To date, studies of MCI have relied almost exclusively on delayed recall or retention measures in rendering the diagnosis (Arnaiz & Almkvist, 2003
), and there has been a relative dearth of research parsing the underlying components of the memory problems that characterize individuals with MCI. Thus, we investigated qualitative differences in learning versus retention, and their relation to morphometric measures and disease progression, in MCI. We presented behavioral evidence showing that MCI individuals can be characterized by impairments either in learning, retention, or both, based on a commonly used verbal memory taskâRAVLT. Furthermore, individuals with different deficit profiles in learning and retention presented distinct patterns of brain morphometry. We then examined the AD progression rate among the MCI groups over a two-year follow-up and found that either impaired learning or impaired retention increased risk of future development of AD, but that individuals with both impaired learning and retention abilities showed the highest risk of AD conversion.
With respect to brain morphometry, we predicted that individuals with impaired learning ability would show a more widespread pattern of brain atrophy involving frontal, temporal, and parietal lobe regions, while individuals with impaired retention ability would demonstrate more circumscribed atrophy primarily involving medial temporal regions. Results based on group comparisons and partial correlation analyses supported our predictions and were consistent with previous neuroimaging and lesion studies (Moscovitch, et al., 2005
; Parsons, et al., 2006
; Powell, et al., 2005
; Rosen, et al., 2005
; Shankle, et al., 2005
; Squire, et al., 2004
; Weintrob, et al., 2007
), suggesting that learning measures involve a broader neural network whereas retention measures show a more focal gray matter involvement.
Interestingly, relative to the healthy control (or HL-HR) group, the HL-LR group showed cortical thinning in medial orbitofrontal areas in addition to temporal lobe areas. The work of Stuss and colleagues have shown that medial orbitofrontal areas are associated with the ability to inhibit irrelevant information (Happaney, Zelazo, and Stuss, 2004
; Stuss and Alexander, 2007
). It is possible that good retention ability requires not only the integrity of medial temporal structures but also the ability to inhibit irrelevant information (e.g., words from the interference trial in the RAVLT) mediated by medial orbitofrontal regions (Stuss and Alexander, 2007
Cortical thinning in PCC was found in all MCI groups relative to the HL-HR group. This was not unexpected given that the PCC is considered part of the limbic system and has reciprocal connections with the medial temporal lobe, including entorhinal cortex and hippocampal formation (Kobayashi & Amaral, 2003
). Hypometabolism and volumetric reduction in PCC has been identified as a feature of early AD (Choo, et al., 2008
; Chua, Wen, Slavin, & Sachdev, 2008
; Pengas, Hodges, Watson, & Nestor, 2008
), and several recent studies have reported PCC hypometabolism or/and volume reduction in individuals with MCI (Choo, et al., 2008
; Chua, et al., 2008
; Fennema-Notestine, et al., 2009
; Pengas, et al., 2008
). Overall, our findings were consistent with prior studies that suggest that PCC abnormality can be detected in a prodromal stage of AD. In addition, we found significant cortical thinning in the lateral temporal lobe regions in all MCI groups relative to the HL-HR group. Lateral temporal areas, particularly middle and inferior temporal gyri, have been implicated in the progression of AD (McEvoy, et al., 2009
; Whitwell, et al., 2007
; Whitwell, et al., 2008
). Although atrophy of the superior temporal gyrus has typically been observed only after a diagnosis of probable AD (Scahill, Schott, Stevens, Rossor, & Fox, 2002
; Whitwell, et al., 2007
), our results, in accord with some recent studies (Chang et al., 2009
; Fan et al., 2008
; McEvoy et al., 2009
), showed significant atrophy in this area in the MCI groups, suggesting that atrophy of the lateral temporal gyrus can occur prior to a diagnosis of probable AD and may be associated with a higher risk of imminent clinical decline.
This possibility is further supported by studies demonstrating that measures of semantic knowledge show significant declines during prodromal AD (Cuetos, Arango-Lasprilla, Uribe, Valencia, & Lopera, 2007
; Powell et al., 2006
; Mickes et al., 2007
), and that these cognitive operations may be relatively independent of episodic memory deficits (Koenig, Smith, Moore, Glosser, & Grossman, 2007
). For example, Mickes and colleagues (2007)
have shown in a detailed neuropsychological study of prodromal AD that both semantic memory and episodic memory functions declined rapidly in a three-year period progressing to AD, whereas executive function deficits were not particularly prominent. Mickes and colleagues concluded that cognitive abilities thought to be subserved by the medial and lateral temporal lobes (episodic and semantic memory, respectively) may be more prominently impaired than cognitive functions subserved by the frontal lobes (executive functions). These findings map nicely onto the known neuropathologic encroachments of AD early on in the disease process (Braak & Braak, 1991
) and are also consistent with recent reports of decreased semantic access in nondemented APOE Î4 older adults (Rosen, Sunderland et al., 2005
) and the ability of language tasks to predict pathologic AD six years later (Powell et al., 2006
Although distinguishable morphometric patterns were found between the poor learning or retention groups and the HL-HR group, the correlation coefficients observed between learning, retention and morphometric measures were generally low (râs = .11 â.41), suggesting that much of the variance in learning and retention scores is not explained by brain morphometry. It is likely that there are some factors of interindividual differences that may have also contributed to the differential morphometric patterns observed among groups. For example, the APOE Î4 allele has been documented as a genetic risk factor for late-onset AD (Bennett, et al., 2003; Bondi, et al., 1994
; Bondi, et al., 1999
; Modrego, 2006
). Some studies suggest that the APOE Î4 genotype, particularly for individuals who progress to AD over time, is associated with more widespread brain atrophy involving areas of medial temporal, frontal, and parietal regions (Hamalainen, et al., 2008
). Consistent with this view, we found that the LL-LR group had the highest frequency of APOE Î4 carriers among the three MCI groups and showed the most widespread pattern of gray matter atrophy relative to the other groups.
Another goal of the current study was to determine the relative utility of learning and retention measures in predicting AD progression among the four groups. Not surprisingly, individuals with both learning and retention impairments at baseline had the highest risk for progression to AD over two years. Learning impairment with intact retention, and retention impairment with intact learning were also each associated with an increased risk for developing AD, although our ability to directly compare the conversion rates of these two important subgroups (i.e., LL-HR vs. HL-LR) was likely underpowered due to their relatively small sample sizes. However, individuals with learning deficits (regardless of the level of their retention abilities) at baseline showed a significantly higher likelihood of developing AD over two years compared to those with a retention deficit (regardless of the level of their learning abilities). These results are consistent with prior studies that have also reported differential sensitivity of learning and retention measures. For example, Grober and Kawas (1997)
, perhaps the first to show the utility of learning measures in prodromal AD, found that individuals in the prodromal stage of dementia recalled significantly fewer words during the learning trials of the free and cued selective reminding procedure than did matched control participants, whereas their retention of material over the 30-min delay period was identical to that of control participants, suggesting that learning variables may be a more sensitive measure for predicting AD conversion than retention. Also, Rabin and colleagues (2009)
investigated the discriminative ability of several widely used clinical memory tests to classify individuals as MCI or healthy older adults. They found that the total learning score on a list-learning task appeared to be the most sensitive diagnostic index for distinguishing MCI from healthy aging. Together with the current results showing that learning impairment is associated with a higher rate of progression to AD than retention deficits in the absence of learning impairments, these findings suggest that learning measures can be as useful as retention measures in predicting progression from MCI to AD, and suggest that the use of only delayed recall or retention measures in studies of amnestic MCI potentially misses an important subset of older adults at risk of developing AD.
Our cross-sectional results showed that individuals with impaired learning or retention could be distinguished from elderly individuals without memory impairment not only from this neuropsychological perspective but also in terms of brain morphometry. Buckner (2004)
suggests that AD pathology, even in the early stages, involves both hippocampal and frontal regions, though via different mechanisms. Some studies have also demonstrated that MCI individuals with more widespread gray matter loss at baseline progress more rapidly to AD relative to those with focal gray matter loss (McEvoy, et al., 2009
; Whitwell, et al., 2008
). Consistent with these studies, relative to the impaired retention group, the impaired learning group showed a more widespread pattern of gray matter loss at baseline involving frontal, temporal, and other cortical regions; and these individuals showed a higher progression rate to AD during the follow-up period. Overall, our finding suggests that learning ability, given its involvement in multiple cortical regions and likely reliance on other neuropsychological mechanisms such as attention and concentration, can be a sensitive indicator of imminent clinical decline in the prodromal period.
Some of the initial factor analytic studies of the neuropsychological measures comprising the MOANS core battery, which includes the RAVLT, also support this notion. Specifically, Smith and colleagues (1992
demonstrated that the RAVLT learning index loaded on a Learning factor along with a number of other learning and working memory measures (WMS-R Logical Memory I, Visual Reproduction I, Visual Associates, Paired Associates), whereas the RAVLT retention index loaded on a more circumscribed Retention factor with other measures of retention only (WMS-R Logical Memory Percent Retention, Visual Reproduction Percent Retention). Other factors on which neither of the RAVLT measures loaded were those relating to Verbal Comprehension, Perceptual Organization, or Attention (WAIS-R Digit Span and Arithmetic, WMS-R Mental Control and Visual Span). The robust psychometric characteristics of the RAVLT variables and their demonstrated stability in factor analytic studies of both normal and clinical dementia samples support the generalizability of our findings with the RAVLT variables to other similar learning and retention measurement strategies.
Although the search for signature cognitive changes in prodromal AD has largely focused on episodic memory, as was the case in our study, several recent reviews and meta-analyses also suggest that there is decline in other cognitive domains in addition to episodic memory in the few years prior to a dementia diagnosis, including deficits in semantic memory, visuospatial skills, executive functions, and attention and speed of processing (Backman, Jones, Berger, Laukka, & Small, 2004). This widespread decline in cognitive abilities mirrors evidence that multiple brain regions (e.g., medial and lateral temporal lobes, frontal and parietal cortices, cingulate cortex) or connectivity between these regions are impaired in prodromal AD (Small, Mobly, Laukka, Jones, & Backman, 2003
). Future studies that more broadly sample cognitive domains beyond episodic memory will be better able to delineate these brain-behavior relationships in MCI and prodromal AD.
Broader conceptualizations of MCI have emerged in recent years to encompass cognitive domains other than episodic memory (Petersen & Morris, 2005
), and clinical subtypes that include amnestic and non-amnestic forms, or single or multiple cognitive domains, have been offered. With the advent of these broader classification schemes, diagnostic challenges related to MCI have understandably increased, and neuropsychological assessment of multiple cognitive domainsâwith sensitive and specific measures of the AD prodromeâwill increasingly play a prominent role in resolving these challenges. For example, a pair of recent studies have shown that, when compared to the typical approach to diagnosing MCI (e.g., recall deficit â 1.5 standard deviations; CDR score of 0.5; normal MMSE score), a comprehensive neuropsychological approach to MCI diagnosis results in more robust associations with expected anatomical and stroke risk findings (Jak et al., 2009a
) as well as better prediction of progression to dementia (Saxton et al., 2009
). Use of comprehensive neuropsychological assessment when diagnosing MCI subtypes will help to improve the stability and reliability of diagnosis, as will the use of multiple measurements (e.g., learning and retention measures) within a cognitive domain such as episodic memory (see Jak et al., 2009b
). Our finding that combined learning and retention impairment was superior to isolated learning or retention impairment in predicting progression to AD supports this notion.
Related to this, differences in the classification of some individuals as MCI or normally aging in the current study relative to classification of these individuals within the ADNI reflects the problems associated with the use of different operational criteria across studies (Busse, Hensel, Guhne, Angermeyer, & Riedel-Heller, 2006
; Jak et al., 2009b
). The internal consistency of MCI diagnosis or prediction of AD progression based on alterations of the classification criteria (i.e., the cut point at which performance was considered impaired) was not the primary interest of the current study. However, differences in the conversion rates among the three MCI groups identified here, and between these MCI groups and the HC group, suggests that diagnostic schemes that incorporate more than delayed recall and global screening measures will increase sensitivity and reliability in predicting diagnostic outcome and likelihood of conversion to AD. Anchoring such sophisticated diagnostic schemes to underlying brain morphometric changes and prediction of AD progression will also provide much needed improvements in MCI diagnostic procedures.
Despite the potential clinical value of our findings, there are limitations that should be noted. First, with the limited number of learning and memory tests available in the ADNI, it is not possible to compare the relative diagnostic and predictive value of visual versus verbal learning and memory tests. Second, with the large sample sizes afforded by the ADNI, it is possible to observe statistically significant group differences, as we did for the bulk of our comparisons, although the clinical impact of these statistically significant findings may not be as clear cut. Fortunately, for at least a subset of the analyses we were able to provide effect size statistics, the bulk of which showed medium to large effect sizes, bolstering the potential clinical import of the findings. Third, 2-year clinical follow-up data was available for only 69% of MCI individuals at the time we conducted this study. This is not uncommon in prospective studies of older adults (Visser, Pluijm, Stel, Bosscher, & Deeg, 2002
), particularly with such a large-scale project. Additional follow-up data over a longer time interval could provide clarifying information on the relative progression rates between the LL-HR and. HL-LR groups. Nevertheless, despite some dropout, MCI participants with or without follow-up data within each group did not significantly differ in any of the baseline demographic (i.e., age, education level, gender distribution, APOE Î4 status) or global cognitive (i.e., MMSE score) characteristics. Thus, it seems unlikely that selective attrition occurred.
In conclusion, we provide evidence in support of the use of both learning and retention measures in the diagnosis of MCI. Furthermore, understanding the underlying qualitative feature of memory deficits in MCI can provide critical information for early detection of AD. Individuals with learning or retention impairment appear to be distinguishable not only neuropsychologically but also morphometrically. That is, individuals with learning deficits appear to show a more widespread pattern of gray matter loss, whereas individuals with retention deficits tend to show more focal gray matter loss with largest effects in medial temporal regions and PCC at baseline. Moreover, both learning and retention measures provide good predictive value for longitudinal clinical outcome, although impaired learning had modestly better predictive power than impaired retention. As expected, use of both measures provided the best predictive power. Hence, the conventional practice relying on the use of delayed recall or retention measures only in most MCI diagnostic schemes misses an important subset of individuals with prodromal AD. Overall, our results highlight the importance of including learning measures in addition to retention measures when making a diagnosis of MCI and for predicting clinical outcome. Knowledge of affected memory processes can also help to tailor specific auxiliary mnemonic strategies in cognitive training in MCI populations.