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Wang J, Sun J, Li C, Tong S, Hong X. The effects of pre-cue alpha and cueing strategy on age-related deficits in post-cue alpha activity and target processing during visual spatial attention. Cereb Cortex 2023; 33:11112-11125. [PMID: 37750338 DOI: 10.1093/cercor/bhad350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/01/2023] [Accepted: 09/02/2023] [Indexed: 09/27/2023] Open
Abstract
Electroencephalography alpha-band (8-13 Hz) activity during visual spatial attention declines in normal aging. We recently reported the impacts of pre-cue baseline alpha and cueing strategy on post-cue anticipatory alpha activity and target processing in visual spatial attention (Wang et al., Cerebral Cortex, 2023). However, whether these factors affected aging effects remains unaddressed. We investigated this issue in two independent experiments (n = 114) with different cueing strategies (instructional vs. probabilistic). When median-splitting young adults (YA) by their pre-cue alpha power, we found that older adults exhibited similar pre-cue and post-cue alpha activity as YA with lower pre-cue alpha, and only YA with higher pre-cue alpha showed significant post-cue alpha activity, suggesting that diminished anticipatory alpha activity was not specific to aging but likely due to a general decrease with baseline alpha. Moreover, we found that the aging effects on cue-related event-related potentials were dependent on cueing strategy but were relatively independent of pre-cue alpha. However, age-related deficits in target-related N1 attentional modulation might depend on both pre-cue alpha and cueing strategy. By considering the impacts of pre-cue alpha and cueing strategy, our findings offer new insights into age-related deficits in anticipatory alpha activity and target processing during visual spatial attention.
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Affiliation(s)
- Jiaqi Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Junfeng Sun
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Chunbo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
- CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai 200030, China
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Shanbao Tong
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiangfei Hong
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
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Han Z, Liu T, Shi Z, Zhang J, Suo D, Wang L, Chen D, Wu J, Yan T. Investigating the heterogeneity within the somatosensory-motor network and its relationship with the attention and default systems. PNAS Nexus 2023; 2:pgad276. [PMID: 37693210 PMCID: PMC10485902 DOI: 10.1093/pnasnexus/pgad276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 06/23/2023] [Accepted: 08/14/2023] [Indexed: 09/12/2023]
Abstract
The somatosensory-motor network (SMN) not only plays an important role in primary somatosensory and motor processing but is also central to many disorders. However, the SMN heterogeneity related to higher-order systems still remains unclear. Here, we investigated SMN heterogeneity from multiple perspectives. To characterize the SMN substructures in more detail, we used ultra-high-field functional MRI to delineate a finer-grained cortical parcellation containing 430 parcels that is more homogenous than the state-of-the-art parcellation. We personalized the new parcellation to account for individual differences and identified multiscale individual-specific brain structures. We found that the SMN subnetworks showed distinct resting-state functional connectivity (RSFC) patterns. The Hand subnetwork was central within the SMN and exhibited stronger RSFC with the attention systems than the other subnetworks, whereas the Tongue subnetwork exhibited stronger RSFC with the default systems. This two-fold differentiation was observed in the temporal ordering patterns within the SMN. Furthermore, we characterized how the distinct attention and default streams were carried forward into the functions of the SMN using dynamic causal modeling and identified two behavioral domains associated with this SMN fractionation using meta-analytic tools. Overall, our findings provided important insights into the heterogeneous SMN organization at the system level and suggested that the Hand subnetwork may be preferentially involved in exogenous processes, whereas the Tongue subnetwork may be more important in endogenous processes.
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Affiliation(s)
- Ziteng Han
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Tiantian Liu
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Zhongyan Shi
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Jian Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Dingjie Suo
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Li Wang
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Duanduan Chen
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Jinglong Wu
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Tianyi Yan
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
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