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Superior temporal sulcus folding, functional network connectivity, and autistic-like traits in a non-clinical population

Molecular Autism volume 15, Article number: 44 (2024) Cite this article

Abstract

Background

Autistic-like traits (ALT) are prevalent across the general population and might be linked to some facets of a broader autism spectrum disorder (ASD) phenotype. Recent studies suggest an association of these traits with both genetic and brain structural markers in non-autistic individuals, showing similar spatial location of findings observed in ASD and thus suggesting a potential neurobiological continuum.

Methods

In this study, we first tested an association of ALTs (assessed with the AQ questionnaire) with cortical complexity, a cortical surface marker of early neurodevelopment, and then the association with disrupted functional connectivity. We analysed structural T1-weighted and resting-state functional MRI scans in 250 psychiatrically healthy individuals without a history of early developmental disorders, in a first step using the CAT12 toolbox for cortical complexity analysis and in a second step we used regional cortical complexity findings to apply the CONN toolbox for seed-based functional connectivity analysis.

Results

Our findings show a significant negative correlation of both AQ total and AQ attention switching subscores with left superior temporal sulcus (STS) cortical folding complexity, with the former being significantly correlated with STS to left lateral occipital cortex connectivity, while the latter showed significant positive correlation of STS to left inferior/middle frontal gyrus connectivity (n = 233; all p < 0.05, FWE cluster-level corrected). Additional analyses also revealed a significant correlation of AQ attention to detail subscores with STS to left lateral occipital cortex connectivity.

Limitations

Phenotyping might affect association results (e.g. choice of inventories); in addition, our study was limited to subclinical expressions of autistic-like traits.

Conclusions

Our findings provide further evidence for biological correlates of ALT even in the absence of clinical ASD, while establishing a link between structural variation of early developmental origin and functional connectivity.

Introduction

Conceptualising psychiatric disorders as disease spectra or dimensions, rather than distinct disease categories, raises the question of dimensional biological correlates. As most psychiatric imaging studies rely on case-control-designs to identify group-level differences of the impact of a particular clinical phenotype, they might be less suited to capture variation related to subclinical phenotypes. For several neuropsychiatric disorders, there is now increasing evidence that such subclinical variation is indeed related to variation in brain structure or function – as shown in affective disorders [1] or psychosis spectrum markers [2, 3], and more recently also with autistic traits [4,5,6,7]. This motivates novel approaches to studying variation related to psychiatric conditions beyond the classical case-control design. In particular, by using dimensional markers such as quantifiable traits (e.g., obtained through behavioural assessments of self-report inventories), which have a biological relation to mechanisms implicated in the respective disease spectrum, one might use a correlational approach to link the variation of behavioural/trait markers with biological parameters. Such an approach might inform on biological processes that underly a spectrum spanning both non-clinical (non-diagnosed) and clinical subjects, yet it might be studied in different cohorts, including those lacking a diagnosis but displaying varying degrees of phenotype expression (including “subclinical” variation). In the present study, we aim to provide additional evidence for the association of subclinical traits / behaviours in individuals without a psychiatric condition with brain structural/functional parameters that are observed to be changed in previous case-control studies, and in our case specifically within the autistic spectrum.

Autism spectrum disorder (ASD) is a severe neurodevelopmental condition, defined by early-onset and persistent differences in social communication and the presence of restricted, repetitive, stereotyped behaviours and interests across multiple situations [8,9,10]. However, some of the phenotypic features identified in ASD individuals are also found in non-affected / non-autistic subjects in the general population, raising the possibility of an overlap or continuum of at least a part of the ASD phenotype. Recent psychometric and clinical findings suggest that autism spectrum disorders (ASD) might also be conceptualised as a combination of categorical and dimensional factors, as conceptualised in hybrid models [11, 12]. The latter might refer to and be conceptualised as behavioural traits, which can be quantified psychometrically, and are intrinsically related to ASD. For example, twin studies suggest moderate to high heritability of autistic traits, which were continuously distributed in a healthy control twin sample [13, 14]; this was particularly the case for the “Social Responsiveness Scale” [14]. Similarly, methylation studies imply shared epigenetics of ASD and autistic-like traits [15]. In addition, several recent studies have found correlations of autistic-like traits, such as those captured using the Autism Spectrum Quotient (AQ) and others, with regional brain volume variation in non-clinical samples, i.e., even in the absence of an ASD diagnosis [4, 16, 17].

While there is still an ongoing discussion whether ASD and its major symptoms and traits, as well as the putative broader autism phenotype [18], are best conceptualised in categorical, dimensional, or hybrid models [12, 19,20,21], these findings make a strong case for investigating the association of subclinical autistic traits and biological parameters. Similar to dimensional approaches within the clinical ASD spectrum (e.g., [22]), subclinical traits offer the opportunity to identify key brain structures and functional networks that may be related to clinically relevant phenotypes. For example, it is unclear whether autistic-like traits (either as a whole or distinct single facets) are related to brain networks overlapping with those identified in ASD case-control studies [23, 24] even though autistic-like traits may not always relate to ASD as they can relate to many other mental disorders [25].

In this present study, we build on previous studies that have identified associations of autistic-like traits in non-clinical populations with either volume-based brain structural parameters [4, 17] or structural connectivity inferred through diffusion tensor imaging (DTI) [5, 26]. While these studies have provided some important initial evidence of subclinical autistic-like trait phenotype correlates in the brain, they have some relevant shortcomings. First, they have focused on structural parameters that have a state-like component and are thus susceptible to additional (e.g., environmental) effects, such as the regional volume of a subcortical or neocortical region of interest. Secondly, not all of them have demonstrated how said structural associations might translate into functional effects (e.g., changes in activation or functional connectivity). We therefore devised a study design that would analyse cortical complexity, a more novel brain structural marker related to the folding of the neocortical surface. We chose cortical complexity as a marker of neocortical folding because it is assumed to reflect a parameter, which is rather static throughout most of adult life; it is thus assumed to be more indicative of early neurodevelopmental impacts, as opposed to cortical volume or thickness markers that might show subsequent changes or “compensation” [27, 28]. Cortical folding is most prominent during intrauterine brain development, probably extending to the first postnatal years, but then staying relatively stable over most of adult life [29, 30]. Hence, any variation detected in cortical folding in (early) adulthood is likely to reflect variation in early brain development in utero (or at least before age 4–6), and possibly related to genetic factors that impact on the formation of gyri. Recent findings show a moderate influence of (SNP-based) genetic risk for major psychiatric disorders on cortical complexity [31] as well as sensitivity to capture early brain development effects seen in pre-term born adult populations [32]. In addition to cortical folding parameters, we aimed to link any potential association of cortical complexity with autistic-like traits to variation in network connectivity using functional magnetic resonance imaging (fMRI). This approach links autistic-like traits, assumed to be associated with social interaction conditions like ASD, ADHD, mood or personality disorders [33,34,35] (van der Meer et al., 2012; van Steenel et al., 2013; May et al., 2021), with both early neurodevelopmental features and resulting variation in functional connectome parameters.

We tested the hypotheses that (a) autistic-like traits (as captured with the AQ) are association with variations in cortical complexity (a measure of early neurodevelopment) in non-clinical subjects, and (b) that the functional connectivity between such identified areas is related to cognitive-behavioural functional networks and altered with increasing autistic-like trait load / expression.

Methods

Subject cohort and phenotyping

We studied a cohort of n = 250 psychiatrically healthy subjects (173 female (69.2%) / 77 male (30.8%); mean age 23.9 yrs (SD = 3.9), average IQ of 116.87 (SD = 14.11), average handedness quotient (based on the Edinburgh Handedness Inventory, EHI [36]) of 79.75 (SD 52.75)) drawn from the general local population through advertisements and word-of-mouth recruitment, as used in a previous study [37]. All participants gave written informed consent to a study protocol approved by the Ethics Committee of the Medical School of Philipps-Universität Marburg (protocol number 61/18), in accordance with the latest version of the Declaration of Helsinki. Inclusion criteria included psychiatric health/well-being (validated with the SCID-I (structured clinical interview for DSM axis I, German version [38]) screening instrument), age 18–40 years and ability to provide informed consent and undergo an MRI scan; exclusion criteria included current or previous psychiatric conditions, traumatic brain injury, neurological (CNS) or uncontrolled general medical conditions potentially impacting on brain structure/function, or an intelligence quotient (IQ) of below 85, estimated with the multiple-choice vocabulary test (Mehrfachwahl-Wortschatz-Intelligenztest [39, 40]).

Resting state analyses were conducted in a slightly smaller subsample, consisting of 233 healthy individuals (161 female (69.1%), 72 male (30.9%) with an average age of 23.79 yrs (SD 3.8), an average IQ of 116.67 (SD 14.03) and an average handedness score of 79.89 (SD 52.84). This sample was slightly smaller owing to exclusion of 17 subjects for either missing data or quality control issues for resting state fMRI. Table 1 shows a detailed overview of the descriptive statistics for both samples.

Table 1 Descriptive statistics for the structural and resting state sample
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Phenotyping of autistic-like traits using AQ (Autism Questionnaire)

As described in our previous study [37], we used the Autism Spectrum Quotient (AQ) to characterise our sample for autistic-like traits. The AQ, a self-administered questionnaire used to measure autistic-like traits in adults [41] has been used widely in a number of clinical and non-clinical studies (meta-analysis in [42]). While the use of AQ in screening for ASD has been discussed, particularly in comparison with other measures [43], the large body of data in non-clinical healthy adults [42] makes it a well-established tool for measuring autistic-like traits in non-clinical populations, including identification of individuals with higher levels of social problems in clinical settings. It has also been widely used for differential comparison with other phenotypes ( [19, 44]) and general population samples [41, 45].

The AQ provides a total score of autistic-like traits, and five subscores, i.e., attention to detail, attention switching, communication, imagination, and social skills. As described previously for this cohort, AQ subscales show satisfactory internal consistency, except for low Cronbach’s alpha in the AQ attention to detail subscale (see inter-item correlations in [4]).

Subjects completed the AQ within a week of MRI scanning using a secured electronic platform, checking for completeness of obtained data.

Magnetic resonance imaging (MRI): data acquisition and pre-processing

We obtained structural and functional MRI data sets on a 3 Tesla scanner (Magnetom Tim Trio, Siemens Healthineers, Erlangen, Germany) with a 12-channel dedicated head matrix Rx-coil.

For structural MRI analysis, we used T1-weighted high-resolution anatomical scans from a three-dimensional MPRAGE sequence with gradient echo, an acquisition time of 4:26 min and the following parameters: repetition time (TR) = 1900ms, echo time (TE) = 2.26ms, inversion time (TI) = 90ms, field-of-view (FoV) = 256 mm, flip angle (FA) = 9°. We acquired 176 slices an isotropic resolution of 1 × 1 × 1mm3.

For functional MRI analysis, we used a resting-state T2*-weighted echo planar imaging (EPI) protocol, which involved subjects to be instructed to rest for 8 min with eyes closed, and room lights turned off. EPI acquisitions used the following sequence parameters: TR = 2000ms, TE = 30ms, FoV = 210 × 210mm2, 33 slices with a slice thickness of 3.8 mm and an in-plane resolution of 3.3 × 3.3mm2.

Cortical complexity analysis

We used the CAT12 toolbox (version rs1720) for pre-processing and analysis of cortical complexity data, applying the recommended default options (Computational Anatomy Toolbox 12, C. Gaser, Structural Brain Mapping Group, University of Jena, Germany).

The cortical complexity measure implemented in CAT12 is based on spherical harmonics modelling of the cortical surface using a fractal dimensions approach [46]. Surface reconstruction includes topology correction and spherical registration [46]. Cortical complexity images were smoothed with a Gaussian kernel of 20 mm FWHM (full width at half maximum).

Previous studies have used the cortical complexity measure to identify cortical folding abnormalities in neuropsychiatric disorders such as schizophrenia [46, 47] and bipolar disorder [48].

Functional brain connectivity analysis

Based on the findings derived from cortical complexity analyses, we performed an analysis of functional connectivity to link the correlation of cortical complexity with AQ scores to potential functional effects.

We used the CONN tool box [49] for pre-processing and time-series analysis of resting-state fMRI scans. Using the default pre-processing pipeline, images were realigned using the SPM12 realign & unwarp procedure [50], co-registered and resampled to a reference image, and normalised into standard MNI space. Images were smoothed with a Gaussian kernel of 4 mm FWHM and denoised with the default denoising pipeline [51]. After denoising, structural and functional normalised images were visually inspected for artefacts and normalisation failures. Further, individuals with less than 75% valid scans (according to the automatic CONN detection algorithm) were excluded (n = 2), leading to the final sample of n = 233.

Statistical analysis

Our hypotheses were tested in two steps. First, we used vertex-wise maps of cortical complexity across the entire neocortex to set up general linear models (GLMs), separately for AQ-total scores and each AQ subscore, with age and sex as covariates, in order to compute vertex-wise correlations between cortical complexity and each AQ subscore. Separate GLMs were used since AQ subscores are often at least moderately inter-correlated with each other, and inclusion of multiple AQ scores in a single GLM thus would have removed variance distorting structure-trait variations. We applied the cluster-level FWE (family-wise error) approach with p < 0.05 thresholding (p < 0.001 uncorrected for cluster definition) to correct for multiple-comparisons. We performed a supplementary analysis including IQ as a nuisance variable in the GLM in order to remove variance related to differences in IQ. IQ was not correlated with AQ total or any of the subscores (AQ total r=-0.07, p = 0.245; AQ Social Skills r = 0.02, p = 0.695; AQ Attention Switching r=-0.05, p = 0.459; AQ Attention to Detail r=-0.07, p = 0.262; AQ Communication r=-0.06, p = 0.314; AQ Imagination r=-0.05, p = 0.426).

Second, we performed a seed-based functional connectivity analysis of resting-state fMRI data. Based on our cortical folding complexity findings, we identified matching regions from the Glasser360 atlas [52], specifically the left and right ventral posterior part of the superior temporal sulcus (STSvp), which were defined as seeds. Using the CONN toolbox and its statistical module, we then applied regression models correcting for age and sex and tested for significant associations of the AQ scores and functional connectivity between each of the seeds with each voxel in the brain. Applying standard setting for cluster-based inferences (random field theory parametric statistics), we again used cluster-level FWE correction to account for multiple comparisons of brain-wide analysis. Again, we performed an additional supplementary analysis correcting for IQ (including IQ as a nuisance variable in the GLM). We corrected for age and sex in all of the GLM analyses of both cortical complexity and resting-state functional connectivity.

Results

Cortical complexity analyses

We found a negative correlation between AQ total score and cortical complexity in the left superior temporal sulcus (ROI: bankssts) (p = 0.009 FWE cluster-level corrected, 190 voxels), as shown in Fig. 1. Higher AQ attention switching scores also showed a negative correlation with cortical complexity in the left superior temporal sulcus. (p = 0.013 FWE cluster-level corrected, 177 voxels).

Fig. 1
figure 1

Correlation of cortical surface complexity (calculated using CAT12 software) and AQ total score (A; significant negative correlation, maximum intensity voxel at co-ordinates x/y/z: -51/-49/+5; p = 0.009); AQ Attention Switching subscore (B; significant negative correlation, maximum intensity voxel at co-ordinates x/y/z: -53/-51/+4); p = 0.013; and AQ communication (C; significant positive correlation, maximum intensity voxel at co-ordinates x/y/z: +6/-5/+42; p = 0.043) resp.; images are thresholded at p < 0.001 uncorrected

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A positive correlation was found for AQ communication scores and cortical complexity in the right posterior cingulate (p = 0.043, FWE cluster-level corrected, 134 voxels).

An overview of significant clusters for cortical complexity analyses providing co-ordinates, significance values and effect size parameters is given in Table 2.

Table 2 Significant clusters in the correlation analyses of cortical surface complexity and AQ scores
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Supplementary analyses correcting for IQ showed almost identical findings (see Supplementary Fig. 1 and Supplementary Table 1 for details).

Functional connectivity analyses

We found a significant negative correlation between the AQ total score and functional connectivity between the right STSvp and a cluster in the left lateral occipital cortex (k = 70 voxels, x/y/z=-38/-60/+44, p = 0.023, FWE cluster-level-corrected), apparently driven by the AQ attention to detail subscale that showed a negative correlation to a very similar cluster (k = 67 voxels, x/y/z=-36/-60/+42, p = 0.029, FWE cluster-level-corrected) with the right STSvp as seed.

We found a positive correlation between the AQ attention switching subscale and functional connectivity between the left STSvp and a cluster in the left inferior/middle frontal gyrus (k = 84 voxels, x/y/z=-50/+28/+20, p = 0.009, FWE cluster-level-corrected).

Lastly, we found a negative correlation between the AQ communication subscale and functional connectivity between the left STSvp and a cluster in the right cuneal cortex (k = 74 voxels, x/y/z = + 4/-80/+32, p = 0.018, FWE cluster-level-corrected). Figure 2 shows the significant clusters obtained from the resting-state analysis. An overview of significant clusters for functional connectivity analyses providing co-ordinates, significance values and effect size parameters is given in Table 3.

Fig. 2
figure 2

Significant clusters in the resting state seed to voxel analysis with right and left STSvp as seeds. (A) AQ Total (negative correlation, maximum intensity voxel at co-ordinates x/y/z: -38/-60/+44; p = 0.023), (B) AQ Attention Switching (positive correlation, maximum intensity voxel at co-ordinates x/y/z: -50/+28/+20; p = 0.009) (C) AQ Attention to Detail (negative correlation, maximum intensity cluster at co-ordinates x/y/z: -36/-60/+42; p = 0.029) (D) AQ Communication (negative correlation, maximum intensity cluster at co-ordinates x/y/z: +4/-80/+32; p = 0.018)

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Table 3 Significant clusters in the resting state seed-to-voxel analysis for the respective AQ scores, with left and right STSvp as seed region
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Again, supplementary analyses including correction for IQ showed mostly similar findings to the main analyses (see Supplementary Fig. 2 and Supplementary Table 2 for details).

Discussion

Subclinical expression of disease-related phenotypes might offer relevant insights into the neurobiological basis of disease spectra, while studying non-clinical cohorts avoids confounding factors such as the effects of psychotropic medication or co-morbidities typically encountered in case-control designs [24]. As this study set out to identify dimensional neural correlates of autistic-like traits in non-clinical subjects, our analyses point to three major findings: first, cortical folding in the left superior temporal sulcus being associated with both AQ total scores and particularly the attention switching subscale; second, changes of functional connectivity from the left STSvp to other brain areas with increasing autistic-like traits, and third, a dissociation of functional connectivity patterns and overall autistic-like traits (AQ total) vs. attention switching AQ traits, where overall autistic-like traits predict functional connectivity of left STS with occipital cortices, but AQ attention switching is related to functional connectivity of the left STS to left lateral prefrontal cortex.

Association of cortical complexity with autistic-like traits

Measures of cortical gyrification capture morphometric brain features different from more conventional parameters such as regional volumes or cortical thickness. Recent basic science findings have added to the general understanding of cortical development [53], but more specifically to the mechanics of cortical folding (e.g. [54]), and the heterochronicity of that process across different cortical areas [55]. On the cellular level, transcriptomics studies have shown that mid-gestational expression and cell differentiation is regulated by genes implicated in ASD [56], while primate studies link genes implicated in ASD to additional postnatal disease-related gene expression [57].

Our gyrification finding implies that subtle variations in this highly developmental parameter can be linked to subclinical autistic-like traits even in the absence of clinical conditions, thus questioning an interpretation of previous ASD case-control studies (e.g., [58, 59]), relating gyrification to the developmental nature of ASD. Rather, at least a part of that variance in gyrification could be related to either an extended phenotype or a dimensional phenotype that spans far beyond clinical and subclinical forms of problems in social interaction. In contrast to most MR morphometric studies in ASD case-control designs (e.g [24, 60, 61]). as well as to more recent dimensional association studies of autistic-like traits in non-clinical subjects (e.g [4, 62]). or mixed samples [63], gyrification is linked to a complex early development of the cortical folds. Our use of cortical complexity as a measure of neocortical folding puts to use a somewhat more recently developed method, which applies a spherical harmonics approach and therefore complements earlier measures of cortical folding.

Three recent studies, although using approaches markedly different from our analysis, merit attention and comparison to our findings: First, a recent study [64] in children and adolescents age 13–16 years has linked gyrification to recent autistic-like traits in a non-autistic cohort (using the parent-assessed Social Responsiveness Scale, SRS) to frontal and temporal areas, including parts of the superior temporal gyrus (more so in the right hemisphere); a second study by Alemany et al. showed in a large cohort of children that autistic-like traits (but not polygenic risk for ASD) are associated with parietal and lateral occipital gyrification [65]. Finally, a study using the Autism Diagnostic Observation Schedule (ADOS-2) showed an association of autistic-like traits, albeit in eating disordered underweight individuals rather than ASD or non-clinical subjects, with gyrification in the postcentral and supramarginal cortices [66]; given the potential effects of weight loss not only on brain water content but possibly also other metrics limits comparability with other studies.

While case-control studies have not consistently shown gyrification abnormalities in ASD (e.g. [67]), more recent stratification within ASD individuals indicates that subgroups with “reciprocal social interaction” and “restricted, repetitive, and stereotyped patterns of behavior” do show increased gyrification [59], echoing previous studies on structural connectivity and repetitive behaviour in ASD [68].

Implications of functional connectivity associations with autistic-like traits

While our gyrification findings are consistent with a (quasi-)dimensional brain structural correlate of autistic-like traits functional connectivity analysis supports this on a functional level. The involvement of posterior STS in social (dys)function and ASD has been shown in previous behavioural studies (e.g. reviews in [69, 70]), as well as meta-analysis of fMRI activation studies across clinical cohorts [71]. While there are few previous studies on fMRI, particularly analyses of functional connectivity, and autistic-like traits in clinically healthy cohorts, our findings share similarities with both a growing number of studies in autism-like traits and task-based fMRI in “neurotypical” subjects. For example, in a cohort of 30 clinically healthy subjects, AQ predicted functional coupling of pSTS activation during eye contact and gaze patterns with the fusiform face area [72] and in another study between STS and dorsomedial prefrontal cortices during mentalisation efforts [73], although aberrant STS connectivity has also been observed in non-visual tasks in non-clinical subjects with autistic-like features [74].

A noteworthy aspect of our functional connectivity findings is the dissociation between connectivity patterns and subscales of the AQ, correlations between pSTS and other brain areas depended on the AQ subscale with significant correlations towards the lateral occipital cortex for the AQ attention to detail subscale, towards the inferior/middle frontal gyrus for AQ attention switching, and towards the cuneus for AQ communication. This suggests that a summary autistic trait score might be limited in detecting associations for particular facets of autism-like traits (see also [75]). This also has implications for interpreting case-control studies, in which categorical comparison is frequently made without considering the heterogeneity within clinical cohorts (i.e., the case-control comparison relies on the overall sum of symptoms/traits, rather than single facets or subscores that might be more useful for cohort stratification or association with brain mechanisms). A recent case control study covering a larger age range of children and adults (age 7–30 years) has suggested that age might be an important factor as connectivities of the pSTS might depend on maturation of underlying circuits [76]. While the age range of our sample precludes direct replication, it should be pointed out that the former findings might rather be driven by the developmental heterogeneity of the ASD cohort.

Different facets autistic-like traits might link to the pSTS because of its unique involvement in multiple brain networks subserving functions that range from socially relevant higher visual integration to “theory of mind” functions [77], emotion recognition [78], and reward functions [79], as well as because of its connections with brain regions involved in attention to visual detail (through its connection with the lateral occipital cortex) and reorienting of visual attention from the current focus towards new stimuli (through its connection with the inferior frontal gyrus) [80, 81].

Limitations

While our study provides several findings that confirm a linear relationship between autistic-like traits in non-clinical subjects and both gyrification (as an indicator of early neurodevelopment) and functional connectivity of posterior STS, there are some limitations to be considered. First, we applied the AQ for assessment of autistic-like traits; although widely used in non-autistic cohorts [42], some subscores have received criticism in recent psychometric studies [75]. Since we did not obtain additional measures of autistic-like traits, replications with additional / complementary inventories as well as studies in well characterised ASD samples to further examine the assumed dimensionality would be desirable. Second, our cohort demographic shows differences to age and IQ distributions typically observed in ASD cohorts [10], which needs to be taken into account. Third, our study did not collect behavioural data that would have allowed us to directly link structural or functional parameters to functions such as eye gaze or social interaction. Despite the consistency with pSTS studies in people with ASD and other psychiatric conditions [77, 82], larger combined study cohorts would be desirable and needed. While we acknowledge recent studies highlighting the difficulties of identifying robust and replicable brain-phenotype correlations [83], it is worth pointing out that global psychopathological markers, such as used in the mentioned study, might be less suitable compared to more specific phenotype facets as given with the AQ subscales (despite some psychometric imperfections, see ). In overcoming the difficulties of insufficient power of brain-behaviour association studies [84], it has been argued that such fine-grained phenotyping might be essential in overcoming the limitations of limited replicability in previous study designs [85].

Conclusions

In conclusion, our study supports a dimensional relationship between the expression of autistic-like traits in the non-clinical spectrum with brain structural variation, in particular cortical folding of the posterior STS, a key region for perception integration and social functioning. The pSTS functional connectivity during rest is furthermore associated with different occipital and frontal brain areas, in dependence of phenotype sub-facets, which highlights the importance of relating particular discernible aspects of a broader autism phenotype to specific circuitry in dimensional models of psychopathology [86].

Data availability

Original data can be made available upon reasonable request and pending local and national data protection and ethics regulation.

References

  1. Besteher B, Gaser C, Nenadic I. Brain structure and subclinical symptoms: a dimensional perspective of psychopathology in the depression and anxiety spectrum. Neuropsychobiology. 2020;79:270–83.

    Article  PubMed  Google Scholar 

  2. Nelson MT, Seal ML, Pantelis C, Phillips LJ. Evidence of a dimensional relationship between schizotypy and schizophrenia: a systematic review. Neurosci Biobehav Rev. 2013;37:317–27.

    Article  PubMed  CAS  Google Scholar 

  3. Sahakyan L, Meller T, Evermann U, Schmitt S, Pfarr JK, Sommer J, Kwapil TR, Nenadic I. Anterior vs posterior hippocampal subfields in an extended psychosis phenotype of Multidimensional Schizotypy in a nonclinical sample. Schizophr Bull. 2021;47:207–18.

    Article  PubMed  Google Scholar 

  4. Schröder Y, Hohmann DM, Meller T, Evermann U, Pfarr JK, Jansen A, Kamp-Becker I, Grezellschak S, Nenadic I. Associations of subclinical autistic-like traits with brain structural variation using diffusion tensor imaging and voxel-based morphometry. Eur Psychiatry. 2021;64:e27.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Jakab A, Emri M, Spisak T, Szeman-Nagy A, Beres M, Kis SA, Molnar P, Berenyi E. Autistic traits in neurotypical adults: correlates of graph theoretical functional network topology and white matter anisotropy patterns. PLoS ONE. 2013;8:e60982.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Yoshimura Y, Kikuchi M, Ueno S, Okumura E, Hiraishi H, Hasegawa C, Remijn GB, Shitamichi K, Munesue T, Tsubokawa T, et al. The brain’s response to the human voice depends on the incidence of autistic traits in the general population. PLoS ONE. 2013;8:e80126.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Fujiwara H, Yoshimura S, Kobayashi K, Ueno T, Oishi N, Murai T. Neural correlates of non-clinical internet use in the Motivation Network and its modulation by subclinical autistic traits. Front Hum Neurosci. 2018;12:493.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Hodges H, Fealko C, Soares N. Autism spectrum disorder: definition, epidemiology, causes, and clinical evaluation. Transl Pediatr. 2020;9:S55–65.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Muhle RA, Reed HE, Stratigos KA, Veenstra-VanderWeele J. The emerging clinical neuroscience of Autism Spectrum disorder: a review. JAMA Psychiatry. 2018;75:514–23.

    Article  PubMed  Google Scholar 

  10. Lord C, Brugha TS, Charman T, Cusack J, Dumas G, Frazier T, Jones EJH, Jones RM, Pickles A, State MW, et al. Autism spectrum disorder. Nat Rev Dis Primers. 2020;6:5.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Wittkopf S, Langmann A, Roessner V, Roepke S, Poustka L, Nenadic I, Stroth S, Kamp-Becker I. Conceptualization of the latent structure of autism: further evidence and discussion of dimensional and hybrid models. Eur Child Adolesc Psychiatry 2022.

  12. Kim H, Keifer C, Rodriguez-Seijas C, Eaton N, Lerner M, Gadow K. Quantifying the optimal structure of the Autism phenotype: a Comprehensive comparison of Dimensional, categorical, and Hybrid models. J Am Acad Child Adolesc Psychiatry. 2019;58:876–e886872.

    Article  PubMed  Google Scholar 

  13. Hoekstra RA, Bartels M, Verweij CJ, Boomsma DI. Heritability of autistic traits in the general population. Arch Pediatr Adolesc Med. 2007;161:372–7.

    Article  PubMed  Google Scholar 

  14. Constantino JN, Todd RD. Autistic traits in the general population: a twin study. Arch Gen Psychiatry. 2003;60:524–30.

    Article  PubMed  Google Scholar 

  15. Massrali A, Brunel H, Hannon E, Wong C, i Baron-Cohen P-MEG, Warrier S. Integrated genetic and methylomic analyses identify shared biology between autism and autistic traits. Mol Autism. 2019;10:31.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Wallace GL, Shaw P, Lee NR, Clasen LS, Raznahan A, Lenroot RK, Martin A, Giedd JN. Distinct cortical correlates of autistic versus antisocial traits in a longitudinal sample of typically developing youth. J Neurosci. 2012;32:4856–60.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Yildiz GY, Vilsten JS, Millard AS, Chouinard PA. Grey-Matter Thickness of the Left but not the right primary visual area correlates with autism traits in typically developing adults. J Autism Dev Disord. 2021;51:405–17.

    Article  PubMed  Google Scholar 

  18. Maxwell CR, Parish-Morris J, Hsin O, Bush JC, Schultz RT. The broad autism phenotype predicts child functioning in autism spectrum disorders. J Neurodev Disord. 2013;5:25.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Abu-Akel A, Philip RCM, Lawrie SM, Johnstone EC, Stanfield AC. Categorical and dimensional approaches to examining the Joint Effect of Autism and Schizotypal personality disorder on sustained attention. Front Psychiatry. 2020;11:798.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Jalbrzikowski M, Ahmed KH, Patel A, Jonas R, Kushan L, Chow C, Bearden CE. Categorical versus dimensional approaches to autism-associated intermediate phenotypes in 22q11.2 microdeletion syndrome. Biol Psychiatry Cogn Neurosci Neuroimaging. 2017;2:53–65.

    PubMed  PubMed Central  Google Scholar 

  21. Frazier TW, Chetcuti L, Al-Shaban FA, Haslam N, Gzahal I, Klingemier EW, Aldosari M, Whitehouse AJO, Youngstrom EA, Hardan AY, Uljarević M. Categorical versus dimensional structure of autism spectrum disorder: a multi-method investigation. JCCP Adv 2023:e12142.

  22. Tang S, Sun N, Floris DL, Zhang X, Di Martino A, Yeo BTT. Reconciling dimensional and categorical models of Autism Heterogeneity: a brain connectomics and behavioral study. Biol Psychiatry. 2020;87:1071–82.

    Article  PubMed  Google Scholar 

  23. Blanken LM, Mous SE, Ghassabian A, Muetzel RL, Schoemaker NK, El Marroun H, van der Lugt A, Jaddoe VW, Hofman A, Verhulst FC, et al. Cortical morphology in 6- to 10-year old children with autistic traits: a population-based neuroimaging study. Am J Psychiatry. 2015;172:479–86.

    Article  PubMed  Google Scholar 

  24. Boedhoe PSW, van Rooij D, Hoogman M, Twisk JWR, Schmaal L, Abe Y, Alonso P, Ameis SH, Anikin A, Anticevic A, et al. Subcortical brain volume, Regional cortical thickness, and cortical Surface Area across disorders: findings from the ENIGMA ADHD, ASD, and OCD Working groups. Am J Psychiatry. 2020;177:834–43.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Lord C, Bishop SL. Let’s be clear that autism spectrum disorder symptoms are not always related to Autism Spectrum Disorder. Am J Psychiatry. 2021;178:680–2.

    Article  PubMed  Google Scholar 

  26. Iidaka T, Miyakoshi M, Harada T, Nakai T. White matter connectivity between superior temporal sulcus and amygdala is associated with autistic trait in healthy humans. Neurosci Lett. 2012;510:154–8.

    Article  PubMed  CAS  Google Scholar 

  27. de Vareilles H, Riviere D, Mangin JF, Dubois J. Development of cortical folds in the human brain: an attempt to review biological hypotheses, early neuroimaging investigations and functional correlates. Dev Cogn Neurosci. 2023;61:101249.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Pfarr JK, Meller T, Brosch K, Stein F, Thomas-Odenthal F, Evermann U, Wroblewski A, Ringwald KG, Hahn T, Meinert S, et al. Data-driven multivariate identification of gyrification patterns in a transdiagnostic patient cohort: a cluster analysis approach. NeuroImage. 2023;281:120349.

    Article  PubMed  Google Scholar 

  29. Striedter GF, Srinivasan S, Monuki ES. Cortical folding: when, where, how, and why? Annu Rev Neurosci. 2015;38:291–307.

    Article  PubMed  CAS  Google Scholar 

  30. Zilles K, Palomero-Gallagher N, Amunts K. Development of cortical folding during evolution and ontogeny. Trends Neurosci. 2013;36:275–84.

    Article  PubMed  CAS  Google Scholar 

  31. Schmitt S, Meller T, Stein F, Brosch K, Ringwald K, Pfarr JK, Bordin C, Peusch N, Steinstrater O, Grotegerd D et al. Effects of polygenic risk for major mental disorders and cross-disorder on cortical complexity. Psychol Med 2021:1–12.

  32. Hedderich DM, Bauml JG, Menegaux A, Avram M, Daamen M, Zimmer C, Bartmann P, Scheef L, Boecker H, Wolke D, et al. An analysis of MRI derived cortical complexity in premature-born adults: Regional patterns, risk factors, and potential significance. NeuroImage. 2020;208:116438.

    Article  PubMed  Google Scholar 

  33. van der Meer JM, Oerlemans AM, van Steijn DJ, Lappenschaar MG, de Sonneville LM, Buitelaar JK, Rommelse NN. Are autism spectrum disorder and attention-deficit/hyperactivity disorder different manifestations of one overarching disorder? Cognitive and symptom evidence from a clinical and population-based sample. J Am Acad Child Adolesc Psychiatry. 2012;51:1160–e11721163.

    Article  PubMed  Google Scholar 

  34. van Steensel FJ, Bogels SM, Wood JJ. Autism spectrum traits in children with anxiety disorders. J Autism Dev Disord. 2013;43:361–70.

    Article  PubMed  Google Scholar 

  35. May T, Pilkington PD, Younan R, Williams K. Overlap of autism spectrum disorder and borderline personality disorder: a systematic review and meta-analysis. Autism Res. 2021;14:2688–710.

    Article  PubMed  Google Scholar 

  36. Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 1971;9:97–113.

    Article  PubMed  CAS  Google Scholar 

  37. Schroder Y, Hohmann DM, Meller T, Evermann U, Pfarr JK, Jansen A, Kamp-Becker I, Grezellschak S, Nenadic I. Associations of subclinical autistic-like traits with brain structural variation using diffusion tensor imaging and voxel-based morphometry. Eur Psychiatry. 2021;64:e27.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Wittchen H-U, Wunderlich U, Gruschwitz S, Zaudig M. SKID-I. Strukturiertes Klinisches interview für DSM-IV. Göttingen: Hogrefe; 1997.

    Google Scholar 

  39. Lehrl S. Mehrfachwahl-Wortschatz-Intelligenztest: MWT-B. 5th ed. Göttingen: Testzentrale; 2005.

    Google Scholar 

  40. Lehrl S, Triebig G, Fischer B. Multiple choice vocabulary test MWT as a valid and short test to estimate premorbid intelligence. Acta Neurol Scand. 1995;91:335–45.

    Article  PubMed  CAS  Google Scholar 

  41. 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.

    Article  PubMed  CAS  Google Scholar 

  42. Ruzich E, Allison C, Smith P, Watson P, Auyeung B, Ring H, Baron-Cohen S. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Conner CM, Cramer RD, McGonigle JJ. Examining the diagnostic validity of autism measures among adults in an Outpatient Clinic Sample. Autism Adulthood. 2019;1:60–8.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Nenadic I, Meller T, Evermann U, Schmitt S, Pfarr JK, Abu-Akel A, Grezellschak S. Subclinical schizotypal vs. autistic traits show overlapping and diametrically opposed facets in a non-clinical population. Schizophr Res. 2021;231:32–41.

    Article  PubMed  Google Scholar 

  45. Abu-Akel A, Allison C, Baron-Cohen S, Heinke D. The distribution of autistic traits across the autism spectrum: evidence for discontinuous dimensional subpopulations underlying the autism continuum. Mol Autism. 2019;10:24.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Yotter RA, Nenadic I, Ziegler G, Thompson PM, Gaser C. Local cortical surface complexity maps from spherical harmonic reconstructions. NeuroImage. 2011;56:961–73.

    Article  PubMed  Google Scholar 

  47. Nenadic I, Yotter RA, Sauer H, Gaser C. Cortical surface complexity in frontal and temporal areas varies across subgroups of schizophrenia. Hum Brain Mapp. 2014;35:1691–9.

    Article  PubMed  Google Scholar 

  48. Nenadic I, Yotter RA, Dietzek M, Langbein K, Sauer H, Gaser C. Cortical complexity in bipolar disorder applying a spherical harmonics approach. Psychiatry Res Neuroimaging. 2017;263:44–7.

    Article  PubMed  Google Scholar 

  49. Whitfield-Gabrieli S, Nieto-Castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2012;2:125–41.

    Article  PubMed  Google Scholar 

  50. Andersson JL, Hutton C, Ashburner J, Turner R, Friston K. Modeling geometric deformations in EPI time series. NeuroImage. 2001;13:903–19.

    Article  PubMed  CAS  Google Scholar 

  51. Nieto-Castanon A. FMRI minimal preprocessing pipeline. In Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN. Edited by Nieto-Castanon A: Hilbert Press; 2020: 3–16.

  52. Glasser MF, Coalson TS, Robinson EC, Hacker CD, Harwell J, Yacoub E, Ugurbil K, Andersson J, Beckmann CF, Jenkinson M, et al. A multi-modal parcellation of human cerebral cortex. Nature. 2016;536:171–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Molnar Z, Clowry GJ, Sestan N, Alzu’bi A, Bakken T, Hevner RF, Huppi PS, Kostovic I, Rakic P, Anton ES, et al. New insights into the development of the human cerebral cortex. J Anat. 2019;235:432–51.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Kroenke CD, Bayly PV. How forces fold the cerebral cortex. J Neurosci. 2018;38:767–75.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Garcia KE, Kroenke CD, Bayly PV. Mechanics of cortical folding: stress, growth and stability. Philos Trans R Soc Lond B Biol Sci 2018, 373.

  56. Polioudakis D, de la Torre-Ubieta L, Langerman J, Elkins AG, Shi X, Stein JL, Vuong CK, Nichterwitz S, Gevorgian M, Opland CK, et al. A single-cell Transcriptomic Atlas of Human Neocortical Development during Mid-gestation. Neuron. 2019;103:785–e801788.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Bakken TE, Miller JA, Ding SL, Sunkin SM, Smith KA, Ng L, Szafer A, Dalley RA, Royall JJ, Lemon T, et al. A comprehensive transcriptional map of primate brain development. Nature. 2016;535:367–75.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Gharehgazlou A, Vandewouw M, Ziolkowski J, Wong J, Crosbie J, Schachar R, Nicolson R, Georgiades S, Kelley E, Ayub M, et al. Cortical gyrification morphology in ASD and ADHD: implication for further similarities or disorder-specific features? Cereb Cortex. 2022;32:2332–42.

    Article  PubMed  Google Scholar 

  59. Ning M, Li C, Gao L, Fan J. Core-symptom-defined cortical gyrification differences in Autism Spectrum Disorder. Front Psychiatry. 2021;12:619367.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Sha Z, van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, Bernhardt B, Bolte S, Busatto GF, Calderoni S, et al. Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium. Mol Psychiatry. 2022;27:2114–25.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Li T, Hoogman M, Roth Mota N, Buitelaar JK, group, E-Aw, Vasquez AA, Franke B, van Rooij D. Dissecting the heterogeneous subcortical brain volume of autism spectrum disorder using community detection. Autism Res 2022, 15:42–55.

  62. Nees F, Banaschewski T, Bokde ALW, Desrivieres S, Grigis A, Garavan H, Gowland P, Grimmer Y, Heinz A, Bruhl R et al. Global and Regional Structural differences and prediction of autistic traits during adolescence. Brain Sci 2022, 12.

  63. Arunachalam Chandran V, Pliatsikas C, Neufeld J, O’Connell G, Haffey A, DeLuca V, Chakrabarti B. Brain structural correlates of autistic traits across the diagnostic divide: a grey matter and white matter microstructure study. Neuroimage Clin. 2021;32:102897.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Durkut M, Blok E, Suleri A, White T. The longitudinal bidirectional relationship between autistic traits and brain morphology from childhood to adolescence: a population-based cohort study. Mol Autism. 2022;13:31.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Alemany S, Blok E, Jansen PR, Muetzel RL, White T. Brain morphology, autistic traits, and polygenic risk for autism: a population-based neuroimaging study. Autism Res. 2021;14:2085–99.

    Article  PubMed  Google Scholar 

  66. Halls D, Leppanen J, Kerr-Gaffney J, Simic M, Nicholls D, Mandy W, Williams S, Tchanturia K. Examining the relationship between autistic spectrum disorder characteristics and structural brain differences seen in anorexia nervosa. Eur Eat Disord Rev. 2022;30:459–73.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Ecker C, Andrews D, Dell’Acqua F, Daly E, Murphy C, Catani M, Thiebaut de Schotten M, Baron-Cohen S, Lai MC, Lombardo MV, et al. Relationship between cortical gyrification, White Matter Connectivity, and Autism Spectrum Disorder. Cereb Cortex. 2016;26:3297–309.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Ecker C, Ronan L, Feng Y, Daly E, Murphy C, Ginestet CE, Brammer M, Fletcher PC, Bullmore ET, Suckling J, et al. Intrinsic gray-matter connectivity of the brain in adults with autism spectrum disorder. Proc Natl Acad Sci U S A. 2013;110:13222–7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Saitovitch A, Bargiacchi A, Chabane N, Brunelle F, Samson Y, Boddaert N, Zilbovicius M. Social cognition and the superior temporal sulcus: implications in autism. Rev Neurol (Paris). 2012;168:762–70.

    Article  PubMed  CAS  Google Scholar 

  70. Riddiford JA, Enticott PG, Lavale A, Gurvich C. Gaze and social functioning associations in autism spectrum disorder: a systematic review and meta-analysis. Autism Res. 2022;15:1380–446.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Patriquin MA, DeRamus T, Libero LE, Laird A, Kana RK. Neuroanatomical and neurofunctional markers of social cognition in autism spectrum disorder. Hum Brain Mapp. 2016;37:3957–78.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Jiang J, von Kriegstein K, Jiang J. Brain mechanisms of eye contact during verbal communication predict autistic traits in neurotypical individuals. Sci Rep. 2020;10:14602.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Moessnang C, Otto K, Bilek E, Schafer A, Baumeister S, Hohmann S, Poustka L, Brandeis D, Banaschewski T, Tost H, Meyer-Lindenberg A. Differential responses of the dorsomedial prefrontal cortex and right posterior superior temporal sulcus to spontaneous mentalizing. Hum Brain Mapp. 2017;38:3791–803.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Avery JA, Ingeholm JE, Wohltjen S, Collins M, Riddell CD, Gotts SJ, Kenworthy L, Wallace GL, Simmons WK, Martin A. Neural correlates of taste reactivity in autism spectrum disorder. Neuroimage Clin. 2018;19:38–46.

    Article  PubMed  PubMed Central  Google Scholar 

  75. English MCW, Gignac GE, Visser TAW, Whitehouse AJO, Maybery MT. A comprehensive psychometric analysis of autism-spectrum quotient factor models using two large samples: model recommendations and the influence of divergent traits on total-scale scores. Autism Res. 2020;13:45–60.

    Article  PubMed  Google Scholar 

  76. Alaerts K, Nayar K, Kelly C, Raithel J, Milham MP, Di Martino A. Age-related changes in intrinsic function of the superior temporal sulcus in autism spectrum disorders. Soc Cogn Affect Neurosci. 2015;10:1413–23.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Yang DY, Rosenblau G, Keifer C, Pelphrey KA. An integrative neural model of social perception, action observation, and theory of mind. Neurosci Biobehav Rev. 2015;51:263–75.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Alaerts K, Woolley DG, Steyaert J, Di Martino A, Swinnen SP, Wenderoth N. Underconnectivity of the superior temporal sulcus predicts emotion recognition deficits in autism. Soc Cogn Affect Neurosci. 2014;9:1589–600.

    Article  PubMed  Google Scholar 

  79. Abrams DA, Lynch CJ, Cheng KM, Phillips J, Supekar K, Ryali S, Uddin LQ, Menon V. Underconnectivity between voice-selective cortex and reward circuitry in children with autism. Proc Natl Acad Sci U S A. 2013;110:12060–5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Dragone A, Lasaponara S, Silvetti M, Macaluso E, Doricchi F. Selective reorienting response of the left hemisphere to invalid visual targets in the right side of space: relevance for the spatial neglect syndrome. Cortex. 2015;65:31–5.

    Article  PubMed  Google Scholar 

  81. Silvetti M, Lasaponara S, Lecce F, Dragone A, Macaluso E, Doricchi F. The response of the left ventral attentional system to invalid targets and its implication for the spatial neglect syndrome: a Multivariate fMRI Investigation. Cereb Cortex. 2016;26:4551–62.

    Article  PubMed  Google Scholar 

  82. Patel GH, Sestieri C, Corbetta M. The evolution of the temporoparietal junction and posterior superior temporal sulcus. Cortex. 2019;118:38–50.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Marek S, Tervo-Clemmens B, Calabro FJ, Montez DF, Kay BP, Hatoum AS, Donohue MR, Foran W, Miller RL, Hendrickson TJ, et al. Reproducible brain-wide association studies require thousands of individuals. Nature. 2022;603:654–60.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flint J, Robinson ES, Munafo MR. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. 2013;14:365–76.

    Article  PubMed  CAS  Google Scholar 

  85. Gratton C, Nelson SM, Gordon EM. Brain-behavior correlations: two paths toward reliability. Neuron. 2022;110:1446–9.

    Article  PubMed  CAS  Google Scholar 

  86. Parkes L, Satterthwaite TD, Bassett DS. Towards precise resting-state fMRI biomarkers in psychiatry: synthesizing developments in transdiagnostic research, dimensional models of psychopathology, and normative neurodevelopment. Curr Opin Neurobiol. 2020;65:120–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

This study was supported by a research grant of the University Medical Center Giessen and Marburg (UKGM), grant 11/2017 MR (to Igor Nenadić) and grant 05/2018 MR (to Sarah Grezellschak and Igor Nenadić), as well as a Forschungscampus Mittelhessen (FCMH) FlexiFund grant (project 2018_2_1_1) to Igor Nenadić and Boris Keil. Igor Nenadić is a PI of and supported by the DYNAMIC center, which is funded by the LOEWE program of the Hessian Ministry of Science and Arts (Grant Number: LOEWE1/16/519/03/09.001(0009)/98). Ahmad Abu-Akel was supported by the Maof Fellowship for the Integration of Outstanding Faculty, Council for Higher Education (2023–2025). We would like to thank all colleagues from the Cognitive Neuropsychiatry Lab, who supported our study, in particular Franziska Hildesheim and Simon Schmitt; their help is appreciated.

Funding

This study was supported by a research grant of the University Medical Center Giessen and Marburg (UKGM), grant 11/2017 MR (to Igor Nenadić) and grant 05/2018 MR (to Sarah Grezellschak and Igor Nenadić). Igor Nenadić is a PI of and supported by the DYNAMIC center, which is funded by the LOEWE program of the Hessian Ministry of Science and Arts (Grant Number: LOEWE1/16/519/03/09.001(0009)/98).

Open Access funding enabled and organized by Projekt DEAL.

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Authors and Affiliations

  1. Cognitive Neuropsychiatry Lab, Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35037, Marburg, Germany

    Igor Nenadić, Yvonne Schröder, Jonas Hoffmann, Ulrika Evermann, Julia-Katharina Pfarr, Aliénor Bergmann, Daniela Michelle Hohmann, Sarah Grezellschak & Tina Meller

  2. Center for Mind, Brain, and Behavior (CMBB), University of Marburg, Justus Liebig University Gießen, and Technical University of Darmstadt, Hans-Meerwein-Straße 6, 35032, Marburg, Germany

    Igor Nenadić, Ulrika Evermann, Julia-Katharina Pfarr, Daniela Michelle Hohmann, Boris Keil, Sanna Stroth, Inge Kamp-Becker, Andreas Jansen & Tina Meller

  3. Marburg University Hospital – UKGM, Marburg, Germany

    Igor Nenadić

  4. Institute of Medical Physics and Radiation Protection, Department of Life Science Engineering, TH Mittelhessen University of Applied Sciences, Giessen, Germany

    Boris Keil

  5. LOEWE Research Cluster for Advanced Medical Physics in Imaging and Therapy (ADMIT), TH Mittelhessen University of Applied Sciences, 35390, Giessen, Germany

    Boris Keil

  6. Department of Diagnostic and Interventional Radiology, University Hospital Marburg, Philipps-Universität Marburg, Marburg, Germany

    Boris Keil

  7. School of Psychological Sciences, University of Haifa, Haifa, Israel

    Ahmad Abu-Akel

  8. The Haifa Brain and Behavior Hub (HBBH), University of Haifa, Haifa, Israel

    Ahmad Abu-Akel

  9. Department of Child and Adolescent Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany

    Sanna Stroth & Inge Kamp-Becker

  10. BrainImaging Core Facility, School of Medicine, Philipps-Universität Marburg, Marburg, Germany

    Andreas Jansen

  11. LOEWE Center DYNAMIC, University of Marburg, Marburg, Germany

    Igor Nenadić

Authors
  1. Igor Nenadić
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Contributions

I.N. conceived of the study, obtained funding, supervised data acquisition and analysis, interpreted findings and wrote the first draft of the manuscript. Y.S. contributed to structural image analysis and subject recruitment. U.E., J.-K.P., A.B. and D.M.H. contributed to subject recruitment. J.H. contributed to fMRI data analysis. S.S., I.K.B, A.J., A.A.A. contributed to interpretation and discussion of data.B.K. obtained funding and contributed to interpretation and discussion of data. S.G. contributed to conception of study and obtained funding. T.M. contributed to conception of the study, subject recruitment and supervision of data analysis. All authors reviewed the manuscript, participated in revision, and approved of the final version.

Corresponding author

Correspondence to Igor Nenadić.

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Ethics approval

All participants gave written informed consent to a study protocol approved by the Ethics Committee of the Medical School of Philipps-Universität Marburg (protocol number 61/18), in accordance with the latest version of the Declaration of Helsinki.

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The authors declare no competing interests.

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Nenadić, I., Schröder, Y., Hoffmann, J. et al. Superior temporal sulcus folding, functional network connectivity, and autistic-like traits in a non-clinical population. Molecular Autism 15, 44 (2024). https://doi.org/10.1186/s13229-024-00623-3

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Keywords

Molecular Autism

ISSN: 2040-2392