Researchers have used network analysis to investigate whether it can uncover the subtle changes related to subjective cognitive decline that cannot be detected through standard test analyses. By conducting a statistical analysis of data combined from two large Canadian datasets, the researchers were able to visualize the strength of relationships between nodes among individuals who are categorized as cognitively normal (CN) or diagnosed with subjective cognitive decline (SCD), mild cognitive impairment (MCI), or Alzheimer’s disease (AD).Some individuals might dismiss cognitive decline as a typical aspect of getting older, but for others, it could be the first sign of a more serious cognitive impairment, such as Alzheimer’s disease. The human brain’s immense complexity makes it challenging to diagnose cognitive decline early, which has significant implications for treatment and prevention. This is particularly true for subjective cognitive decline, where a person expresses worries about their memory or cognitive skills but doesn’t exhibit any abnormalities on cognitive tests conducted by a clinician.The main focus is a recent paper in the journal Cortex, co-authored by Concordia PhD student Nicholas Grunden and Department of Psychology professor Natalie Phillips. In the paper, they explore the use of network analysis as a new technique to examine the subtle changes linked to subjective cognitive decline that may not be evident through standard test analyses.
The network approach involves representing cognitive performance as a complex web of interconnected cognitive abilities that illustrate the connections between a set of variables, or nodes. In this study, the nodes represent the outcomes of various neuropsychological tests, as well as participant characteristics.The researchers used statistical analysis to examine data from two large Canadian data sets, focusing on the relationships between different groups of people based on their cognitive status. These groups included individuals classified as cognitively normal (CN), subjective cognitive decline (SCD), mild cognitive impairment (MCI), or Alzheimer’s disease (AD). The connections between these groups were represented as nodes and edges in a network, with the edges indicating the conditional associations between the variables and whether they are positively or negatively correlated. This approach allowed the researchers to visualize the strength of relationships among different cognitive states and how they work together.These connections are determined by the saturation of the edges. It’s a natural way to visually represent the findings.”
Observing the decrease
Once the networks were created using the combined databases, the researchers pinpointed two nodes that have the greatest impact on the rest of the network: performance on executive function tests and processing speed. Both of these are known to decrease with age.
However, the strength of these two nodes noticeably decreased from the cognitively normal to the subjective cognitive decline to the mild cognitive impairment groups. This progressive gradient demonstrates the decline.aces SCD as an intermediate stage between CN and MCI.
“This discovery is quite intriguing because it reveals aspects of people’s personal concerns that are not usually evident in standard statistical analyses,” Grunden explains.
“Executive function and processing speed are crucial cognitive abilities as they contribute to other skills (e.g., language, attention) and are essential for supporting an individual’s everyday functioning. We understand that efficiency decreases with age, but we also observe it in the early stages of certain types of progressive cognitive decline.”
Age limits</p rnThe researchers found that age plays a crucial role in cognitive decline, especially for those without Alzheimer’s disease. However, its impact diminishes for those with MCI or AD, where other factors become more significant. According to Phillips, age is the most influential factor on cognition for older adults without signs of Alzheimer’s disease.
“However, this is not the case for individuals with a diagnosis of MCI or Alzheimer’s disease. In these cases, cognitive function is more closely related to the progression of the disease, as indicated by general measures of clinical status on standardized cognition tests such as the Montreal Cognitive Assessment Test.”
Grunden explains that network analysis can assist researchers in studying brain function as a system rather than isolated variables acting independently.
“This allows us to delve deeper, as we can examine the interrelationships between all of the variables simultaneously,” he explains. “This enables us to gain insights into the interconnected nature of cognitive function.”The study was funded by the Fonds de recherche du Québec — Nature et technologies (FRQNT), the Fondation Famille Lemaire, and the Centre for Research on Brain, Language and Music. Data for the study was obtained from the Consortium for the early identification of Alzheimer’s disease — Quebec (CIMA-Q) and the Comprehensive Assessment on Neurodegeneration and Dementia (COMPASS-ND) databases. The researchers looked at associations between data elements rather than focusing on individual indicators.
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