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临床研究
脊髓小脑共济失调3型的脑灰质结构异常对运动及非运动功能影响的相关研究
李梦菲 蒋珍珍 陈辉 刘晨 王健

Cite this article as: LI M F, JIANG Z Z, CHEN H, et al. Effect of structural abnormality of gray matter on motor and non-motor function in spinocerebellar ataxia type 3[J]. Chin J Magn Reson Imaging, 2023, 14(5): 60-65, 84.本文引用格式:李梦菲, 蒋珍珍, 陈辉, 等. 脊髓小脑共济失调3型的脑灰质结构异常对运动及非运动功能影响的相关研究[J]. 磁共振成像, 2023, 14(5): 60-65, 84. DOI:10.12015/issn.1674-8034.2023.05.012.


[摘要] 目的 探讨脊髓小脑性共济失调3型(spinocerebellar ataxia type 3, SCA3)患者灰质结构异常及其对非运动性和运动性功能的影响,以期为SCA3的早期诊断、精准治疗提供理论依据。材料与方法 102名受试者[49名患者和53名年龄和性别匹配的健康对照(healthy controls, HC)]被纳入研究。参与者接受了神经心理学量表评估,包括蒙特利尔认知评估(Montreal Cognitive Assessment, MoCA)、简易智力状态检查量表(Mini-mental State Examination, MMSE)、快速词汇测验(Rapid Verbal Retrieval, RVR)、数字跨度测验(Digital Span Test, DST)、日常生活活动能力(Activities of Daily Living, ADL)和汉密尔顿抑郁量表(Hamilton Depression Scale, HAMD)以及MRI评估。采用基于体素的形态学分析(voxel-based morphological analysis, VBM)来分析患者与健康对照组之间灰质结构差异。提取差异脑区的灰质体积(gray matter volume, GMV),并与运动及非运动量表进行偏相关分析,以性别、年龄以及颅内总体积(total intracranial volume, TIV)作为协变量。结果 与正常对照组相比,SCA3组神经量表评分MoCA(Z=-4.578,P<0.001)、MMSE(Z=-4.725,P<0.001)和RVR(Z=-5.773,P<0.001)的分数显著下降,ADL(Z=-6.447,P<0.001)和HAMD(Z=-5.285,P<0.001)分数显著升高。SCA3在小脑蚓Ⅸ叶、双侧小脑、尾状核、扣带回、额叶、海马体、中央前回、壳核、辅助运动区和颞叶、左侧距状裂周围皮层、中央旁小叶和海马旁回、右楔叶、梭状回和枕叶存在体积萎缩(P<0.001,FDR校正);体积增加的为丘脑左侧板内核、背内侧核和右腹前核(P<0.001,FDR校正)。差异脑区体积与神经心理学量表偏相关性分析显示:共济失调等级表(Scale for the Assessment and Rating of Ataxia, SARA)和国际合作共济失调评分表(International Cooperative Ataxia Rating Scale, ICARS)评分与蚓部Ⅸ小叶、双侧小脑、中央前回、颞叶、左侧距状裂周围皮层、右侧尾状核、扣带回、额叶、梭状回和枕叶呈负相关,与左侧丘脑板内核呈正相关;MoCA评分与蚓部Ⅸ小叶和双侧小脑呈正相关;ADL评分与双侧小脑、左侧距状裂周围皮层、颞叶、右梭状回和蚓部Ⅸ小叶呈负相关;HAMD评分与双侧小脑、右额叶、左颞叶呈负相关;RVR评分与双侧小脑、右额叶、左颞叶和蚓部Ⅸ小叶呈正相关。结论 SCA3患者的除了小脑结构损伤外,还涉及广泛的认知、记忆、情绪相关脑区损伤,并且与其运动、非运动功能损伤密切相关,这为SCA3下一步的治疗靶区选择提供了理论指导。
[Abstract] Objective To explore the structural abnormality of gray matter and its effect on non-motor and motor function in patients with spinocerebellar ataxia type 3 (SCA3), so as to provide a theoretical basis for early diagnosis and proper treatment of SCA3.Materials and Methods One hundred and two subjects [49 patients and 53 age- and sex- matched healthy controls (HC) ] were enrolled in the study. The participants were assessed by the neuropsychological scale, including the Montreal Cognitive Assessment (MoCA), the Mini Mental State Examination (MMSE), the Rapid Verbal Retrieve (RVR), Digit Span Test (DST), the Activities of Daily Living (ADL), and the Hamilton Depression Scale (HAMD) . They also participated in the MRI evaluation. Voxel-based morphometry (VBM) was conducted to analyse the gray matter volume (GMV) alterations between the patients and the healthy controls. The partial correlation analysis was performed to identify the partial correlation between GMV and clinical scale scores with sex, age, and total intracranial volume (TIV) as covariables.Results Compared with the healthy control group, the scores of MoCA (Z=-4.578, P<0.001), MMSE (Z=-4.725, P<0.001) and RVR (Z=-5.773, P<0.001) in the SCA3 group decreased significantly, while the scores of ADL (Z=-6.447, P<0.001) and HAMD (Z=-5.285, P<0.001) increased significantly. The brain volume atrophied were discovered in the vermis Ⅸ lobe, bilateral cerebellum, caudate nucleus, cingulate gyrus, frontal lobe, hippocampus, precentral gyrus, putamen, supplementary motor area, and temporal lobe, left calcarine cortex, paracentral lobule, and para-hippocampal gyrus, right cuneus, fusiform gyrus and occipital lobe (P<0.001, FDR corrected). The increased ones were intralaminar nucleus, dorsal medial nucleus of left thalamus and ventral anterior nucleus of right thalamus (P<0.001, FDR corrected). The correlation analysis between differential brain area and the neuropsychological scale showed that Scale for the Assessment and Rating of Ataxia (SARA) and International Cooperative Ataxia Rating Scale (ICARS) scores were negatively correlated with the vermis Ⅸ lobule, bilateral cerebellum, precentral gyrus, and temporal lobe; left calcarine cortex, right caudate nucleus, cingulate gyrus, frontal lobe, fusiform gyrus and occipital lobe, while positively correlated with the intralaminar nucleus of left thalamus. MoCA score was positively correlated with vermis Ⅸ lobule and bilateral cerebellum. ADL score was negatively correlated with vermis Ⅸ lobule, bilateral cerebellum, left calcarine cortex, temporal lobe and right fusiform gyrus; HAMD score was negatively correlated with bilateral cerebellum, right frontal lobe and left temporal lobe; RVR score was positively correlated with bilateral cerebellum, right frontal lobe, left temporal lobe and Ⅸ lobule of vermis.Conclusions In addition to cerebellar structural damage, there are also extensive cognitive, memory and emotional brain damage in SCA3, which is closely related to motor and non-motor functional impairment, which provides theoretical guidance for the next selection of therapeutic targets for SCA3.
[关键词] 脊髓小脑共济失调3型;磁共振成像;基于体素的形态学分析;小脑
[Keywords] spinocerebellar ataxia type 3;magnetic resonance imaging;voxel-based morphometry;cerebellum

李梦菲    蒋珍珍    陈辉    刘晨    王健 *  

陆军军医大学第一附属医院放射科,重庆 400038

通信作者:王健,E-mail:wangjian@aifmri.com

作者贡献声明:王健设计本研究的方案,对稿件重要内容进行了修改,对最终要发表的论文版本进行了全面的审阅和把关;李梦菲起草和撰写稿件,获取、分析或解释本研究的数据;蒋珍珍获取本研究的数据;陈辉分析或解释本研究的数据;刘晨设计本研究的方案,对稿件重要内容进行了修改,获得了国家自然科学基金的资助;全体作者都同意发表最后的修改稿,同意对本研究的所有方面负责,确保本研究的准确性和诚信。


基金项目: 国家自然科学基金 81771814,81601478,81971587
收稿日期:2022-10-17
接受日期:2023-05-06
中图分类号:R445.2  R742.82 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2023.05.012
本文引用格式:李梦菲, 蒋珍珍, 陈辉, 等. 脊髓小脑共济失调3型的脑灰质结构异常对运动及非运动功能影响的相关研究[J]. 磁共振成像, 2023, 14(5): 60-65, 84. DOI:10.12015/issn.1674-8034.2023.05.012.

0 前言

       脊髓小脑共济失调3型(spinocerebellar ataxia type 3, SCA3)又称马查多-约瑟夫病,是一种常染色体显性遗传的神经退行性疾病,由ATXN3基因中的多谷氨酰胺(poly Q)编码的胞嘧啶-腺嘌呤(cytosine-adenine-guanine, CAG)重复的异常扩展引起[1]。SCA3常规MRI上主要表现为小脑萎缩,疾病进展可出现小脑-新纹状体-大脑的渐进性萎缩,进而出现以共济失调为核心症状的运动症状,并逐渐伴发广泛的非运动症状如记忆力减退[2]、空间认知能力下降[3]、言语困难[4]和情感失调[5]。MRI技术在神经系统退行性疾病方面发挥着重要作用,可以对脑结构、功能、代谢异常进行系统的定性、定量研究,并且可以与临床症状进行相关分析,得出神经退行性疾病发展变化规律[6, 7]。目前基于MRI的SCA3结构研究发现其脑灰白质出现了广泛异常[8, 9, 10, 11]。临床上,SCA3患者表现出很多运动和非运动症状,但灰质体积改变特点与其关系尚不清楚。因此,在本研究中,我们使用基于体素的形态学分析(voxel-based morphometry, VBM)来比较SCA3患者和健康对照组之间灰质体积(gray matter volume, GMV)的差异,假设SCA3患者的运动及非运动功能损伤与灰质体积的变化密切相关,得到准确的SCA3灰质损伤区域,以期为SCA3的早期诊断、精准治疗提供理论依据。

1 材料和方法

1.1 一般资料

       根据陆军军医大学第一附属医院内部数据库进行筛选后通过电话进行招募SCA3患者,招募了49名经基因确认的SCA3患者,同时招募了53名年龄和性别匹配的健康对照(healthy controls, HC),两组都进行了MJD1基因的CAG重复数测序[12],其中CAG重复数范围为57~72,诊断为SCA3。每位患者都接受了标准化的临床评估,包括系统的身体和神经系统检查。所有健康对照者在招募前都进行了详细的临床评估,以排除任何神经/精神疾病的家族史、系统性代谢疾病或肿瘤。具体流程详见图1。所有参与者均为右利手,并对所有参与者进行了MRI扫描。本研究遵守《赫尔辛基宣言》,经陆军军医大学第一附属医院医学伦理委员会批准(批件号:KY2020191),所有参与者都签署了知情同意书。

图1  招募流程图。
Fig. 1  Recruitment flowchart.

1.2 临床评估

       所有受试者均由专业的神经心理学医生进行量表量表评估:(1)对SCA3患者进行了共济失调等级表(Scale for the Assessment and Rating of Ataxia, SARA)[13]和 国际合作共济失调评分表(International Cooperative Ataxia Rating Scale, ICARS)[14]评估。HC没有共济失调的症状,所以没有对他们进行SARA和ICARS测试。这两个量表被用来评估SCA3患者的共济失调程度,代表患者运动功能受损的情况。分数越高,症状就越严重。(2)蒙特利尔认知评估(Montreal Cognitive Assessment Scale Bejing version, MoCA-Beijing)[15]和迷你精神状态检查(Mini Mental State Examination, MMSE)[16]用来评估认知能力。(3)数字跨度测试[17](Digit Span Test, DST)是韦氏成人智力量表[18]的一部分,用于测量工作记忆能力[19]。(4)快速词汇测验(Rapid Verbal Retrieve, RVR)[20, 21]用于测试语言流利程度。基础日常生活活动能力(Basic Activities of Daily Living, BADL)[22]和日常生活工具活动(Instrumental Activity of Daily Living, IADL)量表[23] 合为日常生活活动能力量表(Activity of Daily Living, ADL),用于评估参与者的残疾或功能障碍程度,分数越高表示情况越差。汉密尔顿抑郁量表(Hamilton Depression Scale, HAMD)24项[24]用来评估情绪状态。

1.3 MRI采集

       所有来自SCA3患者和HC的影像数据均使用西门子3.0 T TIM Trio磁共振扫描仪(德国埃朗根,8通道)获得,由同一名具有五年以上临床经验的影像科主治医师扫描完成操作。扫描前,受试者戴好防护耳塞并且固定头部。T1加权序列扫描参数如下:TR 1900 ms,TE 2.52 ms,翻转角9º,层厚1 mm,矩阵256×256,体素1 mm×1 mm×1 mm,176层,无间隙。使用三维磁化预处理的快速采集梯度回波获得高分辨率矢状结构T1加权解剖序列。扫描了T2加权和液体衰减反转恢复(fluid attenuated inversion recovery, FLAIR)序列以排除不相关的疾病,如颅内肿瘤、多发性硬化、脑出血等颅内器质性病变。T2加权序列扫描参数如下:TR 4490 ms,TE 113 ms,翻转角129º,层厚1 mm,矩阵220×220,体素1 mm×1 mm×1 mm,176层。FLAIR序列扫描参数如下:TR 7000 ms,TE 79 ms,翻转角120º,层厚1 mm,矩阵240×240,体素1 mm×1 mm×1 mm,176层。

1.4 MRI数据预处理

       数据的预处理是在MATLAB R2019b(https://ww2.mathworks.cn/products/matlab.html)上使用SPM 12(http://www.fil.ion.ucl.ac.uk/spm)中的计算解剖学工具箱CAT 12(http://dbm.neuro.uni-jena.de/cat12/)进行的。在CAT 12中采取了以下步骤对数据进行预处理:(1)T1图像被分割成灰质、白质和脑脊液,并使用DARTEL标准化匹配到蒙特利尔神经研究所(Montreal Neurological Institute, MNI)空间。(2)对图像质量进行人工检查,以确保使用非平滑分割的样本内的均一性,因为这种类型的数据可以提供更多的解剖细节。(3)所有分割的灰质图像使用全半高宽8 mm的高斯核平滑进行平滑处理。在此基础上,在MATLAB上根据AAL3模板用自定义代码提取各脑区的GMV。

1.5 统计学分析

       使用统计学软件IBM SPSS 22.0(https://www.ibm.com/cn-zh/analytics/spss-statistics-software)进行分析,由于两组的人口和临床评估数据不是正态分布,因此使用的卡方检验比较性别分类变量和Mann-Whitney U检验来比较其他连续变量的组间差异,P<0.001认为组间差异具有统计学意义。用SPM 12中的一般线性模型对各组间的GMV进行统计比较,并以年龄、性别和颅内总体积(total intracranial volume, TIV)作为协变量,设定对比差异体素P<0.001,集群P<0.05表示GMV差异有统计学意义,FDR校正以减少Ⅰ型错误的发生(此处FDR校正为SPM 12自动校正)。以年龄、性别和TIV为协变量,在临床特征和重要群组的GMV之间进行偏相关统计分析,将相关的显著性阈值设定为P<0.05(FDR校正,使用matlab中的FDR函数实现)。

2 结果

2.1 人口学和临床特征

       SCA3组的MoCA(Z=-4.578,P<0.001)、MMSE(Z=-4.725,P<0.001)和RVR(Z=-5.773,P<0.001)的评分明显下降,而BADL+IADL(Z=-6.447,P<0.001)和HAMD(Z=-5.285,P<0.001)评分在SCA3组较HC增高。两组之间的DST评分未见明显差异(表1)。

表1  人口统计学和临床评估分析
Tab. 1  Statistical analysis results of demographic and clinical data

2.2 具有显著组间GMV差异的脑区

       与HC相比,SCA3组的GMV显著降低,例如蚓部Ⅸ叶、双侧小脑、小脑下脚,尾状核、扣带回、额叶、海马体、中央前回、壳核、辅助运动区和颞叶、左侧距状裂周围皮层、中央旁小叶和海马旁回、右楔叶、梭状回和枕叶,尤其是小脑。但是,丘脑左侧板内核、背内侧核和右腹前核的GMV增加(表2图2)。

图2  脊髓小脑共济失调3型(SCA3)组与健康对照(HC)组的差异脑区。2A:SCA3组与HC的体积差异横断面显示;2B:SCA3组与HC的体积差异在脑表面的投影。
Fig. 2  Differential brain regions in the spinocerebellar ataxia type 3 (SCA3) group versus the healthy control group. 2A: Cross-sectional display of the volume differences between the SCA3 group and the healthy control group. 2B: The projection of the volume difference on the brain surface between the SCA3 group and the healthy control group.
表2  SCA3组与HC之间灰质体积显著差异的脑区
Tab. 2  Significant differences in grey matter volume between the SCA3 group and controls

2.3 灰质体积与临床运动、非运动症状之间的相关关系

       SARA和ICARS评分与蚓部Ⅸ小叶、双侧小脑、中央前回、颞叶、左侧距状裂周围皮层、右侧尾状核、扣带回、额叶、梭状回和枕叶GMV呈负相关,但与左侧丘脑板内核的GMV呈正相关。MoCA评分与蚓部Ⅸ小叶和双侧小脑的GMV呈正相关。ADL评分与双侧小脑、左侧距状裂周围皮层、颞叶、右梭状回和蚓部Ⅸ小叶的GMV呈负相关。HAMD评分与双侧小脑、右额叶、左颞叶GMV呈负相关。RVR评分与双侧小脑、右额叶、左颞叶和蚓部Ⅸ小叶GMV呈正相关(表3图3)。

图3  SCA3患者灰质体积与临床评估之间的相关性。右侧色柱指示r值,格中数字表示具体r值。*表示P<0.05,**表示P<0.01,***表示P<0.001。SCA3:脊髓小脑共济失调3型;MoCA:蒙特利尔认知评估;MMSE:迷你精神状态检查;ADL:日常生活活动;SARA:共济失调评估和评分表;ICARS:国际合作共济失调评分表;RVR:快速词汇测试;HAMD:汉密尔顿抑郁量表。
Fig. 3  Correlation between gray matter volume and clinical evaluation in SCA3. The right color column indicates the r value, and the number in the cell indicates the specific r value. *: P<0.05, **: P<0.01, ***P<0.001. SCA3: spinocerebellar ataxia type 3; MoCA: Montreal Cognitive Assessment; MMSE: Mini Mental State Examination; ADL: Activities of Daily Living; SARA: Scale for the Assessment and Rating of Ataxia; ICARS: International Cooperative Ataxia Rating Scale; RVR: Rapid Verbal Retrieve; HAMD: Hamilton Depression Scale。
表3  脑区与临床评估之间的相关性
Tab. 3  Correlation between brain regions and clinical evaluation

3 讨论

       本研究使用 VBM 对参与者进行了脑部灰质结构分析,对比了SCA3患者组与HC的大脑灰质结构差异。此外,还使用临床量表来检测这些患者的运动和非运动症状。在研究分析中,SCA3 患者的多个脑区GMV萎缩,涉及小脑、蚓部、尾状核、中央前回、梭状回、距状裂周围皮层、扣带回、额叶、颞叶和枕叶。体积增加区仅见于丘脑。根据相关性分析,SCA3的GMV改变与运动和非运动症状显著相关。

3.1 脑区损伤与运动功能障碍的相关性分析

       在目前招募的SCA3患者队列中,运动症状对患者的生活质量影响比非运动症状更严重。在SCA3患者中,几乎所有与正常人有差异的脑区都会影响运动功能,小脑是最受关注的区域。小脑不启动自主运动,但接收来自脊髓感觉系统和其他脑区的信号,影响运动的协调性、准确性和精确的时间控制[25]。它对运动的感觉后果产生内部预测,并在这些预测不正确时完善运动输出[26]。患者起初表现为不稳定的步态,然后随着疾病的发展,完全失去对身体的控制,无法独立行走,就像他们在SARA和ICARS评估中的表现一样。另一项与帕金森病有关的研究中,发现小脑蚓部损伤会影响患者的步态[27],我们的研究中也发现,小脑蚓部灰质的萎缩也与SARA和ICRS评估的步态表现不佳有关,这表明共济失调的症状与蚓部有关。长期以来,人们认为小脑蚓部与轴向运动控制有关,其皮质部分协调着躯干的运动[28]。因此,SCA3患者有不稳定的步态和语言功能障碍,这进一步支持小脑蚓可能是SCA3的一个关键治疗生物标志物。至于大脑区域,如中央前回[29]、颞叶[30]、距状裂周围皮层[31]、尾状核[32]、扣带回[33]、额叶[34]、梭状回[35]和枕叶[36]参与运动功能的计划和执行或空间意识的产生,而视觉和体感通道将运动错误传达给小脑[37]。因此,我们认为SCA3患者的运动障碍症状是受不同脑区综合影响的。

3.2 脑区损伤与非运动功能障碍的相关性分析

       我们发现小脑、蚓部、额叶和颞叶的体积萎缩与SCA3患者的认知和情感损伤有关。除了运动功能受损外,非运动障碍也给患者的生活带来困扰,如语言、认知障碍和情感障碍。一项研究发现,小脑蚓部可能有助于巩固语言网络中的词汇语义关联,这与我们的结果一致,即小脑蚓部的灰质体积与词汇流畅性呈正相关,词汇流畅性是由RVR量表评估。越来越多的文献表明,小脑与非运动功能也有非常密切的关系[38, 39, 40],部分SCA3患者存在认知障碍和抑郁[41, 42]。此项研究中,SCA3患者的小脑体积与MoCA评分呈正相关,小脑体积萎缩越严重,MoCA评分就越低。小脑-纹状体-皮层网络之间的联系在SCA3患者中受到严重影响,并且纹状体-皮层网络在高阶认知功能中起着重要作用[42]。在一份病例报告中,小脑卒中患者在标准智力测试中得分很高,但在语义知识、联想学习和动词生成方面有明显障碍[43]。小脑的不同部位似乎影响着不同的认知功能,主要局限于小脑Ⅵ、Ⅶ小叶[44]。这些都支持了小脑对SCA3患者认知功能的影响,SCA3患者的小脑认知的系统性改变应在进一步研究中阐明。此外,一些SCA3患者有构音障碍和吞咽困难[45],这可能会影响与语言有关的认知测试的表现。我们还发现,SCA3患者的右额叶和左颞叶与RVR评分有关,这些区域在以前的研究中也被认为与认知能力有明显关系[46, 47, 48]

3.3 丘脑被发现是唯一体积增加的脑区

       在这项研究中,丘脑是唯一被发现灰质体积增加的脑区,我们发现,丘脑体积越大,运动障碍越严重,但与非运动功能无明显相关性。丘脑复合体与大脑皮层相互关联,是小脑与皮层之间联系的关键中继区[49],这可能为丘脑体积的增加提供了一个补偿机制。由于局部小脑和大脑皮层功能的下降,丘脑作为中继站在维持患者的日常活动中需要发挥更大的作用。这种不一致的结果也可能是由我们所招募的患者的个体差异造成的,还需要进一步的研究来证实这一假设。

3.4 不足和展望

       本研究的不足之处在于:首先,我们的患者群体的样本量可能不够大,缺乏代表性,我们将继续招募更多的患者,并尽可能使他们从我们的研究中受益。第二,我们没有做所有的非运动量表,因为很多方面的非运动损伤是由小脑引起的,如睡眠、执行控制能力。我们也许可以丰富评估SCA3的非运动特征的手段。由于小脑损伤带来的一系列特异性脑区受损,SCA3患者会出现一系列运动和非运动症状。与正常人相比,SCA3患者的特定脑区的体积萎缩与不同的临床症状有关。我们的结果证实了先前对SCA3的MRI研究,表明运动症状是与脑萎缩相关。我们还发现,一些受损的大脑区域也与非运动症状显著相关。SCA3的各种表现不能仅归因于小脑损伤。未来的研究还应侧重于非运动功能损伤改变规律及其运动-非运动损伤之间的交互关系。我们未来的研究可能会转向脑区结构的具体功能连接和信息传输途径的整合,这将有助于我们连接整个大脑并确定潜在的治疗方法。

4 结论

       小脑和大脑皮层形成了一个综合网络,运动、认知和情感领域都有联系[50]。大脑皮层中的许多非运动功能区与运动功能密切相关,而这些区域都与小脑相关皮层有关[51]。因此,SCA3是一种广泛影响到整个大脑的疾病。越来越多以前被认为与运动症状有关的脑区现在与非运动症状有关,表明在研究这种疾病时,要探索脑区之间更微妙的联系。期望我们的研究结果对寻找新的治疗靶点和为SCA3患者的全身综合治疗提供理论支持。

[1]
TOULIS V, CASAROLI-MARANO R, CAMÓS-CARRERAS A, et al. Altered retinal structure and function in Spinocerebellar ataxia type 3[J/OL]. Neurobiol Dis, 2022, 170: 105774 [2022-02-20]. https://pubmed.ncbi.nlm.nih.gov/35605759/. DOI: 10.1016/j.nbd.2022.105774">10.1016/j.nbd.2022.105774">10.1016/j.nbd.2022.105774.
[2]
ELYOSEPH Z, MINTZ M, VAKIL E, et al. Selective Procedural Memory Impairment but Preserved Declarative Memory in Spinocerebellar Ataxia Type 3[J]. Cerebellum, 2020, 19(2): 226-234. DOI: 10.1007/s12311-019-01101-w">10.1007/s12311-019-01101-w">10.1007/s12311-019-01101-w.
[3]
MASTAMMANAVAR V S, KAMBLE N, YADAV R, et al. Non-motor symptoms in patients with autosomal dominant spinocerebellar ataxia[J]. Acta Neurol Scand, 2020, 142(4): 368-376. DOI: 10.1111/ane.13318">10.1111/ane.13318">10.1111/ane.13318.
[4]
FENG L, CHEN D B, HOU L, et al. Cognitive impairment in native Chinese with spinocerebellar ataxia type 3[J]. Eur Neurol, 2014, 71(5-6): 262-270. DOI: 10.1159/000357404">10.1159/000357404">10.1159/000357404.
[5]
BRAGA-NETO P, PEDROSO J L, ALESSI H, et al. Cerebellar cognitive affective syndrome in Machado Joseph disease: core clinical features[J]. Cerebellum, 2012, 11(2): 549-556. DOI: 10.1007/s12311-011-0318-6">10.1007/s12311-011-0318-6">10.1007/s12311-011-0318-6.
[6]
NEMOTO K. Understanding Voxel-Based Morphometry[J]. Brain Nerve, 2017, 69(5): 505-511. DOI: 10.11477/mf.1416200776">10.11477/mf.1416200776">10.11477/mf.1416200776.
[7]
WAN N, CHEN Z, WAN L, et al. MR Imaging of SCA3/MJD[J/OL]. Front Neurosci, 2020, 14: 749 [2023-02-20]. https://pubmed.ncbi.nlm.nih.gov/32848545/. DOI: 10.3389/fnins.2020.00749">10.3389/fnins.2020.00749">10.3389/fnins.2020.00749.
[8]
MAAS R, KILLAARS S, VAN DE WARRENBURG B P C, et al. The cerebellar cognitive affective syndrome scale reveals early neuropsychological deficits in SCA3 patients[J]. J Neurol, 2021, 268(9): 3456-3466. DOI: 10.1007/s00415-021-10516-7">10.1007/s00415-021-10516-7">10.1007/s00415-021-10516-7.
[9]
WU Y T, HUANG S R, JAO C W, et al. Impaired Efficiency and Resilience of Structural Network in Spinocerebellar Ataxia Type 3[J/OL]. Front Neurosci, 2018, 12: 935 [2023-02-20]. https://pubmed.ncbi.nlm.nih.gov/30618564/. DOI: 10.3389/fnins.2018.00935">10.3389/fnins.2018.00935">10.3389/fnins.2018.00935.
[10]
HU J, CHEN X, LI M, et al. Pattern of cerebellar grey matter loss associated with ataxia severity in spinocerebellar ataxias type 3: a multi-voxel pattern analysis[J]. Brain Imaging Behav, 2022, 16(1): 379-388. DOI: 10.1007/s11682-021-00511-x">10.1007/s11682-021-00511-x">10.1007/s11682-021-00511-x.
[11]
LOPES T M, D'ABREU A, FRANÇA M C, et al. Widespread neuronal damage and cognitive dysfunction in spinocerebellar ataxia type 3[J]. J Neurol, 2013, 260(9): 2370-2379. DOI: 10.1007/s00415-013-6998-8">10.1007/s00415-013-6998-8">10.1007/s00415-013-6998-8.
[12]
PENG H, LIANG X, LONG Z, et al. Gene-Related Cerebellar Neurodegeneration in SCA3/MJD: A Case-Controlled Imaging-Genetic Study[J/OL]. Front Neurol, 2019, 10: 1025 [2023-02-20]. https://pubmed.ncbi.nlm.nih.gov/31616370/. DOI: 10.3389/fneur.2019.01025">10.3389/fneur.2019.01025">10.3389/fneur.2019.01025.
[13]
SCHMITZ-HÜBSCH T, DU MONTCEL S T, BALIKO L, et al. Scale for the assessment and rating of ataxia: development of a new clinical scale[J]. Neurology, 2006, 66(11): 1717-1720. DOI: 10.1212/01.wnl.0000219042.60538.92">10.1212/01.wnl.0000219042.60538.92">10.1212/01.wnl.0000219042.60538.92.
[14]
STOREY E, TUCK K, HESTER R, et al. Inter-rater reliability of the International Cooperative Ataxia Rating Scale (ICARS)[J]. Mov Disord, 2004, 19(2): 190-192. DOI: 10.1002/mds.10657">10.1002/mds.10657">10.1002/mds.10657.
[15]
ZUO L, DONG Y, ZHU R, et al. Screening for cognitive impairment with the Montreal Cognitive Assessment in Chinese patients with acute mild stroke and transient ischaemic attack: a validation study[J/OL]. BMJ Open, 2016, 6(7): e011310 [2023-02-20]. https://pubmed.ncbi.nlm.nih.gov/27406642/. DOI: 10.1136/bmjopen-2016-011310">10.1136/bmjopen-2016-011310">10.1136/bmjopen-2016-011310.
[16]
MITCHELL A J. A meta-analysis of the accuracy of the mini-mental state examination in the detection of dementia and mild cognitive impairment[J]. J Psychiatr Res, 2009, 43(4): 411-431. DOI: 10.1016/j.jpsychires.2008.04.014">10.1016/j.jpsychires.2008.04.014">10.1016/j.jpsychires.2008.04.014.
[17]
KRAUSE A J, SIMON E B, MANDER B A, et al. The sleep-deprived human brain[J]. Nat Rev Neurosci, 2017, 18(7): 404-418. DOI: 10.1038/nrn.2017.55">10.1038/nrn.2017.55">10.1038/nrn.2017.55.
[18]
LIESTO S, SIPILÄ R, HIETANEN M, et al. Cognitive function is well preserved in a cohort of breast cancer survivors: Roles of cognitive reserve, resilience, and general health[J]. Breast (Edinburgh, Scotland), 2022, 65: 157-163. DOI: 10.1016/j.breast.2022.07.013">10.1016/j.breast.2022.07.013">10.1016/j.breast.2022.07.013.
[19]
LLINÀS-REGLÀ J, VILALTA-FRANCH J, LÓPEZ-POUSA S, et al. The Trail Making Test[J]. Assessment, 2017, 24(2): 183-196. DOI: 10.1177/1073191115602552">10.1177/1073191115602552">10.1177/1073191115602552.
[20]
LUCAS J A, IVNIK R J, SMITH G E, et al. Mayo's Older African Americans Normative Studies: norms for Boston Naming Test, Controlled Oral Word Association, Category Fluency, Animal Naming, Token Test, WRAT-3 Reading, Trail Making Test, Stroop Test, and Judgment of Line Orientation[J]. Clin Neuropsychol, 2005, 19(2): 243-269. DOI: 10.1080/13854040590945337">10.1080/13854040590945337">10.1080/13854040590945337.
[21]
RÜB U, DE VOS R A, BRUNT E R, et al. Spinocerebellar ataxia type 3 (SCA3): thalamic neurodegeneration occurs independently from thalamic ataxin-3 immunopositive neuronal intranuclear inclusions[J]. Brain Pathol (Zurich, Switzerland), 2006, 16(3): 218-227. DOI: 10.1111/j.1750-3639.2006.00022.x">10.1111/j.1750-3639.2006.00022.x">10.1111/j.1750-3639.2006.00022.x.
[22]
POSTUMA R B, BERG D, STERN M, et al. MDS clinical diagnostic criteria for Parkinson's disease[J]. Mov Disord, 2015, 30(12): 1591-1601. DOI: 10.1002/mds.26424">10.1002/mds.26424">10.1002/mds.26424.
[23]
FUKUI S, KAWAKAMI M, OTAKA Y, et al. Activities of daily living among elderly persons with severe aortic stenosis[J]. Disabil Rehabil, 2021, 43(3): 338-344. DOI: 10.1080/09638288.2019.1624838">10.1080/09638288.2019.1624838">10.1080/09638288.2019.1624838.
[24]
HAMILTON M. A rating scale for depression[J]. J Neurol Neurosurg Psychiatry, 1960, 23(1): 56-62. DOI: 10.1136/jnnp.23.1.56">10.1136/jnnp.23.1.56">10.1136/jnnp.23.1.56.
[25]
KOZIOL L F, BUDDING D, ANDREASEN N, et al. Consensus paper: the cerebellum's role in movement and cognition[J]. Cerebellum, 2014, 13(1): 151-177. DOI: 10.1007/s12311-013-0511-x.
[26]
MOONEY R A, NI Z, SHIROTA Y, et al. Age-related strengthening of cerebello-cortical motor circuits[J]. Neurobiol Aging, 2022, 118: 9-12. DOI: 10.1016/j.neurobiolaging.2022.04.016.
[27]
MAITI B, RAWSON K S, TANENBAUM A B, et al. Functional Connectivity of Vermis Correlates with Future Gait Impairments in Parkinson's Disease[J]. Mov Disord, 2021, 36(11): 2559-2568. DOI: 10.1002/mds.28684.
[28]
FUJITA H, KODAMA T, DU LAC S. Modular output circuits of the fastigial nucleus for diverse motor and nonmotor functions of the cerebellar vermis[J/OL]. eLife, 2020, 9: e58613 [2023-02-20]. https://pubmed.ncbi.nlm.nih.gov/32639229/. DOI: 10.7554/eLife.58613.
[29]
TAN H H G, WESTENENG H J, NITERT A D, et al. MRI Clustering Reveals Three ALS Subtypes With Unique Neurodegeneration Patterns[J]. Ann Neurol, 2022, 92(6): 1030-1045. DOI: 10.1002/ana.26488.
[30]
BASAIA S, AGOSTA F, FRANCIA A, et al. Cerebro-cerebellar motor networks in clinical subtypes of Parkinson's disease[J]. NPJ Parkinson's Dis, 2022, 8(1): 113. DOI: 10.1038/s41531-022-00377-w.
[31]
CHEN J, JIN H, ZHONG Y L, et al. Abnormal Low-Frequency Oscillations Reflect Abnormal Eye Movement and Stereovision in Patients With Comitant Exotropia[J]. Front Hum Neurosci, 2021, 15: 754234. DOI: 10.3389/fnhum.2021.754234.
[32]
GUIDA P, MICHIELS M, REDGRAVE P, et al. An fMRI meta-analysis of the role of the striatum in everyday-life vs laboratory-developed habits[J]. Neurosci Biobehav Rev, 2022, 141: 104826. DOI: 10.1016/j.neubiorev.2022.104826.
[33]
KANG N. Increased Cerebellar Gray Matter Volume in Athletes: A Voxel-Wise Coordinate-Based Meta-Analysis[J]. Res Q Exerc Sport, 2022: 1-12. DOI: 10.1080/02701367.2022.2026285.
[34]
NAKAYAMA Y, SUGAWARA S K, FUKUNAGA M, et al. The dorsal premotor cortex encodes the step-by-step planning processes for goal-directed motor behavior in humans[J]. Neuroimage, 2022, 256: 119221. DOI: 10.1016/j.neuroimage.2022.119221.
[35]
ZHANG M, HUANG X, LI B, et al. Gray Matter Structural and Functional Alterations in Idiopathic Blepharospasm: A Multimodal Meta-Analysis of VBM and Functional Neuroimaging Studies[J]. Front Neurol, 2022, 13: 889714. DOI: 10.3389/fneur.2022.889714.
[36]
HEINRICHS-GRAHAM E, WIESMAN A I, EMBURY C M, et al. Differential impact of movement on the alpha and gamma dynamics serving visual processing[J]. J Neurophysiol, 2022, 127(4): 928-937. DOI: 10.1152/jn.00380.2021.
[37]
BONASSI G, PELOSIN E, LAGRAVINESE G, et al. Somatosensory inputs modulate the excitability of cerebellar-cortical interaction[J]. Clin Neurophysiol, 2021, 132(12): 3095-3103. DOI: 10.1016/j.clinph.2021.08.026.
[38]
HWANG K D, KIM S J, LEE Y S. Cerebellar Circuits for Classical Fear Conditioning[J]. Front Cell Neurosci, 2022, 16: 836948. DOI: 10.3389/fncel.2022.836948.
[39]
HABAS C. Functional Connectivity of the Cognitive Cerebellum[J]. Front Syst Neurosci, 2021, 15: 642225. DOI: 10.3389/fnsys.2021.642225.
[40]
SOLSTRAND DAHLBERG L, LUNGU O, DOYON J. Cerebellar Contribution to Motor and Non-motor Functions in Parkinson's Disease: A Meta-Analysis of fMRI Findings[J]. Front Neurol, 2020, 11: 127. DOI: 10.3389/fneur.2020.00127.
[41]
MARUFF P, TYLER P, BURT T, et al. Cognitive deficits in Machado-Joseph disease[J]. Ann Neurol, 1996, 40(3): 421-427. DOI: 10.1002/ana.410400311.
[42]
YAP K H, KESSELS R P C, AZMIN S, et al. Neurocognitive Changes in Spinocerebellar Ataxia Type 3: A Systematic Review with a Narrative Design[J]. Cerebellum (London, England), 2022, 21(2): 314-327. DOI: 10.1007/s12311-021-01282-3.
[43]
FIEZ J A, PETERSEN S E, CHENEY M K, et al. Impaired non-motor learning and error detection associated with cerebellar damage. A single case study[J]. Brain, 1992, 115Pt 1: 155-178. DOI: 10.1093/brain/115.1.155.
[44]
STOODLEY C J, VALERA E M, SCHMAHMANN J D. Functional topography of the cerebellum for motor and cognitive tasks: an fMRI study[J]. Neuroimage, 2012, 59(2): 1560-1570. DOI: 10.1016/j.neuroimage.2011.08.065.
[45]
COSTA M M B. NEURAL CONTROL OF SWALLOWING[J]. Arq Gastroenterol, 2018, 55(Suppl 1): 61-75. DOI: 10.1590/s0004-2803.201800000-45.
[46]
MCLOUGHLIN G, GYURKOVICS M, PALMER J, et al. Midfrontal Theta Activity in Psychiatric Illness: An Index of Cognitive Vulnerabilities Across Disorders[J]. Biol Psychiatry, 2022, 91(2): 173-182. DOI: 10.1016/j.biopsych.2021.08.020.
[47]
MOORE D, JUNG M, HILLMAN C H, et al. Interrelationships between exercise, functional connectivity, and cognition among healthy adults: A systematic review[J/OL]. Psychophysiology, 2022, 59(6): e14014 [2023-02-20]. https://pubmed.ncbi.nlm.nih.gov/35122693/. DOI: 10.1111/psyp.14014.
[48]
VAIDYA A R, BADRE D. Abstract task representations for inference and control[J]. Trends Cogn Sci, 2022, 26(6): 484-498. DOI: 10.1016/j.tics.2022.03.009.
[49]
MA K Y, CAI X Y, WANG X T, et al. Three-Dimensional Heterogeneity of Cerebellar Interposed Nucleus-Recipient Zones in the Thalamic Nuclei[J]. Neurosci Bull, 2021, 37(11): 1529-1541. DOI: 10.1007/s12264-021-00780-y.
[50]
BOSTAN A C, STRICK P L. The basal ganglia and the cerebellum: nodes in an integrated network[J]. Nat Rev Neurosci, 2018, 19(6): 338-350. DOI: 10.1038/s41583-018-0002-7.
[51]
WANG P S, WU Y T, WANG T Y, et al. Supratentorial and Infratentorial Lesions in Spinocerebellar Ataxia Type 3[J]. Front Neurol, 2020, 11: 124. DOI: 10.3389/fneur.2020.00124.

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