Changes in Parahippocampal White Matter Integrity in Amnestic Mild Cognitive Impairment: A Diffusion Tensor Imaging Study
Changes in parahippocampal white matter integrity in amnestic mild cognitive impairment: A diffusion tensor imaging study
E.J. Rogalski 4
C.M. Murphy 2
L. deToledo-Morrell 2
R.C. Shah 0 3
M.E. Moseley 1
R. Bammer 1
G.T. Stebbins 2
0 Department of Rush Alzheimer's Disease Center, Rush University Medical Center , Chicago, IL , USA
1 Department of Radiology Stanford University , Palo Alto, CA , USA
2 Department of Neurological Sciences, Rush University Medical Center , Chicago, IL , USA
3 Department of Family Medicine, Rush University Medical Center , Chicago, IL , USA
4 Cognitive Neurology and Alzheimer's Disease Center at Northwestern University, Feinberg School of Medicine , Chicago, IL , USA
In the present study, changes in the parahippocampal white matter (PWM), in the region that includes the perforant path, were investigated, in vivo, in 14 individuals with amnestic mild cognitive impairment (aMCI) compared to 14 elderly controls with no cognitive impairment (NCI). For this purpose, (1) volumetry; (2) diffusion tensor imaging (DTI) derived measures of mean diffusivity (MD) and fractional anisotropy (FA); and (3) tractography were used. In addition, regression models were utilized to examine the association of PWM measurements with memory decline. The results from this study confirm previous findings in our laboratory and others, showing that compared to controls, individuals with aMCI have PWM volume loss. In addition to volume reduction, participants with aMCI demonstrated a significant increase in MD, but no difference in FA, both in the PWM region and in fibers modeled to pass through the PWM region. Further, the DTI metric of MD was associated with declarative memory performance, suggesting it may be a sensitive marker for memory dysfunction. These results indicate that there is general tissue loss and degradation (decreased volume; increased MD) in individuals with aMCI compared to older people with normal cognitive function. However, the microstructural organization of remaining fibers, as determined by measures of anisotropic diffusion, is not significantly different from that of controls.
MRI; dementia; perforant path; tractography; entorhinal cortex; magnetic resonance imaging; hippocampus; memory; mesial temporal lobe; volumetry
Amnestic mild cognitive impairment (aMCI) refers
to elderly individuals who show a predominant
decline in declarative memory function but do not meet
criteria for Alzheimer’s disease (AD). Individuals
with aMCI do show an increased risk for developing
]. Therefore, studies of individuals with aMCI
compared to normal controls provide a unique
opportunity to learn about neuroanatomical changes associated
with AD at early stages of the disease process, provide
additional information on risk factors for AD, and
perhaps allow for the development of earlier therapeutic
Previous post mortem and in vivo investigations of
the pathophysiology of aMCI and AD have focused on
examining the integrity of the hippocampus (HF) and
the entorhinal cortex (EC), since these mesial temporal
lobe structures are known to be critically important for
successful declarative memory [
Histopathological studies have reported a loss of entorhinal cortex
layer II neurons in patients with AD and in those with
MCI, compared to controls [
with the postmortem findings, quantitative in vivo
structural neuroimaging studies have reported entorhinal and
hippocampal atrophy in patients with AD and aMCI [
]. Entorhinal cortex neurons
receive multimodal sensory information from primary
sensory and association cortices and relay this
information to the hippocampus via the perforant path [
]. It has been hypothesized that the memory deficits
that characterize individuals with aMCI and AD may
partly be a consequence of a degradation in the flow
of information from the entorhinal cortex to the
Recent evidence suggests that damage to the
parahippocampal white matter, in addition to atrophic changes
in the hippocampus and entorhinal cortex, may
contribute to memory dysfunction in aMCI and AD [
]. Specifically, Stoub and colleagues [
] used voxel
based morphometry (VBM) to demonstrate decreased
white matter volume in the anterior medial portion of
the parahippocampal gyrus that includes the perforant
path in individuals with aMCI compared to older
controls. In addition, this reduction in white matter volume
was found to be a significant predictor of memory
function, suggesting that white matter integrity in the region
of the perforant path is important for successful
memory. An important unanswered question is whether
remaining white matter fibers in the perforant path region
of the parahippocampal gyrus show microstructural
alterations in individuals with aMCI. Degraded integrity
of remaining fibers could further hinder impulse
transmission and consequently contribute to the observed
memory deficits in individuals with aMCI.
Diffusion tensor imaging (DTI) makes it possible to
examine the microstructural integrity of white matter
in vivo and is especially indicative for diseases
causing neuronal or axonal damage [
]. This emerging
technique combines MR diffusion-weighted pulse
sequences with tensor mathematics to measure molecular
diffusion in three dimensions. The tensor model allows
for the determination of diffusion direction through
calculation of the primary, secondary and tertiary
eigenvalues with their associated eigenvectors. The primary
direction of diffusion is determined from the primary
eigenvector, and the ratio of the primary eigenvalue
to secondary and tertiary eigenvalues provides a
measure of anisotropy, or the directional dependence of
diffusion. When considered across voxels, the primary
eigenvector can be used to model the path of directional
diffusion, and when used in cerebral white matter, it can
provide a model of putative white matter tracts [
This methodology for modeling white matter pathways
has been termed tractography.
There are several metrics that can be calculated from
DTI scans, but the two most common are mean
diffusivity (MD) and fractional anisotropy (FA). MD provides
a measure of non-directional diffusion and is influenced
by the presence of barriers to free diffusion. As barriers
to free diffusion decrease (i.e., due to degradation of
tissue) MD increases. In contrast to MD, FA provides a
measure of directional diffusion. In intact white matter,
the direction of diffusion is parallel to myelinated and
unmyelinated axonal fibers, thus leading to increased
FA. Damage to white matter, such as demyelination
or axonal damage, decreases FA by allowing increased
diffusion perpendicular to the axonal orientation. Thus,
MD and FA can be used as noninvasive proxy measures
of the general integrity of the tissue and the parallel
organization of remaining white matter fibers
]. Previous studies have found that alterations
in these metrics are present in normal aging [
pathological aging (e.g. Alzheimer’s disease) and
several neurologic and psychiatric disorders [
The primary aim of the present study was to use
DTI and tractography to examine the microstructural
integrity of the remaining white matter fibers in the
parahippocampal gyrus in the region of the perforant
path in individuals with aMCI compared to that of
elderly controls with no cognitive impairment (NCI). A
secondary aim was to determine the contribution of
these changes to declarative memory performance.
Fourteen elderly control subjects with no cognitive
impairment (NCI) and 14 individuals with amnestic
mild cognitive impairment participated in this study
(Table 1). NCI participants were recruited from the
community and aMCI participants were recruited from
the Rush Alzheimer’s Disease Center (RADC;
Chicago, IL) clinic. Both NCI and aMCI participants were
evaluated at the RADC. It is important to note that,
individuals who came to the clinic with memory
complaints, but did not show any cognitive impairment,
were not recruited as controls. Informed consent was
obtained from all participants according to the
guidelines of the Institutional Review Board of Rush
University Medical Center.
2.2. Clinical evaluation
The evaluation carried out by the RADC has been
previously described [
]. Briefly, the evaluation,
which was given to all participants in the study,
incorporated the Consortium to Establish a Registry for
Alzheimer’s Disease (CERAD) [
] procedures and
included a medical history, neurological examination,
neuropsychological testing, informant interview and
blood tests [
Selection as an elderly control subject required a
normal neurological examination, normal cognition as
determined by performance on neuropsychological tests,
a Mini Mental State Examination (MMSE) score
], and age 65. A diagnosis of aMCI was given
to individuals who underwent a standard clinical
evaluation and were found to have an isolated deficit in the
memory domain, but did not meet criteria for
dementia, as previously described [
]. Exclusion criteria for
entry into the study were evidence of other
neurologic, psychiatric or systemic conditions that could cause
cognitive impairment (e.g., stroke, alcoholism, major
depression, a history of temporal lobe epilepsy),
contraindication to MRI scanning (certain metal implants
and cardiac pacemakers, claustrophobia), and age less
than 65 years.
The episodic memory tests administered to all
participants and used to define a memory deficit
consisted of immediate and delayed recall of the East Boston
] and of Story A from the Logical Memory of
the Wechsler Memory Scale – Revised [
additional test involved the learning and retention of a
10-word list from the CERAD battery [
]. The three
scores for this test included Word List Memory (the
total number of words immediately recalled after each of
three consecutive presentations of the list), Word List
Recall (the number of words recalled after a delay) and
Word List Recognition (the number of words correctly
recognized in a four-alternative, forced-choice format,
administered after Word List Recall). Summary scores
were calculated for combined performance on
declarative memory tests by standardizing each of the seven
memory scores. For this study, we used the means and
standard deviations of each test from the baseline visits
of the first wave of 86 control participants entered into
an on-going longitudinal project to construct
individual memory test z-scores for participants in the present
study. For each participant, the seven z-scores were
averaged to construct a declarative memory z-score.
2.3. Acquisition parameters and quantification of
Imaging was performed on a 1.5 T General
Electric scanner (General Electric Medical Systems,
Milwaukee, WI, USA) with LX Horizon high-speed
gradient upgrades (Rev 11.4) at Rush University Medical
Center. Foam padding and tape were used to secure
participants’ head to minimize movement. The
scanning session was completed in one visit and consisted
of a locator scan, a 3D T1-weighted spoiled gradient
recalled (SPGR) scan (124 contiguous 1.6 mm thick
slices acquired in the coronal plane, matrix = 256x192,
field of view = 22 cm, TR/TE = 34/7msec, flip
angle = 35◦ ), and a single-shot echo planar high
resolution diffusion weighted scan (TR/TE = 12100/97ms,
field of view = 25 cm, matrix = 128×128, 38 3 mm
gapless slices, in-plane resolution = 1.95 mm, 2NEX,
3 repetitions). Two diffusion weights (b-values) were
used: b = 0 and b = 800 s/mm2. Diffusion encoding
gradients were applied along a total of 24 non-collinear
directions repeated six times for each slice.
SPGR scans were converted from individual slices
to volumes using the DICOM toolbox in SPM 5
(Wellcome Department of Cognitive Neurology, London,
UK). Post-acquisition processing of DTI images
utilized an open source suite of software developed at
Stanford University (http://sirl.stanford.edu/software)
with modifications developed in our laboratory.
The initial processing of the DTI scans required
unwarping of the eddy current distortions. Simple
coregistration of the DTI images is not optimal for
correcting these distortions; therefore, we utilized a correction
method developed by Rohde and colleagues [
algorithm combines a rigid-body 3D motion
correction (6 parameters) with a constrained non-linear
warping (8 parameters) based on a model of the expected
eddy-current distortions. Calculation of MD and FA
proceeded from the unwarped DTI images through the
calculation of the six diffusion coefficients defining the
six elements of the diffusion tensor [
defining the three principle directions of diffusion for
each voxel, and associated eigenvalues were derived
from the diffusion tensor. MD and FA were derived
from the eigenvalues [
]. The b = 0 scans were
used to construct a T2-weighted image.
2.4. Regions of interest
To better understand the relationship between
volume loss in given mesial temporal lobe gray matter
regions and white matter loss in the region of the
perforant path, three regions of interest (ROI) were
segmented: the parahippocampal white matter (PWM), the
entorhinal cortex (EC) and the hippocampus (HF). The
Analyze software package (Mayo Clinic Foundation,
Rochester, MN, USA) was used for manually
segmenting the ROI boundaries and for estimating the volumes
of each ROI. ROIs were traced bilaterally for each
subject on their high resolution T1 weighted 3D-SPGR
scan. The ROI boundaries were delineated on coronal
slices reformatted to be perpendicular to the long axis
of the HF using two previously described and validated
protocols for the EC and HF [
] and one new
protocol for the PWM, which targets the portion of
parahippocampal white matter that includes the perforant path
as described below. Figure 1 shows sample tracings of
all three ROIs on a single coronal MR image.
Briefly, for determining EC volume, tracing began
with the section in which the gyrus ambiens, amygdala
and white matter of the parahippocampal gyrus first
appeared visible. The superomedial border in rostral
sections was the sulcus semiannularis and in caudal
sections the subiculum. The shoulder of the collateral
sulcus was used as the lateral border. The latter is a
somewhat conservative criterion that allowed consistency in
tracings and avoided the use of different lateral borders
depending on individual differences in the depth of the
collateral sulcus [see for example; 25]. The lateral
border was constructed by drawing a straight line from the
most inferior point of the white matter to the most
inferior tip of the gray matter. The last section measured
was three 1.6 mm sections rostral to the image in which
the lateral geniculate nucleus first appeared visible.
HF tracings started with the first section where it
could be clearly differentiated from the amygdala by
the alveus and included the fimbria, dentate gyrus, the
hippocampus proper and the subiculum. Tracings
continued on all consecutive images until the slice before
the full appearance of the fornix.
Tracing of the PWM began with the slice in which
the gyrus ambiens, amygdala and white matter of the
parahippocampal gyrus first appear. The most caudal
slice traced was one slice rostral to the first appearance
of the lateral geniculate nucleus. The lateral border
of the PWM was defined as the bend that signifies
the junction between the parahippocampal white matter
and the temporal stem. The medial border was defined
as the point at which the white matter meets the gray
matter of the entorhinal cortex. Tracings were carried
out by ER (who was trained to be within 95% of
LdeTM). All tracings were checked, slice by slice, by
To correct for individual differences in brain size, the
ROI volumes (PWM, HF, and EC) were divided by total
intracranial volume derived from sagittally formatted
5 mm slices (i.e., normalized). To compute intracranial
volume, the inner table of the cranium was traced in
consecutive sagittal sections spanning the entire brain.
At the level of the foramen magnum, a straight line
was drawn from the inner surface of the clivus to the
occipital bone. Normalized volume for brain regions
of interest was determined using the formula: absolute
volume in mm3/intracranial volume in mm3 × 1000.
NCI (n = 14) aMCI (n = 14)
Parahippocampal white matter volume 0.754 (+/−0.13) 0.548 (+/−0.22)∗
Hippocampal volume 3.77 (+/−0.41) 3.33 (+/−0.53)∗
Entorhinal cortex volume 0.99 (+/−0.11) 0.79 (+/−0.16)∗
∗Significantly different from controls (p < 0.01). Means and standard deviations
For DTI and tractography analyses, the PWM ROIs
were converted to SPM5 format for co-registration with
the SPGR structural scan and the DTI scan for each
participant. This conversion requires the application
of the transformation matrix from the SPGR to the
ROI for location alignment and re-slicing of the ROI to
backfill the volume. The co-registration of the ROI to
the SPGR and DTI scans utilized a rigid-body method
as implemented in SPM5 which translates and rotates
the SPGR volume to best match the DTI image. The
co-registration parameters were applied to the PWM
ROI to bring them into anatomical alignment with the
DTI data. No DTI metric was shifted during the
coregistration to minimize the addition of noise associated
with the registration process to the DTI images. Each
co-registered SPGR and ROI were manually inspected
for registration error and corrected when required. The
DTI values within the PWM ROIs were extracted using
software developed in our laboratory. While a number
of DTI scalars are available for analysis, we used the
most commonly cited measures of factional anisotropy
(FA) and mean diffusivity (MD).
Following DTI ROI analysis, the PWM ROIs were
used as seed points to generate DTI derived
tractographic models of the white matter pathways to measure the
diffusion properties of the fibers passing through the
PWM region. To generate the tractographic models,
we used an open source suite of software developed at
Stanford University (http://siri.stanford.edu/software)
with modifications developed in our laboratory. These
models were developed using a deterministic
streamline tracking technique [
] with Runge-Kutta
4th order integration . This algorithm seeks to
propagate tracks based on the principal direction
between voxels and on the primary eigenvector. The
program assesses the degree of coherence between
associated voxels in both a forward and a backward
marching method. The model of the underlying white
matter tracts is based on the highest directional association
across voxels with a fiber termination at an FA value
of less than 0.20, or an angular displacement of greater
than 20 degrees. From these models, we quantified
the DTI metrics of FA and MD, as well as the mean
length of the modeled fibers. These values were
calculated individually for each participant based on the
modeled tracts propagated from the individually
determined PWM ROI.
2.5. Statistical analyses
Demographic differences between the two groups of
participants were measured by independent t−tests or
χ2analyses as appropriate in SPSS (SPSS, Chicago,
IL). Separate independent t−tests were used to
examine group differences in volume (PWM, EC, and HF),
DTI metrics (FA and MD) of the PWM ROI and of
the modeled white matter fiber tracts. Because of the
number of repeated comparisons, we adopted a
conservative statistical threshold of p = 0.01 for these
analyses. Pearson correlations were performed to examine
the relationship between FA and MD in our
participants. Multiple regression models were used to assess
the contribution of EC and HF volume loss in
predicting PWM volume. Multiple regression analyses were
also applied to assess the relative contribution of the
in vivo PWM imaging measures to declarative memory
performance. Statistical significance for the regression
analyses was set at p = 0.05.
Demographic data, MMSE scores and episodic
memory z-scores are presented in Table 1. The two
groups of participants did not differ in age or
education, but as expected, there was a difference in MMSE
[t(26) = 4.3, p < 0.001] scores and in episodic memory
z-scores [t(26) = 6.9, p < 0.001].
Total (right + left) hippocampal, entorhinal, and
parahippocampal white matter volumes were
extracted for each subject, adjusted for total intracranial
volume (normalized), and compared by diagnostic group.
Results indicated that total volumes for each structure
were significantly greater for the NCI than the aMCI
group [HF: t(26) = 2.4, p = 0.023; EC: t(26) = 3.8,
p = 0.001; PWM: t(26) = 3.0, p < 0.006; Table 2].
Multiple regression, with PWM volume as the
outcome and EC and HF volume entered as predictors in a
stepwise method revealed total normalized EC volume
as the major predictor of PWM volume loss (F[
17.41, p < 0.001), accounting for approximately 40%
of the variance in PWM volume. Hippocampal volume
did not enter as a predictor into the regression.
White matter integrity of remaining fibers in the
parahippocampal region was measured by comparing
PWM ROI values for mean FA and MD between
diagnostic groups. There were no significant differences in
mean FA by diagnostic group [t(26) = 0.5, p = 0.593].
However, MD [t(26) = −3.5, p = 0.002] was
significantly higher in the aMCI group compared to controls
(Fig. 2). These results suggest a change in general
tissue structure, but not the parallel organization of
remaining fibers in individuals with aMCI compared to
To examine the relationship between memory
performance and DTI measures of white matter integrity,
a multiple regression, with memory z-score as the
outcome and PWM ROI volume and DTI metrics (FA and
MD) entered as predictors in a stepwise method, was
conducted. Results revealed MD as the only significant
predictor in the model (F[
]= 12.34, p = 0.002),
accounting for approximately 32% of the variance in
memory z-scores. This finding suggests that MD is the
strongest predictor of memory performance.
To assess the relationship between the PWM ROI
DTI metrics of FA and MD, Pearson correlations were
used. Results indicated an expected negative
correlation between FA and MD within the NCI group (r
= − 0.57, p = 0.035). However, no significant
relationship between FA and MD was found in the aMCI
group (r = − 0.20, p = 0.485; Fig. 3), demonstrating
a dissociation between the two diagnostic groups.
Up to this point, we have examined changes in the
PWM ROI, but not the tractography results assessing
the fibers passing through this region. We used the
PWM ROIs as seed points to generate maps of fiber
tracts (for an example, see Fig. 4). To examine group
differences as determined by the tractography results,
we assessed mean fiber length, FA and MD of the fibers
passing through the PWM ROI. Measures of the
integrity of the modeled white matter fibers revealed
significant group differences for MD [t(26) = −3.68, p =
0.001], but not FA [t(26) = 0.26, p = 0.8; Table 3].
In addition, there were no significant group differences
for mean length of fibers [t(26) = −0.70, p = 0.49;
Table 3]. These FA and MD results are similar to those
found in the PWM ROI analyses described above.
The relative contribution of tractographic measures
(MD and FA of the fibers, and mean fiber length) to
memory performance was assessed in a stepwise
multiple regression. Fiber MD and FA were significant
predictors of memory performance (F[
] = 10.36,
p = 0.001), accounting for approximately 45% of the
variance in memory z-scores. Mean fiber length did not
enter the regression as a predictor. These results show
that the relationship between FA and MD within the
PWM ROI is different from that of the fibers passing
through the PWM region.
The present study characterized, in vivo, white
matter changes in the parahippocampal region that includes
the perforant path in individuals with aMCI compared
to those of elderly controls with no cognitive
impairment using volumetry, DTI and tractography. The
results from this study confirm and extend previous
findings in our laboratory [
] and others [
that compared to controls, individuals with aMCI have
volume loss in the parahippocampal white matter. We
also found a concomitant increase in MD in the aMCI
group, for both the PWM region including the perforant
path, as well as in the fibers modeled to pass through
this PWM region. In addition to demonstrating group
differences, regression models were used to examine
the relationships between PWM changes and those of
neighboring neuroanatomically connected gray matter
structures (entorhinal cortex and hippocampus), as well
as memory performance.
Total normalized EC, but not HF volume, was a
significant predictor of total normalized PWM volume,
suggesting that volume loss in the PWM may be related
to cell loss in the EC. This finding has been reported
by our laboratory [
] and others [
] and is consistent
with the hypothesis that memory deficits that
characterize individuals with aMCI and AD may partly be a
consequence of a disconnection in the flow of
information from the entorhinal cortex to the hippocampus via
the perforant path [
The relationship between FA and MD in the PWM
region was explored to better understand
microstructural changes in white matter measured by DTI.
Typically, there is an inverse relationship between FA and
MD, so that as tissue is damaged or atrophied, there is
an increase in MD due to increased free diffusion, but
there is usually a concomitant decrease in FA due to
loss of directional diffusion because of a loss of
barriers to isotropic diffusion. In the region of the perforant
path, the aMCI group in this study did not show the
expected relationship between MD and FA values. Their
MD was increased compared to the NCI group, but
there was no difference in FA between the groups. This
finding suggests that although individuals with aMCI
have general diffusivity alterations in the PWM region,
the longitudinal microstructural organization of the
remaining parallel fibers of the perforant path region, as
measures by FA, is comparable to that of controls. This
result is in contrast to a report of decreased FA in the
PWM containing the perforant path in patients with
]. In that study, specific alterations in
anisotropic diffusion parameters associated with myelin
] were found. This disruption of myelin
function in AD may be related to increased susceptibility of
PWM oligodendrocytes to metabolic and free radical
The lack of inverse correlation between FA and MD,
while less common, has been reported in individuals
with MCI [
], as well as in studies of patients with
] and children with 22q deletion syndrome [
Unfortunately, the mechanism for tissue disruption
cannot be definitively determined using DTI. The
neurodegenerative changes taking place in this region are
exceedingly complex and the changes in MD may reflect a
combination of processes including loss of axons,
dendrites and tissue degradation due to anterograde
Wallerian degeneration. This may be a consequence of the
cell loss in layer II of the EC and is consistent with the
EC volume loss reported here and by others [
Controlled pathological studies will be important for
disentangling the temporal course and mechanisms of
A second aim of the study was to examine the
contribution of the in vivo imaging measures from the PWM
ROI to memory performance. Results from the ROI
analyses revealed that MD in the white matter region
containing the perforant path was the only significant
predictor of memory performance, demonstrating the
functional relevance of the diffusivity changes. Our
results also suggest that the memory deficits that
characterize individuals with aMCI do not require PWM
region changes in FA. It is possible that FA may play a
greater role in predicting memory performance as the
disease progresses to meet the criteria for clinical AD.
Future cross-sectional and longitudinal studies,
comparing the PWM region integrity between the three
diagnostic groups (NCI, aMCI and AD), are required to
characterize the temporal changes in the perforant path
The tractography results indicated that both FA and
MD of the fiber tracts generated from the PWM ROI
contributed significantly to memory performance in the
regression models. The significant addition fiber FA
to the prediction of memory performance suggests the
relationship between the fibers in the local PWM ROI
and the fibers that pass through the PWM region is
different. One caveat of tractography estimates from a
single ROI seed point is that it only provides
information about the fibers passing through this region and is
not limited to fibers originating in the PWM region that
includes the perforant path. Despite this limitation, it is
important to note that the diffusion data of the tracked
fibers corroborate the PWM ROI data, which show a
consistent increase in MD, but no change in FA.
Though several studies have examined white matter
changes in MCI and AD, few have targeted the
perforant path as a region of interest using DTI [
]. The results from these studies have been
mixed. The inconsistent findings may be attributed to
the methodological variability between studies,
including different approaches to the acquisition and analysis
of DTI parameters (e.g., diffusion weights, eddy
current correction), inconsistency in the DTI metrics
reported (e.g., coherence index, FA, MD, axial and radial
diffusivity measures) [
], and variability in the
region of interest definitions (e.g. spherical,
The diversity of methodologies used in previous
studies limits generalizability and makes it difficult
to compare the previous results to the present
findings. For example, Kalus and colleagues [
manually traced the perforant path ROI but only examined
the coherence index measure of DTI, leaving out more
common measures of FA or MD from the analysis, thus
preventing parallel comparison to the findings in the
present study. The temporal lobe white matter DTI
results from Fellgiebel and colleagues [
] are consistent
with those reported in this study, i.e., increased MD
but no change in FA. However, their method of ROI
placement (rectangular) was not as anatomically
specific as our targeted manually traced ROI. The present
study applied comprehensive eddy current corrections
to the DTI images, used a clearly defined, anatomically
driven protocol for segmenting the PWM region and
reports data using the two most common DTI metrics,
MD and FA. These methodological considerations
ensured accurate representation of the white matter region
and will allow for replication of the study by future
In summary, the present study used in vivo
imaging to demonstrate regional tissue loss and degradation
as evidenced by decreased volume and increased MD
within the PWM region that includes the perforant path
in individuals with aMCI compared to cognitively
intact elderly controls. However, the parallel
microstructural organization (as measure by FA) of the
remaining fibers within the PWM region was similar between
diagnostic groups. Further, it is not surprising that the
DTI metric of MD, but not FA, was predictive of
memory performance since FA was not significantly different
between the aMCI and control groups. Taken together,
these findings suggest that even with intact
microstructural organization, tissue loss and degradation of the
perforant path fibers contribute to the memory loss that
characterizes individuals with aMCI who are at risk for
developing AD. Though the exact mechanism cannot
be determined, our data are in line with the hypothesis
that parahippocampal white matter degradation causes
a disruption of multimodal input from the entorhinal
cortex to the hippocampus. It is important that future
research characterizes the temporal changes in the
microstructural organization of the PWM throughout the
neurodegenerative process from aMCI to clinical AD.
The research reported here was supported by grants
P01 AG09466 and P30 AG10161 from the national
Institute on Aging, National Institutes of Health. Dr.
Rogalski received support from a Postdoctoral Training
Grant (T32 AG00257) from the National Institute on
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