Pattern of cerebellar grey matter loss associated with ataxia severity in spinocerebellar ataxias type 3: a multi-voxel pattern analysis

Hu, Jianping; Chen, Xinyuan; Li, Mengcheng; Xu, Hao-Ling; Huang, Ziqiang; Chen, Naping; Tu, Yuqing; Chen, Qunlin; Gan, Shi‐Rui; Cao, Dairong

doi:10.1007/s11682-021-00511-x

Cited by 6 publications

(6 citation statements)

References 39 publications

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“…Moreover, traditional statistical analysis is based on mass univariate techniques which process a single voxel independently and ignore the relationship between voxels (Bonnici et al, 2012 ; Samartsidis et al, 2016 ). Furthermore, it defines the common pattern at the level of groups and is unable to predict the unknown sample at the level of individuals (Zhutovsky et al, 2019 ; Hu et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning

Yu-mei

Zhang²,

Luo³

et al. 2022

Front. Hum. Neurosci.

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Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder(ASD), definitive neuroimaging markers remain obscured due to inconsistent or incompatible findings, especially for structural imaging. Furthermore, brain differences defined by statistical analysis are difficult to implement individual prediction. The present study has employed the machine learning techniques under the unified framework in neuroimaging to identify the neuroimaging markers of patients with ASD and distinguish them from typically developing controls(TDC). To enhance the interpretability of the machine learning model, the study has processed three levels of assessments including model-level assessment, feature-level assessment, and biology-level assessment. According to these three levels assessment, the study has identified neuroimaging markers of ASD including the opercular part of bilateral inferior frontal gyrus, the orbital part of right inferior frontal gyrus, right rolandic operculum, right olfactory cortex, right gyrus rectus, right insula, left inferior parietal gyrus, bilateral supramarginal gyrus, bilateral angular gyrus, bilateral superior temporal gyrus, bilateral middle temporal gyrus, and left inferior temporal gyrus. In addition, negative correlations between the communication skill score in the Autism Diagnostic Observation Schedule (ADOS_G) and regional gray matter (GM) volume in the gyrus rectus, left middle temporal gyrus, and inferior temporal gyrus have been detected. A significant negative correlation has been found between the communication skill score in ADOS_G and the orbital part of the left inferior frontal gyrus. A negative correlation between verbal skill score and right angular gyrus and a significant negative correlation between non-verbal communication skill and right angular gyrus have been found. These findings in the study have suggested the GM alteration of ASD and correlated with the clinical severity of ASD disease symptoms. The interpretable machine learning framework gives sight to the pathophysiological mechanism of ASD but can also be extended to other diseases.

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Section: Introductionmentioning

confidence: 99%

Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning

Yu-mei

Zhang²,

Luo³

et al. 2022

Front. Hum. Neurosci.

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“…To better understand the disease, we previously explored GM volume abnormalities in patients with SCA3 from pre‐symptomatic to symptomatic stage, and widespread atrophic GM volumes were observed in symptomatic stage, including bilateral pallidum, brainstem, pons, and cerebellar, 7 then pre‐symptomatic stage suffered from white matter damage but no apparent GM loss/atrophy. These results 2–7 provide a large number of evidences for macromorphological abnormalities in SCA3 patients. However, the brain, especially the cerebral hemisphere, is a complex network composed of interconnected regions 8,9 .…”

Section: Introductionmentioning

confidence: 65%

“…Nowadays, structural neuroimaging has shown that SCA3 suffered from infratentorial cerebellar 2,3 and supratentorial cerebral GM loss/atrophy, [4][5][6] especially cerebellar GM. To better understand the disease, we previously explored GM volume abnormalities in patients with SCA3 from pre-symptomatic to symptomatic stage, and widespread atrophic GM volumes were observed in symptomatic stage, including bilateral pallidum, brainstem, pons, and cerebellar, 7 then pre-symptomatic stage suffered from white matter damage but no apparent GM loss/atrophy.…”

Section: Introductionmentioning

confidence: 99%

Altered large‐scale individual‐based morphological brain network in spinocerebellar ataxia type 3

Su,

Sha,

Qiu

et al. 2023

CNS Neurosci Ther

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BackgroundAccumulating evidences indicate regional gray matter (GM) morphology atrophy in spinocerebellar ataxia type 3 (SCA3); however, whether large‐scale morphological brain networks (MBNs) undergo widespread reorganization in these patients remains unclear.ObjectiveTo investigate the topological organization of large‐scale individual‐based MBNs in SCA3 patients.MethodsThe individual‐based MBNs were constructed based on the inter‐regional morphological similarity of GM regions. Graph theoretical analysis was taken to assess GM structural connectivity in 76 symptomatic SCA3, 24 pre‐symptomatic SCA3, and 54 healthy normal controls (NCs). Topological parameters of the resulting graphs and network‐based statistics analysis were compared among symptomatic SCA3, pre‐symptomatic SCA3, and NCs groups. The inner association between network properties and clinical variables was further analyzed.ResultsCompared to NCs and pre‐symptomatic SCA3 patients, symptomatic SCA3 indicated significantly decreased integration and segregation, a shift to “weaker small‐worldness”, characterized by decreased Cp, lower Eloc, and Eglob (all p < 0.005). Regarding nodal properties, symptomatic SCA3 exhibited significantly decreased nodal profiles in the central executive network (CEN)‐related left inferior frontal gyrus, limbic regions involving the bilateral amygdala, left hippocampus, and bilateral pallidum, thalamus; and increased nodal degree, efficiency in bilateral caudate (all pFDR <0.05). Meanwhile, clinical variables were correlated with altered nodal profiles (pFDR ≤0.029). SCA3‐related subnetwork was closely interrelated with dorsolateral cortico‐striatal circuitry extending to orbitofrontal‐striatal circuits and dorsal visual systems (lingual gyrus‐striatal).ConclusionSymptomatic SCA3 patients undergo an extensive and significant reorganization in large‐scale individual‐based MBNs, probably due to disrupted prefrontal cortico‐striato‐thalamo‐cortical loops, limbic‐striatum circuitry, and enhanced connectivity in the neostriatum. This study highlights the crucial role of abnormal morphological connectivity alterations beyond the pattern of brain atrophy, which might pave the way for therapeutic development in the future.

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“…23 Patients with spinocerebellar ataxia (SCA) type 3 demonstrated atrophy involving almost all cerebellar regions except parts of lobules VIIb, VIIIa, and VIIIb bilaterally. 24 A morphometric study in patients with SCA type 17 revealed cerebellar atrophy predominantly localized in left lobule IX extending to the lobules VIIIA and VIIIb as well as the lobule VIIA and in the bilateral lobule V. 25 Yet, SCA type 10 showed a different atrophy pattern. The cerebellar degeneration was suggested to start in the posterior lobule and, in later stages, expand to the anterior lobe.…”

Section: Discussionmentioning

confidence: 99%

Quantitative brain morphometry identifies cerebellar, cortical, and subcortical gray and white matter atrophy in late‐onset Tay‐Sachs disease

Májovská,

Nestrašil,

Ahmed

et al. 2023

J of Inher Metab Disea

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Cerebellar atrophy is a characteristic sign of late‐onset Tay‐Sachs disease (LOTS). Other structural neuroimaging abnormalities are inconsistently reported. Our study aimed to perform a detailed whole‐brain analysis and quantitatively characterize morphometric changes in LOTS patients. Fourteen patients (8 M/6F) with LOTS from three centers were included in this retrospective study. For morphometric brain analyses, we used deformation‐based morphometry, voxel‐based morphometry, surface‐based morphometry, and spatially unbiased cerebellar atlas template. The quantitative whole‐brain morphometric analysis confirmed the finding of profound pontocerebellar atrophy with most affected cerebellar lobules V and VI in LOTS patients. Additionally, the atrophy of structures mainly involved in motor control, including bilateral ventral and lateral thalamic nuclei, primary motor and sensory cortex, supplementary motor area, and white matter regions containing corticospinal tract, was present. The atrophy of the right amygdala, hippocampus, and regions of occipital, parietal and temporal white matter was also observed in LOTS patients in contrast with controls (p < 0.05, FWE corrected). Patients with dysarthria and those initially presenting with ataxia had more severe cerebellar atrophy. Our results show predominant impairment of cerebellar regions responsible for speech and hand motor function in LOTS patients. Widespread morphological changes of motor cortical and subcortical regions and tracts in white matter indicate abnormalities in central motor circuits likely coresponsible for impaired speech and motor function.

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Pattern of cerebellar grey matter loss associated with ataxia severity in spinocerebellar ataxias type 3: a multi-voxel pattern analysis

Cited by 6 publications

References 39 publications

Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning

Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning

Altered large‐scale individual‐based morphological brain network in spinocerebellar ataxia type 3

Quantitative brain morphometry identifies cerebellar, cortical, and subcortical gray and white matter atrophy in late‐onset Tay‐Sachs disease

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