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Compensatory neural activity distinguishes different patterns of normal cognitive aging.

ABSTRACT: Most cognitive neuroscientific research exploring the nature of age-associated compensatory mechanisms has compared old adults (high vs. average performers) to young adults (not split by performance), leaving ambiguous whether findings are truly age-related or reflect differences between high and average performers throughout the life span. Here, we examined differences in neural activity (as measured by ERPs) that were generated by high vs. average performing old, middle-age, and young adults while processing novel and target events to investigate the following three questions: (1) Are differences between cognitively high and average performing subjects in the allocation of processing resources (as indexed by P3 amplitude) specific to old subjects, or found throughout the adult life span? (2) Are differences between cognitively high and average performing subjects in speed of processing (as indexed by target P3 latency) of similar magnitude throughout the adult life span? (3) Where along the information processing stream does the compensatory neural activity attributed to cognitively high performing old subjects begin to take place? Our results suggest that high performing old adults successfully manage the task by a compensatory neural mechanism associated with the modulation of controlled processing and the allocation of more resources, whereas high performing younger subjects execute the task more efficiently with fewer resources. Differences between cognitively high and average performers in processing speed increase with age. Middle-age seems to be a critical stage in which substantial differences in neural activity between high and average performers emerge. These findings provide strong evidence for different patterns of age-related changes in the processing of salient environmental stimuli, with cognitive status serving as a key mediating variable.

SUBMITTER: Riis JL

PROVIDER: S-EPMC2219544 | biostudies-literature | 2008 Jan

REPOSITORIES: biostudies-literature

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Compensatory neural activity distinguishes different patterns of normal cognitive aging.

Riis Jenna L JL Chong Hyemi H Ryan Katherine K KK Wolk David A DA Rentz Dorene M DM Holcomb Phillip J PJ Daffner Kirk R KR

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Most cognitive neuroscientific research exploring the nature of age-associated compensatory mechanisms has compared old adults (high vs. average performers) to young adults (not split by performance), leaving ambiguous whether findings are truly age-related or reflect differences between high and average performers throughout the life span. Here, we examined differences in neural activity (as measured by ERPs) that were generated by high vs. average performing old, middle-age, and young adults w ...[more]

PMID: 17931892

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Project description:One mechanism that may allow older adults to continue to successfully perform certain cognitive tasks is to allocate more resources than their younger counterparts. Most prior studies have not included individuals beyond their 70s. Here, we investigated whether compensatory increases in neural activity previously observed in cognitively high-performing young-old adults would continue into old-old age. Event-related potentials were recorded from 72 cognitively high performing subjects, aged 18 to 96 years old, while they participated in a subject-controlled novelty oddball paradigm in which they determined viewing duration of standard, target, and novel visual stimuli. Compared to young and middle-aged subjects, both young-old and old-old subjects exhibited an impairment of preliminary mismatch/match detection operations, indexed by an attenuated anterior N2 component. This may have placed a greater burden on the subsequent controlled decision-making process, indexed by the P3, necessitating the allocation of more resources. The relationship between age and resource allocation, as measured by P3 amplitude, from midlife to very old age (45-96 years old) followed an inverted u-shaped curve (quadratic function). It peaked between the late 60s and early 70s. Thereafter, there was an inverse relationship between age and resource appropriation. This relationship remained significant after controlling for differences in task performance and MMSE. Examining the size of the P3 component across different age groups suggests that although cognitively high performing adults in their early 80s exhibit a reduction in P3 amplitude, they have a relatively well-preserved capacity to appropriate resources. However, by the late 80s, there is a robust decline (relative to young-old adults) in the size of the P3. Our results indicate that when carrying out controlled processing linked to directing attention to salient events, cognitively high performers reach the boundary of their capacity, albeit relatively late in life. This limits their ability to appropriate additional resources as compensatory activity for age-related impairments in earlier visual processing, and suggests that such a mechanism does not tend to "survive" old-old age.

Project description:Background: Neuroscience lacks a reliable method of screening the early stages of dementia. Objective: To improve the diagnostics of age-related cognitive functions by developing insight into the proportionality of age-related changes in cognitive subdomains. Materials and Methods: We composed a battery of psychophysiological tests and collected an open-access psychophysiological outcomes of brain atrophy (POBA) dataset by testing individuals without dementia. To extend the utility of machine learning (ML) classification in cognitive studies, we proposed estimates of the disproportional changes in cognitive functions: an index of simple reaction time to decision-making time (ISD), ISD with the accuracy performance (ISDA), and an index of performance in simple and complex visual-motor reaction with account for accuracy (ISCA). Studying the distribution of the values of the indices over age allowed us to verify whether diverse cognitive functions decline equally throughout life or there is a divergence in age-related cognitive changes. Results: Unsupervised ML clustering shows that the optimal number of homogeneous age groups is four. The sample is segregated into the following age-groups: Adolescents ∈ [0, 20), Young adults ∈ [20, 40), Midlife adults ∈ [40, 60) and Older adults ≥60 year of age. For ISD, ISDA, and ISCA values, only the median of the Adolescents group is different from that of the other three age-groups sharing a similar distribution pattern (p > 0.01). After neurodevelopment and maturation, the indices preserve almost constant values with a slight trend toward functional decline. The reaction to a moving object (RMO) test results (RMO_mean) follow another tendency. The Midlife adults group's median significantly differs from the remaining three age subsamples (p < 0.01). No general trend in age-related changes of this dependent variable is observed. For all the data (ISD, ISDA, ISCA, and RMO_mean), Levene's test reveals no significant changes of the variances in age-groups (p > 0.05). Homoscedasticity also supports our assumption about a linear dependency between the observed features and age. Conclusion: In healthy brain aging, there are proportional age-related changes in the time estimates of information processing speed and inhibitory control in task switching. Future studies should test patients with dementia to determine whether the changes of the aforementioned indicators follow different patterns.

Project description:Motor performance and learning have distinct behavioral and neural signatures and can be uniquely modulated by various informational and motivational factors. Contemporary frameworks describe four different motor learning mechanisms mapped onto specific neural regions which are key for motor skill acquisition: error-based learning (cerebellum), reinforcement learning (basal ganglia), cognitive strategies (prefrontal cortex), and use-dependent learning (motor cortex). However, little is known about the neural circuits engaged during skill acquisition that are modulated specifically by practice-based performance improvement and those that predict recall performance. Based on previous work, we hypothesize that brain activity during practice in primary motor cortex and basal ganglia (1) is associated with trial-by-trial practice performance and (2) is predictive of immediate recall performance. Leveraging the contemporary framework, we use a well-known task paradigm that primarily relies upon cognitive strategy, reinforcement, and use-based learning mechanisms to test our hypotheses. Forty neurotypical young adults were asked to practice a pinch force tracking task. Participants received performance feedback after each trial during practice. We used whole brain analysis of functional magnetic resonance imaging (fMRI) and behavioral performance measures (i.e., time-on-target and self-efficacy) during the practice phase to determine which brain activation patterns are (1) associated with trial-by-trial tracking performance and (2) predictive of immediate no-feedback retention performance. We observed brain activations in the frontal orbital cortex, putamen, amygdala, and insula correlated with tracking performance improvement during practice. In contrast, a different set of performance-related activated regions were observed that were associated with immediate retention performance that included the primary motor cortex, superior frontal gyrus, somatosensory cortex, angular gyrus, and parietal gyrus. Our findings demonstrate that improved practice performance and recall of a sensorimotor skill are correlated with distinct neural activity patterns during acquisition, drawing on different motor learning mechanisms during encoding. While motor performance improvements depend on both cortical and subcortical regions, motor skill recall depends primarily on prefrontal and motor cortices. We discuss possible interpretations for why our hypothesis regarding basal ganglia activity and retention performance was not supported. Understanding the different neural mechanisms engaged in motor performance and learning may inform novel interventions to enhance motor skill learning.

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Compensatory neural activity distinguishes different patterns of normal cognitive aging.

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Compensatory neural activity distinguishes different patterns of normal cognitive aging.

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