Alterations of brain structure and function have been associated with psychomotor retardation in major depressive disorder (MDD). However, the association of motor behaviour and white matter integrity of motor pathways in MDD is unclear. The aim of the present study was to first investigate structural connectivity of white matter motor pathways in MDD. Second, we explore the relation of objectively measured motor activity and white matter integrity of motor pathways in MDD. Therefore, 21 patients with MDD and 21 healthy controls matched for age, gender, education and body mass index underwent diffusion tensor imaging and 24 hour actigraphy (measure of the activity level) the same day. Applying a probabilistic fibre tracking approach we extracted connection pathways between the dorsolateral prefrontal cortex (dlPFC), the rostral anterior cingulate cortex (rACC), the pre-supplementary motor area (pre-SMA), the SMA-proper, the primary motor cortex (M1), the caudate nucleus, the putamen, the pallidum and the thalamus. Patients had lower activity levels and demonstrated increased mean diffusivity (MD) in pathways linking left pre-SMA and SMA-proper, and right SMA-proper and M1. Exploratory analyses point to a positive association of activity level and mean-fractional anisotropy in the right rACC-pre-SMA connection in MDD. Only MDD patients with low activity levels had a negative linear association of activity level and mean-MD in the left dlPFC-pre-SMA connection. Our results point to structural alterations of cortico-cortical white matter motor pathways in MDD. Altered white matter organisation of rACC-pre-SMA and dlPFC-pre-SMA pathways may contribute to movement initiation in MDD.
Citation: Bracht T, Federspiel A, Schnell S, Horn H, Höfle O, Wiest R, et al. (2012) Cortico-Cortical White Matter Motor Pathway Microstructure Is Related to Psychomotor Retardation in Major Depressive Disorder. PLoS ONE7(12): e52238. https://doi.org/10.1371/journal.pone.0052238
Editor: Gabriel Alejandro de Erausquin, University of South Florida, United States of America
Received: July 18, 2012; Accepted: November 9, 2012; Published: December 20, 2012
Copyright: © 2012 Bracht 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 authors have no support or funding to report.
Competing interests: Dr. Horn has received speaker's fees from Eli-Lilly, Bristol-Myers-Squibb, Lundbeck, Boehringer-Ingelheim and Vifor. Prof. Strik received speaker's fees from/is advisory board member of Eli-Lilly, Pfizer and Lundbeck. Prof. Müller has received research support from Eli-Lilly, speaker's fees from AstraZeneca, Bristol-Myers-Squibb, Janssen-Cilag, Eli-Lilly, Pfizer, Sanofi-Aventis, Glaxo-Smith-Kline, Desitin-Pharma and Vifor. Prof. Müller is advisory board member of Eli-Lilly, Janssen-Cillag, Board Sandoz and Lundbeck; he has been a consultant to Janssen-Cilag and Glaxo-Smith-Kline. Dr. Walther received speakers fees from Sandoz and Janssen-cilag. There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.
Besides disturbances of mood and affect psychomotor retardation is a key feature of major depressive disorder (MDD) . Psychomotor retardation involves speech, facial expression, posture, as well as pace and extent of movements , , . Psychomotor slowing is of clinical relevance since it may help to distinguish depressive subtypes , , . Furthermore, psychomotor slowing is associated with response to some antidepressants , , , . Nevertheless, to date the neurobiology of psychomotor retardation in MDD is poorly understood.
Most findings of studies investigating associations of motor retardation and brain structure or function have been localised in prefrontal brain regions. For instance, in MDD associations of cerebral blood flow (CBF) and psychomotor slowing were found in the dorsolateral prefrontal cortex (dlPFC), the anterior cingulate cortex or the orbitofrontal cortex , , , , . The rACC is involved in reinforcing behaviour and in anhedonia  whereas the dlPFC is essential for goal directed behaviour . Neurobiological alterations of rACC and dlPFC may therefore contribute to prominent symptoms of MDD such as avolition and anhedonia and result in reduced daily activities . In addition, psychomotor retardation was linked to CBF of the supplemental motor area  and to structural alterations and hypodopaminergic states of the basal ganglia , , . White matter pathways connect these core regions of the motor system. Therefore, those cortico-cortical and cortico-basal ganglia connection pathways are of particular interest to understand psychomotor slowing in depression.
White matter microstructure reflects motor behaviour in healthy subjects. In fact, volitional motor activity, special motor skills and aerobic exercise are associated with white matter integrity , , , . In addition, longitudinal studies demonstrated that training as well as limb immobilization may induce white matter changes of motor pathways , , . Therefore, psychomotor retardation in MDD should be reflected by white matter changes of motor pathways as well.
Indeed, altered white matter microstructure has been demonstrated in MDD and healthy controls at risk for depression in major motor pathways such as the corpus callosum, thalamic projection fibres, the anterior limb of the internal capsule or the superior longitudinal fasciculus , , , , , . However, an assessment of motor behaviour is required to link motor symptoms to underlying neurobiological alterations.
Actigraphy is a reliable measure of gross motor behaviour and repeatedly documented reduced motor activity in MDD , , . Furthermore, actigraphy measures have been linked to symptom severity , . Recently, our group used a voxel-based morphometry (VBM) based DTI approach in combination with actigraphy to relate motor activity to white matter integrity in MDD. Findings point to altered associations of white matter integrity and motor activity in white matter regions of the motor system located close to the dorsal pre-motor cortex and the primary motor cortex . However, the connection pathways involved in these alterations are unclear.
Therefore, the present study was specifically designed to investigate pathways between core regions of the motor system such as the rostral anterior cingulate cortex (rACC), the dlPFC, the pre-supplementary motor area (pre-SMA), the SMA-proper, the M1 and the basal ganglia (thalamus, putamen, caudate nucleus, pallidum). We hypothesized that (i) psychomotor retardation is reflected by reduced volitional motor activity in MDD, (ii) structural connectivity between key areas of the motor system differs in MDD. Those alterations should be reflected by alterations of pathway connection probabilities as well as by reduced fractional anisotropy (FA) and increased mean diffusivity (MD) of the extracted pathways . Furthermore, we performed exploratory analyses of motor pathways to investigate whether associations of quantitative motor behaviour and structural connectivity differ between MDD and healthy controls. Because psychomotor retardation was associated with prefrontal brain regions , , , ,  and the particular importance of dlPFC and rACC for engaging in activities ,  we expect alterations to be most pronounced in dlPFC-pre-SMA and rACC-pre-SMA pathways.
The sample includes subjects who participated in previous studies , . We recruited 21 patients with MDD at the inpatient and outpatient departments of the University Hospital of Psychiatry, Bern, Switzerland and 21 healthy controls. All participants were right handed as assessed with the Edinburgh handedness inventory . Before the scanning procedure participants were assessed with the Beck Depression Inventory (BDI) (controls = 1.6±2.1, patients = 29.9±8.8, Z = −5.58, p<0.001) , the Hamilton Depression Rating Scale (HAMD) (controls = 0.6±1.1, patients = 26.4±5.3, Z = −5.68, p<0.001)  and the Montgomery-Asberg Depression Rating Scale (MADRS) (controls = 0.9±1.4, patients = 26.8±4.9, Z = −5.61, p<0.001) . Diagnoses were given according to DSM-IV following semi-structured clinical interviews. In the MDD group, 9 out of 21 patients had a history of more than 3 episodes. All patients were medicated at the time of scanning. All but two patients received antidepressant drugs (amitriptyline 25–225 mg, n = 2; escitalopram 20 mg, n = 2; clomipramine 75 mg, n = 1; doxepin 100–200 mg, n = 2; mirtazapine 15–45 mg, n = 4; sertraline 100–200 mg, n = 2; venlafaxine 150–300 mg, n = 2; and venlafaxine and mirtazapine combination 150–300 mg/30 mg, n = 4). Six patients received substances for augmentation (lithium n = 2; lamotrigine n = 1, quetiapine n = 3), and 7 patients received zolpidem 10 mg at night. We performed the structured clinical interview for DSM-IV part 2 (SCID-II) and the Unified Parkinson’s disease rating scale (UPDRS)  to exclude comorbid personality disorders or parkinsonian symptoms. Furthermore participants with a history of significant head trauma, electroconvulsive therapy, substance abuse or dependence other than nicotine were excluded from the study. Controls with a lifetime history of depressive episodes or first degree relatives with any affective disorder were excluded. The 21 healthy control subjects were matched for gender (controls = 42.9% male, patients = 52.4% male, Chi2 = 0.38, p = 0.758), age (controls = 41.0±13.7 years, patients = 45.0±13.7 years, T = −0.98, p = 0.332), years of education (controls = 14.7±4.3 years, patients = 13.3±2.6 years, T = 1.28, p = 0.211), body mass index (BMI) (controls = 23.6±3.9 kg/m2, patients = 25.1±5.1 kg/m2, T = −1.04, p = 0.303) and annual income (controls = 43743±22687 CHF, patients = 46101±31280 CHF, T = −0.28, p = 0.781). The study protocol was approved by the local ethics committee (KEK-BE 196/09) and was in accordance with the Declaration of Helsinki. All participants provided written informed consent.
2.2 Data Acquisition
We applied the same methods as in our previous work investigating motor behaviour in neuropsychiatric disorders e.g. , , . After conduction of MRI scans which were performed between 8 and 10 am on Tuesdays and Wednesdays participants wore an actigraph (Actiwatch®, Cambridge Neurotechnology, Inc., UK) on the wrist of the left (nondominant) arm for 24 consecutive hours. Actigraphy of the non-dominant arm reflects total motor activity without interference of manual work . Activity counts were stored in 2 s intervals. Participants provided sleep log information. Activity was analyzed exclusively during wake time. Data were read into a computer and extracted using Sleep analysis 5® software (Cambridge Neurotechnology, Inc., UK). The activity level (AL) (i.e. the cumulated activity counts during wake divided by the netto recording time in hours) was calculated with own Excel® templates .
2.2.2. MRI acquisition.
All images were acquired with a 12-channel signal reception head coil on a 3-Tesla MR scanner (Siemens Magnetom Trio, Erlangen, Germany). High-resolution T1-weighted MR images were obtained using a 3D Modified Driven Equilibrium Fourier Transform (MDEFT) sequence . The optimized acquisition parameters were as follows: 176 sagittal slices, 256×224 matrix (with a non-cubic field of view (FOV) of 256 mm×224 mm, yielding a nominal isotropic resolution of 1 mm3), 7.92 ms repetition time (TR), 2.48 ms echo time (TE), 16° flip angle, inversion with symmetric timing (inversion time 910 ms), fat saturation and a12 min total acquisition time. Identical prescription of MR images was achieved using the Siemens Autoalign sequence, which automatically sets up consistent slice orientation based on a standard MRI atlas.
2.2.3. Diffusion tensor imaging (DTI).
For DTI measurements, we used a spin-echo echo-planar-imaging (EPI) sequence (55 slices, FOV = 256×256 mm2, sampled on a 128×128 matrix resulting in 2 mm3 voxel size, TR/TE = 6000/78 ms) covering the whole brain (40 mT/m gradient, 5/8 partial Fourier, no acceleration factor, bandwidth 1346 Hz/Px). Diffusion-weighted images (DWI) were positioned in the axial plane parallel to the AC-PC line and measured along 42 directions with a b-value = 1300 s/mm2. Four DWI images were measured without diffusion weighting (i.e. b-value = 0, B0 image). These were scanned after the first diffusion-weighted image, and immediately after every 12th subsequent diffusion-weighted image. We used a balanced and rotationally invariant diffusion-encoding scheme over the unit sphere to generate the DTI data .
2.3. Data Analysis
Imaging data were analysed using Statistical Parametric Mapping (SPM8) (www.fil.ion.ucl.ac.uk/spm), implemented in Matlab 7.6.0 (R2008a; Mathworks, Natick, MA, USA). We used a probabilistic fibre tracking method which allows for the extraction of pathways between two regions of interest (ROI)  (www.uniklinik-freiburg,de/mr/live/arbeitsgruppen/diffusion_en.html).
Regions of interest (ROIs) were selected using the WFU-Pick-Atlas implemented in SPM8 . All ROIs were spatially located with regard to the Montreal Neurological Institute (MNI) reference brain. We chose the following bilateral ROIs: dlPFC, rACC, pre-SMA, SMA-proper, M1, thalamus, putamen, caudate nucleus and pallidum. The dlPFC is composed of Brodmann Area (BA) 9 and 46 . We performed a vertical section through the genu of the ACC and only included parts of BA24 which were located rostral to the section for definition of the rACC , . The border of the pre-SMA and the SMA-proper coincides with the vertical anterior commissural line of BA6 . Therefore, we performed a vertical section through the anterior commissure in order to separate the BA6 into the two distinct ROIs.
The 4 B0 images were motion corrected using the SPM8 diffusion toolbox (http://sourceforge.net/projects/spmtools). DTI-images were then realigned to the B0 images. The T1-images were segmented into white and grey matter masks. This procedure calculates normalisation parameters for forward and backward transformation between individual native spaces and the MNI space. Because fibre tracking is done in the native space of each subject, all ROIs were transferred into the native space using the inverse normalisation parameters of each subject . The T1-image and the inverse normalised ROIs of each subject were co-registered to an average image of the 4 corresponding B0-images.
2.3.4. Probabilistic DTI-based fibre tracking.
The applied probabilistic fibre tracking approach is described in depth in  and has been used in previous studies e.g. , , , , . First, the diffusion tensor was computed  and second a Monte Carlo simulation of random walks similar to the probabilistic index of connectivity (Pico) method  was used to calculate probabilistic maps separately for each ROI. In these maps the visiting frequency of a voxel represents a degree of connectivity to the seed region. Random walks were limited to visit at maximum 150 voxels and were repeated 25000 times using Monte-Carlo simulations. The tracking area was restricted to the white matter masks obtained from the segmented T1-images to avoid tracking across anatomical borders. To ensure contact of the cortical ROIs with white matter, a rim of grey matter (10% of the grey matter mask) was included in the white-matter mask . Second, region to region anatomical connectivity was computed using a combination of probability maps . This requires a multiplication of the two resulting probability maps, which takes the directional information about the traversing trajectories into account in order to suppress merging and to preserve connecting fibres . The values obtained from the combination of two probability maps represent a voxel-wise estimation of the probability index that a given voxel is part of the connecting fibre bundle of interest (PIBI).
By combining the probability maps, we bilaterally assessed structural connectivity between prefrontal-pre-SMA pathways (dlPFC - pre-SMA, rACC - pre-SMA), supplemental motor area-M1 pathways (pre-SMA - SMA-proper, SMA-proper - M1) and cortico-basal ganglia pathways (pre-SMA - caudate nucleus, pre-SMA - putamen, pre-SMA – pallidum, pre-SMA – thalamus, M1 - caudate nucleus, M1 - putamen, M1 - pallidum and M1– thalamus).
The combined probability maps of each subject were scaled to between 0 and 1, spatially normalised into the standard MNI space, and smoothed using an isotropic 3-mm full width at half maximum Gaussian kernel.
2.3.6. Statistical analysis.
AL between groups was compared using independent t-tests. We aimed to investigate whether fibre tract organisation such as the course of pathways or pathway width differs between groups. Therefore, we used independent t-tests to compare PIBI values of normalised probability maps. T-tests were calculated in SPM8, using a threshold of p<0.05 with family-wise error (FWE) correction for multiple comparisons.
Furthermore, we compared white matter microstructure between groups by comparing the DTI based diffusion properties FA and MD. For each subject, native space ROIs were defined based on the combined probability maps. Only voxels that were considered to be part of a connection PIBI >0.0148 were included in these native space ROIs e.g. . From the respective ROIs the diffusion properties mean-FA and mean-MD were extracted in order to compare white matter integrity. We calculated separated MANOVAS for 1) bilateral prefrontal-pre-SMA pathways (dlPFC-pre-SMA, rACC-pre-SMA) 2) bilateral supplemental motor area – M1 pathways (pre-SMA-SMA-proper, SMA-proper-M1) and 3) bilateral cortico-basal ganglia pathways (pre-SMA - caudate nucleus, pre-SMA - putamen, pre-SMA - pallidum and pre-SMA – thalamus, M1 - caudate nucleus, M1 - putamen, M1 - pallidum and M1– thalamus). Independent variable was group (controls vs. MDD) and dependent variables were mean-FA and mean-MD values extracted from the respective pathways.
Moreover, we investigated whether different pathways are involved in motor control in MDD. Therefore, we tested for differences between groups regarding the relationship of fibre tract organisation and quantitative motor behavior. For each of the 24 combined probability maps SPM8 was used to calculate a general linear model (GLM). Independent variable was group (controls vs. MDD) and dependent variables were PIBI and AL. A significance threshold of p<0.05, after FWE correction for multiple comparisons was applied.
In addition, we explored if controls and MDD patients differ regarding the relationship of quantitative motor behaviour and white matter integrity. Therefore, GLMs with the independent variable group (controls vs. MDD) and dependent variables mean-FA and AL respectively mean-MD and AL were computed for each of the 24 pathways. Those analyses were performed in SPSS 18 (Chicago, IL, USA). To date there is not sufficient data to generate a priori hypothesis whether or not specific pathways are affected. Therefore, the purpose of our analyses was the identification of candidate pathways and we set a level of significance of p<0.05. After identifying candidate pathways we used a Bonferroni correction for multiple comparisons (p<0.05/24 = 0.002).
Moreover, we performed additional analyses to strengthen our preliminary findings. We assumed that identified differences in associations of structural connectivity and quantitative motor behaviour are predominantly driven by MDD patients with more pronounced psychomotor retardation. Therefore, we generated a more and a less affected MDD group applying a median split of the AL-values. For those pathways where preliminary results revealed different associations of white matter integrity and motor behaviour, we calculated additional GLMs. Dependent variable was group (MDD with low AL, MDD with high AL, controls) and independent variables were AL and mean-FA (mean-MD respectively).
3.1. Group Comparisons of Activity Level
AL was higher in the control group (controls = 19599±7050, patients = 12417±6285, T = 3.485, df = 40, p = 0.001). AL was not correlated with age (r = −0.12, p = 0.453), duration of education (r = 0.07, p = 0.659), BMI (r = −0.15, p = 0.356) or mean depression scale scores (controls: HAMD r = 0.207, p = 0.368; BDI r = −0.084, p = 0.718; MADRS r = 0.012, p = 0.959; patients: HAMD r = −0.181, p = 0.434; BDI r = 0.073, p = 0.752; MADRS r = −0.027, p = 0.908). AL did not differ between antidepressant substance classes ((F(4, 21) = 1.088, p = 0.395).
3.2. Group Comparisons of Structural Connectivity of Motor Pathways
Independent t-tests of PIBI did not reveal differences between groups. Those t-tests were calculated in SPM8, p<0.05, FWE correction for multiple comparisons.
Mean maps averaged on both groups and overlaid on mean-FA images are displayed for the left pre-SMA-SMA-proper (first row) und the right SMA-proper-M1 (second row) connection. The voxel values represent an estimation of the probability that the voxel is part of the fibre bundle of interest (PIBI). To remove random artefacts, only voxels with PIBI values >0.0148 were included in probability maps , . Maximum PIBI values are displayed at the top of each colour bar. The displayed figures illustrate anatomical pathways from where mean-MD values have been extracted.
Calculating post-hoc independent t-tests for mean-MD of supplemental motor area-M1 pathways we found a significant increase of mean-MD in MDD in the left pre-SMA-SMA-proper (controls = 0.71±0.03, patients = 0.73±0.03, T = −2.478, df = 40, p = 0.018) as well as in the right SMA-proper-M1 connection (controls = 0.72±0.02, patients = 0.74±0.03, T = −2.7, df = 40, p = 0.011).
3.3. Associations of Activity Level and Structural Connectivity of Motor Pathways
The GLMs for PIBI did not reveal significant group * AL interactions. Those analyses had been performed in SPM8, p<0.05, FWE correction for multiple comparisons. Furthermore, exploratory tests for associations with AL were performed for the extracted mean-FA and mean-MD values of the 24 pathways, two of them demonstrating significant group*AL interactions:
The GLM for mean-FA of the right rACC-pre-SMA connection indicated different group * AL interactions (F = 4.392, df = 1, p = 0.043), however, this result does not withstand Bonferroni correction (p<0.002). Patients but not controls had a significant positive correlation for mean-FA and AL in the right rACC-pre-SMA connection (controls r = −0.061, p = 0.792; patients r = 0.449, p = 0.041, see Figure 2). Furthermore, we found group * AL interactions for mean-MD for the left dlPFC-pre-SMA pathway (F = 4.292, df = 1, p = 0.045, see Figure 3), though, this result does not withstand Bonferroni correction (p<0.002). In both groups there were no significant correlations between mean-MD and AL (controls r = 0.230, p = 0.316; patients r = −0.340, p = 0.131).
The first column displays mean probability maps of the right rACC-pre-SMA connection averaged on both groups and overlaid on mean-FA images of the subjects. The voxel values represent an estimation of the probability that the voxel is part of the fibre bundle of interest (PIBI) To remove random artefacts, only voxels with PIBI values >0.0148 were included in probability maps , . Maximum PIBI values are displayed at the top of the colour bar. The displayed figures illustrate anatomical pathways from where mean-FA values have been extracted. The second column displays mean-FA values extracted from the displayed connection and the corresponding activity level values of the respective subjects. Controls are displayed as blue circles, patients as red diamonds.
The first column displays mean probability maps of the left dlPFC-pre-SMA connection averaged on both groups and overlaid on mean-FA images of the subjects. The voxel values represent an estimation of the probability that the voxel is part of the fibre bundle of interest (PIBI). To remove random artefacts, only voxels with PIBI values >0.0148 were included in probability maps , . Maximum PIBI values are displayed at the top of the colour bar. The displayed figures illustrate anatomical pathways from where mean-MD values have been extracted. The second column displays mean-MD values extracted from the displayed connection and the corresponding activity level values of the respective subjects. Controls are displayed as blue circles, patients as red diamonds.
Identified associations of AL and diffusion properties have been primarily driven by those MDD patients with more pronounced psychomotor retardation (see Figure S1 and Figure S2 in File S1). Neither were there significant correlations of depression scale scores and mean-FA of the right rACC-pre-SMA connection (HAMD r = −0.042, p = 0.793; BDI r = 0.035, p = 0.824, MADRS r = −0.112, p = 0.418) nor for depression scale scores and mean-MD of the left dlPFC-pre-SMA connection (HAMD r = 0.230, p = 0.316; BDI r = 0.239, p = 0.297; MADRS r = 0.397, p = 0.074).
The main finding of our study was reduced motor activity and altered structural connectivity of pre-SMA-SMA-proper and SMA-proper-M1 pathways in MDD. Furthermore, exploratory analyses point to an altered involvement of rACC-pre-SMA and dlPFC-pre-SMA pathways in motor control in MDD.
4.1. Group Comparison of Activity Level
As expected, AL was lower in MDD than in healthy controls. Our finding is in line with previous studies investigating psychomotor retardation with wrist actigraphy , , , . At the time of the study all but two patients were on stable antidepressive medication. AL did not differ between types of antidepressant, use of mood stabilizers or zolpidem, as reported previously for this sample . Therefore, we assume that medication did not systematically impact AL in the current sample. This is also in line with our previous study investigating 76 MDD patients . Previous studies investigating the short term impact of different antidepressive drugs on AL yielded contradicting results , . In either case, medication is used to treat psychopathology in MDD. Therefore in MDD patients with psychomotor retardation medication may have increased AL, whereas in MDD with anxious agitation AL may have decreased. For a detailed review of medication effects on activity levels we refer to the supplementary material of .
4.2. White Matter Integrity of Motor Pathways
We observed increased mean-MD in pre-SMA-SMA-proper and SMA-proper-M1 pathways in MDD. Pre-SMA and SMA-proper are essential for the initiation of movements, movement sequencing and motor learning , . Therefore structural alterations of pre-SMA, SMA-proper and pathways linking these brain regions might be associated with motor symptoms in MDD. Indeed, previous studies reported volume reductions of the right pre-SMA in a group of patients with MDD who showed motor learning deficits . Furthermore, grey matter volume reductions of the pre-SMA have been found in drug-naïve first episode MDD patients . Pre-SMA, SMA-proper and M1 are interconnected via cortical association fibres as well as via parts of the superior longitudinal fasciculus (SLF) . Our findings of increased mean-MD in MDD are in line with reductions of FA in the SLF in healthy controls at familial risk for depression as well as in MDD , , , . Here, we extend previous work by specifically focussing on fibre tracts connecting pre-SMA, SMA-proper and M1 which are of particular importance for understanding motor behaviour.
Mean-FA and mean-MD did not differ between groups within dlPFC-pre-SMA and rACC-pre-SMA or within cortico-basal ganglia pathways. In contrast, previous studies reported decreased FA in MDD within the dlPFC ,  as well as within the anterior limb of the internal capsule , . However, particularly in cortico-basal ganglia circuits various pathways converge within the anterior limb of the internal capsule . At present, the spatial resolution of DTI does not allow to disentangle pathways in areas of crossing fibres reliably , . Therefore, involvement of other anatomical pathways might explain these discrepancies. Furthermore, one might speculate whether different clinical presentations of MDD might account for those inconsistencies. For instance, a previous study detected reductions of FA only in a subgroup of melancholic depressed patients but not when comparing MDD with healthy controls .
At present, only few studies applied DTI-based fibre tracking approaches to investigate MDD or patients at risk for depression , , , . Our results of unchanged probability of connection which reflects the fibre tract organisation is in line with negative findings of  regarding the geometric characteristics of the cingulum bundle and the uncinate fasciculus in MDD. However, a previous study successfully classified patients with MDD from healthy controls with help of the number of fibres including motor pathways such as the corpus callosum and the cingulum bundle . Therefore, further DTI fibre tracking studies are warranted to raise hypotheses on alterations of organisation of white matter pathways.
4.3. Association of Diffusion Properties and Activity Level
Our results point to altered associations of structural connectivity and motor activity in rACC-pre-SMA and dlPFC-pre-SMA pathways in MDD. Identified associations were more pronounced in patients with low activity level (see Figure S1 and Figure S2 in File S1).
Since neither mean-FA of the right rACC-pre-SMA nor mean-MD of the left dlPFC-pre-SMA connection or AL correlated with depression scale scores we assume an association which is distinct in MDD and specific for motor retardation but not for depression severity.
Functionally, the dlPFC is involved in initiation of motivated behaviour . In MDD white matter microstructure abnormalities in the dlPFC have been reported pointing to neurobiological alterations , . Moreover, in MDD CBF of the ACC was shown to correlate negatively with psychomotor retardation , . Furthermore, the ACC is associated with action monitoring which has been shown to be impaired in MDD , , . The present study identified impaired structural connectivity of pre-SMA-SMA-proper and SMA-proper-M1 pathways which are essential for volitional motor activity , . One might speculate whether alterations of white matter microstructure of rACC -pre-SMA and dlPFC-pre-SMA pathways compensate for those structural and functional impairments in order to initiate movements. Furthermore, cortical engagement might substitute for basal ganglia dysfunction in MDD since reduced extracellular dopamine of caudate and putamen was reported in MDD with motor retardation , . In our previous arterial spin labeling study we suggested a less pronounced impact of the inhibitory indirect pathway ,  as compensation to maintain motor activity . A more efficient use of rACC-pre-SMA and dlPFC-pre-SMA fibre tracts might foster movement initiation as well. This assumption is in line with a more pronounced involvement of further prefrontal brain regions such as the orbitofrontal cortex where we detected a positive association of CBF and motor activity in MDD which was not present in healthy controls .
In our previous VBM-based DTI-study MDD patients but not controls had a negative association of activity level and white matter integrity in a cluster localised underneath M1 . Surprisingly, we could not detect such an association in fibre tracts emanating from the M1. This might be due to methodological differences. Whereas VBM-based DTI approaches aim at detecting subtle white matter pathologies, fibre tracking investigates whole fibre tracts. Likewise, a previous study detected white matter abnormalities with fibre tracking but not with TBSS investigating the same sample . An alternative explanation might be that the identified cluster of our previous study  is not incorporated in the particular fibre tracts investigated in the present study.
This study has some limitations. (i) Since all MDD patients were medicated we cannot rule out an impact of medication on AL. Nevertheless, in the current sample as well as in a larger sample  there was no effect of medication on AL (please refer to the supplementary material of ) (ii) Medication might have influenced white matter microstructure in MDD. However, findings on the effects of antidepressant drugs, benzodiazepines or antipsychotics on white matter microstructure in previous DTI studies and animal models are controversial , , . (iii) The analyses of associations of white matter integrity of motor pathways and activity levels are truly exploratory and do not survive a Bonferroni correction for multiple comparisons. Therefore, identified associations may resemble false positives. However, we emphasize that the aim of this part of the study was to identify candidate pathways involved in psychomotor retardation in MDD. Our findings are reinforced because associations are driven by MDD patients with more pronounced psychomotor retardation (see Figure S1 and Figure S2 in File S1). Nevertheless, those findings must be interpreted with caution and require replication in future studies. (iv) DTI-based measures of FA and MD are unspecific regarding microstructural changes. Future studies should apply novel white matter mapping techniques to quantify axon density, diameter and myelination , .
Altered white matter integrity in motor pathways in MDD connecting pre-SMA, SMA-proper and M1 were identified in the present study. In MDD, dlPFC-pre-SMA and rACC-pre-SMA pathways might be used to initiate movements and to compensate for functional and structural alterations of cortical and subcortical motor regions. The findings suggest that pathways connecting motor cortices with the basal ganglia system do not seem to directly contribute to psychomotor retardation in MDD.
Conceived and designed the experiments: SW HH. Performed the experiments: OH SW. Analyzed the data: TB SW. Contributed reagents/materials/analysis tools: OH SW. Wrote the paper: TB SS. Interpretation of data: TB AF SS HH OH RW TD WS TJM SW. Critical revision and final approval of the article: TB AF SS HH OH RW TD WS TJM SW.
- 1. Schrijvers D, Hulstijn W, Sabbe BG (2008) Psychomotor symptoms in depression: a diagnostic, pathophysiological and therapeutic tool. J Affect Disord 109: 1–20.
- 2. Sobin C, Sackeim HA (1997) Psychomotor symptoms of depression. Am J Psychiatry 154: 4–17.
- 3. Buyukdura JS, McClintock SM, Croarkin PE (2011) Psychomotor retardation in depression: biological underpinnings, measurement, and treatment. Prog Neuropsychopharmacol Biol Psychiatry 35: 395–409.
- 4. Calugi S, Cassano GB, Litta A, Rucci P, Benvenuti A, et al. (2011) Does psychomotor retardation define a clinically relevant phenotype of unipolar depression? J Affect Disord 129: 296–300.
- 5. Caligiuri MP, Gentili V, Eberson S, Kelsoe J, Rapaport M, et al. (2003) A quantitative neuromotor predictor of antidepressant non-response in patients with major depression. J Affect Disord 77: 135–141.
- 6. Mayberg HS, Lewis PJ, Regenold W, Wagner HN Jr (1994) Paralimbic hypoperfusion in unipolar depression. J Nucl Med 35: 929–934.
- 7. Narita H, Odawara T, Iseki E, Kosaka K, Hirayasu Y (2004) Psychomotor retardation correlates with frontal hypoperfusion and the Modified Stroop Test in patients under 60-years-old with major depression. Psychiatry Clin Neurosci 58: 389–395.
- 8. Videbech P (2002) Comment on "Regional cerebral glucose utilization in patients with a range of severities of unipolar depression". Biol Psychiatry 52: 1031; discussion 1031–1032.
- 9. Bench CJ, Friston KJ, Brown RG, Frackowiak RS, Dolan RJ (1993) Regional cerebral blood flow in depression measured by positron emission tomography: the relationship with clinical dimensions. Psychol Med 23: 579–590.
- 10. Walther S, Höfle O, Federspiel A, Horn H, Hügli S, et al. (2012) Neural correlates of disbalanced motor control in major depression. J Affect Disord 136: 124–133.
- 11. Wacker J, Dillon DG, Pizzagalli DA (2009) The role of the nucleus accumbens and rostral anterior cingulate cortex in anhedonia: integration of resting EEG, fMRI, and volumetric techniques. Neuroimage 46: 327–337.
- 12. Haber SN, Knutson B (2010) The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology 35: 4–26.
- 13. Der-Avakian A, Markou A (2012) The neurobiology of anhedonia and other reward-related deficits. Trends Neurosci 35: 68–77.
- 14. Naismith S, Hickie I, Ward PB, Turner K, Scott E, et al. (2002) Caudate nucleus volumes and genetic determinants of homocysteine metabolism in the prediction of psychomotor speed in older persons with depression. Am J Psychiatry 159: 2096–2098.
- 15. Martinot M, Bragulat V, Artiges E, Dolle F, Hinnen F, et al. (2001) Decreased presynaptic dopamine function in the left caudate of depressed patients with affective flattening and psychomotor retardation. Am J Psychiatry 158: 314–316.
- 16. Meyer JH, McNeely HE, Sagrati S, Boovariwala A, Martin K, et al. (2006) Elevated putamen D(2) receptor binding potential in major depression with motor retardation: an [11C]raclopride positron emission tomography study. Am J Psychiatry 163: 1594–1602.
- 17. Walther S, Horn H, Razavi N, Koschorke P, Wopfner A, et al. (2010) Higher motor activity in schizophrenia patients treated with olanzapine versus risperidone. J Clin Psychopharmacol 30: 181–184.
- 18. Marks BL, Katz LM, Styner M, Smith JK (2011) Aerobic fitness and obesity: relationship to cerebral white matter integrity in the brain of active and sedentary older adults. Br J Sports Med 45: 1208–1215.
- 19. Hänggi J, Koeneke S, Bezzola L, Jancke L (2010) Structural neuroplasticity in the sensorimotor network of professional female ballet dancers. Hum Brain Mapp 31: 1196–1206.
- 20. Jancke L, Koeneke S, Hoppe A, Rominger C, Hanggi J (2009) The architecture of the golfer's brain. Plos One 4: e4785.
- 21. Scholz J, Klein MC, Behrens TE, Johansen-Berg H (2009) Training induces changes in white-matter architecture. Nat Neurosci 12: 1370–1371.
- 22. Taubert M, Lohmann G, Margulies DS, Villringer A, Ragert P (2011) Long-term effects of motor training on resting-state networks and underlying brain structure. Neuroimage 57: 1492–1498.
- 23. Langer N, Hanggi J, Muller NA, Simmen HP, Jancke L (2012) Effects of limb immobilization on brain plasticity. Neurology 78: 182–188.
- 24. Zhu XL, Wang X, Xiao J, Zhong MT, Liao JA, 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 Research 1369: 223–229.
- 25. Zou K, Huang X, Li T, Gong Q, Li Z, et al. (2008) Alterations of white matter integrity in adults with major depressive disorder: a magnetic resonance imaging study. J Psychiatry Neurosci 33: 525–530.
- 26. Korgaonkar MS, Grieve SM, Koslow SH, Gabrieli JD, Gordon E, et al. (2011) Loss of white matter integrity in major depressive disorder: evidence using tract-based spatial statistical analysis of diffusion tensor imaging. Hum Brain Mapp 32: 2161–2171.
- 27. Frodl T, Carballedo A, Fagan AJ, Lisiecka D, Ferguson Y, et al. (2012) Effects of early-life adversity on white matter diffusivity changes in patients at risk for major depression. J Psychiatry Neurosci 37: 37–45.
- 28. Cole J, Chaddock CA, Farmer AE, Aitchison KJ, Simmons A, et al. (2012) White matter abnormalities and illness severity in major depressive disorder. Br J Psychiatry 201: 33–39.
- 29. Zuo N, Fang J, Lv X, Zhou Y, Hong Y, et al. (2012) White matter abnormalities in major depression: a tract-based spatial statistics and rumination study. Plos One 7: e37561.
- 30. Teicher MH (1995) Actigraphy and motion analysis: new tools for psychiatry. Harv Rev Psychiatry 3: 18–35.
- 31. Berle JO, Hauge ER, Oedegaard KJ, Holsten F, Fasmer OB (2010) Actigraphic registration of motor activity reveals a more structured behavioural pattern in schizophrenia than in major depression. BMC Res Notes 3: 149.
- 32. Lemke MR, Broderick A, Zeitelberger M, Hartmann W (1997) Motor activity and daily variation of symptom intensity in depressed patients. Neuropsychobiology 36: 57–61.
- 33. Razavi N, Horn H, Koschorke P, Hügli S, Höfle O, et al. (2011) Measuring motor activity in major depression: the association between the Hamilton Depression Rating Scale and actigraphy. Psychiatry Res 190: 212–216.
- 34. Walther S, Hügli S, Höfle O, Federspiel A, Horn H, et al. (2012) Frontal white matter integrity is related to psychomotor retardation in major depression. Neurobiol Dis 47: 13–19.
- 35. Liao Y, Huang X, Wu Q, Yang C, Kuang W, et al. (2012) Is depression a disconnection syndrome? Meta-analysis of diffusion tensor imaging studies in patients with MDD. J Psychiatry Neurosci 37: 110180.
- 36. Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9: 97–113.
- 37. Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J (1961) An inventory for measuring depression. Arch Gen Psychiatry 4: 561–571.
- 38. Hamilton M (1960) A rating scale for depression. J Neurol Neurosurg Psychiatry 23: 56–62.
- 39. Montgomery SA, Asberg M (1979) A new depression scale designed to be sensitive to change. Br J Psychiatry 134: 382–389.
- 40. Fahn S, Elton RL, Members UP (1987) Unified Parkinson's disease rating scale. In: Fahn S, Marsden CD, Goldstein M, Calne DB, editors. Recent developments in Parkinson's disease. Florham Park, NJ: Macmillan Healthcare Information.
- 41. Walther S, Federspiel A, Horn H, Razavi N, Wiest R, et al. (2011) Alterations of white matter integrity related to motor activity in schizophrenia. Neurobiol Dis 42: 276–283.
- 42. Walther S, Federspiel A, Horn H, Razavi N, Wiest R, et al. (2011) Resting state cerebral blood flow and objective motor activity reveal basal ganglia dysfunction in schizophrenia. Psychiatry Res 192: 117–124.
- 43. Middelkoop HA, van Dam EM, Smilde-van den Doel DA, Van Dijk G (1997) 45-hour continuous quintuple-site actimetry: relations between trunk and limb movements and effects of circadian sleep-wake rhythmicity. Psychophysiology 34: 199–203.
- 44. Walther S, Koschorke P, Horn H, Strik W (2009) Objectively measured motor activity in schizophrenia challenges the validity of expert ratings. Psychiatry Res 169: 187–190.
- 45. Deichmann R, Schwarzbauer C, Turner R (2004) Optimisation of the 3D MDEFT sequence for anatomical brain imaging: technical implications at 1.5 and 3 T. Neuroimage. 21: 757–767.
- 46. Hasan KM, Parker DL, Alexander AL (2001) Comparison of gradient encoding schemes for diffusion-tensor MRI. J Magn Reson Imaging 13: 769–780.
- 47. Kreher BW, Schnell S, Mader I, Il'yasov KA, Hennig J, et al. (2008) Connecting and merging fibres: pathway extraction by combining probability maps. Neuroimage 43: 81–89.
- 48. Maldjian JA, Laurienti PJ, Kraft RA, Burdette JH (2003) An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage 19: 1233–1239.
- 49. Nieuwenhuys RVJ, van Huizen C (2008) The Human Central Nervous System. Springer, Fourth Edition.
- 50. Vogt B (2009) Cingulate Neurobiology and Disease. Oxford University Press.
- 51. Habas C (2010) Functional connectivity of the human rostral and caudal cingulate motor areas in the brain resting state at 3T. Neuroradiology 52: 47–59.
- 52. Bracht T, Tüscher O, Schnell S, Kreher B, Rüsch N, et al. (2009) Extraction of prefronto-amygdalar pathways by combining probability maps. Psychiatry Res 174: 217–222.
- 53. Saur D, Kreher BW, Schnell S, Kümmerer D, Kellmeyer P, et al. (2008) Ventral and dorsal pathways for language. Proc Natl Acad Sci U S A 105: 18035–18040.
- 54. Umarova RM, Saur D, Schnell S, Kaller CP, Vry MS, et al. (2010) Structural connectivity for visuospatial attention: significance of ventral pathways. Cereb Cortex 20: 121–129.
- 55. Rüsch N, Bracht T, Kreher BW, Schnell S, Glauche V, et al. (2010) Reduced interhemispheric structural connectivity between anterior cingulate cortices in borderline personality disorder. Psychiatry Res 181: 151–154.
- 56. Vry MS, Saur D, Rijntjes M, Umarova R, Kellmeyer P, et al. (2012) Ventral and dorsal fiber systems for imagined and executed movement. Exp Brain Res 219: 203–216.
- 57. Basser PJ, Mattiello J, LeBihan D (1994) Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103: 247–254.
- 58. Parker GJ, Haroon HA, Wheeler-Kingshott CA (2003) A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements. J Magn Reson Imaging 18: 242–254.
- 59. Todder D, Caliskan S, Baune BT (2009) Longitudinal changes of day-time and night-time gross motor activity in clinical responders and non-responders of major depression. World J Biol Psychiatry 10: 276–284.
- 60. Volkers AC, Tulen JH, Van Den Broek WW, Bruijn JA, Passchier J, et al. (2002) 24-Hour motor activity after treatment with imipramine or fluvoxamine in major depressive disorder. Eur Neuropsychopharmacol 12: 273–278.
- 61. Stanley N, Fairweather DB, Hindmarch I (1999) Effects of fluoxetine and dothiepin on 24-hour activity in depressed patients. Neuropsychobiology 39: 44–48.
- 62. Haggard P (2008) Human volition: towards a neuroscience of will. Nat Rev Neurosci 9: 934–946.
- 63. Nachev P, Kennard C, Husain M (2008) Functional role of the supplementary and pre-supplementary motor areas. Nat Rev Neurosci 9: 856–869.
- 64. Exner C, Lange C, Irle E (2009) Impaired implicit learning and reduced pre-supplementary motor cortex size in early-onset major depression with melancholic features. J Affect Disord 119: 156–162.
- 65. Cheng YQ, Xu J, Chai P, Li HJ, Luo CR, et al. (2010) Brain volume alteration and the correlations with the clinical characteristics in drug-naive first-episode MDD patients: a voxel-based morphometry study. Neurosci Lett 480: 30–34.
- 66. Hua K, Oishi K, Zhang J, Wakana S, Yoshioka T, et al. (2009) Mapping of functional areas in the human cortex based on connectivity through association fibers. Cereb Cortex 19: 1889–1895.
- 67. Huang H, Fan X, Williamson DE, Rao U (2011) White matter changes in healthy adolescents at familial risk for unipolar depression: a diffusion tensor imaging study. Neuropsychopharmacology 36: 684–691.
- 68. Blood AJ, Iosifescu DV, Makris N, Perlis RH, Kennedy DN, et al. (2010) Microstructural abnormalities in subcortical reward circuitry of subjects with major depressive disorder. PLoS One 5: e13945.
- 69. Alexander GE, DeLong MR, Strick PL (1986) Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu Rev Neurosci 9: 357–381.
- 70. Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW (2007) Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage 34: 144–155.
- 71. Jeurissen B, Leemans A, Tournier JD, Jones DK, Sijbers J (2012) Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum Brain Mapp In press.
- 72. Keedwell PA, Chapman R, Christiansen K, Richardson H, Evans J, et al. (2012) Cingulum white matter in young women at risk of depression: the effect of family history and anhedonia. Biol Psychiatry 72: 296–302.
- 73. Zhang A, Leow A, Ajilore O, Lamar M, Yang S, et al. (2012) Quantitative tract-specific measures of uncinate and cingulum in major depression using diffusion tensor imaging. Neuropsychopharmacology 37: 959–967.
- 74. Korgaonkar MS, Cooper NJ, Williams LM, Grieve SM (2012) Mapping inter-regional connectivity of the entire cortex to characterize major depressive disorder: a whole-brain diffusion tensor imaging tractography study. Neuroreport 23: 566–571.
- 75. Ballard IC, Murty VP, Carter RM, MacInnes JJ, Huettel SA, et al. (2011) Dorsolateral prefrontal cortex drives mesolimbic dopaminergic regions to initiate motivated behavior. J Neurosci 31: 10340–10346.
- 76. Schrijvers D, de Bruijn ER, Maas Y, De Grave C, Sabbe BG, et al. (2008) Action monitoring in major depressive disorder with psychomotor retardation. Cortex 44: 569–579.
- 77. Ullsperger M, von Cramon DY (2006) The role of intact frontostriatal circuits in error processing. J Cogn Neurosci 18: 651–664.
- 78. Chiu PH, Deldin PJ (2007) Neural evidence for enhanced error detection in major depressive disorder. Am J Psychiatry 164: 608–616.
- 79. Graybiel AM (2000) The basal ganglia. Curr Biol 10: R509–511.
- 80. Groenewegen HJ (2003) The basal ganglia and motor control. Neural Plast 10: 107–120.
- 81. Brambilla P, Bellani M, Yeh PH, Soares JC (2009) Myelination in bipolar patients and the effects of mood stabilizers on brain anatomy. Curr Pharm Des 15: 2632–2636.
- 82. Benedetti F, Absinta M, Rocca MA, Radaelli D, Poletti S, et al. (2011) Tract-specific white matter structural disruption in patients with bipolar disorder. Bipolar Disord 13: 414–424.
- 83. Versace A, Almeida JR, Quevedo K, Thompson WK, Terwilliger RA, et al. (2010) Right orbitofrontal corticolimbic and left corticocortical white matter connectivity differentiate bipolar and unipolar depression. Biol Psychiatry 68: 560–567.
- 84. Assaf Y, Blumenfeld-Katzir T, Yovel Y, Basser PJ (2008) AxCaliber: a method for measuring axon diameter distribution from diffusion MRI. Magn Reson Med 59: 1347–1354.
- 85. Deoni SC, Rutt BK, Arun T, Pierpaoli C, Jones DK (2008) Gleaning multicomponent T1 and T2 information from steady-state imaging data. Magn Reson Med 60: 1372–1387.