Functional Connectome Hierarchy in Schizotypy and Its associations with Expression of Schizophrenia-Related Genes

doi:10.21203/rs.3.rs-2442195/v1

Download PDF

Article

Functional Connectome Hierarchy in Schizotypy and Its associations with Expression of Schizophrenia-Related Genes

https://doi.org/10.21203/rs.3.rs-2442195/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Schizotype has been conceptualized as a continuum with symptoms of schizophrenia with marked genetic, neurobiological, sensory-cognitive overlaps. Hierarchical organization represents a general organizing principle for both the brain connectome supporting sensation-to-cognition continuum and gene expression patterns. However, the underlying changes in neuroimaging maps reflecting the cortical hierarchy that mechanistically link gene expressions to schizotypy are unclear. Using a large cohort of resting state-fMRI data from 1013 healthy young adults, the present study investigated schizotypy-associated sensorimotor-to-transmodal connectome hierarchy and assessed the connectome hierarchy similarity between schizotypy and schizophrenia. Furthermore, the normative and differential postmortem gene expression data were employed to investigate the transcriptional profiles associated with the schizotypy-associated connectome hierarchy. We found that schizotypy was associated with a compressed functional connectome hierarchy, including compressed global topography and focal alterations in sensory and transmodal cognitive areas, suggesting diminished functional system differentiation. Interestingly, the pattern of schizotypy-related hierarchy is tightly correlated with the pattern of hierarchy organization observed in schizophrenia. Notably, schizotypy-related connectome hierarchy was most closely colocated with expression of schizophrenia-related genes compared with other psychiatric disorders, with the correlated genes being enriched in biological processes well-known involved in schizophrenia, i.e., transsynaptic and receptor signaling, calcium ion binding, and channel activity. These findings not only shed light on the neurobiological and molecular genetic mechanisms underlying the sensory-cognitive deficits in schizotypy, but also provide new insights into the neurobiological continuum of psychosis thus advanced our understanding of how genetic propensity for schizophrenia-alike traits play an enduring role in creating biological vulnerability to psychosis.

Health sciences/Diseases/Psychiatric disorders/Schizophrenia

Biological sciences/Neuroscience

Schizotypy refers to a multidimensional construct spanning a range of cognitive-perceptual experiences (positive), interpersonal behaviors (negative), and cognitive disorganized features that is qualitatively similar to those found in schizophrenia (1, 2). Schizotypy thus putatively represent the liability for schizophrenia in the general population (3). Researchers have conceptualized schizotypy as an important intermediate phenotype of schizophrenia spectrum disorders (4). Recent studies have highlighted that this phenotype is not dichotomous but rather distributed along a continuum that encompasses ranges from psychological well-being to psychosis (3), which is evidenced by marked neurobiological, phenomenological and sensory-cognitive overlaps (5–7). Notably, genetic risk is thought to drive clinical variation on a spectrum of schizophrenia-like traits (8, 9). In line with this, higher level of schizotypy was found among both schizophrenia (10) and their first-degree relatives (11), potentially suggesting its heritable (genetic) origins (8, 12). Thus, schizotypal characteristics can serve as a developmental mediator between very early indicators of risk and progression into high-risk states of schizophrenia, which is associated with changes in neural development at different stages of the life course (4). To this end, the investigation of the gene expression patterns underlying schizotypy-related neuroimaging maps can aid in the elucidation of schizophrenia-related psychopathology.

Schizotypy resembles the wide sensory-cognitive impairments seen in schizophrenia but is associated with a less pronounced level of expression (5). Schizotypy has been associated with impairments in higher-order cognitive functions, such as executive functions and social cognition, as evidenced by the impaired function of higher-order brain regions, such as the prefrontal and temporal cortex (13). In addition to higher-order cognitive deficits, there is also ample research evidence showing deficits in primary perceptual and sensory processing in high levels of schizotypy, as evidenced by the abnormal brain function in visual and sensory cortex (6, 13). On one hand, the failure of higher-order cognitive functions inevitably impedes the modulation to sensory processing (14). On the other hand, it is also essential to note that if early perceptual processing is impaired, it can have a ripple effect by leading to broader consequences at all stages of information processing (15). Although numerous previous studies have demonstrated that schizotypy is associated with aberrant brain structure and function (6, 13), the cortical hierarchy that is spatially organized from primary sensory processing areas to higher-order cognitive areas in schizotypy remains to be elucidated.

Recent progress in neuroscience has shed light on cortical hierarchy as a fundamental functional mechanism for information processing in the brains of primates and mice (16–19), which is thought to directly track the flow of sensory inputs through multiple cortical relays to cognitive/transmodal regions. This hierarchical architecture supports abstract higher-order cognitive functions by separating information reflecting the processing of the current environment from internally generated activities arising in the transmodal regions. The hierarchical structure enables the processing of different specialized functional domains (e.g., sensory and cognitive process), while also allows for the dynamic exchange of information between networks to facilitate more complex metal tasks (17, 18). By applying gradient decomposition to functional connectome derived by resting-state fMRI, researchers can noninvasively investigate the hierarchical architecture of the macroscale functional connectome, following a principal gradient along the axis from primary to transmodal networks (20) and largely analogous to the cortical microstructural myelination (21). Moreover, the principal connectome gradient is sensitive to tracking the development of brain function across lifespan (22, 23) and detecting the abnormality of hierarchical functional systems in psychiatric disorders (24–26). Furthermore, recent studies have quantified the correspondence between neuroimaging maps capturing cortical hierarchy and hierarchical gene expression patterns (16, 27) to inform how specific genes relate to observed neuroimaging neurophenotypes. However, it remains unclear how the neural maps reflect gradients of functional alterations in schizotypy can be associated with the maps of these schizophrenia-related gene expression variation.

It is well documented that schizophrenia is a highly heritable disorder (60–80%, (28)). Evidence from gene-set analyses has indicated a notable concentration of both common and rare genetic variants linked to schizophrenia in synaptic pathways and receptors signaling (29, 30). Since assessing gene expression of the brain tissue in vivo has been a challenge undertaking, combining gene expression profiles from postmortem brain tissue with connectomes from neuroimaging data offers a unique chance to link the micro-level transcriptome profile to the macro-level brain network (31). Using this strategy, researchers revealed that brain functional connectome (e.g., hubs) are preferentially associated with the orchestrated activity of numerous genes related to ion channel activity, metabolism, and synaptic function (27, 32). In addition, researchers have shown that cortical areas with a heightened presence of risk genes associated with schizophrenia form central areas in macroscale disconnectome (33). Considering genetic risk is thought to drive clinical variation on a spectrum of schizophrenia-like traits, i.e., schizotypy (8, 34), and functional hierarchy per se also captures across-cortex variability in molecular and genetic properties (27, 35), it can be anticipated that the macroscale connectome gradient disturbances associated with schizotypy can be linked to transcriptome profiles associated with schizophrenia, such as synaptic pathways. Unravelling such an association can advance our understanding of how genetic propensity for schizophrenia-alike traits play an enduring role in creating biological vulnerability to psychosis.

In this context, we employed a large sample of healthy young people (N = 1013) and postmortem gene expression data from the Allen Institute for Brain Science with the aim to examine how the neuroimaging maps reflect gradients of functional alterations in schizotypy can be associated with the maps of hierarchical gene expression patterns. We tested four hypotheses: (1) global and regional features of primary-to-transmodal hierarchy will be associated with schizotypy; (2) regional pattern of schizotypy-related hierarchy will be associated with multiple functional domains, including perceptual and sensory processing and high-order cognitive functions involving self-referential and executive processing; (3) pattern of schizotypy-related hierarchy will be correlated with the pattern of hierarchy organization observed in schizophrenia; (4) spatial variations of schizotypy-related hierarchy will be associated with genes expression profiles that are particularly enriched in biological processes involved in schizophrenia, such as synaptic pathways and receptors signaling.

Participants

The dataset comprises individuals from the Behavioral Brain Research Project of Chinese Personality (BBP), which launched in September 2019 (still in progress) to recruit participants from that year’s freshmen at Southwest University, Chongqing, China. All procedures involving human subjects were approved by the Ethics Committee of the Southwest University. Written informed consents were obtained from all subjects. In total, 1059 participants completed the cross-sectional neuroimaging protocol, the schizotypal personality questionnaire-Brief (SPQ-B), as well as basic demographical variables, i.e., headedness, age, gender. Psychiatric history of the participants was collected via self-report. Forty-six participants were excluded due to excessive head motion during scanning (with a mean framewise displacement (FD) larger than 0.25 mm), resulting in a final sample of 1013 participants (mean age = 19.11 years old, SD = 0.95; 336 males and 677 females; 90.33% right handedness; mean FD = 0.09, SD = 0.04), see Table S1 for details.

Questionnaire Assessment

The schizotypal personality questionnaire-Brief (SPQ-B) is a 22-item self-report scale that measures schizotypal traits (36). These items can be further grouped into three factors, i.e., cognition-perceptual, interpersonal and disorganization factors. SPQ scale modeled on the DSM-III-R criteria for schizotypal personality disorder. SPQ-B has been widely used to screen for psychometrically defined schizotypal personality disorder and identify people who may be at an elevated risk for prodromal mental illness in the general population (37). The Chinese version of the SPQ-B used in the present study has also been shown to possess good reliability (test-retest reliability = 0.82; (38)). Consistent with previous study in the literature (34, 39), we used the SPQ total score to characterize the schizotypal trait. In the present study, the internal consistency for the whole scale is evidenced by a Cronbach α coefficient of 0.85.

The measurement of family socioeconomic status (SES) consists of two questions answered by the participants: annual family income (1–7 level, Table S1) and highest educational qualification of each parent. We converted the highest educational qualification into years taken to complete the degree. The averaged educational years of both parents was used. Consistent with previous study (40), we used the averaged z-scores for family annual income and averaged parental education length to index SES.

MRI Acquisition And Image Data Preprocessing

All structural and resting-state functional MRI data were acquired on a 3T Siemens Primsa-fit scanner with a standard 32 channel head coil located at the MRI Center of Southwest University. All participants were instructed to stay awake, keep their eyes open, fixate on the displayed crosshair, and move their head as little as possible. See supplementary materials for detailed scanning parameters.

All preprocessing steps were consistent with our previous study(41), See supplementary materials for details. To account for artifact of head motion, we excluded participants with > 0.25 mean framewise displacement (FD) and regarded mean FD as a covariate in statistical analysis.

Connectivity Gradient Mapping

We used the diffusion map embedding approach (42), implemented in BrainSpace Toolbox (43) to construct the connectome gradient for each participant. First, we constructed the parcellation-based functional connectivity matrix using Fisher’s Z-transformed Pearson’s correlations. We used Schaefer's 1000 cortical parcellation (44) and Brainnetome 36 subcortical parcellation (45), resulting in 1036 subregions. We excluded the cerebellum because not all participants’ cerebellum was fully covered during fMRI scanning. For each subject, we obtained a FC matrix (1036*1036), then retained the top 10% of connections per row retained, and further converted the thresholded connectivity matrix into cosine distance-based similarity matrix (24, 25). Finally, diffusion map embedding approach, a nonlinear and unsupervised dimensionality reduction algorithm, was adopted to capture spatial axes (gradient components) in connectivity variation across different areas.

Schizotypy-related Connectome Hierarchy

In order to implement statistical analysis across participants, we performed iterative Procrustes rotation to align the gradients across participants (46). For each subject, four global topographic metrics were used to quantify gradient properties for the two connectome gradients 1 and 2: (1) explanation ratio, which characterizes the percentage of connectivity variance explained by a given gradient (20); (2) gradient range, which characterizes the difference between the greatest positive and negative value in a given gradient (24); (3) gradient variation, which represents the standard deviation of the gradient scores (47); (4) gradient dispersion, which characterizes the summed square of the Euclidean distance from each brain nodal to the centroid of the gradient space determined by the first two gradients (47). Considering the SPQ total score did not follow a normal distirbution (Fig.S1, Kolmogorov-Smirnov test, p < 0.01), the association between connectome gradient metrics (global topography and regional gradient value) and schizotyoy were assessed by using a permutation analysis of general linear model (48) with age, gender, handedness and head motion as covariates. Consistent with previous studies (24, 25), Z-normalization was conduct to gradient map before the regional level association analysis. For global gradient metrics, the threshold of statistical significance was set to p < 0.05. For regional gradient score maps, the threshold of statistical significance was set to FDR q < 0.05.

Meta-analytic Decoding Of The Function Of Significant Connectome Hierarchy Using NeuroSynth

We used NeuroSynth (49), a large-scale database-informed meta-analytic approach to decode the functional properties associated with hierarchy alterations in schizotypy. The top 20 terms (excluding anatomical terms for brain regions) exhibiting the highest correlations with schizotypy-related positive or negative hierarchy associative regions were extracted. These cognitive terms were visualized on a word cloud. The font size of these terms in each word cloud is proportional to their correlation strength with corresponding meta-analytic neuro-maps included in Neurosynth. Considering its nature of exploratory and inverse inference, it should be noted that, the corresponding functional properties should be interpreted cautiously.

Association Between Schizotypy-related And Schizophrenia-altered Connectome Hierarchy

To further assess the similarity between schizotypy-related and schizophrenia-altered connectome hierarchy, we calculated the spatial correlation between schizotypy-related connectome hierarchy and our previous case-control study of schizophrenia-altered cortical connectome hierarchy (Schizophrenia patients, n = 96; Health control, n = 122; (25)). To keep the same dimension, we extracted the regional average T-statistical values for Schaefer's 1000 cortical parcellations from our previous study and computed the Pearson correlation. The significance levels were determined by using permutation tests based on spherical rotations (50), to account for spatial autocorrelation of schizotypy-related connectome hierarchy map (5,000 times).

Estimation Of Regional Gene Expression

First, we downloaded the Allen Human Brain Atlas (AHBA, http://human.brain-map.org), a whole-genome expression atlas of the human brain created by the Allen Institute for Brain Sciences using 6 donors 24 to 57 years of age (51). Then, we followed Allen Human Brain Atlas (AHBA) processing pipeline with the default recommended settings to obtain brain regional gene expression. Briefly, preprocessing for the gene expression microarray data included the following steps: verification of the probe-to-gene annotations, probe selection, tissue sample assignment to each donor’s cortical parcellation with 1000 regions, normalization across donors, gene-set filtering. After that, for each region, gene expression was obtained by calculating mean of all cohorts in a region, resulting in 1,000 × 10,027 regional transcription matrices. See Arnatkevic̆iūtė, Fulcher (52) for more details. Considering AHBA dataset only includes right hemisphere for two donors, in the subsequent analysis, we only used gene expression in left hemisphere, i.e., the region × gene matrix size was 500 × 10,027.

Association between schizotypy-related connectome hierarchy and gene expression profiles

We used Partial Least Squares (PLS) regression to associate the regional schizotypy-related connectome hierarchy to the gene expression measurements for all 10, 027 genes (53, 54). In the PLS regression model, gene expression date was regarded as the predictor variables to predict the regional schizotypy-related connectome hierarchy (response variables). The first PLS component (PLS1) is the linear combination of the predictor variables (i.e., gene expression) that can explain most of the variance in the response variables (i.e., regional schizotypy-related connectome hierarchy). In order to evaluate the significance of the PLS1, we adopted permutation testing based on spherical rotations, to account for spatial autocorrelation of schizotypy-related connectome hierarchy map (5,000 times) to determine whether the variance explained by PLS1 was significantly greater than that expected by chance (50). The variability (standard error) of each gene’s weight on PLS1 was assessed by bootstrapping, and then we divided the weight of each gene by its bootstrap-estimated standard error to obtain the corrected weight for each gene.

We ranked the genes according to their corrected weights, which represent their contribution to the PLS1. Both the ascending and descending gene lists were enrolled for the following gene enrichment analysis. We used GOrial (http://cbl-gorilla.cs.technion.ac.il/, Eden, Navon (55)) to identify the enriched Gene Ontology terms for each ranked list of genes. Consistent with previous studies, we regarded terms with Benjamini-Hochberg false discovery rate (FDR)-corrected q-value less than 0.05 as significant enrichment.

In addition, we directly tested whether there was a significant correlation between PLS1 weights of all genes and the differential expression values in postmortem brain tissue measurements of messenger RNA from case-control studies of schizophrenia. To complement this, we used 2 lists of genes that were transcriptionally dysregulated: (1) 1) the list of genes reported by Gandal, Haney (56) in the prefrontal and parietal brain regions in schizophrenia (n = 159); and 2) the list of genes reported by Fromer, Roussos (57) in the dorsolateral prefrontal cortex in schizophrenia (n = 258). Correlations were calculated using genes that were common across postmortem and PLS1 gene lists. We used repeated random relabeling of genes to test the significance of correlation. To test whether the PLS1 weights gene captured the specificity of schizophrenia, we also used the same procedures to data of differential gene expression from case-control studies of bipolar disorder (BD), autism spectrum disorder (ASD) and major depressive disorder (MDD), which was also reported by Gandal, Haney (56).

Control Analysis

We validated schizotypy-related connectome hierarchy by considering several control analyses. First, considering global signal regression (GSR) is still controversial in the rsfMRI image preprocessing, we repeated core analyses with GSR in the control analyses. Second, we used a different parcellation scheme to validate our results. Specifically, 1078 regions were used to construct connectivity matrix, including 1024 cerebral regions with uniform sizes (58) and 54 subcortical regions (59).

Data And Code Availability

The statistical maps of the associations between schizotypy and connectome hierarchy, and gene list revealed by PLS will be provided once published at GitHub (https://github.com/DeboDong/Hierarchy_schizotypy). The code for spatial permutation testing can be found at https://github.com/frantisekvasa/rotate_parcellation. Connectome gradients were constructed by using BrainSpace toolbox (https://github.com/MICA-MNI/BrainSpace). we followed Allen Human Brain Atlas (AHBA) processing pipeline (https://github.com/BMHLab/AHBAprocessing) with the default recommended settings to obtain brain regional gene expression. The PLS code to associate schizotypy-related connectome hierarchy with gene expression can be found at https://github.com/SarahMorgan/Morphometric_Similarity_SZ. We used GOrial (http://cbl-gorilla.cs.technion.ac.il/) to implement the Gene Ontology enrichment analysis and visualization. Brain maps were visualized using BrainNet Viewer (60), https://www.nitrc.org/projects/bnv/.

Schizotypy associated connectome hierarchy

The principal gradient of connectome explained 11.67% ± 1.2% of the total functional connectivity variance, which showed a primary(visual/sensorimotor)-to-transmodal gradient, a hierarchical axis of cortical organization, see Fig. 1a. The second gradient of connectome explained 10.97% ± 1.4% of the total functional connectivity variance, which was organized along an axis from visual network to sensorimotor network, see Fig. 1b. These two connectome hierarchical gradients replicated the recent observation of the first two connectome gradients extracted from Human Connectome Project healthy adults (20).

Association analysis showed that SPQ total score was negatively correlated with the explained variance of primary-to-transmodal gradient in the functional connectome (r = -0.12, p = 1.5x10^− 4, Fig. 2a). That suggested a downgraded status of the primary-to-transmodal hierarchy in schizotypy. Moreover, individual with high SPQ total score exhibited a narrower range of gradient scores (r = -0.13, p = 4.7x10^− 5, Fig. 2b) and less spatial variation (r = -0.14, p = 1.0x10^− 5, Fig. 2c) in primary-to-transmodal gradient, which suggested a compressed functional connectome hierarchical organization in high levels of schizotypy. We further investigated the regional association between SPQ total scores and gradient scores. We found that SPQ score was negatively associated with gradient scores in the regions of DMN and FPN (e.g., bilateral medial/superior/middle frontal gyrus, anterial cingulate gyrus, angular gyrus, middle temporal gyrus and posterior cingulate/precuneus) but positively associated with gradient scores in the regions localized in the lowest pole of principal gradient, i.e., the regions of SMN and VN (e.g., pre/postcentral gyrus, superior/middle occipital gyrus, lingual gyrus) (Fig. 3a and 3b). Interestingly, we found schizotypy-related primary-to-transmodal gradient regional map showed significant correlation with our previous schizophrenia-compressed primary-to-transmodal gradient regional map (r = 0.53, p < 0.0001, Fig. 3d), suggesting similar global compression of primary-to-transmodal gradient alteration in schizotypy and schizophrenia.

To better characterize the compressed pattern of primary-to-transmodal hierarchical gradient in schizotypy, we divided participants into two groups, i.e., high schizotypy group (n = 106, SPQ total score greater than or equal to 17, the cut-off score has a good sensitivity and specificity for identifying susceptibility to psychosis among general population (36) and Chinese sample (38) ) and low schizotypy control group (n = 506, SPQ total scores were in the bottom 50% (61)). Next, the global histogram analysis was conducted. As can be seen from Fig. 2d, the identified pattern, particularly the regions in the DMN, VIS, and SMN, showed a strong shift from the periphery to the center in primary-to-transmodal gradient space, indicating the less-specialized connectivity profiles among these regions in high schizotypy group.

We did not find any significant associations between SPQ total score and explained variance, range of gradient, spatial variation and regional scores in the second visual-to-sensorimotor gradient.

Sensitivity Analyses Of Connectome Hierarchy In Schizotypy

In the sensitivity analysis, we further examined the association between SPQ total score and connectome hierarchy by further controlling for the other potential confounding factors, i.e., socioeconomic status and total brain volume. In all cases, the key results were conserved (Table S2, Fig.S2 and S3).

In the main analysis, we use SPQ total score to characterize the schizotypy trait as many previous studies adopted. Considering schizotypy is a complex trait consisting of three dimensions: cognitive-perceptual and interpersonal deficits, and disorganization, we also examined the associations between each dimension and connectome hierarchy metrics. We found all the three factors of schizotypy were negatively associated with the explained variance, range, and spatial variation of gradient scores (Table S2). The patterns of regional association between each dimension of SPQ and gradient scores were also similar to the regional association between SPQ total scores and gradient scores (Fig.S4-6).

Functional Properties Associated With Hierarchy Alterations In Schizotypy

Using the online “decoder” function of the Neurosynth, we found the compression of the hierarchical organization in schizotypy were mainly involved in primary sensory process and high-level cognitive function. Specifically, we observed regions showing positive associations with schizotypy were generally related to primary sensorial and perceptual processes (visual and perceptual terms, Fig. 3c), regions showing negative associations with schizotypy were generally related to DMN-related functions (default, autobiographical memory, self-referential, mentalizing and theory of mind, Fig. 3c).

Association between schizotypy-related connectome hierarchy and gene expression profiles

We used PLS regression to investigate the relationship between the association maps of schizotypy-related connectome hierarchy and gene expression profiles. Consist with previous studies (34, 54), we only focus on the first component of the PLS as it explained the most variance, and was most strongly correlated with the connectome hierarchy map of schizotypy. The first component of the PLS accounted for 32.63% of the variance in the schizotypy-related primary-to-transmodal connectome hierarchy, significantly higher than that expected by chance (permutation testing based on spherical rotations: p < 0.0001). The PLS1 defined a weighted sum of whole-genome expression profile mainly characterized by low gene expression in prefrontal and temporal areas but high gene expression in the posterior parietal-occipital areas (Fig.4a). The schizotypy-related primary-to-transmodal gradient map showed positive correlation with the regional gene expression map of PLS1 (r = 0.57, p < 0.0001, Fig.4b).

Interestingly, there was a significant negative correlation between the PLS1 weights of all genes in the genome and the differential expression values for all genes in schizophrenia reported by Gandal, Haney (56) (Spearman’s ρ = −0.14, p < 10⁻⁴, Fig.4c) and Fromer, Roussos (57) (Spearman’s ρ = −0.25, p < 10⁻⁴, Fig.S7). While the negative relationship was present in the other three disorders described in Gandal et al (BD: Spearman’s ρ=-0.05, p<0.001, Fig.4d; ASD: Spearman’s ρ=-0.05, p<0.001, Fig.4e; MDD: Spearman’s ρ=0.06, p<0.001, Fig.4f), the relationship was strongest to data from schizophrenia, suggesting its most close enrichment in schizophrenia.

Our further gene ontology analysis showed that the genes arranged in ascending order based on the first PLS component weight were significantly enriched in relation to biological processes related to receptor signaling pathway and transsynaptic signaling, and molecular function of receptor activity, calcium ion binding, and channel activity (FDR-corrected q < 0.05, Fig.5a and 5b and Table S3).

Control Analysis

Our control analyses found GSR, parcellation choice did not significantly affect the trends of overall results, which ensured the robustness of main results, see online Supplementary Table S2 and Fig.S8 − S9 for details.

Using a large cohort of rs-fMRI data (N = 1013), we found that schizotypy was associated with compressed macroscale functional connectome hierarchy, including compressed global topography and focal alterations of gradient scores in the primary areas involved in sensory processing and transmodal areas involved in high-order cognitive processing. Interestingly, the pattern of schizotypy-related hierarchy was tightly correlated with the pattern of hierarchy organization observed in schizophrenia. Moreover, schizotypy-related connectome hierarchy was most closely covaried with the expression of schizophrenia-related genes, with the most correlated genes being enriched in biological processes involving receptor and transsynaptic signaling, molecular function of receptor activity, calcium ion binding, and channel activity. Combining functional hierarchy and transcriptional data analysis, these findings provide an integrative/holistic neuroscientific perspective to understand the wide-range sensory-cognitive deficits associated with schizotypy, and mechanistic insight into how gene processes may drive alterations in brain functional hierarchy mediating the genetic risk of schzotypy for schizophrenia, thus yielded new insight into a neurobiological continuum of schizophrenia.

Downgraded And Compressed Connectome Hierarchy In Schizotypy

The observed less-explained variance, narrower distribution range and less spatial variation of the principal primary-to-transmodal gradient in schizotypy suggested a downgraded and compressed functional connectome hierarchical organization in schizotypy. Previous studies found individuals with high levels of schizotypy are generally associated with hyperconnectivity. Within such hyperconnectivity pattern, the default mode network constituted the core alterations. In line with this, previous studies found individuals with high schizotypy exhibited increased connectivity within the DMN, between the DMN and the salience network and frontoparietal network compared with the controls (39, 62, 63), and between the DMN and the sensorimotor network (63). Graph theoretical analysis further showed that individuals with higher levels of schizotypy were exhibited with more hub regions in the DMN, such as medial superior frontal gyrus and angular gyrus, suggesting that DMN is highly connected with the other areas in the whole-brain connectome (61) in high schizotypy. Interestingly, individuals with higher levels of schizotypy demonstrated higher structural connectivity in the visual network, sensorimotor and DMN, further suggesting these networks are more highly connected with other brain regions in high schizotypy (64). Brain function is supported by an optimal balance between the segregation of subnetworks and their integration (65). The heightened hyperconnectivity of the brain networks disturbs the balance between the integration and segregation of functions, influencing the hierarchical arrangement of these networks. The overall compression of the principal primary-to-transmodal hierarchy organization thus reflects diminished separation between sensory systems (e.g., visual and sensory regions) involved in the processing of current environment information and transmodal cognitive systems (e.g., DMN) that support complex cognitive inferences. Our findings of the compressed primary-to-tansmodal hierarchy therefore provide an integrative basis to support previous reports of impaired early sensory processing-cognitive function in schizotypy.

Our regional arrangement analysis revealed that certain parts of the SMN/VIS and DMN have migrated from the periphery to the center of the functional hierarchy space, further suggesting a trend towards a reduced differentiation or less divergent in connectivity profiles across the primary-to-transmodal hierarchy organization. In other words, the compression of connectome gradient was not caused by the connection of a single system, but rather by the altered connection of a broad range of systems, from the primary system to the more complex transmodal system. The compressed functional hierarchy involving multiple levels of systems may reflect insufficient bottom-up information processing from the primary systems and inadequate top-down processing from the higher order systems, thus resulting in clinical and cognitive impairments in a variety of domains in schizotypy (14, 15). It should be noted that the DMN subregions showed the preferential alteration among the transmodal pole. The DMN is fundamental to many cognitive processes that are associated with psychiatric symptoms, such as intrinsic stimulus-independent thoughts, pathological thinking and memory, autobiographical memory, monitoring internal and external environment, integrating past experiences, anticipating future events, and forming a unique and integrated “self” (66–68). The decreased network differentiation would unavoidably result in a blurred boundary between external stimuli and internal (e.g., self-related) cognitive processing (69, 70). Such a blurred boundary is considered as the key features of early psychosis proneness including cognitive disturbances and the tendency to prioritize prior subjective knowledge over incoming sensory information (71).

Connectome Hierarchy Similarity Between Schizotypy And Schizophrenia

Interestingly, we found the pattern of schizotypy-related hierarchy was tightly correlated with the pattern of hierarchy observed in schizophrenia. In our previous studies, we found schizophrenia patients showed diminished differentiation (compression) between the sensorimotor and transmodal cognitive systems in corticocortical and cerebellar-cerebral connectome hierarchy (25, 72). In addition, diminished differentiation between the sensorimotor and transmodal cognitive systems along the functional hierarchy has been linked with general psychopathology (73). Our finding on the connectome hierarchy similarity between schizotypy and schizophrenia thus provides new insights into a dimensional neurobiological continuum across the broader psychosis phenotype (7). In other words, the diminished differentiation of functional connectome hierarchy can be considered as a reliable neurobiomarker for the continuum model of psychosis. Considering cortical hierarchy represent a unifying functional mechanism for information processing in the brain and dysfunctional connectivity have been regarded as a key pathophysiological correlate of psychosis, connectome hierarchy can thus be regarded as a unified framework to illustrate the neurodevelopmental trajectories of schizophrenia spectrum continuum.

Covaried Compression Of Connectome Hierarchy In Schizotypy And Expression Of Schizophrenia-related Genes

We found schizotypy-related compression of connectome hierarchy was most closely colocated with expression of schizophrenia-related genes, with the correlated genes being enriched in relation to G protein-coupled receptor and transsynaptic signaling, calcium ion binding, and channel activity. Transsynaptic signaling plays a pivotal role in a wide array of biological processes, including the impact of the formation of synaptic connections, the coordination of the interactions between pre- and postsynaptic neurons, the control of the recruitment of neurotransmitter receptors and postsynaptic scaffolding proteins, and the regulation of synaptic plasticity (74, 75). It has been established that the synaptic signaling in the cerebral cortex is arranged in accordance with the cortical hierarchy (16, 76). Notably, implicated genes and functional genomic data suggest that disturbances of synaptic connectivity act as both a neurodevelopmental antecedent and an ongoing risk mechanism for schizophrenia (77). This was also evidenced by results from a recent study showing that a considerable amount of genetic risk for schizophrenia is present in gene co-expression modules related to synaptic transmission and neuronal excitability, which are sustained or even amplified from birth to adulthood (78).

In addition to transsynaptic signaling, G protein-coupled receptor signaling (activity) is the most enriched terms associated with schizotypy-related functional hierarchy. A considerable body of clinical, pharmacological, and genetic evidence suggests that G protein-coupled receptors (GPCRs) signaling play a critical role in schizophrenia development, progression, and treatment. This pivotal role is further highlighted by the fact that a large number of neurotransmitters involved in schizophrenia act through metabotropic GPCRs (79). GPCRs for serotonin, dopamine, adrenaline, and glutamate are traditionally recognized as molecular targets for antipsychotics (80). The GPCRs mediate slow synaptic transmission by modulating intracellular signal transduction via several downstream pathways, many of them engaging intracellular Ca2 + mobilization (81). Calcium ion binding plays a significant role in intracellular communication, thus regulating many basic aspects of cellular processes. Calcium ion binding is a process that can be triggered by the activation of ion channels or by indirect signal transduction pathways, such as those mediated by GPCRs, to facilitate signal transduction (82). Calcium signaling dysfunction have been well-established in schizophrenia (83). Therefore, our findings provide further evidence that the disrupted functional hierarchy architecture in schizotypy is associated with the expression of schizophrenia-related gene profile related to these tight four molecular mechanisms. This point was further supported by the convergent results of enrichment analyses using two prior and independently discovered lists of genes transcriptionally dysregulated in postmortem studies in schizophrenia. There was a higher significant correlation between the PLS1 weights of all genes in the genome and the differential expression values for all genes reported by Gandal et al. (2018) in schizophrenia than BD, ASD and MDD, suggesting most close enrichment in schizophrenia. Collectively, these closely related molecular mechanisms tucked beneath the compressed hierarchy advance our understanding of how genetic propensity for schizophrenia-alike traits play an enduring role in creating biological vulnerability to psychosis. Individuals with such biological vulnerability can potentially trigger towards high-risk states, or psychopathological outcomes through successive transactions with environmental risk factors identified in relation to schizophrenia (4, 37).

Since the probability of eventually developing schizophrenia in individuals with high schizotypy is only 21–24% (84), most subjects in our study may not develop full-blown psychosis. One possible explanation of the observed schizotypy-related compression of functional hierarchy may help protect or compensate against schizophrenia (37, 85). Future studies combing transcriptomic maps and the longitudinal measurements of functional hierarchy in the same individuals, in particular with high-level schizotypy would provide crucial information for understanding the causal mechanisms underlying the psychosis onset and progression.

Limitation And Future Direction

We should also acknowledge that this study has several limitations. First, we ascertained that none of the participants had a pre-existing psychiatric diagnosis, had not taken any psychiatric medication, and had no history of psychiatric disorders, all based on their self-reported information. Incorporating a structured clinical interview into future studies is likely to result in a more accurate assessment. Second, although SPQ is widely used to assess psychometric schizotypy, it is a self-assessment instrument that gauges schizotypal traits, yet more precise and objective measurements of these characteristics can be advantageous for future studies. Third, the AHBA brain tissue samples used for RNA sequencing were not evenly spread across the cortex, thus, the regional expression estimates are based on a varying number of experimental measurements in each cortical region. Fourth, our cross-sectional research design precluded us from charting the development of the altered functional hierarchy in schizotypy. Longitudinal studies in the future can investigate the evolution of functional hierarchy in schizotypy over time, especially for the individuals with high-level schizotypy, which would illustrate the casual mechanism underling the transmission from psychosis proneness to clinically diagnosis.

The present study highlights the compression of the core connectome hierarchy in schizotypy as well as its linkage with observed hierarchy pattern in schizophrenia, and its association with gene expression profiles that are particularly enriched in biological processes involved in schizophrenia. Our study not only sheds light on the neurobiological and molecular genetic mechanisms underlying the sensory-cognitive deficits in schizotypy but also yielded new insight into a neurobiological continuum of schizophrenia, thus advanced our understanding of how genetic propensity for schizophrenia-alike traits play an enduring role in creating biological vulnerability to psychosis.

Acknowledgements

This work was supported by the grant from the National Nature Science Foundation of China (No. 82202247 and No. 32271087), the Fundamental Research Funds for the Central Universities (No. SWU2209505), and the National Social Science Foundation of China (No. 22&ZD184).

Conflict of Interest

None.

Fonseca-Pedrero E, Ortuño J, Debbané M, Chan RC, Cicero D, Zhang LC, et al. The network structure of schizotypal personality traits. Schizophr Bull. 2018;44(suppl_2):S468-S79.
Raine A, Reynolds C, Lencz T, Scerbo A, Triphon N, Kim D. Cognitive-perceptual, interpersonal, and disorganized features of schizotypal personality. Schizophr Bull. 1994;20(1):191–201.
Grant P, Green MJ, Mason OJ. Models of schizotypy: the importance of conceptual clarity. Schizophr Bull. 2018;44(suppl_2):S556-S63.
Debbané M, Barrantes-Vidal N. Schizotypy from a developmental perspective. Schizophr Bull. 2015;41(suppl_2):S386-S95.
Ettinger U, Meyhöfer I, Steffens M, Wagner M, Koutsouleris N. Genetics, cognition, and neurobiology of schizotypal personality: a review of the overlap with schizophrenia. Front Psychiatry. 2014;5:18.
Ettinger U, Mohr C, Gooding DC, Cohen AS, Rapp A, Haenschel C, et al. Cognition and brain function in schizotypy: a selective review. Schizophr Bull. 2015;41(suppl_2):S417-S26.
Kirschner M, Hodzic-Santor B, Antoniades M, Nenadic I, Kircher T, Krug A, et al. Cortical and subcortical neuroanatomical signatures of schizotypy in 3004 individuals assessed in a worldwide ENIGMA study. Mol Psychiatry. 2022;27(2):1167–76.
Morton SE, O’Hare KJ, Maha JL, Nicolson MP, Machado L, Topless R, et al. Testing the validity of taxonic schizotypy using genetic and environmental risk variables. Schizophr Bull. 2017;43(3):633–43.
Cao H, Zhou H, Cannon TD. Functional connectome-wide associations of schizophrenia polygenic risk. Mol Psychiatry. 2021;26(6):2553–61.
Brosey E, Woodward ND. Schizotypy and clinical symptoms, cognitive function, and quality of life in individuals with a psychotic disorder. Schizophr Res. 2015;166(1–3):92–7.
Lui SS, Hung KS, Wang Y, Ho KK, Yeung HK, Wang Y, et al. Clustering of schizotypal features in unaffected first-degree relatives of schizophrenia patients. Schizophr Bull. 2018;44(suppl_2):S536-S46.
Tarbox SI, Pogue-Geile MF. A multivariate perspective on schizotypy and familial association with schizophrenia: a review. Clin Psychol Rev. 2011;31(7):1169–82.
Tonini E, Quidé Y, Kaur M, Whitford TJ, Green MJ. Structural and functional neural correlates of schizotypy: A systematic review. Psychol Bull. 2021;147(8):828.
Koychev I, Deakin JFW, Haenschel C, El-Deredy W. Abnormal neural oscillations in schizotypy during a visual working memory task: support for a deficient top-down network? Neuropsychologia. 2011;49(10):2866–73.
Javitt DC. When doors of perception close: bottom-up models of disrupted cognition in schizophrenia. Annu Rev Clin Psychol. 2009;5:249.
Burt JB, Demirtaş M, Eckner WJ, Navejar NM, Ji JL, Martin WJ, et al. Hierarchy of transcriptomic specialization across human cortex captured by structural neuroimaging topography. Nat Neurosci. 2018;21(9):1251–9.
Huntenburg JM, Bazin P-L, Margulies DS. Large-scale gradients in human cortical organization. Trends Cogn Sci. 2018;22(1):21–31.
Keller AS, Sydnor VJ, Pines A, Fair DA, Bassett DS, Satterthwaite TD. Hierarchical functional system development supports executive function. Trends Cogn Sci. 2022.
Mesulam M-M. From sensation to cognition. Brain: a journal of neurology. 1998;121(6):1013–52.
Margulies DS, Ghosh SS, Goulas A, Falkiewicz M, Huntenburg JM, Langs G, et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc Natl Acad Sci. 2016;113(44):12574–9.
Paquola C, Vos De Wael R, Wagstyl K, Bethlehem RA, Hong S-J, Seidlitz J, et al. Microstructural and functional gradients are increasingly dissociated in transmodal cortices. PLoS Biol. 2019;17(5):e3000284.
Dong H-M, Margulies DS, Zuo X-N, Holmes AJ. Shifting gradients of macroscale cortical organization mark the transition from childhood to adolescence. Proc Natl Acad Sci. 2021;118(28):e2024448118.
Xia Y, Xia M, Liu J, Liao X, Lei T, Liang X, et al. Development of functional connectome gradients during childhood and adolescence. Sci Bull. 2022;6(10):1049–61.
Hong S-J, Vos de Wael R, Bethlehem RA, Lariviere S, Paquola C, Valk SL, et al. Atypical functional connectome hierarchy in autism. Nat Commun. 2019;10(1):1–13.
Dong D, Yao D, Wang Y, Hong S-J, Genon S, Xin F, et al. Compressed sensorimotor-to-transmodal hierarchical organization in schizophrenia. Psychol Med. 2021:1–14.
Xia M, Liu J, Mechelli A, Sun X, Ma Q, Wang X, et al. Connectome gradient dysfunction in major depression and its association with gene expression profiles and treatment outcomes. Mol Psychiatry. 2022;27(3):1384–93.
Arnatkeviciute A, Fulcher BD, Oldham S, Tiego J, Paquola C, Gerring Z, et al. Genetic influences on hub connectivity of the human connectome. Nat Commun. 2021;12(1):1–14.
Owen MJ, Sawa A, Mortensen PB. Schizophrenia. Lancet. 2016;388(10039):86–97.
Legge SE, Santoro ML, Periyasamy S, Okewole A, Arsalan A, Kowalec K. Genetic architecture of schizophrenia: a review of major advancements. Psychol Med. 2021;51(13):2168–77.
Devor A, Andreassen OA, Wang Y, Mäki-Marttunen T, Smeland OB, Fan CC, et al. Genetic evidence for role of integration of fast and slow neurotransmission in schizophrenia. Mol Psychiatry. 2017;22(6):792–801.
Fornito A, Arnatkevičiūtė A, Fulcher BD. Bridging the gap between connectome and transcriptome. Trends Cogn Sci. 2019;23(1):34–50.
Richiardi J, Altmann A, Milazzo A-C, Chang C, Chakravarty MM, Banaschewski T, et al. Correlated gene expression supports synchronous activity in brain networks. Science. 2015;348(6240):1241–4.
Romme IA, de Reus MA, Ophoff RA, Kahn RS, van den Heuvel MP. Connectome disconnectivity and cortical gene expression in patients with schizophrenia. Biol Psychiatry. 2017;81(6):495–502.
Romero-Garcia R, Seidlitz J, Whitaker KJ, Morgan SE, Fonagy P, Dolan RJ, et al. Schizotypy-related magnetization of cortex in healthy adolescence is colocated with expression of schizophrenia-related genes. Biol Psychiatry. 2020;88(3):248–59.
Goulas A, Changeux J-P, Wagstyl K, Amunts K, Palomero-Gallagher N, Hilgetag CC. The natural axis of transmitter receptor distribution in the human cerebral cortex. Proc Natl Acad Sci. 2021;118(3):e2020574118.
Raine A, Benishay D. The SPQ-B: A brief screening instrument for schizotypal personality disorder. J Pers Disord 1995;9(4):346–55.
Barrantes-Vidal N, Grant P, Kwapil TR. The role of schizotypy in the study of the etiology of schizophrenia spectrum disorders. Schizophr Bull. 2015;41(suppl_2):S408-S16.
Ma W-F, Wu P-L, Yang S-J, Cheng K-F, Chiu H-T, Lane H-Y. Sensitivity and specificity of the Chinese version of the Schizotypal Personality Questionnaire-Brief for identifying undergraduate students susceptible to psychosis. Int J Nurs Stud. 2010;47(12):1535–44.
Wang Y-m, Cai X-l, Zhang R-t, Zhang Y-j, Zhou H-y, Wang Y, et al. Altered brain structural and functional connectivity in schizotypy. Psychol Med. 2022;52(5):834–43.
Takeuchi H, Taki Y, Asano K, Asano M, Sassa Y, Yokota S, et al. Childhood socioeconomic status is associated with psychometric intelligence and microstructural brain development. Commun Biol. 2021;4(1):1–19.
Chen X, Dong D, Zhou F, Gao X, Liu Y, Wang J, et al. Connectome-based prediction of eating disorder-associated symptomatology. Psychol Med. 2022:1–14.
Coifman RR, Lafon S, Lee AB, Maggioni M, Nadler B, Warner F, et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps. Proc Natl Acad Sci. 2005;102(21):7426–31.
Vos de Wael R, Benkarim O, Paquola C, Lariviere S, Royer J, Tavakol S, et al. BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Commun Biol. 2020;3(1):1–10.
Schaefer A, Kong R, Gordon EM, Laumann TO, Zuo X-N, Holmes AJ, et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb Cortex. 2018;28(9):3095–114.
Fan L, Li H, Zhuo J, Zhang Y, Wang J, Chen L, et al. The human brainnetome atlas: a new brain atlas based on connectional architecture. Cereb Cortex. 2016;26(8):3508–26.
Langs G, Golland P, Ghosh SS, editors. Predicting activation across individuals with resting-state functional connectivity based multi-atlas label fusion. Med Image Comput Comput Assist Interv; 2015: Springer.
Bethlehem RA, Paquola C, Seidlitz J, Ronan L, Bernhardt B, Tsvetanov KA, et al. Dispersion of functional gradients across the adult lifespan. Neuroimage. 2020;222:117299.
Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. Neuroimage. 2014;92:381–97.
Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, Wager TD. Large-scale automated synthesis of human functional neuroimaging data. Nat Methods. 2011;8(8):665–70.
Váša F, Seidlitz J, Romero-Garcia R, Whitaker KJ, Rosenthal G, Vértes PE, et al. Adolescent tuning of association cortex in human structural brain networks. Cereb Cortex. 2018;28(1):281–94.
Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012;489(7416):391–9.
Arnatkevic̆iūtė A, Fulcher BD, Fornito A. A practical guide to linking brain-wide gene expression and neuroimaging data. Neuroimage. 2019;189:353–67.
Romero-Garcia R, Warrier V, Bullmore ET, Baron-Cohen S, Bethlehem RA. Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism. Mol Psychiatry. 2019;24(7):1053–64.
Morgan SE, Seidlitz J, Whitaker KJ, Romero-Garcia R, Clifton NE, Scarpazza C, et al. Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia-related genes. Proc Natl Acad Sci. 2019;116(19):9604–9.
Eden E, Navon R, Steinfeld I, Lipson D, Yakhini Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinform. 2009;10(1):1–7.
Gandal MJ, Haney JR, Parikshak NN, Leppa V, Ramaswami G, Hartl C, et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science. 2018;359(6376):693–7.
Fromer M, Roussos P, Sieberts SK, Johnson JS, Kavanagh DH, Perumal TM, et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci. 2016;19(11):1442–53.
Zalesky A, Fornito A, Harding IH, Cocchi L, Yücel M, Pantelis C, et al. Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage. 2010;50(3):970–83.
Tian Y, Margulies DS, Breakspear M, Zalesky A. Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nat Neurosci. 2020;23(11):1421–32.
Xia M, Wang J, He Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PloS one. 2013;8(7):e68910.
Wang Y, Yan C, Yin D-z, Fan M-x, Cheung EF, Pantelis C, et al. Neurobiological changes of schizotypy: evidence from both volume-based morphometric analysis and resting-state functional connectivity. Schizophr Bull. 2015;41(suppl_2):S444-S54.
Wang Y-m, Zou L-q, Xie W-l, Yang Z-y, Zhu X-z, Cheung EF, et al. Altered functional connectivity of the default mode network in patients with schizo-obsessive comorbidity: a comparison between schizophrenia and obsessive-compulsive disorder. Schizophr Bull. 2019;45(1):199–210.
Wang Y-m, Cai X-l, Zhou H-y, Zhang R-t, Zhang Y-j, Wang Y-y, et al. Altered default mode network functional connectivity in individuals with co-occurrence of schizotypy and obsessive-compulsive traits. Psychiatry Res Neuroimaging. 2020;305:111170.
Messaritaki E, Foley S, Barawi K, Ettinger U, Jones DK. Increased sensorimotor structural connectivity in high schizotypy. Netw Neurosci. 2022.
Wig GS. Segregated systems of human brain networks. Trends Cogn Sci. 2017;21(12):981–96.
Whitfield-Gabrieli S, Ford JM. Default mode network activity and connectivity in psychopathology. Annu Rev Clin Psychol. 2012;8:49–76.
Smallwood J, Bernhardt BC, Leech R, Bzdok D, Jefferies E, Margulies DS. The default mode network in cognition: a topographical perspective. Nat Rev Neurosci. 2021;22(8):503–13.
Buckner RL, DiNicola LM. The brain’s default network: updated anatomy, physiology and evolving insights. Nat Rev Neurosci. 2019;20(10):593–608.
Murphy C, Jefferies E, Rueschemeyer S-A, Sormaz M, Wang H-t, Margulies DS, et al. Distant from input: Evidence of regions within the default mode network supporting perceptually-decoupled and conceptually-guided cognition. Neuroimage. 2018;171:393–401.
Northoff G, Duncan NW. How do abnormalities in the brain’s spontaneous activity translate into symptoms in schizophrenia? From an overview of resting state activity findings to a proposed spatiotemporal psychopathology. Prog Neurobiol. 2016;145:26–45.
Teufel C, Subramaniam N, Dobler V, Perez J, Finnemann J, Mehta PR, et al. Shift toward prior knowledge confers a perceptual advantage in early psychosis and psychosis-prone healthy individuals. Proc Natl Acad Sci. 2015;112(43):13401–6.
Dong D, Luo C, Guell X, Wang Y, He H, Duan M, et al. Compression of cerebellar functional gradients in schizophrenia. Schizophr Bull. 2020;46(5):1282–95.
Dong D, Guell X, Genon S, Wang Y, Chen J, Eickhoff SB, et al. Linking cerebellar functional gradients to transdiagnostic behavioral dimensions of psychopathology. NeuroImage Clin. 2022;36:103176.
De Wit J, Ghosh A. Specification of synaptic connectivity by cell surface interactions. Nat Rev Neurosci. 2016;17(1):22–35.
Fossati M, Assendorp N, Gemin O, Colasse S, Dingli F, Arras G, et al. Trans-synaptic signaling through the glutamate receptor Delta-1 mediates inhibitory synapse formation in cortical pyramidal neurons. Neuron. 2019;104(6):1081–94.
Wang X-J. Macroscopic gradients of synaptic excitation and inhibition in the neocortex. Nat Rev Neurosci. 2020;21(3):169–78.
Hall J, Bray NJ. Schizophrenia genomics: convergence on synaptic development, adult synaptic plasticity, or both? Biol Psychiatry. 2021;91(8): 709–17.
Forsyth JK, Nachun D, Gandal MJ, Geschwind DH, Anderson AE, Coppola G, et al. Synaptic and gene regulatory mechanisms in schizophrenia, autism, and 22q11. 2 copy number variant–mediated risk for neuropsychiatric disorders. Biol Psychiatry. 2020;87(2):150–63.
Stępnicki P, Kondej M, Kaczor AA. Current concepts and treatments of schizophrenia. Molecules. 2018;23(8):2087.
Boczek T, Mackiewicz J, Sobolczyk M, Wawrzyniak J, Lisek M, Ferenc B, et al. The Role of G Protein-Coupled Receptors (GPCRs) and Calcium Signaling in Schizophrenia. Focus on GPCRs Activated by Neurotransmitters and Chemokines. Cells. 2021;10(5):1228.
Weis WI, Kobilka BK. The molecular basis of G protein–coupled receptor activation. Annu Rev Biochem. 2018;87:897–919.
Giorgi C, Danese A, Missiroli S, Patergnani S, Pinton P. Calcium dynamics as a machine for decoding signals. Trends Cell Biol. 2018;28(4):258–73.
Nanou E, Catterall WA. Calcium channels, synaptic plasticity, and neuropsychiatric disease. Neuron. 2018;98(3):466–81.
Kwapil TR, Barrantes-Vidal N. Schizotypy: looking back and moving forward. Schizophr Bull. 2015;41(suppl_2):S366-S73.
Siever LJ, Davis KL. The pathophysiology of schizophrenia disorders: perspectives from the spectrum. Am J Psychiatry. 2004;161(3):398–413.

The authors have declared there is NO conflict of interest to disclose

Supplementarymaterials.doc

Download PDF

Version 1

posted

You are reading this latest preprint version

Functional Connectome Hierarchy in Schizotypy and Its associations with Expression of Schizophrenia-Related Genes

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Participants

Questionnaire Assessment

MRI Acquisition And Image Data Preprocessing

Connectivity Gradient Mapping

Schizotypy-related Connectome Hierarchy

Meta-analytic Decoding Of The Function Of Significant Connectome Hierarchy Using NeuroSynth

Association Between Schizotypy-related And Schizophrenia-altered Connectome Hierarchy

Estimation Of Regional Gene Expression

Association between schizotypy-related connectome hierarchy and gene expression profiles

Control Analysis

Data And Code Availability

Results

Schizotypy associated connectome hierarchy

Sensitivity Analyses Of Connectome Hierarchy In Schizotypy

Functional Properties Associated With Hierarchy Alterations In Schizotypy

Association between schizotypy-related connectome hierarchy and gene expression profiles

Control Analysis

Discussion

Downgraded And Compressed Connectome Hierarchy In Schizotypy

Connectome Hierarchy Similarity Between Schizotypy And Schizophrenia

Covaried Compression Of Connectome Hierarchy In Schizotypy And Expression Of Schizophrenia-related Genes

Limitation And Future Direction

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1