{"paper_id":"83cc1cc0-6e8f-45e7-8921-b52ce214f28b","body_text":"Complement receptor C3ar1 deficiency does not alter brain 1 \nstructure or functional connectivity across early life development  2 \nHanna Lemmik1, Eugene Kim1, Eilidh MacNicol1, Davide Maselli2, Michel 3 \nBernanos1, Zhuoni Li3, Dauda Abdullahi1, Esther Walters1, Maria Elisa Serrano 4 \nNavacerrada1, Wuding Zhou4, Aleksandar Ivetic2, Diana Cash1†*, Laura Westacott5†* 5 \n1Department of Neuroimaging, King’s College London, UK 2School of 6 \nCardiovascular and Metabolic Medicine & Sciences, King’s College London, UK 7 \n3Department of Forensic and Neurodevelopmental Sciences, King’s College London, 8 \nUK 4Peter Gorer Department of Immunobiology, King’s College London, UK 9 \n5Neuroscience and Mental Health Innovation Institute, Cardiff University, UK 10 \n†These authors contributed equally 11 \nFor correspondence: westacottlj@cardiff.ac.uk (LJW), diana.cash@kcl.ac.uk (DC) 12 \nAbstract 13 \nPrevious studies suggest that genetic deletion of the complement C3a anaphylatoxin 14 \nchemotactic receptor (C3ar1), a key component of the innate immune response, 15 \ninfluences behaviours associated with psychiatric symptomatology in mice but when 16 \nand where C3ar1 is needed in the brain is not known. These questions are significant 17 \nbecause, as a G-protein-coupled receptor (GPCR), human C3AR1 serves as a 18 \npotential therapeutic target for disorders associated with complement dysregulation, 19 \nsuch as schizophrenia. To provide a brain-wide assessment of developmental C3ar1 20 \nactivity, we used longitudinal tensor-based morphometry (TBM), fractional 21 \nanisotropy (FA) from diffusion-weighted magnetic resonance imaging (dMRI) and 22 \nblood oxygen-level dependent functional MRI (BOLD fMRI) in male and female 23 \nC3ar1-deficient mice and wild-type littermates, with behavioural assessment in 24 \nadulthood. Unexpectedly, we  did not find robust C3ar1-dependent phenotype in 25 \nany of these measures. Therefore, our study does not support neurodevelopmental 26 \nhypotheses for C3ar1 which will likely have implications for targeting this receptor 27 \nin disease.  28 \nIntroduction  29 \nThe complement system is a conserved immune pathway that participates in host 30 \ndefence through pathogen clearance and regulating inflammation (Chaplin 2020) as 31 \nwell as tissue homeostasis (Kunz and Kemper 2021; West and Kemper 2023), with 32 \nemerging roles in neurodevelopment, psychiatric disorders and neurodegeneration 33 \n(Stevens et al. 2007; Hong et al. 2016; Sekar et al. 2016). 34 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n2 \nThe most convincing evidence for the involvement of the complement system in 35 \nneurodevelopment so far is its genetic association with schizophrenia.  Schizophrenia 36 \nis a complex and highly heritable neurodevelopmental disorder characterised by 37 \nhallucinations, delusions, and impaired cognition, with symptoms typically emerging 38 \nin late adolescence or early adulthood (McCutcheon, Reis Marques, and Howes 39 \n2020; Howes, Bukala, and Beck 2024). Neurobiological hallmarks of schizophrenia, 40 \namong others, include grey matter loss (Vita et al. 2012) and a reduction in synaptic 41 \ndensity (Osimo et al. 2019). Genome-wide association studies (GWAS) of 42 \nschizophrenia have identified two complement-related risk loci, the complement 43 \ncomponent 4 A (C4A) structural variant (Sekar et al. 2016), which encodes C4A 44 \nprotein responsible for propagation of complement activation, and the CUB and 45 \nSushi Multiple Domains 1 (CMSD1) mutation which encodes a putative 46 \ncomplement inhibitor protein (Schizophrenia Working Group of the Psychiatric 47 \nGenomics Consortium 2014; Baum et al. 2024). Preclinical studies link these 48 \nmutations to increased brain-specific complement activation and synapse loss (Sekar 49 \net al. 2016; Yilmaz et al. 2021; Baum et al. 2024) potentially tying complement 50 \nactivation to synaptic pathology in schizophrenia. Indeed, the C4A risk locus 51 \nassociates with MRI markers of grey matter loss and reduced cognitive performance 52 \nin humans, even in the absence of neurological disorders (O’Connell et al. 2021). 53 \nComplement pathway proteins represent promising therapeutic targets for disorders 54 \nlinked to abnormal complement activity. Complement modulation therefore has 55 \npotential in addressing unmet therapeutic need in schizophrenia, since elevated 56 \ncomplement proteins correlate with severity of negative symptoms in psychosis 57 \n(Byrne et al. 2024), which do not respond to anti-psychotic medication. 58 \n C3a anaphylatoxin chemotactic receptor (C3aR1), a G-protein coupled receptor 59 \n(GPCR) bound by complement activation product C3a and the granin family 60 \nneuropeptide TLQP-21 (Rodriguez et al. 2023), acts downstream of complement 61 \nactivation and stands out as a pharmacologically tractable target for modifying 62 \ncomplement activity in the brain (Hauser et al. 2017). In mice, its transcript is 63 \npredominantly expressed by microglia, with minimal neuronal expression observed 64 \nin both healthy adult humans and mice according to single-cell transcriptomic 65 \nanalyses (Hammond et al. 2019; Tasic et al. 2016). While its temporal expression 66 \npatterns are not yet well characterised, it appears to be active during early embryonic 67 \ndevelopment, potentially influencing progenitor cell proliferation (Coulthard et al. 68 \n2018; Bénard et al. 2008; Hammond et al. 2019). C3aR1 also appears to play a role in 69 \nfacilitating developmental astrocyte phagocytosis in the retina by microglia 70 \n(Gnanaguru et al. 2023), as well as to regulate microglial reactivity and 71 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n3 \nneuroinflammation more broadly (Gedam et al. 2023; Ge, Guan, and Wang 2024; 72 \nZheng et al. 2021; Lian et al. 2015; Vasek et al. 2016; Chew and Petretto 2019). Brain 73 \nmorphological changes observed in C3ar1-deficient mice further support its 74 \nneurodevelopmental relevance (Westacott et al. 2021; Pozo-Rodrigálvarez et al. 75 \n2021), although there is no consensus on the precise neurodevelopmental actions of 76 \nC3aR1. Addressing this gap is important given its potential as a pharmacological 77 \ntarget. 78 \nC3aR1 may impact brain functions relevant to psychiatric symptomatology since a 79 \nrange of behavioural phenotypes have been reported in C3ar1-deficienct mice. These 80 \ninclude abnormal anxiety-like behaviours (Westacott et al. 2022), hyperactivity 81 \n(Pozo-Rodrigálvarez et al. 2021), cognitive impairment (Coulthard et al. 2018) and 82 \nresilience to depressive-like behaviours induced by chronic stress or inflammation 83 \n(Crider et al. 2018; Zhang et al. 2022; Sun et al. 2024). The involvement of C3aR1 in 84 \nbehaviour suggests that it is needed for healthy brain function, but it remains unclear 85 \nwhether the observed phenotypes arise because C3aR1 is needed during 86 \ndevelopment or from ongoing tonic activity, necessitating longitudinal assessment. 87 \nImportantly, none of the aforementioned studies used control wildtype mice that 88 \nwere littermates of genetically C3ar1-deficient mice, which presents a confound due 89 \nto the rapidly diverging genetic background between mutant and control in small 90 \ninbred colonies (Fitch and Atchley 1985), but also because litter environment affects 91 \nbehaviour and brain development (Crews et al. 2009; Jiménez and Zylka 2021; 92 \nValiquette et al. 2023). 93 \nTo investigate the potential effects of C3ar1 deficiency, we adopted a global, 94 \nunbiased approach, conducting a longitudinal study of male and female C3ar1-95 \ndeficient mice and their wild-type littermates during adolescence and adulthood. 96 \nUsing structural and diffusion magnetic resonance imaging (MRI and dMRI), 97 \nalongside resting state functional connectivity (FC) analysis of the blood oxygen level 98 \ndependent (BOLD rsf) MRI signal, we aimed to assess whether the requirement for 99 \nC3aR1 is implicated in brain structural development during adolescence—a critical 100 \nperiod for psychiatric vulnerability (Westacott and Wilkinson 2022; Paus, Keshavan, 101 \nand Giedd 2008)—or only becomes evident in adulthood.  102 \nOur imaging measures included tensor-based morphometry (TBM) which is a 103 \nsensitive measure for mapping developmental impacts of genetic and immune 104 \nperturbations (Nasseef et al. 2018; Ellegood et al. 2018; Guma et al. 2022; Kielar et al. 105 \n2012), white matter fractional anisotropy (FA) from dMRI to evaluate white matter 106 \norganisation which is influenced by microglial activity in development (Chan et al. 107 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n4 \n2024; Falangola et al. 2023), and other connectivity metrics such as global efficiency 108 \nand clustering coefficient to analyse brain network topology (Zhu et al. 2017; Forlim 109 \net al. 2024; Hadley et al. 2016). These techniques were complemented by behavioural 110 \ntesting in adult mice, measuring cognition and emotional reactivity through 111 \nestablished paradigms like the open field (OF) test, elevated plus maze (EPM), novel 112 \nobject recognition (NOR), and prepulse inhibition (PPI), the three former 113 \nparadigms being previously tested in C3ar1-deficient mice. Unexpectedly, we found 114 \nno robust brain or behavioural phenotype in our datasets, challenging the previous 115 \ninterpretations for a neurodevelopmental role for C3aR1 under physiological 116 \nconditions.  117 \nResults 118 \nValidation of the C3ar1tm1Cge mutation 119 \nAlthough we used an established C3ar1 knockout line, C3ar1tm1Cge  (Humbles et al. 120 \n2000), we performed our own validation of the mutation, for which we designed a 121 \n79-base pair (bp) amplicon targeting the putatively deleted region (Figure 1). PCR 122 \nanalysis of cDNA derived from bone marrow-derived macrophages–a cell type 123 \nconsistently reported to express C3ar1 mRNA (Tao et al. 2021; Mommert et al. 124 \n2018; Mamane et al. 2009)–showed no detectable transcript in this region in the 125 \nmutant animals (Figure 1-i), confirming the absence of the canonical transcript. We 126 \nalso investigated the possibility of an alternative transcript after an alternative start 127 \ncodon but found no transcript there either (Figure 1-ii, Supplemental figure 1). 128 \nThese results were further corroborated by quantitative PCR (qPCR, n = 8) in 129 \nhomozygous knockout animals in either M0-like or interleukin 4 (IL4)-induced M2-130 \nlike macrophages (Supplemental figures 2a-e). Together, these results confirm that 131 \nC3ar1tm1Cge  is a true loss-of-function or a “knockout” allele resulting in no C3ar1 132 \ntranscript. 133 \nC3ar1-deficiency does not influence total or regional brain volume 134 \nWe conducted a longitudinal MRI study (referred to as cohort 1 hereafter or implied 135 \nwhen cohort is not specified) to investigate potential genotype-related differences in 136 \nbrain structure and function using C3ar1tmCge homozygous knockout mice 137 \n(C3ar1-/-, C3ar1-deficient) and their littermate wild-type control animals 138 \n(C3ar1+/+) (Figure 2a) on C57BL6J (Charles River, UK) background. Both groups 139 \nunderwent in vivo MRI in adolescence (postnatal day or PND27-31) and adulthood 140 \n(PND81-92). Structural MR images were additionally collected ex vivo from the 141 \nsame mice sacrificed in adulthood after the in vivo scan to achieve higher isotropic 142 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n5 \nresolution (0.1 mm ex vivo vs 0.15 mm in vivo) and increased signal-to-noise ratio 143 \n(SNR). We also conducted MRI in adulthood in an independent study cohort, 144 \nwhich also represents a replication dataset, rereferred to as cohort 2. 145 \nWe used tensor-based morphometry (TBM) analysis to estimate total brain volume 146 \nand to map regional brain volume differences. There were no genotype dependent 147 \ndifferences in total brain volume in vivo in adolescence (Figure 2b) nor in adulthood 148 \n(Figure 2c) although in both groups female mice exhibited significantly smaller total 149 \nbrain volumes compared to males in adolescence. These sex differences were no 150 \nlonger observed in adulthood which aligns with previous findings (Guma et al. 151 \n2024). 152 \nUsing TBM, no significant genotype-dependent differences in regional brain volume 153 \nwere detected in adolescence (Figure 2d-i). In adulthood (Figure 2d-ii), C3ar1-/- 154 \nmice showed a significant (p < 0.05) volume increase in the right pretectal area, and 155 \nsubthreshold (0.05 < p < 0.5) increases in the left pretectal area and the right lateral 156 \nthalamus in vivo but these differences were no longer observed in the same study 157 \ncohort ex vivo despite improved spatial resolution (not shown), nor did we observe 158 \nany genotype-dependent differences using TBM analysis in cohort 2 (not shown).  159 \nWe did not observe any genotype-by-sex interaction effects in adolescence or in 160 \nadulthood in vivo, or ex vivo in adulthood the same study cohort, nor cohort 2 (not 161 \nshown).  162 \nWhile no reproducibly significant genotype effects in regional volume were observed 163 \nin cohort 1, we detected sexually dimorphic effects (Figure 2d-iii and  2d-iv). In 164 \nadolescence, female mice had significantly larger volumes bilaterally in agranular 165 \ninsular cortex (AI), superior colliculus (SC), medial septum (MS) and in CA1 region 166 \nof the hippocampus, whereas male mice had increased volumes in white matter areas 167 \nincluding the olfactory tract, the corpus callosum, the hippocampal commissure, and 168 \nnotably also in the median preoptic nucleus (MEPO) which is well known to be 169 \nlarger in male rodents (Gorski et al. 1978) (Figure 2d-iii, Supplemental figure 3 170 \nfor absolute volume). Many of these sex differences were no longer observed in 171 \nadulthood (see also Supplemental figure 4 for cohort 2 data), but females showed 172 \nlarger relative volumes in the dorsal anterior cingulate cortex (ACAd), secondary 173 \nmotor cortex (MOs) and primary somatosensory cortex (SSp). Adult males had larger 174 \nvolumes in the medial preoptic area (MPO), medial amygdala (MEA) and the bed 175 \nnucleus of stria terminalis (BST)–differences which are well documented sexual 176 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n6 \ndimorphisms (Hines, Allen, and Gorski 1992) and which were also observed ex vivo 177 \nin this study cohort (Supplemental figure 5).  178 \nThere were also no genotype differences in regional brain volumes over time from 179 \nadolescence to adulthood, nor did we observe any sex-by-genotype interaction (not 180 \nshown). Overall, female somatosensory and motor cortices increased more in volume 181 \nbetween adolescence and adulthood than male (Supplemental figure 6), in line 182 \nwith the observed smaller differences in these areas in adulthood compared to 183 \nadolescence. 184 \nTo evaluate whether our study was sufficiently powered to detect genotype effects 185 \non regional brain volume, we analysed intra-group variability using a region-of-186 \ninterest (ROI)-based approach. The coefficient of variation for grey matter ROI 187 \nvolume showed no differences between genotypes in vivo in adolescence (Figure 2e) 188 \nand adulthood (Figure 2f), nor ex vivo in adulthood (Figure 2g). Variability was 189 \nlow, ranging from 4.2–5.5% in vivo and 4.2% ex vivo, while hippocampal variability 190 \nremained below the neuroimaging gold standard of 5%, at 3.7–3.8% in vivo and 4.2% 191 \nex vivo (Lerch et al. 2012). These values suggest that our study was well-positioned to 192 \ndetect a genotype effect if one had been present.  193 \nC3ar1-deficiency does not influence fractional anisotropy 194 \nTo assess the potential impact of C3ar1 deficiency on white matter integrity, we 195 \nmeasured fractional anisotropy (FA) as an indirect marker of axonal microstructure 196 \n(Figure 3). Using voxel-wise analysis, we observed sub-threshold (0.05 < p < 0.5) 197 \ndecreases in FA in C3ar1-deficient mice compared to wild-types (Figure 3a). In 198 \nadolescence, these sub-threshold reductions were noted in the corpus callosum (CC) 199 \nand optic tract (OPT, Figure 3a, upper panel), while in adulthood, they were 200 \nprimarily localised to the corpus callosum (Figure 3a, bottom panel). These sub-201 \nthreshold differences were not observed in ex vivo scans. Female mice had sub-202 \nthreshold decreases in FA in vivo at PND90 in the internal capsule and in the third 203 \nventricle (Supplemental figure 7) but this was again no longer observed ex vivo. We 204 \ndid not observe any sex-by-genotype interactions in our FA datasets (not shown). 205 \nIn our ROI-based FA analysis, we focused on predefined white matter regions, 206 \nhypothesising that FA alterations would primarily occur in areas containing axonal 207 \ntracts due to increased microglial developmental phagocytosis in C3ar1-deficient 208 \nmice (Gnanaguru et al. 2023; Falangola et al. 2023; Chan et al. 2024). In this analysis 209 \nalso, no significant genotype effects were observed in adolescence (Figure 3b-i) or 210 \nadulthood (Figure 3b-ii). There were no genotype-dependent differences in the 211 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n7 \nextent of change but FA increased in all white matter areas except the olfactory tract 212 \nbetween adulthood and adolescence (Figure 3b-iii), which is in line with the early 213 \nmaturation of the olfaction system in mice (Gretenkord et al. 2019).  214 \nC3ar1-deficiency does not influence global functional brain connectivity 215 \nTo evaluate global brain FC, which has been reported to be altered in other microglia 216 \ngene knockouts (Filipello et al. 2018; Deivasigamani et al. 2023; Zhan et al. 2014), we 217 \nestimated FC through analysis of BOLD signal time-courses with the assumption 218 \nthat  the magnitude of correlation between these time-courses corresponds to the 219 \nstrength of FC. We calculated pairwise correlation coefficients between 36 (18 per 220 \nhemisphere) grey matter regions. Non-zero correlation values were averaged across 221 \nproportional progressively decreasing sparsity thresholds to preserve biologically 222 \nmeaningful weak correlations while minimising noise (Bassett et al. 2009). No 223 \ngenotype-dependent differences in global FC were observed in adolescence (Figure 224 \n4a), adulthood (Figure 4d) or in the change over time (Figure 4g). 225 \nWe applied graph theory to characterise global brain network connectivity across all 226 \nregions, focusing on two key metrics: clustering coefficient (Figure 4b, e and h) and 227 \nglobal efficiency (Figure 4c, f and i), and applying the same strategy for proportional 228 \nthresholding as for FC. The clustering coefficient reflects the tendency of nodes to 229 \nform connected local clusters, with higher values indicating the presence of more 230 \nhighly interconnected subnetworks within the brain (Bullmore and Sporns 2009). 231 \nGlobal efficiency refers to the average of shortest paths linking nodes in a network 232 \nand can be a proxy of information integration abilities since it decreases with 233 \ncognitive deficit (Berlot et al. 2016; Hawkins et al. 2020) and increases with 234 \ndevelopment (Jiang et al. 2023). Clustering coefficient and global efficiency appeared 235 \nhigher in C3ar1-deficient animals at both time-points, but this was not significant 236 \n(Supplemental table 1). Further, we found no effects of genotype across global 237 \nconnectivity measures when we treated males and females as separate groups (Table 238 \n1, for p values, see Supplemental table 2).   239 \nFC, global efficiency and clustering coefficient appeared to increase with brain 240 \nmaturation when groups were combined, but this was only significant in the case of 241 \nglobal efficiency when graph sparsity was lower, that is, when 30-50% of the 242 \nstrongest connections were retained, with small effect sizes observed (Cohen’s d = 243 \n0.30-0.32). These findings indicate that while C3ar1 deficiency did not result in 244 \nalterations in global network properties under our experimental conditions, 245 \ndevelopmental changes in global efficiency were detectable. 246 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n8 \nC3ar1-deficiency has no detectable effect on functional brain networks 247 \nSince C3ar1-deficient mice did not show statistically significant changes in global 248 \nconnectivity metrics, we next examined specific networks after conducting t-tests for 249 \neach FC pair between genotypes. For this we used two hypothesis-free approaches; 250 \nfalse discovery rate (FDR) correction to identify strongly differing edges between 251 \ngenotypes, and network-based statistics (NBS) (Zalesky, Fornito, and Bullmore 252 \n2010) to detect network-level differences while controlling for family-wise error rate. 253 \nThresholding the resulting t-value matrices at |t| ≥ 2 for adulthood and adolescence, 254 \nand their changes over time (adulthood-adolescence; Figure 5a), no edges remained 255 \nsignificant after FDR correction, indicating an absence of strongly differing 256 \nfunctional connections between genotypes. Then, using NBS, which tests the 257 \nlikelihood of detecting a connected component of a specific size, we determined that 258 \nthe network sizes observed after thresholding at |t| ≥ 2 (n = 68 at PND30, n = 15 at 259 \nPND90, and n = 1 for change) could occur by chance in these datasets. 260 \nGiven that neither the hypothesis-free approaches, the FDR correction and NBS, 261 \ndetected genotype-related differences, we focused on anxiety-related regions. This 262 \ndecision was motivated by prior evidence of anxiety-like behaviour in C3ar1-263 \ndeficient animals (Westacott et al. 2022) and the inclusion of anxiety-specific tests in 264 \nour behavioural battery. We calculated the mean absolute connectivity strength of 20 265 \na priori selected anxiety-related regions. We did not detect an effect of genotype 266 \n(Figure 5b), sex (Table 2) or a sex-by-genotype interaction (Supplemental table 267 \n3).  268 \nNext, we examined FC and global efficiency within resting state networks that have 269 \nbeen linked to anxiety and emotionality in humans (Coutinho et al. 2016; Geng et al. 270 \n2015; Schimmelpfennig et al. 2023) and that have been observed in mice (J. 271 \nGrandjean et al. 2020; Sforazzini et al. 2014; Hikishima et al. 2023), the default mode 272 \nnetwork (DMN) and the salience network (SAL; Figure 5c). Additionally, we 273 \nanalysed network connectivity within the predefined anxiety-related regions whose 274 \noverall connectivity was summarised in Figure 5b. Consistent with our earlier 275 \nfindings, we did not observe genotype-dependent differences in these networks at 276 \neither time-point. 277 \nThe only significant genotype-related differences were seen in voxel-wise seed-to-278 \nbrain connectivity analysis. Here, we show two anxiety-related regions, the left 279 \nprefrontal cortex and left ventral hippocampus (Figure 5d; Supplemental figure 8 280 \nfor the right hemisphere) where we observed weak but significant, widespread higher 281 \nconnectivity in C3ar1-deficient animals compared to controls at both time-points. 282 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n9 \nSeed-based analysis further revealed higher FC across the brain when other anxiety-283 \nrelated regions were used as seeds, mostly in adolescence (Supplemental figure 9). 284 \nHowever, this effect was not confined to anxiety-related regions or specific networks 285 \n(Supplemental figure 10). Although the increase of FC in C3ar1-deficient mice 286 \nappears widespread, this is not robust, since our seed-based analysis controls for 287 \nvoxel-wise comparisons within subjects but does not correct for testing multiple 288 \nseeds. 289 \nC3ar1-deficient mice do not have any discernible behavioural phenotypes 290 \nAnother way of assessing functional consequences of genetic manipulations with 291 \nexpected neurodevelopmental sequelae is behavioural testing (Crawley 2007). In this 292 \nstudy, we aimed to evaluate the impact of C3ar1 deficiency on anxiety-like 293 \nbehaviour, locomotion and recognition memory, which were chosen based on prior 294 \nreports of being altered in C3ar1-deficient mice. We used a battery of well-295 \nestablished behavioural tests, including the OF test and EPM for anxiety-like 296 \nbehaviour and locomotion, as well as NOR for recognition memory. We also tested 297 \nPPI which is a sensorimotor reflex consistently found to be attenuated in 298 \nschizophrenia (Ludewig, Geyer, and Vollenweider 2003; Mena et al. 2016) but has 299 \nnot been tested in C3ar1-deficient mice. 300 \nWe performed behavioural testing in both cohorts described in the structural MRI 301 \nresults sections. In cohort 1 (Figure 2a), behavioural testing (EPM, then OF) 302 \noccurred shortly before adulthood MRI scan. Behaviour of cohort 2 was also tested 303 \nin adulthood (OF, NOR, EPM, PPI, in that order) followed by MRI, but they did 304 \nnot undergo an MRI scan in adolescence. Behavioural data were analysed separately 305 \nfor each cohort to account for differences in study design (see also Methods). 306 \nTo assess anxiety-like behaviour and locomotion (Figure 6a–b, Supplemental 307 \ntable 4 for description of anxiety-like metric selection), we calculated z-scores for 308 \nC3ar1-deficient mice relative to wild-type controls (Table 3 for untransformed 309 \nmeans). In cohort 1, no significant genotype effects were detected on anxiety-like 310 \nbehaviour or locomotion at either time-point (Supplemental table 5). To address 311 \npotential confounding from co-housing littermate mutants and wild-types (Kalbassi 312 \net al. 2017), we also examined whether the number of C3ar1-deficient cage-mates 313 \ninfluenced wild-type behaviour (Supplemental figure 11) but found no consistent 314 \npattern or evidence of systematic anxiety-like effects in wild-types.  315 \nIn cohort 2, most anxiety-related and locomotion measures showed no genotype 316 \ndifferences (Supplemental table 6), except for reduced distance travelled in the 317 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n10 \ncentre 70% of the OF arena by C3ar1-deficient mice (uncorrected two-sample t-test p 318 \n< 0.01, Cohen's d = 0.92, Supplemental table 6). However, no genotype effect on 319 \ndistance was detected in the core 30% of the OF arena or in the centre and core of the 320 \nOF arena in cohort 1.  321 \nThe sample size (n = 38) of cohort 2 was insufficient to test sex-by-genotype 322 \ninteractions, limiting statistical power to detect only large effects (Cohen’s f = 0.5 at 323 \nalpha = 0.05 and 80% power), which were clearly not observed across behavioural 324 \nmeasures. No sex-by-genotype interactions were observed in cohort 1 325 \n(Supplemental table 5). 326 \nAdditionally, PPI testing in cohort 2 exclusively showed no effect of C3ar1 deletion, 327 \nthough PPI increased with prepulse intensity as expected (Figure 6c). NOR testing 328 \n(also exclusively in cohort 2) revealed no genotype differences, with all groups 329 \ndemonstrating successful learning based on recognition indices significantly above 330 \nchance levels (Figure 6d). 331 \nOverall, these findings suggest that C3ar1 deficiency does not result in robust 332 \nanxiety-like or hyperactive phenotypes, nor deficits in recognition memory or PPI. 333 \nDiscussion 334 \nHere we  used a longitudinal neuroimaging and behavioural testing to investigate the 335 \nimpacts of C3ar1 deletion on brain structure and function across early life 336 \ndevelopment in mice. We found no robust evidence that C3ar1 deficiency affects 337 \ntotal or regional brain volume, white matter FA, global FC, global efficiency, 338 \nclustering, or specific functional networks although we were able to detect previously 339 \nreported sexual dimorphisms and brain maturation effects unrelated to C3ar1 status 340 \nin these datasets.  341 \nWe also found no evidence of changed behavioural outcomes in male or female 342 \nC3ar1-deficient mice in adulthood. These results raise questions regarding the source 343 \nof discrepancies between these data, our previous behavioural work (Westacott et al. 344 \n2022; Westacott et al. 2021), and that of others (Crider et al. 2018; Coulthard et al. 345 \n2018; Pozo-Rodrigálvarez et al. 2021; Sun et al. 2024), which may be attributed to 346 \nour use of littermate controls, as will be elaborated upon in the following sections. 347 \nOverall, the lack of a behavioural phenotype combined with the paucity of genotype 348 \neffects in brain structure across time-points suggest the role of C3aR1 in 349 \nneurodevelopment and behaviour might not be as significant as originally thought, at 350 \nleast in the absence of exacerbating stress or immune triggers.  351 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n11 \nAbsence of C3ar1-dependent effects on brain structure  352 \nOur morphometric analysis showed that total and regional brain volume was not 353 \nchanged between C3ar1-deficient and wild-type mice. We were, however, able to 354 \ndetect sex-specific neurodevelopmental changes that have previously been reported 355 \nin rodents been such as brain growth occurring later in females and minimal total 356 \nand regional brain volume differences in adulthood (Qiu et al. 2018; Guma et al. 357 \n2024; Gorski et al. 1978; Hines, Allen, and Gorski 1992; de Courten-Myers 1999), 358 \nindicating that our method was sensitive enough to detect these. Given the low 359 \nvariability in our sample and our ability to detect these known sex differences, we 360 \nbelieve our study was well-positioned to identify potential genotype effects, had they 361 \nbeen present. 362 \nAbsence of C3ar1-dependent effects on white matter fractional anisotropy 363 \nC3ar1 is predominantly expressed by microglia in the brain (Quell et al. 2017; Tasic 364 \net al. 2018), where it influences their reactivity and phagocytosis (Vasek et al. 2016; 365 \nGedam et al. 2023; Zheng et al. 2021; Lian et al. 2015; Gnanaguru et al. 2023), and 366 \ndevelopmental microglial characteristics are associated with FA changes in mice and 367 \nhumans (Falangola et al. 2023; Chan et al. 2024). In this study, we found no C3ar1-368 \ndependent changes in white matter while we were able to detect brain maturation 369 \neffects on FA regardless of genotype, which increases during white matter 370 \ndevelopment between adolescence and adulthood in both humans and mice 371 \n(Brouwer et al. 2012; Hagmann et al. 2010; Reynolds et al. 2019; Piekarski et al. 372 \n2023; Chahboune et al. 2007). Lack of a phenotype after the deletion of a 373 \npredominantly microglial gene may not be surprising in the light of recent report 374 \nthat suggest mice that have never had any microglia (achieved by deleting the super 375 \nenhancer for macrophage colony-stimulating factor receptor, Csf1r) do not have 376 \novert neurodevelopmental phenotypes (Rojo et al. 2019), and only show severe 377 \nvulnerability on a neuroinflammatory 5X-FAD background which is used to model 378 \nAlzheimer’s disease (Kiani Shabestari et al. 2022). It could therefore also be that 379 \nC3ar1-deficient neurodevelopmental phenotypes can only be detected in the context 380 \nof neuroinflammation. 381 \nWidespread, weak seed-to-brain functional connectivity increase in C3ar1-382 \ndeficient mice in adolescence 383 \nWe did not detect genotype-dependent global FC changes nor changes to clustering 384 \ncoefficient and global efficiency at either time-point, but we did observe a genotype-385 \nindependent developmental increase in global efficiency with time which has been 386 \nreported before (Jiang et al. 2023; Hagmann et al. 2010; Koenis et al. 2018), 387 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n12 \nparticularly at lower sparsities (Cai, Dong, and Niu 2018), which similarly to out 388 \nstructural results indicates that our method was sensitive enough to pick up 389 \ndevelopmentally relevant effects. 390 \nSimilarly, we did not observe any genotype-dependent changes to specific brain 391 \nnetworks, including anxiety networks (Figure 5), which we previously hypothesised 392 \nto be affected based on our previous behavioural results (Westacott et al. 2022). 393 \nHowever, our anxiety network connectivity findings are internally consistent with 394 \nthe absence of an anxiety-like phenotype in the cohorts tested in this study. 395 \nCombined with a recent null report for an anxiety-like effect in the EPM in C3ar1-396 \ndeficient animals by another group (Sun et al. 2024), our data indicate that the effect 397 \nof C3ar1 on brain and behaviour correlates of anxiety-like behaviour must be 398 \ncontext-dependent and/or minimal.  399 \nAlthough we did not detect network-specific effects, C3ar1-deficient mice exhibited 400 \nwidespread increases in resting-state FC in voxel-wise seed-based analyses during 401 \nadolescence across nearly all seeds examined, with a weaker effect persisting into 402 \nadulthood (Figure 5 and Supplemental figures 8-10). These subtle increases in 403 \nconnectivity may reflect developmental alterations in circuit properties caused by the 404 \nabsence of C3aR1. For instance, similar findings have been reported for other 405 \nmicroglial receptors—such as TREM2, CX3CR1, and CR3—where genetic 406 \ndeficiency altered adult brain FC (Filipello et al. 2018; Zhan et al. 2014; 407 \nDeivasigamani et al. 2023). In these reports, TREM2 or CX3CR1 knockouts led to 408 \nimpaired synapse elimination alongside decreased FC, accompanied by social 409 \nbehaviour deficits and increased repetitive behaviour (Filipello et al. 2018; Zhan et al. 410 \n2014). In contrast, CR3 knockout mice exhibited no deficits in synapse or axon 411 \nrefinement but showed reduced phagocytosis of perinatal cortical neurons and 412 \nhigher cortical FC (Deivasigamani et al. 2023). It is indeed possible that similarly to 413 \nCR3, C3aR1 is also involved in perinatal neuron phagocytosis so that its deletion 414 \nresults in weak brain-wide increase in FC, particularly as C3ar1-deficiency leads to a 415 \ndeficit of developmental astrocyte phagocytosis in the retina (Gnanaguru et al. 2023). 416 \nFor now, we urge caution with this interpretation for several reasons. First, these 417 \nconnectivity changes were not accompanied by any structural or microstructural 418 \nalteration across the brain. Second, the voxel-wise seed-based analysis is the least 419 \nrobust presented here, as the observed changes were largely transient, weak, not 420 \nconfined to a specific subnetwork, and not corrected for multiple comparisons across 421 \nseeds. Future studies investigating FC in C3ar1-deficient mice should incorporate 422 \nrepeat measures from the same individual during adolescence to improve robustness 423 \nas well as measuring spontaneous and miniature excitatory synaptic current (sEPSC 424 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n13 \nand mEPSC respectively) in brain slices, although it is currently still unclear which 425 \nbrain regions should be targeted currently with the latter approach. 426 \nBehavioural outcomes were unaffected by C3ar1 deficiency 427 \nNeither anxiety-like behaviours nor locomotor activity showed significant 428 \ndifferences between genotypes across multiple testing paradigms, apart from a 429 \nspecific decrease in ambulation of C3ar1-deficient mice in the aversive central zone 430 \ncovering 70% of the open field, which in the absence of a locomotor phenotype 431 \ncould index anxiety-like processes. This latter change, however, was not reproducible 432 \nacross study cohorts and was no longer observed in the same study in the core of the 433 \nopen field, covering 30% of the arena. Tests for recognition memory and 434 \nsensorimotor gating, NOR and PPI tests, also yielded null results.  435 \nDifferences between our current findings and previous reports of anxiety-like 436 \nbehaviours (Westacott et al. 2022), hyperactivity (Pozo-Rodrigálvarez et al. 2021) 437 \nand cognitive deficits (Coulthard et al. 2018) may stem from variations in 438 \nexperimental design, particularly environmental factors such as lighting and testing 439 \ntime. Our behavioural experiments were conducted during the active phase (7 PM–440 \n11 PM, lights off) to minimise stress from sleep deprivation, which is known to 441 \nupregulate complement in the brain (Tillmon et al. 2024; Crider et al. 2018; Li et al. 442 \n2023; Tripathi et al. 2021). It is possible that previous studies, which tested animals 443 \nduring their inactive phase, introduced a stress-dependent “second hit” to C3ar1-444 \ndeficiency. However, this seems unlikely since inhibiting complement, including 445 \nC3ar1, has been shown to improve resilience to chronic stress (Li et al. 2023; Crider 446 \net al. 2018; Tripathi et al. 2021; Madeshiya et al. 2022; Tillmon et al. 2024). 447 \nUnlike previous studies of C3ar1-deficient mice, our study employed littermate 448 \ncontrol animals, meaning that wild-type mice were co-housed with their C3ar1-449 \ndeficient siblings. This approach minimises potential environmental differences 450 \nbetween groups, which is especially important in neurobehavioral studies where 451 \nsubtle environmental factors can significantly affect outcomes. While the use of 452 \nlittermate controls is widely regarded as best practice in such contexts (Holmdahl and 453 \nMalissen 2012; Valiquette et al. 2023; Bailey, Rustay, and Crawley 2006), it is not 454 \nwithout its own potential confounds. For instance, abnormal behaviours exhibited 455 \nby genetically altered animals can influence the behaviour of co-housed wild-type 456 \nmice, particularly when the phenotype is pronounced, such as in cases of increased 457 \naggression (Kalbassi et al. 2017). However, as aggression has not been reported in 458 \nC3ar1-deficient mice, and their previously described behavioural phenotype was not 459 \nnotably severe, we considered such an influence unlikely in our study. Nevertheless, 460 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n14 \nwe systematically examined whether the behaviour of wild-type mice correlated with 461 \nthe number of C3ar1-deficient cage-mates and found no significant association 462 \n(Supplemental figure 11).  463 \nPrenatal environment and maternal care significantly influence phenotypic 464 \ndevelopment (McCarty 2017) and the complement system plays an essential role in 465 \nnormal pregnancy and parturition (Girardi et al. 2020). A notable aspect of using a 466 \nlittermate design is that litters of mixed genotype are born from exclusively 467 \nheterozygous parents, whereas in most prior studies of C3ar1-deficiency both the 468 \nmother and offspring were homozygous knockouts. This raises the possibility that 469 \nbehavioural effects in C3ar1-deficient mice may result from altered intrauterine 470 \nenvironments or maternal care by C3ar1-deficient mothers. In line with this, our 471 \ninbred homozygous C3ar1 knockout colony showed increased pre-weaning deaths 472 \nand maternal cannibalism (unpublished data). If this is indeed the reason for 473 \npreviously observed adult behavioural phenotypes, two scenarios should be 474 \nconsidered. In the first scenario, the adult phenotypes are specific and C3ar1-475 \ndeficient offspring are uniquely sensitive to C3aR1-dependent in utero conditions or 476 \nmaternal care deficits. In the second, more likely scenario, however,  the adult 477 \nphenotypes are not specific to C3ar1-deficiency (i.e., wild-type mice would have 478 \nbeen similarly affected). If the latter is true, we were able to avoid measuring these 479 \nnon-specific effects by using a littermate design. 480 \nNull results in our global knockout model could also be due to genetic 481 \ncompensation, where genes with similar function compensate for the loss of a 482 \nmutated gene (El-Brolosy and Stainier 2017). While the mechanism for this is not yet 483 \nresolved, mutated RNA degradation can trigger this process (El-Brolosy et al. 2019). 484 \nWe confirmed that the C3ar1tm1Cge mutant lacks detectable C3ar1 RNA, suggesting 485 \nthe transcript is either not produced or undergoes nonsense-mediated decay, 486 \npotentially activating compensation. Confirming this would require longitudinal 487 \nsingle-cell RNA sequencing in relevant brain regions, which is beyond the scope of 488 \nthis study. 489 \nGenetic background of transgenic models is another factor that varies between 490 \nlaboratories and is known to affect phenotypic expression. For example, 491 \nheterozygous knockout of the autism-associated gene CHD8 has varying effects in 33 492 \n(Tabbaa, Knoll, and Levitt 2023) substrains, mirroring heterogeneity observed in 493 \nhuman CHD8-haploinsufficiency. While that study identified significant variability 494 \nacross substrains, these profiles consistently differed from that of wild-type littermate 495 \ncontrols. In contrast, our study found no robust genotype-dependent differences in 496 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n15 \nthe brain globally and across measures or in behaviour, apart from a single internally 497 \nnon-reproducible behavioural outcome measure, meaning that the effect of C3aR1 498 \non brain development would have to be entirely dependent on modifying genes or 499 \nenvironmental factors. 500 \nFinally, genetic drift could have precluded reproducibility of previously reported 501 \nphenotypes. Mice accumulate spontaneous mutations that rapidly reach 502 \nhomozygosity within small colonies speeding up drift, a phenomenon recognised as 503 \nearly as the 1980s (Fitch and Atchley 1985). So far, our current work is the first to 504 \nstudy C3ar1 deficiency using a littermate design, which minimises the number of 505 \ngenetic loci that differ between the mutant and control mice, all of which could  506 \ninfluence measured phenotypes. Fully understanding the genetic causes of results 507 \npreviously observed by our laboratory and others would require systematic 508 \noutcrossing, whole genome sequencing, and linkage analysis. Given that hundreds of 509 \ngenes likely differ between mutant and control lines last crossed during mutation 510 \ncreation or before, the value of such work is questionable. 511 \nFuture directions 512 \nAside from the role of C3aR1 in brain structure and function, many aspects of the 513 \nbasic biology of C3aR1 remain unclear, including its expression pattern in the 514 \nhealthy brain, with little information available on the cell types, contexts, or time-515 \npoints where it is expressed. It is also unclear which G-proteins C3aR1 signals 516 \nthrough in different brain cell types. Future studies could integrate toolkits like 517 \nTRUPATH, a suite of Gαβγ biosensors for analysing G-protein coupling 518 \npreferences (Olsen et al. 2020), with transcriptomics to address these questions. This 519 \napproach would be particularly valuable, as it could enable the use of chemogenetics 520 \nto activate the same G-protein as C3aR1 in specific cell types and contexts to study 521 \nits function.   522 \nMost importantly, we propose that C3ar1-deficiency in development should be 523 \nstudied after an immune challenge. Unlike humans, laboratory mice live in an 524 \nimmune-privileged environment,  hence investigating the consequences of a 525 \ncombined immune insult with the genetic deficit would be relevant; or better yet, in 526 \nthe context of C4A overexpression, which is a known genetic risk factor for 527 \nschizophrenia (Sekar et al. 2016) and is known to impact white matter integrity 528 \n(Caseras et al. 2024). 529 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n16 \nLimitations 530 \nIn this study, we provided high-level global data on C3ar1-deficient mice. While our 531 \nstudy had notable strengths, including the use of a littermate design and the 532 \ninclusion of female animals, it is not an exhaustive characterisation of the mutant. 533 \nWhile we measured brain region volumes, we cannot definitively address more 534 \nreductionist questions, such as cellular composition which would have required 535 \nantibody staining and microscopy. Similarly, for axonal integrity analysis for which 536 \nwe used the proxy of dMRI-derived FA, the gold standard would have been electron 537 \nmicroscopy. However, a more granular approach would have necessitated a trade-off 538 \nwith throughput–something that is hard to justify in the absence of strong 539 \nhypotheses regarding the developmental expression pattern of C3ar1. Additionally, 540 \nour neuroimaging functional analysis relied on a specific parcellation of the brain 541 \nconsisting of 36 regions, and different parcellations could yield varying results 542 \n(Thirion et al. 2014; Zalesky et al. 2010). We encourage others exploring our datasets 543 \nto therefore experiment with alternative parcellations.  544 \nConclusion 545 \nContrary to expectations, we found no evidence for C3ar1-dependent effects across 546 \nimaging measures at either time-point nor could we replicate previously seen 547 \nbehavioural phenotypes. The absence of detectable phenotypes suggests that C3aR1 548 \nplays a minimal role in brain development, at least in the absence of an immune 549 \ntrigger. These findings challenge prior assumptions about its neurodevelopmental 550 \nsignificance and necessitate further investigation into C3aR1 function on sensitized 551 \nbackgrounds, such as those involving immune activation.  552 \nMethods and materials 553 \nAnimals 554 \nAll animal procedures complied with the UK Animals and Scientific Procedures Act 555 \n1986 and were approved by the local ethical committee at King’s College London 556 \n(KCL). Homozygous C3ar1-/- mice were generated by homologous recombination in 557 \nembryonic stem cells and kindly provided by Dr. Bao Lu and Prof. Craig Gerard 558 \n(Harvard Medical School, Boston, MA) (Humbles et al. 2000). These mice were 559 \nsubsequently backcrossed onto the C57BL/6J strain for at least 12 generations and 560 \nmaintained on a C57BL/6J background in Professor Wuding Zhou’s laboratory at 561 \nKCL. For this study, cryopreserved stocks were rederived at KCL and crossed to 562 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n17 \nC57BL/6J mice purchased from Charles River to refresh the genetic background 563 \nfollowing Jackson’s Laboratories line refreshing protocol. 564 \nExperimental animals (C3ar1-/- and C3ar1+/+ littermates) were generated through 565 \nheterozygote incrosses and resulting genotypes followed Mendelian ratios. The 566 \nheterozygote breeders were generated by outcrossing heterozygous mice to bought 567 \nwild-type Charles River C57BL/6Js. The breeders used to produce the experimental 568 \nanimals were derived either from the first, second or third of these outcrosses. Sibling 569 \ncrosses were not conducted and parental age was between 2-4 months to minimise 570 \ngenetic drift.  571 \nExperimental mice were housed in individually ventilated cages under controlled 572 \ntemperature (20–25°C), humidity (50–60%), and a 12-hour light-dark cycle (lights 573 \non at 7:00 AM, lights off at 7:00 PM). Environmental enrichment included nesting 574 \nmaterials, tunnels, and chew sticks. Mice had ad libitum access to irradiated rodent 575 \nchow and autoclaved water. Animals were group-housed (2–4 mice per cage), with 576 \nmales and females housed separately after weaning (PND21±2). Genotyping was 577 \nconducted on ear biopsy DNA by Transnetyx using probes targeting the neomycin 578 \ncassette for the mutant allele and intron 1 for the wild-type allele. No mismatches 579 \nwere identified through double-genotyping 20% of the study cohorts. 580 \nValidation of mutation 581 \nBone marrow-derived macrophages were obtained from tibias and femurs of eight 582 \nthree-month-old mice (n = 4 C3ar1+/+, n = 4 C3ar1-/-) following standard protocols. 583 \nBone marrow was filtered through a 40 µm mesh, centrifuged at 450 x g for 5 584 \nminutes at 4°C, and treated with NH4CL haemolysis buffer (NH4CL 0.15M, 585 \nK2HCO3 0.01M, EDTA 0.0001M). After a second centrifugation under the same 586 \nconditions, cells were washed with PBS and resuspended in Gibco RPMI 1640 587 \nMedium (Thermo Fisher, #21875-034) supplemented with 50 ng/ml recombinant 588 \nmouse macrophage colony-stimulating factor (M-CSF, R&D Systems, #416-ML-589 \n010/CF), 1% penicillin, 1% streptomycin, and 10% heat-inactivated fetal bovine 590 \nserum (Sigma, #F9665-50ml). 591 \nCells were seeded at 1x106 cells/ml in six-well plates (six wells per animal) and 592 \nincubated at 37°C with 5% CO2 for 72 hours. On day three, fresh medium was 593 \nreplaced, and 50 ng/ml recombinant mouse IL4 (R&D Systems, #404-ML-594 \n010/CF) was added to half of the wells to skew them towards M2 phenotype. 595 \nIncubation continued for an additional 48-72 hours, depending on cell confluence. 596 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n18 \nRNA was extracted using the ReliaPrep™ miRNA Cell and Tissue Miniprep 597 \nSystem (Promega, #Z6211) according to the manufacturer’s instructions. RNA 598 \nconcentration was determined with a NanoDrop spectrophotometer (Thermo 599 \nScientific, NanoDrop 2000), yielding values between 25.5 ng/µl and 227.5 ng/µl. 600 \nReverse transcription PCR (RT-PCR) was performed using the LunaScript® RT 601 \nSuperMix Kit (NEB, #E3010L), again following the manufacturer’s instructions. 602 \nDepending on RNA yield, either 100 or 500 ng of RNA was used per reaction.  603 \nFor cDNA PCR and gel electrophoresis, we used GoTaq® G2 Master Mix (NEB, 604 \n#M7822). We amplified a 79 basepair (bp) fragment in the deleted region alongside a 605 \n372 bp region in the C3ar1 cDNA that was located outside the deleted region and 606 \ndownstream of an alternative start codon identified through Benchling. Gapdh 607 \nprimers were included in each reaction to confirm amplification efficiency. PCR 608 \nproducts were analysed on a 2% agarose gel stained with GelRed. 609 \nFor qPCR, we amplified the previously mentioned 79 basepair (bp) fragment in the 610 \ndeleted region. Samples were analysed in triplicate on 96-well plates (Applied 611 \nBiosystems, #4346906) with Luna® Universal qPCR Master Mix (NEB, #M3003L) 612 \nand readings were obtained using an Applied Biosystems StepOnePlus plate reader. 613 \nThe amplification data were processed with the ΔΔCt method, normalising against 614 \nthe housekeeping gene, Hypoxanthine phosphoribosyltransferase 1 (Hprt). 615 \nGenotypes were arranged alternately across the plate to minimise bias. To verify M2-616 \nlike polarisation, the expression of M2-specific markers Arginase 1 (Arg1) and the 617 \nmannose receptor, Cluster of differentiation 206 (Cd206) were assessed. 618 \nTable 4 | Primer sequences 619 \nPrimer \nname \nForward Reverse Application \nC3ar1 AGGATTTGTTGGTG\nGCTCGCA \nCTCCATGGCTCAGTC\nAAGCACA \nPCR and qPCR deleted \nregion \nC3ar1  GCTTCCTGGTGCCG\nTTTTTC \nAGTTGGTAGAGTGCG\nTGAGC \nPCR of putative alternative \ntranscript (3’ end of exon 2 \nafter alternative start \ncodon) \nHprt AGTCCCAGCGTCGT\nGATTAGCG \nTTGAGCACACAGAGG\nGCCACAA \nqPCR housekeeping gene \nArg1 GGCTTGCGAGACGT\nAGACCC \nGTCCAGCCCGTCGAC\nATCAAA \nqPCR verification of M2 \npolarisation \nCd206 CCGGAGGGTGCAGA\nCAAAGG \nTCGTCCACAGTCCAC\nCGAAAC \nqPCR verification of M2 \npolarisation \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n19 \nGapdh CCTAGACAAAATGG\nTGAA \nGACTCCACGACATAC\nTCAGC \nPCR amplification \nefficiency control \n 620 \nTable 5 | PCR reaction 621 \nAmount (µl) Reagent \n12.5 GoTaq® G2 Master Mix (Green) \n1 C3ar1 forward primer, 10 mM \n1 C3ar1 reverse primer, 10 mM \n1 Gapdh forward primer, 10 mM \n1 Gapdh reverse primer, 10 mM \n7.5 H2O \n1 Template DNA \nFinal volume: 25 µl   \n  622 \nTable 6 | Cycling conditions 623 \nStep Time Temperature \n1. Initial denaturation 5 min 94°C \n2. Denaturation 30 sec 94°C \n3. Annealing 30 sec 60°C \n4. Extension 1 min 72°C \n5. Repeat steps 2-4 Repeat 35 x \n6. Final extension 5 min 72°C \nStudy design 624 \nThis study used two separate cohorts of male and female C3ar1-deficient and 625 \nlittermate wild-type mice. The main, longitudinal MRI cohort termed cohort 1, had 626 \nin vivo MRI performed in adolescence (range 27-31 days) and adulthood (range 81-627 \n92 days), and the adulthood MRI scan was preceded by OF and EPM tests. The 628 \nadolescence time-point was chosen because mice reach puberty approximately 629 \nbetween PND24-34 (Semaan and Kauffman 2015; Brust, Schindler, and 630 \nLewejohann 2015; Pintér et al. 2007). Ex vivo imaging was conducted in cohort 2’s 631 \nperfusion-fixed brains after the final adulthood scan. 632 \nFor cohort 2, behavioural testing was conducted similarly to cohort 1 in adulthood 633 \nonly (range 74-110), and consisted of OF, NOR, EPM and PPI followed by in vivo 634 \nstructural and diffusion MRI (note that MRI was not conducted in adolescence in 635 \nthis cohort). For both cohorts, behavioural testing was conducted 2-7 days before the 636 \nadulthood scan. 637 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n20 \nSample size of cohort 1 was statistically powered to detect medium effect sizes in 638 \nregional volume using TBM across four groups (males and females analysed 639 \nseparately), with a minimum sample size of n = 15 per group based on previously 640 \nobserved variance with this method by our group (Serrano et al. 2023). For cohort 2 , 641 \nstatistical power was calculated to detect medium effect sizes in regional TBM with 642 \nsexes combined, using a sample size of n = 16 per group. 643 \nIn vivo MRI 644 \nTwo to three days after behavioural testing, mice were imaged using a Bruker 645 \nBioSpec 9.4 T scanner with an 86-mm volume resonator for transmission and a 4-646 \nchannel surface array coil. Anaesthesia was induced with 4% isoflurane in medical air 647 \n(1 L/min) and oxygen (0.4 L/min), maintained at 2% but adjusted based on 648 \nrespiration rates. For functional BOLD MRI in cohort 1, we used a medetomidine 649 \nand isoflurane anaesthesia optimised for mouse fMRI (Joanes Grandjean et al. 2014). 650 \nThis consisted of a subcutaneous medetomidine bolus (0.05 mg/kg) followed ten 651 \nminutes later by its continuous infusion (0.1 mg/kg/h), with isoflurane levels 652 \ngradually reduced to 0.45-0.65% over 15 minutes from the start of the infusion after 653 \nBOLD-weighted fMRI was conducted after the structural scans which took a further 654 \n45-60 minutes after reducing isoflurane level. The respiration rate was monitored 655 \nwith a pressure sensor, and temperature was monitored with a rectal thermometer 656 \nand maintained at 36-37°C using a water circulation system. 657 \nEx vivo MRI 658 \nFollowing the adulthood in vivo scan in cohort 1, mice were perfused transcardially 659 \nwith 20 mL phosphate-buffered saline (PBS) followed by 4% paraformaldehyde 660 \n(PFA). Heads were stored in PFA for 48 hours, then transferred to PBS containing 661 \n0.05% sodium azide and 2 mM gadolinium-based contrast agent (Gd-DO3A-butrol). 662 \nBrains were scanned in cranio in groups of four using a custom-made holder 663 \nimmersed in perfluoropolyether (Galden®, Solvay).  664 \nMRI acquisition parameters 665 \nFor in vivo imaging, we first acquired an Actual Flip Angle Imaging (AFI) sequence 666 \nfor B1 mapping. Then we acquired three types of 3D multi-gradient-echo images 667 \nthat were used for creating study-specific templates: magnetization-transfer weighted 668 \n(MTw), proton-density weighted (PDw), and T1-weighted (T1w). Subsequently, 669 \nT2-weighted Rapid Acquisition with Relaxation Enhancement (RARE) images were 670 \nobtained. Diffusion-weighted images were then acquired using a single-shot spin-671 \necho echo planar imaging (EPI) sequence. Finally, for the longitudinal MRI study, 672 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n21 \nBOLD rsfMRI data were acquired using a single-shot gradient-echo EPI sequence 673 \nwith 720 repetitions. Additionally, spin-echo EPI image pairs with opposing phase-674 \nencoding polarity were recorded to enable correction of susceptibility-induced 675 \ndistortions. The in vivo scanning session lasted 1-1.5 hours. 676 \nFor ex vivo morphometric analysis, 3D T2-weighted images were acquired using 677 \nRARE sequences, with a total scan duration of 1 hour and 5 minutes. Ex vivo 678 \ndiffusion-weighted images were obtained using Stejskal-Tanner pulsed gradient spin-679 \necho sequences with a 3D segmented EPI readout. Three b0 images were collected at 680 \nthe beginning of three blocks of 30 diffusion-weighted images. The total scan time 681 \nfor this acquisition was 14 hours and 15 minutes. 682 \nTable 7 | MRI acquisition parameters 683 \nImage \ntype  \nTR (ms)  TE \n(ms)  \nFlip \nangle (°)  \nAver-\nages  \nBand-\nwidth \n(kHz)  \nFOV  Matrix  Other  \nAFI  20/  \n100  \n2.85  55  1  25  16.2 x \n16.2 x 9  \n42 x 42 x \n24  \n   \nMTw  24  2.5  6  2  100  16.2 x \n16.2 x 9  \n108 × \n108 × 60  \n6 echoes with 2.1 ms \nspacing;  \nMT pulse: gaussian, 4 \nms, amplitude 10 µT, \noffset -3 kHz, \nbandwidth 685 Hz  \nPDw  20  2.5  4  2  100  16.2 x \n16.2 x 9  \n108 × \n108 × 60  \n7 echoes with 2.1 ms \nspacing  \nT1w  20  2.5  20  2  100  16.2 x \n16.2 x 9  \n108 × \n108 × 60  \n7 echoes with 2.1 ms \nspacing  \nT2w  \nin vivo  \n5000  42   90/  \n180  \n4  50  16 x 12  128 × 96  32 slices, slice \nthickness 0.5, RARE \nfactor 8  \nT2w  \nex vivo  \n300  30   90/  \n180  \n 1   50  25 x 24 \nx 18  \n250 x \n240 x \n180  \nRARE factor 4, scan \ntime = 1 h 5 min  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n22 \nDWI  \nin vivo  \n3000  21   90/  \n180  \n 2  357  19.2 x12  96 x 60  Single shot spin-echo \nplanar imaging, 30 \nslices, slice thickness \n0.5 mm,  \nthree diffusion shells: \nb = 350 s/mm² with 9 \ndirections, b = 1000 \ns/mm² with 34 \ndirections, b = 2000 \ns/mm² with 78 \ndirections (δ = 3 ms, \nΔ = 11 ms), 3 b0 \nimages per shell  \nDWI  \nex vivo  \n300  28.5  90/  \n180   \n   300  25 × 24 \n× 18  \n200 × \n192 × \n144  \nStejskal-Tanner \npulsed gradient spin \necho sequences with a \n3D segmented EPI \nreadout. 12 segments \nand a total of 90 \ndiffusion-weighted \nimages acquired at a \nb-value of 4000 \ns/mm² (δ = 4 ms, Δ = \n13 ms)  \nBOLD \nfMRI  \n1000  15  55   1  200  20 x 20  64 x 64  Single-shot gradient-\necho EPI sequence, 16 \nslices, slice thickness \n0.5, 720 repetitions  \n 684 \nStructural MR image processing 685 \nFor preprocessing MTw, T1w, and PDw images, de-ringing was conducted using the 686 \nMRtrix3’s (Tournier et al. 2019) mrdegibbs command. MTw, T1w, and PDw 687 \nimages were averaged across echo times, rigidly co-registered using Advanced 688 \nNormalization Tools (ANTS; Avants et al. 2011) antsRegistration, and used for 689 \ntemplate construction (see below).  690 \nFor DWI, MRtrix3 dwidenoise was used for de-noising, MRtrix3 mrdegibbs for de-691 \nringing, and FSL’s (the FMRIB Software Library, Jenkinson et al. 2012; Smith et al. 692 \n2004) topup and eddy for susceptibility and eddy-current distortion and motion 693 \ncorrection. FSL’s dtifit was used for diffusion tensor imaging (DTI) model fitting, 694 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n23 \nenabling the calculation of fractional anisotropy (FA), mean diffusivity (MD) and 695 \naxial diffusivity (AD), the latter two which are not reported in this manuscript for 696 \nbrevity. For a more detailed diffusion processing protocol see Kim et al. (2023).  697 \nStudy templates 698 \nThe antsMultivariateTemplateConstruction2.sh script from ANTs was used to 699 \ncreate study specific templates from processed images. For in vivo scans of cohort 1, 700 \nMTw, T1w, R2* map (generated from the multi-gradient-echo PDw, T1w and 701 \nMTw images using the qi mpm_r2s command in the QUIT package), S0, FA and 702 \nMD images were used. For the ex vivo scans of cohort 1, separate T2w and DTI 703 \n(comprising S0, FA, and MD images) templates were created. For cohort 2, PDw, 704 \nT1w, MTw, S0 (estimated non-diffusion-weighted image from dtifit), FA, and 705 \nMD images were used. 706 \nJacobian determinant maps 707 \nTo estimate volume, Jacobian determinant maps were generated from the 708 \ndeformation fields corresponding to the transformation of each subject to the study 709 \ntemplate using the CreateJacobianDeterminantImage command from ANTs. The 710 \nJacobian determinant values of all voxels within the template brain mask were 711 \nsummed to obtain the total brain volume of each subject. Jacobian determinants 712 \nwere calculated from the combined rigid, affine, and Symmetric Normalization 713 \n(SyN) transforms as well as from only the SyN transforms to obtain maps of absolute 714 \nand relative volume (accounting for differences in global brain volume), respectively. 715 \nFor TBM, the Jacobian determinants were subsequently log-transformed. 716 \nVoxel-wise analysis 717 \nFor voxel-wise statistics, FSL randomise with permutation testing was used (10,000 718 \nfor cohort 1, 5000 iterations for cohort 2) followed by a threshold-free cluster 719 \nenhancement (TFCE) and family-wise error (FWE) correction as described in Wood 720 \net al. (2016) and Kim et al. (2023). Given the absence of genotype differences in total 721 \nbrain volume, TBM regional volumes are reported relative to total brain size for 722 \ngreater accuracy (Lerch et al. 2012), while absolute volume maps are reported in the 723 \nSupplementary files where sex differences in total brain volume were present. 724 \nCommon coordinate space 725 \nThe study template was registered to the Allen Mouse Brain Common Coordinate 726 \nFramework (CCFv3; Wang et al. 2020) using ANTs, and the Allen atlas was 727 \nsubsequently transformed to the study template space with the inverse transform. 728 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n24 \nROI-based analysis of volume 729 \nThe images were segmented using an in house modified version of the Allen atlas of 730 \n72 regions (Serrano et al. 2023; Wang et al. 2020). These segmentations were 731 \nsubsequently used to compute regional volumes by summing the Jacobian 732 \ndeterminants within each parcellation. 733 \nRegional volume variability was estimated by calculating a coefficient of variation 734 \n(CV) for each region with normalised root-mean-square method for each genotype 735 \nin each experiment, using the calculation: 𝐶𝑉\t = \t\n!\n\" , where 𝜎 is the standard 736 \ndeviation and 𝜇 the population mean for each region in the atlas (n = 72 regions). 737 \nGroup differences in CV were calculated with a Kruskal-Wallis test (SciPy.stats, 738 \nkruskal). 739 \nFractional anisotropy 740 \nFor mass-univariate voxel-wise analysis of fractional anisotropy, values from dtifit 741 \nfor cohort 1 were again analysed with FSL randomise with permutation testing 742 \n(10,000 permutations) followed by TFCE and FWE-correction. FA is not reported 743 \nfor cohort 2 in this manuscript for brevity. For ROI-based analysis, voxel FA 744 \nmedians within parcellation were used for all white matter regions.  745 \nTo calculate the change over time in fractional anisotropy in the longitudinal study, 746 \nadolescence values for each voxel value or regional median were subtracted from 747 \nadulthood values. Differences between genotypes were calculated with a mixed 748 \nANOVA with between-subjects factor of genotype and within-subjects factor of 749 \nregion. Change from 0 was calculated with a two-sided one-sample t-test (SciPy.stats, 750 \nttest_1samp), which was corrected with the Benjamini–Hochberg method. 751 \nBOLD fMRI pre-processing 752 \nImages were largely pre-processed using the Analysis of Functional NeuroImages 753 \n(AFNI) toolkit. Slice timing correction was performed using the 3dTshift package, 754 \ndespiking with 3dDespike, and motion correction was applied with 3dvolreg. The 755 \nmotion-corrected time average was then registered to each subject's own T2w image 756 \nusing ANTs, followed by registration to a template T2w image derived from a 757 \nseparate mouse study conducted at the BRAIN Centre (KCL). The use of this 758 \nexternal template was justified by the low resolution of fMRI, which does not 759 \nbenefit from creating a study-specific template. 760 \nThe images were also distortion corrected using FSL’s topup, with the distortion 761 \nestimated using auxiliary phase encoded spin-echo images with otherwise matching 762 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n25 \nacquisition parameters to the gradient echo EPI used for the BOLD signal.  Prior to 763 \nanalysis, corrected images were band-pass filtered at 0.01–0.2 Hz with AFNI’s 764 \n3dTproject to remove low-frequency scanner drift noise and high-frequency 765 \nphysiological noise. Nuisance variables (movement and CSF signal) were also 766 \nsimultaneously regressed out of the signal at this stage. Finally, spatial smoothing was 767 \napplied using AFNI’s 3dBlurInMask with a FWHM kernel. 768 \nFunctional connectivity and graph theory analysis 769 \nFor functional analysis, a high-level parcellation scheme was applied to segment 36 770 \nregions (18+18) excluding white matter from the acquired 3D volume. The BOLD 771 \nsignal time-courses were averaged within each ROI, and the mean time-courses were 772 \nextracted using FSL’s fslmeants tool. Pearson correlation coefficients were 773 \ncalculated for each time-course pair, producing a 36×36 correlation matrix for each 774 \nsubject. These matrices were analysed as functional connectivity (FC) graphs, with 775 \nedge strength determined by the Fisher z transformed Pearson correlation coefficient. 776 \nTo identify the strongest connections, graphs were thresholded at 5% intervals from 777 \n5% to 50%. At each threshold level, referred to as the graph sparsity interval, 778 \nconnections below this threshold were set to zero, generating 10 sparsity graphs per 779 \nsubject. FC was calculated as the average non-zero connectivity at each threshold 780 \nlevel. Global graph metrics, global efficiency and clustering coefficient, were 781 \ncomputed at each sparsity level using Brain Connectivity Toolbox algorithms  782 \n(Rubinov and Sporns 2010) implemented with Network X (3.4.2) 783 \nglobal_efficiency and clustering passing binary thresholded Pearson matrices to 784 \nprioritise topology in the presence of noise. For global efficiency and clustering 785 \ncoefficient, random curves were calculated by shuffling the thresholded binary 786 \nmatrix positions. Area under the curve (AUC) was computed per subject using 787 \ntrapezoidal numerical integration (numpy.trapz), and the likelihood of observed 788 \nvalues was estimated with permutation testing (10,000 permutations).  789 \nTo estimate changes over time, global graph metric values at adolescence were 790 \nsubtracted from adulthood values at each sparsity interval. Group differences were 791 \nagain tested by calculating the AUC and applying permutation testing. Changes 792 \nfrom baseline (zero) were evaluated using two-sided one-sample t-tests (difference 793 \nfrom 0), corrected for multiple comparisons with the Benjamini-Hochberg method. 794 \nFDR correction and network-based statistics 795 \nMatrices for C3ar1-deficient and wild-type mice were compared with Student’s t-796 \ntests for each pairwise connection, resulting in 36×36 t-statistic and p-value matrices. 797 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n26 \nTo control for multiple comparisons, FDR correction was applied to the upper 798 \ntriangle of the p-value matrix using the Benjamini-Hochberg procedure 799 \n(statsmodels.stats.multitest.fdrcorrection, α = 0.05).  800 \nFor network based statistics (NBS), above described t matrices were thresholded at |t| 801 \n≥ 2. To identify connected components within the FC graph, adjacency matrices 802 \nrepresenting significant connections (|t| ≥ 2) were converted into graph objects using 803 \nNetworkX. Regions of interest (ROIs) were treated as nodes, and significant 804 \nconnections as edges. Connected components were identified using a breadth-first 805 \nsearch (BFS) algorithm, which explores all neighbouring nodes before moving deeper 806 \ninto the graph. Only components containing more than one ROI were retained for 807 \nfurther analysis. To generate a null distribution of maximal component sizes, group 808 \nlabels were randomly shuffled across subjects for each permutation while preserving 809 \nmatrix structure (10,000 permutation). The p-value for an observed component was 810 \ncalculated as the proportion of permutations where the maximal component size 811 \nexceeded that of the observed component. 812 \nA priori node strength analysis 813 \nWe selected 20 (10+10 left and right) anxiety and fear related regions and calculated 814 \ntheir average absolute connectivity to all other regions using Fisher z-transformed 815 \nPearson correlation coefficients. We then  used a mixed ANOVA with between-816 \nsubjects factor of genotype and within-subjects factor of region followed by pairwise 817 \ntesting with BH FDR correction of p values.  818 \nWithin-network analysis 819 \nTwo mouse resting state networks were subset from correlation matrices: the default 820 \nmode network (DMN) and the salience network (J. Grandjean et al. 2020; Sforazzini 821 \net al. 2014). The DMN included bilateral prefrontal cortices, cingulate cortices, and 822 \ndorsal hippocampi, while the salience network comprised bilateral cingulate cortex, 823 \namygdala, and striatum. Additionally, a third anxiety-related network was defined, 824 \nconsisting of regions identified in the seed-based analysis described above. 825 \nFor each network, mean FC was calculated as the average of all pairwise connections 826 \nbetween nodes within the network, without applying a threshold. For individual 827 \nnetwork global efficiency analysis, thresholding was not used due to small amount of 828 \nnodes. Instead, weighted Pearson matrices were passed to bctpy (0.6.1) 829 \nefficiency_wei. 830 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n27 \nStudent’s t tests were used to test significance, and the comparisons were corrected 831 \nwithin outcome measure with BH method.  832 \nVoxel-wise seed-to-brain analysis 833 \nWe conducted voxel-wise seed-based FC analyses for anxiety-related regions, as well 834 \nas the colliculus and sensory cortex, which served as control regions not specific to 835 \nanxiety. For each seed region, the time-course of the BOLD signal was extracted and 836 \nregressed with the BOLD signal of every voxel in the brain, resulting in a 3D spatial 837 \nmap of the connectivity with the seed. Group-level comparisons of these maps were 838 \nperformed between genotypes using voxel-wise permutation tests with FSL’s 839 \nrandomise (5000 permutation), converted with TFCE and statistical significance 840 \ncorrected for multiple comparisons using FWE (seed-to-brain), but they were not 841 \ncorrected for the presence of multiple seeds. 842 \nGeneral behavioural procedures 843 \nFor cohort 1, EPM was administered as the first test, followed by OF. For cohort 2, 844 \nOF was the first test, followed by NOR test after two days of low light habituation (4 845 \nlux), EPM, and PPI. 846 \nHandling of the mice began 2-3 days prior to the behavioural testing battery. By the 847 \nstart of the experiments, the mice sat comfortably on the experimenter’s hand. Mice 848 \nwere handled using cardboard tunnels to minimise stress, and tail handling was 849 \navoided. 850 \nBehavioural tests were conducted during the dark phase (between 7:00 PM and 851 \n11:00 PM clock-time) of the light-dark cycle to align with the active period of mice. 852 \nMice were randomised by genotype and counter-balanced by sex, with the 853 \nexperimenter systematically blinded to genotype throughout testing and analysis 854 \nthough the allocation of a study ID and test order ID respectively.   855 \nFor cleaning of the test apparatus, we used 70% EtOH for all arenas except for the 856 \nEPM arena for which we used Virusolve (Amity International) to avoid damage to 857 \nthe material. Behaviour was recorded using a Google Pixel 5a camera at 1080p/60 858 \nfps. Videos were then cropped and down sampled by shell scripting using FFmpeg. 859 \nOpen field test 860 \nMice were placed in a 40 x 40 x 40 cm white arena and allowed to explore freely. For 861 \ncohort 1, dim red light (4 lux) was used, while for cohort 2, the OF test was 862 \nconducted under bright overhead lighting (500 lux). 10-minute videos were analysed 863 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n28 \nusing CleverSys (VA, USA). Outcome parameters included time spent in the centre, 864 \ntotal distance travelled, velocity, and thigmotaxis (edge exploration). 865 \nNovel object recognition 866 \nCohort 2 mice were initially habituated to the arena under low light conditions (4 867 \nlux) over two days, five minutes per session. On the training day, two identical 868 \nobjects were introduced, and mice were allowed to explore for five minutes. After a 869 \none-hour delay, one object was replaced with a novel one. The videos were analysed 870 \nusing CleverSys software stereotypic event “Sniffing” module. Objects were 871 \nmanually outlined with the polygon tool. An interaction with an object was recorded 872 \nwhen the mouse's nose was within 5 mm of the object. The novelty preference was 873 \ndetermined by calculating the proportion of time spent exploring the novel object 874 \nrelative to the total exploration time of both objects, with a recognition index chance 875 \nlevel of 50%. 876 \nElevated plus maze 877 \nFor cohort 1, EPM was administered to behaviourally naïve animals, while in the for 878 \ncohort 2, it was conducted after OF and NOR tests. In both cases, mice were placed 879 \nin the closed arm of the arena (65 x 65 x 55 cm, elevated 40 cm) under full overhead 880 \nlighting (500 lux) and allowed to explore for five minutes. The arena was divided into 881 \nclosed, middle, and open areas for analysis with CleverSys software. The number of 882 \nhead dips and stretch-attend postures was recorded using BORIS software, and 883 \ntesting accuracy was compared with the results of an independent scorer. 884 \nPrepulse inhibition 885 \nLike NOR, PPI was only conducted in cohort 2. We used an acoustic startle 886 \nchamber (SR-LAB, San Diego Instruments, San Diego, CA, USA) with a cylindrical 887 \nPlexiglas enclosure horizontally mounted on a mobile platform within a sound-888 \nproofed isolation chamber. A high-frequency loudspeaker positioned above the 889 \nenclosure emitted continuous background noise at 65 dB, along with the 890 \nexperimental acoustic stimuli. The startle response was recorded by converting 891 \nPlexiglas enclosure vibrations into millivolt signals using a piezoelectric unit. 892 \nEach session started with a five-minute acclimatisation period to the 65 dB 893 \nbackground noise, followed by five startle-alone trials at 120 dB to ensure 894 \nhabituation. Mice then received 10 prepulse stimuli at 3, 6, and 12 dB above 895 \nbackground, each preceding a 120 dB pulse in a pseudo-randomised order, 896 \ninterspersed with 10 no-stimulus trials and 10 startle-alone trials. The session 897 \nconcluded with five final startle-alone pulses. The inter-trial interval was randomised 898 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n29 \nbetween 9-15 seconds to prevent expectation-based modulation of the startle 899 \nresponse. %PPI was calculated for each prepulse using the formula: %𝑃𝑃𝐼\t =900 \n#$%&'\t)%*+' \t(-.!\"#)\t0\t#$%&'\t1234\t#5'#$%&'\t(-.!\"#)\n#$%&'\t)%*+' \t(-.!\"#) . 901 \nStatistical procedure for behavioural outcome measures 902 \nAll statistical analyses were conducted using Python 3.11.7, using relevant libraries 903 \nsuch as SciPy and statsmodels. For both studies, the Shapiro-Wilk test was used to 904 \nassess the normality of data distributions, while Levene’s test was used to evaluate 905 \nhomogeneity of variances. When either assumption of normality or equal variance 906 \nwas violated, Kruskal-Wallis test was used, followed by Dunn’s test for post hoc 907 \npairwise comparisons with Bonferroni correction for multiple testing where 908 \napplicable. For datasets meeting parametric assumptions, different approaches were 909 \nused based on the study design. For cohort 1, a two-way ANOVA was performed to 910 \nanalyse the effect of sex, genotype and sex-by-genotype interaction, followed by 911 \nTukey’s test for post hoc comparisons. Bonferroni correction was applied to adjust 912 \nfor multiple comparisons. Due to the relatively small sample size (n = 38) in cohort 2, 913 \nsex-by-genotype interactions were not analysed. Instead, Student’s t-test was used for 914 \ncomparisons between groups.  915 \nTo balance statistical rigor with preserving power in exploratory contexts, 916 \nbehavioural measures were not universally corrected for multiplicity. Instead, 917 \nrobustness was inferred through replication across independent cohorts and broad 918 \nphenotypic consistency. The threshold for statistical significance was set at p < 0.05. 919 \nIndividual mice served as the experimental units in all analyses. 920 \nCode availability  921 \nData and code to reproduce figures will be added to 922 \nhttps://github.com/hannalemmik upon publication. The MRI processing code is 923 \navailable upon request from Eugene Kim. 924 \nData availability  925 \nBehaviour videos will be added to figshare, and MRI data will be added to 926 \nopenneuro. The intermediary analysis files will be added to github at 927 \nhttps://github.com/hannalemmik. 928 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n30 \nAcknowledgements 929 \nWe thank Bao Lu and Craig Gerard for providing the C3ar1 knockout mice, Dr 930 \nMarija M. Petrinovic for providing prepulse inhibition testing equipment and KCL 931 \nBiological Service Unit staff for animal care. This work was funded by the MRC 932 \ngrant “Complement C3aR in adolescent synaptic pruning and risk for anxiety” 933 \n(MR/W004607/1). Hanna Lemmik was funded by the Wellcome Trust as part of 934 \nthe “Neuro-Immune Interactions in Health & Disease” Wellcome Trust PhD 935 \nProgramme (218452/Z/19/Z). 936 \nAuthor contributions 937 \nHL - writing - original draft, writing - review & editing, conceptualization, 938 \nmethodology, software, formal analysis, data curation, investigation, visualization, 939 \nfunding acquisition; EK - methodology, software, formal analysis, investigation, data 940 \ncuration, writing - review & editing, visualization, funding acquisition; EM - 941 \nmethodology, software, investigation, writing - review & editing; DM - validation, 942 \nmethodology, investigation; MB -  software, formal analysis, data curation, 943 \nvisualization; ZL - investigation, validation; DA - investigation, Esther - investigation; 944 \nMES - funding acquisition; WZ - resources, supervision; AI - resources, supervision; 945 \nDC - writing - original draft, writing - review & editing, supervision, project 946 \nadministration, funding acquisition, conceptualization, methodology; LW - writing - 947 \noriginal draft, writing - review & editing, supervision, funding acquisition, 948 \nconceptualization, methodology. 949 \nReferences 950 \nAvants, Brian B., Nicholas J. 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It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n44 \nZhu, Hongru, Changjian Qiu, Yajing Meng, Minlan Yuan, Yan Zhang, Zhengjia 1373 \nRen, Yuchen Li, et al. 2017. “Altered Topological Properties of Brain Networks in 1374 \nSocial Anxiety Disorder: A Resting-State Functional MRI Study.” Scientific Reports 1375 \n7 (1): 43089. 1376 \nFigures  1377 \n1378 \nFigure 1 | C3ar1tm1Cge  mutant does not make C3ar1 RNA. Schematic shows 1379 \nC3ar1 mRNA with its only protein coding exon and the design of the 79 bp PCR 1380 \namplicon which targets an exon-1/5’UTR-exon-2 junction of the canonical 1381 \nC3ar1+/+ transcript. Also shown is another 372 bp amplicon which targets a region 1382 \ndownstream of the deletion and after an alternative start codon. The start of exon 2 is 1383 \ndeleted in C3ar1-/- mice, so PCR should result in no amplification. Similarly, if no 1384 \nalternative transcript is made, there should be no amplification. (i-ii) Gel images 1385 \nshow PCR products of cDNA from bone-marrow derived interleukin 4 (IL4)-1386 \ninduced M2-like macrophages. Each sample well has a 297 bp Glyceraldehyde 3-1387 \nphosphate dehydrogenase (Gapdh) control band. NEG: no reverse transcriptase 1388 \nnegative control. i) canonical transcript, ii) hypothesised alternative transcript. -/- =  1389 \nC3ar1-/-, +/+ = C3ar1+/+.  1390 \n 1391 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n45 \n 1392 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n46 \nFigure 2 | Sex but not C3ar1 status influences regional brain volume. (a) 1393 \nSchematic of the MRI study shown in (d-g). 69 mice were scanned twice in vivo; in 1394 \nadolescence at ~ postnatal day (PND) 30 (range 27-31) and at adulthood ~ PND90 1395 \n(range 81-92), as well as once ex vivo  after sacrifice after the adulthood scanning 1396 \nsession. Behavioural tests were carried out before the adulthood scanning session. 1397 \nEPM = elevated plus maze, OF = open field. (b) Total brain volume at PND30. 1398 \nTwo-way ANOVA, genotype, sex, genotype * sex, F[1,65]  = 0.35, 16.72, 3.26, p = 0.56, 1399 \n<0.001, 0.76. (c) PND90, two-way ANOVA, genotype, sex, genotype * sex, F[1,65]  = 1400 \n0.74, 0.26, 3.33, p = 0.39, 0.61, 0.07. (b-c) Data presented as mean ± 95% CI. (d) 1401 \nPanels showing relative regional volume changes (%) overlaid on study-specific 1402 \ncoronal templates (grey). Red hues signify areas larger in C3ar1-/- (i-ii) or females (iii-1403 \niv) and blue hues signify areas larger in C3ar1+/+ (i-ii) or males (iii-iv). Transparency 1404 \nof the colour overlay shows the statistical significance, ranging from family wise error 1405 \n(FWE)-corrected p value 0.5 to 0 (transparent to opaque, respectively). Areas where 1406 \nFWE-corrected p value < 0.05 are demarcated with a black line, and where the p value 1407 \n> 0.5 are grey (no overlay), meaning that in adolescence genotype comparison (i), no 1408 \nvoxels had a p value < 0.5. The locations of the coronal slices in relation to bregma in 1409 \nthe left most column from top: -7.6, -4.6, -1.6, 1.4 mm. C3ar1+/+ n = 35, 17 males 1410 \nand 18 females; C3ar1-/- n = 34, 18 males and 16 females. ACAd = dorsal anterior 1411 \ncingulate cortex, AI = agranular insular cortex, BST = bed nucleus of stria terminalis, 1412 \nCA = cornu ammonis, CP = caudoputamen, DG = dentate gyrus, MEA = medial 1413 \namygdala, MEPO = medial preoptic nucleus, MOs = secondary motor cortex (MOs), 1414 \nMPO = medial preoptic area, MS = medial septum, OB = olfactory bulb, PAG = 1415 \nperiaqueductal grey, PRT = pretectal area, SC = superior colliculus, SSp = primary 1416 \nsomatosensory cortex. (e) Coefficient of variation (CV) in adolescence in vivo for 72 1417 \nregional volumes across all animals in the experiment. (f) CV in adulthood in vivo. 1418 \n(g) CV in adulthood ex vivo. (e-g) Data are shown with quartiles and whiskers show 1419 \nthe extent of the distribution. Dotted line shows the intergroup mean excluding 1420 \ncerebrospinal fluid (CSF) areas. Kruskal Wallis p values, all ns. D. hipp = dorsal 1421 \nhippocampus, v. hipp = ventral hippocampus.  1422 \n 1423 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n47 \n 1424 \nFigure 3 | Fractional anisotropy does not depend on C3ar1 status but 1425 \nincreases with age. (a) Panels showing voxel-wise fractional anisotropy analysis in 1426 \nadolescence (top) and adulthood (bottom) corrected for FWE. There are no 1427 \nsignificant voxels (no black contour) where genotype effect is significant (p  < 0.05). 1428 \nCC = corpus callosum, OPT = optic tract. (b) In vivo ROI-based median fractional 1429 \nanisotropy (FA) values in white matter regions for (i) adolescence,  (ii) adulthood 1430 \nand (iii) change over time (adulthood – adolescence). Two-sided one-sample t test 1431 \n(difference from 0) p values that were adjusted with Benjamini-Hochberg (BH) procedure 1432 \n(###  p value < 0.001). (b-i-iii) Mixed ANOVA with genotype and genotype-by-region 1433 \ninteraction effects, all ns. Data are presented as mean ± 95% CI. 1434 \n 1435 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n48 \n1436 \nFigure  4 | Global functional connectivity is not changed in C3ar1-deficient 1437 \nmice. FC, clustering coefficient and global efficiency at decreasing graph sparsity 1438 \nlevels in adolescence (a-c), and adulthood (d-f), and change over time (g-i). (a-i) 1439 \nStatistical significance was determined with sexes combined using AUC permutation 1440 \ntesting (10,000 iterations) and the resulting p values were corrected for multiplicity 1441 \nwith the Benjamini-Hochberg method (n = 3 tests per outcome measure). PND30: 1442 \nC3ar1+/+ n = 32 (15 males, 17 females); C3ar1-/- n = 32 (18 males, 14 females); 1443 \nPND90: C3ar1+/+ n = 33 (16 males, 17 females) and C3ar1-/- n = 32 (17 males, 15 1444 \nfemales); change: C3ar1+/+ n = 30 (14 males and 16 females); C3ar1-/- n = 31 (17 1445 \nmales and 14 females). Data are shown as mean ± 95% CI. (g-i) Two-sided one-1446 \nsample t tests (difference from 0) for global connectivity changes at each sparsity level 1447 \nwith genotypes combined, corrected using the Benjamini-Hochberg procedure (n = 1448 \n10 sparsity levels), # = adjusted p value < 0.05. 1449 \n 1450 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n49 \n1451 \nFigure 5 | C3ar1-deficiency has no detectable effects on functional brain 1452 \nnetworks. (a) Thresholded adjacency matrices comparing C3ar1+/+ vs C3ar1-/- 1453 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n50 \ngroups where |t| ≥ 2 in adolescence and adulthood, and the change between time 1454 \npoints. No connections with |t| ≥ 2 remained significant after the false discovery rate 1455 \n(FDR) correction. Additionally, the number of connections in the |t| ≥ 2 adjacency 1456 \nmatrix components were not significant when assessed using network-based statistics 1457 \n(NBS) correction. (b) Nodal strength (mean absolute connectivity) of a priori 1458 \nanxiety-related seeds (e.g. L cingulate cortex correlation coefficients with all other 1459 \nregions). P values were calculated using a mixed ANOVA with between-subjects 1460 \nfactor of genotype and within-subjects factor of region. Only the region effect was 1461 \nsignificant at both time-points (both p values < 0.001,  ηp2 = 0.69 in adolescence and 1462 \nηp2 = 0.73 in adulthood). (c) Mean functional connectivity (FC) and global efficiency 1463 \n(GE). Student’s t tests within a metric (n = 6) corrected for multiple comparisons 1464 \nusing the Benjamini-Hochberg method. DMN = default mode network, SAL = 1465 \nsalience network, BH = Benjamini-Hochberg. (d) Examples of seed-based FC maps 1466 \nshowing voxel-wise group differences between C3ar1-/- and C3ar1+/+ mice (using t 1467 \ntests) with seeds placed in the left ventral hippocampus and left prefrontal cortex in 1468 \nadolescence and adulthood datasets. The dual scale bar displays contrast value on the 1469 \nx-axis and threshold free cluster enhancement (TFCE) p-values (transformed 0.81-p) 1470 \non the y-axis. The transformed p-values have been re-scaled to range from 0 to 1 for 1471 \nvisualisation, with darker colours representing greater statistical significance. Black 1472 \noutlines demarcate regions where TFCE p-values are below 0.05. 1473 \n 1474 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n51 \n 1475 \nFigure 6 | C3ar1 deficiency does not cause behavioural abnormalities. (a) Z-1476 \nscored (normalised to C3ar1+/+ control mean = 0) locomotion metrics for C3ar1-/- 1477 \nanimals from EPM and OF tests, showing results from two independent study 1478 \ncohorts. Two-way ANOVA for longitudinal study (4 groups, males and females 1479 \nseparated) or Student’s t tests for the adulthood-only study cohort (2 groups, males 1480 \nand females combined due to smaller sample size) where parametric assumptions 1481 \nwere met, otherwise the Kruskal Wallis test (with Dunn in the longitudinal study 1482 \ncohort); uncorrected for multiplicity, all ns. (b) Same as (a) but for anxiety-like 1483 \nmetrics from EPM and OF tests; p values all ns. (c) Prepulse inhibition in the 1484 \nadulthood-only study cohort. PPI increased with increasing prepulse intensity in 1485 \nboth, C3ar1+/+ vs C3ar1-/- mice (paired t tests p value ### < 0.001). Dotted line at 0 1486 \nindicates inhibition threshold. A score above 0 indicates inhibition (shaded area). 1487 \nSlope plot shows means and 95% confidence intervals. Individual mice are plotted 1488 \nthree times at increasing prepulse intensity. Prepulse inhibition did not differ by 1489 \ngenotype at any prepulse intensity (uncorrected Student’s t tests at 3 dB, 6 dB and 12 1490 \ndB, t[37] = 1.58, 1.99, 0.28, p = 0.12, 0.54, 0.17). (d) Novel object recognition (NOR) 1491 \nrecall 1 hr after acquisition in the adulthood-only study cohort. Both groups showed 1492 \nnovelty preference (one-sample t test, value > 50/chance, # = p < 0.05, ## = p < 0.01 1493 \n### = p < 0.001). There were no differences between groups (Student’s t test t[37] = 1494 \n0.42, p = 0.67). The dotted line marks the chance threshold. (a-d) Adulthood-only 1495 \nstudy cohort C3ar1-/- n = 20, C3ar1+/+ n = 19 (males and females combined), 1496 \nlongitudinal study cohort: C3ar1-/- n = 33 (17 male, 16 female), C3ar1+/+ n = 33 (16 1497 \nmale, 17 female). Data are expressed as mean ± 95% CI. 1498 \n 1499 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n52 \nTables 1500 \nTable 1 | Mean network connectivity metrics ± 95% CI in males and females 1501 \nof both genotypes 1502 \nAge Adolescence \n \nAdulthood \nC3ar1 \nstatus \n+/+ -/- +/+ -/- +/+ -/- +/+ -/- \nSex F F M M F F M M \nConnectivity metric (AUC ± 95% CI) \nFC 25.5 \n± 2 \n27.5  \n± 3.1 \n25.9 \n± 5 \n29.5 \n± 4.5 \n27.6 \n± 3.3 \n28.9 \n± 2.9 \n29.9 \n± 5.3 \n31.9 \n± 4.7 \nGE 11.5  \n± 0.8 \n12.7  \n± 1.2 \n11.4 \n± 1.5 \n13.2 \n± 1.7 \n12.17 \n± 1.2 \n12.9 \n± 1.3 \n13.3 \n± 2 \n14.5 \n± 2.1 \nCC 13.5 \n± 1.1 \n14.9 \n± 1.9 \n13.9 \n± 2.8 \n15.9 \n± 2.6 \n14.8 \n± 2 \n15.8 \n± 1.9 \n16.3 \n± 3.2 \n17.4 \n± 2.6 \n 1503 \nTable 2 | Mean anxiety-associated node connectivity in males and females of 1504 \nboth genotypes. 1505 \nAge Adolescence Adulthood \nC3ar1 status +/+ -/- +/+ -/- +/+ -/- +/+ -/- \nSex F F M M F F M M \nBrain area (mean node connectivity strength) \nL cingulate cx 0.39 0.41 0.39 0.44 0.45 0.49 0.48 0.54 \nR cingulate cx 0.38 0.41 0.38 0.43 0.45 0.49 0.48 0.53 \nL prefrontal cx 0.29 0.33 0.32 0.37 0.41 0.44 0.37 0.49 \nR prefrontal cx 0.28 0.28 0.33 0.37 0.41 0.45 0.39 0.47 \nL amygdala 0.14 0.17 0.15 0.2 0.2 0.2 0.24 0.28 \nR amygdala 0.11 0.16 0.15 0.18 0.19 0.2 0.25 0.25 \nL pallidum & accumbens 0.23 0.27 0.23 0.31 0.3 0.29 0.35 0.4 \nR pallidum & accumbens 0.24 0.26 0.24 0.3 0.31 0.31 0.35 0.39 \nL striatum 0.28 0.32 0.3 0.36 0.32 0.36 0.4 0.43 \nR striatum 0.26 0.31 0.32 0.35 0.33 0.36 0.42 0.41 \nL hypothalamus 0.28 0.29 0.27 0.34 0.32 0.32 0.34 0.38 \nR hypothalamus 0.25 0.27 0.26 0.32 0.31 0.32 0.33 0.37 \nL dorsal hippocampus 0.4 0.43 0.42 0.49 0.44 0.46 0.48 0.52 \nR dorsal hippocampus 0.41 0.43 0.42 0.49 0.44 0.47 0.5 0.52 \nL ventral hippocampus 0.18 0.18 0.17 0.26 0.21 0.23 0.2 0.27 \nR ventral hippocampus 0.18 0.23 0.19 0.25 0.22 0.25 0.25 0.26 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n53 \nL PAG 0.28 0.3 0.3 0.38 0.27 0.3 0.32 0.36 \nR PAG 0.28 0.31 0.3 0.39 0.27 0.29 0.32 0.37 \nL brain stem 0.11 0.11 0.12 0.18 0.1 0.14 0.16 0.16 \nR brain stem 0.11 0.09 0.12 0.18 0.11 0.13 0.17 0.18 \n 1506 \nTable 3 | Mean behavioural outcome measures ± 95% CI in males and females 1507 \nof both genotypes 1508 \nCohort Cohort 1 Cohort 2 \nSex F M F M \nC3ar1 status +/+ -/- +/+ -/- +/+ -/- +/+ -/- \nLocomotion-related \nOF locomotion \nspeed (mm/s) \n79.65±\n3.47 \n79.48±\n3.91 \n75.61±\n4.31 \n74.72±\n4.08 \n94.21±\n6.64 \n84.33±\n7.26 \n84.93±\n7.55 \n84.93±\n5.68 \nOF locomotion \nduration (s) \n213.38\n±22.53 \n223.73\n±26.85 \n216.60\n±21.94 \n217.97\n±20.82 \n315.66\n±50.93 \n264.33\n±49.90 \n297.85\n±39.56 \n283.61\n±17.86 \nOF locomotion \ndistance (cm) \n1777.8\n9±210.\n25 \n1847.7\n1±271.\n41 \n1746.4\n5±257.\n47 \n1747.5\n2±225.\n53 \n2984.9\n9±589.\n73 \n2231.4\n5±473.\n78 \n2562.3\n6±515.\n37 \n2412.0\n3±268.\n13 \nEPM speed \n(mm/s) \n67.07±\n3.43 \n65.89±\n2.40 \n67.71±\n3.74 \n69.90±\n2.60 \n75.27±\n5.08 \n75.38±\n6.40 \n73.61±\n4.21 \n76.63±\n3.76 \nEPM locomotion \nduration (s) \n69.25±\n15.36 \n61.22±\n11.89 \n73.29±\n13.14 \n67.01±\n7.48 \n78.93±\n13.23 \n82.53±\n18.41 \n71.11±\n17.69 \n62.51±\n8.88 \nEPM locomotion \ndistance (cm) \n473.32\n±124.8\n7 \n401.52\n±88.01 \n502.39\n±109.2\n2 \n465.16\n±54.60 \n591.26\n±122.2\n0 \n620.88\n±155.6\n9 \n518.89\n±140.0\n6 \n472.57\n±68.28 \nAnxiety-related \nOF duration core \n(s) \n25.92±\n12.12 \n18.74±\n4.60 \n19.81±\n5.35 \n22.99±\n7.73 \n25.06±\n10.65 \n29.96±\n24.36 \n32.19±\n18.93 \n16.71±\n3.87 \nOF duration \ncentre (s) \n106.94\n±18.40 \n102.01\n±16.36 \n97.18±\n13.68 \n109.40\n±17.99 \n105.42\n±40.78 \n120.94\n±39.65 \n133.80\n±31.20 \n95.62±\n13.10 \nOF duration \nperiphery (s) \n493.13\n±18.41 \n498.07\n±16.36 \n502.90\n±13.68 \n490.66\n±17.98 \n493.16\n±42.58 \n478.89\n±39.60 \n466.11\n±31.25 \n504.15\n±13.10 \nEPM duration \nopen (s) \n29.52±\n11.52 \n28.92±\n12.57 \n28.06±\n12.03 \n28.19±\n14.72 \n18.87±\n8.77 \n20.45±\n16.56 \n15.13±\n7.96 \n12.15±\n7.29 \nOF latency core (s) 30.66±\n17.23 \n44.47±\n34.34 \n29.89±\n16.18 \n40.95±\n19.45 \n34.19±\n36.91 \n24.15±\n20.50 \n25.86±\n25.45 \n29.44±\n52.19 \nOF latency centre \n(s) \n10.02±\n3.52 \n13.26±\n8.07 \n12.35±\n7.40 \n11.97±\n4.95 \n5.79±6\n.00 \n7.45±7\n.79 \n5.22±5\n.01 \n5.28±4\n.26 \nEPM duration \nmiddle (s) \n109.53\n±17.89 \n102.17\n±19.15 \n112.33\n±24.24 \n122.79\n±17.27 \n85.38±\n21.11 \n72.59±\n29.02 \n71.13±\n14.54 \n74.93±\n17.85 \nEPM duration \nclosed (s) \n176.11\n±22.18 \n198.69\n±19.25 \n188.78\n±24.17 \n178.29\n±17.22 \n214.24\n±21.04 \n207.31\n±38.82 \n227.88\n±14.86 \n224.17\n±17.79 \nEPM bouts open \n(#) \n4.65±1\n.92 \n3.19±1\n.66 \n3.44±1\n.33 \n2.94±1\n.18 \n2.62±1\n.48 \n3.43±2\n.77 \n2.30±1\n.12 \n1.91±1\n.49 \nEPM head dips (#)  21.53±\n6.20 \n18.12±\n5.17 \n19.62±\n6.29 \n22.31±\n5.36 \n16.75±\n4.55 \n17.57±\n13.16 \n11.90±\n3.14 \n16.73±\n8.52 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint \n\n \n54 \nEPM stretch-\nattend postures (#) \n19.00±\n4.15 \n21.81±\n2.98 \n23.62±\n3.14 \n23.38±\n2.87 \n16.62±\n6.16 \n9.43±6\n.04 \n13.50±\n2.68 \n14.73±\n4.10 \nEPM latency open \n(s) \n65.31±\n27.63 \n106.76\n±53.38 \n92.95±\n48.81 \n117.27\n±51.75 \n75.66±\n81.38 \n83.26±\n103.55 \n95.40±\n71.29 \n137.52\n±70.66 \nOther \nPPI 3 dB (%)     21.11±\n13.2 \n15.31±\n6.40 \n22.87±\n7.41 \n17.14±\n7.70 \nPPI 6 dB (%)     40.63±\n10.16 \n26.36±\n8.74 \n39.49±\n11.24 \n34.37±\n10.65 \nPPI 12 dB (%)     56.81±\n13.18 \n49.81±\n13.28 \n57.07±\n9.51 \n60.40±\n8.38 \nAcoustic startle \nresponse at 120 dB \n(A.U.) \n    876.35\n±432.3\n6 \n1068.3\n2±284.\n31 \n1472.9\n9±360.\n84 \n1908.7\n9±655.\n66 \nNOR recognition \nindex (%) \n    62.49±\n11.20 \n62.60±\n9.77 \n65.02±\n7.66 \n68.16±\n8.87 \nNOR total \nexploration \ntraining (s) \n    39.45±\n15.62 \n38.58±\n10.66 \n38.50±\n12.16 \n41.12±\n11.07 \nNOR total \nexploration test (s) \n    31.68±\n10.07 \n32.41±\n6.83 \n30.81±\n9.17 \n33.58±\n5.39 \n 1509 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 26, 2025. ; https://doi.org/10.1101/2025.04.24.650541doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}