Peripheral oxytocin levels are linked to hypothalamic gray matter volume in autistic adults: a cross-sectional study

doi:10.21203/rs.3.rs-2007632/v1

Download PDF

Research Article

Peripheral oxytocin levels are linked to hypothalamic gray matter volume in autistic adults: a cross-sectional study

https://doi.org/10.21203/rs.3.rs-2007632/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Oxytocin (OXT) is known to modulate social behavior and cognition and has, thus, been expected to be a potential therapeutic agent for individuals with autism spectrum disorder (ASD). However, the underlying neurobiology is far from clear. For instance, relatively little is known about the association between OXT levels and brain structural differences in autistic people. An accumulating body of evidence indicates the hypothalamus to be of particular importance in this regard.

Methods

Here we used region of interest (ROI)- based voxel-based morphometry (VBM) approach to investigate hypothalamic gray matter volume (GMV) in autistic and non-autistic adults. T1-weighted MRI images were obtained from autistic adults without intellectual impairment (n = 29, age 36.03 ± 11.0) and matched non-autistic controls (n = 27, age 30.96 ± 11.2). Peripheral plasma OXT levels and the autism quotient for autistic traits were used for correlation analyses.

Results

GMV of the hypothalamus did not differ between groups. However, the groups showed significant differences in the link between hypothalamic GMV with peripheral OXT levels, such that a positive correlation was found for the ASD group. In addition, hypothalamic GMV showed a positive correlation with autistic traits in the ASD group, while no such correlation was observed in the comparison group.

Limitations

: The relatively small sample size and the wide age range of the participants warrant caution when interpreting the results. Results could be confounded by the use of various psychiatric medications among our autistic participants. At the same time, this adequately represents the adult autistic population in terms of a naturalistic study design.

Conclusion

The results provide new evidence for a potentially important role of the HTH in ASD and its relationship to the OXT system, but also point towards the importance of interindividual differences.

Oxytocin

Autism

Hypothalamus

VBM

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by qualitative impairments of social interaction and communication and restricted and repetitive behaviors. In recent years, neuroimaging studies have reported a broad range of regional and global structural differences in the brains of autistic people. While it has become increasingly evident that there is no single defining neuroanatomical feature of ASD, meta-analyses and reviews have suggested that there may be neuroanatomical alterations that are, at least in parts, characteristic for ASD [1–3]. Consequently, there is hope that regional patterns of neuroanatomical differences could serve as diagnostic, prognostic, and treatment-determining markers [3]. Especially in the case of autistic adults, the possibilities of diagnosis, prognosis and possible beneficial interventions are still quite limited and observer-independent markers would be of great use in this regard [4–6]. Oxytocin (OXT) is known to modulate social behavior and cognition and has, thus, been expected to be a potential therapeutic agent for autistic individuals [7]. Based on this, research has sought to determine whether and how an impairment of the OXT system could be a pathophysiological factor in ASD [8–10]. However, the underlying neurobiology is far from clear and reviews and meta-analyses regarding the efficacy of treatment with OXT are mixed [11–15]. A better understanding of the OXT system at the structural level and the link between brain structure and peripheral OXT levels in ASD could provide important insights to better evaluate the therapeutic potential of OXT and to potentially develop appropriate stratification tools aiming for the identification of subgroups that could benefit from OXT-based interventions.

With regard to a potential brain structural marker of the OXT system, the hypothalamus (HTH) is of particular interest. In the CNS, OXT and the related hormone vasopressin (VP) are synthesized in the HTH in the magnocellular and parvocellular neurons of the supraoptic nucleus (SON) and paraventricular nucleus (PVN). Apart from axonal transport via the hypothalamo-hypophysial tract to the posterior pituitary lobe, the nuclei also project to a variety of brain regions such as the hippocampus, the amygdala and the nucleus accumbens [16]. In addition, distribution of OXT also occurs in the form of neurosecretion directly from dendrites and somata and likely also by secretion into the cerebrospinal fluid of the adjacent 3rd ventricle [17–19]. The OXT receptor has been reported to be expressed throughout the brain, particularly in the HTH and in structures of the limbic system, as well as in various cortical areas associated with social and emotional processes [20–23]. Thus, the anatomy of the OXT system is well compatible with its ascribed function to orchestrate socioemotional processes. Lesions of the HTH have been associated with a range of behavioral and emotional symptoms such as aggressiveness, depression, and social withdrawal [24]. For example, craniopharyngeoma patients, who frequently suffer from a lesion of the HTH caused either directly by the tumor or indirectly by therapeutic resection of the tumor, were found to have a high prevalence of socio-behavioral impairments [25, 26]. Consistent with the hypothesis that OXT regulation may be altered in these patients, Gebert et al. (2018) reported a significant association of decreased OXT levels and hypothalamic injury [27]. Notably, the same patients also showed significantly higher levels of autistic traits and more difficulties in rapid emotion recognition compared to controls [28]. This raises the question whether the socioemotional characteristics found in ASD could be similarly related to OXT and structural alterations in the HTH. Indeed, the three studies which have reported structural findings of the HTH in ASD, have concordantly reported a reduced grey matter volume (GMV) or concentration in autistic children, adolescents and young adults [29–31] as well as an associated enlargement of the 3rd ventricle [30] compared to neurotypical control subjects in the corresponding age range. Shou et al. (2017) further reported a positive association of bilateral hypothalamic volume with peripheral VP concentrations in autistic children (n = 14, mean age = 4.5 years), while they did not measure VP in the control group. VP is a neuropeptide closely related to OXT and produced in the same nuclei. Similar to OXT, it has been suggested to be involved in the regulation of social behavior [10]. Shou et al. (2017) thus hypothesized that impairment of the VP/OXT system is associated with changes in brain morphology and function in ASD. However, they did not examine OXT levels in this context. In line with structural HTH alterations in autistic individuals, studies in healthy carriers of OXTR variants associated with an increased likelihood of ASD have found a significant decrease in GM volume in the HTH in healthy carriers of the rs53576 and rs2254298A alleles [32, 33], for review see [34]. To the best of our knowledge, the link between HTH structure and OXT plasma levels has not been investigated in autistic adults. We, therefore, assessed HTH morphometry by means of a region-of-interest (ROI)-based analysis using voxel-based morphometry (VBM) in a sample of autistic adults without intellectual impairment and matched neurotypical controls. To investigate the relationship between the OXT system and the structural features of the HTH in ASD, peripheral baseline OXT levels and autism quotient (AQ) scores were assessed.

2.1 Participants

Participants were recruited as part of a larger study examining OXT and cortisol levels in autistic and non-autistic adults [35]. Recruitment sources included the “Outpatient and Day Clinic for Disorders of Social Interaction” at the Max Planck Institute of Psychiatry (MPIP) for autistic individuals and an online study application system on the Institute’s website, as well as public advertisements for participants in the comparison group. Of the sixty-four participants included in the larger project, fifty-nine underwent structural MRI. After exclusion of three scans following a quality check protocol (2 due to poor image quality, 1 due to structural abnormalities, see Appendix), the final dataset included brain scans from fifty-six adults aged 18–60 years: twenty-nine autistic adults in the ASD group (17 men; mean age = 36.03 ± 11.0 years) and twenty-seven non-autistic adults in the CG (comparison group) (9 men; mean age = 30.96 ± 11.2 years). Autistic individuals met DSM-V criteria for ASD and had been previously diagnosed in accordance with current guidelines [36]. Autistic participants had no intellectual impairment (IQ scores > 70) and were thus regarded as individuals with high-functioning autism (HFA). A detailed description of the diagnostic assessment is reported in [35]. Test for normal distribution of demographic data using a Shapiro-Wilk test revealed a non-normal distribution for age, handedness scores and verbal IQ (WST [37]). Group comparison for these variables was, therefore, performed with a Man-Whitney U test, whereas Chi² tests were used for categorical variables and t-tests for continuous variables. There were no significant differences (all p > 0.05) between groups in age, sex distribution, handedness score, BMI, verbal IQ, or lifestyle factors (smoking, alcohol, exercise) (Tbl. 1). Thirteen subjects in the ASD group took psychiatric medication regularly. These patients were asked not to take the medication in the morning before the OXT measurements, but after the experiments. General exclusion criteria were severe somatic illness, a current or previous schizophrenia diagnosis, breastfeeding, pregnancy, hormonal contraception, and a contraindication to MRI. The study protocol followed the guidelines of the Declaration of Helsinki and was approved by the ethics committee of the Ludwig-Maximilians-University of Munich. All participants gave written informed consent before participating in the study and received fixed monetary compensation at the end of the experiment.

Table 1 Demographic data.

	CG (n = 27)	ASD (n = 29)	group comparison
	mean (± SD) / N (%)	mean (± SD) / N (%)	t/χ2/U-test	p
Age	30.96 (11.2)	36.03 (11.0)	275.5	0.06
Sex (male:female)	9:18	17:12	3.60	0.06
BMI ^b	22.61 (4.1)	24.2 (4.3)	-1.39	0.17
Handedness score ^b	89.62	66.10	281.50	0.13
Smoker	2 (7.40%)	5 (17.24%)	1.24	0.27
Alcohol	10 (37.03%)	12 (41.37%)	0.11	0.74
Exercise	20 (74.07%)	17 (58.62%)	1.49	0.22
Psychiatric medication	0 (0%)	13 (44.82%)	15.76	< 0.001
AQ-scores ^a	13.89 (5.47)	35.07 (10.27)	-9.6	< 10^− 11

^aData available for 55 participants .

^b Data available for 54 participants.

2.2 Experimental procedures and analyses

2.2.1 Questionnaires

For the quantification of autistic traits, the Autism Spectrum Quotient AQ [38] was used. The AQ is a well-established, self-report questionnaire that provides a scaled measure of the characteristics associated with ASD on a scale of 0 to 50. The autistic traits themselves are regarded as a dimensional construct, which reflects both the autistic and the non-autistic population [39, 40]. Groups were compared using an independent samples t-test in SPSS (IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp). AQ scores were available for n = 55 participants (ASD: n = 28; CG: n = 27).

2.2.2 Oxytocin measurements

For a detailed description of sample acquisition and OXT extraction please refer to the relevant publication [35]. In brief, the participants were asked to abstain from food (> 12h), water (> 1h) and sports the day before the study. After arriving at the outpatient unit of the MPIP at 8:30 am, blood samples were obtained at rest. OXT concentrations were quantified in an external laboratory (RIAgnosis, Sinzing, Germany) using radioimmunoassay (RIA) as previously described [41]. For VBM correlation analyses, which included peripheral OXT baseline levels, the concentrations in pg/ml were used. To confirm that the present subsample of participants showed OXT characteristics similar to the sample included in Albantakis et al. 2021 [35], univariate analyses were performed to test for effects of diagnostic group on baseline OXT concentrations, including age and sex as covariates. Data on baseline OXT levels were available for n = 53 participants (ASD: n = 26; CG: n = 27).

2.2.3 Image Acquisition

Structural brain scans were obtained using a 3 Tesla MRI scanner by GE, model ‘Discovery MR750’. In the scanner room participants received earplugs and an emergency bell in their right hand, the head was then secured firmly in the head coil with inflatable cushions. 3D anatomical T1-weighted MPRAGE (magnetization-prepared rapid gradient echo) magnetic resonance images were acquired with the following parameters: Flip angle 12°, Prep Time 450, echo time (TE) 2.3 ms, frequency 256, voxel resolution 1×1×1 mm.

2.2.4 Image processing

First, T1 images were converted from DICOM into NIfTI format. After checking for correct alignment to the AC/PC axis, images were pre-processed using the publicly available toolbox Cat12 Version 12.7 (1600) (http://www.neuro.uni-jena.de/cat/) implemented in SPM12 (Statistical Parametric Mapping software, http://www.fil.ion.ucl.ac.uk/spm/) using Matlab version R2019a (The MathWorks, Inc., Natick, Massachusetts, USA). Pre-processing was carried out using the standard pipeline and pre-set parameters as suggested in the Cat12 manual (http://www.neuro.uni-jena.de/cat12/CAT12-Manual.pdf). Pre-processing with Cat12 involved bias field inhomogeneity correction and denoising, using the Spatially Adaptive Non-Local Means (SANLM) Filter [42], segmentation into grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in accordance with the unified registration approach [43] and spatial normalization and affine registration to MNI space using a template for high-dimensional DARTEL registration derived from 555 healthy subjects of the IXI- database (http://brain-development.org/) with a final voxel size of 1.5 × 1.5 × 1.5 mm. In version 12.7 of CAT12 used here, this process is extended by refined voxel-based processing using adaptive maximum a posteriori (AMAP) estimation, a Markov Random Field approach (MRF) [44] and accounting for partial volume effects [45]. Finally, segmentations were modulated by multiplication with the Jacobian determinant derived from spatial registrations. This step preserves the original volumes within a voxel, which are altered during registration [46] and is recommended by default. For detailed description of the individual steps in Cat12 we refer to the publishers website. Prior to smoothing, images were checked for correct pre-processing in accordance with the quality check protocol suggested in the Cat12 manual (see Appendix). Following suggestions of applying comparably small kernels for analyses in the HTH due to its small size and size of expected effects [30, 47] images were smoothed with a Gaussian kernel of 4 mm (FWHM). An absolute grey matter threshold masking of 0.1 was applied to account for a possible misclassification of tissues.

2.2.5 ROI-based VBM analysis

For statistical analysis, the general linear model (GLM) approach as implemented in SPM12 was used. Smoothed and modulated GM images were entered in a region of interest (ROI)- based VBM analysis using a HTH mask derived from the subcortical brain nuclei atlas by Pauli et al. (2018) (https://neurovault.org/collections/3145/). The mask was resliced to fit the template space in SPM12 and encompassed 1085 voxels. Clusters were regarded as significant when falling below an initial uncorrected voxel threshold of .001 and an FWE-corrected cluster threshold of .05 inside the ROI. To account for variance due to differences in total intracranial volume (TIV), age and sex, these variables were included as nuisance parameters. Due to co-linearity between TIV and OXT (cos (θ) = r =- 0.35), the correction for TIV in the analysis including OXT levels was applied by global scaling with TIV, as is recommended [49]. Voxel wise group comparison of hypothalamic GMV was conducted using a two-sample t-test for the contrasts ASD > CG and ASD < CG. To test for group differences in associations of GMV and OXT, (i.e. interaction effects), OXT concentrations were included in a full-factorial model and tested for significant positive and negative contrasts inside the HTH mask, i.e. (GMV[ASD] × OXT > GMV[CG] × OXT) and the other way round. To test if hypothalamic GMV is associated with autistic traits, a multiple regression analysis on all subjects was performed with AQ scores as covariate of interest and tested for significant positive and negative associations. Since we did not find a significant association here, we subsequently tested associations of GMV and AQ scores in both groups separately. To investigate whether significant results at the voxel level were also evident at the level of the overall mean hypothalamic GMV, significant statistical models were repeated using the mean hypothalamic GMV. To this end, the unadjusted eigenvariate within the HTH mask was extracted in SPM and implemented in SPSS for further analysis.

3.1 Autistic traits

Comparison of AQ scores between the ASD group (M = 35.07 ± 10.27) and the CG (M = 13.89 ± 5.47) showed significantly higher scores in the ASD group, t(41) = 9.6, p < 10^− 11.

3.2 Peripheral baseline OXT concentrations

As in the original sample [35], the present sub-sample showed no significant main effects of diagnostic group (F(1,49) = 0.104, p = .749), sex (F(1,49) = 0.769, p = .385) or age (F(1,49) = 0.058, p = .810) on plasma OXT baseline concentrations.

3.3 Volumetric group comparison of the HTH

Voxel-wise group comparison of GMV within the HTH mask showed no significant differences.

3.4 Association of hypothalamic GMV and peripheral OXT levels

Voxel-wise group comparison of the link between hypothalamic GMV and OXT revealed significant differences in associations for the contrast (GMV[ASD] × OXT > GMV[CG] × OXT) in a cluster inside the HTH mask at peak-MNI coordinates [5,–9,–2] (FWE corr. p=0.017, T=3.85, Z=3.57, k=46). The cluster was extracted with marsbar [50] and plotted for better illustration (Fig. 2). Likewise repetition of the statistical model using the mean hypothalamic GMV was significant for the interaction term of group and OXT (F(1, 46) = 5.675, p =0.021, η²=0.110) indicating a positive correlation in the ASD group (R=0.326) opposed to a negative correlation in the CG group (R=0.211).

3.5 Association of hypothalamic GMV and autistic traits

There was no significant correlation between GMV and AQ scores within the HTH across the groups. Group-specific correlation analysis revealed a significant positively correlated cluster in the ASD group at peak MNI coordinates [-2,2,9] (FWE corr. p=0.014, T= 4.74, Z=3.92, k=46), while there was no significant correlation in the CG. Likewise, repetition of the model using mean hypothalamic GMV showed a significant association in the ASD group (F(1, 23) = 12.78, p =0.002, η²=0.357), while there was no significant correlation in the CG group (F(1, 22) = 0.215, p =0.648). To determine if the cluster in the ASD group matched the cluster from the previous analysis including the OXT levels, the two clusters were plotted together on the structural mean image (Fig.2). Visual comparison showed only a marginal overlap between the two clusters. Explorative correlation of the cluster from AQ analysis with plasma OXT levels in the ASD group was not significant.

Recognizing the role of the neuropeptide OXT in social behavior, research in recent years has sought to determine if and how OXT might be involved in the pathogenesis of ASD. In this study we investigated structural features of the HTH and its possible link to the OXT system in autistic and non-autistic adults. While voxel-wise comparison showed no significant differences between groups, mean hypothalamic GMV was diminished in autistic compared to non autistic adults. With regard to peripheral OXT plasma levels there was a significant group difference in association with hypothalamic GMV. Moreover there was a positive association with autistic traits in autistic adults only.

The idea that differences in the OXT system may underlie some of the core symptoms of ASD has already been proposed more than two decades ago [51]. Human research in recent years has since highlighted a wide range of potential differences in ASD related to the OXT system. These include the processing of OXT peptides [52], genetic variations in the oxytocin receptor gene (OXTR) [53], the structural gene for OXT (neurophysin-I) [54], and epigenetic alterations [55, 56]. Another often studied marker for an altered OXT system in ASD is peripheral OXT concentrations. Since the first report of decreased basal OXT levels in children with ASD [57], there have been a variety of studies in children with varying and partly inconsistent results. The situation is particularly unclear in adults. While there are reports of lower [58] and higher levels [59] in autistic adults, our group recently added to the existing literature by reporting no differences in basal OXT levels [35], which was also true for the subsample included here. In accordance with our findings and highlighting the importance of age in this context, a recent meta-analysis including 31 studies reported baseline concentrations of OXT to be lower in autistic children compared with control participants, whereas they did not differ in autistic adults [60]. This is suggestive for relevant developmental changes in the OXT system in ASD and possibly for normalization of OXT levels in adulthood. The importance of developmental effects has also recently been shown with regard to OXTR expression patterns [61]. Andari et al. (2014) have previously speculated on a relationship between the OXT system and social behavior, suggesting a reciprocal enhancement of OXT release and social contact during development [62]. Seen that social experiences such as social network size are reflected in brain structure [63, 64], they further suggest that the developmental interplay of OXT and social experiences is also likely to be reflected in structural changes in the brain. Taking into account previous findings on the HTH in autistic children, the results of this adult study may suggest that there may be indeed developmental changes in the HTH brain structure related to the development of the OXT system in ASD. In contrast to previous structural studies reporting diminished hypothalamic GMV in autistic children (mean age 4.1 ± 0.72; n = 28) [31], adolescents (mean age 11.2 ± 3.7; n = 104) [29] and young male adults (mean age 21.0 ± 0.9; n = 20) [30], we found no group differences in our adult sample (mean age 36.03 ± 11.0 range [18;60]; n = 56). These results may suggest that the hypothesized normalization of OXT levels in adulthood in ASD is associated with normalization of hypothalamic volume. While, to the best of our knowledge, our study is the first to explicitly report no volumetric differences in the HTH between autistic and non-autistic individuals, it should be noted that from a formal perspective whole-brain VBM is a method that does not depend on a prior spatial hypothesis, so that VBM studies in ASD that found no abnormalities in the HTH could be considered nonsignificant results with respect to this structure. However, as previously pointed out by Schindler et al. (2012) [65], the sensitivity of VBM to changes in structures as small as the HTH depends largely on the hypothesis-dependent parameter settings. Hence, a lack of discussion of the HTH in previous studies does not automatically imply a lack of effect in this region. Further supporting the notion of a potentially important role of HTH in ASD, but also indicating the importance of individual differences in this regard, we found a significant association between autistic traits and GMV in HTH in the ASD group, but no correlation in the CG. Taken together these results may point to a link between the growth pattern of the HTH and ASD. While further, preferably longitudinal, studies are needed to confirm this hypothesis, the concept of complex growth patterns of regional brain volumes in ASD that are strongly restricted to specific phases of neurodevelopment is not new [66–68]. A growth pattern of volume reduction and convergence to normal volume has for example been reported for the striatum [69]. A ‘reverse’ growth pattern of a decrease with age has been suggested for the amygdala and a range of other structures [70–72]. However, the mechanisms of these structural developments and whether they reflect an ASD inherent growth pattern or might rather reflect compensatory neuroplasticity and adaptations are unclear. In the present study, we found evidence that GMV in the HTH may be linked to peripheral OXT levels and that OXT may therefore be one of the factors underlying structural changes in this region. More precisely, we found a positive association of OXT with hypothalamic GMV in autistic adults as compared to non-autistic adults. As an underlying mechanism for how OXT might be associated with structural changes, the regulatory function of OXT on neuronal plasticity and its role in inhibitory or proliferative cellular processes is well established [73–78]. However, at this stage, it remains unclear whether the here observed volumetric effects are directly attributable to OXT or mirror other factors related to both peripheral OXT concentrations and volume of the HTH. In this context it should be noted that it remains controversial whether and to what extent peripheral OXT measurements can reflect central (dys-)regulation of the OXT system. In particular, a coordination of peripheral and central OXT levels at baseline conditions has been called into question [79]. The inconclusive picture regarding the informativeness of peripheral OXT levels has motivated researchers to investigate genetic variation in the human OXTR gene. Here several single nucleotide polymorphisms (SNPs) such as rs53576 and rs2254298 have been linked to ASD [53]. Interestingly, Tost et al. reported an association of these variants with structural differences in the HTH in healthy carriers [32, 33]. Furthermore, rs2254298, along with other OXTR variants with increased likelihood for ASD, have been reported to be closely linked to peripheral OXT levels [80]. This raises the question of whether changes in the HTH and OXT levels are modulated by common factors such as variations in OXTR. In this context, it also remains to be clarified to what extent other genetic variations of the OXT system such as the structural gene for OXT (oxytocin-neurophysin I) and CD38 (associated with OXT release) play a role [81]. Further investigation of the interrelationships of the HTH and the genetic variations in OXT system may be crucial to understand the significance of OXT for ASD.

Finally, a better understanding of the structure and function of HTH in ASD may also be relevant to other psychiatric disorders associated with socioemotional alterations. For example, using an approach similar to the one used here, Mielke et al. 2018 [82] conducted an analysis in women with early childhood maltreatment guided by the hypothesis that the reported deficits in reward processing in adults who were maltreated as children may be related to OXT and structural differences in the HTH. Interestingly, compared to our results, they found an opposite relationship between peripheral OXT levels and HTH volume in patients compared to controls, possibly indicating a differential relationship of OXT and the HTH compared to ASD. Although it is too early at this stage to draw conclusions here, comparative analyses of the relationship between OXT and the HTH in different psychiatric conditions could be a promising approach for future studies. In addition to the craniopharyngioma patients mentioned above [83], hypothalamic abnormalities have also been reported in schizophrenia [84] and mood-disorders [65]. Given the high prevalence of depression in ASD [85], a comparative analysis in this regard would be particularly relevant to determine the specificity of structural effects in the HTH for ASD.

The results presented here should be interpreted in the context of some methodological considerations and limitations. Given the well-established role of the HTH for the OXT system and the potentially important role of OXT in ASD, a surprisingly small number of studies have focused on this brain structure. This is certainly partly due to the methodological difficulties in proper identification of the HTH in MRI images. Here, we took a relatively straightforward approach by using a HTH mask created on the basis of 168 typical adults [48]. Since this mask was not created specifically for our sample, it can only be considered a rough regional reference. Manual delineation remains the gold standard here, but comes at the cost of a high degree of expertise and time investment [47, 86]. Other studies have used a HTH mask based on the WFU pick atlas [82, 87], which we decided against since it covered the HTH much less accurately in a visual comparison. Since we did not aim for an exact volumetry of the entire HTH but mainly for a voxel-wise comparison between groups in this region, some degree of inaccuracy may not have had such a large impact. Currently, none of the atlases implemented in Cat12, such as AAL3 or Neuromorphometrics, include the HTH, which prevented us from using automated ROI analysis in the native subject space [88]. The implementation and improvement of such atlases as well as the use of deep-learning approaches [89] are promising developments in terms of accurate and user-friendly volumetry in this brain region for future studies. Another methodological issue is that it remains unclear what tissue properties may underlie the altered GM signal in the HTH in VBM studies including our own. In addition to physical cell volume, also nuclear volume, local cell number, and spatial cell grouping of neurons, glia, vessels, and neuropil could play a role [90]. Remarkably, differences in all these morphological and cytoarchitectonic features have been described throughout the brain in ASD [91]. Further limitations concern the sample studied here. Confirming the previous diagnosis of autistic participants, regarded as high-functioning autism without intellectual impairment, there were no group differences in verbal IQ but significant differences in AQ scores, which were well in line with the corresponding reference norms for autistic and non-autistic individuals [40]. As such, our analysis included only ASD patients diagnosed with high-functioning autism. This, naturally, limits the generalizability to the entire autistic spectrum. However, this limitation also appears beneficial in light of reports that hypothalamic abnormality may be strongly implicated in intellectual disability [92], which would certainly complicate the attribution of findings in the HTH to the autistic phenotype in so called low-functioning autists. Although the groups were not significantly different, they were not optimally balanced in terms of age and sex distribution. Particularly in light of reported sex-dependent differences of the HTH in neurotypicals [93] and in associations with OXTR [32, 94], this might warrant sex-specific analyses in a larger sample. While the large age range (18–60 years) of adults tested here may provide a good overview of robust structural effects, it risks overlooking age-specific effects given the apparent age dependency of morphological abnormalities in ASD. Future studies in autistic adults should therefore consider evaluation in groups with smaller age ranges or in a longitudinal framework to determine how structural characteristics of the HTH manifest across the lifespan. Subgroup evaluation was not possible in our study because it would have made the resulting sample size too small. In this sample, 45% of autistic participants received psychiatric medication on a regular basis. This adequately represents the high degree of comorbidities in autistic adults in terms of a naturalistic study design [4]. Due to the various pharmaceutical substances (antispsychotics, antidepressants, stimulants) and different dosages, it did not seem helpful to include medication as confounding variable. While we cannot rule out the possibility that medication may have had an impact on HTH structure, studies to date show no evidence of structural effects in this regard [95, 96].

Bearing in mind the limitations of this study, our results suggest that GMV in the HTH does not differ between autistic and non-autistic adults. However, we observed an association between interindividual differences in autistic traits and GMV in the HTH, but only in autistic adults. Furthermore, in autistic as compared to non-autistic adults, we observed a positive association between peripheral OXT concentration and GMV in the HTH. Although this study does not provide insight into a causal relationship of these associations, our results provide new evidence for a potentially important role of HTH in ASD and its relationship to the OXT system, but also point to the importance of interindividual differences. In conjunction with previous studies our results also raise new questions about possible developmental changes in brain structure and its relationship to OXT in ASD and should motivate further investigation. Finally, a better understanding of the interrelationship between genetic variation of the OXT system, OXT levels, and brain structure may considerably enhance our understanding of the role of OXT in ASD both as pathophysiological factor and potential therapeutic agent.

AQ: Autism Quotient; ASD: Autism spectrum disorder; Cat12: Computational Anatomy Toolbox for SPM, Version 12; CG: Comparison group; DARTEL: Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra; CSF: Cerebrospinal fluid; FWE: Family-wise-Error; GLM: General Linear Model; GM: Gray matter; GMV: Gray Matter Volume; HFA: High-functioning autism; HTH: Hypothalamus; MPIP: Max-Planck-Institute of Psychiatry; MPRAGE: Magnetization-prepared rapid gradient echo; OXT: Oxytocin; OXTR: Oxytocin receptor; PVN: Paraventricular nucleus; ROI: Region of interest; SON: Supraoptic nucleus; TIV: Total intracranical volume; VBM: Voxel based mophometry; VP: Vassopressin; WM: White matter; WMV: White matter volume; WST: Wortschatztest (Vocabulary Scale).

Ethics approval and consent to participate

All study participants provided written informed consent. Ethical approval was granted by the Ethics Committee of the Ludwig-Maximilians-University (LMU) Munich (Project number: 712–15). All procedures were performed in accordance with the Declaration of Helsinki. Participants could withdraw from the study at any time and were financially compensated for their time.

Consent for publication

Not applicable.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author, RH, upon reasonable request.

Competing interests

None

Funding

This work was supported by the Max Planck Society via a grant for an Independent Max Planck Research Group awarded to LS.

Authors' contributions

All named authors contributed to the manuscript. LS designed the experiment and led the project. RH analyzed and interpreted the data and drafted the manuscript. JL, LA, MLB, LS interpreted the data. RH, LA, MLB, LH were responsible for the collection of the data and samples including neuroimaging data. LS acquired funding. All authors read and approved the final manuscript.

Acknowledgements

The authors would like to thank all of our participants for partaking in this study. We would also like to thank our clinical colleagues from the Outpatient and Day Clinic for Disorders of Social Interaction at the Max Planck Institute of Psychiatry (MPIP) for the referral of potential study participants.

Lord C, Elsabbagh M, Baird G, Veenstra-Vanderweele J. Autism spectrum disorder. Lancet 2018;392:508–20. https://doi.org/10.1016/S0140-6736(18)31129-2.
Donovan APA, Basson MA. The neuroanatomy of autism – a developmental perspective. J Anat 2017;230:4–15. https://doi.org/10.1111/joa.12542.
Ecker C. The neuroanatomy of autism spectrum disorder: An overview of structural neuroimaging findings and their translatability to the clinical setting. Autism 2017;21:18–28. https://doi.org/10.1177/1362361315627136.
Lai MC, Baron-Cohen S. Identifying the lost generation of adults with autism spectrum conditions. The Lancet Psychiatry 2015;2:1013–27. https://doi.org/10.1016/S2215-0366(15)00277-1.
Howlin P, Moss P. Adults with Autism Spectrum Disorders. Can J Psychiatry 2012;57:275–83. https://doi.org/10.1177/070674371205700502.
Happé F, Charlton RA. Aging in Autism Spectrum Disorders: A Mini-Review. Gerontology 2012;58:70–8. https://doi.org/10.1159/000329720.
Kirsch P, Meyer-Lindenberg A. The Neurochemical Basis of Autism. Boston, MA: Springer US; 2010. https://doi.org/10.1007/978-1-4419-1272-5.
Modahl C, Fein D, Waterhouse L, Newton N. Does oxytocin deficiency mediate social deficits in autism? J Autism Dev Disord 1992;22:449–51. https://doi.org/10.1007/BF01048246.
Quattrocki E, Friston K. Autism, oxytocin and interoception. Neurosci Biobehav Rev 2014;47:410–30. https://doi.org/10.1016/j.neubiorev.2014.09.012.
Borie AM, Theofanopoulou C, Andari E. The promiscuity of the oxytocin–vasopressin systems and their involvement in autism spectrum disorder. Handb. Clin. Neurol., vol. 182. 1st ed., Elsevier B.V.; 2021, p. 121–40. https://doi.org/10.1016/B978-0-12-819973-2.00009-5.
Guastella AJ, Hickie IB. Review Oxytocin Treatment, Circuitry, and Autism : A Critical Review of the Literature Placing Oxytocin Into the Autism Context. Biol Psychiatry 2016;79:234–42. https://doi.org/10.1016/j.biopsych.2015.06.028.
Alvares GA, Quintana DS, Whitehouse AJO. Beyond the hype and hope: Critical considerations for intranasal oxytocin research in autism spectrum disorder. Autism Res 2017;10:25–41. https://doi.org/10.1002/aur.1692.
Ooi Y, Weng S-J, Kossowsky J, Gerger H, Sung M. Oxytocin and Autism Spectrum Disorders: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Pharmacopsychiatry 2016;50:5–13. https://doi.org/10.1055/s-0042-109400.
Goel R, Hong JS, Findling RL, Ji NY. An update on pharmacotherapy of autism spectrum disorder in children and adolescents. Int Rev Psychiatry 2018;30:78–95. https://doi.org/10.1080/09540261.2018.1458706.
Huang Y, Huang X, Ebstein RP, Yu R. Intranasal oxytocin in the treatment of autism spectrum disorders: A multilevel meta-analysis. Neurosci Biobehav Rev 2021;122:18–27. https://doi.org/10.1016/j.neubiorev.2020.12.028.
Huggenberger S, Moser N, Schröder H, Cozzi B, Granato A, Merighi A. Autonomes Nervensystem: viszeromotorische Bahnen. 2019. https://doi.org/10.1007/978-3-662-56461-5_16.
Landgraf R, Neumann ID. Vasopressin and oxytocin release within the brain: A dynamic concept of multiple and variable modes of neuropeptide communication. Front Neuroendocrinol 2004;25:150–76. https://doi.org/10.1016/j.yfrne.2004.05.001.
Sabatier N, Caquineau C, Dayanithi G, Bull P, Douglas AJ, Guan XMM, et al. α-Melanocyte-Stimulating Hormone Stimulates Oxytocin Release from the Dendrites of Hypothalamic Neurons while Inhibiting Oxytocin Release from Their Terminals in the Neurohypophysis. J Neurosci 2003;23:10351–8. https://doi.org/10.1523/jneurosci.23-32-10351.2003.
Swanson LW, Sawchenko PE. Paraventricular Nucleus:A Site for the Integration of Neuroendocrine and Autonomic Mechanisms. Neuroendocrinology 1980;31:410–7. https://doi.org/10.1159/000123111.
Boccia ML, Petrusz P, Suzuki K, Marson L, Pedersen CA. Immunohistochemical localization of oxytocin receptors in human brain. Neuroscience 2013;253:155–64. https://doi.org/10.1016/j.neuroscience.2013.08.048.
Loup F, Tribollet E, Dubois-Dauphin M, Dreifuss JJ. Localization of high-affinity binding sites for oxytocin and vasopressin in the human brain. An autoradiographic study. Brain Res 1991;555:220–32. https://doi.org/10.1016/0006-8993(91)90345-V.
Insel TR, Young LJ. The neurobiology of attachment. Nat Rev Neurosci 2001;2:129–36. https://doi.org/10.1038/35053579.
Jurek B, Neumann ID. The Oxytocin Receptor: From Intracellular Signaling to Behavior. Physiol Rev 2018;98:1805–908. https://doi.org/10.1152/physrev.00031.2017.
Giustina A, Braunstein GD. Hypothalamic Syndromes∗. Endocrinol. Adult Pediatr., vol. 1–2. Seventh Ed, Elsevier; 2016, p. 174–187.e5. https://doi.org/10.1016/B978-0-323-18907-1.00010-X.
Zada G, Kintz N, Pulido M, Amezcua L. Prevalence of Neurobehavioral, Social, and Emotional Dysfunction in Patients Treated for Childhood Craniopharyngioma: A Systematic Literature Review. PLoS One 2013;8:e76562. https://doi.org/10.1371/journal.pone.0076562.
Müller HL, Merchant TE, Puget S, Martinez-Barbera JP. New outlook on the diagnosis, treatment and follow-up of childhood-onset craniopharyngioma. Nat Rev Endocrinol 2017;13:299–312. https://doi.org/10.1038/nrendo.2016.217.
Gebert D, Auer MK, Stieg MR, Freitag MT, Lahne M, Fuss J, et al. De-masking oxytocin-deficiency in craniopharyngioma and assessing its link with affective function. Psychoneuroendocrinology 2018;88:61–9. https://doi.org/10.1016/j.psyneuen.2017.11.006.
Brandi M, Gebert D, Kopczak A, Auer MK, Schilbach L. Oxytocin release deficit and social cognition in craniopharyngioma patients. J Neuroendocrinol 2020;32:1–13. https://doi.org/10.1111/jne.12842.
Kurth F, Narr KL, Woods RP, O’Neill J, Alger JR, Caplan R, et al. Diminished gray matter within the hypothalamus in autism disorder: A potential link to hormonal effects? Biol Psychiatry 2011;70:278–82. https://doi.org/10.1016/j.biopsych.2011.03.026.
Wolfe FH, Auzias G, Deruelle C, Chaminade T. Focal atrophy of the hypothalamus associated with third ventricle enlargement in autism spectrum disorder. Neuroreport 2015;26:1017–22. https://doi.org/10.1097/WNR.0000000000000461.
Shou X-J, Xu X-J, Zeng X-Z, Liu Y, Yuan H-S, Xing Y, et al. A Volumetric and Functional Connectivity MRI Study of Brain Arginine-Vasopressin Pathways in Autistic Children. Neurosci Bull 2017;33:130–42. https://doi.org/10.1007/s12264-017-0109-2.
Tost H, Kolachana B, Hakimi S, Lemaitre H, Verchinski BA, Mattay VS, et al. A common allele in the oxytocin receptor gene (OXTR) impacts prosocial temperament and human hypothalamic-limbic structure and function. Proc Natl Acad Sci U S A 2010;107:13936–41. https://doi.org/10.1073/pnas.1003296107.
Tost H, Kolachana B, Verchinski BA, Bilek E, Goldman AL, Mattay VS, et al. Neurogenetic effects of OXTR rs2254298 in the extended limbic system of healthy Caucasian adults. Biol Psychiatry 2011;70:e37-9; author reply e41-2. https://doi.org/10.1016/j.biopsych.2011.06.034.
Caria A, Ciringione L, de Falco S. Morphofunctional Alterations of the Hypothalamus and Social Behavior in Autism Spectrum Disorders. Brain Sci 2020;10:435. https://doi.org/10.3390/brainsci10070435.
Albantakis L, Brandi M-L, Brückl T, Gebert D, Auer MK, Kopczak A, et al. Oxytocin and cortisol concentrations in adults with and without autism spectrum disorder in response to physical exercise. Compr Psychoneuroendocrinology 2021;5:100027. https://doi.org/10.1016/j.cpnec.2021.100027.
AWMF. Autismus-Spektrum-Störungen im Kindes-, Jugend- und Erwachsenenalter. AWMF-Registernummer 028 – 018, Interdiszip. S3- leitlin vol. 1, der DGKJP und der DGPPN. Interdiszi, Elsevier; 2015, p. 252. https://doi.org/10.1016/B978-3-437-24911-2.00026-2.
Schmidt K-H, Metzler P. Wortschatztest. WST. Testmappe. Beltz 1992.
Baron-Cohen S, Wheelwright S, Skinner R, Martin J, Clubley E. The Autism-Spectrum Quotient (AQ): Evidence from Asperger Syndrome/High-Functioning Autism, Males and Females, Scientists and Mathematicians. J Autism Dev Disord 2001;31:5–17. https://doi.org/10.1023/A:1005653411471.
Austin EJ. Personality correlates of the broader autism phenotype as assessed by the Autism Spectrum Quotient (AQ). Pers Individ Dif 2005;38:451–60. https://doi.org/10.1016/j.paid.2004.04.022.
Ruzich E, Allison C, Smith P, Watson P, Auyeung B, Ring H, et al. Measuring autistic traits in the general population: a systematic review of the Autism-Spectrum Quotient (AQ) in a nonclinical population sample of 6,900 typical adult males and females. Mol Autism 2015;6:2. https://doi.org/10.1186/2040-2392-6-2.
De Jong TR, Menon R, Bludau A, Grund T, Biermeier V, Klampfl SM, et al. Salivary oxytocin concentrations in response to running, sexual self-stimulation, breastfeeding and the TSST: The Regensburg Oxytocin Challenge (ROC) study. Psychoneuroendocrinology 2015;62:381–8. https://doi.org/10.1016/j.psyneuen.2015.08.027.
Manjón J V., Coupé P, Martí-Bonmatí L, Collins DL, Robles M. Adaptive non-local means denoising of MR images with spatially varying noise levels. J Magn Reson Imaging 2010;31:192–203. https://doi.org/10.1002/jmri.22003.
Ashburner J, Friston KJ. Unified segmentation. Neuroimage 2005;26:839–51. https://doi.org/10.1016/j.neuroimage.2005.02.018.
Rajapakse JC, Giedd JN, Rapoport JL. Statistical approach to segmentation of single-channel cerebral MR images. IEEE Trans Med Imaging 1997;16:176–86. https://doi.org/10.1109/42.563663.
Tohka J, Zijdenbos A, Evans A. Fast and robust parameter estimation for statistical partial volume models in brain MRI. Neuroimage 2004;23:84–97. https://doi.org/10.1016/j.neuroimage.2004.05.007.
Good CD, Johnsrude IS, Ashburner J, Henson RNA, Friston KJ, Frackowiak RSJ. A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains. Neuroimage 2001;14:21–36. https://doi.org/10.1006/nimg.2001.0786.
Boes AD, Fischer D, Geerling JC, Bruss J, Saper CB, Fox MD. Connectivity of sleep- and wake-promoting regions of the human hypothalamus observed during resting wakefulness. Sleep 2018;41:1–12. https://doi.org/10.1093/sleep/zsy108.
Pauli WM, Nili AN, Michael Tyszka J. Data Descriptor: A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei. Sci Data 2018;5:1–13. https://doi.org/10.1038/sdata.2018.63.
Gaser C. Manual Computational Anatomy Toolbox- cat12. Struct Brain Mapp Gr Dep Psychiatry Neurol Univ Jena 2017:1–53.
Brett M, Anton J-L, Valabregue R, Poline J-B. Region of interest analysis using an SPM toolbox [abstract]. 8th Int Conferance Funct Mapp Hum Brain 2002:Abstract 497. https://doi.org/10.1007/978-3-540-75387-2_17.
Waterhouse L, Fein D, Modahl C. Neurofunctional mechanisms in autism. Psychol Rev 1996;103:457–89. https://doi.org/10.1037/0033-295X.103.3.457.
Green L, Fein D, Modahl C, Feinstein C, Waterhouse L, Morris M. Oxytocin and autistic disorder: alterations in peptide forms. Biol Psychiatry 2001;50:609–13. https://doi.org/10.1016/S0006-3223(01)01139-8.
LoParo D, Waldman ID. The oxytocin receptor gene (OXTR) is associated with autism spectrum disorder: a meta-analysis. Mol Psychiatry 2015;20:640–6. https://doi.org/10.1038/mp.2014.77.
Feldman R, Monakhov M, Pratt M, Ebstein RP. Oxytocin Pathway Genes: Evolutionary Ancient System Impacting on Human Affiliation, Sociality, and Psychopathology. Biol Psychiatry 2016;79:174–84. https://doi.org/10.1016/j.biopsych.2015.08.008.
Kumsta R, Hummel E, Chen FS, Heinrichs M. Epigenetic regulation of the oxytocin receptor gene: implications for behavioral neuroscience. Front Neurosci 2013;7:83. https://doi.org/10.3389/fnins.2013.00083.
Andari E, Nishitani S, Kaundinya G, Caceres GA, Morrier MJ, Ousley O, et al. Epigenetic modification of the oxytocin receptor gene: implications for autism symptom severity and brain functional connectivity. Neuropsychopharmacology 2020;45:1150–8. https://doi.org/10.1038/s41386-020-0610-6.
Modahl C, Green LA, Fein D, Morris M, Waterhouse L, Feinstein C, et al. Plasma oxytocin levels in autistic children. Biol Psychiatry 1998;43:270–7. https://doi.org/10.1016/S0006-3223(97)00439-3.
Andari E, Duhamel J-R, Zalla T, Herbrecht E, Leboyer M, Sirigu A. Promoting social behavior with oxytocin in high-functioning autism spectrum disorders. Proc Natl Acad Sci 2010;107:4389–94. https://doi.org/10.1073/pnas.0910249107.
Jansen LMC, Gispen-de Wied CC, Wiegant VM, Westenberg HGM, Lahuis BE, van Engeland H. Autonomic and neuroendocrine responses to a psychosocial stressor in adults with autistic spectrum disorder. J Autism Dev Disord 2006;36:891–9. https://doi.org/10.1007/s10803-006-0124-z.
John S, Jaeggi A V. Oxytocin levels tend to be lower in autistic children: A meta-analysis of 31 studies. Autism 2021;25:2152–61. https://doi.org/10.1177/13623613211034375.
Rokicki J, Kaufmann T, de Lange A-MG, van der Meer D, Bahrami S, Sartorius AM, et al. Oxytocin receptor expression patterns in the human brain across development. Neuropsychopharmacology 2022. https://doi.org/10.1038/s41386-022-01305-5.
Andari E, Schneider FC, Mottolese R, Vindras P, Sirigu A. Osxytocin’s fingerprint in personality traits and regional brain volume. Cereb Cortex 2014;24:479–86. https://doi.org/10.1093/cercor/bhs328.
Von der Heide R, Vyas G, Olson IR. The social network-network: Size is predicted by brain structure and function in the amygdala and paralimbic regions. Soc Cogn Affect Neurosci 2014;9:1962–72. https://doi.org/10.1093/scan/nsu009.
Sallet J, Mars RB, Noonan MP, Andersson JL, O’Reilly JX, Jbabdi S, et al. Social Network Size Affects Neural Circuits in Macaques. Science (80-) 2011;334:697–700. https://doi.org/10.1126/science.1210027.
Schindler S, Geyer S, Strauß M, Anwander A, Hegerl U, Turner R, et al. Structural studies of the hypothalamus and its nuclei in mood disorders. Psychiatry Res Neuroimaging 2012;201:1–9. https://doi.org/10.1016/j.pscychresns.2011.06.005.
Courchesne E, Campbell K, Solso S. Brain growth across the life span in autism: Age-specific changes in anatomical pathology. Brain Res 2011;1380:138–45. https://doi.org/10.1016/j.brainres.2010.09.101.
Greimel E, Nehrkorn B, Schulte-Rüther M, Fink GR, Nickl-Jockschat T, Herpertz-Dahlmann B, et al. Changes in grey matter development in autism spectrum disorder. Brain Struct Funct 2013;218:929–42. https://doi.org/10.1007/s00429-012-0439-9.
Ecker C, Bookheimer SY, Murphy DGM. Neuroimaging in autism spectrum disorder: Brain structure and function across the lifespan. Lancet Neurol 2015;14:1121–34. https://doi.org/10.1016/S1474-4422(15)00050-2.
Langen M, Bos D, Noordermeer SDS, Nederveen H, Van Engeland H, Durston S. Changes in the development of striatum are involved in repetitive behavior in autism. Biol Psychiatry 2014;76:405–11. https://doi.org/10.1016/j.biopsych.2013.08.013.
Nacewicz BM, Dalton KM, Johnstone T, Long MT, McAuliff EM, Oakes TR, et al. Amygdala Volume and Nonverbal Social Impairment in Adolescent and Adult Males With Autism. Arch Gen Psychiatry 2006;63:1417–28. https://doi.org/10.1001/archpsyc.63.12.1417.
Schumann CM, Hamstra J, Goodlin-Jones BL, Lotspeich LJ, Kwon H, Buonocore MH, et al. The amygdala is enlarged in children but not adolescents with autism; the hippocampus is enlarged at all ages. J Neurosci 2004;24:6392–401. https://doi.org/10.1523/JNEUROSCI.1297-04.2004.
Lange N, Travers BG, Bigler ED, Prigge MBD, Froehlich AL, Nielsen JA, et al. Longitudinal Volumetric Brain Changes in Autism Spectrum Disorder Ages 6–35 Years. Autism Res 2015;8:82–93. https://doi.org/10.1002/aur.1427.
Falougy H El, Filova B, Ostatnikova D, Bacova Z, Bakos J. Neuronal morphology alterations in autism and possible role of oxytocin. Endocr Regul 2019;53:46–54. https://doi.org/10.2478/enr-2019-0006.
Gimpl G, Reitz J, Brauer S, Trossen C. Oxytocin receptors: ligand binding, signalling and cholesterol dependence. Prog Brain Res 2008;170:193–204. https://doi.org/10.1016/S0079-6123(08)00417-2.
Lestanova Z, Bacova Z, Kiss A, Havranek T, Strbak V, Bakos J. Oxytocin Increases Neurite Length and Expression of Cytoskeletal Proteins Associated with Neuronal Growth. J Mol Neurosci 2016;59:184–92. https://doi.org/10.1007/s12031-015-0664-9.
Rajamani KT, Wagner S, Grinevich V, Harony-Nicolas H. Oxytocin as a Modulator of Synaptic Plasticity: Implications for Neurodevelopmental Disorders. Front Synaptic Neurosci 2018;10:1–8. https://doi.org/10.3389/fnsyn.2018.00017.
Theodosis DT, Poulain DA. Oxytocin-secreting neurones: a physiological model for structural plasticity in the adult mammalian brain. Trends Neurosci 1987;10:426–30. https://doi.org/10.1016/0166-2236(87)90014-2.
Theodosis DT. Oxytocin-Secreting Neurons: A Physiological Model of Morphological Neuronal and Glial Plasticity in the Adult Hypothalamus. Front Neuroendocrinol 2002;23:101–35. https://doi.org/10.1006/frne.2001.0226.
Valstad M, Alvares GA, Egknud M, Matziorinis AM, Andreassen OA, Westlye LT, et al. The correlation between central and peripheral oxytocin concentrations: A systematic review and meta-analysis. Neurosci Biobehav Rev 2017;78:117–24. https://doi.org/10.1016/j.neubiorev.2017.04.017.
Feldman R, Zagoory-Sharon O, Weisman O, Schneiderman I, Gordon I, Maoz R, et al. Sensitive parenting is associated with plasma oxytocin and polymorphisms in the OXTR and CD38 genes. Biol Psychiatry 2012;72:175–81. https://doi.org/10.1016/j.biopsych.2011.12.025.
Quintana DS, Rokicki J, van der Meer D, Alnæs D, Kaufmann T, Córdova-Palomera A, et al. Oxytocin pathway gene networks in the human brain. Nat Commun 2019;10:668. https://doi.org/10.1038/s41467-019-08503-8.
Mielke EL, Neukel C, Bertsch K, Reck C, Möhler E, Herpertz SC. Alterations of brain volumes in women with early life maltreatment and their associations with oxytocin. Horm Behav 2018;97:128–36. https://doi.org/10.1016/j.yhbeh.2017.11.005.
Fjalldal S, Holmer H, Rylander L, Elfving M, Ekman B, Österberg K, et al. Hypothalamic involvement predicts cognitive performance and psychosocial health in long-term survivors of childhood craniopharyngioma. J Clin Endocrinol Metab 2013;98:3253–62. https://doi.org/10.1210/jc.2013-2000.
Bernstein H-G, Keilhoff G, Steiner J. The implications of hypothalamic abnormalities for schizophrenia, 2021, p. 107–20. https://doi.org/10.1016/B978-0-12-819973-2.00008-3.
Albantakis L, Parpart H, Krankenhagen M, Böhm J, Henco L, Brandi M-L, et al. Autismus-Spektrum-Störungen (ASS) im Erwachsenenalter—Persönlichkeitsprofile und Begleiterkrankungen: Beschreibung einer Stichprobe von Patienten mit ASS aus der Ambulanz für Störungen der sozialen Interaktion des Max-Planck-Instituts in München. PTT Persönlichkeitsstörungen Theor Und Ther 2018;22:56–71.
Bocchetta M, Gordon E, Manning E, Barnes J, Cash DM, Espak M, et al. Detailed volumetric analysis of the hypothalamus in behavioral variant frontotemporal dementia. J Neurol 2015;262:2635–42. https://doi.org/10.1007/s00415-015-7885-2.
Le TM, Liao DL, Ide J, Zhang S, Zhornitsky S, Wang W, et al. The interrelationship of body mass index with gray matter volume and resting-state functional connectivity of the hypothalamus. Int J Obes 2020;44:1097–107. https://doi.org/10.1038/s41366-019-0496-8.
Gaser C, Dahnke R. CAT - A Computational Anatomy Toolbox for the Analysis of Structural MRI Data. 2016.
Billot B, Bocchetta M, Todd E, Dalca A V., Rohrer JD, Iglesias JE. Automated segmentation of the hypothalamus and associated subunits in brain MRI. Neuroimage 2020;223:117287. https://doi.org/10.1016/j.neuroimage.2020.117287.
Asan L, Falfán-Melgoza C, Beretta CA, Sack M, Zheng L, Weber-Fahr W, et al. Cellular correlates of gray matter volume changes in magnetic resonance morphometry identified by two-photon microscopy. Sci Rep 2021;11:4234. https://doi.org/10.1038/s41598-021-83491-8.
Varghese M, Keshav N, Jacot-Descombes S, Warda T, Wicinski B, Dickstein DL, et al. Autism spectrum disorder: neuropathology and animal models. Acta Neuropathol 2017;134:537–66. https://doi.org/10.1007/s00401-017-1736-4.
Swaab DF. Chapter 26 Hypothalamic involvement in psychiatric disorders. Handb. Clin. Neurol. Vol. 80 (3rd Ser. Vol. 2); Hum. Hypothal. Basic Clin. Asp. Part II, vol. 80, Elsevier B.V.; 2004, p. 243–88. https://doi.org/10.1016/S0072-9752(04)80012-5.
Makris N, Swaab DF, van der Kouwe A, Abbs B, Boriel D, Handa RJ, et al. Volumetric parcellation methodology of the human hypothalamus in neuroimaging: Normative data and sex differences. Neuroimage 2013;69:1–10. https://doi.org/10.1016/j.neuroimage.2012.12.008.
Yamasue H, Suga M, Yahata N, Inoue H, Tochigi M, Abe O, et al. Neurogenetic effects of OXTR rs2254298 in the extended limbic system of healthy Caucasian adults. Biol Psychiatry 2011;70:e37-9; author reply e41-2. https://doi.org/10.1016/j.biopsych.2011.07.021.
Scherk H, Falkai P. Effects of antipsychotics on brain structure. Curr Opin Psychiatry 2006;19:145–50. https://doi.org/10.1097/01.yco.0000214339.06507.d8.
Bellani M, Dusi N, Yeh PH, Soares JC, Brambilla P. The effects of antidepressants on human brain as detected by imaging studies. Focus on major depression. Prog Neuro-Psychopharmacology Biol Psychiatry 2011;35:1544–52. https://doi.org/10.1016/j.pnpbp.2010.11.040.

The Appendix is not available with this version.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Peripheral oxytocin levels are linked to hypothalamic gray matter volume in autistic adults: a cross-sectional study

Status:

Version 1

Abstract

Background

Methods

Results

Limitations

Conclusion

Figures

1. Introduction

2. Material And Methods

3. Results

4. Discussion

5. Limitations

6. Conclusion

Abbreviations

Declarations

References

Appendix

Additional Declarations

Status:

Version 1