Structural and functional atypicality in the temporal cortex are associated with auditory perception in maltreated children.

Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Sci Rep . 2026 Mar 2;16:11525. doi: 10.1038/s41598-026-41884-7 Search in PMC Search in PubMed View in NLM Catalog Add to search Structural and functional atypicality in the temporal cortex are associated with auditory perception in maltreated children Natasha Y S Kawata Natasha Y S Kawata 1 Research Center for Child Mental Development, University of Fukui, 23-3 Matsuoka-Shimoaizuki, Eiheiji-Cho, Fukui, 910-1193 Japan 2 Department of Psychology, University of Western Ontario, London, Canada Find articles by Natasha Y S Kawata 1, 2 , Takashi X Fujisawa Takashi X Fujisawa 1 Research Center for Child Mental Development, University of Fukui, 23-3 Matsuoka-Shimoaizuki, Eiheiji-Cho, Fukui, 910-1193 Japan 3 Division of Developmental Higher Brain Functions, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Fukui, Japan 4 Life Science Innovation Center, University of Fukui, Fukui, Japan Find articles by Takashi X Fujisawa 1, 3, 4 , Akiko Yao Akiko Yao 1 Research Center for Child Mental Development, University of Fukui, 23-3 Matsuoka-Shimoaizuki, Eiheiji-Cho, Fukui, 910-1193 Japan Find articles by Akiko Yao 1 , Hidehiko Okazawa Hidehiko Okazawa 4 Life Science Innovation Center, University of Fukui, Fukui, Japan 5 Biomedical Imaging Research Center, University of Fukui, Fukui, Japan Find articles by Hidehiko Okazawa 4, 5 , Akemi Tomoda Akemi Tomoda 1 Research Center for Child Mental Development, University of Fukui, 23-3 Matsuoka-Shimoaizuki, Eiheiji-Cho, Fukui, 910-1193 Japan 3 Division of Developmental Higher Brain Functions, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Fukui, Japan 4 Life Science Innovation Center, University of Fukui, Fukui, Japan 6 Department of Child and Adolescent Psychological Medicine, University of Fukui Hospital, Fukui, Japan Find articles by Akemi Tomoda 1, 3, 4, 6, ✉ Author information Article notes Copyright and License information 1 Research Center for Child Mental Development, University of Fukui, 23-3 Matsuoka-Shimoaizuki, Eiheiji-Cho, Fukui, 910-1193 Japan 2 Department of Psychology, University of Western Ontario, London, Canada 3 Division of Developmental Higher Brain Functions, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Fukui, Japan 4 Life Science Innovation Center, University of Fukui, Fukui, Japan 5 Biomedical Imaging Research Center, University of Fukui, Fukui, Japan 6 Department of Child and Adolescent Psychological Medicine, University of Fukui Hospital, Fukui, Japan ✉ Corresponding author. Received 2025 Jun 25; Accepted 2026 Feb 23; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13056934  PMID: 41772002 Abstract Child maltreatment adversely affects brain development, resulting in vulnerabilities in brain structure and function connectivity, as well as various psychiatric disorders. However, the relationship between structural changes in auditory-related regions and auditory function remains unclear. Therefore, this study investigated the relationship between auditory frequencies associated with brain atypicality and child maltreatment. T1-weighted magnetic resonance imaging (MRI) and functional MRI were used to assess differences in gray matter volume (GMV) and functional connectivity (FC) in maltreated children ( n = 19) compared to those of no maltreatment history ( n = 38) participants. This case–control study focused on the left middle temporal gyrus (L.MTG), a key region in speech perception, and its connectivity with the right temporal pole (R.TP). Additionally, the study analyzed the relationship between these neural alterations and auditory thresholds at pivotal frequencies relevant to speech perception and measures of speech reception thresholds. Maltreatment-related neurodevelopmental adaptations affected GMV (L.MTG; P < 0.001 for peak level, family-wise error [FWE] corrected P = 0.038 for cluster level) and FC (L.MTG-R.TP; P < 0.001 for peak level, FWE corrected P = 0.013 for the cluster level), potentially influencing how abused children process auditory and emotional information. These alterations may have long-term consequences on speech perception, emotional recognition, and social communication. Elucidating these mechanisms will contribute to developing effective therapeutic strategies to improve social and emotional outcomes in maltreated individuals. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-026-41884-7. Keywords: Maltreated children, Voxel-based morphometry, Resting-state fMRI, Temporal cortex, Auditory emotional perception Subject terms: Auditory system, Cognitive neuroscience, Development of the nervous system, Sensory processing Introduction Childhood maltreatment is a substantial social risk factor associated with atypical brain development, increasing the likelihood of psychiatric disorders, including personality disorders, and contributing to rising suicide rates 1 , 2 . The World Health Organization and various nations—including Japan, the United Kingdom, the United States, Australia, and Sweden—have implemented preventive measures 3 , 4 , ranging from epidemiological research and public awareness campaigns to legislative reforms; however, biological approaches to early detection remain limited. Various approaches have been explored for the early identification of childhood maltreatment, including brain magnetic resonance imaging (MRI) 2 , 5 – 7 , epigenomic analyses 8 , hormone measurements 9 , behavioral assessments 10 , and machine learning models 11 . However, many studies are still in the pilot phase and lack defined indicators; thus, researchers are continuously examining various potential methods to accurately detect and elucidate the effects of childhood maltreatment. Functional connectivity (FC) is one approach used to examine how brain regions communicate with each other, including their involvement in auditory perception, and it can provide insights into neural alterations associated with early adversity. Examining sensory organs and their peripheral receptors (e.g., in the visual and auditory systems), as well as how their inputs are integrated into cortical networks is essential for understanding how childhood maltreatment influences neurodevelopment. Sensory pathways, such as the visual and auditory systems, provide crucial inputs to the brain regions responsible for perception, cognition, and emotional regulation 12 – 14 . Adverse experiences such as abuse disrupt the development of these sensory systems, resulting in structural changes in the corresponding brain areas 15 , 16 . Previous studies have demonstrated that early adversity can affect sensory systems, including visual and auditory pathways 15 , 16 . Structural alterations have been reported in primary sensory cortices 15 , suggesting that early environmental stress may shape sensory-related brain development. However, given the central role of auditory input in language acquisition and socio-emotional communication during childhood, disruptions in auditory processing may have widespread developmental implications 17 , 18 . As verbal interactions are primary vehicles for nurturing and adverse experiences, alterations in auditory-related neural systems may directly influence cognitive development, emotion regulation, and socio-emotional functioning 19 . Similarly, Tomoda et al. 20 investigated the relationship between childhood maltreatment and structural changes in auditory-related brain regions. They reported increased GMV in the superior temporal gyrus (STG), suggesting a potential association between abuse exposure and altered auditory information processing. Voxel-based morphometry (VBM) analysis was used to examine 21 unmedicated, right-handed individuals aged 18–25 years with histories of parental verbal abuse. The findings revealed a 14.1% increase in GMV in the left STG among those exposed to verbal abuse. These results suggest that early exposure to harsh verbal environments may alter the development of the auditory association cortex, which plays a pivotal role in language processing. Research on adversity-related neuroplasticity suggests that early-life stress can induce long-lasting changes in auditory processing regions, leading to hyper-reactivity in the neural circuits responsible for perceiving and interpreting sound stimuli 16 . This hyper-reactivity may lead to heightened sensitivity to harsh tones, impaired language processing, and difficulties in distinguishing subtle speech cues 19 . Relating audiometric examinations of brain structure and FC in maltreated children is essential for understanding the interplay between sensory perception and neural development. Audiometry is a valuable tool for identifying disruptions in auditory sensitivity that result from early adverse experiences. These disruptions often correspond to structural and functional changes in brain regions such as the STG, primary auditory cortex, and neural networks 20 – 22 . Furthermore, as the auditory system is closely associated with language, memory, and emotional regulation, abnormalities in auditory perception may reflect broader neural impairments resulting from maltreatment 21 , 23 , 24 . Investigating these relationships provides valuable insights into how environmental stressors reshape the developing brain and auditory perception. This approach facilitates the early identification of sensory and cognitive deficits and guides interventions that target both sensory function and neural rehabilitation to support improved outcomes in affected children. However, the association between auditory perceptual abilities and FC within auditory-related temporal networks remains unclear. Investigating this association could provide a more robust understanding of the effects of maltreatment-related neural alterations may influence auditory processing systems. Furthermore, although structural changes in auditory-related regions, such as the temporal cortex, have been documented, the relationship between these changes and auditory function remains unclear. Investigating this correlation could clarify how maltreatment disrupts auditory sensory function and its downstream effects on cognitive and emotional development, highlighting the importance of integrative studies that combine sensory assessments with neuroimaging. Temporal cortical regions exhibit well-established functional asymmetry, with the left hemisphere predominantly supporting linguistic and semantic processing, and the right hemisphere contributing more strongly to prosodic and affective aspects of auditory perception 23 , 25 . Given this hemispheric specialization, structural alterations in the left-lateralized auditory-language regions may have distinct functional consequences compared to the right temporal regions, particularly in the context of speech-relevant frequency processing. Therefore, investigating GMV alterations in auditory-related temporal regions, and specifically examining their functional connectivity, provides a theoretically grounded approach to understand how maltreatment-related structural changes may influence auditory and language-related neural networks. This study hypothesized that atypical GMV in auditory-related regions could lead to hyperconnectivity or hypoconnectivity in associated functional networks, reflecting downstream vulnerabilities in auditory-related neural networks. To test this hypothesis, this study aimed to identify GMV and FC changes in the brains of children who have experienced maltreatment using T1-weighted structural and functional MRI (fMRI). Results Behavioral data The clinical, demographic, psychological, and auditory assessments of the participants in the final dataset are summarized in Table 1 . Handedness did not differ between groups (χ 2 = 0.84, P = 0.36), and the small number of left-handed participants in each group makes it unlikely that lateralization differences influenced the observed brain structure or FC results. The participants in the children maltreatment (CM) group had substantially more difficulties with psychosocial behavior and cognitive dysfunction than the no maltreated group (non-CM). In contrast, the normal pure tone audiometry threshold according to the Japan Audiological Society was noted for participants in both groups. Table 1. Demographic and clinical characteristics of the children maltreatment (CM) and no maltreated history (non-CM) groups. Mean (SD) CM non-CM Statistics Participants ( n ) 19 38 Age ( y ) 14.4 (3.0) 13.3 (3.5) t (55) = 1.15 P = 0.25 Sex (girl/boy) 9/10 21/17 χ 2 (1) = 0.32 P = 0.57 Right-handedness (yes/no) 16/3 35/3 χ 2 (1) = 0.84 P = 0.36 Physical abuse 8 – – – Emotional abuse 10 – – – Neglect 14 – – – Sexual abuse 0 – – – Duration of maltreatment ( y ) 4.5 (3.7) – – – Elapsed duration ( y ) 7.9 (4.2) – – – FSIQ [WISC or WAIS] 95.2 (7.9) 105.7 (12.7) t (55) = –3.32 P = 0.002 SDQ [total score] 10.8 (5.4) 6.6 (4.6) t (55) = 3.03 P = 0.004 DSRS-C 11.3 (6.2) 9.6 (5.7) t (54) = 1.05 P = 0.30 PTA-R (dB) 6.6 (2.6) 6.1 (5.0) t (55) = 0.40 P = 0.70 PTA-L (dB) 7.2 (3.6) 4.7 (4.4) t (55) = 2.14 P = 0.04 SRT-R (dB) 6.3 (5.0) 6.6 (6.3) t (55) = –0.16 P = 0.87 SRT-L (dB) 5.3 (6.1) 5.0 (5.6) t (55) = 0.16 P = 0.87 Open in a new tab SD, standard deviation; FSIQ, Full-Scale Intelligence Quotient; WISC, Wechsler Intelligence Scale for Children; WAIS, Wechsler Adult Intelligence Scale; SDQ, Strength and Difficulties Questionnaire; DSRS-C, Depression Self-Rating Scale for Children; PTA-R/L, Pure Tone Average Right/Left; SRT-R/L, Speech Reception Threshold Right/Left. Underlining denotes P < 0.05. ROI of temporal cortex results The GMV in the left middle temporal gyrus (L.MTG; Montreal Neurological Institute [MNI] coordinates: x = −64, y = −26, z = −4; T = 4.13; P < 0.001 for peak level, family-wise error (FWE) corrected P = 0.038 for cluster level) was significantly reduced in the CM group compared with that of the non-CM group, emphasizing the statistical significance after accounting for multiple comparisons. Extracting the beta values for coordinates and assessing the power to identify significant differences between groups confirmed sufficient power for the coordinates presented in the main findings (a = 0.05, d = –1.14 , power [1–b] = 0.98; Fig. 1 ). No other significant temporal cortical regions were identified with corrected peak probabilities. Fig. 1. Open in a new tab Structural differences in the regional GMV between the CM and non-CM groups. Smaller regional GMV of L.MTG between the CM and non-CM groups according to ROI analysis. Significantly smaller voxels ( P < 0.001 at the voxel level and P < 0.05 with FWE-corrected for multiple comparisons at the cluster level) identified in the CM group compared to those in the non-CM group are depicted in yellow (MNI coordinates x = −66, y = −26, z = −4, L.MTG). GMV, gray matter volume; CM, children maltreatment; non-CM, no maltreatment history; VBM, voxel-based morphometry; FWE-corrected, family-wise error-corrected; MNI, Montreal Neurological Institute; L.MTG, left middle temporal gyrus; ROI, region-of-interest. Resting-state FC results VBM analysis was used to identify the L.MTG as an atypical brain structure; hence, it was set as a seed for seed-based correlations (SBC) analysis, and significant changes in the right temporal pole (R.TP) were observed (Fig. 2 a). The R.TP indicated that FC from the L.MTG was significantly greater (MNI coordinates: x = 32, y = 16, z = –38; cluster size = 237 voxels; P < 0.001 for peak level, FWE-corrected P = 0.013 for the cluster level) in the CM group than in the non-CM group. Extracting the eigenvariate values for the coordinates and assessing the power to detect significant differences between groups confirmed sufficient power for the coordinates presented in the main findings (a = 0.05, d = 1.81, power [1–b] = 0.99). Fig. 2. Open in a new tab FC from the L.MTG of the CM group compared with that of the non-CM group. (a) The L.MTG was set as the seed. The R.TP with re-colored pixels represents increased connectivity. The color bar denotes the t -statistic range. (b) Correlation plots between the hearing threshold of the left ear at 2 kHz (L_2000) and the FC to the L.MTG-R.TP. Closed circle, CM; open circle, non-CM; shaded beige, 95% confidence intervals of the regression line (dark yellow). FC, functional connectivity; L.MTG, left temporal gyrus; CM, children maltreatment; non-CM, no maltreatment history; R.TP, right temporal pole. Correlation between brain atypicality of GMV and resting-state FC and auditory measures No significant correlations were observed between the auditory assessment and the GMV of the L.MTG. The eigenvariate representing FC between the L.MTG and the R.TP was extracted and correlated with the pure-tone auditory thresholds, revealing a significant association with the 2 kHz threshold measured in the left ear (R 2 = 0.29, FDR corrected P = 0.03; Fig. 2 b). Discussion The results of this study showed that the early-life maltreatment group had significant GMV differences in the L.MTG compared to the non-CM group. Atypical FC patterns were also observed. However, a key finding was the increased FC between the R.TP region when the L.MTG was used as the seed region. These findings suggest that early-life maltreatment affects the brain structure as well as its functional communication. The effect of the atypical L.MTG as a brain structural feature in the CM group is consistent with that observed in previous studies exploring atypical brain structures associated with commonly observed clinical symptoms, including post-traumatic stress disorder, depressive symptoms, and emotional-cognitive difficulties in maltreated children 26 – 29 . The MTG is highly responsive to human voices and speech sounds, playing a crucial role in voice recognition, phoneme processing, and spoken language interpretation 23 , 30 , 31 . It is closely connected to the primary auditory cortex and interacts with the inferior frontal gyrus, a part of Broca’s area, to support speech production and comprehension 32 , 33 . Notably, the MTG exhibits well-established hemispheric asymmetry. The L.MTG is primarily involved in linguistic processing, whereas the R.MTG plays a greater role in prosody and affective cue interpretation 23 – 25 , 34 . Thus, alterations emerging specifically in L.MTG may disproportionately affect left-lateralized auditory-language networks. Despite examining various auditory aspects, no association was found between the observed reduction in the GMV of the L.MTG and auditory outcomes. However, this reduction may not have directly affected the audiometric outcomes. The extensive connections of the L.MTG with the auditory cortex, frontal regions, and multimodal sensory areas make it a crucial center for understanding spoken language and processing auditory stimuli 23 , 31 , 34 . Therefore, the atypical GMV observed in the CM group may have indirectly contributed to reduced speech perception. However, further studies are required to confirm these findings. In contrast, significant changes were observed in the FC analysis when the L.MTG, which exhibited GMV atypicality, was used as the seed region. Specifically, the CM group showed a greater FC pattern between the L.MTG and R.TP than the non-CM group. The functional role of the TP is hemispherically asymmetric. The L.TP is primarily involved in language, semantic memory, and naming, whereas the R.TP is more strongly associated with socioemotional processing, including the interpretation of emotional prosody and affective cues 35 – 38 . In addition to identifying speech content, the R.TP assists in interpreting vocal intonation, facilitating the recognition of emotions such as happiness, anger, and sadness—an ability crucial for effective social communication and emotional understanding. Portnova et al. 39 used an electroencephalogram to demonstrate that children with autism spectrum disorder (ASD) exhibit atypical responses to emotional vocalizations, such as crying and laughter, compared to controls. These findings suggest that deficits in emotion perception extend beyond visual expressions to auditory stimuli, further emphasizing the importance of auditory emotional perception in social interactions. Hence, accurate emotion recognition is crucial, as misclassification can lead to inappropriate social responses. Therefore, the presence of altered GMV in the L.MTG, together with enhanced FC between the L.MTG and the R.TP, may represent neurodevelopmental adaptations affecting both language-related and socioemotional auditory pathways, which may be particularly sensitive to early adverse experiences. Children exposed to maltreatment often face high levels of stress and adversity, which can lead to heightened demands on specific brain regions involved in processing social and emotional cues 21 , 40 . The L.MTG is associated with language processing, semantic processing, and word production 23 , 34 , 41 . Although reduced GMV in the L.MTG may suggest alterations in its structural integrity, this does not necessarily imply diminished function. Instead, such reduction may reflect adaptive neurodevelopmental changes shaped by early adverse experiences, potentially influencing language-related or socioemotional processing. Furthermore, children exposed to maltreatment may develop heightened sensitivity to vocal cues and emotional nuances as part of a survival mechanism. Enhanced FC between L.MTG and R.TP may reflect this increased need for real-time integration of auditory and emotional information, supporting rapid detection of social threats or changes in vocal tone. Childhood maltreatment is associated with impaired emotion recognition 42 , potentially linked to altered auditory emotional perception. This study observed a significant correlation between an auditory frequency of 2 kHz and FC L.MTG-R.TP, suggesting that subtle differences in auditory perception may influence auditory emotional perception. A frequency of 2 kHz is commonly used to assess hearing sensitivity and estimate speech understanding 43 . Frequencies around 2 kHz are common in emotionally charged sounds such as infant cries, alarms, and high-pitched vocalizations 44 , 45 . These sounds are evolutionarily significant, prompting heightened attention and neural responses, which might explain the observed correlation at this frequency 45 , 46 . In addition, the auditory cortex might process different frequencies with varying sensitivity. Such as, specific brain regions may be more finely tuned to frequencies around 2 kHz due to its prominence in everyday speech 23 , particularly for vowel articulation and certain consonant discriminations. In typical hearing loss patterns, higher frequencies, above 4 kHz, are often affected first 47 . Adverse childhood experiences can impact auditory processing pathways, potentially affecting sensitivity to specific frequency ranges, such as 2 kHz. This neuroplasticity may lead to heightened or diminished responses to sounds within this key range. Such changes could signal an early-stage deficit before more widespread impairments become noticeable. Thus, although both groups exhibited normal hearing thresholds, the CM group had a higher threshold than the non-CM group. The significant correlation between subtle auditory threshold differences and FC in the CM group likely reflects a subtle decline in hearing sensitivity. These differences may indicate alterations in the sensitivity of maltreated children to auditory emotional information, pointing to neurodevelopmental adaptations in the auditory and affective processing pathways. A possible explanation for atypical auditory perception in maltreated children lies in early parent–child interactions. Typically, caregivers engage in infant-directed speech, characterized by exaggerated prosodic cues that enhance sensitivity to suprasegmental speech features, thereby facilitating efficient emotional perception 19 . However, maltreated children may lack consistent and enriching social interactions, potentially hindering the development of auditory-emotional perception and affecting their ability to interpret and respond to social-emotional cues. In the CM group, greater functional connectivity between the L.MTG and R.TP was significantly correlated with auditory sensitivity at 2 kHz, a frequency critically involved in speech perception. These findings suggest that maltreated children may exhibit altered sensitivities to auditory emotional information, potentially reflecting neurodevelopmental adaptations to early adversity. This study has some limitations. Child maltreatment often coincides with other adverse factors, such as poverty or malnutrition 48 , complicating efforts to isolate its direct influence on brain development. Additionally, the findings may have limited applicability to broader populations because of the potential influence of social interventions targeting child maltreatment. Although FDR correction was applied for the associations between functional connectivity measures and auditory behavioral performance, it was implemented only for those results that initially reached nominal significance, following the analytical approach used in Kawata et al. 5 . As the correction was not applied across the full set of tests, this procedure may increase the risk of type I errors. Therefore, these exploratory results should be interpreted with caution. Furthermore, although handedness did not differ between groups, and only a small number of participants were left-handed, we acknowledge that variability in hemispheric lateralization cannot be entirely excluded as a potential influence on language-related findings 49 – 51 . Additionally, because formal screening for ASD/attention-deficit hyperactivity disorder (ADHD) was not performed for all participants, undetected neurodevelopmental conditions may have contributed to individual variability. However, given the high comorbidity between maltreatment and neurodevelopmental disorders, and the small number of documented cases in our sample, we retained all participants to preserve conservation validity. In conclusion, this study provides compelling evidence that childhood maltreatment is associated with alterations in brain structure and FC in the brain regions involved in auditory emotional information perception. The atypical GMV in the L.MTG, along with increased FC between the L.MTG and R.TP, suggest that maltreatment-related neurodevelopmental adaptations may affect how children perceive and interpret auditory emotional information. Notably, although hearing thresholds remained within the normal range, subtle differences in auditory perception influenced emotional perceptions. These findings highlight the crucial role of early life experiences in shaping auditory-affective neural networks, with potential consequences for speech perception, emotional recognition, and social communication. Given the implications for emotional and cognitive development, future studies should investigate the longitudinal trajectories of auditory emotional perception in maltreated children and explore targeted interventions that could mitigate these neurodevelopmental alterations. Furthermore, future research should adopt robust study designs and comprehensive data harmonization to better understand the effects of maltreatment on brain development. Understanding these mechanisms will contribute to the development of effective therapeutic strategies, ultimately improving social and emotional outcomes in individuals with a history of maltreatment. Methods Ethics statement This study was conducted in accordance with the Declaration of Helsinki and the Ethical Guidelines for Clinical Studies issued by the Ministry of Health, Labor, and Welfare of Japan. The study protocol was approved by the Ethics Committee of the University of Fukui, Japan (approval numbers: 20220044, 20220034, and 20210004). All parents or directors of child welfare facilities provided written informed consent. Participants This study used a subset of a longitudinal archival T1-weighted MRI and fMRI dataset involving 19 maltreated Japanese children and adolescents aged 9–18 years (10 boys, nine girls; mean age ± standard deviation [SD] = 14.5 ± 3.0 years). The recruitment sources included the University of Fukui Hospital and local child welfare facilities (Table 1 ). All CM participants experienced physical, emotional, and neglect, resulting in care by a child protective service or equivalent. In the CM group, 19 children lived in stable environments within the child welfare facilities. Two children were diagnosed with neurodevelopmental disorders according to the DSM-5 criteria: one with ADHD and one with ASD with ADHD. The non-CM group consisted of 38 Japanese children (17 boys, 21 girls; mean age ± SD = 13.3 ± 3.5 years) with no maltreatment history, recruited from the local community via advertisements, and matched for age and sex. The exclusion criteria for the non-CM group included psychiatric diagnoses (mood-related, anxiety, and stress disorders) or neurodevelopmental disorders (ASD, ADHD, and learning disorders). For all participants, the exclusion criteria included a Full-Scale Intelligence Quotient (FSIQ) of < 70 on the Wechsler Intelligence Scale for Children-Fourth Edition or the Wechsler Adult Intelligence Scale-Third Edition; a history of head trauma with loss of consciousness; perinatal or neonatal complications; neurological disorders; sleep disturbances; or medical conditions adversely affecting growth and development. In-scanner motion was also controlled using strict quality criteria: participants were excluded if mean framewise displacement (FD) exceeded 0.3 mm, maximum displacement exceeded 2.5 mm, or if more than 20% of volumes were identified as outlier scans. A total of 10 participants were excluded based on these motion criteria. The independent t -test and χ 2 test were used to assess the potential differences between the CM and non-CM groups for all demographic variables. The present study adopted the same methodology as described in Kawata et al. 5 Pure-tone audiometry Pure tone audiometry is a behavioral hearing test used to detect an individual’s hearing threshold levels. Conventional audiometric tests were performed at frequencies of 250 Hz, 500 Hz, 1 kHz, 2 kHz, 4 kHz, and 8 kHz to investigate the effect of auditory sensitivity between the groups. The level of these pure tones is then reduced to quietest level at which a patient can detect the tone, which is defined as the hearing threshold. The participants’ assessments were initiated with the right ear. Threshold of 25 dB or better is commonly considered normal hearing 52 . Pure-tone average Pure-tone average (PTA) is calculated by simply averaging air-conduction hearing thresholds at 500 Hz, 1 kHz, and 2 kHz. Speech-recognition threshold The speech-recognition threshold is defined as the lowest hearing level at which 50% of the words presented can be identified correctly. The Japan Audiological Society guidelines use single-digit numbers, which were created and used to measure the threshold for listening speech by speech (speech-understanding threshold). The participants were asked to write the single-digit number presented to their ears. The aim was to determine the softest sound level at which a participant could hear and writes approximately half of the compound words correctly. T1-weighted image acquisition, preprocessing, and statistical analysis Images of the brains of the 57 participants were acquired using a 3.0 Tesla General Electric SIGNA MRI system (SIGNA PET/MR; GE Healthcare, Milwaukee, WI, USA) equipped with an 8-channel head coil. A T1-weighted anatomical scan was obtained using a fast spoiled-gradient recalled imaging sequence with the following parameters: repetition time (TR) = 8.488 ms, echo time (TE) = 3.248 ms, flip angle (FA) = 11°, field of view (FOV) = 256 mm, matrix size = 256 × 256, volume dimensions = 1.0 × 1.0 × 1.0 mm 3 , slice thickness = 1.0 mm), with a total of 172 slices. Visual quality control of T1-weighted images was performed. The present study adopted the same methodology as described in Kawata et al. 5 Structural brain image data preprocessing and statistical analyses were performed using Statistical Parametric Mapping 12 software version 7771 (SPM12, Wellcome Department of Cognitive Neurology, London, UK) implemented in MATLAB (R2022b, MathWorks Inc., Natick, MA, USA). Further details regarding structural image data preprocessing are summarized by Kawata et al. 5 . Spatially normalized gray matter images were smoothed using a Gaussian kernel with a full width at half maximum (8 mm). Regional differences in GMV were analyzed using two-sample models in SPM12. ROI analysis was conducted specifically targeting the temporal cortex based on prior hypotheses of atypical brain region development 20 . The ROIs were defined using the WFU PickAtlas toolbox (version 3.0.5). Corrections for multiple comparisons at the cluster level were applied to examine GMV differences between the groups. The potential confounding effects of age, sex, and GMV were modeled, and variances attributable to confounders were excluded. The statistical threshold was set at P < 0.001 at the voxel level and P < 0.05 with FWE correction for multiple comparisons at the cluster level. fMRI acquisition, preprocessing and statistical analysis The fMRI parameters were as follows: TR = 2.300 ms, TE = 3.000 ms, FA 81°; FOV, 100 mm; and slice thickness = 3.5 mm. Resting-state FC preprocessing and analysis were performed using a standard pipeline in the CONN toolbox 53 (version 22a), implemented in MATLAB. Further details regarding fMRI data preprocessing are summarized by Kawata et al. 5 . SBC was used to analyze group differences for the second-level analysis. The potential confounding effects of age and sex were modeled, and the variances attributable to them were excluded from the analysis. Based on our prior VBM findings, the L.MTG ROI was selected as a candidate seed region and used for the subsequent SBC analysis. The statistical threshold was set at P < 0.001 at the voxel cluster level and P < 0.05 with FWE correction for multiple comparisons at the cluster level. Statistical analyses For group comparisons of clinical variables, Pearson’s correlation analysis was conducted to examine the eigenvariate values of GMV and FC identified in the group comparisons, adjusting for control variables in the main analysis to determine their association with pure-tone audiometry outcomes. The adjusted eigenvariates values represented linearly transformed estimates of GMV and FC. The GMV and FC eigenvariates values were extracted across all voxels within the corresponding cluster from the GMV and FC values for each participant. All statistical analyses were performed using R software (version 2024.09.0, R Core Development Team, Toulouse, France). The significance level was set at FDR corrected P < 0.05. Supplementary Information Below is the link to the electronic supplementary material. Supplementary Material 1 (235KB, docx) Acknowledgements We thank the staff at the Research Center for Child Mental Development, University of Fukui, for their clerical support. Author contributions NYSK contributed to the conceptualization, methodology, data collection, formal analysis, visualization, writing—original draft, writing—review, and editing. AY and HO contributed to the collected the data. TXF and AT contributed to the conception, methodology, and revised the manuscript critically for intellectual content. All authors approved of the final manuscript. Funding This work was supported by Early-Career Scientists [grant number 22K13677 to NYSK], Fostering Joint International Research [grant number 22KK0218 to NYSK], JSPS KAKENHI Scientific Research (C) [grant number 21K02352 to AT], JSPS KAKENHI Scientific Research (C) [grant number 23K10129 to NYSK and AT], and JSPS KAKENHI Scientific Research (B) [grant number 23K25644 to TXF]. Data availability The datasets generated during and/or analyzed during the current study are not publicly available due to the risk of subject identification, subjects whose anonymity should be carefully protected. However, data and code may be provided to interested researchers upon reasonable request to the corresponding author, after clearance from the Research Ethics Committee. Declarations Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Lippard, E. T. C. & Nemeroff, C. B. The devastating clinical consequences of child abuse and neglect: Increased disease vulnerability and poor treatment response in mood disorders. Am. J. Psychiatry. 177 , 20–36. 10.1176/appi.ajp.2019.19010020 (2020). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Tomoda, A. et al. The neurobiological effects of child maltreatment on brain structure, function, and attachment. Euro. Arch. Psych. Clin. Neuro. 10.1007/s00406-024-01779-y (2024). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. Organization, W. H. INSPIRE: Seven Strategies for Ending Violence Against Children. https://www.who.int/publications/i/item/inspire-seven-strategies-for-ending-violence-against-children (2016). 4. UNICEF, W. H. O. Global Status Report on Preventing Violence Against Children 2020. https://www.who.int/publications/i/item/9789240004191 (2020). 5. Kawata, N. Y. S. et al. Brain structures and functional connectivity in neglected children with no other types of maltreatment. Neuroimage 292 , 120589. 10.1016/j.neuroimage.2024.120589 (2024). [ DOI ] [ PubMed ] [ Google Scholar ] 6. Kawata, N. Y. S. et al. White matter microstructure abnormalities in children experiencing neglect without other forms of maltreatment. Sci. Rep. 15 (1), 27282. 10.1038/s41598-025-13363-y (2025). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 7. Kawata, N. Y. S., Fujisawa, T. X., Yao, A., Okazawa, H. & Tomoda, A. Subcortical regional volume vulnerability linked to child neglect without other forms of maltreatment. Brain Res. Bull. 236 , 111771. 10.1016/j.brainresbull.2026.111771 (2026). [ DOI ] [ PubMed ] [ Google Scholar ] 8. Chang, O. D., Meier, H. C. S., Maguire-Jack, K., Davis-Kean, P. & Mitchell, C. Childhood maltreatment and longitudinal epigenetic aging: NIMHD social epigenomics program. JAMA Netw. Open 7 (7), e2421877-e2421877. 10.1001/jamanetworkopen.2024.21877 (2024). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Wen, S. et al. Childhood maltreatment and risk of endocrine diseases: An exploration of mediating pathways using sequential mediation analysis. BMC Med. 22 (1), 59. 10.1186/s12916-024-03271-9 (2024). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Roeders, M. et al. Early identification and awareness of child abuse and neglect among physicians and teachers. BMC Pediatr. 24 , 302. 10.1186/s12887-024-04782-3 (2024). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 11. Landau, A. Y. et al. Developing machine learning-based models to help identify child abuse and neglect: Key ethical challenges and recommended solutions. J. Am. Med. Inform. Assoc. 29 , 576–580. 10.1093/jamia/ocab286 (2022). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 12. Elgueda, D., Ayala, Y. A. & Delano, P. H. Editorial: Listening in action: Attention, emotions and cognition in the auditory system. Front. Neurosci. 16 , 1007095. 10.3389/fnins.2022.1007095 (2022). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. de Boer, M. J., Jürgens, T., Başkent, D. & Cornelissen, F. W. Auditory and visual integration for emotion recognition and compensation for degraded signals are preserved with age. Trends Hear. 25 , 23312165211045306. 10.1177/23312165211045306 (2021). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 14. Sievers, B. et al. Visual and auditory brain areas share a representational structure that supports emotion perception. Curr. Biol. 31 , 5192-5203.e5194. 10.1016/j.cub.2021.09.043 (2021). [ DOI ] [ PubMed ] [ Google Scholar ] 15. Yao, A. et al. Subclinical structural atypicality of retinal thickness and its association with gray matter volume in the visual cortex of maltreated children. Sci. Rep. 14 , 11465. 10.1038/s41598-024-62392-6 (2024). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Ye, Y., Mattingly, M. M., Sunthimer, M. J., Gay, J. D. & Rosen, M. J. Early-life stress impairs perception and neural encoding of rapid signals in the auditory pathway. J. Neurosci. 43 , 3232–3244. 10.1523/jneurosci.1787-22.2023 (2023). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Teicher, M. H. & Samson, J. A. Annual research review: Enduring neurobiological effects of childhood abuse and neglect. J. Child Psychol. Psychiatry 57 , 241–266. 10.1111/jcpp.12507 (2016). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Leipold, S., Abrams, D. A., Karraker, S. & Menon, V. Neural decoding of emotional prosody in voice-sensitive auditory cortex predicts social communication abilities in children. Cereb. Cortex 33 (3), 709–728. 10.1093/cercor/bhac095 (2022). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. Yeshoda, K., Raveendran, R. & Konadath, S. Perception of vocal emotional prosody in children with hearing impairment. Int. J. Pediatr. Otorhinolaryngol. 137 , 110252. 10.1016/j.ijporl.2020.110252 (2020). [ DOI ] [ PubMed ] [ Google Scholar ] 20. Tomoda, A. et al. Exposure to parental verbal abuse is associated with increased gray matter volume in superior temporal gyrus. Neuroimage 54 (Suppl 1), S280-286. 10.1016/j.neuroimage.2010.05.027 (2011). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 21. Teicher, M. H., Samson, J. A., Anderson, C. M. & Ohashi, K. The effects of childhood maltreatment on brain structure, function and connectivity. Nat. Rev. Neurosci. 17 (10), 652–666. 10.1038/nrn.2016.111 (2016). [ DOI ] [ PubMed ] [ Google Scholar ] 22. Bick, J. & Nelson, C. A. Early adverse experiences and the developing brain. Neuropsychopharmacology 41 , 177–196. 10.1038/npp.2015.252 (2016). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 23. Hickok, G. & Poeppel, D. The cortical organization of speech processing. Nat. Rev. Neurosci. 8 , 393–402. 10.1038/nrn2113 (2007). [ DOI ] [ PubMed ] [ Google Scholar ] 24. Frühholz, S. & Grandjean, D. Processing of emotional vocalizations in bilateral inferior frontal cortex. Neurosci. Biobehav. Rev. 37 , 2847–2855. 10.1016/j.neubiorev.2013.10.007 (2013). [ DOI ] [ PubMed ] [ Google Scholar ] 25. Schirmer, A. & Kotz, S. A. Beyond the right hemisphere: Brain mechanisms mediating vocal emotional processing. Trends Cogn Sci. 10 , 24–30. 10.1016/j.tics.2005.11.009 (2006). [ DOI ] [ PubMed ] [ Google Scholar ] 26. De Bellis, M. D. et al. Brain structures in pediatric maltreatment-related posttraumatic stress disorder: A sociodemographically matched study. Biol. Psychiatry 52 , 1066–1078. 10.1016/S0006-3223(02)01459-2 (2002). [ DOI ] [ PubMed ] [ Google Scholar ] 27. Shen, X. et al. Brain structural associations with depression in a large early adolescent sample (the ABCD study®). eClinicalMedicine 42 , 101204. 10.1016/j.eclinm.2021.101204 (2021). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Voss, S. et al. Interaction of childhood abuse and depressive symptoms on cortical thickness: A general population study. Eur. Arch. Psychiatry Clin. Neurosci. 272 , 1523–1534. 10.1007/s00406-022-01387-8 (2022). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. Wan, Z., Rolls, E. T., Feng, J. & Cheng, W. Brain functional connectivities that mediate the association between childhood traumatic events, and adult mental health and cognition. EBioMedicine 79 , 104002. 10.1016/j.ebiom.2022.104002 (2022). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. Belin, P., Zatorre, R. J., Lafaille, P., Ahad, P. & Pike, B. Voice-selective areas in human auditory cortex. Nature 403 , 309–312. 10.1038/35002078 (2000). [ DOI ] [ PubMed ] [ Google Scholar ] 31. Rauschecker, J. P. & Scott, S. K. Maps and streams in the auditory cortex: Nonhuman primates illuminate human speech processing. Nat. Neurosci. 12 , 718–724. 10.1038/nn.2331 (2009). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 32. Tyler, L. K. & Marslen-Wilson, W. Fronto-temporal brain systems supporting spoken language comprehension. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363 , 1037–1054. 10.1098/rstb.2007.2158 (2008). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 33. Friederici, A. D. The brain basis of language processing: From structure to function. Physiol. Rev. 91 , 1357–1392. 10.1152/physrev.00006.2011 (2011). [ DOI ] [ PubMed ] [ Google Scholar ] 34. Binder, J. R., Desai, R. H., Graves, W. W. & Conant, L. L. Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cereb. Cortex 19 , 2767–2796. 10.1093/cercor/bhp055 (2009). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 35. Olson, I. R., Plotzker, A. & Ezzyat, Y. The enigmatic temporal pole: A review of findings on social and emotional processing. Brain 130 , 1718–1731. 10.1093/brain/awm052 (2007). [ DOI ] [ PubMed ] [ Google Scholar ] 36. Bonilha, L. et al. Temporal lobe networks supporting the comprehension of spoken words. Brain 140 , 2370–2380. 10.1093/brain/awx169 (2017). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 37. Tiesinga, P. et al. Uncovering the fast, directional signal flow through the human temporal pole during semantic processing. Sci. Rep. 13 (1), 6831. 10.1038/s41598-023-33318-5 (2023). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Metternich, B. et al. Dynamic facial emotion recognition and affective prosody recognition are associated in patients with temporal lobe epilepsy. Sci. Rep. 14 (1), 3935. 10.1038/s41598-024-53401-9 (2024). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 39. Portnova, G. V., Skorokhodov, I. V. & Mayorova, L. A. The levels of auditory processing during emotional perception in children with autism. J. Integr. Neurosci. 10.31083/j.jin2205112 (2023). [ DOI ] [ PubMed ] [ Google Scholar ] 40. McCrory, E., De Brito, S. A. & Viding, E. The impact of childhood maltreatment: A review of neurobiological and genetic factors. Front. Psychiatry 2 , 48. 10.3389/fpsyt.2011.00048 (2011). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 41. Price, C. J. A review and synthesis of the first 20 years of PET and fMRI studies of heard speech, spoken language and reading. Neuroimage 62 , 816–847. 10.1016/j.neuroimage.2012.04.062 (2012). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 42. Kavanagh, E., Whitehouse, J. & Waller, B. The face in everyday social interaction: social outcomes and personality correlates of facial behaviour. 10.31234/osf.io/7tbyr (2022). 43. Wiseman, K. B. & McCreery, R. W. Quantifying access to speech in children with hearing loss: The influence of the work of Pat Stelmachowicz on measures of audibility. Semin. Hear. 44 , S17-s28. 10.1055/s-0043-1764136 (2023). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 44. Soltis, J. The signal functions of early infant crying. Behav. Brain Sci. 27 , 443–458. 10.1017/S0140525X0400010X (2004). [ PubMed ] [ Google Scholar ] 45. Arnal, L. H., Flinker, A., Kleinschmidt, A., Giraud, A.-L. & Poeppel, D. Human screams occupy a privileged niche in the communication soundscape. Curr. Biol. 25 (15), 2051–2056. 10.1016/j.cub.2015.06.043 (2015). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 46. Pessoa, L. A network model of the emotional brain. Trends Cogn Sci 21 , 357–371. 10.1016/j.tics.2017.03.002 (2017). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 47. Hamiter, M. Hearing Loss , < https://www.msdmanuals.com/professional/ear-nose-and-throat-disorders/hearing-loss/hearing-loss > (2024). 48. Hock, R. S. et al. Relationship between infant malnutrition and childhood maltreatment in a Barbados lifespan cohort. Vulnerable Child. Youth Stud. 12 , 304–313. 10.1080/17450128.2017.1371817 (2017). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 49. Knecht, S. Handedness and hemispheric language dominance in healthy humans. Brain 123 , 2512–2518. 10.1093/brain/123.12.2512 (2000). [ DOI ] [ PubMed ] [ Google Scholar ] 50. Mazoyer, B. et al. Gaussian mixture modeling of hemispheric lateralization for language in a large sample of healthy individuals balanced for handedness. PLoS ONE 9 , e101165. 10.1371/journal.pone.0101165 (2014). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 51. Tzourio-Mazoyer, N., Josse, G., Crivello, F. & Mazoyer, B. Interindividual variability in the hemispheric organization for speech. Neuroimage 21 , 422–435. 10.1016/j.neuroimage.2003.08.032 (2004). [ DOI ] [ PubMed ] [ Google Scholar ] 52. Kassjański, M. et al. Automated hearing loss type classification based on pure tone audiometry data. Sci. Rep. 14 , 14203. 10.1038/s41598-024-64310-2 (2024). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 53. Whitfield-Gabrieli, S. & Nieto-Castanon, A. Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2 , 125–141. 10.1089/brain.2012.0073 (2012). [ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplementary Material 1 (235KB, docx) Data Availability Statement The datasets generated during and/or analyzed during the current study are not publicly available due to the risk of subject identification, subjects whose anonymity should be carefully protected. However, data and code may be provided to interested researchers upon reasonable request to the corresponding author, after clearance from the Research Ethics Committee. Articles from Scientific Reports are provided here courtesy of Nature Publishing Group ACTIONS View on publisher site PDF (1.4 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top