Decoding emotional prosody: a unified brain network integrating gender and task type effect

Decoding emotional prosody: a unified brain network integrating gender and task type effect | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Decoding emotional prosody: a unified brain network integrating gender and task type effect Suyu Zhong, Pinyuan Hu, Xiaochen Sun, Xingyu Ouyang, Xinyu Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6155286/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Communications Biology → Version 1 posted You are reading this latest preprint version Abstract Emotional prosody processing is vital for social communication. Despite numerous neuroimaging studies exploring emotional prosody, results remain inconsistent across studies, and the factors influencing these inconsistencies are unclear. Here, we identified a unified brain network for emotional prosody processing using activation network mapping. We evaluated how gender and task type influence this network. Results showed broader activation networks in females compared to males, regardless of the emotional prosody type. Moreover, the comparison of task type revealed stage processing mode of emotional prosody. Additionally, analyses link emotional prosody to specific receptors/transporters ( \(\:{5HT}_{1A}\) , \(\:{CB}_{1}\) , \(\:{mGluR}_{5}\) , and \(\:NET\) ) and physiological processes such as synapse extension, energy metabolism, active transmembrane transport, along with diseases like autistic disorder, Alzheimer's disease, and general disease progression. In conclusion, these findings underscore the importance of considering gender and task type effects on emotional processing research and provide a deeper understanding of the complex neural mechanisms underlying emotional prosody. Biological sciences/Neuroscience Biological sciences/Neuroscience/Cognitive neuroscience/Language Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Emotion, as a fundamental aspect of human beings, plays a vital role in social interactions and communication. Accurate perception of emotional information serves as an essential cornerstone for effective social interaction. Numerous studies have demonstrated that individuals with a range of neuropsychiatric disorders, including autism, aphasia, Asperger's syndrome, and Alzheimer's disease, frequently exhibit atypical social interaction abilities 1 . These abilities may be linked to deficits in emotion perception 2 – 4 . Emotional prosody (EP), as one of the important emotional cues and an important way to convey verbal emotion during communication 5 , 6 , plays a crucial role in enhancing the meaning of spoken language, allowing listeners to interpret the speaker's feelings and intentions better. Emotional prosody accurate perception is essential for successful social interactions, as it aids in the recognition of emotional states, facilitating empathy and appropriate responses in conversations 7 . Therefore, investigating the neural underpinnings of emotional prosody processing may facilitate the understanding of the neural mechanisms underlying emotional processes and provide insights into disease pathogenesis. Despite numerous studies exploring the neural underpinnings of emotional prosody using various lesion and neuroimaging methods 8 , 9 , these studies have presented highly heterogeneous results in the activation patterns of emotional prosody processing 10 – 12 . This inconsistency may be influenced by a variety of factors. One of the factors is the task type, implying differences in processing levels, from implicit processing to explicit judgments (implicit emotional prosody, IEP; explicit emotional prosody, EEP) 13 , 14 . Additionally, the gender of the participants may also be a factor, with some studies finding that the left frontal area is more strongly activated in females than in males during emotional prosody processing 15 . Recently, resting state functional connectivity (rs-FC) has become a popular tool to assess brain functions and activities in various fields 16 , including emotional processing. In healthy children, studies concerning the relationship between rs-FC and emotional voice processing have indicated that children with better performance in emotion recognition tasks exhibit a stronger rs-FC between IFG and motor regions 17 . Meanwhile, for patients with emotional processing deficits, weak rs-FC between certain areas of the brain networks have also been found 16 , 18 , 19 . Moreover, evidence suggests that the resting human brain networks are strikingly similar to the activation patterns of task-based brain networks 20 . Based on this similarity, a method known as activation network mapping (ANM), which was developed from the lesion network mapping technique, has been proposed to obtain brain networks by integrating inconsistent neuroimaging studies 21 . By using the activation coordinates reported in previous studies as seeds and then investigating the common pattern between the normative seed-based functional connectivity maps of each experiment, the ANM approach has been shown to integrate previous heterogeneous studies better and provide higher reproducibility across these studies compared with traditional meta-analysis. Based on previous findings, we put forward two hypotheses: 1) The EEP and IEP processing exhibit different activation patterns; 2) There is a gender difference in the activation patterns of EP processing. To test these hypotheses, we adopted the ANM technique to investigate the neural networks of EP processing from a network perspective based on human connectome project dataset (HCP) 22 . First, we converged the heterogeneous fMRI results into a common emotional prosody network under ANM analysis. Second, we assessed the effect of the two factors, gender and task type, on emotional prosody processing. Finally, we explored the neurotransmitter and molecular mechanism underlying emotional prosody. Results Articles selection Detailed information about the article selection is shown in Table 1 and Table S1 . Here, different experiments or contrasts for the same article were counted separately. For the emotional prosody analysis, a total of 40 articles were included, with 54 experiments, 725 subjects (334 females, 391 males), and 474 activity coordinates. To explore the difference in activation networks between EEP and IEP, we categorized the total experiments into EEP experiments and IEP experiments. There were 32 EEP articles, with 41 experiments, 587 subjects (270 females, 317 males), 390 coordinates, and 9 IEP articles with 11 experiments, 162 subjects (78 females, 84 males), and 59 coordinates. Notice that 2 experiments were excluded from EEP and IEP analysis for their inappropriate contrast. Table 1 The detail information about the article selection. EP EEP IEP No. of articles 40 32 9 No. of experiments 54 41 11 No. of participants (F/M) 725 (334/391) 587 (270/317) 162 (78/84) No. of activation coordinates 474 390 59 Inconsistency of neuroimaging results in EP processing The degree of consistency in the findings on EP processing across studies was quantified by calculating the overlap proportion of studies for each region of the AICHA. For EP, the activation coordinates reported in previous studies were widely distributed across almost all brain regions, with the right superior temporal gyrus (STG) showing the highest overlap proportion. However, even in this region, the overlap was less than 60% (31%, Fig. 1 A), indicating significant variability. For EEP and IEP, the right STG also showed a top overlap proportion, but only with proportions of 34% and 36%, respectively (Fig. 1 B, C). These findings highlight the heterogeneity in neuroimaging results for EP processing. Activation networks of EP processing The activation networks from ANM are shown in Fig. 2 A. For EP, our analysis revealed a widespread activation brain network, including somatomotor network (SMN, bilateral Precentral and postcentral gyrus [PreCG and PostCG], bilateral planum temporal [PT], bilateral posterior part of STG [STGp], bilateral superior temporal sulcus [STS] and left Heschl's gyrus [HG]), ventral attention network (VAN, bilateral Insular), default mode network (DMN, right posterior middle temporal gyrus [MTGp]), frontoparietal network (FPN, bilateral opercular and triangular part of inferior frontal gyrus [IFGpo and IFGpt], bilateral middle frontal gyrus [MFG] and bilateral posterior supramarginal gyrus [SMGp]), dorsal attention network (DAN, temporooccipital part of the bilateral MTG [MTGtp]), limbic network (LN, bilateral orbitofrontal cortex [OFC]), and subcortical network (Amygdala). For EEP, the unthresholded activation network was highly correlated to that of EP ( \(\:r\) = 0.992, \(\:{P}_{spin}\) < 1.0×10 −4 ) (Fig. 3 C). In contrast, the activation network of IEP was more localized, primarily involving regions such as the bilateral Insular, bilateral SMGp, bilateral PT, bilateral STGp and STS, resulting in a relatively lower spatial correlation with that of EP ( \(\:r\:\) = 0.86, \(\:{P}_{spin}\) < 1.0×10 −4 ) (Fig. 3 C). The overlap between activation networks of EEP and IEP showed that the EEP activation network encompassed the network of IEP (Fig. 2 B), with an EEP-specific network primarily including regions such as the bilateral IFGpo, left IFGpt, bilateral MFG, bilateral OFC, right MTGtp, bilateral PreCG and bilateral PostCG. These findings indicate that EEP engages a more extensive neural network compared to IEP, especially in areas associated with higher-order cognitive processing. Widespread activation networks of EP in females To test gender differences in brain networks involved in EP, we separately estimated the activation networks for male and female cohorts using activation seeds and fMRI scans of corresponding subjects of the HCP. As illustrated in Fig. 3 A, D, the ANM results indicated that females engaged in a broader brain network than males during EP processing. Specifically, for EP and EEP, the gender-shared activation network was mainly in the bilateral PT and IFGpo/IFGpt, while IEP showed activation exclusively in the bilateral PT. The female-specific network involved in EP and EEP was mainly distributed in regions including bilateral MFG, right MTGtp, bilateral Insular, left SMC, bilateral PreCG, and bilateral PostCG. Moreover, for IEP, brain regions including right Insular were female-specific. Additionally, for almost all subnetworks, the overlap proportion (the proportion of experiments whose binarized normative rs-FC map showed activation for each voxel) of the activation networks in each sub-network 23 was significantly higher in females than in males (Fig. 3 B) (see detailed Wilcoxon rank-sum test results in Supplementary Table 2). Neuroreceptor mechanisms underlying EP processing To investigate the underlying neuroreceptor mechanism for the activation networks of emotional prosody, we conducted a spatial correlation analysis between receptors and the activation networks. A common neuroreceptor set related to emotional prosodies was found, including \(\:{5HT}_{1A}\) , \(\:{CB}_{1}\) , \(\:{mGluR}_{5}\) and \(\:NAT\) ( \(\:{5HT}_{1A}\) : \(\:r\) = 0.32 ± 0.037, \(\:{P}_{spin}\) = 0.014 ± 0.012; \(\:{CB}_{1}\) : \(\:r\:\) = 0.34 ± 0.074, \(\:{P}_{spin}\) = 0.028 ± 0.012; \(\:{mGluR}_{5}\) : \(\:r\) = 0.31 ± 0.026, \(\:{P}_{spin}\) = 0.020 ± 0.021; \(\:NAT\) : \(\:r\) = 0.33 ± 0.013, \(\:{P}_{spin}\) = 0.00013 ± 5.0×10^-5). Additionally, another set of neuroreceptors exhibited a gender-specific effect. For EP and EEP, \(\:{5HT}_{1B}\) , \(\:{5HT}_{2A}\) , and \(\:{5HT}_{6}\) were significantly positively associated with spatial activation patterns in females ( \(\:{5HT}_{1B}\) : [EP: \(\:r\) = 0.27, \(\:{P}_{spin}\) = 0.018; EEP: \(\:r\) = 0.27, \(\:{P}_{spin}\) = 0.013]; \(\:{5HT}_{2A}\) : [EP: \(\:r\) = 0.28, \(\:{P}_{spin}\) = 0.036; EEP: \(\:r\) = 0.28, \(\:{P}_{spin}\) = 0.032]; \(\:{5HT}_{6}\) : [EP: \(\:r\) = 0.21, \(\:{P}_{spin}\) = 0.044; EEP: \(\:r\) = 0.20, \(\:{P}_{spin}\) = 0.0496]). In contrast, \(\:VAChT\) showed significant positive correlations with spatial activation patterns in males (EP: \(\:r\) = 0.31, \(\:{P}_{spin}\) = 0.01; EEP: \(\:r\) = 0.34, \(\:{P}_{spin}\) = 0.0030) (Fig. 4 C). Genetic mechanisms underlying the EP processing All first three PLS components were significant after 10,000 times permutation. For each activation network, the first three components of the PLS regression totally explained 32.48%~43.42% of the variance (Supplementary Fig. 1). Supplementary Fig. 2 illustrates that within each component of the PLS regressions (PLS1, PLS2, and PLS3), the distributions are highly correlated, while the correlations between different components across all activation networks are relatively low. Specifically, PLS1 exhibited a transcriptional profile marked by under-expression predominantly in the bilateral LG and para-hippocampal regions. PLS2 displayed overexpression primarily in the bilateral Insula, TP, PreCG, and PostCG, with concurrent under-expression in the occipital lobe. PLS3 revealed overexpression chiefly in the bilateral PreCG, PostCG, posterior STG, and occipital lobe, while showing under-expression in the frontal lobe. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis revealed that the genes were enriched in biological processes (BP), cell components (CC), molecular function (MF) and pathways related to energy metabolism, synapse extensions and active transportation (Fig. 4 A). Energy metabolism included fatty acid metabolic process [GO:0006631] in BP, mitochondrial matrix [GO:0005759] in CC, ATP hydrolysis activity [GO:0016887] in MF, and fatty acid degradation [hsa00071] in pathways. Synapse extensions included cell leading edge [GO:0031252] in CC and metallopeptidase activity [GO:0008237] in MF). Active transportation included active transmembrane transporter activity [GO:0022804] in MF and ABC transporters [hsa02010] in pathways (Fig. 4 A). Additional Disease Gene Network (DisGeNET) disease enrichment analyses showed that genes associated with emotional prosody processing are linked to Disease Progression [umls:C0242656], Autistic Disorder [umls:C0004352] and Alzheimer's Disease [umls:C0002395] (Fig. 4 B). Discussion By integrating the previous neuroimaging findings, we uncovered the common activation brain network involved in EP processing, its gender difference, and its association with the transcriptional profiles and neurotransmitter receptor patterns. Our analysis revealed a widespread activation brain network for EP processing included networks including DMN, DAN, LN, SMN and subcortical network. Besides, we found that females exhibited relatively broader activation networks associated with EP processing compared to males. Interestingly, these activation networks exhibited correlations with the spatial patterns of receptors/transporters ( \(\:{5HT}_{1A}\) , \(\:{CB}_{1}\) , \(\:{mGluR}_{5}\) , and \(\:NET\) ) and gene expression profiles (energy metabolism, synapse extension, active transmembrane transport, along with diseases such as autistic disorder, Alzheimer's disease, and general disease progression). These findings enhance our understanding of the neurobiological and modular mechanisms underlying emotional prosody processing and the influence of gender on emotional prosody processing. The common activation networks of EP processing We utilized ANM analysis to reveal common activation networks of EP. The EP networks included the regions of bilateral STGp, STS, DMN (right MTGp), DAN (MTGtp), FPN (bilateral IFGpo and IFGpt), LN (bilateral OFC), bilateral SMGp, SMN (bilateral PreCG and PostCG), bilateral PT, left HG, VAN (bilateral Insular), and subcortical network (Amygdala). Most of those areas align with existing literature, highlighting their roles in emotional voice perception. By analyzing the activation networks, we found that most of the activated brain regions of IEP (STG, STS) and part of the activated brain regions of EEP (IFG, MFG, OFC) align with the three-stage model of emotional prosody processing 9 , 13 . IEP-specific activated areas are mainly involved in the first two stages of EP processing. For instance, in the sensory processing stage, the auditory cortex is involved in analyzing acoustic information. The STG has been shown to track variations in the amplitude envelope of incoming sound, thus contributing to the extraction and identification of vocally expressed emotional sounds 24 – 26 . Brück et al. 13 further emphasized the key role of the middle superior temporal gyrus (m-STG) in the decoding of emotional intonation, a view that was supported by fMRI experimental data. The activation of the m-STG is associated with acoustic parameters (e.g., loudness, duration, or fundamental frequency), which collectively define emotional intonation 27 – 29 . The STS region is also implicated in this process and functions as a pivotal node in the auditory "what" pathway, integrates emotional information and connects with frontal regions 9 , 30 . The EEP processing, on the other hand, encompassed the IEP-activated regions and involved a more extensive network, including the IFG, MFG and OFC, which played crucial role in the higher-order cognitive processes, such as evaluative judgments of emotional information. These higher-order cognitive processes can be triggered by top-down mechanisms that increase listeners' attention to emotional prosody 9 . Later neuroimaging studies have revealed that the IFG is crucial for the evaluation of vocal emotional cues and often co-activate with the temporal auditory areas 31 , 32 , the MFG is linked to working memory load 24 , 33 – 35 , while the OFC is involved in evaluating emotional prosody and selecting emotional connotations 34 , 36 , 37 . Besides those brain areas involved in the three stages, some other brain regions were also found active during EP processing, both in our and previous results. For example, during IEP processing, we found the activation of bilateral SMGp and PT. Previous studies have reported that PT is specifically involved in the processing of emotional semantics and word content 34 , 38 , while the SMG has been found responsible for auditory–motor integration in speech perception, and supports phonological encoding–recoding processes in a variety of tasks 39 . Additional regions have also been found during EEP processing, including MTG, PreCG and PostCG. For MTG, studies have reported that it is related to recognizing slow sound changes and may be involved in the integration process of prosodic and semantic information 34 , 39 . PreCG and PostCG have been identified as regions responsible for somatosensory monitoring and feedback during phoneme perception 40 , 41 . This indicates that the sensorimotor cortex is also engaged in EP processing. This is highly consistent with the dual-stream model of speech processing 42 , supporting the possible existence of a similar dual-stream model of EP processing. Comparing the activated brain regions of IEP and EEP, EEP processing involved a more extensive network. The involvement of executive frontal regions (IFG, MFG and OFC) implies higher cognitive demands during EEP processing, The involvement of dorsal sensorimotor regions (e.g., PreCG and PostCG) provides further evidence that the increased cognitive demands are associated with explicit emotional tasks 6 , 43 . Our results revalidate the three-stage model of EP processing from the perspective of brain networks and provide additional evidence for other potential sub-processes. EP processing involves multiple sub-stages, each engaging a complex network of brain regions. Broader emotional prosody activation network in female This study revealed significant gender differences in the neural mechanisms underlying emotional prosody processing. The female group showed more broader activation in bilateral MFG, right MTGtp, bilateral Insular, left SMC, bilateral PreCG, and bilateral PostCG regions, which are critical for emotion processing and speech perception 13 , 14 , 24 , 37 , 44 . This finding is consistent with previous neuroimaging studies on gender differences in emotion processing. Numerous studies have indicated significant differences between males and females in emotion perception. Research showed that females tend to perform better in emotion recognition tasks, including the perception of vocal affect, facial expressions, and body language 45 – 49 . From an evolutionary perspective, these gender differences in emotion processing may be attributed to the distinct social roles in primeval conditions 50 . Historically, females have often been responsible for childcare and social bonding, which requires heightened emotional sensitivity. In contrast, males have been more attuned to detecting threats, such as anger from competitors or predators 51 – 53 . Furthermore, previous studies suggest that the gender differences in emotion processing were a result of genetically mediated adaptation to primeval conditions 50 , 54 – 56 . The broader frontal activation, along with the involvement of additional brain areas and greater functional connectivity during EP processing, suggest that females may utilize more complex neural networks to handle emotional tasks, resulting in a higher cognitive and emotional integration demand 57 . The emotional prosody related receptors/transporters Our findings provided critical insights into the molecular underpinnings of emotional prosody, highlighting both shared and gender-specific neuroreceptor contributions. The identification of shared receptors, including the \(\:{5HT}_{1A}\) receptor, \(\:{CB}_{1}\) receptor, \(\:{mGluR}_{5}\) , and \(\:NET\) , suggests that emotional prosody processing may involve a foundational set of neuroreceptors linked to broader emotional regulation. These findings align with prior research showing that these receptors play critical roles in fear and anxiety regulation, emotional perceptual biases 58 – 60 . For example, \(\:{5HT}_{1A}\) receptor agonists reduce fear recognition in facial expressions, and \(\:{CB}_{1}\) receptor agonists modulate anxiety responses, supporting their involvement in processing emotionally salient prosodic cues 61 – 63 . This shared receptor set underscores the likelihood of conserved molecular mechanisms underlying various emotional processing tasks, extending their significance to the domain of emotional prosody. In addition to the shared neuroreceptors, our results revealed gender-specific receptor effects, providing a nuanced understanding of the neurochemical basis of emotional prosody. In females, serotonin receptors ( \(\:{5HT}_{1B}\) , \(\:{5HT}_{2A}\) , and \(\:{5HT}_{6}\) ) were significantly associated with activation patterns for both EP and EEP, suggesting heightened serotonergic modulation in emotional prosody. These findings align with previous studies demonstrating that serotonergic signaling is modulated by sex hormones such as estrogen 64 , which may enhance serotonin receptor sensitivity in females. Conversely, the cholinergic system, as indicated by the significant correlation of \(\:VAChT\) with activation patterns in males, points to a distinct neurochemical pathway in male emotional prosody processing. This divergence could reflect structural or functional brain differences between genders, such as the density of cholinergic innervation or hormonal effects on acetylcholine receptor regulation 65 , 66 . The molecular mechanism underlay emotional prosody GO and KEGG pathway enrichment analyses revealed that the genes correlated with emotional prosody processing are enriched in pathways related to energy metabolism, synapse extension, and active transmembrane transport. These findings suggest that energy-demanding processes and synaptic modifications are crucial for the neural mechanisms underlying emotional prosody. For example, the enrichment of mitochondrial matrix processes and ATP hydrolysis activities underscores the metabolic demands of regions actively engaged in emotional prosody tasks. Additionally, the involvement of metallopeptidase activity and synapse extension pathways may reflect the structural plasticity required for efficient neural signaling during emotional communication. These molecular pathways represent potential targets for further functional validation studies. The identified genes were also enriched in diseases such as autistic disorder, Alzheimer's disease, and general disease progression. While these associations are intriguing, they should be interpreted with caution. The enrichment results do not establish causality but rather suggest that emotional prosody processing shares molecular mechanisms with these disorders. For instance, deficits in emotional prosody are well-documented in autism and Alzheimer's disease. Future research could investigate whether these shared molecular signatures contribute to the impaired emotional communication observed in these conditions. Limitation There were some limitations. First, it should be noticed that the activation networks for males and females were estimated using activation data from the overall cohorts because of the limited study that investigated the activation of EP for a single gender. As a result, our integrated findings may not fully reflect the actual network patterns unique to each gender. Nevertheless, using the same activation data from the overall cohort, we still observed distinct activation networks for males and females, highlighting the different activation patterns when processing emotional prosody. Second, like the ANM research study, no brain regions connected to 100% of the activation seeds were found, and a less conservative overlapping threshold (60%) was used. However, the highest overlap proportion in consistency analysis was 36%, illustrating that EP processing is supported by a distinct network. Third, given the high heterogeneity observed across studies on emotional prosody, a larger number of experiments could yield more robust results. Future research can further explore the receptor involvement and genetic regulatory mechanisms in emotional prosody processing, to gain a deeper understanding of the neural mechanisms underlying emotional prosody. Materials and Methods Articles selection A systematic review was conducted to identify relevant articles with two independent reviewers. The primary search included a comprehensive examination of the PubMed database, as well as a thorough review of existing meta-analyses. Studies from January 1, 1993, to July 10, 2024, were searched using terms related to emotional prosodic studies, including "prosody", "emotional prosody", and "affective prosody", in conjunction with terms related to neuroimaging techniques, including "fMRI", "functional MRI", "functional magnetic resonance imaging", "PET", "positron emission tomography", "neuroimaging", and "BOLD". As a result, a total of 248 research articles were obtained from both a PubMed search and earlier meta-analyses 24 , 43 , 44 , 67 . Further, screening processes were conducted to include the articles upon the fulfillment of the following criteria: 1) Studies that employed tasks to assess the auditory processing of emotional prosody; 2) Studies that included a rest or control task; 3) Studies that involved healthy adult participants, or case-control studies with a healthy adult control group; 4) Studies that utilized standardized 3D spatial neuroimaging coordinates from functional magnetic resonance imaging (fMRI) or positron emission tomography (PET). Additionally, studies that employed multi-modal or dichotic listening methodologies were excluded. As a result of the screening process, 40 peer-reviewed articles were identified. The process of the literature search is graphically represented in Fig. 5 . Data extraction Data extracted from each experiment included the number of participants and the activation coordinates (in MNI or Talairach space). Only coordinates reported significant activation in emotional prosody processing were considered. To unify the coordinates in the same space system coordinates, those initially reported in Talairach space were transformed to MNI using the MNI2Tal web tool ( https://bioimagesuiteweb.github.io/webapp/mni2tal.html ). To examine the differences between EEP and IEP, we categorized these experiments into two groups based on the specific tasks performed and the contrasts analyzed. MRI processing for HCP data To construct a normative functional connectome, the MRI dataset from the HCP S1200 release was included in this study 68 . Here, 1084 participants with two runs including a left-to-right (LR) and a right-to-left (RL) phase encoding direction were used. All subjects provided written informed consent, and the research protocol was approved by the Institutional Review Board of Washington University. Please refer to Van Essen et al. 69 for more details about the dataset. MRIs were acquired using a Siemens Connectome Skyra 3 T scanner with a 32-channel head coil housed at Washington University in St. Louis. For more specific scanning parameters please find them in Supplementary materials or refer to Glasser et al. 70 . The rs-fMRI and T1-weighted images were preprocessed using the HCP minimal-preprocessing pipelines 70 . The following preprocess procedures were further performed on the minimal preprocessing pipeline data. For the T1 images, skull-stripping was first performed using FSL bet2 (f = 0.5). Then skull-stripped T1-weighted images were segmented into three tissue types including white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). For each individual, the averaged WM signal and CSF signal were extracted based on the segmentation results. For the fMRI images, detrending was first executed to minimize the effects of the low-frequency drift. Then, to eliminate potential confounding effects on the signal of gray matter, 24 movement parameters, signals of WM and CSF were regressed. Finally, all images were bandpass filtered (0.01-0.1Hz) to eliminate the impact of high-frequency physiological noise and low-frequency drift. Activation network mapping To uncover the emotional prosody activation network hidden in inconsistent neuroimaging results, we conducted an ANM analysis. Here, the "overlap" approach was used. The steps were summarized as follows. First, we identified the activation seed for each experiment by generating 4-mm-radius spheres centered on each coordinate. The activation seed was identified by combining the spheres associated with the same experiment. Next, normative functional connectivity maps were generated for each experiment. Specially, the seed based rs-FC map was computed for each subject in HCP. Voxel-wise one-sample t-tests were then performed on these rs-FC maps, with age and gender effects controlled. Notably, the above step was performed for both LR scans and RL scans, resulting in LR t map and RL t map. The normative functional connectivity map was obtained by averaging the LR t map and RL t map, followed by smoothing with a 6 mm full-width at half-maximum (FWHM) Gaussian kernel. Finally, we calculated the activation network by binarizing the normative rs-FC map for each experiment with a threshold of Bonferroni-corrected \(\:p\) < 0.05. The activation network was finally generated by overlapping these binary maps and thresholding at 60%. Here, the overlap approach was employed due to the following advantages: 1) higher interpretability, 2) reduction of bias induced by autocorrelation, 3) mitigation of bias driven by extreme values in a small number of experiment-level maps, and 4) corresponding to our consistency analysis. To validate the results, the "t-test" approach was employed. For the details of the "t-test" approach please refer to Supplementary Methods or Peng et al. 21 . Mapping neurotransmitter receptor maps to the activation networks of EP processing To identify the molecular foundation of emotional prosody, we calculated the spatial correlation between activation networks and the distribution of receptors. The publicly available receptors dataset, Hansen receptors, was used 71 ( https://github.com/netneurolab/hansen_receptors ). Hansen receptors dataset included PET images of 19 different neurotransmitter receptors and transporters, across 9 neurotransmitter systems: serotonin ( \(\:{5HT}_{1A}\) , \(\:{5HT}_{1B}\) , \(\:{5HT}_{2A}\) , \(\:{5HT}_{4}\) , \(\:{5HT}_{6}\) , \(\:5HTT\) ), dopamine ( \(\:{D}_{1}\) , \(\:{D}_{2}\) , \(\:DAT\) ), norepinephrine ( \(\:NAT\) ), histamine ( \(\:{H}_{3}\) ), acetylcholine ( \(\:{\alpha\:}_{4}{\beta\:}_{2}\) , \(\:{M}_{1}\) , \(\:VAChT\) ), cannabinoid ( \(\:{CB}_{1}\) ), opioid ( \(\:MU\) ), glutamate ( \(\:NMDA\) , \(\:{mGluR}_{5}\) ), and GABA ( \(\:{GABA}_{A/BZ}\) ) systems. All these PET images were collected and registered to MNI152 space. To explore the relationship between activation networks and the distribution of receptor/transporter, we performed Spearman's correlation analysis after normalization 72 . Notably, 10,000 spin test permutations while controlling for spatial autocorrelation were used to test the significance of these correlations. Association between activation networks and gene expression To identify the gene foundation of emotional prosody, the public gene expression data, Allen Human Brain Atlas (AHBA) ( http://human.brain-map.org ) were used. It offers transcriptomic data of 58,692 probes corresponding to 29,131 genes, derived from 3702 spatially distinct tissue samples collected from six postmortem adult brains. The gene expression data was processed as follows: intensity-based probes filtering, representative probe selection, matching tissue samples to atlas (The Atlas of Intrinsic Connectivity of Homotopic Areas, AICHA) 73 , normalization and aggregation. More details please find in Supplementary methods. As a result, a 15633×384 gene expression matrix was created. Here the abagen toolbox ( https://abagen.readthedocs.io/ ) was used. To detect genes whose expression levels were significantly correlated to the activation networks of EP, PLS (Partial Least Squares) regression was used, with z-scored region-level gene expression matrix as the independent variable (X) and the z-scored parcellated activation network as the dependent variable (Y). A 10,000 times spatial permutation test was utilized to examine the significance level for each PLS component 74 . Ultimately, the first three components of all PLS regressions passed the spin test ( \(\:{P}_{spin}\) < 0.01). Therefore, subsequent studies are based on the first three components of all PLS regression. Genes whose absolute gene weight ranked in the top 10% (top 1563) in each PLS component were selected for subsequent enrichment analyses. Gene enrichment analysis The extracted gene sets and their signed gene weights were selected to perform enrichment analysis to identify enriched GO ( https://geneontology.org/ ) terms 75 , 76 , KEGG ( https://www.genome.jp/kegg/pathway.html ) pathways 77 and DisGeNET ( https://www.disgenet.com ) diseases 78 by using the Web-based Gene Set Analysis Toolkit (WebGestalt: https://www.webgestalt.org ) 79 . For GO terms, all three ontology categories, including BP, CC and MF were considered. Notably, only the top 5 positively and negatively related terms that were significantly enriched at FDR \(\:p\) < 0.05 with 10000 permutations were identified as enriched terms for each PLS component. Statistics and Reproducibility For the computational part of ANM, statistical analyses including calculating Pearson correlation, one - sample t - test, and Bonferroni correction were all performed using built - in functions of MATLAB R2023a. The Spearman correlation and spin test in the receptor analysis were implemented with functions from the Python scipy ( https://docs.scipy.org ) and netneurotools ( https://github.com/netneurolab/netneurotools ) libraries. The extraction of differential genes (PLS regression and spatial permutation) was based on MATLAB R2023a. All statistical analyses for gene enrichment were carried out using the WebGestalt web tool. Sample sizes and the number of replicates were determined according to the experimental requirements, and relevant data and codes are available upon reasonable request from the corresponding author to ensure the reproducibility of the study. Declarations Data availability: All data are available in the main text or the supplementary materials. Code availability: The ANM algorithm is open - source and can be freely accessed at ( https://github.com/sailingpeng/2021_ActivationNetworkMapping.git ). Competing interests: Authors declare that they have no competing interests. Author contributions: Conceptualization: SZ, XS Methodology: SZ, SP, PH, YS, ML Investigation: XS, XO, XZ Visualization: PH, XO Supervision: SZ, XS Writing—original draft: PH, SZ, XS, XO Writing—review & editing: PH, SZ, XS, XO, SP, YS Acknowledgments This research project is supported in part by the National Natural Science Foundation of China (81801782, 81701783 to S.Z.), New Talent Project of Beijing University of Posts and Telecommunications (2021RC40, 2023RC59), STI 2030–Major Projects (2021ZD0200500), Science Foundation of Beijing Language and Culture University (supported by “the Fundamental Research Funds for the Central Universities”) (19YBB39, 20YJ090001). References Eddy, C. M. & Cook, J. L. Emotions in action: The relationship between motor function and social cognition across multiple clinical populations. Progress in Neuro-Psychopharmacology and Biological Psychiatry 86 , 229–244 (2018). Blonder, L. X., Gur, R. E. & Gur, R. C. 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The Gene Ontology knowledgebase in 2023. Genetics 224 , iyad031 (2023). Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat Genet 25 , 25–29 (2000). Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research 28 , 27–30 (2000). Piñero, J. et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Research gkz1021 (2019) doi:10.1093/nar/gkz1021. Elizarraras, J. M. et al. WebGestalt 2024: faster gene set analysis and new support for metabolomics and multi-omics. Nucleic Acids Research 52 , W415–W421 (2024). Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6155286","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":432310411,"identity":"94a455ec-ecb3-478e-916e-ebd70d69b9a6","order_by":0,"name":"Suyu Zhong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDACCRiDvQfKOEC0Fp4zQCKBJC0SOURqkZ/dfOzhlz+HEzfcfHtMuvAHgxzfjQTGzwV4tDDOOZZuLNsG1HI7L016RgKDseSNBGbpGXi0MEvkmElLNoC0ABk8CQyJG24ksDHz4NHCBtIiAXbYGbCWeoJaeIBaJD+wAbXc4AFrSTAgpEVCIi1NmrEt3Xjmmbxk6xlpEoYzzzxslsanRX5G8jHJH3+sZfuOnz14u8DGRp7vePLBz/i0gADIGY4NIAYkmhgbCGgAKvnBwGDPANEyCkbBKBgFowATAAAoDksg1hB9cAAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":true,"prefix":"","firstName":"Suyu","middleName":"","lastName":"Zhong","suffix":""},{"id":432310412,"identity":"e11182a1-dab2-474b-ad30-75f73204ec9e","order_by":1,"name":"Pinyuan Hu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Pinyuan","middleName":"","lastName":"Hu","suffix":""},{"id":432310413,"identity":"a9076b35-2e1b-433d-b422-762855b28870","order_by":2,"name":"Xiaochen Sun","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xiaochen","middleName":"","lastName":"Sun","suffix":""},{"id":432310414,"identity":"a521a31c-2729-44d0-aec1-8ee5915275db","order_by":3,"name":"Xingyu Ouyang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xingyu","middleName":"","lastName":"Ouyang","suffix":""},{"id":432310415,"identity":"7fde2694-b96a-4e8e-976d-f9e32d3e7cca","order_by":4,"name":"Xinyu Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Zhang","suffix":""},{"id":432310416,"identity":"af071573-9582-4cb6-b1f1-5da2d5b523ac","order_by":5,"name":"Shaoling Peng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shaoling","middleName":"","lastName":"Peng","suffix":""},{"id":432310417,"identity":"c8f2f433-bc5e-4c4b-b337-ccc9826c5768","order_by":6,"name":"Yuwei Su","email":"","orcid":"https://orcid.org/0000-0002-4412-094X","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Yuwei","middleName":"","lastName":"Su","suffix":""},{"id":432310418,"identity":"c4286a4f-0bad-4b5a-9139-c1b3e968a8cf","order_by":7,"name":"Min Lan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Lan","suffix":""}],"badges":[],"createdAt":"2025-03-04 14:20:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6155286/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6155286/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s42003-026-09625-8","type":"published","date":"2026-02-02T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79571565,"identity":"d6515023-8b7d-4a62-a1e1-504105d400d1","added_by":"auto","created_at":"2025-03-31 10:39:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":305233,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe distribution of activation coordinates from previous neuroimaging studies.\u003c/strong\u003e The percentage of studies for EP (A), EEP (B) and IEP (C) in each ROI in AICHA. All percentages are below 40%.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6155286/v1/2b4ad89705a2f1efcd50a9ce.png"},{"id":79571568,"identity":"bda8ad67-b459-463c-9710-63f649ec0695","added_by":"auto","created_at":"2025-03-31 10:39:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":354956,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe activation networks of emotional prosody based on overall cohort.\u003c/strong\u003e (A) The activation networks of EP, EEP and IEP, (B) The overlap of EEP and IEP.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6155286/v1/b48fa5de416cb76b528c664a.png"},{"id":79571570,"identity":"18cb2c46-588a-41e3-832b-141b9d412240","added_by":"auto","created_at":"2025-03-31 10:39:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":688556,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGender effects on emotional prosody activation network.\u003c/strong\u003e (A) The activation networks of EP processing (including EP, EEP and IEP) and the overlap of EEP and IEP based on female cohorts (left) and male cohorts (right) respectively. (B) The overlap proportion of thresholded activation networks across 7 Yeo networks. (C) The spatial correlation between unthresholded activation networks. (D) The overlap maps of female and male for EP, EEP and IEP. O-EP, F-EP, M-EP: Emotional prosody activation networks for overall, female and male cohort; O-EEP, F-EEP, M-EEP: Explicit emotional prosody activation networks for overall, female and male cohort; O-IEP, F-IEP, M-IEP: Implicit emotional prosody activation networks for overall, female and male cohort.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6155286/v1/ef8324a4c1e8950d41ae3177.png"},{"id":79571569,"identity":"43ecea52-b4f7-410a-aa38-93110df19d31","added_by":"auto","created_at":"2025-03-31 10:39:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":667677,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceptor/transporter and gene enrichment analysis.\u003c/strong\u003e(A) Gene enrichment analysis results based on BP, CC and MF. (B) Gene enrichment analysis results based on Pathways and Disease. Please refer to Supplementary Table 3 to get the corresponding term. Only the enrichments with a significance of FDR p \u0026lt; 0.05 are displayed. Terms colored blue are related to the physiological process of Energy metabolism; those in green pertain to Synapse extensions; and the red - colored terms are associated with Active transportation. (C) The correlation between activation networks and the receptor/transporter maps. Only the correlation with a significance of P spin \u0026lt; 0.01 are displayed. (D) The three-stage model of emotional prosody processing we proposed.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6155286/v1/d5deb194af260aea1192ef86.png"},{"id":79572010,"identity":"b91f1285-6157-4d82-84c1-094a90ea34f2","added_by":"auto","created_at":"2025-03-31 10:47:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":303832,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe flowchart showing the process of identifying the 40 articles included in the analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6155286/v1/e332d9f5428e17bd2601f430.png"},{"id":104122361,"identity":"9033967c-5b60-480f-91f5-0136723a7b3c","added_by":"auto","created_at":"2026-03-07 08:11:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3511988,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6155286/v1/b5daa767-4208-4662-a16c-1116d1749f44.pdf"},{"id":79571566,"identity":"134ce7ce-c7e7-46aa-a28c-0f64fcc408df","added_by":"auto","created_at":"2025-03-31 10:39:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1682491,"visible":true,"origin":"","legend":"supplementary material","description":"","filename":"Supplementarymaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6155286/v1/bb3a5a72e27b2748eab77e83.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Decoding emotional prosody: a unified brain network integrating gender and task type effect","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEmotion, as a fundamental aspect of human beings, plays a vital role in social interactions and communication. Accurate perception of emotional information serves as an essential cornerstone for effective social interaction. Numerous studies have demonstrated that individuals with a range of neuropsychiatric disorders, including autism, aphasia, Asperger's syndrome, and Alzheimer's disease, frequently exhibit atypical social interaction abilities\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. These abilities may be linked to deficits in emotion perception\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Emotional prosody (EP), as one of the important emotional cues and an important way to convey verbal emotion during communication\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, plays a crucial role in enhancing the meaning of spoken language, allowing listeners to interpret the speaker's feelings and intentions better. Emotional prosody accurate perception is essential for successful social interactions, as it aids in the recognition of emotional states, facilitating empathy and appropriate responses in conversations\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Therefore, investigating the neural underpinnings of emotional prosody processing may facilitate the understanding of the neural mechanisms underlying emotional processes and provide insights into disease pathogenesis.\u003c/p\u003e \u003cp\u003eDespite numerous studies exploring the neural underpinnings of emotional prosody using various lesion and neuroimaging methods\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, these studies have presented highly heterogeneous results in the activation patterns of emotional prosody processing\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. This inconsistency may be influenced by a variety of factors. One of the factors is the task type, implying differences in processing levels, from implicit processing to explicit judgments (implicit emotional prosody, IEP; explicit emotional prosody, EEP)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Additionally, the gender of the participants may also be a factor, with some studies finding that the left frontal area is more strongly activated in females than in males during emotional prosody processing\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecently, resting state functional connectivity (rs-FC) has become a popular tool to assess brain functions and activities in various fields\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, including emotional processing. In healthy children, studies concerning the relationship between rs-FC and emotional voice processing have indicated that children with better performance in emotion recognition tasks exhibit a stronger rs-FC between IFG and motor regions\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Meanwhile, for patients with emotional processing deficits, weak rs-FC between certain areas of the brain networks have also been found\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Moreover, evidence suggests that the resting human brain networks are strikingly similar to the activation patterns of task-based brain networks\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Based on this similarity, a method known as activation network mapping (ANM), which was developed from the lesion network mapping technique, has been proposed to obtain brain networks by integrating inconsistent neuroimaging studies\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. By using the activation coordinates reported in previous studies as seeds and then investigating the common pattern between the normative seed-based functional connectivity maps of each experiment, the ANM approach has been shown to integrate previous heterogeneous studies better and provide higher reproducibility across these studies compared with traditional meta-analysis.\u003c/p\u003e \u003cp\u003eBased on previous findings, we put forward two hypotheses: 1) The EEP and IEP processing exhibit different activation patterns; 2) There is a gender difference in the activation patterns of EP processing. To test these hypotheses, we adopted the ANM technique to investigate the neural networks of EP processing from a network perspective based on human connectome project dataset (HCP)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. First, we converged the heterogeneous fMRI results into a common emotional prosody network under ANM analysis. Second, we assessed the effect of the two factors, gender and task type, on emotional prosody processing. Finally, we explored the neurotransmitter and molecular mechanism underlying emotional prosody.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eArticles selection\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDetailed information about the article selection is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Here, different experiments or contrasts for the same article were counted separately. For the emotional prosody analysis, a total of 40 articles were included, with 54 experiments, 725 subjects (334 females, 391 males), and 474 activity coordinates. To explore the difference in activation networks between EEP and IEP, we categorized the total experiments into EEP experiments and IEP experiments. There were 32 EEP articles, with 41 experiments, 587 subjects (270 females, 317 males), 390 coordinates, and 9 IEP articles with 11 experiments, 162 subjects (78 females, 84 males), and 59 coordinates. Notice that 2 experiments were excluded from EEP and IEP analysis for their inappropriate contrast.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe detail information about the article selection.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEEP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIEP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of articles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of experiments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of participants (F/M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e725 (334/391)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e587 (270/317)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162 (78/84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of activation coordinates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInconsistency of neuroimaging results in EP processing\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe degree of consistency in the findings on EP processing across studies was quantified by calculating the overlap proportion of studies for each region of the AICHA. For EP, the activation coordinates reported in previous studies were widely distributed across almost all brain regions, with the right superior temporal gyrus (STG) showing the highest overlap proportion. However, even in this region, the overlap was less than 60% (31%, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), indicating significant variability. For EEP and IEP, the right STG also showed a top overlap proportion, but only with proportions of 34% and 36%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, C). These findings highlight the heterogeneity in neuroimaging results for EP processing.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eActivation networks of EP processing\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe activation networks from ANM are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. For EP, our analysis revealed a widespread activation brain network, including somatomotor network (SMN, bilateral Precentral and postcentral gyrus [PreCG and PostCG], bilateral planum temporal [PT], bilateral posterior part of STG [STGp], bilateral superior temporal sulcus [STS] and left Heschl's gyrus [HG]), ventral attention network (VAN, bilateral Insular), default mode network (DMN, right posterior middle temporal gyrus [MTGp]), frontoparietal network (FPN, bilateral opercular and triangular part of inferior frontal gyrus [IFGpo and\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIFGpt], bilateral middle frontal gyrus [MFG] and bilateral posterior supramarginal gyrus [SMGp]), dorsal attention network (DAN, temporooccipital part of the bilateral MTG [MTGtp]), limbic network (LN, bilateral orbitofrontal cortex [OFC]), and subcortical network (Amygdala).\u003c/p\u003e \u003cp\u003eFor EEP, the unthresholded activation network was highly correlated to that of EP (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e = 0.992, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{spin}\\)\u003c/span\u003e\u003c/span\u003e \u0026lt; 1.0\u0026times;10\u003csup\u003e\u0026minus;4\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In contrast, the activation network of IEP was more localized, primarily involving regions such as the bilateral Insular, bilateral SMGp, bilateral PT, bilateral STGp and STS, resulting in a relatively lower spatial correlation with that of EP (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\:\\)\u003c/span\u003e\u003c/span\u003e= 0.86, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{spin}\\)\u003c/span\u003e\u003c/span\u003e \u0026lt; 1.0\u0026times;10\u003csup\u003e\u0026minus;4\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The overlap between activation networks of EEP and IEP showed that the EEP activation network encompassed the network of IEP (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), with an EEP-specific network primarily including regions such as the bilateral IFGpo, left IFGpt, bilateral MFG, bilateral OFC, right MTGtp, bilateral PreCG and bilateral PostCG. These findings indicate that EEP engages a more extensive neural network compared to IEP, especially in areas associated with higher-order cognitive processing.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eWidespread activation networks of EP in females\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo test gender differences in brain networks involved in EP, we separately estimated the activation networks for male and female cohorts using activation seeds and fMRI scans of corresponding subjects of the HCP. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, D, the ANM results indicated that females engaged in a broader brain network than males during EP processing. Specifically, for EP and EEP, the gender-shared activation network was mainly in the bilateral PT and IFGpo/IFGpt, while IEP showed activation exclusively in the bilateral PT. The female-specific network involved in EP and EEP was mainly distributed in regions including bilateral MFG, right MTGtp, bilateral Insular, left SMC, bilateral PreCG, and bilateral PostCG. Moreover, for IEP, brain regions including right Insular were female-specific.\u003c/p\u003e \u003cp\u003eAdditionally, for almost all subnetworks, the overlap proportion (the proportion of experiments whose binarized normative rs-FC map showed activation for each voxel) of the activation networks in each sub-network\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e was significantly higher in females than in males (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) (see detailed Wilcoxon rank-sum test results in Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eNeuroreceptor mechanisms underlying EP processing\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo investigate the underlying neuroreceptor mechanism for the activation networks of emotional prosody, we conducted a spatial correlation analysis between receptors and the activation networks. A common neuroreceptor set related to emotional prosodies was found, including \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{1A}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CB}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{mGluR}_{5}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:NAT\\)\u003c/span\u003e\u003c/span\u003e (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{1A}\\)\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e = 0.32 \u0026plusmn; 0.037, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{spin}\\)\u003c/span\u003e\u003c/span\u003e = 0.014 \u0026plusmn; 0.012; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CB}_{1}\\)\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\:\\)\u003c/span\u003e\u003c/span\u003e= 0.34 \u0026plusmn; 0.074, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{spin}\\)\u003c/span\u003e\u003c/span\u003e = 0.028 \u0026plusmn; 0.012; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{mGluR}_{5}\\)\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e = 0.31 \u0026plusmn; 0.026, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{spin}\\)\u003c/span\u003e\u003c/span\u003e = 0.020 \u0026plusmn; 0.021; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:NAT\\)\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e = 0.33 \u0026plusmn; 0.013, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{spin}\\)\u003c/span\u003e\u003c/span\u003e = 0.00013 \u0026plusmn; 5.0\u0026times;10^-5). Additionally, another set of neuroreceptors exhibited a gender-specific effect. For EP and EEP, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{1B}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{2A}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{6}\\)\u003c/span\u003e\u003c/span\u003e were significantly positively associated with spatial activation patterns in females (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{1B}\\)\u003c/span\u003e\u003c/span\u003e: [EP: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e = 0.27, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{spin}\\)\u003c/span\u003e\u003c/span\u003e = 0.018; EEP: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e = 0.27, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{spin}\\)\u003c/span\u003e\u003c/span\u003e = 0.013]; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{2A}\\)\u003c/span\u003e\u003c/span\u003e: [EP: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e = 0.28, \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{spin}\\)\u003c/span\u003e \u003c/span\u003e = 0.036; EEP: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e = 0.28, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{spin}\\)\u003c/span\u003e\u003c/span\u003e = 0.032]; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{6}\\)\u003c/span\u003e\u003c/span\u003e: [EP: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e = 0.21, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{spin}\\)\u003c/span\u003e\u003c/span\u003e = 0.044; EEP: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e = 0.20, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{spin}\\)\u003c/span\u003e\u003c/span\u003e = 0.0496]). In contrast, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:VAChT\\)\u003c/span\u003e\u003c/span\u003e showed significant positive correlations with spatial activation patterns in males (EP: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e = 0.31, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{spin}\\)\u003c/span\u003e\u003c/span\u003e = 0.01; EEP: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e = 0.34, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{spin}\\)\u003c/span\u003e\u003c/span\u003e = 0.0030) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGenetic mechanisms underlying the EP processing\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAll first three PLS components were significant after 10,000 times permutation. For each activation network, the first three components of the PLS regression totally explained 32.48%~43.42% of the variance (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eSupplementary Fig.\u0026nbsp;2 illustrates that within each component of the PLS regressions (PLS1, PLS2, and PLS3), the distributions are highly correlated, while the correlations between different components across all activation networks are relatively low. Specifically, PLS1 exhibited a transcriptional profile marked by under-expression predominantly in the bilateral LG and para-hippocampal regions. PLS2 displayed overexpression primarily in the bilateral Insula, TP, PreCG, and PostCG, with concurrent under-expression in the occipital lobe. PLS3 revealed overexpression chiefly in the bilateral PreCG, PostCG, posterior STG, and occipital lobe, while showing under-expression in the frontal lobe.\u003c/p\u003e \u003cp\u003eThe Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis revealed that the genes were enriched in biological processes (BP), cell components (CC), molecular function (MF) and pathways related to energy metabolism, synapse extensions and active transportation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Energy metabolism included fatty acid metabolic process [GO:0006631] in BP, mitochondrial matrix [GO:0005759] in CC, ATP hydrolysis activity [GO:0016887] in MF, and fatty acid degradation [hsa00071] in pathways. Synapse extensions included cell leading edge [GO:0031252] in CC and metallopeptidase activity [GO:0008237] in MF). Active transportation included active transmembrane transporter activity [GO:0022804] in MF and ABC transporters [hsa02010] in pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eAdditional Disease Gene Network (DisGeNET) disease enrichment analyses showed that genes associated with emotional prosody processing are linked to Disease Progression [umls:C0242656], Autistic Disorder [umls:C0004352] and Alzheimer's Disease [umls:C0002395] (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBy integrating the previous neuroimaging findings, we uncovered the common activation brain network involved in EP processing, its gender difference, and its association with the transcriptional profiles and neurotransmitter receptor patterns. Our analysis revealed a widespread activation brain network for EP processing included networks including DMN, DAN, LN, SMN and subcortical network. Besides, we found that females exhibited relatively broader activation networks associated with EP processing compared to males. Interestingly, these activation networks exhibited correlations with the spatial patterns of receptors/transporters (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{1A}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CB}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{mGluR}_{5}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:NET\\)\u003c/span\u003e\u003c/span\u003e) and gene expression profiles (energy metabolism, synapse extension, active transmembrane transport, along with diseases such as autistic disorder, Alzheimer's disease, and general disease progression). These findings enhance our understanding of the neurobiological and modular mechanisms underlying emotional prosody processing and the influence of gender on emotional prosody processing.\u003c/p\u003e\n\u003ch3\u003eThe common activation networks of EP processing\u003c/h3\u003e\n\u003cp\u003eWe utilized ANM analysis to reveal common activation networks of EP. The EP networks included the regions of bilateral STGp, STS, DMN (right MTGp), DAN (MTGtp), FPN (bilateral IFGpo and IFGpt), LN (bilateral OFC), bilateral SMGp, SMN (bilateral PreCG and PostCG), bilateral PT, left HG, VAN (bilateral Insular), and subcortical network (Amygdala). Most of those areas align with existing literature, highlighting their roles in emotional voice perception.\u003c/p\u003e \u003cp\u003eBy analyzing the activation networks, we found that most of the activated brain regions of IEP (STG, STS) and part of the activated brain regions of EEP (IFG, MFG, OFC) align with the three-stage model of emotional prosody processing\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. IEP-specific activated areas are mainly involved in the first two stages of EP processing. For instance, in the sensory processing stage, the auditory cortex is involved in analyzing acoustic information. The STG has been shown to track variations in the amplitude envelope of incoming sound, thus contributing to the extraction and identification of vocally expressed emotional sounds\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Br\u0026uuml;ck et al.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e further emphasized the key role of the middle superior temporal gyrus (m-STG) in the decoding of emotional intonation, a view that was supported by fMRI experimental data. The activation of the m-STG is associated with acoustic parameters (e.g., loudness, duration, or fundamental frequency), which collectively define emotional intonation\u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The STS region is also implicated in this process and functions as a pivotal node in the auditory \"what\" pathway, integrates emotional information and connects with frontal regions\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The EEP processing, on the other hand, encompassed the IEP-activated regions and involved a more extensive network, including the IFG, MFG and OFC, which played crucial role in the higher-order cognitive processes, such as evaluative judgments of emotional information. These higher-order cognitive processes can be triggered by top-down mechanisms that increase listeners' attention to emotional prosody\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Later neuroimaging studies have revealed that the IFG is crucial for the evaluation of vocal emotional cues and often co-activate with the temporal auditory areas\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, the MFG is linked to working memory load\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, while the OFC is involved in evaluating emotional prosody and selecting emotional connotations\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBesides those brain areas involved in the three stages, some other brain regions were also found active during EP processing, both in our and previous results. For example, during IEP processing, we found the activation of bilateral SMGp and PT. Previous studies have reported that PT is specifically involved in the processing of emotional semantics and word content\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, while the SMG has been found responsible for auditory\u0026ndash;motor integration in speech perception, and supports phonological encoding\u0026ndash;recoding processes in a variety of tasks\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Additional regions have also been found during EEP processing, including MTG, PreCG and PostCG. For MTG, studies have reported that it is related to recognizing slow sound changes and may be involved in the integration process of prosodic and semantic information\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. PreCG and PostCG have been identified as regions responsible for somatosensory monitoring and feedback during phoneme perception\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. This indicates that the sensorimotor cortex is also engaged in EP processing. This is highly consistent with the dual-stream model of speech processing\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, supporting the possible existence of a similar dual-stream model of EP processing.\u003c/p\u003e \u003cp\u003eComparing the activated brain regions of IEP and EEP, EEP processing involved a more extensive network. The involvement of executive frontal regions (IFG, MFG and OFC) implies higher cognitive demands during EEP processing, The involvement of dorsal sensorimotor regions (e.g., PreCG and PostCG) provides further evidence that the increased cognitive demands are associated with explicit emotional tasks\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Our results revalidate the three-stage model of EP processing from the perspective of brain networks and provide additional evidence for other potential sub-processes. EP processing involves multiple sub-stages, each engaging a complex network of brain regions.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBroader emotional prosody activation network in female\u003c/h2\u003e \u003cp\u003eThis study revealed significant gender differences in the neural mechanisms underlying emotional prosody processing. The female group showed more broader activation in bilateral MFG, right MTGtp, bilateral Insular, left SMC, bilateral PreCG, and bilateral PostCG regions, which are critical for emotion processing and speech perception\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. This finding is consistent with previous neuroimaging studies on gender differences in emotion processing.\u003c/p\u003e \u003cp\u003eNumerous studies have indicated significant differences between males and females in emotion perception. Research showed that females tend to perform better in emotion recognition tasks, including the perception of vocal affect, facial expressions, and body language\u003csup\u003e\u003cspan additionalcitationids=\"CR46 CR47 CR48\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. From an evolutionary perspective, these gender differences in emotion processing may be attributed to the distinct social roles in primeval conditions\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Historically, females have often been responsible for childcare and social bonding, which requires heightened emotional sensitivity. In contrast, males have been more attuned to detecting threats, such as anger from competitors or predators\u003csup\u003e\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Furthermore, previous studies suggest that the gender differences in emotion processing were a result of genetically mediated adaptation to primeval conditions\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe broader frontal activation, along with the involvement of additional brain areas and greater functional connectivity during EP processing, suggest that females may utilize more complex neural networks to handle emotional tasks, resulting in a higher cognitive and emotional integration demand\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe emotional prosody related receptors/transporters\u003c/h2\u003e \u003cp\u003e Our findings provided critical insights into the molecular underpinnings of emotional prosody, highlighting both shared and gender-specific neuroreceptor contributions. The identification of shared receptors, including the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{1A}\\)\u003c/span\u003e\u003c/span\u003e receptor, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CB}_{1}\\)\u003c/span\u003e\u003c/span\u003e receptor, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{mGluR}_{5}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:NET\\)\u003c/span\u003e\u003c/span\u003e, suggests that emotional prosody processing may involve a foundational set of neuroreceptors linked to broader emotional regulation. These findings align with prior research showing that these receptors play critical roles in fear and anxiety regulation, emotional perceptual biases\u003csup\u003e\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. For example, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{1A}\\)\u003c/span\u003e\u003c/span\u003e receptor agonists reduce fear recognition in facial expressions, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CB}_{1}\\)\u003c/span\u003e\u003c/span\u003e receptor agonists modulate anxiety responses, supporting their involvement in processing emotionally salient prosodic cues\u003csup\u003e\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. This shared receptor set underscores the likelihood of conserved molecular mechanisms underlying various emotional processing tasks, extending their significance to the domain of emotional prosody.\u003c/p\u003e \u003cp\u003eIn addition to the shared neuroreceptors, our results revealed gender-specific receptor effects, providing a nuanced understanding of the neurochemical basis of emotional prosody. In females, serotonin receptors (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{1B}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{2A}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{6}\\)\u003c/span\u003e\u003c/span\u003e) were significantly associated with activation patterns for both EP and EEP, suggesting heightened serotonergic modulation in emotional prosody. These findings align with previous studies demonstrating that serotonergic signaling is modulated by sex hormones such as estrogen\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, which may enhance serotonin receptor sensitivity in females. Conversely, the cholinergic system, as indicated by the significant correlation of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:VAChT\\)\u003c/span\u003e\u003c/span\u003e with activation patterns in males, points to a distinct neurochemical pathway in male emotional prosody processing. This divergence could reflect structural or functional brain differences between genders, such as the density of cholinergic innervation or hormonal effects on acetylcholine receptor regulation\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe molecular mechanism underlay emotional prosody\u003c/h2\u003e \u003cp\u003eGO and KEGG pathway enrichment analyses revealed that the genes correlated with emotional prosody processing are enriched in pathways related to energy metabolism, synapse extension, and active transmembrane transport. These findings suggest that energy-demanding processes and synaptic modifications are crucial for the neural mechanisms underlying emotional prosody. For example, the enrichment of mitochondrial matrix processes and ATP hydrolysis activities underscores the metabolic demands of regions actively engaged in emotional prosody tasks. Additionally, the involvement of metallopeptidase activity and synapse extension pathways may reflect the structural plasticity required for efficient neural signaling during emotional communication. These molecular pathways represent potential targets for further functional validation studies.\u003c/p\u003e \u003cp\u003eThe identified genes were also enriched in diseases such as autistic disorder, Alzheimer's disease, and general disease progression. While these associations are intriguing, they should be interpreted with caution. The enrichment results do not establish causality but rather suggest that emotional prosody processing shares molecular mechanisms with these disorders. For instance, deficits in emotional prosody are well-documented in autism and Alzheimer's disease. Future research could investigate whether these shared molecular signatures contribute to the impaired emotional communication observed in these conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eThere were some limitations. First, it should be noticed that the activation networks for males and females were estimated using activation data from the overall cohorts because of the limited study that investigated the activation of EP for a single gender. As a result, our integrated findings may not fully reflect the actual network patterns unique to each gender. Nevertheless, using the same activation data from the overall cohort, we still observed distinct activation networks for males and females, highlighting the different activation patterns when processing emotional prosody. Second, like the ANM research study, no brain regions connected to 100% of the activation seeds were found, and a less conservative overlapping threshold (60%) was used. However, the highest overlap proportion in consistency analysis was 36%, illustrating that EP processing is supported by a distinct network. Third, given the high heterogeneity observed across studies on emotional prosody, a larger number of experiments could yield more robust results. Future research can further explore the receptor involvement and genetic regulatory mechanisms in emotional prosody processing, to gain a deeper understanding of the neural mechanisms underlying emotional prosody.\u003c/p\u003e \u003c/div\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eArticles selection\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA systematic review was conducted to identify relevant articles with two independent reviewers. The primary search included a comprehensive examination of the PubMed database, as well as a thorough review of existing meta-analyses. Studies from January 1, 1993, to July 10, 2024, were searched using terms related to emotional prosodic studies, including \"prosody\", \"emotional prosody\", and \"affective prosody\", in conjunction with terms related to neuroimaging techniques, including \"fMRI\", \"functional MRI\", \"functional magnetic resonance imaging\", \"PET\", \"positron emission tomography\", \"neuroimaging\", and \"BOLD\". As a result, a total of 248 research articles were obtained from both a PubMed search and earlier meta-analyses\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Further, screening processes were conducted to include the articles upon the fulfillment of the following criteria: 1) Studies that employed tasks to assess the auditory processing of emotional prosody; 2) Studies that included a rest or control task; 3) Studies that involved healthy adult participants, or case-control studies with a healthy adult control group; 4) Studies that utilized standardized 3D spatial neuroimaging coordinates from functional magnetic resonance imaging (fMRI) or positron emission tomography (PET). Additionally, studies that employed multi-modal or dichotic listening methodologies were excluded. As a result of the screening process, 40 peer-reviewed articles were identified. The process of the literature search is graphically represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eData extraction\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eData extracted from each experiment included the number of participants and the activation coordinates (in MNI or Talairach space). Only coordinates reported significant activation in emotional prosody processing were considered. To unify the coordinates in the same space system coordinates, those initially reported in Talairach space were transformed to MNI using the MNI2Tal web tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioimagesuiteweb.github.io/webapp/mni2tal.html\u003c/span\u003e\u003cspan address=\"https://bioimagesuiteweb.github.io/webapp/mni2tal.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To examine the differences between EEP and IEP, we categorized these experiments into two groups based on the specific tasks performed and the contrasts analyzed.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMRI processing for HCP data\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo construct a normative functional connectome, the MRI dataset from the HCP S1200 release was included in this study\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Here, 1084 participants with two runs including a left-to-right (LR) and a right-to-left (RL) phase encoding direction were used. All subjects provided written informed consent, and the research protocol was approved by the Institutional Review Board of Washington University. Please refer to Van Essen et al. \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e for more details about the dataset.\u003c/p\u003e\u003cp\u003eMRIs were acquired using a Siemens Connectome Skyra 3 T scanner with a 32-channel head coil housed at Washington University in St. Louis. For more specific scanning parameters please find them in Supplementary materials or refer to Glasser et al.\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. The rs-fMRI and T1-weighted images were preprocessed using the HCP minimal-preprocessing pipelines\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. The following preprocess procedures were further performed on the minimal preprocessing pipeline data. For the T1 images, skull-stripping was first performed using FSL bet2 (f\u0026thinsp;=\u0026thinsp;0.5). Then skull-stripped T1-weighted images were segmented into three tissue types including white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). For each individual, the averaged WM signal and CSF signal were extracted based on the segmentation results. For the fMRI images, detrending was first executed to minimize the effects of the low-frequency drift. Then, to eliminate potential confounding effects on the signal of gray matter, 24 movement parameters, signals of WM and CSF were regressed. Finally, all images were bandpass filtered (0.01-0.1Hz) to eliminate the impact of high-frequency physiological noise and low-frequency drift.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eActivation network mapping\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo uncover the emotional prosody activation network hidden in inconsistent neuroimaging results, we conducted an ANM analysis. Here, the \"overlap\" approach was used. The steps were summarized as follows. First, we identified the activation seed for each experiment by generating 4-mm-radius spheres centered on each coordinate. The activation seed was identified by combining the spheres associated with the same experiment. Next, normative functional connectivity maps were generated for each experiment. Specially, the seed based rs-FC map was computed for each subject in HCP. Voxel-wise one-sample t-tests were then performed on these rs-FC maps, with age and gender effects controlled. Notably, the above step was performed for both LR scans and RL scans, resulting in LR t map and RL t map. The normative functional connectivity map was obtained by averaging the LR t map and RL t map, followed by smoothing with a 6 mm full-width at half-maximum (FWHM) Gaussian kernel. Finally, we calculated the activation network by binarizing the normative rs-FC map for each experiment with a threshold of Bonferroni-corrected \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e \u0026lt; 0.05. The activation network was finally generated by overlapping these binary maps and thresholding at 60%.\u003c/p\u003e \u003cp\u003eHere, the overlap approach was employed due to the following advantages: 1) higher interpretability, 2) reduction of bias induced by autocorrelation, 3) mitigation of bias driven by extreme values in a small number of experiment-level maps, and 4) corresponding to our consistency analysis. To validate the results, the \"t-test\" approach was employed. For the details of the \"t-test\" approach please refer to Supplementary Methods or Peng et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMapping neurotransmitter receptor maps to the activation networks of EP processing\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo identify the molecular foundation of emotional prosody, we calculated the spatial correlation between activation networks and the distribution of receptors. The publicly available receptors dataset, Hansen receptors, was used\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/netneurolab/hansen_receptors\u003c/span\u003e\u003cspan address=\"https://github.com/netneurolab/hansen_receptors\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Hansen receptors dataset included PET images of 19 different neurotransmitter receptors and transporters, across 9 neurotransmitter systems: serotonin (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{1A}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{1B}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{2A}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{4}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{6}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:5HTT\\)\u003c/span\u003e\u003c/span\u003e), dopamine (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{D}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{D}_{2}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:DAT\\)\u003c/span\u003e\u003c/span\u003e), norepinephrine (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:NAT\\)\u003c/span\u003e\u003c/span\u003e), histamine (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}_{3}\\)\u003c/span\u003e\u003c/span\u003e), acetylcholine (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{4}{\\beta\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:VAChT\\)\u003c/span\u003e\u003c/span\u003e), cannabinoid (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CB}_{1}\\)\u003c/span\u003e\u003c/span\u003e), opioid (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:MU\\)\u003c/span\u003e\u003c/span\u003e), glutamate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:NMDA\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{mGluR}_{5}\\)\u003c/span\u003e\u003c/span\u003e), and GABA (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{GABA}_{A/BZ}\\)\u003c/span\u003e\u003c/span\u003e) systems. All these PET images were collected and registered to MNI152 space.\u003c/p\u003e \u003cp\u003eTo explore the relationship between activation networks and the distribution of receptor/transporter, we performed Spearman's correlation analysis after normalization\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Notably, 10,000 spin test permutations while controlling for spatial autocorrelation were used to test the significance of these correlations.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between activation networks and gene expression\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo identify the gene foundation of emotional prosody, the public gene expression data, Allen Human Brain Atlas (AHBA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://human.brain-map.org\u003c/span\u003e\u003cspan address=\"http://human.brain-map.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used. It offers transcriptomic data of 58,692 probes corresponding to 29,131 genes, derived from 3702 spatially distinct tissue samples collected from six postmortem adult brains. The gene expression data was processed as follows: intensity-based probes filtering, representative probe selection, matching tissue samples to atlas (The Atlas of Intrinsic Connectivity of Homotopic Areas, AICHA)\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e, normalization and aggregation. More details please find in Supplementary methods. As a result, a 15633\u0026times;384 gene expression matrix was created. Here the abagen toolbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://abagen.readthedocs.io/\u003c/span\u003e\u003cspan address=\"https://abagen.readthedocs.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used.\u003c/p\u003e \u003cp\u003eTo detect genes whose expression levels were significantly correlated to the activation networks of EP, PLS (Partial Least Squares) regression was used, with z-scored region-level gene expression matrix as the independent variable (X) and the z-scored parcellated activation network as the dependent variable (Y). A 10,000 times spatial permutation test was utilized to examine the significance level for each PLS component\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Ultimately, the first three components of all PLS regressions passed the spin test (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{spin}\\)\u003c/span\u003e\u003c/span\u003e \u0026lt; 0.01). Therefore, subsequent studies are based on the first three components of all PLS regression. Genes whose absolute gene weight ranked in the top 10% (top 1563) in each PLS component were selected for subsequent enrichment analyses.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eGene enrichment analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe extracted gene sets and their signed gene weights were selected to perform enrichment analysis to identify enriched GO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://geneontology.org/\u003c/span\u003e\u003cspan address=\"https://geneontology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) terms\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e, KEGG (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/pathway.html\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/pathway.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) pathways\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e and DisGeNET (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.disgenet.com\u003c/span\u003e\u003cspan address=\"https://www.disgenet.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) diseases\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e by using the Web-based Gene Set Analysis Toolkit (WebGestalt: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.webgestalt.org\u003c/span\u003e\u003cspan address=\"https://www.webgestalt.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e79\u003c/sup\u003e. For GO terms, all three ontology categories, including BP, CC and MF were considered. Notably, only the top 5 positively and negatively related terms that were significantly enriched at FDR \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e \u0026lt; 0.05 with 10000 permutations were identified as enriched terms for each PLS component.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eStatistics and Reproducibility\u003c/h2\u003e \u003cp\u003eFor the computational part of ANM, statistical analyses including calculating Pearson correlation, one - sample t - test, and Bonferroni correction were all performed using built - in functions of MATLAB R2023a. The Spearman correlation and spin test in the receptor analysis were implemented with functions from the Python scipy (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://docs.scipy.org\u003c/span\u003e\u003cspan address=\"https://docs.scipy.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and netneurotools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/netneurolab/netneurotools\u003c/span\u003e\u003cspan address=\"https://github.com/netneurolab/netneurotools\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) libraries. The extraction of differential genes (PLS regression and spatial permutation) was based on MATLAB R2023a. All statistical analyses for gene enrichment were carried out using the WebGestalt web tool. Sample sizes and the number of replicates were determined according to the experimental requirements, and relevant data and codes are available upon reasonable request from the corresponding author to ensure the reproducibility of the study.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eData availability:\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAll data are available in the main text or the supplementary materials.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eCode availability:\u003c/h2\u003e \u003cp\u003eThe ANM algorithm is open - source and can be freely accessed at (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/sailingpeng/2021_ActivationNetworkMapping.git\u003c/span\u003e\u003cspan address=\"https://github.com/sailingpeng/2021_ActivationNetworkMapping.git\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eAuthors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: SZ, XS\u003c/p\u003e\n\u003cp\u003eMethodology: SZ, SP, PH, YS, ML\u003c/p\u003e\n\u003cp\u003eInvestigation: XS, XO, XZ\u003c/p\u003e\n\u003cp\u003eVisualization: PH, XO\u003c/p\u003e\n\u003cp\u003eSupervision: SZ, XS\u003c/p\u003e\n\u003cp\u003eWriting\u0026mdash;original draft: PH, SZ, XS, XO\u003c/p\u003e\n\u003cp\u003eWriting\u0026mdash;review \u0026amp; editing: PH, SZ, XS, XO, SP, YS\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis research project is supported in part by the National Natural Science Foundation of China (81801782, 81701783 to S.Z.), New Talent Project of Beijing University of Posts and Telecommunications (2021RC40, 2023RC59), STI 2030\u0026ndash;Major Projects (2021ZD0200500), Science Foundation of Beijing Language and Culture University (supported by \u0026ldquo;the Fundamental Research Funds for the Central Universities\u0026rdquo;) (19YBB39, 20YJ090001).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEddy, C. 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KEGG: Kyoto Encyclopedia of Genes and Genomes. \u003cem\u003eNucleic Acids Research\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 27\u0026ndash;30 (2000).\u003c/li\u003e\n\u003cli\u003ePi\u0026ntilde;ero, J. \u003cem\u003eet al.\u003c/em\u003e The DisGeNET knowledge platform for disease genomics: 2019 update. \u003cem\u003eNucleic Acids Research\u003c/em\u003e gkz1021 (2019) doi:10.1093/nar/gkz1021.\u003c/li\u003e\n\u003cli\u003eElizarraras, J. M. \u003cem\u003eet al.\u003c/em\u003e WebGestalt 2024: faster gene set analysis and new support for metabolomics and multi-omics. \u003cem\u003eNucleic Acids Research\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, W415\u0026ndash;W421 (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6155286/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6155286/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEmotional prosody processing is vital for social communication. Despite numerous neuroimaging studies exploring emotional prosody, results remain inconsistent across studies, and the factors influencing these inconsistencies are unclear. Here, we identified a unified brain network for emotional prosody processing using activation network mapping. We evaluated how gender and task type influence this network. Results showed broader activation networks in females compared to males, regardless of the emotional prosody type. Moreover, the comparison of task type revealed stage processing mode of emotional prosody. Additionally, analyses link emotional prosody to specific receptors/transporters (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{5HT}_{1A}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CB}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{mGluR}_{5}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:NET\\)\u003c/span\u003e\u003c/span\u003e) and physiological processes such as synapse extension, energy metabolism, active transmembrane transport, along with diseases like autistic disorder, Alzheimer's disease, and general disease progression. In conclusion, these findings underscore the importance of considering gender and task type effects on emotional processing research and provide a deeper understanding of the complex neural mechanisms underlying emotional prosody.\u003c/p\u003e","manuscriptTitle":"Decoding emotional prosody: a unified brain network integrating gender and task type effect","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 10:38:57","doi":"10.21203/rs.3.rs-6155286/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-biology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsbio","sideBox":"Learn more about [Communications Biology](http://www.nature.com/commsbio/)","snPcode":"","submissionUrl":"","title":"Communications Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2398a385-fae5-49b8-98ae-d85669aace82","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46049376,"name":"Biological sciences/Neuroscience"},{"id":46049377,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Language"}],"tags":[],"updatedAt":"2026-03-07T08:11:28+00:00","versionOfRecord":{"articleIdentity":"rs-6155286","link":"https://doi.org/10.1038/s42003-026-09625-8","journal":{"identity":"communications-biology","isVorOnly":false,"title":"Communications Biology"},"publishedOn":"2026-02-02 05:00:00","publishedOnDateReadable":"February 2nd, 2026"},"versionCreatedAt":"2025-03-31 10:38:57","video":"","vorDoi":"10.1038/s42003-026-09625-8","vorDoiUrl":"https://doi.org/10.1038/s42003-026-09625-8","workflowStages":[]},"version":"v1","identity":"rs-6155286","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6155286","identity":"rs-6155286","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}