Measurement of Brain Age: Conceptual Issues and Neurobiological Indices
Richard C. Adelman, George S. Roth in Endocrine and Neuroendocrine Mechanisms of Aging, 2017
The major concern in selecting measures of brain aging, of course, is whether or not a particular variable correlates with age. That is, a brain variable must obviously be measurably different in aged subjects vs. young-mature subjects if it is to be used to assess brain age. Moreover, the more discrimination between age groups that it provides (i.e., the less overlap) the more useful it is presumed to be. (See Reference 11 for a discussion of the effect of variance and population size on the measurement of aging.) It is also often assumed that the earlier in life at which an aging correlate can be detected, the more valuable it is. However, this latter assumption may not be completely valid since, as noted above, all correlates of brain aging may not be directly linked, and some may proceed independently of others. Conceivably, therefore, some later appearing correlates may be of greater relevance to, for example, functional decline. Nevertheless, since brain aging appears to begin by mid-life, age-correlated variables that can be detected early in mature life seem of potential interest in terms of assessing "primary" mechanisms.
Changes in Cognitive Function in Human Aging
David R. Riddle in Brain Aging, 2007
Age-related changes in cognitive function vary considerably across individuals and across cognitive domains, with some cognitive functions appearing more susceptible than others to the effects of aging. Much of the basic research in cognitive aging has focused on attention and memory, and indeed it may be that deficits in these fundamental processes can account for much of the variance observed in higherlevel cognitive processes. The mapping of cognitive processes onto neural structures constitutes a relatively recent research enterprise driven largely by advances in neuroimaging technology (see Chapter 12, this volume). Early work in this area focused on establishing brain regions associated with different kinds of cognitive performance and revealed that normally aging older adults often appear to activate different brain structures than young people when performing cognitive tasks. The reasons for these differences are a matter of considerable debate. Ultimately, the understanding of age-related changes in cognition will require a parallel understanding of the age-related changes in the brain and the underlying mechanisms responsible for those changes. This volume explores the current state of research on the aging brain, providing some initial hypotheses concerning how changes in the nervous system may be related to the kinds of age-related cognitive changes that are outlined in this chapter.
Ageing, Neurodegeneration and Alzheimer's Disease
James N. Cobley, Gareth W. Davison in Oxidative Eustress in Exercise Physiology, 2022
In the ageing brain, antioxidant and cellular waste removal systems become less effective (Daniele et al., 2018), and redox imbalance becomes apparent as a shift towards a pro-oxidative environment prevails. The oxidation of biomolecules can significantly impact cell survival as dysfunctioning biomolecules impact cellular processes essential for life such as metabolic function, perturbed signal transduction, or induce apoptosis and eventual cell death (Trachootham et al., 2008). In the brain, oxidative protein aggregation has been linked with neurodegeneration (Butterfield and Boyd-Kimball, 2018). Although brain ageing is an inevitable process, the rate of decline is variable. That is, some people who are matched for their chronological age do not share the same features associated with their biological or ‘brain age’ (Cole, 2020) (i.e., chronological and biological age are often asynchronous). This may, in part, explain why some individuals experience significant cognitive decline and neurodegeneration early as they age, whereas some people age with excellent cognitive health, free from apparent neurodegenerative disease.
Precision neuroimaging biomarkers for bipolar disorder
Published in International Review of Psychiatry, 2022
Delfina Janiri, Sophia Frangou
The investigation of accelerated ageing in BD benefits from machine learning methods applied to neuroimaging data that can harness the multidimensional nature of age-related brain changes (Franke & Gaser, 2019). The application of machine learning methods enables the prediction of the biological age of the brain from neuroimaging phenotypes based on the population norms for these phenotypes. The ‘brain-age-gap-estimation (brainAGE) is an individualized measure of brain ageing based on the discrepancy between neuroimaging-predicted age and chronological age (Franke & Gaser, 2019). A positive brainAGE indicates that the biological age of an individual's brain appears ‘older’ than their actual age, and a negative brainAGE reflects the inverse. Individual studies examining brainAGE in BD using sMRI or DTI-derived brain features have been generally conflicting (Ballester et al., 2022) (Table 2 and Supplementary Table 2). The largest study to date used morphometric data from 459 patients with BD whose brainAGE was approximately 2 years older than that of their healthy counterparts (Kaufmann et al., 2019). The underlying mechanisms remain unclear but are likely to involve cardiometabolic dysregulation (Ryan et al., 2022) while lithium may confer a protective effect (Van Gestel et al., 2019) via a yet unspecified mechanism.
Considering patient age when treating multiple sclerosis across the adult lifespan
Published in Expert Review of Neurotherapeutics, 2021
Dejan Jakimovski, Svetlana P Eckert, Robert Zivadinov, Bianca Weinstock-Guttman
Another way of determining the biological brain age includes machine learning analysis of neuroimaging data. A pattern recognition procedure is used to determine brain-predicted age from a large set of healthy individuals and later applied to pwMS. In one study, when compared to healthy controls, the MS brains were on average 6 years older than their chronologically age-matched controls with significant MS phenotype effect (SPMS brains were up to 9 years older) [36]. Furthermore, the predicted brain age could predict future disability worsening and resulted in additional marginal gap widening [36]. In a similar analysis, the cross-sectional MS brain age was 4.4 years older when compared to chronological age-matched healthy controls [37]. pwMS also experienced significantly faster brain aging with an increase of 0.4 additional biological years for every chronological year [37]. Both studies suggest that MS accelerates neurological and immunological aging when compared to chronologically-matched controls.
The aging brain: impact of heavy metal neurotoxicity
Published in Critical Reviews in Toxicology, 2020
Omamuyovwi M. Ijomone, Chibuzor W. Ifenatuoha, Oritoke M. Aluko, Olayemi K. Ijomone, Michael Aschner
Aging is an inevitable biological process that occurs with time. The aging brain is associated with an attendant morphological, functional, and cellular alterations known to be responsible for the cognitive and motor decline observed in the elderly. The normal process of aging presents hallmarks (which we have highlighted in this review) that triggers age-related neurodegenerative diseases, including Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease. However, continuously increasing industrialization results in frequent exposure to environmental toxicants, particularly metals. Many of these metals are toxic to the brain, and have been shown to trigger oxidative stress, mitochondrial dysfunction, DNA damage, thus could further exacerbate the brain’s aging processes and hasten onset of neurodegenerative diseases. Hence, the current review focused on elucidating the possible impacts of metals on the aging brain from recent scientific evidence. The commonalities in neurotoxic impact induced by these metals on the aging brain include oxidative stress, accumulation of damaging molecules, neuroinflammation, and ultimately neurodegeneration. In summary, this review highlights the consequences of the dyshomeostasis of these metals in the aging brain. More studies are necessary for a better understanding of the mechanism involved in how these hasten aging in the brain, which may identify target sites for pharmacological treatments to mitigate neurologic disorders caused by these metals during aging.
Related Knowledge Centers
- Ageing
- Brain
- Old Age
- Life Extension
- Rejuvenation
- Neurodegenerative Disease
- Mild Cognitive Impairment
- Dementia
- Alzheimer's Disease
- Cerebrovascular Disease