Elsevier

Neurobiology of Aging

Volume 94, October 2020, Pages 185-195
Neurobiology of Aging

Regular article
The role of cognitive reserve on prefrontal and premotor cortical activity in visuo-motor response tasks in healthy old adults

https://doi.org/10.1016/j.neurobiolaging.2020.06.002Get rights and content

Highlights

  • High cognitive reserve is associated with enhanced brain preparatory activities.

  • Enhanced behavioral performance was observed in individuals with high cognitive reserve.

  • Event-related potentials and behavioral results suggest neural compensation and maintenance effects.

Abstract

Cognitive reserve (CR) is a key factor to mitigate the cognitive decline during the aging process. Here, we used event-related potentials to target the preparatory brain activities associated with different levels of CR during visuo-motor simple response tasks (SRTs) and discriminative response tasks (DRTs). EEG was recorded from 28 healthy old (Age: 72.2 ± 4.7 years) and 14 young (Age: 22.2 ± 2.4 years) individuals during an SRT and a DRT. Depending on the CR median score, old participants were divided into either a high (High-CR) or a low CR (Low-CR) group. Behavioral performance and electrophysiological data were compared across the 3 groups. Compared with the Low-CR, the High-CR group showed larger prestimulus prefrontal (prefrontal negativity) and premotor activity (Bereitschaftspotential–BP), in the SRT, and increased premotor readiness (BP), in the DRT. The High-CR was faster and more accurate than the Low-CR group in the DRT and SRT, respectively. The High-CR group revealed enhanced brain preparatory activities that, paralleled to their behavioral performance, might reflect neural compensation and maintenance effects possibly counteracting the age-related decline in cognitive functioning.

Introduction

Aging is a continuous process inducing, in the majority of individuals, a general decline of biological and cognitive functions. Although the deterioration of cognitive functions cannot be completely reversed, diverse factors may contribute to slow down the physiological decline observed through normal aging. Among these factors, physical exercise (Berchicci et al., 2014, 2013) and cerebral reserve (Katzman, 1993; Mortimer et al., 1981) play a major role. The cerebral reserve theory stemmed from the initial observation that, in some cases, individuals with central nervous system diseases do not show the typical clinical symptoms (Stern, 2002). According to this theory, passive and active models of cerebral reserve have been postulated (e.g., Stern, 2009). The “brain reserve” model (Katzman, 1993) is a typical example of a passive model, referring to the brain structure in terms of size or neurons' numerosity. In contrast, active models, such as the “cognitive reserve (CR) model” refer to brain functions as the ability to use the available neural resources in a flexible and efficient way (e.g., Stern et al., 2005). CR can be estimated from different proxies, such as educational level, intelligence quotient (IQ), literacy, complexity of job, and leisure time activities (Nucci et al., 2012; Valenzuela and Sachdev, 2005), through the use of specific questionnaires, such as the Cognitive Reserve Index questionnaire (CRIq; Nucci et al., 2012). Stern (2006) identified 3 different mechanisms whereby the CR is implemented in the brain: neural reserve, neural maintenance, and neural compensation. According to Cabeza and colleagues (2018), “neural reserve is the cumulative enrichment, intended as the increase in neural resources promoted by environmental or genetic factors, occurring prior to the onset of a disease or age-related cognitive decline; neural maintenance refers to the preservation of neural resources by a continuous repair and replenishment of brain resources in response to neural damage; neural compensation refers to the deployment of neural resources to cope with task demands. Neural compensation can be achieved in diverse ways: by selection, reorganization, and upregulation. Compensation by selection consists of the recruitment of neural circuitry that are available but usually not recruited by young adults. Conversely, compensation by reorganization consists of recruiting a neural mechanism that is not available to young adults to cope with the same task demand. The latter mechanism, compensation by upregulation, consists of a compensatory overactivation of the cortical areas usually activated by young individuals for a given task (Cabeza et al., 2002). Similar compensatory mechanisms have been considered also in the scaffolding theory of aging and cognition (STAC) postulating that behavioral performance is preserved by recruiting additional circuitry, especially the prefrontal cortex, supporting structures that have become inefficient (Park and Reuter-Lorenz, 2009). According to the STAC model, higher CR may positively influence the quality, quantity, and effectiveness of the compensatory activity (Park and Reuter-Lorenz, 2009).

Growing neuroimaging and neuropsychological literature has focused on CR, highlighting that in healthy older adults, larger CR is associated with stronger functional connectivity within the resting-state network and between specific brain areas involved in action preparation, such as the anterior cingulate and the posterior parietal cortices. These studies are in line with the hypothesis of CR-related compensatory brain functions in older adults (for recent reviews, see Anthony and Lin, 2018; Cabeza et al., 2018; Colangeli et al., 2016).

The event-related potential (ERP) method is a noninvasive neurophysiological technique with high-temporal resolution, which can dynamically measure the activity of the cerebral cortex related to a given event. ERP literature on the CR in healthy older adults is still scarce, and the few available studies proposed a relationship between the CR and neural efficiency, focusing their measures on the P3 component, representing multiple postperceptual cognitive resource allocation related to task evaluation and closure (Donchin and Coles, 1988; Polich, 2007). Indeed, Gu et al. (2018) showed that the increase in P3 amplitude across tasks of increasing demand was reduced in participants with higher CR. Similar findings were reported by Speer and Soldan (2015). Other ERP research on the CR was conducted on a sample of young participants only and focused on the relationship between the P3 component and IQ, which is one of the proxies that is commonly used for the estimation of CR. These studies showed that higher CR could be associated with larger and earlier P3 during auditory (De Pascalis et al., 2008; Jaušovec and Jaušovec, 2000; Wronka et al., 2013) and visual (Jaušovec and Jaušovec, 2000; Liu et al., 2011) discrimination response tasks. Notwithstanding the growing interest on brain activity underpinning the CR, most studies focused only on the reactive (poststimulus) P3 and did not investigate the effects of CR on brain preparatory activities or proactive functions. These latter functions have been proposed as a potential mechanism to increase speed and accuracy (Aron, 2011; Di Russo et al., 2019), through increased motor preparedness and proactive inhibitory control that allow for the anticipation of upcoming events and actions (e.g., Bar, 2009). It is worth noting that in everyday life, most of the time, there is a need to withhold the response until the decision-making process is completed and the response can be emitted or not (Aron, 2011) as opposed to purely reactive inhibition, which is quite rare (Aron, 2011). With this in mind, the investigation of proactive brain functions is warranted to gain a comprehensive view of the effects of CR on brain functions. The quantification of the CR effects on the proactive ERP components may represent a cutting-edge topic, as the mean age of the population is continuously increasing and a deeper understanding of possible mechanisms promoting an enhanced quality of life among older adults is of paramount importance.

In the present study, we aimed to investigate whether CR can affect proactive brain functions involved in motor and cognitive task preparation. In addition, we sought to confirm the CR effect on the P3. To this aim, a group of healthy older adults with high CR has been compared with healthy older adults with low CR and to young individuals, while performing a simple and a complex visuo-motor task. In these tasks, prior to stimulus presentation, 2 slow-rising ERP negative activities can be observed over the medial central (the Bereitschaftspotential–BP; Kornhuber and Deecke, 1965) and prefrontal (prefrontal negativity–pN (Berchicci et al., 2012); electrodes. Combining the neuroimaging and ERP measures, the BP origin has been localized in premotor areas (cingulate and supplementary motor areas), whereas the cortical source of pN has been localized in the inferior frontal gyrus (Di Russo et al., 2016; Ragazzoni et al., 2019; Sulpizio et al., 2017). The BP has been associated with motor preparation, intended as the progressive excitability of premotor areas culminating with action initiation, whereas the pN has been associated with cognitive preparation, intended as top-down executive processing and, especially, proactive inhibition to avoid undesired responses. These 2 preparatory components can be viewed as a proactive accelerator/brake system allowing anticipatory balancing of action/inhibition tendencies to proactively regulate the upcoming behavioral performance in terms of speed, accuracy, and consistency (e.g., Bianco et al., 2017b,a; Perri et al., 2014). In fact, larger BP usually predicts shorter response time (RT), and changes in the pN have been associated with error-prone tendencies (for a review, see Di Russo et al., 2017, and for normative data, see Di Russo et al., 2019). Further, the amplitude of these components has been shown to vary across age: while the pN and the BP tend to be smaller in preadolescents than in young adults (Quinzi et al., 2018), the pN increases as a function of age after 35 years (Berchicci et al., 2014, 2013, 2012), and the BP displays an earlier onset in older compared with young adults (Falkenstein et al., 2006). In old people, physical activity seems to moderate the effects of aging on the pN enhancement and on response speed (Berchicci et al., 2014, 2013).

In agreement with the previous studies investigating the effects of CR in healthy old adults, we hypothesize slower response times in older adults compared with young individuals (Berchicci et al., 2012; Falkenstein et al., 2006); further, the behavioral performance of healthy old adults with high CR should be less affected by the physiological decline of cognitive functions than that of old adults with low CR. At the neural level, according to Cabeza et al. (2018) and to Park and Reuter-Lorenz (2009), we expect to find compensatory effects in individuals with high CR compared with their low CR peers, consisting in a larger prefrontal activity (pN) associated with less error-prone tendencies. In addition, according to our hypothesis, individuals with high CR may show a BP amplitude similar to young individuals and larger than low CR individuals, possibly suggesting neural maintenance effect of the CR on readiness potential. If our hypothesis on the behavioral performance is confirmed, concerning the P3 of individuals with high CR an increase in amplitude might reflect a compensatory effect (Jaušovec and Jaušovec, 2000; Liu et al., 2011), whereas a decrease in amplitude might be associated with neural efficiency (Gu et al., 2018; Speer and Soldan, 2015).

Section snippets

Participants

After explanation of the aims and procedures of the test, 28 healthy and physically active old participants (Age: 72.2 ± 4.7 years; CRI: 109.7 ± 18.1; 20 females) volunteered to participate in this study. In addition, 14 young participants, matched for gender and physical activity to the older group, were also recruited for the control group (Age: 22.2 ± 2.4 years; 10 females). None of them reported neurological disorders, all participants had a normal or corrected to normal vision and reported

Behavioral data

Fig. 2 shows the behavioral performance of the 3 groups. The ANOVA on the RT (Fig. 2A) showed significant effects of Group (F(2,39) = 19.3; p < 0.0001; η2p = 0.497), with slower RT in the older groups, Task (F(1,39) = 909.8; p < 0.0001; η2p = 0.859), with slower RT in the DRT, and Group × Task interaction (F(2,39) = 7.2; p = 0.0023; η2p = 0.270). Post-hoc comparisons on the interaction effect showed that in the DRT the Young group was faster than the other 2 groups (Low-CR: p = 0.0216, d =

Discussion

In this study, we investigated the effect of CR of healthy old adults (over 65 years of age) on behavioral performance and on proactive and reactive brain activities during an SRT and a DRT. At the behavioral level, the main finding indicated faster response time in the DRT for the old adults with higher CR compared with their low CR peers. Moreover, we showed that in the SRT, high CR may mitigate the physiological age-related RT slowing, as shown by the comparable RT between high CR and young

Conclusions

In the present study, we investigated the effect of CR on brain processing during the performance of visuo-motor response tasks. Depending on the CR level, proactive brain activities varied across tasks. High CR was associated with larger proactive cognitive preparation in the SRT, and this result can be interpreted as a sign of neural compensation. In addition, individuals with high CR showed preserved premotor excitability in both SRT and DRT. The preserved premotor excitability might be

CRediT authorship contribution statement

Federico Quinzi: Investigation, Writing - original draft, Visualization. Marika Berchicci: Investigation, Writing - review & editing. Valentina Bianco: Investigation, Writing - review & editing. Gloria Di Filippo: Conceptualization, Resources, Writing - review & editing. Rinaldo Livio Perri: Investigation, Writing - review & editing. Francesco Di Russo: Conceptualization, Methodology, Resources, Writing - review & editing, Supervision.

Acknowledgements

FDR and GDF conceived the study; FQ, MB, VB, and RLP collected the data; FQ and FDR analyzed the data; and FQ drafted the manuscript. FDR, MB, VB, RLP and GDF reviewed the manuscript.

Declarations of interest: none.

References (53)

  • F. Di Russo et al.

    Spatiotemporal brain mapping during preparation, perception, and action

    Neuroimage

    (2016)
  • M. Falkenstein et al.

    Effects of aging on slowing of motor-response generation

    Int. J. Psychophysiol.

    (2006)
  • D. Friedman et al.

    An overview of age-related changes in the scalp distribution of P3b. Electroencephalogr

    Clin. Neurophysiol.

    (1997)
  • S. Gauthier et al.

    Mild cognitive impairment

    Lancet

    (2006)
  • L. Gu et al.

    Cognitive reserve modulates attention processes in healthy elderly and amnestic mild cognitive impairment: an event-related potential study

    Clin. Neurophysiol.

    (2018)
  • N. Jaušovec et al.

    Correlations between ERP parameters and intelligence: a reconsideration

    Biol. Psychol.

    (2000)
  • T. Liu et al.

    Response preparation and cognitive control of highly intelligent children: a Go-Nogo event-related potential study

    Neuroscience

    (2011)
  • R. Oldfield

    The assessment and analysis of handedness: the edinburgh inventory

    Neuropsychologia

    (1971)
  • J. Polich

    Updating P300: an integrative theory of P3a and P3b

    Clin. Neurphysiol.

    (2007)
  • F. Quinzi et al.

    Weak proactive cognitive/motor brain control accounts for poor children’s behavioral performance in speeded discrimination tasks

    Biol. Psychol.

    (2018)
  • M.E. Speer et al.

    Cognitive reserve modulates ERPs associated with verbal working memory in healthy younger and older adults

    Neurobiol. Aging

    (2015)
  • Y. Stern

    Cognitive reserve

    Neuropsychologia

    (2009)
  • V. Sulpizio et al.

    Hemispheric asymmetries in the transition from action preparation to execution

    Neuroimage

    (2017)
  • E. Wronka et al.

    Psychometric intelligence and P3 of the event-related potentials studied with a 3-stimulus auditory oddball task

    Neurosci. Lett.

    (2013)
  • A.R. Aron

    From reactive to proactive and selective control: developing a richer model for stopping inappropriate responses

    Biol. Psychiatry

    (2011)
  • M. Anthony et al.

    A systematic review for functional neuroimaging studies of cognitive reserve across the cognitive aging spectrum

    Arch. Clin. Neuropsychol.

    (2018)
  • Cited by (0)

    Declarations of interest: none.

    View full text