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Network structure of brain atrophy in de novo Parkinson's disease

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Cite as: eLife 2015;4:e08440 doi: 10.7554/eLife.08440

Abstract

We mapped the distribution of atrophy in Parkinson's disease (PD) using magnetic resonance imaging (MRI) and clinical data from 232 PD patients and 117 controls from the Parkinson's Progression Markers Initiative. Deformation-based morphometry and independent component analysis identified PD-specific atrophy in the midbrain, basal ganglia, basal forebrain, medial temporal lobe, and discrete cortical regions. The degree of atrophy reflected clinical measures of disease severity. The spatial pattern of atrophy demonstrated overlap with intrinsic networks present in healthy brain, as derived from functional MRI. Moreover, the degree of atrophy in each brain region reflected its functional and anatomical proximity to a presumed disease epicenter in the substantia nigra, compatible with a trans-neuronal spread of the disease. These results support a network-spread mechanism in PD. Finally, the atrophy pattern in PD was also seen in healthy aging, where it also correlated with the loss of striatal dopaminergic innervation.

https://doi.org/10.7554/eLife.08440.001

eLife digest

Although Parkinson's disease is the second most common neurodegenerative disorder, its cause is not known and there is no cure. The symptoms of Parkinson's disease, which include tremor and slowing of voluntary movements, get progressively worse over time. The numbers of neurons in certain brain regions also decrease, causing those parts of the brain to shrink; this is known as ‘atrophy’. However, no conclusive signs of atrophy have been found in the brains of people in the early stages of the disease.

One theory suggests that Parkinson's disease is caused by a toxic protein that is able to spread from neuron to neuron. Recent advances in brain imaging have made it possible to map networks in the living human brain—the so-called brain connectome. These networks could form the ‘highways’ through which a disease-causing agent might spread.

The Parkinson's Progression Markers Initiative (PPMI) is a large study that collects data from hundreds of people in an effort to identify the causes of Parkinson's disease. Zeighami et al. have now analyzed MRI scans that were collected as part of this initiative, which show the structure of the brains of 230 people in the early stages of Parkinson's disease. Comparing these scans to those from age-matched healthy individuals allowed Zeighami et al. to identify the set of brain regions that show atrophy in the early stages of Parkinson's disease. These regions correspond to a normal brain network, and the relative extent of atrophy in each brain region supports the theory that the disease spreads through the connectome.

The patients who were enrolled in this study will continue to be evaluated on a yearly basis. Zeighami et al. plan to continue mapping how the disease progresses throughout the brain and to relate this to the development of new symptoms of Parkinson's disease.

https://doi.org/10.7554/eLife.08440.002

Introduction

Parkinson's disease (PD) is the second most common neurodegenerative illness. Its clinical hallmarks are due to loss of dopamine neurons in the substantia nigra (SN); however, post-mortem studies have shown that PD pathology affects several other brain areas (Goedert et al., 2013). Surprisingly, however, magnetic resonance imaging (MRI) studies have failed to consistently demonstrate regional brain atrophy at least in the earlier stages of the disease. This is possibly due to studies consisting mostly of small numbers of subjects from single centers and the use of methodological tools that are relatively insensitive to subcortical atrophy, coupled with univariate methods that suffer from lack of statistical power.

The distribution of Lewy body pathology in post-mortem samples led Braak et al. to propose that a disease process spreads from the brainstem to subcortical areas and then to the cerebral hemispheres along neuronal pathways (Braak et al., 2003, 2006; Goedert et al., 2013). More generally, the network-spread hypothesis suggests that all neurodegenerative diseases target spatially distributed intrinsic brain networks (Warren et al., 2013). At a molecular level, neurodegenerative diseases are now thought to involve prion-like spread of toxic misfolded protein aggregates (Frost and Diamond, 2010; Jucker and Walker, 2013). Alpha-synuclein fibrils, implicated in PD pathogenesis, have recently been shown to spread from cell to cell via neuronal pathways after inoculation in mouse brain (Luk et al., 2012; Masuda-Suzukake et al., 2013).

Neuroimaging studies have revealed that the spatiotemporal organization of the brain takes the form of intrinsic connectivity networks (ICNs) (Fox and Raichle, 2007; Smith et al., 2009), which are consistent across studies, in test–retest analysis, and during both rest and task states (Damoiseaux et al., 2006; Smith et al., 2009; Zuo et al., 2010). Intrinsic networks have also been thought to be potential targets for AD since the earliest reports of default mode network dysfunction in the disease (Greicius et al., 2004), and analysis of large MRI data sets in AD and other dementias have supported the network-vulnerability hypothesis (Seeley et al., 2009; Raj et al., 2012; Zhou et al., 2012); however, it has yet to be tested in PD. Here, we analyzed data from newly diagnosed PD patients (n = 232) and an age-matched control group (n = 117) obtained from the Parkinson's Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data), an observational, multicenter longitudinal study designed to identify PD progression biomarkers (Marek et al., 2011). We used deformation-based morphometry (DBM) and tensor probabilistic independent component analysis (ICA) to identify brain regions demonstrating atrophy in early PD.

We also sought to provide support for the network-spread hypothesis in PD by showing that the disease, in humans, targets intrinsic brain networks. We compared the set of atrophic regions in PD patients to ICNs from young healthy subjects and tested them for spatial overlap. To further investigate the model of disease propagation through brain networks, we used resting state functional MRI (fMRI) and diffusion-weighted MRI (DW-MRI) of healthy subjects to define the normal brain connectome and determined whether the pattern of atrophy in PD was compatible with spread via this network from a presumed disease epicenter in the SN.

Results

ICA detects a PD-specific pattern of deformation

DBM was used as the measure of local brain atrophy. It is a measure of the change in the shape of each voxel that results from applying non-linear spatial normalization of the entire brain to a standard template (Aubert-Broche et al., 2013). For each subject, we obtained one parametric image of MRI-determined voxel-wise difference in volume, compared to the template brain. ICA was conducted on these DBM images using FSL MELODIC software (Beckmann and Smith, 2004). No constraint was imposed on the number of components, and probabilistic ICA estimated 30 independent components of deformation in the PPMI data set (PD patients and controls combined). Each ICA component consists of a spatial map and the average deformation value in that map for each subject.

In each of these 30 independent components, the average deformation between PD and control groups was compared using an unpaired t-test. PD patients had significantly lower DBM values in one and only one of the 30 deformation components (p = 0.0001 uncorrected, p = 0.003 with Bonferroni correction). The next spatial component in terms of statistical significance (p = 0.06, uncorrected) consisted of cerebellar areas VIIIA, VIIB, CrusII, and IX known for their involvement in motor and executive function (Stoodley and Schmahmann, 2009). None of the other components demonstrated a difference between PD and controls (p > 0.05 uncorrected for all remaining components). We will call the deformation pattern showing a group difference the PD-ICA network from this point on. Ten of the other ICA components demonstrated an effect of age in the entire group. In three of these, there was a positive correlation between the component expression and age (meaning increased volume with age) and all three consisted of ventricle or cerebrospinal fluid space enlargement (Figure 1—figure supplement 1). The other seven age-related components demonstrated a negative correlation (volume loss with age) and consisted of areas of gray or white matter (Figure 1—figure supplement 2). The PD-ICA network also demonstrated greater atrophy with increasing age in both PD patients (r = −0.38, p < 10−9) and controls (r = −0.53, p < 10−9).

Spatial analysis of PD-ICA network

Each spatial map was converted to a z-statistic image via a normalized mixture model and then thresholded at z ≥ 3. Regions were identified using the atlases of Talairach and Tournoux (1988) and Mai et al. (2003). The PD-ICA network includes all components of the basal ganglia (substantia nigra, subthalamic nucleus, nucleus accumbens, putamen, caudate nucleus, and internal and external globus pallidus), the pedunculopontine nucleus, basal forebrain, including bed nucleus of the stria terminalis and an area containing the nucleus basalis of Meynert, the hypothalamus, amygdala, hippocampus, parahippocampal gyrus, and two thalamic regions, the ventrolateral nucleus and pulvinar. Cortical regions in this network are the insula, occipital cortex Brodmann area 19, superior temporal gyrus, rostral anterior cingulate cortex, premotor and supplementary areas, and parts of lateral prefrontal cortex (Figure 1, Tables 1, 2; see also Figure 1—figure supplement 4).

Figure 1 with 4 supplements see all
Distribution of atrophy in Parkinson's disease.

This image displays the only one of the 30 independent component analysis (ICA) networks showing a significant difference between Parkinson's disease (PD) and Controls (p = 0.003 after correction for multiple comparison). The ICA spatial map was converted to a z-statistic image via a normalized mixture–model fit and then thresholded at z = 3. Selected sections in Montreal Neurological Institute (MNI) space at coordinates z = −16, z = −12, z = −7, z = −2, z = 8, z = 14, z = 20, z = 70. See Tables 1, 2 for anatomical localization. Note that the value at each voxel is the z-score of the ICA component, not the group difference.

https://doi.org/10.7554/eLife.08440.003
Table 1

PD-ICA subcortical anatomical areas

https://doi.org/10.7554/eLife.08440.008
Brain areaSub-areasZ-score L/RPeak coordinate L/R
Entorhinal cortexna (2.94)/3.1−20, −7, −32/19, −4, −34
Claustrumna/4.3na/36, 0, −21
AmygdalaBasolateral3.8/4.1−23, −5, −21/22, −4, −20
HippocampusDentate Gyrus4.7/4.2−34, −18, −14/34, −15, −17
Substantia nigra5/4.9−8, −18, −15/7, −17, −15
Periaqueductal gray5.6/5.4−3, −34, −12/3, −33, −12
Pedunculopontine nucleus4.7/4.6−6, −30, −11/6, −29, −11
Hypothalamus3.4/4.2−5, −3, −11/4, −3, −11
HippocampusCA1, CA2, CA35.3/4.5−30, −27, −10/31, −27, −11
Subthalamic nucleus5.2/5.2−8, −16, −10/9, −16, −10
Nucleus accumbens5/4.9−9, 11, −9/8, 11, −8
Basal forebrainBNST3.3/3.6−6, 4, −8/9, 3, −10
Basal forebrainExtended amygdala6/5.8−16, −6, −8/10, −6, −8
Basal forebrainSubstantia innominata3.6/5.1−8, 0, −8/8, 0, −8
PutamenAnterior putamen5.6/4.8−25, 11, −5/25, 11, −5
PutamenPosterior putamen6.1/4.8−30, −12, −6/31, −15, −2
Globus pallidusInternal + external5.7/4.7−20, 1, −1/21, −3, −3
Caudate nucleusHead8.2/6.2−10, 12, 4/10, 10, 2
PulvinarMedial/Lateral5.3/4.5−19, −31, 5/11, −26, −4
ThalamusVentrolateral/Ventroanterior5.3/3.6−17, −14, 11/14, −14, 12
CaudateBody4/4.8−15, 11, 12/17, 10, 15
  1. List of subcortical regions belonging to the PD-ICA network and their peak z-scores. (BA: Brodmann area, na: not applicable, BNST: bed nucleus of the stria terminalis, ICA: independent component analysis, PD: Parkinson's disease).

Table 2

PD-ICA cortical anatomical areas

https://doi.org/10.7554/eLife.08440.009
Brain areaSub-areasZ-score L/RPeak coordinate L/R
Superior temporal gyrusTemporal pole BA 385.6/3.6−50, 11, −18/50, 10, −12
Occipital lobeBA 193.1/3.4−39, −77, −18/35, −79, −14
InsulaMid-insula4.5/4.6−39, 0, −5/38, 5, −2
Inferior frontal gyrusBA 453.4/4.4−38, 26, 19/53, 26, 15
Anterior cingulate cortexRostral ACC4.3/na−6, 31, 18/na
Middle frontal gyrusDLPFC BA 9/464.1/na−22, 51, 19/na
Superior frontal gyrusBA 64/3.7−18, −10, 66/23, −10, 54
Supplementary motor areana/3.4na/5, −12, 67
  1. List of cortical regions belonging to the PD-ICA network and their peak z-scores. (BA: Brodmann area, na: not applicable, ACC: anterior cingulate cortex, DLPFC: dorsolateral prefrontal cortex, ICA: independent component analysis, PD: Parkinson's disease).

Clinical correlation

To confirm that the PD-ICA network identified above was disease-related, we compared individual deformation values in the network to measures of disease severity. The two clinical measures used were the striatum binding ratio (SBR) measured with single photon emission computed tomography (SPECT) using the tracer [123I]FP-CIT (Booij and Knol, 2007) to measure dopamine nerve terminal density, and the score on the Movement Disorder Society revised Unified Parkinson's Disease Rating Scale (UPDRS) part III (Goetz et al., 2008), an objective measure of motor disability. For SBR, we used the average value of left and right putamen. There was a significant correlation between individual SBR and DBM values in the PD-ICA network in the PD group (r = 0.23, p < 0.0005, Figure 2). This shows that the greater the loss of dopamine nerve terminals, the greater the volume loss in the PD-ICA network. There was also a significant correlation between these two measures in the control group (r = 0.33, p < 0.0005).

PD-ICA network, dopamine denervation, and severity of disease.

Left: Unified Parkinson's Disease Rating Scale (UPDRS) part III (a measure of motor function and disease severity—higher value means more severe disease) was significantly correlated with the degree of atrophy in the network (r = −0.22, p < 0.001). Right: plot of [123I]FP-CIT striatum binding ratio (SBR) vs deformation value in the PD-ICA (Figure 1). Correlation: r = 0.23, p < 0.0005 for PD patients, and r = 0.33, p < 0.0005 for age-matched controls.

https://doi.org/10.7554/eLife.08440.010

There was a significant correlation between DBM values within the PD-ICA network and UPDRS III in the PD patients (r = −0.22, p < 0.001; Figure 2). SBR was not significantly correlated with age in the PD subjects (r = −0.10, p = 0.12) but it was in controls (r = −0.35, p < 0.0001). Also, in PD subjects, SBR was significantly correlated with UPDRS III (r = 0.20, p = 0.002). We also tested whether UPDRS III was correlated with DBM values obtained from any one of the other 29 ICA components. There was only one other component marginally correlated with disease severity (r = −0.2, p = 0.048, Bonferroni corrected) consisting of the previously mentioned cerebellar network, areas VIIIA, VIIIB, CrusII, and IX. Because age, UPDRS III, and SBR all correlated with PD-ICA DBM, we performed multiple linear regressions. In the PD group, the model (PD-ICA ~ 1 + Age + UPDRS III + SBR) showed an effect of age (p = 2.4 e−08), UPDRS III (p = 0.06) and SBR (p = 0.01). In the controls, the model (PD-ICA ~ 1 + Age + SBR) demonstrated an effect of age (p = 4.7 e−08) but not SBR (p = 0.17).

Finally, multivariate analysis was used to look for an effect of scanning site. For each obtained DBM-network, we applied the model: DBM ~ Group (PD/Control) + Age + Gender + Site. There was no significant effect of site after correcting for multiple comparisons (p > 0.1).

Comparing disease-related atrophy to functional networks in health

We next tested the hypothesis that the PD-ICA deformation network represents an intrinsic functional network. We compared the PD-ICA network as well as the other 29 ICA maps obtained from the DBM ICA analysis to intrinsic brain networks in healthy brain. In order to increase confidence in the results, we determined normal intrinsic brain networks in several different ways:

  1. A seed-based functional connectivity map obtained from resting-state fMRI (rsfMRI) in 51 healthy volunteers with an a priori region of interest in the SN.

  2. Two sets of 70 and 100 typical functional resting-state networks from healthy volunteers previously identified with MELODIC ICA by Smith et al. (2009) and Smith et al. (2013).

First, we compared the resting-state SN seed-based map (Figure 3B) with all 30 structural maps obtained from the PPMI ICA analysis. Only two ICA networks passed the threshold of |r| > 0.25. These two networks correspond to very similar subcortical networks in basal ganglia and brainstem, one of which is mostly white matter areas (r = 0.36), while the other is mostly gray matter areas (r = 0.30). The latter is the aforementioned PD-ICA network in which atrophy correlated with disease severity (Figure 3—figure supplement 1).

Figure 3 with 3 supplements see all
PD atrophy resembles normal intrinsic connectivity networks.

Selected sections for (A) PD-ICA network from the Parkinson's Progression Markers Initiative (PPMI) data set thresholded at z = 3. (B) Seed-based resting-state functional MRI (fMRI) connectivity with substantia nigra as a priori seed. (C) Intrinsic connectivity network (ICN) correlated with PD-ICA from Smith et al. (2009). (D) Regions responding to stimulus value during fMRI (meta-analysis of Bartra et al., 2013) (Selected slices in MNI space z = −2, x = −8, x = −23, y = 10.)

https://doi.org/10.7554/eLife.08440.011

Then, we compared the PD-ICA network to the 70 rsfMRI ICNs in normal brain provided by Smith et al. (2009). One of the 70 networks passed the threshold (with r = 0.32). This network has been related to reward tasks, interoceptive functions, and motor/sensory processing (Figure 3C). We assessed statistical significance by generating 1000 permutations of each of the 70 ICNs by reassigning the coordinates of each voxel randomly (Figure 3—figure supplement 2). We then repeated this comparison using a finer decomposition of 100 resting-state ICNs from the Human Connectome Project (HCP) (Smith et al., 2013) using MELODIC. Four components showed spatial overlap to the PD-ICA network using spatial correlation (Figure 3—figure supplement 3). The mean fMRI time series from these components were then used to determine whether they themselves belonged to one larger ICN. These time series demonstrated significant inter-correlation (p < 0.0016 by permutation testing). Finally, hierarchical clustering (Smith et al., 2013) confirmed that all four components clustered together.

We also compared the PD-ICA network to a map of regions responding to stimulus value during fMRI experiments as identified by meta-analysis (Bartra et al., 2013). In these experiments, subjects typically evaluated an offered item (say a food) and experimenters identified brain regions where the fMRI signal tracked subjective value (e.g., willingness to pay for the item). The premise is that PD may affect dopamine projection sites that encode aspects of stimulus value. Only one of the 30 networks passed the threshold (r = 0.28), namely the aforementioned PD-ICA network (Figure 3D).

In summary, the PD-ICA network exhibited significant spatial overlap with presumed intrinsic brain networks determined by three different methods.

Testing the propagation model

The sequential propagation model predicts that the spatial progression of the disease process will be determined by brain network topology. Connectivity between any region and the presumed disease epicenter will determine how severely it is affected. Here, we evaluated this assumption by exploring whether the gray matter atrophy patterns observed in PD patients could be explained by functional and geodesic distance (i.e., the number of edges separating two nodes in a graph) to the hypothetical pathogenic epicenter. We chose to use the SN as the epicenter based on known PD pathology. Note that the SN is unlikely to be the first affected site in the central nervous system (CNS) (Braak et al., 2003, 2004); however, we postulate that it is likely to function as a source for propagation to the supratentorial brain. Network connectivity in health can be defined functionally, using rsfMRI, or structurally, using DW-MRI. The influence of disease on each node can be estimated from the statistical difference in deformation between PD and control groups. We parcellated the brain into 112 regions of interest (ROIs, Figure 4—figure supplement 1) and computed the degree of deformation in each region. We also generated two connectomes from these ROI using rsfMRI and DW-MRI data from two different pools of healthy subjects. The connection strength between each pair of regions was computed as described in the ‘Materials and methods’.

There was a significant correlation between resting-state functional connectivity of each node with SN (in healthy brain) and the PD-related deformation (PD minus control t-score), (r = 0.40, p < 0.0001). We repeated the same analysis controlling for spatial proximity between each region and SN by entering Euclidean distance as a covariate. The correlation was unchanged (r = 0.38, p < 0.0001), suggesting that the relationship cannot be explained by spatial proximity. The correlation implies that higher functional connection between a given region and SN is related to higher PD-related atrophy in that region (Figure 4). When using an anatomical measure of connectivity (DW-MRI) (Figure 4), we also observed a significant relationship between the level of atrophy of each region and its geodesic distance to the SN (r = −0.28, p < 0.003). These results were not different after controlling for Euclidean distance to SN (r = −0.25, p < 0.005). Finally, we repeated this analysis using every ROI as a potential disease propagator and found that SN was the likeliest disease propagator when using the rsfMRI connectome (Table 3). However, the red nucleus (r = 0.28) and subthalamic nucleus (r = 0.28) were also identified as potential propagators. Repeating this analysis using a tractography-derived connectome also revealed that the SN was one of the likeliest propagators, but numerous cerebellar regions also emerged as potential propagators (Figure 4—source data 2). This may be due to difficulty in accurate identification of the targets of brainstem white matter tracts using DW-MRI (Ford et al., 2013).

Figure 4 with 1 supplement see all
Relationship between atrophy in different brain regions and functional and structural connectivity with SN.

The brain was parcellated into 112 regions (Figure 4—figure supplement 1). SN was chosen a priori as the region of interest, and the functional and structural connectivities between each given region and SN were calculated. The statistical difference (t-score) between the average deformation in PD and controls in each region was used as an atrophy measure. Using correlation, the relationship between regional atrophy and both regional functional connectivity with SN using resting-state fMRI (rsfMRI) (left) and regional anatomical distance using diffusion-weighted imaging (DW-MRI) (right) was examined. There was significantly greater atrophy with proximity to the SN determined functionally (r = 0.4, p < 0.0001) and anatomically (r = −0.28, p < 0.003). Note that the connectivity measure in rsfMRI is correlation, resulting in greater values for more connected regions, whereas the connectivity measure in DW-MRI is distance, resulting in smaller values for more connected regions.

https://doi.org/10.7554/eLife.08440.015
Table 3

Best propagators (resting-state fMRI connectome)

https://doi.org/10.7554/eLife.08440.019
Seed regionrSeed regionr
Substantia nigra0.40Cerebellum VIIIb0.05
Subthalamic nucleus0.28Insula0.04
Red nucleus0.28Anterior temporal lobe (lateral part)0.02
Cerebellum dentate0.27Cerebellum CrusI0.01
Pallidum0.23Superior temporal gyrus (anterior part)0.01
Hippocampus0.22Caudate nucleus−0.06
Cerebellum vermis X0.21Superior temporal gyrus (posterior part)−0.07
Cerebellum vermis VIIIa0.20Middle and inferior temporal gyrus−0.07
Cerebellum interposed0.20Lingual gyrus−0.08
Cerebellum fastigial0.20Postcentral gyrus−0.08
Cerebellum vermis IX0.20Precentral gyrus−0.09
Cerebellum vermis VIIIb0.18Posterior temporal lobe−0.09
Cerebellum I IV0.18Inferior frontal gyrus−0.10
Cerebellum vermis VIIb0.17Middle frontal gyrus−0.10
Parahippocampal gyrus0.16Cuneus−0.10
Cerebellum V0.16Anterior cingulate gyrus−0.12
Anterior temporal lobe (medial part)0.15Occipital lobe (lateral part)−0.12
Cerebellum vermis CrusII0.14Lateral orbital gyrus−0.16
Occipitotemporal gyrus (lateral part)0.14Superior frontal gyrus−0.16
Cerebellum VIIb0.13Parietal lobe (Inferiolateral)−0.16
Cerebellum CrusII0.13Superior parietal gyrus−0.20
Cerebellum IX0.12Pre-subgenual frontal cortex−0.20
Cerebellum VI0.12Posterior orbital gyrus−0.23
Amygdala0.12Posterior cingulate gyrus−0.23
Cerebellum X0.11Medial orbital gyrus−0.27
Cerebellum vermis CrusI0.10Straight gyrus−0.31
Putamen0.10Anterior orbital gyrus−0.33
Cerebellum vermis VI0.08Subgenual frontal cortex−0.34
Cerebellum VIIIa0.07Nucleus accumbens−0.38
Thalamus0.06Subcallosal area−0.42
  1. Each brain region from the atlas was used as a potential propagator. The statistical difference (t-value) between the average deformation in PD and controls in each region was used as an atrophy measure. The correlation between this atrophy measure and the functional connectivity to the potential propagator was used as a measure of propagation strength. The potential propagator regions are sorted by correlation values.

  2. fMRI: functional MRI, PD: Parkinson's disease.

Discussion

Pattern of atrophy in PD

The combination of DBM and ICA of an unprecedentedly large set of MRI data at a magnetic field strength of 3T allowed us to map out the brain regions affected in de novo PD. Patients included in this study were diagnosed on average 7 months prior to the investigation and had no evidence of dementia (Table 4). Most MRI studies to date using T1-weighted images in PD have reported normal volumes of brainstem, basal ganglia, and cerebral cortex. In the SN, measurements of volume and shape have been inconclusive, reporting either no change, a decrease, or increase in volume depending on the method used (Pyatigorskaya et al., 2014). However, most studies using parametric mapping have failed to report a difference in SN. Reduced putamen or caudate volume has been reported but only in advanced cases with mild cognitive impairment or dementia (Apostolova et al., 2010; Silbert and Kaye, 2010; Pyatigorskaya et al., 2014). Similarly, hippocampus and amygdala atrophy are typically linked to cognitive impairment (for review see Silbert and Kaye, 2010; Ibarretxe-Bilbao et al., 2011). With regard to cortical areas, studies using voxel-based morphometry (VBM), DBM, or cortical thickness measurements have reported significant differences between PD and controls in parahippocampal gyrus, inferior, middle and frontal temporal gyrus, parietal lobe and occipital lobe, but, once again, typically in advanced patients with cognitive impairment (Silbert and Kaye, 2010; Ibarretxe-Bilbao et al., 2011; Camicioli, 2013; Pyatigorskaya et al., 2014). Our finding of an atrophy network including brainstem and subcortical regions in these de novo patients is likely due to the larger number of participants available through the PPMI database compared to previous relatively underpowered investigations.

Table 4

Clinical characteristics of subjects

https://doi.org/10.7554/eLife.08440.020
Control (n = 117)PD (n = 232)p value
Age (years)59.7 ± 11.361.2 ± 9.10.1
Years of education (years)15.7 ± 2.9115.4 ± 2.8NS
Sex (M/F/% males)74/43/63.2155/77/66.8NS
Handedness–R/L/A98/11/8210/17/5NS
Striatum binding ratio2.6 ± 0.61.4 ± 0.4<0.0001
MoCA28.2 ± 1.227.3 ± 2.2<0.0001
Disease duration (months)6.9 ± 7.1
MDS UPDRS part III21.9 ± 9.1
H&Y stage1.6 ± 0.5
  1. M = male, F = female, NS = not significant, H&Y: Hoehn and Yahr, PD: Parkinson's disease, MoCA, Montreal Cognitive Assessment, UPDRS, Unified Parkinson's Disease Rating Scale. Statistical differences analyzed through an unpaired t-test or chi square test. Listed values are the mean ± standard deviation.

Eidelberg and colleagues (Eidelberg, 2009) were the first to use data reduction techniques (principal component analysis) to map PD pathology using [18F]fluorodeoxyglucose (FDG) positron emission tomography. They identified a PD-related pattern (PDRP) implicating several regions identified in our analysis, namely globus pallidus, putamen, thalamus, premotor and supplementary motor areas. The expression of this PDRP differentiates patients from healthy controls and correlates with measures of disease severity. However, it probably reflects functional effects of the disease more than neuronal atrophy, as evidenced by the fact that it is normalized by levodopa or deep brain stimulation (Eidelberg, 2009).

PD targets an intrinsic brain network

We found that the set of regions demonstrating atrophy in the PD group corresponded spatially to one ICN in normal brain. rsfMRI and anatomical imaging have identified consistent sets of regions that act as functional networks, by virtue of anatomical connectivity and temporal covariance of neuronal activity. We showed that our PD-ICA set of regions corresponded to a normal ICN identified by seed-based resting-state functional connectivity and ICA (Smith et al., 2009, 2013). The finding that PD targets a set of connected brain regions supports the network-spread hypothesis, although an alternate explanation is that neurons in one network could share a common vulnerability. The brain regions that make up the PD-ICA network are involved in reward and motivation, as demonstrated by the fact that they respond to the subjective value of a perceived stimulus during fMRI (Bartra et al., 2013). All the regions of this value network receive projections from midbrain dopamine neurons, in keeping with this neurotransmitter's role in signaling incentive value (Salamone et al., 2005; Berridge, 2006).

Network spread in PD

Recent evidence supports a hypothesis originally put forward by Braak (Braak et al., 2003, 2006; Goedert et al., 2013), according to which pathogenic forms of the protein alpha-synuclein spread throughout the nervous system leading to a stereotypical step-wise pattern of neurological impairment in PD. Misfolded synuclein fibrils injected focally into rodent brains spread to neuronally connected but not adjacent areas (Luk et al., 2012; Masuda-Suzukake et al., 2013) (see also Figure 1—figure supplement 3).

Here, we tried to identify a putative best propagator or epicenter of the disease by creating functional and anatomical connectomes from an anatomical brain atlas. We showed that the SN could act as a propagator, since the rate of atrophy in any brain region was proportional to its effective topological distance from the SN, as determined by either functional (rsfMRI) or anatomical (DW-MRI) connectivity. We note that, while the SN contains dopamine neurons whose degeneration accounts for all the key clinical features of early PD, the Braak scheme identifies the dorsal motor nucleus (DMN) of the vagus in the medulla as the first affected CNS structure. Indeed, recent evidence supports the potential transfer of pathogenic alpha-synuclein from the intestine to the DMN via the vagus nerve (Holmqvist et al., 2014). In the current study, the DMN did not demonstrate atrophy in PD compared to control subjects. Perhaps this is due to insensitivity of T1-weighted MRI-based anatomical methods in identifying neuronal loss in lower brainstem structures. The DMN is much smaller that the SN (approximately 1/10–1/20 in volume) and has limited contrast in T1 MRI. Our results, however, suggest that the SN could act as the propagator of the disease to the supratentorial CNS. Note, however, that spatial resolution limitations for all of the imaging modalities used here, T1-weighted MRI, fMRI, and DW-MRI, make it difficult to localize atrophy to small nuclei with complete certainty. Spatial inaccuracy in atlas generation and normalization of subject images to Montreal Neurological Institute (MNI) space may compound this problem. This is especially relevant to small structures of the basal forebrain and midbrain. For example, the propagator analysis identified SN, subthalamic nucleus, and red nucleus as potential propagators, but it is clear that the imaging techniques used here do not allow us to fully resolve these structures, either anatomically or functionally. Furthermore, because T1-weighted MRI is sensitive to iron content, changes in iron accumulation, for example, in SN, globus pallidus, or red nucleus, may be interpreted as volume changes by the DBM methodology.

Nonetheless, comparing the atrophy pattern identified here to the stages described by Braak et al. (2003), Braak et al. (2006), we note that all the areas identified in stage 3 (pedunculopontine nucleus, amygdala, basal forebrain, and substantia nigra), stage 4 (temporal mesocortex and hippocampus), and stage 5 (insula, cingulate cortex, and temporal neocortex) belong to our PD-ICA network. Braak hypothesized that the medial temporal lobe served as a beachhead for further propagation to the remaining cortex, and interestingly three of the best supratentorial propagators identified here were the parahippocampal gyrus, anterior medial temporal lobe, and hippocampus (Table 3).

Another prediction of the network-spread model is that connectome hubs should be especially vulnerable to disease spread (Zhou et al., 2012). Hubs are defined as brain regions that are highly connected using graph theoretical metrics such as degree (Crossley et al., 2014) or betweenness (He et al., 2009). In theory, hubs, being highly connected, should have greater exposure to a toxic agent that is spread trans-neuronally. In a recent meta-analysis, Crossley et al. (2014) found that most neurodegenerative diseases, including AD and fronto-temporal dementia, did indeed target hubs. However, one salient exception was PD. One possibility is that the PD studies included in the meta-analysis were too small in scale or methodologically incapable of detecting the true extent of damage. It is intriguing that our study identified numerous hub regions (Bassett et al., 2008; He et al., 2009; Crossley et al., 2014) in the PD-ICA network, including the medial temporal lobe, putamen, insula, occipital cortex, anterior cingulate, superior frontal gyrus, and middle frontal gyrus. One notable hub region absent from our PD-ICA network is the posterior cingulate gyrus/precuneus, an area typically affected in AD (Buckner et al., 2009). It would be interesting to see if this area eventually develops atrophy as PD progresses, and whether this is associated with cognitive impairment.

Atrophy in healthy aged subjects

The PD-ICA network demonstrated a correlation between atrophy and dopamine denervation measured with SPECT, in both PD patients and age-matched control subjects. Atrophy and dopamine denervation also both correlated with age in the control group. In sum, the control subjects demonstrated age-related loss of dopamine innervation and atrophy in the PD-ICA network. This finding is also novel. It is known that healthy aging is associated with a progressive loss of dopamine neurotransmission (Fearnley and Lees, 1991), and that this may account for motor slowing and executive cognitive impairment that occurs with age (Jagust, 2013). Indeed, subtle Parkinsonian signs such as stooped posture, bradykinesia, and reduced facial expression in the healthy elderly were associated with SN neuron loss at post-mortem (Ross et al., 2004). Our results extend these findings by showing that neurodegeneration of the extended dopamine network also occurs in healthy aging, albeit without attaining the severity of outright PD, possibly via loss of neurotrophism, or perhaps, due to low-level mitochondrial dysfunction or synucleinopathy (Olanow and Brundin, 2013). PD might then target the PD-ICA network due to a dual hit effect of pathology superimposed upon normal aging.

Materials and methods

Subjects and data collection

Data used in this article were primarily obtained from the PPMI database. In addition, rsfMRI and DW-MRI in healthy subjects were used to generate normal human connectomes for investigation of the network propagation model.

PPMI data set

The PPMI is described at www.ppmi-info.org. PPMI is a public–private partnership funded by the Michael J Fox Foundation for Parkinson's Research and funding partners listed at www.ppmi-info.org/fundingpartners. It is an observational, multicenter longitudinal study designed to identify PD biomarkers (Marek et al., 2011). Each participating PPMI site received approval from a local research ethics committee before study initiation and obtained written informed consent from all subjects participating in the study.

For this study, we used the initial visit 3T high-resolution T1-weighted MRI scans acquired from September 2013 to January 2014. MRI data were acquired in 16 centers participating in the PPMI project, using scanners from three different manufacturers (GE medical systems, Siemens, and Philips medical systems). The acquisition parameters are detailed in the data set Website: http://www.ppmi-info.org/wp-content/uploads/2015/03/PPMI-MRI-Operations-Manual-V7-0-20JAN2015-FINAL.pdf.

We also made use of the following data for each participant: age, sex, disease duration, score on the Movement Disorder Society—UPDRS III (Goetz et al., 2008) while off medications, score on the Montreal Cognitive Assessment battery and striatal binding ratio (SBR) from SPECT measurements with the tracer [123I]FP-CIT, a measure of dopamine neuron terminal density (Booij and Knol, 2007). In total data from 355 subjects (237 PD patients and 118 age-matched controls) were used. Six subjects, five PD patients and one age-matched control, were excluded from analysis due to failure in MRI processing. Clinical characteristics are shown in Table 4.

rsfMRI

We acquired rsfMRI in 51 healthy, right-handed volunteers (mean age: 23.6 ± 5.9, range: 18–47, 32 men, 63%). None of the subjects reported a history of drug abuse, neurological or psychiatric disorder. The experimental protocol was reviewed and approved by Research Ethics Board of MNI. All subjects gave informed consent. Scans were acquired using a Siemens MAGNETOM Trio 3T MRI system at the MNI. High-resolution, T1-weighted, three-dimensional volume acquisition for anatomic localization (1-mm3 voxel size) and resting-state echoplanar T2*-weighted images with blood oxygenation level-dependent (BOLD) contrast (3.5-mm isotropic voxels, TE 30 ms, TR 2 s, flip angle 90°) were acquired from all participants. Each resting-state scan was 5-min long (150 vol). Subjects were instructed to rest quietly with eyes open.

DW-MRI

To obtain white matter connectivity maps of normal brain, we used the Illinois Institute of Technology Human Brain Atlas v.3 (Varentsova et al., 2014) (http://www.nitrc.org/projects/iit2/). This atlas contains structural (T1) and high angular resolution DW-MRI data, and probabilistic gray matter maps of the adult human brain in MNI space. The atlas was generated from MRI data from 72 human subjects (42 females (59%): mean age 26.6 ± 4.8 years, range 20–39 years; 30 males: mean age 31.9 ± 4.9 years, range 22–40 years).

DBM

Local change in tissue density was calculated using DBM. DBM consists in spatially transforming each MRI non-linearly to a stereotaxic template, and using the local deformation as a measure of tissue expansion or atrophy. There are other methods to detect population differences in brain structure. VBM measures local gray matter density by transforming the brain to stereotaxic space, segmenting the tissue into gray and white matter and cerebrospinal fluid, and performing spatial smoothing on the gray matter maps so that local image intensity reflects gray matter density (Ashburner and Friston, 2000). We decided against VBM as it does not preserve the entirety of the MRI data, and there is some suggestion that it is less sensitive than DBM to subcortical atrophy (Borghammer et al., 2010; Scanlon et al., 2011). Another approach is to measure cortical thickness from the MRI (Pereira et al., 2014), but this would also make it impossible to detect subcortical changes.

For DBM, we registered every brain non-linearly to the MNI152-2009c template (available at http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009) and computed the deformation applied at each voxel using the procedure explained in Aubert-Broche et al. (2013). Pre-processing of MRI included denoising using optimized non-local means filtering (Coupe et al., 2008), correction for intensity inhomogeneity (Sled et al., 1998), and linear intensity scaling using histogram matching to the MNI152-2009c template.

The resulting images were registered to MNI space using the MNI152-2009c template, in two steps: (1) A hierarchical nine-parameter linear registration was computed between native MRI images and the template by maximizing the cross correlation of intensities as the similarity measure (Collins et al., 1994). The resulting transformation was applied to the MR image to resample it onto a 1-mm3 voxel grid and bring it into MNI space. (2) A hierarchical non-linear registration was performed on the linearly resampled scan to align it with the MNI152-2009c template (Collins and Evans, 1997). The resulting non-linear transformation field was inverted to generate a map of the deformations in template (MNI) space for each subject. Quality control was performed on each individual data set: the brain mask, and linear and non-linear registrations were visually inspected, and data sets with faulty registration were discarded.

After the registration procedure, for each MRI and for each position in the brain x = x1, x2, x3, we obtain a displacement value in each direction to generate a field of vectors: U(x) = (u1(x), u2(x), u3(x)); that is, during the registration procedure position, x is displaced to the new position x + U(x) in the template space. This shows how much each voxel was moved from the MNI152-2009c template to match the subject's brain. To estimate local atrophy, an extra step is needed. Since a completely uniform displacement results in no change of volume, we are interested in the derivative of the displacement in each direction, that is, the determinant of the local Jacobian matrix of displacement.

J=Ux=[u1x1u1x2u1x3u2x1u2x2u2x3u3x1u3x2u3x3].

This Jacobian matrix is estimated using a first order approximation:

u3x2u3(x1,x2+δ,x3)u3(x1,x2δ,x3)2δ,

where δ is the voxel dimension along the x2 axis. To calculate the relative ratio of the local volume change, we use the determinant of the Jacobian matrix |J| minus 1, that is, |J| − 1. By performing this calculation at each voxel, we obtain a map of local relative volumetric difference between each subject image and the MNI152-2009c template, reported in the MNI152-2009c template Talairach-like coordinate system (Chung et al., 2001).

Anatomical atlas

We created a composite anatomical atlas of gray matter regions in the cerebral hemispheres, cerebellum, and midbrain. For the supratentorial regions, we used the Hammers atlas (Copyright Imperial College of Science, Technology and Medicine, Alexander Hammers and University College London 2011. All rights Reserved) (Hammers et al., 2003) excluding the brainstem, cerebellum, and white matter. For the cerebellum, we used a public-domain high-resolution digital cerebellar atlas (Diedrichsen et al., 2009). These two atlases do not have adequate segmentation of three midbrain structures, the SN, subthalamic nucleus, and red nucleus. We therefore manually segmented these three structures using the Display software tool (McConnell Brain Imaging Centre) and three sources of anatomical information: the high-resolution MRI template (T1-weighted ICBM 2009c template, resolution = 0.5 mm3), the BigBrain (Amunts et al., 2013), a 20-micron resolution digital brain atlas in MNI space, and the brainstem anatomical atlas of Duvernoy et al. (1995). The three structures were manually drawn on the high-resolution ICBM 2009c template. We then confirmed the segmentation of these three regions with a recently developed subcortical atlas based on ultra high-field MRI (Keuken et al., 2014). The composite atlas thus created contains 112 cortical and subcortical structures (Figure 4—figure supplement 1, Figure 4—source data 1) and excludes all brainstem regions caudal to the SN.

Extracting independent components of the structural deformation

We used ICA to extract patterns of deformation in PD patients and age-matched controls. ICA is a statistical method to decompose multivariate data into statistically independent components (Calhoun et al., 2001; Beckmann and Smith, 2004; Hyvärinen et al., 2004). In this case, we used ICA to decompose the deformation maps into spatially distinct subcomponents. ICA was performed with MELODIC (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MELODIC), a toolbox that is part of the FSL analysis package (Beckmann and Smith, 2004, 2005; Smith et al., 2004; Douaud et al., 2014). All the individual DBM images (patients and controls combined) were concatenated to create a 4-D image in which the first three dimensions are the individual 3-D deformation maps and the fourth dimension consists of the subjects. MELODIC applies an initial principal component analysis-based dimension estimation to find the optimal number of independent components and then uses this number in the decomposition procedure to identify the independent spatial components. Each of these components consists of a vector of normalized DBM values (one per subject) as well as a corresponding 3-D spatial map. The spatial maps were converted to z-statistic images via normalized mixture-model fitting, and thresholded at z = 3 (Beckmann and Smith, 2004; Smith et al., 2009). The ICA algorithm in MELODIC is sensitive to sparsely distributed (super-Gaussian) data (Daubechies et al., 2009) as typically seen in fMRI. We confirmed that the DBM data used here possessed this super-Gaussian property (kurtosis >4).

Statistical analysis of independent networks

To identify regions showing greater atrophy in PD, the average DBM values for each ICA component and each subject were entered in an unpaired t-test comparing PD subjects and age-matched controls (Bonferroni corrected for multiple comparisons). Also, the DBM values from each component were correlated with the age of each subject to identify patterns of deformation associated with aging. Again, Bonferroni corrections were applied. The DBM values from the component(s) that demonstrated a statistically significant group difference were compared to two disease-related clinical measures using linear regression: SBR and UPDRS III.

rsfMRI analysis

In order to test the theory that PD targets normal brain networks, we analyzed rsfMRI data from 51 young healthy individuals. The rsfMRI data were preprocessed using the Neuroimaging Analysis Kit (NIAK) (Bellec et al., 2010, 2012), to perform slice timing correction, rigid body motion correction, and removal of slow temporal drift using a high-pass filter with 0.01 Hz cut-off (Perlbarg et al., 2007). Physiological noise was accounted for by including white matter and cerebrospinal fluid signals as covariates. In the next step, the mean motion-corrected volume of each subject's fMRI data was first linearly (6 parameters: 3 translations, 3 rotations) registered to the native individual T1 image and then non-linearly registered to the MNI152 non-linear template (http://www.bic.mni.mcgill.ca/ServicesAtlases/HomePage). All data were resampled and smoothed with a 6-mm Gaussian kernel. All fMRI time series further underwent level one auto-regression (AR1) temporal de-noising.

The mask of the SN described above was used to extract the average BOLD time series of each individual scan. The time series from right and left SN were averaged. Seed-based functional connectivity analysis was performed (Worsley et al., 2002; Worsley, 2005) as implemented in fmristat (http://www.math.mcgill.ca/keith/fmristat/). The SN average BOLD signal was entered as a regressor in the design matrix and its correlation calculated with all voxels in the brain. A mixed effects model was applied to generate a t-map for the group. This was thresholded based on random field theory to achieve a p value of 0.05 after correction for multiple comparisons.

Comparing PD-related structural atrophy networks and functional networks in healthy brain

We quantified the spatial similarity between PD-ICA atrophy maps and intrinsic networks in the healthy brain. We compared the PD-ICA map to intrinsic brain networks obtained from three different sources: the functional connectivity map obtained from the SN seed region, and ICNs from two published sources (Smith et al., 2009, 2013). We also compared the PD-ICA network map to a map derived from a meta-analysis of fMRI experiments where subjects tracked the value of rewarding stimuli (Bartra et al., 2013).

First, the functional connectivity map obtained from the SN seed-based analysis was compared to the 30 different ICA maps from the deformation analysis. Second, we compared the PD atrophy map (PD-ICA network) to the 70 ICNs identified by Smith et al. based on ICA analysis of resting-state data in healthy subjects (Smith et al., 2009). We further compared the PD-ICA map to 100 ICNs identified from the HCP rsfMRI data (Smith et al., 2013). Then, we compared the 30 ICA maps from the deformation analysis to a map identified in a meta-analysis of value-related fMRI studies, reasoning that dopamine networks implicated in PD would be similar to value networks in healthy brains (i.e., regions where BOLD signal tracks the value of experimental stimuli). Spatial cross-correlation (Pearson) was used as the measure of similarity. We chose |r| > 0.25 as indicative of similarity between two spatial maps as this has been argued to guard against false positives in a similar comparison between ICA-derived spatial maps (Smith et al., 2009).

Relating deformation to the normal connectome

We generated the normal connectome of our 112 region brain atlas (52 paired bilateral regions, 8 midline regions), in two different ways, with rsfMRI and with DW-MRI. The goal here was to test the theory that geodesic (synaptic) proximity to the SN in healthy brain would predict the distribution of regional atrophy in PD.

Generation of the functional connectome

The modified Hammers-Cerebellum-Brainstem atlas was used to extract the average BOLD time-series, after correction for physiological noise, for all regions for each of the 51 rsfMRI acquisitions. All 112 regions in the atlas were used as separate masks and average time series were extracted for each. The time series used were the residuals of the linear model after correction for physiological noise and head motion. As a result, we obtained the correlation between all region pairs, which gives a 112 × 112 connectivity matrix for each acquisition. Then, a common group-based matrix called Rgroup was calculated. To do so, we followed steps explained in Carbonell et al. (2014): each individual subject's (j) correlation matrix Rindj was converted to a standard normal metric using the Fisher transformation

Zindj=12log(1+Rindj1Rindj).

Then, all the Fisher transformed results were averaged.

Zgroup=j=1NZindjN,

where N is the number of subjects. Finally, the group Z correlation matrix result was converted back to the correlation space using an inverse Fisher transform

Rgroup=e2Zgroup1e2Zgroup+1.

The modified Hammers-Suit-Brainstem atlas was also used to extract the average deformation values (Jacobian) for each region for each PD and age-matched control subject. A t-test was performed in each region to assess the difference between the PD and control groups, corrected for age. These t-values are a measure of the difference between the two groups and the sign of the t-value shows the direction of the effect, with negative values chosen to mean greater deformation (atrophy) in PD.

The relationship between functional connectivity of each brain region with the selected seed region (SN) and the t-value of the deformation in each region (PD minus age-matched controls) was investigated using correlation analysis.

Generation of the DW-MRI connectome

Probabilistic anatomical connectivity values between each pair of atlas regions were estimated using a fully automated fiber tractography algorithm (Iturria-Medina et al., 2007) and the intravoxel fiber orientation distribution functions from the IIT Human Brain Atlas v.3 (Varentsova et al., 2014). A maximum of 500-mm trace length and a curvature threshold of ±90° were imposed as tracking parameters. Based on the resulting voxel-region connectivity maps, the anatomical connection probability (ACP) between any pair of regions i and j (ACPij = ACPji) was calculated as the maximum voxel-region connectivity value between both regions. The ACP measure (Iturria-Medina et al., 2007) reflects the degree of evidence supporting the existence of each hypothetical white matter connection, independently of the density/strength of this connection, and is thus a measure of low susceptibility to gross fiber degeneration related to the aging processes. Then, effective anatomical distances between each region i and all the other ROI were estimated as the lengths of the shortest paths (in terms of ACP values) linking that region to all the other regions (Iturria-Medina et al., 2014).

Then, as for the functionally derived connectome above, we tested whether the PD minus control atrophy value in each atlas region depended on the geodesic distance to the SN in the DW-MRI connectome. We calculated the non-linear correlation between the regional T value of the difference in Jacobian between patients and controls and the effective anatomical distance to the SN.

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Decision letter

  1. David C Van Essen
    Reviewing Editor; Washington University in St Louis, United States

eLife posts the editorial decision letter and author response on a selection of the published articles (subject to the approval of the authors). An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.

Thank you for submitting your work entitled “Parkinson's disease targets an intrinsic brain network” for peer review at eLife. Your submission has been favorably evaluated by Timothy Behrens (Senior Editor), a Reviewing Editor, and two reviewers.

The reviewers have discussed the reviews with one another and the Reviewing editor has drafted this decision to help you prepare a revised submission.

This study is a very interesting and well thought out multimodal analysis of brain atrophy and functional connectivity in a multi-site cohort of de novo PD patients. It benefits from a (multi-centric) large cohort of rare de novo PD patients and from the methodological approaches used for the analysis of local atrophy (namely DBM+ICA). The fact that the authors also looked for a spread of the disease via structural and functional networks (using both diffusion imaging and [resting-state] fMRI) is another strength of this study. It provides intriguing evidence in support of the hypothesis that atrophy initiated in or near the substantia nigra may spread to other subcortical and cortical regions in a pattern that at least to some degree reflects network connectivity as revealed by resting-state fMRI.

Both reviewers were enthusiastic about many aspects of the study, but both raised substantial concerns that are largely complementary to one another. Hence, we consider the manuscript to be potentially acceptable, but only after a major revision that addresses the key points raised by the reviewers.

Some of the recommendations would entail significant additional analysis, and we do not insist that all of them necessarily be carried out. For example, suggestion #8 is to carry out the rsfMRI analysis of functional connectivity using the freely available HCP dataset. We encourage you to consider this, but it is not essential for the revised manuscript. For the other recommended re-analyses, including #1 (seed-based analysis), #11 (higher-dimensional ICA decomposition) and #13 (FLICA analysis) it is important that you either follow the recommendation or provide a cogent response as to why this was not done.

Essential revisions:

1) A strength of this paper is that multiple different methods were used to measure connectivity patterns in normal subjects (resting state seed based, resting state ICA, DTI). A relative weakness is that only one method was used to define the atrophy pattern in PD, the central finding in the paper. There are numerous techniques for detecting and quantifying atrophy, so why did the authors choose the one they did (DBS). Do they get similar results using an alternative technique? Similarly, they only used one method to identify the atrophy pattern (ICA). Why not use a seed based approach to identify regions whose atrophy correlates with atrophy in the SN? The authors do not need to perform every methodological possibility, but the reasons for their choices need to be more clearly justified. Further, they should make it clear when and why their methods deviate from prior work with similar goals (e.g. Bill Seeley's work).

2) Results are a bit overstated at times which could detract from the importance of the findings. The authors convincingly show that a specific pattern of atrophy is related to PD, aging, dopamine binding in the striatum, and UPDRS score. This alone is very worthy of publication. Whether this atrophy network is an “intrinsic brain network”, as defined by resting state fcMRI, or validates the “network spread model” of PD, this is indeed an important question, but it is weakly supported by the present data. The authors may be better served to focus on their strongest findings and relegate the others to the Discussion.

3) The authors put great emphasis on the fact that their atrophy pattern matches an “intrinsic connectivity network”, including making this the title, but the data supporting this claim are weak. Specifically, the criteria for a “match” are arbitrary. The authors chose a threshold of r = 0.25. If they had chosen a threshold of 0.35 instead, we would conclude that the atrophy pattern fails to match any intrinsic connectivity networks. Rather than concluding that the atrophy matches or fails to match an intrinsic connectivity pattern, the authors could make better claims on comparative matching. In other words, they can claim that their atrophy pattern matches a specific network better than other atrophy patterns and they can conclude that their atrophy pattern matches a specific network better than other networks.

4) It is a bit unclear whether the PD-ICA network (Figure 1) shows the full ICA component identified combining PD and controls or if only those voxels within the component that showed significant differences between PD and controls. I believe it's the former, but this should be made a bit clearer and it would be helpful to also show that latter. What part of this network shows the greatest difference between PD and controls?

5) There are concerns regarding the correlations across the 135 ROIs. The authors already have atrophy and connectivity measures at the voxel level so why not do the analysis across voxels? By combining different pieces of various brain atlases with their own hand-drawn atlas of brainstem structures, the authors introduce the possibility of bias into their ROI analysis. Is there no suitable existing atlas such as the WFU-Pickatlas? If the authors must use a custom atlas, some criteria regarding which brainstem structures were included versus excluded are needed.

6) Atrophy in the PD-ICA, SBR, UPDRS, and age all appear to be somewhat correlated. It would be interesting to know which are independently correlated after accounting for the others using a multivariate analysis.

7) In testing whether the PD-ICA overlaps with an intrinsic connectivity network, the authors include comparison to a meta-analysis of regions responding to stimulus value. Although potentially interesting, this map should not be referred to as an “intrinsic connectivity network”.

8) The resolution used for the rsfMRI connectivity analyses in healthy young subjects (both seed-based and “propagation model”) is of 3.5 mm isotropic, which makes it impossible to distinguish (a seed in) the substantia nigra from the subthalamic nucleus, and probably also the red nucleus. This might explain why the authors found that the latter two structures were as likely to be propagators as the substantia nigra.

To alleviate these major concerns, the authors should probably re-do these analyses with an improved resolution dataset, which is for instance readily available in the HCP in a young and healthy population (∼500 subjects at 2 mm isotropic).

9) The authors should explain clearly how they manually defined their ROI in the substantia nigra and other small structures (only names of anatomical atlases are specified in the Methods), and extensively discuss the inherent limitations coming with such a resolution for both their seed-based analysis and propagation model.

10) Similarly, in the subsection “Spatial Analysis of PD-ICA network”, it is not clear that the location of the T1 weighted results obtained from DBM can be so precisely identified (substantia nigra vs. subthalamic nucleus, PPN, bed nucleus of the stria terminalis, etc.), so the authors should make this point clear in their manuscript.

11) The spatial cross-correlation between the 3 different networks seems to some extent arbitrarily set up at |r|>0.25. The authors should possibly report whether other significant cross-correlations were found for |r|<=0.25. The authors might also want to use the higher dimension ICA decomposition (d=70 instead of d=20) provided in Smith et al., 2009, as their high dimension ICA yields more specific basal ganglia networks.

12) Regarding the DBM analysis, the use of ICA is quite ingenious, especially considering the multi-centric aspect (16 sites, 3 different scanners) of this imaging cohort. Could the authors please specify whether they found site-specific or scanner-specific artefactual ICs in their results? What about a direct comparison of their DBM maps between the 2 populations? Presumably, this provided no significant result, which therefore sends a strong methodological message about an optimised approach for multi-centric T1 weighted volumetric studies.

13) There is concern about the specific use of MELODIC on structural data. The reason is that MELODIC is “tuned” to identifying sparse data and is inherently more suited for fMRI rather than structural data. It can therefore be the case that some more “global” components explaining the largest variance across the subjects can be missed sometimes. It seems unlikely here as the inputs used for TICA were DBM maps and not GM maps, and the main IC is reassuringly highly relevant to the pathology studied. It might be worth however for the authors to run FLICA (another data-driven ICA tool available in FSL) on their data to make sure results are similar.

14) Regarding their correlations with clinical measures, could the authors maybe justify in the manuscript why they have not used the MoCA and other parts of the UPDRS than part III, or alternatively carry out these correlation analyses?

15) Could the authors please provide the list of best propagators using diffusion imaging (similar to Table 3)? It would also be interesting to discuss the strong negative correlations reported in Table 3.

https://doi.org/10.7554/eLife.08440.021

Author response

[…] Some of the recommendations would entail significant additional analysis, and we do not insist that all of them necessarily be carried out. For example, suggestion #10 is to carry out the rsfMRI analysis of functional connectivity using the freely available HCP dataset. We encourage you to consider this, but is not essential for the revised manuscript. For the other recommended re-analyses, including #1 (seed-based analysis), #11 (higher-dimensional ICA decomposition) and #13 (FLICA analysis) it is important that you either follow the recommendation or provide a cogent response as to why this was not done.

Here is a quick summary of the changes made. All the suggested analyses have been attempted, and most are now included:

1) Seed based structural covariance was carried out. Results were similar but possibly less informative than ICA.

2) HCP data used:

a) For rsfMRI connectome, the results very similar, but HCP data suffered from subcortical signal loss when corrected for physiological noise with the FIX algorithm;

b) HCP rsfMRI data were used for more fine-grained ICA decomposition (N=100).

3) Higher dimensional decomposition performed at 70 and 100 components.

4) Greater justification for the claim that atrophy network matches an intrinsic brain network:

a) Finer decompositions were used.

b) Permutation testing was carried out to guard against false positives.

5) FLICA was tried, but we found to be less useful than MELODIC for DBM data. We also confirmed that DBM data have the super-Gaussian distribution (high kurtosis) required by FastICA algorithm used in MELODIC.

6) We have changed the title in accordance with the change of emphasis from proving the network hypothesis to mapping atrophy in de novo PD.

7) Subjectivity and bias from homemade brain atlas were mitigated by using three published atlases for ROI delineation (this doesn’t rule out error, but reduces the possibility of observer bias).

8) Multiple linear regression for clinical scores (age, UPDRS, SBR) were carried out.

9) Tractography-based connectome was added to the resting state fMRI connectome for the epicenter analysis.

10) Voxelwise epicenter analysis was also carried out.

Essential revisions:

1) A strength of this paper is that multiple different methods were used to measure connectivity patterns in normal subjects (resting state seed based, resting state ICA, DTI). A relative weakness is that only one method was used to define the atrophy pattern in PD, the central finding in the paper. There are numerous techniques for detecting and quantifying atrophy, so why did the authors choose the one they did (DBS). Do they get similar results using an alternative technique?

There are essentially three commonly used data-driven methods to detect brain atrophy: Deformation based morphometry (DBM) as used here, voxel based morphometry (VBM), and cortical thickness measurements. It is also possible to automatically or manually segment the brain into anatomical structures and estimate their size, but we were interested in a whole brain parametric mapping approach for use with ICA.

We were especially interested in the subcortical and brainstem structures, given the evidence from post-mortem studies that these are the earliest targets in PD. This ruled out cortical thickness analysis.

VBM uses segmentation of the brain into grey matter, white matter and CSF followed by low-dimensional spatial registration of the grey matter map to a template. The variable used in parametric analysis is the percent grey matter in each voxel. It has the advantage of being fairly robust and easy to implement. Disadvantages include the fact that both changes in MR signal intensity and tissue density may equally affect VBM measurements, and that only data labelled as grey matter are used in the analysis (i.e. not all the MR image data are used). VBM does not preserve shape information. VBM also requires considerable spatial smoothing (to control for inter-individual cortical variability), to generate maps in which intensity reflects tissue grey matter density. Smoothing reduces spatial resolution. Also it renders the data more Gaussian, making it less suitable for MELODIC (see below). Most importantly however, VBM does poorly in the brainstem and subcortical areas.

DBM retains all of the MRI data, and uses high-dimensional nonlinear registration to the template. It is sensitive to local shape changes due to atrophy. The main disadvantage of DBM lies in the complexity of the method and requirement for accurate quality control (QC). Our group has implemented and validated a DBM pipeline method that easily allows trained users to perform QC on every scan.

There are few studies directly comparing DBM to VBM. Scanlon et al. (Scanlon et al., 2011) showed that DBM was superior to VBM in detecting subtle subcortical abnormalities in temporal lobe epilepsy. Borghammer et al. in a pilot study found that DBM might be superior to VBM in detecting atrophy in PD (Borghammer et al., 2010). In sum, DBM was used because the preliminary evidence supports its use to detect subcortical atrophy, and because it retains all of the MR information and does not require additional spatial smoothing, making it preferable for ICA analysis. We used a validated pipeline (Aubert-Broche et al., 2013) and performed extensive QC (every registration verified visually).

We investigated the correlation between DBM and VBM in different areas of the brain, in the PPMI dataset. In cortical areas as well as cerebellum there is a high degree of similarity between the two methods; however in subcortical areas and brainstem VBM appears to be less sensitive than DBM (and not sensitive at all in the Brainstem). We compared the t-value of the difference between PD and control at each voxel for the two procedures. The correlation between voxelwise DBM and VBM t-values for whole brain is r = 0.54, (p<.001), the correlation for cerebellum and cortical areas are r = 0.65 and r = 0.6, respectively. For Basal ganglia the correlation is r = 0.46 and for brainstem there is no significant correlation between the two measures (r = -0.01, p = 0.26). The graphs below (Author response image 1) show that there is less variability in t-values for brainstem and basal ganglia for VBM than DBM. This supports the notion that VBM is relatively insensitive to atrophy in subcortical areas. A new paragraph detailing this was also added to the Methods section (“DBM consists in spatially transforming each MRI […] make it impossible to detect subcortical changes”).

Similarly, they only used one method to identify the atrophy pattern (ICA). Why not use a seed based approach to identify regions whose atrophy correlates with atrophy in the SN? The authors do not need to perform every methodological possibility, but the reasons for their choices need to be more clearly justified.

ICA has two advantages over seed-based methods: (1) it is data driven and hence not limited by a-priori hypotheses; (2) data-reduction increases the ability to detect changes. Also it has the power to distinguish independent sub-components that might be considered part of the same map using seed based analysis.

We performed the seed based analysis of the DBM maps, with SN as a seed region. (This looks for areas that show structural covariance with the seed across the group.) This results in a map (see Author response image 2, panel A, thresholded at t=3.0) that is very similar to our ICA result. The PD group has significantly greater atrophy than controls within the map (t=2.04, p=0.04). However, with ICA, there are two components (Author response image 2, panel B, coloured in red and blue) that overlap with the seed-based map: (1) the PD-ICA (Author response image 2, panel C) and (2), a component that consists of cerebellum and white matter near the basal ganglia (Author response image 2, panel D). Deformation in the first ICA differed between PD and controls (t=3, p=0.003), however, there was no significant difference in the second ICA (t=1.2, p = 0.20). This suggests that the seed-based analysis yields somewhat similar but less specific results that may include components not specifically PD related.

We also tried the more traditional univariate approach: comparing DBM values at each voxel. The image is show here (Author response image 3) with thresholded at z=2.5:

Only the substantia nigra and small peaks in basal ganglia in medial temporal lobe survive rigorous statistical thresholding. The univariate approach suffers from low power.

Further, they should make it clear when and why their methods deviate from prior work with similar goals (e.g. Bill Seeley's work).

A few groups have attempted to relate brain atrophy to neurodegeneration using a network-based framework/approach. The Seeley group published two papers on Alzheimer’s disease (AD) and frontotemporal dementia (FTD) that were a major influence on our current work. The ideas of (1) comparing a disease atrophy map to a normal resting state (Seeley et al., 2009) and (2) using graph theory to identify an epicenter for disease spread (Zhou et al., 2012), both of which support the network spread hypothesis, originated from these two papers.

In the first they showed that atrophy maps from 5 different dementing syndromes each showed overlap with ICNs constructed using seed-based connectivity mapping (the seeds selected from the atrophy maps). This is similar to what we did here; they compared two maps: the atrophy statistical map for each disease and an ICN seeded from the most affected voxel of that atrophy map (we used completely data-driven ICNs). They used a Goodness of Fit measure between these ICNs and a binarized version of the atrophy map. We think our approach is quite similar to theirs except that because they had 5 diseases, they were able to show that the ICN generated from disease i fit the atrophy pattern from disease i better than that from the other 4 diseases. Strictly speaking this does not prove that the atrophy map is a resting state network. We went somewhat further to demonstrate that the PD atrophy map corresponded to a set of connected brain regions.

Other papers have attempted to relate ICA components from different types of data, as we have done here (Smith et al., 2009; Segall et al., 2012). As discussed below these papers used the same approach as us, by setting a somewhat arbitrary threshold for spatial correlation. Another recent paper (Douaud et al., 2014) compared ICA components obtained in normal aging (using VBM rather than DBM) to atrophy maps from AD and schizophrenia. They also used spatial correlation but tried to assess statistical significance by generating 1000 random maps and demonstrating that the correlations between the true maps were outside the confidence interval of the correlations to the random maps. We have now implemented a version of this approach as well (Figure 3–figure supplement 2).

Finally, two recent papers have used graph theory and epidemiological spread models to support network spread models in AD (Raj et al., 2012; Iturria-Medina et al., 2014). We similarly used graph theory to identify a supposed disease epicenter, but both papers pushed this approach further in ways that are beyond our current work.

2) Results are a bit overstated at times which could detract from the importance of the findings. The authors convincingly show that a specific pattern of atrophy is related to PD, aging, dopamine binding in the striatum, and UPDRS score. This alone is very worthy of publication. Whether this atrophy network is an “intrinsic brain network” as defined by resting state fcMRI or validates the “network spread model” of PD are important questions, but more weakly supported by the present data. The authors may be better served to focus on their strongest findings and relegate the others to the Discussion.

We now temper our claims regarding evidence of PD as a “nexopathy”, and rather emphasize the use of ICA and DBM to map atrophy in early PD.

For example the Results section originally started with “We also tested the hypothesis that the pattern of neurodegeneration in PD is consistent with spread of a pathogen through intrinsic brain networks…”. It now starts with the deformation-based morphometry results. The Introduction has also been reconfigured.

The Abstract, which originally started with: “We tested the network-spread model of neuro-degeneration in Parkinson’s Disease (PD) using MRI and clinical data…”, now begins with: “We mapped the distribution of atrophy in Parkinson’s Disease (PD) using MRI and clinical data…”.

The title has been changed to “Network Structure of Brain Atrophy in de novo Parkinson’s Disease”. The word “network” only refers to the fact that we use a brain network framework in the analysis.

In sum, the atrophy findings are emphasized, and the comparison to intrinsic networks secondary.

3) The authors put great emphasis on the fact that their atrophy pattern matches an “intrinsic connectivity network”, including making this the title, but the data supporting this claim are weak. Specifically, the criteria for a “match” are arbitrary. The authors chose a threshold of r = 0.25. If they had chosen a threshold of 0.35 instead, we would conclude that the atrophy pattern fails to match any intrinsic connectivity networks. Rather than concluding that the atrophy matches or fails to match an intrinsic connectivity pattern, the authors could make better claims on comparative matching. In other words, they can claim that their atrophy pattern matches a specific network better than other atrophy patterns and they can conclude that their atrophy pattern matches a specific network better than other networks.

We now use the finer decomposition (N=70 and N=100). We show that there are other ICNs that correlate with the atrophy map, but that these ICNs are themselves inter-connected (see Figure 3–figure supplement 3).

However, the question “is the set of PD affected regions an intrinsic network in healthy brain?” is perhaps not answerable unequivocally in a statistical sense. (Note however that the epicenter analysis in our paper showing that PD related atrophy is predicted by distance from the SN in the normal connectome also fits with the network spread hypothesis.)

We agree that the threshold choice is to a certain extent arbitrary. It was based on (Smith et al., 2009) and (Segall et al., 2012). These authors used Pearson correlation with |r|<=0.25 and |r|<=0.20 respectively, to look at the correspondence between sets of ICA-derived networks. In both cases the threshold was chosen based on the estimated degrees of freedom (number of resels) in the images. Smith et al. calculated that |r|<=0.25 effectively protected against multiple comparisons in a dataset that, like ours, compared two sets of ICA networks derived from MELODIC (one of the two sets being the 70 network decomposition used here). If one accepts the Smith et al. (2009) argument, then |r|<=0.25 protects against false positives for these data.

We add two other pieces of evidence. First, to show that the correlation between the PD atrophy map and the resting-state ICN exceeds chance levels we generated 1000 sets of 70 ICNs by randomly reassigning voxel values in each ICN (i.e. keeping the same z-scores but reassigning them spatially). We then calculated the spatial correlation between the PD atrophy map and each of the 70 maps thus generated, 1000 times. In the figure below we plot the correlation between the PD atrophy map and the true ICNs from the 70-network decomposition in red, and the confidence interval for the correlations with the random networks. This shows that that in the true 70 ICN decomposition one network is more significantly correlated than the others, and that this pattern is significantly above chance levels.

Note that the higher dimension ICA decomposition (d=70 instead of d=20) provided in Smith et al., 2009 is used as suggested by the reviewers (in point #11). This in fact improved the similarity measure between the network of interest (PD-ICA) and the most similar network from r=0.28 to r=0.32.

Note that with this finer decomposition there are three other networks showing correlation (although less than 0.15). To test if these are themselves correlated we used a different rsfMRI decomposition: the 100 ICN decomposition from the human connectome project (Smith et al., 2013). We identified four networks that correlated spatially with the PD-ICA network. (We could not do this with the original decomposition from Smith et al. 2009 because the BOLD timeseries are not available.) Because HCP also provides the fMRI time course of each component we were able to show that these four networks (1) demonstrate significant covariance with each other and (2) cluster together in a hierarchical arrangement of all 100 components. This is now Figure 3–figure supplement 3.

Figure 3–figure supplement 3 shows that the PD-ICA atrophy map correlates better with 4 ICNs that are inter-connected than with other ICNs. To assess significance we performed permutation testing by randomizing the order of the 100 networks 1000 times and found that the covariance between these first four was significantly greater than chance (p<0.0016).

4) It is a bit unclear whether the PD-ICA network (Figure 1) shows the full ICA component identified combining PD and controls or if only those voxels within the component that showed significant differences between PD and controls. I believe it's the former, but this should be made a bit clearer and it would be helpful to also show that latter. What part of this network shows the greatest difference between PD and controls?

The PD-ICA map (Figure 1) consists of all voxels in the ICA component (thresholded at z=3) i.e. it is not restricted to the voxels showing a group difference at the voxel level. This is now clarified in the legend. We have generated a second similar figure displaying the T-stat of the group difference at each voxel within the ICA. It is now included as a subfigure of Figure 1 (Figure 1–figure supplement 4).

5) There are concerns regarding the correlations across the 135 ROIs. The authors already have atrophy and connectivity measures at the voxel level so why not do the analysis across voxels?

We preformed the same correlation as in Figure 4 at a voxel level: i.e. functional connectivity between each voxel and the SN seed region (resting state fMRI data) versus atrophy t-score at each voxel. The correlation coefficient was 0.13, which is highly significant (80,000 voxels). However, we think an anatomical ROI-based connectome is more advantageous. The voxel level connectome suffers from the high spatial correlation of fMRI data between neighbouring voxels, and from the increased noise of voxel as opposed to ROI fMRI data. Also the voxelwise analysis is not possible for a tractography-based connectome.

By combining different pieces of various brain atlases with their own hand-drawn atlas of brainstem structures, the authors introduce the possibility of bias into their ROI analysis. Is there no suitable existing atlas such as the WFU-Pickatlas? If the authors must use a custom atlas, some criteria regarding which brainstem structures were included versus excluded are needed.

There is no MRI atlas of the entire brainstem currently available. We agree with the criticism of potential bias. Indeed, when rethinking this issue, we concluded that the atlas regions below the midbrain (caudal to the SN) actually serve no purpose in this study. First, measuring atrophy in the caudal brainstem is difficult using T1-MRI, even with DBM. Second, both rsfMRI and tractography do poorly in this region: fMRI because it is prone to respiratory and pulse artifacts, and tractography because it is difficult to trace white matter pathways to their real target (the brainstem being a dense structure with numerous small nuclei). The brainstem is also especially prone to MRI susceptibility artifacts. These issues are outlined in Ford et al., 2013. Therefore, regions from pons and medulla are expected to contribute mostly noise to the correlation analysis intended to test the epicentre model of disease spread.

We have now repeated all the ROI analyses using only the Hammers and Cerebellum atlases plus three regions not present in these atlases, namely the substantia nigra (SN), subthalamic nucleus (STN) and red nucleus. These three regions were drawn, as before, on the Big Brain (Amunts et al., 2013), and Duvernoy (Duvernoy et al., 1995) atlases in MNI space. Since our submission we became aware of an MRI atlas of these three structures based on ultra-high resolution Flash MR imaging at 7T (Keuken et al., 2014), which we used to confirm the location and extent of our atlas regions.

This has actually improved our results. Now the SN emerges as a much better disease propagator (see reply to point 8 below). As before, this says nothing about a presumed initial disease epicenter in the medulla (based on Braak). It only suggests that SN is the best propagator of the disease to the supratentorial brain among regions tested.

6) Atrophy in the PD-ICA, SBR, UPDRS, and age all appear to be somewhat correlated. It would be interesting to know which are independently correlated after accounting for the others using a multivariate analysis.

8) The resolution used for the rsfMRI connectivity analyses in healthy young subjects (both seed-based and “propagation model”) is of 3.5mm isotropic, which makes it impossible to distinguish (a seed in) the substantia nigra from the subthalamic nucleus, and probably also the red nucleus. This might explain why the authors found that the latter two structures were as likely to be propagators as the substantia nigra.

To alleviate these major concerns, the authors should probably re-do these analyses with an improved resolution dataset, which is for instance readily available in the HCP in a young and healthy population (∼500 subjects at 2mm isotropic).

We performed the suggested analysis using the HCP dataset. The results are not different (the three midbrain structures are still equally good propagators), possibly because we are still using the same low-resolution anatomical data for atrophy calculation (i.e. only the fMRI data has the improved resolution). These data are with the new atlas (112 regions, caudal brainstem excluded). Interestingly, we now find a greater correlation with SN in our 51 rsfMRI data. We note a problem with the HCP data: the “FIX’d” resting state data (i.e. corrected for physiological artifacts using FIX) loses much signal in the brainstem and subcortical areas. We therefore chose to retain our original resting state data for this comparison (but we do use HCP to look for resting state ICNs – see above). Here are the correlations (atrophy versus connectivity) for each region and dataset:

rsfMRI-51 subjects

rsfMRI-HCP

Substantia Nigra

0.40

Red Nucleus

0.30

Subthalamic Nucleus

0.28

Substantia Nigra

0.26

Red Nucleus

0.28

Subthalamic Nucleus

0.22

Cerebellum Dentate

0.27

Thalamus

0.08

Pallidum

0.23

Cerebellum Vermis VIIb

0.07

Hippocampus

0.22

Cerebellum Vermis CrusII

0.05

Cerebellum Vermis X

0.21

Cerebellum Fastigial

0.05

Cerebellum Vermis VIIIa

0.20

Cerebellum Vermis CrusI

0.03

Cerebellum Interposed

0.20

Cerebellum X

0.03

Cerebellum Fastigial

0.20

Cerebellum Vermis VI

0.02

Cerebellum Vermis IX

0.20

Cerebellum VIIb

0.01

Cerebellum Vermis VIIIb

0.18

Cerebellum VI

0.01

Cerebellum I IV

0.18

Cerebellum V

-0.01

Cerebellum Vermis VIIb

0.17

Cerebellum I IV

-0.01

Parahippocampal gyrus

0.16

Cerebellum Interposed

-0.01

Cerebellum V

0.16

Cerebellum Vermis VIIIa

-0.03

Anterior temporal lobe (medial part)

0.15

Cerebellum VIIIb

-0.04

Cerebellum Vermis CrusII

0.14

Cerebellum VIIIa

-0.04

occipitotemporal gyrus (lateral part)

0.14

Cerebellum Vermis IX

-0.04

Cerebellum VIIb

0.13

Cerebellum Vermis VIIIb

-0.05

Cerebellum CrusII

0.13

Cerebellum CrusII

-0.06

Cerebellum IX

0.12

Pallidum

-0.06

Cerebellum VI

0.12

Cerebellum Dentate

-0.08

Amygdala

0.12

Anterior temporal lobe (medial part)

-0.09

Cerebellum X

0.11

occipitotemporal gyrus (lateral part)

-0.10

Cerebellum Vermis CrusI

0.10

Cerebellum IX

-0.10

Putamen

0.10

Occipital lobe (lateral part)

-0.12

Cerebellum Vermis VI

0.08

Insula

-0.12

Cerebellum VIIIa

0.07

Cuneus

-0.12

Thalamus

0.06

Cerebellum Vermis X

-0.13

Cerebellum VIIIb

0.05

Cerebellum CrusI

-0.14

Insula

0.04

Parahippocampal gyrus

-0.14

Anterior temporal lobe (lateral part)

0.02

Caudate nucleus

-0.14

Cerebellum CrusI

0.01

Precentral gyrus

-0.14

Superior temporal gyrus (anterior part)

0.01

Postcentral gyrus

-0.14

Caudate nucleus

-0.06

Putamen

-0.16

Superior temporal gyrus (posterior part)

-0.07

Lingual gyrus

-0.17

Middle and inferior temporal gyrus

-0.07

Inferior frontal gyrus

-0.17

Lingual gyrus

-0.08

Hippocampus

-0.18

Postcentral gyrus

-0.08

Parietal lobe (Inferiolateral)

-0.18

Precentral gyrus

-0.09

Amygdala

-0.19

Posterior temporal lobe

-0.09

Posterior temporal lobe

-0.19

Inferior frontal gyrus

-0.10

Superior parietal gyrus

-0.21

Middle frontal gyrus

-0.10

Superior temporal gyrus (posterior part)

-0.21

Cuneus

-0.10

Middle frontal gyrus

-0.22

Anterior cingulate gyrus

-0.12

Anterior temporal lobe (lateral part)

-0.23

Occipital lobe (lateral part)

-0.12

Anterior cingulate gyrus

-0.23

Lateral orbital gyrus

-0.16

Superior temporal gyrus (anterior part)

-0.23

Superior frontal gyrus

-0.16

Posterior cingulate gyrus

-0.24

Parietal lobe (Inferiolateral)

-0.16

Lateral orbital gyrus

-0.28

Superior parietal gyrus

-0.20

Nucleus accumbens

-0.28

Pre-subgenual frontal cortex

-0.20

Middle and inferior temporal gyrus

-0.31

Posterior orbital gyrus

-0.23

Straight gyrus

-0.33

Posterior cingulate gyrus

-0.23

Subcallosal area

-0.34

Medial orbital gyrus

-0.27

Posterior orbital gyrus

-0.36

Straight gyrus

-0.31

Medial orbital gyrus

-0.36

Anterior orbital gyrus

-0.33

Pre-subgenual frontal cortex

-0.36

Subgenual frontal cortex

-0.34

Superior frontal gyrus

-0.37

Nucleus accumbens

-0.38

Subgenual frontal cortex

-0.41

Subcallosal area

-0.42

Anterior orbital gyrus

-0.44

9) The authors should explain clearly how they manually defined their ROI in the substantia nigra and other small structures (only names of anatomical atlases are specified in the Methods), and extensively discuss the inherent limitations coming with such a resolution for both their seed-based analysis and propagation model.

In the revised manuscript, we only use three manually delineated structures (SN, STN, Red Nucleus), and explain the method more clearly (please see: “These two atlases do not have adequate segmentation […] and excludes all brainstem regions caudal to the SN”).

10) Similarly, in the subsection “Spatial Analysis of PD-ICA network”, it is not clear that the location of the T1 weighted results obtained from DBM can be so precisely identified (substantia nigra vs. subthalamic nucleus, PPN, bed nucleus of the stria terminalis, etc), so the authors should make this point clear in their manuscript.

Regarding the SN, the atrophy map (Figure 1) convincingly shows the highest peak in the SN. In answer to these two points we added the following caveat: “Note, however, that spatial resolution limitations for all of the imaging modalities […] may be interpreted as volume changes by the DBM methodology.”

11) The spatial cross-correlation between the 3 different networks seems to some extent arbitrarily set up at |r|>0.25. The authors should possibly report whether other significant cross-correlations were found for |r|<=0.25. The authors might also want to use the higher dimension ICA decomposition (d=70 instead of d=20) provided in Smith et al., 2009, as their high dimension ICA yields more specific basal ganglia networks.

Please see our response to concern #3.

12) Regarding the DBM analysis, the use of ICA is quite ingenious, especially considering the multi-centric aspect (16 sites, 3 different scanners) of this imaging cohort. Could the authors please specify whether they found site-specific or scanner-specific artefactual ICs in their results?

To investigate whether there is an effect of center, multivariate analysis was used. The analysis was used for each obtained DBM-network separately using DBM ∼ Group (PD/Control) + Age + Gender + Site. There was no significant effect of site after correcting for multiple comparisons (p > 0.1). This is now included in the Results section.

What about a direct comparison of their DBM maps between the 2 populations? Presumably, this provided no significant result, which therefore sends a strong methodological message about an optimised approach for multi-centric T1 weighted volumetric studies.

This is correct. When correcting for multiple comparisons, the univariate voxel-wise approach yields few significant results (shown in Author response image 4).

13) There is concern about the specific use of MELODIC on structural data. The reason is that MELODIC is “tuned” to identifying sparse data and is inherently more suited for fMRI rather than structural data. It can therefore be the case that some more “global” components explaining the largest variance across the subjects can be missed sometimes. It seems unlikely here as the inputs used for TICA were DBM maps and not GM maps, and the main IC is reassuringly highly relevant to the pathology studied. It might be worth however for the authors to run FLICA (another data-driven ICA tool available in FSL) on their data to make sure results are similar.

We confirm that the DBM data posses the super-Gaussian (high kurtosis) quality which is a precondition for using MELODIC. As mentioned above, GM maps and DBM maps have different data structure. GM maps from VBM having a sub-Gaussian distribution in general (due to spatial smoothing), do not possess the sparsity of fMRI data and indeed when we apply MELODIC to VBM maps from the PPMI dataset we obtain a single component (basically the entire brain). DBM data on the other hand are even sparser than fMRI and this makes MELODIC suitable for analyzing them. In order to formally check for sparsity we performed a kurtosis analysis of the PPMI DBM and VBM data as well as our fMRI dataset for comparison. In Author response image 5, we present the distributions of kurtosis values computed for each subject.

FLICA has recently been used by the FMRIB group to analyze VBM data. We contacted the first author, Dr Douaud, who states that FLICA is more tuned to sub-Gaussian signals, which explains why it succeeds with VBM data. Presumably this would make it less useful for DBM with its high sparsity. We added the following text to the Methods: “The ICA algorithm in MELODIC is sensitive to sparsely distributed (super-Gaussian) data (Daubechies et al., 2009) as typically seen in fMRI. The DBM data used here possessed this super-Gaussian property (kurtosis >4).”

14) Regarding their correlations with clinical measures, could the authors maybe justify in the manuscript why they have not used the MoCA and other parts of the UPDRS than part III, or alternatively carry out these correlation analyses?

PPMI has numerous clinical assessment tools. We needed to limit the number of correlations investigated to those measures of disease severity that might best predict brain atrophy to avoid a multiple comparison problem. The two best measures of disease severity are UPDRS part III and SPECT binding (SBR).

The UPDRS has four parts but only part III is an objective assessment of motor function. Parts I and II refer to the patient’s subjective experience of the disease (non-motor and motor). Part I loads onto sleep problems, depression and cognitive impairment. Part II refers to activities of daily living such as speaking, dressing and eating. Example questions are “do you have trouble remembering things or paying attention” or “do you have problems dressing.” While these quality of life assessments are quite relevant to clinical practice, they are likely less closely correlated to neurodegeneration than UPDRS part III, in which a trained examiner measures motor function objectively. Part IV is not relevant as it refers to complications of medications, and all the participants here were unmedicated.

The reason not to study MoCA was different. Entry into the trial consisted mostly of non-demented individuals. The mean MoCA was 27.1 (‘normal’ = 27-30), and only 20% of patients had a score in the ‘abnormal’ range (MoCA<26). There may therefore not be enough variability in MoCA to expect correlations.

Moreover, one paper has already been published on MoCA and cortical thickness in the PPMI database (although only 123 patients were included) (Pereira et al., 2014). Not surprisingly, there was cortical thinning in cognitively impaired patients.

Nonetheless we performed the following correlations:

First we investigated the relationship between the deformation in the PD-ICA network and MOCA score in both patients and controls. There was a correlation between MoCA and atrophy for the two groups together (r=0.13, p=0.01) however this relationship disappeared after accounting for the effect of age (r=-.002, p=0.96). This phenomenon held when examining PD and control subjects separately, with age as a confound (i.e. the correlation between atrophy and MoCA was entirely explained by age in both groups).

We also investigated the relationship between MOCA and deformation in the remaining 29 networks, controlling for age, and again there were no significant correlations, even at a trend p=0.1 level.

In summary, the MoCA predicted brain atrophy, but the effect was entirely age-related, and there were no differences between PD and controls in any of the brain networks. This is likely due to the fact that most patients had a MoCA in the normal range.

15) Could the authors please provide the list of best propagators using diffusion imaging (similar to Table 3)?

The list is provided here and in the revised manuscript. With diffusion imaging, SN remains the best propagator except for cerebellar regions. The presence of many cerebellar regions as good propagators in the diffusion-based connectome is possibly artifactual. Cerebellar fibres pass through the pons, forming the cortico-ponto-cerebellar tract. Tractography may be poor at resolving these cortico-ponto-cerebellar connections from ascending fibres whose origin is in brainstem nuclei, and from corticospinal tract fibres, due to partial volume effects (contamination among numerous densely packed adjacent pathways) and decussation within the brainstem. Thus the brainstem and cerebellar portion of the connectome is likely to suffer from imprecise assignment of edges. This could explain the low specificity obtained for the different cerebellar regions as possible propagators (i.e. all cerebellum regions are grouped with very similar correlation values, according to the DW-MRI data). Nonetheless we now present the data and discuss potential pitfalls.

rsfMRI

DW-MRI

Substantia Nigra

0.40

Cerebellum VIIb

-0.28

Subthalamic Nucleus

0.28

Substantia Nigra

-0.28

Red Nucleus

0.28

Cerebellum X

-0.28

Cerebellum Dentate

0.27

Cerebellum VIIIa

-0.27

Pallidum

0.23

Cerebellum VIIIb

-0.27

Hippocampus

0.22

Cerebellum Vermis VIIb

-0.27

Cerebellum Vermis X

0.21

Cerebellum CrusII

-0.26

Cerebellum Vermis VIIIa

0.20

Cerebellum Vermis VIIIa

-0.25

Cerebellum Interposed

0.20

Cerebellum Vermis CrusII

-0.25

Cerebellum Fastigial

0.20

Cerebellum CrusI

-0.25

Cerebellum Vermis IX

0.20

Cerebellum Vermis VIIIb

-0.24

Cerebellum Vermis VIIIb

0.18

Cerebellum Vermis CrusI

-0.24

Cerebellum I IV

0.18

Cerebellum IX

-0.23

Cerebellum Vermis VIIb

0.17

Cerebellum VI

-0.23

Parahippocampal gyrus

0.16

Cerebellum Vermis IX

-0.23

Cerebellum V

0.16

Cerebellum Vermis VI

-0.22

Anterior temporal lobe (medial part)

0.15

Cerebellum Dentate

-0.21

Cerebellum Vermis CrusII

0.14

Cerebellum Interposed

-0.20

occipitotemporal gyrus (lateral part)

0.14

Cerebellum Fastigial

-0.19

Cerebellum VIIb

0.13

Cerebellum V

-0.18

Cerebellum CrusII

0.13

Parahippocampal gyrus

-0.18

Cerebellum IX

0.12

Cerebellum I IV

-0.18

Cerebellum VI

0.12

Cerebellum Vermis X

-0.18

Amygdala

0.12

Middle and inferior temporal gyrus

-0.15

Cerebellum X

0.11

Occipital lobe (lateral part)

-0.14

Cerebellum Vermis CrusI

0.10

Lingual gyrus

-0.14

Putamen

0.10

occipitotemporal gyrus (lateral part)

-0.13

Cerebellum Vermis VI

0.08

Posterior temporal lobe

-0.13

Cerebellum VIIIa

0.07

Amygdala

-0.13

Thalamus

0.06

Anterior temporal lobe (medial part)

-0.12

Cerebellum VIIIb

0.05

Subthalamic Nucleus

-0.11

Insula

0.04

Cuneus

-0.10

Anterior temporal lobe (lateral part)

0.02

Red Nucleus

-0.09

Cerebellum CrusI

0.01

Hippocampus

-0.09

Superior temporal gyrus (anterior part)

0.01

Anterior temporal lobe (lateral part)

-0.08

Caudate nucleus

-0.06

Superior temporal gyrus (posterior part)

-0.06

Superior temporal gyrus (posterior part)

-0.07

Parietal lobe (Inferiolateral)

-0.05

Middle and inferior temporal gyrus

-0.07

Superior temporal gyrus (anterior part)

-0.01

Lingual gyrus

-0.08

Thalamus

0.02

Postcentral gyrus

-0.08

Superior parietal gyrus

0.03

Precentral gyrus

-0.09

Pallidum

0.03

Posterior temporal lobe

-0.09

Insula

0.12

Inferior frontal gyrus

-0.10

Putamen

0.14

Middle frontal gyrus

-0.10

Caudate nucleus

0.17

Cuneus

-0.10

Postcentral gyrus

0.18

Anterior cingulate gyrus

-0.12

Nucleus accumbens

0.20

Occipital lobe (lateral part)

-0.12

Subcallosal area

0.23

Lateral orbital gyrus

-0.16

Posterior orbital gyrus

0.23

Superior frontal gyrus

-0.16

Posterior cingulate gyrus

0.24

Parietal lobe (Inferiolateral)

-0.16

Medial orbital gyrus

0.27

Superior parietal gyrus

-0.20

Straight gyrus

0.29

Pre-subgenual frontal cortex

-0.20

Subgenual frontal cortex

0.29

Posterior orbital gyrus

-0.23

Lateral orbital gyrus

0.31

Posterior cingulate gyrus

-0.23

Precentral gyrus

0.32

Medial orbital gyrus

-0.27

Inferior frontal gyrus

0.32

Straight gyrus

-0.31

Anterior orbital gyrus

0.33

Anterior orbital gyrus

-0.33

Superior frontal gyrus

0.35

Subgenual frontal cortex

-0.34

Anterior cingulate gyrus

0.35

Nucleus accumbens

-0.38

Middle frontal gyrus

0.35

Subcallosal area

-0.42

Pre-subgenual frontal cortex

0.36

It would also be interesting to discuss the strong negative correlations reported in Table 3.

A significant negative correlation (in the rsfMRI graph) is more difficult to interpret than a positive one (which identifies best propagators of the disease). It could indicate a region affected at later disease stages: e.g. if the disease marches in a single direction, a region at the edge of the advancing wave would exhibit negative correlation. Another possibility is that a region does not accumulate the pathogenic protein, or accumulates but does not propagate: nodes connected to it would be protected. This is speculative however.

https://doi.org/10.7554/eLife.08440.022

Article and author information

Author details

  1. Yashar Zeighami

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Contribution
    YZ, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  2. Miguel Ulla

    1. McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    2. Service de Neurologie A, CHU Clermont-Ferrand, Clermont-Ferrand, France
    Contribution
    MU, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  3. Yasser Iturria-Medina

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Contribution
    YI-M, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  4. Mahsa Dadar

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Contribution
    MD, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  5. Yu Zhang

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Contribution
    YZ, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  6. Kevin Michel-Herve Larcher

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Contribution
    KM-HL, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  7. Vladimir Fonov

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Contribution
    VF, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  8. Alan C Evans

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Contribution
    ACE, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  9. D Louis Collins

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Contribution
    DLC, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  10. Alain Dagher

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
    Contribution
    AD, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    For correspondence
    alain.dagher@mcgill.ca
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon 0000-0002-0945-5779

Funding

Michael J. Fox Foundation for Parkinson's Research (MJFF) (320897)

  • D Louis Collins
  • Alain Dagher

W. Garfield Weston Foundation (320897)

  • D Louis Collins
  • Alain Dagher

Alzheimer's Association (320897)

  • D Louis Collins
  • Alain Dagher

Canadian Institutes of Health Research (Instituts de recherche en santé du Canada) (MOP-136776)

  • Alain Dagher

Natural Sciences and Engineering Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie du Canada) (436259-13)

  • Alain Dagher

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was funded by grants from the Michael J Fox Foundation for Parkinson's Research, the W Garfield Weston Foundation, and the Alzheimer's Association, the Canadian Institutes for Health Research, and the Natural Sciences and Engineering Research Council of Canada. We thank Christian Beckmann and Simon Eickhoff for their advice on data analysis. Data used in this article were obtained from the Parkinsons Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org. PPMI is sponsored and partially funded by the Michael J Fox Foundation for Parkinsons Research and funding partners, including AbbVie, Avid Radiopharmaceuticals, Biogen, Bristol-Myers Squibb, Covance, GE Healthcare, Genentech, GlaxoSmithKline (GSK), Eli Lilly and Company, Lundbeck, Merck, Meso Scale Discovery (MSD), Pfizer, Piramal Imaging, Roche, Servier, and UCB (www.ppmi-info.org/fundingpartners). Some data used in this paper were also provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

Ethics

Human subjects: For the Parkinson's Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). Each participating PPMI site received approval from a local research ethics committee before study initiation, and obtained written informed consent from all subjects participating in the study. For the resting state fMRI data collected in our lab, We acquired resting state fMRI in 51 healthy, right-handed volunteers. The experimental protocol was reviewed and approved by Research Ethics Board of Montreal Neurological Institute. All subjects gave informed consent.

Reviewing Editor

  1. David C Van Essen, Reviewing Editor, Washington University in St Louis, United States

Publication history

  1. Received: April 30, 2015
  2. Accepted: September 5, 2015
  3. Accepted Manuscript published: September 7, 2015 (version 1)
  4. Version of Record published: October 8, 2015 (version 2)

Copyright

© 2015, Zeighami et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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