Introduction
In recent years, advances in acquisition and analysis of high resolution magnetic resonance imaging (MRI) data have allowed for the precise and reliable visualization and quantification of normal and abnormal cerebral white matter. Two important MRI sequences that can be used to quantify white matter include diffusion tensor imaging (DTI), and T2-weighted imaging. Both imaging modalities have been used in the context of cognitive aging and dementia research, but few studies have considered them simultaneously.
Diffusion tensor imaging provides clues about white matter microstructure by exploiting the differential diffusion of water across tissue types (Mori & Barker, Reference Mori and Barker1999; Pierpaoli & Basser, Reference Pierpaoli and Basser1996). Because fractional anisotropy (FA), the most common DTI metric, reflects water molecule coherence, it can be used to estimate the orientation and integrity of fiber bundles (Basser, Pajevic, Pierpaoli, Duda, & Aldroubi, Reference Basser, Pajevic, Pierpaoli, Duda and Aldroubi2000; Mori & Barker, Reference Mori and Barker1999; Mori, Crain, Chacko, & van Zijl, Reference Mori, Crain, Chacko and van Zijl1999). Decreased FA has been shown to be related to poorer performance across several cognitive domains in healthy older individuals (Madden, Bennett, & Song, Reference Madden, Bennett and Song2009; Penke & Deary, Reference Penke and Deary2010; Vernooij et al., Reference Vernooij, Ikram, Vrooman, Wielopolski, Krestin, Hofman and Breteler2009) and across the adult lifespan (Grieve, Williams, Paul, Clark, & Gordon, Reference Grieve, Williams, Paul, Clark and Gordon2007), but the exact mechanisms linking cognitive functioning with white matter microstructural integrity are not fully understood. Lowered FA could result from localized microstructural processes that affect single tracts in some individuals, but it could also be to some extent a global phenomenon that affects many tracts simultaneously (Wahl et al., Reference Wahl, Li, Ng, Lahue, Cooper, Sherr and Mukherjee2010).
Diffusion tensor imaging has also been used to examine white matter microstructural changes associated with neurodegenerative conditions, such as Alzheimer's disease (AD). Measures of white matter integrity have been reported to be lower among patients with AD relative to controls in several regions throughout the brain (Bozzali et al., Reference Bozzali, Falini, Franceschi, Cercignani, Zuffi, Scotti and Filippi2002; Damoiseaux et al., Reference Damoiseaux, Smith, Witter, Sanz-Arigita, Barkhof, Scheltens and Rombouts2009; Huang & Auchus, Reference Huang and Auchus2007; Medina et al., Reference Medina, DeToledo-Morrell, Urresta, Gabrieli, Moseley, Fleischman and Stebbins2006; Naggara et al., Reference Naggara, Oppenheim, Rieu, Raoux, Rodrigo, Dalla Barba and Meder2006; Takahashi et al., Reference Takahashi, Yonezawa, Takahashi, Kudo, Inoue and Tohgi2002; Xie et al., Reference Xie, Xiao, Gong, Zang, Wang, Wu and Jiang2006), and to correlate with the degree of cognitive impairment (Bozzali et al., Reference Bozzali, Falini, Franceschi, Cercignani, Zuffi, Scotti and Filippi2002; Duan et al., Reference Duan, Wang, Xu, Lin, Chen, Kang and Yao2006). Despite observations of FA reduction among patients with AD, findings regarding the regional specificity of these observations have been less consistent. As variance in white matter integrity estimated with FA is highly collinear across tracts, some authors have suggested deriving summary measures of total white matter integrity to maximize the reliability of these measures (Penke et al., Reference Penke, Munoz Maniega, Murray, Gow, Hernandez, Clayden and Deary2010). In the current study, we derive a general white matter integrity summary factor as the primary DTI measure of interest.
Whereas DTI can be used to estimate variability in the integrity of white matter due to aging, disease, or inter-individual variability, white matter hyperintensities (WMH) are a reflection of frank macrostructural white matter pathology. White matter hyperintensities most likely reflect small vessel cerebrovascular disease, as shown in pathological studies (Fazekas et al., Reference Fazekas, Kleinert, Offenbacher, Schmidt, Kleinert, Payer and Lechner1993; Gouw et al., Reference Gouw, Seewann, van der Flier, Barkhof, Rozemuller, Scheltens and Geurts2011; C.D. Smith, Snowdon, Wang, & Markesbery, Reference Smith, Snowdon, Wang and Markesbery2000), and suggested by their strong associations with vascular risk factors, such as high or fluctuating blood pressure, diabetes, and heart disease (Brickman et al., Reference Brickman, Reitz, Luchsinger, Manly, Schupf, Muraskin and Mayeux2010; Brickman, Schupf, et al., Reference Brickman, Schupf, Manly, Luchsinger, Andrews, Tang and Brown2008; DeCarli et al., Reference DeCarli, Miller, Swan, Reed, Wolf, Garner and Carmelli1999; Liao et al., Reference Liao, Cooper, Cai, Toole, Bryan, Hutchinson and Tyroler1996, Reference Liao, Cooper, Cai, Toole, Bryan, Burke and Heiss1997; Manolio et al., Reference Manolio, Kronmal, Burke, Poirier, O'Leary, Gardin and Bryan1994; Meyer et al., Reference Meyer, Rauch, Crawford, Rauch, Konno, Akiyama and Haque1999; Raz, Rodrigue, Kennedy, & Acker, Reference Raz, Rodrigue, Kennedy and Acker2007; Strassburger et al., Reference Strassburger, Lee, Daly, Szczepanik, Krasuski, Mentis and Alexander1997; Viswanathan, Rocca, & Tzourio, Reference Viswanathan, Rocca and Tzourio2009). They are more prevalent and severe among patients with AD than controls (Barber et al., Reference Barber, Gholkar, Scheltens, Ballard, McKeith and O'Brien1999; Capizzano et al., Reference Capizzano, Acion, Bekinschtein, Furman, Gomila, Martinez and Starkstein2004; Scheltens et al., Reference Scheltens, Barkhof, Valk, Algra, van der Hoop, Nauta and Wolters1992; Tanabe et al., Reference Tanabe, Amend, Schuff, DiSclafani, Ezekiel, Norman and Weiner1997) and have been shown to be associated with a precipitous cognitive decline among patients with prevalent AD (Brickman, Honig, et al., Reference Brickman, Honig, Scarmeas, Tatarina, Sanders, Albert and Stern2008; Burns et al., Reference Burns, Church, Johnson, Xiong, Marcus, Fotenos and Buckner2005; Yoshita et al., Reference Yoshita, Fletcher, Harvey, Ortega, Martinez, Mungas and DeCarli2006). Individuals at greatest risk for the development of AD have increased WMH volume relative to those at lesser risk (Luchsinger et al., Reference Luchsinger, Brickman, Reitz, Cho, Schupf, Manly and Brown2009). White matter hyperintensities are distributed disproportionately in frontal (Capizzano et al., Reference Capizzano, Acion, Bekinschtein, Furman, Gomila, Martinez and Starkstein2004) and parietal regions (Gootjes et al., Reference Gootjes, Teipel, Zebuhr, Schwarz, Leinsinger, Scheltens and Hampel2004), and while generally emerging as homogenous signal on MRI, the regional distribution might point to distinct underlying pathological features that might play a unique role in AD (Brickman, Muraskin, & Zimmerman, Reference Brickman, Muraskin and Zimmerman2009).
While most previous studies have examined the relation of either WMH or FA with cognition or AD, few have examined whether one is a better predictor of cognitive function among older adults than the other in the same sample. Our overall aim was to examine the role of white matter microstructure variability (estimated with DTI) and markers of white matter pathology (appreciated through examination of WMH) in cognitive aging and dementia. First, we were interested in examining the association between FA and WMH to determine to what extent the two measures reflect the same process. Next, we examined the utility of regional WMH and DTI-derived FA to predict cognitive functioning among older adults; we were interested in testing whether FA and WMH were independently related to specific cognitive functions, or whether one marker of white matter was a better predictor of cognitive function than the other one. Finally, we compared WMH volume and DTI-derived FA across three diagnostic categories (i.e., normal control, mild cognitive impairment [MCI], and AD) that were determined independently of imaging findings. Based on findings from the extant literature (Brickman et al., Reference Brickman, Muraskin and Zimmerman2009; Luchsinger et al., Reference Luchsinger, Brickman, Reitz, Cho, Schupf, Manly and Brown2009; Smith et al., Reference Smith, Egorova, Blacker, Killiany, Muzikansky, Dickerson and Guttmann2008), we expected higher WMH burden to be associated with a more severe degree of cognitive impairment and vary as a function of diagnostic groups. Specifically, although WMH burden is often linked to executive dysfunction (Gunning-Dixon & Raz, Reference Gunning-Dixon and Raz2000), an emerging literature suggests a role of WMH in the pathogenesis of AD, a neurodegenerative condition marked primarily by memory impairment. Thus, we hypothesized that there would be an association between WMH and the AD “phenotype” as a quantitative trait (i.e., memory function as a continuous measure) or as a diagnostic entity. Similarly, we expected lower FA to be associated with poorer cognitive functioning, either defined continuously or as diagnostic entity. As FA and WMH ostensibly reflect distinct aspects of white matter (i.e., micro- and macrostructure), we hypothesized that each to be independently associated with cognition.
This imaging substudy is part of a larger effort that seeks to elucidate unique genes involved with AD pathogenesis among African Americans. Increased incidence and prevalence rates of AD and cognitive dysfunction among African Americans have been reported in the literature (Gurland et al., Reference Gurland, Wilder, Lantigua, Stern, Chen, Killeffer and Mayeux1999; Perkins & Schisterman, Reference Perkins and Schisterman2006; M.X. Tang et al., Reference Tang, Cross, Andrews, Jacobs, Small, Bell and Mayeux2001; Unverzagt, Hall, Torke, & Rediger, Reference Unverzagt, Hall, Torke and Rediger1996). Our belief is that one potential source of discrepancies across racial groups in diagnosis could be due to differences in cerebrovascular disease. While we do not include members of different racial groups in the current study for explicit comparison, African Americans have been severely under-represented in most large-scale neuroimaging studies of cognitive aging and dementia and we believe that it is important to establish brain-behavior relationships in this group.
Methods
The parent study is a multi-site effort that includes the participation of investigators at Columbia University, Duke University, North Carolina A&T State University, Vanderbilt University, and University of Miami. A subset of participants at the Columbia University site was recruited for participation in an MRI sub-study. The current report focuses on these individuals. This study was approved by an Institute Institutional Review Board at Columbia University Medical Center.
Subjects
Inclusion criteria for the parent study included age greater than 50 years; self-identification as Black or African American and non-Hispanic; and fluent in English. Participants in the MRI sub-study gave written informed consent for MRI scanning and did not have contraindications.
Neuropsychological Evaluation
Participants were administered a neuropsychological battery, which included representative tests from several cognitive domains. The battery comprised the following tests: Mini-Mental State Exam (Folstein, Folstein, & McHugh, Reference Folstein, Folstein and McHugh1975); Digit Symbol subtest of the Wechsler Adult Intelligence Scale - Revised (Wechsler, Reference Wechsler1997); Trailmaking Test Parts A and B (Reitan, 1978); Digit Span Forward and Backward (Wechsler, Reference Wechsler1997); California Verbal Learning Test-2 (CVLT-II; Trials 1–5 free recall, short delayed free recall, long delayed free recall, recognition hits-false positives) (Delis, Kramer, Kaplan, & Ober, Reference Delis, Kramer, Kaplan and Ober2000); Logical Memory Test (immediate recall, delayed recall) (Wechsler, Reference Wechsler1997); Rey-Osterrieth Complex Figure Test (immediate recall, delayed recall) (Osterrieth, Reference Osterrieth1944); Controlled Oral Word Association Test (CFL; (Benton & Hamsher, Reference Benton and Hamsher1976)); Category Fluency Test (animal and vegetable naming; (Goodglass, Reference Goodglass and Kaplan1983; Morris et al., Reference Morris, Heyman, Mohs, Hughes, van Belle, Fillenbaum and Clark1989); and 30-item Boston Naming Test (Kaplan, Goodglass, & Weintraub, Reference Kaplan, Goodglass and Weintraub1983; Weintraub et al., Reference Weintraub, Salmon, Mercaldo, Ferris, Graff-Radford, Chui and Morris2009). All neuropsychological data were analyzed in raw form.
Participants underwent a diagnostic work-up, including a medical history, assessment of functional status and memory complaints, and a neurological exam. Informants were interviewed whenever available and asked about functional status (Pfeffer, Kurosaki, Harrah, Chance, & Filos, Reference Pfeffer, Kurosaki, Harrah, Chance and Filos1982), neuropsychiatric symptoms (Cummings, Mega, Gray, Rosenberg-Thompson, & Carusi, Reference Cummings, Mega, Gray, Rosenberg-Thompson and Carusi1994), and symptom onset (Sano, Stern, Mayeux, Hartman, & Devanand, Reference Sano, Stern, Mayeux, Hartman and Devanand1987). These data, along with performance on the neuropsychological battery, were reviewed at a diagnostic consensus conference that included at least two attending neuropsychologists with expertise in this area. Neuroimaging data were not considered in the diagnostic formulation. Participants were categorized as normal controls, mild cognitive impairment (MCI) (Petersen et al., Reference Petersen, Smith, Waring, Ivnik, Tangalos and Kokmen1999), or as meeting criteria for dementia (American Psychiatric, Reference American Psychiatric1994). Probable AD was diagnosed following established research criteria (McKhann et al., Reference McKhann, Drachman, Folstein, Katzman, Price and Stadlan1984). Some participants had evidence of some cognitive impairment which the diagnosticians did not believe that they met MCI criteria, but rather that the pattern of cognitive performance reflected lifelong functioning. For these participants, a diagnosis of “cognitive impairment not MCI” was assigned.
Participants were asked whether they had ever been diagnosed with or whether they were currently being treated for hypertension, diabetes mellitus, myocardial infarction, congestive heart failure, any other heart disease, and/or stroke. These dichotomous variables were summed to create a single peripheral vascular disease summary score, similar to what we have done in previous studies (Brickman, Schupf, et al., Reference Brickman, Schupf, Manly, Luchsinger, Andrews, Tang and Brown2008).
Neuroimaging
Magnetic resonance imaging (MRI) was acquired with a 1.5 Tesla Philips Intera scanner at Columbia University. The following images were acquired in the axial orientation: high-resolution three-dimensional (3D) T1-weighted anatomical (echo time/repetition time [TE/TR]: 2.1/20, field of view [FOV]: 240, Matrix: 256 × 256, flip angle: 20°, slice thickness: 1.3 mm, slices: 105), fluid attenuated inverse recovery (FLAIR) T2-weighted (TE/TR: 5500/144, IR: 1900, FOV: 250, Matrix: 192 × 256, slice thickness: 3 mm, slices: 47 no gap), 16-direction DTI (TR/TE: 10624, FOV: 224, Matrix: 112 × 112, slice thickness: 2 mm, slices: 70 no gap). Other MRI sequences were collected as part of the study, including gradient echo and T2-weighted (without fluid attenuation), but were not used in the current analyses.
White Matter Hyperintensity Quantification
White matter hyperintensity volumes were derived on T2-weighted FLAIR images as described previously (Brickman et al., Reference Brickman, Muraskin and Zimmerman2009, Reference Brickman, Sneed, Provenzano, Garcon, Johnert, Muraskin and Roose2011; Oliveira, Brickman, Provenzano, Muraskin, & Reference Oliveira, Brickman, Provenzano, Muraskin and LouisLouis, in press). Briefly, T1-weighted images were segmented into grey matter, white matter, and cerebrospinal fluid tissue classes with SPM. The normalization parameters were applied to the T2-weighted and T1-weighted images to transform them into standardized atlas space. FLAIR images were skull stripped/brain extracted using the segmented tissue classes from the T1-weighted image. On each FLAIR image, WMH seeds were defined as areas that were 2.7 SD or greater above the mean intensity value of the entire image. Once seeds were placed, each one passed through a mean intensity-based region-growing algorithm that used a 10-point connectivity scheme to label adjacent voxels that fell within 5% of the mean intensity value for the seed. The newly labeled voxels were added to the labeled voxels and a new intensity mean was calculated. This process continued iteratively until all hyperintense voxels were labeled. White matter hyperintensity volume was the sum of all labeled voxels multiplied by voxel dimensions. To calculate regional (i.e., frontal, temporal, parietal, and occipital) WMH volumes, an anatomical atlas (Admiraal-Behloul et al., Reference Admiraal-Behloul, Olofesen, Van den Heuvel, Schmitz, Reiber and Van Buchem2004) was spatially normalized with the inverse transform matrix generated from the segmentation of the T1-weighted image to each participant's labeled FLAIR image. Each region within the anatomical atlas was defined by a unique identification parameter and intersection of the labeled WMH with the unique anatomical label defined the regional WMH volume. Figure 1 displays an example of WMH labeling for a single subject.
Fig. 1 Example of regional white matter hyperintensities (WMH) quantification for one subject. Left top: raw T2-weighted FLAIR image. Other panels: Orthogonal view of labeled image. White matter hyperintensities are labeled with red/yellow. Colors correspond to cerebral lobes (green: frontal, brown: parietal, dark green: temporal, blue: occipital, mauve: cerebellum).
Diffusion Tensor Imaging
Diffusion tensor imaging data were processed with the fMRI Software Library (FSL 4.1.3. release; www.fmrib.ox.ac.uk/fsl) fMRIB Diffusion Toolbox and tract based spatial statistics (TBSS) (Smith et al., Reference Smith, Jenkinson, Woolrich, Beckmann, Behrens, Johansen-Berg and Matthews2004, Reference Smith, Jenkinson, Johansen-Berg, Rueckert, Nichols, Mackay and Behrens2006). Briefly, after individual subjects raw DTI images were aligned to the b = 0 image (Jenkinson, Bannister, Brady, & Smith, Reference Jenkinson, Bannister, Brady and Smith2002), a mean FA skeleton was created by thinning the group mean FA image. This skeleton is a representation of the centers of the white matter tracts common to the group. All FA images were then nonlinearly transformed into the space of the mean image. For each subject, local FA maxima were projected onto the group template skeleton (Smith et al., Reference Smith, Jenkinson, Johansen-Berg, Rueckert, Nichols, Mackay and Behrens2006). Next, we used JHU White Matter Label Map included with FSL to identify sections of the white matter skeleton that represent a number of white matter fiber tracts in both brain hemispheres (listed in Figure 2 caption). Average FA values for each white matter tract were calculated from the white matter skeleton and values from the left and right hemispheres were averaged together where applicable. Figure 2 displays the regions of interest superimposed on the skeletonized map.
Fig. 2 “Skeletonized” diffusion tensor imaging (DTI) map indicating fractional anisotropy (FA) regions of interest (ROIs; colors) used to derive the summary FA score superimposed onto the mean FA skeleton (green) of the DTI subjects. The summary score comprised the following tracts: middle cerebellar peduncles, pontine crossing tracts part of the middle cerebellar peduncles, genu of the corpus callosum, body of the corpus callosum, splenium of the corpus callosum, fornix column and fornix body, corticospinal tract, medial lemniscus, inferior cerebellar peduncles, superior cerebellar peduncles, cerebral peduncles, anterior limb of the internal capsule, posterior limb of the internal capsule, retrolenticular part of the internal capsule, anterior corona radiata, superior corona radiata, posterior corona radiata, posterior thalamic radiation including optic radiation, sagittal striatum including the inferior longitudinal fasciculus, external capsule, cingulum-cingulate gyrus, cingulum hippocampus, fornix cres stria terminalis, superior longitudinal fasciculus, uncinate fasciculus, and the tapetum.
Given the collinearity among FA measurements in multiple tracts, our interest in global measures of white matter integrity, and the lack of reported regional consistency in the extant literature, we focused on a summary tract measure as a marker of global white matter integrity. Furthermore, a derived summary measure for FA increases the reliability of measurement because it comprises several tracts and helps minimize the number of statistical comparisons. Following procedures put forth by Penke and colleagues (Penke et al., Reference Penke, Munoz Maniega, Murray, Gow, Hernandez, Clayden and Deary2010), mean FA values from each ROI were subjected to a principal component analysis and the first derived factor, which explained 54.9% of the total variance, was used in subsequent analyses. Apart from four tracts, factor loadings were all greater than 0.64. Factor loadings that were less than 0.64 came from pontine crossing-tracts part of the middle cerebellar peduncles, corticospinal tract, inferior cerebellar peduncles, and the tapetum, which ranged in factor loading from 0.284 to 0.482. In post hoc exploratory analyses we did, however, also examine individual tracts that have been implicated in AD, including inferior longitudinal fasciculus, uncinate fasciculus, fornix, and genu of the corpus callosum (Kiuchi et al., Reference Kiuchi, Morikawa, Taoka, Nagashima, Yamauchi, Makinodan and Kishimoto2009; Liu et al., Reference Liu, Spulber, Lehtimaki, Kononen, Hallikainen, Grohn and Soininen2011; Pievani et al., Reference Pievani, Agosta, Pagani, Canu, Sala, Absinta and Filippi2010; Stricker et al., Reference Stricker, Schweinsburg, Delano-Wood, Wierenga, Bangen, Haaland and Bondi2009).
Relative Brain Volume
A measure of total relative brain volume was derived with SPM5 (Wellcome Department of Cognitive Neurology, London, UK). Total relative brain volume was the sum of voxels labeled as grey matter and white matter as a ratio to the sum of grey matter, white matter, and cerebrospinal fluid, which were derived using default settings. This measure was used in statistical analyses to control for individual differences in atrophy.
Statistical analysis
Descriptive demographic data, including age, sex distribution, and number of years of education, were generated for the entire imaging sample and compared across diagnostic subgroups with analysis of variance (ANOVA) and χ2 tests. Demographic features were also compared between the MRI sub-study participants and the remaining Columbia University site participants who did not participate in the MRI sub-study (n = 325, at the time of analysis). Descriptive statistics for the neuroimaging markers and performance on the neuropsychological tests were also generated and compared with ANOVA across groups. For statistical tests to address the study hypotheses, both parametric and non-parametric procedures were used. The relationship between regional WMH volumes and the FA factor score was examined with bivariate Pearson and Spearman correlations. A series of multiple linear regression analyses was run to examine associations of the white matter variables with the neuropsychological outcomes. By including the white matter measures (i.e., WMH and FA) simultaneously in the multiple regression analyses, we were able to compare explicitly the relative and independent association of each with cognition. For these analyses, all subject subgroups were combined and the lobar WMH volumes and FA measure were entered simultaneously and each of the neuropsychological outcomes was examined as dependent variables in separate analyses. Each regression model controlled for age, years of education, and relative brain volume by entering these factors as additional predictors. For all analyses, non-parametric tests were used as secondary analyses, as WMH volumes are typically not normally distributed. Given that covariate analyses are not possible with non-parametric statistical procedures, we considered the parametric tests as primary and the non-parametric tests (without covariates) as confirmatory. Although it is often possible to normalize positively skewed distributions by taking the log transformation of the data, in our case there was a large proportion of individuals with zero values for WMH volumes in temporal and occipital lobe, making transformation to normality of distribution impossible. In a post hoc exploratory manner, we re-ran the analyses with four fiber tracts previously implicated in AD entered individually or together instead of the FA summary measure.
To examine group differences across controls, those with MCI, and those with AD in regional WMH and global FA we constructed two general linear models. Participants classified as “impaired not MCI” were excluded from these analyses. We chose to exclude these individuals from the group analyses because they did not fit into the pre-defined diagnostic categories. We included them in the individual-differences analyses (described above) because we were interested examining white matter correlates of cognition among older adults in general. For regional WMH analysis, Diagnostic Group (NC, MCI, and AD) was a between-groups factor and Lobe (frontal, temporal, parietal, and occipital) was a within-subjects factor. For FA, we ran a similar analysis with Diagnostic Group as the independent variable and the FA factor score as the dependent variable. This analysis was re-run with the four AD-specific FA fiber tracts instead of the FA summary measure. Both analyses controlled for age, education, and relative brain volume. Non-parametric Kruskal-Wallis tests were used to verify significant group differences.
Results
Subject Characteristics
Table 1 displays demographic data for the entire sample and cognitive subgroups. Impaired not MCI subjects were younger than all other groups, normal controls and MCI subjects were similar in age, and patients with AD were older than all other groups (significant main effect of Diagnostic Group, F(3,108) = 7.724; p < .001). The groups were similar in sex distribution (χ2(3) = 0.457; p = .928) and vascular disease histories (F(3,99) = 0.848; p = .471). Patients with AD and MCI had fewer years of education than controls, whereas Impaired not MCI, MCI, and AD participants had similar number of years of education (significant main effect of Diagnostic Group, F(3,106) = 4.865; p < .001). As expected, AD patients had lower Mini-Mental State Examination (MMSE) scores than MCI participants, who in turn, had lower MMSE than controls. Impaired not MCI subjects and controls had similar MMSE scores (main effect of Diagnostic Group F(3,105) = 10.17; p < .001).
Table 1 Subject demographic data
In terms of representativeness of the MRI sample in the Columbia site sample, t-tests showed no differences for education (t(434) = 0.592; p = .554) and MMSE total score (t(435) = 1.552; p = .121), but subjects receiving MRI scans were slightly older (t(435) = 3.048; p = .002). χ2 tests showed no difference for sex of the participants (χ2(1) = 0.181, p = 0.671), but there was a difference in the distribution of diagnostic groups (χ2(3) = 13.403, p = .004). In the imaging sample, there was a greater representation of individuals with AD (8% vs. 5%) and individuals who were classified as Impaired not MCI (10% vs. 2%) and lesser representation of normal controls (54% vs. 63%) compared with the remaining subjects.
Omnibus differences for the morphological measures and neuropsychological variables are reported in Table 2. The groups differed in frontal, temporal, and occipital WMH volume as well as FA and performance across all neuropsychological tests. Note that these comparisons were made for descriptive purposes and did not include relevant covariates necessary for inference.
Table 2 Descriptive subject neuroimaging and neuropsychological data
Note. Omnibus effects across the four diagnostic groups are reported. Note that no covariates are included in these statistical analyses.
Of the 112 subjects included in the study, DTI data were available for 89 subjects. Diffusion tensor imaging was not available for subjects either due to scan artifacts or because fatigued subjects requested to discontinue the imaging study before the acquisition of the DTI sequence. Individuals with and without DTI available were similar in terms of age (t(110) = 0.423; p = .673), education (t(108) = 1.183; p = .239), sex distribution (χ2(1) = 0.457; p = .499), and distribution of diagnosis (χ2(3) = 1.490; p = .685).
Relationship Between Regional WMH and FA
Lower FA was associated with higher WMH volume in frontal (r = −0.509; p < .001), temporal (r = 0.464; p < .001), parietal (r = −0.509; p < .001), and occipital (r = −0.275; p = .009) lobes. Results of the non-parametric comparisons yielded similar effect sizes, although FA comparisons with temporal and occipital WMH were reduced to non-significance.
Relationship of Regional WMH and FA With Cognition
Increased WMH volume in the frontal lobes was associated with poorer performance on the CVLT long delayed free recall trial (overall model F(8,78) = 2.632; p = .014). For illustration, the plot of the association between frontal lobe WMH and CVLT long delayed free recall is displayed in Figure 3. This association remained significant when tested with simple bivariate correlations (Spearman's r = −0.213; p = .022). When the vascular disease summary score was entered into the regression analysis, the overall model was reduced to non-significance, but the relationship between frontal WMH volume and performance remained of similar magnitude (i.e., standardized beta of −0.423 vs. −0.329). Similarly, increased WMH volume was associated with poorer performance on the CVLT recognition trial (overall model (F(8,78) = 3.889; p = .001), which was also verified with simple bivariate correlations (Spearman's r = −0.195; p = .050). This association remained statistically significant when the vascular disease summary score was entered into the regression analysis. Increased WMH volume in the temporal lobes was associated with worse performance on the Trailmaking Test Part A and increased WMH volume in the occipital lobes was associated with better performance on the Trailmaking Test Part A (overall model F(8,78) = 20.737; p < .001). However, when examining these associations with simple bivariate correlations, neither temporal lobe WMH (Spearman's r = 0.036; p = .717) nor occipital lobe WMH (Spearman's r = 0.013; p = .895) was associated with performance on the Trailmaking Test Part A. Increased frontal lobe WMH was associated with poorer performance on the Digit Span forward test, although the overall model was not statistically significant (F(7,32) = 1.581; p = .187) nor was the bivariate correlation (Spearman's r = −0.257; p = .115). However, as the Digit Span test was only available for a subset of subjects, the lack of statistical significance may reflect insufficient power. Increased WMH in the temporal lobes was unexpectedly associated with better performance on the Logical Memory delayed trial (overall model F(8,77) = 4.472; p < .001) but this association was not confirmed with simple bivariate correlations (Spearman's r = −0.179; p = .074). Similarly, increased WMH in temporal lobes was associated with poorer performance on the Boston Naming Test (overall model F(8,77) = 4.472; p < .001), this association was not confirmed with simple bivariate correlations (Spearman's r = −0.101; p = .316). Results from all of the regression analyses are presented in Table 3. The FA summary measure was not associated with cognitive test performance in any of the analyses. When the analyses were re-run with the four AD-specific fiber tracts, none was associated reliably with cognitive test performance either when entered individually or together in a single model. In summary, only the significant relationship between higher WMH burden and lower delayed free memory recall was confirmed by non-parametric statistics.
Fig. 3 Scatterplot showing relationship between frontal lobe white matter hyperintensities (WMH) volume and performance on the long delay recall trial of the California Verbal Learning Test (CVLT). The plot is a partial plot displaying the results from the multiple regression analysis with WMH volumes from the other regions, the FA summary score, relative brain volume, age, and education included as additional covariates. The relationship between frontal lobe WMH volume and performance on the long delay recall trial of the CVLT remained even after removal of data from the subject who appears in the upper left side of the scatterplot.
Table 3 Standardized beta weights and p values (in parentheses) for neuropsychological test performance across diagnostic groups
Note. Results are from separate multiple regression analyses run for each of the outcomes (columns). Significant effects are emphasized in bold.
Differences in Regional WMH and FA Between NC, MCI, and AD
The regional distribution pattern of WMH differed across diagnostic groups (significant Diagnostic Group by Lobe interaction, F(6,273) = 2.325; p = .033). The pattern of the omnibus interaction (see Figure 4) suggests a “dose response” of increased WMH volume in frontal and parietal lobes among patients with AD, followed by subjects with MCI, and normal controls. Formal post hoc analyses were conducted with separate ANOVAs for each lobe separately. For frontal lobes, AD patients had significantly greater WMH volume than controls and subjects with MCI. In all other lobes, WMH volume was statistically similar across groups. The frontal lobe differences were confirmed with non-parametric Kruskal-Wallis Test (p = .028 for frontal lobes). The overall model comparing FA among the three diagnostic groups showed a trend toward significance (F(2,71) = 2.893; p = .062). Post hoc examination showed that patients with AD had significantly (p < .05) lower FA than controls and subjects with MCI, who were similar to each other. This same pattern emerged for FA in the inferior longitudinal fasciculus and genu of the corpus callosum (i.e., significant reduction among AD patients relative to MCI patients and controls), but not in the fornix or uncinate fasciculus. When the vascular disease summary score was entered as an additional covariate, the findings for both WMH and FA remained unchanged.
Fig. 4 White matter hyperintensities (WMH) volume differences across diagnostic groups. Error bars are standard errors.
Discussion
We showed that increased WMH burden is related to decreased DTI-derived FA, suggesting that both capture aspects of white matter structure. However, the relationship of these white matter measurements to cognition or diagnostic group differed. In general, WMH severity, a marker of small vessel cerebrovascular disease, was more consistently related to cognitive function and AD than FA, a marker of white matter microstructural integrity. Specifically, we found that WMH volume in the frontal lobes was associated with poorer memory and that both frontal and parietal WMH volume is elevated among individuals with AD and at risk for AD (i.e., the MCI group). Global FA, on the other hand, in our sample was not associated with either cognitive functioning or with diagnostic group, although there was a non-significant trend for deceased FA among patients with AD and post hoc analyses did reveal statistically reliable diminution of FA among AD patients. Our findings suggest that WMH volume is a more sensitive radiological correlate of cognitive functioning among older adults than FA and, particularly when distributed in frontal and parietal lobes, may play a specific role in AD.
Although, to our knowledge, previous studies have not combined macrostructural and microstructural white matter imaging techniques to examine their differential associations with cognition and diagnosis, our finding of a relationship between the two is in line with previous investigations showing an inverse relationship in clinical samples (Engelhardt et al., Reference Engelhardt, Moreira, Alves, Lanna, Alves, Ericeira-Valente and Laks2009; Taylor et al., Reference Taylor, Payne, Krishnan, Wagner, Provenzale, Steffens and MacFall2001). Despite the shared variance between the two measures, only WMH were related to cognitive function reliably in our study. As FA captures both normal variability in white matter structure as well as disease- or age-related changes (Furutani, Harada, Minato, Morita, & Nishitani, Reference Furutani, Harada, Minato, Morita and Nishitani2005; Grieve et al., Reference Grieve, Williams, Paul, Clark and Gordon2007; Stricker et al., Reference Stricker, Schweinsburg, Delano-Wood, Wierenga, Bangen, Haaland and Bondi2009), the measure may be less sensitive than frank markers of pathology, such as WMH.
It was somewhat surprising that the measures of FA did not emerge as a significant predictor of cognitive function in this study and there are several possibilities for this negative result. First, our interest was in capturing a general measure of white matter integrity. Indeed the derived FA factor accounted for 54.9% in the variance. We expected that these data reduction approach would provide the most reliable general measure of FA that would be related to cognitive function (Penke et al., Reference Penke, Munoz Maniega, Murray, Gow, Hernandez, Clayden and Deary2010) but it is possible that subtle regional differences were not captured. Second, our DTI sequences comprised 16 directions, which may have obscured more subtle relationships with cognition. Third, it is possible that among older adults with significant vascular disease histories, measurements of macrostructure that reflect cerebrovascular disease may simply be more relevant to cognitive outcomes than subtle variation in white matter microstructure.
However, we did observe a trend in the data – decreased FA among patients with AD compared to healthy controls – which has been observed in previous studies (Bozzali et al., Reference Bozzali, Falini, Franceschi, Cercignani, Zuffi, Scotti and Filippi2002; Takahashi et al., Reference Takahashi, Yonezawa, Takahashi, Kudo, Inoue and Tohgi2002). Findings have not been entirely consistent, though, particularly with regard to the regional distribution of FA reduction in AD; reduced FA among AD patients has been reported in several regions, including periventricular frontal white matter (Choi, Lim, Monteiro, & Reisberg, Reference Choi, Lim, Monteiro and Reisberg2005), posterior cingulate (Yoshiura et al., Reference Yoshiura, Mihara, Ogomori, Tanaka, Kaneko and Masuda2002), columns of the fornix (Ringman et al., Reference Ringman, O'Neill, Geschwind, Medina, Apostolova, Rodriguez and Bartzokis2007), thalamus, parietal white matter, posterior limbs of the internal capsule (Rose, Janke, & Chalk, Reference Rose, Janke and Chalk2008), and genu of the corpus callosum (Salat et al., Reference Salat, Tuch, Greve, van der Kouwe, Hevelone, Zaleta and Dale2005). Thus, while reduction in FA among patients with AD has been observed, there is a relative paucity of replication regarding specific regions; our findings suggest that when overall white matter microstructural integrity is estimated, there is little evidence of a statistically reliable general reduction among patients with AD.
Our findings are consistent with the increased regional distribution of WMH in frontal and parietal lobes, also found by Yoshita and colleagues (Yoshita et al., Reference Yoshita, Fletcher, Harvey, Ortega, Martinez, Mungas and DeCarli2006) as well as by Leys and colleagues (Reference Leys, Pruvo, Parent, Vermersch, Soetaert, Steinling and Clarisse1991) among patients with AD. These areas are typically associated with memory and are known to be susceptible to AD pathology (Brun & Englund, Reference Brun and Englund1981; Laakso et al., Reference Laakso, Soininen, Partanen, Helkala, Hartikainen, Vainio and Riekkinen1995). In the context of AD, WMH may result from perfusion abnormalities, vascular burden, and from vascular deposition of beta amyloid (Brickman et al., Reference Brickman, Muraskin and Zimmerman2009), or possibly from Wallerian degeneration to cortical-cortical fiber connections. Frontal and parietal lobes are more vulnerable to perfusion abnormalities (Matsuda, Reference Matsuda2001) and vascular deposition of beta amyloid (Esiri & Wilcock, Reference Esiri and Wilcock1986) than the remaining regions of the human brain. This consistent regional distribution of WMH suggests a possible pathological link of AD with vascular disease on a mechanistic level, on the one hand, and/or suggests that a cerebrovascular disease burden in addition to “primary” AD pathology can contribute to the clinical presentation of AD, on the other.
While several studies have reported associations between WMH and cognition (Dufouil, Alperovitch, & Tzourio, Reference Dufouil, Alperovitch and Tzourio2003; Smith et al., Reference Smith, Salat, Jeng, McCreary, Fischl, Schmahmann and Greenberg2011), the specific relationship between WMH and memory performance we observed is not entirely consistent with previous studies. Among neurologically healthy older adults, WMH are typically reported to be related with speeded tasks of executive function (Gunning-Dixon & Raz, Reference Gunning-Dixon and Raz2000). However, there is an emerging literature linking WMH to MCI and AD, which are characterized primarily by an amnestic syndrome (Smith et al., Reference Smith, Salat, Jeng, McCreary, Fischl, Schmahmann and Greenberg2011). The current study is consistent with previous reports, which have shown that WMH are not only more common in AD patients, but their appearance is also elevated in people with MCI (Delano-Wood et al., Reference Delano-Wood, Bondi, Sacco, Abeles, Jak, Libon and Bozoki2009; Luchsinger et al., Reference Luchsinger, Brickman, Reitz, Cho, Schupf, Manly and Brown2009; Yoshita et al., Reference Yoshita, Fletcher, Harvey, Ortega, Martinez, Mungas and DeCarli2006).
Results from the current study may reflect the unique sample of exclusively African American participants. Increased prevalence and incidence rates of dementia and cognitive dysfunction among older African Americans (Gurland et al., Reference Gurland, Wilder, Lantigua, Stern, Chen, Killeffer and Mayeux1999; Perkins & Schisterman, Reference Perkins and Schisterman2006; Tang et al., Reference Tang, Cross, Andrews, Jacobs, Small, Bell and Mayeux2001; Unverzagt et al., Reference Unverzagt, Hall, Torke and Rediger1996) may be due in part to increased cerebrovascular disease among this population (Brickman, Schupf, et al., Reference Brickman, Schupf, Manly, Luchsinger, Andrews, Tang and Brown2008; Reitz et al., Reference Reitz, Schupf, Luchsinger, Brickman, Manly, Andrews and Mayeux2009), though previous studies have not found differential brain-behavior relationships across ethnic/racial groups (DeCarli et al., Reference DeCarli, Reed, Jagust, Martinez, Ortega and Mungas2008). Future studies should further explicitly examine whether the mediators of cognitive function and diagnostic group vary as a function of ethnic/racial group. Relatively poor specificity of neuropsychological measures used to measure cognitive function and diagnosis patients among African Americans may be another important source of variance in our study, contributing to both relatively weak associations between the MRI-derived measures of white matter and cognition and to differences between our study and results reported in the extant literature. Mapping the longitudinal course of cognitive change would certainly contribute to a more clear understanding of the link between white matter abnormalities and progressive cognitive change.
There are weaknesses in the current study that are important to highlight. First, the number of statistical comparisons was quite high, increasing the likelihood of Type I statistical error. On the other hand, the number of AD and MCI participants was relatively low, possibly masking group differences due to diminished power and potentially increasing the Type II statistical error rate. Replication of our findings in a separate and larger cohort is clearly warranted. A group of participants did not clearly meet criteria for MCI, AD, or normal cognition and were thus labeled “cognitively impaired not MCI.” It is unclear what the nature of the cognitive impairment is and it is possible they have had cognitive difficulties for most of their adult lives. Again, longitudinal follow-up of participants will be critical to elucidate better whether this subgroup of participants have a neurodegenerative condition. Furthermore, because we were interested in understanding the relationship between imaging markers and cognition or diagnosis and to maintain consistency with the parent study, we did not use the neuroimaging data in the diagnostic formulation of each subject. Future studies may wish to incorporate radiological data (e.g., presence of infarct, regional atrophy) into the diagnostic procedures. Finally, future studies may wish to consider other DTI metrics, which may provide complementary information or may increase sensitivity for detection of relationships between cognition and white matter microstructure.
In summary, we showed that among African American older adults, FA and WMH are associated with each other, but that only WMH were significant predictors of memory and AD diagnosis.
Acknowledgment
This work was supported by NIH grants AG028786 and AG034189, and by a grant from Gottfried und Julia Bangerter-Rhyner-Stiftung. This work was completed, in part, toward the requirements for a master's degree at the University of Zurich (IBM). The authors report no conflicts of interest.
