High angular resolution diffusion imaging correlates of depression in Parkinson’s disease: a connectometry study

Abstract

Depression is a significant disabling feature in Parkinson’s disease (PD). However, the neuropathology of this comorbidity is still unclear. In fact, few studies have tried to elucidate the neural correlates of depression in PD and have mostly examined specific regions of interest. In this study, we applied diffusion MRI connectometry, a powerful complementary approach to investigate alterations in whole white matter pathways regarding the severity of depressive symptoms. Using a multiple regression model, the correlation of severity of depressive symptoms assessed by the Hospital Anxiety and Depression Scale (HADS) with white matter connectivity was surveyed in 27 non-demented PD patients related to 26 age, sex, and educational level-matched healthy subjects. Results revealed areas, where white matter quantitative anisotropy (QA) was correlated with depression score in PD patients, without any significant association in healthy controls. The analysis showed a significant negative association (false discovery rate < 0.05) between scores on depression subscale of HADS in PD patients and QA of left Cingulum, Genu, and Splenium of the Corpus Callosum, and anterior and posterior limbs of the right internal capsule. This finding might improve our understanding of the neural basis of depression and its severity in PD.

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Acta Neurologica Belgica
https://doi.org/10.1007/s13760-018-0937-5
ORIGINAL ARTICLE
High angular resolution diffusion imaging correlates ofdepression
inParkinson’s disease: aconnectometry study
FarzanehGhaziSherbaf1,2· KavehSame1,2· MohammadHadiAarabi1,2
Received: 19 July 2017 / Accepted: 26 April 2018
© Belgian Neurological Society 2018
Abstract
Depression is a significant disabling feature in Parkinson’s disease (PD). However, the neuropathology of this comorbidity is
still unclear. In fact, few studies have tried to elucidate the neural correlates of depression in PD and have mostly examined
specific regions of interest. In this study, we applied diffusion MRI connectometry, a powerful complementary approach
to investigate alterations in whole white matter pathways regarding the severity of depressive symptoms. Using a multiple
regression model, the correlation of severity of depressive symptoms assessed by the Hospital Anxiety and Depression Scale
(HADS) with white matter connectivity was surveyed in 27 non-demented PD patients related to 26 age, sex, and educational
level-matched healthy subjects. Results revealed areas, where white matter quantitative anisotropy (QA) was correlated with
depression score in PD patients, without any significant association in healthy controls. The analysis showed a significant
negative association (false discovery rate < 0.05) between scores on depression subscale of HADS in PD patients and QA of
left Cingulum, Genu, and Splenium of the Corpus Callosum, and anterior and posterior limbs of the right internal capsule.
This finding might improve our understanding of the neural basis of depression and its severity in PD.
Keywords Depression· Hospital anxiety and depression scale· Parkinson’s disease· Connectometry· Diffusion MRI
Introduction
Depression is a common and disabling non-motor symptom
(NMS) of Parkinson’s disease (PD), affecting nearly half
of the patients [1]. Often accompanied by anxiety, it can
aggravate other features of PD such as motor symptoms and
cognitive behavior, accelerate disease progression and is the
main culprit in lowering the quality of life in these patients
[2, 3]. Although it is expected to encounter mood-related
responses in patients suffering from chronic and debilitating
disorders such as PD, it is believed that depressive symp-
toms are mainly attributed to the causative neurodegenera-
tive processes [4–7]. More interestingly, depression tell-tale
the onset of the underlying neurodegeneration which may
emerge several years before the onset of cardinal motor
symptoms of PD [8]. This has, therefore, sparked hopes for
early diagnosis and application of neuroprotective measures
prior to initiation of disabling motor symptoms [9]. How-
ever, the neural basis of comorbidity of depression in PD
(dPD) is still unclear, and only a few studies have explored
the neural correlates of dPD [10].
Current evidence suggests multi-regional destruction
in cortical and subcortical regions, such as the basal gan-
glia and limbic system, while exploring the disconnection
hypothesis of involved neural networks in dPD [4]. Involve-
ment of cholinergic, serotonergic, noradrenergic despite as
well as dopaminergic neurotransmitter systems, well dis-
cussed in the literature, illustrate the complexity of neural
circuits in dPD [9, 11, 12]. Moreover, different aspects of
depressive symptoms are shown to be related to different
neuroimaging alterations. Thus, heterogeneous structural
or functional neuroimaging findings in dPD are expected,
with the great need for further studies to achieve a coherent
model [4, 9, 13].
Depressive symptoms are difficult to assess in PD patients
mainly because of the overlapping motor, affect, and cogni-
tive morbidities. Although current scales have shortcomings
when applied to probe depression in PD patients, several
rating scales have been used to assess dPD dependent on
* Mohammad Hadi Aarabi
mohammadhadiarabi@gmail.com
1 Faculty ofMedicine, Tehran University ofMedical Sciences,
Tehran, Iran
2 Students’ Scientific Research Center, Tehran University
ofMedical Sciences, Tehran, Iran

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the research targets. The depression subscale of the Hospi-
tal Anxiety and Depression Scale (HADS) is a valid test to
screen mild-to-moderate depressive symptoms in PD and
due to limited involvement of motor features in scaling, is
a useful measure to rate depression severity in a spectrum
of PD severity [14]. In fact, the HADS questionnaire has no
somatic items making it more appropriate to identify depres-
sive symptoms in PD patients with overlapping somatic
symptoms inherent to the Parkinson’s disease in comparison
with other existing depression rating scales [15]. Further-
more, HADS mainly searches for core traits in dPD, mood,
and anhedonia, while ignoring guilt and suicidal thoughts
which are less frequent in dPD [15, 16]. Though, it does not
meet the internal consistency threshold to be diagnostic in
the clinical setting [15].
In the present study, we examined the neural correlates
of the severity of depressive symptoms assessed by HADS
in PD patients. For this, we used high angular resolution
single-shell diffusion MRI and employed diffusion MRI
connectometry to characterize the changes in white matter
microstructure. Previous DTI studies have mostly relied on
conventional region-of-interest or end-to-end fiber track-
ing to assess white matter correlates of dPD [17–20]. The
reliability of these approaches has recently been questioned
especially in regions adjacent to grey matter [21, 22]. Dif-
fusion MRI connectometry is a novel approach in tracking
associations of areas with similar connectivity patterns or
track differences in these patterns between study groups.
Connectometry improves the power of analysis using the
notion of “local connectomes” and tracking only the signifi-
cantly related fiber bundle to the study variable instead of
pre-assigning regions or tracks inevitably containing irrel-
evant branches [23]. Furthermore, connectometry relies on
Spin Distribution Function (SDF), a numeric measure of the
density of water diffusion for any given direction of a voxel,
which serves as a “local connectome fingerprint” to reliably
identify each individual [24], reflecting its higher sensitivity
and specificity than conventional diffusivity indices [23].
Methods
Participants
27 patients clinically diagnosed with Parkinson’s disease
(mean disease duration of 5years) and 26 age, sex, and edu-
cational level-matched healthy controls were recruited from
a previous study by Ziegler etal. [25]. The main study factor,
depressive symptoms, was evaluated by the depression part
of Hospital Anxiety and Depression Scale. Disease stage
and severity within the patient population were assessed in
the “on” state using the Hoehn & Yahr (H&Y) staging and
the Unified Parkinson’s Disease Rating Scale (UPDRS).
PD patients were asked to complete the Parkinson’s Dis-
ease Questionnaire (PDQ39) to measure their quality of life.
Twenty-four subjects in the diseased group were on the anti-
parkinsonian regiment at the time of imaging acquisition.
To compare treatment dosages, total levodopa equivalent
daily dose was calculated (ranging from 0 to 900mg) [26].
All study subjects were assessed by Mattis Dementia Rating
Scale and mini-mental state examination (MMSE) to evalu-
ate their global cognitive function. Demographic and clinical
features are outlined in Table1.
The study was performed according to the guidelines of
the Ethics Committee of the University of Liège and written
informed consent was obtained from all participating sub-
jects in accordance with the Declaration of Helsinki.
Imaging data acquisition
This data set was acquired on a 3T Siemens scanner,
producing 120 DWI (repetition time = 6800 ms, echo
time = 91 ms; voxel size: 2.4 × 2.4 × 2.4 mm3; field of
view = 211 × 211mm) at b value of 2500s/mm2 [25].
Table 1 Demographic information and comparison of clinical out-
comes between HC and patients with PD
Values indicate mean (standard deviation). Between group differences
were analyzed using Chi square test for sex and handedness, and two-
tailed t test for other variables. P value < 0.05 was considered statisti-
cally significant
LEDD L-DOPA equivalent daily dose, UPDRS Unified Parkinson’s
Disease Rating Scale, PDQ-39 Parkinson’s disease Questionnaire,
MMSE Mini-mental State Examination, HADS Hospital Anxiety and
Depression Scale
PD patients (n = 27) Healthy
controls
(n = 26)
P value
Age 65.6 (7.5) 64.3 (7.7) 0.549
Sex (M:F) 14:13 14:12 0.884
Years of education 11.2 (2.5) 12.5 (3.4) 0.130
Handedness (L:R) 2:25 2:24 0.968
Most affected side
(L:R)
10:17
Hoehn & Yahr stage 1.5 (0.6)
Disease duration
(years)
5.3 (2.9)
LEDD (mg) 322.5 (255.3)
UPDRS2 9.4 (6.2)
UPDRS3 13.7 (6.5)
PDQ39 188.5 (114.3)
MMSE 27.7 (1.3) 28.6 (1.4) 0.022
Mattis 135.5 (3.9) 139 (4.48) 0.004
HADS depression 4.8 (3) 3.6 (2.1) 0.103
HADS anxiety 8 (4.2) 6.6 (2.6) 0.143
HADS total 12.8 (5.9) 10.2 (4.1) 0.067

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Diffusion MRI data processing andconnectometry
analysis
Diffusion MRI data were corrected for subject motion, eddy
current distortions, and susceptibility artifacts due to the
magnetic field inhomogeneity using ExploreDTI toolbox
[27]. Diffusion data were reconstructed in the MNI space
using q-space diffeomorphic reconstruction to obtain the
spin distribution function. A diffusion sampling length ratio
of 1.25 was used, and the output resolution was 2mm.
Diffusion MRI connectometry[23] was used to study the
effect of depression. A multiple regression model was used
to investigate correlation of depression score with white mat-
ter quantitative anisotropy (QA), in 27 PD patients and 26
healthy controls, considering the age, sex, score on anxiety
subscale of the HADS, MMSE, and Mattis as covariates of
the model for both groups, and the quality of life, duration of
the disease, UPRDS-III, and L-DOPA equivalent daily dose
as covariates for the PD group. QA of each fiber orientation
gives the peak value of water density and signifies the degree
of connectivity for white matter connectomes. A t threshold
of 3 was assigned to select local connectomes, which were
then tracked using a deterministic fiber tracking algorithm
[28]. The HCP-842 template [29] was used for analysis. All
tracks generated from bootstrap resampling were included.
A length threshold of 40mm was used to select tracks. The
seeding density was 50 seeds per mm3. To estimate the false
discovery rate, a total of 2000 randomized permutation was
applied to the group label to obtain the null distribution of
the track length. The analysis was conducted using DSI Stu-
dio (http://dsi-studi o.labso lver.org).
Results
Clinical measures
The UPDRS-III score indicated only mild motor impair-
ment in on-drug status (13.7 ± 6.5). Fifteen patients had
unilateral motor involvement, three had unilateral plus axial
involvement, eight had bilateral involvement without balance
impairment, and only three had mild-to-moderate disability
with impaired postural reflexes, according to the H&Y stag-
ing of motor involvement. The HADS score revealed only
one patient with a definite diagnosis of depression based on
criteria by Zigmond in 1983 [30]. There was no significant
difference in HADS score (total, depression and anxiety sub-
scales) between PD patients and control subjects. Although
PD subjects had significantly lower scores on cognitive
assessments (Mattis and MMSE) compared to controls, none
of the participants in both groups scored below the cut-off
thresholds to be marked as demented (Table1).
Connectometry
Results from multiple regression models in Diffusion MRI
connectometry revealed areas, where white matter QA cor-
related with depression score in PD patients, without any
significant association in healthy controls. The analysis
showed a significant negative association [false discovery
rate (FDR) < 0.05] between HADS scores in PD patients and
Genu, Splenium, right anterior limb of the internal capsule,
right posterior limb of the internal capsule, and left cingu-
lum (Fig.1).
Discussion
The principal finding of this study is that the higher scores
on the depression subscale of the HADS test in non-
demented PD patients are associated with lower connectivity
in specific white matter regions including the left cingulum,
genu and splenium of the corpus callosum, and the ante-
rior and posterior limbs of the right internal capsule, while
healthy controls with the same HADS scores did not reveal
such association. In other words, more severe depressive
symptoms are contributed to lower white matter integrity of
fiber tracts in the aforementioned regions of the brain only
in PD patients. The effect of confounders such as anxiety,
cognitive disturbances, duration and severity of the disease
and the treatment dosages were controlled.
Cingulum, CC, and internal capsule are among those
white matter regions which contribute to emotional regula-
tion, and their impaired integrity is found to have significant
correlations with depression [31, 32]. The Lewy neuritis as
the main pathological feature of PD spreads into the limbic
system, the most famous network involved in depression
and dPD [33], relatively early in the course of the disease
[34]. The cingulum, which anatomically encircles the CC
and is considered a major part of the limbic system, encom-
passes highly complex neural interconnections with other
regions of the brain and plays a key role in emotional, cog-
nitive, motor, and sensory regulations [35]. Damage to the
neural networks of emotional processing which are highly
integrated into the cingulum is shown to result in a set of
symptoms including apathy, mood depletion, inattention,
and emotional instability [35, 36]. Growing evidence has
shown the role of distinct parts of cingulum in the patho-
genesis of depressive symptoms in PD. Two voxel-based
morphometry studies have demonstrated the contribution
of tissue loss in the cingulate region with depression scores
[37, 38]. Functional imaging studies have also shown lower
perfusions in this area in depressed PD patients, which
were increased after administration of antidepressants [39].
In addition, a resting state fMRI study discussed increased
functional connectivity in the right posterior cingulate cortex

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in PD patients with confirmed diagnosis of major depressive
disorder versus non-depressed PD and controls as a result
of failure to dampen the activity of a cognitive dominated
emotional network in this region seen in depressed individu-
als [36]. Decreased dopaminergic innervation in the limbic
system, including the anterior cingulate cortex, is as well
documented regarding the pathogenesis of depression and
anxiety in PD [33]. In this study, we found lower connec-
tivity in the left cingulum in correlation with depression
severity in PD. This is consistent with the hypothesis of left
frontal dysfunction as the underlying neural mechanism of
depression [40]. Furthermore, right-onset PD patients more
likely develop severe depressive symptoms [41]. The same
lateralization of degeneration in connective white matter fib-
ers is reported in a previous whole white matter diffusion
tensor imaging of dPD [19].
The Corpus Callosum serves as the greatest neural path-
way connecting the two cerebral hemispheres and has a
major role in integrating emotional, memory, and cognitive
information [42]. The genu of the CC relays information
between corresponding prefrontal and orbitofrontal cortices,
which are believed to play a key role in emotional stabil-
ity and executive function, respectively [43, 44]. Disrup-
tion of the genu is demonstrated in association with dPD
[45, 46], impulse control disorders [47, 48], dementia [49]
and deficits in different domains of cognitive performance
such as executive function, attention and memory in PD [50,
51]. The posterior part of CC, the splenium, interconnects
the high-order association areas of temporo-parietal lobes
which are also shown to be disrupted in depressed individu-
als [52–54]. Although one study using DTI tractography
found intact interhemispheric connectivity comparing dPD
to ndPD and healthy controls though with a sample size
of 6 in each group [20], several studies have demonstrated
that reduced integrity of CC is associated with more severe
depressive symptoms [55–57]. Remarkably, L.T. Lacerda
etal. found that alteration in genu and splenium is attrib-
uted to only severe or familial subtypes of major depres-
sion [58]. Despite mood disorders and cognitive decline, the
importance of neurodegeneration of CC is reflected through
its role in differentiating PD patients with malignant motor
features, such as freezing of gait [59] and postural instabil-
ity and gait difficulty [60]. This is in line with the frequent
observation of mood dysregulations in more aggressive non-
tremor phenotypes of PD [61–63].
Internal capsule is a unique white matter structure,
where all afferent and efferent fibers of cortical–subcortical
pathways converge [64]. Disturbed connectivity of the inter-
nal capsule implicates the contribution of the disruption of
neural networks involved in emotional regulations, which
is well discussed in the literature regarding depression [44,
65–68]. The anterior limb of the internal capsule takes part
in medial and basolateral limbic circuits [69, 70]. The pos-
terior limb also serves as a bridge between low thalamic and
high somatosensory cortical levels of interoceptive process-
ing networks [64, 71]. Thus, disrupted connectivity in these
structures may result in the impairment of neural circuits
involved in depression. Interestingly, investigating the same
cohort, we have also demonstrated the role of the posterior
limb of the internal capsule in predicting the poor quality
of life in PD [72].
In sum, disturbed tissue organization in the left cingu-
lum, corpus callosum and right internal capsule supports the
hypothesis that neurodegeneration of fibers connecting inter-
cortical and cortico-subcortical regions of the brain, involv-
ing complicated neural circuits, manifests mood deregula-
tions in PD patients. Lack of follow-up comparison and a
small sample of patients highlight the dire need for further
studies with follow-up data of PD patients with depres-
sive symptoms. It is worthy to note that caution should be
exercised when discussing our results due to lack of HADS
validity to diagnose depression in the clinical setting.
Acknowledgements The data set of this work was supported by
the Belgian National Fund for Scientific Research, the University
of Liège, the Queen Elisabeth Medical Foundation, the Léon Fred-
ericq Foundation, the Belgian Inter-University Attraction Program,
the Walloon Excellence in Life Sciences and biotechnology pro-
gram, and the Marie Curie Initial Training Network in Neurophysics
(PITN-GA-2009-238593).
Author contributions FGS and MHA were involved in the design of
this study. KS and MHA contributed to the analysis of the data. FGS
and MHA contributed to the writing the manuscript. All authors criti-
cally reviewed and approved the final manuscript.
Compliance with ethical standards
Conflict of interest The authors have no conflict of interest.
Ethical approval All procedures performed here including human par-
ticipants were in accordance with the ethical standards of the institu-
tional research committee and with the 1964 Helsinki Declaration and
its later amendments or comparable ethical standards.
Informed consent Informed consent was obtained from all individual
participants included in the study.
References
1. Reijnders JS etal (2008) A systematic review of prevalence studies
of depression in Parkinson’s disease. Mov Disord 23(2):183–189
Fig. 1 White matter pathways significantly associated with depres-
sion severity in PD patients; a genu of the corpus callosum, b sple-
nium of the corpus callosum, c right anterior limb of the internal
capsule, d right posterior limb of the internal capsule, and e left cin-
gulum
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Acta Neurologica Belgica
1 3
2. van Mierlo TJ etal (2015) Depressive symptoms in Parkinson’s
disease are related to decreased hippocampus and amygdala vol-
ume. Mov Disord 30(2):245–252
3. Scalzo P etal (2009) Depressive symptoms and perception of
quality of life in Parkinson’s disease. Arquivos de neuro-psiquia-
tria 67(2A):203–208
4. Cummings JL (1992) Depression and Parkinson’s disease: a
review. Am J psychiatry 149(4):443
5. Burn DJ (2002) Depression in Parkinson’s disease. Eur J Neurol
9(s3):44–54
6. McDonald WM, Richard IH, DeLong MR (2003) Prevalence,
etiology, and treatment of depression in Parkinson’s disease. Biol
psychiatry 54(3):363–375
7. Nilsson FM etal (2002) Major depressive disorder in Parkin-
son’s disease: a register-based study. Acta Psychiatr Scand
106(3):202–211
8. Schuurman A etal (2002) Increased risk of Parkinson’s dis-
ease after depression A retrospective cohort study. Neurology
58(10):1501–1504
9. Chagas MHN etal (2013) Neuroimaging of depression in Parkin-
son’s disease: a review. Int Psychogeriatr 25(12):1953–1961
10. Hall JM etal (2016) Diffusion alterations associated with Parkin-
son’s disease symptomatology: a review of the literature. Parkin-
sonism Relat Disord 33:12–26
11. Bohnen N etal (2007) Cortical cholinergic denervation is associ-
ated with depressive symptoms in Parkinson’s disease and parkin-
sonian dementia. J Neurol Neurosurg Psychiatry 78(6):641–643
12. Barone P (2010) Neurotransmission in Parkinson’s disease:
beyond dopamine. Eur J Neurol 17(3):364–376
13. Benoit M, Robert PH (2011) Imaging correlates of apathy and
depression in Parkinson’s disease. J Neurol Sci 310(1):58–60
14. Schrag A etal (2007) Depression rating scales in Parkin-
son’s disease: critique and recommendations. Mov Disord
22(8):1077–1092
15. Marinus J etal (2002) Evaluation of the hospital anxiety and
depression scale in patients with Parkinson’s disease. Clin Neu-
ropharmacol 25(6):318–324
16. Schwarz J etal (2011) Depression in Parkinson’s disease. J Neurol
258(2):336
17. Matsui H etal (2007) Depression in Parkinson’s disease. J Neurol
254(9):1170–1173
18. Li W etal (2010) White matter microstructure changes in the
thalamus in Parkinson disease with depression: a diffusion tensor
MR imaging study. Am J Neuroradiol 31(10):1861–1866
19. Huang P etal (2014) Disrupted white matter integrity in depressed
versus non-depressed Parkinson’s disease patients: a tract-based
spatial statistics study. J Neurol Sci 346(1):145–148
20. Surdhar I etal (2012) Intact limbic-prefrontal connections and
reduced amygdala volumes in Parkinson’s disease with mild
depressive symptoms. Parkinsonism Relat Disord 18(7):809–813
21. Reveley C etal (2015) Superficial white matter fiber systems
impede detection of long-range cortical connections in diffusion
MR tractography. Proc Natl Acad Sci 112(21)E2820–E2828
22. Thomas C etal (2014) Anatomical accuracy of brain connections
derived from diffusion MRI tractography is inherently limited.
Proc Natl Acad Sci 111(46):16574–16579
23. Yeh FC, Badre D, Verstynen T (2016) Connectometry: a statistical
approach harnessing the analytical potential of the local connec-
tome. NeuroImage 125:162–171. https ://doi.org/10.1016/j.neuro
image .2015.10.053
24. Yeh F-C etal (2016) Quantifying differences and similarities in
whole-brain white matter architecture using local connectome
fingerprints. PLoS Comput Biol 12(11):e1005203
25. Ziegler E etal (2014) Mapping track density changes in nigrostri-
atal and extranigral pathways in Parkinson’s disease. Neuroimage
99:498–508
26. Hobson DE etal (2002) Excessive daytime sleepiness and sud-
den-onset sleep in Parkinson disease: a survey by the Canadian
Movement Disorders Group. Jama 287(4):455–463
27. Leemans A, Jeurissen B, Sijbers J, Jones D (2009) ExploreDTI:
a graphical toolbox for processing, analyzing, and visualizing
diffusion MR data. In: Proceedings of the International Society
for Magnetic Resonance in Medicine, p 3537
28. Yeh F-C etal (2013) Deterministic diffusion fiber tracking
improved by quantitative anisotropy. PLOS ONE 8(11):e80713
29. Yeh F-C, Panesar S, Fernandes D, Meola A, Yoshino M, Fernan-
dez-Miranda JC, Vettel J, Verstynen T (2018) Population-aver-
aged atlas of the macroscale human structural connectome and
its network topology. bioRxiv. https ://doi.org/10.1101/13647 3
30. Zigmond AS, Snaith RP (1983) The hospital anxiety and depres-
sion scale. Acta Psychiatr Scand 67(6):361–370
31. Murphy ML, Frodl T (2011) Meta-analysis of diffusion tensor
imaging studies shows altered fractional anisotropy occurring
in distinct brain areas in association with depression. Biol Mood
Anxiety Disord 1(1):3
32. Kieseppä T etal (2010) Major depressive disorder and white
matter abnormalities: a diffusion tensor imaging study with
tract-based spatial statistics. J Affect Disord 120(1):240–244
33. Remy P etal (2005) Depression in Parkinson’s disease: loss of
dopamine and noradrenaline innervation in the limbic system.
Brain 128(6):1314–1322
34. Braak H, Braak E (2000) Pathoanatomy of Parkinson’s disease.
J Neurol 247:II3-II10
35. Bush G, Luu P, Posner MI (2000) Cognitive and emotional
influences in anterior cingulate cortex. Trends Cogn Sci
4(6):215–222
36. Lou Y etal (2015) Altered brain network centrality in depressed
Parkinson’s disease patients. Mov Disord 30(13):1777–1784
37. Kostić V etal (2010) Regional patterns of brain tissue loss
associated with depression in Parkinson disease. Neurology
75(10):857–863
38. Feldmann A etal (2008) Morphometric changes of gray matter in
Parkinson’s disease with depression: a voxel-based morphometry
study. Mov Disord 23(1):42–46
39. Fregni F etal (2006) Effects of antidepressant treatment with
rTMS and fluoxetine on brain perfusion in PD. Neurology
66(11):1629–1637
40. Hama S etal (2007) Post-stroke affective or apathetic depression
and lesion location: left frontal lobe and bilateral basal ganglia.
Eur Arch Psychiatry Clin Neurosci 257(3):149–152
41. Foster PS etal (2011) Anxiety and depression severity are related
to right but not left onset Parkinson’s disease duration. J Neurol
Sci 305(1):131–135
42. Bloom JS, Hynd GW (2005) The role of the corpus callosum in
interhemispheric transfer of information: excitation or inhibition?
Neuropsychol Rev 15(2):59–71
43. Tekin S, Cummings JL (2002) Frontal–subcortical neuronal cir-
cuits and clinical neuropsychiatry: an update. J Psychosom Res
53(2):647–654
44. Zhu X etal (2011) Altered white matter integrity in first-episode,
treatment-naive young adults with major depressive disorder: a
tract-based spatial statistics study. Brain Res 1369:223–229
45. Ansari M, Adib Moradi S, Ghazi Sherbaf F, Hedayatnia A, Aarabi
MH (2018) Comparison of structural connectivity in Parkinson’s
disease with depressive symptoms versus non-depressed: a diffu-
sion MRI connectometry study. Int Psychogeriatr, 1–8. https ://doi.
org/10.1017/s1041 61021 80001 70
46. Ghazi Sherbaf F, Rahmani F, Jooyandeh SM, Aarabi MH (2018)
Microstructural changes in patients with Parkinson disease and
REM sleep behavior disorder: depressive symptoms versus non-
depressed. Acta Neurolog Belg. https ://doi.org/10.1007/s1376
0-018-0896-x

Acta Neurologica Belgica
1 3
47. Yoo HB etal (2015) Whole-brain diffusion-tensor changes in par-
kinsonian patients with impulse control disorders. J Clin Neurol
11(1):42–47
48. Mojtahed Zadeh M, Ashraf-Ganjouei A, Ghazi Sherbaf F, Hagh-
shomar M, Aarabi MH (2018) White matter tract alterations in
drug-naive Parkinson’s disease patients with impulse control
disorders. Front Neurol 9:163. https ://doi.org/10.3389/fneur
.2018.00163
49. Kamagata K etal (2013) Relationship between cognitive impair-
ment and white-matter alteration in Parkinson’s disease with
dementia: tract-based spatial statistics and tract-specific analysis.
Eur Radiol 23(7):1946–1955
50. Zheng Z etal (2014) DTI correlates of distinct cognitive impair-
ments in Parkinson’s disease. Hum Brain Mapp 35(4):1325–1333
51. Melzer TR et al (2013) White matter microstructure deterio-
rates across cognitive stages in Parkinson disease. Neurology
80(20):1841–1849
52. Biver F etal (1994) Frontal and parietal metabolic disturbances
in unipolar depression. Biol Psychiatry 36(6):381–388
53. Freedman M (1994) Frontal and parietal lobe dysfunction in
depression: delayed alternation and tactile learning deficits. Neu-
ropsychologia 32(8):1015–1025
54. Klemm E etal (1996) Temporal lobe dysfunction and correlation
of regional cerebral blood flow abnormalities with psychopathol-
ogy in schizophrenia and major depression—a study with single
photon emission computed tomography. Psychiatry Res Neuro-
imaging 68(1):1–10
55. Won E etal (2016) Association between reduced white matter
integrity in the corpus callosum and serotonin transporter gene
DNA methylation in medication-naive patients with major depres-
sive disorder. Transl Psychiatry 6(8):e866
56. Tham MW et al (2011) White matter abnormalities in major
depression: evidence from post-mortem, neuroimaging and
genetic studies. J Affect Disord 132(1):26–36
57. Di Paola M etal (2015) Corpus callosum structure is topographi-
cally correlated with the early course of cognition and depression
in Alzheimer’s disease. J Alzheimers Dis 45(4):1097–1108
58. Lacerda AL etal (2005) Anatomical MRI study of corpus cal-
losum in unipolar depression. J Psychiatr Res 39(4):347–354
59. Canu E etal (2015) Brain structural and functional connectivity
in Parkinson’s disease with freezing of gait. Hum Brain Mapp
36(12):5064–5078
60. Chan L-L etal (2014) Transcallosal diffusion tensor abnormalities
in predominant gait disorder parkinsonism. Parkinsonism Relat
Disord 20(1):53–59
61. Lewis S etal (2005) Heterogeneity of Parkinson’s disease in the
early clinical stages using a data driven approach. J Neurol Neu-
rosurg Psychiatry 76(3):343–348
62. Reijnders J etal (2009) The association between motor subtypes
and psychopathology in Parkinson’s disease. Parkinsonism Relat
Disord 15(5):379–382
63. Fereshtehnejad S-M etal (2015) New clinical subtypes of Par-
kinson disease and their longitudinal progression: a prospec-
tive cohort comparison with other phenotypes. JAMA Neurol
72(8):863–873
64. Wakana S etal (2004) Fiber tract–based atlas of human white
matter anatomy. Radiology 230(1):77–87
65. Xiao J etal (2015) Altered white matter integrity in individuals
with cognitive vulnerability to depression: a tract-based spatial
statistics study. Sci Rep 5:9738
66. Jia Z etal (2010) High-field magnetic resonance imaging of suici-
dality in patients with major depressive disorder. Am J Psychiatry
167(11):1381–1390
67. Gutman DA etal (2009) A tractography analysis of two deep brain
stimulation white matter targets for depression. Biol Psychiatry
65(4):276–282
68. Ghazi Sherbaf F, Same K, Ashraf-Ganjouei A, Aarabi MH
(2018) Altered white matter microstructure associated with mild
and moderate depressive symptoms in young adults, a diffusion
tensor imaging study. Neuroreport 29(8):685–689. https ://doi.
org/10.1097/wnr.00000 00000 00101 7
69. Papez JW (1937) A proposed mechanism of emotion. Arch Neurol
Psychiatry 38(4):725–743
70. Livingston KE, Escobar A (1971) Anatomical bias of the lim-
bic system concept: a proposed reorientation. Arch Neurol
24(1):17–21
71. Kim Y-H etal (2008) Corticospinal tract location in internal cap-
sule of human brain: diffusion tensor tractography and functional
MRI study. Neuroreport 19(8):817–820
72. Ghazi Sherbaf F, Mojtahed Zadeh M, Haghshomar M, Aarabi
MH (2018) Posterior limb of the internal capsule predicts poor
quality of life in patients with Parkinson’s disease: connectom-
etry approach. Acta Neurol Belg. https ://doi.org/10.1007/s1376
0-018-0910-3