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

Coppen, Emma M.; Hafkemeijer, Anne; Rombouts, Serge A.R.B.; Roos, Raymund A.C.

doi:10.1016/j.nicl.2016.10.009

Cited by 43 publications

(58 citation statements)

References 49 publications

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“…Moreover, the effect of memantine was stronger in the BGN and hippocampal network than in the sensorimotor network and DMN. This is consistent with human studies which showed reduced structural covariance in sensorimotor, hippocampus, BGN and DMN . Nevertheless, it is noteworthy that the increase of structural covariance by memantine did not alleviate brain atrophy .…”

Section: Discussionsupporting

confidence: 91%

“…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.…”

Section: Discussionmentioning

confidence: 64%

“…Using diffusion tensor imaging, early studies have found white matter abnormality in premanifest gene carriers (pre‐HD) . Structure covariance analysis also indicates network patterns of gray matter atrophy in pre‐HD . Resting‐state functional connectivity MRI (rs‐fcMRI), which detects temporal coherence of blood oxygenation level dependent (BOLD) signals across brain regions, on HD patients showed increased short‐range resting‐state functional connectivity (rsFC) in several cortical and subcortical areas but reduced long‐range connectivity between parietal areas .…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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Recent studies suggest that neurodegenerative diseases could affect brain structure and function in disease‐specific network patterns; however, how spontaneous activity affects structural covariance network (SC) is not clear. We hypothesized that hyper‐excitability in Huntington disease (HD) disrupts the coordinated structural and functional connectivity, and treatment with memantine helps to reduce excitotoxicity and normalize the connectivity. MRI was conducted to measure somatosensory activation, resting‐state functional‐connectivity (rsFC), SC, amplitude of low frequency fluctuation (ALFF) and ALFF covariance (ALFFC) in the YAC128 mouse model of HD. We found somatosensory activation was unchanged but the subcortical ALFF was increased in HD mice, indicating subcortical but not cortical hyperactivity. The reduced sensorimotor rsFC but spared hippocampal and default mode networks in the HD mice was consistent with the more pronounced impairment in motor function compared with cognitive performance. The disease suppressed SC globally and reduced ALFFC in the basal ganglia network as well as its anti‐correlation with the default mode network. By comparing these connectivity measures, we found that the originally coupled rsFC‐SC relationship was impaired whereas SC‐ALFFC correlation was increased by HD, suggesting disease facilitated covariation of brain volume and activity amplitude but not neural synchrony. The comparison with mono‐synaptic axonal projection supports the hypothesis that rsFC, but not SC or ALFFC, is highly dependent on structural connectivity under healthy conditions. Treatment with memantine had a strong effect on normalizing the SC and reducing ALFF while slightly increasing other connectivity measures and restoring the rsFC‐SC coupling, which is consistent with its effect on alleviating hyper‐excitability and improving the coordinated neural growth. These results indicate that HD affects the cerebral structure–function relationship which could be partially reverted by NMDA antagonism. These connectivity measures provide unique insights into pathological and pharmaceutical effects in brain circuitry, and could be translatable biomarkers for evaluating drug effect and refining its efficacy.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 64%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…In the current literature of ICA in T1‐weighted data, the dimensionality has ranged considerably across studies. Values chosen include 7 (Eckert, ), 9 (Hafkemeijer et al, ; Xu et al, ), 10 (Coppen et al, ), and 75 (Segall et al, ). We selected a dimensionality of 20.…”

Section: Methodsmentioning

confidence: 99%

“…Here, ICA's exploratory nature is fully leveraged with BrainMap VBM in that many diseases (43) showing grey matter morphology are processed together in a pseudo time‐series for pattern identification. ICA has shown success in identifying SCov components from T1, intersubject covariance data in healthy and diseased populations (Coppen et al, ; Eckert, ; Hafkemeijer et al, ; Kharabian Masouleh et al, 2017; Segall et al, ; Xu et al, ). In this study, we demonstrate that ICA of BrainMap VBM coordinates—blind to any diagnostic information—can extract spatially independent components of inter‐experiment SCov.…”

Section: Introductionmentioning

confidence: 99%

BrainMap VBM: An environment for structural meta‐analysis

Vanasse

Fox

Barron

et al. 2018

Human Brain Mapping

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The BrainMap database is a community resource that curates peer-reviewed, coordinate-based human neuroimaging literature. By pairing the results of neuroimaging studies with their relevant meta-data, BrainMap facilitates coordinate-based meta-analysis (CBMA) of the neuroimaging literature en masse or at the level of experimental paradigm, clinical disease, or anatomic location. Initially dedicated to the functional, task-activation literature, BrainMap is now expanding to include voxel-based morphometry (VBM) studies in a separate sector, titled: BrainMap VBM. VBM is a whole-brain, voxel-wise method that measures significant structural differences between or within groups which are reported as standardized, peak x-y-z coordinates. Here we describe BrainMap VBM, including the meta-data structure, current data volume, and automated reverse inference functions (region-to-disease profile) of this new community resource. CBMA offers a robust methodology for retaining true-positive and excluding false-positive findings across studies in the VBM literature. As with BrainMap's functional database, BrainMap VBM may be synthesized en masse or at the level of clinical disease or anatomic location. As a use-case scenario for BrainMap VBM, we illustrate a trans-diagnostic data-mining procedure wherein we explore the underlying network structure of 2,002 experiments representing over 53,000 subjects through independent components analysis (ICA). To reduce data-redundancy effects inherent to any database, we demonstrate two data-filtering approaches that proved helpful to ICA. Finally, we apply hierarchical clustering analysis (HCA) to measure network- and disease-specificity. This procedure distinguished psychiatric from neurological diseases. We invite the neuroscientific community to further exploit BrainMap VBM with other modeling approaches.

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An automatic self‐initialized clustering method for brain tissue segmentation and pathology detection from magnetic resonance human head scans with graphics processing unit machine

Kalaiselvi

Nagarajan

Sriramakrishnan

2020

Concurrency and Computation

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The proposed work introduces a fully automatic modified fuzzy c-means (MFCM) algorithm for segmenting brain tissue into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) which identifies the pathological conditions of magnetic resonance human head scans. The present work implements histogram smoothing using Gaussian distribution for finding the number of clusters (K) and cluster centers (C) to initialize modified FCM algorithm (MFCM). The modification includes the local impact of each pixel based on the median of local neighborhoods. This needs more computational power to reduce the processing time and requires a parallel programming environment like the Graphics Processing Unit. The parallel MFCM is performed with the help of compute unified device architecture language and reduced the processing time up to 80 speedup folds than the serial implementation in Matlab and 20 speedup folds than C programming implementation. The method is examined with the Internet Brain Segmentation Repository (IBSR20) T1W dataset. The quantitative and qualitative results of the proposed method are compared with state-of-the-art-methods using the Dice coefficient (DC). Proposed method yields high DC 0.84 ± 0.03 for GM, 0.83 ± 0.04 for WM, and 0.41 ± 0.12 for CSF segmentation. In post-processing, 3D volumes of segmented regions have been constructed and compared with the gold standard quantitatively and qualitatively. K E Y W O R D S cerebrospinal fluid, CUDA, fuzzy c-means, graphics processing unit, gray matter, parallel processing, white matter 1 INTRODUCTION Magnetic resonance (MR) images are the widely used medical imaging technique in diagnosing soft tissue organs like human brain because the MR imaging (MRI) technique produces high quality of details for the soft tissues. 1 MRI technique makes use of the proton density of the hydrogen atoms in the human body placed in a heavy magnetic field produces quality images of the soft tissue organs using non-ionizing radio waves and thus used for better diagnosing processes. Based on the echo (TE) and relaxation (TR) time during image acquisition, the obtained images are of T1-weighted (T1W), T2-weighted (T2W), and proton density (PD) types. 2 The T1W and T2W images are negative to each other. That is the darker region in T1 images are of the brighter region in T2 images and vice versa. Thus segmentation algorithms process based on this variation between these two types

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

Cited by 43 publications

References 49 publications

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

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

BrainMap VBM: An environment for structural meta‐analysis

An automatic self‐initialized clustering method for brain tissue segmentation and pathology detection from magnetic resonance human head scans with graphics processing unit machine

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