One compelling notion regarding brain asymmetry is that ‘functional lateralization is guided by structural asymmetry,’ hypothesizing a critical role of structural asymmetries in the neuroanatomical basis of functional lateralization (Ocklenburg and Güntürkün, 2018; Wada, 2009). As a well-known cortical area posterior to the Heschl’s gyrus (HG), which includes most of the primary auditory cortex, the PT putatively plays an essential role in the functional lateralization of language processing (Albouy et al., 2020; Galaburda et al., 1978; Hickok and Poeppel, 2000; Hickok and Poeppel, 2007; Moffat et al., 1998; Price, 2010; Steinmetz, 1996; Tzourio-Mazoyer et al., 2018), and its lateralization in functional activation during auditory- or language-related tasks has been well demonstrated (Albouy et al., 2020; Hunter et al., 2003; Pahs et al., 2013; Shapleske et al., 1999; Zatorre et al., 2002). Additionally, the PT shows distinct leftward asymmetries in a variety of structural aspects, e.g., length, gray matter concentration/volume, surface area, cortical thickness, and columnar neuronal units, although the results might conflict between studies (Anderson et al., 1999; Bloom et al., 2013; Chance et al., 2008; Dorsaint-Pierre et al., 2006; Eckert et al., 2006; Fukutomi et al., 2018; Galuske et al., 2000; Greve et al., 2013; Josse et al., 2003; Knaus et al., 2006; Moffat et al., 1998; Ocklenburg et al., 2018; Schmitz et al., 2019; Sigalovsky et al., 2006; Tzourio-Mazoyer et al., 2019). Due to the role of the PT in language processing, the idea that these asymmetries may be related to leftward functional lateralization in speech processing is highly intuitive. Unexpectedly, examining the functional association of PT macrostructural asymmetries at the individual level almost always showed negative results, except for only a couple of studies demonstrating correlations between PT asymmetry of surface area and some non-PT-related indices of language lateralization, e.g., the lateralization of dichotic listening (Guadalupe et al., 2022; Hugdahl et al., 2003; Dos Santos Sequeira et al., 2006).

Several factors possibly account for such an unexpected majority of negative results. First, the sample size in previous studies is too small for such association analyses across individuals, compared to the recently suggested thousands of individuals for capturing a reproducible brain-wide association (Marek et al., 2022). Next, structural asymmetries were confined to PT macrostructural measures that are biologically nonspecific, e.g., surface area and thickness. Recent studies have used novel MRI techniques to quantify PT microstructure on the level of axons or dendrites and further revealed PT asymmetries in myelin content, neurite density, and neurite orientation dispersion (Fukutomi et al., 2018; Ocklenburg et al., 2018; Schmitz et al., 2019; Sigalovsky et al., 2006). Importantly, postmortem work on the microstructure of the temporal cortex has led to the hypothesis that asymmetries in the organization of the intrinsic microcircuitry of the PT and other areas may be crucial for functional lateralization (Galuske et al., 2000). In line with this idea, asymmetries in PT microstructure have been linked to electrophysiological correlates of auditory speech, an indirect measure of functional language lateralization (Ocklenburg et al., 2018). However, the crucial experiment would be to show a direct link between PT microstructure and PT functional lateralization measured with fMRI. Finally, duplicated HG (dHG) in the left or right hemisphere occurs substantially in healthy individuals, although less frequently than single HG (sHG) (Campain and Minckler, 1976; Leonard et al., 1998; Marie et al., 2015). In particular, such an HG gyrification pattern is accompanied by significant differences in PT structural properties and their asymmetries compared with the single HG (Tzourio-Mazoyer and Mazoyer, 2017). The HG gyrification pattern could further relate to the functional-structural association of PT asymmetries, therefore, confounding the interindividual correlation between PT asymmetries. Thus, the interindividual variation in the HG gyrification pattern should be taken into account when investigating the functional-structural association of PT asymmetries, but this has been largely overlooked in previous studies.

In the present study, we included a large cohort of 907 right-handed healthy young adults to assess whether and how PT lateralization of speech-related functional activation relates to its underlying structural asymmetries. Particularly, we chose to manually delineate bilateral PTs on the virtually reconstructed cortical surface for each individual, which is very labor intensive but can localize such an anatomically highly variable structure with minimized errors. Moreover, a variety of macrostructural and microstructural measures were applied to quantify PT structural asymmetries, including surface area, cortical thickness, myelin content, neurite density index (NDI), and orientation dispersion index (ODI). Here, cortical myelin content mainly reflects the degree of intracortical axonal myelination and was measured using a noninvasive T1w/T2w mapping approach (Glasser et al., 2016; Glasser and Van Essen, 2011). Cortical NDI and ODI mainly represent the density of intracortical neurite and the complexity of dendritic arborization, respectively (Ocklenburg et al., 2018), and were estimated using the diffusion MRI-based neurite orientation dispersion and density imaging (NODDI) model (Zhang et al., 2012). Using these data, we evaluated (1) the functional and structural PT asymmetries at the group level, (2) the functional-structural coupling of PT asymmetries at the individual level, and (3) the within-hemispheric functional-structural coupling of PT at the individual level and its asymmetry. In these analyses, the influence of the HG gyrification pattern was considered.

Two experienced raters manually delineated PT and determined the HG gyrification pattern for 907 healthy right-handed young adults (Figure 1), with each rater assessing about half of the subjects.

Figure 1

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As shown in Table 1, the manual operation showed excellent interrater reliability and imaging test-retest reproducibility for all PT measures. Among these young adults, left and right HG duplications were identified in 229 and 263 subjects, respectively (occurrence rate: left, 25.3%, right, 29.0%). In terms of the HG gyrification pattern in both hemispheres, all subjects were divided into four groups: 503 individuals with bilateral sHGs (L1/R1, 55.5%), 175 individuals with left sHG but right dHG (L1/R2, 19.3%), 141 individuals with left dHG but right sHG (L2/R1, 15.6%), and 88 individuals with bilateral dHGs (L2/R2, 9.7%). Age and sex did not differ among these four groups (Table 2).

The differences in age and sex distribution across the four groups were evaluated using one-way ANOVA and the Kruskal-Wallis test, respectively. HG, Heschl’s gyrus. L1/R1, single HG on the left and single HG on the right; L1/R2, single HG on the left and duplicated HG on the right; L2/R1, duplicated HG on the left and single HG on the right; L2/R2, duplicated HG on the left and duplicated HG on the right. F, female; M, male.

For each subject, fMRI activation of the left and right PT during an auditory-language comprehension task was estimated. Both left and right PTs showed significant group-level activation for all three contrasts (i.e. ‘story – baseline,’ ‘math – baseline,’ and ‘story – math’) within the task (Figure 2—figure supplement 1), indicating a strong functional involvement of both PTs in such auditory-language processing. Factor analysis of the activation T values of the three contrasts revealed two factors in each hemisphere: one representing PT functional activation of speech perception and the other representing PT functional activation of speech comprehension.

We evaluated the correlation of functional and structural measures of the left and right PT with two available behavioral language test scores (the oral reading recognition test, measuring the ability to reade decoding; the picture vocabulary test, measuring the ability of linguistic comprehension), but found no significant results (all P_FWE >0.05).

PT functional and structural asymmetries at the group level

For each of the four groups above, PT functional lateralization of speech perception and comprehension activation were evaluated while controlling for age, gender, and brain size. As shown in Figure 2, significant leftward PT lateralization was observed for both speech perception and comprehension in the L1/R1 (Cohen’s d: speech perception = 0.35, speech comprehension = 0.36) and L1/R2 (Cohen’s d: speech perception = 0.52, speech comprehension = 0.57) groups but not in the L2/R1 and L2/R2 groups (Supplementary file 1a), suggesting a specific influence of the HG gyrification pattern on speech-related functional lateralization of the PT. For each hemisphere, we further compared these PT activations between subjects with sHG and dHG. The results showed that HG duplication was largely accompanied by decreased functional activation in the ipsilateral PT, and the degree of such a decrease varied between the left and right PTs (Figure 2—figure supplement 2).

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Similar analyses were applied to PT structural measures, i.e., surface area, thickness, myelin content, NDI, and ODI. As shown in Figure 3, ODI and cortical thickness showed consistent leftward and rightward asymmetry across all 4 groups, suggesting a minimal influence of the HG gyrification pattern on the group-level PT asymmetry of these particular measures. In contrast, there was the more or less confounding influence of the HG gyrification pattern on the PT asymmetry of the other four structural measures. Specifically, we observed (1) significant leftward asymmetry of PT surface area in the L1/R1, L1/R2, and L2/R2 groups (Cohen’s d: L1/R1 = 0.42, L1/R2 = 1.48, L2/R2 = 0.73) but no asymmetry in the L2/R1 group; (2) significant leftward and rightward asymmetry of PT myelin content only in the L1/R2 and L2/R1 groups (Cohen’s d: L1/R2 = 0.58, L2/R1 = –0.68); and (3) significant leftward NDI asymmetry in the L1/R2 group (Cohen’s d: L1/R2 = 0.40) but rightward NDI asymmetry in both the L1/R1 and L2/R1 groups (Cohen’s d: L1/R1 = –0.21, L2/R1 = –0.48) (Supplementary file 1a). In addition, the comparison of these PT structural measures between subjects with sHG and dHG indicated that HG duplication was accompanied by a decrease in surface area, myelin content, NDI, and ODI of the ipsilateral PT, with the left and right PT also showing variable degrees of such a decrease (Figure 3—figure supplement 1).

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We also evaluated whether functional and structural PT asymmetries correlate with the two behavioral language test scores. The AI of PT speech comprehension activation was found significantly correlated with picture vocabulary test scores (R = 0.10, P_FWE = 0.047). No any other correlation was observed.

Structure-function associations of PT asymmetries at the individual level

For each functional or structural PT measure, an asymmetry index (AI) was calculated for each subject. For each functional-structural pair of PT measures (2 × 5 pairs in total), we evaluated the interindividual correlation of their AIs with consideration of the HG gyrification pattern (including a group factor, i.e., L1/R1, L1/R2, L2/R1, or L2/R2), while controlling for age, gender, and brain size. No significant group effect (i.e. the interaction with the HG gyrification pattern in the general linear model) was observed for any functional-structural AI pair.

As shown in Figure 4, among these functional-structural pairs, the functional AI of speech perception activation showed significant positive correlations with the AIs of myelin content (R = 0.26, P_FWE = 1.22 × 10^–13), NDI (R = 0.13, P_FWE = 2.00 × 10^–3), and ODI (R = 0.22, P_FWE = 1.75 × 10^–9), regardless of the HG gyrification pattern. In contrast, functional AI of speech comprehension activation significantly correlated with the AIs of surface area (R = 0.21, P_FWE = 2.66 × 10^–9), myelin content (R = 0.20, P_FWE = 4.66 × 10^–8), and NDI (R = 0.11, P_FWE = 1.47 × 10^–2), regardless of the HG gyrification pattern. Notably, the observed interindividual correlations were consistently positive, strongly supporting an individual-level coupling of PT functional and structural asymmetries.

Figure 4

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Within-hemispheric PT structure-function associations at the individual level

In addition to PT functional and structural AIs, we evaluated the correlations between PT functional and structural measures of each hemisphere separately, while controlling for age, gender, and brain size (Figure 5). For speech perception, functional activation correlated positively with myelin content and ODI for either the left (myelin content: R = 0.16, P_FWE = 6.47 × 10^–5; ODI: R = 0.17, P_FWE = 2.88 × 10^–5) or right PT (myelin content: R = 0.20, P_FWE = 1.30 × 10^–7; ODI: R = 0.18, P_FWE = 7.18 × 10^–6), regardless of the HG gyrification pattern. The permutation test, however, showed no significant difference in the degree of functional-structural correlations between the left and right PTs. Moreover, speech perception activation showed significant correlations with surface area (R = –0.11, P_FWE = 2.33 × 10^–2) and NDI (R = 0.14, P_FWE = 2.16 × 10^–3) for the right PT but not the left PT. Regarding speech comprehension, functional activation significantly correlated with myelin content for the right PT (R = 0.16, P_FWE = 2.59 × 10^–5) but not the left PT. We also observed a significant correlation between functional activation of speech comprehension and NDI for the left PT (R = –0.12, P_FWE = 1.62 × 10^–2) but not the right PT.

Figure 5

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Using a large cohort of healthy adults with high-quality multimodal MRI data and highly accurate PT determination, the present study shows clear associations between structural and functional asymmetries in the PT. The observed functional-structural associations of PT asymmetries are highly specific to the within-PT functional activation of auditory-language processing.

As one of the most prominent structural asymmetries across the entire human brain, PT structural asymmetries have been well recognized and widely believed to play a critical role in human auditory or language processing (Bloom et al., 2013; Eckert and Leonard, 2000; Keller et al., 2019; Ocklenburg et al., 2018; Steinmetz, 1996; Yuan et al., 2021; Zatorre et al., 2002). The anatomical leftward asymmetry of PT emerges at an early stage of life, prior to any environmental influences such as language exposure (Dubois et al., 2010; Witelson and Pallie, 1973). This provides evidence of an early functional lateralization in the speech-processing capacities of the two hemispheres (Dehaene-Lambertz et al., 2002). In concordance, anomalies of PT asymmetries in surface area or thickness have been reported in various brain disorders with auditory or language deficits, e.g., dyslexia, autism, and schizophrenia (Altarelli et al., 2014; Bloom et al., 2013; Floris et al., 2016; Hynd and Semrud-Clikeman, 1989; Knaus et al., 2018; Pahs et al., 2013; Sumich et al., 2002; Sumich et al., 2005; Vanderauwera et al., 2018), suggesting that asymmetric PT anatomy was an optimal architecture for the setting up of efficient speech networks. Moreover, there exists evolutionary evidence supporting the role of the PT as an anatomical substrate for language lateralization. For example, the leftward structural asymmetry of the PT has been observed in multiple non-human primates, including chimpanzees, macaques, and baboons (Becker et al., 2024; Gannon et al., 1998; Xia et al., 2020). Particularly, recent studies on baboons further demonstrated that PT structural leftward asymmetry in newborn baboons could predict future development of communicative gestures, implying a key role of PT structural asymmetry in the lateralized communication system for human and non-human brain evolution (Becker et al., 2021; Becker et al., 2024).

To understand the role of PT structural asymmetries in language, it is critical to determine how they relate to functional patterns of language-related processing, e.g., language-related functional lateralization. Some evidence has demonstrated an association of PT structural asymmetry with language-related functional lateralization, but these functional lateralization were not measured directly from the PT (Greve et al., 2013; Josse et al., 2003; Seghier et al., 2011; Tzourio-Mazoyer et al., 2018). For instance, PT asymmetry in surface area was correlated with regional lateralization of language-related functional activation around the Sylvian fissure but not the activation of the PT (Tzourio-Mazoyer et al., 2018). Our present study provides the first empirical evidence of interindividual associations between functional and structural asymmetries within the PT, implying a functional pathway from PT structural asymmetry to PT functional lateralization to effective speech processing. A large sample size and accurate PT localization based on the well-trained manual operation were crucial in obtaining these results. Our present manually delineating method should be able to measure the PT more reliably and accurately, compared with previous atlas-based methods (Chai et al., 2021; Kuiper et al., 2020; Ocklenburg et al., 2018). The previously observed negative results of structural-functional correlation for PT asymmetries might partly be attributed to coarse locating of PT regions (Greve et al., 2013; Liem et al., 2014; Tzourio-Mazoyer et al., 2018). For PT-related studies with large sample sizes, however, it is difficult to apply such a labor-intensive manual PT labeling approach, and therefore, an accurate automatic labeling approach needs to be developed. Notably, our observed PT functional-structural associations were across healthy right-handed young adults, and it is unclear whether these associations could be extrapolated to children, left-handed adults, or patients. Therefore, future studies are required to evaluate the functional-structural association of PT asymmetries in other populations, which should provide insight into the modulating factor of functional-structural associations of PT.

In the context of brain asymmetries, our currently observed functional-structural coupling of PT asymmetries at the individual level empirically proves the role of structural gray matter asymmetries in the functional lateralization of the same brain area, supporting the compelling hypothesis that ‘functional lateralization is guided by structural asymmetry’ (Wada, 2009). For a specific brain area, however, its gray matter asymmetries are unlikely to be the only determinant for its functional lateralization. The relevant corpus callosum (interhemispheric connection) and structural white matter asymmetries should also play important roles in its functional lateralization, and integrating the three factors is encouraged for detangling structural mechanisms underlying functional lateralization (Allendorfer et al., 2016; Catani et al., 2005; Catani et al., 2007; Ocklenburg et al., 2013; Ocklenburg and Güntürkün, 2018).

The observed functional-structural coupling of PT asymmetries strongly depends on functional and structural measures. On the functional side, only PT functional activation of auditory speech processing showed such coupling with PT structural asymmetries, indicating the functional and spatial specificity of PT lateralization in auditory-language processing (Forseth et al., 2020; Gernsbacher and Kaschak, 2003; Hickok and Poeppel, 2000; Steinmetz, 1996). Speech perception and comprehension also showed functional-structural coupling with different structural measures, compatible with the dissociation of these two speech-processing components (Dehaene-Lambertz et al., 2005; Price, 2010). It is possible that hemispheric lateralization of different functional processing are generally related to distinct structural mechanisms. However, it should be noted that the fMRI language task used in the present study was not specifically designed to study PT activation and its lateralization (Binder et al., 2011), and therefore, the observed functional lateralization of speech perception and speech comprehension in the PT was not measured directly by a specific language task. Functional lateralization estimated indirectly by a data-driven approach is less specific in nature, which may partly account for the modest effect size of our observed functional-structural correlations. This also makes it difficult to further interpret the functional-structural correlation’s differential pattern observed between speech perception and speech comprehension. Similarly, our specificity analyses were not based on other specifically designed language tasks for PT-associated non-speech auditory processing (e.g. reading or word production) but on available task-fMRI data from the HCP. Future investigations with more specifically designed fMRI tasks on PT activation are warranted to verify and extend the PT functional-structural coupling results. On the structural side, the observed functional-structural coupling of PT asymmetries mainly involves microstructural measures, e.g., myelin content, neurite density, and neurite orientation dispersion, except for the association of speech comprehension with surface area. These microstructural measures represent specific aspects of cortical composition on the level of axons or dendrites (Glasser and Van Essen, 2011; Zhang et al., 2012), therefore, implying a dominant role of within-PT microconnectional or microcircuitry asymmetries in functional asymmetries of PT. Specific microstructural asymmetry between bilateral PTs might cause less recruitment of the nondominant hemisphere in particular speech processing, therefore, resulting in more pronounced functional lateralization (Galaburda et al., 1990; Tzourio-Mazoyer et al., 2018). Another possible explanation could be that higher myelin content and larger surface area in left PT potentially indicated more white matter connection with other language-related regions such as Broca’s area, and therefore, is more involved in language tasks than its right homolog (Allendorfer et al., 2016; Catani et al., 2005; Giampiccolo and Duffau, 2022).

Compatibly with the functional-structural coupling of PT asymmetries, most microstructural measures, including myelin content, neurite density, and neurite orientation dispersion, showed an association with speech-related activation for the left or right PT per se. Similar functional-structural associations have been observed in other non-PT cortical areas, e.g., between myelin content and task-evoked activities (Glasser et al., 2014; Grydeland et al., 2016; Helbling et al., 2015; Hunt et al., 2016; Kim and Knösche, 2016; Ma and Zhang, 2017). These findings together indicate an important contribution of intracortical microcircuits to functional activity: cortical areas with greater intracortical fiber density and more complex dendritic structures are likely accompanied by stronger local activation during the task (Hall et al., 2003; Hunt et al., 2016). As for macrostructural measures, the asymmetric PT surface area was also associated with speech comprehension AI. Given that the within-hemispheric coupling tendency between surface and speech comprehension existed only in the left PT, it was possible that the larger surface area of the left PT led to a less recruitment of its right homologous, and therefore, the lateralization of functional activity would be more pronounced. Additionally, an opposite tendency was found between the correlation of speech perception and comprehension with surface area, potentially implying the segregation of the different speech processing in the PT area. Intriguingly, within-hemispheric PT functional-structural coupling could be asymmetric, e.g., existing only in one hemisphere but not in the other. For instance, EEG-measured neurophysiological processing of speech perception correlated with neurite density of the left PT but not the right PT, as recently revealed by Ocklenburg and colleagues (Ocklenburg et al., 2018). Consistent with this, within-hemispheric PT functional-structural coupling of neurite density was also asymmetric in our study, highlighting the distinct role of PT neurite density in speech processing between the two hemispheres. The distinct roles of left and right PT in speech processing have been well-documented. A number of studies substantiated that PT of the left hemisphere responded more strongly to lexical-semantic and syntactic aspects of sentence processing, whereas the right hemisphere demonstrated a greater involvement in the speech melody (Albouy et al., 2020; Meyer et al., 2002). These findings are consistent with those reported for the arcuate fasciculus (AF). The left AF has been identified as a crucial structure for language function (Giampiccolo and Duffau, 2022; Zhang et al., 2021). Disruption to this pathway has been linked to multimodal phonological and semantic deficits (Agosta et al., 2010), while injuries in the right AF did not affect language function (Zeineh et al., 2015).

The functional-structural coupling of PT asymmetries may be simply driven by the asymmetry of within-hemispheric functional-structural coupling between the left or right PT per se, but this is not the case. The coupling of PT asymmetries could be accompanied by either asymmetric (e.g. speech perception vs. neurite density) or non asymmetric within-hemispheric functional-structural couplings (e.g. speech perception vs. myelin content or neurite orientation dispersion). On the other hand, asymmetric within-hemispheric structure-function association does not necessarily lead to a coupling of PT asymmetries (e.g. speech perception activation vs. surface area). Finally, PT functional and structural asymmetries could be associated, even though there was no within-hemispheric functional-structural coupling for both left and right PTs (e.g. speech comprehension activation vs. surface area). This implied that PT functional and structural asymmetries and their coupling might represent unique structural and functional information, independent of within-hemispheric PT measures. Overall, there was no simple causal relationship between these functional-structural couplings, and they likely capture distinct aspects of PT functional-structural associations and, therefore, should be investigated separately.

In line with previous observations (Tzourio-Mazoyer et al., 2019; Tzourio-Mazoyer and Mazoyer, 2017), HG duplication occurred considerably in healthy adults. Regardless of the HG gyrification pattern, both PT functional and structural AI showed an unimodal frequency distribution (Guadalupe et al., 2022; Packheiser et al., 2020). This contrasts with the well-documented bimodal frequency distribution of functional lateralization for speech production (Bruckert et al., 2021; Knecht et al., 2000), suggesting that the populational frequency distribution of language lateralization depends on a specific language processing module. Intriguingly, the occurrence of a left HG duplication was accompanied by a lack of the typically observed leftward asymmetry of PT activation, independent of the right HG duplication, which is consistent with previous studies (Tzourio-Mazoyer et al., 2015; Tzourio-Mazoyer et al., 2018). This highlights a nontrivial role of the left HG duplication in the functional reorganization of auditory language processing between bilateral PTs. In contrast, the HG duplication changes the PT structural asymmetries in a very complex manner, strongly depending on specific structural measures. Therefore, the impact of HG duplication on PT functional lateralization cannot be simply attributed to its impact on PT structural asymmetries. While the influence of the HG duplication on PT asymmetries has been well observed, they are largely overlooked in specific investigations, possible accounting for the result discrepancy between previous studies on PT asymmetries. Notably, no matter considering the HG duplication or not, how the asymmetries of the Heschl’s gyrus and planum temporale are related remain largely unknown, and specific investigation on this topic is needed in future studies. On the other hand, the functional-structural coupling of both PT asymmetries and within-hemispheric PT measures were not significantly affected by the HG gyrification pattern. Therefore, HG duplication-induced individual changes in PT measures or their asymmetry do not necessarily lead to changes in the interindividual association between these measures or asymmetries, suggesting the robustness of these PT functional-structural couplings.

In conclusion, the association between specific PT functional and microstructural asymmetries provides direct empirical support for the contribution of structural asymmetry to functional lateralization of the same cortical area. Moreover, the findings highlight the critical role of microstructural PT asymmetries in auditory-language processing.

The list of HCP-subject-ID with a morphological group (L1/R1, L1/R2, etc.) is available in the Supplementary file 2. The datasets analysed during the current study are available at https://www.humanconnectome.org/study/hcp-young-adult. Other data generated during this study are included in the manuscript and supplementary files.

1. 1. Ugurbil K
   2. Essen D

   (2017) Human Connectome Project

   ID hcp-young-adult. HCP Young Adult.

   https://www.humanconnectome.org/study/hcp-young-adult

1. 1. Abdul-Kareem IA
   2. Sluming V

   (2008) Heschl gyrus and its included primary auditory cortex: structural MRI studies in healthy and diseased subjects

   Journal of Magnetic Resonance Imaging 28:287–299.

   https://doi.org/10.1002/jmri.21445

   • PubMed
   • Google Scholar
2. 1. Agosta F
   2. Henry RG
   3. Migliaccio R
   4. Neuhaus J
   5. Miller BL
   6. Dronkers NF
   7. Brambati SM
   8. Filippi M
   9. Ogar JM
   10. Wilson SM
   11. Gorno-Tempini ML

   (2010) Language networks in semantic dementia

   Brain 133:286–299.

   https://doi.org/10.1093/brain/awp233

   • PubMed
   • Google Scholar
3. 1. Albouy P
   2. Benjamin L
   3. Morillon B
   4. Zatorre RJ

   (2020) Distinct sensitivity to spectrotemporal modulation supports brain asymmetry for speech and melody

   Science 367:1043–1047.

   https://doi.org/10.1126/science.aaz3468

   • PubMed
   • Google Scholar
4. 1. Allendorfer JB
   2. Hernando KA
   3. Hossain S
   4. Nenert R
   5. Holland SK
   6. Szaflarski JP

   (2016) Arcuate fasciculus asymmetry has a hand in language function but not handedness

   Human Brain Mapping 37:3297–3309.

   https://doi.org/10.1002/hbm.23241

   • PubMed
   • Google Scholar
5. 1. Altarelli I
   2. Leroy F
   3. Monzalvo K
   4. Fluss J
   5. Billard C
   6. Dehaene-Lambertz G
   7. Galaburda AM
   8. Ramus F

   (2014) Planum temporale asymmetry in developmental dyslexia: Revisiting an old question

   Human Brain Mapping 35:5717–5735.

   https://doi.org/10.1002/hbm.22579

   • PubMed
   • Google Scholar
6. 1. Anderson B
   2. Southern BD
   3. Powers RE

   (1999)

   Anatomic asymmetries of the posterior superior temporal lobes: a postmortem study

   Neuropsychiatry, Neuropsychology, and Behavioral Neurology 12:247–254.

   • PubMed
   • Google Scholar
7. 1. Barch DM
   2. Burgess GC
   3. Harms MP
   4. Petersen SE
   5. Schlaggar BL
   6. Corbetta M
   7. Glasser MF
   8. Curtiss S
   9. Dixit S
   10. Feldt C
   11. Nolan D
   12. Bryant E
   13. Hartley T
   14. Footer O
   15. Bjork JM
   16. Poldrack R
   17. Smith S
   18. Johansen-Berg H
   19. Snyder AZ
   20. Van Essen DC
   21. WU-Minn HCP Consortium

   (2013) Function in the human connectome: task-fMRI and individual differences in behavior

   NeuroImage 80:169–189.

   https://doi.org/10.1016/j.neuroimage.2013.05.033

   • PubMed
   • Google Scholar
8. 1. Becker Y
   2. Sein J
   3. Velly L
   4. Giacomino L
   5. Renaud L
   6. Lacoste R
   7. Anton JL
   8. Nazarian B
   9. Berne C
   10. Meguerditchian A

   (2021) Early left-planum temporale asymmetry in newborn monkeys (Papio anubis): a longitudinal structural MRI study at two stages of development

   NeuroImage 227:117575.

   https://doi.org/10.1016/j.neuroimage.2020.117575

   • PubMed
   • Google Scholar
9. 1. Becker Y
   2. Phelipon R
   3. Marie D
   4. Bouziane S
   5. Marchetti R
   6. Sein J
   7. Velly L
   8. Renaud L
   9. Cermolacce A
   10. Anton JL
   11. Nazarian B
   12. Coulon O
   13. Meguerditchian A

   (2024) Planum temporale asymmetry in newborn monkeys predicts the future development of gestural communication’s handedness

   Nature Communications 15:4791.

   https://doi.org/10.1038/s41467-024-47277-6

   • PubMed
   • Google Scholar
10. 1. Binder JR
    2. Gross WL
    3. Allendorfer JB
    4. Bonilha L
    5. Chapin J
    6. Edwards JC
    7. Grabowski TJ
    8. Langfitt JT
    9. Loring DW
    10. Lowe MJ
    11. Koenig K
    12. Morgan PS
    13. Ojemann JG
    14. Rorden C
    15. Szaflarski JP
    16. Tivarus ME
    17. Weaver KE

    (2011) Mapping anterior temporal lobe language areas with fMRI: a multicenter normative study

    NeuroImage 54:1465–1475.

    https://doi.org/10.1016/j.neuroimage.2010.09.048

    • PubMed
    • Google Scholar
11. 1. Blau V
    2. Reithler J
    3. van Atteveldt N
    4. Seitz J
    5. Gerretsen P
    6. Goebel R
    7. Blomert L

    (2010) Deviant processing of letters and speech sounds as proximate cause of reading failure: a functional magnetic resonance imaging study of dyslexic children

    Brain 133:868–879.

    https://doi.org/10.1093/brain/awp308

    • PubMed
    • Google Scholar
12. 1. Bloom JS
    2. Garcia-Barrera MA
    3. Miller CJ
    4. Miller SR
    5. Hynd GW

    (2013) Planum temporale morphology in children with developmental dyslexia

    Neuropsychologia 51:1684–1692.

    https://doi.org/10.1016/j.neuropsychologia.2013.05.012

    • PubMed
    • Google Scholar
13. 1. Bruckert L
    2. Thompson PA
    3. Watkins KE
    4. Bishop DVM
    5. Woodhead ZVJ

    (2021) Investigating the effects of handedness on the consistency of lateralization for speech production and semantic processing tasks using functional transcranial Doppler sonography

    Laterality 26:680–705.

    https://doi.org/10.1080/1357650X.2021.1898416

    • PubMed
    • Google Scholar
14. 1. Campain R
    2. Minckler J

    (1976) A note on the gross configurations of the human auditory cortex

    Brain and Language 3:318–323.

    https://doi.org/10.1016/0093-934x(76)90026-2

    • PubMed
    • Google Scholar
15. 1. Catani M
    2. Jones DK
    3. ffytche DH

    (2005) Perisylvian language networks of the human brain

    Annals of Neurology 57:8–16.

    https://doi.org/10.1002/ana.20319

    • PubMed
    • Google Scholar
16. 1. Catani M
    2. Allin MPG
    3. Husain M
    4. Pugliese L
    5. Mesulam MM
    6. Murray RM
    7. Jones DK

    (2007) Symmetries in human brain language pathways correlate with verbal recall

    PNAS 104:17163–17168.

    https://doi.org/10.1073/pnas.0702116104

    • PubMed
    • Google Scholar
17. 1. Chai Y
    2. Liu TT
    3. Marrett S
    4. Li L
    5. Khojandi A
    6. Handwerker DA
    7. Alink A
    8. Muckli L
    9. Bandettini PA

    (2021) Topographical and laminar distribution of audiovisual processing within human planum temporale

    Progress in Neurobiology 205:102121.

    https://doi.org/10.1016/j.pneurobio.2021.102121

    • PubMed
    • Google Scholar
18. 1. Chance SA
    2. Casanova MF
    3. Switala AE
    4. Crow TJ

    (2008) Auditory cortex asymmetry, altered minicolumn spacing and absence of ageing effects in schizophrenia

    Brain 131:3178–3192.

    https://doi.org/10.1093/brain/awn211

    • PubMed
    • Google Scholar
19. 1. Chen Y
    2. Li P
    3. Gu B
    4. Liu Z
    5. Li X
    6. Evans AC
    7. Gong G
    8. Zhang Z

    (2015) The effects of an APOE promoter polymorphism on human cortical morphology during nondemented aging

    The Journal of Neuroscience 35:1423–1431.

    https://doi.org/10.1523/JNEUROSCI.1946-14.2015

    • PubMed
    • Google Scholar
20. 1. Daducci A
    2. Canales-Rodríguez EJ
    3. Zhang H
    4. Dyrby TB
    5. Alexander DC
    6. Thiran JP

    (2015) Accelerated microstructure imaging via convex optimization (AMICO) from diffusion MRI data

    NeuroImage 105:32–44.

    https://doi.org/10.1016/j.neuroimage.2014.10.026

    • PubMed
    • Google Scholar
21. 1. Dehaene-Lambertz G
    2. Dehaene S
    3. Hertz-Pannier L

    (2002) Functional neuroimaging of speech perception in infants

    Science 298:2013–2015.

    https://doi.org/10.1126/science.1077066

    • PubMed
    • Google Scholar
22. 1. Dehaene-Lambertz G
    2. Pallier C
    3. Serniclaes W
    4. Sprenger-Charolles L
    5. Jobert A
    6. Dehaene S

    (2005) Neural correlates of switching from auditory to speech perception

    NeuroImage 24:21–33.

    https://doi.org/10.1016/j.neuroimage.2004.09.039

    • PubMed
    • Google Scholar
23. 1. Dorsaint-Pierre R
    2. Penhune VB
    3. Watkins KE
    4. Neelin P
    5. Lerch JP
    6. Bouffard M
    7. Zatorre RJ

    (2006) Asymmetries of the planum temporale and Heschl’s gyrus: relationship to language lateralization

    Brain 129:1164–1176.

    https://doi.org/10.1093/brain/awl055

    • PubMed
    • Google Scholar
24. 1. Dos Santos Sequeira S
    2. Woerner W
    3. Walter C
    4. Kreuder F
    5. Lueken U
    6. Westerhausen R
    7. Wittling RA
    8. Schweiger E
    9. Wittling W

    (2006) Handedness, dichotic-listening ear advantage, and gender effects on planum temporale asymmetry--a volumetric investigation using structural magnetic resonance imaging

    Neuropsychologia 44:622–636.

    https://doi.org/10.1016/j.neuropsychologia.2005.06.014

    • PubMed
    • Google Scholar
25. 1. Dubois J
    2. Benders M
    3. Lazeyras F
    4. Borradori-Tolsa C
    5. Leuchter RHV
    6. Mangin JF
    7. Hüppi PS

    (2010) Structural asymmetries of perisylvian regions in the preterm newborn

    NeuroImage 52:32–42.

    https://doi.org/10.1016/j.neuroimage.2010.03.054

    • PubMed
    • Google Scholar
26. 1. Eckert MA
    2. Leonard CM

    (2000) Structural imaging in dyslexia: the planum temporale

    Mental Retardation and Developmental Disabilities Research Reviews 6:198–206.

    https://doi.org/10.1002/1098-2779(2000)6:3<198::AID-MRDD7>3.0.CO;2-1

    • PubMed
    • Google Scholar
27. 1. Eckert MA
    2. Leonard CM
    3. Possing ET
    4. Binder JR

    (2006) Uncoupled leftward asymmetries for planum morphology and functional language processing

    Brain and Language 98:102–111.

    https://doi.org/10.1016/j.bandl.2006.04.002

    • PubMed
    • Google Scholar
28. 1. Engqvist L

    (2005) The mistreatment of covariate interaction terms in linear model analyses of behavioural and evolutionary ecology studies

    Animal Behaviour 70:967–971.

    https://doi.org/10.1016/j.anbehav.2005.01.016

    • Google Scholar
29. 1. Floris DL
    2. Lai MC
    3. Auer T
    4. Lombardo MV
    5. Ecker C
    6. Chakrabarti B
    7. Wheelwright SJ
    8. Bullmore ET
    9. Murphy DGM
    10. Baron-Cohen S
    11. Suckling J

    (2016) Atypically rightward cerebral asymmetry in male adults with autism stratifies individuals with and without language delay

    Human Brain Mapping 37:230–253.

    https://doi.org/10.1002/hbm.23023

    • PubMed
    • Google Scholar
30. 1. Forseth KJ
    2. Hickok G
    3. Rollo PS
    4. Tandon N

    (2020) Language prediction mechanisms in human auditory cortex

    Nature Communications 11:5240.

    https://doi.org/10.1038/s41467-020-19010-6

    • PubMed
    • Google Scholar
31. 1. Fukutomi H
    2. Glasser MF
    3. Zhang H
    4. Autio JA
    5. Coalson TS
    6. Okada T
    7. Togashi K
    8. Van Essen DC
    9. Hayashi T

    (2018) Neurite imaging reveals microstructural variations in human cerebral cortical gray matter

    NeuroImage 182:488–499.

    https://doi.org/10.1016/j.neuroimage.2018.02.017

    • PubMed
    • Google Scholar
32. 1. Galaburda AM
    2. LeMay M
    3. Kemper TL
    4. Geschwind N

    (1978) Right-left asymmetrics in the brain

    Science 199:852–856.

    https://doi.org/10.1126/science.341314

    • PubMed
    • Google Scholar
33. 1. Galaburda AM
    2. Rosen GD
    3. Sherman GF

    (1990) Individual variability in cortical organization: its relationship to brain laterality and implications to function

    Neuropsychologia 28:529–546.

    https://doi.org/10.1016/0028-3932(90)90032-j

    • PubMed
    • Google Scholar
34. 1. Galuske RA
    2. Schlote W
    3. Bratzke H
    4. Singer W

    (2000) Interhemispheric asymmetries of the modular structure in human temporal cortex

    Science 289:1946–1949.

    https://doi.org/10.1126/science.289.5486.1946

    • PubMed
    • Google Scholar
35. 1. Gannon PJ
    2. Holloway RL
    3. Broadfield DC
    4. Braun AR

    (1998) Asymmetry of chimpanzee planum temporale: humanlike pattern of Wernicke’s brain language area homolog

    Science 279:220–222.

    https://doi.org/10.1126/science.279.5348.220

    • PubMed
    • Google Scholar
36. Book

    1. Geffroy D
    2. Rivière D
    3. Denghien I
    4. Souedet N
    5. Laguitton S
    6. Cointepas Y

    (2011)

    BrainVISA: A Complete Software Platform for Neuroimaging

    SicenceDirect.

    • Google Scholar
37. 1. Gernsbacher MA
    2. Kaschak MP

    (2003) Neuroimaging studies of language production and comprehension

    Annual Review of Psychology 54:91–114.

    https://doi.org/10.1146/annurev.psych.54.101601.145128

    • PubMed
    • Google Scholar
38. 1. Giampiccolo D
    2. Duffau H

    (2022) Controversy over the temporal cortical terminations of the left arcuate fasciculus: a reappraisal

    Brain 145:1242–1256.

    https://doi.org/10.1093/brain/awac057

    • PubMed
    • Google Scholar
39. 1. Glasser MF
    2. Van Essen DC

    (2011) Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI

    The Journal of Neuroscience 31:11597–11616.

    https://doi.org/10.1523/JNEUROSCI.2180-11.2011

    • PubMed
    • Google Scholar
40. 1. Glasser MF
    2. Sotiropoulos SN
    3. Wilson JA
    4. Coalson TS
    5. Fischl B
    6. Andersson JL
    7. Xu J
    8. Jbabdi S
    9. Webster M
    10. Polimeni JR
    11. Van Essen DC
    12. Jenkinson M
    13. WU-Minn HCP Consortium

    (2013) The minimal preprocessing pipelines for the human connectome project

    NeuroImage 80:105–124.

    https://doi.org/10.1016/j.neuroimage.2013.04.127

    • PubMed
    • Google Scholar
41. 1. Glasser MF
    2. Goyal MS
    3. Preuss TM
    4. Raichle ME
    5. Van Essen DC

    (2014) Trends and properties of human cerebral cortex: correlations with cortical myelin content

    NeuroImage 93 Pt 2:165–175.

    https://doi.org/10.1016/j.neuroimage.2013.03.060

    • PubMed
    • Google Scholar
42. 1. Glasser MF
    2. Coalson TS
    3. Robinson EC
    4. Hacker CD
    5. Harwell J
    6. Yacoub E
    7. Ugurbil K
    8. Andersson J
    9. Beckmann CF
    10. Jenkinson M
    11. Smith SM
    12. Van Essen DC

    (2016) A multi-modal parcellation of human cerebral cortex

    Nature 536:171–178.

    https://doi.org/10.1038/nature18933

    • PubMed
    • Google Scholar
43. 1. Greve DN
    2. Van der Haegen L
    3. Cai Q
    4. Stufflebeam S
    5. Sabuncu MR
    6. Fischl B
    7. Brysbaert M

    (2013) A surface-based analysis of language lateralization and cortical asymmetry

    Journal of Cognitive Neuroscience 25:1477–1492.

    https://doi.org/10.1162/jocn_a_00405

    • PubMed
    • Google Scholar
44. 1. Grydeland H
    2. Westlye LT
    3. Walhovd KB
    4. Fjell AM

    (2016) Intracortical posterior cingulate myelin content relates to error processing: results from T1- and T2-weighted mri myelin mapping and electrophysiology in healthy adults

    Cerebral Cortex 26:2402–2410.

    https://doi.org/10.1093/cercor/bhv065

    • PubMed
    • Google Scholar
45. 1. Guadalupe T
    2. Kong XZ
    3. Akkermans SEA
    4. Fisher SE
    5. Francks C

    (2022) Relations between hemispheric asymmetries of grey matter and auditory processing of spoken syllables in 281 healthy adults

    Brain Structure & Function 227:561–572.

    https://doi.org/10.1007/s00429-021-02220-z

    • PubMed
    • Google Scholar
46. 1. Hall DA
    2. Hart HC
    3. Johnsrude IS

    (2003) Relationships between human auditory cortical structure and function

    Audiology & Neuro-Otology 8:1–18.

    https://doi.org/10.1159/000067894

    • PubMed
    • Google Scholar
47. 1. Helbling S
    2. Teki S
    3. Callaghan MF
    4. Sedley W
    5. Mohammadi S
    6. Griffiths TD
    7. Weiskopf N
    8. Barnes GR

    (2015) Structure predicts function: combining non-invasive electrophysiology with in-vivo histology

    NeuroImage 108:377–385.

    https://doi.org/10.1016/j.neuroimage.2014.12.030

    • PubMed
    • Google Scholar
48. 1. Hickok G
    2. Poeppel D

    (2000) Towards a functional neuroanatomy of speech perception

    Trends in Cognitive Sciences 4:131–138.

    https://doi.org/10.1016/S1364-6613(00)01463-7

    • Google Scholar
49. 1. Hickok G
    2. Poeppel D

    (2007) The cortical organization of speech processing

    Nature Reviews. Neuroscience 8:393–402.

    https://doi.org/10.1038/nrn2113

    • PubMed
    • Google Scholar
50. 1. Hugdahl K
    2. Heiervang E
    3. Ersland L
    4. Lundervold A
    5. Steinmetz H
    6. Smievoll AI

    (2003) Significant relation between MR measures of planum temporale area and dichotic processing of syllables in dyslexic children

    Neuropsychologia 41:666–675.

    https://doi.org/10.1016/s0028-3932(02)00224-5

    • PubMed
    • Google Scholar
51. 1. Hunt BAE
    2. Tewarie PK
    3. Mougin OE
    4. Geades N
    5. Jones DK
    6. Singh KD
    7. Morris PG
    8. Gowland PA
    9. Brookes MJ

    (2016) Relationships between cortical myeloarchitecture and electrophysiological networks

    PNAS 113:13510–13515.

    https://doi.org/10.1073/pnas.1608587113

    • PubMed
    • Google Scholar
52. 1. Hunter MD
    2. Griffiths TD
    3. Farrow TFD
    4. Zheng Y
    5. Wilkinson ID
    6. Hegde N
    7. Woods W
    8. Spence SA
    9. Woodruff PWR

    (2003) A neural basis for the perception of voices in external auditory space

    Brain 126:161–169.

    https://doi.org/10.1093/brain/awg015

    • PubMed
    • Google Scholar
53. 1. Hynd GW
    2. Semrud-Clikeman M

    (1989) Dyslexia and brain morphology

    Psychological Bulletin 106:447–482.

    https://doi.org/10.1037/0033-2909.106.3.447

    • PubMed
    • Google Scholar
54. 1. Jespersen SN
    2. Leigland LA
    3. Cornea A
    4. Kroenke CD

    (2012) Determination of axonal and dendritic orientation distributions within the developing cerebral cortex by diffusion tensor imaging

    IEEE Transactions on Medical Imaging 31:16–32.

    https://doi.org/10.1109/TMI.2011.2162099

    • PubMed
    • Google Scholar
55. 1. Josse G
    2. Mazoyer B
    3. Crivello F
    4. Tzourio-Mazoyer N

    (2003) Left planum temporale: an anatomical marker of left hemispheric specialization for language comprehension

    Brain Research. Cognitive Brain Research 18:1–14.

    https://doi.org/10.1016/j.cogbrainres.2003.08.007

    • PubMed
    • Google Scholar
56. 1. Keller M
    2. Neuschwander P
    3. Meyer M

    (2019) When right becomes less right: Neural dedifferentiation during suprasegmental speech processing in the aging brain

    NeuroImage 189:886–895.

    https://doi.org/10.1016/j.neuroimage.2019.01.050

    • PubMed
    • Google Scholar
57. 1. Kim SG
    2. Knösche TR

    (2016) Intracortical myelination in musicians with absolute pitch: quantitative morphometry using 7-T MRI

    Human Brain Mapping 37:3486–3501.

    https://doi.org/10.1002/hbm.23254

    • PubMed
    • Google Scholar
58. 1. Knaus TA
    2. Bollich AM
    3. Corey DM
    4. Lemen LC
    5. Foundas AL

    (2006) Variability in perisylvian brain anatomy in healthy adults

    Brain and Language 97:219–232.

    https://doi.org/10.1016/j.bandl.2005.10.008

    • PubMed
    • Google Scholar
59. 1. Knaus TA
    2. Kamps J
    3. Foundas AL
    4. Tager-Flusberg H

    (2018) Atypical PT anatomy in children with autism spectrum disorder with expressive language deficits

    Brain Imaging and Behavior 12:1419–1430.

    https://doi.org/10.1007/s11682-017-9795-7

    • PubMed
    • Google Scholar
60. 1. Knecht S
    2. Deppe M
    3. Dräger B
    4. Bobe L
    5. Lohmann H
    6. Ringelstein E
    7. Henningsen H

    (2000) Language lateralization in healthy right-handers

    Brain 123 ( Pt 1):74–81.

    https://doi.org/10.1093/brain/123.1.74

    • PubMed
    • Google Scholar
61. 1. Kuiper JJ
    2. Lin YH
    3. Young IM
    4. Bai MY
    5. Briggs RG
    6. Tanglay O
    7. Fonseka RD
    8. Hormovas J
    9. Dhanaraj V
    10. Conner AK
    11. O’Neal CM
    12. Sughrue ME

    (2020) A parcellation-based model of the auditory network

    Hearing Research 396:108078.

    https://doi.org/10.1016/j.heares.2020.108078

    • PubMed
    • Google Scholar
62. 1. Leonard CM
    2. Puranik C
    3. Kuldau JM
    4. Lombardino LJ

    (1998) Normal variation in the frequency and location of human auditory cortex landmarks: heschl’s gyrus: where is it?

    Cerebral Cortex 8:397–406.

    https://doi.org/10.1093/cercor/8.5.397

    • PubMed
    • Google Scholar
63. 1. Liem F
    2. Hurschler MA
    3. Jäncke L
    4. Meyer M

    (2014) On the planum temporale lateralization in suprasegmental speech perception: evidence from a study investigating behavior, structure, and function

    Human Brain Mapping 35:1779–1789.

    https://doi.org/10.1002/hbm.22291

    • PubMed
    • Google Scholar
64. 1. Ma Z
    2. Zhang N

    (2017) Cross-population myelination covariance of human cerebral cortex

    Human Brain Mapping 38:4730–4743.

    https://doi.org/10.1002/hbm.23698

    • PubMed
    • Google Scholar
65. 1. Marcus DS
    2. Harwell J
    3. Olsen T
    4. Hodge M
    5. Glasser MF
    6. Prior F
    7. Jenkinson M
    8. Laumann T
    9. Curtiss SW
    10. Van Essen DC

    (2011) Informatics and data mining tools and strategies for the human connectome project

    Frontiers in Neuroinformatics 5:4.

    https://doi.org/10.3389/fninf.2011.00004

    • PubMed
    • Google Scholar
66. 1. Marek S
    2. Tervo-Clemmens B
    3. Calabro FJ
    4. Montez DF
    5. Kay BP
    6. Hatoum AS
    7. Donohue MR
    8. Foran W
    9. Miller RL
    10. Hendrickson TJ
    11. Malone SM
    12. Kandala S
    13. Feczko E
    14. Miranda-Dominguez O
    15. Graham AM
    16. Earl EA
    17. Perrone AJ
    18. Cordova M
    19. Doyle O
    20. Moore LA
    21. Conan GM
    22. Uriarte J
    23. Snider K
    24. Lynch BJ
    25. Wilgenbusch JC
    26. Pengo T
    27. Tam A
    28. Chen J
    29. Newbold DJ
    30. Zheng A
    31. Seider NA
    32. Van AN
    33. Metoki A
    34. Chauvin RJ
    35. Laumann TO
    36. Greene DJ
    37. Petersen SE
    38. Garavan H
    39. Thompson WK
    40. Nichols TE
    41. Yeo BTT
    42. Barch DM
    43. Luna B
    44. Fair DA
    45. Dosenbach NUF

    (2022) Reproducible brain-wide association studies require thousands of individuals

    Nature 603:654–660.

    https://doi.org/10.1038/s41586-022-04492-9

    • PubMed
    • Google Scholar
67. 1. Marie D
    2. Jobard G
    3. Crivello F
    4. Perchey G
    5. Petit L
    6. Mellet E
    7. Joliot M
    8. Zago L
    9. Mazoyer B
    10. Tzourio-Mazoyer N

    (2015) Descriptive anatomy of Heschl’s gyri in 430 healthy volunteers, including 198 left-handers

    Brain Structure & Function 220:729–743.

    https://doi.org/10.1007/s00429-013-0680-x

    • PubMed
    • Google Scholar
68. 1. Meyer M
    2. Alter K
    3. Friederici AD
    4. Lohmann G
    5. von Cramon DY

    (2002) FMRI reveals brain regions mediating slow prosodic modulations in spoken sentences

    Human Brain Mapping 17:73–88.

    https://doi.org/10.1002/hbm.10042

    • PubMed
    • Google Scholar
69. 1. Moffat SD
    2. Hampson E
    3. Lee DH

    (1998) Morphology of the planum temporale and corpus callosum in left handers with evidence of left and right hemisphere speech representation

    Brain 121:2369–2379.

    https://doi.org/10.1093/brain/121.12.2369

    • PubMed
    • Google Scholar
70. 1. Ocklenburg S
    2. Hugdahl K
    3. Westerhausen R

    (2013) Structural white matter asymmetries in relation to functional asymmetries during speech perception and production

    NeuroImage 83:1088–1097.

    https://doi.org/10.1016/j.neuroimage.2013.07.076

    • PubMed
    • Google Scholar
71. 1. Ocklenburg S
    2. Friedrich P
    3. Fraenz C
    4. Schlüter C
    5. Beste C
    6. Güntürkün O
    7. Genç E

    (2018) Neurite architecture of the planum temporale predicts neurophysiological processing of auditory speech

    Science Advances 4:eaar6830.

    https://doi.org/10.1126/sciadv.aar6830

    • PubMed
    • Google Scholar
72. Book

    1. Ocklenburg S
    2. Güntürkün O

    (2018) The Lateralized Brain: The Neuroscience and Evolution of Hemispheric Asymmetries

    Elsevier/Academic Press.

    https://doi.org/10.1016/B978-0-12-803452-1.00009-6

    • Google Scholar
73. 1. Packheiser J
    2. Schmitz J
    3. Arning L
    4. Beste C
    5. Güntürkün O
    6. Ocklenburg S

    (2020) A large-scale estimate on the relationship between language and motor lateralization

    Scientific Reports 10:13027.

    https://doi.org/10.1038/s41598-020-70057-3

    • PubMed
    • Google Scholar
74. 1. Pahs G
    2. Rankin P
    3. Helen Cross J
    4. Croft L
    5. Northam GB
    6. Liegeois F
    7. Greenway S
    8. Harrison S
    9. Vargha-Khadem F
    10. Baldeweg T

    (2013) Asymmetry of planum temporale constrains interhemispheric language plasticity in children with focal epilepsy

    Brain 136:3163–3175.

    https://doi.org/10.1093/brain/awt225

    • PubMed
    • Google Scholar
75. 1. Price CJ

    (2010) The anatomy of language: a review of 100 fMRI studies published in 2009

    Annals of the New York Academy of Sciences 1191:62–88.

    https://doi.org/10.1111/j.1749-6632.2010.05444.x

    • PubMed
    • Google Scholar
76. Book

    1. Rivière D
    2. Geffroy D
    3. Denghien I
    4. Souedet N
    5. Cointepas Y

    (2011)

    Anatomist: A Python Framework for Interactive 3D Visualization of Neuroimaging Data

    BrainVISA.

    • Google Scholar
77. 1. Schmitz J
    2. Fraenz C
    3. Schlüter C
    4. Friedrich P
    5. Jung RE
    6. Güntürkün O
    7. Genç E
    8. Ocklenburg S

    (2019) Hemispheric asymmetries in cortical gray matter microstructure identified by neurite orientation dispersion and density imaging

    NeuroImage 189:667–675.

    https://doi.org/10.1016/j.neuroimage.2019.01.079

    • PubMed
    • Google Scholar
78. 1. Seghier ML
    2. Kherif F
    3. Josse G
    4. Price CJ

    (2011) Regional and hemispheric determinants of language laterality: implications for preoperative fMRI

    Human Brain Mapping 32:1602–1614.

    https://doi.org/10.1002/hbm.21130

    • PubMed
    • Google Scholar
79. 1. Shapleske J
    2. Rossell SL
    3. Woodruff PW
    4. David AS

    (1999) The planum temporale: a systematic, quantitative review of its structural, functional and clinical significance

    Brain Research. Brain Research Reviews 29:26–49.

    https://doi.org/10.1016/s0165-0173(98)00047-2

    • PubMed
    • Google Scholar
80. 1. Sigalovsky IS
    2. Fischl B
    3. Melcher JR

    (2006) Mapping an intrinsic MR property of gray matter in auditory cortex of living humans: a possible marker for primary cortex and hemispheric differences

    NeuroImage 32:1524–1537.

    https://doi.org/10.1016/j.neuroimage.2006.05.023

    • PubMed
    • Google Scholar
81. 1. Steinmetz H

    (1996) Structure, functional and cerebral asymmetry: in vivo morphometry of the planum temporale

    Neuroscience and Biobehavioral Reviews 20:587–591.

    https://doi.org/10.1016/0149-7634(95)00071-2

    • PubMed
    • Google Scholar
82. 1. Sumich A
    2. Chitnis XA
    3. Fannon DG
    4. O’Ceallaigh S
    5. Doku VC
    6. Falrowicz A
    7. Marshall N
    8. Matthew VM
    9. Potter M
    10. Sharma T

    (2002) Temporal lobe abnormalities in first-episode psychosis

    The American Journal of Psychiatry 159:1232–1235.

    https://doi.org/10.1176/appi.ajp.159.7.1232

    • PubMed
    • Google Scholar
83. 1. Sumich A
    2. Chitnis XA
    3. Fannon DG
    4. O’Ceallaigh S
    5. Doku VC
    6. Faldrowicz A
    7. Sharma T

    (2005) Unreality symptoms and volumetric measures of Heschl’s gyrus and planum temporal in first-episode psychosis

    Biological Psychiatry 57:947–950.

    https://doi.org/10.1016/j.biopsych.2004.12.041

    • PubMed
    • Google Scholar
84. 1. Tzourio-Mazoyer N
    2. Marie D
    3. Zago L
    4. Jobard G
    5. Perchey G
    6. Leroux G
    7. Mellet E
    8. Joliot M
    9. Crivello F
    10. Petit L
    11. Mazoyer B

    (2015) Heschl’s gyrification pattern is related to speech-listening hemispheric lateralization: FMRI investigation in 281 healthy volunteers

    Brain Struct Funct 220:1585–1599.

    https://doi.org/10.1007/s00429-014-0746-4

    • Google Scholar
85. 1. Tzourio-Mazoyer N
    2. Mazoyer B

    (2017) Variations of planum temporale asymmetries with Heschl’s Gyri duplications and association with cognitive abilities: MRI investigation of 428 healthy volunteers

    Brain Structure & Function 222:2711–2726.

    https://doi.org/10.1007/s00429-017-1367-5

    • PubMed
    • Google Scholar
86. 1. Tzourio-Mazoyer N
    2. Crivello F
    3. Mazoyer B

    (2018) Is the planum temporale surface area a marker of hemispheric or regional language lateralization?

    Brain Structure & Function 223:1217–1228.

    https://doi.org/10.1007/s00429-017-1551-7

    • PubMed
    • Google Scholar
87. 1. Tzourio-Mazoyer N
    2. Maingault S
    3. Panzieri J
    4. Pepe A
    5. Crivello F
    6. Mazoyer B

    (2019) Intracortical myelination of heschl’s gyrus and the planum temporale varies with heschl’s duplication pattern and rhyming performance: an investigation of 440 healthy volunteers

    Cerebral Cortex 29:2072–2083.

    https://doi.org/10.1093/cercor/bhy088

    • PubMed
    • Google Scholar
88. 1. Vanderauwera J
    2. Altarelli I
    3. Vandermosten M
    4. De Vos A
    5. Wouters J
    6. Ghesquière P

    (2018) Atypical structural asymmetry of the planum temporale is related to family history of dyslexia

    Cerebral Cortex 28:63–72.

    https://doi.org/10.1093/cercor/bhw348

    • PubMed
    • Google Scholar
89. 1. Van Essen DC
    2. Ugurbil K
    3. Auerbach E
    4. Barch D
    5. Behrens TEJ
    6. Bucholz R
    7. Chang A
    8. Chen L
    9. Corbetta M
    10. Curtiss SW
    11. Della Penna S
    12. Feinberg D
    13. Glasser MF
    14. Harel N
    15. Heath AC
    16. Larson-Prior L
    17. Marcus D
    18. Michalareas G
    19. Moeller S
    20. Oostenveld R
    21. Petersen SE
    22. Prior F
    23. Schlaggar BL
    24. Smith SM
    25. Snyder AZ
    26. Xu J
    27. Yacoub E
    28. WU-Minn HCP Consortium

    (2012) The human connectome project: a data acquisition perspective

    NeuroImage 62:2222–2231.

    https://doi.org/10.1016/j.neuroimage.2012.02.018

    • PubMed
    • Google Scholar
90. 1. Van Essen DC
    2. Smith SM
    3. Barch DM
    4. Behrens TEJ
    5. Yacoub E
    6. Ugurbil K
    7. WU-Minn HCP Consortium

    (2013) The WU-minn human connectome project: an overview

    NeuroImage 80:62–79.

    https://doi.org/10.1016/j.neuroimage.2013.05.041

    • PubMed
    • Google Scholar
91. 1. Wada JA

    (2009)

    Is functional hemispheric lateralization guided by structural cerebral asymmetry?

    The Canadian Journal of Neurological Sciences. Le Journal Canadien Des Sciences Neurologiques 36:S25–S31.

    • PubMed
    • Google Scholar
92. 1. Wilke M
    2. Schmithorst VJ

    (2006) A combined bootstrap/histogram analysis approach for computing A lateralization index from neuroimaging data

    NeuroImage 33:522–530.

    https://doi.org/10.1016/j.neuroimage.2006.07.010

    • PubMed
    • Google Scholar
93. 1. Wilke M
    2. Lidzba K

    (2007) LI-tool: a new toolbox to assess lateralization in functional MR-data

    Journal of Neuroscience Methods 163:128–136.

    https://doi.org/10.1016/j.jneumeth.2007.01.026

    • PubMed
    • Google Scholar
94. 1. Williams CM
    2. Peyre H
    3. Toro R
    4. Ramus F

    (2022) Comparing brain asymmetries independently of brain size

    NeuroImage 254:119118.

    https://doi.org/10.1016/j.neuroimage.2022.119118

    • PubMed
    • Google Scholar
95. 1. Witelson SF
    2. Pallie W

    (1973) Left hemisphere specialization for language in the newborn: neuroanatomical evidence of asymmetry

    Brain 96:641–646.

    https://doi.org/10.1093/brain/96.3.641

    • PubMed
    • Google Scholar
96. 1. Xia J
    2. Wang F
    3. Wu Z
    4. Wang L
    5. Zhang C
    6. Shen D
    7. Li G

    (2020) Mapping hemispheric asymmetries of the macaque cerebral cortex during early brain development

    Human Brain Mapping 41:95–106.

    https://doi.org/10.1002/hbm.24789

    • PubMed
    • Google Scholar
97. 1. Yuan D
    2. Luo D
    3. Kwok VPY
    4. Zhou Y
    5. Tian H
    6. Yu Q
    7. An J
    8. Gao JH
    9. Qiu S
    10. Tan LH

    (2021) Myeloarchitectonic asymmetries of language regions in the human brain

    Cerebral Cortex 31:4169–4179.

    https://doi.org/10.1093/cercor/bhab076

    • PubMed
    • Google Scholar
98. 1. Zatorre RJ
    2. Belin P
    3. Penhune VB

    (2002) Structure and function of auditory cortex: music and speech

    Trends in Cognitive Sciences 6:37–46.

    https://doi.org/10.1016/S1364-6613(00)01816-7

    • Google Scholar
99. 1. Zeineh MM
    2. Kang J
    3. Atlas SW
    4. Raman MM
    5. Reiss AL
    6. Norris JL
    7. Valencia I
    8. Montoya JG

    (2015) Right arcuate fasciculus abnormality in chronic fatigue syndrome

    Radiology 274:517–526.

    https://doi.org/10.1148/radiol.14141079

    • PubMed
    • Google Scholar
100. 1. Zhang H
     2. Schneider T
     3. Wheeler-Kingshott CA
     4. Alexander DC

     (2012) NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain

     NeuroImage 61:1000–1016.

     https://doi.org/10.1016/j.neuroimage.2012.03.072

     • PubMed
     • Google Scholar
101. 1. Zhang J
     2. Zhong S
     3. Zhou L
     4. Yu Y
     5. Tan X
     6. Wu M
     7. Sun P
     8. Zhang W
     9. Li J
     10. Cheng R
     11. Wu Y
     12. Yu Y
     13. Ye X
     14. Luo B

     (2021) Correlations between dual-pathway white matter alterations and language impairment in patients with aphasia: a systematic review and meta-analysis

     Neuropsychology Review 31:402–418.

     https://doi.org/10.1007/s11065-021-09482-8

     • PubMed
     • Google Scholar
102. 1. Zhao C
     2. Yang L
     3. Xie S
     4. Zhang Z
     5. Pan H
     6. Gong G

     (2019) Hemispheric module-specific influence of the x chromosome on white matter connectivity: evidence from girls with turner syndrome

     Cerebral Cortex 29:4580–4594.

     https://doi.org/10.1093/cercor/bhy335

     • PubMed
     • Google Scholar

Author details

1. Peipei Qin

   State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

   Contribution

   Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Qiuhui Bi

   Competing interests

   No competing interests declared

2. Qiuhui Bi

   School of Artificial Intelligence, Beijing Normal University, Beijing, China

   Contribution

   Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Peipei Qin

   Competing interests

   No competing interests declared

3. Zeya Guo

   State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

4. Liyuan Yang

   State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

   Contribution

   Formal analysis, Validation

   Competing interests

   No competing interests declared

5. Haokun Li

   State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

   Contribution

   Formal analysis, Validation, Investigation

   Competing interests

   No competing interests declared

6. Peng Li

   State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

   Contribution

   Formal analysis, Validation, Investigation

   Competing interests

   No competing interests declared

7. Xinyu Liang

   State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

   Contribution

   Formal analysis, Validation

   Competing interests

   No competing interests declared

8. Junhao Luo

   State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

   Contribution

   Formal analysis, Validation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3207-1597

9. Xiangyu Kong

   State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

   Contribution

   Formal analysis, Validation

   Competing interests

   No competing interests declared

10. Yirong Xiong

    State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

    Contribution

    Formal analysis, Validation

    Competing interests

    No competing interests declared

11. Bo Sun

    School of Artificial Intelligence, Beijing Normal University, Beijing, China

    Contribution

    Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

12. Sebastian Ocklenburg

    1. Department of Psychology, Medical School Hamburg, Hamburg, Germany
    2. ICAN Institute for Cognitive and Affective Neuroscience, Medical School Hamburg, Hamburg, Germany
    3. Institute of Cognitive Neuroscience, Biopsychology, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany

    Contribution

    Validation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

13. Gaolang Gong

    1. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
    2. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
    3. Chinese Institute for Brain Research, Beijing, China

    Contribution

    Conceptualization, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    gaolang.gong@bnu.edu.cn

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5788-022X

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