Abstract
Normal aging is related to a decline in specific cognitive processes, in particular in executive functions and memory. In recent years a growing number of studies have focused on changes in brain functional connectivity related to cognitive aging. A common finding is the decreased connectivity within multiple resting state networks, including the default mode network (DMN) and the salience network. In this study, we measured resting state activity using fMRI and explored whether cognitive decline is related to altered functional connectivity. To this end we used a machine learning approach to classify young and old participants from functional connectivity data. The originality of the approach consists in the prediction of the performance and age of the subjects based on functional connectivity by using a machine learning approach. Our findings showed that the connectivity profile between specific networks predicts both the age of the subjects and their cognitive abilities. In particular, we report that the connectivity profiles between the salience and visual networks, and the salience and the anterior part of the DMN, were the features that best predicted the age. Moreover, independently of the age of the subject, connectivity between the salience network and various specific networks (i.e., visual, frontal) predicted episodic memory skills either based on a standard assessment or on an autobiographical memory task, and short-term memory binding. Finally, the connectivity between the salience and the frontal networks predicted inhibition and updating performance, but this link was no longer significant after removing the effect of age. Our findings confirm the crucial role of episodic memory and executive functions in cognitive aging and suggest a pivotal role of the salience network in neural reorganization in aging.
Highlights
The cognitive and neural changes accompanying healthy aging are a crucial topic in cognitive neuroscience
The principal aim of this study was to further characterize the brain functional reorganization related to cognitive aging in order to shed light on the network reorganization related to cognitive decline in older adults, in particular linked to episodic memory and executive functions
We found significant differences for all measures in favor of better performance in young adults than in older adults, except for FLU [t(25) = 1.71, p = 0.1]: episodic autobiographical memory (EAM) [t(25) = 7.56, p < 0.001], EPI [t(25) = 3.34, p < 0.01], TMTB-A [t(25) = 4.24, p < 0.001], INHIB [t(25) = 6.72, p < 0.001], UP-D [t(25) = 2.41, p < 0.05], VSS [t(25) = 4.57, p < 0.001], and STB [t(25) = 3.07, p < 0.01]
Summary
The cognitive and neural changes accompanying healthy aging are a crucial topic in cognitive neuroscience. There is compelling evidence that executive functions and memory are the most severely impaired cognitive domains in this population (Salthouse et al, 2003). Executive functions are seen as high-level cognitive processes responsible for flexible and adaptive behavior (Miyake et al, 2000). They play a fundamental role in dealing with complex situations in everyday life. They largely contribute to the effective functioning of other cognitive processes, such as memory. This decline may be accounted by the functional and structural reorganization of the frontal lobes with aging (Moscovitch and Winocur, 1995; Cabeza, 2002; Grady et al, 2005; Fjell and Walhovd, 2010)
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