White Matter Abnormalities and Structural Hippocampal
Disconnections in Amnestic Mild Cognitive Impairment
and Alzheimer’s Disease
Jared Rowley1, Vladimir Fonov4, Ona Wu5, Simon Fristed Eskildsen6, Dorothee Schoemaker1, Liyong
Wu1,2, Sara Mohades1, Monica Shin1, Viviane Sziklas4, Laksanun Cheewakriengkrai1, Amir Shmuel3, Alain
Dagher3, Serge Gauthier1, Pedro Rosa-Neto1,3*, for the Alzheimer's Disease Neuroimaging Initiative
1 Translational Neuroimaging Laboratory, McGill Centre for Studies in Aging (MCSA), McGill University, Montreal, Quebec, Canada , 2 Department of
Neurology, Xuan Wu Hospital, Capital Medical University, Beijing, China, 3 McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University,
Montreal, Quebec, Canada, 4 Montreal Neurological Institute, McGill University, Montreal, QC, Canada, 5 Athinoula A. Martinos Center for Biomedical Imaging,
Charlestown, Massachusetts, United States of America, 6 Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
The purpose of this project was to evaluate white matter degeneration and its impact on hippocampal structural
connectivity in patients with amnestic mild cognitive impairment, non-amnestic mild cognitive impairment and
Alzheimer’s disease. We estimated white matter fractional anisotropy, mean diffusivity and hippocampal structural
connectivity in two independent cohorts. The ADNI cohort included 108 subjects [25 cognitively normal, 21 amnestic
mild cognitive impairment, 47 non-amnestic mild cognitive impairment and 15 Alzheimer’s disease]. A second cohort
included 34 subjects [15 cognitively normal and 19 amnestic mild cognitive impairment] recruited in Montreal. All
subjects underwent clinical and neuropsychological assessment in addition to diffusion and T1 MRI. Individual
fractional anisotropy and mean diffusivity maps were generated using FSL-DTIfit. In addition, hippocampal structural
connectivity maps expressing the probability of connectivity between the hippocampus and cortex were generated
using a pipeline based on FSL-probtrackX. Voxel-based group comparison statistics of fractional anisotropy, mean
diffusivity and hippocampal structural connectivity were estimated using Tract-Based Spatial Statistics. The
proportion of abnormal to total white matter volume was estimated using the total volume of the white matter
skeleton. We found that in both cohorts, amnestic mild cognitive impairment patients had 27-29% white matter
volume showing higher mean diffusivity but no significant fractional anisotropy abnormalities. No fractional anisotropy
or mean diffusivity differences were observed between non-amnestic mild cognitive impairment patients and
cognitively normal subjects. Alzheimer’s disease patients had 66.3% of normalized white matter volume with
increased mean diffusivity and 54.3% of the white matter had reduced fractional anisotropy. Reduced structural
connectivity was found in the hippocampal connections to temporal, inferior parietal, posterior cingulate and frontal
regions only in the Alzheimer’s group. The severity of white matter degeneration appears to be higher in advanced
clinical stages, supporting the construct that these abnormalities are part of the pathophysiological processes of
Citation: Rowley J, Fonov V, Wu O, Eskildsen SF, Schoemaker D, et al. (2013) White Matter Abnormalities and Structural Hippocampal Disconnections in
Amnestic Mild Cognitive Impairment and Alzheimer’s Disease. PLoS ONE 8(9): e74776. doi:10.1371/journal.pone.0074776
Editor: Hideyuki Sawada, National Hospital of Utano, Japan
Received February 21, 2013; Accepted August 7, 2013; Published September 27, 2013
Copyright: © 2013 Rowley et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The MCSA data, analysis and writing of this paper were supported by Canadian institutes of Health Research (CIHR) (MOP-11-51-31);
Alzheimer's Association (NIRG-08-92090); National Nature Science Foundation of China (NSFC) (3070024); Beijing Scientific and Technological New Star
Program (2007B069); Nussia & André Aisenstadt Foundation,Fonds de la recherche en santé du Québec, and the Scotia Bank Trust. Additionally, data
collection and sharing for this project was funded by the Alzheimer's disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01
AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous
contributions from the following: Alzheimer’s Association, Alzheimer’s Drug Discovery Foundation, BioClinica, Inc., Biogen Idec Inc., Bristol-Myers Squibb
Company, Eisai Inc., Elan Pharmaceuticals, Inc., Eli Lilly and Company, F. Hoffmann-La Roche Ltd. and its affiliated company, Genentech, Inc., GE
Healthcare, Innogenetics, N.V., IXICO Ltd., Janssen Alzheimer Immunotherapy Research & Development, LLC., Johnson & Johnson Pharmaceutical
Research & Development LLC., Medpace, Inc., Merck & Co., Inc., Meso Scale Diagnostics, LLC., NeuroRx Research, Novartis Pharmaceuticals
Corporation, Pfizer Inc., Piramal Imaging, Servier, Synarc Inc., and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is
providing funds to support ADNI clinical sites in Canada. Private sector contributions are Rev November 7, 2012 facilitated by the Foundation for the
National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is
coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for
Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129 and K01 AG030514. The
funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
PLOS ONE | www.plosone.org1 September 2013 | Volume 8 | Issue 9 | e74776
Competing interests: The authors would like to emphasize that none of the authors have employment, consultancy, patents and products in development
or marketing products competing or conflicting with the results reported in this manuscript. In contrast, the large list of potential conflicts of interest reflects
a requirement from ADNI. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.
Alzheimer’s disease (AD) has been conceptualized by a
succession of pathophysiological events beginning with
progressive extracellular accumulation of amyloid followed by a
variety of neurodegenerative changes such as intracellular
accumulation of neurofibrillary inclusions, brain atrophy and cell
depletion . In AD, neurodegenerative changes (i.e. tau
hyperphosphorylation and cell depletion) follows a typical 6-
stage topographic pattern starting in the entorhinal cortex,
propagating to the limbic cortex and subsequently to the
polymodal association cortex . In fact, the asymptomatic AD,
mild cognitive impairment (MCI) and dementia stages nearly
correspond to the severity of AD neuropathology propagation
From pathophysiological perspective, there is a growing
consensus that white matter (WM) abnormalities in MCI
constitute an integral part of the degenerative process
associated with AD pathophysiology. Chen and colleagues
proposed that white matter pathology, as measured in-vivo in
dementia patients, may be a sign of ‘anterograde Wallerian
degeneration’, in which gray matter pathology could be
preceded by axonal dysfunction . WM structural changes
such as myelin breakdown, loss of myelin basic protein ,
neuroinflammation as well as abnormal axonal transport have
been recognized as part of AD WM neuropathological features
The role of WM degeneration in AD has been explored in
vivo with Magnetic Resonance Imaging (MRI; see review
[9,10]). Mean diffusivity (MD) and fractional anisotropy (FA) are
MRI diffusion tensor imaging (DTI) outcome measures
informative of microstructural organization of water in WM
compartments. High MD conveys local increase of free water
diffusivity in WM, which possibly is linked to reduction in myelin
content, axonal depletion or declines on extracellular matrix
. Low FA indicates loss of diffusion directionality, which is
imposed by abnormal axonal membranes. In fact, post mortem
data show a correlation between FA axonal and myelin WM
contents . Advances in image processing allow the
estimations of WM pathways, which are derived from Bayesian
mathematical models (probabilistic tractography) . These
techniques provide a metric to estimate the degree in which
WM abnormalities disrupt long pathways connecting distinct
brain regions. Thus, assessment of WM abnormalities using
MRI can expand classic neuropathological approach by
estimating WM structural connectivity in major WM pathways
MCI due to AD is a condition characterized by objective
cognitive deficits which minimally interfere with activities of
daily living . Peterson and colleagues outlined a
classification of MCI as amnestic mild cognitive impairment
(aMCI) and non-amnestic mild cognitive impairment (naMCI),
based on the predominance of memory deficits over other
cognitive domains . It has been established that local WM
disconnections between the
hippocampus (i.e. perforant path) are involved in AD and MCI
pathophysiology, however large-scale hippocampal WM
connectivity has never been systematically assessed in these
populations [17,18]. Large-scale hippocampus structural
connectivity indicates the severity of disconnections between
limbic and polymodal association cortex. Here, we aimed to
compare patterns of brain FA and MD abnormalities as well as
hippocampal connectivity among aMCI, naMCI and AD
individuals. We hypothesized that there will be greater severity
of MD and FA abnormalities in AD. In addition, we predict
disconnections on large-scale hippocampal WM networks in
AD but not in aMCI or naMCI.
entorhinal cortex and
Two cohorts were analyzed in this study. The first cohort of
aMCI and cognitively normal (CN) individuals was recruited at
the McGill Centre for Studies in Aging (MCSA cohort) located
in Montreal, Quebec, Canada. An independent cohort of CN,
MCI, and AD was obtained from Alzheimer’s Disease
Neuroimaging Initiative Go /2 (ADNI cohort). Informed written
consent was obtained from each subject in accordance with
local institutions’ Research Ethic Boards (REB) . The McGill
University Research and Ethics committee approved these
MCSA cohort data acquisition
The McGill Centre for Studies in Aging (MCSA) staff was
responsible for patient recruitment, screening and enrollment in
the MCSA cohort. Patients with subjective memory complaints,
substantiated by a knowledgeable informant were clinically
assessed (SG). Subsequently, patients underwent a full
medical, neurological examination
neuropsychological tests including the standard Mini Mental
State Examination (MMSE) and Rey Auditory Verbal Learning
Test . Diagnosis of MCI was achieved by a consensus in a
clinical diagnosis meeting based on the Peterson criteria .
Age and gender matched controls enrolled in this study,
referred here as CN, were recruited by advertisements in local
newspapers. CN exclusion criteria were (1) presence of current
or past neurological or psychiatric condition and (2) history of
memory complaints. Exclusion criteria for all subjects included
a history of psychological problems, intellectual inability, past
psycho stimulant drug use or brain vascular lesions on the
Fluid attenuation inversion recovery (FLAIR) MRI.
MRI data was acquired on a Siemens 3T Trio MR scanner
(Siemens Medical Systems, Erlangen, Germany) using a 32-
channel phased-array head coil. Diffusion encoding was
achieved using a single-shot spin-echo echo planar sequence
with twice-refocused balanced diffusion encoding gradients.
and battery of
White Matter Abnormalities in AD
PLOS ONE | www.plosone.org2September 2013 | Volume 8 | Issue 9 | e74776
High angular resolution reconstruction was acquired with 99
diffusion encoding and 10 resting (b0) directions, 2mm isotropic
voxel size, 63 slices, b=1000 s/mm2, TE=89ms, TR=8.3s. A
1mm isotropic resolution T1-weighted anatomical scan was
also acquired (TR=18ms, TE=10ms, FA=30 degrees). The two
datasets were registered using a mutual information based
algorithm  to remove image misregistration from echo
planar induced image shifts and motion. All scans were
conducted at the Montreal Neurological Institute.
ADNI cohort data description
A second dataset used in the preparation of this article was
obtained from the Alzheimer’s Disease Neuroimaging Initiative
(ADNI) database (adni.loni.ucla.edu). The ADNI was launched
in 2003 by the National Institute on Aging (NIA), the National
Institute of Biomedical Imaging and Bioengineering (NIBIB), the
Food and Drug Administration (FDA), private pharmaceutical
companies and non-profit organizations, as a $60 million, 5-
year public-private partnership. The primary goal of ADNI has
been to test whether serial magnetic resonance imaging (MRI),
positron emission tomography (PET), other biological markers,
and clinical and neuropsychological assessment can be
combined to measure the progression of mild cognitive
impairment (MCI) and early Alzheimer’s disease (AD).
Determination of sensitive and specific markers of very early
AD progression is intended to aid researchers and clinicians to
develop new treatments and monitor their effectiveness, as
well as lessen the time and cost of clinical trials.
The Principal Investigator of this initiative is Michael W.
Weiner, MD, VA Medical Center and University of California –
San Francisco. ADNI is the result of efforts of many co-
investigators from a broad range of academic institutions and
private corporations, and subjects have been recruited from
over 50 sites across the U.S. and Canada. The initial goal of
ADNI was to recruit 800 subjects but ADNI has been followed
by ADNI-GO and ADNI-2. To date these three protocols have
recruited over 1500 adults, ages 55 to 90, to participate in the
research, consisting of cognitively normal older individuals,
people with early or late MCI, and people with early AD. The
follow up duration of each group is specified in the protocols for
ADNI-1, ADNI-2 and ADNI-GO. Subjects originally recruited for
ADNI-1 and ADNI-GO had the option to be followed in ADNI-2.
For up-to-date information, see www.adni-info.org.
From the ADNI-GO and ADNI-2 dataset, we selected all
participants aged 55 to 90 years of age (inclusive) who had
completed, during the course of a single visit, the following
clinical, imaging and neuropsychological assessments: T1 MRI,
DTI, Mini Mental State Examination (MMSE), Clinical Dementia
Rating scale (CDR), Wechsler Memory Scale Logical Memory
II, Alzheimer’s disease assessment scale (ADAS)-cog, Rey
auditory verbal learning test (RAVLT). Selected individuals
were classified as CN, MCI (divided into aMCI, naMCI) and AD,
on the basis of clinic-behavioral measures put forth by ADNI.
The data was acquired from 14 centers around the USA and
Canada between 2010 and 2012. The scanning parameters
were as follows. All diffusion images were scanned on GE 3
tesla scanners. There were 41 diffusion encoding and 5 resting
(b0) directions, 1.4mm x 1.4mm x 2.7mm voxel size, b=1000
s/mm2. All scans used were already EPI-eddy current corrected
by ADNI. T1 scans were acquired on the same GE 3T scanner
with a 1.2 mm x 1mm x 1mm voxel size. Further acquisition
details are available from ADNI website (ADNI-INFO.org)
Clinical operational definitions
The criteria for CN included an MMSE score ranging
between 24-30 (inclusive), and a CDR score of 0 [22,23].
ADNI2 and ADNIGO define MCI as individuals with subjective
memory complaint, an MMSE score between 24-30 (inclusive),
objective memory loss as shown on scores on delayed recall of
one paragraph from the Wechsler Memory Scale Logical
Memory II, a CDR of 0.5, preserved activities of daily living,
and the absence of dementia [24,25].
We reclassified ADNI MCI individuals in aMCI or naMCI as
defined as 1.5 std. below CN on 30 min auditory verbal
learning test (AVLT) delay recall . In addition to the
NINCDS/ADRDA criteria for probable AD, mild AD dementia
subjects had MMSE scores between 20-26 (inclusive) and a
CDR of 1 .
Individual T1 MRIs were skull stripped , non-uniformity
corrected  and registered to MNI152 space nonlinearly .
The T1 was then classified into grey matter, white matter, and
cerebrospinal fluid with a validated automated classification
algorithm  and subsequently automatically labeled using
Automatic, Nonlinear, Imaging Matching and Anatomical
Labeling (ANIMAL) . Individual segmented hippocampus
was used as a seed region for the hippocampal structural
FA and MD maps were generated using FSL-DTIFIT from
the skull-stripped  eddy current corrected images in MRI
HSC maps were generated using a pipeline based on FSL
4.1-FDT (Figure 1). In brief (1), DTI images were skull-stripped
using BET2 and Eddy current-corrected  ; (2) At each voxel
of individual DTI images, a probability distribution of fiber
direction was generated using FSL-bedpostx with a maximum
of two fiber directions per voxel  ; (3) single voxel level
probabilistic maps were generated for every voxel of the seed
region using FSL-probtrackx (approx. 400 maps per region;
see animation in the supplementary materials). These maps
represented the probability that any given voxel in the brain
was connected to a seed voxel. Finally (4) a single HSC map of
the entire hippocampus was computed via the max function
derived from the single voxel level maps. Thus a given voxel
value in the HSC map represents the likelihood of this voxel
being connected to the hippocampus. HSC maps were
generated with seed regions in both the left and right
hippocampus (see link https://www.youtube.com/watch?
White Matter Abnormalities in AD
PLOS ONE | www.plosone.org3September 2013 | Volume 8 | Issue 9 | e74776
Figure 1. Summary of DTI imaging acquisition, processing and statistical outcomes. Note that DTI acquisition parameters
differ for the two cohort, however, the analytical pipeline is identical for the ADNI and MCSA cohorts. Arrows indicate the flow of
data through the pipeline. *Hippocampal VOI was estimated using individualized brain segmentations.
White Matter Abnormalities in AD
PLOS ONE | www.plosone.org4 September 2013 | Volume 8 | Issue 9 | e74776
FA, MD and HSC statistical group differences were created
using the track-based spatial statistic tool (TBSS) . Firstly,
all FA images were aligned to the MNI 152 standard space
. Then a skeleton was created from the mean FA > 0.2.
Local maxima of FA images of each subject were then
projected onto the skeleton. At each voxel in the skeleton
statistical group differences
permutations tests (FSL randomize) . MD and HSC maps
were projected onto the same skeletons to determine statistical
group differences. The statistical results were thickened to
allow better visualization of group differences. Correction for
multiple corrections was estimated using family wise error
(FWE) and the threshold-free cluster enhancement (TFCE)
Global averages for FA, MD and HSC were calculated for
each subject using the WM skeleton as a region of interest
(ROI). In brief, for each statistical contrast comparing two
groups, an FA TBSS skeleton was generated by using voxels
greater than 0.2. The volume of FA skeleton was then
calculated. The proportion of abnormal voxels was computed
as the ratio between the volume of voxels showing statistical
differences between groups and the total skeleton volume. This
strategy intended to avoid the contamination grey matter
signal. Regional values of FA, MD and HSC were calculated for
each subject using masks based on statistical significant voxels
derived from various contrast of interest. To estimate the
normalized MD or FA volumes, we computed the ratio between
the volume of abnormal voxels when compared to CN as
defined by TBSS (corrected p <0.05) and the total WM skeleton
In addition, in order to compare the magnitude of change
across patient populations, FA, MD and HSC absolute scores
were transformed as z-scores. Transformed FA, MD and HSC
values were compared across groups using one-way-ANOVA.
were determined using
In the MCSA cohort an unpaired one-tailed t-test showed
that CN and aMCI groups did not differ in terms of gender, age,
and educations (Table 1a). As expected, MMSE, RAVLT, and
APOE status was different between groups.
In the ADNI cohort a 1-way ANOVA of the ADNI
demographics showed the CN, aMCI, naMCI and AD groups
did not differ in terms of gender, age, and education (table 1b).
Post-hoc comparisons using Tukey’s HSD indicated significant
differences in MMSE, AVLT, and APOE status. There was no
demographic difference between the aMCI group and the
naMCI group other than the AVLT score.
DTI outcome measures
Global and regional FA and MD z-score values are
represented in Figure 2A and 2B. Average FA and MD were
significantly different between cohorts (FA CNMCSA=0.41 vs
CNADNI=0.36). The magnitude of global MD, FA and HSC
abnormalities does not differ among patient groups (Figure 2A).
In addition, while regional MD abnormalities predominate in
aMCI, FA abnormalities are higher in AD (Figure 2B). Average
HSC obtained in the CN, depicting patterns of hippocampal
connectivity known from experiments obtained in post mortem
tissue, such as projections to the entire temporal neocortex as
well as to cingulate cortex as well as associative parietal
occipital and frontal cortices (Figure 3).
DTI group differences
In the MCSA aMCI cohort, 29% of estimated WM had
significantly elevated MD compared to the CN group. Corpus
callosum, arcuate, uncinate, superior and inferior longitudinal
fascicles were affected (Figure 4A). Voxels with abnormal MD
in aMCI were on average 7.1% higher than CN. Global WM MD
was elevated 5.5% in aMCI (p=0.003) compared to CN. None
of the clusters of reduced FA were significant after correcting
for multiple comparisons. HSC revealed no hippocampal
connectivity abnormalities in aMCI.
Table 1. Summary of Demographic and memory scores for all groups in the ADNI and MCSA cohorts.
Weight (kg)76±12.664.9±12.479.9±14.4 76.3±11.679.3±13.5 76±15.9
* t-test in the MCSA cohort revealed significant difference (p<0.05).
One-way ANOVA Post-hoc Tukey HSD tests are indicated by abbreviations. ap<0.05, when compared to CN; bp<0.05, when compared to aMCI; cp<0.05, when compared to
naMCI; dp<0.05, when compared to AD.
1/8 7/104/136/1015/41 4/6
White Matter Abnormalities in AD
PLOS ONE | www.plosone.org5September 2013 | Volume 8 | Issue 9 | e74776
In the ADNI cohort (Figure 4B) aMCI (aMCI>CN) showed
abnormal mean diffusivity in 27.6% of estimated WM,
particularly on the corpus callosum, splenium, superior and
inferior longitudinal fascicles, and arcuate fascicles. These
abnormal voxels had an average of 5.8% higher MD than CN.
Global WM MD was elevated 4.1% in aMCI compared to CN
(p=0.039) (Table 2). Similarly to the MCSA cohort, ADNI aMCI
had no significant declines in FA. No WM abnormalities were
observed in the reverse statistical comparison [aMCI< CN]. No
hippocampal connectivity abnormalities were observed in
In naMCI, part of ADNI cohort, neither FA nor MD values
differed from CN.
The AD patients in the ADNI cohort (Figure 4C) showed
abnormally elevated MD in 66.3% of estimated WM compared
to CN. These abnormalities were observed particularly in the
Figure 2. Global and regional magnitude of WM
abnormalities in patient population. A shows that
magnitude of global MD, FA and HSC abnormalities does not
differ among patient groups (One way ANOVA (p=0.0018;
F=3.212; df=8). In contrast, regional MD abnormalities
predominates in aMCI (purple box) while FA abnormalities
arises in AD (Figure 2B -- one way ANOVA (p<0.0001; F=7.74;
Student-Newman-Keuls Post-hoc test ***, p=0.001; **,p=0.01.
genu and splenium of the corpus callosum, uncinate, superior
and inferior longitudinal fascicles and cingulate bundle.
Abnormal voxels in these pathways had an average of 8.5%
higher MD than CN. Global WM MD was elevated 7.7% in AD
(p=0.0001) (Table 2). In addition, FA was abnormally low in
54.3% of the estimated WM. In these abnormal voxels, FA was
11.3% lower than CN. Interestingly, 75% of WM regions with
abnormally lower FA also had abnormally higher MD. Global
WM FA was reduced by 7.9% (p=0.0004) (Table 2). No FA
elevations or MD declines were observed in AD.
HSC showed reduced hippocampal connectivity (Figure 5A
and 5B) in the temporal lobe (angular bundle, inferior
longitudinal and uncinate fascicles), limbic projections
(cingulate bundle and fornix), inferior parietal cortex (arcuate
fascicles) and frontal (inferior occipitofrontal and superior
longitudinal fascicles) in AD patients compared to CN (Figure
5; see link https://www.youtube.com/watch?v=LuRgO0I4TZU).
All other contrasts were not significant after correcting for
The contrasts between AD and naMCI patients of the ADNI
cohort revealed abnormally high MD in 54% of AD estimated
WMs (Figure 4D). Abnormal estimated WM in AD had an
average of 7% higher MD than naMCI. Global WM MD was
elevated 5.2% in AD as compared to naMCI (p=0.001) (Table
2). In comparison with naMCI, AD patients had abnormally
reduced FA in 57.7% of the estimated WM. These abnormal
voxels had an average of 10.6% lower FA in AD in comparison
with naMCI. Global WM FA was reduced by 6% (p=0.003) in
AD in comparison with naMCI (Table 2).
The AD to aMCI contrast of the ADNI cohort (Figure 4A)
revealed no abnormal MD voxels after correcting for multiple
comparisons. Global WM MD was elevated 3.5% in AD as
compared to naMCI (p=0.047), however this was just barely
significant. FA was abnormal in 0.34% of the normalized WM
voxels. Abnormal voxels had 12.5% lower FA than CN. Global
WM FA was reduced by 5.3% (p=0.03) (Table 2).
Figure 3. Hippocampal structural connectivity. Average
maps expressing the hippocampal to brain structural
connectivity for the CN group.
White Matter Abnormalities in AD
PLOS ONE | www.plosone.org6 September 2013 | Volume 8 | Issue 9 | e74776
Figure 4. FA and MD group statistics. All parts display voxels showing abnormal FA and MD on TBSS skeleton. Statistically
significant group differences of MD (yellow), FA (blue) are shown. A shows the aMCI>CN contrast for the MCSA cohort while B-E
show contrasts from the ADNI cohort. Notice C and D show overlap of MD and FA in red. Contrasts not shown were not significant.
White Matter Abnormalities in AD
PLOS ONE | www.plosone.org7 September 2013 | Volume 8 | Issue 9 | e74776
Both FA and MD values in ADNI cohort, showed no
abnormalities in naMCI (aMCI vs. naMCI) after correcting for
Brain WM forms the backbone of a large network connecting
multiple segregated cortical regions, which occupies nearly as
much of brain volume as the gray matter . There is a
growing body of the literature suggesting active WM
degeneration as part of the repertoire of pathophysiological
processes underlying AD. Although somehow neglected, the
impact of WM abnormalities in AD has been reported to be
Here, we report increased MD (6-7%) occurring in
approximately 28% of estimated WM and no FA declines in
aMCI. In contrast, the WM of AD individuals had slightly higher
MD (8.5%) and lower FA (11.3%) affecting a larger proportion
(66%) of estimated WM. The magnitudes of MD or FA group
differences reported here are above expected variability as
revealed by previous DTI test-retest studies . In addition,
we report, for the first time, brain areas disconnected from the
hippocampus due to WM abnormalities in AD patients using
FSL’s probabilistic tractography method . This technique
provides the likelihood of
hippocampus and any given brain region. Our results support
the hypothesis that AD brain undergoes a progressive WM
degeneration characterized firstly by increased MD, followed by
declines in FA and reduction of hippocampal connectivity.
The modest but significant increase of MD found in aMCI
(both MCSA and ADNI cohorts) supports the construct that
increased WM water diffusivity
neurodegenerative event associated to AD pathophysiological
processes. In fact, high MD has been frequently reported in
aMCI populations [41-49]. In contrast using TBSS, Agosta and
colleagues only found widespread changes in axial diffusivity
but no significant MD differences in 15 aMCI individuals,
however, these disparities
methodological issues such as MRI magnetic strength as well
as low in-plane resolution [47,48,50].
connectivity between the
constitutes an early
could be explained by
Table 2. Summary of magnitude of global WM differences
across group contrasts.
MCSA CohortADNI Cohort
CN vs. aMCI 2.73 5.47*12.232.45 4.09* 7.65
CN vs. naMCI 1.74 2.401.78
CN vs. AD 7.88*7.69* 9.17
naMCI vs. aMCI 0.70 1.65 5.97
naMCI vs. AD 6.04*5.16* 7.52
aMCI vs. AD 5.3*3.45*1.65
Both MCSA and ADNI cohort indicate MD abnormalities on aMCI. FA
abnormalities are present in AD groups. *p<0.05
Absence of FA reduction in aMCI, as reported here, is
consistent with previous studies using similar methodology (i.e.
same MCI inclusion criteria, 3T acquisitions and strict TBSS
analysis) [41,42,50-52]. However, FA declines in MCI have
been previously reported using VOI and voxel-based
techniques [43,45,46,53-59]. Such discrepancy might be
associated with low sample size, diagnostic criteria for MCI,
analytical methods (VOI vs. voxel-based) or correction for
multiple comparisons (no correction, FDR, random field).
At least two previous studies focusing on FA and MD
abnormalities in naMCI provide conflicting results [42,60]. In
fact, older age and the presence of multiple pathologies could
account for these conflicting results .
The two-fold increase of estimated WM showing increased
MD in demented patients (as compared to aMCI) supports the
hypothesis that WM pathology also progresses in AD. Higher
estimated WM showing lower FA in demented individuals in
comparison with aMCI or naMCI further corroborates WM
progressive degeneration in AD. Reduced FA in AD has been
previously reported using VOI and voxel-based techniques
[52,54,61-64]. [56,57,65-68] In fact, progression of WM
abnormalities has been previously suggested by cross
sectional and longitudinal DTI studies [41,42,51,69]. As a
whole, these findings suggest increased MD as an early WM
degenerative event in AD.
In post-mortem tissue, increase of tissue water diffusivity, as
measured as m2s-1, might occur due the reduction of tissue
barriers imposed by various causes (i.e. tissue atrophy,
neuroinflammation), however empirical evidence supporting
this claim is undermined by the effects of tissue fixation
typically utilized in post-mortem / DTI correlation studies .
Possibly increased MD in aMCI might represent an early event
of WM degeneration present in AD pathophysiology secondary
to the reduction of brain extracellular matrix and shrinkage of
WM tissue in preclinical AD [70,71].
In addition to the axonal degeneration originating due to
death of cortical cell lesions,
pathophysiological mechanisms underlying FA and MD
abnormalities described in AD can be partially attributable to
independent WM neurodegeneration. A growing body of
literature suggests that WM DTI abnormalities might be
secondary to neuroinflammatory factors . Corroborating this
hypothesis, imaging studies with PET and molecular imaging
agents show an increase of microglia activation and
astrocytosis in the WM of aMCI [73-75]. In addition, it is
noteworthy that ventricular enlargement (a surrogate of WM
atrophy) is able to accurately distinguish between MCI and AD
and has been proposed as tool for measuring disease
progression in the short term .
We also demonstrated hippocampal WM connectivity
abnormalities in AD dementia. Hippocampal connectivity
abnormalities affected hippocampal connections predominantly
to the temporal, parietal, occipital and frontal polymodal
associative areas (Figure 5). The structural connectivity
outcome measure described here (HSC) represents the
probability of a given brain area to be connected with the
hippocampal seed region. The hippocampal seed typically
encompasses all cornus amonius (CA) sectors, the dentate
recent advances in
White Matter Abnormalities in AD
PLOS ONE | www.plosone.org8September 2013 | Volume 8 | Issue 9 | e74776
Figure 5. Hippocampal structural connectivity group statistics. Part A displays voxels showing reduction of hippocampal
connectivity on the TBSS skeleton [AD<CN] after correction from multiple comparisons. Note the reduction in hippocampal
connectivity on angular bundle, fornix, superior longitudinal, inferior longitudinal, cingulate, uncinate and arcuate fascicles.
Part B shows areas of reduced connectivity in AD projected on WM pathways on a transparent brain surface space highlighting
regions with reduced connectivity (See 3D animation of this image at http://www.youtube.com/watch?v=LuRgO0I4TZU).
White Matter Abnormalities in AD
PLOS ONE | www.plosone.org9September 2013 | Volume 8 | Issue 9 | e74776
gyrus and subiculum, since the limits between these areas
remains below the resolution of the present DTI acquisition. For
example, connectivity declines observed in the inferior
occipitofrontal and superior longitudinal fascicles possibly
reflect hippocampal connectivity declines mediated via
subiculum, which constitutes a critical hub between the
hippocampal system and the cortex [77-82]. WM areas with
significant decline of probability to be connected to the
hippocampus were interpreted as depleted from normal
The results from our HSC CN maps are in excellent
agreement with the post-mortem data describing hippocampal
connectivity [83,84]. Individual connectivity maps generated by
this study in CN captured the classical reciprocal connections
between the hippocampal system and the temporal, cingulate,
inferior parietal or frontal cortices. Since the nature of DTI does
not permit the inference of directionality, one cannot
discriminate between hippocampal-petal and hippocampal-
fugal fibers. Areas with reduced structural connectivity revealed
by the HSC technique in AD was consistent with functional
disconnections frequently reported by the literature. For
example, reduced hippocampal connectivity in the cingulate
bundle might explain functional disconnection reported
between the hippocampus and the posterior cingulate /
precuneus frequently described by numerous AD rsfMRI
studies [85,86]. Moreover, the WM connectivity reduction of the
arcuate fascicles, inferior longitudinal, uncinate fascicles and
superior longitudinal fascicles is a possible mechanism
underling the [18F]FDG signature of AD (hypometabolism in the
posterior cingulate, inferior parietal, temporal and prefrontal
cortices) [87-89]. Hippocampal WM disconnections as
described here corroborate the theoretical framework that
emphasizes cortical disconnections as a key feature of AD .
In fact, neuropathological, functional neuroimaging and
neuropsychological evidence indicate WM disconnections as a
pathophysiological mechanism involved in AD.
The relative integrity of hippocampal connections in aMCI or
naMCI, as predicted in our hypothesis, is supported by
histopathology evidence showing tangle pathology and WM
disconnection affecting predominantly
projections to the dentate gyrus (perforant path) in aMCI, while
projections from the hippocampus and subiculum and the rest
of entorhinal cortex are affected in more advanced stages of
the disease . Possibly, a seed point in the transentorhinal
cortex could better capture perforant path depletion in MCI
stage, however transentorhinal connectivity is beyond the
scope of this specific study.
Some methodological issues limit the interpretations of the
present study. Since this is a cross sectional study, inferences
regarding progression of WM pathology in the spectrum of AD
clinical manifestations are speculative. The hypothesis posing
MD increases as an early WM AD pathophysiology change
followed by FA declines should be assessed by appropriate
Vascular pathology is certainly a potential confounder in all
studies of this nature, since vascular insults and small vessel
disease constitute a frequent finding in the MRI of elderly
populations. For example, white matter intensities detected in
T2 or FLAIR images may have an impact on various DTI
outcome measures. However, since all patients recruited in this
study, had Hachinski scores lower than 4, the impact of these
lesions are unlikely to be clinically significant. In contrast the
impact of vascular pathology on DTI outcomes is always a
limitation for dementia studies since Hachinski score < 4 does
not preclude vascular lesions. Particularly on the MCSA cohort,
the presence of white matter intensities was minimal and
monitored with 3D FLAIR MRI. Since the results obtained from
the MCSA and ADNI cohorts were identical, it seems that
vascular pathology affects these two populations in a similar
Although ADNI provides a powerful database for AD
research, the drawback of utilizing DTI acquisitions acquired in
14 different scanners might represent a limitation for this study.
While there were large efforts taken to cross-validate MRI
scanners, multiple scanners acquisition is an undeniable
confounding factor. Although, using the same analytical
pipeline, the MCSA cohort yielded higher FA and MD control
values in comparison with the same outcome measures from
ADNI cohort. High FA and MD values on the MCSA cohort
were obtained due to higher signal to noise ratio from the
utilization a 32-channel head coil as well as 99 diffusion
Regarding statistical analysis, TBSS is a conservative but
extensively utilized method to compare WM change in
numerous experimental populations [42,91,92]. Particularly in
the case of this study, the results using voxel-based non-
parametric statistics provide similar results to TBSS (data not
shown). Analytical protocols can potentially constitute a bias
particularly for those studies utilizing voxel-based parametric
statistical analysis without correcting for multiple comparisons.
In conclusion, we found in aMCI WM abnormalities are
characterized by high MD, which are possibly secondary to
brain inflammatory changes or WM axons or myelin content
depletion. Furthermore, the concomitance of MD and FA
abnormalities observed in AD suggests higher degree of WM
microstructural lesion, which impacts in large-scale brain
structural connectivity. Further longitudinal studies are
necessary to corroborate whether a progression of WM
disease occurs in the spectrum of clinical manifestation of
Data used in preparation of this article were obtained from the
Alzheimer’s disease Neuroimaging Initiative (ADNI) database
(adni.loni.ucla.edu). As such, the investigators within the ADNI
contributed to the design and implementation of ADNI and/or
provided data but did not participate in analysis or writing of
this report. A complete listing of ADNI investigators can be
found at: http://adni.loni.ucla.edu/wp-content/uploads/
White Matter Abnormalities in AD
PLOS ONE | www.plosone.org 10September 2013 | Volume 8 | Issue 9 | e74776
Conceived and designed the experiments: JR PR SG.
Performed the experiments: JR DS SM. Analyzed the data: JR
PR. Contributed reagents/materials/analysis tools: VF SE VS
MS SM LW AS AD LC. Wrote the manuscript: JR PR SG OW
1. Jack CR Jr, Knopman DS, Jagust WJ, Shaw LM, Aisen PS et al. (2010)
Hypothetical model of dynamic biomarkers of the Alzheimer’s
pathological cascade. Lancet Neurol 9: 119–128. doi:10.1016/
S1474-4422(09)70299-6. PubMed: 20083042.
2. Braak H, Braak E (1991) Neuropathological staging of Alzheimer-
related changes. Acta Neuropathol 82: 239-259.
3. Mufson EJ, Binder L, Counts SE, DeKosky ST, de Toledo-Morrell L et
al. (2012) Mild cognitive impairment: pathology and mechanisms. Acta
Neuropathol 123: 13-30. doi:10.1007/s00401-011-0884-1. PubMed:
4. Chen TF, Chen YF, Cheng TW, Hua MS, Liu HM et al. (2009)
Executive dysfunction and periventricular diffusion tensor changes in
amnesic mild cognitive impairment and early Alzheimer’s disease. Hum
Brain Mapp 30: 3826-3836. doi:10.1002/hbm.20810. PubMed:
5. Wang DS, Bennett DA, Mufson EJ, Mattila P, Cochran E et al. (2004)
Contribution of changes in ubiquitin and myelin basic protein to age-
related cognitive decline. Neurosci Res 48: 93-100. doi:10.1016/
j.neures.2003.10.002. PubMed: 14687885.
6. Bartzokis G, Cummings JL, Sultzer D, Henderson VW, Nuechterlein KH
et al. (2003) White matter structural integrity in healthy aging adults and
patients with Alzheimer disease: a magnetic resonance imaging study.
Arch Neurol 60: 393–398. doi:10.1001/archneur.60.3.393. PubMed:
7. Lue LF, Rydel R, Brigham EF, Yang LB, Hampel H et al. (2001)
Inflammatory repertoire of Alzheimer’s disease and nondemented
elderly microglia in vitro. Glia 35: 72-79. doi:10.1002/glia.1072.
8. Stokin GB, Goldstein LS (2006) Axonal transport and Alzheimer’s
disease. Annu Rev Biochem 75: 607-627. PubMed: 16756504.
9. Clerx L, Visser PJ, Verhey F, Aalten P (2012) New MRI Markers for
Alzheimer’s Disease: A Meta-Analysis of Diffusion Tensor Imaging and
a Comparison with Medial Temporal Lobe Measurements. J Alzheimers
Dis 29: 405-429. PubMed: 22330833.
10. Sexton CE, Mackay CE, Ebmeier KP (2009) A systematic review of
diffusion tensor imaging studies in affective disorders. Biological
11. Gouw AA, Seewann A, Vrenken H, Van Der Flier WM, Rozemuller JM
et al. (2008) Heterogeneity of white matter hyperintensities in
Alzheimer’s disease: post-mortem
neuropathology. Brain 131: 3286-3298. doi:10.1093/brain/awn265.
12. Behrens TE, Woolrich MW, Jenkinson M, Johansen-Berg H, Nunes RG
et al. (2003) Characterization and propagation of uncertainty in
diffusion-weighted MR imaging. Magn Reson Med 50: 1077-1088. doi:
10.1002/mrm.10609. PubMed: 14587019.
13. Charlton RA, Barrick TR, McIntyre DJ, Shen Y, O’Sullivan M et al.
(2006) White matter damage on diffusion tensor imaging correlates with
age-related cognitive decline.
10.1212/01.wnl.0000194256.15247.83. PubMed: 16434657.
14. Duan JH, Wang HQ, Xu J, Lin X, Chen SQ et al. (2006) White matter
damage of patients with Alzheimer‚ Äôs disease correlated with the
decreased cognitive function. Surgical and Radiologic Anatomy 28:
15. Gauthier S, Reisberg B, Zaudig M, Petersen RC, Ritchie K et al. (2006)
Mild cognitive impairment. Lancet 367: 1262-1270. doi:10.1016/
S0140-6736(06)68542-5. PubMed: 16631882.
16. Petersen RC (2004) Mild cognitive impairment as a diagnostic entity. J
Intern Med 256: 183-194. doi:10.1111/j.1365-2796.2004.01388.x.
17. Schneider JA, Arvanitakis Z, Leurgans SE, Bennett DA (2009) The
neuropathology of probable Alzheimer disease and mild cognitive
impairment. Ann Neurol 66: 200-208. doi:10.1002/ana.21706. PubMed:
18. Petersen RC, Parisi JE, Dickson DW, Johnson KA, Knopman DS et al.
(2006) Neuropathologic features of amnestic mild cognitive impairment.
Arch Neurol 63: 665-672. doi:10.1001/archneur.63.5.665. PubMed:
19. Beattie BL (2007) Consent in Alzheimer’s disease research: risk/benefit
factors. Can J Neurol Sci 34 Suppl 1: S27-S31. PubMed: 17469678.
quantitative MRI and
Neurology 66: 217-222. doi:
20. Schmidt M (1996) Rey Auditory Verbal Learning Test: RAVLT: a
Handbook: Western. Psychological Services.
21. Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P (1997)
Multimodality image registration by maximization of mutual information.
IEEE Trans Med Imaging 16: 187-198. PubMed: 9101328.
22. Berg L (1988) Clinical dementia rating (CDR). Psychopharmacol Bull
24: 637–639. PubMed: 3249765.
23. Folstein MF, Folstein SE, McHugh PR (1975) "Mini-mental state". A
practical method for grading the cognitive state of patients for the
clinician. J Psychiatr
10.1016/0022-3956(75)90026-6. PubMed: 1202204.
24. Aisen PS, Petersen RC, Donohue MC, Gamst A, Raman R et al. (2010)
Clinical Core of the Alzheimer’s Disease Neuroimaging Initiative:
progress and plans. Alzheimers Dement 6: 239-246. doi:10.1016/j.jalz.
2010.05.778. PubMed: 20451872.
25. Wechsler D (1987) WMS-R: Wechsler Memory Scale -- Revised:
manual. San Antonio: Psychological Corporation.
26. Lucas JA, Ivnik RJ, Smith GE, Bohac DL, Tangalos EG et al. (1998)
Mayo’s older Americans normative studies: category fluency norms. J
Clin Exp Neuropsychol 20: 194-200. doi:10.1076/jcen.220.127.116.113.
27. Tierney MC, Fisher RH, Lewis AJ, Zorzitto ML, Snow WG et al. (1988)
The NINCDS-ADRDA Work Group criteria for the clinical diagnosis of
probable Alzheimer’s disease A clinicopathologic study of 57 cases.
Neurology 38: 359-359. doi:10.1212/WNL.38.3.359. PubMed: 3347338.
28. Eskildsen SF, Coupé P, Fonov V, Manjón JV, Leung KK et al. (2012)
BEaST: Brain extraction based on nonlocal segmentation technique.
NeuroImage 59: 2362-2373. doi:10.1016/j.neuroimage.2011.09.012.
29. Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for
automatic correction of intensity nonuniformity in MRI data. Medical
Imaging, IEEE Transactions on 17: 87-97. doi:10.1109/42.668698.
30. Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC et al. (2011)
Unbiased average age-appropriate atlases for pediatric studies.
NeuroImage 54: 313–327. doi:10.1016/j.neuroimage.2010.07.033.
31. Zijdenbos A, Forghani R, Evans A (1998) Automatic quantification of
MS lesions in 3D MRI brain data sets: Validation of INSECT. Medical
Image Computing and Computer-Assisted Interventation—MICCAI’ 98.
Heidelberg: Springer Verlag Berlin pp. 439-448
32. Collins DE, Evans AC (1997) ANIMAL: validation and applications of
non-linear registration-based segmentation. Int J Pattern Recognit Artif
Intell 11: 1271-1294. doi:10.1142/S0218001497000597.
33. Jenkinson M, Pechaud M, Smith S (2005) ET2: MR-based estimation of
brain, skull and scalp surfaces.
34. Jenkinson M, Pechaud M, Smith S (2005) ET2: MR-based estimation of
brain, skull and scalp surfaces pp. 12-16.
35. Behrens TE, Johansen-Berg H, Woolrich MW, Smith SM, Wheeler-
Kingshott CA et al. (2003) Non-invasive mapping of connections
between human thalamus and cortex using diffusion imaging. Nat
Neurosci 6: 750-757. doi:10.1038/nn1075. PubMed: 12808459.
36. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE et
al. (2006) Tract-based spatial statistics: voxelwise analysis of multi-
subject diffusion data. NeuroImage 31: 1487-1505. doi:10.1016/
j.neuroimage.2006.02.024. PubMed: 16624579.
37. Collins DL, Neelin P, Peters TM, Evans AC (1994) Automatic 3D
intersubject registration of MR volumetric data in standardized
Talairach space. J Comput Assist Tomogr 18: 192–205. doi:
10.1097/00004728-199403000-00005. PubMed: 8126267.
38. Nichols TE, Holmes AP (2001) Nonparametric permutation tests for
functional neuroimaging: a primer with examples. Hum Brain Mapp 15:
1-25. PubMed: 11747097.
39. Miller AK, Alston RL, Corsellis JA (2008) Variation with age in the
volumes of grey and white matter in the cerebral hemispheres of man:
measurements with an image analyser. Neuropathol Appl Neurobiol 6:
119-132. PubMed: 7374914.
40. Vollmar C, O’Muircheartaigh J, Barker GJ, Symms MR, Thompson P et
al. (2010) Identical, but not the same: intra-site and inter-site
reproducibility of fractional anisotropy measures on two 3.0T scanners.
Neuroimage 51: 1384-1394.
Res 12: 189-198. doi:
White Matter Abnormalities in AD
PLOS ONE | www.plosone.org 11 September 2013 | Volume 8 | Issue 9 | e74776
41. Bosch B, Arenaza-Urquijo EM, Rami L, Sala-Llonch R, Junqué C et al.
(2012) Multiple DTI index analysis in normal aging, amnestic MCI and
AD. Relationship with neuropsychological performance. Neurobiol
Aging 33: 61-74. doi:10.1016/j.neurobiolaging.2010.02.004. PubMed:
42. O’Dwyer L, Lamberton F, Bokde AL, Ewers M, Faluyi YO et al. (2011)
Multiple indices of diffusion identifies white matter damage in mild
cognitive impairment and Alzheimer’s disease. PLOS ONE 6: e21745.
doi:10.1371/journal.pone.0021745. PubMed: 21738785.
43. Cho H, Yang DW, Shon YM, Kim BS, Kim YI et al. (2008) Abnormal
Integrity of Corticocortical Tracts in Mild Cognitive Impairment: A
Diffusion Tensor Imaging Study. J Korean Med Sci 23: 477–483. doi:
10.3346/jkms.2008.23.3.477. PubMed: 18583886.
44. Chua TC, Wen W, Slavin MJ, Sachdev PS (2008) Diffusion tensor
imaging in mild cognitive impairment and Alzheimer’s disease: a
review. Curr Opin Neurol
0b013e3282f4594b. PubMed: 18180656.
45. Fellgiebel A, Wille P, M uuml ller MJ, Winterer G, Scheurich A, et al
(2004) Ultrastructural Hippocampal and White Matter Alterations in Mild
Cognitive Impairment: A Diffusion Tensor Imaging Study. Dement
Geriatr Cogn Disord 18: 101-108. doi:10.1159/000077817. PubMed:
46. Rose SE, Chen F, Chalk JB, Zelaya FO, Strugnell WE et al. (2000)
Loss of connectivity in Alzheimer’s disease: an evaluation of white
matter tract integrity with colour coded MR diffusion tensor imaging. J
Neurol Neurosur Ps 69: 528-530. doi:10.1136/jnnp.69.4.528. PubMed:
47. Good CD, Scahill RI, Fox NC, Ashburner J, Friston KJ et al. (2002)
Automatic Differentiation of Anatomical Patterns in the Human Brain:
Validation with Studies of Degenerative Dementias. NeuroImage 17:
29-46. doi:10.1006/nimg.2002.1202. PubMed: 12482066.
48. Good CD (2001) A Voxel-Based Morphometric Study of Ageing in 465
Normal Adult Human Brains. NeuroImage 14: 21-36. doi:10.1006/nimg.
2001.0786. PubMed: 11525331.
49. Yasmin H, Nakata Y, Aoki S, Abe O, Sato N et al. (2008) Diffusion
abnormalities of the uncinate fasciculus in Alzheimer’s disease:
diffusion tensor tract-specific analysis using a new method to measure
the core of the tract. Neuroradiology 50: 293-299. doi:10.1007/
s00234-007-0353-7. PubMed: 18246334.
50. Agosta F, Pievani M, Sala S, Geroldi C, Galluzzi S et al. (2011) White
matter damage in Alzheimer disease and its relationship to gray matter
atrophy. Radiology 258: 853-863.
51. Douaud G, Jbabdi S, Behrens TE, Menke RA, Gass A et al. (2011) DTI
measures in crossing-fibre areas: increased diffusion anisotropy
reveals early white matter alteration in MCI and mild Alzheimer’s
disease. NeuroImage 55: 880-890.
2010.12.008. PubMed: 21182970.
52. Fellgiebel A, Wille P, Müller MJ, Winterer G, Scheurich A et al. (2004)
Ultrastructural hippocampal and white matter alterations in mild
cognitive impairment: a diffusion tensor imaging study. Dement Geriatr
Cogn Disord 18: 101-108.
53. Chua TC, Wen W, Chen X, Kochan N, Slavin MJ et al. (2009) Diffusion
tensor imaging of the posterior cingulate is a useful biomarker of mild
cognitive impairment. The American journal of geriatric psychiatry:
official journal of the American Association for Geriatric Psychiatry 17:
602-613. doi:10.1097/JGP.0b013e3181a76e0b. PubMed: 19546655.
54. Kiuchi K, Morikawa M, Taoka T, Nagashima T, Yamauchi T et al.
(2009) Abnormalities of the uncinate fasciculus and posterior cingulate
fasciculus in mild cognitive impairment and early Alzheimer's
disease: A diffusion tensor tractography study. Brain Res 1287:
184-191. doi:10.1016/j.brainres.2009.06.052. PubMed: 19559010.
55. Medina D, DeToledo-Morrell L, Urresta F, Gabrieli JD, Moseley M et al.
(2006) White matter changes in mild cognitive impairment and AD: A
diffusion tensor imaging study. Neurobiol Aging 27: 663-672. doi:
10.1016/j.neurobiolaging.2005.03.026. PubMed: 16005548.
56. Parente DB, Gasparetto EL, da Cruz LC Jr, Domingues RC, Baptista
AC, et al. (2008) Potential role of diffusion tensor MRI in the differential
diagnosis of mild cognitive impairment and Alzheimer's disease.
American Journal of Roentgenology 190: 1369-1374. doi:10.2214/AJR.
07.2617. PubMed: 18430857. doi:10.2214/AJR.07.2617 PubMed:
57. Stahl R, Dietrich O, Teipel SJ, Hampel H, Reiser MF et al. (2007) White
Matter Damage in Alzheimer Disease and Mild Cognitive Impairment:
Assessment with Diffusion-Tensor MR Imaging and Parallel Imaging
Techniques. Radiology 243: 483-492. doi:10.1148/radiol.2432051714.
21: 83-92. doi:10.1097/WCO.
58. Ukmar M, Makuc E, Onor ML, Garbin G, Trevisiol M et al. (2008)
Risonanza magnetica con tensori di diffusione nella valutazione delle
alterazioni della sostanza bianca nei pazienti con malattia di Alzheimer
e nei pazienti con mild cognitive impairment. Radiol Med 113: 915-922.
doi:10.1007/s11547-008-0286-1. PubMed: 18618077.
59. Zhang Y, Schuff N, Jahng GH, Bayne W, Mori S et al. (2007) Diffusion
tensor imaging of cingulum fibers in mild cognitive impairment and
Alzheimer disease. Neurology
0000259403.31527.ef. PubMed: 17200485.
60. Zhuang L, Wen W, Zhu W, Trollor J, Kochan N et al. (2010) White
matter integrity in mild cognitive impairment: a tract-based spatial
statistics study. Neuroimage 53: 16-25. doi:10.1016/j.neuroimage.
2010.05.068. PubMed: 20595067.
61. Bozzali M, Falini A, Franceschi M, Cercignani M, Zuffi M et al. (2002)
White matter damage in Alzheimer’s disease assessed in vivo using
diffusion tensor magnetic resonance imaging. J Neurol Neurosurg,
Psychiatr 72: 742-746. doi:10.1136/jnnp.72.6.742. PubMed: 12023417.
62. Choi SJ (2005) Diffusion Tensor Imaging of Frontal White Matter
Microstructure in Early Alzheimer's Disease: A Preliminary
Study. J Geriatr Psychiatry
10.1177/0891988704271763. PubMed: 15681623.
63. Ibrahim I, Horacek J, Bartos A, Hajek M, Ripova D et al. (2009)
Combination of voxel based morphometry and diffusion tensor imaging
in patients with Alzheimer's disease. Neuro endocrinology letters
64. Medina D, Detoledo-Morrell L, Urresta F, Gabrieli JDE, Moseley M et
al. (2006) White matter changes in mild cognitive impairment and AD: A
diffusion tensor imaging study. Neurobiol Aging 27: 663-672. doi:
10.1016/j.neurobiolaging.2005.03.026. PubMed: 16005548.
65. Stricker NH, Schweinsburg BC, Delano-Wood L, Wierenga CE, Bangen
KJ et al. (2009) Decreased white matter integrity in late-myelinating
fiber pathways in Alzheimer’s disease supports retrogenesis.
NeuroImage 45: 10-16. doi:10.1016/j.neuroimage.2008.11.027.
66. Takahashi E, Ohki K, Kim DS (2007) Diffusion tensor studies
dissociated two fronto-temporal pathways in the human memory
system. NeuroImage 34: 827-838.
2006.10.009. PubMed: 17123836.
67. Taoka T, Iwasaki S, Sakamoto M, Nakagawa H, Fukusumi A et al.
(2006) Diffusion anisotropy and diffusivity of white matter tracts within
the temporal stem in Alzheimer disease: evaluation of the " tract
of interest" by diffusion tensor tractography. AJNR Am J
Neuroradiol 27: 1040-1045. PubMed: 16687540.
68. Teipel SJ, Stahl R, Dietrich O, Schoenberg SO, Perneczky R et al.
(2007) Multivariate network analysis of fiber tract integrity in
Alzheimer’s disease. NeuroImage
j.neuroimage.2006.07.047. PubMed: 17166745.
69. Salat DH, Tuch DS, van der Kouwe AJ, Greve DN, Pappu V et al.
(2010) White matter pathology isolates the hippocampal formation in
Alzheimer’s disease. Neurobiol Aging 31: 244-256. doi:10.1016/
j.neurobiolaging.2008.03.013. PubMed: 18455835.
70. Brückner G, Hausen D, Härtig W, Drlicek M, Arendt T et al. (1999)
Cortical areas abundant in extracellular matrix chondroitin sulphate
proteoglycans are less affected by cytoskeletal changes in Alzheimer’s
S0306-4522(99)00071-8. PubMed: 10426522.
71. de la Monte SM (1989) Quantitation of cerebral atrophy in preclinical
and end-stage alzheimer’s disease. Ann Neurol 25: 450-459. doi:
10.1002/ana.410250506. PubMed: 2774485.
72. Krstic D, Knuesel I (2012) Deciphering the mechanism underlying late-
onset Alzheimer disease. Nat. Rev Neurol.
73. Cagnin A, Kassiou M, Meikle SR, Banati RB (2006) In vivo evidence for
microglial activation in neuro degenerative dementia. Acta Neurol
74. Okello A, Edison P, Archer HA, Turkheimer FE, Kennedy J et al. (2009)
Microglial activation and amyloid deposition in mild cognitive
impairment: a PET study. Neurology 72: 56-62. doi:10.1212/01.wnl.
0000345004.84188.b9. PubMed: 19122031.
75. Carter SF, Schöll M, Almkvist O, Wall A, Engler H et al. (2012)
Evidence for astrocytosis in prodromal Alzheimer disease provided by
11C-deuterium-L-deprenyl: a multitracer PET paradigm combining 11C-
Pittsburgh compound B and 18F-FDG. J Nucl Med 53: 37-46. doi:
10.2967/jnumed.110.087031. PubMed: 22213821.
76. Nestor SM, Rupsingh R, Borrie M, Smith M, Accomazzi V et al. (2008)
Ventricular enlargement as a possible measure of Alzheimer’s disease
progression validated using the Alzheimer’s Disease Neuroimaging
Initiative database. Brain 131: 2443-2454. doi:10.1093/brain/awn146.
68: 13-19. doi:10.1212/01.wnl.
Neurol 18: 12-19. doi:
34: 985-995. doi:10.1016/
92: 791-805. doi:10.1016/
White Matter Abnormalities in AD
PLOS ONE | www.plosone.org12September 2013 | Volume 8 | Issue 9 | e74776
77. Goldman-Rakic PS, Selemon LD, Schwartz ML (1984) Dual pathways
connecting the dorsolateral prefrontal cortex with the hippocampal
formation and parahippocampal cortex in the rhesus monkey.
Neuroscience 12: 719-743.
78. Augustinack JC, Magnain C, Reuter M, van der Kouwe AJW, Boas D et
al. (2013) MRI Parcellation of Ex Vivo Medial Temporal Lobe.
NeuroImage. PubMed: 23702414
79. Barbas H, Blatt GJ (1995) Topographically specific hippocampal
projections target functionally distinct prefrontal areas in the rhesus
monkey. Hippocampus 5: 511-533. doi:10.1002/hipo.450050604.
80. Blatt GJ, Rosene DL (1998) Organization of direct hippocampal efferent
projections to the cerebral cortex of the rhesus monkey: projections
from CA1, prosubiculum, and subiculum to the temporal lobe. J Comp
Neurol 392: 92-114. doi:10.1002/(SICI)1096-9861(19980302)392:1.
81. Morris R, Pandya DN, Petrides M (1999) Fiber system linking the mid-
dorsolateral frontal cortex with the retrosplenial/presubicular region in
the rhesus monkey. J Comp Neurol 407: 183-192. doi:10.1002/
(SICI)1096-9861(19990503)407:2. PubMed: 10213090.
82. Seltzer B, Van Hoesen GW (1979) A direct inferior parietal lobule
projection to the presubiculum in the rhesus monkey. Brain Res 179:
157–161. doi:10.1016/0006-8993(79)90499-2. PubMed: 116714.
83. Insausti R, Amaral DG (2004) Insausti: Hippocampal formation -
Google Scholar. The human nervous system.
84. Duvernoy HM (2005) The human hippocampus: functional anatomy,
vascularization and serial sections with MRI. Springer Verlag.
85. Wang K, Liang M, Wang L, Tian L, Zhang X et al. (2006) Altered
functional connectivity in early Alzheimer’s disease: A resting-state
fMRI study. Hum Brain Mapp 28: 967-978.
86. Wang L, Zang Y, He Y, Liang M, Zhang X et al. (2006) Changes in
hippocampal connectivity in the early stages of Alzheimer’s disease:
evidence from resting state fMRI. NeuroImage 31: 496-504. doi:
10.1016/j.neuroimage.2005.12.033. PubMed: 16473024.
87. Herholz K, Salmon E, Perani D, Baron JC, Holthoff V et al. (2002)
Discrimination between Alzheimer dementia and controls by automated
analysis of multicenter FDG PET. NeuroImage 17: 302-316. doi:
10.1006/nimg.2002.1208. PubMed: 12482085.
88. Villain N, Desgranges B, Viader F, De La Sayette V, Mézenge F et al.
(2008) Relationships between hippocampal atrophy, white matter
disruption, and gray matter hypometabolism in Alzheimer’s disease. J
Neurosci 28: 6174-6181. doi:10.1523/JNEUROSCI.1392-08.2008.
89. Villain N, Fouquet M, Baron JC, Mézenge F, Landeau B et al. (2010)
Sequential relationships between grey matter and white matter atrophy
and brain metabolic abnormalities in early Alzheimer’s disease. Brain
133: 3301-3314. doi:10.1093/brain/awq203. PubMed: 20688814.
90. Delbeuck X, Van der Linden M, Collette F (2003) Alzheimer’Disease as
a Disconnection Syndrome? Neuropsychol Rev 13: 79-92. doi:
10.1023/A:1023832305702. PubMed: 12887040.
91. Giorgio A, Watkins KE, Chadwick M, James S, Winmill L et al. (2010)
Longitudinal changes in grey and white matter during adolescence.
Neuroimage 49: 94-103. doi:10.1016/j.neuroimage.2009.08.003.
92. Roosendaal SD, Geurts JJ, Vrenken H, Hulst HE, Cover KS et al.
(2009) Regional DTI differences in multiple sclerosis patients.
Neuroimage 44: 1397-1403. doi:10.1016/j.neuroimage.2008.10.026.
White Matter Abnormalities in AD
PLOS ONE | www.plosone.org13 September 2013 | Volume 8 | Issue 9 | e74776