Gray matter volume changes in chronic subcortical stroke: A cross-sectional study

Diao, Qingqing; Liu, Jingchun; Wang, Caihong; Cao, Chen; Guo, Jun; Han, Tong; Cheng, Jingliang; Zhang, Xuejun; Yu, Chunshui

doi:10.1016/j.nicl.2017.01.031

Cited by 37 publications

(28 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accordingly, the volumetric loss of the infratentorial regions may result from the Wallerian degeneration of the distal parts in the nervous systems following the injury of proximal axon or cell body (Yu et al, 2009). These results were in line with previous studies that reported the structural damage in remote regions in subcortical stroke, which was caused by axonal degeneration secondary to the occurrence of stroke lesions (Diao et al, 2017a, Diao et al, 2017b). However, volumetric alterations of infratentorial regions were rarely reported in patients with subcortical stroke.…”

Section: Discussionsupporting

confidence: 93%

“…For instance, the functional magnetic resonance imaging (MRI) demonstrated altered activation within the motor network and in other functional network and their interaction between the functional networks after subcortical stroke (Wang et al, 2014). The brain morphometric studies based on structural MRI revealed multiregional gray matter volume changes in subcortical stroke patients, not only in brain regions adjacent to stroke lesions but also in the areas remote to lesions during recovery (Anderson et al, 2002; Zhang et al, 2014; Diao et al, 2017a, Diao et al, 2017b). However, the existing morphometric studies mostly focus on cortical gray matter structural changes, and the structural alterations of subcortical regions and infratentorial regions are rarely studied.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, a multitude of neuroimaging studies have shown that the cerebral hemispheric asymmetry (Chiu and Damasio, 1980; LeMay, 1986; Corballis, 2014) may result in different structural reorganization following stroke in the homologous region of the left and right hemispheres, and the lesion-side effect has been found in gray matter volume changes in subcortical stroke patients (Diao et al, 2017a, Diao et al, 2017b). In this case, the analyses in our study were further refined according to the side of the lesions, as it would be also interesting to investigate whether patients with lesions in the left and right hemispheres exhibit similar or different patterns in brain structural damage and volumetric structural covariance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Structural covariance in subcortical stroke patients measured by automated MRI-based volumetry

Wang

Zhao

Luo

et al. 2019

NeuroImage: Clinical

Self Cite

View full text Add to dashboard Cite

A network-level investigation of the volumetric changes of subcortical stroke patients is still lacking. Here, we explored the alterations of structural covariance caused by subcortical stroke with automated brain volumetry. T1-weighed brain MRI scans were obtained from 63 normal controls (NC), 46 stroke patients with infarct in left internal capsule (CI_L), 33 stroke patients with infarct in right internal capsule (CI_R). We performed automatic anatomical segmentation of the T1-weighted brain images with AccuBrain. Volumetric structural covariance analyses were first performed within the basal ganglia structures that were both identified by voxel-based morphometry with AAL atlas and AccuBrain. Subsequently, we additionally included the infratentorial regions that were particularly quantified by AccuBrain for the structural covariance analyses and investigated the alterations of anatomical connections within these subcortical regions in CI_L and CI_R compared with NC. The association between the regional brain volumetry and motor function was also evaluated in stroke groups. There were significant and extensive volumetric differences in stroke patients. These significant regions were generally symmetric for CI_L and CI_R group depending on the side of stroke, involving both regions close to lesions and remote regions. The structural covariance analyses revealed the synergy volume alteration in subcortical regions both in CI_L and CI_R group. In addition, the alterations of volumetric structural covariance were more extensive in CI_L group than CI_R group. Moreover, we found that the subcortical regions with atrophy contributed to the deficits of motor function in CI_R group but not CI_L group, indicating a lesion-side effect of brain volumetric changes after stroke. These findings indicated that the chronic subcortical stroke patients have extensive disordered anatomical connections involving the whole-brain level network, and the connections patterns depend on the lesion-side.

show abstract

Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Structural covariance in subcortical stroke patients measured by automated MRI-based volumetry

Wang

Zhao

Luo

et al. 2019

NeuroImage: Clinical

Self Cite

View full text Add to dashboard Cite

show abstract

“…Dynamic remodeling may lead to changes in brain structure, including atrophy and/or expansion of specific brain regions after ischemic stroke ( 5 – 7 ). One study reported that gray matter volume (GMV) was not correlated with motricity index scores, but that patients with decreased GMV exhibited a small degree of motor improvement after stroke ( 8 ).…”

Section: Introductionmentioning

confidence: 99%

Correlation of Longitudinal Gray Matter Volume Changes and Motor Recovery in Patients After Pontine Infarction

et al. 2018

View full text Add to dashboard Cite

The mechanisms of motor functional recovery after pontine infarction (PI) remain unclear. Here, we assessed longitudinal changes in gray matter volume (GMV) and examined the relationship between GMV and clinical outcome. Fifteen patients with unilateral PI underwent magnetic resonance imaging and neurological exams five times during a period of 6 months. Another 15 healthy participants were enrolled as the normal control (NC) group and were examined with the same protocol. The MR exam included routine protocol and a 3D T1-weighted magnetization-prepared rapid acquisition gradient echo scan. Changes in GMV were assessed using voxel-based morphometry. Furthermore, the correlations between GMV changes in regions of interest and clinical scores were assessed. Compared with NCs, the decreased GMVs in the contralateral uvula of cerebellum and the ipsilateral tuber of cerebellum were detected at third month after stroke onset. At the sixth month after stroke onset, the decreased GMVs were detected in the contralateral culmen of cerebellum, putamen, as well as in the ipsilateral tuber/tonsil of cerebellum. Compared with NC, the PI group exhibited significant increases in GMV at each follow-up time point relative to stroke onset. Specifically, the significant GMV increase was found in the ipsilateral middle frontal gyrus and ventral anterior nucleus of thalamus at second week after stroke onset. At first month after stroke onset, the increased GMVs in the ipsilateral middle temporal gyrus were detected. The significant GMV increase in the ipsilateral mediodorsal thalamus was noted at third month after stroke onset. At the end of sixth month after stroke onset, the GMV increase was found in the ipsilateral mediodorsal thalamus, superior frontal gyrus, and the contralateral precuneus. Across five times during a period of 6-month, a negative correlation was observed between mean GMV in the contralateral uvula, culmen, putamen, and ipsilateral tuber/tonsil and mean Fugl-Meyer (FM) score. However, mean GMV in the ipsilateral mediodorsal thalamus was positively correlated with mean FM score. Our findings suggest that structural reorganization of the ipsilateral mediodorsal thalamus might contribute to motor functional recovery after PI.

show abstract

“…The T1 structural MRI data of each participant were analyzed using the software package VBM8 3 to calculate the voxel-wise gray matter volume (GMV) and white matter volume (WMV). More detailed information can be found in our previous papers (Zhang et al, 2014;Diao et al, 2017;Liu et al, 2018).…”

Section: Applications Of Mvpanimentioning

confidence: 99%

MVPANI: A Toolkit With Friendly Graphical User Interface for Multivariate Pattern Analysis of Neuroimaging Data

Peng

Zhang

et al. 2020

Front. Neurosci.

Self Cite

View full text Add to dashboard Cite

With the rapid development of machine learning techniques, multivariate pattern analysis (MVPA) is becoming increasingly popular in the field of neuroimaging data analysis. Several software packages have been developed to facilitate its application in neuroimaging studies. As most of these software packages are based on command lines, researchers are required to learn how to program, which has greatly limited the use of MVPA for researchers without programming skills. Moreover, lacking a graphical user interface (GUI) also hinders the standardization of the application of MVPA in neuroimaging studies and, consequently, the replication of previous studies or comparisons of results between different studies. Therefore, we developed a GUIbased toolkit for MVPA of neuroimaging data: MVPANI (MVPA for Neuroimaging). Compared with other existing software packages, MVPANI has several advantages. First, MVPANI has a GUI and is, thus, more friendly for non-programmers. Second, MVPANI offers a variety of machine learning algorithms with the flexibility of parameter modification so that researchers can test different algorithms and tune parameters to identify the most suitable algorithms and parameters for their own data. Third, MVPANI also offers the function of data fusion at two levels (feature level or decision level) to utilize complementary information contained in different measures obtained from multimodal neuroimaging techniques. In this paper, we introduce this toolkit and provide four examples to demonstrate its usage, including (1) classification between patients and controls, (2) identification of brain areas containing discriminating information, (3) prediction of clinical scores, and (4) multimodal data fusion.

show abstract

Gray matter volume changes in chronic subcortical stroke: A cross-sectional study

Cited by 37 publications

References 37 publications

Structural covariance in subcortical stroke patients measured by automated MRI-based volumetry

Structural covariance in subcortical stroke patients measured by automated MRI-based volumetry

Correlation of Longitudinal Gray Matter Volume Changes and Motor Recovery in Patients After Pontine Infarction

MVPANI: A Toolkit With Friendly Graphical User Interface for Multivariate Pattern Analysis of Neuroimaging Data

Contact Info

Product

Resources

About