Large-scale brain network abnormalities in Huntington's disease revealed by structural covariance

Abstract

Huntington's disease (HD) is a progressive neurodegenerative disorder that can be diagnosed with certainty decades before symptom onset. Studies using structural MRI have identified grey matter (GM) loss predominantly in the striatum, but also involving various cortical areas. So far, voxel-based morphometric studies have examined each brain region in isolation and are thus unable to assess the changes in the interrelation of brain regions. Here, we examined the structural covariance in GM volumes in pre-specified motor, working memory, cognitive flexibility, and social-affective networks in 99 patients with manifest HD (mHD), 106 presymptomatic gene mutation carriers (pre-HD), and 108 healthy controls (HC). After correction for global differences in brain volume, we found that increased GM volume in one region was associated with increased GM volume in another. When statistically comparing the groups, no differences between HC and pre-HD were observed, but increased positive correlations were evident for mHD, relative to pre-HD and HC. These findings could be explained by a HD-related neuronal loss heterogeneously affecting the examined network at the pre-HD stage, which starts to dominate structural covariance globally at the manifest stage. Follow-up analyses identified structural connections between frontoparietal motor regions to be linearly modified by disease burden score (DBS). Moderator effects of disease load burden became significant at a DBS level typically associated with the onset of unequivocal HD motor signs. Together with existing findings from functional connectivity analyses, our data indicates a critical role of these frontoparietal regions for the onset of HD motor signs.

Connectomic imaging reveals Huntington‐related pathological and pharmaceutical effects in a mouse model

“…Structural covariance network analysis provides another angle of brain network change by examining the inter‐regional co‐variation of brain volume across the population . Altered patterns of structural covariance have been found in many brain disorders, such as AD, schizophrenia, epilepsy and autism but to a lesser extent in Huntington disease . We found that Huntington disease greatly reduced the structural covariance in almost all the networks and memantine treatment ameliorated this reduction.…”

Early grey matter changes in structural covariance networks in Huntington's disease

“…Here, in all pre-specified motor, working memory, cognitive flexibility, and social-affective networks there were no differences between controls and pre-HD observed (Minkova et al, 2016). In our study, however, we found evidence for early grey matter volume changes in two structural covariance networks in pre-HD compared to controls.…”

“…In HD, network-based analysis has been applied in one other recent study that investigated structural covariance networks in brain regions that are functionally related (Minkova et al, 2016). Here, in all pre-specified motor, working memory, cognitive flexibility, and social-affective networks there were no differences between controls and pre-HD observed (Minkova et al, 2016).…”

BrainMap VBM: An environment for structural meta‐analysis

“…Accumulating research demonstrates that lifespan changes to brain structures do not occur independently but follow multi-regional, coherent patterns that show unique chronological trajectories of integrity and organization (Alexander-Bloch et al, 2013;Li, Pu, Fan, Niu, Li, & Li, 2013;V a sa et al, 2017;Zhu et al, 2012;Zielinski, Gennatas, Zhou, & Seeley, 2010). SCov has proven to be a sensitive method in studying disease, and alterations exist in a range of disorders that are neurodegenerative (Coppen, van der Grond, Hafkemeijer, Rombouts, & Roos, 2016;Minkova et al, 2016), psychiatric (Palaniyappan et al, 2015;Wu et al, 2017;Xu, Groth, Pearlson, Schretlen, & Calhoun, 2009), developmental (Bethlehem, Romero-Garcia, Mak, Bullmore, & Baron-Cohen, 2017;Dziobek, Bahnemann, Convit, & Heekeren, 2010), systemic (e.g., cardiovascular risk factors) (Kharabian Masouleh et al, 2017), and even chemotherapy-related (i.e., cognitive impairment) (Hosseini, Koovakkattu, & Kesler, 2012). A seed-to-whole-brain and seed-to-target transdiagnostic SCov application using BrainMap VBM was recently published by Kotkowski, Price, Mickle Fox, Vanasse, and Fox (2018).…”