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(p. 125) Training the Aging Brain: Is Cognitive Decline Inevitable? 

(p. 125) Training the Aging Brain: Is Cognitive Decline Inevitable?
Chapter:
(p. 125) Training the Aging Brain: Is Cognitive Decline Inevitable?
Author(s):

Amir Raz

, and Sheida Rabipour

DOI:
10.1093/med-psych/9780198789673.003.0004
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date: 23 July 2019

About every three seconds, the world inherits a new case of dementia.

prince et al., 2015

Many people wonder whether cognitive decline necessarily occurs as we grow older. The answer to this question depends, at least to some extent, on perspective. Recently we ran into an expert on Alzheimer’s disease at a social function. When the topic of conversation turned to his opinion on age-related dementia, he recommended drinking coffee, eating chocolate, and speaking multiple languages. On a different occasion, another expert recommended sleeping and eating well, being physically active, reducing anxiety, socializing, and continuing to learn throughout life. Alas, specialists often regurgitate old wisdom that consumers find dissatisfying, even disappointing. An editorial summarizing clinical wisdom for preventing cognitive decline in older adults offered the following simple guidance: avoid products and activities that make cognitive decline worse, stay physically active, and engage in cognitive exercises perceived as challenging and stimulating—without expecting rapid or drastic improvements (Lenze and Bowie, 2018). In this chapter, we present the impressions of a few leading experts along with the latest scientific insights on training and preserving function in the senescent brain. We conclude with a summary of the factors that experts agree play a role in the conundrum of whether cognitive decline accompanies age.

The average consumer expects doctors and scientists to unravel little-known truths that would result in marked improvement. However, many (p. 126) factors contribute to physical and mental health throughout life, and the outcomes of aging can be unpredictable. With this backdrop, the public hungrily reaches for new-age trends, vivid brain images coupled with broad claims, and obscure terminology intimating scientific validity. Some of these fads have potential, although most constitute modern variations on old snake oil.

Even experts find it difficult to separate the wheat from the chaff. For example, Robert Bilder, Professor of Psychiatry and Biobehavioral Sciences at the University of California, Los Angeles (UCLA), recounts discovering advertisements for a familiar product that made surprising claims. “They claimed the product had been studied by leading universities around the world, and was proven to lead to significant improvement in 80% of users.” Bilder had previously read the scientific literature and didn’t remember such evidence, so he called the company for clarification. “I found out I had been so naïve to assume that, because there were two clauses in the sentence connected with the word ‘and’, they would somehow be connected!” As it turned out, 80% of users who chose to comment on the product said that it had led to improvements, and the studies in question were unrelated to these consumer reports. In other words, with careful maneuvering, the company presented facts that would lead readers to erroneously infer that scientific studies support the effectiveness of the product.

Many companies use such deceptive marketing. Advertisements may truthfully state that products are the subject of study or based on scientific research, without clarifying what the research shows or how closely the studies relate to the product. Coupled with handpicked testimonials, such marketing can effectively lead consumers to make inaccurate inferences about a product, to the benefit of the company. In early 2016, for example, Lumosity (Lumos Labs Inc., 2017)—one of the most aggressively marketed commercial brain-training programs to date—agreed to a $2 million settlement with the Federal Trade Commission for “charges alleging that they deceived consumers with unfounded claims that Lumosity games can help users perform better at work and in school, and reduce or delay cognitive impairment associated with age and other serious health conditions” (Federal Trade Commission, 2016; Box 4.1). Without proper guidance, figuring out the accuracy of each bit of advice, the relevance of specific arguments to your situation, and the feasibility of a particular method in your life can prove difficult. (p. 127)

The Case of Lumosity

Lumosity (Lumos Labs Inc., 2017) represents one of the most visibly growing brain-training companies in North America. For about US$15 per month, or a lifetime membership of US$300, Lumosity granted you access to an expanding variety of games through its web application: matching games aiming to engage memory and processing speed, visual searches targeting attention, and games demanding timed mental calculation or rotation. From Internet ads to television and radio commercials, the company thrust a forceful marketing campaign suggesting that Lumosity games can help delay the onset of dementia by up to 10 years. Their exploding number of consumers and continued growth is a testament to the effectiveness of deceptive marketing.

(p. 128) Brain-training research in older adults soon began to challenge Lumosity’s marketing claims. In 2015, less than a year before the Lumosity settlement, the Canadian Broadcasting Corporation (CBC) approached neuroscientist Adrian Owen to examine Lumosity as part of an investigation on “mind games” (“Brain Training: Mind Games,” 2015) to help determine whether advertised claims are accurate. Over 2–4 weeks, a group of 54 adults played Lumosity for at least 15 minutes per day, three times per week. Before beginning the training, and again after completing the program, the group underwent a series of neuropsychological tests to assess their cognitive abilities. Owen was no stranger to web-based cognitive training: in 2010, he publicly criticized online computer games as part of the British Broadcasting Corporation (BBC) television series “Bang Goes the Theory.” The episode presented research drawing on over 11,000 adults 18–60 years of age, who played a series of brain games from the comfort of their homes over 6 weeks. Findings of this highly publicized—and widely criticized—study, published in Nature magazine (Owen et al., 2010), stunned participants and viewers of the program with the allegation that computerized brain training does not improve general cognitive abilities. In the 2015 investigation with the CBC, the pattern repeated. For the second time, Owen publicly broadcast the message that computerized brain-training games are yet to show real-world benefits.

Merely Another Cog in the Brain-training Wheel?

How does the CBC investigation fare against the scientific literature on Lumosity? Outside of reports published in-house, results from independent studies targeting healthy adults are, as you may have guessed, mixed. One randomized clinical trial in older adults, led by researchers in Spain, reported improvements in alertness as well as decreased distractibility (i.e. the ability to filter out information irrelevant to a particular task) in healthy older adults who played 20 hour-long sessions, but not in the control group who attended meetings with other members of the group (Mayas et al., 2014). The trial further revealed training-related improvements in immediate and delayed recall of family pictures as well as in self-ratings of affection and assertiveness (Ballesteros et al., 2014). Nevertheless, the study found little or no benefit of training over the control conditions in the other measures of executive function, attention, working memory, facial recognition memory, or psychological wellbeing. To supplement the relatively low sample size (only 27 participants in total) and passive control condition, the authors conducted a larger follow-up trial comparing Lumosity training (p. 129) to commercially available simulation strategy video games (The Sims and SimCity Build), which showed similar results: little benefit of Lumosity for untrained cognitive tasks (Ballesteros et al., 2017). Results of Lumosity training have been mixed, even within the same research group: in a similar study, the same Spanish researchers examined 15 1-hour sessions of Lumosity training in healthy older adults who regularly participated in cultural activities at a local seniors center. Compared to controls who simply maintained their regular activities at the seniors center, those who completed Lumosity training demonstrated improvements in memory tests (Toril et al., 2016). A separate study focusing on undergraduate students examined 8 hours of training with Lumosity compared to Portal 2 (Valve Corporation, 2011), a commercially available action video game. The researchers observed no measurable improvements following Lumosity training, whereas participants who played Portal 2 displayed comparably heightened problem-solving, spatial skill, and persistence (Shute et al., 2015).

The hype surrounding Lumosity seems to be ending. Nevertheless, the CBC investigation shared the same pitfalls as other media reports, popular blogs, and articles published in non-scientific sources. While the investigation raised many valid and important points, the investigation was not rigorous enough to present an accurate story. On the one hand, the CBC investigation—similar to the BBC experiment—demonstrated that playing web-based games for a few weeks, at leisure, is probably insufficient to produce any meaningful benefits to otherwise healthy individuals. However, both the CBC and BBC experiments had weak control over the conditions under which participants played the games, included insufficient amounts of training compared to other reported studies, and generalized one example to an entire field. For now, the investigation remains another chapter in the unfolding story of cognitive training.

Expert advice on maintaining brain function in aging tends to be conservative. Specialists, including cognitive neurologist Howard Chertkow, Chair in Cognitive Neurology and Innovation at Baycrest’s Rotman Research Institute in Toronto, Canada, recommend daily habits that promote a balanced lifestyle. Achieving this balance means getting enough sleep, eating vegetables, keeping stress and negative emotions low, and moving around physically. In his experience working with adults who have mild cognitive impairment and dementia, he has seen no compelling evidence that taking vitamins, following specific diets, or favoring one type of exercise regimen over another is beneficial in preventing or slowing down age-related neurological disease. Chertkow advocates, generally, salubrious nutrition, social interaction, and fitness—although how much exercise a person needs remains uncertain and likely varies between individuals. (p. 130) Similarly, according to Robert Bilder, physical exercise remains the most validated approach to maintaining brain structure and function, and cognitive performance. Among numerous accolades, Bilder is also the Director of the Mind Well program at University of California, Los Angeles (UCLA), part of the larger Healthy Campus Initiative, which promotes wellness of the mind, brain and spirit, creativity, and social connectedness across the UCLA community. The primary concern in prescribing any intervention, notes Bilder, is to understand individual values and goals. Only then is it possible to articulate personalized strategies that will fit into people’s lifestyles and motivate them to engage in the required effort that will ultimately cause a change in behavior.

What Science Can Tell Us About the Aging Brain

Training the Aging BrainIs Cognitive Decline Inevitable?

Cognitive impairment is common in the later stages of life. Many become familiar with the medical lexicon as loved ones grow older and begin to battle the physical and mental effects of aging. Although satisfaction, positive thinking, and emotional stability often increase with age (Carstensen and Mikels, 2005; Williams et al., 2006), and memories with positive associations tend to be preserved better over time (Henson et al., 2016), people become increasingly concerned with their brain function as time goes by—whether or not their mental capacities have changed. A nationwide study conducted across Canada identified memory loss as the most common category of (p. 131) complaints about brain function among healthy adults over 65 years of age (Graham et al., 1997). Moreover, almost 20% of people in the study reported experiencing cognitive impairment in the absence of dementia.

According to some sources, personal sense of memory loss may represent the first warning signs of dementia. A series of studies presented at the Alzheimer’s Association International Conference (Alzheimer’s Association, 2013b; Wang et al., 2004) indicated that self-reports of problems with memory or cognitive processes may represent early signs of neurodegenerative disease. Such subjective perceptions of memory loss are greater than occasional bouts of forgetfulness (e.g. misplaced keys or forgetting the name of a new acquaintance). Rather, self-identified problems reflective of a more serious issue may involve trouble remembering how to do something routine (e.g. dialing a telephone) or forgetting something well known (Alzheimer’s Association, 2017).

For individuals with Alzheimer’s disease or other dementias, impairments are more prominent. Lapses in mental function may extend to attention, reasoning, and information processing, hindering the ability to make informed decisions and live independently (Jak et al., 2013). Increased need for medical attention means that close family members and friends must often make substantial changes in their professional lives, including taking time off and working fewer hours, to care for loved ones entering this battle. Despite the personal and communal consequences of aging, preventative methods continue to elude the scientific community. In addition, available treatment options—primarily drug-based—remain scarce, limited in their effectiveness, and often trigger adverse side effects (Bentham et al., 2004; Ihl et al., 2011; Schneider et al., 2006). These factors demand better alternatives to improving the mental health and lifestyle of people facing cognitive decline (Box 4.2).

So, is cognitive impairment our destiny? Not necessarily.

The nuances of age-related cognitive decline:

During the last session of our brain-training study, carried out in the Neuropsychology Lab. at the University of Ottawa, Mrs S read a passage her friend had sent her. “The brains of older people are slow because they know so much. [ . . . ] The brains of older people do not get weak. On the contrary, they simply know more.” At almost 90 years of age, she had driven herself to our facilities for over a month. She told us about her volunteering activities, get-togethers with her bridge group, and the piano lessons she had started just a few months before. Like many of the seniors who participate in our research, Mrs S was old and retired—and loving every minute of it. “They say retirement is when your life really begins,” she would say, smiling; “I’m doing what I can to keep the ol’ brain going.” (p. 132)

(p. 133) As we grow older, our brains naturally change (Davidson and Winocur, 2017). The overall volume of brain cells decreases (Drag and Bieliauskas, 2010), activity becomes increasingly shared across the two hemispheres (Cabeza, 2002), and frontal regions tend to be more involved in mental processes that require greater thought (Davis et al., 2008). The aging brain also processes information in different ways. Changes in information processing can potentially affect the speed with which we perceive and make sense of visual and auditory information (Ritchie et al., 2014), our ability to focus on relevant information (Cabeza et al., 2004), the degree to which we block out distractions (Park et al., 1989), and how well we can multitask (Clapp et al., 2011). Processes involved in remembering past and future events tend to decrease (Uttl, 2008). Cognitive functions affected by age can hinder daily tasks such as grocery shopping, preparing a meal, managing finances, and driving (Castel, 2005). Losing the freedom to perform these tasks can be devastating; to some seniors, a revoked license feels like losing an arm (Thomson et al., 2013). For this reason, many fear the end of their independence as cognitive decline becomes more pronounced.

Age-related changes in brain function and structure are not necessarily negative (Davidson and Winocur, 2017). Older adults tend to have a richer vocabulary and greater “semantic memory,” or memory for universal facts (Drag and Bieliauskas, 2010). Memories with an emotional component, particularly when the association is pleasant, are more likely to stick with us as we age (Fernandes et al., 2008). This effect of remembering and experiencing fewer negative emotions, termed the “positivity effect” (Mather and Carstensen, 2005), associates with increased brain activity in areas related to processing and regulating emotions (Sakaki et al., 2013), and may be most pronounced when remembering personal life events. Insights about our own thought processes and behaviors, a skill known as metacognition, may also improve with age (Box 4.3; Hertzog and Dunlosky, 2011).

Even the “mind fog” or cognitive slowing that people begin to feel as they grow older has become the subject of debate. In a provocative article, (p. 134) a research group led by Michael Ramscar at the University of Tübingen in Germany proposed that cognitive decline may simply result from the fact that older brains carry more knowledge, and therefore need more time to sift through all that information—the sentiment shared by Mrs S (Ramscar et al., 2014). The theory arose from a mathematical model predicting the natural progression of cognitive function throughout life. In a series of simulations, Ramscar and his team programmed a computer to learn words, letters, or names, and then tested how well the information was learned. In their article, they described the results as analogous to human performance. Specifically, as the amount of knowledge grew larger and subject to social cues, akin to what humans typically encounter throughout a lifetime, and as the context of the simulations changed (e.g. to account for life events such as retirement), the computer’s performance became slower and increasingly resembled the results of an older adult (Ramscar et al., 2014). In the article describing these experiments, the authors further proposed that people largely forget irrelevant information in old age; whereas young adults typically remember a larger range of knowledge, older adults tend to retain only the information that is pertinent to a given context. Some evidence supports this idea. For example, older adults seem to be more efficient at prioritizing important information and remembering facts of higher personal value (Castel, 2007). In a more recent series of simulations, a separate group found that older adults select different memory strategies compared to young adults and that performance differences arise because seniors choose strategies that are suboptimal in certain situations (Blanco et al., 2016). These findings have led Ramscar and colleagues to conclude that age-related cognitive decline, in the absence of disease, is merely a myth, and that researchers need better tools to assess the cognitive abilities of older adults.

Members of the scientific community have criticized the idea that age-related cognitive decline is a myth. Patrick Rabbitt, a British psychologist and professor emeritus at the University of Manchester, argues that the “Ramscar model” fails to account for many factors that influence the way we process, store, and retrieve information in the real world. Moreover, Rabbitt points out that the scientific literature on the aging brain contradicts many assumptions of the model. In a final note to the Ramscar group, Rabbitt cautioned against accepting this view:

“We can only assess cognitive changes by measuring peoples’ performance on laboratory tasks or at their real life skills or professions. Sadly, when we (p. 135) do this we find that all of us perform less well as we age. Documenting these negative changes is useful because it is only by describing precisely what is going wrong that we may find ways to make things better. Refusal to use the word ‘decline’ to describe negative change seems unhelpful superstition.” (Rabbitt, 2014)

Perhaps we should revisit our notion of the term “decline.”

Many factors can contribute to healthy aging, and no reliable predictors currently exist. Studies from the Research Group for Lifespan Changes in Brain and Cognition, based in Oslo, Norway, suggest that age, in itself, predicts less than half of the changes in memory, and that many contributors to its variability include early-life factors such as birth weight and parental education (Walhovd et al., 2016). Moreover, the relationship between brain imaging patterns and cognitive performance may remain relatively stable throughout life (Walhovd et al., 2016). Aside from objective biological factors (Erdo et al., 2017), subjective perceptions of health and mood may also influence age-related cognitive changes in healthy older adults (Salthouse, 2014). For example, subjective perceptions of memory deterioration in cognitively healthy adults may predict their future development of dementia (Wang et al., 2004). And, while emotional stability and wellbeing tend to increase later in life, older adults who live in greater social isolation are significantly more likely to experience emotional distress and cognitive decline compared to those who remain socially engaged (Charles and Carstensen, 2010). Evidence suggests that people who maintain an active, social lifestyle and engage in mentally stimulating activities may experience better physical and mental health, and report greater quality of life (Merriam and Kee, 2014).

What has long stymied scientists is the discrepancy between levels of brain pathology and changes in behavior (Stern, 2002). Despite the widely recognized symptoms of aging, a sizeable proportion of the elderly remains lucid and relatively healthy until their final days. Conversely, many reach the later stages of life with a marked decline in their cognitive capacities that does not categorize as dementia (Chapman et al., 2015). In Canada, for example, an early survey revealed that 17% of adults over 65 years of age report experiencing cognitive impairment in the absence of dementia (Graham et al., 1997)—a proportion that will likely continue to rise with increased societal concern over the prospect of mental decline. Worldwide, 3–19% of adults over 65 years of age have mild cognitive impairment, a syndrome characterized by cognitive decline greater than that expected at a certain age and education level, but that does not interfere with daily (p. 136) activities (Gauthier et al., 2006). More than half of people diagnosed with mild cognitive impairment develop dementia within 5 years. Moreover, significant individual differences exist in the cognitive abilities affected as well as the degree to which those abilities decline (Glisky, 2007; Nyberg et al., 2012). Similarly, age does not appear to affect different types of cognitive skills (e.g. explicit vs. implicit memory) to the same degree (Henson et al., 2016). While researchers have proposed a number of theories to account for age-related changes in cognition (Dennis and Cabeza, 2008), none can entirely account for age-related declines in cognitive function and the degree of interindividual variability in these changes (Hedden and Gabrieli, 2004). According to Aubrey de Grey, co-founder and Chief Scientific Officer at the Strategies for Engineered Negligible Senescence Research Foundation, aging is akin to a curable infectious disease that will disappear with the help of modern medicine. The inconsistencies in age-related brain and behavioral changes raise the question: can we prevent, or at least delay, the seemingly inevitable course of mental decline?

Neuroenhancement: Digital Interfaces, Drugs, and Diets

Enhancing Productivity

One of the most enticing aspects of brain training is the prospect of enhancing productivity or providing preventative health benefits for the average middle-aged or older adult. In recent years, employers have jumped on the brain fitness bandwagon, offering workplace wellness programs that often include cognitive exercises to reduce stress, mitigate unconstructive emotional responses, and increase job performance (“Employers turning to ‘brain health’ tools to reduce stress, increase productivity,” 2013). Because of their accessibility and increasing commercial presence, cognitive training software and biofeedback devices are among the most popular components in these wellness programs. A 2013 market review by SharpBrains identified HeartMath (Institute of HeartMath, 2013) and Brain Resource (Brain Resource Ltd., 2013) as the two market leaders who then most successfully integrated their products into corporations. The market survey further revealed that individual consumers perceived the greatest results from services offered by HeartMath, Lumos Labs, and Posit Science (p. 137) (Fernandez et al., 2013)—companies that emphasized some aspect of productivity.

Do these high-rated programs, in all their technological glory, actually boost workplace productivity? At the University of New South Wales in Sydney, Australia, a group of researchers carried out an experiment in over 100 public sector employees to find out (Borness et al., 2013). For 20 minutes three times a week, volunteers 18–65 years of age played with the HAPPYneuron software, an extensive sequence of games targeting memory, attention, language, reasoning, and visuospatial perception (Scientific Brain Training Corporation, 2017). To offset possible subsidiary effects of participating in the study (i.e. improvements that occur for reasons unrelated to the program itself), a control group watched nature documentaries and completed questionnaires on their content over the same timeframe. The results were unexpected; following the 16-week intervention, individuals who watched the nature documentaries improved to a greater extent than those who played the cognitive training games on measures of wellbeing as well as mental performance. Specifically, the control group reported a greater increase in quality of life and psychological wellbeing, and outperformed the cognitive training group on tests of attention and language skills—although researchers contend that this latter result may reflect higher emphasis on language in the group that watched and answered questions on the documentaries. Finally, the study found no improvements in work-related productivity in either of the groups. These findings suggest that, for the average working adult, isolating specific brain functions—in this case, attention and language processing—won’t necessarily increase work-related efficiency or improve daily life. However, the study did reveal that doing activities you find enjoyable, such as watching nature documentaries, may benefit other aspects of life. The common denominator across successful approaches seems to be customized training, in which the selected exercises and level of challenge fit with individual preferences and goals.

Beyond Productivity: Enhancing Thought and Behavior in Aging

Notwithstanding notable issues in research design (see Chapters 6 and 7), the literature on computerized brain training is rife with reports of enhancement in mental processing and cognitive domains such as memory, attention, or speed of processing in older adults (Kelly et al., 2014; (p. 138) Kueider et al., 2012; Reijnders et al., 2013). Specific approaches, such as perceptual training, may increase the performance of healthy older adults on working memory tasks and increase the efficiency of neural activation patterns, as evidenced by neural signals over posterior visual areas measured using electroencephalography (Berry et al., 2010). Improvements in cognition following brain training have also been correlated with the plasticity of neural networks, including increased cerebral blood flow and structural connectivity in regions associated with resting state activity and executive function, as well as increased white matter integrity (Chapman et al., 2015).

Although the study of mental capacity in older adults is a vast and continually expanding field, a few interventions stand out in the brain-training literature. The Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study (Jobe et al., 2001)—one of the longest-running and most stringent brain-training studies to date—demonstrated longitudinal evidence for the benefits of training verbal episodic memory, reasoning ability, and attention-based speed-of-processing independently of pharmacological intervention. A portion of the initially reported effects, mainly in outcomes proximal to the training conditions, remained at follow-up after 2 (Ball et al., 2002), 5 (Wolinsky et al., 2006), and even 10 years (Rebok et al., 2014), particularly in the groups that trained reasoning ability and speed of processing (Willis et al., 2006). The researchers further noted that participants largely retained their independence, as well as the ability to continue driving in old age despite decreased scores on cognitive tests (Ross et al., 2017). Similarly, the Improvement in Memory with Plasticity-based Adaptive Cognitive Training (IMPACT) study—evaluating a commercial web-based training program focusing on auditory discrimination in 487 healthy older adults (Posit Science, 2015a)—found improvements in the trained tasks as well as enhanced performance on measures of auditory memory and attention (Smith et al., 2009). The authors reported sustained improvements in some of the trained and transfer tasks following a 3-month no-contact period (Zelinski et al., 2011). In the COGITO study, 100 hours of training with lab-designed tasks of processing speed, memory of events (referred to as “episodic” memory), and working memory correlated with post-training improvements in tests similar to the trained tasks as well as in general cognitive abilities such as working memory and fluid intelligence (Schmiedek et al., 2010). More recently, the Iowa Healthy and Active Minds Study (IHAMS) reported improvements in daily life activities as well as depressive symptoms in middle-aged and older adults following speed-of-processing training (Wolinsky et al., 2015). The benefits of computerized (p. 139) brain training in older adults appear to be attainable regardless of technological prowess or prior experience with specific consoles (Kueider et al., 2012).

Training with multi-modal programs that combine different types of cognitive tasks—particularly those designed under the direction of independent research groups rather than commercial vendors—appears to improve memory, processing speed, and visuospatial skills (Lampit et al., 2014). More recent studies using custom-designed or commercially available products (see Table of Products) have reported improvements in distractibility, sustained attention, working memory, and enhanced patterns of neural activity related to cognitive control, following multitask training and generalized cognitive exercises (Chadick et al., 2014; Mayas et al., 2014; Simons, 2013). Commercial video games may facilitate similar, albeit more specific, enhancements in cognitive performance. Older adults who report solving brainteasers such as Sudoku or Tetris exhibit higher speeds of visual processing compared to non-gamers (Wilms and Nielsen, 2014), although improvements in other domains are unsubstantiated. Likewise, in young adults, playing action video games appears to correlate with faster reaction times in a variety of cognitive tasks (Dye et al., 2009). Complex action games such as “Medal of Honor” and “Space Fortress” may further facilitate improvements in cognitive abilities such as processing speed and executive control in older adults (Basak et al., 2008). The magnitude of such improvements in cognitive function, moreover, appears to increase with age (Toril et al., 2014).

Brain-training games, particularly digital “gamified” versions of tasks, offer numerous advantages. These playful variations allow repetitive tasks to become more engaging and fun, which helps motivate us to stick with a program and benefit from its potential (Boot et al., 2013a; Drobics and Smith, 2014). Computerized cognitive training has the additional benefit of providing optimal levels of challenge to a large number of users by adapting to performance in real time. In this respect, for those who enjoy a digital interface, computerized brain training may promote the optimal “flow” state—that perfect balance between challenge and engagement in a task—which enhances learning and cognition.

A newer approach combines brain training with physical exercise. These “exergames” integrate haptic interfaces and motion trackers to enable physical activity while participants complete cognitively demanding tasks (Moreau and Conway, 2013). Exergame providers include both commercial (e.g. Wii [Nintendo, 2014], EyeToy [Sony Computer Entertainment America LLC, 2014], and X-Box [Microsoft, 2014]) and not-for-profit organizations (p. 140) (e.g. The Long Lasting Memories [LLM] Project [2009], [Bamidis, 2013]). Such platforms rely on the theory that increasing the complexity of tasks leads to greater improvement (Erickson et al., 2007), particularly when the program includes both a motor and a cognitive component (Wu et al., 2013). Despite excitement over the potential implications of exergames in seniors, few studies have evaluated these programs. One early report of the Nintendo Wii platform found improvements in cardiorespiratory fitness and executive function and processing speed after 24 hours of training over 2 weeks, although the comparison was a no-treatment condition (Maillot et al., 2012).

More generally, merging physical with cognitive exercise shows only tenuous evidence of benefit in seniors (Rabipour et al., 2017). Following 10 weeks of simultaneous physical exercise—walking on a treadmill—and verbal working memory training, one study found that healthy older adults improved on measures of learning, executive control, reasoning, memory span, and speed of information processing, compared to controls (Theill et al., 2013). However, improvements were comparable on most of the cognitive tasks regardless of whether participants completed the physical exercise alone, working memory training alone, or both (see also Linde and Alfermann, 2014; Shatil, 2013). Another study (McDaniel et al., 2014) examined the separate and combined effects of aerobic exercise—a choice between supervised treadmill walking or cycling—and cognitive training in healthy community-dwelling older adults and used more ecological outcome measures (i.e. everyday tasks adapted for laboratory administration), including cooking breakfast (Craik and Bialystok, 2006), planning a Virtual Week (Rendell and Craik, 2000), and remembering important health information. Members of the aerobic exercise groups showed cardiovascular improvement following the training, but no improvements on the cognitive outcome measures. Similarly, cognitive training led to modest improvement only on a real-world measure of prospective memory (the Virtual Week). Moreover, combining the physical and cognitive exercises provided no special advantage compared to the other groups.

What Makes Some Programs Work Better than Others?

The brain-training market is replete with games that seem to differ in theme, storyline, or interface, but not necessarily in their underlying mechanism. In older adults, often less familiar with computer games and digital interfaces, one possible factor contributing to improved cognition draws on the novelty of digital technology (Rabipour and Davidson, in press). (p. 141) For example, in older adults, learning to use social media or electronic devices seems comparable to engaging in stimulating daily activities and socializing (Chan et al., 2014; Myhre, 2013). Studies, moreover, suggest that using digital technology—regardless of the reason—supports independence and perceived quality of life in seniors (Mynatt and Rogers, 2001), and may heighten mood (Cotten et al., 2014) as well as cognitive prowess (Kaufman et al., 2014; Tun and Lachman, 2010). Even those less familiar with digital interfaces appear to benefit from such programs (Kueider et al., 2012).

Subjective perceptions of a program can substantially influence observed results through indirect factors (see also Chapter 7). The success of any program relies on its ability to produce the desired outcomes effectively. In addition to effectiveness, however, participants must also be willing and able to engage with the program for an extended period of time. The perceived benefit of an intervention can influence both of these. Unsurprisingly, people are more willing to begin an intervention and adhere to the program until completion if they expect the program to be beneficial in some way. This issue of intervention adherence is particularly prominent in older adults, both in therapeutic settings and in the context of brain training (Boot and Kramer, 2014). For example, one study showed that older adults were less likely to engage with an action video game, which they didn’t expect to be beneficial, compared to a more explicit brain-training program (Boot et al., 2013a). Conversely, participants were more likely to stick to the explicit brain-training program, which they believed would help their everyday functioning. Thus, despite its advantages in certain contexts, digital game-based training—and any kind of program subjectively perceived to be ineffective—may be suboptimal for those who prefer to engage in other types of activities. In other words, if video games aren’t your cup of tea, there are other ways to engage your brain and benefit performance.

Explicitly training strategy use is another approach to brain training (Morrison and Chein, 2011). Rather than exercise multiple cognitive abilities through games, strategy training focuses on exercising a specific skill (e.g. remembering a list of items), often to compensate for an ability lost after injury or disease (Li et al., 2016). Studies have demonstrated benefits of various applied memory strategies applicable to various contexts, particularly for older adults who have experienced some form of cognitive deficit (Gross et al., 2012; Reijnders et al., 2013), although even those who feel fully functional may benefit (Mowszowski et al., 2016). For example, researchers at the Center for Lifespan Changes in Brain and Cognition at the University (p. 142) of Oslo in Norway showed fewer age-related declines in brain structure and improvements in measures of memory performance in healthy older adults who completed 20 weeks of memory training using a mnemonic technique for verbal recall, compared to controls who participated in popular science lectures once per week for 10 weeks (de Lange et al., 2018). The program involved a combination of group and home sessions, with individually adjusted difficulty to maintain challenge, and a rest period of 10 weeks in between two 10-week training periods. Such strategy training programs can be independent or added to digital training platforms (McCabe et al., 2016). For those who enjoy such applications, strategy training combined with engaging media such as video games may also enhance motivation and engagement with the task, encouraging the deep, enjoyable concentration of the “flow state” (Astell et al., 2014).

In addition to the nature of a program, training schedules may influence outcomes. The study at the Center for Lifespan Changes in Brain and Cognition found that the effects of training on brain structure subsided over the course of the 10-week rest period, although the memory improvements appeared consistent (de Lange et al., 2018). Moreover, results from the ACTIVE trial suggest that booster sessions can increase—and may even be necessary for—the sustained benefits of training (Ballesteros et al., 2015). Similarly, a study in people with amnesic mild cognitive impairment suggested sustained benefits of the MEMO memory- and attention-training program (Bier et al., 2015), which offered a booster session to review the procedures and strategies learned during the intervention (Belleville et al., 2018). The notion of booster sessions aligns well with the finding that a larger number of training sessions may increase the benefits of training, particularly in people with more pronounced declines in cognitive function (Bamidis et al., 2015).

The Cost of Cognitive Training

The reported benefits of certain types of brain training may come at a price. In a series of Spanish studies of Lumosity, for example, participants in the training condition declined in their performance on a task of visuospatial working memory following the intervention, whereas those in the control group improved (Ballesteros et al., 2017; 2014). Similarly, a study published by Sandia Laboratories—a Federally Funded Research and Development Center in the US—found that improvements in working memory performance came at the expense of recognition memory capacity, (p. 143) which significantly decreased in participants who completed 12–14 sessions of training (Matzen et al., 2014). In contrast, participants who did not complete any training, or who trained on a mental imagery task requiring use of strategies, did not display the same decreases despite showing similar improvement on measures of working memory. The authors hypothesized that repeatedly practicing tasks that did not encourage strategy use might impair or eliminate the tendency to create memory strategies useful in other contexts (e.g. to recognize previously seen images). Whether these patterns result from using suboptimal strategies, different allocation of brain resources, or other reasons remains unclear.

Costs related to limited brain resources are hardly unique to cognitive training software. In a historic study on London taxi drivers, researchers observed an increase in the size of a brain region associated with navigation ability, proportional to the navigational training of drivers. However, the increase in volume of this particular brain area, the posterior hippocampus, came at the expense of the anterior hippocampus, an adjacent area associated with the ability to acquire or remember new visuospatial information. This latter region appeared to shrink in proportion to the growth seen in the posterior hippocampus (Maguire et al., 2000; 2006). The brain seems, quite literally, to be telling us to “use it or lose it.” In other words, our brains accommodate the functions and responses most important to success, while optimizing resources. In training desired functions, therefore, we may inadvertently weaken other mental functions.

Cognitive Enhancement Through Supplements

The discourse on enhancing cognitive function with age would hardly be complete without mentioning pharmacological and nutritional supplements, sometimes used alongside cognitive training regimens. Widely propagated myths about underused brain power have laid the foundation for many a science fiction novel and screenplay pondering what might happen if humans were to use 100% of their brain at once (hint: think more along the lines of seizure rather than superhuman performance). Those most determined to preserve their mental prowess become prime targets for such marketing campaigns, and propel a growing business of brain health supplies. The more experienced players in the study of cognition are unimpressed with the state of current health fads and the implications their representatives make.

(p. 144) A grain of sensibility weaves through the fabric of many fads; however, we find it difficult to discern. To provide a few overarching examples we can pick and choose from a wide array of options. From crossword puzzles to omega-3 fatty acids, the media provide “sure-fire” advice on what we can or should do. “The Memory Cure”—a catchy, albeit misleading title—lists the benefit of Mediterranean diets and regular exercise. Dr Oz talks about ways to prevent everything from hair loss to cancer. SharpBrains, a market research company interested in healthcare and educational applications of neuroscience, releases guides to maintaining brain fitness throughout age. Popular books (e.g. New York Times bestseller Brain Rules, written by molecular biologist John Medina) outline rules or practices that promise to optimize health and functioning at home, in school, and at work. Some of these sources communicate rather sound advice whereas others obfuscate half-truths with confusing and misleading representations.

Do “Smart Drugs” Really Make You Smarter?

Nootropics—so called “smart drugs”—and other brain-boosting supplements promise heightened cognition with little effort on your part (see Box 3.11 for context and arguments for and against their use in the general population). The idea is that ingesting the active compounds in such drugs will increase the circulation of chemicals in the brain, known as neurotransmitters, which play a role in our ability to learn, make decisions, and motivate ourselves to work towards a goal. A vast scientific literature drawing on animal models as well as clinical trials examines the potential of chemical compounds to enhance cognition. Many of these studies examine the protective effect of nootropics for clinical cognitive decline (Box 4.4). An even larger popular literature, comprising personal blogs and commentaries, magazine articles, and popular books, showcases a range of opinions on medications and commercially available supplements—from believers who swear by the transformative effects of nootropics to skeptics who feel the concept is ineffective, unsafe, and unethical.

The effectiveness of off-label medication use in healthy adults is inconsistent, and scientific knowledge about the effects of nootropics in healthy individuals, particularly over the long term, is limited (Sahakian et al., 2015). Experimental and clinical studies show relatively modest effects, due in part to the large variability in response across and within individuals (Husain and Mehta, 2011). Furthermore, some of the observed effects may stem from increased arousal (e.g. with drugs such as modafinil) or (p. 145) motivation, which indirectly improve cognition. As with any drug or experimental technique, side effects represent an important consideration. Regardless of the pharmacological effect or “efficacy,” the effectiveness of nootropics also depends on the overall response to the drug, including possible adverse drug reactions. In the case of nootropics, an Internet search (p. 146) reveals myriad reported side effects ranging from nausea, vomiting, diarrhea, and dizziness, to weight loss, anxiety, and even death—a risk that seems hardly worth the possible, temporary neural enhancement in the healthy. The scientific literature, however, paints a more ambiguous picture. Reports of side effects are conflicting in patients (Rao et al., 2013) and healthy adults (Battleday and Brem, 2015) who take certain medications, or in those who mix nutritional supplements with pharmaceuticals (Schulz, 2003). Moreover, research on off-label use of psychostimulants and other nootropics is scarce (Emanuel et al., 2013), and the long-term safety of these drugs is uncertain.

Safety represents a particularly important concern for commercially available nootropics. Drugs approved for preserving cognitive function in clinical populations undergo rigorous testing, which often begins with research on animal models or observations from unrelated studies, and ends with randomized clinical trials. In such trials, participants are randomly assigned to receive the drug or a placebo, and closely monitored for safety and efficacy on pre-determined measures. Once the drugs are deemed safe and effective, regulatory agencies monitor their distribution for specific populations. Conversely, commercially available nootropics are scarcely studied and often have little, if any, scientific evidence supporting their safety and efficacy. Many of the most popular products among lay consumers offer vague instructions regarding dosage and frequency of use, and provide ambiguous explanations regarding mechanisms of action. Interested consumers often resort to the guidance of personal blogs, other commercial distributors, or informal reviews. Nootriment (2015), for example, posts web reviews drawing inferences about commercial products—advertised to improve focus, mood, longevity, body-building, weight loss, and sexual health—based on scientific evidence from isolated chemical compounds. Creators may capitalize on former associations with reputable academic institutions, in some cases adding school logos to imply collaboration where none exists. Importantly, commercial nootropics have little scientific support for efficacy. Intellux, once advertised as the latest craze among celebrities and respected scientific minds such as Stephen Hawking, seems to draw entirely on false advertising (Miller, 2015). Fine print on their website—like many others—indicated that the “all-natural” supplement had not been evaluated by the US Food and Drug Administration (FDA), and that claims of the distributors did not reflect the opinions of medical professionals. Numerous similarly deceptive products exist, sometimes identified as a scam by independent consumers. (p. 147) However, not all companies are so blatantly misleading. The creators of Brain Octane (Bulletproof Digital Inc., 2015), a product that may mimic a ketogenic diet (see the following section), and truBrain (“truBrain: (re)Designing Focus,” 2015), a mix of piracetam and oxiracetam, base their claims on testimonials and scientific theory with equivocal applicability to the products themselves (Neuroskeptic, 2015). At an estimated US$85 per month, truBrain is a pricey investment with unclear benefit and also unevaluated by the FDA. While such commercial products may turn out to benefit cognitive function, elevated health risks and weak scientific support for effectiveness prompt many to opt for less invasive approaches.

You Are What You Eat!

Everyone knows the importance of eating right, but few can describe with certainty what “right” means. Research on nutrition and cognitive health is vast and convoluted. Aside from personal preferences, what you eat depends on your culture, society, socioeconomic status, and health requirements. Once upon a time, our ancestors had to hunt wild animals and gather uncultivated plants for sustenance. Since the agricultural revolution 10,000 years ago—and to a greater extent since the industrial revolution about 200 years ago—humans have tended towards a diet rich in fats, simple sugars, and salt, and low in fiber, potassium, and calcium (Eaton and Konner, 1985). Fast forward to the twenty-first century, where the abundance and easy access of such foods has become endemic among developed nations.

Diets adopted by Western cultures may be particularly detrimental. Paradoxically, despite accessibility to a greater amount and diversity of food, mainstream diets have borne “affluent malnutrition”—poor nutrition caused by modern dietary and lifestyle habits in affluent societies, including substitution of coarse grains by polished rice and refined wheat, increased intake of hydrogenated fat and sugar, and the intake of more energy than consumed throughout the day (Gopalan, 2000). These first-world habits associate with health problems such as obesity, heart disease, hypertension, diabetes, and certain types of cancer (Frassetto et al., 2001; Gopalan, 2000). As age increases, factors such as declining metabolism and lower tolerance to carbohydrates (e.g. glucose) may add to health risks and call for a change in diet (Rowe and Kahn, 1987).

What you eat can influence mental acuity. Having breakfast and maintaining regular eating habits can be a boon to memory, cognitive (p. 148) performance, and mood (Rampersaud et al., 2005; Zilberter and Zilberter, 2013). Studies further suggest that intermittent fasting, eating a ketogenic (i.e. high-fat, low-carbohydrate) breakfast, and restricting calories in general may protect cognitive function (Zilberter and Zilberter, 2013). Ingesting antioxidant-rich nutrients, including those found in berries and leafy green vegetables, may improve age-related deficits in brain cell function and cognitive behavior (Joseph et al., 1999; Sarubbo et al., 2018). The benefits of antioxidants, at least for memory improvements in people with neurodegenerative disease, do not appear related to vitamin E functioning (McDaniel et al., 2003; Petersen et al., 2005). Fatty acids (e.g. omega-3) found in fish, nuts, vegetables, oils, meat, and dairy products may assist in glucose digestion, thereby feeding the brain and helping maintain neural structure as well as cognitive function in humans and animals (Gomez-Pinilla, 2008). However, while including fatty acids within the diet may slow down cognitive decline in adults without dementia, it does not appear to prevent or effectively treat dementia (Fotuhi et al., 2009). Similarly, Mediterranean diets—high in plant foods, olive oil, and fish, with moderate amounts of wine, and low amounts of red meat—correlate with better health outcomes, including decreased mortality and neurodegenerative disease (Shah, 2013; Sofi et al., 2010). However, the specific components of the diet—or the lifestyles of those who follow the diet—that influence health outcomes are unclear.

Nutritional supplements are fashionable among cognitive enhancement diets. Food constituents such as essential vitamins and nutrients, as well as plant derivatives, are often marketed alongside nootropics as a “natural” way to boost mental and physical energy. A common example is caffeine, widely recognized to boost arousal temporarily. While the cause of caffeine-related cognitive enhancement is still subject to scientific debate, caffeine is also associated with improvements in performance on tasks of vigilance, memory, and general cognitive functioning (Smith, 2002). Once ingested, caffeine stimulates brain activity and neural plasticity by blocking receptors that inhibit neural activity. Consuming caffeine appears to activate regions of the brain associated with working memory in older adults, both healthy and those diagnosed with mild cognitive impairment, and has been suggested—although not proven—to delay the onset of Alzheimer’s disease (Haller et al., 2014). Ginseng, an ancient herbal treatment originating in eastern Asia, is a component of traditional Chinese medicine posited to promote longevity and invigorate the aging body (Lieberman, 2001). Derived from the Panax ginseng root, this supplement has gained popularity in (p. 149) Western cultures with claims of elevated energy and increased resistance to stress when taken regularly, although validated evidence shows improved mental calculation (Lieberman, 2001). The effects of ginseng on physical functioning are inconclusive to date. Evidence supporting the effectiveness of ginkgo biloba extracts, commonly used for preventing and treating memory-related issues, are conflicting. Effects of the extract appear limited in healthy young and older adults, and academics have criticized the methodology of human studies demonstrating benefits of ginkgo biloba in individuals with Alzheimer’s disease (Burns et al., 2006; LeBars et al., 1997). Dietary choline, a precursor of the neurochemical acetylcholine found in a variety of foods (e.g. eggs, meat, fish, legumes), correlates with better memory performance and may help preserve white matter in the brain (Poly et al., 2011). The mechanisms of action of choline are unknown, but one possibility is that supplementing choline in the diet increases acetylcholine in the brain, thereby enhancing its effects on attention, memory, and learning (McDaniel et al., 2003).

As with any lifestyle habit, moderation is important. High-fat diets may increase brain inflammation—related to impaired cognition—and could be deleterious to physical health in the long term (Brinkworth et al., 2009; Pistell et al., 2010). Moreover, restricting food intake may have psychological costs that impact thought and behavior. A study in young women demonstrated poorer cognitive performance in participants dieting to lose weight, compared to women who were not on a diet, perhaps owing to the mental cost of thinking about food, weight, and body shape (Kemps et al., 2005). Stimulants such as caffeine may induce unwanted side effects such as tolerance and dependence. When consumed in excess (e.g. 10 cups of coffee per day or highly caffeinated energy drinks), caffeine may increase blood pressure and could lead to anxiety and mood disorders, altered behavior, as well as heart conditions, including elevated heart rate in healthy people and cardiac arrest in those with pre-existing heart conditions (Cappelletti et al., 2015). Notably, taking multiple supplements may trigger interactions between the compounds; these are difficult to predict and could potentially have detrimental effects on health. Finally, reports concerning the effects of diet and nutritional supplements on cognitive function and health largely draw on observations and animal studies, and do not necessarily reflect a direct or causal relationship between diet and disease. In other words, the “right” foods and even the most scientifically corroborated supplements are unlikely to halt brain degeneration, even if you feel better after ingesting them.

(p. 150) The Battle Against Dementia

In 2010 and 2017, Scientific panels at the National Institutes of Health and National Academies of Science, Engineering, and Medicine, respectively, reviewed medical literature and expert testimonies on preventing the signs of Alzheimer’s disease (Daviglus et al., 2010; Downey et al., 2017). Despite being nearly a decade apart, the reports were similar in many ways. Both uncovered little evidence for pharmaceutical or dietary preventative measures for cognitive decline, certainly not enough to formulate clinical recommendations. Moreover, both panels—to date, unparalleled in their scope—concluded that further research should include large-scale, population-based studies and randomized-controlled trials. The complexity of Alzheimer’s and other dementias has shrouded our ability to find a cure or even understand the precise pathophysiology of these diseases. Changes in brain anatomy and functioning represent a gradual transformation over several years; reversing those changes cannot happen overnight. The panel reviews highlighted the unsatisfying reality that scientists have yet to deliver concrete evidence on preventing or reversing cognitive decline and dementia. The panels nevertheless conceded that further study may support cognitive and physical engagement for preventing the onset of Alzheimer’s disease and delaying its progression in diagnosed individuals. Albeit preliminary, such endorsement from a conservative review suggests that training the brain is not yet a lost cause in the fight against cognitive decline.

The appeal of cognitive enhancement has propelled the growth of a billion-dollar commercial market, projected to increase to over $6 billion by 2020 (SharpBrains, 2013). Older adults are the strategic target of “anti cognitive aging” advertising by this flourishing industry. Programs such as Lumosity (Lumos Labs Inc., 2017), BrainHQ (Posit Science, 2015a), CogniFit (CogniFit Inc., 2014), and Brain Age (Nintendo DS, 2017), which provide computerized cognitive exercises, typify commercialized cognitive training software and are increasingly prevalent: Lumosity, for example, boasted an annual growth rate of 150% in 2013 (Lumos Labs Inc., 2013) and over 60 million users worldwide in the following year (Venkatesh, 2014).

Prescribing Games?

Brain games are making their way into modern medicine as a potential new tool in the doctor’s armamentarium against dementia (Bluestein, 2014). In an industry dominated by Big Pharma, prescription of brain training represents a paradigm shift. Nevertheless, the prospect of using cognitive (p. 151) training software in medicine has caught the interest of pharmaceutical companies. Other companies are following suit. Pear Therapeutics, a Massachusetts-based company, created an entire business model around “eFormulations”—the pairing of current medication with digital applications to enhance drug efficacy (Pear Therapeutics, 2015).

One of the pioneering scientists in this effort is Adam Gazzaley, Founding Director of Neuroscape, a translational neuroscience center bridging work from multiple scientists working in various fields at the University of California, San Francisco. Neuroscape is a medley of cutting-edge, often consumer-facing, technology, and traditionally distinct areas such as cognitive neuroscience and gaming. With collaboration from Akili Interactive Labs, a San Francisco based company he co-founded, Gazzaley has integrated the mechanics of three-dimensional video games, art, music, and captivating storyline, to get players deeply engaged and immersed in the game in a way that is sustainable over time. The recipe entails enough moment-to-moment distraction to maintain engagement and immersion, but without becoming overly consuming, in a conscientious effort to manage potential addiction and interference in other aspects of life. In 2013, the academic journal Nature featured Gazzaley’s work—a study evaluating Neuroracer, an early multitasking training program that eventually evolved into Project: EVO evaluating brain-training games for attention deficit hyperactivity disorder (see Chapter 6)—in an issue entitled “Game Changer,” alluding to the significance of the emerging field.

Having run clinical trials of cognitive training programs funded by pharmaceutical companies such as Pfizer and Shire (Temple, 2014), Gazzaley is no stranger to the “prescription games” revolution. Gazzaley was among the first to believe that doctors should be able to prescribe cognitive training software alongside—or even instead of—a pill, for certain psychiatric or neurological conditions. Such interventions may improve the shortcomings of current non-specific, side effect–laden therapeutics by offering a tailored approach based on individual responsiveness and feedback.

In the emerging field of game-based therapy, the voices with the greatest knowledge and expertise are often the most conservative, yet rarely the loudest. Proper research takes patience and rigor. Experiments are expensive to conduct and there isn’t enough money in science to create a paradigm shift; having companies that are motivated to help spur a field into something legitimate is critical. The major players in the field either collaborate with companies, create their own, or both. Nevertheless, the partnership between video game developers, private companies, and clinical therapy is tricky. The risk of propagating false claims and abusing public (p. 152) trust exists whenever consumer products are not regulated by agencies. As a step towards accountability, researchers, including Michael Merzenich and Adam Gazzaley, were among the first to call for FDA approval of their brain-training products. Even with this oversight, managing conflicts of interest and maintaining transparency will remain important when presenting content to the public.

Gazzaley, like other scientists in the field, has since devoted more attention to follow-up once a training program is complete, to determine how long training gains can last, and what factors underlie and maximize this sustainability. He hopes to discover vulnerabilities in how our brains operate at the highest level, and to develop innovative tools that are readily accessible and can help enhance cognition. But, like many others, he recommends caution: “It takes patience and time. We are in the infancy of this field, and that’s how it should be viewed.”

Evaluating the Evidence

Evidence for the benefits of training the aging brain, while promising, is hardly conclusive (Rabipour, in press). In a large-scale effort, researchers from Australia and the UK, in association with the Cochrane Collaboration, conducted a review of cognitive training as a rehabilitative technique in individuals with Alzheimer’s and other forms of dementia (Bahar-Fuchs et al., 2013). The review included only studies that randomly assigned participants to groups (as opposed to selecting which specific people would end up in a certain group systematically) and that included “active” controls—i.e. people who participated in an analogous form of intervention, but without what was believed to be the “active ingredient” contributing to the training effects. These important considerations in study design help to account for the possibility that the observed effects of a study could result from people’s expectations that an intervention will work, rather than from the intervention itself. The outcomes evaluated were both for the individuals participating in the studies as well as family caregivers. The authors concluded that current evidence in support of cognitive training, at least for people with dementia, remains limited. They also argued that the rigor of scientific studies, in addition to the appropriate classification of trials, must improve. The authors noted that trials might use assessment measures that do not properly capture all of the training gains.

Limitations in the design of video game and cognitive training studies likely contribute to the variability of reported findings (see also Chapter 7). One prominent critic of existing cognitive training studies, Daniel Simons, (p. 153) Professor of Psychology at the University of Illinois at Urbana-Champaign, expounds on some of the shortcomings in his Had I Been A Reviewer blog. These weaknesses include low numbers of participants, inadequate comparison groups and assessment tools, and an unrepresentative demographic (Simons, 2013). In addition, Simons and others have speculated about the extent to which participant expectations fuel positive outcomes (e.g. see Motter et al., 2016; Simons et al., 2016). In other words, engaging in cognitive training likely harbors anticipatory improvement amongst participants concerning their attention, memory, processing speed, or other cognitive function. The widely recognized tendency of participants to behave in a way that would affirm the expected result, the psychological phenomenon of “demand characteristics” (Kihlstrom, 2008), likely confounds many studies; brain training is no exception (Boot et al., 2013b).

So, what’s the verdict?

Intuitively, maintaining brain function by “using” the brain—practicing cognitively challenging tasks—makes sense. Clinicians and program developers often have inspiring stories about the changes their training regimens made in people’s lives. However, these claims are largely anecdotal and, in many cases, have yet to be proven on a broader scale. But research on cognitive training, especially in older adults, is elusive. Considerable variability exists in study design, training regimens and approaches, and outcome measures across the scientific literature. The average duration and frequency of training between studies can range from 10 to 60 minutes, one to five times per week, with varying overall training durations (i.e. lasting a few days to several months). For example, the IHAMS study comprised 10 hours of brain training, with some groups receiving an additional four booster sessions, whereas the IMPACT study involved a more intensive 40-hour training program over 8 weeks. While evidence suggests that 15–25 hours of training can facilitate performance improvements (Jobe et al., 2001), the benefits reported immediately following training may wane unless reinforced with booster sessions (Willis and Caskie, 2013). Moreover, the measures included in individual studies may not be best suited to evaluate a particular sample or to capture training gains properly (Bahar-Fuchs et al., 2013). For example, many studies screen or assess healthy older adults using the Mini Mental State Examination (Folstein et al., 1975), which is less sensitive to minor declines in cognitive function compared to the newer Montreal Cognitive Assessment (Nasreddine et al., 2004). Such disparities limit the extent to which the scientific community can interpret findings and compare studies conducted from different groups.

(p. 154) Many underestimate how difficult conducting adequate studies can be in the field of brain training. Determining whether particular observations or stories replicate on a large scale is resource-intensive, expensive, and often logistically difficult to coordinate. The field of psychology’s “reproducibility crisis,” first reported in 2015, serves as a case in point: members of the Center for Open Science attempting to reproduce 100 of the field’s most prominent studies were only able to replicate 39 successfully (Aarts et al., 2015). Heightened scrutiny of scientific research stemming from this reproducibility crisis, coupled with the advent of mass-distributed computers and easily accessible online applications, has helped resolve some of these issues. Digital technologies are a boon to behavioral studies, especially for long-term involvement. Web-based training programs, particularly those administered through commercial venues, have the advantage of pooling data from thousands, sometimes millions, of users. The Human Cognition Project, an initiative of the creators of Lumosity, is one example where data mining hundreds of millions of cognitive training results can help examine behavioral traits and processes related to the human mind at different stages in life (Sternberg et al., 2013). Other resources are also available. The accessibility of such resources is transforming the nature of research on cognitive performance. With time, scientific evidence will better elucidate the extent to which hope and discretion are warranted.

Calling in the Brain Reserves

Studies in the mid–late twentieth century began documenting an intriguing observation: when examined at autopsy, the brain structures of lucid, behaviorally healthy elderly individuals showed histological signs of incipient dementia, including Alzheimer’s disease—even though their cognitive abilities remained largely intact throughout life (Katzman et al., 1988; Kramer et al., 2011). Post-mortem examinations revealed that these individuals had heavier brains compared to their peers, suggesting that the cognitively healthy elderly who presented with Alzheimer’s pathology either lost fewer brain cells or started with a larger amount, leaving them with more brain “reserve” (Katzman et al., 1988). Studies have identified genetic associations between cognitive health and Alzheimer’s neuropathology. Some of these genes may disturb brain pathways affected by pathological protein development and help the brain cope with the toxic effects of Alzheimer’s-related (p. 155) changes and thereby build up brain as well as cognitive reserve (Kramer et al., 2011).

Theories surrounding the existence of brain and cognitive reserve extend beyond a genetic basis. Emerging research suggests that factors potentially accounting for the “reserve” phenomenon may include higher education and continued mental stimulation achieved through intellectually challenging careers, engaging leisure activities, or involvement with social circles. Such factors have been associated with lower decline in memory and other cognitive functions, as well as decreasing the risk for developing Alzheimer’s symptoms (Daviglus et al., 2010; Scarmeas et al., 2001). One theory proposes that people who engage in a mentally stimulating lifestyle can compensate for pathological traits by using the brain more flexibly and efficiently, thereby evading diagnoses of dementia or clinical cognitive decline despite rigorous and repeated longitudinal assessments (Tucker and Stern, 2011). Similarly, the use of strategies that engage the frontal lobes has been suggested as a compensatory or rehabilitative method in patients with amnesia (Davidson et al., 2006). Evidence supporting the benefits of such compensatory techniques, particularly in the current climate of scant effective treatment options, has rendered cognitive training an appealing exploratory avenue for fighting memory loss.

For some, age-related brain changes become pervasive enough to steal independence and autonomy. No known method currently exists to reverse the cognitive deterioration that results from dementia. By the time most people receive a clinical diagnosis, the damage is too severe for meaningful long-term treatment. In other words, for best effect, treatment should ideally begin before impairments become pronounced. Brain-training programs that aim to enhance or rehabilitate cognitive functions may offer a drug-free method to maximize the use of memory and other mental capacities (Bahar-Fuchs et al., 2013). Overwhelmingly, however, evidence suggests that cognitive training—be it through targeted interventions or generally stimulating activities—does little to augment brain function in healthy people who, presumably, perform at their maximal capacity. But what about sustaining it to protect against prospective damage before the first noticeable signs?

In the context of aging, the distinction between programs intended to maintain healthy functioning versus restore a lost function becomes particularly relevant. Although scientific writers tend to use the terminology interchangeably in both academic and popular literature, training to ameliorate a functional capacity is not quite the same as rehabilitation to mend a lost function. In the former case, terms such as brain training or, perhaps more accurately, cognitive training are appropriate; in the latter case, cognitive (p. 156) rehabilitation, retraining, or remediation may further specify the restorative goal of a particular method (see Chapters 5 and 6). Moreover, in the meticulous vernacular of scientists, cognitive stimulation generally refers to a branch of technology meant to augment—or, in some cases, dampen—activity in the cells of a target brain region, often through magnetic or electric currents. In this respect, we can think of cognitive stimulation as a more direct form of brain training; whereas most programs typically aim to “stimulate” brain function indirectly through a series of games or tasks, cognitive stimulation per se involves tools that draw on electrical or magnetic forces to alter brain signals directly.

Targeted cognitive exercises hold the potential to maintain, enhance, and rehabilitate cognitive function in healthy older adults (Anguera et al., 2013) as well as those with more prominent signs of memory loss (Clare and Woods, 2004). Whether training benefits are more pronounced in people who begin with higher functioning vs. those with more marked impairments remains controversial. Whereas some evidence supports a “Matthew Effect” wherein the “cognitively rich” become even “richer,” other studies have demonstrated that seniors with more marked impairments may improve more over the course of training. For example, two research groups from Spain reported contradictory findings on this topic: whereas one study found fewer improvements of a combined cognitive and physical training program in people with more severe neurocognitive disorders (Bamidis et al., 2015), another suggested that the benefits of brain training may act by enhancing inhibition and working memory in people who began the program with lower scores on these measures (Lopez-Higes et al., 2018).

As with educational and clinical programs, interventions targeting older adults come in many varieties. Programs targeting attention and information processing in older adults often incorporate driver training, for example. Age-related declines in driving ability are no secret. Driving a car calls for the ability to filter through distractors—background chatter, interesting signage or scenery, electronic devices—and pay attention to a varying number of important cues, including stop signals, traffic lights, unpredictable pedestrians, and other, potentially reckless, drivers. In addition to public institutions that offer driver safety courses, such as the American Association of Retired Persons, academic and commercial brain-training modules borrow the fast-paced context of driving as a real-world application of cognitive software. Training driving-related skills is one of the biggest selling points of popular brain-training modules for older adults. Commercially available products include InSight, a set of driving-related cognitive exercises incorporated in the BrainHQ program of Posit Science (Posit Science, 2015b). A separate (p. 157) training approach is the three-dimensional “multiple object tracking” task designed by CogniSens (CogniSens Inc., 2016). The goal of this task is to follow the independent motion of one or more target objects—often circles or spheres—on a digital screen as they move around distractors that are identical to the targets. Simple in design, the task requires dividing attention among the targets while ignoring the distractors, and can become extremely challenging. Studies suggest that practicing the multiple object tracking task can improve perception of visual scenes, an important skill for navigating traffic, which tends to decline with age (Legault and Faubert, 2012). For those seeking to enhance cognitive skills required to complete specific daily tasks, such targeted training may be a promising approach.

Leisure and Lifestyle: Fighting Cognitive Decline Through Daily Habits

The pursuit of maintaining a youthful mind need not center on digital technology. For those who prefer to spend time off-screen, programs such as the Active Cognitive Stimulation-Prevention in the Elderly (AKTIVA) may be more appealing. The German-founded intervention involves pursuing leisurely activities that provide some form of cognitive stimulation (Tesky et al., 2011). These activities may include playing chess, going for a leisurely stroll, reading, or playing music. Participants enrolled in the AKTIVA program also receive information about dementia risk factors, age-related changes in cognition and general lifestyle, and the importance of motivation and self-awareness. In a randomized-controlled study, researchers found significant improvements in speed of processing in participants over 75 years of age, and lower subjective ratings of age-related memory decline in participants younger than 75 years of age. Overall, however, the study did not find a benefit of the AKTIVA program over the comparison group, which completed AKTIVA plus nutrition and exercise counseling, or compared to the no-contact controls (Tesky et al., 2011). More recently, researchers examined the effects of sustained engagement in learning new skills as part of the Synapse Project (Park et al., 2014). For a minimum of 15 hours per week over 3 months, seniors 60–90 years of age performed productive-engagement tasks, which required continual learning of new and complex tasks, or receptive-engagement activities, which offered novelty of situation but no acquisition of new skills. A final group simply provided weekly checklists of daily activities as part of a no-treatment control (p. 158) condition. In the productive-engagement condition, participants learned digital photography and computer skills using photo-editing software, learned to design and sew quilts, or both. In the receptive-engagement condition, participants engaged in facilitator-led socializing activities or at-home tasks that appeared to be cognitively beneficial but had no substantiated link to cognitive improvement (e.g. listening to classical music, completing word-meaning puzzles). Findings from the Synapse Project revealed a significant benefit of the productive-engagement condition—i.e. participants who acquired new and complex skills through quilting or digital photography—on “episodic memory,” our memory for events. Moreover, participants who learned digital photography, either alone or in combination with quilting, displayed the greatest benefits on cognition. By combining engaging, cognitively challenging daily-life activities, programs such as AKTIVA and the Synapse Project offer a fun and leisurely way potentially to sustain cognitive function throughout old age.

What if the biggest factor was your own mind? People often become increasingly wary of memory problems as they grow older, but it’s important to keep things in perspective: you may not be great at remembering names or dates as you age, but were you ever good at remembering them? Are you simply more distractible because now you’re preoccupied with other things in life, or because you are more wary of your age? In the absence of any other conditions or pathological traits, focusing on increasing age and the negative societal implications associated with being “old” can, in and of itself, worsen memory performance and make you “age” in a matter of minutes (Hughes et al., 2013). This phenomenon relates to the psychological concept of stereotype threat, the fear of confirming a negative stereotype associated with a personal characteristic (Steele, 1997). In the case of seniors, alluding to age-related stereotypes may trigger a sense of self-consciousness which, in itself, can decrease cognitive performance (Chasteen et al., 2005). While stereotype threat is highly specific to context and does not consistently affect all older adults, your environment can subtly affect the way you perceive yourself and your cognitive health.

The good news, however, is that psychological barriers such as stereotype threat are surmountable. Ellen Langer, Professor of Psychology at Harvard University, believes that age is simply a matter of perspective. Her research shows that actively noticing new things and being sensitive to context—a process she calls “mindfulness” (distinct from, although related to, mindfulness meditation; see Chapter 3)—can help us remain in the present and reverse the typical mental and even physical effects of aging. In one study, Langer observed an improvement in physical fitness—including (p. 159) decreased weight, body fat, body mass index, and blood pressure—in hotel housekeeping staff after guiding them to view their daily work as a good example of exercise and calorie-burning (Crum and Langer, 2007). In her book, Counterclockwise, Langer recounts her success at turning back the biological clock in a group of 70-year-old workers by having them change their view of work and their language use to thinking of it as exercise. These effects were sustained in people in the US, UK, South Korea, and the Netherlands. Although it is unclear whether adopting a new perspective led these workers subtly to change their behavior (e.g. by putting more energy into their daily tasks and thereby burning more calories), the studies prove that changing mindset can lead to observable outcomes.

Psychological factors such as enjoyment and subjective self-perception—similar to anticipated benefits, as discussed above—can confound outcomes if not accounted for by researchers. Simply including a social aspect, for example, can enhance the effects of brain training in both healthy and cognitively impaired older adults (Rabipour and Davidson, in press). Aside from the motivation of interacting with a community, group-based brain training brings the added challenge of communicating and problem-solving within teams. The benefits of training as part of a social group extend to multi-player online games that promote social interactions and competition (Drobics and Smith, 2014). In the spirit of building such social opportunity in the context of preventing age-related cognitive impairment, the European government created the ElderGames Project (Gamberini et al., 2009). The initiative encourages people of different cultures and generations to work together in solving cognitive tasks through an interactive, multi-player tabletop. Similarly, studies have demonstrated that interacting with a coach enhances the effectiveness of home-based training (Wadley et al., 2006) and may even be necessary for meaningful improvement (Lampit et al., 2014).

Alan Castel, Professor of Cognitive Psychology at UCLA, notes that studies of memory and behavior in older adults often overlook subtle, but crucial, psychological factors that can support or undermine a program’s success. In the same way that wellbeing and performance increased more for control participants in the HAPPYneuron study, factors related to personality, optimism, and attitudes towards aging can shape cognitive vitality (Castel, 2009; McGillivray et al., 2015). Castel recalls how the insights of the late Coach John Wooden, former basketball player and coach at UCLA, capture this sentiment: “Stay busy, stay active, enjoy every day like it is your masterpiece, have some variety and try to learn something new every day [ . . . ] Staying positive and upbeat, having a loving family that you cherish, (p. 160) having strong social support, having interests, working hard [by maintaining an active work schedule]—all of these help.” Experts share the same perspective, and most commonly advise to be active, read, and remain socially involved. Progressing in efforts towards healthy aging calls for a better understanding of age-related changes that need to be remedied to improve quality of life. Incorporating these psychological factors into any training regimen—and, indeed, in the daily routine—seems key.

Conclusions on Training and Aging

To answer the question in the title of this chapter: cognitive decline is likely inevitable, at least to a certain extent. However, the degree of decline varies considerably between different people. Some factors contributing to decline are controllable—in other words, related to lifestyle habits. Others are related to genes and family history, which are less easy to control. In the end, you can do everything right and still develop dementia. Conversely, you can do everything wrong and live a long life with lucidity. In this respect, the prospect of slipping away into senescence is daunting because of its implications as well as its unpredictability.

Certain activities and lifestyle habits benefit mental status. People who continue to learn and pursue physical, intellectual, and social engagement tend to do better than people who stagnate. However, knowing to what extent specific habits help or harm you can be difficult; you cannot be certain of the outcomes of an approach you decided not to take. In pursuit of the approach that best suits you, it is important to understand there is no “holy grail,” “magic bullet,” or “one size fits all.” To have a shot at being helpful, cognitive training should do what its name implies: train. In other words, programs must provide challenge and adapt to performance, but also target the functions that you specifically want to improve in a way that can realistically fit into your daily life. People believe in crosswords and Sudoku because they engross the mind in problem-solving. But after a while, solving the same type of puzzle gets old. Just like physical training, cognitive training must be engaging so you stick with it, and vary enough that your improvement doesn’t plateau over time. Finally, time investment is necessary for effects to last. Nobody expects a physical workout to transform the body in a matter of days, or for the effects of physical exercise to last after stopping the routine. Expectations should be no different for cognitive training, which requires persistent and continuous effort for meaningful benefit.

(p. 161) Cognitive training regimens may help, but it is unclear to what extent. In late 2014, a panel of scientists published a consensus statement (The Stanford Center on Longevity, 2014) urging caution when evaluating and investing in software-based cognitive training. But even among scientists, there is no clear consensus. A few months later, an even larger group of academics published an open letter (CognitiveTrainingData.org, 2014) defending the promise of such programs, perhaps to soften the blow and prevent undue dismissal of the field. If we were to follow the Hype cycle, a graphical presentation by the Gartner group, brain training is probably somewhere between the “peak of inflated expectations” and the “trough of disillusionment.” The scientific community has a long way to go before fully understanding how the brain works. Similarly, eradicating myths about aging, mental function, and brain training will take time. For the rest of us, having a positive attitude and finding satisfaction in day-to-day life are important to healthy aging. Even if a particular technique doesn’t turn out to preserve cognitive ability in the long term, feeling like you’re doing something productive can be beneficial if the alternative is to wait passively for what seems inevitable. The question is whether the method, in itself, is worthwhile to you.

As with any new approach, caution is warranted. In 1949, António Egas Moniz won the Nobel prize for his work with lobotomies—part of the then desperate attempt to treat mental illness effectively. Harvard mathematician and satirist Tom Lehrer joked that the nicest Christmas present he received was a gift certificate to get one. Now, of course, the technique is less popular. Similarly, before you buy your aging parents a gift certificate to the latest brain-training program or IQ-boosting pill, do your homework—maybe read this chapter again.

References

Aarts, A. A., Anderson, J. E., Anderson, C. J., et al. (2015). Estimating the reproducibility of psychological science. Science, 349(6251):aac4716.Find this resource:

Ahmed, A. H., and Oswald, R. E. (2010). Piracetam defines a new binding site for allosteric modulators of alpha-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid (AMPA) receptors. Journal of Medicinal Chemistry, 53(5), 2197–203.Find this resource:

Alzheimer’s Association. (2013a). 2013 Alzheimer’s disease facts and figures. Alzheimer’s and Dementia, 9, 208–45.Find this resource:

Alzheimer’s Association. (2013b). Subjective Cognitive Decline May Be the Earliest Clinical Indicator of Alzheimer’s Disease. Copenhagen, DK: Alzheimer’s Association International Conference.Find this resource:

(p. 162) Alzheimer’s Association. (2015). 2015 Alzheimer’s disease facts and figures. Alzheimer’s and Dementia, 11(2), 332+.Find this resource:

Alzheimer’s Association. (2017). 2017 Alzheimer’s disease facts and figures. Alzheimer’s and Dementia, 13, 325–73.Find this resource:

Anguera, J. A., Boccanfuso, J., Rintoul, J. L., et al. (2013). Video game training enhances cognitive control in older adults. Nature, 501(7465), 97.Find this resource:

Astell, A., Alm, N., Dye, R., Gowans, G., Vaughan, P., and Ellis, M. (2014). Digital video games for older adults with cognitive impairment. In: K. Miesenberger, D. Fels, D. Archambault, P. Peňáz and W. Zagler (eds). Computers Helping People with Special Needs. New York: Springer International Publishing, pp. 264–71.Find this resource:

Bahar-Fuchs, A., Clare, L., and Woods, B. (2013). Cognitive training and cognitive rehabilitation for mild to moderate Alzheimer’s disease and vascular dementia. Cochrane Database of Systematic Reviews, 6. doi: Artn Cd003260.Find this resource:

Ball, K., Berch, D. B., Helmers, K. F., et al. (2002). Effects of cognitive training interventions with older adults. JAMA: The Journal of the American Medical Association, 288(18), 2271–81.Find this resource:

Ballesteros, S., Prieto, A., Mayas, J., et al. (2014). Brain training with non-action video games enhances aspects of cognition in older adults: a randomized controlled trial. Frontiers in Aging Neuroscience, 6. doi: ARTN 277 10.3389/fnagi.2014.00277Find this resource:

Ballesteros, S., Mayas, J., Prieto, A., et al. (2015). A randomized controlled trial of brain training with non-action video games in older adults: results of the 3-month follow-up. Frontiers in Aging Neuroscience, 7. doi: UNSP 45 10.3389/fnagi.2015.00045Find this resource:

Ballesteros, S., Mayas, J., Prieto, A., Ruiz-Marquez, E., Toril, P., and Reales, J. M. (2017). Effects of video game training on measures of selective attention and working memory in older adults: results from a randomized controlled trial. Frontiers in Aging Neuroscience, 9, 354.Find this resource:

Bamidis, P. (2013). Cognitive and Physical Games for the Elderly: How Effective Are They. Aristotle University of Thessaloniki.Find this resource:

Bamidis, P. D., Fissler, P., Papageorgiou, S. G., et al. (2015). Gains in cognition through combined cognitive and physical training: the role of training dosage and severity of neurocognitive disorder. Frontiers in Aging Neuroscience, 7, 152.Find this resource:

Basak, C., Boot, W. R., Voss, M. W., and Kramer, A. F. (2008). Can training in a real-time strategy video game attenuate cognitive decline in older adults? Psychology and Aging, 23(4), 765–77.Find this resource:

Bates, C. (2012). Dementia cases “to double by 2030” says World Health Organization. Daily Mail, 11 April 2012. http://www.who.int/mediacentre/news/releases/2012/dementia_20120411/en/

(p. 163) Battleday, R. M., and Brem, A. K. (2015). Modafinil for cognitive neuroenhancement in healthy non-sleep-deprived subjects: a systematic review. European Neuropsychopharmacology, 25(11), 1865–81.Find this resource:

Belleville, S., Hudon, C., Bier, N., et al. (2018). MEMO plus: efficacy, durability and effect of cognitive training and psychosocial intervention in individuals with mild cognitive impairment. Journal of the American Geriatrics Society, 66(4), 655–63.Find this resource:

Bentham, P., Gray, R., Raftery, J., et al. (2004). Long-term donepezil treatment in 565 patients with Alzheimer’s disease (AD2000): randomised double-blind trial. Lancet, 363(9427), 2105–15.Find this resource:

Berry, A. S., Zanto, T. P., Clapp, W. C., et al. (2010). The influence of perceptual training on working memory in older adults. PLoS ONE, 5(7), e11537.Find this resource:

Bier, N., Grenier, S., Brodeur, C., et al. (2015). Measuring the impact of cognitive and psychosocial interventions in persons with mild cognitive impairment with a randomized single-blind controlled trial: rationale and design of the MEMO plus study. International Psychogeriatrics, 27(3), 511–25.Find this resource:

Blanco, N. J., Love, B. C., Ramscar, M., Otto, A. R., Smayda, K., and Maddox, W. T. (2016). Exploratory decision-making as a function of lifelong experience, not cognitive decline. Journal of Experimental Psychology—General, 145(3), 284–97.Find this resource:

Bluestein, A. (2014). Will doctors soon prescribe videogames? How researchers are using computer games to treat pain, aging, ADHD, and other ailments. Fast Company, 2015.Find this resource:

Boot, W. R., and Kramer, A. F. (2014). The Brain-Games Conundrum: Does Cognitive Training Really Sharpen the Mind? Retrieved from http://www.dana.org/Cerebrum/2014/The_Brain-Games_Conundrum__Does_Cognitive_Training_Really_Sharpen_the_Mind_/-sthash.iPYa8y4b.dpuf

Boot, W. R., Champion, M., Blakely, D. P., Wright, T., Souders, D. J., and Charness, N. (2013a). Video games as a means to reduce age-related cognitive decline: attitudes, compliance, and effectiveness. Frontiers in Psychology, 4. doi: Artn 31 Doi 10.3389/Fpsyg.2013.00031Find this resource:

Boot, W. R., Simons, D. J., Stothart, C., and Stutts, C. (2013b). The pervasive problem with placebos in psychology: why active control groups are not sufficient to rule out placebo effects. Perspectives on Psychological Science, 8(4), 445–54.Find this resource:

Borness, C., Proudfoot, J., Crawford, J., and Valenzuela, M. (2013). Putting brain training to the test in the workplace: a randomized, blinded, multisite, active-controlled trial. Plos One, 8(3). doi: 10.1371/journal.pone.0059982Find this resource:

Brain Resource Ltd. (2013). Brain Products for Everyday Life, 2013, from http://www.brainresource.com/

Brain Training: Mind Games and V. Smart (Director). (2015) [Television]. In: L. Burgess, K. Coughlin, T. Foxcroft, G. Sadler, V. Smart, J. Stiglic and A. Wesley (Producer), CBC Marketplace: Canadian Broadcasting Corporation.Find this resource:

(p. 164) Bredesen, D. E. (2014). Reversal of cognitive decline: a novel therapeutic program. Aging-Us, 6(9), 707–17.Find this resource:

Brinkworth, G. D., Noakes, M., Buckley, J. D., Keogh, J. B., and Clifton, P. M. (2009). Long-term effects of a very-low-carbohydrate weight loss diet compared with an isocaloric low-fat diet after 12 mo. American Journal of Clinical Nutrition, 90(1), 23–32.Find this resource:

Bulletproof Digital Inc. (2015). Brain Octane Retrieved December 31, 2015, from http://www.bulletproof.com/nutrition/quality-fats/?gclid=CjwKEAiAwZO0BRDvxs_1w-qFnhkSJABo10ggVn0kAfA0AwJ2yBOYD1SeV0V6Z6AnwQ3Lt5kHQLWX0RoCBSnw_wcB

Burns, N. R., Bryan, J., and Nettelbeck, T. (2006). Ginkgo biloba: no robust effect on cognitive abilities or mood in healthy young or older adults. Human Psychopharmacology—Clinical and Experimental, 21(1), 27–37.Find this resource:

Cabeza, R. (2002). Hemispheric asymmetry reduction in older adults: the HAROLD model. Psychology and Aging, 17(1), 85–100.Find this resource:

Cabeza, R., Daselaar, S. M., Dolcos, F., Prince, S. E., Budde, M., and Nyberg, L. (2004). Task-independent and task-specific age effects on brain activity during working memory, visual attention and episodic retrieval. Cerebral Cortex, 14(4), 364–75.Find this resource:

Cappelletti, S., Daria, P., Sani, G., and Aromatario, M. (2015). Caffeine: cognitive and physical performance enhancer or psychoactive drug? Current Neuropharmacology, 13(1), 71–88.Find this resource:

Carstensen, L. L., and Mikels, J. A. (2005). At the intersection of emotion and cognition—aging and the positivity effect. Current Directions in Psychological Science, 14(3), 117–121.Find this resource:

Castel, A. (2009). Memory and successful aging: a conversation with coach John Wooden. Association for Psychological Science, 22(2), 13–15.Find this resource:

Castel, A. D. (2005). Memory for grocery prices in younger and older adults: the role of schematic support. Psychology and Aging, 20(4), 718–21.Find this resource:

Castel, A. D. (2007). The adaptive and strategic use of memory by older adults: evaluative processing and value-directed remembering. Skill and Strategy in Memory Use, 48, 225–70.Find this resource:

Chadick, J. Z., Zanto, T. P., and Gazzaley, A. (2014). Structural and functional differences in medial prefrontal cortex underlie distractibility and suppression deficits in ageing. Nature Communications, 5. doi: Artn 4223 Doi 10.1038/Ncomms5223Find this resource:

Chan, M. Y., Haber, S., Drew, L. M., and Park, D. C. (2014). Training older adults to use tablet computers: does it enhance cognitive function? Gerontologist, 1–11. doi: 10.1093/geront/gnu057Find this resource:

(p. 165) Chapman, S. B., Aslan, S., Spence, J. S., et al. (2015). Neural mechanisms of brain plasticity with complex cognitive training in healthy seniors. Cerebral Cortex, 25(2), 396–405.Find this resource:

Charles, S. T., and Carstensen, L. L. (2010). Social and emotional aging. Annual Review of Psychology, 61, 383–409.Find this resource:

Chasteen, A. L., Bhattacharyya, S., Horhota, M., Tam, R., and Hasher, L. (2005). How feelings of stereotype threat influence older adults’ memory performance. Experimental Aging Research, 31(3), 235–60.Find this resource:

Clapp, W. C., Rubens, M. T., Sabharwal, J., and Gazzaley, A. (2011). Deficit in switching between functional brain networks underlies the impact of multitasking on working memory in older adults. Proceedings of the National Academy of Sciences of the United States of America, 108(17), 7212–17.Find this resource:

Clare, L., and Woods, R. T. (2004). Cognitive training and cognitive rehabilitation for people with early-stage Alzheimer’s disease: a review. Neuropsychological Rehabilitation, 14(4), 385–401.Find this resource:

CogniFit Inc. (2014). CogniFit 2014, from https://www.cognifit.com

CogniSens Inc. (2016). CogniSens, 2016, from https://neurotracker.net/

CognitiveTrainingData.org. (2014). Cognitive Training Data: An Open Letter, from http://www.cognitivetrainingdata.org/

Concurring Statement of Commissioner Julie Brill (2016), from https://www.ftc.gov/public-statements/2016/01/concurring-statement-commissioner-julie-brill-matter-lumos-lab-inc

Cotten, S. R., Ford, G., Ford, S., and Hale, T. M. (2014). Internet use and depression among retired older adults in the United States: a longitudinal analysis. Journals of Gerontology Series B—Psychological Sciences and Social Sciences, 69(5), 763–71.Find this resource:

Craik, F. I. M., and Bialystok, E. (2006). Planning and task management in older adults: cooking breakfast. Memory and Cognition, 34(6), 1236–49.Find this resource:

Crum, A. J., and Langer, E. J. (2007). Mind-set matters: exercise and the placebo effect. Psychological Science, 18(2), 165–71.Find this resource:

Davidson, P. S. R., and Winocur, G. (2017). Aging and cognition. Reference Module in Neuroscience and Biobehavioral Psychology, 1–7. doi: 10.1016/B978-0-12-809324-5.00252-2Find this resource:

Davidson, P. S. R., Troyer, A. K., and Moscovitch, M. (2006). Frontal lobe contributions to recognition and recall: linking basic research with clinical evaluation and remediation. Journal of the International Neuropsychological Society, 12(2), 210–23.Find this resource:

Daviglus, M. L., Bell, C. C., Berrettini, W., et al. (2010). National Institutes of Health state-of-the-science conference statement: preventing Alzheimer disease and cognitive decline. Annals of Internal Medicine, 153(3), 176–81.Find this resource:

(p. 166) Davis, S. W., Dennis, N. A., Daselaar, S. M., Fleck, M. S., and Cabeza, R. (2008). Que PASA? The posterior-anterior shift in aging. Cerebral Cortex, 18(5), 1201–9.Find this resource:

de Lange, A. M. G., Brathen, A. C. S., Rohani, D. A., Fjell, A. M., and Walhovd, K. B. (2018). The temporal dynamics of brain plasticity in aging. Cerebral Cortex, 28(5), 1857–65.Find this resource:

Dennis, N. A., and Cabeza, R. (2008). Neuroimaging of healthy cognitive aging. In: F. I. M. Craik and T. A. Salthouse (eds). The Handbook of Aging and Cognition, 3rd edn. New York, NY: Psychology Press, pp. 1–54.Find this resource:

Downey, A., Stroud, C., Landis, S., and Leshner, A. I. (2017). Preventing Cognitive Decline and Dementia: A Way Forward. Washington (DC): The National Academies Press.Find this resource:

Drag, L. L., and Bieliauskas, L. A. (2010). Contemporary review 2009: cognitive aging. Journal of Geriatric Psychiatry and Neurology, 23(2), 75–93.Find this resource:

Drobics, M., and Smith, S. (2014). Game-based IT solutions for active and healthy aging. Games for Training, Education, Health and Sports, 8395, 8–11.Find this resource:

Dye, M. W. G., Green, C. S., and Bavelier, D. (2009). Increasing speed of processing with action video games. Current Directions in Psychological Science, 18(6), 321–6.Find this resource:

Eaton, S. B., and Konner, M. (1985). Paleolithic nutrition—a consideration of its nature and current implications. New England Journal of Medicine, 312(5), 283–9.Find this resource:

Emanuel, R. M., Frellsen, S. L., Kashima, K. J., Sanguino, S. M., Sierles, F. S., and Lazarus, C. J. (2013). Cognitive enhancement drug use among future physicians: findings from a multi-institutional census of medical students. Journal of General Internal Medicine, 28(8), 1028–34.Find this resource:

Employers Turning to ‘Brain Health’ Tools to Reduce Stress, Increase Productivity. (2013). HR.BLR.com. From http://hr.blr.com/HR-news/Benefits-Leave/Employee-Wellness/Employers-turning-to-brain-health-tools-to-reduce-

Engelter, S. T. (2013). Safety in pharmacological enhancement of stroke rehabilitation. European Journal of Physical and Rehabilitation Medicine, 49(2), 261–7.Find this resource:

Erdo, F., Denes, L., and de Lange, E. (2017). Age-associated physiological and pathological changes at the blood–brain barrier: a review. Journal of Cerebral Blood Flow and Metabolism, 37(1), 4–24.Find this resource:

Erickson, K. I., Colcombe, S. J., Wadhwa, R., et al. (2007). Training-induced functional activation changes in dual-task processing: an fMRI study. Cerebral Cortex, 17(1), 192–204.Find this resource:

Federal Trade Commission. (2016). Lumosity to Pay $2 Million to Settle FTC Deceptive Advertising Charges for Its “Brain Training” Program Company Claimed Program Would Sharpen Performance in Everyday Life And Protect Against (p. 167) Cognitive Decline, from https://www.ftc.gov/news-events/press-releases/2016/01/lumosity-pay-2-million-settle-ftc-deceptive-advertising-charges

Fernandes, M., Ross, M., Wiegand, M., and Schryer, E. (2008). Are the memories of older adults positively biased? Psychology and Aging, 23(2), 297–306.Find this resource:

Fernandez, A., Goldberg, E., and Michelon, P. (2013). The SharpBrains Guide to Brain Fitness, 2nd edn. San Francisco: SharpBrains, Inc.Find this resource:

Folstein, M. F., Folstein, S. E., and McHugh, P. R. (1975). Mini-mental state–practical method for grading cognitive state of patients for clinician. Journal of Psychiatric Research, 12(3), 189–98.Find this resource:

Fotuhi, M., Mohassel, P., and Yaffe, K. (2009). Fish consumption, long-chain omega-3 fatty acids and risk of cognitive decline or Alzheimer disease: a complex association. Nature Clinical Practice Neurology, 5(3), 140–52.Find this resource:

Frassetto, L., Morris, R. C., Sellmeyer, D. E., Todd, K., and Sebastian, A. (2001). Diet, evolution and aging—the pathophysiologic effects of the post-agricultural inversion of the potassium-to-sodium and base-to-chloride ratios in the human diet. European Journal of Nutrition, 40(5), 200–13.Find this resource:

Gamberini, L., Martino, F., Seraglia, B., et al. (2009). Eldergames Project: an innovative mixed reality table-top solution to preserve cognitive functions in elderly people. Hsi: 2009 2nd Conference on Human System Interactions, pp. 161–6.Find this resource:

Gauthier, S., Reisberg, B., Zaudig, M., et al. (2006). Mild cognitive impairment. Lancet, 367(9518), 1262–70.Find this resource:

Giurgea, C. (1973). The “nootropic” approach to the pharmacology of the integrative activity of the brain. Conditional Reflex, 8(2), 108–15.Find this resource:

Glisky, E. L. (2007). Changes in cognitive function in human aging. In: D. R. Riddle (ed.). Brain Aging: Models, Methods, and Mechanisms. Boca Raton (FL): CRC Press/Taylor & Francis; Chapter 1.Find this resource:

Gomez-Pinilla, F. (2008). Brain foods: the effects of nutrients on brain function. Nature Reviews Neuroscience, 9(7), 568–78.Find this resource:

Gopalan, S. (2000). Malnutrition: causes, consequences, and solutions. Nutrition, 16(7–8), 556+.Find this resource:

Graham, J. E., Rockwood, K., Beattie, B. L., et al. (1997). Prevalence and severity of cognitive impairment with and without dementia in an elderly population. Lancet, 349(9068), 1793–6.Find this resource:

Gross, A. L., Parisi, J. M., Spira, A. P., et al. (2012). Memory training interventions for older adults: a meta-analysis. Aging Mental Health, 16(6), 722–34.Find this resource:

Grossberg, G. T., Edwards, K. R., and Zhao, Q. Y. (2006). Rationale for combination therapy with galantamine and Memantine in Alzheimer’s disease. Journal of Clinical Pharmacology, 46(7), 17s–26s.Find this resource:

(p. 168) Haller, S., Montandon, M. L., Rodriguez, C., et al. (2014). Acute caffeine administration effect on brain activation patterns in mild cognitive impairment. Journal of Alzheimers Disease, 41(1), 101–12.Find this resource:

Hansen, R. A., Gartlehner, G., Webb, A. P., Morgan, L. C., Moore, C. G., and Jonas, D. E. (2008). Efficacy and safety of donepezil, galantamine, and rivastigmine for the treatment of Alzheimer’s disease: a systematic review and meta-analysis. Clinical Interventions in Aging, 3(2), 211–25.Find this resource:

Hedden, T., and Gabrieli, J. D. E. (2004). Insights into the ageing mind: a view from cognitive neuroscience. Nature Reviews Neuroscience, 5(2), 87–U12.Find this resource:

Henson, R. N., Campbell, K. L., Davis, S. W., et al. (2016). Multiple determinants of lifespan memory differences. Scientific Reports, 6. doi: ARTN 32527 10.1038/srep32527Find this resource:

Hertzog, C., and Dunlosky, J. (2011). Metacognition in later adulthood: spared monitoring can benefit older adults’ self-regulation. Current Directions in Psychological Science, 20(3), 167–73.Find this resource:

Hughes, M. L., Geraci, L., and De Forrest, R. L. (2013). Aging 5 years in 5 minutes: the effect of taking a memory test on older adults’ subjective age. Psychological Science, 24(12), 2481–8.Find this resource:

Husain, M., and Mehta, M. A. (2011). Cognitive enhancement by drugs in health and disease. Trends in Cognitive Sciences, 15(1), 28–36.Find this resource:

Ihl, R., Frolich, L., Winblad, B., et al. (2011). World Federation of Societies of Biological Psychiatry (WFSBP) Guidelines for the Biological Treatment of Alzheimer’s disease and other dementias. World Journal of Biological Psychiatry, 12(1), 2–32.Find this resource:

Institute of HeartMath. (2013). Connecting Hearts and Minds, 2013, from http://www.heartmath.org/

Jak, A. J., Seelye, A. M., and Jurick, S. M. (2013). Crosswords to computers: a critical review of popular approaches to cognitive enhancement. Neuropsychology Review, 23(1), 13–26.Find this resource:

Jobe, J. B., Smith, D. M., Ball, K., et al. (2001). Active: a cognitive intervention trial to promote independence in older adults. Controlled Clinical Trials, 22(4), 453–79.Find this resource:

Jones, R. W., Soininen, H., Hager, K., et al. (2004). A multinational, randomised, 12-week study comparing the effects of donepezil and galantamine in patients with mild to moderate Alzheimer’s disease. International Journal of Geriatric Psychiatry, 19(1), 58–67.Find this resource:

Joseph, J. A., Shukitt-Hale, B., Denisova, N. A., et al. (1999). Reversals of age-related declines in neuronal signal transduction, cognitive, and motor behavioral deficits with blueberry, spinach, or strawberry dietary supplementation. Journal of Neuroscience, 19(18), 8114–21.Find this resource:

(p. 169) Katzman, R., Terry, R., DeTeresa, R., et al. (1988). Clinical, pathological, and neurochemical changes in dementia: a subgroup with preserved mental status and numerous neocortical plaques. Annals of Neurology, 23(2), 138–44.Find this resource:

Kaufman, D., Sauve, L., Renaud, L., and Duplaa, E. (2014). Cognitive Benefits of Digital Games for Older Adults. Paper presented at the EdMedia, Tampere, Finland.Find this resource:

Kelly, M. E., Loughrey, D., Lawlor, B. A., Robertson, I. H., Walsh, C., and Brennan, S. (2014). The impact of cognitive training and mental stimulation on cognitive and everyday functioning of healthy older adults: a systematic review and meta-analysis. Ageing Research Reviews, 15, 28–43.Find this resource:

Kemps, E., Tiggemann, M., and Marshall, K. (2005). Relationship between dieting to lose weight and the functioning of the central executive. Appetite, 45(3), 287–94.Find this resource:

Kihlstrom, J. F. (2008). Placebo: feeling better, getting better, and the problems of mind and body. Mcgill Journal of Medicine: MJM: An International Forum for the Advancement of Medical Sciences by Students, 11(2), 212–14.Find this resource:

Kramer, P. L., Xu, H., Woltjer, R. L., et al. (2011). Alzheimer disease pathology in cognitively healthy elderly: a genome-wide study. Neurobiology of Aging, 32(12), 2113–22.Find this resource:

Kueider, A. M., Parisi, J. M., Gross, A. L., and Rebok, G. W. (2012). Computerized cognitive training with older adults: a systematic review. PLoS One, 7(7), e40588.Find this resource:

Lampit, A., Hallock, H., and Valenzuela, M. (2014). Computerized cognitive training in cognitively healthy older adults: a systematic review and meta-analysis of effect modifiers. PLoS Med, 11(11), e1001756.Find this resource:

LeBars, P. L., Katz, M. M., Berman, N., Itil, T. M., Freedman, A. M., and Schatzberg, A. F. (1997). A placebo-controlled, double-blind, randomized trial of an extract of Ginkgo biloba for dementia. JAMA—Journal of the American Medical Association, 278(16), 1327–32.Find this resource:

Legault, I., and Faubert, J. (2012). Perceptual-cognitive training improves biological motion perception: evidence for transferability of training in healthy aging. Neuroreport, 23(8), 469–73.Find this resource:

Lenze, E. J., and Bowie, C. R. (2018). Cognitive training for older adults: what works? Journal of the American Geriatrics Society, 66(4), 645–7.Find this resource:

Li, B., Zhu, X. Y., Hou, J. H., Chen, T. J., Wang, P. Y., and Li, J. (2016). Combined cognitive training vs. memory strategy training in healthy older adults. Frontiers in Psychology, 7. doi: ARTN 834 10.3389/fpsyg.2016.00834Find this resource:

Lieberman, H. R. (2001). The effects of ginseng, ephedrine, and caffeine on cognitive performance, mood and energy. Nutrition Reviews, 59(4), 91–102.Find this resource:

Linde, K., and Alfermann, D. (2014). Single versus combined cognitive and physical activity effects on fluid cognitive abilities of healthy older adults: a 4-month (p. 170) randomized controlled trial with follow-up. Journal of Aging and Physical Activity, 22(3), 302–13.Find this resource:

Long Lasting Memories Project. (2009). Long Lasting Memories: Mind and Body Fitness for Life. Retrieved December 2, 2014, from http://www.longlastingmemories.eu

Lopez-Higes, R., Martin-Aragoneses, M. T., Rubio-Valdehita, S., et al. (2018). Efficacy of cognitive training in older adults with and without subjective cognitive decline is associated with inhibition efficiency and working memory span, not with cognitive reserve. Frontiers in Aging Neuroscience, 10, 23.Find this resource:

Lumos Labs Inc. (2013). Lumosity Grows 150 Percent in the Past Year Reaching Over 35 Million Members Worldwide: Mobile and International Expansion Planned in 2013. San Francisco: Lumosity Press.Find this resource:

Lumos Labs Inc. (2017). Lumosity, from http://www.lumosity.com

Maguire, E. A., Gadian, D. G., Johnsrude, I. S., et al. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences of the United States of America, 97(8), 4398–403.Find this resource:

Maguire, E. A., Woollett, K., and Spiers, H. J. (2006). London taxi drivers and bus drivers: a structural MRI and neuropsychological analysis. Hippocampus, 16(12), 1091–101.Find this resource:

Maillot, P., Perrot, A., and Hartley, A. (2012). Effects of interactive physical-activity video-game training on physical and cognitive function in older adults. Psychology and Aging, 27(3), 589–600.Find this resource:

Mather, M., and Carstensen, L. L. (2005). Aging and motivated cognition: the positivity effect in attention and memory. Trends in Cognitive Sciences, 9(10), 496–502.Find this resource:

Matzen, L. E., Trumbo, M. C., Haass, M. J., et al. (2016). Practice Makes Imperfect: Working Memory Training Can Harm Recognition Memory Performance: Sandia National Laboratories. Memory & Cognition, 44(8), 1168–82.Find this resource:

Mayas, J., Parmentier, F. B. R., Andres, P., and Ballesteros, S. (2014). Plasticity of attentional functions in older adults after non-action video game training: a randomized controlled trial. Plos One, 9(3). doi: ARTN e92269 DOI 10.1371/journal.pone.0092269Find this resource:

McCabe, J. A., Redick, T. S., and Engle, R. W. (2016). Brain-training pessimism, but applied-memory optimism. Psychological Science in the Public Interest, 17(3), 187–91.Find this resource:

McDaniel, M. A., Maier, S. F., and Einstein, G. O. (2003). “Brain-specific” nutrients: a memory cure? Nutrition, 19(11–12), 957–75.Find this resource:

(p. 171) McDaniel, M. A., Bugg, J. M., Waldum, E. R., et al. (2014). Effects of cognitive training with and without aerobic exercise on cognitively demanding everyday activities. Psychology and Aging, 29(3), 717–30.Find this resource:

McGillivray, S., Murayama, K., and Castel, A. D. (2015). Thirst for knowledge: the effects of curiosity and interest on memory in younger and older adults. Psychology and Aging, 30(4), 835–41.Find this resource:

Merriam, S. B., and Kee, Y. (2014). Promoting community wellbeing: the case for lifelong learning for older adults. Adult Education Quarterly, 64(2), 128–44.Find this resource:

Microsoft. (2014). X Box, 2014, from http://www.xbox.com

Miller, R. (2015). Intellux and Geniux Review—Miracle or Myth? From https://www.supplementcritique.com/intellux-and-geniux-review-miracle-or-myth/

Moreau, D., and Conway, A. R. A. (2013). Cognitive enhancement: a comparative review of computerized and athletic training programs. International Review of Sport and Exercise Psychology, 6(1), 155–83.Find this resource:

Morrison, A. B., and Chein, J. M. (2011). Does working memory training work? The promise and challenges of enhancing cognition by training working memory. Psychonomic Bulletin and Review, 18(1), 46–60.Find this resource:

Motter, J. N., Devanand, D. P., Doraiswamy, P. M., and Sneed, J. R. (2016). Clinical trials to gain FDA approval for computerized cognitive training: what is the ideal control condition? Frontiers in Aging Neuroscience, 8. doi: ARTN 249 10.3389/fnagi.2016.00249Find this resource:

Mowszowski, L., Lampit, A., Walton, C. C., and Naismith, S. L. (2016). Strategy-based cognitive training for improving executive functions in older adults: a systematic review. Neuropsychological Review, 26(3), 252–70.Find this resource:

Myhre, J. W. (2013). Effects of Online Social Networking on the Cognitive, Social, and Emotional Health of Older Adults. (Ph.D.) The University of Arizona.Find this resource:

Mynatt, E. D., and Rogers, W. A. (2001). Developing technology to support the functional independence of older adults. Ageing International, 27(1), 24–41.Find this resource:

Nasreddine, Z. S., Chertkow, H., Phillips, N., Whitehead, V., Collin, I., and Cummings, J. L. (2004). The Montreal Cognitive Assessment (MoCA): a brief cognitive screening tool for detection of mild cognitive impairment. Neurology, 62(7), A132.Find this resource:

Neuroskeptic. (2015). What’s The Truth About “TruBrain”? Retrieved from http://blogs.discovermagazine.com/neuroskeptic/2015/01/14/truth-about-trubrain/-.VoTvL5MrJsM

Nintendo. (2014). Wii U, 2014, from http://www.nintendo.com/wiiu

Nintendo DS. (2017). Brain Age: Concentration Training, from http://brainage.nintendo.com

(p. 172) Nootriment. (2015). Nootriment Retrieved December 31, 2015, from http://nootriment.com/

Nyberg, L., Lovden, M., Riklund, K., Lindenberger, U., and Backman, L. (2012). Memory aging and brain maintenance. Trends in Cognitive Sciences, 16(5), 292–305.Find this resource:

Owen, A. M., Hampshire, A., Grahn, J. A., et al. (2010). Putting brain training to the test. Nature, 465(7299), 775–U776.Find this resource:

Park, D. C., Smith, A. D., Dudley, W. N., and Lafronza, V. N. (1989). Effects of age and a divided attention task presented during encoding and retrieval on memory. Journal of Experimental Psychology—Learning Memory and Cognition, 15(6), 1185–91.Find this resource:

Park, D. C., Lodi-Smith, J., Drew, L., et al. (2014). The impact of sustained engagement on cognitive function in older adults: The Synapse Project. Psychological Science, 25(1), 103–12.Find this resource:

Pear Therapeutics. (2015). eFormulation Retrieved October 30, 2015, from http://peartherapeutics.com/

Petersen, R. C., Thomas, R. G., Grundman, M., et al. (2005). Vitamin E and donepezil for the treatment of mild cognitive impairment. New England Journal of Medicine, 352(23), 2379–88.Find this resource:

Pistell, P. J., Morrison, C. D., Gupta, S., et al. (2010). Cognitive impairment following high fat diet consumption is associated with brain inflammation. Journal of Neuroimmunology, 219(1–2), 25–32.Find this resource:

Poly, C., Massaro, J. M., Seshadri, S., et al. (2011). The relation of dietary choline to cognitive performance and white-matter hyperintensity in the Framingham Offspring Cohort. American Journal of Clinical Nutrition, 94(6), 1584–91.Find this resource:

Posit Science. (2015a). Brain HQ, from http://www.brainhq.com/

Posit Science. (2015b). InSight. Retrieved December 31, 2015, from http://www.brainhq.com/brain-training-products/insight

Prince, M., Bryce, R., Albanese, E., Wimo, A., Ribeiro, W., and Ferri, C. P. (2013). The global prevalence of dementia: a systematic review and metaanalysis. Alzheimers and Dementia, 9(1), 63–75.Find this resource:

Prince, M., Wimo, A., Guerchet, M., Ali, G.-C., Wu, Y.-T., and Prina, M. (2015). World Alzheimer Report 2015: The Global Economic Impact of Dementia, from https://www.alz.co.uk/research/world-report-2015

Rabbitt, G. (2014). Age and the Overstuffed Mind. Retrieved from https://outlookfromhutch.com/2014/02/01/hello-world/

Rabipour, S. (in press). The (dis)enchantment of brain training. In: A. Raz and R. Thibault (eds). The Dark Side of Brain Imaging: What a Scanner Won’t Tell You. Elsevier.Find this resource:

(p. 173) Rabipour, S., and Davidson, P. S. R. (in press). Using digital technology for cognitive assessment and enhancement in older adults. In: D. Faust, K. Faust and M. Potenza (eds). The Oxford Handbook of Digital Technologies and Mental Health. Oxford, UK: Oxford University Press.Find this resource:

Rabipour, S., Miller, D., Taler, V., Messier, C., and Davidson, P. S. R. (2017). Physical and cognitive exercise. In: Rigby, L. (ed.). Aging: Handbook of Gerontology Research Methods: Understanding Successful Aging. Abington: Routledge.Find this resource:

Rampersaud, G. C., Pereira, M. A., Girard, B. L., Adams, J., and Metzl, J. D. (2005). Review—breakfast habits, nutritional status, body weight, and academic performance in children and adolescents. Journal of the American Dietetic Association, 105(5), 743–60.Find this resource:

Ramscar, M., Hendrix, P., Shaoul, C., Milin, P., and Baayen, H. (2014). The myth of cognitive decline: non-linear dynamics of lifelong learning. Topics in Cognitive Science, 6(1), 5–42.Find this resource:

Rao, M. G., Holla, B., Varambally, S., Raveendranathan, D., Venkatasubramanian, G., and Gangadhar, B. N. (2013). Piracetam treatment in patients with cognitive impairment. General Hospital Psychiatry, 35(4), 451.e5–451.e6.Find this resource:

Rebok, G. W., Ball, K., Guey, L. T., et al. (2014). Ten-year effects of the advanced cognitive training for independent and vital elderly cognitive training trial on cognition and everyday functioning in older adults. Journal of the American Geriatrics Society, 62(1), 16–24.Find this resource:

Reijnders, J., van Heugten, C., and van Boxtel, M. (2013). Cognitive interventions in healthy older adults and people with mild cognitive impairment: a systematic review. Ageing Research Reviews, 12(1), 263–75.Find this resource:

Rendell, P. G., and Craik, F. I. M. (2000). Virtual week and actual week: age-related differences in prospective memory. Applied Cognitive Psychology, 14, S43–S62.Find this resource:

Ritchie, S. J., Tucker-Drob, E. M., and Deary, I. J. (2014). A strong link between speed of visual discrimination and cognitive ageing. Current Biology, 24(15), R681–3.Find this resource:

Ross, L. A., Freed, S. A., Edwards, J. D., Phillips, C. B., and Ball, K. (2017). The impact of three cognitive training programs on driving cessation across 10 years: a randomized controlled trial. The Gerontologist, 57(5), 838–46Find this resource:

Rowe, J. W., and Kahn, R. L. (1987). Human aging–usual and successful. Science, 237(4811), 143–9.Find this resource:

Sahakian, B. J., Bruhl, A. B., Cook, J., et al. (2015). The impact of neuroscience on society: cognitive enhancement in neuropsychiatric disorders and in healthy people. Philosophical Transactions of the Royal Society B—Biological Sciences, 370(1677).Find this resource:

(p. 174) Sakaki, M., Nga, L., and Mather, M. (2013). Amygdala functional connectivity with medial prefrontal cortex at rest predicts the positivity effect in older adults’ memory. Journal of Cognitive Neuroscience, 25(8), 1206–24.Find this resource:

Salthouse, T. A. (2014). Correlates of cognitive change. Journal of Experimental Psychology—General, 143(3), 1026–48.Find this resource:

Sarubbo, F., Moranta, D., and Pani, G. (2018). Dietary polyphenols and neurogenesis: molecular interactions and implication for brain ageing and cognition. Neuroscience Biobehavoral Review, 90, 456–70.Find this resource:

Scarmeas, N., Levy, G., Tang, M. X., Manly, J., and Stern, Y. (2001). Influence of leisure activity on the incidence of Alzheimer’s disease. [Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, P.H.S.]. Neurology, 57(12), 2236–42.Find this resource:

Schmiedek, F., Lovden, M., and Lindenberger, U. (2010). Hundred days of cognitive training enhance broad cognitive abilities in adulthood: findings from the COGITO Study. Frontiers in Aging Neuroscience, 2. doi: 10.3389/fnagi.2010.00027Find this resource:

Schneider, L. S., Tariot, P. N., Dagerman, et al. (2006). Effectiveness of atypical antipsychotic drugs in patients with Alzheimer’s disease. New England Journal of Medicine, 355(15), 1525–38.Find this resource:

Schulz, V. (2003). Ginkgo extract or cholinesterase inhibitors in patients with dementia: what clinical trials and guidelines fail to consider. Phytomedicine, 10, 74–9.Find this resource:

Scientific Brain Training Corporation. (2017). HAPPYneuron, 2013, from http://www.happy-neuron.com

Shah, R. (2013). The role of nutrition and diet in Alzheimer disease: a systematic review. Journal of the American Medical Directors Association, 14(6), 398–402.Find this resource:

Shatil, E. (2013). Does combined cognitive training and physical activity training enhance cognitive abilities more than either alone? A four-condition randomized controlled trial among healthy older adults. Frontiers in Aging Neuroscience, 5. doi: 10.3389/Fnagi.2013.00008Find this resource:

SharpBrains. (2013). Market Intelligence: Digital Platforms for Brain/Cognitive Assessment, Monitoring and Enhancement, from https://sharpbrains.com/market-report/

Shute, V. J., Ventura, M., and Ke, F. F. (2015). The power of play: the effects of Portal 2 and Lumosity on cognitive and noncognitive skills. Computers and Education, 80, 58–67.Find this resource:

Simons, D. (2013). 19 questions about video games, multitasking, and aging (a HI-BAR commentary on a new Nature paper). Retrieved from http://blog.dansimons.com/2013/09/19-questions-about-video-games.html

(p. 175) Simons, D. J., Boot, W. R., Charness, N., et al. (2016). Do “brain-training” programs work? Psychological Science in the Public Interest, 17(3), 103–86.Find this resource:

Smith, A. (2002). Effects of caffeine on human behavior. Food and Chemical Toxicology, 40(9), 1243–55.Find this resource:

Smith, G. E., Housen, P., Yaffe, K., et al. (2009). A cognitive training program based on principles of brain plasticity: results from the Improvement in Memory with Plasticity-based Adaptive Cognitive Training (IMPACT) Study. Journal of the American Geriatrics Society, 57(4), 594–603.Find this resource:

Sofi, F., Abbate, R., Gensini, G. F., and Casini, A. (2010). Accruing evidence on benefits of adherence to the Mediterranean diet on health an updated systematic review and meta-analysis. American Journal of Clinical Nutrition, 92(5), 1189–96.Find this resource:

Sony Computer Entertainment America LLC. (2014). Eyetoy: Kinetic, 2014, from http://www.playstation.com/en-us/games/eyetoy-kinetic-ps2/

Statistics Canada. (2017). Age and sex, and type of dwelling data: key results from the 2016 Census, from https://www150.statcan.gc.ca/n1/daily-quotidien/170503/dq170503a-eng.htm

Steele, C. M. (1997). A threat in the air—how stereotypes shape intellectual identity and performance. American Psychologist, 52(6), 613–29.Find this resource:

Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve concept. Journal of the International Neuropsychological Society, 8(3), 448–60.Find this resource:

Sternberg, D. A., Ballard, K., Hardy, J. L., Katz, B., Doraiswamy, P. M., and Scanlon, M. (2013). The largest human cognitive performance dataset reveals insights into the effects of lifestyle factors and aging. Frontiers in Human Neuroscience, 7. doi: Artn 292 Doi 10.3389/Fnhum.2013.00292Find this resource:

Tan, C. C., Yu, J. T., Wang, H. F., et al. (2014). Efficacy and safety of donepezil, galantamine, rivastigmine, and memantine for the treatment of Alzheimer’s disease: a systematic review and meta-analysis. Journal of Alzheimers Disease, 41(2), 615–31.Find this resource:

Temple, J. (2014). Brainteaser: Can an iPad Game Detect Alzheimer’s? Re/code. Retrieved from http://recode.net/2014/01/09/brainteaser-can-an-ipad-game-detect-alzheimers/

Tesky, V. A., Thiel, C., Banzer, W., and Pantel, J. (2011). Effects of a group program to increase cognitive performance through cognitively stimulating leisure activities in healthy older subjects. GeroPsych: The Journal of Gerontopsychology and Geriatric Psychiatry, 24(2), 83–92.Find this resource:

The Stanford Center on Longevity. (2014). A Consensus on the Brain Training Industry from the Scientific Community, from http://longevity3.stanford.edu/blog/2014/10/15/the-consensus-on-the-brain-training-industry-from-the-scientific-community-2/

(p. 176) Theill, N., Schumacher, V., Adelsberger, R., Martin, M., and Jancke, L. (2013). Effects of simultaneously performed cognitive and physical training in older adults. BMC Neuroscience, 14. doi: Artn 103 Doi 10.1186/1471-2202-14-103Find this resource:

Thomson, J., Ahluwalia, M., and Weatherley, K. (2013). For Seniors, Losing Driver’s Licence Like Having ‘Arm Cut Off’. CBC NewsCanada. Retrieved from http://www.cbc.ca/news/canada/for-seniors-losing-driver-s-licence-like-having-arm-cut-off-1.1346585

Toril, P., Reales, J. M., and Ballesteros, S. (2014). Video game training enhances cognition of older adults: a meta-analytic study. Psychology and Aging, 29(3), 706–16.Find this resource:

Toril, P., Reales, J. M., Mayas, J., and Ballesteros, S. (2016). Video game training enhances visuospatial working memory and episodic memory in older adults. Frontiers in Human Neuroscience, 10. doi: ARTN 206 10.3389/fnhum.2016.00206Find this resource:

truBrain: (re)Designing Focus. (2015), from https://trubrain.com/

Tucker, A. M., and Stern, Y. (2011). Cognitive reserve in aging. Current Alzheimer Research, 8(4), 354–60.Find this resource:

Tun, P. A., and Lachman, M. E. (2010). The association between computer use and cognition across adulthood: use it so you won’t lose it? Psychology and Aging, 25(3), 560–8.Find this resource:

Uttl, B. (2008). Transparent meta-analysis of prospective memory and aging. Plos One, 3(2). doi: ARTN e1568 10.1371/journal.pone.0001568Find this resource:

Valve Corporation. (2011). Portal 2: Perpetual Testing Initiative. Retrieved October 30, 2015, from http://www.thinkwithportals.com/

Venkatesh, G. (2014). Brain wave. MyDigitalFC (Financial Chronicle). Retrieved from http://www.mydigitalfc.com/knowledge/brain-wave-519

Wadley, V. G., Benz, R. L., Ball, K. K., Roenker, D. L., Edwards, J. A., and Vance, D. E. (2006). Development and evaluation of home-based speed-of-processing training for older adults. Archives of Physical Medicine and Rehabilitation, 87(6), 757–63.Find this resource:

Walhovd, K. B., Krogsrud, S. K., Amlien, I. K., et al. (2016). Neurodevelopmental origins of lifespan changes in brain and cognition. Proceedings of the National Academy of Sciences of the United States of America, 113(33), 9357–62.Find this resource:

Wang, L., van Belle, G., Crane, P. K., et al. (2004). Subjective memory deterioration and future dementia in people aged 65 and older. Journal of the American Geriatrics Society, 52(12), 2045–51.Find this resource:

Williams, L. M., Brown, K. J., Palmer, D., et al. (2006). The mellow years? Neural basis of improving emotional stability over age. Journal of Neuroscience, 26(24), 6422–30.Find this resource:

(p. 177) Willis, S. L., and Caskie, G. I. (2013). Reasoning training in the ACTIVE study: how much is needed and who benefits? Journal of Aging Health, 25(8 Suppl.), 43S–64S.Find this resource:

Willis, S. L., Tennstedt, S. L., Marsiske, M., et al. (2006). Long-term effects of cognitive training on everyday functional outcomes in older adults. JAMA—Journal of the American Medical Association, 296(23), 2805–14.Find this resource:

Wilms, I. L., and Nielsen, S. (2014). Normative perceptual estimates for 91 healthy subjects age 60–75: impact of age, education, employment physical exercise, alcohol, and video gaming. Frontiers in Psychology, 5. doi: Artn 1137 Doi 10.3389/Fpsyg.2014.01137Find this resource:

Wolinsky, F. D., Unverzagt, F. W., Smith, D. M., Jones, R., Wright, E., and Tennstedt, S. L. (2006). The effects of the ACTIVE cognitive training trial on clinically relevant declines in health-related quality of life. Journals of GerontologySeries B Psychological Sciences and Social Sciences, 61(5), S281–7.Find this resource:

Wolinsky, F. D., Vander Weg, M. W., Howren, M. B., Jones, M. P., and Dotson, M. M. (2015). The effect of cognitive speed of processing training on the development of additional IADL difficulties and the reduction of depressive symptoms: results from the IHAMS randomized controlled trial. Journal of Aging Health, 27(2), 334–54.Find this resource:

Wu, T., Liu, J., Hallett, M., Zheng, Z., and Chan, P. (2013). Cerebellum and integration of neural networks in dual-task processing. Neuroimage, 65, 466–75.Find this resource:

Zelinski, E. M., Spina, L. M., Yaffe, K., et al. (2011). Improvement in memory with plasticity-based adaptive cognitive training: results of the 3-month follow-up. Journal of the American Geriatrics Society, 59(2), 258–65.Find this resource:

Zilberter, T., and Zilberter, E. Y. (2013). Breakfast and cognition: sixteen effects in nine populations, no single recipe. Frontiers in Human Neuroscience, 7. doi: ARTN 631 10.3389/fnhum.2013.00631 (p. 178) Find this resource: