Revealing The Brain s Hidden Potential: Cognitive Training & Neurocognitive Plasticity. Introduction

Revealing The Brain s Hidden Potential: Cognitive Training & Neurocognitive Plasticity. Introduction Global aging poses significant burdens as age-related impairments in cognitive function affect quality of life, the ability to live independently, and they increase the risk for neurodegenerative diseases. As the population of older adults accelerates domestically and globally, the scientific community must address factors that contribute to healthy aging (i.e. the maintenance of optimal cognitive and intellectual integrity in late life). Healthy aging will affect not only an individual s quality of life, but also participation in economic and societal concerns. For example, the ability of baby boomers to work during retirement can generate the US economy an estimated 12.9 trillion by the year 2025 (Greenwood & Parasuraman, 2012). Contrary to the traditional view that progressive, irreversible cognitive decline occurs with age, recent research shows that it is neither universal nor inevitable. While aging is inevitable, cognitive decline is not. As evidenced in the literature (e.g. Hedden & Gabrieli, 2004) and anecdotally, significant individual variability exists in the rate of decline in late adulthood. Indeed, recent evidence shows that the human brain retains a substantial amount of plasticity. Whether diseased, aged, or healthy, the brain is capable of changing in response to experience throughout life (Kramer and Erickson, 2007). By understanding the mechanisms underlying age-related cognitive decline, my long term goal is to provide scientific evidence for developing interventions for older adults to lead rich and cognitively vital lives. The central hypothesis of this study is that older adults retain the capacity for brain plasticity and that a minimal amount of training is capable of diminishing agerelated differences and influencing the allocation of resources in response to cognitive task demands. 1

Cognition is an umbrella term for a collective group of mental processes that can be broken down into basic mechanisms, such as attention, sensory memory, working memory, and processing speed. Reductions in these cognitive processes serve as bottlenecks to overall, higher order cognitive function. Indeed, researchers have carefully documented the age-related decline in these basic processes and their interaction with global cognitive decline (Baltes & Lindenberger, 1997; Park et al., 2002; Salthouse, 1996). These changes are exemplified in a cross-sectional analysis (Figure 1) of 301 adults ranging from age 20 to 90 (Park et al., 2002). This study focuses on the working memory reductions that occur in normal aging (for a review, cf. Verhaeghen, 2011). The concept of working memory (Baddeley, 1986; Craik & Byrd, 1982) involves active maintenance of limited information in a temporary and accessible state readily available for higher-level cognitive processes (e.g. driving a car in high traffic while engaging in a conversation). Several theories explain the reductions in working memory (WM) function; for example, Salthouse (1996) proposed that age-related reductions in processing speed renders it more difficult to rapidly access WM s on-line information. Additionally, a deficit in attention control inappropriately focuses on irrelevant, environmental stimuli that competes with task-relevant stimuli, diminishing task performance (Hasher, Lustig, & Zacks, 2008). These interrelationships among basic cognitive processes predict cognitive function. The advent of the neuroimaging epoch has greatly informed cognitive aging literature. Perhaps the most intriguing finding from functional neuroimaging studies is the over-recruitment of additional brain areas in older adults when compared to younger adults who perform similarly or better during the same WM task. Drawing upon this phenomenon of over-recruitment, Reuter- Lorenz proposed the Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH; Reuter-Lorentz & Cappell, 2008) where, in general, as task load increases, people 2

will progressively engage in compensatory activity in the form of recruiting additional neural resources to assist in task performance. However, for older adults, who have a lower threshold for task loads, it may be necessary to over-recruit neural resources at lower task loads. The CRUNCH model continues to state that following over-recruitment, a ceiling is reached in which all neural resources are depleted, thus task performance drops (hence, crunch ; see Figure 2). Following this hypothesis, manipulating WM load (by increasing the amount of maintained and processed information ) taxes the WM system until reaching its limited capacity, or crunch-point. Further increases in WM load should result in low performance. CRUNCH holds strong implications for substantial flexibility and reorganization of neural resources in the aging brain. This leads to the specific hypothesis that with enough training an individual s crunch-point can be extended, suggesting that WM capacity can increase (see Figure 3). CRUNCH has been qualitatively supported by only a few studies (Daffner et al., 2011; Schneider-Garces et al., 2010); however, to date, no studies have examined the flexibility of the crunch point with the effect of training. Testing this hypothesis requires multiple sessions of training. Additionally, the WM paradigm needs to include multiple levels of task load spanning from very low to very high to allow room for the crunch-point to be extended. The objectives of the current study are 1) to quantitatively support the cognitive predictions of CRUNCH, and 2) to examine the extent to which the crunch-point is influenced by training. The Sternberg (1966) memory search task, used by Schneider-Garces et al. (2010), effectively examines the relationship between task load and cognitive performance by parametrically varying task load in the form of manipulating memory set sizes. In this simple Sternberg task, a memory set of items is presented for a duration. Following a delay interval, a probe stimulus is presented and the test participant responds with whether or not the probe item 3

was part of the original memory set. To allow room for the crunch-point to extend between sessions, the present study used memory set sizes varying from 2-8 items. Methods Participants: Fifty-six participants from ages 55 89 were included in the present experiment; however, a total of 14 subjects were excluded due to various equipment issues during recording. Thus, only the data from 42 participants are presented in the current report. Additionally, the participants were divided into three age groups. Group 1 included 10 participants (age range = 55-64 yrs, mean age = 59 yrs, 6 women); Group 2 included 18 participants (age range 65-74 yrs, mean age = 69.72 yrs, 10 women); and Group 3 included 14 participants (age range = 75+ yrs, mean age = 80.21, 7 women). The age groups were chosen to span a large segment of the aging population with a wide spectrum of age-related changes. The participants were screened for a history of major neurological and psychiatric disorders and alcohol/drug abuse. Each participant signed an informed consent form and was compensated $15 per hour. All participants were right-handed and had normal or corrected-to-normal vision. Paradigm: In the present experiment, we used a modified version of the Sternberg memory search task (Sternberg, 1966) and parametrically manipulated memory set sizes varying from 2 to 8 items. Set sizes 2 to 8 were chosen on the basis of the limited WM capacity (4 + 1 items) proposed by Cowan s model of WM (Cowan, 2001), thus, allowing the observation of sub-limit, at limit, and supra-limit effects. Each memory set contained randomly assigned letters with no identical letters in one set. Four memory set size conditions (2, 4, 6, or 8) were presented in blocks, consisting of 8 trials per condition for a total of 4 task blocks per run. The order of the blocks was counterbalanced across 4 runs. This yielded a total of 128 trials per session (see Figure 4). For each trial, the participants were presented with a memory set of 2, 4, 6, or 8 upper- 4

case letters to be encoded. The memory set was presented for 3 sec, followed by a screen containing only a fixation cross for 1 sec. Next, a lower-case probe letter was presented for 500 msec, followed by another fixation cross for 1.5 sec. The probes were presented in lower-case to avoid a direct visual match. During the 2 sec interval, participants indicated whether or not the probe was included in the memory set of that particular trial by pressing the corresponding response pad held in their right or left hand. The response hand was counterbalanced across subjects. The probe was part of the memory set on 50% of the trials. Participants were asked to respond as accurately and quickly as possible. Procedures: Each participant came into the lab for three different sessions (1-wk apart). During the first session participants were introduced to and trained on the memory paradigm as well as administered neuropsychological testing. Upon completion of the first session, participants were familiarized with all testing procedures. Before each subsequent testing session, participants were trained once more to ensure that the details of the task were clear. Behavioral Analysis: Participants with complete cognitive data from all three sessions were included in the analyses. Trials where the participant did not respond were excluded. Cognitive performance was measured using reaction times (RT), d (e.g. a measure of signal detection) and throughput. For correct trials, RT was computed as the interval duration between the onset of the stimulus probe and the button press. Participants d scores were determined by: z(hit Rate) z(false Alarm Rate). The participants reaction times and d scores were analyzed with mixed-design ANOVAs. A repeated-measures ANOVA tested the significance of the three age groups at four memory load conditions and across the three sessions. A slope analysis then estimated the participants behaviors at memory loads above and below their WM span. 5

To characterize WM capacity, each participant s information throughput was calculated based on performance level. Throughput estimates the amount of information processed in WM given the number of items presented in a memory set and computed from the following formula: Throughput = [(ACC - 0.5) / 0.5] * N items. For each memory set condition, the number of items (N) presented in the memory set is the maximum amount of information throughput (i.e 100% accuracy for the trial; see ideal line in Figure 7). The chance level of 0.5 is subtracted from uncorrected overall accuracy (ACC), then range-corrected by 0.5 and finally multiplied by the number of items (N) presented in the memory set for the trial. Measuring throughput across all memory loads allowed for an estimate of each participant s WM capacity as the maximum amount of information transmitted across set sizes. The WM capacity computed from the throughput measure is significantly correlated with the measurements from traditional memory span tests (see Schneider-Garces et al., 2010). Results The cognitive data from 42 participants are first presented separately for the third session and then presented for all three sessions combined. Sensitivity to Targets: Sensitivity was based on each participant s ability to detect targets from non-targets (d scores). Results from the mixed-design repeated-measures ANOVA show that overall, the oldest adults had lower d scores than younger adults [Main Effect of Age Group: F(2, 39) = 3.64, p = 0.036], and that all participants were less sensitive to detect targets at higher memory loads [Main Effect of Load: F(3, 117) = 137.90, p < 0.001]. The Memory Load x Age Group interaction was not significant [F(6, 117) = 0.72, ns], indicating that the decreased d scores with increasing memory load were not significantly different across age groups. 6

Reaction Time: All age groups had significantly slower reaction times as memory load increased [Main Effect of Memory Load: F(3, 117) = 154.48, p < 0.001]. Overall, the oldest adults were not significantly slower than younger adults [Main Effect of Age Group: F(2, 39) = 0.165, ns]. The increase in reaction time with increasing memory load was not significantly different across age groups [Memory Load x Age Group: F(6, 117) = 0.958, ns]. Throughput: A repeated-measures ANOVA revealed that overall, the oldest adults had lower throughput scores than younger adults [Main Effect of Age Groups: F(2, 39) = 3.19, p = 0.052]. The Memory Load x Age Group interaction for throughput was not significant [F(6, 117) = 0.729, ns], indicating that the decrease in information throughput with increasing memory load was not significantly different across age groups. WM Capacity: WM capacity was also computed for all participants based on the throughput data. An ANOVA revealed a main effect of age to be marginally significant [F(2, 50) = 3.0714, p < 0.10], indicating that as age increases, WM capacity decreases [r = 0.469, t(40) = 3.36, p < 0.001] (see Figure 5). The younger adults could retain significantly more information. Effects of Multiple Sessions: The effect of multiple sessions on throughput is presented in Figure 6. A mixed-design ANOVA was performed to analyze the effect of multiple sessions on throughput, with one between-subjects factor (Age Group) and two within-subjects factors (Session and Memory Load). This ANOVA revealed a main effect of Session [F(2, 78) = 3.51, p < 0.05], a Session by Age Group interaction [F(4, 78) = 2.13, p < 0.10], and a three-way interaction between Session, Age Group, and Memory Load [F(12, 234) = 2.41, p <.01]. According to Figure 6, the interactions appeared to be explained by more improvement for the oldest adults at higher loads. To test this hypothesis, an improvement index was derived, in which the throughput in session 1 was subtracted from the throughput in session 3 for all 7

memory loads (see Figure 7). Planned t-tests were performed to test for improvement, separately for all age groups at all memory loads. From the improvement indices: the oldest age group significantly improved their throughput at memory loads of 2, 6, and 8 items, the middle age group improved their throughput at memory loads of 2 and 4 items, while the youngest age group showed no significant improvements in their throughput from session 1 to session 3. Discussion Motivation for the current study originated from the cognitive predictions of the CRUNCH model and also from reports on neurocognitive plasticity and training. The CRUNCH model proposes that older adults reach a crunch-point (i.e. breakdown in cognitive performance) at lower task loads than younger adults while recruiting more brain resources to maintain proficiency in task performance. By parametrically manipulating WM load in a modified Sternberg paradigm (1966), age-related performance was calculated as a function of WM load and presented in the present report. The reported results demonstrated quantitative support for CRUNCH s prediction in cognitive performance. Additionally, the oldest adults showed significant improvements after three training sessions. The first objective was to quantitatively support the age-related cognitive performance deficits in CRUNCH s WM model. The results from multiple sessions show a significant breakdown in cognitive performance at lower memory loads for the oldest group when compared to the younger groups in the first two sessions; however, the third session revealed no significant age by load interaction (see Figure 6). On average, the oldest adults performed more poorly than the younger groups, especially at the higher memory loads. The data from the WM capacity analysis showed that regardless of the memory task load, age was a main effect, supporting the age-related decline in WM capacity reported in other studies (e.g. Park et al., 2002). 8

The second objective was to determine whether the CRUNCH-related WM decrements would be consistent across multiple sessions of training. Results from the first two sessions showed that the oldest adults reached their crunch-point at low task loads. The age-related differences in cognitive performance, however, ameliorated through practice. During the third session, no significant Age Group by Memory Load interactions were found. Across sessions, the oldest group showed substantially more improvement at the higher memory loads. The throughput data showed that with each session, the maximum number of items transmitted into the WM system increased. Through practice, the oldest adults extended their behavioral crunchpoint, suggesting that they can significantly increase the number of items processed by their WM systems. This study expands upon the original CRUNCH model by demonstrating the contributions of training. Moreover, consistent with theoretical understanding of cognition, improvements in WM function should lead to improvements in overall cognition. Plasticity and the Effect of Training: The duration of the reported improvements is unknown. After a 10-session large-scale intervention program (Advanced Cognitive Training for Independent and Vital Elderly, ACTIVE) that trained various cognitive processes, the older adults showed cognitive improvements that were reported to last for five years (Ball et al., 2002). However, the benefits of training are often specific to the trained task, resulting in a limited transfer to other tasks and everyday activities (Lustig et al., 2009). Most of the findings from cognitive training studies have involved an extensive amount of training (i.e. 10-sessions for the ACTIVE program). The results from the present study show that performance, in older adults compared to younger adults, can be changed even after just three sessions of training. This supports the hypothesis that older adults retain the capacity for brain 9

plasticity and only minimal amounts of training are necessary for altering the allocation of resources in response to varying cognitive task demands. Future Directions and Conclusion: CRUNCH accounts for the effects of task load on brain activity and cognitive performance, positing that older adults over-recruit neural resources at lower task loads compared to younger adults in order to maintain proficiency in task performance, and, therefore, reach an earlier resource ceiling, or crunch-point. In reaching their ceiling, older adults perform lower at higher task loads and show under-activation of neural resources compared to younger adults. A future direction of the study is to acquire and examine neuroimaging data from all three sessions for CRUNCH-related effects in brain activation. The conclusions of the present study lead to strong predictions for the neuroimaging study. It is expected that the oldest group will show over-recruitment of brain resources at earlier memory loads than younger groups. Furthermore, with enough training, the improvement in cognitive performance for the oldest adults shown in the present study is predicted to be accompanied by significant changes in neural utilization (i.e. the amount of compensatory recruitment available is extended to be allocated to higher task loads). Cognitive components govern everyday tasks like financial management or medication adherence, and the inability to perform these tasks adequately results in negative consequences. Repetition, practice, and training are easily implemented, and there is promising data that capitalizes on the brain s plastic capabilities to support improved cognitive and brain function. Further advancements in the understanding of brain aging and its impact on cognitive function will prove beneficial for an enriched life for older adults. 10

Figure 1. Figure reproduced from Park et al. (2009) representing the vast amount of cognitive decline with age. Figure 2. Figure modified from Reuter-Lorenz & Cappell (2008) showing the predictions of the CRUNCH model, in which compared to younger adults, older adults reach an earlier breakpoint in the recruitment of neural resources (Panel 1) and behavioral performance (Panel 2). Figure 3. Behavioral predictions of extending crunch-point, which allows for greater information throughput (i.e. greater capacity) into the WM system. Figure 4. Figure representing the Sternberg paradigm used in the present study. 11

Figure 5. Correlation of individual WM capacity with age. r = 0.469, t(40) = 3.36, p < 0.001 Figure 6. Measures of throughput across all 3 sessions and memory loads for all age groups. The ideal throughput function is provided as reference. Figure 7. Improvement index for throughput at each memory load condition calculated by subtracting the session 1 throughput from session 3 throughput. 12

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