NeuroImage: Clinical 23 (2019) 101900 Contents lists available at ScienceDirect NeuroImage: Clinical journal homepage: www.elsevier.com/locate/ynicl Specific patterns of brain alterations underlie distinct clinical profiles in T Huntington's disease Clara Garcia-Gorroa,b,1, Alberto Llerac,1, Saul Martinez-Hortad,e, Jesus Perez-Perezd,e, Jaime Kulisevskyd,e,f, Nadia Rodriguez-Dechichag, Irene Vaquerg, Susana Subirag,h, Matilde Calopai, Esteban Muñozj,k,l, Pilar Santacruzj, Jesus Ruiz-Idiagom,n, Celia Marecam, ⁎ ⁎ Christian F. Beckmannc,o, Ruth de Diego-Balaguera,b,p,q, ,2, Estela Camaraa, a Cognition and Brain Plasticity Unit, L'Hospitalet de Llobregat (Barcelona), IDIBELL (Institut d'Investigació Biomèdica de Bellvitge), Spain b Department of Cognition, Development and Educational Psychology, University of Barcelona, Barcelona, Spain c Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands d Movement Disorders Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau (IIB-Sant Pau), Barcelona, Spain e CIBERNED (Center for Networked Biomedical Research on Neurodegenerative Diseases), Carlos III Institute, Madrid, Spain f Universidad Autónoma de Barcelona, Barcelona, Spain g Hestia Duran i Reynals, Hospital Duran i Reynals, Hospitalet de Llobregat (Barcelona), Spain h Department of Clinical and Health Psychology, Universitat Autònoma de Barcelona, Barcelona, Spain i Movement Disorders Unit, Neurology Service, Hospital Universitari de Bellvitge, Barcelona, Spain j Movement Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain k IDIBAPS (Institut d'Investigacions Biomèdiques August Pi i Sunyer), Barcelona, Spain l Facultat de Medicina, University of Barcelona, Barcelona, Spain m Hospital Mare de Deu de la Mercè, Barcelona, Spain n Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain o Donders Institute for Brain, Cognition and Behaviour, Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands p The Institute of Neurosciences, University of Barcelona, Barcelona, Spain q ICREA (Catalan Institute for Research and Advanced Studies), Barcelona, Spain A R T I C LE I N FO A B S T R A C T Keywords: Huntington's disease (HD) is a genetic neurodegenerative disease which involves a triad of motor, cognitive and Linked ICA psychiatric disturbances. However, there is great variability in the prominence of each type of symptom across Data fusion individuals. The neurobiological basis of such variability remains poorly understood but would be crucial for Huntington's disease better tailored treatments. Multivariate multimodal neuroimaging approaches have been successful in disen- Neurodegeneration tangling these profiles in other disorders. Thus we applied for the first time such approach to HD. We studied the Clinical profiles Structural MRI relationship between HD symptom domains and multimodal measures sensitive to grey and white matter structural alterations. Forty-three HD gene carriers (23 manifest and 20 premanifest individuals) were scanned and underwent behavioural assessments evaluating motor, cognitive and psychiatric domains. We conducted a multimodal analysis integrating different structural neuroimaging modalities measuring grey matter volume, cortical thickness and white matter diffusion indices – fractional anisotropy and radial diffusivity. All neuroi- maging measures were entered into a linked independent component analysis in order to obtain multimodal components reflecting common inter-subject variation across imaging modalities. The relationship between multimodal neuroimaging independent components and behavioural measures was analysed using multiple linear regression. We found that cognitive and motor symptoms shared a common neurobiological basis, whereas the psychiatric domain presented a differentiated neural signature. Behavioural measures of different Abbreviations: ACC, anterior cingulate cortex; CC, corpus callosum; CST, corticospinal tract; CT, cortical thickness; FA, fractional anisotropy; ICA, independent component analysis; IFOF, inferior fronto-occipital fasciculus; ILF, inferior longitudinal fasciculus; OFC, orbitofrontal cortex; PBA-s, problem behaviours assessment - short version; RD, radial diffusivity; SDMT, symbol digit modality tests; SLF, superior longitudinal fasciculus; SMA, supplementary motor area; TMT, trail making test; UF, uncinated fasciculus; UHDRS, unified Huntington's disease rating scale; VBM, voxel based morphometry ⁎ Corresponding author at: Cognition and Brain Plasticity Unit, L'Hospitalet de Llobregat (Barcelona), IDIBELL (Institut d'Investigació Biomèdica de Bellvitge), Spain; Department of Cognition, Development and Educational Psychology, University of Barcelona, Barcelona, Spain E-mail address: ruth.dediego@ub.edu (R. de Diego-Balaguer). 1 The authors contributed equally to this work 2 Lead contact: Ruth de Diego-Balaguer, 'Cognition and Brain Plasticity Unit, Feixa Llarga s/n, 08907 L'Hospitalet de Llobregat, Barcelona, Spain. https://doi.org/10.1016/j.nicl.2019.101900 Received 29 April 2019; Received in revised form 11 June 2019 Available online 15 June 2019 2213-1582/ © 2019 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/). C. Garcia-Gorro, et al. NeuroImage: Clinical 23 (2019) 101900 symptom domains correlated with different neuroimaging components, both the brain regions involved and the neuroimaging modalities most prominently associated with each type of symptom showing differences. More severe cognitive and motor signs together were associated with a multimodal component consisting in a pattern of reduced grey matter, cortical thickness and white matter integrity in cognitive and motor related networks. In contrast, depressive symptoms were associated with a component mainly characterised by reduced cortical thickness pattern in limbic and paralimbic regions. In conclusion, using a multivariate multimodal approach we were able to disentangle the neurobiological substrates of two distinct symptom profiles in HD: one characterised by cognitive and motor features dissociated from a psychiatric profile. These results open a new view on a disease classically considered as a uniform entity and initiates a new avenue for further research considering these qualitative individual differences. 1. Introduction are analysed separately, novel multimodal fusion approaches take ad- vantage of the common information across modalities, estimating Huntington's disease (HD) is a neurodegenerative disease caused by useful features from the different modalities independently (Groves a mutation in the HTT gene that produces an abnormal expansion of a et al., 2011, 2012). This can reveal data variations not shown through cytosine-adenine-guanine (CAG) trinucleotide repeat. Symptoms in- the independent analyses (Abrol et al., 2017). Analysing data in a clude motor disturbances such as chorea, dystonia and bradykinesia, multimodal way permits the investigation of brain variability between cognitive deficits, especially in executive functions, and psychiatric individuals that is common across different neuroimaging modalities, symptoms, such as apathy and depression. Despite the monogenic thus possibly reflecting a common pathophysiologic mechanism nature of HD, there is a high degree of heterogeneity in the prominence (Francx et al., 2016). Linked independent component analysis (Linked and evolution of each type of symptoms experienced by HD mutation ICA) has been successfully used to characterise brain changes in ageing carriers. (Douaud et al., 2014) and attention-deficit/hyperactivity disorder One possible source of such interindividual differences among HD (Francx et al., 2016; Wolfers et al., 2017). It uses a data-driven multi- patients could be the variability in the degree of neurodegeneration of variate approach to elucidate the hidden structure of covariance across different neural circuits. In this regard, neuroimaging studies can con- different modalities. This type of method reduces the dimensionality of tribute to the understanding of the neurobiological basis of phenotypic the data from thousands of voxels to a manageable number of in- heterogeneity. However, the vast majority of neuroimaging studies in dependent components, which can be interpreted individually and the HD literature has focused on differences from healthy controls, characterise a biophysically plausible form of variability (Groves et al., overlooking the interindividual differences among HD patients.. 2012). These components can then be related to specific behavioural Multivariate approaches including measures of the three symptom do- measures. mains simultaneously are needed to establish the specificity between Thus, given the importance of distinguishing the neurobiological brain alterations and each symptom domain (Garcia-Gorro et al., 2017). basis linked to different profiles in a disease such as HD with no current Shedding light on the specificity of this relationship is vital in order to cure, the aim of this study was to investigate the relationship between understand disease (Aylward et al., 2013; Marquand et al., 2016). Im- interindividual differences in brain structure and the prominence of the portantly, this may also enable the development of more personalised three main types of symptoms in HD. We used linked ICA to merge treatments since patients with greater prominence of motor, cognitive structural neuroimaging data from different modalities including grey or psychiatric impairments may have different neurobiological char- matter and white matter measures in order to improve the potential acteristics even though they have the same underlying genetic mutation sensitivity in distinguishing HD profiles. We hypothesised that inter- (de Diego-Balaguer et al., 2016). With this purpose, many studies have individual differences in the damage in grey and white matter regions tried to stratify patients of different disorders into subgroups. Inter- could explain the variability in the severity of each symptom domain. estingly, cellular studies on post-mortem brain tissue of HD patients have found distinctive patterns of neuronal loss associated with motor, psychiatric and mixed (motor and psychiatric) profiles (Tippett et al., 2. Material and methods 2007; Thu et al., 2010; Nana et al., 2014; Mehrabi et al., 2016). Neu- roimaging techniques have been employed in neurodegenerative, psy- 2.1. Participants chiatric and neurodevelopmental disorders in order to subdivide pa- tients in different profiles (Hrdlicka et al., 2005; Fair et al., 2012; Gates Forty-three HD mutation carriers participated in the current study et al., 2014; Costa Dias et al., 2015; Drysdale et al., 2016; Lubeiro et al., between 2013 and 2017 in the area of Barcelona, Spain. HD mutation 2016; Park et al., 2017). However, this has never been applied to carriers were defined as carriers of the HTT mutation with ≥39 CAG stratify HD profiles. repeats. Since cognitive and psychiatric symptoms as well as neural Combining clinical measures of the three main types of symptoms degeneration can precede the onset of motor symptoms we included along with different neural measures could enable us to tease apart the manifest and premanifest individuals. Twenty-three of the gene carriers neural substrates of the different types of symptoms experienced by HD were manifest HD patients and 20 were premanifest individuals. The individuals.. Different neuroimaging modalities can provide com- criterion to classify a gene carrier as manifest or premanifest was the plementary information of brain structure and microstructure to study diagnostic confidence score (DCS) of the Unified Huntington's Disease the neuropathological substrates of HD in the same patients. Given the Rating Scale (UHDRS) (Huntington Study Group, 1996), with a score of clinical complexity and the alterations found in both grey and white four indicating manifest stage and a score of less than four considered matter in HD, integrating within-subject information from different as premanifest stage. Participants' demographic information is included imaging modalities may provide a more complete view of the neural in Table 1. substrates underlying each symptom. None of the patients had previous history of neurological disorder Multimodal neuroimaging fusion approaches enable the study of the other than HD. All participants signed an informed consent to partici- brain from different perspectives, making use of different modalities in pate in this study, which was approved by the ethics committee of a single analysis, thus offering a more comprehensive view. In contrast Bellvitge Institute for Biomedical Research (IDIBELL) and Bellvitge to classical multimodal studies, where data from different modalities Hospital. All procedures followed were in accordance with the Helsinki Declaration of 1975. 2 C. Garcia-Gorro, et al. NeuroImage: Clinical 23 (2019) 101900 Table 1 angle = 9°, 1 mm isotropic voxels, 208 sagittal slices, ma- Mean and standard deviation of demographic and clinical information. trix = 208 × 256 × 256, field of view (FOV) = 256 mm × 256 mm, N total (preHD/manifest) 43 (20/23) N slice thickness = 1 mm). Gender (F/M) 30/13 43 Diffusion weighted MRI (DW-MRI) data were acquired using a dif- Age 43.9 (11.9) 43 fusion tensor imaging sequence using a dual spin-echo diffusion ima- Education 11.7 (3.4) 42 ging sequence with GRAPPA (reduction factor of 4) cardiac gating, with CAG 44.2 (3.0) 42 CAP 97.6 (23.5) 42 TE = 92 ms, 2 mm isotropic voxels, no gap, 60 axial slices, TFC 12 (1.7) 42 FOV = 23.6 cm. In order to obtain the diffusion tensors, diffusion was measured along 64 non-collinear directions, using a single b-value of Motor domain UHDRS-motor 13.1 (14.1) 42 1500 s/mm2 and interleaved with 9 non-diffusion b = 0 images. To avoid chemical shift artefacts, frequency-selective fat saturation was Cognitive domain Verbal fluency 31.4 (15.1) 41 used to suppress fat signal. TMT B-A 93.9 (79.2) 38 SDMT 36.7 (15.7) 41 2.4. MRI data processing Stroop interference 3.4 (10) 39 Psychiatric domain 2.4.1. Grey matter structure measures Lille Apathy −7.5 (4.6) 36 2.4.1.1. Voxel-based morphometry. A voxel-based morphometric (VBM) Delay discounting k 0.02 (0.03) 33 analysis was carried out using the vbm8 toolbox (http://dbm.neuro. Depression PBA 1.8 (3) 41 Anxiety PBA 1.7 (2.5) 41 uni-jena.de/vbm/) in the SPM8 software package (Welcome Sensitivity to reward 6.2 (3.9) 41 Department of Imaging Neuroscience Group, London, UK) running on Sensitivity to punishment 10 (6.6) 41 MATLAB (v12.b, Mathworks, Natick, MA). Specifically, unified segmentation (Ashburner and Friston, 2005) was applied to the Age and education are given in years. PreHD = premanifest Huntington's dis- structural T1-weighted images of each subject to estimate tissue grey ease individuals; M = males; F = females; CAP = CAG-Age product; matter probability maps. During this segmentation step, spatial TFC = Total functional capacity; UHDRS-motor = Unified Huntington's disease rating scale total motor score; TMT B-A = Trail Making Tests B-A; regularisation (regularisation: 0.02, discrete cosine transform warp SDMT = Symbol Digit Modality Test. PBA = Problem Behaviours Assessment. frequency cut-off of 22) was adapted to account for striatal neurodegeneration and ventricle dilatation. The resulting grey matter 2.2. Symptom assessments maps were then imported and fed into DARTEL (Ashburner, 2007) to achieve spatial normalisation into MNI space (Ashburner and Friston, Participants were evaluated in the three symptom domains (motor, 2009). These images were modulated by a non-linear component of the cognitive and psychiatric) using a battery of clinical scales and ques- Jacobian determinant, allowing direct comparison of regional tionnaires. The clinical assessment was carried out using the UHDRS, differences in the volume of grey matter corrected for individual which comprises motor, cognitive and behavioural subscales. The brain sizes. Images were downsampled from 1.5 mm to 4 mm UHDRS total motor score was selected as a measure of motor disability, isotropic voxel size to reduce computational complexity. Finally, a with higher scores indicating more severe motor impairment. The 9.4 mm FWHM surface-based smoothing kernel was applied. cognitive domain was assessed with measures of cognitive flexibility (trail making test (TMT) B-A (Tombaugh, 2004)), verbal fluency 2.4.1.2. Cortical thickness. Vertex-wise cortical thickness (CT) maps (phonemic letter fluency test FAS (Butters et al., 1986)), inhibitory were estimated using Freesurfer v6.0 software(http://surfer.nmr.mgh. control (Stroop interference (Golden, 1978)), psychomotor speed harvard.edu/) by means of a fully automated surface reconstruction of (symbol digit modalities test, SDMT (Benedict et al., 2017)). In this the grey/white matter boundary and the pial surface (Dale et al., 1999; case, higher scores in TMT B-A and Stroop interference indicate worse Fischl et al.,1999). Then, CT was calculated as the closest distance performance, while for verbal fluency and SDMT poorer performance between the grey/white matter boundary and the pial surface at each corresponds to lower scores. Psychiatric measures included the De- vertex across the surface (Fischl & Dale, 2000). CT maps were projected pression and Anxiety subscales of the short version of the Problem onto the average subject surface (fsaverage) to align the cortical folding Behavioural Assessment (PBA-s (McNally et al., 2015)), multiplying patterns. Finally, a 10 mm FWHM surface-based smoothing kernel was frequency by severity, with higher scores indicating more severe applied. symptoms, as well as the Spanish version of the sensitivity to punish- ment and sensitivity to reward questionnaire (SPSRQ (Torrubia et al., 2.4.2. White matter microstructure measures 2001)), with higher scores indicating more sensitivity, and the short Brain extraction was performed using the FSL Brain Extractor Tool Lille apathy scale (Sockeel et al., 2006), with higher scores indicating (Smith, 2002). Head motion and eddy-current correction were then more severity. Due to time constrains, some of the behavioural vari- performed using the FMRIB's Diffusion Toolbox (FDT) in FMRIB's ables were not acquired in some participants. From the behavioural Software Library (FSL, http://www.fmrib.ox.ac.uk/fsl/fdt) and the database, we only included those variables in which < 15% of the data gradient matrix was rotated (Leemans and Jones, 2009). were missing. Behavioural data were windsorised in order to deal with The diffusion tensor was then reconstructed using Diffusion outliers and standardised to z-scores. Missing values for each beha- Toolkit's least-squares estimation algorithm for each voxel provided in vioural variable were replaced by the mean. Diffusion Toolkit (http://www.trackvis.org/dtk) and its corresponding eigenvalues and eigenvectors were extracted to calculate the fractional anisotropy (FA) and radial diffusivity (RD) maps. Using Tract-Based 2.3. MRI data acquisition Spatial Statistics (TBSS, Smith et al., 2006) FA volumes were non- linearly aligned to the FMRIB-58_FA MNI template and skeletonised, MRI data were acquired using a 3 T whole-body MRI scanner building a skeletonised mean FA image, which contained the centres of (Siemens Magnetom Trio; Hospital Clínic, Barcelona) with a 32-channel all tracts common to all participants in the study. Subsequently, each phased array head coil. Structural images comprised a conventional participant's normalised FA map was projected onto this skeleton FA high-resolution 3D T1 image (magnetisation-prepared rapid-acquisition group thresholded at FA ≥ 0.2. This process was also applied for the RD gradient echo sequence (MPRAGE), repetition time (TR) = 1970 ms, map using the transformations previously calculated for the FA maps. echo time (TE) = 2.34 ms, inversion time (TI) = 1050 ms, flip Finally, the resolution of the skeleton was downsampled from 1 mm to 3 C. Garcia-Gorro, et al. NeuroImage: Clinical 23 (2019) 101900 2 mm isotropic voxel size. The remaining components were a combination of different neu- roimaging variables, with diffusion measures dominating components 2.5. Linked independent component analysis 3, 5 and 6, whilst cortical thickness was predominant in component 7. Linked independent component analysis (linked ICA) (Groves et al., 3.2. Relationship between neuroimaging components and behavioural 2011) is a multimodal multivariate analysis that allows the fusing of measures MRI data from different modalities into a single model to obtain in- dependent components of inter-subject variability. Linked ICA takes After correcting for multiple comparisons, two neuroimaging com- into account the spatial correlation of each modality to determine the ponents were significantly correlated with behavioural measures. optimal weighting of each neuroimaging modality, which reflects the Component 2 was a clearly multimodal component with a roughly si- relative contribution of each modality to a given independent compo- milar variance explained by CT (32%), FA (30%) and VBM (24%) and nent. Each resulting component consists of a spatial pattern for each to a slightly lesser extent RD (13%). The subjects' loadings of this neuroimaging modality and a single subject-course, with the loadings of component significantly correlated with motor deficits reflected in the each participant indicating the contribution to each component. UHDRS total motor score (r = −0.69, P < 0.001) and also with cog- In the current study we used linked ICA combining the four mea- nitive measures related with processing speed (SDMT: r = 0.59, sures described above: VBM grey matter volume, cortical thickness, FA P < 0.001) and executive functions such as cognitive flexibility (TMT and RD. In order to obtain a robust model, an order smaller than 1/4 of B-A: r = −0.71, P < 0.001) and phonemic fluency (verbal fluency the number of subjects is commonly advised (Groves et al., 2011, test, FAS: r = 0.63, P = 0.003 (see Fig. 2). The only cognitive measure 2012). Given our sample size (n = 43), we initially ran the linked ICA that did not correlate with this component was the measure of sup- model to estimate 10 components(n = 43). Note that the automatic pression of interference (Stroop interference). Regarding the contribu- relevant determination (ARD) prior automatically identified a model tion of HD individuals at different stages to this component, the Chi- order of 9 ICAs, using our initial choice of 10 as model order more and square test of independence with Yate's continuity correction showed letting then the model itself decide on the number of components that, in this component, there was a significant association between present in the data. disease stage and subject loadings (χ2 (1) = 25.60, P < 0.001). Spatial patterns of the independent components were converted to In this cognitive-motor component, cortical thickness and grey pseudo-z-statistics and thresholded at z = 2.3. matter volume measures provided complementary information that overlapped in different areas. The pattern of reduced cortical thickness 2.6. Relationship between neuroimaging components and behavioural associated to more severe cognitive and motor disturbances extended to measures multiple regions, including areas related to executive functions and sensorimotor and visuospatial processing (Fig. 3). In particular, the In order to study the association between each of the resulting in- most significant thinning was found in left superior temporal gyrus, the dependent components and the behavioural measures, a multiple linear left supplementary motor area (SMA) and left premotor cortex. regression analysis was performed for each behavioural measure, in- While all the regions described so far showed cortical thinning as- cluding each component as a predictor. Age was also included as a sociated with cognitive and motor impairments, it is worth noting that predictor with the aim of modelling its effect. Bonferroni correction was this component was associated at the same time with increased cortical used in order to account for multiple comparisons, taking into account thickness restricted to the anterior cingulate cortex (ACC) (Fig. 3). the number of behavioural variables (n = 10), resulting in a threshold The results of VBM were consistent with the cortical thickness maps, of P < 0.005. Furthermore, we conducted a Chi-square test of in- showing a pattern of reduced grey matter volume associated with worse dependence in those components where statistically significant corre- cognitive and motor disturbances in regions involved in executive lations with behavioural measures were found in order to analyse functions and sensorimotor processing, covering the caudate bilaterally, whether disease stage (manifest = 1, premanifest = 2) was associated SMA, bilateral somatosensory cortex and superior parietal cortex ex- with the contribution to this component (high = 1, low = 2). In order tending to lateral occipital cortex. In contrast, the medial occipital to establish two groups according to the subject's contribution to the component, the median subject loading in each component was calcu- lated and all subjects with a lower loading were included in the “low” group, while subjects with higher loadings were included in the “high” group. The subject corresponding to the median loading was therefore excluded in each component. 3. Results 3.1. Linked independent component analysis From the initial ten dimensional factorisation, the linked ICA au- tomatically provided through the automatic relevant determination (ARD) priors a model order equal to nine, resulting in nine independent components, each of them reflecting a different combination of mod- alities' contributions. Fig. 1 illustrates the relative loading of each component, sorting the components by the relative amount of total Fig. 1. Relative weight of each modality within each component. Components variance explained, in descending order. are sorted from 1 to 10 by the amount of variance they explain. Components After visual inspection of the spatial maps of the different compo- that significantly correlated with behavioural measures are indicated (2 and 7). nents, the first unimodal component was discarded. This first compo- VBM = Voxel Based Morphometry; FA = Fractional Anisotropy; RD = Radial nent, found in previous studies (Llera Arenas et al., 2018), reflected Diffusivity; CT = Cortical Thickness. Green colours relate to grey matter mea- brain size and significantly correlated with age (r = −0.57, sures and purple colours to white matter measures. (For interpretation of the P < 0.001). Thus, we removed this component and included age as a references to color in this figure legend, the reader is referred to the web ver- predictor in the multiple linear regression analysis. sion of this article.) 4 C. Garcia-Gorro, et al. NeuroImage: Clinical 23 (2019) 101900 Fig. 2. Relationship between subject loadings of Component 2 and both motor (in red) and cognitive (in yellow) deficits. The plots represent betas associated with each behavioural measure in the multiple linear regression analysis. UHDRS TMS: Unified Huntington's disease Rating Scale Total Motor Scale; SDMT = Symbol Digit Modality Test; TMT B-A = Trail Making Test B-A; FAS = Phonemic fluency test (letters F, A, S). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) cortex presented increased grey matter volume in this component cingulate cortex (ACC) and posterior cingulate cortex (PCC) also pre- (Fig. 3). sented reduced thickness in individuals with higher depression scores. Furthermore, reduced FA values were also associated to more severe Interestingly, some partial overlap with some regions associated with cognitive and motor disturbances in a main motor-related projection, the previous component was also observed. Regions involved in sen- the corticospinal tract (CST) bilaterally, and in connections that are sorimotor processing such as left precentral and postcentral gyri and involved in cognitive processing like the bilateral fornix, superior regions involved in cognitive processing, such as the left supramarginal longitudinal fasciculus (SLF), inferior fronto-occipital fasciculus (IFOF) gyrus also presented a pattern of reduced thickness in this component. and forceps minor and body of the corpus callosum (CC) as well as in On the other hand, increased cortical thickness related to more se- the left arcuate fasciculus. vere depressive symptoms was observed in areas related to visuospatial On the other hand, more severe cognitive and motor disturbances processing, such as the left lateral occipital cortex and left fusiform were at the same time associated to increased FA in the bilateral cor- gyrus extending to the left inferior temporal gyrus as well as the sec- tico-ponto-cerebellar tract (Fig. 3). Notice that the spatial maps of RD ondary somatosensory cortex (Fig. 5). values showed, as expected, a similar inverse pattern of FA maps, where Although VBM contributed less to this component, it is worth noting regions of reduced FA corresponded with increased RD. that the reduced grey matter volume related to more severe depression The whole multimodal pattern of this cognitive-motor component is resembled the pattern of reduced cortical thickness, although in this convergent, since the white matter tracts showing microstructural case the pattern was not left-lateralised. Similarly to the CT pattern, changes connect the grey matter areas associated to this component in reduced grey matter volume was observed in areas involved in emo- the CT and VBM modalities. tional processing, such as the left temporal pole and ventromedial In contrast to the previous component, a second component (com- prefrontal cortex and lateral orbitofrontal cortices bilaterally, as well as ponent 7) was largely dominated by one modality: cortical thickness areas involved in cognition, such as the bilateral supramarginal gyrus (CT = 78%, FA = 16%, VBM = 4%). Subjects loadings in this compo- and the right inferior frontal and middle temporal gyri as well as areas nent were significantly correlated with depression scores (PBA-s de- related to motor function, such as bilateral precentral and right post- pression subscale: r = −0.45, P = 0.001) (see Fig. 4). Furthermore, the central gyri (Fig. 5). Chi-square test of independence with Yate's continuity correction Interestingly, this pattern of reduced grey matter volume was ac- showed that this psychiatric component was equally represented by companied by increased grey matter volume in the right caudate, SMA, manifest and premanifest individuals not showing a significant asso- cerebellum and bilateral superior parietal lobules and the bilateral fu- ciation between disease stage and subject loading in this component (χ2 siform gyri extending to the inferior temporal gyri. (1) = 0.86, P = 0.354). Regarding white matter measures, more depressive symptoms were The pattern of cortical thickness was notably left lateralised (Fig. 5). associated with reduced FA in the forceps major of the CC, bilateral More severe depressive symptoms were associated with reduced cor- arcuate fasciculus IFOF, inferior longitudinal fasciculus (ILF), left un- tical thickness in limbic and paralimbic regions such as the left tem- cinate fasciculus (UF) and cingulum, altogether connecting grey matter poral pole, left ventromedial prefrontal cortex and bilateral insula. areas found to be affected in this component. On the other hand, this Furthermore, regions of the default mode network (DMN) such as the pattern of reduced FA was accompanied by increased FA in right UF, left superior frontal gyrus, left middle temporal gyrus, left anterior inferior cerebellar peduncle and in the forceps minor of the CC (Fig. 5). 5 C. Garcia-Gorro, et al. NeuroImage: Clinical 23 (2019) 101900 Fig. 3. Spatial maps of each modality in component 2. The percentage of variance explained by each modality in this component is indicated. Hot and cold colours indicate lower and higher values on each MRI measure associated with more cognitive and motor disturbances, respectively. Colour bars indicate Z-statistics. The right side of the brain is shown in the left side of the image. MNI coordinates are shown for each slice. R = Right; VBM = Voxel-based morphometry; FA = Fractional Anisotropy; RD = Radial Diffusivity; CT = Cortical Thickness. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 4. Discussion alterations in the disease. This approach also allowed us to see which neuroimaging modality was more sensitive in capturing the unique The aim of the present study was to investigate the neurobiological variance associated with each symptom domain. basis of the three main symptom domains of HD: motor, cognitive and We obtained four main findings. Firstly, cognitive and motor psychiatric disturbances. Although all of them characterise the disease, symptoms in HD shared a common neurobiological basis, whereas the there is a high degree of heterogeneity in the prominence of each type psychiatric domain presents a differentiated neural signature. Secondly, of symptom. Thus, we hypothesised that interindividual differences in cognitive and motor symptoms were associated with grey matter re- the damage in grey and white matter regions could explain the differ- gions and white matter tracts involved in executive functions, language, ential severity of each symptom domain. Given the complex nature of and visuospatial and sensorimotor processing, whereas psychiatric HD, with alterations in both grey and white matter, we used a multi- symptoms were mainly associated with cortical areas and white matter variate and multimodal approach that allowed us to fuse images from tracts associated with emotional processing. Thirdly, whilst both grey different neuroimaging techniques and relate them with each type of and white matter equally contributed to explaining the cognitive-motor symptom, in order to gain a more complete picture of the brain domain, in the case of the psychiatric domain, cortical thickness was 6 C. Garcia-Gorro, et al. NeuroImage: Clinical 23 (2019) 101900 correlated with scores corresponding to both motor and cognitive symptoms (processing speed, cognitive flexibility and verbal fluency), whereas component 7 correlated with a variable of the psychiatric domain: depression (Depression subscale of the PBA-s). Our findings of a common neural basis for cognitive and motor symptoms that is dif- ferent from that related with the psychiatric domain is in line with a recent study (Kim et al., 2015) following symptom progression in pre- manifest HD individuals. In this longitudinal study, more severe motor signs were associated with worse cognitive deficits, but not always by higher levels of depressive symptoms. 4.2. Spatial maps associated with symptom domains Fig. 4. Relationship between subject loadings of Component 7 and depressive symptoms. The plot represent betas associated the PBA depression behavioural The second main result of this study is the difference between the measure in the multiple linear regression analysis. PBA = Problem Behaviours spatial maps associated with the cognitive-motor domain and those of Assessment. associated with the psychiatric domain. In the cognitive-motor com- ponent, more severe cognitive and motor disturbances were associated strikingly relevant compared to the other modalities. Fourthly, in the with reduced cortical thickness, volume and structural connectivity in cognitive-motor component there was a higher contribution of manifest grey matter areas and white matter tracts involved in both cognitive patients, while the psychiatric component was characterised by an and motor functions. Regarding cognitive functions, the pattern of grey equal contribution of both manifest and premanifest individuals. and white matter alterations involved structures related to cognitive control (bilateral caudate nucleus, dorsolateral prefrontal cortex, su- 4.1. Relationship between symptom domains and neuroimaging independent perior parietal areas), attention (SLF), visuospatial processing (occipital components cortex and IFOF), language (inferior frontal areas and arcuate fasci- culus) and memory (fornix). Thus, the pattern of cognitive structures The first main result was that component 2 from the linked ICA involved in this component is consistent with its relationship to the Fig. 5. Spatial maps of each modality in component 7. The percentage of variance explained by each modality in this component is indicated. Hot and cold colours indicate lower and higher values on each MRI measure associated with more depressive symptoms, respectively. Colour bars indicate Z-statistics. The right side of the brain is shown in the left side of the image. MNI coordinates are shown for each slice. R = Right; VBM = Voxel-based morphometry; FA = Fractional Anisotropy; RD = Radial Diffusivity; CT = Cortical Thickness. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 7 C. Garcia-Gorro, et al. NeuroImage: Clinical 23 (2019) 101900 deficits in processing speed, cognitive flexibility and verbal fluency. showed increased volume and thickness associated with higher de- Regarding the structures involved in motor function, this component pression scores in the psychiatric component. On the other hand, in the showed thinning and reduced volume of the SMA and the somato- cognitive-motor component, the middle occipital cortex and the ACC sensory cortex, two key areas in motor planning and execution. The CST showed increased volume and thickness, respectively, while the inverse – a major motor tract – and the body of the CC – which connects sen- pattern was observed in the psychiatric component. sorimotor regions from one hemisphere to the other – showed reduced The results of the spatial maps partially converge with the model of structural connectivity in this component too. The involvement of segregated motor, associative and limbic cortico-striatal circuits (Parent motor areas and white matter tracts in this component is therefore in and Hazrati, 1995; Humphries and Prescott, 2010), characterised by a line with the observed association with motor signs. partial overlap between motor and associative networks and a more It is important to point out that regions associated with emotional clearly segregated limbic loops. processing such as the temporal poles or the orbitofrontal cortex were not involved in this cognitive-motor component. Indeed, the only 4.3. Contribution of neuroimaging modalities in each symptom profile limbic structure involved, the ACC, showed a reverse pattern of cortical thickness to that of the other regions involved. Thus, the increased Regarding the third main finding of the current study, there were cortical thickness in this structure could suggest that patients with a differences regarding the contribution of each modality to each com- cognitive-motor profile may be less prone to emotional disturbances. ponent. In the case of the cognitive-motor component, both grey and In contrast, the spatial map of the psychiatric component was pri- white matter measures had a similar weight, whereas the psychiatric marily characterised by a pattern of reduced grey matter thickness and component was clearly dominated by a single modality: cortical volume and white matter connectivity in structures related to emo- thickness. tional processing. Depressive symptoms were associated with altera- These results may suggest differences in the pathophysiology un- tions in limbic and paralimbic structures previously associated with derlying different symptoms in HD. On the one hand, cognitive and depression, such as the ACC, medial OFC, medial temporal gyrus, motor symptoms seem to be caused equally by a reduction in grey temporal pole and anterior insula (Lee et al., 2011; Sprengelmeyer matter volume, cortical thickness and white matter integrity. In con- et al., 2011; Grieve et al., 2013; Ramasubbu et al., 2014; Webb et al., trast, the neural underpinning of depression in HD seems to be much 2014) as well as the cingulum bundle and the left UF (Taylor et al., more restricted, primarily involving cortical thickness with grey matter 2007; Dalby et al., 2010; Schermuly et al., 2010; Keedwell et al., 2012; volume only explaining 4% of the variance. Zhang et al., 2012). It is important to note that, even though cortical thickness influ- In the cortical thickness modality, which explained a great majority ences grey matter volume, these two measures are independent, since of the variance of the psychiatric component, the pattern was clearly grey matter volume also depends of surface area, which is thought to left-lateralised, with the exception of the insula, which was found in contribute to a larger extent than cortical thickness (Winkler et al., both hemispheres. This is in line with an extensive literature on de- 2010). Although the neurobiological basis of each metric is still unclear, pression that shows an imbalance in interhemispheric grey matter it has been observed that in many, but not all, cortical regions of the (Koolschijn et al., 2009; Tu et al., 2012) and dynamics with hy- brain thicker areas have reduced neural density (la Fougère et al., poactivity of the left hemisphere and hyperactivity of the right hemi- 2011), which is thought to indicate an increased number of dendrites sphere (for a review see Hecht, 2010). and synapses per neuron (Wagstyl et al., 2015). Thus, in many brain In addition, the pattern of cortical thickness included the ACC, PCC, regions, cortical thickness could be related to cortical connectivity superior frontal gyrus and middle temporal gyrus, all areas pertaining (Chiarello et al., 2016). to the DMN. Our results, therefore, are consistent with the known as- sociation between hyperactivity of the DMN and depression, possibly 4.4. Contribution of premanifest and manifest individuals in each symptom due to a maladaptive self-oriented rumination (Berman et al., 2011; domain Hamilton et al., 2011, 2015). Besides structures classically associated with the processing and The fourth main finding of the study showed that subject loadings regulation of emotion, we also found alterations in some grey matter which contributed more to the cognitive-motor component included regions and white matter tracts associated with cognitive processing significantly more manifest patients than premanifest individuals, while such as the left supramarginal gyrus and bilateral IFOF, arcuate fasci- in the psychiatric component the subjects who contributed more to the culus and ILF. Cortical thinning was also found in sensorimotor pro- component were a mix of manifest and premanifest individuals. This is cessing areas such as the precentral and postcentral gyri. Interestingly, in line with the previous studies that have found that whilst cognitive cognitive and sensorimotor areas have also been found to be affected in and motor deficits tend to worsen as the disease progresses (Mahant depression, in terms of both grey matter structure (Tu et al., 2012; et al., 2003; Stout et al., 2011), depressive symptoms can vary across Grieve et al., 2013) and functional connectivity (Drysdale et al., 2016). different disease stages and do not correlate with disease progression On the other hand, the psychiatric component was also associated (Craufurd et al., 2001; Julien et al., 2007; Kim et al., 2015). with a pattern of increased grey matter. This was observed in areas related with visual processing, such as the lateral occipital and lingual 4.5. Integration of main findings and concluding remarks cortices and cognitive control, such as the right caudate, precuneus, superior parietal cortex, cerebellum and SMA. These results suggest that Considering the four main findings of our study which show dif- these areas tend to be preserved in HD patients with depressive ferences between cognitive-motor and psychiatric domains regarding i) symptoms. the associations with independent components, ii) brain structures in- In summary, the second main finding showed large differences be- volved, iii) contributions of each neuroimaging modality and iv) con- tween the spatial maps of the cognitive-motor and the psychiatric tribution of patients in premanifest and manifest stages, we propose components, with a low degree of overlapping. All in all, the regions that there are two distinct symptom profiles in HD with differentiated that we found to be associated with each symptom component are in neural substrates. line with its known functions. Importantly, some regions showed op- Our results suggest that depression in HD is independent of striatal posite patterns. For instance, whilst in the cognitive-motor component degeneration, which would explain the lack of correlation between more severe deficits were associated with reduced volume in right depression and disease progression. This division of psychiatric symp- caudate, SMA and bilateral superior parietal gyrus as well as reduced toms from cognitive and motor disturbances could originate from the cortical thickness in lateral occipital and lingual cortices, these areas differences in the cortico-striatal circuits involved. In both cognitive 8 C. Garcia-Gorro, et al. NeuroImage: Clinical 23 (2019) 101900 and motor circuits, cortical areas (dorsolateral prefrontal and sensor- relevant in the context of clinical trials, since they could be used to imotor cortices, respectively) project to the dorsal striatum (caudate define specific biomarkers for each symptom profile, even before clin- and putamen), while in the limbic circuit, cortical areas (mainly the ical signs appear. Having more homogenous groups would potentially ACC) project to the ventral striatum. In addition, the output of basal increase the likelihood of detecting successful interventions and help to ganglia nuclei are also distinct. Whilst in cognitive and motor circuits find individualised treatments that target specific cognitive, motor and projections from the dorsal striatum connect to the dorsal portions of psychiatric disturbances. Furthermore, our findings underscore the the globus pallidus, the limbic circuit projects to the ventral pallidum. value of multimodal fusion approaches in characterising heterogeneous Furthermore, the dopaminergic pathways involved are also different. In patterns of neurodegeneration. the cognitive and motor circuits, dopaminergic projections from the substantia nigra pars compacta connect to the dorsal striatum through Funding the nigrostriatal pathway. In the case of the limbic circuit, the ventral striatum receives dopaminergic projections from the ventral tegmental This work was supported by the Instituto de Salud Carlos III, which area through the mesolimbic pathway. is an agency of the MINECO, co-funded by European Regional The interaction between the cognitive and the motor circuits has Development Fund (ERDF), a way to Build Europe (CP13/00225 and been repeatedly observed in the literature. Some instances are the role PI14/00834, both to EC). This study was also funded by Ministerio de of executive prefrontal regions in gait speed control (Suzuki et al., Ciencia, Innovación y Universidades, which is part of Agencia Estatal de 2004) and the involvement of pre-SMA in cognitive motor control Investigación (AEI), through the project BFU2017-87109-P to RdD (Co- (Ikeda et al., 1999), being an area at the intersection of cognitive and funded by ERDF, a way to build Europe). We thank CERCA Programme motor domains. Furthermore, it has been observed that there is greater / Generalitat de Catalunya for institutional support. influence of cognitively demanding contexts in motor performance during cognitive-motor dual tasks in older adults (Oliveira et al., 2018) Declaration of Competing interests and different neurological populations, such as stroke (Plummer et al., 2013) and Parkinson's disease patients (Kelly et al., 2012), which fur- The authors declare no competing interests. ther highlights the possibility that shared neural circuits could underlie the relationship between cognitive and motor functions. Another ex- Acknowledgements ample of this interaction is the engagement of both executive and motor corticostriatal circuits during learning (Wang et al., 2017). 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