THE EFFECTS OF AGE AND WORKING MEMORY ABILITY ON FRONTAL LOBE OXYGENATION DURING WORKING MEMORY TASKS. A Thesis. Presented to

Transcription

1 THE EFFECTS OF AGE AND WORKING MEMORY ABILITY ON FRONTAL LOBE OXYGENATION DURING WORKING MEMORY TASKS A Thesis Presented to The Honors Tutorial College Ohio University In Partial Fulfillment of the Requirements for Graduation from the Honors Tutorial College with the degree of Bachelor of Science in Biological Sciences by Marie Y. Braasch June 2010

2 2 This thesis has been approved by The Honors Tutorial College and the Department of Biological Sciences Dr. Julie Suhr Professor, Psychology Thesis Advisor Dr. Soichi Tanda Associate Professor, Biological Sciences Honors Tutorial College, Director of Studies Dr. Jeremy Webster Associate Professor, English Dean, Honors Tutorial College

3 3 Acknowledgments I owe my deepest gratitude to my thesis advisor, Dr. Julie Suhr, without whom this project would not have been possible. I greatly appreciate my research assistants, Nicholas Baer, Elizabeth Brink, and Krystyna Chisholm, for all of their hard work. I am also indebted to my director of studies, Dr. Soichi Tanda, as well as Dean Jeremy Webster and Assistant Dean Jan Hodson of the Honors Tutorial College. Finally, I want to thank the multitude of people who have positively impacted my academic journey, especially my parents.

4 4 Table of Contents Page Acknowledgments...3 List of Figures...5 List of Tables...6 List of Abbreviations...7 Abstract...8 Introduction...9 Methods...24 Results...36 Discussion...38 References...43 Figures...54 Tables...59 Appendix...61

5 5 List of Figures Page Figure 1: Location of the frontal lobe in the brain Figure 2: Brodmann areas Figure 3: Inverted-U pattern of activation Figure 4: Electrodes from the NIRS machine attached to a participant s forehead Figure 5: Using NIRS to measure rso 2 change scores for each task... 58

6 6 List of Tables Page Table 1: Mean behavioral performance on the n-back and Stroop Color-Word Test as a function of age and working memory ability Table 2: Mean change in average oxygenation level from control to working memory task on the n-back and Stroop Color-Word Test as a function of age and working memory ability... 60

7 7 List of Abbreviations CRUNCH compensation-related utilization of neural circuits hypothesis fmri functional magnetic resonance imaging NIRS near-infrared spectroscopy PET positron emission tomography RBANS Repeatable Battery for the Assessment of Neuropsychological Status rso 2 regional cerebral oxygen saturation WAIS Wechsler Adult Intelligence Scale WM working memory

8 8 Abstract Previous research on differences between frontal lobe activation of young and older adults during working memory tasks have yielded conflicting results, which may be due to a failure to control for task difficulty or for the working memory ability of the participants. In the current study, young and older adults were divided into good and bad working memory groups, and near-infrared spectroscopy (NIRS) was used to measure oxygenation changes in the dorsolateral prefrontal cortex during n-back and Stroop tasks. Age did not have a main effect on oxygenation change in either task, but working memory group did. On the n-back, the bad working memory group had a greater increase in oxygenation than the good working memory group. On the Stroop, the bad working memory group had a decrease in oxygenation, while the good working memory group had an increase. These results may be explained through an inverted-u pattern of activation hypothesis for the relationship between frontal activation, task difficulty, and working memory capability.

9 9 Introduction As people grow older, their mental abilities undergo tremendous changes. Aging affects us all, so understanding the relationship between aging and cognition is of paramount importance. The present study focuses on the relationship between aging, a specific type of mental process (working memory), and cerebral oxygenation patterns in a region of the brain relevant to that mental process (the dorsolateral prefrontal cortex). Aging and the brain Aging profoundly changes the brain, both structurally and functionally. Physically, as people get older, certain brain regions actually shrink, either due to cell loss or shrinkage or reduction in dendritic branching. Examples of these regions include the lateral prefrontal cortex, which steadily becomes smaller throughout the lifespan, and the hippocampus, which loses considerable volume after age 50 (Raz et al., 2004). One study showed a mean brain tissue volume loss of 5.4 cm 3 per year, involving decreases in both grey and white matter (Resnick et al., 2003). Other researchers have confirmed that the frontal lobe undergoes pronounced shrinkage with aging (Tisserand et al., 2002). The brain also experiences significant neurochemical and molecular changes with aging, as levels of important neurotransmitter receptors (dopamine and serotonin) and neurotransmitter transporters (dopamine) decrease (Volkow et al., 2000; Volkow et al., 1998; Wang et al., 1995). The frontal lobe s glucose metabolism also declines (Volkow et al., 2000). The physical and

10 10 neurochemical changes that occur with aging are likely to be related to changes in cognitive functioning. Older adults consistently perform worse than younger adults on a variety of cognitive tasks, exhibiting either slower reaction times, lower accuracy, or both. Cognitive processes that show reliable decline with age include processing speed, visuospatial attention, visuospatial and verbal memory, episodic memory, working memory, and a variety of other frontally-mediated tasks (Grady, 2000; Grady et al., 1998; Greenwood, 2000; Park et al., 2002; Parkin & Java, 1999). One prominent idea, known as the frontal lobe hypothesis of aging, states that the frontal lobes, along with frontal lobe-mediated cognitive processes, are among the first areas of the brain to degenerate, and they experience more severe degeneration than other brain regions (West, 2000; West, 1996). As the most reliable age-related cognitive findings relevant to frontal lobe theories of aging are in the domain of working memory, the present study focused on how aging affects performance on working memory tasks. Working memory Working memory is defined as a cognitive process that involves maintaining, updating, and using pertinent information during a task (Greenwood, 2000). An everyday example of working memory is complex mental math, during which one has to remember numbers in addition to manipulating them in order to achieve a goal. Thus, working memory differs from short-term memory, which is simply holding an item or a set of items in storage for a short time.

11 11 Working memory, as it was first conceptualized by Baddeley & Hitch in 1974, comprises a set of systems: two slave systems (the phonological loop and visuospatial sketch pad, which receive verbal or visual stimuli, respectively, and hold it in short-term memory) and the central executive (responsible for directing attention as well as integrating information from both the verbal and visual systems). Later, a fourth system was added, the episodic buffer, which also integrates various sensory stimuli from an event, as well as retrieving information from and putting information into long-term memory (Baddeley, 2000). A variety of cognitive paradigms have been used to study working memory. One common type of working memory test is a delayed match-to-sample item recognition task. A first set of stimuli is presented and then, after a short period of time (generally several seconds), a new item is presented, and the participant has to determine whether the new item matches the items in the first set. Letters are the most commonly used stimuli, both in terms of their identity (Cappell, Gmeindl, & Reuter- Lorenz, 2010; Jonides et al., 2000; Rypma et al., 2001) and/or their spatial location (Reuter-Lorenz et al., 2000). Faces have also been used (Grady et al., 1998). The delayed match-to-sample task primarily tests the storage and maintenance component of working memory, rather than the manipulation component. However, one variant of the delayed match-to-sample paradigm included manipulation; the stimuli were colored patterns, and participants were required to alternate between matching colors or matching patterns (Klingberg, O Sullivan, & Roland, 1997).

12 12 A different kind of working memory test involves remembering and manipulating strings of stimuli; examples of this paradigm include Digit Span, Spatial Span, and Letter-Number Sequencing. In Digit Span and Spatial Span, a participant hears a string of digits or sees an experimenter tapping a sequence of blocks (respectively) and either has to repeat the sequence in the same order or in the reverse order (Wechsler, 2008; Wechsler, 1997). In Letter-Number Sequencing, a participant hears a string of mixed-up numbers and letters and has to repeat the sequence with the numbers first in ascending order and then the letters in alphabetical order (Wechsler, 2008). These sorts of tasks have been well validated as measures of working memory (Beigneux, Plaie, & Isingrini, 2007; Bor et al., 2006; Haut et al., 2005). Besides those two types of tasks, many other kinds of working memory tests are available. One example is the n-back, which involves observing a sequence of stimuli and comparing each presented item to the item that was n back in the sequence (e.g. 1 back, 2 back, etc.) (Owen et al., 2005). Another example is Operation Span, in which a participant is given a series of items, with each item containing a word and a math equation. Throughout the task, the participant has to remember all the words while checking the accuracy of each math equation (Smith et al., 2001). These more complex working memory tasks likely involve the central executive component of working memory, because they require directing attention to competing information over a sustained period of time. Working memory performance shows a significant age effect. Older adults often perform worse than young adults on a variety of working memory tasks,

13 13 including letter-number sequencing and letter-number spans (Haut et al., 2005), spatial span tasks (Beigneux, Plaie, & Isingrini, 2007), and letter delayed match-to-sample item recognition (Cappell, Gmeindl, & Reuter-Lorenz, 2010; Jonides et al., 2000; Reuter-Lorenz, 2000). Working memory and Neuroimaging Researchers have measured brain activation during working memory tasks in order to understand why older adults exhibit decline in working memory ability and to relate these findings to frontal lobe theories of aging. One of the brain regions predominately responsible for working memory is the prefrontal cortex (Narayanan et al., 2005), located in the frontal lobe (see Figure 1). The dorsolateral prefrontal cortex seems more involved with the manipulation component of working memory, while the ventral prefrontal cortex is more involved with the maintenance component (Park et al., 2002). Working memory is associated with executive functioning, an umbrella term for a variety of frontally-mediated tasks that involve managing, initiating, and inhibiting other cognitive processes (Carpenter, Just, & Reichle, 2000). In order to study changes in activation in the prefrontal cortex during working memory tests, various forms of functional neuroimaging have been used. The types of functional neuroimaging described below rely on the principle of cerebral hemodynamics. When a certain brain region becomes more active, blood flow to that region increases. This results in activated brain regions exhibiting a measurable increase in blood oxygen concentration (Raichle & Mintun, 2006). Neuroimaging

14 14 techniques like functional magnetic resonance imaging (fmri), positron emission tomography (PET), and near-infrared spectroscopy (NIRS) all rely on this principle. fmri takes advantage of the fact that oxygenated hemoglobin (the protein in red blood cells that carries oxygen) resonates differently in a magnetic field than deoxygenated hemoglobin does. Measuring changes in oxygenated hemoglobin levels is used as a proxy for brain activation. In PET, a radioactive tracer is injected into the bloodstream, so that increased amounts of blood flowing to an active brain region can be detected by locating the tracer (Mehagnoul-Schipper et al., 2002). NIRS is less well known than fmri and PET but is just as valid as a neuroimaging technique and similarly relies on cerebral hemodynamics. NIRS utilizes the fact that oxygenated hemoglobin and deoxygenated hemoglobin absorb near-infrared light differently (Mehagnoul-Schipper et al., 2002). As with fmri, the change in oxygenation levels is used as a proxy for brain activation. The present study used NIRS to assess oxygenation in the dorsolateral prefrontal cortex during working memory tasks. Neuroimaging, Working Memory, and Aging Numerous studies have used neuroimaging techniques to measure the effects of aging on brain activation. Overall, older adults seem less able to produce increased blood oxygenation levels, for example, in response to breath-holding or a motor task (Mehagnoul-Schipper et al., 2002; Safanova et al., 2004). Possible reasons for this include changes to the blood vessels themselves, in terms of elasticity or the ability to regulate dilation, or other changes in vascular regulation (Safanova et al., 2004).

15 15 However, the brain activation patterns elicited by cognitive tasks are much more complicated. No blanket statement can compare changes in older adult brain oxygenation levels to those of young adults under all circumstances. In aging and neuroimaging studies, under-activation in older adults (meaning they exhibit lower neural activation levels than young adults) is generally thought to show that the older brain is unable to respond or perform as well, which is evinced by the lower activity levels. Alternatively, over-activation in older adults (meaning they exhibit higher neural activation levels than young adults) is thought to reflect compensatory mechanisms; perhaps the older brain has to use more neural resources than young brains to complete the same task (Cappell et al., 2010). This latter interpretation has been referred to as the compensatory hypothesis. If those are the two possibilities, which do older adults show during cognitive tasks, over-activation or under-activation? First, activation patterns vary depending on the brain region examined. For example, in an fmri study assessing comprehension of complex sentences, which can be considered a type of verbal working memory, older adults showed increased activation in the left dorsal inferior frontal lobe but showed decreased activation in the left inferior parietal lobe, relative to younger adults. This was interpreted as the upregulation of the rehearsal mechanisms in the frontal lobe compensating for deficits in the storage processes of the parietal lobe (Grossman et al., 2002). However, even if one particular lobe of the brain is chosen as a point of focus in an imaging study, results vary widely. For example, out of ten studies that

16 16 compared younger and older adults on frontal lobe activation during working memory or other frontally-mediated cognitive tasks, four studies found that older adults showed less frontal activation (Jonides et al., 2000; Kameyama et al., 2004; Kwee & Nakada, 2003; Schroeter et al., 2003), two studies found that older adults showed higher frontal activation (Grossman et al, 2002; Smith et al., 2001), and three studies found either higher or lower activation for different subdivisions of the frontal lobe (e.g. left dorsolateral prefrontal cortex vs. right ventrolateral prefrontal cortex) (Grady et al., 1998; Nagahama et al., 1997; Rypma et al., 2001). One study did not find differences in frontal activation between young and older adults, although they did find that older adults with lower levels of education had greater bilateral frontal activation than older adults with higher levels of education (Haut et al., 2005). When results are confined to the studies that specifically examined the dorsolateral prefrontal cortex (see Figure 2), lower activation in older adults is shown in this brain region by several studies (Kwee & Nakada, 2003; Nagahama et al., 1997; Rypma et al., 2001), but not all (Grady et al., 1998). As the use of neuroimaging grew in sophistication, researchers began to assess the laterality of cerebral activation during cognitive tasks. Some studies found that during working memory tests, older adults actually show more bilateral frontal activation compared to young adults, who show unilateral frontal activation (e.g. only the left hemisphere for verbal working memory tasks) (Reuter-Lorenz et al., 2001), and this bilaterality is indeed linked to better performance for older adults, as would be expected by the compensation hypothesis (for a review, see Park & Reuter-Lorenz,

17 ). This interpretation is further supported by the finding that young adults also show more bilateral activation when a working memory task becomes very challenging (Klingberg, O Sullivan, & Roland, 1997; Park & Reuter-Lorenz, 2009; Rypma et al., 1999). The above findings on laterality imply that older adults find working memory tasks to be more neurologically challenging at lower task loads than young adults do, and thus need to recruit both hemispheres for tasks that young adults only need one hemisphere to complete. This idea is supported by studies that have specifically examined task load in working memory studies. In general, when working memory load increases, the dorsolateral prefrontal cortex becomes more active (Narayanan et al., 2005). Callicott et al. (1999) used fmri to demonstrate that, on an n-back task, dorsolateral prefrontal cortex activation actually followed an inverted-u shape, where activation increased with increasing task difficulty (from 1-back to 2-back) until a certain point, after which it decreased (from 2-back to 3-back). The decrease in activation from the 2-back to 3-back was accompanied by a decrease in task performance. Thus, the authors suggested that the dorsolateral prefrontal cortex increases its activity until the task is too difficult and is beyond the brain s capacity, and then activity levels drop (see Figure 3). These results might help to explain the inconsistencies in the prior literature s older adult findings, which often do not control for task difficulty or working memory ability levels. When Mattay et al. (2006) used fmri to compare young and older adult activation during the n-back task, they found that young adults showed this inverted-u

18 18 in the left prefrontal cortex, but the older adults only showed decreasing activation with increasing task load. For the 1-back, while the older adults had equivalent accuracy to the young adults, their activation level was actually greater than the young adults, but for the 2-back and 3-back, the older adults had worse performance and lower activation. The authors interpreted this to mean that the older adults started on the downward side of the inverted-u; i.e. the lower load levels already met the ceiling of the older adult s ability and thus required high levels of dorsolateral prefrontal cortex activation, and then with increasing task load, the older adults s activation levels dropped. Another fmri study by Cappell, Gmeindl, and Reuter-Lorenz (2010) also compared young and older adult activation in the dorsolateral prefrontal cortex with increasing task difficulty using a letter delayed match-to-sample task. The older adults showed equivalent performance on the medium difficulty level and higher activation in the right dorsolateral prefrontal cortex compared to the young adults. However, on the hardest difficulty level, the older adults performed worse on the task and also showed lower activation in the right dorsolateral prefrontal cortex. The relationship between age, performance, and activation in these results is very similar to that found in the Mattay et al. (2006) study. Cappell and colleagues described these results as fitting their compensation-related utilization of neural circuits hypothesis (CRUNCH), which is the same as the above description of the aging and inverted-u activation hypothesis.

19 19 These findings demonstrate that the effects of task difficulty on activation patterns are related to the performance of the subjects or their actual working memory ability. Prior studies of aging and cerebral activation have generally contained older adults who performed more poorly than the young adults at moderate or high working memory load levels; thus, the older adults showed lower working memory ability overall. As was mentioned earlier, older adults do generally have poorer working memory capabilities; however, this is not necessarily true for all older adults. Almost no studies have considered comparing older adults who have high working memory performance to young adults with that same level of performance. Many studies have not even considered performance at all; the authors of one study, which did not measure performance, explicitly said in their methods section, It was not essential that subjects gave the correct answer (Kwee & Nakada, 2003, p. 526). The underlying idea was that, in order to study oxygenation patterns, participants simply needed to engage in the task that would activate that area of the brain, regardless of how good they were at the task. However, ability and performance may have a great impact on brain activation patterns. Grady (2000) reviewed several studies on memory and aging and found that, overall, in the studies where the older adults showed higher activation levels in the left prefrontal cortex than the young adults, the older and young adults did equally well on the tasks. In contrast, in studies in which older adults showed lower activation levels in the prefrontal cortex during the cognitive tests, the older adults also performed more poorly. It is possible that older adults with lower frontal activation levels are suffering

20 20 from brain deterioration and thus are unable to do as well on the tasks, while older adults with higher frontal activation are compensating for any age-deterioration effects, in order to continue to perform well. Another study that was published after the Grady (2000) review supports this idea. Grossman and colleagues (2002) ensured that the young and older adults performed at the same level of accuracy on their complex sentence comprehension working memory task, and fmri showed that the older adults had higher levels of activation in the left dorsal inferior frontal lobe. In order to consider task performance when interpreting functional neuroimaging findings, Smith et al. (2001) divided their participants into old, younggood performance, and young-bad performance on an operation span task (which, as mentioned earlier, involved doing math problems and memorizing word lists simultaneously). Using PET, they found that the young-bad performance participants and older participants both showed higher levels of activation in the left dorsolateral prefrontal cortex when they completed the working memory task compared to when they did the control task, while the young-good performance participants showed no such higher activation. This illustrates the importance of considering working memory ability in addition to age in cerebral activation studies. An fmri study by Nyberg et al. (2009) also examined older adults, young adults with good performance (young-high), and young adults with poor performance (young-low). They used an n-back task; thus, they were able to consider both task difficulty (similar to the Mattay et al. (2006) study mentioned earlier) as well as working memory ability. At the 1-back level, the older adults showed higher levels of

21 21 left frontal activation than both groups of younger adults. At the 2-back, the older adults did not show increased activation but the younger adults did, going up to a level that was higher than the older adults, and the young-low group actually had (nonsignificantly) greater activation than the young-high group. At the 3-back, the older and young-low adults either had the same or decreased activation as they did on the 2- back, while the young-high group had a further increase in frontal activation. Accompanying these activation findings was the behavioral performance; the older adults had a significant accuracy drop from the 1-back to the 2-back, while the younglow adults showed a significant accuracy drop from the 2-back to the 3-back. Thus, the combination of the performance and activation findings support the explanation that the interaction between working memory capacity and task load results in an inverted-u pattern of activation, with activation increasing until the subject is unable to perform well (see Figure 3). Neither the Smith et al. (2001) or the Nyberg et al. (2009) studies included older adult-high performance and older adult-low performance groups. To my knowledge, only one neuroimaging study has compared old-high, old-low, younghigh, and young-low participants on a working memory task (Nagel et al., 2009). They used a spatial delayed match-to-sample task and fmri to compare how activation in the dorsolateral prefrontal cortex and a few other brain regions associated with spatial working memory changed across three difficulty levels: load 1, 3, and 7. The younghigh group showed increasing activation, especially in the left dorsolateral prefrontal cortex, across the loads. Across the brain regions, the old-low group generally showed

22 22 increasing activation from load 1 to 3, then decreasing activation from load 3 to 7. Interestingly, the old-high group did not show much change in activation either way, although they did generally have a higher level of activation across all loads than the old-low group. The young-low group also did not show much change in activation across loads. The authors suggested that, on the inverted-u model of activation, the young-high group were on the ascending side, the old-low were on the descending side, and the old-high and young-low groups were flat-lined at the top of the inverted- U. The study by Nagel et al. (2009) was limited in several ways. First, they based their findings on only one working memory task, a spatial paradigm. Also, the groups were selected based on the task that was used in the study, which presented a potential confound when the groups and their performance were compared to the neuroimaging data. Finally, their young high and low performance groups were selected by picking the top 10 and bottom 10 participants from among 30 young participants, and the same was done for the old participants. Thus, the young-high and old-high groups did not truly have equivalent levels of performance. More research is needed in which working memory ability is truly equated between the age groups, in order to better understand the functional neuroimaging results. The present study was designed to address this need, dividing participants by age and working memory ability into young-good working memory, young-bad working memory, old-good working memory, and old-bad working memory groups. My study included a variety of working memory tasks and selected the working

23 23 memory groups using tasks that were separate from the tasks used for analyzing frontal activation. Thus, the research question in the present study is the following: if young and older adults of low and high working memory skill complete working memory tasks, what hemodynamic activation patterns in the dorsolateral prefrontal cortex would result as a main effect of age, as a main effect of working memory ability, or as an interaction between age and ability?

24 24 Methods Participants The participant groups consisted of 14 older adults (mean age: 64.1 yrs, SD = 5.9) and 9 young adults (mean age: 19.0 yrs, SD = 0.7). The age groups had similar levels of education (mean = 16.2 yrs, SD = 2.8 for older adults; mean = 12.6 yrs, SD = 0.7 for young adults), with the older adults having a mean education level equivalent to a bachelor s degree and the young adults currently enrolled in a college degree program. Of the 14 older adults, 2 were male, and of the 9 young adults, 1 was male. The gender distributions for the two age groups were not significantly different (χ 2 (1) = 0.05, p = 0.83). The older adults were healthy, independently-living community dwellers in a small Midwestern town. The young adults were undergraduates at a Midwestern university. People with self-reported neurological conditions (e.g. brain tumor, stroke, dementia) were not invited to participate, as such extremes in brain structure and function could have confounded the results, and this study was meant to compare healthy older and younger adults. Participants were recruited from ongoing studies in Dr. Suhr s laboratory. The older adults had originally participated in a free community memory screen, while the young adults had participated in a different neuropsychological study for undergraduate psychology course credit. In the present study, older adults were compensated $20, while the young adults either received $20 or 2 extra credit points for an undergraduate psychology class.

25 25 The current study was designed to test differences between people with high or low working memory (WM) ability. Thus, we wished to have a good WM and a bad WM group. In order to conserve resources, individuals were pre-screened based on their working memory scores on certain tests completed in their prior study participation (Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) Digit Span, Letter-Number Sequencing, and Arithmetic, which comprise the WAIS Working Memory Index). In order to be invited back for the present study, participants either had to receive a standardized score of 8 or below on two of the three tests (to potentially qualify for the bad WM group) or an 11 or above on two of the three tests (to potentially qualify for the good WM group), with the third test score not placing them in the opposite group. Standardized scores were taken from the WAIS reference group, based on a population of ages 20-34, in order to generally equate the young and older adults on the working memory tasks. After participants came back for the current study, their WM group placement was further confirmed based on their current Digit Span Backward and Letter-Number Sequencing scores. A participant qualified for the bad WM group if at least one of these scores was an 8 or below, and they qualified for the good WM group if at least one of these scores was a 12 or above, with the other score not falling in the range for the opposite group. If one score was an 8 or below and the other score was a 12 or above, the participant s data was excluded from the analysis. The participant s data was also excluded if both scores fell in the 9-11 range, because in the general population, the mean score is a 10, and the current study was designed to test extreme

26 26 groups. These exclusion criteria resulted in a loss of 9 participants who participated in the study but are not included in the current analyses. Because standardized reference group scores were not available for the Digit Span Backward subtest, the standardized scores were based on the age groups closest to the reference group (ages and 30-34). In the end, the good WM group consisted of 6 young adults and 6 older adults, while the bad WM group consisted of 3 young adults and 7 older adults. Procedure Upon arriving in the laboratory, each participant was informed about the study and signed a consent form (as an example, see Appendix for the older adult consent form). The participant was then attached to the NIRS machine via two adhesive electrodes on his or her forehead (see Figure 4). A baseline oxygenation level was recorded for 5 minutes, for which the participant was told to relax and informed that conversation during the rest periods would be avoided. Following the baseline, the participant completed a series of neuropsychological tasks (described below), while cerebral oxygenation continued to be recorded. Between each task was a 3-minute rest to re-establish baseline oxygenation levels, with the exception of one task requiring a 5-minute baseline due to the participant being wheeled from a table to a nearby computer. After completion of the final task and a final 3-minute rest period, the participants were thanked and compensated. Overall, the procedure lasted approximately 1 hour and 45 minutes. Materials

27 27 Digit Span subtest of the Wechsler Adult Intelligence Scale Fourth Edition (WAIS-IV; Wechsler, 2008). Digit Span is commonly used as an index of attention and working memory. Digit Span consists of three parts Forward, Backward, and Sequencing although in the present study, only Digits Forward and Digits Backward were used. In Digits Forward, strings of digits of increasing length are read to participants, and the participant repeats each string back to the experimenter. This is a test of rote short-term memory/attention span. In Digits Backward, the participant has to repeat the string of digits backward (Wechsler, 2008). This is a test of working memory, as the participant has to remember the digits and mentally manipulate them. Digit Span has a very high internal consistency reliability coefficient (average across ages is r = 0.93, with the reliability coefficient remaining almost unchanged from ages 16 to 90). Overall, Digit Span exhibits good test-retest reliability (r = 0.82) with an inter-test period of 8-82 days (mean of 22 days), although this varies somewhat depending on the age group (r = 0.71 for ages 16-29, r = 0.89 for ages 55-69) (Wechsler, 2008). Digit Span shows good validity as a measure of attention and working memory skills. Digit Span scores are correlated to scores on the working memory index of the Wechsler Memory Scale-III (WMS-III) (r = 0.57). Digit Span is also highly correlated with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) attention index (r = 0.65) and somewhat correlated with the immediate memory index (r = 0.33) (Wechsler, 2008).

28 28 Performance on both Digits Forward and Digits Backward decrease with age (Hester, Kinsella, & Ong, 2004), with some, but not all studies suggesting the decline for Digits Backward is greater than that for Digits Forward (Babcock & Salthouse, 1990; Hester, Kinsella, & Ong, 2004; Myerson et al., 2003). People with traumatic brain injury (TBI), which often especially affects the frontal lobes, do moderately worse on Digit Span than matched controls do, and this difference is worse on Digits Sequencing than on Digits Forward or Digits Backward (Wechsler, 2008). The dependent variable for the present study was the standardized score on Digits Backward, which was used to qualify and place participants in a WM group. Letter-Number Sequencing subtest of the Wechsler Adult Intelligence Scale Fourth Edition (WAIS-IV; Wechsler, 2008). Letter-Number Sequencing is used as an index of working memory. In this task, the participant hears a string of jumbled-up numbers and letters and then repeats it back with the numbers first in increasing order and then the letters in alphabetical order (Wechsler, 2008). This is a working memory task because it involves both immediate memory and manipulation of the items through ordering. The internal consistency reliability coefficient of Letter-Number Sequencing is quite good (r = 0.88), and the test-retest reliability is reasonable (r = 0.76) with an inter-test period of 8-82 days (mean of 22 days). Letter-Number Sequencing scores correlate well with Digit Span scores (r = 0.69), as would be expected because they are both working memory tests. Letter-Number Sequencing scores are correlated to scores on the Working Memory Index of the Wechsler Memory Scale-III (WMS-III)

29 29 (r = 0.63). Letter-Number Sequencing is correlated with the RBANS attention index (r = 0.52), but only weakly correlated with the immediate memory index (r = 0.21) (Wechsler, 2008). People with mild cognitive impairment (MCI) do much worse on Letter- Number Sequencing than matched controls. People with traumatic brain injury (TBI) do moderately worse (Wechsler, 2008). Haut et al. (2005) did a study comparing older adults and younger adults on a Letter-Number Sequencing task while using PET to measure brain oxygenation and found that the task activated the frontal and parietal lobes, though the laterality differed depending on age and education. Young adults also performed significantly better on Haut et al. s version of Letter-Number Sequencing than the older adults did. Myerson et al. (2003) found that performance on Letter-Number Sequencing declined sharply after age 60, more so than Digit Span Forward or Backward scores did. The dependent variable for the present study was the standardized score on Letter-Number Sequencing, which was used to qualify and place participants in a WM group. Stroop Color and Word Test. The Stroop Color and Word Test (Golden, 1978) is an interference and executive function task. The test consists of three parts. The Word page consists of a participant reading as many color words (red, green, blue) as he or she can in 45 seconds; the words are printed in black ink. The Color page consists of a participant naming as many colors (each item consists of X s printed in red, green, or blue ink) as he or she can in 45 seconds. The Color-Word page consists

30 30 of a participant seeing color words printed in other colors (e.g. the word red printed in green ink) and naming the color instead of reading the word, completing as many items as he or she can in 45 seconds. The Color-Word page is the most difficult because the strong inclination to read the word must be suppressed. An interference score is determined by comparing the Color-Word score to the scores on Word and Color (Golden, 1978) and is the best indicator of vulnerability to interference and inhibition. In order to allow enough time to see a brain oxygenation response, in the present study, participants performed the Color-Word test for 90 seconds instead of 45 seconds, although the behavioral performance on the test was only scored for the first 45 seconds. Test-retest reliability for the Golden Stroop Test is high for young adults (r = 0.83 for Word, r = 0.74 for Color, and r = 0.67 for Color-Word with an inter-test interval of one or two weeks (Franzen et al., 1987)) and is also high for older adults (r = 0.75 for Word, r = 0.82 for Color, and r = 0.82 for Color-Word with an inter-test interval of 14 days (Lemay et al., 2004)). The Stroop interference score is correlated to performance on another kind of inhibition test, the stop-signal task (for stopping probability, r = 0.33 for younger and older adults; for stopping time, r = 0.16 for younger adults and r = 0.56 for older adults) (May & Hasher, 1998). Performance on the Stroop Test appears related to working memory; a study by Kane & Engels (2003) found that low working memory participants (as judged by an operation span task) did worse on their version of the Stroop Test than high working memory participants did.

31 31 They suggest that working memory is used in this task for goal maintenance (i.e. continuing to remember to name the color, not read the word). The literature is unclear as to whether the Stroop interference effect worsens with age, and even if it does, whether that is actually due to relevant cognitive processes (Strauss, Sherman, & Spreen, 2006). The frontal lobes are involved during the Stroop Test (Strauss, Sherman, & Spreen, 2006), and people with frontal lobe lesions perform more poorly than those with lesions in other brain areas (Demakis, 2004). The dependent variable for the present study was the Stroop interference score. The Stroop interference score is calculated by comparing the predicted Color-Word score (CW ) to the actual Color-Word score (CW). CW = (W*C)/(W+C), where W is the Word score and C is the Color score. The Stroop interference score = CW CW. N-Back Test. The n-back test is widely used as a working memory test. Although the test can be presented in many forms, the basic premise is the same. Items are presented in a sequence, and the participant must determine whether each item matches the item that was n-back in the sequence (Owen et al., 2005). For example, in the present study, the auditory 1-back and 2-back were used. The participant heard a computerized voice reading a series of letters at a rate of 3 seconds per letter, with the stimulus presented for 500 ms and an inter-stimulus interval of 2500 ms (Jaeggi et al., 2008). In the 1-back condition, if the letter the participant heard matched the last letter he/she heard (e.g., F, B, B ), then he/she pressed a key. In the 2-back condition, if the participant heard a letter that matched the letter 2 back in the sequence (e.g. F, B, C,

32 32 B ), then he/she pressed the key. This computerized version of the auditory n-back only scored accuracy; reaction times were not measured. A study using spatial n-back (in which each item is presented in a different area of the screen, and participants have to determine if the location of the item matches the location of the item n-back) found low or moderate test-retest reliability, with a 1 week inter-test interval, for accuracy on the 1-back (r = 0.493) and the 2-back (r = 0.538). Test-retest reliabilities were better for n-back reaction times (r = for 1-back, r = for 2-back) (Hockey & Geffen, 2004). The n-back contains face validity as a working memory test, as it clearly seems to involve manipulating remembered items by keeping track of when items appeared in the sequence and whether they match the current item. However, few studies have tested the concurrent validity of this test. A study by Kane et al. (2007) used a visual verbal n-back test (where the sequence of letters was seen on the computer screen rather than heard) and found that performance on the 3-back did not correlate very well with performance on operation span, another well-known working memory task. A meta-analysis of 24 studies did find that, in addition to several other brain regions, the dorsolateral and mid-ventrolateral prefrontal cortex is reliably activated bilaterally during various forms of the n-back test (Owen et al., 2005). Concerning age and performance, some studies found that while young and older adults performed comparably on the 1-back, older adults were less accurate than young adults on the 2- back (Mattay et al., 2005; Verhaegen & Basak, 2005). A study comparing young adults of high and low working memory ability to older adults (who were specifically

33 33 chosen to perform below the young-high group on the 3-back) found that older adults performed worse than both the young-high and young-low groups on a visual verbal 1- back and 2-back. In the left frontal lobe, older adults had more activation than both young adult groups on the 1-back. However, on the 2-back, older adults had slightly decreased activation while both young adult groups had increased activation that surpassed the older adults (Nyberg et al., 2009). Another study found older adults showed higher prefrontal cortex activation than young adults during the 1-back but lower prefrontal cortex activation during the 2-back. The older adults and young adults had the same accuracy on the 1-back, but the older adults performed more poorly on the 2-back (Mattay et al., 2005). The dependent variable for the present study was the accuracy percentage on both the 1-back and the 2-back. A change in accuracy score was also computed by subtracting the 2-back accuracy percentage from the 1-back accuracy percentage for each participant; thus, higher scores indicated a greater drop in accuracy. Near-infrared spectroscopy (NIRS). NIRS is used to measure cerebral oxygenation. The particular device used in the present study was a 2-channel NIRS machine, the INVOS 5100 Cerebral Oximeter (Somanetics Corporation, Troy, MI, USA). The INVOS measures regional cerebral oxygen saturation (rso 2 ), using the fact that oxygenated and deoxygenated hemoglobin absorb near-infrared light differently, and thus, a version of the Beer-Lambert Law can be used to measure changes in rso 2 based on light absorption measurements. Two disposable sensors are used (see Figure 4), each of which contains a light emitting diode (LED), which alternates between

34 34 emitting light of wavelengths 730 and 810 nm, and two optodes that receive the reflected light, located 3 and 4 cm away from the LED. The system measures the light absorption 15 times per second, and when 50 samples are gathered (every 3.3 seconds), the number is averaged and sent to the display (Thavasothy et al., 2002). The device measures oxygenation from the brain tissue a few centimeters underneath the sensors (Somanetics Corporation), hence, given that the sensors are placed on the forehead, they measure activity in the dorsolateral prefrontal cortex. The INVOS calculates rso 2 by assuming an arterial to venous blood ratio of 25% : 75%. Research on the INVOS has shown that, rather than using the INVOS to obtain absolute measurements of cerebral oxygenation, more accurate data can be obtained by looking at trends (i.e. measuring how cerebral oxygenation has changed over a certain period of time) (Thavasothy et al., 2002). Studies comparing NIRS recordings to fmri data have found the two methods to yield similar results, supporting the validity of NIRS as a measure of cerebral oxygenation and functional neuroimaging (Mehagnoul-Schipper et al., 2002; Toronov et al., 2001). NIRS has been used in a variety of studies on aging (Safanova et al., 2004; Mehagnoul-Schipper et al., 2002), including research specifically on the frontal lobes (Kameyama et al., 2004; Kwee & Nakada, 2003; Schroeter et al., 2003). The dependent variable for the present study is the rso 2 change score for each task (see Figure 5). Change scores were calculated by taking the average rso 2 level for a control task and subtracting it from the average rso 2 level for the working memory task. For the n-back, the rso 2 level during the 1-back was subtracted from the

35 35 rso 2 level during the 2-back. For the Stroop Color and Word test, the rso 2 level during the Stroop Word and Stroop Color tasks (combined) was subtracted from the rso 2 level during the Stroop Color-Word task. Given that the control tasks were the same as the working memory tasks in terms of reading, listening, etc., any change in activation between the tasks should have been due to the addition of the working memory component.

36 36 Results N-back Behavioral performance. Performance on the 1-back showed a trend towards a main effect of age, F(1,18) = 3.58, p = 0.08, with the older adults performing less accurately than the young adults (see Table 1). There was no main effect of WM group, F(1,18) = 0.26, p = 0.62, and no WM group by age interaction, F(1,18) = 1.05, p = For the 2-back, however, there was no main effect of age, F(1,19) = 1.14, p = 0.30, but there was a main effect of WM group, F(1,19) = 7.06, p = 0.02, with the bad WM group performing less accurately than the good WM group. There was no WM group by age interaction, F(1,19) = 0.30, p = The bad WM group showed a significantly greater drop in accuracy from the 1-back to the 2-back than the good WM group, F(1,18) = 8.60, p = There was no main effect of age, F(1,18) = 0.03, p = 0.87, and no WM group by age interaction, F(1,18) = 1.71, p = Cerebral oxygenation. The average oxygenation level during the 1-back was subtracted from the average oxygenation level during the 2-back to yield an oxygenation change score; thus, higher scores indicate increased activation from the control task to the working memory task. There was no main effect of age on the oxygenation change score in either the left, F(1,18) = 1.66, p = 0.21, or right, F(1, 18) = 1.77, p = 0.20, hemispheres (see Table 2). There was a trend towards a main effect of WM group in the left hemisphere, with the bad WM group showing a larger increase in oxygenation than the good WM group, F(1,18) = 3.20, p = However, the difference between the WM groups was not significant in the right hemisphere,

37 37 F(1,18) = 1.51, p = There was no WM group by age interaction in either the left, F(1,18) = 0.12, p = 0.74, or right, F(1,18) = 0.34, p = 0.57, hemispheres. Stroop Color and Word Test Behavioral performance. Performance on the Stroop Color and Word Test was judged by the interference score. There was a main effect of age, F(1,19) = 5.24, p = 0.03, with young adults scoring better than older adults (see Table 1). There was also a main effect of WM group, F(1,19) = 5.90, p = 0.03, with good WM participants scoring better than bad WM participants. There was no WM group by age interaction, F(1,19) = 2.42, p = Cerebral oxygenation. The average oxygenation level during the Stroop Word and Stroop Color tasks combined was subtracted from the average oxygenation level during the Stroop Word-Color (interference) task to yield an oxygenation change score. Thus, higher scores indicate increased activation from the control task to the working memory/interference task. There was no main effect of age in either the left, F(1,19) = 0.11, p = 0.74, or right, F(1,18) = 0.08, p = 0.79, hemispheres (see Table 2). There was a main effect of WM group in the left hemisphere, with the bad WM group showing a slight decrease in oxygenation from the control task to the interference task, while the good WM group showed a slight, but significantly different, increase in oxygenation, F(1,19) = 4.78, p = However, the difference between the WM groups was not significant in the right hemisphere, F(1,18) = 0.35, p = There was no WM group by age interaction in either the left, F(1,19) = 0.11, p = 0.75, or right, F(1,18) = 0.02, p = 0.89, hemispheres.

38 38 Discussion In the present study, young and older adults were divided into good or bad WM groups, and the effects of age and working memory ability were evaluated on an n-back and Stroop Color-Word Test. No main effect of age was seen for the changes in dorsolateral prefrontal cortex activation from the 1-back to the 2-back or from the Stroop control (Color and Word) to the Stroop interference (Color-Word) tasks. Instead, both tasks showed a main effect of working memory ability in the left hemisphere. These findings suggest that some of the cerebral activation differences attributed to aging in prior research may have been related to differences in working memory skills, rather than the effects of age per se. According to both the inverted-u pattern of activation hypothesis (Callicott et al., 1999) and the CRUNCH hypothesis (Cappell, Gmeindl, & Reuter-Lorenz, 2010), after an individual reaches his or her maximum capability to perform a task, frontal activation should decrease. Previous studies on aging and task difficulty have found this (Cappell, Gmeindl, & Reuter-Lorenz, 2010; Mattay et al., 2006; Nyberg et al., 2009). The current study s Stroop results also conform to this pattern. Both young and older adults with bad working memory had significantly worse Stroop interference scores than the participants in the good WM group, and both young and older adults with bad working memory showed a decrease in dorsolateral prefrontal cortex activation from the Stroop control to the Stroop interference task, while young and older adults in the good WM group showed an increase in activation.

39 39 The current study s n-back results, however, differ somewhat from the expected pattern. Both Mattay et al. (2006) and Nyberg et al. (2009) found that older adults, who showed poorer performance on the 2-back compared to the 1-back, either had no change in activation or a decrease. However, in the present study, both young and older adults with bad WM and n-back performance decline actually showed an increase in frontal activation, larger than that of the young and older adults with good WM and less n-back performance decline. It is possible that this discrepancy is due to prior studies not dividing their older adults into good and bad WM groups. In order to understand why the bad WM group in the present study had such a high increase in dorsolateral prefrontal cortex activation even though they were performing poorly on the task, further research will be needed to determine what actually causes the proposed decline in activation after subjects reach their maximum capability on task performance. The results of this current study only found significant differences in oxygenation in the left hemisphere. The right hemisphere showed a similar pattern for the differences between the WM groups, but the differences were not statistically significant. Finding significant differences only in the left hemisphere is consistent with several other aging studies (Grady, 2000; Grossman et al., 2002; Mattay et al., 2006; Nyberg et al., 2009; Smith et al., 2001), although not all (Cappell, Gmeindl, & Reuter-Lorenz, 2010), and is actually contradictory to those in support of the bilaterality hypothesis of aging (Park & Reuter-Lorenz, 2009; Reuter-Lorenz et al., 2001). More research will be needed to reconcile these two bodies of literature.

40 40 To my knowledge, the present study is the first of its kind in two respects. First, instead of merely evaluating performance, the WM groups were truly matched on working memory ability because the groups were based on scores from tests (Digit Span Backward and Letter-Number Sequencing) that were separate from the tests used to evaluate oxygenation change scores (n-back test and Stroop Color and Word test). The finding that the bad WM group performed significantly worse on the 2-back and Stroop interference tests than the good WM group confirms that participants were properly assigned to the groups. Second, the young and older good WM groups were originally chosen so that they had truly equivalent working memory scores, as were the bad WM groups. Thus, instead of simply taking the best performers from a young sample and the best from an older sample, the current study ensured that the working memory ability of the participants in the WM groups was equated across the age groups. Several methodological issues should be considered when evaluating the results of the current study. First, the sample size is rather small, especially for the young-bad WM group. More participants will be needed to have greater confidence in the present findings. Second, a 2-channel NIRS was used, and thus the only brain region that could be evaluated was the dorsolateral prefrontal cortex. Given the compensatory hypotheses of aging, it would be important to use other forms of neuroimaging (e.g. fmri, NIRS with a greater number of channels) that would show if activation changes in other areas of the brain are linked to the changes found in the dorsolateral prefrontal cortex. Finally, each of our tasks only involved two difficulty

41 41 levels (1-back vs. 2-back, Stroop control vs. Stroop interference). The inverted-u pattern of activation may be more clearly seen if more task difficulty levels are included. Future studies could use 0-back and 3-back conditions in addition to the 1- back and 2-back for the n-back task. For the Stroop, another difficulty level could be added by using the Delis-Kaplan Executive Function System (D-KEFS) version of the Stroop test, which adds a fourth test that involves switching back and forth between saying the color or reading the word for the Color-Words (Strauss, Sherman, & Spreen, 2006). Research on aging, working memory ability and the underlying brain activation patterns has implications beyond simply understanding how aging affects this particular cognitive process. The deterioration of executive functions, including working memory, contributes to dementia (Brandt et al., 2009), which is defined as major impairment in mental capabilities that disrupts daily living (Cleveland Clinic, 2007). Working memory declines are also seen in people with mild cognitive impairment (MCI), a potential precursor to dementia (Brandt et al., 2009). Given that an estimated 29 million people worldwide have dementia (Wimo et al., 2007), and that number will only increase as the global population as a whole grows older, it is imperative to understand the relationship between aging, declines in working memory, and the brain mechanisms that link the two. Possible benefits of this kind of research include discovering treatments to enhance cognitive performance in aging or ways to prevent dementia. One study found that after participants spent a few weeks practicing a certain kind of working memory

42 42 task (a dual n-back), they had increased fluid intelligence scores. These higher scores were measured on tests separate from the task they practiced, showing that the benefits actually transferred more generally to other cognitive functions (Jaeggi et al., 2008). Perhaps future research could compare the effects of working memory training on frontal lobe activation between young and older adults, promoting our understanding of how to achieve healthy aging in the brain.

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54 54 Figures Figure 1. Location of the frontal lobe in the brain. The frontal lobe is responsible for executive functioning. The prefrontal cortex, responsible for working memory, is located in the anterior section of the frontal lobe (the far right in this diagram). Source: University of Queensland, Australia, School of Psychology.

55 55 Figure 2. Brodmann areas. The dorsolateral prefrontal cortex is Brodmann areas 9 and 46 (Rypma et al., 1999). Image source: Grahn (2010).

56 56 Figure 3. Inverted-U pattern of activation. Recent studies on task difficulty and performance suggest that prefrontal cortex activation increases with increasing task difficulty, until the subject is unable to perform the task well, at which point activation decreases. It appears that, in general, older adults are more challenged by lower task difficulty levels than young adults, and thus start at greater levels of activation and decline from there.

57 57 Figure 4. Electrodes from the NIRS machine attached to a participant s forehead. This image was taken before a headband was placed over the electrodes to block out excess light and help hold the electrodes in place. The NIRS machine used in the present study was the INVOS 5100 Cerebral Oximeter.