About this Research Topic
Despite differences in definition, most theories illustrate the cause of individual differences through the impact of inheritable genes, life experiences, and new events on brain structure and function changes. Though the theories have been supported by a variety of studies, it has long been questioned whether the reserve is measurable? With the advance of machine learning algorithms, the related applications on predicting age, education, cognition, and even mortality using neuroimaging markers grow rapidly since the last decade, which brings the studies from group to the individual level. However, prediction approaches based on neuroimaging to detect an age-dependent individual difference as a proxy measure beyond static metrics like education and IQ remain insufficient and need to be moving forward. Moreover, it is critical whether the reserve measurement is changeable in response to intervention, lifestyle, or environmental demands? If so, could the measurable reserve help to predict or evaluate one's brain health status? Lastly, could the protective ability against neural loss be personalized to clarify the mechanisms of conversion/reversion-turning points across the lifespan?
Accordingly, this Research Topic aims to promote studies moving from observation to prediction and intervention study, which focuses on the personalized measure of reserve, compensation, and maintenance. Besides mechanism study, this Research Topic particularly welcomes the study highlighting the dynamic of reserve using prediction approaches and showing the modifiable of the reserve is associated with behavior or cognitive changes in the normal aging process. Instead of discussing inconsistencies between reserve, compensation, and maintenance, we welcome both cross-sectional and longitudinal studies that go beyond them and encourage using the prediction derivatives to probe the status of an individual's brain among the normal aging trajectories.
Keywords: Cognitive Reserve, Neuroimaging Biomarkers, Normal Aging Process
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