The complexity of the human brain makes it difficult to diagnose cognitive decline early, which can have significant implications for treatment and prevention. Subjective cognitive decline, in which individuals report concerns about memory or cognitive ability but show no deviation on cognitive tests, is particularly challenging to detect. A new paper by Concordia PhD student Nicholas Grunden and professor Natalie Phillips explores whether network analysis can reveal subtle changes associated with subjective cognitive decline that are not identified through standard test analyses.
Using a network approach, the researchers analyzed data from two large Canadian datasets to visualize the relationships between cognitive abilities and participant characteristics among individuals classified as cognitively normal, subjective cognitive decline, mild cognitive impairment, or Alzheimer’s disease. They identified executive function and processing speed as the nodes that exerted the strongest influence on the network. Both abilities are known to decline with age, with marked decreases in strength from cognitively normal to subjective cognitive decline to mild cognitive impairment, placing SCD as an intermediate stage between CN and MCI.
The researchers also found that age was a significant predictor of cognitive decline, with substantial influence on cognitive abilities among those classified as CN and SCD. However, its influence waned among those classified as MCI or AD, where other nodes measuring cognitive ability took on more weight. This suggests that cognitive function in individuals with MCI or Alzheimer’s disease is more associated with disease progression rather than age, as indicated by clinical status on standardized cognition tests.
Network analysis allows researchers to examine brain function as a system, focusing on interrelationships between variables rather than isolated elements of data. By looking at associations between variables simultaneously, researchers can identify indicators that may not be apparent in individual data elements. The study was funded by the Fonds de recherche du Québec, Fondation Famille Lemaire, and the Centre for Research on Brain, Language, and Music, and used data from the CIMA-Q and COMPASS-ND databases.
In conclusion, early diagnosis of cognitive decline is challenging due to the complexity of the human brain. Subjective cognitive decline presents a unique set of difficulties in detection, as individuals may report concerns about cognitive abilities that are not reflected in cognitive tests. Network analysis offers a promising approach to identifying subtle changes associated with subjective cognitive decline, providing insights into the interconnected nature of cognitive abilities and participant characteristics. Ultimately, this research has important implications for understanding the progression of cognitive decline and informing strategies for treatment and prevention.
