1 Howard Hughes Medical Institute, One Brookings Drive, St Louis, MO 63130, USA, 2 Department of Psychology, Washington University, St Louis, MO 63130, USA, 3 Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO 63110, USA, 4 Department of Neurology, Washington University School of Medicine, St Louis, MO 63110, USA, 5 Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO 63110, USA and 6 Department of Anatomy and Neurobiology, Washington University School of Medicine, St Louis, MO 63110, USA
Address correspondence to Denise Head, HHMI at Washington University, Psychology Department Campus Box 1125, One Brookings Drive, St Louis, MO 63130, USA. email: dhead{at}artsci.wustl.edu.
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Abstract |
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Key Words: corpus callosum dementia MCI MRI white matter
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Introduction |
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The degenerative changes in AD begin in medial temporal lobe structures and later involve adjacent temporal, parietal and frontal neocortex (Braak and Braak, 1991; Price et al., 2001
). Magnetic resonance image (MRI) studies of AD consistently reveal marked volume reductions in the hippocampus using cross-sectional and longitudinal approaches (e.g. Jack et al., 1992
, 1997
; Convit et al., 1993
; Killiany et al., 1993
). In contrast, structural MRI (Salat et al., 1999
; Raz, 2000
) and diffusion tensor imaging (DTI) cross-sectional studies (O'Sullivan et al., 2001
; Head et al., 2004
) suggest that atrophy in nondemented aging may be relatively greater in anterior regions (gray and white matter) and striatal structures. With respect to cognitive function, the hallmark of early-stage AD is memory impairment (Albert, 1998
; Storandt et al., 2002
) possibly accompanied by deficits in attentional control (Balota and Faust, 2001
). In contrast, the cognitive change in nondemented aging is mostly in executive control (Moscovitch and Winocur, 1995
; Craik and Grady, 2002
) accompanied by memory problems related more to executive dysfunction than rapid forgetting (Albert, 1998
; Storandt et al., 2002
).
Here we provide direct evidence that nondemented aging and AD are distinct entities by demonstrating an anatomical double dissociation. The structures selected for study were the hippocampus (HC) and the corpus callosum (CC). Although neuropathological investigations (Braak and Braak, 1991) suggest that AD-related changes may begin in the entorhinal cortex and subsequently spread to the hippocampus, we elected to measure the hippocampus as the MRI-based procedures for hippocampal delineation are better established than for the entorhinal cortex and there is a substantial literature on AD- and nondemented age-related effects on the hippocampus (for reviews, see Jack and Petersen, 2000
; Raz et al., 2000
). The CC is topographically organized, with anterior and posterior portions connecting anterior and posterior lobar regions, respectively (de Lacoste et al., 1985
; Pandya and Seltzer, 1986
). Studies in nondemented aging reveal a trend for anterior regions to exhibit greater atrophic effects than posterior regions (e.g. Weis et al., 1991
; Aboitz et al., 1996
; Janowsky et al., 1996
; see also O'Sullivan et al., 2001
; Head et al., 2004
), which is similar to the anterior-to-posterior gradient observed for white matter lobar regions (Head et al., 2004
). In contrast, early-stage AD may differentially affect posterior regions of the CC (Jack and Petersen, 2000
). Later stages of AD include more widespread effects (Braak and Braak, 1991
) and anterior portions of the CC may then be involved (e.g. Pantel et al., 1999
; Teipel et al., 1999
; Hensel et al., 2002
). We first obtained measurements in 25 young adults, 25 nondemented older adults and 25 individuals with very mild-to-mild dementia of the Alzheimer type (DAT) (Table 1). The two older groups were matched for age and gender. Then, measurements were repeated in a second, independent sample that was age- and gender-matched to the initial cohort. A final set of analyses combined data across the two cohorts and formally explored the double dissociation between aging and early-stage AD.
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Materials and Methods |
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Young adults were undergraduate students at Washington University screened for neurologic illness or injury and use of psychoactive medications. Older adults were recruited from the Washington University Alzheimer's Disease Research Center (ADRC) screened for neurologic illness, head injury, current depression, use of psychoactive medications and medical conditions that might produce cognitive impairment (e.g. cerebrovascular disease and Parkinson's disease). All participants were right-handed native English speakers. Older adults were classified as demented or nondemented based on the Washington University Clinical Dementia Rating (CDR; Morris, 1993). This a validated, interview-based measure that examines the participant's abilities in memory, orientation, judgement and problem solving, community affairs and functions in the home, hobbies and personal care (Morris et al., 1988
). Separate interviews are conducted with the participant and a collateral source. The clinical distinction between nondemented (CDR = 0) and demented (CDR
0.5) has been validated by neuropathological examination (Berg et al., 1998
), including at the very mildest (CDR = 0.5) stages of dementia (Morris et al., 2001
). All participants consented to participation in accordance with guidelines of the Washington University Human Studies Committee. Demographic characteristics of participants are listed in Table 1.
MR Acquisition
All imaging was performed using a Siemens 1.5 Tesla Vision scanner (Erlangen, Germany). Cushions and a thermoplastic mask were used during scanning to reduce head movement. A scout image (TR = 15 ms, TE = 6 ms, flip angle = 30°, 2.34 x 1.17 x 8 mm resolution) was acquired first in order to center the field of view on the brain. Four T1-weighted sagittal MP-RAGE (Mugler and Brookeman, 1991) scans (TR = 9.7 ms, TE = 4 ms, flip angle = 10°, TI = 20 ms, TD = 200 ms, 1 x 1 x 1.25 mm resolution) were acquired in each subject. Inter- and intra-scan motion correction and averaging were accomplished off-line. All raw MRI data are available to research investigators.
Image Processing
Image processing prior to regional analysis included several image registration steps ultimately resulting in registered structural data resampled to 1 mm3 voxels in the atlas space of Talairach and Tournoux (1988). The following describes the image registration steps carried out for each individual. First, a 12-parameter affine atlas transform was computed for one MP-RAGE. The atlas representative target image included 12 (six female) young adult and 12 (nine female) nondemented old (mean age 75 years) subjects (Buckner et al., 2000
). Results from our laboratory indicate that atlas normalization, when using this youngold target atlas, is equivalent to normalization based on intracranial volume (r = 0.93) and is minimally biased by global atrophy typical of aging and dementia (Buckner et al., 2004
). For each participant, the remaining MP-RAGE images were registered to the first (allowing xyz stretch). Atlas transforms for all MP-RAGE images were computed by transform composition (matrix multiplication). Each participant's averaged, atlas-transformed MP-RAGE image was then produced using a single interpolation per scan. Intensity inhomogeneity was corrected using an algorithm minimizing intensity variation within continuous regions with the bias field modeled as a general second-order polynomial in x, y, z (10 free parameters) (Styner, Brechbuhler, Szekel and Gerig, 2000
). MP-RAGE images were segmented into gray matter, white matter and cerebrospinal fluid using fuzzy-class means. These segmented images in conjunction with the unsegmented T1-weighted images were used to manually obtain regional measurements of the corpus callosum.
Regions-of-Interest Measurement Procedures
The following measurement procedures were conducted using Analyze software (Version 4.0, Mayo Clinic). Images were displayed on an 18-inch interactive display monitor and each region-of-interest (ROI) was manually outlined on the screen with the accompanying grip pen. One operator (D.H.), blind to participants' exact age, gender and cognitive status, manually outlined the ROIs. Intrarater reliability was assessed on 10 randomly selected brains measured on two occasions separated by two weeks. All reliability coefficients, with intraclass correlations presuming random selection of raters [ICC(2); Shrout and Fleiss, 1979], exceeded 0.90. One nondemented older adult participant was replaced because their data for several measurements were >3 SD above the mean.
Corpus Callosum
The entire corpus callosum (CC) was measured on segmented images in the sagittal plane. The T1-weighted nonsegmented image was also displayed during measurement to allow for exclusion of large blood vessels and the fornices. Measurements were made on the midsagittal slice and five slices lateral to the midsagittal slice in both hemispheres, giving a total of 11 slices. The midsagittal slice was determined primarily by the clarity of the cerebral aqueduct, using the septum pellucidum as a second landmark when the cerebral aqueduct was less clearly visualized. As in previous studies (Janowsky et al., 1996; Pantel et al., 1999
), the CC was separated into five subregions, each 20% of the rostralcaudal length: genu and rostrum (CC1), rostral body (CC2), midbody (CC3), isthmus (CC4) and splenium (CC5). Examples of the tracings of the ROIs are depicted in Figure 1a.
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Measurement procedures were similar to those employed in previous work on hippocampal volume in aging and AD (Jack et al., 1992; Killiany et al., 1993
). The hippocampus (HC) was measured on 2330 coronal T1-weighted nonsegmented images aligned perpendicular to the long axis of the left hippocampus post-acquisition. Images were resampled to 0.5-mm-thick slices and measurements were made on every third slice, i.e. on sections separated by 1.5 mm. The most anterior slice on which the HC emerged inferior to the amygdala formed the rostral border. The caudal border was the slice on which the fornices are seen as they rise after leaving the fimbria. Measured volumes included the hippocampal formation, dentate gyrus, alveus, fimbria and portions of the subiculum. Examples of the tracing of the ROIs are depicted in Figure 1b.
Bias
When using MRI measurements of volume there is always the potential for bias because tissue parameters change with age (Seong et al., 1997) and also because tissue boundaries are influenced by voxels that contain multiple tissue classes. This issue was approached here by tracing outlines based on our best estimate of where the anatomy of the structure began and ended, using knowledge of structure shape across adjacent sections as a guide. In ambiguous cases, such as low-intensity voxels (likely including partial contributions of CSF) that were adjacent to clear tissue-containing voxels, we conservatively included these voxels in the region being traced.
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Results |
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Regional Variability in Age- and Dementia-related Effects
Anterior portions of the CC showed aging effects independent of dementia (Fig. 2). This was supported by a significant group x brain region interaction [F(8,284) = 3.34, P = 0.001]. Follow-up, independent samples t-tests revealed all subregions of the CC were significantly smaller in nondemented older adults compared with young adults [CC1: t(48) = 4.65, P < 0.0001; CC2: t(48) = 6.45, P < 0.0001; C3: t(48) = 4.55, P < 0.0001; CC4: t(48) = 2.82, P < 0.01; CC5: t(48) = 2.34, P < 0.05]. However, the age differences were greater in CC1 compared with CC5 as indicated by a significant group (young versus nondemented old) by brain region (CC1 versus CC5) interaction [F(1,48) = 9.85, P < 0.01]. There were no significant differences between the nondemented and demented older adults for any of the subregions (all P > 0.12). The main effect of sex was not significant (F < 1). However, there was a significant sex x brain region interaction [F(4,284) = 2.63, P < 0.05]. Follow-up, independent samples t-tests did not reveal significant sex differences in any subregions (all P > 0.19), but the interaction appeared to reflect numerically larger volumes in males in CC1, CC2, CC3 and CC4, but a larger CC5 volume in females.
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Thus, age-dependent volume differences were present in all regions of the CC, but greater in anterior than posterior regions. There were no effects of early-stage AD compared with age-matched controls in the CC. In direct contrast, significant AD-related effects were apparent bilaterally in the HC.
Replication of the Anatomical Double Dissociation between Nondemented Aging and AD
The above-noted differential effects of aging and AD on anatomic structures are strongly suggestive of distinct mechanisms underlying nondemented aging and AD. The results are consistent with previous findings of HC volumetric reduction in AD (for a review, see Jack and Petersen, 2000) and a recent report of greater atrophy in hippocampus/amygdala compared with CC area in AD (Teipel et al., 2003
). However, other studies have reported AD-specific effects in the CC, including CC1 (e.g. Janowsky et al., 1996
; Pantel et al., 1999
; Teipel et al., 1999
), and mild-to-moderate age effects in the HC (Raz, 2000
; Salat et al., 1999
). Measurement of the second sample was therefore undertaken to substantiate the dissociation observed in the first sample (Table 1).
Analyses of the second sample confirmed the initial findings in all critical particulars (Fig. 3). The group x brain region interaction was again significant [F(8,284) = 4.56, P < 0.0001] for the CC. Follow-up, independent samples t-tests indicated significant nondemented age-related differences in CC1 [t(48) = 3.24, P < 0.01] and the three truncal regions [CC2: t(48) = 4.71, P < 0.0001; CC3: t(48) = 2.79, P < 0.01; CC4: t(48) = 2.07, P < 0.05], but not in CC5 (t < 1). There were no significant AD-related effects on the volume of the CC subregions compared with nondemented older adults (all P > 0.15). There also was a significant main effect of sex [F(1, 71) = 6.36, P = 0.01], but the sex x brain region interaction was not significant [F(4,284) = 1.78, ns]. For the hippocampus, there was a main effect of group [F(2,71) = 15.82, P < 0.0001]. The nondemented age-related volume differences in the HC were more pronounced in this sample, but still nonsignificant [left HC: t(48) = 1.51, P = 0.14; right HC: t(48) = 1.32 P = 0.19]. In contrast, AD status was associated with significant HC volume differences compared with nondemented older adults [left HC: t(48) = 3.53, P < 0.001; right HC: t(48) = 3.62, P < 0.001]. Neither the main effect of sex (F < 1) nor the sex x brain region interaction [F(1,71) = 2.19, ns] was significant.
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Additional analyses were conducted to examine the influence of aging in the 100 older individuals in the combined cohorts (nondemented and early-stage AD). This analysis is independent of the earlier analyses, as variance within the older individuals did not contribute to the group effects described above. However, the impact of advancing age in the early-stage AD group should be interpreted with caution as this is a cross-sectional design and the most appropriate assessment of aging effects on AD progression requires a longitudinal design. Again, analysis of the CC revealed a marked effect of age with an anterior-to-posterior gradient. A mixed general linear model with age as a continuous variable, group (early-stage AD versus nondemented) and sex as between-subjects variables and brain region (CC1 versus CC5) as a within-subjects variable was conducted (Fig. 4a). Nonsignificant interactive terms in the full linear model were removed. In the reduced model, the age x brain region interaction was significant [F(1,96) = 3.96, P < 0.05]. This interaction reflected a larger effect of age on CC1 [Pearson's r(100) = 0.44, P < 0.0001] than CC5 [r(100) = 0.29, P < 0.01]. Based on the regression equations, there is an estimated loss of 1.01% and 0.60% per year in the anterior and posterior regions of the CC, respectively, in the older adults (nondemented and early-stage AD). Neither the main effect of group (F < 1), the group x age interaction (F < 1) nor the group x brain region interaction [F(1,96) = 1.35, ns] was significant, indicating no significant differences in CC1 or CC5 volume between the early-stage AD adults and the nondemented older adults.
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In order to further examine the relationship between nondemented aging and early-stage AD, the combined sample of older adults was split into subgroups based on whether they were above or below an age criterion of 77 years and their dementia status (e.g. 77-nondemented,
78-nondemented,
77-demented and
78-demented; see Table 2 for demographics). The logic of this analysis is that it allows exploration of the formal double-dissociation between effects of aging and AD by contrasting the
77-demented group and the
78-nondemented group. In this manner, the two effects (aging and dementia) are structured to oppose one another. Thus, the effect of dementia on the hippocampus must significantly exceed the effect of aging and, separately, the effects of aging on the anterior corpus callosum must significantly exceed any effect of dementia status. This analysis represents a conservative balance between the desire to directly test the two predictions of the double dissociation and the constraint that the current study has insufficient power to explore a three-way interaction.
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Discussion |
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Across two independent samples, the magnitudes of nondemented age-related differences were greater in the most anterior portions of the CC (genu and rostrum) than in the splenium. There were no additional reductions in callosal subregions associated with AD status, suggesting that this was entirely an aging phenomenon. The pattern of macrostructural volume loss observed in the current study likely parallels findings apparent at the microstructural level. Recent studies assessing white matter microstructure with diffusion tensor imaging note differential age-associated degeneration of anterior corpus callosum and lobar regions (O'Sullivan et al., 2001; Head et al., 2004
). The cellular basis of the losses observed in these neuroimaging studies is uncertain. Post-mortem investigations document decreases in length and diameter of myelinated fibers and loss of small diameter myelinated fibers in subcortical white matter with advancing age (Tang et al., 1997
). Subcortical white matter damage in nondemented older adults, as evidenced by white matter hyperintensities, may contribute to callosal atrophy (Pantel et al., 1999
). Critically, the present study strongly suggests that the anatomical changes associated with normal aging and AD are due to distinct mechanisms.
It is possible that the regional atrophy of the CC observed here is related to regional neocortical degeneration (Pandya and Seltzer, 1986; Teipel et al., 2003
). The distribution of age-related callosal differences is consistent with a tendency for greater vulnerability of anterior compared with posterior regions of the cortex (Kemper, 1994
; Raz, 2000
). It has been reported that the gray matter losses may precede white matter changes followed by acceleration of white matter degeneration (Miller et al., 1980
; Jernigan et al., 2001
). However, the temporal sequence and functional relationship of gray and white matter changes require further investigation. It is also possible that white matter is preferentially vulnerable to age-associated processes, such as hypertension, that influence cortical functioning indirectly.
The temporoparietal association cortices are affected by AD prior to other neocortical sites (Braak and Braak, 1991; Price and Morris, 1999
). As the splenium contains fibers from these regions, it was expected to be particularly affected by AD. Previous reports have noted atrophy of posterior callosal regions in early-stages of AD (Teipel et al., 1999
) that may be similar in magnitude to medial temporal atrophy, even in early-stage AD (Teipel et al., 2003
). In the current study the AD group was not significantly different from the nondemented group in splenial atrophy. The present sample consisted of very mildly to mildly demented individuals, with a preponderance of very mildly demented. It is possible that neocortical degeneration sufficient to result in callosal effects may be present in later stages of AD. There were nonsignificant trends for mildly demented individuals (CDR = 1) to show reductions in splenial volume compared with very mildly demented (CDR = 0.5) individuals and from age-matched controls.
As expected from the substantial literature (Jack and Petersen, 2000), hippocampal volume was considerably reduced in AD individuals. The volume reductions likely correspond to well-known neuropathological effects (Jack et al., 2002
). Conversely, nondemented older adults did not have significantly smaller hippocampal volumes compared with younger adults, although there was a 0.47% per year age-associated volume reduction within the nondemented older adults (aged 6593 years). The nature and degree of nondemented age-related volume loss in the hippocampus remains unsettled. A recent quantitative review of 15 cross-sectional studies revealed a median age by hippocampus correlation of r = 0.27, with a range of r = 0.03 to r = 0.63 (Raz, 2000
). Reasons for such variability include intrinsically high variance of the r statistic, inconsistent screening for pre-clinical dementia or other health-related problems that may impact the hippocampus, and disparate rules for regional demarcation. In addition, our morphometric methods conservatively estimate volume decline because ambiguous voxels are often included within the structure (such as occur at the juncture between CSF and hippocampus and increase with age). Recent longitudinal studies converge on an
12% annual age-related hippocampal decline (Jack and Petersen, 2000
; Cardenas et al., 2003
; Raz et al., 2004
). The decline may be characterized by a constant slope during younger ages and accelerated decline in later life (Raz et al., 2004
). Such a pattern might explain the present findings and also failures to find significant age effects in samples with restricted age ranges. Our results, and those reported in the literature, all imply that hippocampal volume loss in AD is prominent and disproportionate to that observed in nondemented aging (Jack and Petersen, 2000
; Cardenas et al., 2003
).
The present results complement a growing body of literature reporting differences in the effects of nondemented aging and AD. Ohnishi et al. (2001) noted differences in hippocampal and entorhinal regions in AD and also age-related differences in frontal, temporal and parietal cortex. Differential effects have also been observed within the hippocampus. Nondemented aging affects the dentate gyrus and subiculum whereas AD is particularly associated with CA1 and entorhinal cortex changes (Small et al., 2000
, 2002
; Uylings and de Brabander, 2002
). At the behavioral level, memory problems in AD present as rapid forgetting whereas in nondemented aging the memory problems may relate more to executive dysfunction secondary to impairments in prefrontal cortical function (Albert, 1998
; Storandt et al., 2002
).
The possibility that AD represents accelerated aging has been based in part on the observation of hippocampal atrophy in both nondemented aging and AD. However, the present data in aggregate lead to the conclusion that at least one of the mechanisms underlying nondemented aging disproportionately affects anterior regions of the cerebrum and is independent of AD. Furthermore, although mild hippocampal atrophy may occur in nondemented aging, there appears to exist a separate pathologic mechanism that results in substantial hippocampal volume loss at the very earliest stages of AD (e.g. CDR = 0.5/1). The double dissociation implies that AD and nondemented aging are separable entities and provides strong support for a multiple-component model of brain aging: certain aspects of aging and AD reflect distinct underlying processes.
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Acknowledgments |
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References |
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![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Albert M (1998) Normal and abnormal memory: aging and Alzheimer's disease. In: Handbook of the aging brain (Wang E, Snyder D, eds) pp. 113. San Diego, CA: Academic Press.
Balota D, Faust ME (2001) Attention in dementia of the Alzheimer type. In: Handbook of neuropsychology, 2nd Edition (Boller F, Cappa SF, eds) pp. 5180. New York: Elsevier Science.
Berg L, McKeel DW, Miller JP, Storandt M, Rubin EH, Morris JC, Baty J, Coats M, Northon J, Goate AM, Price JL, Gearing M, Mirra SS, Saunders AM (1998) Clinicopathologic studies in cognitively healthy aging and Alzheimer disease: relation of histologic markers to dementia severity, age, sex, and apolipoprotein E genotype. Arch Neurol 55:326335.
Braak H, Braak E (1991) Neuropathological staging of Alzheimer-related changes. Acta Neuropathol 82:239259.[CrossRef][ISI][Medline]
Buckner RL (2004) Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate. Neuron (in press).
Buckner RL, Snyder AZ, Sanders AL, Raichle ME, Morris JC (2000) Functional brain imaging of young, nondemented and demented older adults. J Cogn Neurosci 12(Suppl 2):2434.[CrossRef][ISI][Medline]
Buckner RL, Head D, Parker J, Fotenos A, Marcus D, Morris JC, Snyder AZ (2004) A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. Neuroimage (in press).
Cardenas VA, Du AT, Hardin D, Ezekiel F, Weber P, Jagust WJ, Chui HC, Schuff N, Weiner M (2003) Comparison of methods for measuring longitudinal brain change in cognitive impairment and dementia. Neurobiol. Aging 24:537544.[CrossRef][ISI][Medline]
Cho S, Jones D, Reddick WE, Ogg RJ, Steen RG (1997) Establishing norms for age-related changes in proton T1 of human brain tissue in vivo. Magn Reson Imag 15:11331143.[CrossRef][ISI][Medline]
Convit A, de Leon MJ, Golomb J, George AE, Tarshish CY, Bobinski M, Tsui W, De Santi S, Wegiel J, Wisniewski H (1993) Hippocampal atrophy in early Alzheimer's disease: anatomic specification and validation. Psychiatr Q 64:371387.[CrossRef][Medline]
Craik FIM, Grady CL (2002) Aging, memory, and frontal lobe functioning. In: Principles of frontal lobe function (Stuss DT, Knight RT, eds), pp. 528540. Oxford: Oxford University Press.
de Lacoste MC, Kirkpatrick JB, Ross ED (1985) Topography of the human corpus callosum. J Neuropathol Exp Neurol 44:578591.[ISI][Medline]
Folstein MF, Folstein SE, McHugh PR (1975) Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189198.[CrossRef][ISI][Medline]
Hauw JJ, Duyckaerts C (2001) Alzheimer's Disease. In Pathology of the aging human nervous system, 2nd edn (Duckett S, De La Torre JC, eds), pp. 207263. New York: Oxford University Press.
Head D, Buckner RL, Shimony JS, Girton LE, Akbudak E, Conturo TE, McAvoy M, Morris JC, Snyder AZ (2004) Differential vulnerability of anterior white matter in nondemented aging with minimal acceleration in dementia of the Alzheimer type: evidence from diffusion tensor imaging. Cereb Cortex 14:410423.
Hedden T, Gabrieli JDE (2004) Insights into the ageing mind: a view from cognitive neuroscience. Nat Rev Neurosci 5:8797.[CrossRef][ISI][Medline]
Hensel A, Wolf H, Kruggel F, Riedel-Heller SG, Nikolaus C, Arendt T, Gertz HJ (2002) Morphometry of the corpus callosum in patients with questionable and mild dementia. J Neurol Neurosurg Psychiatry 73:5961.
Huppert FA, Brayne C (1994) What is the relationship between dementia and normal aging? In: Dementia and normal aging (Huppert FA, Brayne C, eds), pp. 311. Cambridge: Cambridge University Press.
Jack CR Jr, Petersen RC (2000) Structural imaging approaches to Alzheimer's disease. In: Early diagnosis of Alzheimer's disease. (Scinto LFM, Daffner KR, eds), pp. 127148. Totawa, NJ: Humana Press.
Jack CR Jr, Sharbrough FW, Cascino GD, Hirschorn KA, O'Brien PC, Marsh WR (1992) Magnetic resonance image-based hippocampal volumetry: correlation with outcome after temporal lobectomy. Ann Neurol 31:138146.[CrossRef][ISI][Medline]
Jack CR Jr, Petersen RC, Xu YC, Waring SC, O'Brien PC, Tangalos EG, Smith GE, Ivnik RJ, Kokmen E (1997) Medial temporal atrophy on MRI in normal aging and very mild Alzheimer's disease. Neurology 49:786794.[Abstract]
Jack CR Jr, Dickson DW, Parisi JE, Zu YC, Cha RH, O'Brien PC, Edland SD, Smith GE, Boeve BF, Tangalos EG, Kokmen E, Petersen RC (2002) Antemortem MRI findings correlate with hippocampal neuropathology in typical aging and dementia. Neurology 58:750757.
Janowsky JS, Kaye JA, Carper RA (1996) Atrophy of the corpus callosum in Alzheimer's disease versus healthy aging. J Am Geriatr Soc 44:798803.[ISI][Medline]
Jernigan TL, Archibald SL, Fennema-Notestine C, Gamst AC, Stout JC, Bonner J, Hesselink JR (2001) Effects of age on tissues of the cerebrum and cerebellum. Neurobiol Aging 22:581594.[CrossRef][ISI][Medline]
Kemper TL (1994) Neuroanatomical and neuropathological changes during aging and dementia. In: Clinical neurology of aging (Albert ML, Kusefel J, eds), pp. 367. New York: Oxford University Press.
Killiany RJ, Moss MB, Albert MS, Sandor T, Tieman J, Jolesz F (1993) Temporal lobe regions on magnetic resonance imaging identify patients with early Alzheimer's disease. Arch Neurol 50:949954.[Abstract]
Miller AKH, Alston RL, Corsellis JA (1980) Variation with age in the volumes of grey and white matter in the cerebral hemispheres of man: measurements with an image analyser. Neuropathol Appl Neurol 6:119132.
Morris JC (1993) The clinical dementia rating scale (CDR): current version and scoring rules. Neurology 43:24122414.[Medline]
Morris JC (1999) Is Alzheimer's disease inevitable with age? Lessons from clinicopathologic studies of healthy aging and very mild Alzheimer's disease. J Clin Invest 104:11711173.
Morris JC, McKeel DW Jr, Fulling K, Torack RM, Berg L (1988) Validation of clinical diagnostic criteria for Alzheimer's disease. Ann Neurol 24:1722.[CrossRef][ISI][Medline]
Morris JC, Storandt M, Miller JP, McKeel DW, Price JL, Rubin EH, Berg L (2001) Mild cognitive impairment represents early-stage Alzheimer disease. Arch Neurol 58:397405.
Morrison JH, Hoff PR (1997) Life and death of neurons in the aging brain. Science 278:412419.
Moscovitch M, Winocur G (1995) Frontal lobes, memory, and aging. Ann N Y Acad Sci 769:119150.[ISI][Medline]
Mugler III JP, Brookeman JR (1991) Rapid three-dimensional T1-weighted MR imaging with the MP-RAGE sequence. J Magn Reson Imaging 1:561567.[Medline]
Ohnishi T, Matsuda J, Tabira T, Asada T, Uno M (2001) Changes in brain morphology in Alzheimer disease and normal aging: is Alzheimer disease and exaggerated aging process? Am J Neuroradiol 22:16801685.
O'Sullivan M, Jones DK, Summers PE, Morris RG, Williams SCR, Markus HS (2001) Evidence for cortical disconnection as a mechanism of age-related cognitive decline. Neurology 57:632638.
Pandya DM, Seltzer B (1986) The topography of commissural fibers. In: Two hemispheres one brain: functions of the corpus callosum (Lepore F, Ptito M, Jasper HH, eds), pp. 4773. New York: Wiley.
Pantel J, Schroder J, Jauss M, Essig M, Minakaran R, Schonknecht P, Schneider G, Schad LR, Knopp MV (1999) Topography of callosal atrophy reflects distribution of regional cerebral volume reduction in Alzheimer's disease. Psychiatry Res Neuroimag 90:181192.[CrossRef][ISI]
Price JL, Morris JC (1999) Tangles and plaques in nondemented aging and preclinical Alzheimer's disease. Ann Neurol 45:358368.[CrossRef][ISI][Medline]
Price JL, Ko AI, Wade MJ, Tsou SK, McKeel DW, Morris JC (2001) Neuron number in the entorhinal cortex and CA1 in preclinical Alzheimer disease. Arch Neurol 58:13951402.
Raz N (2000) Aging of the brain and its impact on cognitive performance: integration of structural and functional findings. In: Handbook of aging and cognition (Craik FIM, Salthouse TA, eds), vol. 2, pp. 190. Mahwah, NJ: Erlbaum.
Raz N, Rodrigue KM, Head D, Kennedy KM, Acker JD (2004) Differential aging of the medial temporal lobe: a study of a five-year change. Neurology 10:433438.
Salat DH., Kaye JA, Janowsky JS (1999) Prefrontal gray and white matter volumes in healthy aging and Alzheimer disease. Arch Neurol 56:338344.
Shrout PE, Fleiss JL (1979) Intraclass correlations: uses in assessing raters reliability. Psychol Bull 86:420428.[CrossRef][ISI]
Small S, Nava, AS, Perera GM, DeLaPaz R, Stern Y (2000) Evaluating the function of hippocampal subregions with high-resolution MRI in Alzheimer's disease and aging. Microsc Res Tech 51: 101108.[CrossRef][ISI][Medline]
Small S, Tsa WY, DeLaPaz R, Mayeux R, Stern Y (2002) Imaging hippocampal function across the human life span: is memory decline normal or not? Ann Neurol 51:290295.[CrossRef][ISI][Medline]
Storandt M, Grant EA, Miller P, Morris JC (2002) Rates of progression in mild cognitive impairment and early Alzheimer's disease. Neurology 59:10341041.
Styner M, Brechbuhler C, Szekely G, Gerig G (2000) Parametric estimate of intensity inhomogeneities applied to MRI. IEEE Trans Med Imaging 19:153165.[CrossRef][ISI][Medline]
Sullivan EV, Adalsteinsson E, Hedehus M, Ju C, Moseley M, Lim KO, Pfefferbaum A (2001) Equivalent disruption of regional white matter microstructure in ageing healthy men and women. Neuroreport 12:99104.[CrossRef][ISI][Medline]
Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain. New York: Thieme Medical Publishers.
Tang Y, Nyengaard JR, Pakkenberg B, Gundersen HJG (1997) Age-induced white matter changes in the human brain: a stereological investigation. Neurobiol Aging 18:609615.[CrossRef][ISI][Medline]
Teipel SJ, Hampel D, Pietrini P, Alexander GE, Horwitz B, Daley E, Moller H-J, Schapiro MB, Rapoport SI (1999) Region-specific corpus callosum atrophy correlates with the regional pattern of cortical glucose metabolism in Alzheimer disease. Arch Neurol 56:467473.
Teipel SJ, Bayer W, Alexander GE, Bokde AL, Zebuhr Y, Teichberg D, Muller-Spahn F, Schapiro MB, Moller HJ, Rapoport SI, Hampel H (2003) Regional pattern of hippocampus and corpus callosum atrophy in Alzheimer's disease in relation to dementia severity: evidence for early neocortical degeneration. Neurobiol Aging 24:8594.[CrossRef][ISI][Medline]
Uylings HBM, de Brabander JM (2002) Neuronal changes in normal human aging and Alzheimer's disease. Brain Cogn 49:268276.[CrossRef][ISI][Medline]
Weis S, Jellinger K, Wenger, E. (1991) Morphometry of the corpus callosum in normal aging and Alzheimer's disease. J Neural Transm Suppl 33:3538.[Medline]
Whalley LJ (2002) Brain ageing and dementia: what makes the difference? Br J Psychiatry 181:369371.
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