Age-related changes of node degree in the multiple-demand network predict fluid intelligence.
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 %,
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper investigates age-related changes in brain connectivity and their correlation with fluid intelligence, which is relevant to understanding cognitive decline associated with aging. However, it primarily focuses on correlational findings rather than addressing the root causes of aging or proposing interventions that could significantly alter the aging process. Thus, while it contributes to the field of cognitive aging, its impact is limited and more incremental than transformative.
Author of this paper? Send a correction.