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. 2024 Jun 14:17:245-251.
doi: 10.1016/j.ibneur.2024.06.005. eCollection 2024 Dec.

Age-related changes of node degree in the multiple-demand network predict fluid intelligence

Lizhi Yu  1 , Qin Zhang  1 , Xiaoyang Li  1 , Mei Zhang  1 , Xiaolin Chen  2 , Mingchun Lu  1 , Zhen Ouyang  1
Affiliations

Age-related changes of node degree in the multiple-demand network predict fluid intelligence

Lizhi Yu et al. IBRO Neurosci Rep. .

Abstract

Fluid intelligence is an individual's innate ability to cope with complex situations and is gradually reduced across adults aging. The realization of fluid intelligence requires the simultaneous activity of multiple brain regions and depends on the structural connection of distributed brain regions. Uncovering the structural features of brain connections associated with fluid intelligence decline will provide reference for the development of intervention and treatment programs for cognitive decline. Using structural magnetic resonance imaging data of 454 healthy participants (18-87 years) from the Cam-CAN dataset, we constructed structural similarity network for each participant and calculated the node degree. Spearman correlation analysis showed that age was positively correlated with degree centrality in the cingulate cortex, left insula and subcortical regions, while negatively correlated with that in the orbito-frontal cortex, right middle temporal and precentral regions. Partial least squares (PLS) regression showed that the first PLS components explained 32 % (second PLS component: 20 %, p perm < 0.001) of the variance in fluid intelligence. Additionally, the degree centralities of anterior insula, supplementary motor area, prefrontal, orbito-frontal and anterior cingulate cortices, which are critical nodes of the multiple-demand network (MDN), were linked to fluid intelligence. Increased degree centrality in anterior cingulate cortex and left insula partially mediated age-related decline in fluid intelligence. Collectively, these findings suggest that the structural stability of MDN might contribute to the maintenance of fluid intelligence.

Keywords: Aging; Fluid intelligence; Multiple-demand network; Nodal degree; SMRI.

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Conflict of interest statement

All authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Analysis flowchart of this study. First, gray matter volume of all voxels in each brain region was extracted from the processed structural magnetic resonance images based on AAL atlas and used to estimate the probability distribution function. Second, KL divergence was used to estimate the similarity of the probability distribution of gray matter volume between each two brain regions, so as to construct the individual similarity matrix. Third, individual binary matrix was created by thresholding the similarity matrix. Fourth, graph-based degree of each node was calculated for each participant. Finally, partial least square regression was performed to predict the individual fluid intelligence score, and node degrees were entered as predictors. MRI, magnetic resonance imaging; AAL, anatomical automatic labeling; KL, Kullback–Leibler; PLS, partial least squares.
Fig. 2
Fig. 2
Correlation between PLS1 scores and individual fluid intelligence scores and contribution of each cortical node. A. The first PLS component was positively correlated with fluid intelligence score (Spearman correlation). B. The weight of each node, i.e. the contribution to the first PLS component.
Fig. 3
Fig. 3
Correlation between PLS2 scores and individual fluid intelligence scores and contribution of each cortical node. A. The second PLS component was positively correlated with fluid intelligence score (Spearman correlation). B. The weight of each node, i.e. the contribution to the second PLS component.
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