Investigating the somatosensory-motor network heterogeneity associated with higher-order systems
Poster No:
1828
Submission Type:
Abstract Submission
Authors:
Ziteng Han1, Jinglong Wu1, Tianyi Yan1
Institutions:
1Beijing Institute of Technology, Beijing, Beijing
First Author:
Co-Author(s):
Introduction:
The somatosensory-motor network (SMN) not only plays an important role in primary somatosensory and motor processing but also serves as a transdiagnostic hub, whose upward pathways with the attention and default systems are strongly associated with general psychopathology, cognitive impairment and impulsivity [1, 2]. However, the SMN heterogeneity related to higher-order systems still remains unclear. Here, we first delineate a finer-grained cortical parcellation to characterize the SMN substructures in more detail. We then examine how the distinct attention and default streams are carried forward into the functions of SMN, to test for SMN heterogeneity.
Methods:
We used ultra-high-field neuroimaging data from the HCP S1200 release. Seventeen of the original 184 subjects with 7T fMRI data were excluded due to major acquisition artifacts or incomplete scans, yielding a total of 167 healthy young subjects (102 female, age range 22–35 years) for further analyses. Then, these individuals were randomly split into two groups, which we termed Dataset 1 (dataset for parcellation, n = 85) and Dataset 2 (dataset for parcellation validation and individual-level analyses, n = 82).
(i) First, a hard boundary mapping-based parcellation approach was employed to delineate a finer-grained cortical parcellation[3]. The new population-level parcellation map was then personalized to account for inter-individual variability[4]. (ii) Following this, the network architecture of the parcel-wise graph was assessed using the Infomap algorithm in individuals. For each subject, we identified the SMN, anterior dorsal attention network (aDAN) and anterior frontoparietal control network (aFPCN) by matching the clustering-derived community to the Yeo network[5]. The SMN subnetworks were identified based on task-evoked activity (motor task). (iii) Subsequently, we calculated the resting-state functional connectivity (RSFC) between each SMN subnetwork and the remaining SMN regions, aDAN and aFPCN, respectively. The connector hubness and centrality of each SMN subnetwork were computed to represent their network roles. Furthermore, spectral dynamic causal modelling (DCM) was implemented to infer the effective connections[6]. Finally, a Neurosynth meta-analytic coactivation analysis was performed to validate the generalizability of the SMN fractionation[7].
(i) First, a hard boundary mapping-based parcellation approach was employed to delineate a finer-grained cortical parcellation[3]. The new population-level parcellation map was then personalized to account for inter-individual variability[4]. (ii) Following this, the network architecture of the parcel-wise graph was assessed using the Infomap algorithm in individuals. For each subject, we identified the SMN, anterior dorsal attention network (aDAN) and anterior frontoparietal control network (aFPCN) by matching the clustering-derived community to the Yeo network[5]. The SMN subnetworks were identified based on task-evoked activity (motor task). (iii) Subsequently, we calculated the resting-state functional connectivity (RSFC) between each SMN subnetwork and the remaining SMN regions, aDAN and aFPCN, respectively. The connector hubness and centrality of each SMN subnetwork were computed to represent their network roles. Furthermore, spectral dynamic causal modelling (DCM) was implemented to infer the effective connections[6]. Finally, a Neurosynth meta-analytic coactivation analysis was performed to validate the generalizability of the SMN fractionation[7].
Results:
(i) The new finer-grained cortical parcellation contains 430 parcels (Fig. 1a-b), slightly more than the Gordon333 parcellation. The SMN and its subnetworks are shown in Fig. 1c.
(ii) Statistical analyses show that the Hand subnetwork is central within the SMN (Fig. 2a-b) and exhibits stronger RSFC with the attention systems (i.e., aDAN) than the other subnetworks(Fig. 2c), whereas the Tongue subnetwork exhibits stronger RSFC with the default systems (i.e., aFPCN; Fig. 2d). Moreover, both the Hand and Tongue subnetworks serve as connector hubs (Fig. 2e).
(iii) Direct interactions were observed between the Hand subnetwork and attention systems, as well as between the Tongue subnetwork and default systems (Fig. 2f).
(iv) We further found that two distinct behavioral domains are preferentially associated with specific SMN subnetworks. Perceptual attention processes, such as "visual attention", load more strongly onto the Hand subnetwork than the Tongue subnetwork, while introspective processes, such as "affective processing", load more strongly onto the Tongue subnetwork (Fig. 2g).
(ii) Statistical analyses show that the Hand subnetwork is central within the SMN (Fig. 2a-b) and exhibits stronger RSFC with the attention systems (i.e., aDAN) than the other subnetworks(Fig. 2c), whereas the Tongue subnetwork exhibits stronger RSFC with the default systems (i.e., aFPCN; Fig. 2d). Moreover, both the Hand and Tongue subnetworks serve as connector hubs (Fig. 2e).
(iii) Direct interactions were observed between the Hand subnetwork and attention systems, as well as between the Tongue subnetwork and default systems (Fig. 2f).
(iv) We further found that two distinct behavioral domains are preferentially associated with specific SMN subnetworks. Perceptual attention processes, such as "visual attention", load more strongly onto the Hand subnetwork than the Tongue subnetwork, while introspective processes, such as "affective processing", load more strongly onto the Tongue subnetwork (Fig. 2g).
Conclusions:
(i) Our parcellation result suggests that ultrahigh spatial resolution (1.6 mm) data, combined with the state-of-the-art algorithm, have the potiential to provide deeper insights into the functional organization of the cortex[8].
(ii) Our findings reveal a heterogeneous SMN organization that may in part emerge from separable attention and default processing streams[9]. The Hand subnetwork may be preferentially involved in exogenous processes, whereas the Tongue subnetwork may be more important in endogenous processes.
(ii) Our findings reveal a heterogeneous SMN organization that may in part emerge from separable attention and default processing streams[9]. The Hand subnetwork may be preferentially involved in exogenous processes, whereas the Tongue subnetwork may be more important in endogenous processes.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Neuroinformatics and Data Sharing:
Brain Atlases 2
Keywords:
Cortex
FUNCTIONAL MRI
Motor
Other - brain networks; somatosensory-motor network
1|2Indicates the priority used for review
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