Association of microstructural white matter abnormalities with cognitive dysfunction
in geriatric patients with major depression
Gilberto Sousa Alvesa,c,⁎,1, Tarik Karakayaa,1, Fabian Fußera,⁎,1, Martha Kordullaa, Laurence O'Dwyera,
Julia Christla, Jörg Magerkurthb, Viola Oertel-Knöchela, Christian Knöchela, David Prvulovica,
Alina Jurcoaneb, Jerson Laksc, Eliasz Engelhardtc, Harald Hampela, Johannes Panteld
aDepartment of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe-University, Frankfurt, Germany
bDepartment of Neuroradiology, Goethe-University, Frankfurt, Germany
cCenter for Alzheimer's Disease, Cognitive and Behavioral Neurology Unit, Federal University of Rio de Janeiro, Brazil
dInstitute of General Practice, Geriatric Medicine, Goethe-University, Frankfurt, Germany
a b s t r a c ta r t i c l e i n f o
Received 3 June 2011
Received in revised form 15 December 2011
Accepted 16 December 2011
Diffusion tensor imaging
White matter lesions
Tract-based spatial statistics (TBSS)
Major depression disorder (MDD) is one of the most common causes of disability in people over 60 years of
age. Previous studies have linked affective and cognitive symptoms of MDD to white matter (WM) disruption
in limbic-cortical circuits. However, the relationship between clinical cognitive deficits and loss of integrity
in particular WM tracts is poorly understood. Fractional anisotropy (FA) as a measure of WM integrity
was investigated in 17 elderly MDD subjects in comparison with 18 age-matched controls using tract-
based spatial statistics (TBSS) and correlated with clinical and cognitive parameters. MDD patients revealed
significantly reduced FA in the right posterior cingulate cluster (PCC) compared with controls. FA in the right
PCC (but not in the left PCC) showed a significant positive correlation with performance in a verbal naming
task, and showed a non-significant trend toward a correlation with verbal fluency and episodic memory per-
formance. In control subjects, no correlations were found between cognitive tasks and FA values either in the
right or left PCC. Results provide additional evidence supporting the neuronal disconnection hypothesis in MDD
and suggest that cognitive deficits are related to the loss of integrity in WM tracts associated with the disorder.
© 2012 Elsevier Ireland Ltd. All rights reserved.
Major depression disorder (MDD) is a chronic disease with a prev-
alence rate of 6.5–9% in people over 60 years of age and is considered
one of the most important causes of disability (Greenberg et al., 1993;
Lyness et al., 2002). Along with mood alterations, cognitive symptoms
are present in a substantial proportion of MDD patients (Lesser et al.,
1996; Alexopoulos et al., 2008a) and can be a persistent symptom
even after effective treatment of a depressive episode (Elderkin-
Thompson et al., 2006). Previous investigations suggest that execu-
tive dysfunction is associated with lower response to antidepressant
therapy (Alexopoulos et al., 2008b), and longitudinal studies estimate
that 13–20% of those with moderate to severe MDD develop mild cog-
nitive impairment within a period of 3–6 years (Barnes et al., 2006;
Geda et al., 2006). In addition to executive dysfunction a variety of
other cognitive skills related to executive control may be affected,
such as set-shifting, processing speed, episodic memory and verbal
fluency (Herrmann et al., 2007).
Genetic (Taylor et al., 2005), neuropathological (Thomas et al.,
2002) and functional magnetic resonance imaging studies (Bae et al.,
2006; Drevets, 2007) have suggested that MDD results from system-
level disorder that affects functionally integrated pathways involving
limbic, subcortical and cortical areas. Functional and pathological
studies are supported by structural magnetic resonance imaging
(MRI) results showing brain volumetric reductions in the frontal
cortices, amygdala, hippocampus and cingulate regions of depressed
patients (Bae et al., 2006; Koolschijn et al., 2009). These anatomical
regions are interconnected by a few major white matter tracts such
as the cingulum bundle, the fornix and the uncinate fasciculus
(Schermuly et al., 2010). These results support the limbic-cortical
network dysfunction model proposed to describe the biological under-
pinnings of MDD (Mayberg, 2003). In the last decade an increasing
number of MRI studies with depressed patients have applied diffusion
tensor imaging (DTI) to investigate the role of specific white matter
(WM) tracts in the limbic-cortical networks (Sexton et al., 2009). One
of the most common indices of DTI to assess the WM structural
organization is fractional anisotropy (FA), a scalar measure ranging
from 0 to 1 that rates the degree of anisotropy in diffusion (Gupta et al.,
2006). Because of its properties, particularly the possibility of revealing
Psychiatry Research: Neuroimaging 203 (2012) 194–200
⁎ Corresponding authors at: Klinik für Psychiatrie, Psychosomatik und Psychotherapie,
Klinikum der J.W.Goethe-Universität, Heinrich-Hoffmann-Str. 10, 60528 Frankfurt am
E-mail addresses: firstname.lastname@example.org (G.S. Alves), email@example.com
0925-4927/$ – see front matter © 2012 Elsevier Ireland Ltd. All rights reserved.
Contents lists available at SciVerse ScienceDirect
Psychiatry Research: Neuroimaging
journal homepage: www.elsevier.com/locate/psychresns
microstructural changes in cerebral networks associated with MDD
(Murphy et al., 2007), DTI is an important tool for investigating age-
related affective disorders (Burzynska et al., 2010).
DTI findings observed that depressioncan accelerate the loss of WM
integrity (Shimony et al., 2009) and that increasing WM abnormalities
were found are related to limbic and dorsal cortical communication in
geriatric MDD (Tekin and Cummings, 2002; Rogers et al., 2004;
sion, and existing evidence indicates an association of low FA with
deficits in response inhibition (Murphy et al., 2007), executive control
(Yuan et al., 2007), and processing speed (Shimony et al., 2009).
Most of the earlier DTI studies in MDD used a region-of-interest
(ROI) approach, with brain areas being delineated manually or with
semi-automated methods (Alexopoulos et al., 2002; Nobuhara et al.,
2004; Taylor et al., 2004; Bae et al., 2006). However, the ROI approach
has been criticized in the recent literature (Sexton et al., 2009;
Stricker et al., 2009; Zhu et al., 2011), namely because of the difficulty
in precisely replicating ROIs, difficulties in the anatomical delineation
of the ROI, and use of pre-selected brain regions rather than consider-
ing diffusion changes in the whole brain. To improve the objectivity
and interpretability of DTI studies, the technique of tract-based
spatial statistics (TBSS) was developed to enable DTI scans to be com-
pared across subjects more robustly (Smith et al., 2006); furthermore,
TBSS reduces the problem of misalignment (Snook et al., 2007) and is
based on voxelwise analysis, which approaches the whole brain with-
out any a priori selection of regions. Therefore, TBSS is a promising
approach to identify more accurately anatomical changes in MDD
throughout the WM structure. TBSS results may have important
repercussions for clinical practice, as they can help in developing bio-
markers for the diagnosis and treatment based on diffusivity changes
across time in specific brain networks of MDD (Alexopoulos et al.,
In the current study, we investigated WM microstructural integ-
rity in a sample of non-demented elderly individuals with MDD in
comparison with age-matched healthy controls. Our objectives
were twofold: 1) to investigate WM abnormalities in the MDD
group using TBSS; and 2) to examine if cognitive performance in
MDD was associated with global and regional WM abnormalities,
particularly in the tracts that have previously been identified as
compromised in MDD.
We expected to identify decreased anisotropy in the MDD group
in comparison with non-depressed subjects, specifically in the
major WM tracts connecting limbic-cortical circuits. It was also hy-
pothesized that cognitive deficits in MDD subjects would be correlat-
ed with reduced FA in these WM tracts, as a component of disrupted
connectivity in depression.
2. Materials and methods
2.1. Clinical assessment
All subjects (n=40) were examined by two members of the Department of
Psychiatry (FF and TK) with experience in Geriatric Psychiatry. Medical assessment
was based on the Structured Clinical Interview for DSM-IV (SCID) (American
Psychiatric Association, 1994) for major depression in the patient group and lifetime
absence of psychiatric illness in the control group. The entire cohort was screened to
exclude mild cognitive impairment or dementia using the Petersen criteria (Petersen,
2004) and DSM-IV, respectively. All subjects included in the study had Clinical Demen-
tia Rating Scale (Hughes et al., 1982) scores of 0. All individuals were evaluated with
the Hamilton Depression Rating Scale (HAMD; Hamilton, 1960), a 21-item rating
scale, and with a shorter version of the Geriatric Depression Scale (GDS) with 15
items (Sheikh and Yesavage, 1986).
Exclusion criteria for all participants were a history of seizures, psychotic symp-
toms, neurological diseases, dementia, impaired thyroid function, abuse of alcohol or
substance abuse or dependence. The study protocol was prepared in accordance with
ethical standards laid down in the declaration of Helsinki and was approved by the
local ethics committee. Patients and controls signed a written consent following a
full oral description of the study.
2.2. Neuropsychological assessment
Forneuropsychologicalassessment,a test batterywasusedtoexamine severalcogni-
tive domains: executive function, episodic memory, working memory, attention, verbal
fluency, visual constructional praxis and language skills. In addition to the Mini-Mental
State Examination (MMSE; Folstein et al., 1975), all participants were assessed with the
battery of the Consortium to Establish a Registry for Alzheimer's Disease — CERAD
constructional praxis (figure copying), language (a reduced version from the Boston
Naming Test — BNT) and episodic memory (word list learning, delayed free recall and
word recognition). Visual memory and working memory were assessed by recall of geo-
metric figures presented earlier in the CERAD test. The Trail Making Test (TMT), which
evaluates psychomotor speed (TMT A) and executive function (TMT B) (Reitan, 1958)
was also included.
For statistical analysis, raw scores from the following cognitive variables were
taken from CERAD tests: immediate recall for words (sum of lists 1, 2 and 3), figure
copying (circle, triangle, rhombus, rectangle, cube); and delayed recall for words and
for visual memory (geometric figures). Raw scores (time in seconds) were also taken
from TMT A and B and Verbal Fluency (number of animals and words).
As most variables were not normally distributed, non-parametric tests were used.
Two-tailed correlations and independent group comparisons were performed with
Spearman's rank correlation and the Mann–Whitney-U test, respectively. In order to
control for the effects of education on cognitive tasks, analysis of covariance (ANCOVA)
was employed. A p valueb0.05 was adopted as statistically significant. All statistical an-
alyses were performed with SPSS version 15.0.
2.3. MRI data acquisition
Imaging was performed on a 3-T MRI scanner (Trio, Siemens Medical Solutions,
Erlangen, Germany). DTI scans were acquired using a gradient echo sequence with the
following parameters: repetition time (TR)=8200 ms, echo time (TE)=99 ms, acquisi-
tion voxel size=2×2×2 mm3, 60 transaxial slices, 60 diffusion encoding directions
(b=1000 s/mm2), slice thickness=2 mm, field of view=192 mm, acquisition ma-
trix=96×96; total acquisition time: 9 min 42 s. Ten images with no diffusion gradient
(B0) were acquired. We allowed for parallel acquisition of independently reconstructed
images using generalized auto-calibrating partially parallel acquisitions [GRAPPA
(Griswold et al., 2002)]. For each subject a total of three consecutive DTI scans were
2.4. Control for white matter lesions
A fluid attenuated inversion recovery sequence (FLAIR) was conducted to identify
subjects with WM lesions, using the following parameters: TR=10000 ms; TE=
105 ms, 1×1×3 mm3, 38 slices. All FLAIR images were visually inspected by one investi-
gator (CK) blind to any clinical data. In order to exclude patients with macrostructural
subcortical vascular disease, the severity of WM lesions was estimated using the Fazekas
scale (Fazekas et al., 1987), and the parameters of WM volume estimation of the LADIS
study (Inzitari et al., 2009). Five subjects with severe WM lesions (>20 mm diameter
and grade=3) were excluded.
2.5. Demographic and clinical characteristics of the sample
A total of 35 subjects remained for further analysis, as shown by Table 1. The two
groups comprised 17 patients diagnosed with a MDD (8 females, mean age=65.5,
S.D.=5.5; range=59–78 years) and 18 subjects (11 females, mean age=66.4,
S.D.=3.5, range=61–74 years) assessed as a control group. Depressed patients and
controls did not differ in gender, age or subcortical vascular lesions, but did differ in
years of education (Table 1). Twelve (70.58%) patients were currently receiving
antidepressant therapy. The remaining patients (n=5, 29.42%) had their first
depressive episode and were drug naive at the time of measurement. Four patients
(23.52%) received co-therapy with antipsychotics and four with (23.52%) low-dose
benzodiazepines, mainly prescribed for sedation. One patient had augmentation of
selective serotonin reuptake inhibitor (SSRI) treatment with lithium; none of the pa-
tients had received electroconvulsive therapy. Mean age of disease onset was 46.88
2.6. DTI preprocessing
DTI processing and voxelwise statistical analysis were performed using tools from
the Oxford Centre for Functional MRI of the Brain — FMRIB free software library (FSL —
http://www.fmrib.ox.ac.uk/fsl/). The three DTI datasets acquired for each subject were
first merged into a single volume. Motion and eddy current correction, as well as an
affine registration to the reference volume (b0), were then performed (Jenkinson
and Smith, 2001). The volumes of each of the three scans were extracted from the
merged image providing three motion and eddy current corrected datasets which
were averaged to produce a single DTI image. FSL's Brain Extraction Tool (BET)
(Smith, 2002) was applied to the averaged DTI image, and a DTI model, including
maps of FA using the FMRIB Diffusion Toolbox. The preprocessing steps were per-
formed automatically using an in-house script pipeline (MR Imaging and Spectroscopy
Toolbox, Institute of Neuroradiology, University Hospital, Frankfurt/Main, Germany).
G.S. Alves et al. / Psychiatry Research: Neuroimaging 203 (2012) 194–200
2.7. DTI statistical analysis with TBSS
TBSS scripts were used to perform a non-linear registration that aligned each FA
image to every other one. This created a calculation of the amount of warping needed
for the images to be aligned. The most representative image was determined as the one
needing the least warping for all other images to align to it. This target image was
affine-aligned into 1×1×1 mm3Montreal Neurological Institute (MNI) 152 standard
space. Each FA image was then transformed into MNI152 space by applying their
respective nonlinear transforms to the target and then the affine transform to MNI
space. The aligned FA images were averaged to create a mean FA image which was
thinned using an FA skeletonization program (threshold FA value of 0.2). This identified
all fiber pathways consistently across all subjects. FA data were then projected onto the
mean FA skeleton that is common to all participants (Smith et al., 2006).
A standard approach with the simple permutation function (Randomize, v 2.1) in
FSL was used on the skeletonized data to calculate voxelwise differences between
depressed patients and healthy controls. Voxelwise statistics were carried out using
two sample t-tests and a General Linear Model (GLM). As the mean years of education
were statistically lower in the depressed group, this variable was included in the anal-
ysis as a confounding regressor. The number of permutations was set to 5,000 and
clusters were defined with the threshold-free cluster enhancement option (tfce),
which avoids the need for an arbitrary initial cluster-forming threshold (Smith and
Nichols, 2009). The level of significance was adopted at pb0.05 level and corrected
for multiple comparisons with family-wise error correction (FWE).
Following analysis with Randomize, two ROIs were created by drawing a mask
in the WM tracts: the first ROI was drawn in the region with statistical differences in
voxelwise analysis; a second ROI was then mirrored in corresponding fiber tracts on
the contra-lateral side, using as anatomical reference a DTI color map human atlas
(Oishi et al., 2011). Finally, FA values of both ROIs were extracted from each
3.1. Cognitive performance between groups
Depressed patients performed significantly worse in the following
cognitive tasks: semantic verbal fluency, immediate recall tests and
Trail Making B. Groups did not differ for MMSE scores (Table 1).
3.2. TBSS results
Voxelwise statistical analysis revealed significantly reduced FA
in MDD patients in comparison with healthy controls in the right pos-
terior cingulate cluster (PCC). This region was composed mainly of
WM tracts belonging to the posterior cingulate and, to a lesser extent,
the cingulum bundle and the posterior limb of the internal capsule,
namely the corticospinal tract (Fig. 1). Mean FA values for the entire
WM skeleton in MDD patients (mean FA=0.369, S.D.=0.018) were
decreased in relation to controls (mean FA=0.379, S.D.=0.015),
and this difference was statistically significant after adjusting for
education using ANCOVA (F=5.245, d.f.=1, p=0.03). FA values for
the right PCC were significantly lower than for the left PCC in the
MDD group, but not in the control group (Fig. 2).
Fig. 1. Significantly decreased FA in depressed patients relative to controls. The mean FA skeleton (green voxels) is projected on the standard MNI 152 template brain. Upper row:
red voxels on the right hemisphere in coronal (A) and sagittal (B) slices denote regions where FA was significantly reduced in depressed patients compared with controls for
the posterior cingulate cluster after voxelwise statistical analysis (pb0.05, FWE). Lower row: a detail of the skeleton in the posterior cingulate is shown in coronal (C) and axial
(D) slices, depicting fiber tracts with significantly reduced FA (red voxels, right hemisphere) and the yellow masked ROI, with equivalent symmetric tracts on the contralateral
side (without statistical significance). FWE: family-wise error; ROI: region of interest.
Socio-demographic and cognitive variables.
Years of education
Semantic verbal fluency
Boston naming test
Delayed recall — wordsa,b
Delayed recall — figuresa,c9.83±1.34
Trail Making Test Ba
0.05117.24±15.33 167.57±15.87 F=4.33, d.f.=1
HAMD: Hamilton Depression Scale; GDS: Geriatric Depression Scale; MMSE: Mini
Mental State Examination; CERAD: Consortium to Establish a Registry for Alzheimer's
aANCOVA adjusting for education.
bComposite scores: immediate recall word lists 1, 2 and 3.
cComposite scores: delayed recall for circle, rhombus, rectangle and cube.
G.S. Alves et al. / Psychiatry Research: Neuroimaging 203 (2012) 194–200
3.3. Correlation between FA values and cognitive tests for the entire
Spearman analysis in Table 2 shows a significant positive correla-
tion between FA in the right PCC and the performance in the follow-
ing CERAD tests: verbal fluency (r=0.36, pb0.05), immediate word
recall (r=0.41, pb0.05) and delayed recall for visual memory-cube
(r=0.41, pb0.01); no significant correlations were found between
socio-demographic, clinical variables and anisotropy values in the
left PCC. FA values did not correlate with age or subcortical hyperin-
tensities rated using the Fazekas scale.
3.4. DTI correlations within clinical group
Statistical analysis between FA and cognitive tasks were analyzed
in each clinical group. In MDD patients, difficulties in naming were
accompanied by a statistically significant decrease in FA in the right
PCC (Table 2). Furthermore, a trend for a positive correlation was
found between DTI parameters and the number of words generated
in the verbal fluency (p=0.06), words recalled in episodic memory
(p=0.06), and delayed recall for cube (p=0.07). No significant cor-
relations between DTI indices and cognitive tasks were found in the
In the current study DTI data from elderly depressed patients and
healthy controls were investigated with TBSS, and related to cognitive
performance in both groups. MDD patients showed a significant FA
decrease in WM tracts mainly including the posterior cingulate and,
to a minor degree, parieto-occipital tracts of the corpus callosum
(CC) and the posterior limb of the internal capsule. DTI indices were
positively correlated with cognitive scores in the MDD group, but
not in controls. We also investigated whether clinical cognitive
features of depression were more associated with FA decrease in the
global WM or in particular WM tracts. Our results reveal that all
correlations with cognitive tasks occurred solely with FA in the right
PCC. Anisotropy values in this cluster showed significant positive cor-
relations with an object naming task and reached a trend of positive
statistical significance with task performance in executive function
(indicated by verbal fluency test), working memory (figure delayed
recall test), and episodic memory (word list test). Taken together,
the results suggest that cognitive disturbances may be associated
with regional rather than a global WM damage.
Our results are in line with the majority of DTI studies reporting
significant FA changes in elderly MDD patients compared to healthy
controls (Nobuhara et al., 2004; Taylor et al., 2004; Bae et al., 2006;
Nobuhara et al., 2006; Murphy et al., 2007; Yang et al., 2007). Not-
withstanding the majority of findings have been related to frontal
lobe areas (Nobuhara et al., 2006; Murphy et al., 2007; Shimony
et al., 2009), DTI abnormalities have also been reported in other neu-
roanatomical areas, such as the anterior cingulate (Bae et al., 2006),
the temporal lobe (Nobuhara et al., 2006; Yang et al., 2007), limbic
areas (Murphy et al., 2007), and the right inferior parietal lobe
(Yuan et al., 2007). Similar results for the right PCC were previously
described, with findings for the internal capsule (Bae et al., 2006)
and posterior cingulate (Murphy et al., 2007; Shimony et al., 2009).
Although most of the DTI studies in MDD have related FA
changes to disruption of WM integrity in cortical-subcortical connec-
tions (Shimony et al., 2009; Kieseppä et al., 2010; Korgaonkar et al.,
2011), the anatomical findings across them showed a large discrep-
ancy, possibly reflecting methodological differences in the DTI tech-
nique and different pathological processes underlying WM changes,
such as demyelination, small vessel ischemic disease and perivascu-
lar dilatation (Thomas et al., 2002; Black et al., 2009). Additionally
these discrepancies can also be suggestive of distributed network
dysfunction ultimately resulting in the clinical symptoms of MDD.
Multiple possibilities of disrupted connectivity between limbic and
cortical regions may exist, providing heterogeneous presentations
of geriatric depression based on different arrays of mood and cogni-
tive features (Laks and Engelhardt, 2010).
Fig. 2. MDD patients and controls are compared in relation to ROI FA values in the right
and left posterior cingulate clusters. Significant differences were found for the right
posterior cingulate (F=12.894, d.f.=1,*pb0.001) but not for the left posterior cingu-
late (F=3.185, d.f.=1, p=0.084); FA values were adjusted for education by ANCOVA.
AU: arbitrary unit; FA: fractional anisotropy; MDD: major depression disorder; ROI:
region of interest.
Spearman rank correlations between FA values and socio-demographic and clinical variables.
Age Education FazekasMMSE VF BNTEpisodic memory recallTMT B
FA: fractional anisotropy; PCC: posterior cingulate cluster; WM: white matter; MMSE: Mini Mental State Examination; VF: verbal fluency (animal category); BNT: Boston naming
test; TMT: Trail Making Test.
G.S. Alves et al. / Psychiatry Research: Neuroimaging 203 (2012) 194–200
Although cognitive deficits frequently coexist with MDD, few
DTI studies analyzed the cognitive outcomes of WM changes
(Alexopoulos et al., 2002; Murphy et al., 2007; Yuan et al., 2007;
Shimony et al., 2009; Schermuly et al., 2010). All findings explored
the association between DTI abnormalities and executive dysfunction
and did not clearly demonstrate whether other cognitive domains
would also be implicated in this process. Our results extend the
anatomical-clinical evidence on the cognitive disorders in geriatric
MDD by showing that deficits not directly related to executive func-
tioning, but with episodic memory and language skills, were associat-
ed with disrupted connectivity in the PCC. Likewise, our findings
provide additional evidence to previous studies showing that degra-
dation of posterior WM tracts may hamper the transmission among
limbic, frontal and temporal regions (Sullivan et al., 2006; Kennedy
and Raz, 2009), and can be associated with MDD symptoms (Bae et
al., 2006; Murphy et al., 2007; Shimony et al., 2009). The current re-
sults are supported by the functional anatomy of the PCC and evi-
dence from MRI studies with MDD. The corticospinal tract is part of
the projection fibers, which connect the brain stem, cingulum and
dorsolateral prefrontal cortex (Wakana et al., 2004); a higher
corticospinal excitability in the primary motor cortex on the right
hemisphere (in contrast with the hypoactivity on the contralateral
side) is thought to lead to inter-hemispheric imbalance, thus affecting
mood and cognitive regulation in acute depression (Bajwa et al.,
2008); the WM in the corpus callosum vicinity includes the callosal
fibers which connect striatum, thalamus and inter-hemispheric
areas; lower anisotropy in these tracts has been associated with
poor antidepressant response (Alexopoulos et al., 2008a, 2008b)
and slowing in processing speed (Shimony et al., 2009). Finally, WM
in the posterior cingulate comprises the limbic system fibers (Oishi
et al., 2011) and connects important limbic-cortical networks in de-
pression (Schermuly et al., 2010); those fibers receive projections
from the nearby cingulate gyrus, extending to the middle temporal
lobe and hippocampus (Wakana et al., 2004; Oishi et al., 2011); in
healthy subjects the posterior cingulate cortex has shown a higher
activation during tasks with emotional valence, such as the visual
stimuli expressing anger and fear, learning within a motivational con-
text and the description of words with emotional meaning (Maddock
et al., 2003; Maletic et al., 2007); in contrast, elderly subjects with
acute depression have shown an enhanced deactivation in the poste-
rior cingulate during executive and emotional processing tasks
(Wang et al., 2008) and decreased activation in temporo-limbic struc-
tures with episodic memory tasks (Grön et al., 2002). Indeed, func-
tional MRI studies showed that the posterior portion of the
posterior cingulate (Brodmann's area 30) is closely connected with
the retrosplenial and the hippocampal cortices and has been implicat-
ed in self-consciousness and memory retrieval (Buckner et al., 2005;
Nielsen et al., 2005). The network integrating limbic and callosal
fibers with temporal and frontal areas is also required for successful
word production (Stamatakis et al., 2011). One study reported an
association between age-related anisotropic changes in the corpus
callosum, internal capsule and superior longitudinal fasciculus with
word finding difficulties (Stamatakis et al., 2011).
The posterior cingulate cortex is also known as a key region of
the default mode network (DMN), a set of brain regions typically
showing more activity during rest than in response to external
stimulation, for example, during cognitive tasks (Zhang and Raichle,
2010). Growing evidence from functional and structural MRI studies
implicatesdisturbances inDMN regionsasa possibleunderlying patho-
physiological mechanism in psychiatric diseases like schizophrenia,
Alzheimer's disease (AD), and depression (Zhang and Raichle, 2010;
Wu et al., 2011). In particular, the posterior cingulate cortex is a critical
hub with the highest degree and centrality in cortical networks
(Buckner et al., 2009; Bullmore and Sporns, 2009). Impairing network
efficiency, microstructural lesions of these hubs might lead to path-
ological processes like functional or metabolic changes. Despite
increasing reports of WM changes in the posterior cingulate, it is
still a matter of debate whether these abnormalities represent a
state or a trait marker of geriatric MDD (Schermuly et al., 2010). Cog-
nitive symptoms are a common feature of both depression and neu-
rodegenerative disorders like Alzheimer's disease. The finding of WM
abnormalities in the posterior cingulate bundle raises the question of a
possible common dysfunctional pathway underlying these two
conditions. However, our findings do not permit us to reach definitive
conclusions on the matter, and further studies are necessary to investi-
gate the default network activity in late life depression.
Other possible variables that could explain DTI changes did not
show a significant correlation in our sample. FA was not associated
with the severity of symptoms in HAMD (p=0.98), GDS (p=0.63)
or age of disease onset (p=0.92). Previous studies obtained equivo-
cal findings on the issue, with some studies finding an absence of
association between FA and depression severity rated by HAMD
(Yang et al., 2007) and MADRS (Alexopoulos et al., 2002; Bae et al.,
2006), while others encountered positive results (Nobuhara et al.,
2006; Dalby et al., 2010). Some characteristics of the sample, such
as the effect of antidepressant treatment, a larger proportion of pa-
tients with mild to moderate symptoms as assessed by the HAMD
(n=11, 64.70%) and a relatively low number of depressive episodes
(mean=2.71; S.D.=2.80) might explain the absence of association
with FA; however, due to the cross-sectional nature of our study, it
is not possible to rule out the influence of these variables on DTI
changes in our sample.
The principal limitation of this study is the lack of specificity in the
findings of the right PCC, because not all fibers within an ROI belong
to a particular circuit. Indeed, it is claimed by many authors that
the brain connections are variable, and one entire WM tract might
be disorganized by multiple possibilities of neuronal disconnection
in cortical and subcortical areas (Bae et al., 2006; Alexopoulos et al.,
2008a). A promising approach is DTI-based fiber tracking, which al-
lows the reconstruction of WM tracts according to an anatomical or
a pathophysiological hypothesis (Price et al., 2007). Hence, based on
the evidence of the global picture of white matter, fiber tracking is
certainly a step further for future studies in the field of neuropsychi-
atry. Another constraint was the small sample size in our study,
which reduced the power of statistical analysis.
Our study showed WM structural deficits in posterior areas of the
brain that were associated with clinical cognitive deficits in elderly
MDD patients. The results contribute to the existing evidence on the
limbic-cortical WM disconnection in depression and suggest, further-
more, that disruption of WM tracts located in the posterior cingulum
may affect executive function, episodic memory and verbal language
domains. Our future goals are to extend these preliminary findings in
a larger sample and with additional DTI measures such as radial diffu-
sivity and axial diffusion and fiber tracking in order to enhance sensi-
tivity and to better understand the impact of WM structural changes
on cognitive symptoms of MDD.
MRI was performed at the Frankfurt Brain Imaging Center, sup-
ported by the German Research Council (DFG) and the German
Ministry for Education and Research (BMBF; Brain Imaging Center
Frankfurt am Main, DLR 01GO0203). Jerson Laks receives a grant from
the Conselho Nacional de Desenvolvimento Científico e Tecnológico
(CNPq), Brazil (Researcher 2). G.A. has been supported by a scholarship
from CNPq, Brazil (Nr. 290012/2009-0) in a cooperative exchange
G.S. Alves et al. / Psychiatry Research: Neuroimaging 203 (2012) 194–200
program with the Deutscher Akademischer Austauschdienst (DAAD),
Alexopoulos, G.S., Kiosses, D.N., Choi, S.J., Murphy, C.F., Lim, K.O., 2002. Frontal white
matter microstructure and treatment response of late-life depression: a prelimi-
nary study. The American Journal of Psychiatry 159, 1929–1932.
Alexopoulos, G.S., Gunning-Dixon, F., Latoussakis, V., Kanellopoulos, D., Murphy, C.F.,
2008a. Anterior cingulate dysfunction in geriatric depression. International Journal
of Geriatric Psychiatry 23, 347–355.
Alexopoulos, G.S., Murphy, C.F., Gunning-Dixon, F.M., Latoussakis, V., Kanellopoulos, D.,
Klimstra, S., Lim, K.O., Hoptman, M.J., 2008b. Microstructural white matter abnor-
malities and remission of geriatric depression. The American Journal of Psychiatry
American Psychiatric Association, 1994. Diagnostic and Statistical Manual of Mental
Disorders, DSM-IV, 4th ed. American Psychiatric Publishing, Inc., Washington, DC.
Bae, J.N., MacFall, J.R., Krishnan, K.R.R., Payne, M.E., Steffens, D.C., Taylor, W.D., 2006.
Dorsolateral prefrontal cortex and anterior cingulate cortex white matter alter-
ations in late-life depression. Biological Psychiatry 60, 1356–1363.
Bajwa, S., Bermpohl, F., Rigonatti, S.P., Pascual-Leone, A., Boggio, P.S., Fregni, F., 2008.
Impaired interhemispheric interactions in patients with major depression. The
Journal of Nervous and Mental Disease 196, 671–677.
Barnes, D.E., Alexopoulos, G.S., Lopez, O.L., Williamson, J.D., Yaffe, K., 2006. Depressive
symptoms, vascular disease, and mild cognitive impairment: findings from the
Cardiovascular Health Study. Archives of General Psychiatry 63, 273–279.
Black, S., Gao, F., Bilbao, J., 2009. Understanding white matter disease: imaging-
pathological correlations in vascular cognitive impairment. Stroke 40, 48–52.
Buckner, R.L., Snyder, A.Z., Shannon, B.J., LaRossa, G., Sachs, R., Fotenos, A.F., Sheline,
Y.I., Klunk, W.E., Mathis, C.A., Morris, J.C., Mintun, M.A., 2005. Molecular, structural,
and functional characterization of Alzheimer's disease: evidence for a relationship
between default activity, amyloid, and memory. Journal of Neuroscience 25,
Buckner, R.L., Sepulcre, J., Talukdar, T., Krienen, F.M., Liu, H., Hedden, T., Andrews-Hanna,
J.R., Sperling, R.A., Johnson, K.A., 2009. Cortical hubs revealed by intrinsic functional
connectivity: mapping, assessment of stability, and relation to Alzheimer's disease.
Journal of Neuroscience 29, 1860–1873.
Bullmore, E., Sporns, O., 2009. Complex brain networks: graph theoretical analysis of
structural and functional systems. Nature Reviews Neuroscience 10, 15–28.
Burzynska, A.Z., Preuschhof, C., Bäckman, L., Nyberg, L., Li, S., Lindenberger, U.,
Heekeren, H.R., 2010. Age-related differences in white matter microstructure:
region-specific patterns of diffusivity. NeuroImage 49, 2104–2112.
Dalby, R.B., Frandsen, J., Chakravarty, M.M., Ahdidan, J., Sorensen, L., Rosenberg, R.,
Videbech, P., Ostergaard, L., 2010. Depression severity is correlated to the integrity
of white matter fiber tracts in late-onset major depression. Psychiatry Research:
Neuroimaging 184, 38–48.
Drevets, W.C., 2007. Orbitofrontal cortex function and structure in depression. Annals
of the New York Academy of Sciences 1121, 499–527.
Elderkin-Thompson, V., Mintz, J., Haroon, E., Lavretsky, H., Kumar, A., 2006. Executive
dysfunction and memory in older patients with major and minor depression.
Archives of Clinical Neuropsychology 21, 669–676.
Fazekas, F., Chawluk, J., Alavi, A., Hurtig, H., Zimmerman, R., 1987. MR signal abnormal-
ities at 1.5 T in Alzheimer's dementia and normal aging. American Journal of
Roentgenology 149, 351–356.
Folstein, M.F., Folstein, S.E., McHugh, P.R., 1975. “Mini-mental state”. A practical meth-
od for grading the cognitive state of patients for the clinician. Journal of Psychiatric
Research 12, 189–198.
Geda, Y.E., Knopman, D.S., Mrazek, D.A., Jicha, G.A., Smith, G.E., Negash, S., Boeve, B.F.,
Ivnik, R.J., Petersen, R.C., Pankratz, V.S., Rocca, W.A., 2006. Depression, apolipopro-
tein E genotype, and the incidence of mild cognitive impairment: a prospective
cohort study. Archives of Neurology 63, 435–440.
Greenberg, P.E., Stiglin, L.E., Finkelstein, S.N., Berndt, E.R., 1993. The economic burden
of depression in 1990. The Journal of Clinical Psychiatry 54, 405–418.
Griswold, M.A., Jakob, P.M., Heidemann, R.M., Nittka, M., Jellus, V., Wang, J., Kiefer, B.,
Haase, A., 2002. Generalized autocalibrating partially parallel acquisitions (GRAPPA).
Magnetic Resonance in Medicine 47, 1202–1210.
Grön, G., Bittner, D., Schmitz, B., Wunderlich, A.P., Riepe, M.W., 2002. Subjective
memory complaints: objective neural markers in patients with Alzheimer's disease
and major depressive disorder. Annals of Neurology 51, 491–498.
Gupta, R.K., Saksena, S., Hasan, K.M., Agarwal, A., Haris, M., Pandey, C.M., Narayana, P.A.,
2006. Focal Wallerian degeneration of the corpus callosum in large middle cerebral
artery stroke: serial diffusion tensor imaging. Journal of Magnetic Resonance
Imaging 24, 549–555.
Hamilton, M., 1960. A rating scale for depression. Journal of Neurology, Neurosurgery,
and Psychiatry 23, 56–62.
Herrmann, L.L., Goodwin, G.M., Ebmeier, K.P., 2007. The cognitive neuropsychology of
depression in the elderly. Psychological Medicine 37, 1693–1702.
Hughes, C.P., Berg, L., Danziger, W.L., Coben, L.A., Martin, R.L., 1982. A new clinical scale
for the staging of dementia. The British Journal of Psychiatry 140, 566–572.
Inzitari, D., Pracucci, G., Poggesi, A., Carlucci, G., Barkhof, F., Chabriat, H., Erkinjuntti, T.,
Fazekas, F., Ferro, J.M., Hennerici, M., Langhorne, P., O'Brien, J., Scheltens, P., Visser,
M.C., Wahlund, L., Waldemar, G., Wallin, A., Pantoni, L., on behalf of the LADIS
Study Group, 2009. Changes in white matter as determinant of global functional
decline in older independent outpatients: three year follow-up of LADIS (leukoar-
aiosis and disability) study cohort. British Medical Journal 339, b2477.
Jenkinson, M., Smith, S., 2001. A global optimization method for robust affine registra-
tion of brain images. Medical Image Analysis 5, 143–156.
Kennedy, K.M., Raz, N., 2009. Aging white matter and cognition: differential effects of
regional variations in diffusion properties on memory, executive functions, and
speed. Neuropsychologia 47, 916–927.
Kieseppä, T., Eerola, M., Mäntylä, R., Neuvonen, T., Poutanen, V., Luoma, K., Tuulio-
Henriksson, A., Jylhä, P., Mantere, O., Melartin, T., Rytsälä, H., Vuorilehto, M.,
Isometsä, E., 2010. Major depressive disorder and white matter abnormalities: a
diffusion tensor imaging study with tract-based spatial statistics. Journal of Affec-
tive Disorders 120, 240–244.
Koolschijn, P.C.M.P., vanHaren, N.E.M., Lensvelt-Mulders, G.J.L.M., Pol, H.E.H.,
Kahn, R.S., 2009. Brain volume abnormalities in major depressive disorder: a
meta analysis of magnetic resonance imaging studies. Human Brain Mapping
spatial statistical analysis of diffusion tensor imaging. Human Brain Mapping 32 n/a.
Laks, J., Engelhardt, E., 2010. Peculiarities of geriatric psychiatry: a focus on aging and
depression. CNS Neuroscience and Therapeutics 16, 374–379.
Lesser, I., Boone, K., Mehringer, C., Wohl, M., Miller, B., Berman, N., 1996. Cognition and
white matter hyperintensities in older depressed patients. The American Journal of
Psychiatry 153, 1280–1287.
Lyness, J.M., Caine, E.D., King, D.A., Conwell, Y., Duberstein, P.R., Cox, C., 2002. Depres-
sive disorders and symptoms in older primary care patients: one year outcomes.
The American Journal of Geriatric Psychiatry 10, 275–282.
Maddock, R.J., Garrett, A.S., Buonocore, M.H., 2003. Posterior cingulate cortex activation
by emotional words: fMRI evidence from a valence decision task. Human Brain
Mapping 18, 30–41.
Maletic, V., Robinson, M., Oakes, T., Iyengar, S., Ball, S.G., Russell, J., 2007. Neurobiology
of depression: an integrated view of key findings. International Journal of Clinical
Practice 61, 2030–2040.
Mayberg, H.S., 2003. Modulating dysfunctional limbic-cortical circuits in depression:
towards development of brain-based algorithms for diagnosis and optimized treat-
ment. British Medical Bulletin 65, 193–207.
Morris, J.C., Heyman, A., Mohs, R.C., Hughes, J.P., van Belle, G., Fillenbaum, G., Mellits,
E.D., Clark, C., 1989. The Consortium to Establish a Registry for Alzheimer's Disease
(CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer's dis-
ease. Neurology 39, 1159–1165.
Murphy, C.F., Gunning-Dixon, F.M., Hoptman, M.J., Lim, K.O., Ardekani, B., Shields, J.K.,
Hrabe, J., Kanellopoulos, D., Shanmugham, B.R., Alexopoulos, G.S., 2007. White-
matter integrity predicts stroop performance in patients with geriatric depression.
Biological Psychiatry 61, 1007–1010.
Nielsen, F.A., Balslev, D., Hansen, L.K., 2005. Mining the posterior cingulate: segregation
between memory and pain components. NeuroImage 27, 520–532.
Nobuhara, K., Okugawa, G., Minami, T., Takase, K., Yoshida, T., Yagyu, T., Tajika, A.,
Sugimoto, T., Tamagaki, C., Ikeda, K., Sawada, S., Kinoshita, T., 2004. Effects of elec-
troconvulsive therapy on frontal white matter in late-life depression: a diffusion
tensor imaging study. Neuropsychobiology 50, 48–53.
Nobuhara, K., Okugawa, G., Sugimoto, T., Minami, T., Tamagaki, C., Takase, K., Saito, Y.,
Sawada, S., Kinoshita, T., 2006. Frontal white matter anisotropy and symptom
severity of late-life depression: a magnetic resonance diffusion tensor imaging
study. Journal of Neurology, Neurosurgery, and Psychiatry 77, 120–122.
Oishi, K., Faria, A.V., van Zijl, P.C.M., Susumu Mori, S., 2011. MRI Atlas of Human White
Matter, 2nd ed. Elsevier, London, pp. 174–178.
Petersen, R.C., 2004. Mild cognitive impairment as a diagnostic entity. Journal of Inter-
nal Medicine 256, 183–194.
Price, G., Cercignani, M., Parker, G.J., Altmann, D.R., Barnes, T.R., Barker, G.J., Joyce, E.M.,
Ron, M.A., 2007. Abnormal brain connectivity in first-episode psychosis: a diffusion
MRI tractography study of the corpus callosum. NeuroImage 35, 458–466.
Reitan, R., 1958. Validity of the Trail Making Test as an indicator of organic brain
damage. Perceptual and Motor Skills 8, 271–276.
Rogers, M.A., Kasai, K., Koji, M., Fukuda, R., Iwanami, A., Nakagome, K., Fukuda, M., Kato,
N., 2004. Executive and prefrontal dysfunction in unipolar depression: a review of
neuropsychological and imaging evidence. Neuroscience Research 50, 1–11.
Schermuly, I., Fellgiebel, A., Wagner, S., Yakushev, I., Stoeter, P., Schmitt, R., Knickenberg,
R.J., Bleichner, F., Beutel, M.E., 2010. Association between cingulum bundle structure
and cognitive performance: an observational study in major depression. European
Psychiatry 25, 355–360.
Sexton, C.E., Mackay, C.E., Ebmeier, K.P., 2009. A systematic review of diffusion tensor
imaging studies in affective disorders. Biological Psychiatry 66, 814–823.
Sheikh, J.I., Yesavage, J.A., 1986. Geriatric Depression Scale: recent evidence and devel-
opment of a shorter version. Clinical Gerontologist: The Journal of Aging and
Mental Health 5, 165–173.
Shimony, J.S., Sheline, Y.I., D'Angelo, G., Epstein, A.A., Benzinger, T.L.S., Mintun, M.A.,
McKinstry, R.C., Snyder, A.Z., 2009. Diffuse microstructural abnormalities of
normal-appearing white matter in late life depression: a diffusion tensor imaging
study. Biological Psychiatry 66, 245–252.
Smith, S.M., 2002. Fast robust automated brain extraction. Human Brain Mapping 17,
Smith, S.M., Nichols, T.E., 2009. Threshold-free cluster enhancement: addressing prob-
lems of smoothing, threshold dependence and localization in cluster inference.
NeuroImage 44, 83–98.
Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E.,
Watkins, K.E., Ciccarelli, O., Cader, M.Z., Matthews, P.M., Behrens, T.E.J., 2006.
Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.
NeuroImage 31, 1487–1505.
G.S. Alves et al. / Psychiatry Research: Neuroimaging 203 (2012) 194–200
Snook, L., Plewes, C., Beaulieu, C., 2007. Voxel based versus region of interest analysis in Download full-text
diffusion tensor imaging of neurodevelopment. NeuroImage 34, 243–252.
Stamatakis, E.A., Shafto, M.A., Williams, G., Tam, P., Tyler, L.K., 2011. White matter
changes and word finding failures with increasing age. PLoS One 6, e14496.
Stricker, N.H., Schweinsburg, B.C., Delano-Wood, L., Wierenga, C.E., Bangen, K.J.,
Haaland, K.Y., Frank, L.R., Salmon, D.P., Bondi, M.W., 2009. Decreased white matter
integrity in late-myelinating fiber pathways in Alzheimer's disease supports retro-
genesis. NeuroImage 45, 10–16.
Sullivan, E.V., Adalsteinsson, E., Pfefferbaum, A., 2006. Selective age-related degradation
of anterior callosal fiber bundles quantified in vivo with fiber tracking. Cerebral
Cortex 16, 1030–1039.
Taylor, W.D., MacFall, J.R., Payne, M.E., McQuoid, D.R., Provenzale, J.M., Steffens, D.C.,
Krishnan, K.R.R., 2004. Late-life depression and microstructural abnormalities in
dorsolateral prefrontal cortex white matter. The American Journal of Psychiatry
Taylor, W.D., Steffens, D.C., Payne, M.E., MacFall, J.R., Marchuk, D.A., Svenson, I.K.,
Krishnan, K.R.R., 2005. Influence of serotonin transporter promoter region poly-
morphisms on hippocampal volumes in late-life depression. Archives of General
Psychiatry 62, 537–544.
Tekin, S., Cummings, J.L., 2002. Frontal-subcortical neuronal circuits and clinical neuro-
psychiatry: an update. Journal of Psychosomatic Research 53, 647–654.
Thomas, A.J., Perry, R., Barber, R., Kalaria, R.N., O'Brien, J.T., 2002. Pathologies and path-
ological mechanisms for white matter hyperintensities in depression. Annals of the
New York Academy of Sciences 977, 333–339.
Wakana, S., Jiang, H., Nagae-Poetscher, L.M., van Zijl, P.C.M., Mori, S., 2004. Fiber tract-
based atlas of human white matter anatomy. Radiology 230, 77–87.
Wang, L., Krishnan, K.R., Steffens, D.C., Potter, G.G., Dolcos, F., McCarthy, G., 2008.
Depressive state- and disease-related alterations in neural responses to affective
and executive challenges in geriatric depression. The American Journal of Psychia-
try 165, 863–871.
Wu, M., Andreescu, C., Butters, M.A., Tamburo, R., Reynolds, C.F., Aizenstein, H., 2011.
Default-mode network connectivity and white matter burden in late-life depres-
sion. Psychiatry Research: Neuroimaging 194, 39–46.
Yang, Q., Huang, X., Hong, N., Yu, X., 2007. White matter microstructural abnormalities
in late-life depression. International Psychogeriatrics 19, 757–766.
Yuan, Y., Zhang, Z., Bai, F., Yu, H., Shi, Y., Qian, Y., Zang, Y., Zhu, C., Liu, W., You, J., 2007.
White matter integrity of the whole brain is disrupted in first-episode remitted
geriatric depression. Neuroreport 18, 1845–1849.
Zhang, D., Raichle, M.E., 2010. Disease and the brain's dark energy. Nature Reviews
Neurology 6, 15–28.
Zhu, X., Wang, X., Xiao, J., Zhong, M., Liao, J., Yao, S., 2011. Altered white matter integ-
rity in first-episode, treatment-naive young adults with major depressive disorder:
a tract-based spatial statistics study. Brain Research 1369, 223–229.
G.S. Alves et al. / Psychiatry Research: Neuroimaging 203 (2012) 194–200