Developmental Aspects and Neurobiological Correlates of Working and Associative Memory

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Developmental Aspects and Neurobiological Correlates of Working and Associative Memory

                                        Gerald Goldstein                                                             Daniel N. Allen

         VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania                                         University of Nevada Las Vegas

                                       Nicholas S. Thaler                                                            James F. Luther

                      University of Oklahoma Health Sciences                                VA VISN IV Mental Illness Research, Educational, and Clinical

Center, VA Pittsburgh Healthcare System, Pittsburgh,

Pennsylvania

Kanagasabai Panchalingam and Jay W. Pettegrew

University of Pittsburgh School of Medicine

Objective: It has been shown that verbal working and associative memory have different developmental trajectories with working memory, taking a linear course from early childhood to adolescence, whereas associative memory takes a curvilinear course asymptoting at about age 12. This study made a determination of whether these trajectories tracked with 2 magnetic resonance spectroscopy imaging (MRSI) variables: phosphocreatine level (PCr) and gray matter percentage (GM%). Method: In a cross-sectional study, 94 children ranging in age from 6–14 years were administered tests of verbal working and associative memory and underwent an MRSI procedure evaluating 6 major brain regions. The study considered PCr levels and GM% in the 6 regions. Loess curves were constructed plotting the memory tests and MRSI variables across age, and trajectories were evaluated. Results: PCr showed a linear increase with age, particularly in the left superior temporal lobe with this increase closely tracking improvement in working memory but not associative memory scores. GM% did not increase with age in any brain region, and there was no tracking with either of the memory tests. Conclusion: Verbal working memory and verbal associative memory have differing age trajectories, with working memory showing close tracking with PCr level, mainly in the left superior temporal lobe. No such tracking was found for the associative memory tests. GM% curves were flat across regions, showing no association with age.

Keywords: working memory, MR spectroscopy, Loess curves

Working memory is a limited capacity memory system that provides temporary storage to manipulate information for complex cognitive tasks, such as those involved in learning and reasoning (Baddeley & Hitch, 1974). The distinction has been made between verbal and other forms of working memory such as spatial working memory, and different measures are used to evaluate these abilities separately. This study is concerned with verbal working memory, which has been operationally defined by both psychometric tests and experimental procedures that assess it in various ways. For

Gerald Goldstein, VISN IV Mental Illness Research, Educational, and Clinical Center, VA Pittsburgh Healthcare System, Pittsburgh, PA; Daniel N. Allen, Department of Psychology, University of Nevada Las Vegas; Nicholas S. Thaler, Department of Psychiatry and Behavioral Sciences, University of Oklahoma Health Sciences; now at UCLA Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Behavioral Sciences James F. Luther, VISN IV Mental Illness Research, Educational, and Clinical Center, VA Pittsburgh Healthcare System, Pittsburgh, PA; Kanagasabai Panchalingam, Department of Psychiatry, University of

Pittsburgh School of Medicine; Jay W. Pettegrew, Department of Psychi1

example, IQ and memory batteries often incorporate working memory indices for clinical use (Sheslow & Adams, 2003; Wechsler, 2008, 2009), whereas laboratory procedures such as the n-back and Sentence Memory Test are also available (Daneman & Carpenter, 1980; Jaeggi et al., 2010). Typically, verbal working memory tests use auditory stimuli as test items, as is the case in this study. Although this form of memory is generally characterized as verbal working memory, it is usually evaluated with spoken language material. Its definition, first proposed by Baddeley (1992,

atry, Department of Neurology, Department of Behavioral and Community Health Services, University of Pittsburgh School of Medicine and Department of Bioengineering, University of Pittsburgh.

This work was supported in part by an NIHCD/NIH HD-39799 grant (JWP) Indebtedness is expressed to the Medical Research Service and the VA VISN-IV Mental Illness Research, Education and Clinical Center (MIRECC) Depertment of Veterans Affairs.

Correspondence concerning this article should be addressed to Jay W. Pettegrew, Department of Psychiatry, University of Pittsburgh School of Medicine, RIDC Park, 2600 Kappa Drive, Pittsburgh PA 15238. E-mail:

pettegrew@phfrinc.com

2003), is the ability to maintain and manipulate a limited amount of information over a period of time, generally while solving a problem. Associative memory is defined as the process of storage and retrieval of information beyond the initial few seconds. Working memories disappear after that time interval while associative memories are stored in the long-term memory system.

It is well-established that there are substantial maturational changes in the brain throughout childhood and early adulthood. These changes have been studied with a variety of methods with Volpe (1995), indicating that the major processes are neuronal proliferation, migration of neurons to specific sites, and organization and myelination of the neural circuitry. Longitudinal studies of maturational trajectories of typically developing youth have found that gray matter as a whole develops in an inverted U-shaped trajectory, though there are differential rates of peak gray matter density with the dorsolateral prefrontal cortex and inferior parietal cortex among the regions that peak in later adolescence. In contrast, white matter density generally increases through childhood and adolescence, which may result in greater connectivity of neural circuitry. Individual differences in trajectory have been ascribed to genetic, environmental, and biological factors including gender (Giedd et al., 2009; Lenroot et al., 2007; Wallace et al., 2006), which in turn predict general intellectual functioning (Shaw et al., 2006). This study of verbal memory focuses on two critical aspects of neurodevelopment; synaptic proliferation and pruning and structural heterochronicity. In animals, normal synaptic elimination occurs synchronously in all regions and is said to be homochromous, but in humans it occurs at different ages in different regions and is said to be heterochronous (Huttenlocher & Dabholkar, 1997).

In addition to neurodevelopment of general cognition there exist differences between associative and working memory development in children that also relate to the neural circuits that underlie these components (Hötting et al., 2010). Associative memory for facts has been linked to medial temporal structures while working memory relies on frontal and parietal regions (Bledowski, Rahm, & Rowe, 2009; Curtis & D’Esposito, 2003; Klingberg, 2010; Linden, 2007; Townsend, Richmond, Vogel-Farley, & Thomas, 2010). Memory abilities associated with these structures may demonstrate different developmental trajectories through childhood and adolescence, particularly as the prefrontal cortex has a prolonged maturational course (Huttenlocher & Dabholkar, 1997; Raznahan et al., 2012).

The design of our study of the neurodevelopment of memory may be viewed as pertinent to the above considerations in the following context. We have already completed a MRS–MRI study of a comprehensive battery of neuropsychological tests. In this cross-sectional study of age differences, we used magnetic resonance spectroscopy imaging (MRI–MRSI) as a neurobiological procedure with children and adolescents over a wide age range (Goldstein et al., 2009; Pettegrew, McClure, & Panchalingam, 2008). They were evaluated with a battery of neurocognitive tests and numerous MRI and MRSI measures. The study showed that two measures were particularly sensitive to age differences in cognitive function. The neurochemical measure identified was resting phosphocreatine (PCr), a critical high energy phosphate in the brain. PCr is a storage form of ATP and can be converted to ATP by the creatine kinase enzyme (E.C.2.7.3.2). ATP synthesis is stimulated by a reduction in ATP levels or an increase in adenosine diphosphate (ADP) or H levels. The reaction is in equilibrium with no net flux in either direction (Siesjo, 1978, p. 12). Therefore, PCr functions as a spatial energy “shuttle” (Andres, Ducray, Schlattner, Walliman, & Wilder, 2008) bridging ATP generation and consumption. In the brain, the synaptic membrane is the site of highest ATP consumption being used to repolarize the depolarized synaptic membrane. Thus, PCr levels are very sensitive to regional synaptic activity and numbers, as reviewed by Pettegrew, McClure, and Panchalingam (2011). PCr and ATP also are consumed in the process of glutamate uptake into synaptic vesicles (Xu et al., 1996). The use of resting PCr is noted because the values of PCr change during activation procedures such as fMRI, which were not used in this research.

The other variable considered was percentage of gray matter (GM%) in a particular brain region. The MRI procedure segments the brain into gray, white, and CSF–extracortical space producing the percentage of gray matter in a particular brain region. Reductions in GM% reflect synaptic pruning, a normal process in the brain associated with developmental growth (Huttenlocher & Dabholkar, 1997). Although GM% may decrease in a region because of an increase in white matter, MRI findings from this study of healthy individuals indicated that GM% decreases without any increase in white matter, supporting the view that it is associated with normal synaptic pruning. This previous study found that age differences in PCr and GM% systematically corresponded with changes in cognitive development (Goldstein et al., 2009). As cognitive test scores increased with age, PCr levels became higher and GM% levels became lower reflecting synaptic pruning. Numerous other MRI–MRSI measures were used, but only PCr and GM% were found to be clearly associated with cognitive function. Memory was assessed in this study but was not considered in detail, but only as one cognitive domain studied within the framework of a comprehensive neuropsychological battery evaluating numerous domains. Also the study considered a single axial slice of brain and a number of cognitive domains that were not evaluated separately.

The results of this research indicated that there appeared to be particularly striking differences in the developmental trajectories of working and associative memory as assessed by the Wide Range Assessment of Memory and Learning (WRAML; Sheslow & Adams, 2003). There is ample evidence that different brain regions have different age related trajectories whether demonstrated with longitudinal or cross-sectional studies, with the frontal lobes developing later than the temporal lobes (Bauer, 2008; Huttenlocher & Dabholkar, 1997; Lenroot & Giedd, 2006; Squire, 2004). Memory abilities associated with these structures therefore also can be expected to demonstrate different developmental trajectories through childhood and adolescence. Therefore, as a follow-up, Thaler et al. (2013), who were able to obtain access to the standardization sample of the Test of Memory and Learning (TOMAL; Reynolds & Bigler, 1994), demonstrated that working memory and associative memory have different developmental trajectories using that sample. The TOMAL and WRAML have very similar verbal working and associative memory tasks; one measured by two backward span tasks and the other by a list learning task. The working memory task took on a linear course until age 11, whereas the trajectory for the associative memory task was curvilinear with an inflection point at age 8 and an asymptote at age 11. Loess (local regression) curves obtained from scatterplots of the data points were plotted demonstrating these trajectories and the interaction term of an analysis of variance (ANOVA) involving age group and memory system was highly significant. Thus, the difference in age trajectory between working and associative memory was demonstrated statistically at a high level of significance.

The Goldstein et al. (2009) and Thaler et al. (2013) articles, which used entirely different participants from each other, led to this study because of the particularly interesting findings they respectively produced regarding PCr and working memory. Our study was designed to evaluate regional neurobiological associations between the MRSI-based trajectories found in the Goldstein et al. (2009) study involving PCr and GM% and the memory trajectories found in the Thaler et al. (2013) study. The opportunity to do this was provided by the similarities between the WRAML and TOMAL working and associative memory tests, the access to a large stratified sample for the Thaler at al. study, and the availability of both memory test and regional MRSI data in the Goldstein et al. (2009) study. Thus, the context of our investigation is the interesting finding concerning a developmental difference between working and associative memory derived from a broader investigation of numerous cognitive abilities, the identification of PCr as a specific metabolite associated with these differing developmental patterns, and the opportunity to examine brain regional differences not accomplished previously. An initially general finding led to an evaluation of more specific effects.

The three studies including this one may be viewed as a single project concerning memory development. The Thaler et al. (2013) study confirmed the observation of the different memory trajectories based on a large representative sample but did not involve neurobiological data. The missing link, therefore, became the association between neurobiological phenomena and these differing memory trajectories and, thus, became the topic of our study. The availability of the large sample of subjects for the Thaler et al. study encouraged the use of a cross-sectional study because the sample was carefully demographically stratified so as to avoid the cohort effects that often compromise cross-sectional studies. Also, cohort effects are intrinsically associated with longitudinal studies because these studies are necessarily based on a single cohort unless a difficult-to-implement, mixed-longitudinal, cross-sectional design is used (Botwinick, 1981).

A study that compares age differences between associative and working memory in association with regional MRI–MRSI data would address those matters and is the purpose of this investigation. Such a study would provide potentially important findings about the relationships among different forms of memory and possible differences in neurobiological relationships in the brain and about how these relationships change with neurodevelopment. Therefore, the MRI–MRSI data used in the Goldstein et al. (2009) study were reanalyzed to provide regional data for six major brain regions, and the results of the WRAML Number–Letter and Verbal Learning subtests were used as the memory measures in an effort to identify specific relationships between the developmental course of verbal working and associative memory and that of two neurobiological measures, PCr and GM%, in six major brain regions. The memory tests used were chosen because they are representative of tests commonly used in the memory literature to assess working and associative memory substantially without the influence of other cognitive functions such as attention, executive function, and language abilities often involved in more complex tests (e.g., Daneman & Carpenter, 1980). We have data on other memory tests, some of which involve working and associative memory, but our intent was to use tests that were relatively pure measures of those abilities without the complicating effects of other abilities.

The method used aims toward assessing how closely cognitive age changes track neurobiological age changes. The aim of the study was that of identifying age difference tracking patterns that demonstrated correspondences between measures of verbal working and associative memory and PCr and GM% age differences in six brain regions. Our investigation therefore constitutes an extension of the Goldstein et al. (2009) study, which used a multidomain comprehensive battery of tests of cognitive abilities, and the Thaler et al. (2013) study in which specific aspects of memory were considered in detail. This study completes this sequence by considering neurobiological changes in specific brain regions associated with two forms of memory. It was specifically designed to test hypotheses based on the findings of those studies suggesting that basic verbal working memory and associative memory skills will have different age related trajectories, as clearly demonstrated by the Thaler at al. study and that these trajectories will track with age differences in PCr and GM%. On the basis of evidence reviewed above that molecular neurodevelopment is heterochronous across brain regions, level of performance on tests of verbal working and associative memory as well as PCr and GM% will show differing trajectories across six major brain regions that are generally thought to be associated with learning and memory, including the right and left prefrontal areas, superior temporal lobes, and inferior parietal lobes. Data were available for the occipital lobes but were not analyzed because there is no evidence the authors know of that indicates that the occipital lobes play a role in auditory verbal working or associative memory. Clark et al. (2001) report on a relationship between occipital lobe function and verbal working memory and ERP activity, but visual target stimuli were used. The occipital lobes were involved in early visual processing, which would not be relevant here.

Method

Participants

The study involved a sample of 94 (50 female and 44 male) healthy normal children and adolescents who had received a memory battery, the Wide Range Assessment of Memory and Learning (WRAML-2; Sheslow & Adams, 2003) and who went through an MRI structural and magnetic resonance spectroscopy imaging (MRI–MRSI) procedure. This sample was the same as the one used in Goldstein et al. (2009). The age distribution covered the ages of 6–14 years, thereby including the neurodevelopmental stages of childhood, pubescence, and adolescence. The group is of average intelligence, with average scores on tests of academic achievement. The parents were generally well educated with educational levels ranging from 12–22 years for the mothers and 11–22 years for the fathers.

The study was approved by the University of Pittsburgh institutional review board. All participants (and parents when appropriate) gave informed written consent to participate. Personal interviews, including the participant and family, were conducted by a psychologist, and following the informed consent procedure and execution of the appropriate consent form, procedures were accomplished to assure that recruited participants were healthy, normally developing individuals who met all inclusion–exclusion criteria: These included a review of the study inclusion–exclusion criteria and completion and review of the Devereux Scale for Mental Disorders (Naglieri, LeBuffe, & Pfeiffer, 1994), a cognitive test battery, an interview in which magnetic resonance (MR) exclusion criteria were reviewed by an MR center nurse or technician, and by subject participation in an MR simulator, and a physical examination conducted by a pediatrician. If all entry criteria were met by participants, the full battery was completed, followed by the MRI–MRSI examination within 1 month after completion of the cognitive neurodevelopmental battery.

Psychometric and MRI–MRSI Procedures

Memory tests considered in this study were Verbal Learning and Number–Letter subtests of the WRAML-2 used as measures of auditory verbal associative and working memory. Because this was an age-effect study, raw scores were used rather than age-corrected standard scores. A variety of memory and other cognitive tasks were used in an exploratory manner in the Goldstein et al. (2009) study. However, the Thaler et al. (2013) and this study focused on basic auditory verbal working memory as contrasted with verbal associative memory with the intent of evaluating those tests that evaluated the clearest procedures we had available of these two forms of memory. Therefore, single tasks of each of these forms of memory were used rather than multiple tasks, as was the case in the Goldstein et al. study, in which several memory tests were used that did not represent clear instances of particular forms of memory. The test of working memory were chosen on the basis of a substantial literature beginning with Baddeley, who is generally credited with inventing the concept of working memory and based his experimental work mainly on span tests, Mirsky et al. (1991) who performed a widely accepted factor analysis that identified a factor containing span tests that were identified as a “working memory” factor, and the inclusion of span tests on the Wechsler Intelligence and Memory scales as a major part of what was characterized as a “Working Memory Index”(WMI; Holdnack, Drozdick, & Iverson, 2013). The common acceptance of span tests as indicators of working memory is indicated by use of the terms “working memory span measures” (Baddeley, Jarrold, & VargaKhadem, 2011) and “short term and working memory (as measured by simple and complex span tests” (Siegert, Weatherall, Taylor, & Abernethy, 2008) suggest the common acceptance of these tasks as measures of working memory that do not share the characteristics of other tests that involve a number of memory and other cognitive abilities. Measures of what is generally agreed to involve associative memory have existed since the 19th century, reviewed in Woodworth (1938).

MRS–MRI Procedures

The MRS–MRI procedures were conducted using a doubletuned transmit–receive volume head coil on a GE LX 1.5-T wholebody MRI system. A three-dimensional volume of T1-weighted images covering the entire brain was then collected for tissuesegmentation analysis of the 31P spectroscopy voxels. To prescribe the MRSI slice location, a three-plane MRI localizer image was first collected, followed by a set of sagittal and axial scout images using the two-dimensional fast spin-echo sequence. Using the midsagittal image, we defined the anterior commissure–posterior commissure (AC–PC) line, and a 30-mm axial slice was positioned parallel to and superior to the AC–PC line for the spectroscopy. A single-slice selective excitation radio frequency (RF) pulse followed by phase-encoding pulses to spatially encode the two dimensions of the slice was used to acquire the multivoxel in vivo 31 P spectroscopy data.

The acquisition method combined the PRESS sequence (Bottomley, 1987) with the phase encoding steps of a chemical shift imaging (CSI) sequence. In this study the region of interest (ROI) is positioned in the axial plan and the left-right and anteriorposterior dimensions will vary accordingly to ensure the ROI covers the brain in the defined axial plane. To minimize the partial volume effect for the sampled regions, we applied six different voxel grid shift schemes to both 1H and 31P MRSI prior to two-dimensional inverse Fourier transformation (2D IFT). These grid schemes provided voxels that include left and right: prefrontal cortex, superior temporal cortex, and inferior parietal cortex. The acquisition method in other regions were recorded not involved in this study.

The reliability of the PCr quantitation was demonstrated by a coefficient of variance (CV) of 13% which is quite good. The 31P MRSI acquisition, postprocessing, spectral fitting, and quantitation were performed as discussed in Goldstein et al. (2009). The postprocessing was fully automated, required no operator input, and quantitation results were 100% reproducible.

Morphometry

Segmentation of MRI data was performed as follows. First, the 152 average T1 Montreal Neurological Institute template was coregistered to each structural MRI image, using a 12-parameter affine model. This transformation was subsequently applied a priori to gray matter, white matter, and cerebrospinal fluid (CSF) segmentation images derived from the MRI data. Next, images were segmented into gray, white, and CSF compartments using SPM is standard terminology SPM software SPM is a modified mixture-model clustering algorithm, which also corrects for intensity bias across the image (Ashburner & Friston, 2000) resulting in probabilistic segmentation images. Slices in the MRI image corresponding to the MRSI slab were extracted and reduced to a single MRI slab as follows. For each voxel in the MRI slab and each tissue type, a percentage of tissue score was computed, which ranged between 0 and 100 (where 100 indicates all voxels in the MRI slab contained a given tissue completely, e.g., gray matter relative concentrations of 1). Once the MRI slab voxels were computed, they were further summarized to obtain the percentage of gray, white, and CSF matter in the MRSI voxels of interest. A grid-shift scheme identical to the one used for the MRSI grid shift was used for all three segmented images. As indicated, decreases in GM% were not associated with increase in white matter percentage.

Data Analysis

As was the case in the Goldstein et al. (2009) study the Loess curve-fitting method (Cleveland, 1979) was used with cognitive and MRI–MRSI data placed on the same plot. It is a nonparametric regression method applied when a suitable parametric form of the regression surface is not known. A Loess curve is a locally weighted scatterplot smoothing function. We used the Akaike information criterion (Hurvich, Simonoff, & Tsai, 1998) to select the smoothing parameter for each curve. This method of fitting curves seemed reasonable because it was already found in the Thaler et al. (2013) study that the curves for the two memory tests were very similar to those used here and were quite different from each other, one being mainly linear and the other curvilinear. All data were converted to z scores so that all measures could be placed on the same scale.

Initially, scatterplots and associated Loess curves were constructed for age in years as a continuous variable just for the memory tests to demonstrate the differences in age trajectory between the two tests. Then MRI–MRSI z-score data and age in years as a continuous variable were plotted against the z scores for the two memory tests, PCr, and GM%. These sets of scatterplots and Loess curves were constructed for PCr and GM% so that tracking patterns could be observed comparing the memory and MRI–MRSI data. That is, the WRAML-2 Number–Letter and Verbal Learning subtests were placed on the same scatterplot with PCr and GM%. These scatterplots were constructed for the six brain regions as a way of determining whether the curves for the associative and working memory WRAML-2 scores had the same or different trajectories. The memory test data were the same for each of the plots but were nevertheless included for ease of comparison with the MRI–MRSI data, which varied across the six plots.

Results

The Loess curves for the memory tests alone are presented in Figure 1. The contrast in curves between the Verbal Learning and Number–Letter tests is quite clear. We also present these separate scatterplots with individual bivariate data points to provide a more precise description of the relationship. For the Number–Letter test the presence of a linear pattern is clear with a small number of outlying cases. For Verbal Learning there is a clear inflection at about age 12 years marked by an initial increase and then a flattening of the curve. The individual data points do not form a clearly coherent pattern with numerous outliers. These patterns obtained from the WRAML are consistent with those reported by Thaler et al. (2013) based on the TOMAL. Loess curves for the memory test and MRI–MRSI data for the left and right prefrontal, inferior parietal and superior temporal regions are presented in Figures 2, 3, 4, and 5. Figure 2 and 3 are for PCR and Figures 4 and 5 are for the GM%. These scatterplots contain age in years on the horizontal axis and data in the form of z scores for the Verbal Learning and Number–Letter subtests and for the MRI–MRSI variables PCr and GM% in the regions studied. The figures allow for determining whether the various measures track each other across ages and whether these tracking trajectories differ among regions. The curves for the memory tests are the same for each region but are presented in each of them for comparative purposes.

For the brain regions, the PCr curve for the prefrontal region is essentially flat across the ages studied. In sharp contrast the tra-

Figure 1. Scatterplots for age and scores of the Number Letter and Verbal Learning Test.

jectory for the superior temporal lobe is linear increasing with age. The curve for the inferior parietal lobe is linear but not to as great an extent as is the case for the temporal region. Substantial differences were not noted between the left and right hemisphere curves. With regard to tracking between the memory tests and PCr, the tracking is very close between the Number–Letter subtest and PCr in the superior temporal lobe and to a somewhat lesser extent in the inferior parietal region. There is essentially no tracking with the prefrontal regions. Curves for GM% had a slight downward trend with increasing age in all cases, as was found to a greater extent in the Goldstein et al. (2009) study, where a single axial slice involving the whole brain and a wider age range extending to 18 were considered. There was no clear suggestion of tracking across the age range studied for any region in the case of the Verbal Learning subtest and GM% did not track with either of the memory tests.

The two major observations are therefore that PCr level increases systematically with age in the superior temporal regions and the inferior parietal regions to a lesser extent, but not in the prefrontal regions. A test of working memory tracks highly consistently with PCr level in the superior temporal lobe and to a lesser extent with the inferior parietal lobe but not with the prefrontal regions. A test of associative memory does not track across

Figure 2. Loess curves for left hemisphere phosphocreatine level for memory tests and brain regions.

the age range studied in any brain region. In general, the differences between hemispheres are minor, but there are major differences among the regions. GM% showed a slight decline with advancing age, apparently associated with synaptic pruning, but did not track with the memory tests.

Discussion

This study was conducted to describe age trajectories in verbal working in contrast to verbal associative memory in healthy children and adolescents ranging in age from 6 to 14 years.

The Goldstein et al. (2009) and Thaler et al. (2013) studies reported results of tests of statistical significance between inflection points on the Loess curves for the MRI–MRSI variables and for tests contrasting associative with working memory using the TOMAL. The Goldstein et al. (2009) study reported significant age-group differences in PCr, which became higher with age, and GM%, which became lower with age. With regard to the memory tests, Thaler et al. (2013) found a significant interaction between

Figure 3. Loess curves for right hemisphere phosphocreatine level for memory tests and brain regions.

Figure 4. Loess curves for left hemisphere gray matter percentage for memory tests and brain regions.

age and type of memory, indicating a linear pattern in the case of the working memory test and a curvilinear pattern for the associative memory test. Thus, differences in PCr levels become significantly higher as age increases, and there is a significantly widely separating performance level between working and associative memory tests as age increases up until early adolescence. Because the same data were used for the MRI–MRSI analyses and data from very similar tests were used for the memory test analyses, and because the focus of this study was on trajectories rather than significance of differences among age groups, tests of significance are not reported here.

Inspection of scatterplot-based Loess curves showed different patterns of age differences between the two memory tests and the six brain regions. With regard to the memory tests we found that the relationship between age and score on the working memory test is mainly linear whereas the relationship with the associative memory test was curvilinear accelerating until an inflection point at about age 10, continuing to accelerate at a slower rate until the

Figure 5.  Loess curves for right hemisphere gray matter percentage for memory tests and brain regions.

age of 12, and then dropping off. The clearest visible tracking on the Loess curves was between the Number Letter subtest and PCr in the left superior temporal lobe. Of interest, Paus et al. (1999) reported that the left temporal region shows more increased white matter density than the right superior temporal region. White matter density is thought to be associated with greater brain connectivity (Marsh, Gerber, & Peterson, 2008), which may explain the strong relationship identified here.

In contrast, there was no tracking at all in the prefrontal region. Even in the left superior temporal lobe, there was no visible tracking across age ranges for the Verbal Learning subtest. The GM% curves declined with age in all regions, but only to a modest extent not comparable to the substantial changes noted for PCr. The present data therefore suggested that while PCr level is age related in certain brain regions, GM% is not strongly age related, except for a slight decline across age in all regions, thought to be associated with synaptic pruning and consonant with the inverse U-shaped curve identified in longitudinal neuroimaging studies (Gogtay et al., 2004). The decline in GM% was substantially sharper in the Goldstein et al. (2009) study for two likely reasons. In that study the age range went up to 18. Thus, the older ages not evaluated in this study are just when one would anticipate the greatest degree of synaptic pruning. In addition, Pettegrew, Panchalingam, Stanley, and McClure (2008) have shown with other data that GM% reduction is particularly pronounced in late adolescence involving the occipital lobes, regions that were not studied here. However, in both studies there was no tracking between this decline and cognitive variables, which likely reflects the linear increase in cognitive performance and the inverse curve of GM%. A more refined understanding of the relationships among age, brain region, and the neurochemical indices can only be obtained through more detailed curve-fitting studies involving a wide age range and additional metabolites so that relationships among the indices studied here and other variables can be ascertained. This matter goes well beyond the limitations of this study.

A question may be raised about the nature of the neurobiological correlates of developing memory. A suggestion comes from inspection of the Loess curves for the left superior temporal lobe. It appears that there is good tracking between PCr and the Verbal Learning subtest until close to age 12 at which point PCr continues to increase while the curve for Verbal Learning plateaus after which there is no further improvement in test scores. Therefore, it is suggested that increase in PCr is associated with development of both associative and working memory as long as those memory abilities are developing, but the relationship ends at the point at which the cognitive ability stops developing. It is also possible that white matter density may play a role given previous findings (Paus et al., 1999) though we were unfortunately not able to examine white matter density in this study. Regardless, the phenomenon of measurable improvement in associative memory to about age 12 is supported by both the data in the MRI–MRSI study and from the Thaler et al. (2013) study, which is based on a large, stratified, national sample for the TOMAL.

As noted in Goldstein et al. (2009) ATP is primarily consumed at synaptic membranes for repolarizing those that have been depolarized, and PCr is the buffer for ATP (Buchli, Martin, Bosiger, & Rumpel, 1994; Chugani, Phelps, & Mazziotta, 2002; Frey, 1994; Hein, Krieglstein, & Stock, 1975; Hess, 1961; Jansson, Harkonen, & Helve, 1979; Kadekaro, Crane, & Sokoloff, 1985; Kennedy & Sokoloff, 1957; McCandless & Wiggins, 1981; Sokoloff, 1966, 1991). The increase in PCr could be caused by synaptic elimination, resulting in fewer synapses plus decreased activity of remaining synapses in older subjects. PCr shows a heterochronous pattern with differing age related patterns among the regions. Notably, the left temporal lobe in particular shows an increase in PCr level with age, whereas the frontal lobes do not. In the case of GM%, the age pattern is similar among regions, suggesting homochronous development over the 6–14 year age range. Thus, the developmental pattern for PCr is heterochronous with increases beginning in the temporal lobes at the earliest ages studied while GM% appears to have a homochronous developmental profile across regions. Therefore, the increase in PCr levels with increasing age (6–14 years) in this participant sample, without decreases in GM% volume over the same age group, suggests a reduction in synaptic activity in the temporal lobes without a substantial reduction in GM% volume.

This study has several limitations including not having the availability of a large sample and of longitudinal data. Large sample and longitudinal studies with the procedures used here are unfeasible because of the expense and limited capability of maintaining a long-term longitudinal study with the same MRI–MRSI equipment used across many years in the face of rapidly changing technology. There is also the conclusion made by Heaton and Drexler (1987) after reviewing numerous studies that in the area of neurobehavioral aging research, the results coming from crosssectional studies are essentially the same as those coming from longitudinal studies. In the case of this study, although there would be advantages of a longitudinal design, there are formidable problems with feasibility. Maintaining the same intact sample over a 15-year period that goes through the identical laboratory procedure conducted by the same individuals is not likely to be possible. There is loss of subjects for varying reasons including moving or development of illness making them no longer healthy, turnover of data analysts, and the inevitable changes in MRI–MRS technology that will occur. These considerations essentially eliminate the possibility of a viable longitudinal study. Even if such a study were conducted unavoidable subject attrition and technology changes would make it difficult to interpret. A contemporary evaluation of Heaton and Drexler’s conclusion might be that longitudinal and cross-sectional studies each have advantages and disadvantages. However, the obvious advantage of obtaining data about actual development of the same individual over time may be somewhat compromised by the considerations that simple longitudinal studies involve sampling of only a single cohort and that practical concerns such as selective attrition of the sample, development of health problems in some individuals, and feasibility of accomplishing a lengthy longitudinal study unaffected by technological changes in the procedures used can produce limitations on conclusions reached.

Another consideration is that neurodevelopmentally based tissue studies cannot be done longitudinally but were found to be consistent with longitudinal developmental studies in living subjects (Smiley & Goldman-Rakic, 1993). Pursuing this consideration was viewed as being of particular significance because of the tissue studies of Huttenlocher (1979, 1990), which involved a wide age range that documented changes in synaptic density that were later supported by our developmental MRS studies. Results showing consistencies between tissue and cross-sectional studies therefore also support the credibility of cross-sectional data. In this study, we have avoided making inferences about cause-and-effect relationships in individual cases, consistent with what is common practice among published cross-sectional age difference studies. We have characterized this study as age difference research comparable to numerous age difference studies in the literature. Efforts were made not to interpret the findings in terms of sequences of events in individuals, but rather as reflecting characteristics associated with varying ages in a single cohort.

This study may be understood in the context of this research being part of a three-study program with the same goal, in one case using the same database. Increased detailed information could be obtained by performing curve-fitting analyses contrasting different models and identifying significant inflection points to better characterize the trajectories. Aside from the overall trajectories it is important to document the locations of inflection points at which important developmental changes take place.

Despite its limitations, the results of the study may have relevance to the differing developmental patterns between working and associative memory. Apparently PCr plays a role in the development of ability such that it tracks across ages until the ability is reasonably fully developed, at which point there is a parting of the ways, and PCr may continue to increase while the ability remains stable. The clinical implications of this finding are that memory test results indicating a discrepancy between working and associative memory tests may now be understood to reflect developmental differences in growth of these two forms of memory. Thus, a child showing unanticipated scores on tests of these two forms of memory may have developmental difficulties. Beyond age 12 lack of improvement in associative memory would be normally anticipated, but lack of improvement in working memory would not occur normally because working memory should not plateau at age 12 but should improve into adolescence and young adulthood. Thus, findings based on studies of healthy normal individuals may have implications for individuals with developmental difficulties.

The reason why associative and working memory have different trajectories is not made clear by this study, but the process of working memory involving short-term storage of information during the performance of an often complex task may require more elaborate circuitry than the relatively more simple encoding, storage, and recall of information involved in associative memory. It is noted that while the bulk of the studies of neural correlates of working memory involve subregions of the prefrontal cortex, our findings were that there was no association between PCr and GM% age-related changes in the frontal lobes with substantial changes in other regions for PCr. Possibly, the prefrontal cortex has not yet been engaged in humans in the age range studied, and processing of working memory in that age range may be increased in the frontal lobes in adults. An associated possible consideration is that findings based on animal studies may not apply completely to humans. The neural correlate literature does indicate that verbal working memory is associated with a complex system involving prefrontal cortex and possibly Broca’s region (Rottschy et al., 2012). Such systems may take many years to fully develop. It would therefore be of great interest to do studies with older individuals to track further development of the frontal lobes.

References

Andres, R. H., Ducray, A. D., Schlattner, U., Walliman, T., & Widner, H. R. (2008). Functions and effects of creatine in the central nervous system. Brain Research Bulletin, 76, 329–343. doi:10.1016/j

.brainresbull.2008.02.035

Ashburner, J., & Friston, K. J. (2000). Voxes-based Morphometry—The methods. NeuroImage, 11, 805–821. doi:10.1006/nimg.2000.0582

Baddeley, A. (1992, January 31). Working memory. Science, 255, 556– 559. doi:10.1126/science.1736359

Baddeley, A. (2003). Working memory: Looking back and looking forward. Nature Reviews. Neuroscience, 4, 829–839. doi:10.1038/nrn1201 Baddeley, A. D., & Hitch, G. J. (1974). Working memory. In G. A. Bower, (Ed.), Recent advances in learning and motivation (Vol. 8, pp. 47–89). New York, NY: Academic Press.

Baddeley, A., Jarrold, C., & Vargha-Khadem, F. (2011). Working memory and the hippocampus. Journal of Cognitive Neuroscience, 23, 3855– 3861. doi:10.1162/jocn_a_00066

Bauer, P. J. (2008). Toward a neuro-developmental account of the development of declarative memory. Developmental Psychobiology, 50, 19– 31. doi:10.1002/dev.20265

Bledowski, C., Rahm, B., & Bowe, J. B. (2009). What works in working memory? Separate systems for the selection and updating of critical information. The Journal of Neuroscience, 29, 13735–13741. doi: 10.1523/JNEUROSCI.2547-09.2009

Bottomley, P. A. (1987). Spatial localization in NMR spectroscopy in vivo.

Annals of the New York Academy of Sciences, 508, 333–348. doi:

10.1111/j.1749-6632.1987.tb32915.x

Botwinick, J. (1981). Neuropsychology of aging. In S. B. Filskov & T. J. Boll, (Eds.), Handbook of clinical neuropsychology (pp. 135–171). New York, NY: Wiley.

Buchli, R., Martin, E., Bosiger, P., & Rumpel, H. (1994). Developmental changes of phosphorus metabolite concentrations in the human brain: A 31

P brain magnetic resonance spectroscopy study in vivo. Pediatric Research, 35, 431–435. doi:10.1203/00006450-199404000-00009

Chugani, H. T., Phelps, M. E., & Mazziotta, J. C. (2002). Positron emission tomography study of human brain functional development. In M. H. Johnson, Y. Munakata, & R. O. Gilmore (Eds.), Brain development and cognition: A reader (2nd ed., pp. 101–116). Malden, MA: Blackwell

Publishing.

Clark, C. R., Moores, K. A., Lewis, A., Weber, D. L., Fitzgibbon, S., Greenblatt, R., . . . Taylor, J. (2001). Cortical network dynamics during verbal working memory function. International Journal of Psychophysiology, 42, 161–176. doi:10.1016/S0167-8760(01)00164-7

Cleveland, W. S. (1979). Robust locally-weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74, 829–836. doi:10.1080/01621459.1979.10481038

Curtis, C. E., & D’Esposito, M. (2003). Persistent activity in the prefrontal cortex during working memory. Trends in Cognitive Sciences, 7, 415– 423. doi:10.1016/S1364-6613(03)00197-9

Daneman, M., & Carpenter, P. A. (1980). Individual differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior, 19, 450–466. doi:10.1016/S0022-5371(80)90312-6

Frey, K. A. (1994). Positron emission tomography. In G. J. Siegel, B. W. Agranoff, R. W. Albers, & P. B. Molinoff (Eds.), Basic neurochemistry: Molecular, cellular, and medical aspects (5th ed., pp. 935–955). New York, NY: Raven Press.

Giedd, J. N., Lalonde, F. M., Celano, M. J., White, S. L., Wallace, G. L., Lee, N. R., & Lenroot, R. K. (2009). Anatomical brain magnetic resonance imaging of typically developing children and adolescents. Journal of the American Academy of Child & Adolescent Psychiatry, 48, 465– 470. doi:10.1097/CHI.0b013e31819f2715

Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., & Thompson, P. M. (2004). Dynamic mapping of human cortical development during childhood through early adulthood.

Proceedings of the National Academy of Sciences of the United States of America, 101, 8174–8179. doi:10.1073/pnas.0402680101

Goldstein, G., Panchalingam, K., McClure, R. J., Stanley, J. A., Calhoun, V. D., Pearlson, G. D., & Pettegrew, J. W. (2009). Molecular neurodevelopment: An in vivo 31P-1H MRSI study. Journal of the International Neuropsychological Society, 15, 671–683. doi:10.1017/ S1355617709990233

Heaton, R. K., & Drexler, M. (1987). Clinical neuropsychological findings in schizophrenia and aging. In N. E. Miller & G. D. Cohen (Eds.), Schizophrenia and aging: Schizophrenia, paranoia, and schizophreniform disorders in later life (pp. 145–161). New York, NY: Guilford

Press.

Hein, H., Krieglstein, J., & Stock, R. (1975). The effects of increased glucose supply and thiopental anesthesia on energy metabolism of the isolated perfused rat brain. Naunyn Schmiedebergs Archives of Pharmacology, 289, 399–407. doi:10.1007/BF00508413

Hess, H. (1961). The rates of respiration of neurons and neurogliain human cerebrum. In S. S. Kety & J. Elkes (Eds.), Regional neurochemistry (pp. 200–202). Oxford, England: Pergamon Press.

Holdnack, J. A., Drozdick, L. W., & Iverson, G. L. (2013). WAIS-IV, WMS-IV, and ACS: Advanced clinical interpretation. Amsterdam, the Netherlands: Elsevier.

Hötting, K., Katz-Biletzky, T., Malina, T., Lindenau, M., & Bengner, T. (2010). Long-term versus short-term memory deficits for faces in temporal lobe and generalized epilepsy patients. Journal of the International Neuropsychological Society, 16, 574–578. doi:10.1017/ S1355617710000275

Hurvich, C. M., Simonoff, J. S., & Tsai, C. L. (1998). Smoothing parameter selection in nonparametric regression. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 60, 271–293. doi: 10.1111/1467-9868.00125

Huttenlocher, P. R. (1979). Synaptic density in human frontal cortex— Developmental changes and effects of aging. Brain Research, 163, 195–205. doi:10.1016/0006-8993(79)90349-4

Huttenlocher, P. R. (1990). Morphometric study of human cerebral cortex development. Neuropsychologia, 28, 517–527. doi:10.1016/00283932(90)90031-I

Huttenlocher, P. R., & Dabholkar, A. S. (1997). Regional differences in synaptogenesis in human cerebral cortex. Journal of Comparative Neurology, 387, 167–178. doi:10.1002/(SICI)1096-9861(19971020)387: 2167::AID-CNE13.0.CO;2-Z

Jaeggi, S. M., Studer-Luethi, B., Buschkuehl, M., Su, Y., Jonides, J., & Perrog, W. J. (2010). The relationship between n-back performance and matrix reasoning—Implications for training and transfer. Intelligence, 38, 625–635. doi:10.1016/j.intell.2010.09.001

Jansson, S. E., Harkonen, M. H., & Helve, H. (1979). Metabolic properties of nerve endings isolated from rat brain. Acta Physiologica Scandinavica, 107, 205–212. doi:10.1111/j.1748-1716.1979.tb06464.x

Kadekaro, M., Crane, A. M., & Sokoloff, L. (1985). Differential effects of electrical stimulation of sciatic nerve on metabolic activity in spinal cord and dorsal root ganglion in the rat. Proceedings of the National Academy of Sciences of the United States of America, 82, 6010–6013.

Kennedy, C., & Sokoloff, L. (1957). An adaptation of the nitrous oxide method to the study of the cerebral circulation in children: Normal values for cerebral blood flow and cerebral metabolic rate in childhood. Journal of Clinical Investigation, 36, 1130–1137. doi:10.1172/ JCI103509

Klingberg, T. (2010). Training and plasticity of working memory. Trends in Cognitive Sciences, 14, 317–324. doi:10.1016/j.tics.2010.05.002

Klunk, W. E., Xu, C. J., Panchalingam, K., McClure, R. J., & Pettegrew, J. W. (1994). Analysis of magnetic resonance spectra by mole percent: Comparison to absolute units. Neurobiology of Aging, 15, 133–140.

doi:10.1016/0197-4580(94)90153-8

Kreis, R. (1997). Quantitative localized 1 H MR spectroscopy for clinical use. Journal of Progress in Nuclear Magnetic Resonance, 31, 155–195. doi:10.1016/S0079-6565(97)00014-9

Lenroot, R. K., & Giedd, J. N. (2006). Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging. Neuroscience Biobehavioral Reviews, 30, 718–729.

Lenroot, R. K., Gogtay, N., Greenstein, D. K., Wells, E. M., Wallace, G. L., Clasen, L. S., & Giedd, J. N. (2007). Sexual dimorphism of brain developmental trajectories during childhood and adolescence. NeuroImage, 36, 1065–1073. doi:10.1016/j.neuroimage.2007.03.053

Linden, D. E. (2007). The working memory networks of the human brain. Neuroscientist, 13, 257–267. doi:10.1177/1073858406298480

Marsh, R., Gerber, A. J., & Peterson, B. S. (2008). Neuroimaging studies of normal brain development and their relevance for understanding childhood neuropsychiatric disorders. Journal of the American Academy of Child & Adol.

McCandless, D. W., & Wiggins, R. C. (1981). Cerebral energy metabolism during the onset and recovery from halothane anesthesia. Neurochemical Research, 6, 1319–1326. doi:10.1007/BF00964353

McIlwain, H., & Bachelard, H. S. (1985). Biochemistry and the central nervous system (5th ed.). Edinburgh, Scotland: Churchill Livingstone.

Mirsky, A. F., Anthony, B. J., Duncan, C. C., Ahearn, M. B., & Kellam, S. G. (1991). Analysis of the elements of attention a neuropsychological approach. Neuropsychology Review, 2, 109–145. doi:10.1007/ BF01109051

Naglieri, J. A., LeBuffe, P. A., & Pfeiffer, S. I. (1994). Devereux Scale for Mental Disorders. San Antonio, TX: Psychological Corporation.

Paus, T., Zijdenbos, A., Worsley, K., Collins, D. L., Blumenthal, J., Giedd, J. N., . . . Evans, A. C. (1999, March 19). Structural maturation of neural pathways in children and adolescents: In vivo study. Science, 283, 1908–1911.

Pettegrew, J. W., Keshavan, M. S., Panchalingam, K., Strychor, S., Kaplan, D. B., Tretta, M. G., & Allen, M. (1991). Alterations in brain high energy phosphate and phospholipid metabolism in first episode, drug naive schizophrenia. A pilot study of the dorsal prefrontal cortex by in vivo 31P NMR spectroscopy. Archives of General Psychiatry, 48, 563– 568. doi:10.1001/archpsyc.1991.01810300075011

Pettegrew, J. W., Keshavan, M. S., Stanley, J. A., McClure, R. J., Johnson, C. R., Pouwels, P. J., & Frahm, J. (1997). Differential distribution of NAA and NAAG in human brain as determined by quantitative localized proton MRS. Nuclear Magnetic Resonance in Biomedicine, 10, 73–78.

Pettegrew, J. W., McClure, R. J., & Panchalingam, K. (2011). Sprectroscopic imaging in schizophrenia. In M. E. Shenton & B. I. Turetsky (Eds.), Understanding neuropsychiatric disorders (pp. 48–77). Cambridge, England: Cambridge University Press.

Pettegrew, J. W., Panchalingam, K., Stanley, J. A., & McClure, R. J. (2008). Molecular biomarkers of regional brain development. Neuroscience Meeting Planner. Washington, DC. Society for Neuroscience.

Provencher, S. W. (1993). Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magnetic Resonance in Medicine, 30, 672–679. doi:10.1002/mrm.1910300604

Rakic, P., Bourgeois, J.-P., Eckenhoff, M. F., Zecevic, N., & GoldmanRakic, P. S. (1986, April 11). Concurrent overproduction of synapses in diverse regions of the primate cerebral cortex. Science, 232, 232–235.

doi:10.1126/science.3952506

Raznahan, A., Greenstein, D., Lee, N. R., Clasen, L. S., & Giedd, J. N. (2012). Prenatal growth in humans and postnatal brain maturation into late adolescence. Proceedings of the National Academy of Sciences, 109, 11366–11371.

Reynolds, C. R., & Bigler, E. D. (1994). Test of Memory and Learning. Austin, TX: Pro-Ed.

Rottschy, C., Langner, R., Dogan, I., Reetz, K., Laird, A. R., Schulz, J. B.,

& Eickhoff, S. B. (2012). Modelling neural correlates of working memory: A coordinate-based meta-analysis. NeuroImage, 60, 830–846. doi: 10.1016/j.neuroimage.2011.11.050

Roux, L. (1998). Magnetic resonance. Journal of Magnetic Resonance Imaging, 8, 1022–1023.

Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., & Giedd, J. (2006). Intellectual ability and cortical development in children and adolescents. Nature, 440, 676–679.

Sheslow, D., & Adams, W. (2003). Wide Range Assessment of Memory and Learning second edition administration and technical manual. Lutz, FL: Psychological Assessment Resources.

Siegert, R. J., Weatherall, M., Taylor, K. D., & Abernethy, D. A. (2008). A meta-analysis of performance on simple span and more complex working memory tasks in Parkinson’s disease. Neuropsychology, 22(4), 450.

Siesjo, B. K. (1978). Brain energy metabolism. New York, NY: Wiley.

Simmons, M. L., Frondoza, C. G., & Coyle, J. T. (1991). Immunocytochemical Localization of N-acetyl-aspartate with monoclonal antibodies. Neuroscience, 45, 37–45. doi:10.1016/0306-4522(91)90101-S

Smiley, J. F., & Goldman-Rakic, P. S. (1993). Heterogeneous targets of dopamine synapses in monkey prefrontal cortex demonstrated by serial section electron microscopy: A laminar analysis using the silver enhanced diaminobenzidine-sulfide (SEDS) immunolabeling technique. Cerebral Cortex, 3, 223–238. doi:10.1093/cercor/3.3.223

Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., . . . Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(Suppl. 1), S208–219. doi:10.1016/j.neuroimage.2004 .07.051

Sokoloff, L. (1966). Cerebral circulatory and metabolic changes associated with aging. Research Publications–Association for Research in Nervous and Mental Disease, 41, 237–254.

Sokoloff, L. (1991). Measurement of local cerebral glucose utilization and its relation to local functional activity in the brain. Advances in Experimental Medicine and Biology, 291, 21–42. doi:10.1007/978-1-46845931-9_4

Sokoloff, L. (1993). Function-related changes in energy metabolism in the nervous system: Localization and mechanisms. Keio Journal of Medicine, 42, 95–103. doi:10.2302/kjm.42.95

Squire, L. R. (2004). Memory systems of the brain: A brief history and current perspective. Neurobiology of Learning and Memory, 82, 171– 177. doi:10.1016/j.nlm.2004.06.005

Stanley, J. A., Drost, D. J., Williamson, P. C., & Thompson, R. T. (1995). The use of a priori knowledge to quantify short echo in vivo1h mr spectra. Magnetic Resonance in Medicine, 34, 17–24. doi:10.1002/mrm .1910340105

View publication stats

Stanley, J. A., & Pettegrew, J. W. (2001). Post-processing method to segregate and quantify the broad components underlying the phosphodiester spectral region of in vivo 31 P brain spectra. Magnetic Resonance in Medicine, 45, 390–396. doi:10.1002/1522-2594(200103)45:3390:: AID-MRM10513.0.CO;2-D

Suzuki, K. (1965). The pattern of mammalian brain gangliosides—III: Regional and developmental differences. Journal of Neurochemistry, 12, 969–979. doi:10.1111/j.1471-4159.1965.tb10256.x

Thaler, N. S., Goldstein, G., Pettegrew, J. W., Luther, J. F., Reynolds, C. R., & Allen, D. N. (2013). Developmental aspects of working and associative memory. Archives of Clinical Neuropsychology, 28, 348– 355. doi:10.1093/arclin/acs114

Townsend, E. L., Richmond, J. L., Vogel-Farley, V. K., & Thomas, K. (2010). Medial temporal lobe memory in childhood: Developmental transitions. Developmental Science, 13, 738–751. doi:10.1111/j.14677687.2009.00935.x

Truckenmiller, M. E., Namboodiri, M. A. A., Brownstein, M. J., & Neale, J. H. (1985). N-acetylation of L-aspartate in the nervous system: Differential distribution of a specific enzyme. Journal of Neurochemistry, 45, 1658–1662. doi:10.1111/j.1471-4159.1985.tb07240.x

Volpe, J. J. (1995). Neuronal proliferation, migration, organization, and myelination. Neurology of the Newborn, 1, 43–92.

Wallace, G. L., Eric Schmitt, J., Lenroot, R., Viding, E., Ordaz, S., Rosenthal, M. A., & Giedd, J. N. (2006). A pediatric twin study of brain morphometry. Journal of Child Psychology and Psychiatry, 47, 987– 993. doi:10.1111/j.1469-7610.2006.01676.x

Wechsler, D. (2008). Wechsler Adult Intelligence Scale—Fourth Edition: Administration and scoring manual. San Antonio, TX: Psychological Corporation.

Wechsler, D. (2009). Wechsler Memory Scale—Fourth Edition: Administration and scoring manual. San Antonio, TX: Pearson.

Woodworth, R. S. (1938). Experimental psychology. New York, NY: Holt.

Woolrich, M. W., Jbabdi, S., Patenaude, B., Chappell, M., Makni, S., Behrens, T., & Smith, S. M. (2009). Bayesian analysis of neuroimagimg data in FSL. NeuroImage, 45(Suppl. 1), S173–S186. doi:10.1016/j .neuroimage.2008.10.055

Xu, C. J., Klunk, W. E., Kanfer, J. N., Xiong, Q., Miller, G., & Pettegrew, J. W. (1996). Phosphocreatine-dependent glutamate uptake by synaptic vesicles. A comparison with atp dependent glutamate uptake. Journal of Biological Chemistry, 271, 13435–13440.

Received April 12, 2013

Revision received November 20, 2013

Accepted November 27, 2013

Developmental Aspects and Neurobiological Correlates of Working and Associative Memory