In a recent study posted to the eLife preprint server, researchers performed complete-brain voxel-wise functional magnetic resonance imaging (fMRI) to identify brain areas with functional-type compensation. They also investigated neurophysiological changes that maintain cognitive function in older adults.
*Important notice: Preprints publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.
Background
Age-related functional compensatory mechanisms in the cognitive neurobiology of healthy aging are controversial, according to which older individuals increase brain activity to compensate for decreased cognitive ability. However, whether the additional brain activity helps cognitive performance is uncertain. Neuroimaging reveals that the human brain can adapt to tissue losses by increasing brain activities to sustain cognitive functioning. Age similarly influences fluid intelligence, a cognition skill.
About the study
In the present study, researchers used fMRI data from a fluid intelligence test to identify brain areas involved in functional compensation and understand brain responses to tissue loss. They also explored the relationship between age-related changes in brain activation and cognitive performance, specifically in fluid intelligence tasks.
The team analyzed data from 223 adult participants of the Cambridge Centre for Ageing Neuroscience (Cam-CAN) study to examine the relationship between age, cognitive performance, and brain activation patterns. Participants were aged 19 to 87 years, fluent in English, and mentally and physically fit, excluding those with MRI contraindications, poor mini-mental state examination (MMSE) scores, and psychiatric, medical, visual, or hearing impairments.
The team performed functional and structural neuroimaging to study the relationship between age, cognitive performance, and brain activation patterns. They performed a problem-solving task based on the modified Cattell Culture Fair Intelligence test during fMRI. They scanned participants during the Cattell fluid intelligence task, completing puzzles from two difficulty levels, to determine whether the candidate compensation regions exhibited multivariate evidence of compensation.
The dependent variables were the differences in functional MRI activation for hard vs. easy task blocks. The team used multivariate Bayesian decoding (MVB) to explore the role of multivoxel patterning in providing additional data related to task difficulty. They predicted that regions associated with functional compensation would have more data related to tasks with age. MVB was used to identify areas with additional multivariate data and support functional-type compensation, which involves the brain increasing activity to support cognitive functions in response to tissue loss.
To identify patterns of brain activation, the team overlaid maps testing for positive influences of age and performance on brain function, assessed using the hard vs. easy contrast. They used multiple regressions for analysis, with activation maps reflecting the unique effects of each. The team repeated multiple regression after scaling the influence of Cattell activation by estimating the resting state fluctuation amplitude (RSFA) for each region of interest (ROI) from an independent resting-state scan for each participant.
The team analyzed participant data using boxcar functions and statistical parametric mapping (SPM) hemodynamic response functions, fitting a model to each voxel. They defined functional compensation ROIs, the cuneal and frontal cortex by the empirical Bayes approach. They standardized and treated age and behavioral performance variables as linear predictors.
Results
Bilateral cuneal cortical activity increased with performance and age for hard vs. easy problems, even after adjusting for age-associated disparities in cerebrovascular reactivation. The cuneus region showed multivariate data supporting functional compensation, and age enhanced the likelihood of activation patterns, providing non-redundant data beyond the MDN work usually activated in the task.
The modified Cattell task showed a decrease in behavioral performance with age during fMRI scans. A strong correlation was found between fMRI and standard Cattell task performance measures when performed one to three years prior. Bilateral activation in multiple demand network (MDN) regions, including the intraparietal sulcus, middle/inferior frontal gyri, anterior cingulate cortical region, anterior insula, and lateral and ventral occipital temporal cortical region, was observed, probable due to the visual type of tasks like problem-solving and fluid intelligence.
Age-association increase in activity in the middle area of the frontal gyrus, precuneus, and motor supplementary areas was positively associated with performance in regions with higher activity for hard vs. easy tasks.
Two brain regions, the bilateral cuneal and frontal cortical regions, exhibited spatially overlapping positive influences of performance and age, indicating age-associated compensatory responses. However, the frontal area demonstrated additive influences of both study variables, while the cuneus area exhibited signs of interaction. The study found that age significantly influences performance as older individuals engage in compensatory patterns.
Conclusion
Overall, the study findings showed that healthy older individuals compensate for fluid intelligence during visual problem-solving tasks by increasing the recruitment of the bilateral cuneal cortex. The compensation allows the brain to react to the loss of tissue by increasing cognitive functions, known as functional compensation. Fluid intelligence, which involves solving abstract problems, declines with age. The MDN involvement in fluid-intelligence tasks tends to decrease with age. The cuneus region may play a role in functional compensation, and its activation increases with age.
*Important notice: Preprints publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.
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