Corticolimbic Degeneration and Treatment of Dementia
Phase: PHASE4
Conditions: Dementia
Sponsor: Washington University School of Medicine

 
 
 
 
 
 
 
 
RESEARCH PROTOCOL 
 
CORTICOLIMBIC DEGENERATION AND TREATMENT 
OF DEMENTIA 
 
AUGUST 26, 2008 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
 
 
 
PRINCIPAL INVESTIGATOR 
 
John G. Csernansky, M.D. 
Gilman Professor and Chairman 
Department of Psychiatry and Behavioral Sciences 
Northwestern Feinberg School of Medicine 
446 E. Ontario St., Suite 7-200 
Chicago, Illinois   60611 
Phone: (312) 926-2323 
Fax: (312) 695-6276 
E-mail: jgc@northwestern.edu 
 
 
STUDY SITES 
 
Data Collection 
Washington University School of Medicine 
Alzheimer’s Disease Research Center (ADRC) 
4488 Forest Park Ave #130 
St. Louis, MO 63108 
 
Data Analysis of De-identified Data 
Northwestern University 
Department of Psychiatry and Behavioral Sciences 
446 E. Ontario, Suite 7-200 
Chicago, IL 60611 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
 
 
 
 
TABLE OF CONTENTS 
 
PURPOSE AND OBJECTIVES 
 1 
BACKGROUND 
 2 
 
CHOLINESTERASE INHIBITORS AS TREATMENTS FOR DAT 
2 
 
NMDA ANTAGONISTS AS TREATMENTS FOR DAT 
3 
 
NEUROANATOMICAL MEASURES AS MARKERS OF DISEASE PROGRESSION 
3 
 
 
CLINICAL SIGNIFICANCE OF THE PROPOSED STUDIES 
PRELIMINARY STUDIES 
4 
 
DISCRIMINATION OF SUBJECTS WITH VERY MILD DAT AND CONTROLS USING        
        NEUROANATOMICAL MARKERS 
4 
 
ASSESSMENT OF DISEASE PROGRESSION USING NEUROANATOMICAL MARKERS 
5 
  
HIPPOCAMPAL SHAPE PREDICTS RESPONSE TO DONEPEZIL 
6 
 
 
STUDY DESIGN 
7 
 
SPECIFIC AIMS 1&2 – CORRELATIONS BETWEEN CG LEVELS AND DISEASE PROGRESSION IN  
 
DAT SUBJECTS 
7 
   
          OVERALL DESIGN AND SOURCE OF SUBJECTS 
7 
 
                    INCLUSION CRITERIA 
9 
 
                    EXCLUSION CRITERIA 
9 
 
          SELECTION OF INSTRUMENTS FOR CLINICAL EVALUATION 
10 
 
          NEUROANATOMICAL RULES FOR BRAIN STRUCTURE BOUNDARIES 
10 
 
          MR SCANNING PROTOCOLS AND IMAGE PREPROCESSING 
10 
 
          LARGE DEFORMATION HIGH DIMENSIONAL BRAIN MAPPING (HDBM-LD)  TO ASSESS  
 
          THE VOLUME AND SHAPE OF THE HIPPOCAMUS  
12 
 
          METHODS FOR LABELED CORTICAL DEPTH MAPPING (LCDM) OF THE  
 
          PARAHIPPOCAMPAL AND CINGULATE GYRI 
14 
 
          ASSESSMENT OF TOTAL INTRACRANIAL AND CEREBRAL VOLUMES 
15 
 
          DATA ANALYSIS 
15 
 
          SAMPLE SIZE AND POWER CONSIDERATIONS 
16 
HUMAN SUBJECTS 
16 
DATA MANAGEMENT  
18 
REFERENCES  
 
 
 
 
 
 
 
 
 
          19 
 

1                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
PURPOSE AND OBJECTIVES 
 
Amyloid plaques, neurofibrillary tangles and other indications of neuronal degeneration in the hippocampus and 
cortical structures that are functionally related to it, such as the parahippocampal and cingulate gyri, are among 
the neuropathological hallmarks of AD (Hyman, et al, 1984 and 1987; Arnold, et al, 1991; Gomez-Isla, et al, 
1996; Berg, et al, 1998). Since these corticolimbic structures have been shown in humans to play a critical role 
in declarative memory (Squire and Zola-Morgan, 1991), neurodegeneration within them has been hypothesized 
to be the basis for memory losses in patients with dementia of the Alzheimer type (DAT). Moreover, 
progressive structural degeneration of the hippocampus occurs in parallel with clinical worsening of the disease 
(Jobst, et al, 1994; Fox, et al, 1996; Jack, et al, 1998). 
 
Treatment with cholinesterase inhibitors is now considered routine for patients with very mild-to-mild DAT 
(Knopman and Morris, 1997; Samuels and Davis, 1998).  A number of cholinesterase inhibitors (e.g., tacrine, 
donepezil, rivastigmine, and galantamine) are commercially available, and there is ample evidence of their 
capacity to ameliorate cognitive deficits associated with AD (Corey-Bloom, et al, 1998; Davis, et al, 1992; 
Farlow, et al, 1992; Knapp,et al, 1994; Morris, et al, 1998; Rogers, et al, 1998; Rogers and Friedhoff, 1996; 
Thal, et al, 1996; Wilcock and Wilkinson, 1996). However, these studies provide little evidence that 
cholinesterase inhibitors have the capacity to alter the progression of the disease. Recently, the first drug 
treatment for DAT other than a cholinesterase inhibitor was approved for use in the U.S. Memantine, a voltage 
dependent, uncompetitive NMDA antagonist, has also been shown to ameliorate the cognitive deficits 
associated with AD (Reisberg, et al, 2003), and in a trial comparing combined memantine/donepezil treatment 
to donepezil alone, the combination of memantine and donepezil was demonstrated to have superior efficacy 
(Tariot, et al, 2004). Because the mechanism of action of this drug involves modulation of the potentially 
excitotoxic neurotransmitter, glutamate, it has been hypothesized that this drug may slow progression of the 
disease process in AD (Jarvis and Figgitt, 2003). However, there is no direct evidence of memantine’s capacity 
to slow the progression of neurodegeneration in DAT patients. 
 
During the initial funding period of this grant, we tested the hypothesis that the volume and shape of the 
hippocampus could predict the outcome of treatment with donepezil in patients with very mild-to-mild DAT. 
The rationale for this experiment was that hippocampal neurons receive cholinergic afferent projections 
(Umbriaco, et al, 1995), and so became the indirect targets of cholinomimetic treatments. We now propose to 
extend this investigation of the relationships between neuroanatomical structure and treatment response in 
patients with DAT by 1) determining what neuroanatomical changes are most strongly correlated with the 
progression of cognitive deficits in patients with DAT (Specific Aim 1), and 2) determining whether treatment 
with cholinesterase inhibitors and/or adjunctive memantine can slow progression of the neuroanatomical and 
cognitive/behavioral changes associated with AD (Specific Aim 2).  
  
The specific aims and hypotheses of the project are as follows:  
 
Specific Aim 1: 
To determine what neuroanatomical measures are most strongly correlated with the progression of cognitive 
deficits in patients with DAT, we propose to use high resolution magnetic resonance (MR) imaging and the 
tools of computational anatomy to assess changes in the structure of the hippocampus, parahippocampal gyrus 
and cingulate gyrus in subjects with very mild-to-mild DAT over two years. During the first funding period of 
this project, we collected data using MR imaging and large-deformation high dimensional brain mapping 
(HDBM-LD) that shows that progressive hippocampal volume loss and shape change discriminates subjects 
with very mild DAT from nondemented controls (see Progress Report). The volume, thickness, and surface area 
of the parahippocampal and cingulate gyri will be assessed in the proposed work using labeled cortical depth 
mapping (LCDM), a new computational tool that was developed during the last funding period (see Progress 
Report). (Hypothesis 1: Over two years, decreases in hippocampal and cortical volumes, thinning of the 
cingulate and parahippocampal cortices, and changes in hippocampal shape will be correlated with the 
rate of cognitive decline in subjects with very mild-to-mild DAT. Moreover, in a stepwise regression 

2                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
model, each of these variables will predict the rate of cognitive decline in DAT subjects independently of 
each other and independently of the effects of age, gender and apoE allelic status.) 
 
Specific Aim 2:  
To determine whether cholinesterase inhibitors and memantine can slow the progression of disease in DAT 
subjects, we will use HDBM-LD and LCDM to compare the rate of change in the neuroanatomical measures 
listed above in three groups – 1) untreated DAT subjects, 2) DAT subjects treated with donepezil alone, and 3) 
DAT subjects treated with the combination of donepezil and memantine.  Past, present and future subjects 
recruited from the ADRC as well as the Memory Diagnostic Center (MDC) at Washington University will be 
used for this study, and the period of treatment will be two years.  Due to ethical considerations (see Research 
Design and Methods), drug treatment will be naturalistic and open label. (Hypothesis 2: The rate of 
decreasing hippocampal and cortical volumes, thinning of the cingulate and parahippocampal cortices, 
and changing hippocampal shape will be equal in DAT subjects that are untreated and in DAT subjects 
that are treated with donepezil alone, but greater than the rate of volume loss, thinning and hippocampal 
shape change in DAT subjects that are treated with the combination of donepezil and memantine.) 
 
BACKGROUND 
 
Cholinesterase Inhibitors as Treatments for DAT 
 
Several cholinesterase inhibitors are now commercially available for the treatment of DAT, and have been 
shown to have at least ameliorative effects on the symptoms of DAT, including memory loss (Corey-Bloom, et 
al, 1998; Davis, et al. 1992; Farlow, et al, 1992; Knapp,et al, 1994; Morris, et al, 1998; Rogers, et al., 1998; 
Rogers and Friedhoff, 1996; Thal, et al, 1996; Wilcock and Wilkinson, 1996). Cholinergic projections from the 
nucleus basalis are dispersed widely throughout the brain, and include the hippocampus, parahippocampal 
gyrus, cingulate gyrus and other neocortical areas.  However, because of the central role played by the 
hippocampus and structures related to it in memory (Zola-Morgan and Squire, 1993), and the high frequency 
with which these structures are affected early in the course of Alzheimer’s disease (Hyman, et al, 1984 and 
1987; Arnold, et al, 1991; Gomez-Isla, et al, 1996; Berg, et al, 1998), these structures may be especially 
involved in symptom production and treatment response in subjects with DAT.   
 
The precise mechanism of action of cholinesterase inhibitors for the treatment of DAT remains under 
investigation. Terminals for acetylcholine are found throughout the CA1 subfield of the hippocampus 
(Umbriaco, et al, 1995), and studies of neurotransmitter receptors show that the hippocampus contains a variety 
of receptors for acetylcholine. Receptors for both muscarinic (M1) and nicotinic ACh receptors are expressed 
throughout the CA1 subfield of the hippocampus. In particular, M1 receptors are found on the terminals of 
pyramidal neurons where they suppress excitatory activity (Dutar and Nicoll, 1988; Sheridan and Sutor, 1990), 
and on the terminals of inhibitory/GABA interneurons where they suppress GABA release (Behrends and Ten 
Bruggencate, 1993).  However, while acetylcholine has various sites of action within the hippocampus, the net 
result of these effects appears to be the excitation of hippocampal pyramidal neurons (Steckler and Sahgal, 
1995). In rats, injection of the prototypical cholinesterase inhibitor, physostigmine, directly into the 
hippocampus reverses the memory deficits induced by scopolamine (Ohno, et al, 1996). RU47213, another 
cholinesterase inhibitor, similarly reverses the working memory deficits of rats in the radial arm maze induced 
by scopolamine (M’Harzi, et al, 1997). These studies suggest that cholinomimetic drugs are effective in 
reversing cognitive deficits due to experimentally-induced cholinergic deficits. 
 
Relatively few studies have addressed the question of whether treatment with cholinesterase inhibitors can 
slow the progression of the AD process. In many clinical studies of cholinesterase inhibitors in patients with 
DAT, improvements in cognition are only temporary and when declines in cognition resume, they proceed 
similarly in patients that have received active treatment and placebo (Burns, et al, 1999; Feldman, et al, 2001; 
Raskind, et al, 2000; Rogers, et al, 1998). Moreover, there is no obvious neural mechanism by which 
acetylcholine might exert neuroprotective effects. Instead, acetylcholine is generally considered to be an 

3                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
excitatory neurotransmitter (Steckler and Sahgal, 1995), and elevations in the release of acetylcholine have even 
been reported to have neurotoxic effects (Kim, et al, 1999).  
 
NMDA Antagonists as Treatments for DAT 
 
 
Hippocampal pyramidal neurons are glutamatergic and are interconnected via a trisynaptic excitatory circuit 
(Wyss and van Groen, 1989). When experimentally-induced increases in the release of glutamate occur, 
ongoing destruction of the hippocampus can proceed through a cascade of excitotoxicity (Choi, et al, 1988; 
Lipton and Rosenberg, 1994). Of particular importance to the pathogenesis of AD and to the mechanism of 
action of memantine, Aβ may also initiate the excitotoxic cascade. For example, Harkany, et al (2000), reported 
that Aβ depolarizes astroglial membranes and blocks glutamate uptake by such cells.  Increases in the resultant 
levels of glutamate then trigger neuronal damage, which is blocked by the non-competitive NMDA antagonist, 
MK-801. Barger and Basile (2001) have also suggested that glial cells may be a source of the glutamate 
released by Aβ.  Excitotoxic stimulation, in turn, promotes the processing of APP so that more pathogenic 
forms of Aβ, such as Aβ42, accumulate (Mattson, et al, 1993). Finally, Parks, et al (2001) and Ikezu, et al 
(2003) have shown that the NMDA antagonist, MK-801, blocks the release of reactive oxygen species triggered 
by Aβ. 
 
Memantine is a voltage-dependent, uncompetitive antagonist at NMDA-type glutamate receptors that does 
not interfere with the physiological actions of glutamate required for learning and memory (see Kilpatrick and 
Tillbrook, 2002 and Jarvis and Figgitt, 2003 for review). Because of this unusual pharmacology, it is devoid of 
the anesthestic and psychotogenic effects that characterize non-competitive NMDA antagonists, such as 
phencyclidine and MK-801 (Frankiewicz, et al, 1996). Twenty years ago, memantine was observed to have 
alerting effects in comatose patients (Miltner, 1982), and more than 10 years ago, memantine was first 
investigated for use in patients with AD (Ditzler, 1991). Memantine’s hypothesized mechanism of action as an 
anti-dementia drug is the use-dependent (i.e., voltage-dependent) blockade of NMDA receptors that mediate 
excitotoxicity (Erdo and Schafer, 1991; Weller, et al, 1993) and neuronal degeneration triggered by Aβ 
(Miguel-Hidelgo, et al, 2002).  Also, memantine increases the expression of brain-derived neuroprotective 
factor (BDNF) and trkB in the mammalian brain (Marvanova, et al, 2001).  
 
The efficacy of memantine in patients with DAT has been recently been demonstrated in a major multi-
center study in the US (Reisberg, et al, 2003), and in the fall of 2003, memantine was approved for use in the 
U.S. as a treatment for DAT. Also, combination therapy of memantine and donepezil has been shown to be 
superior to treatment with donepezil alone (Farlow, et al, 2003; Tariot, et al, 2004). However, while reports of 
memantine’s efficacy have been impressive, it remains uncertain as to whether the drug can alter the 
progression of AD (Reisberg, et al, 2003). The potential of memantine to alter the course of AD rests on its 
capacity to block excitotoxicity (Jarvis and Figgitt, 2003). However, clinical trials alone cannot provide 
conclusive evidence of its ability to block excitotoxic neurodegeneration and so alter the underlying disease 
process of AD (see Areosa and Sherriff, 2003 for review).  
   
 
Neuroanatomical Measures as Markers of Disease Progression 
 
 
Substantial volume losses in medial temporal lobe brain structures, such as the hippocampus, have been 
reported using manual analysis of MR scans in DAT subjects (Jack, et al, 1992; Pearlson, et al, 1992; Scheltens, 
et al, 1992; Ikeda, et al, 1994).  These studies are consistent with the results of post-mortem studies, which 
demonstrate substantial neuronal loss in the same brain areas (Gomez-Isla, et al, 1996). Substantial volume 
losses in the hippocampus and parahippocampal gyrus are seen in people with even mild DAT, and the 
progression of such volume losses correlates with symptomatic worsening (Jobst, et al, 1994; Fox, et al, 1996; 
Killiany, et al, 1993; Jack, et al, 1998; Laakso, et al, 1996; Murphy, et al, 1993; Mungas, et al, 2002; Wang, et 
al, 2003). In fact, progression of hippocampal volume loss over time has been suggested as a method of 
discriminating patients with DAT from patients with normal aging and other brain diseases (Fox, et al, 1996; 

4                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
Jack, et al, 1998). However, to date, there have been no studies that have answered the question of whether drug 
treatment alters the progression of neuroanatomical changes in patients with DAT. 
 
Computerized methods for MR image analysis are being increasingly applied to the problem of identifying 
early forms of AD and other neuropsychiatric diseases (Iosifescu, et al, 1997; Thompson, et al, 1997; 
Ashburner, et al, 2003). We have recently completed several studies of the hippocampus using one of these 
methods (i.e., HDBM-LD) in subjects with very mild DAT, showing that hippocampal shape and volume can be 
used to discriminate DAT and control subjects (Csernansky, et al, 2000; Wang, et al, 2003) and that antemortem 
hippocampal volume predicted post-mortem tangle density in the hippocampus (Csernansky, et al, 2004). The 
results of these studies indicate that subjects with very mild DAT symptoms have highly characteristic shape 
deformations of the hippocampus as well as substantial hippocampal volume losses. These methods also hold 
promise for improving our capacity to study disease progression in DAT patients during drug treatment. In drug 
treatment studies, one is chiefly interested in assessing the degree of change within individuals.  This problem is 
ideally addressed by methods that can transform one MR scan (i.e., at baseline) onto another (i.e., after drug 
treatment), thus quantifying subtle changes in neuroanatomical volumes and shapes. Fox and colleagues have 
performed a number of studies of this kind in subjects with and without DAT using a methodology called voxel 
compression mapping (VBM) (Fox, et al, 1996 and 2001; Fox and Freeborough, 1997; Freeborough and Fox, 
1998).  Also, this group has shown compelling evidence that VBM as compared to conventional manual 
volumetry significantly reduces the sample size required to detect the effects of drug treatment in patients with 
DAT (Fox, et al, 2000). During the first funding period of this project, we used HDBM-LD to compare patterns 
of change in hippocampal volume and shape in untreated patients with very mild DAT and non-demented 
controls (Wang, et al, 2003). In this study, we demonstrated that hippocampal shape change and the rate of 
volume loss provided complementary information for distinguishing subjects with DAT from the non-demented 
controls. 
 
Clinical Significance of the Proposed Studies 
 
There are two major areas of clinical significance to the proposed studies. First, the human study proposed in 
this application represent direct tests of the capacity of memantine and donepezil to slow the progression of 
DAT in patients. Second, we will be able to identify neuroanatomical measures that are most highly correlated  
for assessing the clinical progression of the AD process. By identifying neuroanatomical measures that are most 
sensitive to disease progression and to the potential effects of drug treatment, we will be identifying measures 
likely to be sensitive to the disease-altering effects of other therapeutic approaches to be developed in the future.  
 
PRELIMINARY STUDIES 
Discrimination of Subjects with Very Mild DAT and Controls Using Neuroanatomical Markers 
 
To select neuroanatomical markers optimal for tracking the progression of disease in patients with DAT, 
one can first determine which neuroanatomical markers discriminate patients with early forms of DAT from 
controls. In our first study of this kind, we compared 18 subjects with very mild DAT and 18 elderly controls 
matched for age and gender using measures of hippocampal volume and shape generated by HDBM-LD 
(Csernansky, et al, 2000).  The elderly subjects were categorized as having either very mild DAT (i.e. CDR 0.5) 
or no dementia (i.e., CDR 0) using the Clinical Dementia Rating Scale (CDR) (Morris, 1993). MR scans from a 
younger control group previously recruited for a study of schizophrenia (Csernansky, et al, 1998) were included 
so that we could also assess the effects of aging alone on hippocampal structure. Measures of hippocampal 
volume and shape both discriminated the CDR 0.5 and CDR 0 subjects. There was no significant difference in 
hippocampal volume between the elder non-demented subjects and the younger control subjects; however, the 
shape of the hippocampus also discriminated these two groups. Interestingly, the patterns of hippocampal shape 
deformation that discriminated CDR 0.5 and CDR 0 subjects and the CDR 0 subjects from younger controls 
were markedly different, indicating that the neurobiological effects of early DAT and healthy aging on 
hippocampal structure were distinct.  

5                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
 
 
Figure 1.    Visualizing the pattern of 
hippocampal surface deformation 
associated with DAT.   The left and 
right hippocampi are viewed from above 
and to the right of the paired structures. 
The structure seen represents the com-
posite hippocampus of the non-demented 
subjects and the shading indicates the 
degree of displacement of the hippocam-
pal surface that was present in the DAT 
subjects (cooler colors (e.g. blue) 
represent inward deformation and 
warmer colors represent outward 
deformation). Areas of blue shading 
indicate regions of the hippocampal 
surface where there is localized volume 
loss (i.e., inward deformation) in the 
DAT subjects.  
R 
L 
We have recently replicated the results of our first study of hippocampal structure in DAT in a larger, non-
overlapping group of 46 subjects with DAT and 65 non-demented controls (Csernansky, et al, in preparation).  
The DAT and non-demented subjects were again matched with regard to gender (20/26 [m/f] for the DAT 
subjects and 19/46 [m/f] for the non-demented subjects) and age (mean (SD) 74.6 (7.9) years for the DAT 
subjects and 78.2 (7.7) years for the non-demented subjects).  The mean (SD) sum-of-box score derived from 
the CDR was 4.05 (2.04) for the DAT subjects, indicating that the subjects had very mild-to-mild dementia. The 
non-demented subjects had a mean (SD) hippocampal volume of 2142 mm3 (344) on the left and 2588 mm3 
(416) on the right, while the DAT subjects had a mean (SD) hippocampal volume of 1833 mm3 (359) on the left 
and 2242 mm3 (462) on the right. 
 
Analysis of variance (ANOVA) showed that the difference in hippocampal volumes between these two subject 
groups was highly significant (F = 21.38, df = 1,109, p < .0001). As in our first study (Csernansky, et al, 2000), 
the difference in hippocampal shape between the two groups was 
evaluated using eigenvectors derived from the HDBM-LD 
transformations (see Research Design and Methods for further 
explanation). The first 20 eigenvectors explained 87.5% of the variance 
in hippocampal shape, and a MANOVA using these variables revealed a 
significant group difference (F = 3.34, df = 20,90, p < .0001). Moreover, 
in a "leave-one-out" (jackknife) discriminant function analysis, 56 of the 
65 non-demented subjects (86% sensitivity) and 36 of 46 DAT subjects 
could be correctly classified (78% specificity). In this study, the group 
discrimination achieved by hippo-campal shape information alone was 
equivalent to the discrimination achieved when both hippocampal 
volume and shape information was used. Not surprisingly, eigenvector 1 
was correlated with both left (rho = .69, p < .0001) and right (rho = .69, 
p < .0001) hippocampal volumes.   
The pattern of hippocampal shape abnormality found in these DAT 
subjects (see Figure 1) was highly similar to the pattern of deformity we 
observed in our first study (Csernansky, et al, 2000), and again showed 
inward deformation of the head and lateral prominence of the 
hippocampus. This pattern of hippocampal deformation is highly 
consistent with patterns of plaque and tangle deposition in the CA1 
hippocampal subfield observed in post-mortem studies of AD (Arnold, 
et al, 1991; Price and Morris, 1999). 
 
 
 
 
 
 
Assessment of Disease Progression Using Neuroanatomical Markers   
 
During the first funding period, we completed a longitudinal study comparing changes in hippocampal 
structure in CDR 0.5 and CDR 0 subjects matched for age and gender over approximately two years (mean 
interval in years (range) - CDR 0.5 – 2.0 (1.0-2.6) and CDR 0 subjects– 2.2 (1.4-4.1) (Wang, et al, 2003). The 
results of this study are critical for this application, as they demonstrate that our approach to assessing disease 
progression can discriminate between subjects with and without DAT, and so may also be sensitive to 
differences between groups of DAT patients treated with different drugs. This study included 18 CDR 0.5 
subjects and 26 CDR 0 subjects that were partially overlapping with the subjects included in our previously 
published cross-sectional study (Csernansky, et al, 2000). The 18 CDR 0.5 subjects were not treated with 
cholinesterase inhibitors or memantine during the period of study. Measures of hippocampal shape and 

6                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
R 
L 
CDR 0.5 
CDR 0 
Figure 2.  Comparisons from 
Baseline to Follow-up (two 
years) in CDR 0.5 and CDR 0 
Elder Subjects. Displacement 
maps were used to visualize the 
across-time difference in hippo-
campal structure in the two sub-
ject groups. Cooler colors (blue to 
purple) signify areas of inward 
deformation over time. In the 
CDR 0.5 subjects, areas of inward 
deformation were more wide-
spread and included more of the 
lateral edge of the hippocampus, 
while in the CDR 0 subjects, areas 
of inward deformation were 
smaller and confined to the head 
f h hi
volume were generated across both groups and time as in the cross-sectional study. At baseline, the CDR 0 
subjects had a mean (SD) hippocampal volume of 2081(354) mm3 on the left side and 2600 (481) mm3 on the 
right side, while the CDR 0.5 subjects had a mean hippocampal volume of 1717 (224 mm3) on the left side 
and 2186 (370 mm3) on the right side.  At follow-up, the CDR 0 subjects showed a hippocampal volume 
reduction of 82 mm3 on the left side and 142 mm3 on the right side, while the CDR 0.5 subjects showed a 
hippocampal volume reduction of 164 mm3 on the left side and 236 mm3 on the right side. ANOVA indicated 
a significant group effect on the rate of hippocampal volume loss (F = 7.81, df = 1,42, p = 0.008). This 
difference persisted after covarying hippocampal volumes for total cerebral brain volume (p = 0.007), total 
intra-cranial volume (p = 0.008) and scan interval (p = 0.015). 
There was also a group difference in the rate of progression of hippocampal 
shape deformity. The first 12 eigenvectors explained 75% of the variance in 
hippocampal shape, and using these 12 eigenvectors as variables in a 
MANOVA, we found a significant group effect (F = 2.66, df = 12,31, p = 
0.014; effect size = 2.0). In a follow-up logistic regression analysis, the 
combination of eigenvectors 1, 2, 4, and 11 were found to be optimally useful 
for discriminating the patterns of hippocampal shape change in the two groups 
of subjects (χ2 = 19.4).  Using these four eigenvectors, a significant group x 
time interaction was found (F = 7.81, df = 4,39, p < 0.008), and in a “take-one-
out'' discriminant function analysis, 22 out of 26 (84.6%) CDR 0 subjects and 
15 out of 18 (83.3%) CDR 0.5 subjects were correctly classified.  When 
hippocampal volume change was included in this logistic regression analysis, 
only eigenvectors 2, 4, and 11 were selected (χ2=20.1) (eigenvector 1 was again 
correlated with the change in hippocampal volume across subject groups - r = - 
0.75, p < 0.0001). Visualization of the changes in the shape of the hippocampus 
in the two groups revealed that areas of inward deformities over time were 
more widespread in the CDR 0.5 than in the CDR 0 subjects (see Figure 2). 
 
Hippocampal Shape Predicts Response to Donepezil  
 
A major objective during the first funding period was to test the hypothesis 
that an assessment of hippocampal structure could be used to predict the 
outcome of donepezil treatment in DAT patients. As proposed in our original 
application, estimates of cognitive decline over time were generated using CDR 
sum of-boxes scores, ADAS-Cog total scores, MMSE total scores and 
Neuropsychiatric Inventory (NPI) total scores (see Research  Design and 
Methods section below for more description of these measures). Fifty-one 
subjects with DAT were included in this analysis and, to date, data were 
available for a minimum of 48 weeks in 38 subjects (this trial will be completed 
in the final year of the present funding period). In almost all cases, the subjects 
had been treated with donepezil, 10 mg/day, for the period of study – in rare 
subjects that could not tolerate this dose of donepezil, 5 mg/ day, was used.  In 
addition, we included data from 65 non-demented subjects (CDR 0) in the 
analysis, so that we could evaluate variation in hippocampal shape related to 
drug response in the context of normative structure. To estimate the rate of annual change in the selected 
clinical measures, a slope value was calculated for each subject using a growth curve model (Laird and Ware, 
1982). The mean (SD) annual rate of change for the sum-of-boxes scores was 0.13 (± 0.11, range -0.06 to 
+0.41) per year. Other mean (SD) annual rates of change were as follows: ADAS-Cog total score -  0.15 (± 
0.29, range -0.29 to +1.32); MMSE total score -  -0.084 (± 0.17, range - -0.61 to +0.16); the NPI total score -  
+0.027 (± 0.06, range  -0.03 to +0.37). Thus, in general, mild deterioration was observed in the DAT subjects 

7                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
Figure 3.  Pattern of Hippo-
campal Shape Variation 
Correlated with the 
Outcome of Donepezil 
Treatment.  
The two panels illustrate the 
extremes of hippocampal 
shape variation in eigenvector 
3, that were characteristic of 
DAT subjects who showed 
clinical worsening (top panel) 
and improvement (bottom 
panel) during donepezil 
treatment. Cooler colors (blue 
to purple) signify areas of 
inward deformation and 
warmer colors signify areas of 
outward deformation relative 
to the mean shape of all 
subjects.  
R 
L 
R 
treated with donepezil (i.e., slopes for sum-of-boxes scores, ADAS-Cog and NPI scores were positive, while 
MMSE slopes were negative). 
Hippocampal volumes, total cerebral volumes and total intracranial volumes were not significantly 
correlated with any of the measures of cognitive decline.  However, among the first 10 eigenvectors (which 
explained  > 80% of the variance in hippocampal shape), eigenvector 3 coefficients were correlated with two 
measures of the rate of cognitive decline. More negative eigenvector 3 coefficient values were significantly 
correlated with increasing sum-of-boxes slope values (r = -0.34, p = 0.03, two-tailed) and with increasing NPI 
slope values (r = -0.31, p = 0.05, two-tailed).  A trend correlation was observed between more negative 
eigenvector 3 coefficient values and decreasing MMSE total scores (r = 0.29, p = 0.07, two-tailed), but no 
correlation was observed between eigenvector 3 coefficient values and ADAS-Cog total scores (r = -0.11, p = 
.50, two-tailed). These correlations were unchanged after taking into account total cerebral brain volume, total 
intracranial volume, and age.   
The pattern of hippocampal shape variation represented by eigenvector 3 is 
shown in Figure 3. The pattern of shape variation associated with greater 
cognitive decline during donepezil treatment was similar to the pattern of shape 
deformation previously shown to discriminate CDR 0.5 and CDR 0 subjects (see 
Figure 1), and to be associated with disease progression changes CDR 0.5 
subjects over time (see Figure 2) (i.e., inward deformation of the lateral 
prominence of the hippocampus). In turn, the relative absence of this pattern of 
deformation in hippocampal shape was associated a more favorable outcome 
during donepezil treatment. These results suggest that hippocampal shape may 
be a predictor of the capacity of DAT patients to respond to donepezil.  
 
 
STUDY DESIGN 
 
Clinical data and digital MR scans from the Specific Aims of this study will be 
de-identified and obtained for analysis from the clinical database maintained by 
the Alzheimer’s Disease Research Center (ADRC) at Washington University via 
an electronically secure file transfer protocol (ftp). At Northwestern University, 
the data will be downloaded to a Pentium D based PC managed by Dr. Lei 
Wang. The data will be physically secure and will have continuous power via a 
UPS and connection to an emergency power supply. The NMFF’s internet 
security system secures its network from the Internet via firewalls and a network 
intrusion detection system. Analysis of the data will be conducted by the Principle 
and Co-investigator. The confidentiality of the participants at Washington 
University will be protected as indicated in the “Code Access Agreement” signed 
by the PI and Washington University personnel such that Northwestern 
University personnel will not have access to subject identifiers or a master code 
list under any circumstances (see attached code access agreement). 
 
Specific Aims 1& 2 - Monitoring Disease Progression During Treatment with 
Donepezil and Memantine in Subjects with Very Mild-to-Mild DAT 
 
a. Overall Design and Source of Subjects. 
 
 Changes in specific neuroanatomical markers will be compared over a two 
year period in four groups of subjects – Group 1) subjects with very mild (CDR 
0.5) to mild (CDR 1) DAT that are untreated with either cholinesterase inhibitors or memantine, Group 2) 
subjects with very mild (CDR 0.5) to mild (CDR 1) DAT that are treated with donepezil, Group 3) subjects with 
R 
L 

8                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
very mild (CDR 0.5) to mild (CDR 1) DAT that are treated with the combination of donepezil and memantine, 
and Group 4) nondemented comparison subjects. MR scans and clinical data from Group 1 and Group 4 
subjects will be obtained from the clinical database and MR image archive maintained by the ADRC at 
Washington University. The ADRC is directed by Dr. John Morris, who is also a Co-Investigator on this 
project. At present, repeat MR scans and clinical data are available for 21 Group 1 subjects and 44 Group 4 
subjects. MR scans and clinical data from Group 2 subjects will be obtained from data collected during the 
initial funding period of this project. At present, complete sets of clinical data and repeat MR scans are available 
for 38 such subjects; however, 9 additional subjects are still completing the protocol – thus, the total number of 
Group 2 subjects could be as high as 47. Finally, repeat MR scans and clinical data from Group 3 subjects will 
be obtained during the proposed funding period of the project. Specifically, we propose to treat 50 new DAT 
subjects with the combination of memantine and donepezil during the proposed funding period, and again 
projecting an attrition rate of approximately 20% (see Data Analysis section below), this plan should yield 
approximately 40 Group 3 subjects for data analysis.  
New subjects to be studied during the proposed funding period will be recruited from DAT patients enrolled 
at the Memory Diagnostic Center (MDC) at Washington University School of Medicine, also directed by Drs. 
John C. Morris and James Galvin. These subjects are referred to the MDC from community neurologists and 
from the ADRC at Washington University. Presently, MDC patients are prescribed donepezil as the 
cholinesterase inhibitor of first choice, because of its tolerability and ease of use (single daily dosing).  Other 
cholinesterase inhibitors are offered only if the patient cannot tolerate donepezil or has a strong personal 
preference. A New Drug Application for memantine was submitted to the FDA in January of 2003, and 
memantine was approved for use in October, 2003. In February, 2003, the drug was distributed to U.S. 
pharmacies, and launch occurred in March, 2003. Thus, memantine will be clinically available in advance of the 
beginning of the proposed study. The MDC has evaluated the data available for memantine and plans to use this 
new drug almost exclusively as an adjunctive agent to donepezil (John Morris, M.D., personal communication).  
Clinical diagnostic criteria for DAT at the ADRC and MDC at Washington University correspond to those 
established by the NINCDS-ADRDA Work Group (McKhann, et al, 1984), and have a 93% accuracy for the 
histopathological presence of AD at autopsy (Morris, et al, 1996; Berg, et al, 1998). All subjects are assessed 
annually using clinical, psychometric, and behavioral measures that are valid for the detection of very mild 
stages of DAT (Berg, et al, 1998).  The clinical measures include staging of dementia severity using the CDR 
(Morris, 1993).  
Treatment will be administered in this study in an open-label fashion.  All DAT subjects selected for this 
study will already have been selected for donepezil therapy alone or the combination of donepezil and 
memantine, regardless of their participation in the proposed study. Therefore, randomization to a lesser form of 
drug therapy or placebo would be below the standard of care and therefore unethical (Kawas, et al, 1999; 
Knopman, et al, 1998).  Also, by selecting subjects for this study who have already been identified as candidates 
for drug therapy, our sample should be representative of clinical populations prescribed such treatments.  
All clinical evaluations will be performed blind to the results of neuroanatomical assessments. Therefore, no 
systematic bias will be introduced related to our main hypotheses involving correlations between the results of 
these biological assessments and treatment response.  Subjects will be excluded from this study if they have 
already had a substantial period of treatment (i.e., more than 8 weeks) with a cholinesterase inhibitor. Vitamin E 
treatment (400-800 U/day), non-steroidal drugs and Cox-2 inhibitor drugs will be permitted during the 
treatment period, since this situation is typical of clinical settings in which cholinesterase inhibitors and 
memantine are administered, so long as the doses of these treatments remain constant throughout the treatment 
period. However, other drug treatments under investigation for their efficacy in DAT will not be permitted 
during the treatment period.  
Prior to initiation of treatment, all subjects will undergo a comprehensive diagnostic evaluation using 
NINCDS-ADRDA criteria (McKhann, et al, 1984), as well as the CDR (Morris, 1993), the Alzheimer Disease 
Assessment Scale (ADAS; Rosen, et al, 1984), which includes a concise battery of cognitive tests (i.e., the 
ADAS-Cog), the Mini-Mental State Examination (MMSE; Folstein, et al, 1975), and the Neuropsychiatric 
Inventory (NPI), to assess the severity of dementia and behavioral symptoms related to dementia.  Emergent 

9                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
side-effects of donepezil and memantine treatment will be recorded using an instrument standard to clinical 
trials (Treatment Emergent Side-effect Scale [TESS]).  
Drug treatment will begin with 5 mg/day of donepezil for six weeks.  After six weeks of such treatment, the 
subjects’ symptoms will be re-evaluated and any side-effects of treatment assessed and recorded.  If no serious 
side-effects of donepezil are encountered, the dose of donepezil will be increased to 10 mg/day. For subjects 
prescribed the combination of donepezil and memantine, memantine (20 mg/day) will be added to the drug 
treatment regimen after the dose of donepezil has been established (i.e., at six weeks). Again, memantine will be 
initially started at 10 mg/day and increased to its full dose only if no serious side-effects are encountered. 
Subsequent evaluations will then be performed every 12 weeks for two years. All evaluations after 6 weeks will 
include the complete set of clinical and cognitive (i.e., ADAS-Cog) assessments, and a full diagnostic 
evaluation will be repeated annually (see Flowchart below). MR scans will be collected at baseline and after 
two years of treatment for all subjects who complete the entire period of treatment. For any subject that 
discontinues their participation in the study prematurely, the second MR scan will be collected at that time. 
_______________________________________________________________________________________________ 
 
Clinical Trial Flowchart 
 
Baseline  
 
       Six Weeks   
          12 Weeks                      Every 12 Weeks For Two Years 
 
Donepezil        Donepezil                      
(5 mg/d) 
 
      (5 to 10 mg/d) 
 
 
 
 
      Memantine                      
 
 
 
      (10 to 20 mg/d) 
Diagnosis 
 
 
 
 
 
 
 
         Repeat Diagnosis at 1 and 2 Yrs 
 
 
       
 
 
CDR 
 
 
       CDR 
 
 
CDR 
 
 
                 CDR 
ADAS-Cog 
 
       ADAS-Cog             ADAS-Cog 
 
      
     ADAS-Cog 
MMSE  
        
       MMSE 
 
 
MMSE  
                  
     MMSE 
 
NPI 
 
 
       NPI 
 
 
NPI  
 
 
      
     NPI 
 
 
 
       TESS 
 
 
TESS  
      
      
     TESS 
 
MRI 
 
 
 
 
 
   
 
 
 
      Repeat MRI at 2 Yrs 
_______________________________________________________________________________________________ 
 
Subjects will be selected for this study if they meet NINCDS-ADRDA criteria for DAT at baseline 
(McKhann, et al, 1984), and have a score of 0.5 or 1 on the CDR (Morris, 1993).  Subjects who no longer meet 
diagnostic criteria for DAT after one or two years of treatment, or who are diagnosed with another dementing 
illness, will still be followed for the remainder of the study, but their data will be evaluated separately from 
those subjects who continue to meet all inclusion and exclusion criteria.  Based upon our prior experience with 
this population of subjects in the ADRC (Berg, et al, 1992), we expect such changes in diagnosis to be rare.  
The great majority of DAT subjects rated as 0.5 or 1 using the CDR are proven at autopsy to show the 
neuropathological signs of AD (Morris, et al, 1996; Berg, et al, 1998).  Although it is possible that there will be 
subjects whose symptoms progress to the point where they can no longer be evaluated, this again should be rare 
given the demonstrated reliability and validity of the CDR across a wide range of severity of dementia 
symptoms (Berg, et al, 1992; Morris, et al, 1993). 
Inclusion Criteria: 1) meets NINCDS-ADRDA criteria for DAT, 2) CDR score of 0.5 or 1, 3) 50-80 years 
of age, 4) able to give informed consent or has a primary caregiver or legal guardian, who can give informed 
consent.  
Exclusion Criteria: 1) other psychiatric (e.g., depression) or neurological (e.g., CVA) disorders that would 
confound the assessment of dementia symptoms, 2) history of loss of consciousness, and 3) unstable or severe 

10                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
medical illness (e.g., hepatotoxicity) that would make donepezil or memantine treatment or participation in 
other aspects of the study unsafe.  
 
b. Selection of Instruments for Clinical Evaluation 
 
Total sum-of-box scores from the CDR (Morris, 1993) will be used as the primary outcome measure.  In our 
prior long-term studies (i.e., 4 to 10 years) of DAT subjects with very mild-to-mild DAT (i.e., CDR 0.5 to CDR 
1), sum-of-boxes scores have been shown to be highly sensitive to disease progression and to have minimal 
ceiling or floor effects (Berg, et al, 1988; Berg, et al, 1992).  In addition, so that the results of this study can be 
compared with large clinical treatment trials of DAT subjects, ADAS-Cog scores, total MMSE scores and total 
NPI scores will be used as secondary outcome measures (see below). 
To perform the CDR, a clinician trained in its use rates the subject’s cognitive abilities and function in six 
categories:  memory, orientation, judgment and problem solving, community affairs, home and hobbies, and 
personal care.  Impairment is rated 0-5 in the direction of increasing severity in each of these categories or 
boxes. The sum of these box scores is then compiled to yield CDR scores as follows: CDR 0 (no dementia); 
CDR 0.5 (very mild dementia); CDR 1 (mild dementia); CDR 2 (moderate dementia); and CDR 3 (severe 
dementia). Interrater reliability for the CDR is very high (Burke, et al, 1988), and subjects categorized with 
DAT over time using the CDR, including the CDR 0.5 category, are confirmed at autopsy as having AD with 
96% accuracy (Morris, et al, 1988; Morris, et al, 1991; Berg and Morris, 1994).  Clinicians at our Center all 
routinely receive extensive training in the use of the CDR, and interrater reliability is routinely monitored.  
Finally, sum-of-boxes scores have been shown to be a highly useful an index of dementia severity over time 
(Berg, et al, 1988; Berg, et al, 1992). In a study of 68 DAT subjects with beginning CDR scores of 0.5 or 1 
followed for 4 to 10 years, sum-of-boxes scores declined in a relatively linear fashion and had very few ceiling 
or floor effects (i.e., all 68 DAT subjects could be rated using this measure at baseline and 24 months later) 
(Berg, et al, 1992).  
As mentioned above, the ADAS-Cog (Rosen, et al, 1984), the MMSE total score (Folstein, et al, 1975), and 
the NPI will be used as secondary outcome measures.  These measures have been previously used in treatment 
trials of DAT subjects as both primary and secondary measures of symptom severity, and have been 
demonstrated to be sensitive to the effects of cholinesterase inhibitors (Farlow, et al, 1998; Rogers, et al, 1996) 
and memantine (Reisberg, et al, 2003). 
 
c. Neuroanatomical Rules for Brain Structure Boundaries 
 
The hippocampus, parahippocampal gyrus and cingulate gyrus have been selected as the neuroanatomical 
variables to be assessed during the proposed funding period based on a review of the literature and our own 
prior results. Explicit neuroanatomical rules were used to define the boundaries of these brain structures. To 
generate templates, structural boundaries were outlined using semiautomated routines that combine autotracing 
and manual tracing techniques available in AnalyzeTM.  AnalyzeTM  permits the simultaneous viewing of any 
point in the scan from three orthogonal planes.  Autotracing functions encircle a selected region of uniform 
density by enclosing all contiguous pixels of similar density.  The selection of tissue of a particular density is 
then achieved by setting thresholds that define the brightness of the pixels in the area of interest.  Surrounding 
tissue of a different density (threshold) is deleted from the picture, leaving only the tissue of interest.  When 
autotracing fails to segment tissues of similar density, the tissue is manually traced.   
 
d. MR Scanning Protocols and Image Preprocessing 
 
MR scans will be collected at the baseline assessment and after two years using a 1.5T VISION system 
(Siemens Medical Systems) with a UNIX based host computer (Sun Microsystems), actively shielded gradients, 
and echo-planar capability.  Subjects will be positioned on the table with their canthomeatal line perpendicular 
to the table; head position stability is facilitated with adjustable cushioned head supports.  A standardization 
object is placed in the field of view on the left side of the head for each MR scan.  Prior to the scan, the 

11                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
procedure is explained in detail to each subject, and techniques to tolerate small spaces and avoid 
claustrophobia are described. The MRI scans do not involve the administration of any contrast agent. 
All MR scans will be acquired using a turbo-FLASH sequence (TR=20, TE=5.4, Flip angle=30o ACQ-1, 
Matrix=256X256, Scanning time=13.5 minutes), that 3D datasets with 1 mm x 1 mm x 1 mm isotropic 
resolution across the entire cranium (Venkatesan and Haacke, 1997). A 2D set of proton density and T2 
weighted images with standard 5-mm thicknesses will also be acquired for clinical interpretation of any 
unsuspected disease conditions, and to assess the number of white matter hyperintensities in each subject. Dr. 
Tom Conturo, Associate Professor of Radiology, oversees MR scan acquisition at Washington University 
School of Medicine, and together with his team, monitors scanner drift on a daily basis.  Frequency, receiver 
gain, reference transmitter voltage, and signal-to-noise are examined.  Phantoms of known dimensions are 
imaged and the gradient calibration is checked to make certain the correct size is registered. Protocols are also 
employed to check for ghosting artifacts. Further tests and calibration adjustments are performed when needed.  
Shims are used when needed to correct for field inhomogeneities. 
For image analysis, the raw MR data are transferred to a SGI workstation at Washington University where 
3D reconstructions of brains are obtained using AnalyzeTM software (Rochester, MN).  MR data files are 
identified by number only to maintain blind conditions for image analysis. Because the turbo-FLASH MR 
sequence yields 1 mm isotropic voxels, rescaling to correct discrepancies due to voxel isotropy is not necessary. 
Signed 16 bit MR datasets are compressed to unsigned 8 bit MR datasets by linearly rescaling the voxel 
intensities, such that voxels with intensity levels at two standard deviations above the white matter mean 
(corpus callosum) are mapped to 255, and voxels with intensity levels at two standard deviations below the CSF 
mean (third ventricle) are mapped to 0.  
As a final step in the preprocessing of MR images, neuroanatomical landmarks are placed by Dr. Lei Wang.  
In each scan, twelve landmarks are placed at the external boundaries of the brain, and at the points where the 
anterior and posterior commissures intersect the midsagittal plane (Haller, et al, 1997).  Additional landmarks 
are also placed at selected points along the principal axis of the hippocampus (Csernansky, et al, 1998).  
i) Hippocampus.   The most posterior slice containing the hippocampus is defined when the hippocampus 
first appears adjacent to the trigone of the lateral ventricle.  A narrow band of gray matter along the medial 
aspect of the trigone opens to a thicker complex of gray matter and is separated from the trigone by a strip of 
white matter, i.e., the junction of the fimbria and fornix.  The coronal slice shows an elongated gray matter 
shape because of the transverse orientation of the hippocampal tail.  This complex is the cornu ammonis (CA), 
dentate gyrus and subiculum. It is difficult to select a point of separation between the CA and the subiculum 
because the superior component of the CA does not extend medially far enough to be the landmark of the 
medial border of the hippocampus, the inferior component of CA is continuous with the subiculum, and there is 
no gross anatomical separation of the subiculum, presubiculum, and parasubiculum, the lattermost being part of 
the parahippocampal gyrus.  For these reasons, the subiculum and CA are combined in the volume called 
hippocampus, as has been done by others (Shenton, et al, 1992), and the inferior border of the CA-subiculum is 
continued medially with a straight horizontal line across the cortex of the parahippocampal gyrus. The cortex 
below this line is thus considered the parahippocampal gyrus, and the cortex above this line the hippocampus. 
Progressing anteriorly from the tail, the orientation of the hippocampus changes from the medial-to-lateral 
alignment of the tail to the posterior-to-anterior alignment of the body.  Thus, the body is cross-sectioned in the 
coronal slices perpendicular to its long axis, and appears to be divided by the white matter of the fimbria into a 
lower part that is the CA-subiculum and an upper part formed by the tail of the caudate nucleus.  The thalamus 
appears medial and superior to the CA-subiculum complex. 
The thalamus and caudate nucleus form the superior borders of the hippocampus in its posterior aspect.  The 
white matter boundary (alveus and fimbria) between the hippocampus and caudate nucleus as well as any CSF 
lateral to the hippocampus is not included. The superior and lateral borders of the hippocampal body and head 
are identified by the contrast against the white matter or CSF. Vessels at the medial aspect of the hippocampus 
are excluded from the hippocampus.  Where the amygdala appears at the superior boundary of the hippocampal 
head, it as well as the white matter boundary are excluded.  However, the vertical digitation of the hippocampal 

12                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
head which curves up and medial to the amygdala in coronal sections is included.  The separation of the 
amygdala and hippocampus is best achieved by viewing sagittal and transverse sections.    
ii) Parahippocampal Gyrus (including entorhinal cortex).  Delineation of the structure is made primarily 
using MR slices oriented in the coronal plane, perpendicular to the AC-PC line.  Posteriorly, the 
parahippocampal gyrus blends imperceptibly with the lingual gyrus of the occipital lobe. It is well known that 
temporo-occipital separation is not definable.  Therefore, the posterior limit of the parahippocampal gyrus is 
defined as the anterior end of the calcarine sulcus.  The same coronal section also defines the posterior limit of 
the cingulate gyrus, and the parahippocampal gyrus is situated between the notch of the calcarine sulcus below 
and the cingulate gyrus above.  
Moving anteriorly, in coronal sections, the parahippocampal gyrus is limited by the subiculum medially and 
the collateral sulcus laterally.  The anterior limit is defined by the rhinal sulcus which runs from lateral to 
medial, separating the parahippocampal gyrus from the temporal pole.  The rhinal sulcus is identified in the 
axial section that shows the amygdala, head of hippocampus and uncal recess of the temporal horn. The sweep 
of the ambient gyrus over the medial aspect of amygdala in the axial section ends at a notch where the temporal 
pole begins.  This notch is the rhinal sulcus. In coronal sections anterior to the amygdala, the superior limit of 
the parahippocampal gyrus is defined as the inflection point of the white matter at the inferior border of the gray 
matter of the amygdala. 
iii) Cingulate Gyrus.  The cingulate gyrus is delimited at its ventral aspect by the callosal sulcus, which 
separates it from the corpus callosum.  Dorsally, it is delimited by the cingulate sulcus. Delineation of the 
structure is made primarily from MR slices oriented in the coronal plane, perpendicular to the AC-PC line.   
The cingulate gyrus can be separated into anterior and posterior segments. The posterior limit of the anterior 
cingulate gyrus is taken to be in the coronal section perpendicular to the midpoint of the AC-PC line.  As the 
cingulate gyrus extends anteriorly and wraps around the genu and rostrum of the corpus callosum, it tapers off 
gradually. Therefore, the anterior limit of the anterior cingulate gyrus is defined as the most rostral coronal 
section through the septum pellucidum, and the medial limit of the cingulate gyrus is the cortical surface in the 
interhemispheric fissure.  The superior limit of the cingulate gyrus is the cortical surface forming the inferior 
bank of the cingulate sulcus (the superior bank being the superior frontal gyrus). This surface extends 
horizontally in a lateral direction to the fundus of the cingulate gyrus.  At this point, a straight horizontal line is 
drawn through the thickness of the cortex to indicate the separation of the cingulate gyrus and superior frontal 
gyrus.  From the lateral end of this horizontal line in the deep limit of the cortex, a straight line is drawn to the 
lateral limit  of the cortex abutting the superior surface of the corpus callosum and separated from it by the 
callosal sulcus.  The inferior limit of the cingulate gyrus is the inferior surface of the cortex forming the superior 
bank of the callosal surface (the inferior bank being the corpus callosum). 
The posterior cingulate gyrus wraps around the splenium of the corpus callosum.  In the coronal sections 
which run through the posterior end of thalamus, the anterior limit of the sub-splenium segment of the cingulate 
gyrus is defined as the most rostral extent through the anterior end of the calcarine sulcus.  Indeed, the same 
coronal section also defines the posterior limit of the parahippocampal gyrus (see above). The posterior 
cingulate gyrus then starts at the anterior end of the calcarine sulcus, moving posteriorly, follow its bank until 
reaching the bifurcation point where the calcarine sulcus branches into the posterior calcarine sulcus and the 
parietal-occipital sulcus.  A straight line is then drawn from this point upwards to the nearest point on the sub-
parietal sulcus.  The cortex below this line is also delineated as the cingulate gyrus. The posterior cingulate 
gyrus follows the bank of sub-parietal sulcus as it traces out a curve parallel to the corpus callosum (and the 
brain derma). The lateral extent of the posterior cingulate follows the upper bank of the calcarine sulcus. 
 
e. Large Deformation High Dimensional Brain Mapping (HDBM-LD) to Assess the Volume and Shape of the 
Hippocampus 
 
MR scans from different subject groups or from the same subject group at different points in time will be 
used to construct and compare composite volumes and shape representations.   In statistical terms, these 
representations reflect the coordinate systems of the scans for the subject groups or time points that are 
associated with the smallest average squared error.  In practical terms, these representations allow us to generate 

13                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
automatic volume measurements of selected brain structures and to quantify neuroanatomical shapes. 
Composite representations for the subject groups are first created by forming the MR scans from the subject 
groups or time points into sets.  Composite coordinate systems for the various sets of scans are then calculated 
by finding the correspondence of the coordinate system of each of the members of each group with a standard 
coordinate system.  The composite coordinate system most representative of each population is then constructed 
from the average of these correspondences.   
This approach relies fundamentally on our ability to calculate the correspondence of highly variable MR 
images with a common coordinate system.  Since the earliest introduction of our brain mapping methods 
(Miller, et al, 1993; Christensen, et al, 1994; Grenander and Miller, 1994), we have studied the variability of 
mammalian brain anatomy by generating smooth correspondences (i.e. maps) from a single template onto 
families of target images.  In our approach, the template represents the typical structure. Anatomical variation is 
then accommodated by performing transformations of the coordinate system of the template.  The translation of 
every point in the template allows us to change the coordinate system in as many ways as there are points in the 
coordinate system (i.e. the number of voxels in the MR image).  It is the high dimensionality of these 
transformations that differentiates our method from other mapping algorithms, and allows for the analysis of 
subtle characteristics of shape in small brain structures.  
Our method uses a coarse-to-fine approach that fuses the elegant landmark work of Bookstein (Bookstein 
and Green, 1992; Bookstein, 1978) with the pioneering volume-based deformation work of Bajcsy and 
colleagues (Bajcsy, et al, 1983; Dann, et al, 1989), but extends these methods to accommodate local 
deformations so that the precise shape of brain structures can be studied.  The importance of this innovation is 
that it allows neuroanatomists to embed information into brain maps that is optimally meaningful with regard to 
subtle anatomical variation. To help ensure that the transformations preserve key features of brain geometry 
(e.g. unbroken gyral surfaces), we force the transformations to obey physical laws of elasticity and fluidity.  The 
continuum mechanics-based mathematical derivations that correspond to these constraints may be found in 
several previously published works (Grenander, et al, 1992; Miller, et al, 1993; Christensen, et al, 1994 and 
1995; Grenander and Miller, 1994; Joshi, et al, 1997; Miller, et al, 1997).  
There are limitations to any method, including HDBM-LD. First, our methods depend heavily on the quality 
of the gray scale image data.  Currently, we are using the state-of-the-art high resolution MR images; however, 
as better resolution scanning sequences become available, we will employ them and very likely increase the 
precision of our assessments.  Also, non-uniformities in the MR image data due to poor calibration and field 
inhomogeneities may result in mismatches of gray and white matter, because our current tools are not driven 
solely by information about geometric structure.  We have worked hard to control these sources of error using 
conventional strategies.  In addition, we have recently begun to develop transformation tools, which are driven 
solely by geometry and will therefore be invariant to non-uniformities in the MR images.  Second, our methods 
are not entirely automatic, and the expertise of the expert neuroanatomist continues to play a key role.  The 
expert creates the information in the template, and landmarks placed by experts may bias the initial manifold 
transformations.  However, as regards the hippocampus, we have shown (see Haller, et al, 1997) that the 
transformations are robust to this potential source of error (i.e. the automatic results were more repeatable than 
the results obtained from manual outlining). Another potential limitation of our methods is that we make the 
assumption that the anatomies in the template and targets are diffeomorphic.  Diffeomorphism can be roughly 
defined as the requirement that an analogous anatomical features can be found in every target and in the 
template. Moreover, as the transformations occur, the movements of contours and surfaces in the template never 
intersect. Thus, our mapping algorithms would currently fail if the template or the targets contained non-
analogous features (e.g., tumor). 
The quantification of hippocampal volume and shape using HDBM-LD begins with superimposing a 
triangulated graph of points onto the gray matter surface of the hippocampus within the template using a 
computer aided design program for fitting closed surfaces to connected subvolumes.  This surface is then 
treated as a two-dimensional closed manifold and carried along as the template is transformed onto the target 
MR scans.  When the transformations are completed, a vector field and the surface that it represents are 
generated for each selected brain structure in each of the target scans. Composite surfaces for each structure in 
each group of subjects are then produced by averaging the individual vector fields generated by the 

14                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
transformations.  Brain structure volumes are measured in the subject groups by calculating the gray matter 
volumes enclosed by the transformation-generated surfaces. Quantification of hippocampal shape is performed 
using the deformation vector fields produced by the transformations. A pooled covariance matrix is first 
computed using the vector fields from the transformed surfaces in all subjects. This covariance matrix is then 
reduced in its dimensionality by computing the complete orthonormal set of eigenvectors, and a subset of 
eigenvectors that accounts for ~75 % of the variance in hippocampal shape is used in group comparisons. 
Recently, we have adapted these methods to specifically assess the rate of change in hippocampal shape 
over time (Wang, et al, 2003). From the pairs of MR images collected at different time points, the assessment of 
a change in the shape of the hippocampus over time is accomplished by mapping the transformations from the 
first time point onto the second time point via rigid-motion registration (rotation and translation). If no change 
in the shape of the hippocampus has occurred, the result of this transformation is an identity map (modulo 
rotation and translation). Conversely, any shape change that has occurred is defined as the deviation of the 
result of the transformation away from this identity map. To compare shape change in different groups of 
subjects, the covariance of the resulting transformation vector fields across all subjects is used, and shape 
change is assessed in each group by 1) obtaining a set of eigenvectors via the singular value decomposition of 
the covariance matrix, 2) computing coefficients associated with these eigenvectors for each subject, and 3) 
performing a MANOVA using the eigenvectors across all subjects. A post-hoc logistic regression is used to 
determine which of the eigenvectors are most discriminating of the groups being compared. Since the 
covariance of the shape change vector fields is empirical, repeat neuroanatomical data from nondemented (CDR 
0) subjects are required in order to account for “normal” shape change during the analysis of shape change 
associated with DAT and its treatment. 
  
f. Methods for Labeled Cortical Depth Mapping (LCDM) of the Parahippocampal and Cingulate Gyri 
 
When the brain structure of interest is a subregion of the cerebral cortex (e.g., cingulate or parahippocampal 
gyrus), it is first necessary to create a segmentation of the various tissues (i.e., gray matter, white matter, and 
CSF) within that portion of the MR dataset, and in relationship to the geometry of the neocortex. We have 
recently developed specific algorithms for this purpose (see Figure 5). 
Construction of the surface between gray and white matter then places us in a position to determine the 
probabilities of CSF, gray matter and white matter voxels within the segmented subvolume as a function of the 
closest distance from the voxel to this surface (see embedded surface represented by the yellow line in Figure 5, 
panel C). Specific algorithms to compute these distances were first developed in Miller, et al (2000), for several 
types of cortical subvolumes, and then refined by Ratnanather, et al (2003), for specific cortical regions. Figure 
5, panel D shows an example of a profile that shows the probabilities of encountering CSF, gray matter and 
white matter voxels as a function of the distance to the gray matter/white matter surface in a single subject. 
 
 
 
Figure 5. Computational Algorithms for Assessing the Neuroanatomical 
Structure of Cortical Subregions. The first step in the assessment of a subregion of 
the cerebral cortex is tissue segmentation (panel A) followed by identification of the 
iso-surface between gray and white matter (panel B). The probabilities of voxels at 
every distance from this surface (yellow line in panel C) is then determined, along 
with the cumulative probability of accounting for all voxels (panel D). The distance at 
A 
C 
D 
B 

15                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
which a predetermined percentage of voxels are accounted for (e.g., 80%, 90%, etc.) can then be used as an estimate of cortical 
thickness across the region. 
 
 
Cortical metrics such as gray matter volume and thickness are then generated from the probability profiles.   
The gray matter volume is equivalent to the cumulative sum of gray matter data in the profile. In turn, 
graymatter thickness is based on the distances from the gray matter/white matter surface where pre-assigned 
percentiles of voxels (e.g., 85%, 90% or 95%) are reached. Finally, gray matter surface area is computed by 
summing up all the triangles of the triangulated graph of the surface within the selected subvolume. 
 
g. Assessment of Total Intracranial and Cerebral Volumes 
 
Assessment of total intracranial and brain volumes is needed to control for generalized brain volume 
reduction, as has been reported by some investigators. Mathalon, et al (1993), have examined the issue of 
correction for head size and brain volume in such circumstances and shown that such corrections decrease 
unwanted variance while improving the ability to detect meaningful biological variation (e.g. correlations 
between brain structure volumes and age).  
A template image is prepared by an expert using manual outlining techniques, and includes, in this case, 
segmentation data for the total intracranial and brain volumes.  The entire MR image of the template is first 
globally registered with the MR image of each target using a landmark-based transformation and scalar points at 
the boundaries of the brain and in the midline. An elastic transformation (with eight basis vectors having 2187 
basis coefficients) is then performed, which matches the templates for the total intracranial space and total brain 
to the target.  As with the transformations developed to study the hippocampus, the manifold and the elastic 
transformations used for this measurement are concatenated together and all the anatomical features of the 
segmented template brain are carried through the transformation. 
 
h. Data Analysis 
 
To test hypothesis 1 (Decreases in hippocampal and cortical volumes, thinning of the cingulate and 
parahippocampal cortices, and changes in hippocampal shape will be correlated with the rate of cognitive 
decline in subjects with very mild-to-mild DAT.), growth curve models will be developed to assess the rate of 
cognitive decline for the principal (i.e., CDR sum-of-boxes scores) and secondary (i.e., ADAS-cog total scores, 
MMSE total scores, and NPI total scores) outcome variables (Laird and Ware, 1982).  The underlying 
framework for the Laird and Ware model is essentially the same as for a traditional univariate repeated 
measures analysis of variance (ANOVA).  However, the traditional ANOVA framework is difficult to apply 
when assessments are not available for all subjects at all time points. We have previously examined the 
advantages and disadvantages of this statistical model in characterizing  rates of cognitive decline in a group of 
CDR 0.5 and CDR 1 DAT subjects highly similar to the one recruited for this project (Berg, et al, 1992). The 
CDR sum-of-boxes score was found to be entirely adequate as an outcome variable in that analysis (Berg, et al, 
1992), because of its minimal ceiling and floor effects. The Laird and Ware model generates a slope value and 
intercept (i.e., estimate of baseline severity) for each subject.  
Variables to assess changes in structural volume, thickness, and shape (for the hippocampus) will be 
generated as described above. Bivariate correlations between changes in clinical and neuroanatomical variables 
will be estimated using non-parametric statistics (Spearman’s rho).  To assess whether observed bivariate 
relationships are independent of each other, a stepwise multiple regression model will be developed including 
all independent neuroanatomical variables  and slope values for the CDR sum-of-boxes scores as the dependent 
variable.   
To test hypothesis 2 (The rate of decreasing hippocampal and cortical volumes, thinning of the cingulate 
and parahippocampal cortices, and changing hippocampal shape will be equal in DAT subjects that are 
untreated and in DAT subjects that are treated with donepezil alone, but greater than the rate of volume loss, 
thinning and hippocampal shape change in DAT subjects that are treated with the combination of donepezil and 

16                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
memantine.), rates of change for the various neuroanatomical variables will be compared in DAT subjects that 
are untreated, that are treated with donepezil alone, that are treated with the combination of donepezil and 
memantine, and in nondemented comparison subjects. These analyses will be performed using simple 
difference scores (follow-up minus baseline) for volume and thickness variables.  To compare rates of change  
in hippocampal shape across groups, a MANOVA using eigenvectors derived from the time 1-to-time 2 
transformation covariance will be performed (see above).  The distributional characteristics of each of these 
variables will be examined prior to hypothesis testing. Finally, non-parametric (Wilcoxon rank sum tests) 
statistics will be used to compare the groups. 
 
i. Sample Size and Power Considerations 
 
Based upon previous work by our group in similar groups of CDR 0.5 and CDR 1 DAT subjects (Berg, et 
al, 1988; Berg, et al, 1992), and on our experience with the subjects recruited and studied during the initial 
funding period, we estimate that the attrition rate of subjects who cannot continue in the study for the full two 
years due to pronounced deterioration, drug side-effects, or refusal to participate will be approximately 20% or 
less, leaving at least 40 subjects who would provide data for the entire two-year period. However, the number of 
subjects entered into the analysis may exceed this somewhat, since a slope value can be estimated for each 
subject who remains in the treatment trial for at least five assessments (one year).  Estimating 40 subjects are 
used in the analysis used to test Hypothesis 1, we will have a power of  .80 to detect a correlation (or partial 
correlation) of .44 or more with a 2-tailed significance level of .05 (Borenstein and Cohen, 1988; Kraemer and 
Thiemann, 1987). 
For the testing of Hypothesis 2, we will be measuring the rate of change of neuroanatomical variables over 
time; therefore, the test-retest reliability for these variables must be high.  To estimate the normative test-retest 
reliability of the neuroanatomical measures proposed in this application, we scanned eight healthy control 
subjects on two separate occasions approximately one month apart, and then estimated differences between 
volumes and surface contours of the hippocampus. At the first time point, the mean left hippocampal volume 
was 2,233 mm3 and the mean right hippocampal volume was 2,699 mm3; at the second time point, these 
volumes were 2,209 and 2,686 mm3, respectively. Using repeated measures ANOVA, there were no significant 
test-retest differences in these volumes (on the left F = .14, p = .72; on the right F = .07, p = .80) and the 
intraclass correlation coefficient between these two datasets was very high (ICC = 0.93).  To compare the 
similarity of surface contours at the two time points, we then registered all the scans with respect to the template 
and then calculated the percent overlap of voxels. The average percent overlap in the eight subjects was 87.4%, 
which is comparable to the overlap achieved by a single expert performing repeated manual segmentations of 
the hippocampus from the same MR scan (Haller, et al, 1997). To the test the reliability of shape assessment for 
the hippocampus, we generated the eigenvectors associated with this brain structure based on the eight control 
subjects at time point 1. The coefficients associated with these eigenvectors were then computed for each 
subject at both time 1 and time 2. The mean ICC for all eigenvector coefficients was very high - 0.89 (range of 
0.83 to 0.94), suggesting very high repeatability. Finally, the standard deviation for the estimation of 
hippocampal volumes on the two occasions was 225 mm3, and an annual rate of decrease in hippocampal 
volumes was 2 mm3. 
Assuming that the between-group effect size for comparisons of the rate of change of neuroanatomical 
variables is moderate (i.e., 0.5 or higher), and if the correlations in neuromorphometric measures within our 
proposed sample sizes at the different time points continue to be high (i.e., > .75), changes in brain structure 
shapes of the magnitude observed to date (see Progress Report and Wang, et al, 2003), and volume decreases of 
50 mm3 or greater over two years, would be detectable with greater than 85% power (Borenstein and Cohen, 
1988; Kraemer and Thiemann, 1987).  
 
HUMAN SUBJECTS 
 
50 new subjects who meet NINDS-ADRDA criteria for probable AD (i.e. DAT), and who have been 
registered with the MDC for treatment will be recruited for participation in the project during the proposed 

17                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
funding period. The age range of the subjects will be 50 to 80 years.  Any subjects will be excluded if they have 
medical problems that would contraindicate treatment with donepezil or memantine or make administration of a 
MR scan or any other aspect of the protocol hazardous. 
With regard to the inclusion of both genders and various minorities, the samples will be equally balanced for 
gender and include approximately 15% African-American subjects. This percentage is similar to the distribution 
of African-Americans who present to the ADRC and MDC. This distribution of subjects should also allow 
inclusion of gender as a covariate in data analyses. 
2. Collected clinical data will include information about demographics, dementia symptoms, other 
behavioral disturbances, and performance on tests of cognition (i.e., the ADAS-Cog). In all cases, verbal or 
written information obtained directly from the patients and involved caregivers will be used. These evaluations 
will take place in an ordinary office setting.  MR scan data will be collected according to established protocols 
at the Mallinckrodt Institute of Radiology at Washington University. 
3. Patients will be recruited for participation in this project as they present to the MDC for assessment and 
treatment. Several neurologists work together to assess and treat patients at the MDC - Drs. Morris, Galvin, 
Holtzman, and Snider. As Director of the MDC and Co-Investigator on this project, Dr. Morris will work with 
the other treating neurologists to select possible subjects for this project. However, the consent of the individual 
treating neurologist, who has been informed of the goals and procedures of the project, and of the patients’ 
families will always be obtained before any patient is approached to obtain their informed consent.   For those 
patients who are incompetent to give informed consent, legal guardians will be approached to seek their 
informed consent for the subject’s participation.  All subjects shall give their ongoing assent for participation in 
the project, regardless of whether they have provided written informed consent as well.  
4. The potential risks of this project include those associated with routine clinical interviews.  The clinical 
interview and cognitive testing instruments to be used for the project could produce boredom and fatigue.  
However, breaks in interview sessions will be given whenever the subject requests or obvious fatigue is 
observed.  
There are also risks in obtaining MRI scans.  However, so long as subjects are properly screened to exclude 
individuals with metal embedded within their bodies, these risks are minimal and include feelings of 
claustrophobic fear while being scanned, and minor physical discomfort because of remaining still throughout 
the scanning procedure. Subjects will also be warned that should they have unremembered metal objects within 
their bodies, these objects could become heated by the radiofrequency energy of the scanner.  
The risks of donepezil therapy include increased bowel movements, diarrhea, nausea, vomiting, dyspepsia, 
diminished appetite, and weight loss in approximately 10% of subjects.  Less commonly, slight decreases in 
heart rate, insomnia, and psychomotor agitation have occurred. These side-effects can generally be managed by 
reducing the dose of donepezil from 10 to 5 mg/day.  The side effects of memantine include diarrhea, insomnia, 
dizziness, headache, and rarely hallucinations. Also, as with any drug, idiocyncratic hypersensitivity reactions 
can rarely occur. Again, subjects will be warned about these risks, and excluded when they have a history of 
serious or unstable medical disorders. 
5. To minimize these risks, clinical interviews and scanning sessions will be interrupted or discontinued if 
necessary.  Subjects and their caregivers will also be carefully interviewed to exclude the possibility of 
including anyone with metal embedded in their body. Finally, all drug treatment will be performed in the 
context of an expert clinic. The DAT subjects will be closely monitored for any drug side-effects, and in 
subjects who experience such side-effects, the protocol will be discontinued. Personnel trained by the ADRC at 
Washington University in interviewing and testing DAT subjects will conduct all research-related procedures.   
6. The risks of this project are relatively small, and not appreciably greater than the risks of ordinary 
inpatient assessment and non-invasive radiologic procedures.  The drug treatment will be administered to these 
subjects in the context of ordinary clinical care in the MDC. If successful, knowledge may be gained about 
neuromorphometric predictors of treatment response in patients with DAT, and perhaps about the 
pathophysiology of AD in general.  In consideration of the small risks and substantial scientific benefit of this 
work, we consider that risk/benefit ratio to be favorable. 
7. All key personnel involved in the design or conduct of the research involving the human subjects will 
receive the required education on the protection of human research participants prior to funding of this project. 

18                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
 
 
DATA MANAGEMENT 
  
 
All data will be de-identified and then entered at Washington University. The Northwestern University 
research team will not have access to any identified data – all subjects in this study are given a random study 
number that will be used. All identifying data will be kept at Washington University. A distributed data entry 
system for this project will be used, with computers for data entry at all Washington University clinical data 
collection sites. Brain structure measures are entered into datasets using PC-type workstations. All computers 
currently use Excel data entry screens, formatted to simulate the hard copy data entry forms.  All data are 
double-entered, and error checking programs are used to identify and correct errors. Once entered into these 
spreadsheets and checked, the data files are consolidated into the main SAS project database, which is 
maintained on a PC microcomputer under the direct supervision of Dr. Paul Thompson from Washington 
University, the project biostatistician.  Back-ups are routinely performed to assure the preservation of the 
database. Data are checked for range, consistency, missing values, etc.  Whenever errors are detected, reports 
are sent to the data entry sites, where the data are checked again. 
Data entry system at Washington University is transitioning to a web-based data entry system for easier 
data management.  This system uses SAS/IntrNet methods for data entry and formulates data entry tools using 
HTML screens (Thompson, 2003a, 2003b).  This system integrates data entry with data analysis when 
necessary, and includes programs to check for completeness of data entry and other data monitoring tools to be 
run as data are entered, so that data evaluations and comparisons can be synchronous with data entry.  This 
system is used on several other projects in which Dr. Thompson has management responsibilities, and is thus 
well tested and highly reliable. 
The project database is maintained using SAS.  Data from individual clinical or cognitive instruments are 
maintained in separate datasets, indexed by subject ID number, date and originating clinic.  During data 
analysis, combined datasets are created as needed.  Data are maintained in the master system datasets using a 
source/derived data approach, where source datasets are maintained unmodified, while the derived datasets are 
modified as needed.  Thus, alterations that prove to be incorrect are easily rectified. The confidentiality of all 
data is maintained using the HIPPA compliance standards of Washington University. These standards involve 
retaining confidential information (name, address, telephone numbers, and social security number) in 
encrypted datasets or on removable media.  No confidential information is posted to the web under any 
circumstances.  All possible efforts are made to retain only the necessary information in all cases; that is, ID 
numbers are used in all possible cases, and actual identification information are used only when necessary and 
will not be included in the data that is transferred to Northwestern University. 
Neuroimaging data produced by high dimensional transformations of the neuroanatomical template consists 
of volumes (in mm
3), symmetry metrics (no units), and shape metrics (e.g., individual eigenvector coefficient 
values). These data are also collected into Excel datasets, checked, and then merged into the project database. 
      As a first step in the data analysis process, exploratory data analyses will be undertaken for all 
neuroanatomical variables to assess their distributional properties and to identify outliers. Variables intended for 
use in MANOVA and other parametric analyses will be tested for deviations from distributional assumptions of 
the tests.  If deviations are identified, we will attempt to find appropriate transformations, such as power or log 
transformations, which give the variables the required distributional properties.  If no simple transformation can 
be used, alternative analyses with less rigid distributional requirements will be used.  For example, rank-ordered 
group comparisons (e.g., Mann-Whitney U and Wilcoxon tests) and correlations (i.e. Spearman rho) may be 
used for ordered data for which we cannot demonstrate normality. Additionally, other methods (i.e., generalized 
estimating equations), which are compatible with other error distributions, will be considered. 
 

19                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
REFERENCES 
 
Areosa SA, Sherriff F. (2003) Memantine for dementia. Cochrane Database Syst Rev 1:CD003154. 
Arnold SE, Hyman BT, Flory J, Damasio AR, Hoesen GW (1991) The topological and neuroanatomical 
distribution of neurofibrillary tangles and neuritic plaques in the cerebral cortex of patients with 
Alzheimer’s disease. Cerebral Cortex 1:103-116.  
Ashburner J, Csernansky JG, Davatzikos C, et al. (2003) Computer-assisted imaging to assess brain structure in 
healthy and diseased brains. Lancet: Neurology 2:79-88. 
Bajcsy R, Lieberson R, Reivich M. (1983) A computerized system for the elastic matching of deformed 
radiographic images to idealized atlas images. J Comp Assisted Tomography 7:618-625.  
Bardgett ME, Jackson JL, Taylor BM, Csernansky JG. (1998) The effects of kainic acid lesions on locomotor 
responses to haloperidol and clozapine. Psychopharmacology, 135:270-278. 
Bardgett ME, Jacobs PS, Jackson JL, Csernansky JG. (1997). Kainic acid lesions enhance locomotor responses 
to novelty, saline, amphetamine, and MK-801. Behav Brain Res, 84:47-55. 
Barger SW, Basile AS. (2001) Activation of microglia by secreted amyloid precursor protein evokes release of 
glutamate by cystine exchange and attenuates synaptic function. J Neurochem 76:846-854.  
Behrends JC, Ten Bruggencate G. (1993) Cholinergic modulation of synaptic inhibition in the guinea pig 
hippocampus in vitro. J Neurophysiology 69:626-629. 
Berg L, McKeel DW, et al. (1998) Clinicopathologic studies in cognitively healthy aging and Alzheimer 
disease. Arch Neurol 55:326-335.  
Berg L, Miller JP, et al. (1988) Mild senile dementia of the Alzheimer type. 2. Longitudinal assessment. Ann 
Neurol 23:477-484. 
Berg L, Miller JP, et al. (1992) Mild senile dementia of the Alzheimer type. 4. Evaluation of intervention. Ann 
Neurol 31:242-249.  
Berg L, Morris JC (1994) Diagnosis. In: Alzheimer disease. Terry RD, Katzman R, Bick KL, eds. Raven Press, 
New York.  
Bookstein FL, Green WDK. (1992) Edge information at landmarks in medical images. In Visualization in 
Biomedical Computing (RA Robb, Ed.) pp. 242-258. SPIE 1808. 
Bookstein FL. (1978) The Measurement of Biological Shape and Shape Change. Vol 24. Springer-Verlag: 
Lecture Notes in Biomathematics, New York.  
Borenstein M, Cohen J. (1988) Statistical Power Analysis: A Computer Program. L. Erlbaum Assoc, NJ. 
Burke WJ, Miller JP, et al. (1988) Reliability of the Washington University Clinical Dementia Rating (CDR). 
Arch Neurol 45:31-42.  
Burns A, Rossor M, et al. (1999) The effects of donepezil in Alzheimer’s disease – results form a multinational 
trial. Dement Geriatr Cogn Disord 10:237-244.  
 
Choi DW. (1988) Glutamate neurotoxicity and diseases of the nervous system. Neuron 1:623-634. 
Christensen GE, Rabbitt RD, Miller MI. (1994) 3D brain mapping using a deformable neuroanatomy. Physics 
in Medicine and Biology 39:609-618.  
Christensen GE, Rabbitt RD, et al. (1995) Topological Properties of Smooth Anatomical Maps. Kluwer 
Academic Publishers. 
Corey-Bloom J, Anand R, Veach J, for the ENA 713B352 Study Group (1998) A randomized trial evaluating 
the efficacy and safety of ENA 713 (rivastigimine tartrate), a new acetylcholinesterase inhibitor, in 
patients with mild to moderately severe Alzheimer’s disease. Int J Geriatr Psychopharm 1:55-65.  
Csernansky JG, Hamstra J, et al. (2004) Correlations between antemortem hippocampal volume and 
neuropathology in AD subjects. Alz Disease Assoc Disorders.  In press. 
Csernansky JG, Joshi S, et al. (1998) Hippocampal morphometry in schizophrenia by high dimensional brain 
mapping. Proc Nat’l Acad Sci 95:11406-11411.   
Csernansky JG, Wang L, et al. (2000) Early DAT is distinguished from aging by high-dimensional mapping of 
the hippocampus. Neurology 55:1636-1643. 
Cullen WK, Suh YH, Anwyl R, Rowan MJ (1997) Block of LTP in rat hippocampus in vivo by beta amyloid 
precursor protein fragments. Neuroreport 8:3213-3217. 

20                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
Dann R, Hoford J, Kovacic S, Reivich M, Bajcsy. (1989) Evaluation of elastic matching systems for anatomic 
(CT, MR) and functional (PET) cerebral images. J Comp Assisted Tomogr 13:603-611. 
Davis KL, Thal LJ, et al. (1992) A double-blind, placebo-controlled multicenter study of tacrine for 
Alzheimer’s disease. N Engl J Med 327:1253-1259.  
Ditzler K. (1991) Efficacy and tolerability of memantine in patients with dementia syndrome: A double-blind, 
placebo-controlled study. Arzneimittelforschung 42:904-913.  
Dutar P, Nicoll RA. (1988) Classification of muscarinic responses in hippocampus in terms of receptor subtypes 
and second messenger systems. J Neuroscience 8:4212-4224. 
Erdo SL, Schafer M. (1991) Memantine is highly potent in protecting cortical cultures against excitotoxic cell 
death evoked by glutamate and N-methyl-D-aspartate. Eur J Pharmacol 198:215-217. 
Farlow MR, Gracon SI, et al. (1992) A controlled trial of tacrine in Alzheimer’s disease. JAMA 268:2523-2529.  
Farlow MR, Lahiri DK, Poirier J, Davignon J, Schneider L, Hui SL (1998) Treatment outcome of tacrine 
therapy depends on apolipoprotein genotype and gender of the subjects with Alzheimer’s disease. 
Neurology 50:669-677.  
Farlow MR, Tariot PN, et al. (2003) Memantine/donepezil dual therapy is superior to placebo/donepezil therapy 
for treatment of moderate to severe Alzheimer’s disease. Neurology 60 (Suppl 1):A412. 
Feldman H, Gauthier S, et al. (2001) A 24-week, randomized, double-blind study of donepezil in moderate to 
severe Alzheimer’s disease. Neurology 57:613-620.  
Folstein MF, Folstein SE, McHugh PR. Mini-Mental State. (1975) A practical method for grading the cognitive 
state of patients for the clinician. J Psychiatric Res 12:189-198.  
Forstl H, Sahakian B (1993) Thalamic radiodensity and cognitive performance in mild and moderate dementia 
of the Alzheimer’s type. J Psychiatry Neurosci 18:33-37. 
Fox NC, Cousens S, Scahill R, Harvey RJ, Rossor MN. (2000) Using serial registered brain magnetic resonance 
imaging to measure disease progression in Alzheimer disease: power calculations and estimates of 
sample size to detect treatment effects. Arch Neurol 57: 339-344. 
Fox NC, Crum WR, et al. (2001) Imaging of onset and progression of Alzheimer's disease with voxel-
compression mapping of serial magnetic resonance images. Lancet  358: 201-205. 
Fox NC, Freeborough PA. (1997) Brain atrophy progression measured from registered serial MRI: validation 
and application to Alzheimer's disease. J Magn Reson Imaging 7: 1069-1075. 
Fox NC, Freeborough PA, Rossor MN. (1996) Visualization and quantification of rates of atrophy in 
Alzheimer’s disease. Lancet 348:94-97.  
Frankiewicz T, Potier B, et al. (1996) Effects of memantine and MK-801 on NMDA-induced currents in 
cultured neurons and on synaptic transmission and LTP in area CA1 of the rat hippocampal slices. Br J 
Pharmacol 117:689-697. 
Frautschy SA, Baird A, Cole GM (1991) Effects of injected Alzheimer b-amyloid cores in rat brain. Proc. Natl. 
Acad. Sci. USA 88:8362-8366. 
Freeborough PA, Fox NC. (1998) Modeling brain deformations in Alzheimer's disease by fluid registration of 
serial 3D MR images. J Comput Assist Tomogr 22: 838-843. 
Freedman M, Oscar-Berman M. (1989) Spatial and visual learning deficits in Alzheimer’s and Parkinson’s disease. Brain 
Cogn 11:114-126. 
Geinisman Y, Disterhoft JF, et al. (2000) Remodeling of hippocampal synapses after hippocampus-dependent associative 
learning.  J Comp Neurol 417:49-59. 
Gomez-Isla T, Price JL, et al. (1996) Profound loss of layer II entorhinal cortex neurons occurs in very mild 
Alzheimer’s disease. J Neuroscience 16:4491-4500.  
Grenander U, Miller MI, Christensen G. (1992) Deformable anatomical databases using global shape models. In 
Proc. Cooperative Working Group on Whole Body 3-D Electronic Imaging of the Human Body. Crew 
System Ergonomics Information Analysis Center - Wright Patterson Air Force Base (3/9-11). 
Grenander U, Miller MI. (1994) Representations of knowledge in complex systems. J Royal Statistical Soc Bull 
56:549-603. 
Gundersen HJ, Jensen EB. (1987) The efficiency of systematic sampling in stereology and its prediction. J 
Microsc 147:229-63. 

21                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
Gundersen HJ, Jensen EB, Tandrup T (1997) The optical rotator. J Microsc 186:108-20. 
Haller JW, Banerjee A, et al. (1997) 3D hippocampal morphometry by high dimensional tranformation of a 
neuroanatomical atlas. Radiology. 202:504-510. 
Harkany T, Abraham I, et al. (2000) β−amyloid neurotoxicity is mediated by a glutamate-triggered excitotoxic 
cascade in rat nucleus basalis. Eur J Neuroscience 12:2735-2745.  
Hyman BT, Damasiao AR, VanHoesen GW, Barnes CL. (1984) Alzheimer’s disease: Cell-specific pathology 
isolates the hippocampal formation. Science 225:1168-1170.  
Hyman BT, Van Hoeren GW, Damasio AR (1987) Alzheimer's disease: glutamate depletion in the hippocampal 
perforant pathway zone. Ann Neurol 22:37-38. 
Ikeda M, Tanabe H, et al. (1994) MRI-based quantitative assessment of the hippocampal region in very mild 
and moderate Alzheimer’s disease. Neuroradiology 36:7-10.  
Ikezu T, Luo X, et al. (2003) Amyloid precursor protein-processing products affect mononuclear phagocyte 
activation: Pathways for sAPP- and Aβ-mediated neurotoxicity. J Neurochem 85:925-934. 
Iosifescu DV, Shenton ME, Warfield SK, Kikinis R, Dengler J, Jolesz FA, McCarley RW (1997) An automated 
registration algorithm for measuring MRI subcortical brain structures. NeuroImage 6:13-25.  
Jack CR, Peterson RC, O’Brien PC, Tangalos EG. (1992) MR-based hippocampal volumetry in the diagnosis of 
Alzheimer’s disease. Neurology 42:183-188. 
Jack CR, Petersen RC, et al. Rate of medial temporal lobe atrophy in typical aging and Alzheimer’s disease. 
(1998) Neurology 51:993-999.  
Jarvis B, Figgitt DP. (2003) Memantine. Drugs Aging 20:465-476. 
Jobst KA, Smith AD, et al. (1994) Rapidly progresive atrophy of medial temporal lobe in Alzheimer’s disease. 
Lancet 343:829-930.  
Joshi S, Miller MI, Grenander U. (1997) On the geometry and shape of brain sub-manifolds. IJPRAI (Special 
Issue). 
Kawas C, Clark CM, Farlow MR, et al. (1999) Clinical trials in Alzheimer’s Disease: Debate on the use of 
placebo contols. Alz Dis Assoc Disord 13:124-129.  
Killiany RJ, Moss MB, et al. (1993) Temporal lobe regions in magnetic resonance imaging identify patients 
with early Alzheimer’s disease. Arch Neurol 50:949-954. 
Kilpatrick GJ, Tilbrook GS. (2002) Curr Opin Investig Drugs 3:798-806. 
Kim SH, Price MT, Olney JW, Farber NB. (1999) Excessive cerebrocortical release of acetylcholine induced by 
NMDA antagonists is reduced by GABAergic and alpha2-adrenergic agonists. Molecular Psychiatry 
4:344-352. 
Kim J, Fanselow MS (1992) Modality-specific retrograde amnesia of fear. Science 256:675-677. 
Knapp MJ, Knopman DS, et al. (1994) A 30-week randomized controlled trial of high-dose tacrine in patients 
with Alzheimer’s disease. J Am Med Assoc 271:985-991. 
Knopman D, Kahn J, Miles S. (1998) Clinical research designs for emerging treatments for Alzheimer disease: 
        
moving beyond placebo-controlled trials. Arch Neurol 55:1425-1429. 
Knopman D, Morris JC. (1997) An update on primary drug therapies for Alzheimer’s disease. Arch Neurol 
54:1406-1409.  
Kraemer HC, Thiemann S. (1987) How Many Subjects? Sage Publications, New York. 
Laakso MP, Partanen K, et al. (1996) Hippocampal volumes in Alzheimer’s disease, Parkinson’s disease with 
and without dementia, and vascular dementia: an MRI study. Neurology 46:678-681.  
Laird NM, Ware JH (1982) Random-effects models for longitudinal data. Biometrics 38:963-974.  
Lipton SA, Rosenberg PA. (1994) Excitatory amino acids as a final common pathway for neurologic disorders. 
New Eng J Med 330:613-621.  
Marvanova M, Lakso M, et al. (2001) The neuroprotective agent memantine induces brain-derived neurotrophic 
factor and trkB expression in rat brain. Mol Cell Neurosci 18:247-258. 
Mathalon DH, Sullivan EV, Rawles JM, Pfefferbaum A. (1993) Correction for head size in brain-imaging 
measurements. Psychiatry Research: Neuroimaging 50:121-139.  

22                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
Mattson MP, Barger SW, et al. (1993) β-amyloid precursor protein metabolites and loss of neuronal Ca2+ 
homeostasis in Alzheimer’s disease.  Trends Neurosci 16:409-414.  
McKhann G, Drachmann D, et al. (1984) Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-
ARDDA work group under the auspices of Department of Health and Human Services Task Force on 
Alzheimer’s disease. Neurology 34:939-944.  
M’Harzi M, Gieules C, Palou A-M, Oberlander C, Barzaghi F (1997) Ameliorating effects of RU 47213, a 
novel oral and long-lasting cholinomimetic agent, on working memory impairment in rats. Pharmacol 
Biochem Behav 56:663-668. 
Miguel-Hidalgo JJ, Alvarez XA, Cacabelos R, Quack G. (2002) Neuroprotection by memantine against 
neurodegeneration induced by β-amyloid (1-40). Brain Res 958:210-221. 
Miller MI, Banerjee A, et al. (1997) Statistical methods in computational anatomy. Statistical Methods in 
Medical Research 6:267-299.  
Miller MI, Christensen GE, Amit Y, Grenander U.  (1993) Mathematical textbook of deformable 
neuroanatomies. Proc Natl Acad Sci 90:11944-11948. 
Miller MI, Hosakari M, Priebe C, Ratnanather JT, Wang L, Gado M, Csernansky JG. (2003) Labeled cortical 
depth maps quantifying cortical change during aging and DAT. Proc Nat’l Acad Sci USA 100:15172-
15177. 
Miller MI, Massle A, Ratnanather JT, Botteron K, Csernansky JG. (2000). Bayesian construction of 
geometrically bases cortical thickness metrics. Neurology 12:676-687. 
Miltner FO. (1982) Use of symptomatic therapy with memantine in cerebral coma. I. Correlation of coma stages 
and EEG spectral patterns. Arzneimittelforschung 32:1268-1270. 
Monarch ES, Rick J, Goldstein FC, Hamann SB (1999) Fear conditioning is impaired in early Alzheimer’s 
disease. Soc Neurosci Abst 25:99. 
Morris JC. (1993) The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology 43:2412-
2414. 
Morris JC, Edland S, Clark C, et al. (1993) The consortium to establish a registry for Alzheimer’s Disease 
(CERAD). Part IV. Rates of cognitive change in the longitudinal assessment of probable Alzheimer’s 
disease. Neurology 43:2457-2465. 
Morris JC, Cyrus PA, et al. (1998) Effects of metrifonate on the cognitive, global, and behavioral function of 
Alzheimer’s disease patients: Results of a randomized, double-blind, placebo-controlled study. 
Neurology 50:1222-1230. 
Morris JC, McKeel DW, et al. (1988) Validation of clinical diagnostic criteria for Alzheimer’s disease. Ann 
Neurol 24:17-22. 
Morris JC, McKeel DW, et al. (1991) Very mild Alzheimer’s disease: Informant-based clinical, psychometric, 
and pathological distinction from normal aging. Neurology 41:469-478.  
Morris JC, Storandt M, et al. (1996) Cerebral amyloid deposition and diffuse plaques in “ normal” aging: 
Evidence for presymptomatic and very mild Alzheimer’s disease. Neurology 46:707-719.  
Mungas D, Reed BR, Jagust WJ, et al. (2002) Volumetric MRI predicts rate of cognitive decline related to AD 
and cerebrovascular disease. Neurology 59:867-873.  
Murphy DGM, DeCarli CD, et al. (1993) Volumetric magnetic resonance imaging in men with dementia of the 
Alzheimer’s type: correlations with disease severity. Biol Psych 34:612-621.  
Ohno M, Kishi A, Watanabe S. (1996) Effect of cholinergic activation by physostigmine on working memory 
failure caused in rats by pharmacological manipulation of hippocampal glutamatergic and 5-HTergic 
neurotransmission. Neurosci Lett 217:21-24. 
Parks JK, Smith TS, et al. (2001) Neurotoxic Aβ peptides increase oxidative stress in vivo through NMDA-
receptor and nitric-oxide-synthase mechanisms, and inhibit complex IV activity and induce a 
mitochrondrial permeability transition in vitro. J Neurochem 76:1050-1056. 
Pearlson GD, Harris GJ, et al. (1992) Quantitative changes in mesial temporal volume, regional cerebral blood 
flow, and cognition in Alzheimer’s disease. Arch Gen Psych 49:402-408. 

23                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
Phillps RG, LeDoux JE (1992) Differential contribution of amygdala and hippocampus to cued and contextual 
fear conditioning. Behav Neurosci, 106:274-285. 
Price JL, Morris JC. (1999) Tangles and plaques in nondemented aging and preclinical Alzheimer’s disease. 
Ann Neurol  45:358-368. 
Raskind SL, Peskind ER, et al. (2000) Galantamine in AD: A 6-month, randomized, placebo-controlled trial 
with a 6-month extension. Neurology 54:2261-2268.  
Ratnanather JT, Priebe CE, Miller MI (2003) Semi-automated segmentation of cortical subvolumes via 
hierarchical mixture modeling. In: Medical Imaging 2003: Image Processing (eds. Sonka M and 
Fitzpatrick JM). Proc SPIE 5032:1602-1612. 
Reisberg B, Doody R, Stoffler A, Schmitt F, Ferris S, Mobius HJ and the Memantine Study Group (2003) 
Memantine in moderate-to-severe Alzheimer’s disease. New England J Medicine 348:1333-1341. 
Rogers SL, Farlow MR, et al. (1998) A 24-week, double-blind, placebo-controlled trial of donepezil in patients 
with Alzheimer’s disease. Neurology 50:136-145.  
Rogers SL, Friedhoff LT (1996) The efficacy and safety of donepezil in patients with Alzheimer’s disease: 
Results of a US multicentre, randomized, double-blind, placebo-controlled trial. Dementia 7:293-303.  
Rosen WG, Mohs RC, Davis KL. (1984) A new rating scale for Alzheimer disease. Am J Psychiatry 141:1356-
1364.  
Samuels SC, Davis KL (1998) Experimental approaches to cognitive disturbance in Alzheimer’s disease. 
Harvard Rev Psychiatry 6:11-22.  
Scheltens P, Leys D, et al.. (1992) Atrophy of the medial temporal lobes on MRI in “probable Alzheimer’s 
disease and normal aging: diagnostic value and neuropsychological correlates. J Neuro Neurosurg Psych 
55:967-972.  
Shenton ME, Kikinis R, et al. (1992) Abnormalities of the left temporal lobe and thought disorder in 
schizophrenia. N Engl J Med 327:604-612. 
Sheridan RD, Sutor B. (1990) Presynaptic M1 muscarinic cholinoceptors mediate inhibition of excitatory 
synaptic transmission in the hippocampus in vitro. Neurosci Lett 108:273-278. 
Squire LR, Zola-Morgan S. (1991) The medial temporal lobe memory system. Science 253:1380-1386. 
Steckler T, Sahgal A. (1995) The role of serotonergic-cholinergic interactions in the mediation of cognitive 
behaviour. Beh Brain Res 67:165-199.  
Tariot PN, Farlow MR, et al. (2004) Memantine treatment in patients with moderate to severe Alzheimer 
Disease who are already receiving donepezil. J Am Med Assoc 291:317-324. 
Thal LJ, Schwartz G, Sano M, et al. (1996) A multicenter double-blind study of controlled-release 
physostigmine for the treatment of symptoms secondary to Alzheimer’s disease. Neurology 47:1389-
1395.  
Thompson PM, MacDonald D, Mega MS, Holmes CJ, Evans AC, Toga AW. (1997) Detection and mapping of 
abnormal brain structure with a probabilistic  atlas of cortical surfaces. J Comp Asst Tomography 
21:567-581.  
Umbriaco D, Garcia S, Beaulieu C, Descarries L. (1995) Relational features of acetylcholine, noradrenaline, 
serotonin and GABA axon terminals in the striatum radiatum of adult rat hippocampus (CA1). 
Hippocampus 5:605-620. 
Venkatesan R, Haacke EM. (1997) Role of high resolution in magnetic resonance (MR) imaging: Applications 
fo MR angiography, intracranial T1-weighted imaging, and image interpolation. Int J Imaging Syst 
Technol 8:529-543.  
Wang L, Swank J, et al. (2003) Changes in hippocampal volume and shape across time distinguish dementia of 
the Alzheimer type from healthy aging. NeuroImage  20:667-682. 
Weller M, Finiels-Marlier F, Paul SM. (1993) NMDA receptor-mediated glutamate toxicity of cultured 
cerebeller, cortical and mesenencephalic neurons: Neuroprotective properties of amantidine and 
memantine.  Brain Res 613:143-148. 
West MJ, Slomianka L, Gundersen HJG. (1991) Unbiased stereological estimation of the total number of 
neurons in the subdivisions of the rat hippocampus using the optical fractionator. Anat Record 231:482-
497. 

24                    Corticolimbic Degeneration and Treatment of Dementia 8-26-2008     
Wilcock G, Wilkinson D. (1996) Galanthamine hydrobromide: Interim results of a group comparative, placebo 
controlled study of efficacy and safety in patients with a diagnosis of senile dementia of the Alzheimer 
type. Neurobiol Aging 17:S144. 
Wyss JM, van Groen R. (1989) Development of the hippocampus. In: The Hippocampus - New Vistas (Eds: V. 
Chan-Palay, C. Kohler) Neurology and Neurobiology, Vol. 52, Alan R. Liss, Inc. pp. 1-17.  
Zola-Morgan S, Squire L. (1993) Neuroanatomy of memory. Annual Rev Neurosci 16:547-563.