Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging.
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ABSTRACT:
SUBMITTER: Anaturk M
PROVIDER: S-EPMC7978127 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Multimodal imaging enables sensitive measures of the architecture and integrity of the human brain, but the high-dimensional nature of advanced brain imaging features poses inherent challenges for the analyses and interpretations. Multivariate age prediction reduces the dimensionality to one biologically informative summary measure with potential for assessing deviations from normal lifespan trajectories. A number of studies documented remarkably accurate age prediction, but the differential age trajectories and the cognitive sensitivity of distinct brain tissue classes have yet to be adequately characterized. Exploring differential brain age models driven by tissue-specific classifiers provides a hitherto unexplored opportunity to disentangle independent sources of heterogeneity in brain biology. We trained machine-learning models to estimate brain age using various combinations of FreeSurfer based morphometry and diffusion tensor imaging based indices of white matter microstructure in 612 healthy controls aged 18-87 years. To compare the tissue-specific brain ages and their cognitive sensitivity, we applied each of the 11 models in an independent and cognitively well-characterized sample (n = 265, 20-88 years). Correlations between true and estimated age and mean absolute error (MAE) in our test sample were highest for the most comprehensive brain morphometry (r = 0.83, CI:0.78-0.86, MAE = 6.76 years) and white matter microstructure (r = 0.79, CI:0.74-0.83, MAE = 7.28 years) models, confirming sensitivity and generalizability. The deviance from the chronological age were sensitive to performance on several cognitive tests for various models, including spatial Stroop and symbol coding, indicating poorer performance in individuals with an over-estimated age. Tissue-specific brain age models provide sensitive measures of brain integrity, with implications for the study of a range of brain disorders.
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Project description:Neuroimaging studies of the aging brain provide support that the strongest predictor of preserved memory and cognition in older age is brain maintenance, or relative lack of brain pathology. Evidence for brain maintenance comes from different levels of examination, but up to now relatively few studies have used a longitudinal design. Examining factors that promote brain maintenance in aging is a critical task for the future and may be combined with the use of new techniques for multimodal imaging.
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Project description:ObjectivesAge-related cognitive changes can be influenced by both brain maintenance (BM), which refers to the relative absence over time of changes in neural resources or neuropathologic changes, and cognitive reserve (CR), which encompasses brain processes that allow for better-than-expected behavioral performance given the degree of life-course-related brain changes. This study evaluated the effects of age, BM, and CR on longitudinal changes over 2 visits, 5 years apart, in 3 cognitive abilities that capture most of age-related variability.MethodsParticipants included 254 healthy adults aged 20-80 years at recruitment. Potential BM was estimated using whole-brain cortical thickness and white matter mean diffusivity at both visits. Education and intelligence quotient (IQ; estimated with American National Adult Reading Test) were tested as moderating factors for cognitive changes in the 3 cognitive abilities.ResultsConsistent with BM-after accounting for age, sex, and baseline performance-individual differences in the preservation of mean diffusivity and cortical thickness were independently associated with relative preservation in the 3 abilities. Consistent with CR-after accounting for age, sex, baseline performance, and structural brain changes-higher IQ, but not education, was associated with reduced 5-year decline in reasoning (β = 0.387, p = .002), and education was associated with reduced decline in speed (β = 0.237, p = .039).DiscussionThese results demonstrate that both CR and BM can moderate cognitive changes in healthy aging and that the 2 mechanisms can make differential contributions to preserved cognition.
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Project description:Peripheral insulin resistance is a key component of metabolic syndrome associated with obesity, dyslipidemia, hypertension, and type 2 diabetes. While the impact of insulin resistance is well recognized in the periphery, it is also becoming apparent in the brain. Recent studies suggest that insulin resistance may be a factor in brain aging and Alzheimer's disease (AD) whereby intranasal insulin therapy, which delivers insulin to the brain, improves cognition and memory in AD patients. Here, we tested a clinically relevant delivery method to determine the impact of two forms of insulin, short-acting insulin lispro (Humalog) or long-acting insulin detemir (Levemir), on cognitive functions in aged F344 rats. We also explored insulin effects on the Ca(2+)-dependent hippocampal afterhyperpolarization (AHP), a well-characterized neurophysiological marker of aging which is increased in the aged, memory impaired animal. Low-dose intranasal insulin improved memory recall in aged animals such that their performance was similar to that seen in younger animals. Further, because ex vivo insulin also reduced the AHP, our results suggest that the AHP may be a novel cellular target of insulin in the brain, and improved cognitive performance following intranasal insulin therapy may be the result of insulin actions on the AHP.
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Project description:Cerebral white matter hyperintensities (WMH) reflect accumulating white matter damage with aging and impair cognition. The role of childhood intelligence is rarely considered in associations between cognitive impairment and WMH. We studied community-dwelling older people all born in 1936, in whom IQ had been assessed at age 11 years. We assessed medical histories, current cognitive ability and quantified WMH on MR imaging. Among 634 participants, mean age 72.7 (SD 0.7), age 11 IQ was the strongest predictor of late life cognitive ability. After accounting for age 11 IQ, greater WMH load was significantly associated with lower late life general cognitive ability (? = -0.14, p < 0.01) and processing speed (? = -0.19, p < 0.001). WMH were also associated independently with lower age 11 IQ (? = -0.08, p < 0.05) and hypertension. In conclusion, having more WMH is significantly associated with lower cognitive ability, after accounting for prior ability, age 11IQ. Early-life IQ also influenced WMH in later life. Determining how lower IQ in youth leads to increasing brain damage with aging is important for future successful cognitive aging.
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Project description:The Apple polysaccharides (AP), extracted from the fruit of apple, has been used to treat multiple pathological diseases. In this study, we evaluated the effects of AP on cognitive impairment and intestinal aging in naturally aging mice. As a result, it was found that AP could improve spatial learning and memory impairment in aging mice through the Morris water maze experiment. Additionally, AP intervention can upregulate the expression of nerve growth factor (BDNF), postsynaptic marker (PSD95), and presynaptic marker (SYP) proteins. Moreover, AP can enhance total antioxidant capacity, reduce the level of pro-inflammatory cytokine, and inhibit the activation of the NF-κB signaling pathway, exerting anti-inflammatory and antioxidant functions. And the administration of AP restored intestinal mucosal barrier function, reduced the expression of aging and apoptosis related proteins. The administration of AP also altered the gut microbiota of mice. At the genus level, AP decreased the abundance of Helicobacter and Bilophila, while increased the abundance of Lactobacillus and Bacteroides. In summary, these data demonstrate that AP treatment can alleviate cognitive impairment, oxidative stress, and inflammatory reactions, repair the intestinal mucosal barrier, reduce intestinal aging, and alter specific microbial characteristics, ultimately improving the health of the elderly.
| S-EPMC11246462 | biostudies-literature
Project description:Cognitive control impairments in healthy older adults may partly reflect disturbances in the ability to actively maintain goal-relevant information, a function that depends on the engagement of lateral prefrontal cortex (PFC). In 2 functional magnetic resonance imaging studies, healthy young and older adults performed versions of a task in which contextual cues provide goal-relevant information used to bias processing of subsequent ambiguous probes. In Study 1, a blocked design and manipulation of the cue-probe delay interval revealed a generalized pattern of enhanced task-related brain activity in older adults but combined with a specific delay-related reduction of activity in lateral PFC regions. In Study 2, a combined blocked/event-related design revealed enhanced sustained (i.e., across-trial) activity but a reduction in transient trial-related activation in lateral PFC among older adults. Further analyses of within-trial activity dynamics indicated that, within these and other lateral PFC regions, older adults showed reduced activation during the cue and delay period but increased activation at the time of the probe, particularly on high-interference trials. These results are consistent with the hypothesis that age-related impairments in goal maintenance abilities cause a compensatory shift in older adults from a proactive (seen in young adults) to a reactive cognitive control strategy.
| S-EPMC2904686 | biostudies-literature
Project description:BackgroundDNA methylation (DNAm) age acceleration (AgeAccel) has been shown to be predictive of all-cause mortality but it is unclear what functional aspect(s) of aging it captures. We examine associations between four measures of AgeAccel in adults aged 45-87 years and physical and cognitive performance and their age-related decline.MethodsAgeAccelHannum, AgeAccelHorvath, AgeAccelPheno, and AgeAccelGrim were calculated in the Medical Research Council National Survey of Health and Development (NSHD), National Child Development Study (NCDS) and TwinsUK. Three measures of physical (grip strength, chair rise speed, and forced expiratory volume in one second [FEV1]) and two measures of cognitive (episodic memory and mental speed) performance were assessed.ResultsAgeAccelPheno and AgeAccelGrim, but not AgeAccelHannum and AgeAccelHorvath were related to performance in random effects meta-analyses (n = 1,388-1,685). For example, a 1-year increase in AgeAccelPheno or AgeAccelGrim was associated with a 0.01 mL (95% confidence interval [CI]: 0.01, 0.02) or 0.03 mL (95% CI: 0.01, 0.05) lower mean FEV1 respectively. In NSHD, AgeAccelPheno and AgeAccelGrim at 53 years were associated with age-related decline in performance between 53 and 69 years as tested by linear mixed models (p < .05). In a subset of NSHD participants (n = 482), there was little evidence that change in any AgeAccel measure was associated with change in performance conditional on baseline performance.ConclusionsWe found little evidence to support associations between the first generation of DNAm-based biomarkers of aging and age-related physical or cognitive performance in midlife to early old age. However, there was evidence that the second generation biomarkers, AgeAccelPheno and AgeAccelGrim, could act as makers of an individual's healthspan as proposed.
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Project description:Cognitive deficits are important predictors for outcome, independence and quality of life after stroke, but often remain unnoticed and unattended because other impairments are more evident. Computerized cognitive training (CCT) is among the candidate interventions that may alleviate cognitive difficulties, but the evidence supporting its feasibility and effectiveness is scarce, partly due to the lack of tools for outcome prediction and monitoring. Magnetic resonance imaging (MRI) provides candidate markers for disease monitoring and outcome prediction. By integrating information not only about lesion extent and localization, but also regarding the integrity of the unaffected parts of the brain, advanced MRI provides relevant information for developing better prediction models in order to tailor cognitive intervention for patients, especially in a chronic phase. Using brain age prediction based on MRI based brain morphometry and machine learning, we tested the hypotheses that stroke patients with a younger-appearing brain relative to their chronological age perform better on cognitive tests and benefit more from cognitive training compared to patients with an older-appearing brain. In this randomized double-blind study, 54 patients who suffered mild stroke (>6 months since hospital admission, NIHSS≤7 at hospital discharge) underwent 3-weeks CCT and MRI before and after the intervention. In addition, patients were randomized to one of two groups receiving either active or sham transcranial direct current stimulation (tDCS). We tested for main effects of brain age gap (estimated age - chronological age) on cognitive performance, and associations between brain age gap and task improvement. Finally, we tested if longitudinal changes in brain age gap during the intervention were sensitive to treatment response. Briefly, our results suggest that longitudinal brain age prediction based on automated brain morphometry is feasible and reliable in stroke patients. However, no significant association between brain age and both performance and response to cognitive training were found.
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Project description:Neuroimaging signatures based on composite scores of cortical thickness and hippocampal volume predict progression from mild cognitive impairment to Alzheimer's disease. However, little is known about the ability of these signatures among cognitively normal adults to predict progression to mild cognitive impairment. Towards that end, a signature sensitive to microstructural changes that may predate macrostructural atrophy should be useful. We hypothesized that: (i) a validated MRI-derived Alzheimer's disease signature based on cortical thickness and hippocampal volume in cognitively normal middle-aged adults would predict progression to mild cognitive impairment; and (ii) a novel grey matter mean diffusivity signature would be a better predictor than the thickness/volume signature. This cohort study was part of the Vietnam Era Twin Study of Aging. Concurrent analyses compared cognitively normal and mild cognitive impairment groups at each of three study waves (ns = 246-367). Predictive analyses included 169 cognitively normal men at baseline (age = 56.1, range = 51-60). Our previously published thickness/volume signature derived from independent data, a novel mean diffusivity signature using the same regions and weights as the thickness/volume signature, age, and an Alzheimer's disease polygenic risk score were used to predict incident mild cognitive impairment an average of 12 years after baseline (follow-up age = 67.2, range = 61-71). Additional analyses adjusted for predicted brain age difference scores (chronological age minus predicted brain age) to determine if signatures were Alzheimer-related and not simply ageing-related. In concurrent analyses, individuals with mild cognitive impairment had higher (worse) mean diffusivity signature scores than cognitively normal participants, but thickness/volume signature scores did not differ between groups. In predictive analyses, age and polygenic risk score yielded an area under the curve of 0.74 (sensitivity = 80.00%; specificity = 65.10%). Prediction was significantly improved with addition of the mean diffusivity signature (area under the curve = 0.83; sensitivity = 85.00%; specificity = 77.85%; P = 0.007), but not with addition of the thickness/volume signature. A model including both signatures did not improve prediction over a model with only the mean diffusivity signature. Results held up after adjusting for predicted brain age difference scores. The novel mean diffusivity signature was limited by being yoked to the thickness/volume signature weightings. An independently derived mean diffusivity signature may thus provide even stronger prediction. The young age of the sample at baseline is particularly notable. Given that the brain signatures were examined when participants were only in their 50 s, our results suggest a promising step towards improving very early identification of Alzheimer's disease risk and the potential value of mean diffusivity and/or multimodal brain signatures.
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