Background
Positron emission tomographic studies of patients with Alzheimer disease (AD) suggest a loss of metabolic functional interactions between different cortical regions. Atrophy of the corpus callosum as the major tract of intracortical connective fibers may reflect decreased cortical functional integration in AD.
Objectives
To investigate whether regional atrophy of the corpus callosum is correlated with regional reductions of cortical glucose metabolism, as shown by positron emission tomography, and whether primary white matter degeneration is a possible cofactor of corpus callosum atrophy in AD.
Patients and Methods
We measured total and regional cross-sectional areas of the corpus callosum on midsagittal magnetic resonance imaging scans from 12 patients with AD and 15 age-matched control subjects. Regional cerebral metabolic rates for glucose in cortical lobes were measured by positron emission tomography using fludeoxyglucose F 18. White matter hyperintensities were rated in T2-weighted magnetic resonance imaging scans.
Results
The total cross-sectional area of corpus callosum was significantly reduced in patients with AD, with the most prominent changes in the rostrum and splenium and relative sparing of the body of the corpus callosum. Frontal and parietal lobe metabolism was correlated with the truncal area of the corpus callosum in AD. The ratios of frontal to parietal and of frontal to occipital metabolism were correlated with the ratio of anterior to posterior corpus callosum area in the group with AD. White matter hyperintensities did not correlate with corpus callosum atrophy in the patients with AD.
Conclusion
The regional pattern of corpus callosum atrophy correlated with reduced regional glucose metabolism independently of primary white matter degeneration in the patients with AD.
THREE LINES of evidence support the notion of Alzheimer disease (AD) as a progressive neocortical disconnection syndrome.1-3 Neuropathological investigations3-6 find neurofibrillary tangles, a marker for cellular degeneration in AD, predominantly in large pyramidal neurons in cortical layers 3 and 5. These neurons give rise to long corticocortical connections within and between the hemispheres.7-9 They are detected in a clustered organization within the cortex of the primate brain.10 There is evidence2 for a similar clustered pattern of β-amyloid deposits in AD that would specifically connect these hallmarks of pathology with the corticocortical projecting neurons.
The corpus callosum is the major commissure connecting both cerebral hemispheres. Its fibers arise from a subset of the large pyramidal neurons in cortical layer 3.11,12 Several magnetic resonance imaging (MRI) studies13-18 report substantial atrophy of the midsagittal cross-sectional area of the corpus callosum in patients with AD. Primary white matter degeneration has been proposed13 to contribute to corpus callosum atrophy in subjects with AD who have a high prevalence of white matter hyperintensities on T2-weighted MRI scans.
Positron emission tomographic (PET) studies19-24 using fludeoxyglucose F 18 show reductions in the regional cerebral metabolic rates for glucose in AD in association neocortex, with a relative sparing of primary neocortex and subcortical areas. In addition, left-right asymmetries of regional cerebral metabolic rates for glucose and intrahemispheric metabolic uncoupling, evidenced by fewer statistically significant correlations between regional metabolic rates, have been reported in several studies.25-30 Haxby et al31 also demonstrate a significantly greater variance of anterior-posterior metabolic ratios in mildly demented patients with AD than in healthy controls. In a recent study, Yamauchi et al14 report a significantly positive correlation between the oxygen metabolism measured by PET using oxygen 15 and the cross-sectional area of the corpus callosum in AD.
In this study, we measured the degree of corpus callosum atrophy in 12 patients with AD and in 15 age-matched healthy controls using a new MRI-based method to measure cross-sectional areas of corpus callosum, and we correlated the measures with regional patterns of cortical glucose metabolism obtained with PET studies using fludeoxyglucose F 18. We also evaluated the white matter hyperintensity load32 in patients with AD and controls to see if primary white matter degeneration might correlate with corpus callosum atrophy in AD. A correlation between the regional cortical metabolic pattern and corpus callosum atrophy in AD would indicate that the corpus callosum cross-sectional area may serve as an indirect marker of cortical disconnection in AD.
Twelve patients having clinically diagnosed AD (3 women and 9 men; mean±SD age, 67.3±8.2 years [range, 55-81 years]) were investigated. Nine patients were diagnosed as having probable and 2 patients as having possible AD, according to the criteria of the National Institute of Neurological and Communicative Disorders and Stroke (now the National Institute of Neurological Disorders and Stroke) and the Alzheimer's Disease and Related Disorders Association.33 One patient had biopsy-proven AD. Three patients exhibited predominantly visuospatial impairment. The degree of cognitive impairment was assessed using the Mini-Mental State Examination (MMSE).34 Four patients with an MMSE score of less than 10 were classified as having severe dementia, 5 patients with an MMSE score between 10 and 20 had moderate dementia, and 3 patients with a score of 20 or higher had mild dementia. The mean±SD MMSE score was 12.5±10.0. Eight patients had early-onset AD, and 4 patients had late-onset AD (cutoff, 65 years).
For comparison, 15 healthy volunteers (7 women and 8 men; mean±SD age, 70.0±8.7 years [range, 53-84 years]) were studied. All control subjects scored 29 or above on the MMSE (mean±SD score, 29.7±0.5). Magnetic resonance imaging and neuropsychological data from a subset of 15 subjects, 7 patients with AD and 8 controls, had already been included in a previous study18 on corpus callosum atrophy in AD.
Substantial comorbidity in the patients with AD and in controls was excluded by history and the results of physical and neurological examinations, psychiatric evaluation, chest x-ray film, electrocardiography, electroencephalography, brain MRI scans, and laboratory tests (complete blood count, sedimentation rate, serum electrolytes, glucose, urea nitrogen, creatinine, liver-associated enzymes, cholesterol, high-density-lipoprotein cholesterol, triglyceride, antinuclear antibody, rheumatoid factor, VDRL, human immunodeficiency virus, vitamin B12, folate, and thyroid function test levels and urinalysis). Intracerebral disease, such as cerebral infarction, neoplasms, and chronic cerebrovascular lesions, was excluded for all subjects by normal results on an MRI scan, except for overall brain atrophy in the patients with AD. No subject had a history of diabetes mellitus, stroke, or head trauma. Four subjects, however, 2 patients with AD and 2 controls, showed mild systolic hypertension with a systolic blood pressure not above 165 mm Hg and a diastolic blood pressure below 90 mm Hg during the clinical examination.
The maximum period between MRI and PET measurement was 4 months in the patients with AD and 38 months in the controls. After a complete description of the study to the subjects, written informed consent was obtained from all subjects or the holders of their durable power of attorney to undergo MRI and PET examinations and neuropsychological assessment for clinical investigation and research. The protocol was approved by the National Institute on Aging's Institutional Review Board.
Magnetic resonance imaging
Eighteen T2-weighted and 18 proton-weighted axial slices (slice thickness, 6 mm; repetition time [TR]/echo time [TE], 2000/80 milliseconds and 2000/20 milliseconds, respectively), 32 proton-weighted coronal slices (slice thickness, 6 mm, and TR/TE, 2000/20 milliseconds) were obtained on a 0.5-T tomograph (Picker Instruments, Cleveland, Ohio). Furthermore, 1 patient with AD was administered a 64-slice, T1-weighted, and sagittally oriented volumetric sequence (slice thickness, 2.50 mm in plane resolution 1×1 mm; TR/TE, 36/6 milliseconds; and flip angle, 30°). All remaining 25 subjects were investigated with a 90-slice, T1-weighted, and sagittally oriented volumetric sequence (slice thickness, 2 mm; TR/TE, 20/6 milliseconds; and flip angle, 45°).
Positron emission tomography
Fifteen images were obtained parallel to the inferior orbitomeatal line using a PET scanner (model PC2048-15B; Scanditronix, Uppsala, Sweden) with reconstructed transverse and axial resolutions of 6.5 mm each. Subjects were placed in the scanner under resting conditions with eyes covered, the ears occluded, and the head fixed in a thermoplastic mask to minimize movement. Further details of the procedure are described elsewhere.35
Each subject was administered the Mattis Dementia Rating Scale36 by trained psychometricians to obtain an overall assessment of cognitive function.
Areas of the total corpus callosum and 5 callosal subregions were measured by one investigator (S.J.T.) blind to the subject's diagnosis in the sagittal T1-weighted MRI slice that best represented the midsagittal section according to a previously described method18 and using commercial software for measuring region of interest (Analyze; Biomedical Imaging Resource, Mayo Foundation, Rochester, Minn) on a Sun workstation (Sun Microsystems, Mountain View, Calif). Interrater and intrarater reliability of this method, assessed with the intraclass correlation coefficient,37 has been previously reported.18 The number of pixels within each region was added automatically and multiplied by the pixel area to obtain absolute values (square millimeters) for the areas of total corpus callosum and of 5 subregions (labeled C1 to C5 in the rostral-occipital direction) (Figure 1).
The values of the 2 most anterior regions of the corpus callosum (C1 and C2) were added to obtain the frontal callosal area, and the values of the areas C3 to C5 were added to obtain the posterior callosal area. The corpus callosum ratio was calculated as the frontal callosal area divided by the posterior callosal area. Each of the absolute values was divided by the total intracranial volume to normalize callosal measurements for head size.
Total Intracranial Volume
The total intracranial volume was measured on the proton-weighted coronal slices using locally developed software (Quantas; Laboratory of Neurosciences, National Institute on Aging, National Institutes of Health, Baltimore, Md) for manual tracing and volumetry of intracranial structures. The total intracranial volume was traced at the inner edge of the dura. Each pixel represents a volume element (voxel) of 1×1 mm in the coronal plane and 6 mm in the rostral-occipital axis. An approximation of the total intracranial volume can be obtained by automatically adding the voxels belonging to the traced regions of interest over all slices.
A template of circular regions of interest, 8 mm in diameter and encompassing 12 pixels, was constructed for all 15 horizontal PET slices. The regions of interest were evenly spaced throughout the cortex and subcortical and thalamic nuclei according to anatomical sections, parallel to the inferior orbitomeatal line, of an atlas of the human brain.38 This template was adjusted to the individual brain. The template was selected that best corresponded to the individual PET slice by visual inspection. Regions of interest of the template then were manually adjusted to the individual slice.
Metabolic values of regions of interest in the frontal, temporal, parietal, and occipital lobes were averaged to obtain lobar mean values of regional cerebral metabolic rates for glucose. Regions of interest covering the precentral and postcentral areas and the primary visual cortex (Brodmann area 17) were excluded. Values obtained for the regions of interest of both thalami were added separately to obtain mean metabolic rates for the left and right thalamus.
All lobar metabolic values were normalized to the metabolic rate of the thalamus as an internal reference region that is relatively preserved in AD.39 Because functional deactivation from the more severely affected hemisphere could decrease metabolic activity in the ipsilateral thalamus, the side with the higher mean metabolic value was used for normalization. Thalamic normalization was done by dividing each lobar metabolic value by this thalamic value.
The values for all regions of interest belonging to parietal, temporal, and occipital cortical areas were added to obtain a mean parietotemporo-occipital cortical metabolic rate,14 which then was normalized to the thalamic metabolism.
Metabolic ratios were defined as the frontal lobe metabolism divided by the temporal, parietal, and occipital metabolic rates.31
White Matter Hyperintensity Rating
Periventricular and deep white matter hyperintensities were graded by one investigator (S.J.T.) on the T2- and proton-weighted axial MRI slices using a modification of a reported rating scale.32 Signal changes on the corpus callosum were added as an extra item to this scale. Regional periventricular and deep white matter hyperintensity subscores were added to obtain a total periventricular and total deep white matter hyperintensity score.
Patient and control groups were compared in age using the Student t test and for the extent of periventricular and deep white matter hyperintensity scores using the Mann-Whitney U test. The sex ratio was compared between groups using the χ2 test. Differences in absolute and normalized total callosal areas between patients with AD and controls were assessed with the Student t test. Group differences in the distribution of callosal areas were tested using repeated-measures analysis of variance with groups as the between-subjects factor and the 5 callosal subregions as the within-subject factor. A significant group-by-subregion interaction was followed up by pairwise single-effect analysis using the Student t test.
Further analyses were performed on the normalized corpus callosum areas. In a multiple regression model, we predicted the normalized total corpus callosum area as a dependent variable by periventricular and deep white matter hyperintensity scores, age, and in the group with AD, by the Mattis Dementia Rating Scale score as an overall assessment of dementia severity. Four stepwise multiple regression models were performed separately for patient and control groups using the 5 normalized callosal subregions as predictors for each normalized lobar regional cerebral metabolic rate for glucose.
Correlations between metabolic and corpus callosum ratios, between frontal corpus callosum and frontal lobe metabolism, and between posterior corpus callosum and posterior cortical metabolism were assessed using the Pearson product moment correlation. The homogeneity of variance of the metabolic ratios between groups was tested using the Fisher F test. A value of P<.05 was considered significant.
The group with AD and the control group were matched for age (P≤.43), but the ratio of women to men was significantly higher in the control group (P≤.01). Patients with AD and controls did not differ in deep white matter or periventricular hyperintensity scores (P≤.98 and P≤.13, respectively) (Table 1).
Absolute and normalized mean total corpus callosum areas were significantly smaller in the group with AD compared with controls (P≤.05 and P≤.005, respectively). Furthermore, a repeated-measures analysis of variance revealed a significant difference (P≤.001) in the distribution of callosal area between patients with AD and controls. To further elucidate the regional pattern of corpus callosum atrophy, areas of the 5 callosal subregions were compared separately between groups. The most severe changes were seen in the splenium area, C5. The C1 area, the most rostral subregion, showed a trend toward atrophy (P≤.06). After normalization, the decrease in the mean areas of regions C1 and C2 reached significance (P≤.05). On the other hand, areas of the 2 callosal subregions that represented the posterior part of the callosal body (C3 and C4) did not differ significantly between patients with AD and controls (Table 2 and Table 3). Normalized total callosal areas were not correlated with age, deep white matter and periventricular hyperintensity scores, and in the group with AD, the Mattis Dementia Rating Scale score.
In the group with AD, the correlation of the values between rostral corpus callosum and normalized frontal lobe metabolism was not significant (r=0.45; P≤.15); similarly, the correlation between posterior corpus callosum and normalized posterior metabolism was not significant (r=0.52; P≤.08). The corresponding correlations in the control group also did not reach significance.
The anterior-posterior corpus callosum ratio in the group with AD was correlated significantly (r=0.64; P≤.03) with the frontal-to-parietal metabolic ratio and with the frontal-to-occipital metabolic ratio (r=0.72; P≤.01) (Figure 2 and Figure 3). The frontal-to-temporal metabolic ratio was not correlated with the corpus callosum ratio.
In controls, there was no significant correlation between any of the metabolic ratios and the corpus callosum ratio. The variance of all 3 metabolic ratios was significantly greater in subjects with AD than in controls (P≤.01).
In the group with AD, a normalized area of callosal region C3 explained a significant (P≤.03) proportion of variance of normalized frontal lobe and normalized parietal lobe metabolism (Figure 4 and Figure 5). No other corpus callosum subregion contributed to the predictive power of the multiple regression model. Neither normalized occipital lobe metabolism nor temporal lobe metabolism was correlated with the area of any corpus callosum region.
In controls, the multiple regression explained no significant proportion of variance of any normalized lobar metabolic value predicted by any corpus callosum subregion.
We investigated whether regional corpus callosum atrophy in AD was correlated with cortical glucose metabolism, to evaluate the use of corpus callosum area as an indirect marker of cortical disconnection in AD. We found atrophy of the corpus callosum, predominantly in the callosal rostrum and splenium, in patients with AD with a relatively wide range of white matter hyperintensities. This pattern remained stable after normalization to total intracranial volume to control for a possible effect of different sex ratios between groups. The frequency of white matter hyperintensities did not differ between patients with AD and controls, and white matter hyperintensities did not contribute to corpus callosum atrophy in patients with AD. This is generally consistent with our previously published report18 of corpus callosum atrophy in patients with AD with only minimal white matter changes. Because parietal and occipital cortical areas project mainly through the posterior corpus callosum,40,41 the inclusion of 3 patients with AD with Balint syndrome who exhibited predominantly occipitoparietal cortical disease42 may account for the predominance of posterior corpus callosum atrophy in our sample with AD. In contrast to our findings, Vermersch et al13 reported more white matter changes in their group with AD than in healthy controls and a significant correlation between corpus callosum atrophy and white matter hyperintensities. The mean±SD age in their sample with AD, however, was higher than in ours (75.6±9.5 years vs 67.3±8.2 years) (P≤.04)). Because there is evidence43 of specific white matter changes in late-onset AD, a greater contribution of primary white matter degeneration to corpus callosum atrophy might occur in late-onset than in early-onset AD.
We found a significant correlation between the anterior-posterior corpus callosum ratio in patients with AD and the frontal-to-occipital and frontal-to-parietal metabolic ratios but not the frontal-to-temporal metabolic ratio. Reduced metabolism in occipital and parietal relative to frontal association cortical areas was accompanied by an analogous pattern of reduced posterior corpus callosum relative to frontal corpus callosum area. The anterior-posterior metabolic pattern has previously been established31 as a sensitive marker of cortical disconnection in AD. Thus, the significant correlation between the corpus callosum and metabolic ratios supports our notion that corpus callosum atrophy in AD may indicate a loss of intracortical connective neurons due to primary cortical disease rather than primary subcortical fiber loss. The anterior two thirds of the temporal lobe, including the allocortical areas of its mesial aspect, have no direct contralateral projections through the corpus callosum, which may account for the lack of a significant correlation between the temporal metabolic and corpus callosum ratio.44,45 The inclusion of 3 subjects with AD with primary visuospatial impairment who were diagnosed as having the Balint syndrome increased the variance of the data, without introducing a statistically distinct subgroup (Figure 1 and Figure 2). By visual inspection, the subjects with the Balint syndrome exhibited both parieto-occipital hypometabolism and posterior corpus callosum atrophy. The sample, however, was too small to allow for statistical testing of this pattern.
In controls, we did not find any correlation between metabolic and corpus callosum ratios. This likely reflects the narrow range of the metabolic ratio data in the control group.
The normalized frontal callosal cross-sectional area in our group with AD was not correlated significantly with normalized frontal metabolism, and the normalized posterior corpus callosum area did not correlate with normalized parietotemporo-occipital cortical metabolism. In contrast, Yamauchi et al14 reported a correlation between frontal and parietotemporo-occipital cortical metabolism and frontal and posterior corpus callosum areas in AD. Significant correlations, however, between 2 large regions in PET studies and the corpus callosum may reflect a global reduction of cortical glucose metabolism parallel to overall brain atrophy, rather than a specific correlation between a regional pattern of cortical hypometabolism and corpus callosum atrophy. Looking at smaller cortical and corpus callosum regions, we did find a significant correlation of the frontal and parietal lobes cortical metabolic rates with region C3, representing the middle truncus of the corpus callosum. This finding is surprising. Both cortical regions may send a small proportion of their interhemispheric fibers through this callosal region, but the major part of projections runs through callosal areas whose atrophy was not related to metabolism in both lobes.40,41,46,47
A significant correlation between metabolic and corpus callosum ratios suggests a region-specific loss of interhemispheric projecting neurons in neocortex as the main cause of corpus callosum atrophy in AD, independent of primary white matter pathology. Metabolic impairment in PET cannot be explained only by damage to callosally projecting neurons. However, because these neurons form a subgroup of the intracortical projecting large pyramidal neurons in cortical layers 3 and 5,7,8,10,11 the correlation between corpus callosum atrophy and the anterior-posterior metabolic pattern in PET studies may be representative for the temporal course and regional distribution of AD pathology to corticocortical projecting neurons.
To establish the corpus callosum as a possible in vivo marker for the progress and regional distribution of neocortical degeneration in AD, future studies on a larger AD sample are warranted to allow the further investigation of the strength of correlations between corpus callosum atrophy and the pattern of cortical metabolic impairment in the dependency of disease progression.
Accepted for publication August 6, 1998.
This work was supported in part by the Ernst Jung Foundation, Hamburg, Germany (Dr Hampel).
Part of the presented material originates from a doctoral thesis (Dr Teipel, Ludwig-Maximilian-University, Munich, Germany; in preparation).
We thank H. Hippius, MD, Department of Psychiatry, Ludwig-Maximilian-University, for his generous support and Ms Marion Teipel for technical support.
Reprints: Stefan J. Teipel, MD, Geriatric Psychiatry Branch, Research Unit D2, Department of Psychiatry, Ludwig-Maximilian-University, School of Medicine, Nussbaumstr 7, 80336 Munich, Germany (e-mail: stt@psy.med.uni-muenchen.de).
1.van Hoesen
GW The dissection by Alzheimer's disease of cortical and limbic neural systems relevant to memory. McGaugh
JWeinberger
Neds.
Brain Organization and Memory Cells, Systems and Circuits. New York, NY Oxford University Press1990;234- 261
Google Scholar 2.Armstrong
RA The spatial pattern of discrete β-amyloid deposits in Alzheimer's disease reflects synaptic disconnection.
Dementia. 1996;786- 90
Google Scholar 3.Morrison
JHScherr
SLewis
DACampbell
MJBloom
FE The laminar and regional distribution of neocortical somatostatin and neuritic plaques: implications for Alzheimer's disease as a global neocortical disconnection syndrome. Scheibel
ABWeschler
AFeds.
The Biological Substrates of Alzheimer's Disease. New York, NY Academic Press, Inc1986;115- 131
Google Scholar 4.Pearson
RCAEsiri
MMHiorns
RWWilcock
GKPowell
TPS Anatomical correlates of the distribution of the pathological changes in the neocortex in Alzheimer's disease.
Proc Natl Acad Sci U S A. 1985;824531- 4534
Google ScholarCrossref 5.Lewis
DACampbell
MJTerry
RDMorrison
JH Laminar and regional distributions of neurofibrillary tangles and neuritic plaques in Alzheimer's disease: a quantitative study of visual and auditory cortices.
J Neurosci. 1987;71799- 1808
Google Scholar 6.Rogers
JMorrison
JH Quantitative morphology and regional and laminar distributions of senile plaques in Alzheimer's disease.
J Neurosci. 1985;52801- 2808
Google Scholar 7.Jones
EG Laminar distribution of cortical efferent cells. Peters
AJones
EGeds.
Cerebral Cortex Cellular Components of the Cerebral Cortex. Vol 1 New York, NY Plenum Publishing Corp1984;521- 553
Google Scholar 8.Glickstein
MWhitteridge
D Degeneration of layer III pyramidal cells in area 18 following destruction of callosal input.
Brain Res. 1976;104148- 151
Google ScholarCrossref 9.Tigges
JTigges
MAnschel
SCross
NALetbetter
WDMcBride
RL Areal and laminar distribution of neurons interconnecting the central visual cortical areas 17, 18, 19 and MT in squirrel monkey (
Saimiri).
J Comp Neurol. 1981;202539- 560
Google ScholarCrossref 10.Hiorns
RWNeal
JWPearson
RCAPowell
TPS Clustering of ipsilateral cortico-cortical projection neurons to area 7 in the rhesus monkey.
Proc R Soc Lond. 1991;2461- 9
Google ScholarCrossref 11.Conti
FManzoni
T The neurotransmitters and postsynaptic actions of callosally projecting neurons.
Behav Brain Res. 1994;6437- 53
Google ScholarCrossref 12.Innocenti
GM General organization of callosal connections in the cerebral cortex. Jones
EGPeters
Aeds.
Cerebral Cortex Sensory Motor Areas and Aspects of Cortical Connectivity. Vol 5 New York, NY Plenum Publishing Corp1986;291- 353
Google Scholar 13.Vermersch
PRoche
JHamon
M
et al. White matter magnetic resonance imaging hyperintensity in Alzheimer's disease: correlations with corpus callosum atrophy.
J Neurol. 1996;243231- 234
Google ScholarCrossref 14.Yamauchi
HFukuyama
HHarada
K
et al. Callosal atrophy parallels decreased cortical oxygen metabolism and neuropsychological impairment in Alzheimer's disease.
Arch Neurol. 1993;501070- 1074
Google ScholarCrossref 15.Janowsky
JSKaye
JACarper
RA Atrophy of the corpus callosum in Alzheimer's disease versus healthy aging.
J Am Geriatr Soc. 1996;44798- 803
Google Scholar 16.Biegon
AEberling
JLRichardson
BC
et al. Human corpus callosum in aging and Alzheimer's disease: a magnetic resonance imaging study.
Neurobiol Aging. 1994;15393- 397
Google ScholarCrossref 17.Cuénod
CADenys
AMichot
JL
et al. Amygdala atrophy in Alzheimer's disease: an in vivo magnetic resonance imaging study.
Arch Neurol. 1993;50941- 945
Google ScholarCrossref 18.Hampel
HTeipel
SJAlexander
GE
et al. Corpus callosum atrophy is a possible indicator for region- and cell type–specific neuronal degeneration in Alzheimer disease: a magnetic resonance imaging analysis.
Arch Neurol. 1998;55193- 198
Google ScholarCrossref 19.Grady
CLRapoport
SI Cerebral metabolism imaging and dementia. Birren
JESloane
RBeds.
Handbook of Mental Health and Aging. New York, NY Academic Press, Inc1992;201- 223
Google Scholar 20.Duara
RGrady
CHaxby
J
et al. Positron emission tomography in Alzheimer's disease.
Neurology. 1986;36879- 887
Google ScholarCrossref 21.Kumar
ASchapiro
MBGrady
CL
et al. High resolution PET studies in Alzheimer's disease.
Neuropsychopharmacology. 1991;435- 46
Google Scholar 22.Foster
NLChase
TNFedio
PPatronas
NJBrooks
RADi Chiro
G Alzheimer's disease: focal cortical changes shown by positron emission tomography.
Neurology. 1983;33961- 965
Google ScholarCrossref 23.Frackowiak
RSJPozzilli
CLegg
NJ
et al. Regional cerebral oxygen supply and utilization in dementia: a clinical and physiological study with oxygen-15 and positron tomography.
Brain. 1981;104
((pt 4))
753- 778
Google ScholarCrossref 24.Friedland
RPBrun
ABudinger
TF Pathological and positron emission tomographic correlations in Alzheimer's disease [letter].
Lancet. 1985;1228
Google ScholarCrossref 25.Haxby
JVGrady
CLKoss
E
et al. Longitudinal study of cerebral metabolic asymmetries and associated neuropsychological patterns in early dementia of the Alzheimer type.
Arch Neurol. 1990;47753- 760
Google ScholarCrossref 26.Rapoport
SIHorwitz
BHaxby
JVGrady
CL Alzheimer's disease: metabolic uncoupling of associative brain regions.
Can J Neurol Sci. 1986;13(suppl 4)540- 545
Google Scholar 27.Horwitz
BGrady
CLSchlageter
NLDuara
RRapoport
SI Intercorrelations of regional cerebral glucose metabolic rates in Alzheimer's disease.
Brain Res. 1987;407294- 306
Google ScholarCrossref 28.Azari
NPRapoport
SIGrady
CL
et al. Patterns of interregional correlations of cerebral glucose metabolic rates in patients with dementia of the Alzheimer type.
Neurodegeneration. 1992;1101- 111
Google Scholar 29.Haxby
JVDuara
RGrady
CLCutler
NRRapoport
SI Relations between neuropsychological and cerebral metabolic asymmetries in early Alzheimer's disease.
J Cereb Blood Flow Metab. 1985;5193- 200
Google ScholarCrossref 30.Azari
NPPettigrew
KDSchapiro
MB
et al. Early detection of Alzheimer's disease: a statistical approach using positron emission tomographic data.
J Cereb Blood Flow Metab. 1993;13438- 447
Google ScholarCrossref 31.Haxby
JVGrady
CLKoss
E
et al. Heterogeneous anterior-posterior metabolic patterns in dementia of the Alzheimer type.
Neurology. 1988;381853- 1863
Google ScholarCrossref 32.Scheltens
PBarkhof
FLeys
D
et al. A semiquantitative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging.
J Neurol Sci. 1993;1147- 12
Google ScholarCrossref 33.McKhann
GDrachman
DFolstein
MKatzman
RPrice
DStadlan
EM Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA work group under the auspices of the Department of Health and Human Services Task Force on Alzheimer's Disease.
Neurology. 1984;34939- 944
Google ScholarCrossref 34.Folstein
MFFolstein
SEMcHugh
PR "Mini-mental-state": a practical method for grading the cognitive state of patients for the clinician.
J Psychiatr Res. 1975;12189- 198
Google ScholarCrossref 35.Pietrini
PDani
AFurey
ML
et al. Low glucose metabolism during brain stimulation in older Down's syndrome subjects at risk for Alzheimer's disease prior to dementia.
Am J Psychiatry. 1997;1541063- 1069
Google Scholar 36.Mattis
S Mental status examination for organic mental syndrome in the elderly patient. Bellak
LKarasu
TBeds.
Geriatric Psychiatry. New York, NY Grune & Stratton Inc1976;
Google Scholar 38.Eycleshymer
ACShoemaker
DM A Cross-section Anatomy. New York, NY Appleton1911;
39.Minoshima
SFrey
KAKoeppe
RAFoster
NLKuhl
DE A diagnostic approach in Alzheimer's disease using three-dimensional stereotactic surface projections of fluorine-18-FDG PET.
J Nucl Med. 1995;361238- 1248
Google Scholar 40.Pandya
DNSeltzer
B The topography of commissural fibres. Lepre
FPtito
MJasper
HHeds.
Two Hemispheres—One Brain Functions of the Corpus Callosum. New York, NY Alan R Liss Inc1986;47- 73
Google Scholar 41.de Lacoste
MCKirkpatrick
JBRoss
ED Topography of the human corpus callosum.
J Neuropathol Exp Neurol. 1985;44578- 591
Google ScholarCrossref 42.Levine
DNLee
JMFisher
CM The visual variant of Alzheimer's disease: a clinicopathologic case study.
Neurology. 1993;43305- 313
Google ScholarCrossref 43.Scheltens
PBarkhof
FValk
J
et al. White matter lesions on magnetic resonance imaging in clinically diagnosed Alzheimer's disease.
Brain. 1992;115735- 748
Google ScholarCrossref 44.Demeter
SRosene
DLvan Hoesen
GW Interhemispheric pathways of the hippocampal formation, presubiculum, and entorhinal and posterior parahippocampal cortices in the rhesus monkey: the structure and organization of the hippocampal commissures.
J Comp Neurol. 1985;23330- 47
Google ScholarCrossref 45.Demeter
SRingo
JLDoty
RW Morphometric analysis of the human corpus callosum and anterior commissure.
Hum Neurobiol. 1988;6219- 226
Google Scholar 46.Pandya
DNKarol
EAHeilbronn
D The topographical distribution of interhemispheric projections in the corpus callosum of the rhesus monkey.
Brain Res. 1971;3231- 43
Google ScholarCrossref 47.Yu-ling
TBing-huan
CJiong-da
Y
et al. Localization of functional projections from corpus callosum to cerebral cortex.
Chin Med J (Engl). 1991;104851- 857
Google Scholar