-vivo metabolomics is highly complementary.

In-Vivo Spectroscopy Thus far, imaging has provided information on the physical location of SPIONs, and relaxation weighted 2D has provided clues as to their molecular interactions. However, one missing aspect is the toxic-mode-of-action of SPIONs, or in simple terms, "how" SPIONs are toxic. In-vivo spectroscopy is ideal to address this gap. To our knowledge, nanoparticle impacts have not been studied using in-vivo NMR in Daphnia, but previous in-vitro assays have shown a decrease in energy creating metabolites, including glucose, glutamate, glutamine, tyrosine, histidine, and ATP production as a mechanism to increase molting, a documented response of the organism to the presence of externally interacting nanoparticles. [10,11] In the current study, organisms are first allowed to acclimatize to the conditions in the flow system as per previous protocols [43] without the presence of SPIONs. After 6 hours the SPIONs are introduced and monitored for an additional 18 hours, resulting in a 24-hour total experiment time. HSQC detection is used to provide spectral dispersion required for metabolite assignment. All cross peaks were screened using manual peak-picking, and assignments were confirmed as previously described. [22] The metabolites with the most significant changes are plotted in Figure 6. As in previous reports, decreases in relative concentration of glycine, glutamine, and histidine were seen. These are key metabolites related to increased energy demand and oxidative stress. [11,16] It has also been shown in previous work that the disturbance of nanoparticles increased lactate because of anaerobic metabolism. [11] However, similar trends were not seen here, likely due to the low nanoparticle concentration. It is hypothesized that sustained exposure would eventually cause a shift to anaerobic metabolism as energy stores are depleted, and food and oxygen uptake slow due to aggregation of nanoparticles on the outside the organisms. An additional metabolite methionine, not previously reported in nanoparticle studies on Daphnia, was seen to cycle with larger impact, relative to control organisms. Methionine is a key metabolite, which is replenished in the TCA cycle, indicating that energy production is being significantly impacted due to SPION exposure. [17] This could be due to oxidative stress caused from a build-up of electrons in the electron transport chain due to the new energy requirements. The increased electrons create reactive oxygen species (ROS) in the organisms, which have been directly implicated in cell aging and death. [10,44] L-glutathione was also measured to decrease relative to the control. This metabolite is used to help remove ROS by creating a protein which aids in their removal from the body. [45] This decrease indicates that both processes mentioned above may be taking place simultaneously in the organism. One additional component to note is lysine. Previous studies have indicated that lysine decreases as a response to oxidative stress response, [11] however, in this study it increased significantly in the presence of SPIONs relative to the control. As can be seen in Figure 6 (purple box), the response was quite variable and thus harder to quantify in the presence of the nanoparticles. This may be because lysine is known to bind strongly to PEG. [46,47] Historically, the combination of PEG and lysine have been used to help keep biological implants from being rejected due to their stability and ease of binding. This binding would impact the TCA pathway which has potential impacts with respect to DNA repair and protein degradation due to oxidative stress. [11,48] These results indicate that even at sub-lethal concentrations, SPIONs cause a large impact to Daphnia magna from which they may be unable to recover.

Conclusion

This research describes a three-pronged approach to examine toxicity in living organisms, which provide holistic understanding of how contaminants interact in complex organisms. Using all three NMR experiments together was critical to gain a complete understanding of the toxicmode-of-action, answering "why" SPIONs are toxic. Confirmation of SPION uptake was provided by MRI, and accumulation in the area of the heart was consistent with SPIONs entering the Daphnia's hemolymph. Relaxation weighted spectroscopy supported two main "targets" of interaction. One with the chitin in the carapace and the other with protein, likely within the hemolymph. The organism's response manifested as an increase in energy and oxidative stress related metabolites. The results suggest that even at sub-lethal concentrations, SPIONs cause a large negative impact to Daphnia magna. Continued study on the longer-term impacts of SPIONs is recommended given the present environmental concentrations and projected future increases. Combining different magnetic resonance techniques provides insight into any changes in macroscopic structural characteristics and molecular processes induced by exposure to SPIONs. Similar approaches may be of value for evaluating the impact of the wide range of paramagnetic and ferromagnetic nanoparticles, and metal-organic complexes that are being released into the environment.

Imaging 1 H

 1 2D/3D NMR imaging measurements were performed on a Bruker Avance III HD 500 MHz spectrometer equipped with Bruker micro-imaging accessory including a GREAT-60 amplifier and

Figure 1 .

 1 Figure 1. Confirmation of nanoparticle core and ligand size. a) Transmission electron microscopy (TEM) of the SPION cores to measure average sizing, determined to be 12.4 nm. b) Dynamic light scattering analyses of ligand (PEG)-stabilized SPION particle suspensions in H 2 O. The average d hyd size (core plus PEG ligands) progressively increases from 16.5 to 19.1 nm. The three suspensions were used to image Daphnia magna to determine the most impactful uptake based on the change in T 2 relaxation within the organism.

  . A 3D video with all the internal structures of a Daphnia magna is linked in the supporting information file, while representative still images are shown in Figure2to provide an overview of Daphnia physiology.

Figure 2 .

 2 Figure 2. MRI of dead Daphnia magna. a) Standard spin echo image of a Daphnia magna with internal components labelled including compound eye, external carapace (uncalcified shell surrounding the Daphnia), and the spaces between the embryos inside the brood pouch. The dark space at the bottom of the Daphnia magna is a vacant spot due to the lifting of the thoracopods ('feet'). Note: the antennae labelled in the reference light microscope image would also be present in the MRI, but in this organism, it is tucked along the carapace rather than facing outwards. b-d) 2D sagittal, coronal, and axial planes extrapolated from the 3D image organism allowing for additional visualization of the labelled components in the 3D image.

Figure 3 .

 3 Figure 3. MRI of dead Daphnia magna using SPIONs as a T 2 contrast agent. The right represents an organism that was not exposed to SPIONs. The left represents a Daphnia which had been exposed to 10% of the LC 50 for 48 hours prior to imaging. In the exposed organism the areas in proximity to the SPIONs are darkened.Darkening can be seen in the internal organs, stomach, and especially the heart. The regions least darkened are the carapace (the outer exoskeleton) and the brood pouch. In past studies nanoparticles have been seen to bind to the outside of the carapace, this has been reduced here via washing so that internal uptake is easier to discern.

  different ligand sizes impact Daphnia consumption. The changes in the organism averaged T 2 relaxation times are shown in Figure

Figure 4 .

 4 Figure 4. Changes in T 2 relaxation inside dead Daphnia magna. Control Daphnia were not exposed to nanoparticles and their T 2 relaxation was measured as an average of the whole organism. Relaxation of exposed organisms was then measured, and the change in the relaxation relative to the controls plotted.In the 1 kDa ligand there was no obvious decrease in the relaxation time (orange), indicating no uptake of nanoparticles, whereas in both 5 and 10 kDa there was a decrease. 12 nm:5 kDa nanoparticles were determined to be most impactful due to the highest average decrease in relaxation time (~30 ms) based on uptake and were chosen for further study. Each experiment was conducted in triplicate and error bars represent standard error in the measured changes.

  experiments, here the T 1 capabilities are applied. Due to the overlap in the 1 H NMR of Daphnia, 1 H-

Figure 5 .

 5 Figure 5. Changes in the T 1 relaxation, relative to the controls, inside Daphnia magna (dosed with 5kDagrafted 12 nm SPIONs) as a heat map with specific regions highlighted of the intact, ex-vivo organism reswollen and unwashed. Aliphatics and aromatics are boxed in green and purple respectively with most signals arising from amino acids, including alanine, asparagine, histidine, leucine, proline and tyrosine. This is most likely as a result of general protein binding. Carbohydrates are contained in the blue box, which is consistent with Chitin, the main component of the organism's carapace (shell).

Figure 6 .

 6 Figure 6. Metabolite responses in Daphnia magna due to nanoparticle exposure in-vivo. The upper green box shows the down regulation of metabolites resulting from increased energy metabolism, likely due to the nanoparticles both aggregating to the outside of the organisms resulting in increased molting requirements, and through consumption impacting the circulatory system. The lower left blue box indicates the down regulation of L-glutathione, an important metabolite in the removal of ROS. A decrease in this metabolite indicates it is likely being utilized to counteract ROS created from the increased energy requirements. Loss of this metabolite introduces DNA damage caused by cell aging and death. The lower right purple box indicates an increase in lysine. In each graph blue represents exposed Daphnia, and orange represents the control. Results are plotted as percent change relative to the first control point. The X-axis shows one point prior to exposure (-2 hours). Point 0 represents when SPIONs were exposed (also represented by a red arrow). Each experiment was conducted in triplicate and error bars represent standard error.

Wiley -UK Magnetic Resonance in Chemistry

Acknowledgements

We would like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) [Alliance (ALLRP 549399), Alliance (ALLRP 555452) and Discovery Programs (RGPIN-2019-04165)], the Centre national de la recherche scientifique (CNRS) PhD Mobility Funding Programme, the Canada Foundation for Innovation (CFI), the Ontario Ministry of Research and Innovation (MRI), the Krembil Foundation for providing funding, and the Government of Ontario for an Early Researcher Award.

Wiley -UK Magnetic Resonance in Chemistry Wiley -UK Magnetic Resonance in Chemistry Wiley -UK Magnetic Resonance in Chemistry Wiley -UK Magnetic Resonance in Chemistry Wiley -UK Magnetic Resonance in Chemistry


Fanny Dégeilh
email: fanny.degeilh@inserm.fr

Tilmann Von Soest

Lia Ferschmann

Joanne C Beer

Malo Gaubert

Inga K Koerte

Christian K Tamnes

Social problems and brain structure development following childhood mild traumatic brain injury

Keywords: Traumatic brain injury, Concussion, Brain development, Social problems, Cortical thickness

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Social problems and brain structure development following childhood mild traumatic brain injury

Introduction

Traumatic brain injury (TBI) is highly prevalent in children and can lead to long-term behavioral problems, including social problems such as aggressive behaviors and social cognitive deficits (Anderson et al., 2022;Catroppa et al., 2015;[START_REF] Dégeilh | Developmental trajectories of adaptive functioning following early mild traumatic brain injury[END_REF]Li & Liu, 2013;Ryan et al., 2021;Zamani et al., 2019). Childhood TBI, even of mild severity (mTBI) which represent 90% of TBI cases (Zemek et al., 2013), may profoundly impact the child's quality of life and academic achievements (Anderson et al., 2009). While the consequences of TBI on children's social functions have received increased attention over the past decade, the brain mechanisms underlying the social dysfunction following childhood TBI remain poorly understood (Ryan, Catroppa, Godfrey, et al., 2016).

Social cognitive functions emerge early in development, but also show a protracted development, continuing across childhood and adolescence, coinciding with the extended maturation of the 'social brain', an anatomically distributed set of cortical regions underlying social cognition and behavior (Blakemore, 2012;Frith & Frith, 2003;Mills et al., 2014). The social brain comprises the posterior superior temporal sulcus, the anterior temporal cortex, the medial prefrontal cortex (medial Brodmann Area 10), and the inferior parietal cortex (also referred as the temporoparietal junction; Frith and Frith, 2003;Blakemore, 2012;Mills et al., 2014). Cortical thickness decreases in these brain regions from childhood through late adolescence (Mills et al., 2014;Tamnes et al., 2017). Childhood TBI may disrupt the expected brain structural developmental trajectories of social brain regions, that could manifest as development of social problems.

Despite the heterogeneity of the brain injury profiles following TBI, common causes of injury involve similar mechanisms, and some brain regions are more vulnerable than others to injury, and therefore more commonly damaged (Bigler, Abildskov, et al., 2013). Particularly vulnerable brain regions include parts of the social brain network. Indeed, given the shape of the skull and how the brain is held in situ, focal contusions often occur in frontotemporal regions and lead to a frontotemporal distribution of cortical atrophy (Bigler, Abildskov, et al., 2013). Moreover, evidence from neuroimaging studies suggest an association between structural social brain alterations (i.e., brain lesion, reduced cortical volume or thickness in frontal, temporal, and parietal regions) and persistent social deficits following pediatric moderate-tosevere TBI (Bigler, Yeates, et al., 2013;Levan et al., 2015;Ryan, Catroppa, Beare, et al., 2016;Ryan, Catroppa, Godfrey, et al., 2016;Ryan, van Bijnen, et al., 2016;Zamani et al., 2019). Concerning mTBI, the very few studies conducted so far show conflicting results, with some reporting reduced gray matter volumes and cortical thickness in frontal, temporal and parietal regions following pediatric mTBI (Mayer et al., 2015;Urban et al., 2017), while others report no changes (King et al., 2019;Ryan, Catroppa, Beare, et al., 2016).

The interaction between injury physiopathology and brain maturation may underpin the long-term functional impact of pediatric TBI (King et al., 2019).

Longitudinal studies of brain maturation following pediatric TBI are however scarce.

The three available studies explored longitudinal changes in regional brain volumes and cortical thickness, and suggested deviation from the expected developmental pattern, particularly in frontal and temporal regions, following mTBI or moderate-to-severe TBI sustained in late childhood and adolescence (Dennis et al., 2017;Mayer et al., 2015;Wilde et al., 2012). However, the small sample sizes (all Ns < 21 TBI) and analytic methods not handling challenges associated with longitudinal data, such as partially missing data, limit the generalization of these findings. In addition, none of these studies included children who sustained a TBI at a younger age than 8 years.

The present study aimed to characterized longitudinal changes in social problems and cortical thickness in social brain regions following pediatric mTBI using data from the large Adolescent Brain Cognitive Development (ABCD) Study (Casey et al., 2018). We used an analytic method sensitive to inter-individual variability in intra-individual change, namely latent change score models (LCSM), to test for group differences in developmental changes in social problems and cortical thickness from ages 10 years to ages 12 years in 345 children with a history of childhood mTBI and 7,089 controls.

Methods

No part of the study procedures or analyses was pre-registered prior to the research being conducted. Below, we report how we determined our sample size, all data exclusions, all inclusion/exclusion criteria, whether inclusion/exclusion criteria were established prior to data analysis, all manipulations, and all measures in the study.

Study design

Data used in the current analyses was collected as part of the ABCD Study®. The ABCD study is a large 21-site longitudinal study of child development in the United States, which recruited 11,876 children aged 8.9 to 11.1 years between 2016 and 2018 and follow them annually (Barch et al., 2018;Garavan et al., 2018). All ABCD study procedures were approved by the local human research ethics committees. All families provided written informed consent for participation.

Measures

The current study used baseline, 1-year follow-up and 2-year follow-up demographic, physical health, mental health (Barch et al., 2018), and neuroimaging (Casey et al., 2018) data from ABCD 4.0 data release (http://dx.doi.org/10.15154/1527902). Only measures included in the current analyses are described here.

Demographic Variables.

Demographic variables, including child age at each visit, sex assigned at birth, ethnicity, family income, and parental education (i.e., highest grade or level of school completed or highest degree received), were reported by parents via the demographic questionnaire (Barch et al., 2018).

Brain injury history.

The Modified Ohio State University TBI Screen-Short Version (Barch et al., 2018;Bogner et al., 2017;Corrigan & Bogner, 2007) is a parent-report questionnaire designed to measure lifetime history of TBI in children. Parents are asked to recall events related to the occurrence of injury to the head or neck (e.g., car accident, fall, being hit by someone or something). In case of positive response to any occurrence, follow-up questions are asked to determine the age of the injury and whether the injury was associated with loss of consciousness (LOC) or memory loss. At baseline, parents were asked about the child's entire life (i.e., the first 10 years of life). At follow-ups, parents were asked to report on any new injury to the head or neck that may have occurred since the previous visit.

The summary score ('tbi_ss_worst_overall') was used to identify participants as mTBI or controls. The summary score rates injury severity on a five-point scale:

(1) Improbable TBI (no TBI or TBI without LOC or memory loss), (2) Possible mild TBI (TBI without LOC, but with memory loss), (3) Mild TBI (TBI with LOC ≤ 30 minutes), (4) Moderate TBI (TBI with LOC between 30 minutes to 24 hours), and (5)

Severe TBI (TBI with LOC ≥ 24 hours). Children with a moderate or severe TBI (score > 3) at baseline, who reported sustaining a TBI between baseline and 2-year follow-up visit, or which the score was missing were excluded from the analyses.

Children with a score of 2 or 3 at baseline were included in the mTBI group. Children with a summary score of 1 without history of neck or head injury at baseline or at follow-ups were included in the control group.

Social problems.

Baseline and 2-year follow-up age-corrected t-scores from the social problem subscale on the Child Behavior Checklist-Parent form (CBCL; Achenbach and Rescorla, 2000) were used as measures of children's social problems. CBCL is a parent report of emotional and behavioral problems in children and adolescents.

Social problems subscale contains 11 items related to several areas of social functioning, including peer rejection (e.g., "Not liked by other kids", "Feels others are out to get him/her", "Complains of loneliness") and social interaction difficulties (e.g., "Clings to adults or too dependent", "Prefers being with younger kids"). Each item is rated on a three-point rating scale (0 = Not true; 1 = Somewhat or sometimes true; 2 = Very true or often true), and the social problems subscale includes 11 items.

Higher scores suggest more social problems, with score ≤64 corresponding to nonclinical scores, [65][66][67][68][69] corresponding to borderline scores, and ≥ 70 corresponding to clinical scores.

Social brain cortical thickness.

Baseline and 2-year follow-up cortical thickness was computed on T1-weighted images by the ABCD group (Hagler et al., 2019) using FreeSurfer v5.3 (Fischl, 2012) within the Desikan-Killiany regions (Desikan et al., 2006). The following left and right hemisphere regions of interest (ROIs) corresponding to core region of the social brain network (Mills et al., 2014) were used in our analyses: medial orbitofrontal cortex, anterior temporal cortex (temporal pole), inferior parietal cortex, and banks of the superior temporal sulcus (Fig. 1). Scanner effects (n scanners = 26) were corrected before statistical analyses using longitudinal ComBat (long-ComBat; Beer et al., 2020). Long-ComBat is an empirical Bayesian method for harmonizing longitudinal multi-scanner MRI data removing both additive and multiplicative scanner effects. ComBat is more robust to outliers in the case of small within-scanner sample sizes than other harmonization methods (Johnson et al., 2007). Long-ComBat model included explanatory variables child sex (0 = female; 1 = male) and parental education (ranging from 0 to 18), group, time x group interaction, and a subject-level random intercept. Fig. S1 (see supplemental) shows residual boxplots by scanner before and after data harmonization using long-ComBat.

Statistical analyses

Group differences on demographic variables were tested using Chi-squared test (categorial data) and t-test (continuous data).

Multigroup LCSM (Kievit et al., 2018) were constructed with the lavaan 0.6-12 package (Rosseel, 2012) in R (version R4.1.2) to estimate latent change scores between baseline and follow-up for social problems and cortical thickness (for each ROI -Combat-corrected cortical thickness values) for all individuals (see Fig. 2). Group (mTBI versus controls) differences in four parameters of interestthe mean of the baseline score for behavior or cortical thickness, the mean of the latent change factor (reflecting rate of change over time), the variance of the baseline score (reflecting individual differences in scores at baseline) and the variance of the latent change factors (reflecting individual differences in rate of change)were tested by means of equality constraints (Kievit et al., 2018). More specifically, each parameter of interest was individually constrained to be equal in the two groups and this constrained model was compared to a model where the parameter of interest was freely estimated using scaled Chi-squared difference tests (Satorra & Bentler, 2001). A significant difference between the two models reflects that the constrained parameter is significantly different between the two groups. Both the baseline measure and the latent change factor were controlled for child sex (0 = female; 1 = male) and parental education (ranging from 0 to 18). Covariates included in the models were selected a priorybefore any group comparisonbased on previous work published that reported sex and socio-economic status (family income or parent education) impact on child's recovery and outcomes following childhood TBI (Gerring & Wade, 2012;Gupte et al., 2019), or brain development (DeCasien et al., 2022;Farah, 2017). Parental education was preferred to family income as a socio-economic status proxy as it in the ABCD study is a continuous variable and has less missing data than the income variable (0.1% of missing data for the parental education variable vs. 8% for the income variable).

When the multigroup LCSM reveled significant results, a secondary univariate LCSM was constricted to the mTBI group to explore if the changes observed following mTBI were associated with age at injury.

Results

Participants final sample

From the 11,876 participants of ABCD data release 4.0, 4,318 were not eligible for the current study or met an exclusion criterion, 351 children were categorized as mTBI and 7,207 children were categorized as controls (Fig. 3). Of these 7,558 eligible participants, 124 were excluded from the analyses because of the following criteria: Structural MRI data were missing at both timepoint for 79 controls and 2 mTBI, and did not pass the ABCD quality control (Hagler et al., 2019) for 15 controls. To reduce imbalance in scanners between the two groups, 15 controls were excluded because their MRI data were collected on a scanner used for acquiring less than two scans per group (Beer et al., 2020). One participant was mislabeled as mTBI, and 3 had repetitive head impact. Finally, parental education (used as covariate in the analyses) was missing for 9 controls. The final sample for our analyses thus included 345 children with mTBI and 7,089 controls (cf. Table 1 for demographic information and group comparison). The two groups were significantly different regarding sex and parental education; thus, these two variables were entered as covariates in the analyses.

Social problems

The two-group LCSM without constraints across groups (i.e., all parameters freely estimated) fits the data well: χ² (4) = 19.12, Comparative Fit Index (CFI) = 0.99, Turkey-Lewis Index (TLI) = 0.98, Root Mean Square Error of Approximation (RMSEA) = 0.032 [0.019 0.047]. Constraining the parameters of interest one by one to be equal across groups significantly reduced the model fit in all models (all ps ≤

.002) reflecting group differences in both means and variances of the baseline value of and change in social problems (Table 2; Fig. 4).

Children with a history of mTBI showed higher levels of social problems at baseline (estimated mean = 56.4) than the control group (estimated mean = 54.4;

Table 3), with levels of social problems for both groups falling in the non-clinical range. Moreover, social problems score decreased faster in children with history of mTBI (estimated mean = -1.2, p = .007) than in controls in which social problems score stayed stable over time (estimated mean: -0.3, p = .33; Table 3). A linear regression model accounting for sex and parental education revealed that social problems score at 2-year follow-up remained significantly higher by 1.3 +/-0.3 points (p < .0001) in children with an history of mTBI than in controls (estimated mean = 54.7 +/-0.4), still falling in the non-clinical range for both groups. In addition, the proportion of children with clinically significant social problems score was higher in mTBI than in controls at both timepoint (Table 4).

Children with mTBI showed greater individual difference at baseline (variance = 45.7) and in change (variance = 26.9) than the controls (baseline variance = 17.8, change variance = 15.6; Table 3). Of note, the results were similar when covariates (sex and parental education) were not included in the model (see supplemental Tables S1 andS2).

Sex had significant association with both baseline levels and change in social problems (all ps <.0001; Table 3). Males showed higher baseline scores and faster decrease over time in social problems than females. Parent education had a negative significant association with baseline levels in social problems (all ps <.0001; Table 3).

Secondary univariate LCSM in the mTBI group revealed no association between the age at injury and the means of the baseline value (p = .20) of and change in (p = .63) social problems.

Social brain cortical thickness

For all ROIs (medial orbitofrontal cortex, anterior temporal cortex, inferior parietal cortex, banks of the superior temporal sulcus), the two-group latent change score model without constraints across groups fit the data well: CFI were all equal to 1.00, TLI were between 0.99 and 1.00, and RMSEA were between 0.000 and 0.016 (Table 5). For all ROIs, but the left medial orbitofrontal cortex, constraining the parameters of interest to be equal between groups did not significantly reduce the model fit (all ps ≥ .12), reflecting no group difference on means and variances of the baseline value and change of the social brain cortical thickness (Table 6; Figure 5). Concerning the left medial orbitofrontal cortex, constraining the latent change factor variance to be equal across groups significantly reduced the model fit (p = .03), reflecting group differences in the variances of change in cortical thickness (Table 6; Figure 5).

Children with mTBI showed significantly lower latent change variance (0.011) than controls (0.014; Table 7). In both group and for all ROIs, except for the left and right anterior temporal cortex, thickness significantly decreased by 0.06 to 0.08 mm over the two years of the study period (Table 7). Sex was significantly associated with cortical thickness at baseline in the left and right banks of the superior temporal sulcus, inferior parietal cortex, and anterior temporal cortex, with males showing thicker cortex than females (Table 7). There were also significant sex differences in the rate of change of cortical thickness bilaterally in the banks of the superior temporal sulcus, the inferior parietal cortex, and the medial orbitofrontal cortex, with a faster decrease in males than in females. Parental education was significantly associated with cortical thickness in all ROIs at baseline, with higher parental education associated with thicker cortex (Table 7). Parental education was also significantly associated with the rate of change of cortical thickness in the left and right banks of the superior temporal sulcus and the left medial orbitofrontal cortex, with higher parental education associated with lower decrease.

Of note, the results were similar when covariates were not entered in the model (Table S3), and the analyses were conducted on the raw (not harmonized for the scanners) MRI data (Table S4). Partial correlations accounting for sex and parental education, revealed no significant association between cortical thickness and social problems (r = [-.02 -.01], all ps > .07).

Discussion

This study examined the longitudinal development of social problems and cortical thickness in social brain regions following childhood mTBI. Using a large sample with retrospective information on history of TBI and multigroup latent change score models, an analytic method sensitive to longitudinal change at both individual and group level, we found different developmental changes in the levels of social problems between children with and without history of mTBI. Children with mTBI showed higher levels of social problems than children without mTBI at age 10, although remaining mostly within the non-clinical range. Then, the level of social problems decreased over 2 years but remained higher, but still mostly within the non-clinical range, in children with mTBI than in controls in which they stayed stable. No group difference in the trajectory of the social brain cortical thickness was observed. Cortical thickness significantly decreased between age 10 and 12 years in both groups, except for the anterior temporal cortex where the thickness remained stable over the 2 years.

The present behavioral findings are consistent with previous studies showing persistent differences in social functioning in children with TBI compared to their noninjured peers (Anderson et al., 2022;Catroppa et al., 2015;[START_REF] Dégeilh | Developmental trajectories of adaptive functioning following early mild traumatic brain injury[END_REF]Li & Liu, 2013;Ryan et al., 2021;Zamani et al., 2019). Here, we further observed that mTBI during childhood was associated with higher, but non-clinical, levels of social problems, with a decrease over time that may suggest a slow return to a level similar to their non-injured peers.

Additional follow-up of the ABCD cohort will allow to test this hypothesis and explore whether or not adolescents with mTBI reach a similar level as their non-injured peers by the end of the adolescence.

While it is believed that the brain development and integrity may underpin social impairments developing following a pediatric TBI (Beauchamp & Anderson, 2010a), no significant difference in the developmental trajectory of the cortical thickness in the social brain was observed in the current study. This result echoes recent findings by Anderson and colleagues (Anderson et al., 2022) showing no difference in gray matter volume of largescale brain networks (i.e., Central Executive Network, Default Mode Network, Mentalizing Network, Mirror Neuron Empathy Network, and Salience Network) 3 to 8 weeks post-injury between TBI children with different profiles of social impairment recovery trajectory. However, it is not possible to conclude whether the absence of significant difference reflects recovery or absence of social brain cortical alteration following childhood mTBI, or that the assessment (here cortical thickness from T1-w) is not sensitive enough to detect existing alteration. Future studies should explore if changes in social brain function or other brain structure metrics may underly the changes in the level of social problems after pediatric TBI.

In both groups, cortical thickness significantly decreased over the two years of the study in all the regions of the social brain but the anterior temporal cortex. This developmental trajectory of the social brain structure is consistent with what has been observed in longitudinal studies showing that the cerebral cortex undergoes widespread and regionally variable nonlinear decreases in thickness across childhood and adolescence (Mills et al., 2014;Tamnes et al., 2017). Concerning the social brain regions, cortical thickness in the posterior superior temporal sulcus, the medial prefrontal cortex, and the inferior parietal cortex has in previous studies consistently been found to decreased with age, while the findings for the anterior temporal cortex are less consistent, with both increase and decrease observed.

The present study was strengthened by its large sample of children who sustained a mTBI (n = 225, against n < 21 in published neuroimaging studies) between ages 0 to 9 years old (none of the published neuroimaging studies included children who sustained a mTBI before aged of 8 years old), and its longitudinal design. We applied a longitudinal analytic method well-suited to model individual differences in change across two time points and to handle missing data by means of full information maximum likelihood estimation. Furthermore, special attention was paid to scanner effects correction using a suitable method for longitudinal data.

Nonetheless, the current results must be interpreted in the context of some study limitations. Most notably, the assessment of TBI status was based on parent retrospective report and thus reflects parents' memory of their child lifetime history of TBI, which is subject to recall bias in parental response to the questionnaire. In addition, the Modified Ohio State University TBI Screen-Short Version provides only an indirect index of child TBI history with limited information on mechanisms of injury and acute symptoms. Furthermore, the current mTBI definition (from Modified Ohio State University TBI Screen) is based on LOC or or memory loss duration allowing to identify only children having experienced more severe mTBI (complicated mTBI) and does not allow to identify children having experienced head or neck injury (excluded from the current analyses) in the absence of alterations in mental status for which acute symptoms is need to conclude on the presence or absence of a mTBI diagnose. Given the absence of social problems measures prior to TBI, the possibility that higher social problems were already present in children prior to TBI cannot be excluded.

Prospective longitudinal studies with more detailed information on the mechanisms and type of injury, acute symptoms and brain lesion, and objective assessment of the injury severity (i.e., Glasgow coma scale, symptoms measurement) providing a detailed picture of the pediatric TBI heterogeneity, are needed to better understand the neural mechanisms underpinning social impairment after a pediatric TBI. Finally, external (e.g., SES, ethnicity, or culture) and internal family factors (e.g., parent-child interactions, parental practices, or family functioning) are known to play a particularly important role in social (Beauchamp & Anderson, 2010b;Tuerk et al., 2021) and brain (Farah, 2017;Ilyka et al., 2021) development, as well as to both influence child's recovery following TBI (Gerring & Wade, 2012) and be impacted by pediatric TBI (Lalonde et al., 2016(Lalonde et al., , 2020)). Here we observed higher levels of social problems in children with mTBI compared to non-injured peers, beyond external family factors (i.e., parental education). Further studies should explore the role of internal family factors (e.g., parent-child interaction) on social problems development following childhood TBI. https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Authors Contributions

FD conceptualized the study, conducted the analyses and drafted the original manuscript.

TvS contributed to defining the statistical models, conducting the analyses and writing the manuscript. LF contributed to conducting the analyses and commented on the manuscript. JB contributed the data processing and commented on the manuscript. MG organized the data collection and commented on the manuscript. IKK contributed to conceptualizing the study, provided access to the data, and commented on the manuscript. CKT contributed to conceptualizing the study and writing the manuscript, and supervised the project. All authors contributed to the editing of the paper.

Competing Interests

IKK receives grant funding from the National Institutes of Health, The European Research Council, and the German Ministry for Research and Education. She receives funding for a collaborative project and serves as a paid scientific advisor for Abbott. She receives royalties for scientific book chapters. Her spouse is an employee at Siemens AG. The other authors declare no competing interests.

Figure 1 .

 1 Figure 1. The social brain. Social brain regions of interest. medial orbitofrontal

Figure 2 :

 2 Figure 2: Latent Change Score Model. Social problems and cortical thickness(X)

Figure 3 :

 3 Figure 3: Participants selection.

Figure 4 .

 4 Figure 4. Visualization of changes in social problems scores in controls (CTL)

Figure 5 .

 5 Figure 5. Visualization of changes in social brain cortical thickness in control (CTL) and mTBI

Table 1 :

 1 Demographic characteristics of the final sample

	TBI	Controls	t/χ2	p
	N = 345	N = 7,089

Table 2 :

 2 Social problems -Model fit comparisons

	df χ²	χ²	p

Table 3 :

 3 Social problems -Model estimates (all parameters freely estimated)

	Controls TBI

Table 5 :

 5 Social brain cortical thickness -Model fit (all parameters freely estimated)

	χ² (df = 4) CFI TLI RMSEA [90CI]

Table 6 :

 6 Social brain cortical thickness -Models fit comparisons

	Df χ²	χ²	p

Table 7 :

 7 Social brain cortical thickness -Model estimates (all parameters freely estimated)

	Left	Right

Acknowledgments

Funding: This work was supported by the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant scheme [#101024023, to F.D.], the European Research Council under the ERC Starting Grant scheme [#804326, to I.K.K.], and the Research Council of Norway [#288083]. ABCD Study: Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development SM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at

Data availability

The data that support the findings are available from the NIMH Data Archive (NDA). The ABCD data used in this report came from the ABCD 3.0 data release [NIMH Data Archive Digital Object Identifier (DOI): http://dx.doi.org/10.15154/1527902].

Code availability

The code that supports the findings of this study is available as supplementary material.