Microstructural asymmetry of the planum temporale guides lateralization of speech processing
Poster No:
1040
Submission Type:
Abstract Submission
Authors:
Peipei Qin1, Qiuhui Bi2, Gaolang Gong3
Institutions:
1BeijIng Normal University, BeijIng, Hiadian,Beijing, 2Beijing Normal University, Beijing, Beijing, 3State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Resea, Beijing, Beijing
First Author:
Co-Author(s):
Gaolang Gong
State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Resea
Beijing, Beijing
State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Resea
Beijing, Beijing
Introduction:
A classic neuroscience hypothesis posits a fundamental role of structural asymmetries in functional lateralization [1], but the empirical evidence remains scarce. As a well-known structurally asymmetric region [2], the planum temporal (PT) is involved in speech processing [3], a lateralized function. Thus, this region has been much studied to elucidate the relationship between structural and functional asymmetries, but the results are mixed [4,5]. The present study aims to revisit this important issue using both macrostructural and microstructural imaging approach with a large cohort of healthy young adults.
Methods:
We included 907 right-handed subjects from HCP S1200 release (Mean age ± SD: 28.8 ± 3.7 years, 507 females and 398 males) [6]. The study manually outlined the PT and manually determined the type of the Heschl's gyrus (HG) (single or duplicated) by two trained raters [7].
Functional activation was assessed for three contrasts ('story – baseline,' 'math – baseline,' and 'story - math') in a story-telling task, which were then factor-analyzed into two components: speech perception and speech comprehension. The modified LI-toolbox [8] was then used to calculate the functional activation and its asymmetry index (AI).
Structural metrics of PT, including macrostructural (surface area, thickness) and microstructural metrics (myelin content, neurite density index(NDI), and orientation dispersion index(ODI) [9]), were calculated. Structural AIs were defined as (Left - Right) / (Left + Right).
We tested the correlation between the 5 structural and 2 functional AIs using 5*2 general linear models (GLM), with the functional AI as the response variable and structural AI as the fixed effect in the model. Furthermore, intra-hemispheric structural-functional correlations were examined by another 2*5*2 GLMs, with the functional activation as the response variable and structural metric as the fixed effect in the model. Covariates included age, sex, brain size, and HG type in each GLM. Significance levels were Bonferroni-corrected P < 0.05.
Functional activation was assessed for three contrasts ('story – baseline,' 'math – baseline,' and 'story - math') in a story-telling task, which were then factor-analyzed into two components: speech perception and speech comprehension. The modified LI-toolbox [8] was then used to calculate the functional activation and its asymmetry index (AI).
Structural metrics of PT, including macrostructural (surface area, thickness) and microstructural metrics (myelin content, neurite density index(NDI), and orientation dispersion index(ODI) [9]), were calculated. Structural AIs were defined as (Left - Right) / (Left + Right).
We tested the correlation between the 5 structural and 2 functional AIs using 5*2 general linear models (GLM), with the functional AI as the response variable and structural AI as the fixed effect in the model. Furthermore, intra-hemispheric structural-functional correlations were examined by another 2*5*2 GLMs, with the functional activation as the response variable and structural metric as the fixed effect in the model. Covariates included age, sex, brain size, and HG type in each GLM. Significance levels were Bonferroni-corrected P < 0.05.
Results:
As shown in Figure 1, the functional AI for speech perception was positively correlated with the AI of myelin (R=0.26, PFWE <10-12), NDI (R=0.13, PFWE<10-2), and ODI (R=0.22, PFWE<10-9). On the other hand, the functional AI for speech comprehension was significantly correlated with the AI for surface area (R=0.21, PFWE<10-9), myelin (R=0.20, PFWE<10-8), and NDI (R=0.11, PFWE<0.01). As shown, the significant correlations were mainly observed the AI of micro- metrics (myelin, NDI, ODI).
The correlations between PT functional activation and structural metrics for each hemisphere are shown in Figure 2. For the activation of speech perception, significant correlations were observed for the myelin (R=0.16, PFWE <10-4) and ODI (R=0.17, PFWE <10-5) in the left PT, and for the surface area (R=-0.11, PFWE <0.02), myelin (R=0.20, PFWE <10-6), NDI (R=0.14, PFWE <0.01) and ODI (R=0.18, PFWE <10-6) in the right PT. For speech comprehension, functional activation was significantly correlated with the NDI (R=-0.12, PFWE <0.02) in the left PT, and with the myelin (R=0.16, PFWE <10-5) in the right PT.
The correlations between PT functional activation and structural metrics for each hemisphere are shown in Figure 2. For the activation of speech perception, significant correlations were observed for the myelin (R=0.16, PFWE <10-4) and ODI (R=0.17, PFWE <10-5) in the left PT, and for the surface area (R=-0.11, PFWE <0.02), myelin (R=0.20, PFWE <10-6), NDI (R=0.14, PFWE <0.01) and ODI (R=0.18, PFWE <10-6) in the right PT. For speech comprehension, functional activation was significantly correlated with the NDI (R=-0.12, PFWE <0.02) in the left PT, and with the myelin (R=0.16, PFWE <10-5) in the right PT.
Conclusions:
Our results revealed significant association of functional PT asymmetries with several microstructural asymmetries, such as intracortical myelin content, neurite density, and neurite orientation dispersion. The PT microstructure per se also showed hemispheric-specific coupling with PT functional activity. These results suggest that microstructural asymmetry guides functional lateralization of the same brain area and highlight a critical role of microstructural PT asymmetries in auditory-language processing.
Language:
Speech Perception 1
Novel Imaging Acquisition Methods:
Anatomical MRI
BOLD fMRI 2
Diffusion MRI
Keywords:
FUNCTIONAL MRI
Hemispheric Specialization
Myelin
NORMAL HUMAN
Perception
STRUCTURAL MRI
1|2Indicates the priority used for review
Provide references using author date format
[2] Galaburda, A. M. et al. (1978), “Right-left asymmetrics in the brain,” Science, vol. 199, no. 4331, pp. 852–856
[3] Sajid, N. et al. (2023), “Degeneracy in the neurological model of auditory speech repetition,” Communications Biology, vol. 6, no. 1, Art. number 1
[4] Trimmel, K. et al. (2021), “Decoupling of functional and structural language networks in temporal lobe epilepsy,” Epilepsia, vol. 62, no. 12, pp. 2941–2954
[5] Tzourio, N. et al. (1998), “Left planum temporale surface correlates with functional dominance during story listening,” Neuroreport, vol. 9, no. 5, pp. 829–833
[6] Van Essen, D. C. et al. (2013), “The WU-Minn Human Connectome Project: An overview,” NeuroImage, vol. 80, pp. 62–79
[7] Abdul-Kareem, I. A. et al. (2008), “Heschl gyrus and its included primary auditory cortex: Structural MRI studies in healthy and diseased subjects,” Journal of Magnetic Resonance Imaging, vol. 28, no. 2, pp. 287–299
[8] Wilke, M. et al. (2007), “LI-tool: A new toolbox to assess lateralization in functional MR-data,” Journal of Neuroscience Methods, vol. 163, no. 1, pp. 128–136
[9] Zhang, H. et al. (2012), “NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain,” NeuroImage, vol. 61, no. 4, pp. 1000–1016