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Journal of Alzheimer’s Disease 41 (2014) 421–430 DOI 10.3233/JAD-131682 IOS Press

Associative Learning and Regional White Matter Deficits in Mild Cognitive Impairment Erin L. Boespfluga,∗ , James Eliassena , Jeffrey Welgea,b and Robert Krikoriana a Department

of Psychiatry and Behavioral Neuroscience, University of Cincinnati Academic Health Center, Cincinnati, OH, USA b Department of Environmental Health (Division of Epidemiology & Biostatistics), University of Cincinnati Academic Health Center, Cincinnati, OH, USA Handling Associate Editor: Peter Bayley

Accepted 29 January 2014

Abstract. Background: While diagnostic criteria for Alzheimer’s disease (AD) include neuroimaging biomarkers, there remains no definitive biomarker of mild cognitive impairment (MCI). MCI is a risk factor for AD that may be amenable to early intervention. Early decline in white matter (WM) integrity identified by diffusion tensor imaging (DTI) is a predictor of future progression of neurodegeneration. Objective: Identify regionally specific WM differences between individuals with MCI and those with age-associated memory impairment (AAMI) and relationships with specific memory decrements. Methods: DTI and neuropsychological data were acquired from 38 participants (23 MCI and 15 AAMI). A region of interest approach was used to evaluate regional differences between groups and correlative relationships with performance on memory tasks. Results: Fornix WM had higher mean (MD), radial (DR), and axial (DA) diffusivity in MCI participants relative to AAMI. Temporal stem (TS) WM had higher MD and DR in MCI than in AAMI. In MCI, TS MD and DR varied, while fornix MD and DR was uniformly high, and in AAMI, TS MD and DR were uniformly low and fornix MD and DR varied. In MCI, TS MD and DA were inversely associated with associative learning but not list learning. Conclusions: In addition to supporting prior evidence implicating the fornix in early AD pathology, these data implicate a profile of neurodegeneration associated with early MCI. Further, they suggest that associative learning tasks are more sensitive to early neurodegeneration and may be useful in identifying individuals at risk for AD. Keywords: Alzheimer’s disease, biomarker, diffusion tensor imaging, fornix, mild cognitive impairment, paired-associate learning

INTRODUCTION The ability to identify individuals at increased risk for Alzheimer’s disease (AD) is of particular rele∗ Correspondence to: Erin L. Boespflug, PhD, Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati Academic Health Center, Rm. E685 Medical Sciences Services Bldg., 231 Albert Sabin Way, PO Box 670583, Cincinnati, OH 45267-0583, USA. Tel.: +1 513 558 7168; Fax: +1 513 558 7164; E-mail: [email protected].

vance, as there is evidence that intervention may be effective in predementia conditions [1–3], whereas effective treatment of AD pathology is not available. The recently refined diagnostic criteria for AD include cognitive assessment and biomarker evidence [4]. Diffusion tensor imaging (DTI), a magnetic resonance imaging (MRI) modality, utilizes the propensity of water molecules to diffuse differentially in tissue depending on its condition to obtain information about tissue type, structure, and integrity. The most

ISSN 1387-2877/14/$27.50 © 2014 – IOS Press and the authors. All rights reserved

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commonly reported DTI metrics are mean diffusivity (MD) and fractional anisotropy (FA), which describe the magnitude and directionality of the diffusion, respectively [5]. These measures have been found to reflect postmortem histological status [6, 7], indicating that they are a correlate of in vivo pathological status. Emerging evidence suggests that analysis of the components of the diffusion tensor parallel to (axial, or DA) and perpendicular to (radial, or DR) the principal diffusion vector provide additional insight into underlying pathology with respect to information regarding neuronal and myelin dysfunction, respectively [8, 9]. The involvement of white matter (WM) in early AD pathology has been recognized as an early marker. In research focusing on mild cognitive impairment (MCI), a clinical condition representing increased risk for AD, DTI has revealed early and progressive loss of integrity of WM that is temporally antecedent to clinical manifestation of neurocognitive decline [10–12]. Studies have demonstrated loss of integrity in WM of the temporal lobes [13] and more extensive damage in AD relative to MCI [13, 14] as well as early loss of integrity in the fornix in preclinical AD. Reduced integrity (increased MD, DR, and/or DA, and decreased FA) of the fornix has been shown to be associated with MCI and AD status [15–17] as well as progressive cognitive decline in MCI and AD [12, 15, 18–22] and in pre-clinical familial AD [11]. Just as imaging studies implicate neuroanatomical foci in the early pathology associated with MCI, clinical evaluation has identified impairment of long-term memory ability. Identification of individuals with MCI who ultimately progress to AD has relied on tests of episodic memory [23]. Verbal list learning and verbal associative learning measures, both of which probe episodic memory, have been shown to be sensitive to cognitive decline in AD [24, 25]. Further, reduction in fornix integrity, as measured by DTI, has been linked to episodic memory loss and hippocampal atrophy in MCI subjects [17]. New associative learning among semantically unrelated terms depends upon the hippocampi and parahippocampal structures to a greater extent than list learning or recall of semantically related word pairs [26–30], which is consistent with the involvement of medial temporal lobe structures in mediating associative learning and the recognized atrophy of these structures in AD pathobiology. These findings indicate that associative learning tasks can be sensitive to the neuropathology observed in MCI and AD, and this sensitivity makes such tasks well suited for identification of individuals at increased risk of progressive neurodegeneration.

While clinical classification of individuals with MCI provides a means of identifying those at greater risk for progression, not all persons with MCI progress to AD [31]. The joint use of neuroimaging and memory evaluation therefore may lead to better prediction of risk of progression to AD. Motivated by emerging evidence of targeted WM involvement, as well as specific (associative) learning decrements in early AD pathology, we sought to investigate relationships between these variables in individuals at increased risk of AD. We present data that suggest a specific WM profile for those at risk for progression to AD, as well as a particular relationship between regional WM integrity and medial-temporal lobe dependent learning performance in these individuals. The aim of this work is to inform research concerning markers of MCI and very early AD, with the long-term goal of identifying individuals with MCI who have greatest risk for progression to AD.

METHODS Participants were recruited from the greater Cincinnati/Northern Kentucky region with print advertising soliciting participation of older adults with memory complaints. Memory complaints consisted of increasing frequency of forgetfulness for everyday recent events, as well as prospective forgetfulness, such as forgetting appointments. Study procedures were approved by the Medical Institutional Review Board of the University of Cincinnati and written informed consent was obtained from all study participants. The Academic and Medical History Questionnaire [32] provided a means of gathering self-reported information on academic achievement, medical history, medication, supplement, and substance use. Exclusionary conditions included diabetes, liver and kidney disease, substance abuse disorder, diagnosis of a psychiatric or neurological condition which could impact cognitive performance or neural integrity, current use of medications that might affect clinical measures (e.g., benzodiazepines), and contraindication to high-field MRI (e.g., metallic objects in body, claustrophobia). Participants were classified as having MCI or age associated memory impairment (AAMI), the latter denoting expectable age-related decline in memory function [33] primarily on the basis of the Clinical Dementia Rating (CDR) [34], as well as objective memory measures. The CDR assesses functional decline in six domains including memory, orientation, judgment/problem solving, community affairs, hobbies, and personal care with the memory domain

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weighted most heavily. The CDR assessment was determined with the use of a structured clinical examination to obtain information from an informant about the prospective participant. All enrolled MCI participants were classified as having memory decline but not dementia (CDR global score = 0.5), consistent with MCI classification [35]. Participants with CDR = 0.0 were enrolled in the AAMI group. In order to avoid inclusion of a subject with advanced impairment who achieved qualifying performance on the CDR, we used performance on the Montreal Cognitive Assessment [36], a measure of general intellectual function, as well as the California Verbal Learning Test- II (CVLT-II) [37] as corroborative evidence of cognitive status. Prospective participants who exhibited grossly divergent scores on these objective measures were excluded. To assess the possibility of memory performance affected by mood, the 30-item Geriatric Depression Scale (GDS) [38], was administered. Individuals with scores indicative of a possible depressive disorder (>16) were further evaluated and excluded from the study and referred for further evaluation and treatment if indicated. Imaging data were acquired from 24 MCI and 15 AAMI participants, although one MCI dataset was excluded due to excessive distortion. Summary demographic and clinical information for the final sample is found in Table 1. Both groups were predominately Caucasian and female. The MCI group was significantly older than the AAMI group, in keeping with study design, and had fewer years of education. As would be expected, the MCI group scored significantly lower on both the CVLT-II and the Verbal Paired Associate Learning Test (V-PAL). Although the AAMI group was significantly heavier than the MCI group, no significant difference was evident for blood pressure, fasting insulin, and fasting glucose values. Level of depressive symptoms of this study sample was low and not significantly different between groups, mitigating concern over this possible confound. Neurocognitive testing Long-term memory function was assessed with the V-PAL. This task has been used in non-clinical standardization studies [39], in clinical research across the lifespan [40], and in intervention studies with MCI participants [2, 3]. The V-PAL also has been shown to be sensitive to neuropathology associated with cognitive decline in MCI [41]. The task consists of four learning/testing trials in which participants are asked to learn ten word pairs including five semanti-

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Table 1 Age1 Gender (% female) Race Education GDS& CVLT- II Cumul. Total1 V-PAL Hard Total1 Insulin (mg/dL)1 Glucose (uU/mL)1 Weight, kg1 BP systolic1 BP diastolic1

MCI (n = 23)

AAMI (n = 15)

75.3 (6.4) 74 19 W, 4 AA 14.1 (2.3) 3.9 (3.2) 40.6 (7.2) 6.7 (4.2) 17.0 (9.8) 103.0 (13.2) 70.9 (8.7) 133.4 (19.4) 76.7 (10.2)

69.6 (5.5) 67 14 W, 1 AA 16.3 (2.3) 3.5 (3.0) 51.0 (11.3) 11.3 (5.2) 13.6 (4.2) 104.5 (9.5) 80.3 (10.4) 129.5 (16.0) 78.4 (12.7)

p ∗∗

− −

∗∗

ns ∗∗ ∗∗

ns ns ∗∗

ns ns

1 Mean (SD), ∗∗ p ≤ 0.01, & one observation excluded. MCI, mild cognitive impairment; AAMI, age associated memory impairment; GDS, Geriatric Depression Scale; CVLT- II, California Verbal Learning Test; V-PAL, Verbal Paired Associate Learning Test; BP, blood pressure.

cally related, or easy associates, (e.g., “north-south”), and five semantically unrelated, or hard associates, (e.g., “village-copy”). Exposure to the easy associates involves priming existing semantic relationships to facilitate their recall, while learning the semantically unrelated hard associates requires new associative learning. The cumulative number of hard associates recalled across the four learning trials determined the V-PAL score, as this score reflected ability to form new associations. The CVLT-II [37], a list learning task consisting of presentation of 16 common words over five learning and testing trials, was an additional episodic memory measure that does not depend on associative learning. The total number of words recalled during the five trials served as the outcome measure for the CVLT-II, and this score paralleled the V-PAL cumulative learning scores in that it is the total of items recalled and does not incorporate a delay. Magnetic resonance imaging data Brain imaging and cognitive testing occurred during the same study visit. All imaging was performed at the University of Cincinnati Center for Imaging Research (CIR) using a 4.0 tesla Varian Unity INOVA whole body MRI/MRS system (Varian Inc., Palo Alto, CA). Padding was inserted around each participant’s head to minimize movement. A high-resolution, T1-weighted 3-D brain scan was obtained using a modified driven equilibrium Fourier transform (MDEFT) sequence (TMD = 1.1 s, TR = 13 ms, TE = 5.3 ms, FOV = 25.6 × 19.2 × 19.2 cm, matrix 256 × 192 × 96 pixels, flip angle = 20◦ ) [42]. Diffusion-weighted spin-echo echo planar-images were acquired using a 30 direction

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diffusion-encoding scheme with six non-diffusionweighted (B0) images [43]. A midsagittal localizer scan was obtained to place 30 slices that extend from the inferior cerebellum to encompass the entire brain (TR = 10s, TE = 89.2 ms, slice thickness = 4 mm, FOV = 25.6 cm × 25.6 cm, flip angle = 90◦ , slice orientation = axial, matrix size = 64 × 64, maximum b-value = 1000.65). A multi-echo reference scan was obtained and used for reduction of Nyquist ghosting and geometric distortion correction [44]. Raw scanner data were reconstructed and converted to AFNI (Analysis of Functional NeuroImages) [45] format using in-house software developed in IDL (Interactive Data Language). DTI data were co-registered using scanner coordinates to the highresolution anatomical scan, visually inspected for accurate co-registration, and manually aligned if necessary. Images were motion corrected with a twelve-parameter rigid body transformation and mutual information cost function [46]. Anisotropy measures were calculated in AFNI via the 3dDWItoDT and 3dDTeig programs. A focused, region of interest (ROI) approach was used, and regions were chosen a priori based on our previous studies in these populations [41]. Further, we chose an ROI approach to minimize the spatial warping that may occur in normalization of data [47]. Warping and resampling might alter the voxel-wise representation of the fornix, given its small size. For this reason and because of its proximity to the ventricular system (Fig. 1), we manually traced the fornix for each participant using an established protocol providing guidelines to define the posterior boundary [19, 48]. In brief, the tract was defined from the anterior commissure to “the point where the crus of the fornix can be seen in its entirety, extending inferiorlaterally to connect with the hippocampus” [48]. To exclude voxel contamination from cerebrospinal fluid (CSF), the high-resolution T1-weighted anatomical data was segmented using MATLAB and SPM5 Unified Segmentation software (Wellcome Department of Cognitive Neurology, UK [49]) to classify brain tissue into grey matter, WM, or CSF compartments. Voxels containing greater than 20% CSF in each individual’s fornix mask were excluded from analyses [50]. This resulted in ROIs with possible CSF contamination in the fornix of less than 2% for all individual datasets. The fornix, particularly when near the hippocampal formation, runs close to the stria terminalis, which contains fibers projecting from multiple regions to the amygdala. The manual tracing methods employed here avoided this region, because tracing does not include

Fig. 1. Regions of interest. Representative sample of principal regions of interest, the bilateral temporal stems and fornix. Inset is cross section at the level of the temporal stem region of interest.

the region that would likely include stria terminalis fibers. This also reduced concern over errant inclusion of grey matter of the hippocampus in the fornix ROI. Mean diffusion values for each ROI were computed, so participant-to-participant variation in ROI volume would not confound analysis. Other ROIs included regions known to be spared and affected early in MCI and AD pathology. Given the established sparing of the sensorimortor tracts and their function in MCI and early AD pathology [51–53], we sampled bilaterally in the posterior limb of the internal capsule (PLIC) as negative control. These ROIs were placed lateral to the thalamus and medial to the lateral globus pallidus and putamen. We also placed ROIs in the WM of the temporal stems (TS) based on protocols in previously published work [41]. There are multiple fiber bundles running through this region, namely Meyer’s loop, the inferior occipitofrontal fasciculus, and the uncinate fasiculus, all of which show reduced integrity in AD [14, 54]. ROI placement in the TS was based on methods previously described [41, 55]. We used established anatomical landmarks described as “the level of the amygdala anteriorly to the level of the lateral geniculate body posteriorly” [54] with preferential placement in the more anterior segment to avoid the optic radiations (Fig. 1). Non-fornix ROIs were 4 mm spheres. All ROIs were placed by a single rater blind to neurocognitive test performance. To determine reliability of the tracing, ten participant datasets were sampled randomly and traced by a second rater. Pearson correlations of MD values between the two raters showed

E.L. Boespflug et al. / Associative Learning and Fornix Decrement in MCI

significant correspondence between tracers (ß = 0.718, p = 0.019). Statistical approach Unpaired t-tests were used to compare clinical values by group (Table 1). All tests unless otherwise stated were performed with a maximum Type I error rate of ␣ = 0.05, two-sided. Lateralized homologues (PLIC, TS) were evaluated for collapsibility with paired samples t-tests, with right/left differences at p ≥ 0.05 considered significant. Neither of these regions was significantly different across homologues for MD values, so these data were collapsed into single (mean) variables for each subject and used in subsequent analyses. Comparison of diffusivity values for regions across groups was done using more conservative, nonparametric tests because of the difference in group sizes and non-normal distribution of diffusivity values for some of the regions. No systematic basis for the nonnormality of the data, such as misplacement of ROIs or excessive inclusion of non-WM voxels, was identified. To investigate the relationship between diffusivity in the TS and fornix regions, we co-plotted diffusivity values for each region and evaluated possible differences in variance via a ratio of variances between the two data pools using Levene’s test of equal variance. The implementation of this method allowed us to determine whether the difference in variability between groups for each region was statistically significant. Given that DTI is an indirect measure of pathology, we sought the functional significance associated with regionally disparate diffusion profiles between groups. We evaluated the relationship between diffusivity measures, MD, DR, and DA, in each ROI (TS, fornix, PLIC) and performance on the CVLT-II and V-PAL memory tasks. Specifically, we evaluated the relationship between the diffusivity measures and the total number of words recalled during the CVLT-II list learning trials and the cumulative number of semantically unrelated associates recalled during the V-PAL

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learning trails, as discussed. Given the age difference between these groups, our findings were also evaluated in the context of this possible confound. We expected that there would be an association between MTLdependent memory impairment in MCI (TS pathology present) but not in AAMI (TS WM sparing). RESULTS As can be seen in Table 2, group-wise comparisons of diffusivity revealed significant region-specific effects. In the fornix, there was higher mean (12.67 versus 23.96 median rank, Mann-Whitney U = 70.0, p < 0.01), radial (12.33 versus 24.17 median rank, Mann-Whitney U = 65.0, p < 0.01), and axial diffusivities (12.00 versus 24.39 median rank, Mann-Whitney U = 60.0, p < 0.01) in MCI relative to AAMI. In TS WM, there was higher mean (14.80 versus 22.57 median rank, Mann-Whitney U = 102.0, p = 0.04) and radial (14.80 versus 22.57, U = 98.0. p = 0.03) diffusivities in MCI, but no significant difference in DA. In contrast, there was no significant difference in MD in the sensorimotor region, but lower radial (24.47 versus 16.26, U = 247.0. p = 0.03) and higher axial (12.13 versus 24.30, U = 62.0 p < 0.01) diffusivities in MCI. We further explored the data by co-plotting diffusivity of the fornix and temporal stem regions (Fig. 2). In MCI, fornix MD values were consistently elevated and TS MD varied, whereas in AAMI, TS MD was uniformly low, while fornix MD varied (Fig. 2a). We evaluated possible differences in variance via a ratio of variances between the two data pools. Using Levene’s test of equal variance, we found that in both the fornix and TS, the distribution of MD values was significantly different across groups (F(36) = 7.99, p < 0.01 and F(36) = 7.63 and p < 0.01, respectively). To test the influence of age on these ratios, we first obtained ageadjusted values for fornix and TS MD via the residuals resulting from a regression analysis between age and either TS or fornix MD values. We then re-tested differences in variance using these age-corrected values.

Table 2 Group-based differences in regional diffusivity. Fornix MD, DR, and DA, and temporal stem MD and DR are higher in MCI relative to control. High MD, DR, and DA are reflective of poor tract health Fornix MD DR DA

Temporal Stem

AAMI

MCI

D&

12.67 12.33 12.00

23.96 24.17 24.39

+ + +

p ∗∗ ∗∗ ∗∗

AAMI

MCI

D&

14.80 14.53 16.80

22.57 22.74 21.26

+ + ns

Sensorimotor p ∗ ∗

ns

AAMI

MCI

D&

p

19.87 24.47 12.13

19.26 16.26 24.30

ns − +

ns ∗

∗∗

AAMI, age associated memory impairment; MCI, mild cognitive impairment; MD, mean diffusivity; DR radial diffusivity; DA, axial diffusivity. & Difference, MCI relative to AAMI; ∗∗ p ≤ 0.01 ∗ p ≤ 0.05.

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Fig. 2. Diffusivity profile. In MCI (circles), fornix MD is consistently mid/high (disease) while TS MD is varied. In AAMI (squares), TS MD is uniformly low (healthy) while fornix MD is varied (a). The variance of age-corrected values are significantly different across groups (F(36) = 7.71, p = 0.01 and F(36) = 7.22 and p = 0.01, for the TS and fornix, respectively). Analyses of diffusion components reveals the distribution pattern is largely attributable to radial (b) (fornix F = 6.62 p = 0.017, TS F = 7.74, p < 0.01), and not axial (c) (fornix F = 3.00, p = 0.09, TS F = 3.89, p = 0.06) diffusivities. Increased radial diffusivity is implicative of myelin pathology. Note ellipsoids are manually placed.

Fig. 3. Memory performance and temporal stem diffusivity. In MCI increasing MD (a) is associated with decreasing paired associate learning performance (p = 0.04, r = −0.44). Analyses of diffusion components in the MCI group found TS DA (p < 0.01, r = −0.63), but not DR (p = 0.14, r = −0.33) to be associated with memory performance (b, c, respectively). Together, these findings implicate a group-based profile for TS WM degradation associated with specific neurocognitive decline, such that TS WM degradation is concurrent with decrement in new relational learning in MCI. The stronger association of DA, rather than DR, implicates neuronal pathology in this learning decrement. Comparisons are of age-corrected values.

The variance of age-corrected values remained significantly different across groups (F(36) = 7.71, p = 0.01 and F(36) = 7.22 and p = 0.01, for the TS and fornix, respectively). This was notable given the observed difference in mean age between groups. These steps were also performed for DA and DR in these regions. It appeared that the majority of the MD effect was due to DR, as the distribution of DR but not DA in fornix and TS WM mirrored that of MD (Fig. 2). Levene’s Test confirmed the significant difference in distri-

bution between groups for DR (fornix F(36) = 9.09, p = 0.005, TS F = 8.33 p < 0.01). These relationships remained significant even when corrected for age (fornix F = 6.62 p = 0.017, TS F = 7.74 p < 0.01). In contrast, the distribution patterns of DA were not significantly distinct between groups. While Levene’s test for DA showed significantly different distributions for fornix (F = 5.10 p = 0.03) but not for TS (F = 3.55 p = 0.07), this effect was eliminated when controlling for age (fornix F = 3.00 p = 0.09, TS F = 3.89, p = 0.06).

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We performed partial correlation analyses controlling for age and found V-PAL performance to be associated with TS MD in MCI (p = 0.04, r = −0.44) and AAMI (p < 0.01, r = 0.67). In the MCI group, increasing TS MD is associated with decreasing memory performance (Fig. 3a), whereas in AAMI, there is a very narrow distribution of low TS MD values (data not shown), which results in the strong positive r value. Subsequent analyses in the MCI group revealed that age-corrected DA (p = 0.002, r = −0.63), but not DR (p = 0.14, r = −0.33) was associated with V-PAL performance (Fig. 3b, c, respectively). We found no significant association between TS or fornix MD, DA, or DR and CVLT-II performance, and no association between fornix MD, DR, or DA and VPAL performance. We found no significant association between sensorimotor WM and CVLT-II or V-PAL performance.

DISCUSSION We present data indicating a clinical subgroupspecific profile of WM tracts serving the medial temporal lobe. In addition, we report specific decrement of new associative learning in MCI associated with this WM profile. This corroborates the cognitive effect of early WM decline and the potential usefulness of this type of memory task in clinical assessment of age-related neurocognitive decline. We identified group-wise differences in regional diffusivity based on disease status (Table 2). Specifically, we found MD, DR, and DA in the fornix to be higher in individuals with MCI relative to those with AAMI. We also observed significantly higher MD and DR in the TS in MCI relative to AAMI. High MD, DR, and DA, are indicative of WM pathology [6–9]. These results are consistent with previous reports of altered diffusivity profiles in fornix and TS WM associated with progressive loss of memory function in MCI and AD [12, 15, 18–22]. Subsequent analyses provide strikingly different patterns of the relative distribution of MD and DR in fornix and TS WM. Figure 2a shows that in AAMI, TS MD is uniformly low (indicative of relative WM health), while the distribution of fornix MD values is varied. In contrast, in MCI, the distribution of TS MD is varied, while fornix MD values are consistently elevated. Evaluation of the diffusion components DR and DA further elucidate a potential mechanism, as MD findings are attributable predominately to the radial components of the tensor (Fig. 2b, c). Alterations in DR

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are associated with myelin-specific pathology [56–58]. Based on these data, one might speculate that myelinspecific pathology affecting WM is principally evident in the fornix (as we see in AAMI), and at clinical threshold, that is, when a clinically significant decline in cognitive function is detected, we see pathology in the TS WM (as seen in the distribution pattern of MCI subjects). Confirmation of these considerations will require longitudinal evaluation of these tracts over the course of disease progression. The notion of regional WM involvement in MCI was reinforced in the evaluation of the clinical implication of our diffusivity findings. In MCI, we found a negative relationship between TS WM diffusivity and performance on a task involving new associative learning (Fig. 3), but not list learning. While both tasks assess verbal episodic learning and show diminished performance in dementia, new associative learning requires integrated activation of the hippocampus and parahippocampal structures that are vulnerable in early neurodegeneration [60]. TS MD and DA, but not DR, were associated with performance on the V-PAL (Fig. 3b, c). In contrast, a strongly positive r value and consistently low MD was found in the AAMI group, indicating that the TS MD values were too low (indicative of health) to have a meaningful distribution with respect to the clinical scores. In MCI, but not AAMI, we see evidence of a neuronal-based (as opposed to myelin-based) pathology of the TS associated with specific decrement in new associative learning. Interestingly, although MCI participants showed high fornix diffusivity, we did not observe significant associations between fornix diffusivity and performance on the memory tasks. However, we did find functional decrement associated with neuronal pathology in TS WM in participants with clinical impairment (MCI) while this was not observed in AAMI. Regional differences between clinical groups (Fig. 2) and clinical correlates of diffusion indices (Fig. 3) could be interpreted as early fornix permeability (consistently high MD and DR values), followed by neuronal pathology of the TS, the latter being manifest in functional decrement in associative learning. It would be of interest to track clinical progression and associated changes in these regions over time in this cohort. A number of groups have identified WM disease progression that is consistent with the proposed mechanism (early fornix pathology associated with progressive cognitive decline in MCI) in longitudinal designs [12, 20]. Given the increasing number of studies identifying WM decline, particularly hippocampal afferents including the fornix, in early AD

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pathology, additional investigation into the clinical significance of this fiber tract in early cognitive decline is warranted. Novel imaging and analyses tools, including fiber tracking and fiber orientation-based analyses [59], might be utilized in resolving specific WM tracts and their involvement in clinical changes. Of particular interest to interpretation of these data is that both regional WM diffusivity and the relationships between TS mean diffusivity and specific cognitive decline persist beyond the effects of age. That is, even when the effect of age (the strongest predictor of AD symptoms) was controlled, we observed a specific profile of regional diffusivity and specific cognitive ability. This suggests that other etiological factors (environmental and genetic) may account for the risk profile of WM degradation. The distinct distribution profiles (Fig. 2) as well as the relationships between memory performance and diffusivity (Fig. 3) was found for MD but not FA, another commonly reported composite diffusivity measure. It is possible that although there is measureable change in diffusivity, there is not sufficient disruption of tract orientation (indexed by FA) in the very early stages of cognitive decline. This interpretation is consistent with other reports identifying MD, as opposed to FA, as a more sensitive marker of decreased WM integrity in this population [15]. As discussed, medial temporal lobe structures are vulnerable to early pathology, so it is not surprising that we found effects specific to this region and tracts connecting these regions to the rest of the brain. However, novel findings include 1) the clinical group-specific profile of TS and fornix pathology, with myelin-specific fornix degradation implicated in MCI, and 2) the correlation between diffusivity implicating neuronal pathology in the TS and paired associative learning, but not with list learning exclusively in MCI participants. In addition to reinforcing the implicated role of WM in disease progression, this finding is pertinent to the development of clinical tools for establishing the presence of MCI.

bility of the authors and does not necessarily represent the official views of the NIH. The authors would like to gratefully acknowledge the research study participants as well as Elizabeth M. Fugate, Marcelle Shidler, Jonathan Dudley, Amanda Stover, and Judd Storrs for their assistance in data acquisition and processing. Authors’ disclosures available online (http://www.jalz.com/disclosures/view.php?id=2131). REFERENCES [1]

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This research was supported by grants from the NIH National Institute on Aging (R01 AG 0341516), the US Highbush Blueberry Council, and Welch Foods, Inc., and the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant 8 UL1 TR000077-05. The content is solely the responsi-

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