Frontal lobes and aging: Deterioration and Compensation

Roberto Cabeza & Nancy A. Dennis

Until the mid 1990s, available evidence regarding neurocognitive aging could be summarized in a simple statement: cognitive deficits in healthy older adults are largest for tasks that are highly dependent on executive control processes because these processes are mediated by the prefrontal cortex (PFC), which is the region most disrupted by healthy aging. This view, known as the frontal lobe hypothesis, rested on the assumption that both cognitive and cerebral aging occurred mainly in one direction: a decline in function. This simple version of the frontal lobe hypothesis was challenged by the first crop of positron emotion tomography (PET) studies, which clearly showed that aging can have two opposing effects on brain activity: compared to young adults, older adults showed reduced activity in some brain regions but increased activity in other brain regions (e.g, Grady et al., 1994; Cabeza et al., 1997b). Age-related increases in neural activity were prominent within PFC, where they often involved regions that were not reliably recruited by younger adults. These findings challenged the standard assumption that aging is associated with a simple pattern of cognitive and neural decline, and supported the notion that cognitive processing in the aging brain is not just a weaker version of cognitive processing in the young brain, it is different.

The obvious question was why do older adults recruit additional PFC regions, and the first answer was compensation: the aging brain attempts to counteract neural decline by reorganizing its functions (e.g, Grady et al., 1994; Cabeza et al., 1997b). The concept of compensation captured the imagination of many researchers, making it the one of the most popular ideas in the growing field of cognitive neuroscience of aging. Despite its popularity, the concept of compensation remains rather speculative and vague. Greater activity is not always associated with better cognitive performance, and it is unclear whether increased activity in older adults reflects compensation, non-selective recruitment (an inability to recruit specialized brain regions), or just a confound in task difficulty. Thus, it is critical for the field to agree on clear criteria for using this term. It is also important to note that compensatory changes in the aging brain may not always occur as a net increase in brain activity, but as an increase in functional connectivity. Although most functional neuroimaging studies of aging have focused on age effects on regional activity, there is evidence functional connectivity is also modulated by aging, including increases in PFC connectivity. If age-related increases in PFC activity have been attributed to compensation, it is possible that age-related increases in PFC connectivity are also compensatory.

The present chapter has three main sections. The first section focuses on evidence for age- related deterioration supporting the frontal lobe hypothesis, including executive control deficits, PFC atrophy, white matter decline, and dopamine decline. The second section describes a simple model of age-related compensation and proposes four criteria for using this term. The third section reviews consistent patterns of age-related increases in PFC activity and connectivity that have been attributed to compensation, and considers how well they fulfill the proposed criteria for compensation.

Evidence for Age-Related Frontal Lobe Deterioration

One of the most influential ideas in cognitive neuroscience of aging is the frontal lobe hypothesis, which postulates that cognitive deficits in older adults are primarily due to the anatomical and functional deterioration of the frontal lobes (e.g., Moscovitch and Winocur, 1992; West, 1996) This hypothesis is supported by several lines of evidence including the following: (1) cognitive deficits in older adults are more pronounced in tasks that are highly dependent on executive control processes, which are assumed to be mediated by PFC, (2) age- related reductions in brain volume (atrophy) are more pronounced in PFC than in other brain regions, (3) age-related white matter deterioration is most pronounced in anterior brain regions including PFC, and (4) PFC is affected by age-related deficits in dopamine function. These four lines of evidence are briefly reviewed in separate sections below.

Age-related executive control deficits

Age-related deficits in executive functioning can be observed both in standardized neuropsychological tests assumed to assess frontal function, such as the Wisconsin Card Sorting Task (WCST) (e.g., Kramer et al., 1994; Parkin and Lawrence, 1994) and the verbal fluency test (e.g., Perlmuter et al., 1987; Tomer and Levin, 1993; Parkin and Lawrence, 1994; Baltes and Lindenberger, 1997), as well as in experimental paradigms focused on various executive processes. Although there several different ways of classifying executive processes (Miyake et al., 2000), it is generally agreed that they include (1) inhibiting irrelevant information, (2) coordinating multiple simultaneous operations, (3) manipulating information within working memory, and (4) controlling episodic memory operations. All of these processes exhibit significant decline in healthy aging.

Age-related deficits in inhibiting irrelevant information have been found in a variety of tasks, including negative priming, visual search, Stroop, attentional control, and directed forgetting (e.g., Hasher et al., 1991; McDowd and Oseas-Kreger, 1991; Connelly and Hasher, 1993; Kane et al., 1994; Kramer et al., 1994; Hasher et al., 1997; McDowd, 1997; Andres et al., 2008; Darowski et al., 2008). For example, younger adults take longer to name a letter in the current trial when the letter was a distractor in the previous trial, but this negative priming effect is reduced in older adults, possible reflecting a failure to inhibit distractor stimuli (Hasher et al., 1991). The failure to inhibit irrelevant information may lead to a clutter of information within working memory, reducing overall processing efficiency. According to the inhibition deficit theory (Hasher and Zacks, 1988), this mechanism plays a major role in cognitive aging. The link between inhibitory control deficits and PFC dysfunction is supported by evidence that similar deficits are displayed by patients with frontal lobe damage (e.g., Perret, 1974; Knight et al., 1989; Richer et al., 1993; Chao and Knight, 1998; Jonides et al., 2000; Thompson-Schill et al., 2002).

Age-related deficits in coordinating multiple simultaneous cognitive operations have been demonstrated using dividing attention paradigms (Somberg and Salthouse, 1982; McDowd and Craik, 1988; Park et al., 1989; Tun et al., 1992; Naveh-Benjamin et al., 2005). Older adults' sensitivity to divided attention tasks is consistent with the resources deficit theory (Craik and Byrd, 1982; Craik, 1986), which postulates that cognitive aging deficits are partly due to a reduction in attentional resources. In keeping with this theory, there is evidence that young adults under divided attention show patterns of performance that are qualitatively similar to those displayed by older adults under full attention (Rabinowitz et al., 1982; Jennings and Jacoby, 1993; Anderson et al., 1998). Also in agreement with the resource deficit theory, age-related cognitive deficits tend to be smaller in tasks that provide greater environmental support, which refers to guidance that reduces the need for self-initiated mental operations (Craik, 1983; Craik, 1986). In the episodic memory domain, for example, age-related deficits tend to be more pronounced for free recall than for cued recall and for cued recall than for item recognition, with free recall providing the least environmental support and item recognition, the most (e.g., Perlmutter, 1979; Craik and McDowd, 1987).

Age-related deficits in manipulating information within working memory are evidenced by larger age effects in complex working memory tasks than in simple maintenance tasks (Craik, 1977; Reuter-Lorenz and Sylvester, 2005), For instance, Dobbs and Rule (1989) found substantial age-related deficits on the N-back task, which requires working memory updating, but minimal age-related differences on simple forward and backward digit span tasks. Similarly, Wiegersma & Meertse (1989) found age-related decline in tasks that required reordering sequence of items within working memory but not in tasks that only required a simple reproduction of the item sequence. The link between the frontal lobes and manipulation of information within working memory is supported by abundant functional neuroimaging studies (Curtis and D'Esposito, 2006), including the finding that dorsolateral PFC shows greater activity when working memory contents are reordered than when they are maintained in the original format (e.g., D'Esposito et al., 1999; Blumenfeld and Ranganath, 2006)

Finally, age-related deficits in episodic memory control processes are evident in several paradigms, including associative memory and false memory paradigms. Regarding associative memory paradigm, large meta-analyses (Spencer and Raz, 1995; Old and Naveh-Benjamin, 2008) indicate that older adults are more impaired in remembering context (who, where, when, how?) than content (what?) information, and in remembering relationships between items (e.g., word pairs) than single items (e.g., single words). This evidence is consistent with the associative deficit hypothesis (Naveh-Benjamin, 2000), which postulates that a difficulty in binding different pieces of information is a major component of age-related cognitive decline, particularly in the memory domain. Although the associative deficit is partly due to medial temporal lobe (MTL) decline, PFC is also important for associative memory since PFC-mediated executive processes organize information during encoding and memory search during retrieval (Moscovitch, 1992; Johnson et al., 1993). In fact, a recent study found that source memory performance in older adults was associated with performance on neuropsychological tests assumed to measure PFC function rather than with those assumed to measure MTL function (Glisky and Kong, 2008). Turning to false memory paradigms, there is evidence that older adults are more prone than younger adults to falsely remember events that never happened when they fit with the general gist of previously experienced events (LaVoie and Malmstrom, 1998; Tun et al., 1998; Budson et al., 2003; Pierce et al., 2005b, a; Pierce et al., 2008). Avoiding false memories depends on evaluation and monitoring processes assumed to depend on PFC, and the link between age- related false memory increases and PFC dysfunction is supported by evidence of increased false memory rates in frontal lobe patients (Parkin et al., 1996; Rapcsak et al., 1996; Curran et al., 1997; Moscovitch and Melo, 1997) and older adults with poorer frontal lobe functioning (Butler et al., 2004).

In sum, consistent with the frontal lobe hypothesis, cognitive deficits in healthy older adults are greater for tasks that are highly dependent on executive control processes, such as inhibiting irrelevant information, coordinating multiple simultaneous operations, manipulating information within working memory, and controlling episodic memory operations. All these processes have been linked to PFC, which—as reviewed below—is a brain region with substantial age-related decline.

Age-related PFC atrophy

Both post-mortem and in vivo studies universally show that the brain shrinks with age. Cross-sectional studies have shown that PFC is affected more than other cortical regions (for detailed reviews see Raz, 2000; Raz, 2004). Results also indicate differential patterns of decline within the PFC. Specifically, grey matter volume in lateral PFC regions show the greatest amount of loss and steepest declines (Raz et al., 2004), while no differences in asymmetry of grey (or white) matter decline have been observed (Takao et al., 2010) (but see Bennett et al., 2010). Longitudinal studies often report higher estimates of PFC decline than cross-sectional studies, possibly because the longitudinal studies control for individual differences and cohort effects (Raz et al., 2005). Like cross-sectional studies, longitudinal studies show that age-related atrophy is greater in the frontal lobes (0.9-1.5% per year) than in posterior brain regions (Pfefferbaum et al., 1998; Resnick et al., 2003; Raz et al., 2005; Driscoll et al., 2009). Some evidence suggests inferior PFC regions show the steepest rates of decline (Resnick et al., 2003). One finding, shown more clearly by longitudinal than cross-sectional studies is that whole-brain and PFC atrophy are not linear across the lifespan but become steeper in old age (Raz, 2005; Raz et al., 2007). Consistent with the aforementioned assumption that age-related executive control deficits reflect PFC dysfunction, significant correlations have been reported between executive function and PFC atrophy in older adults (Raz et al., 1998; Head et al., 2002; Gunning-Dixon and Raz, 2003; Gong et al., 2005; Kennedy and Raz, 2005; Head et al., 2008; Zimmerman et al., 2008; Head et al., 2009; Kennedy et al., 2009; Paul et al., 2009). For example, Gunning-Dixon & Raz (2003) found that in a large group of older adults, the number of perseveration errors in the WCST was negatively correlated with PFC volume. Likewise, Head et al. (2009) found that PFC volume was a significant mediator of age-related deficits in the WCST, as well as in working memory tasks. Assessing longitudinal changes, Cardenas et al. (2009) found that smaller lateral PFC volume predicted executive function decline. Finally, there is some evidence of regional specificity. For example, one study (Elderkin-Thompson et al., 2008) found that in older adults the volume of the anterior cingulate cortex was positively correlated with inhibitory control performance whereas the volume of the gyrus rectus was positively correlated with inductive reasoning performance.

Despite evidence for disproportionate frontal atrophy, it should be noted that other brain regions, including the basal ganglia (Bugiani et al., 1978; Schwartz et al., 1985) and the thalamus (Xu et al., 2000; Gallo et al., 2004; de Rover et al., 2008) also show pronounced age-related atrophy. In fact, during the last decades of healthy aging, volumetric changes in PFC do not differ from those in other neocortical areas (Salat et al., 1999; Resnick et al., 2000). Thus, when considering the impact on frontal decline in aging, one should not overlook the fact that these changes do not occur in isolation from changes in other brain regions.

Age-related white matter decline

Postmortem studies show that age-related white matter loss occurs throughout the brain but is more pronounced within PFC, where atrophy in white matter tends to be more pronounced than atrophy in gray matter (Esiri, 1994; Kemper, 1994; Double et al., 1996). In vivo studies using volumetric MRI measures have confirmed substantial age-related white matter atrophy (Sullivan et al., 1995; Raz, 1996; Raz et al., 1997), with some studies finding this atrophy mainly within PFC (Raz et al., 1997; Salat et al., 1999). Several studies examining white matter differences across the lifespan (Pfefferbaum et al., 1994; Courchesne et al., 2000; Sullivan et al., 2004) indicate a decline beginning only in the seventh decade, while others indicate an increased rate of decline beginning in the fifth decade (Kennedy and Raz, 2009) (but see Liu and Cooper, 2003). Whereas studies on age-related gray matter decline have typically focused on volumetric measures, studies on age-related white matter decline often examine measures of white matter integrity, such as white matter hyperintensities (WMHs) and diffusion tensor imaging (DTI).

WMHs are areas of increased brightness in structural MRI scans, which are assumed to reflect white matter damage from different causes, including ischemia, myelin degradation, fiber loss, arteriosclerosis, and micro-vascular disease (Fazekas et al., 1993; Fernando et al., 2006; Holland et al., 2008). Recent studies have suggested that the presence of WMHs are greatest in deep compared to periventricular white matter (Sachdev et al., 2007) and within PFC (Smith et al., 2000; Raz et al., 2003; Fazekas et al., 2005; Yoshita et al., 2006; Sachdev et al., 2007). While few longitudinal studies of WMH growth have been conducted, there is evidence for greater age- related increases in WMH within periventricular and frontal regions than other brain regions (Prins et al., 2004; Raz et al., 2007). Several studies have found an association between the prevalence of WMH within the PFC and performance on PFC-mediated cognitive tasks. For example, Gunning-Dixon & Raz (2003) found that WMH within PFC are independently associated with perseveration errors on the WCST. Additionally, a large study by Soderlund and colleagues (2003) found that periventricular WMHs were associated with poor performance in the Stroop task. Yet another study (Raz et al., 2007) found that the prevalence and rate of increase of WMHs were associated with measures of fluid intelligence. Research also suggests an association between the prevalence of WMH and reductions in PFC activation (Nordahl et al., 2006; Venkatraman et al., 2010). For example, Nordahl and colleagues found that age-related increases in both global WMH and WMH within the dorsolateral PFC were associated with decreased PFC activation during both episodic retrieval and verbal working memory.

DTI measures the movement of water molecules, which in healthy myelinated fiber tracts tends to occur along the fiber walls rather than across these walls. With degradation of myelin sheath in normal aging, the probability and speed of diffusion along the fiber walls diminishes. Fractional anisotropy (FA) quantifies these changes, and in turn, white matter integrity. Reduced FA is indicative of degraded micro-structural tissue integrity. Although age-related FA reductions can be found throughout the brain, they tend to be most pronounced within PFC (Salat et al., 1999; O'Sullivan et al., 2001; Madden et al., 2004; Raz et al., 2005; Madden et al., 2007). Recent evidence suggests rather than a PFC-specific effect, these reductions show an more global anterior-to-posterior gradient (e.g., Pfefferbaum and Sullivan, 2003; Head et al., 2004; Salat et al., 2005; Sullivan et al., 2008; Davis et al., 2009; Bennett et al., 2010). For example, Davis et al. (2009) found that the magnitude of age-related FA reductions decreased gradually from anterior to posterior segments of longitudinal fiber tracts (see Fig. 1). In keeping with the fact that anterior regions are the last to develop, it has been suggested the anterior-to-posterior gradient is indicative of a “last-in-first-out” pattern of brain aging (Raz, 2000; Bartzokis et al., 2004; Davis et al., 2009). While the underlying mechanism mediating this gradient is unclear, it may reflect in part a pattern of vascular decline (Kennedy and Raz, 2009). Within PFC, there is also evidence that ventromedial regions exhibit greater age-related FA reductions than other frontal regions (Salat et al., 2005) (but see Zahr et al., 2009).

Fig 1 about here

In addition to using FA as a global measure of white matter integrity, a few studies have investigated two FA components, radial diffusivity (RD) and axial diffusivity (AD), which are assumed to respectively index myelin and axonal integrity (Song et al., 2003; Budde et al., 2007). Most studies support the idea that age effects are larger for RD than for AD (Fjell et al., 2008; Zhang et al., 2008; Davis et al., 2009; Madden et al., 2009). For example, Davis et al. (2009) found that in many tracts, including the genu of the corpus callosum, the cingulate bundle, and the uncinate fasciculus, age effects were significant for RD but not for AD (see Fig. 1). This finding supports the idea that white matter deficits in older adults reflect mainly demyelination rather than axonal decline. It may also be the case that age-related changes in these measures are region-specific (Bennett et al., 2010; Burzynska et al., 2010). More generally, whereas some evidence strongly links age-related white matter decline to demyelination, this mechanism alone cannot account for all age effects on white matter integrity.

Research investigating the relationship between FA and function have found that measures of working memory, executive functioning and fluid intelligence are associated with measures of white matter integrity (O'Sullivan et al., 2001; Deary et al., 2006; Sullivan et al., 2006; Grieve et al., 2007; Madden et al., 2007; Davis et al., 2009; Kennedy and Raz, 2009; Madden et al., 2009). For example, Deary et al. (2006) found reduced frontal FA to be associated with poorer performance on several measures of frontal functioning including choice reaction time, working memory, and verbal fluency in older adults. Another study, Davis et al. (2009) found that prefrontal FA was correlated with older adults' performance in an executive function task (see Fig. 1-B). Furthermore, the FA correlation from Davis et al. was mirrored in RD, but not AD, suggesting that RD is more sensitive to age-related changes in cognition.

Dopamine deficits and PFC

In addition to atrophy and white matter deficits, PFC function in older adults is affected by deficits in dopamine (DA) function in the striatum and PFC, including reductions in DA concentration, receptor density, and transporter availability (Volkow et al., 2000). For example, D2 receptor loss is estimated to be around 13% per decade in the anterior cingulate cortex and 11% in the frontal cortex (Kaasinen et al., 2000). Given abundant evidence that DA modulates activity within PFC (e.g., Williams and Goldman-Rakic, 1995; Luciana et al., 1998), it has been proposed that decreases in DA projections and availability within PFC mediate age-related cognitive deficits, including difficulties with context processing and working memory (Braver and Barch, 2002). Consistent with this idea, significant correlations between DA functioning and cognitive performance in older adults have been shown for a variety of tasks (Volkow et al., 1998; Backman et al., 2000; Erixon-Lindroth et al., 2005). For example, Volkow and colleagues (1998) found that across healthy individuals ranging in age between 24 and 86 years availability of D2 receptors correlated with tasks assumed to be mediated by PFC, such as WCST and Stroop.

In addition to DA dysfunction in the PFC, declines in striatal DA availability and function may also have significant impact on frontally-mediated cognitive processes, given the vast interconnections of the striatum and the PFC (Cummings, 1993; Eslinger and Grattan, 1993). In support of this view, several studies find that patients with DA deficits (Huntington’s and Parkinson’s disease) often show cognitive deficits - which can be modulated by dopamine agonists and antagonists. For example, DA deficits in Parkinson’s disease (within the fronto- striatal system) are accompanied by reduction in processing speed and working memory span (Owen et al., 1992; Gabrieli et al., 1996). Thus it has been argued that this form of pathological aging may be integral in composing a model for fronto-striatal cognitive deficits observed in normal aging (Hedden and Gabrieli, 2004; Backman et al., 2006). Summary

In sum, there is abundant evidence of age-related decline in behavioral measures of executive control processes associated with PFC as well as measures of PFC anatomy and physiology. Anatomical changes shows similar patterns of age-related decline, appearing most prominent in advanced aging and in anterior than in posterior regions. Also underlying the observed behavioral differences, there is evidence that dopamine availability and function within PFC decreases in aging. Given the prominent role of DA in modulating PFC function, this deficit combined with substantial anatomical decline provides a basis for understanding the executive function deficits observed in aging at the behavioral level. Taken together, this evidence supports a theory of cognitive aging focused on the a role of declining functionality of the frontal cortex as the underlying cause of age-related cognitive decline (see West, 1996) and greater cognitive decline in advanced aging (for a review see Park, 2002). The frontal lobe hypothesis does not dismiss age-related changes in other brain regions, but postulates that age-related deficits are primarily the result of frontal decline. Given the aforementioned structural and behavioral evidence, as well as the vast interconnections of the PFC with other cortical regions, the frontal lobe hypothesis carries substantial merit in the realm of cognitive aging.

The concept of compensation

Despite the vast literature reviewing age-related deficits in both structure and function of the PFC, there exists an equally vast amount of evidence documenting age-related increases in PFC activation (see below for a review). Taken together these finding represent one of the more paradoxical relationships in the aging literature. With regard to both sets of results, functional neuroimaging studies often attribute age-related increases in activity to functional compensation (Grady et al., 1994; Cabeza et al., 1997b; Reuter-Lorenz et al., 2000a; Cabeza, 2002). Unfortunately, the term compensation is both vague and ambiguous, and can lead to contradictory predictions. In an attempt to clarify this term, the next section describes an age- related compensation model and the following section suggests four criteria for defining compensation. A simple model of age-related compensation

Fig. 2 depicts an extremely simple (i.e., oversimplified) model of age-related compensation. The model consists of three hypothetical constructs (rectangles) and four measurable variables (ovals). The construct neural resources refers to the total capacity of the brain for cognitive processing. Neural resources can be operationalized in terms of anatomical and physiological integrity, which can assessed in vivo using neuroimaging measures (e.g., MRI measures of brain atrophy, DTI measures of white matter integrity, PET measures of neurotransmitter function and amyloid deposits). Neural resources include those typically engaged for a particular task, which are called here standard resources for the task, as well as those engaged when the main resources are not sufficient to perform the task, which are called here reserve resources. The construct neural supply refers to the amount of neural resources being deployed for cognitive processing at a particular point in time, including main and alternative resources. Neural supply can be operationalized in terms of neural activity and connectivity, which can be assessed in vivo using functional neuroimaging measures (e.g., fMRI, event-related potentials—ERPs, magneto- encephalography—MEG). The construct cognitive processing refers to the hypothetical cognitive operations and strategies being performed (attention, memory, etc.), which can be operationalized in terms of patterns of performance (accuracy, response times) in behavioral tests. Finally, the construct task demands refer to quantity and quality of cognitive processing required by a particular cognitive task. Task demands are difficult to measure independently of task performance but they can be manipulated by the experimenter (e.g., number of items to be maintained in a working memory task, to be scanned in a visual search task, or to be recalled in an episodic memory task).

Fig 2 about here

As evidenced by the anatomical and physiological deficits reviewed in the previous section, aging is associated with a reduction in standard neural resources. The model assumes that the resource reduction leads to a decrease in neural supply, which in turn leads to a decline in cognitive processing. As a result, older adults have difficulty coping with task demands in the real world and in the laboratory; there is a mismatch between available cognitive processing and task demands. To meet task demands, older adults attempt to compensate for the aforementioned resource deficit by recruiting reserve neural resources (see grey arrow from mismatch in Fig. 2), thereby increasing neural supply (grey arrows from reserve resources to neural supply). We use the term attempted compensation to refer to this mismatch-triggered recruitment of reserve resources and associated increase in neural supply. Thus, not every increase in activity reflects attempted compensation; it must be an increase that can be linked to a mismatch between available processing resources and task demands. The increase in neural supply due to attempted compensation may be observed as increased activity in the same brain regions or in alternative brain regions and/or as increased connectivity among regions. The term "attempted compensation" can be also applied to similar changes displayed by young adults in response to increased task demands (which also create a demand-processing mismatch, see Fig. 2). Thus, attempted compensation is a natural response of the brain to deal with insufficient cognitive processing- resources (Reuter-Lorenz and Cappell, 2008). In a broader sense, attempted compensation occurs in all body systems. In the motor system, for example, although we have two hands available, we typically lift light objects using one hand (main resource). However, when the object is heavy (increased demands), we may compensate by using both hands (main plus reserve).

Turning to the right side of Fig. 2, the attempt to compensate for the mismatch between cognitive processing and task demands by increasing neural activity or connectivity may lead to enhanced cognitive processing (successful compensation) or it may lead to no change in performance or even worse performance (unsuccessful compensation). The distinction between attempted compensation and successful compensation is critical because these two concepts can lead to different predictions in functional neuroimaging studies. Thus, we need clear criteria for defining attempted and successful compensation.

Criteria for defining attempted and successful compensation

Here we propose four criteria necessary for increased activity/connectivity to be labeled as compensatory, two for attempted compensation and two for successful compensation. We explain these criteria using eyeglasses as a metaphor: eyeglasses are compensatory devices for declining vision, and hence, the criteria that define compensatory activity can be illustrated using the criteria one uses for the use of eyeglasses. Although eyeglasses are external compensatory devices whereas compensatory activity is a natural response of the brain, the metaphor is useful to explain the criteria. Criterion 1: Attempted-compensation activity has an inverted-U relationship with brain decline

Because the attempt to compensate originates in a mismatch between cognitive processing capacity and task demands, it is reasonable to expect that at first attempted-compensation activity increases as a function of structural decline (see Fig 3-A). In terms of the eyeglasses metaphor, this idea is obvious: the strength of the eyeglasses prescription should be strongest for those with the worst vision. In other words, attempted compensation should be most prominent in those who need compensation the most. Consistent with this idea, there is evidence of negative correlations between increased activity and brain integrity in older adults (Fig. 2, dashed arrow 1). Likewise, there is evidence of increased PFC activity in Alzheimer Disease (AD) patients (Grady et al., 2003b) and mild cognitive impairment (MCI) patients (Yetkin et al., 2006) compared to healthy elderly, and in APOE4 carriers compared to non-carriers (Bookheimer et al., 2000). There is also evidence in aging that the over-recruitment of one brain region is often associated with the under-recruitment of a different region (negative correlation between regions: Fig. 2, dashed arrow 2). For example, increased PFC recruitment is frequently coupled with occipital under- recruitment, a pattern we called Posterior-Anterior Shift with Aging or PASA (Davis et al., 2008) and discuss below.

Although attempted-compensation is at first likely to increase with brain decline (Fig. 3-A- 1), eventually, when brain deterioration has disrupted both main and reserve resources, attempted-compensation activity is likely to decline (Fig. 3-A-2), yielding an inverted-U function. In the eyeglasses metaphor, this could be described as a shift from not needing eyeglasses, to needing eyeglasses and using them, to a point of decline where eyeglasses are no longer helpful. The idea of an inverted-U function was suggested by Dickerson et al. (2005) to explain their finding that medial-temporal lobe (MTL) activity during memory encoding increased from healthy aging to MCI but then decreased from MCI to AD. Likewise, Wierenga and Bondi (2007) suggested that PFC activity increases from healthy aging to mild MCI and then decreased for severe MCI. The inverted-U hypothesis predicts that the correlation between brain activity and brain integrity measures (dashed arrow 1 in Fig. 2) should flip from negative at mild levels of brain decline (Fig. 3-A-1: worse integrity  more activity) to positive at more severe levels of brain decline (Fig. 3-A-2: worse integrity  less activity). Of course, determining the peak of this inverted-U function is very difficult, particularly since it may vary across brain regions. For example, Wierenga and Bondi (2007) suggested that MTL activity peaks in mild MCI whereas PFC activity peaks in severe MCI. Moreover, besides brain deterioration, attempted-compensation activity is likely to be influenced by several other factors not considered in our model, including education, motivation, and personality (Stern, 2002).

Fig 3 about here

Criterion 2: Attempted-compensation activity has an inverted-U relationship with task demands

Given that attempted compensation is triggered by the mismatch between cognitive processing capacity and task demands, it should be more prominent not only when cognitive processing declines, but also when task demands increase. As noted previously, this is the reason why the notion of attempted compensation applies not only to older adults but also to young adults, who also show greater activity in response to increased task demands. Again, the eyeglasses metaphor is straightforward: stronger eyeglasses are needed not only when the vision is worse (first criterion) but when the letters are smaller (second criterion). Whereas the first criterion for attempted compensation can be assessed by linking increased activity in a certain region to measures of anatomy or physiology (Fig. 2, dashed arrow 1) or activity in different regions (Fig. 2, dashed arrow 2), the second criterion can be assessed by linking increased activity to levels of task demands (Fig 2., dashed arrow 3).

Functional neuroimaging studies that parametrically varied task demands in younger adults (for a review, see D'Esposito, 2001) suggest that the function linking activity and task demands shows an inverted-U function (see Fig. 3-B): as task demands increase, neural activity tends to increase (possibly tracking the recruitment of additional neural resources, i.e., increase neural supply) until it reaches asymptote (perhaps reflecting the limit of available neural resources) and then starts to decline (possibly signaling the breakdown of cognitive operations when capacity is exceeded). Reuter-Lorenz and collaborators (Reuter-Lorenz and Mikels, 2006; Reuter-Lorenz and Cappell, 2008) have proposed that aging shifts the demand-activity function to the left, as indicated by the grey line in Fig. 3-B. Because of this leftward shift, at low levels of task demands, activity grows faster in older than in younger adults (attempted compensation) but as task demands keep increasing, activity in older adults reaches asymptote earlier (fewer resources) and starts to decline earlier (breakdown) than in younger adults. Evidence for these age-related changes has been provided by several fMRI studies (Rypma and D'Esposito, 2000; Mattay et al., 2006; Schneider-Garces et al., 2009; Cappell et al., 2010). For example, a recent study (Cappell et al., 2010) found that older adults showed more right dorsolateral PFC activity than younger adults when maintaining 5 letters in working memory but less activity when maintaining 7 letters (see Fig. 3-C). According to Reuter-Lorenz and collaborators, the age- related leftward shift in the demand-activity function, which they call Compensation-Related Utilization of Neural Circuits Hypothesis or CRUNCH, occurs because older adults engage more neural resources at lower levels of task demands and, as a result, they have fewer resources available to meet higher levels of task demand.

The inverted-U function linking brain activity and brain deterioration (Fig. 3-A) and the one linking brain activity and task demands (Fig. 3-B) are obviously related, and the compensation model in Fig. 2 explains why: they both reflect a mismatch between available cognitive processing and task demands. Although the activation increase (left side of inverted-U function) could reflect similar mechanisms in both cases (i.e., recruitment of additional neural resources), the activation decrease (right side of inverted-U function) seems to reflect different mechanisms. In the case of excessive brain deterioration, the activity decrease is likely to reflect the dwindling of neural resources. In the case of excessive task demands, an explanation in terms of capacity limits is not enough because such explanation predicts an asymptote rather than an activity decrease. To account for the drop, one needs to postulate an additional mechanism. In the case of working memory, for example, we could postulate that additional items beyond one’s working memory span are not only not processed but interfere with current maintenance. An alternative motivational account is that when task demands become excessive, participants stop performing the task at least part of the time, and hence, task-relevant brain regions are partly disengaged. Further research is required to distinguish between these interference and motivational accounts, and to investigate graded task demand manipulations beyond the working memory domain (Rypma and D'Esposito, 2000; Mattay et al., 2006; Schneider-Garces et al., 2009; Cappell et al., 2010).

Criterion 3: Successful-compensation activity is positively correlated with cognitive performance

An obvious requirement for employing the term "successful compensation" is a positive association between the increased activity or connectivity and successful task performance (Fig. 2, dashed arrow 4). The eyeglasses metaphor highlights two important clauses for this criterion to work. The first clause is that a compensatory change is likely to enhance performance only if the change is required. In terms of the metaphor, if a person needs to use the eyeglasses, wearing them is likely to improve visual performance, but if a person does not need to use eyeglasses, then wearing them is unlikely to improve vision and it could actually make it worse. For example, there is evidence that during working memory increased PFC activity is associated with faster reaction times in older adults but slower reaction times in younger adults (Rypma and D'Esposito, 2000). Of course, unlike the case of eyeglasses, it is very difficult to determine whether an individual or a group "needs" compensatory activity. A possible indirect measure of "need for compensation" is reduced brain integrity, which was previously identified as a criterion for attempted compensation (Criterion 1). Thus, there is a reason to assume that the brain regions showing successful-compensation activity should be a subset of regions showing attempted- compensation activity. We return below to the question of how to distinguish attempted- and successful-compensation activity.

The second clause is that a compensatory mechanism is likely to enhance performance only if the mechanism is effective. In terms of the eyeglass metaphor, if the eyeglasses prescription is incorrect, visual performance is not going to improve and it could decline. Likewise, if older adults activate a different region than younger adults but the activation reflects the use of an ineffective strategy, then the activity-performance correlation is likely to be negative or non- significant rather than positive. While ineffective, such activation shifts do not discount the fact that the older adults are attempting to compensate, hence the label attempted compensation. For example, Schacter et al. (1996) found that older adults recruited more posterior PFC activations during memory recall, showing no performance benefits. This activity was attributed to an inefficient phonology-based strategy and without any associated performance boost, this would be an example of attempted compensation without successful compensation. Of course, it is very difficult to know in brain imaging studies what strategies are being used by older and younger adults, what brain regions are associated with each strategy, and which strategy is more effective.

Criterion 4: Successful-compensation activity is found in regions whose alteration affects cognitive performance in older adults

The fourth criteria for successful compensation is that if the function of the region showing increased activity in older adults is disrupted or enhanced, then cognitive performance in older adults is also likely to be disrupted or enhanced. The criterion is clear in the eyeglasses metaphor: blocking or facilitating the use of eyeglasses in those who need them is likely to affect their visual performance.

A non-invasive method for altering the function of brain regions assumed to mediate successful compensation is transcranial magnetic stimulation (TMS). This technique, which alters neural function in a small cortical area (e.g., 0.5cm) with the magnetic field generated by a coil placed on the scalp, produces effects that are transient and disappear after a few seconds or minutes. The main advantage of using TMS to investigate compensation is that, unlike previously discussed links (Fig 2., dashed arrows 1-3) which are all correlational, TMS can test for a causal link between compensatory activity and cognitive performance. As reviewed below, there is evidence that TMS-based disruption of cognitive function associated with PFC regions known to be over-recruited by older adults impairs cognitive performance to a greater extent in older than younger adults or in high- than low performing older adults (Rossi et al., 2004; Manenti et al., 2011). In terms of our model, this TMS effect may be described as transiently disrupting the reserve neural resources used by older adults for attempted and successful compensation (see TMS in Fig. 2). It is worth noting that depending on the parameters used, TMS may also enhance neural function. If so, the Criterion 4 is that TMS on the region associated with compensation should enhance performance, particularly in older adults (Sole- Padulles et al., 2006; Cotelli et al., 2008). For both impairing and enhancing TMS effects, one should be cautious about attributing behavioral effects solely to the region stimulated (e.g., PFC) because these behavioral effects may also reflect the function of brain areas modulated by the region stimulated with TMS (e.g., MTL).

A more indirect method for altering the function of successful-compensation regions is to use cognitive tasks known to engage these regions. For example, one could prime these regions beforehand, enhancing their compensatory contributions during the task of interest. Alternatively, one could disrupt the compensatory role of the region by interfering with the function of the region using a secondary task. For example, if during Task X, young adults using region A and older adults, regions A and B, then a simultaneous task Y that depends on region B should impair older adults to a greater extent than younger adults. This logic points to the possible cost of compensation: recruiting additional regions for one task makes them less available for other simultaneous tasks. Given that PFC contributes to many different cognitive tasks, the trend of more widespread PFC activity in older adults could partly explain why older adults are more impaired by divided attention manipulations. The main problem of altering brain function using cognitive tasks is that it is very difficult to target individual brain regions. In contrast, TMS can be selectively applied to the specific regions over-recruited in an older individual.

Finally, a third method for testing Criterion 4 is to compare the effects of brain damage in young and older adults. For example, in a strongly left-lateralized task in young adults (e.g., linguistic processing) in which older adults engage also the right hemisphere, the effects of right hemisphere damage should be more pronounced in older adults. Likewise, given the aforementioned PASA pattern, it is reasonable to expect that PFC damage is likely to have greater impact on a simple visual tasks in older than younger adults. To avoid simple explanation in terms of greater sensitivity to brain damage in older adults, it is important to show that damage of a different (non-compensatory) region does not yield disproportionate deficits in older adults. Of course, TMS provides better solution of this problem because the contribution of several regions can be assessed within participants (and in the absence of permanent impairment).

Summary

Here we propose a relatively simple model for defining age-related compensation. The model takes into account both attempted compensation and successful compensation. With regard to attempted compensation, we propose two criteria for increased activations to be labeled compensatory. First, attempted–compensation activity will exhibit an inverted-U relationship with brain decline, such that attempted compensation will be most prominent in those that need it the most, but then decline with advanced brain deterioration. Second, attempted–compensation activity will exhibit an inverted-U relationship with task demands, such that attempted compensation will increase as task demands increase, but only up to a certain level of task difficulty at which point the availability of neural resources will no longer be sufficient to sustained increased compensation. While the proposed inverted-U function is not unique to older adults, the model does predict that the slope of this function (with regard to increasing task demands) will be steeper in older, compared to younger adults and that the overall extent of compensatory-related neural activity (height of the function) will be less for older, compared to younger adults. For activity to be attributed to successful-compensation, we further propose that that the activity will positively correlate with cognitive performance. And finally, successful- compensation activity should be found in regions whose alteration affects cognitive performance in older adults.

Age-Related Increases in PFC Activity and Connectivity

Having described in the previous section a simple model for age-related compensation (Fig. 2) and several criteria for linking activation and connectivity changes to attempted compensation or to successful compensation, we now turn to specific examples of age-related increases in PFC activity and PFC connectivity that might reflect compensation. Age-related increases in PFC activity can take several forms. Here we discuss two commonly observed forms identified in the literature. In one form, older adults show greater activity in the PFC hemisphere that is less activated in younger adults, yielding an age-related reduction in hemispheric asymmetry. This pattern is known as Hemispheric Asymmetry Reduction in Older Adults or HAROLD). In the other form, the age-related increase in PFC is coupled with an age-related decrease in occipital cortex. This second pattern is known as Posterior-Anterior Shift with Aging (PASA). Whereas HAROLD and PASA involves changes in mean levels of activity, a few recent studies have reported age-related increases in functional connectivity between PFC and posterior brain regions. The three sub-sections below summarize evidence for HAROLD, PASA, and functional connectivity increases. Each of these sections examines whether these age-related changes fit with the aforementioned compensation criteria.

Hemispheric Asymmetry Reduction in Older Adults (HAROLD)

As illustrated by Table 1, the HAROLD pattern has been found in many PET and fMRI studies in a variety of cognitive domains, including episodic memory retrieval, episodic memory encoding, and working memory (Cabeza, 2002). HAROLD was first reported in the episodic retrieval domain in a PET study in which young adults activated right PFC during recall (Cabeza et al., 1997c) while older adults activated PFC bilaterally (see Fig. 4-A). Soon afterwards, this finding was replicated for recognition memory (Fig. 4-B) and for nonverbal stimuli (Fig. 4-C). A sharp unilateral vs. bilateral difference is more difficult to find in fMRI studies because PFC activations are often bilateral in young adults (Cabeza et al., 2004b). However, the HAROLD model does not require a sharp unilateral vs. bilateral difference and applies also to cases in which activity is bilateral in both groups but less asymmetric in younger adults. This is the pattern typically found in episodic encoding studies, in which young adults often show greater activity in left than right PFC whereas older adults show a symmetric pattern (Cabeza et al., 1997b; Anderson et al., 2000; Grady et al., 2002; Logan et al., 2002; Rosen et al., 2002; Stebbins et al., 2002; Daselaar et al., 2003). A noteworthy study in the episodic encoding domain is the blocked fMRI study by Logan et al. (2002) , which found that compared to a condition in which encoding strategies were not provided (intentional encoding), providing participants with a specific encoding strategy enhanced PFC activity in older adults but did not eliminate the HAROLD pattern. This result suggests that HAROLD does not reflect a deficit in cognitive strategies, and is more likely a consequence of a change in the underlying neural network. It is worth noting that the fact that HAROLD may result from either left PFC increases, as in several retrieval studies, or from right PFC increases, as in several encoding studies, suggests that the change is better described as an increase in bilateral recruitment (or reduced asymmetry) rather than as increase in one hemisphere.

The idea that HAROLD may reflect either a right or a left PFC increase is very clear in the working memory domain. For example, Reuter-Lorenz et al. (2000) examined simple maintenance operations in aging using both verbal and spatial stimuli. Whereas in the verbal task, aging reduced activity in left PFC but increased activity in right PFC, in the spatial task, aging reduced activity in right PFC but increased activity in left PFC. Yet, in both tasks, older adults showed a more bilateral pattern of PFC activity consistent with the HAROLD model (see Table1). This finding supports the generalizability of the HAROLD model to various stimuli and more generally to visuospatial cognitive processing.

Besides episodic retrieval, episodic encoding, and working memory domains, HAROLD has been observed in several other domains, including perception, language, inhibitory control, decision making, and, very recently, conceptual priming (see Table 1). The conceptual priming finding is very interesting because this is a process assumed to occur automatically without conscious control. In this study (Bergerbest et al., 2009), older adults exhibited weaker repetition-related reductions in left inferior PFC than young adults, while also exhibiting stronger repetition-related reductions in right inferior and middle PFC.

Fig 4 about here When is HAROLD compensatory?

If one assumes the age-related compensation model in Fig. 2, then this question must be separately addressed for attempted and successful compensation and the answers should be based on the proposed criteria for these two forms of compensation. Thus, in this section we consider how well HAROLD fits each of the 4 criteria.

The first criterion is that attempted-compensation activity has an inverted-U relationship with brain decline (Fig. 3-A). To our knowledge, no functional neuroimaging study has measured brain activity in the same task across the whole range of brain integrity (young adults, high-performing older adults, low-performing older adults, mild MCI, severe MCI, mild AD, severe AD), and hence, available evidence covers only a fraction of the inverted-U function. Consistent with the left side of the function (Fig. 3-A-1), some studies have found that contralateral PFC recruitment is negatively correlated with brain integrity For example, Persson et al. (2006) found than bilateral PFC recruitment was more pronounced among elderly with greater hippocampal atrophy and greater white matter decline. Also consistent with the left side of the function, Logan et al. (2002) found HAROLD among old-older adults (mean age = 80) but not among young-older adults (mean age = 67). Consistent with the right side of the inverted-U function (Fig. 3-A-2), other studies have found a positive correlation between brain integrity and brain activity. For example, Cabeza et al. (2002) found HAROLD for high- but not for low- performing older adults (see Fig. 4-D). Likewise, HAROLD during episodic encoding was found for high but not low-memory elderly (Rosen et al., 2002). Moreover, in the aforementioned priming study, priming effects in the right hemisphere were positively correlated with an independently acquired measure of semantic knowledge. Of course, these two sets of findings would fit an inverted-U function only if they were respectively sampling older adults with lower (Fig. 3-A-1) or higher (Fig. 3-A-2) levels of brain deterioration, which is very difficult to determine across studies. Also, given that the inverse-U function depends also on task demands and may differ across tasks, comparing results across studies is very difficult.

The second criterion for attempted compensation activity is an inverted-U relationship with task demands (Fig. 3-A). Fulfilling this criterion does not require comparing young and older adults, and hence, it can be examined in the domain of cognition in young adults, where more data are available. Do young adults recruit contralateral brain regions when the task becomes more demanding? There is evidence that this is the case (Banich, 1998; Nolde et al., 1998; Reuter-Lorenz et al., 1999; Passarotti et al., 2002; Cairo et al., 2004). In the episodic memory retrieval domain, for example, a meta-analysis of PFC activity found that simple tasks tended to yield unilateral activations whereas complex tasks tended to yield bilateral activations (Nolde et al., 1998; Cairo et al., 2004). To our knowledge, no evidence is available that PFC activations become unilateral again when task demands becomes very high (Fig. 3-A-2).

Turning to successful compensation, the third criterion is that successful-compensation activity is positively correlated with cognitive performance. In addition to the aforementioned findings linking HAROLD to high-performing older adults (Fig. 4-D), which may be interpreted either as reflecting attempted compensation or successful compensation, there are several examples of positive correlations between contralateral recruitment and better performance. For example, Reuter-Lorenz et al. (2000b), found that older adults who showed bilateral recruitment during working memory task showed faster reaction times. Likewise in the aforementioned priming study (Bergerbest et al., 2009), the activation in the contralateral PFC region recruited by older adults, right PFC, was positively correlated with the magnitude of behavioral priming in the older adults.

Finally, the fourth criterion is that successful-compensation activity should be found in regions whose alteration affects cognitive performance in older adults. As mentioned above, an ideal method for assessing this criterion is to use TMS, which temporarily disrupts the function of a brain region. In an important study, Rossi and collaborators (2004) briefly interfered with the function of left and right PFC while participants were trying to remember previously encoded pictures. Given the right-lateralization of PFC activity during retrieval found in functional neuroimaging studies (see Fig. 4-A to D), one would expect that retrieval performance in young should be more impaired by TMS of right PFC than left PFC. In contrast, if older adults recruit PFC bilaterally (HAROLD), TMS in older adults should disrupt performance in older adults not only when applied to right PFC but also when applied to left PFC. This is exactly what Rossi et al. found (see Fig. 4-E). This finding is very important because it provides evidence that disrupting the presumably compensatory region in left PFC leads to performance decrements in older adults, consistent with the idea that this region does indeed help performance in a compensatory way when over-recruited. This finding was recently replicated by Manenti et al. (2011) using rTMS (repetitive TMS) and verbal stimuli. In an extension of the previous work, Manenti et al. also separated their older adults sample into high and low performers. The asymmetry in neural activity predicted by TMS disruption was observed in both groups of older adults during retrieval, but only in high performing older adults during encoding. Importantly these results support the compensation account of HAROLD in that results show that only high not low-performing older adults exhibit both intact functioning of the specialized hemisphere and enhanced performance associated with increased neural activation in the contralateral hemisphere.

As noted above, depending on how it is used, TMS stimulation can also produce enhanced neural effects. In accord with such treatment, repetitive (r)TMS has recently been shown to enhance encoding-related neural activity in the right prefrontal cortex, resulting in a more bilateral frontal activation pattern in older adults who received the treatment compared to those receiving a sham treatment (Sole-Padulles et al., 2006). The benefits of the TMS treatment was found in low-performing older adults who presented with memory complaints and resulted in improved cognitive performance in this population. Results suggest that rTMS may facilitate the recruitment of compensatory PFC activity during memory encoding, and may be beneficial to older adults who are experiencing declines in memory-related functioning. Similar application of rTMS has also been used in patients with mild to severe AD during a semantic naming task (Cotelli et al., 2008) – who again, are marked by disease-related deficits in memory performance. Results showed that while the benefits of rTMS was limited to left-lateralized frontal stimulation in normal controls, performance in patients with mild to severe AD benefited from bilateral stimulation. This bilateral effect can be attributed to the effect of compensatory right-lateralized recruitment in the patient group, supporting naming performance.

In sum, HAROLD fulfills all 4 criteria for compensation. First, HAROLD is modulated by brain integrity and available results could fit an inverted-U function: in some studies HAROLD is associated with brain decline (left side of inverted-U function—Fig. 3-A-1) whereas in other study HAROLD is associated with brain integrity (right side of inverted-U function—Fig. 3-A- 2). Further research is required to demonstrate that these separate findings actually fit an inverted-U function. To do so, one would need to investigate a large sample of individuals covering the whole range of brain integrity, from young adults, to MCI, to AD, and directly link brain activity to accurately measures of brain integrity, such as volumetric MRI and DTI. Second, while evidence of bilateral recruitment in response to increasing task demands remains an area to be investigated in older adults, there is evidence of such a relationship in young adults, with young adults exhibiting contralateral PFC recruitment increases as a function of task demands (Nolde et al., 1998; Reuter-Lorenz et al., 1999). Third, several studies have linked HAROLD to enhanced cognitive performance. These associations have been made both across- participants, by comparing preselected high- vs. low-performing older adults (Cabeza et al., 2002) and by correlating activity and performance (Reuter-Lorenz et al., 2002). Finally, arguably the strongest evidence for compensation is evidence that disrupting the function of the PFC hemisphere over-recruited by older adults disrupts performance to a greater extent in older than younger adults. This finding has been reported for episodic retrieval (Rossi et al., 2004; Manenti et al., 2011), and episodic encoding (Manenti et al., 2011). Thus, there is substantial evidence that HAROLD is associated not only with attempted compensation but also with successful compensation.

Posterior-Anterior Shift with Aging (PASA)

One of the most consistent findings in functional neuroimaging studies of cognitive aging using visual stimuli is an age-related decrease in occipital activity coupled with an age-related increase in PFC activity, which we have called posterior-anterior shift in aging (PASA). Grady et al. (1994) were the first to noticed the PASA pattern (see Fig. 5-A), and they suggested that older adults compensated for visual processing deficits (occipital decrease) by recruiting higher- order cognitive processes (PFC increase). A few years later Madden et al. (1997) found a very similar PASA pattern (see Fig. 5-A). In Grady et al.'s study, older and younger adults were matched in accuracy but differed in RTs, so the authors further suggested that additional recruitment of PFC functions allows older adults to maintain a good accuracy level at the expense of slower reaction times. Most subsequent studies that found PASA endorsed Grady et al.'s compensatory account of age-related PFC increases.

Fig 5 about here

Like HAROLD, PASA can be found across a variety of cognitive functions (see Table 2), including visual perception (Grady et al., 1994; Grady et al., 2000; Levine et al., 2000; Iidaka et al., 2002; Gunning-Dixon et al., 2003; Davis et al., 2008), attention (Madden et al., 1997; Cabeza et al., 2004a; Solbakk et al., 2008), working memory (Cabeza et al., 2004a), problem solving (Nagahama et al., 1997), encoding (Meulenbroek et al., 2004; Gutchess et al., 2005; Dennis et al., 2007; Leshikar et al., 2010), and retrieval (Anderson et al., 2000; Cabeza et al., 2000; Grady et al., 2002; Cabeza et al., 2004a; Davis et al., 2008). In all aforementioned studies (see also Table 2) older adults exhibit both an age-related increase in PFC activity associated with an age- related decrease in posterior activation. One limitation of comparisons across studies (e.g., Table 2) is that it is unclear if the PFC and occipital regions showing the PASA effect are actually the same across domains. To investigate this issue, we directly compared brain activity in young and older adults across three very different cognitive tasks, a working memory (WM) task in which participants maintained four words and their locations in working memory, a visual attention (VA) task in which they sustained attention on fixation to detect possible blips, and an episodic retrieval task (ER) in which they recognized words studied before scanning. As illustrated by Fig. 5-B, in all these tasks the same occipital regions showed age-related decreases and the same PFC regions showed age-related increases. This finding confirms that PASA is a very general and task independent change in brain activity. While the observed decreases in occipital activity are consistent with age-related decline in sensory processes (Baltes and Lindenberger, 1997), the observed PFC increases suggest the attempt to compensate for this decline, which leads us to the next question.

When is PASA compensatory?

As noted above, the first interpretation of the PASA pattern by Grady et al. (1997) was in terms of functional compensation: older adults attempt to counteract deficits in sensory-related occipital under-recruitment by over-recruiting higher-order cognitive processes mediated by PFC. Has available evidence confirmed Grady et al.'s hypothesis? As we did for HAROLD, we answer this question separately for attempted and successful compensation, using our four compensation criteria.

The first criterion is that attempted-compensation activity has an inverted-U relationship with brain decline (Fig. 3-A). Regarding the PFC component of PASA, this criterion is consistent with aforementioned evidence that, compared to controls, patients with MCI (Yetkin et al., 2006) and APOE4 carriers compared to non-carriers (Bookheimer et al., 2000) tend to exhibit increased PFC activity. But, as age-related impairment increases [e.g., severe AD (Wierenga and Bondi, 2007)], PFC activity eventually exhibits declines. However, given that PASA involves not only the PFC increase but also the occipital decrease, more direct evidence for attempted compensation would be evidence that in older adults the magnitude of PFC activity is negatively correlated with the magnitude of occipital activity. (In terms of the eyeglasses metaphor: the worse the vision, the stronger the prescription.) This evidence was provided by Davis et al. (2008) in an fMRI study comparing activity in older adults during perception and memory tasks. In both tasks, older adults showed stronger PFC activity but weaker occipital activity than young adults. Critically, the strength of PFC activity in older adults was negatively correlated with the strength of occipital activity in this group (see Fig. 5-C). In other words, the older adults who showed the strongest PFC activations were the ones showing the weakest occipital activation. This finding is consistent with the idea that older adults attempt to compensate for the occipital decline by over-recruiting PFC.

The second criterion for attempted compensation activity is an inverted-U relationship with task demands (Fig. 3-B). In terms of the PFC component of PASA, this criterion is supported by aforementioned evidence that as task demands increase PFC activity tends to increase, possibly reflecting resource recruitment, but eventually declines, perhaps signaling capacity limitations. (Rypma and D'Esposito, 2000; Mattay et al., 2006; Schneider-Garces et al., 2009; Cappell et al., 2010). As reviewed before, the inverted-U function tends to be shifted to the left in older adults (Fig. 3-B), a pattern that has been described as CRUNCH (Reuter-Lorenz and Cappell, 2008) and interpreted as evidence that older adults spend neural resources at lower levels of task demands, leaving fewer resources available to meet higher levels of task demand. Given that PASA involves also an occipital component, more direct evidence for the second criterion would be to show, for example, that the magnitude of the leftward shift is associated with the occipital decrease. To our knowledge, this evidence is not yet available.

Turning to successful compensation, the third criterion is that successful-compensation activity is positively correlated with cognitive performance. In terms of the PFC component of PASA, this criterion is consistent with aforementioned evidence that age-related increases PFC activity are often associated with better cognitive performance. In some case, the PFC increases that are positively correlated with performance reflect an increase in bilateral recruitment and hence are more specific to HAROLD (e.g., Reuter-Lorenz et al., 2000; Cabeza et al., 2002; Rosen et al., 2002). In other cases, however, the performance-related PFC increases are not specific to hemispheric contributions and are more generally consistent with PASA (e.g., Rypma & D'Esposito, 2000). Given that PASA involves also an occipital component, more direct evidence that PASA is compensatory requires showing that the same PFC regions that coupled with occipital under-recruitment are the ones associated with better performance. This finding was reported by Davis et al. (2008) in the aforementioned fMRI study including both memory and perception tasks. As illustrated by Fig. 5-D, the same PFC activations that were negatively correlated with occipital activations (Fig. 5-C) were positively correlated with cognitive performance in both perception and memory tasks. In terms of the eyeglasses metaphor, the strength of the prescription is not only negatively correlated with visual acuity but it is associated with better visual performance.

Finally, the fourth criterion is that successful-compensation activity should be found in regions whose alteration affect cognitive performance in older adults. As illustrated by Fig. 4-E, this evidence is available for HAROLD (Rossi et al., 2004; Manenti et al., 2011). Finding TMS evidence more specific to PASA, would require combining TMS with fMRI and showing that TMS disruption in older adults is negatively correlated with the degree of occipital under- recruitment. In other words, older adults with the weakest occipital recruitment are the ones who are more dependent on PFC over-recruitment to maintain performance. To our knowledge, this evidence is not yet available.

Age-Related Changes in Functional Connectivity

In addition to studying age-related differences in overall patterns of neural activation, functional neuroimaging allows also the study of age effects on functional connectivity among brain regions (Cabeza et al., 1997a; Grady et al., 2003a; Andrews-Hanna et al., 2007; Grady et al., 2010; Leshikar et al., 2010; Park et al., 2010) . Although most of these studies have focused on age effects specific to particular cognitive tasks, it appears that some age-related connectivity changes generalize across different tasks. Interestingly, these global age effects on functional connectivity resemble the two global activation patterns in older adults, PASA and HAROLD. As in PASA, there is evidence of a an age-related reduction in functional connectivity involving posterior brain regions coupled with an age-related increase in functional connectivity with PFC regions (Grady et al., 2003a; Daselaar et al., 2006; Dennis et al., 2008; St Jacques et al., 2009). As in HAROLD, there is evidence that aging is not only associated with more bilateral activation pattern but also with an increase in functional connectivity homologous regions in the two hemispheres (Davis et al., in press). Below, we describe a few examples of these PASA-like and HAROLD-like age-related increases in functional connectivity, and in the next section we consider how these increases fit with the proposed criteria for attempted and successful compensation.

A PASA-like age-related increase in functional connectivity has been found in at least three different studies (Fig. 6-A to 6-C). The fMRI study by Daselaar and colleagues (2006) investigated the effects of aging on activity during a word recognition test. Functional connectivity was assessed within-participants by assessing fluctuations in activity across trials. In association with successful retrieval, older adults exhibited reduced connectivity between an MTL region (hippocampus) and a posterior cortical regions (parietal and retrosplenial cortices) but increased connectivity between another MTL region (rhinal cortex) and bilateral dorsolateral PFC regions (Fig 6-A). Thus, as in PASA, there was an age-related shift from stronger functional connectivity with posterior regions in young adults to stronger connectivity with PFC regions in older adults.

A similar PASA-like effect in connectivity was found in a different memory domain by St. Jacques and colleagues (2009). In this study, young and older participants were scanned while encoding emotional and neutral pictures. Given the emotional nature of the stimuli, the focus of connectivity analysis was on the amygdala. Functional connectivity was assessed again within- participants and across-trials. While both age groups exhibited equal engagement of the amygdala during successful emotional encoding, young adults exhibited stronger amygdalar connectivity with a posterior region, the hippocampus, whereas older adults exhibited stronger amygdalar connectivity with bilateral dorsolateral PFC regions (Fig. 6-B). In both this and the Daselaar et al. (2006) study, researchers suggested that the age-related increase in PFC connectivity represents a strategic shift in memory processing – with older adults engaging more top-down control mechanisms when executing successful memory tasks, potentially to compensate for decreased functioning of the MTL. A similar conclusion was drawn in the encoding study by Grady and colleagues (2003a) which observed an age-related shift in MTL connectivity with ventral PFC regions in younger adults to more dorsal PFC regions in older adults. This ventral/dorsal distinction was interpreted as a shift in cognitive strategy in aging from more perceptually based processes to those involved in executive functioning. A similar PASA-like effect on functional connectivity as those described above has also been observed in a difficult source memory encoding task (Dennis et al., 2008). Again, using a common hippocampal seed region involved in successful source encoding (and focusing only on connectivity associated with successful trials), researchers found age-related reductions in functional coupling between the hippocampus and inferior temporal cortices involved in perceptual processing, but greater connectivity between the hippocampus and PFC (Fig. 6-C). An important feature of this study is that although the older adults showed stronger PFC connectivity than young adults, they did not show stronger PFC activity overall. Actually, PFC activity was stronger for young than for older adults. This finding is important because it suggest that the PASA-like effect in functional connectivity is not merely a consequence of a PASA effect on brain activity.

Fig 6 about here

Finally, there is also evidence of HAROLD-like age-related increases in functional connectivity (Cook et al., 2007; Tyler et al., 2010; Davis et al., in press). Although several studies have found HAROLD (Table 1) it is not always clear if the contralateral PFC regions recruited by older adults are integrated into the core network mediating task performance. To investigate age effects on cross-hemispheric communication, Davis et al. (Davis et al., in press) scanned young and older participants using fMRI and DTI while they matched pairs of words presented to either the same or opposite visual fields (unilateral vs. bilateral). Cross-hemispheric communication was measured behaviorally as greater accuracy for bilateral than unilateral trials (bilateral processing advantage—BPA). At the neural level, hemispheric communication was indexed by functional and structural connectivity between contralateral PFC regions. The study yielded four findings. First, consistent with previous behavioral evidence (Reuter-Lorenz et al., 1999), the BPA was greater for older than younger adults, in keeping with the idea that older adults distribute processing across hemispheres to a greater extent than younger adults. Second, compared to younger adults, older adults showed greater activity in contralateral PFC regions, consistent with HAROLD, and interestingly this effect occurred for bilateral but not for unilateral trials. This finding suggests that only when cognitive processing can be "outsourced" to the contralateral hemisphere, older adults show contralateral over-recruitment. Third, functional connectivity between left and right PFC was greater for older than younger adults during bilateral trials, suggesting that contralateral PFC are not only recruited more than younger adults but they are also integrated more into the network underlying task performance. Finally, fractional anisotropy in the corpus callosum was correlated with both BPA and functional connectivity between PFC regions, indicating that older adults' ability to distribute processing across hemispheres is constrained by white matter decline.

In sum, age-related increases in functional connectivity show not only a posterior-anterior pattern consistent with PASA but also a cross-hemispheric pattern consistent with HAROLD. The next question is whether these connectivity increases in older adults are compensatory, as defined by the four proposed criteria.

Are age-related increases in functional connectivity compensatory?

Age-related increases and shifts in functional connectivity are more rare than increases in neural recruitment alone. As such, finding evidence of connectivity shifts and linking these shifts to compensatory processing are also more difficult. The first criterion in regards to connectivity- related compensation is that attempted-compensation functional connectivity has an inverted-U relationship with brain decline (Fig. 3-A). While no study, to our knowledge, has simultaneously measured both brain integrity and functional connectivity, we can infer this relationship based upon studies examining connectivity in healthy and abnormal aging (i.e., MCI/AD patients) samples. Consistent with the left side of the function (Fig. 3-A-1), several studies have found increases in frontal connectivity with the MTL during memory tasks in healthy aging compared to young adults (Daselaar et al., 2006; Dennis et al., 2008; St Jacques et al., 2009). Also consistent with the left side of the attempted-compensation activity function (Fig. 3-A-1), studies investigating functional connectivity in MCI and mild AD patients have observed increased frontal functional connectivity in patients compared to normal controls (e.g., Yang et al., 2009; Gili et al., 2011). For example, Yang et al. (2009) found that an inductive reasoning task elicited equitable performance across groups, yet higher functional connectivity between bilateral DLPFC for MCI patients compared to healthy age-matched controls. However, consistent with the right side of the function (Fig. 3-A-2), as disease progresses (e.g., moderate to advanced AD) and brain atrophy continues to increase, studies find very little connectivity between various brain regions (Sanz-Arigita et al., 2010; Petrella et al., in press). Taken together, results suggest that while moderate brain declines found in healthy aging and mild cognitive impairment are accompanied by increases in frontal connectivity, connectivity decreases arise when atrophy becomes more severe as in cases of advanced dementia.

The second criterion for compensation is an inverted-U relationship with task demands (Fig. 3-B). As in the case of activation findings, we can turn to young data to fulfill this criterion. Connectivity studies in the domain of working memory have found increased coupling within the PFC and between the PFC and parietal cortex as memory load increases (Honey et al., 2002; Narayanan et al., 2005). For example, Honey and colleagues (2002) observed enhanced inferior frontal-parietal and prefrontal-prefrontal connectivity as working memory load increased from 0 to 3 items in an N-back task. The authors suggest that this increase reflects greater demand for maintenance and executive processes associated with increases in task complexity. A recent study by Nagel and colleagues (in press) examined age differences in WM load. While results in young adults replicated previous findings, older adults exhibited a load-dependent change in coupling only with the left DLPFC, medial PFC, and medial temporal visual regions. Results suggest that cortical/subcortical connectivity in older adults may also be modulated by task demands, but on a more limited scale – which is consistent with the proposed inverted-U relationship.

In regards to successful compensation, the third criterion states that successful- compensatory connectivity is positively correlated with cognitive performance. In support of this criterion, the foregoing study by Nagel and colleagues (in press) found that, across age, individuals with more flexible adjustments in DLPFC connectivity were those that exhibited the greatest performance benefits. Older adults who performed poorly in response to increases in load were also those who were not able to modulate connectivity within this region. Though the foregoing memory connectivity studies do not measure this connectivity-behavior correlation directly, the connectivity analyses in the Daselaar et al. (2006) and St. Jacques et al. (2010) studies focused their connectivity analyses on successful memory trials. Thus, the observed age- related increases in MTL-PFC connectivity were tied to successful cognitive performance. Further studies are needed to compare the relationship between connectivity between unsuccessful as well as successful performance in order to fully assess this third criterion. Finally, the fourth criterion states that successful-compensatory connectivity should be found in regions whose alterations affect cognitive performance in older adults. Again, an ideal of test of this theory would utilize TMS. Finding TMS evidence to support compensatory connectivity differences would require combining TMS and fMRI/connectivity analyses to show that inactivation of a frontal region involved in an enhanced connectivity network in older adults would disrupt both connectivity and performance. To our knowledge no study has been conducted investigating this possibility.

Conclusions The first section of this chapter reviewed resting neuroimaging studies of aging investigating age-related decline in anatomical and physiological measures. Results indicate that while the brain undergoes significant structural change with age, the greatest declines are found within the frontal lobes. Regarding volumetric decline, both gray and white matter show significant decline across the lifespan, with the PFC showing the greatest amount of loss and the steepest declines. Supporting the frontal aging hypothesis, behavioral studies also find age- related decline in function to be greatest when tasks are dependent on frontal function. Supporting this relationship, several studies are able to link structural and behavioral declines in aging. Similar to such structural declines, DA dysfunction in the PFC has also shown to increase with age and correlate with deficits in cognitive performance across the lifespan. In the second section of the chapter we review a model for age-related functional compensation. The model includes four criteria, two in support of attempted-compensation and two defining successful-compensation. Our compensatory model attempts to make sense of the paradoxical finding in the aging literature that the PFC, which exhibits the most significant age- related deterioration is also a brain region that exhibits the greatest amount of age-related increases in functional activation during cognitive tasks. First we posit that the relationship between attempted-compensation and brain decline will be one of an inverted-U, where attempted compensation will be greatest in those that need it the most, but decline with advanced brain deterioration. Second we posit that the relationship between attempted-compensation and task demands will follow a similar inverted-U function, where attempted-compensation will increase as task demands increase, but only to a certain level of task difficulty. Moreover, this decline in attempted compensation will occur at lower levels of task difficulty for older than younger adults. Third, with respect to successful-compensation, we propose that activity will positively correlate with cognitive performance. Finally, we propose that successful- compensation will be found in brain regions whose function can be altered to affect cognitive performance in older adults. In the third section we briefly reviewed the available evidence regarding age-related increases in PFC activity and functional connectivity. In regards to age-related increases in PFC activity we focused on two regularly observed patterns of PFC compensation, HAROLD and PASA. We also allowed these two patterns to help shape our review of the connectivity results. After reviewing the overall findings of each compensation-pattern, we applied these four criteria to the foregoing findings. For each compensatory pattern of activation we find evidence to support the model’s four criteria - and when unavailable, we propose future studies that can be undertaken to investigate whether age-related increases in PFC activity and connectivity represents attempted or successful compensation.

Acknowledgments: This work was supported by NIH grant R01 AG19731 to RC and NSF grant 1025709 to NAD. This chapter was written while NAD was an AFAR grant recipient. The authors would like to thank Patti Reuter-Lorenz and Cheryl Grady for helpful comments on earlier versions of this chapter.

Figure captions

Figure 1. A. Age-related differences in FA, RD, and AD for the genu of the corpus callosum, uncinate fasiculi (UF), cingulum bundles (CB), inferior longitudinal fascicule (ILF), and splenium of the corpus callosum. FA differences are displayed along mean tracts from a representative subject while RD and AD differences within these target sections are displayed in graphs corresponding to specific long association tracts. *p< 0.01; **p<0.005; ***p<0.001. B. Representative correlation between diffusivity measures extracted from the right uncinate fasciculus and performance on the Intra-Extra Dimensional Set Shift (IED) task. Scatterplots show significant relationships in older adults between IED performance and FA (A) and RD (B), but not AD (C). *p<0.05; ***p<0.005. (Courtesy of Davis et al., 2009)

Figure 2. Model of Age-related compensation. . The model consists of three hypothetical constructs (rectangles) and four measurable variables (ovals). Solid arrows refer to the operation of the model whereas dashed arrows refer to relationships between observable variables, which allow testing of the model. A mismatch between available cognitive processing resources and current task demands leads to the recruitment of reserve neural resources, which increase neural supply (attempted compensation). The increase in neural supply may (successful compensation) or may not (unsuccessful compensation) enhance cognitive processing. Turning to relationships between measurable variables, the model predicts that dashed arrows 1 and 3 are inverted-U functions (as neural resources or task demands increase, activity/connectivity first increase but eventually declines). Dashed arrow 2 refers to relationship between activity in different brain regions, such as a negative correlation between regions showing compensatory activity (e.g., PFC) and regions showing deficits (e.g., occipital cortex). Dashed arrow 4 is critical for distinguishing between successful compensation (positive correlation) and unsuccessful compensation (correlation absent or negative). Finally, dashed arrow 5 refers to the link between brain resources (e.g., PFC volume) and task performance (e.g., executive control).

Figure 3

A.Proposed relationship between attempted-compensation and brain atrophy, (1) increasing with initial brain decline and (2) decreasing with more advance deterioration

B. Proposed relationship between functional activation and task demands in younger and older adults (adapted from Reuter-Lorenz and Cappell, 2008)

C. Right dorsolateral PFC activity in young and older adults as working memory load (task demands) increases (adapted from Cappell, et al. 2010)

Figure 4 A-D: Results of functional neuroimaging studies in which prefrontal activity during episodic memory retrieval was lateralized in younger adults but bilateral in older adults (HAROLD): (a) Cabeza, et al. (1997); (b) Madden, et al. (1999); (c) Grady et al. (2002); (d) Cabeza et al. (2002); E: Hemispheric interaction of rTMS effects and age groups during retrieval [adapted from Rossi et al. (2004)]

Figure 5 A: Age-related shifts in neural activity from occipital cortex to frontal cortex (PASA): (a) Grady et al. (1994); (b) Madden et al. 1997)

B: task independent age effects. Compared to young, older adults exhibit weaker activity in occipital cortex and greater activity in frontal cortex for working memory (WM), visual attention (VA), and episodic retrieval (ER), (Cabeza et al., 2004)

C: Correlation between occipital activations and frontal activations in older adults, consistent with the compensation account of PASA. (Davis et al., 2008)

D: Correlation between frontal activity and performance in perception and memory tasks (based on composite performance) in older adults, consistent with the compensation account of PASA. (Davis et al., 2008)

Figure 6A-C: Age-related shifts in functional connectivity between the medial temporal lobe and frontal cortex during (a) episodic retrieval, Daselaar et al., 2006; (b) source encoding, Dennis et al., 2008; (c) emotional encoding, St. Jacques et al., 2009.

D: Age group x Visual field interactions within the frontal cortex and connectivity parameter estimates of the left middle frontal gyrus activation; below, scatterplots in younger and older adults with older adults showing a significantly more positive relationship between white matter connectivity and both functional connectivity, and residualized accuracy on the bilateral task.

Table captions

Table 1: Plus signs represent significant activity in the left and right PFC, and minus signs represent non-significant activity. The number of plus signs is an approximate index of the relative amount of activity in the left and right PFC in each study, and it cannot be compared across studies. BF=blocked fMRI; BP=blocked PET; EF=event-related fMRI. Table 2: Significant activation in older compared to younger adults. Plus signs represent age-related activity increases and minus signs, decreases. BF=blocked fMRI; BP=blocked PET; EF=event-related fMRI.

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Table 1 Younger Older HAROLD (Hemispheric Assymmetry Reduction in Older Adults) L R L R BF Encoding Stebbins 02 ++ + + + BF Encoding-inc Logan 02 ++ + ++ ++ BF Encoding-inc Rosen 02 ++ + + + EF Encoding-inc/Dm Morcom 03 + - ++ ++ BF Encoding-int Logan 02 + + + + BP Encoding-int Cabeza 97 ++ - + + EF Decision Making Lee 08 + - - + EF Priming Begerbest 09 ++ - ++ ++ EF Inhibitory Control Nielson 02 - + + + EF Language Perrson 04 + - + + EF Language Tyler 10 ++ - ++ ++ EF Semantic Processing Wierenga 08 ++ - ++ ++ BP Perception Grady 94 exp 2 - + ++ ++ BP Perception Grady 00a + +++ ++ ++ BP Retrieval Cabeza 97 - ++ + + BP Retrieval Bäckman 97 - + + + BP Retrieval Madden 99 - + ++ ++ BP Retrieval Grady 00b - ++ + + BP Retrieval Cabeza 02 - + + + EF Retrieval Maguire 03* - + + + BP Working Memory(letter) Reuter-Lorenz 00 + - + + BP Working Memory(location) Reuter-Lorenz 00 - + + + BP Working Memory Dixit 00 + +++ ++ ++ EF Working Memory Cabeza 04b + + + + BP Working Memory Grady 98 + ++ + +

Table 2 PASA (Posterior-Anterior Shift in Aging)Occipital Frontal BP Perception Grady 94 - + BP Perception Grady 94 +/- + BP Perception Grady 00 - +/- BP Perception Levine 00 +/- + BF Perception GunningDixon 03 - + EF Perception Iidaka 02 - +/- EF Perception Davis 08 - +/- BP Attention Madden 97 - + EF Attention Cabeza 04 - + EF Attention Solbakk 08 - + EF Working Memory Cabeza 04 - + BP Prolem Solving Nagahama 97 - +/- EF Encoding Meulenbroek 04 - +/- EF Encoding Gutchess 05 - + EF Encoding Dennis 07 - + EF Encoding Leshikar 10 - + EF Retrieval Cabeza 04 +/- + BP Retrieval Grady 02 - + BP Retrieval Anders 00 +/- +/- BP Retrieval Cabeza 00 - +/- EF Retrieval Davis 08 - +/- EF Emotional St. Jacques 10 - + EF Emotional Ritchey 10 - +