Magnetic resonance imaging (MRI)

MRI scans were performed on a 3 T Verio scanner (n = 30) or a 3 T Prisma scanner (n = 50) using the following parameters: Verio/Prisma: repetition time = 1900 ms, echo time = 2.32/2.58 ms, inversion time = 900 ms, flip angle = 9°, in-plane matrix = 256 × 256, in-plane resolution = 0.9 × 0.9 mm, 224 slices and a slice thickness of 0.9 mm, no gap. The images were segmented using Voxel Based Morphometry (VBM8) implemented in Statistical Parametric Mapping (SPM8) (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/)^{Reference Ashburner, Barnes, Chen, Daunizeau, Flandin and Friston22} to mask grey matter in the subsequent extraction of tissue time-activity curves (TACs).

PET data acquisition

Procedures for PET acquisition have been described in detail elsewhere.^{Reference Ettrup, Svarer, Mcmahon, Da Cunha-Bang, Lehel and Møller23} Briefly, scans were conducted using a high-resolution research tomography (HRRT) PET scanner with parameters: matrix = 256 × 256 × 207 voxels, voxel size = 1.22 × 1.22 × 1.22 mm. All participants underwent a transmission scan using the built-in source for attenuation and scatter correction followed by a 120 min emission scan which started at the time of the intravenous bolus injection of [¹¹C]Cimbi-36 (mean injected activity: 499 ± 109 MBq; injected mass of Cimbi-36: 0.72 ± 0.44 μg). [¹¹C]Cimbi-36 scan data were reconstructed into 45 dynamic frames (6 × 10 s, 6 × 20 s, 6 × 60 s, 8 × 120 s and 19 × 300 s) using the methods described previously.^{Reference Keller, Svarer and Sibomana24}

PET processing and quantification

PET processing was performed as previously described.^{Reference Ettrup, Svarer, Mcmahon, Da Cunha-Bang, Lehel and Møller23} Briefly, motion-corrected PET images were co-registered with the participants’ T1-weighted image using SPM8. Automatic delineation of regions of interest was performed using PVElab (MATrix LABoratory (MATLAB), Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark).^{Reference Svarer, Madsen, Hasselbalch, Pinborg, Haugbøl and Frøkjær25} A global neocortical region of interest (ROI) was defined as a volume-weighted mean of the following cortical ROIs: orbitofrontal cortex, superior, middle and inferior frontal gyrus; superior, middle and inferior temporal gyrus, sensorimotor cortex, parietal cortex and occipital cortex. A composite neocortex ROI was used because the signal is highly correlated between the neocortical regions.^{Reference Spies, Nasser, Ozenne, Jensen, Knudsen and Fisher26}

Kinetic modelling using the simplified reference tissue model (SRTM)^{Reference Lammertsma and Hume27} with the cerebellum (excluding vermis) as the reference tissue was performed with in-house MATLAB scripts. The calculated non-displaceable binding potential (BP _ND) served as the outcome measure for quantifying 5-HT_2A binding. BP _ND is defined as:

$${BP}_{{\rm ND}} = \displaystyle{{V_T-V_{{\rm ND}}} \over {V_{{\rm ND}}}} $$

where V _T and V _ND are the distribution volumes in the ROI and reference region (cerebellum), respectively.

In addition to extracting mean 5-HT_2AR BP _ND values in the neocortex using the methods described above, we generated vertex-wise (surface) cortical 5-HT_2AR BP _ND maps to visualise the distribution of cortical 5-HT_2AR BP _ND associated with the inward facets of neuroticism. The vertex-wise cortical 5-HT_2AR BP _ND maps were generated using the PETSurfer^{Reference Greve, Svarer, Fisher, Feng, Hansen and Baare28} processing stream in FreeSurfer (version 7.3.2; see http://surfer.nmr.mgh.harvard.edu),^{Reference Fischl29} as described previously.^{Reference Beliveau, Ganz, Feng, Ozenne, Højgaard and Fisher30} FastSurfer (version 2.0.1; see https://doi.org/10.1016/j.neuroimage.2020.117012) was used in place of FreeSurfer to process the structural images (recon-all). Briefly, PET–MRI co-registration was estimated using linear registration between the structural MRI and the time-weighted average of the dynamic PET images. Cortical vertex-wise TACs were obtained by resampling the dynamic PET data onto the common fsaverage surface space registration.^{Reference Postelnicu, Zollei and Fischl31} Cortical TACs were surface smoothed by 10 mm full-width at half-maximum.

Kinetic modelling of the parametric BP _ND was performed using the Multilinear Reference Tissue Model 2 (MRTM2)^{Reference Ichise, Liow, Lu, Takano, Model and Toyama32} with cerebellar grey matter as the reference region. Cerebellar grey matter masks were obtained by taking the intersection of the cerebellar grey matter segmentation defined by FastSurfer and a cerebellar grey matter segmentation, excluding vermis, derived using SUIT 3.1 (_python3, Brain and Mind Institute, University of Western Ontario, London, UK, and Ontario, Canada).^{Reference Diedrichsen33} The reference region washout rate (k ₂′) was computed using MRTM^{Reference Ichise, Ballinger, Golan, Vines, Luong and Tsai34} and a noise-free high-binding TAC was obtained from a surface-weighted average of the neocortical regions for this purpose.

Neuroticism

Participants completed the Danish version of the 240-item NEO-Personality Inventory-Revised (NEO-PI-R); this version has been normed in a sample of 600 individuals.^{Reference Skovdahl, Mortensen and Schiøtz35} The NEO-PI-R is a self-reported personality questionnaire that evaluates the broad personality traits of neuroticism, extraversion, openness, agreeableness and conscientiousness. Each trait is made up of six constituent facets which for the neuroticism trait include anxiety, depression, self-consciousness, vulnerability, angry hostility and impulsiveness. Each facet score is derived by summing the scores of six constituent items. The outcome measures of neuroticism for this study were the facets of anxiety, depression, self-consciousness and vulnerability to stress, as these facets have previously been shown to be associated with 5-HT_2AR binding.^{Reference Frokjaer, Vinberg, Erritzoe, Baaré, Holst and El11}

Cortisol awakening response

For the assessment of CAR, participants were instructed to collect saliva samples at home immediately after awakening and after 15, 30, 45 and 60 min. Saliva samples were collected in Salivette tubes (Sarstedt, Neubringen, Germany) by chewing a swab until it was completely saturated with saliva. Participants were instructed to avoid food intake, drinking, brushing their teeth and smoking during the first hour after waking up. If the first saliva sample was collected more than 10 min after awakening, the data-set was not included in our analyses. The participants noted whether the CAR samples were collected on a work day, study day or weekend/day off, and also noted the time of awakening and collection of the saliva sample. The saliva samples collected at home were stored in the refrigerator and returned to the laboratory the day after the completion of sampling, and if collected during the weekend, a maximum of 3 days after collection. The samples were subsequently stored at −80°C until analysis. Samples were shipped on dry ice to an external laboratory, and cortisol concentrations were analysed in four different batches across the collection period (i.e. 2012–2021) to minimise laboratory-dependent noise. The analysis of the first batch (37% of the total sample) was performed using the electroluminescence immunoassay (ECLIA) method on Modular Analytics E170 equipment (Roche, Mannheim, Germany), whereas the three remaining batches were analysed using the chemiluminescence immunoassay (CLIA) method on the IDS-iSYS automatic analyser (IDS plc, Boldon, UK). The intra- and inter-assay variations of both immunoassay methods were less than 15%. We also had internal saliva samples that were run with each batch for quality control.^{Reference Høgsted, Borgsted, Dam, Nasser, Rye Jørgensen and Ozenne36} The CAR measurements were computed as the area under the curve (AUC) with respect to the increase from baseline at waking (AUCi). The AUCi captures change over time and is thereby an index of reactivity of the HPA axis.^{Reference Fekedulegn, Andrew, Burchfiel, Violanti, Hartley and Charles37} There was no significant difference in AUCi values among the collection days (P = 0.14).

Analyses of demographic, clinical, scanner and radiotracer data

Analyses were conducted in R (version 4.3.12022.2.0)^{Reference R Core Team38} using RStudio. Group differences between males and females in continuous (age, weight-adjusted [¹¹C]Cimbi-36 injected mass (μg/kg)) and categorical variables (magnetic resonance scanner type) were evaluated using Mann–Whitney U-tests and Fisher's exact tests, respectively. All results were considered significant at P < 0.05.

Analysis of the association between neocortical 5-HT_2AR BP _ND and neuroticism

We applied a linear latent variable model (LVM) using the lava package (version 7.2.1)^{Reference Holst and Budtz-Jørgensen39} implemented in R to first examine the association between 5-HT_2AR binding in the neocortex and the inward-directed facets of neuroticism in 80 healthy individuals. LVM is used to model the association between neuroticism and PET with a single parameter via a latent variable.

This latent variable, denoted Inward-Neuroticism_LV captures the shared signal between facets and can be seen as a weighted average of the facets where the weights are estimated via maximum likelihood. The PET outcome variable was log-transformed 5-HT_2AR BP _ND in the neocortex, and the neuroticism outcome variable was a latent variable (termed Inward-Neuroticism_LV) representing the inward-directed facets of neuroticism, namely anxiety, depression, self-consciousness and vulnerability. Inward-Neuroticism_LV was adjusted for age and gender, while neocortical 5-HT_2AR BP _ND values were adjusted for age, gender and magnetic resonance scanner type. The association between 5-HT_2AR BP _ND and the Inward-Neuroticism_LV is described in terms of regression coefficients (‘parameter estimate’). The 5-HT_2AR BP _ND vertex maps were input into the same LVM model to aid the visualisation of this association at each cortical vertex. The parameter estimate and P-value maps were created using the oro.nifti package^{Reference Whitcher, Schmid and Thornton40} in R, and visualised using FreeSurfer.^{Reference Fischl29}

A sensitivity analysis using structural equation modelling examined the association between 5-HT_2AR BP _ND and the inward facets of neuroticism when the latter was calculated as the sum of its constituent traits (rather than as a latent variable). As in the LVM, neuroticism was adjusted for age and gender and 5-HT_2AR BP _ND was adjusted for age, gender and magnetic resonance scanner type.

CAR as a moderator of the association between neocortical 5-HT_2AR BP _ND and neuroticism

In individuals with data on CAR, secondary analysis with LVM was performed to determine if cortisol dynamics, as measured with CAR, moderated the association between 5-HT_2AR BP _ND and Inward-Neuroticism_LV. To perform this moderation analysis with LVM, CAR values were split into three equally sized categories.

Gender as a moderator of the association between neocortical 5-HT_2AR BP _ND and neuroticism

Lastly, an exploratory analysis using an LVM investigated whether gender moderated the association between 5-HT_2AR BP _ND and Inward-Neuroticism_LV. Gender differences in neuroticism have been repeatedly observed, with females reporting higher levels of this trait relative to males.^{Reference Schmitt, Realo, Voracek and Allik41,Reference South, Jarnecke and Vize42} Although 5HT_2AR binding has not consistently been shown to differ by gender,^{Reference Soloff, Price, Mason, Becker and Meltzer43–Reference Adams, Pinborg, Svarer, Hasselbalch, Holm and Haugbøl45} 5HT_2AR binding has been found to be positively associated with endogenous estradiol levels.^{Reference Frokjaer, Erritzoe, Juul, Nielsen, Holst and Svarer46} The evidence for the effect of gender on neuroticism and potentially on 5-HT_2AR BP _ND motivated this exploratory analysis.

For all the above-mentioned LVMs, the relevance of additional model paths was assessed using score tests (via the modelsearch function within the lava package). The path with the lowest P-value was added to the model if the adjusted P-value was below 0.05. A Bonferroni adjustment was used over all possible paths to control the family-wise error rate at 0.05. This procedure was repeated until no new model paths were supported.