基于独立成分分析的时间自相关方法在功能磁共振激活区自适应提取中的应用

传统基于ICA的激活区检测手段是将分离后的独立成分与参考信号做相关性分析。实际问题中,不同区域的脑血流动力学响应情况不同,因此往往得不到标准的参考信号。针对此类问题,提出时间自相关方法(TSC)与ICA方法结合,在不需要参考信号的情况下,通过检测体素点各周期的时间序列相关性,对fMRI数据进行激活区提取。应用5 邻域ICA方法对fMRI数据逐点处理,然后应用时间自相关算法检测各时间序列周期间的相关性,选择最大的自相关系数作为该体素点的信号值。再通过Z变换将相关系数分布转换为服从N(0,1)的Z分布,提取出具有显著性差异（a=0.05）的激活区。将自相关算法应用于仿真数据和12组双手握拳运动的真实fMRI数据的处理,结果表明该方法能够准确提取出仿真数据中的激活区。对真实数据的处理,该方法在空间准确性上与GLM方法无显著性差别(0.4653±0.1368 vs 0.4905±0.1341),在时间准确性上显著优于GLM方法 (0.6364±0.0111 vs 0.3692±0.0109),具有良好的脑功能激活区检测及空间定位能力。     

多序列MR影像组学融合模型预测胶质瘤分级的价值

目的:探讨多序列MR影像组学特征及融合模型预测胶质瘤分级的价值。方法:回顾性分析2016年1月~2021年6月间梅州市人民医院经病理证实且行MR检查的胶质瘤病人资料。将多序列MR DICOM格式的原始图像导入ITK-SNAP软件进行VOI勾画，采用GE A.K分析软件进行影像组学特征提取，用ANOVA+Mann-Whiney、Spearman相关分析和LASSO模型进行特征筛选。选择Logistic回归（LR）算法构建单序列模型，选择LR、线性判别分析（LDA）、支持向量机（SVM）3种机器学习算法构建融合序列模型。通过受试者操作特征曲线评估不同模型的预测效能，并计算其曲线下面积（AUC）。结果:共有150例患者纳入本研究，其中低级别胶质瘤（LGG）41例，高级别胶质瘤（HGG）组109例。LGG组与HGG组病人间的性别、年龄差异无统计学意义（P>0.05）。共获得5个最优特征集，其中T1WI特征集包含15个特征，T2WI特征集包含31个特征，Flair特征集包含25个特征，DWI特征集包含12个特征，T1C包含4个特征，融合序列集包含44个特征。基于T1WI、T2WI、Flair、DWI及T1C各序列MR影像组学预测模型在训练集和测试集中的AUC分别为0.719 0、0.651 4，0.769 5、0.711 4，0.7410、0.610 2，0.721 9、0.747 0，0.815 7、0.754 5。基于LR、LDA、SVM算法的融合模型在训练集和测试集中的AUC分别为0.952 4、0.767 0，0.894 8、0.688 1，0.928 6、0.704 5。结论:基于多序列MRI影像组学特征的单序列模型中，T1C单序列预测模型对胶质瘤分级诊断效能最高；相较于单序列预测模型，多序列融合预测模型具有更高的诊断效能，且基于LR算法构建的融合模型较LDA算法及SVM算法构建的融合模型显示出更高的预测效能。     

T1WI增强纹理参数联合临床病理学特征对脑胶质瘤患者术后1年内复发的预测价值研究

目的 探讨T₁WI增强纹理参数联合临床病理学特征对脑胶质瘤患者术后1年内复发的预测价值。方法 回顾性队列研究。纳入2018年1月—2021年2月川北医学院附属医院及重庆市人民医院经术后病理确诊的脑胶质瘤患者90例。其中男60例、女30例,年龄17~76（47±14）岁,术后1年内复发46例、未复发44例。按照2∶1比例将患者随机分为训练集与验证集。采用MaZda软件于术前MR T₁WI增强图像上提取胶质瘤纹理参数。在训练集中对纹理参数进行降维筛选并利用最小绝对收缩和选择算子（LASSO）算法分析建立预后纹理积分模型。同时在训练集中对术后1年内复发与未复发患者的临床病理学特征及纹理积分进行影响因素分析,构建可视化联合预测模型。观察项目：（1）分析影响脑胶质瘤术后1年内复发的危险因素,并建立联合预测模型。（2）在验证集中,对纹理积分模型及联合预测模型进行验证。采用受试者工作特征（ROC）曲线、校准曲线及决策曲线分析法评价模型的诊断效能与临床净获益。结果 单因素分析显示,肿瘤WHO分级、异柠檬酸脱氢酶-1突变、术后放化疗、纹理积分是脑胶质瘤术后1年内复发的影响因素。多因素分析结果显示,WHO分级、异柠檬酸脱氢酶-1突变、术后放化疗及纹理积分均为脑胶质瘤术后1年内复发的独立危险因素[风险比（OR）=6.527、0.160、0.052、6.300,95％可信区间（CI）=1.201~35.485、0.031~0.827、0.004~0.708、1.905~20.841,P=0.030、0.029、0.026、0.003]。联合预测模型预测脑胶质瘤术后1年内复发的效能高于纹理积分模型,其训练集与验证集的曲线下面积（AUC）分别为0.92（95％CI 0.86~0.99）和0.86（95％CI 0.74~0.99）,灵敏度分别为0.90、0.88,特异度分别为0.83、0.79;纹理积分模型在训练集与验证集中的预测AUC分别为0.85（95％CI 0.75~0.95）和0.82（95％CI 0.67~0.97）,灵敏度分别为0.73、0.63,特异度分别为0.90、0.99。在两种模型中,预测概率与实际概率间的一致性及临床净获益均较高。结论 基于术前T₁WI增强图像的纹理参数联合临床病理学特征建立的联合预测模型对脑胶质瘤全切术后1年内的复发具有一定的预测价值。     

基于独立成分分析和时间相关分析的脑功能连通研究方法

目的：利用静息状态功能磁共振成像（functional magnetic resonance imaging，fMRI）技术，提出联合独立成分分析（independent component analysis，ICA）和时间相关分析的人脑功能连通性研究方法。方法：首先采用空间ICA定位任务激活的脑区；然后选择一个激活区作为感兴趣区域（region of interest，ROI），采用时间相关分析方法检测静息状态大脑特定皮层的功能连通性，并通过检测人脑运动皮层的功能连通性验证方法的有效性。结果：大脑运动皮层功能连通网络包括初级运动区、辅助运动区、初级感觉皮层、背侧前运动区和后顶骨体觉区。实验结果表明，静息状态下。时间相关分析检测到的运动皮层的功能连通网络与已知的解剖连通相一致。结论：利用静息fMRI。结合空间ICA和时间相关分析方法。检测了静息时人脑运动皮层的功能连通网络。为脑区间功能连通的研究提供了一种简便的、无损的、有效的研究方法。     

功能磁共振脑连接度中心性对精神分裂症症状的预测评估

精神分裂症是一种严重影响患者身心健康的精神类疾病,绝大部分患者需要终身服药以控制症状,因此药物疗效的评估预测对于患者治疗方法的选择十分重要。采用功能磁共振的脑连接度中心度(DC)指标,分析临床常用抗精神病药物利培酮和氯氮平对大脑功能的影响,并尝试建立预测评估两种药物疗效的方法。基于44例精神分裂症患者(20例利培酮治疗组、24例氯氮平治疗组)和30例健康志愿者的脑静息态功能磁共振成像数据,首先采用方差分析得到患者组与正常组间的差异脑区,然后根据这些脑区的DC值,用多变量支持向量回归算法构建回归模型,探讨用DC值评测患者症状的可行性。结果发现,两个患者组的丘脑、脑岛及初级感知运动相关皮层的功能连接特征存在显著的异常(P< 0.05),且可以有效地预测氯氮平组患者的阴性症状(r=0.448, P<0.05),但未能较好地预测利培酮患者的症状。在统计对比分析的基础上,利用回归模型,标识出抗精神疾病药物作用的部分重要脑区,因而有助于将来患者治疗的药物选择。     

缺血性卒中患者脑运动区域协调性功能影像学研究

目的　评估缺血性脑卒中患者颅脑神经解剖学与脑区功能学的关联性,进一步挖掘缺血性卒中发生的神经机制。 方法　采集15例健康对照组和19例亚急性缺血性脑卒中上肢偏瘫患者,利用计算神经影像学计算个性化颅脑内部半球功能连接指标,采集治疗前后临床康复评分作统计相关分析。 结果　（1）与对照组相比,卒中患者大脑半球功能连接显著降低,差异有统计学意义(T=-10.077, P<0.001);（2）病灶异常集中于补充运动区（P=0.02）和中央前回(P=0.03),差异有统计学意义;（3）药物治疗前后患者组中央前回中的功能连接值与康复评分量表呈正相关,差异有统计学意义（r=0.29,P=0.03）。 结论　皮质下缺血性脑卒中患者大脑运动区域的半球间功能连接减少,可作为临床诊断和康复评估提供新的途径。     

增强恶性胶质瘤树突状细胞疫苗疗效的策略

恶性胶质瘤是最常见的颅内恶性肿瘤.近年来,树突状细胞(DC)疫苗在治疗恶性胶质瘤方面取得了一定进展.研究证实,DC疫苗能延长恶性胶质瘤患者的生存期.负载胶质瘤抗原的DC疫苗有效克服胶质瘤细胞引起的免疫抑制,激活机体免疫系统清除胶质瘤的同时对正常细胞不造成伤害,故其在恶性胶质瘤治疗上有良好的应用前景.高特异性抗原的选择、对肿瘤微环境中调节性因子及DC表面分子的调节等多种免疫策略的采用可有效提高DC疫苗激活机体免疫系统的能力.     

基于密度分布特征及机器学习诊断COVID-19相关性肺炎

目的:基于密度分布特征及机器学习诊断新型冠状病毒（COVID-19）相关性肺炎。方法:回顾性收集经荧光逆转录聚合酶链反应检测确诊COVID-19的患者42例（COVID-19组）,社区获得性肺炎43例（对照组）。共获得211份胸部CT图像,以6:4比例分层抽样为训练集（126份）及验证集（85份）。采用一种CAD软件中的肺炎模块获得肺炎不同密度区间所占全肺体积的百分比（P/L%）。密度分布特征降维后采用支持向量机（SVM）建模,并评价4种核函数的SVM模型的诊断效能。结果:两组患者的年龄、性别及出现胸膜腔积液的构成比差异均无统计学意义（P>0.05）。肺炎密度分布特征降维后获得32个特征。基于该32个特征建立的4种核函数SVM模型中,多项式SVM模型在验证集的效能最高,受试者特征曲线（ROC）的曲线下面积为0.897（95%可信区间0.828~0.966）,P<0.001。准确性为0.906（95%可信区间0.823~0.959）,敏感性为0.906,特异性为0.906。结论:基于密度分布特征及机器学习诊断COVID-19相关性肺炎有较高的效能,有助于快速筛选COVID-19患者。     

乙型肝炎患者骨髓造血干细胞来源的树突状细胞表型分析

目的 检测乙型肝炎患者骨髓造血干细胞（HSC）诱导的树突状细胞（DC）的表面分子的表达,评价其相关因素及临床意义.方法 收集慢性乙型肝炎患者的骨髓液9例,健康者7例后用磁珠分离仪分离纯化骨髓液CD34＋细胞,在含有干细胞生长因子（SCF）、酪氨酸激酶受体家族Ⅲ的配体（FLT3）、促血小板生成素（TPO）、IL-3和10%FBS的IMDM培养基中孵育并进行扩增,在干细胞扩增基础上,在GM-CSF和IL-4作用下诱生DC,通过流式细胞仪分析其表面分子的表达并对DC进行形态学观察.结果 乙型肝炎患者HSC经诱导为DC后,其免疫表型CD80、CD86、CD1a的表达均低于正常对照组,差异有统计学意义：CD1a（t=3.94,P＜0.05）,CD80（t=7.08,P＜0.01）,CD86（t=3.65,P＜0.05）,而HLA-DR表达与正常组比较差异无统计学意义（t=0.34,P＞0.05）.结论 慢性乙型肝炎患者HSC来源的部分DC的表面分子表达低下可能与HBV感染HSC后出现病态的免疫细胞分化有关.     

9种机器学习模型预测幕上深部自发性脑出血早期血肿扩张及预后不良的比较

目的 比较9种机器学习模型对幕上深部自发性脑出血（SICH）患者发生早期血肿扩张及预后不良情况的预测性能。方法 回顾性研究。纳入2015年1月—2019年5月4家医院幕上深部SICH患者420例。其中男275例、女145例,年龄25~90（61.0±12.9）岁。420例患者按照7∶3的比例,采用完全随机法分为训练集294例和验证集126例。患者在72 h内复查CT,若血肿体积比初始体积增长>6 mL或>33%,判定存在早期血肿扩张。采用改良的Rankin评分量表（mRS）评估预后,以mRS>3分判定为预后不良。比较训练集和验证集的基线资料。采用随机森林、极限梯度提升算法（XGboost）、梯度爬升决策树、自适应提升算法、朴素贝叶斯、logistic回归、支持向量机、K近邻、多层感知机9种机器学习算法对早期血肿扩张及预后不良分别构建预测模型;在训练集中,依据各模型的灵敏度和特异度绘制受试者操作特征曲线,采用3折交叉验证取曲线下面积（AUC）,比较各模型对早期血肿扩张及预后不良情况的预测性能,并在验证集测试模型的可靠性。结果 训练集和验证集患者基线资料比较差异均无统计学意义（P值均>0.05）。420例患者中,早期脑血肿扩张的患者有117例（27.86%）;399例患者获随访,其中预后不良的患者有210例（52.63%）。预测早期血肿扩张：训练集中,9种机器学习模型的AUC为0.590~0.685,其中以XGboost模型最高,AUC为0.685±0.024;在验证集中,XGboost模型AUC为0.686[95%可信区间（CI）0.578~0.721]。预测预后不良：9种机器学习模型的AUC为0.703~0.852,其中logistic回归模型最高,AUC为0.852±0.041;而在验证集中,logistic回归模型AUC为0.894（95%CI 0.862~0.912）。结论 9种机器学习算法模型中,XGboost对幕上深部SICH早期血肿扩张的预测性能最佳,而logistic回归模型对预后不良的预测性能最高;对于不同临床结局的预测,应选用合适的机器学习模型。     

Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network

In recent years, the use of advanced magnetic resonance (MR) imaging methods such as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) has recorded a great increase in neuropsychiatric disorders. Deep learning is a branch of machine learning that is increasingly being used for applications of medical image analysis such as computer-aided diagnosis. In a bid to classify and represent learning tasks, this study utilized one of the most powerful deep learning algorithms (deep belief network (DBN)) for the combination of data from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets. The DBN was employed so as to focus on the combination of resting-state fMRI (rs-fMRI), gray matter (GM), and white matter (WM) data. This was done based on the brain regions that were defined using the automated anatomical labeling (AAL), in order to classify autism spectrum disorders (ASDs) from typical controls (TCs). Since the diagnosis of ASD is much more effective at an early age, only 185 individuals (116 ASD and 69 TC) ranging in age from 5 to 10 years were included in this analysis. In contrast, the proposed method is used to exploit the latent or abstract high-level features inside rs-fMRI and sMRI data while the old methods consider only the simple low-level features extracted from neuroimages. Moreover, combining multiple data types and increasing the depth of DBN can improve classification accuracy. In this study, the best combination comprised rs-fMRI, GM, and WM for DBN of depth 3 with 65.56% accuracy (sensitivity?=?84%, specificity?=?32.96%, F1 score?=?74.76%) obtained via 10-fold cross-validation. This result outperforms previously presented methods on ABIDE I dataset.     

Analysis of the Structural-Functional Organization of a Counting Task in the Context of a Study of Executive Functions

Neuroscience and Behavioral Physiology - fMRI data indicate that backward mental counting is accompanied by activation of structures in the left hemisphere: the supplementary motor area, the...     

基于多目标优化约束独立成分分析方法的fMRI数据分析研究

约束独立成分分析(CICA)通过加入先验信息,可极大地提高独立成分分析(ICA)的盲源信号分析性能,但还存在先验信息难以获取、先验信息约束条件阈值参数难以选择以及先验信息难以被有效利用等问题,需要进一步研究和解决。在多目标优化框架的基础上,建立一种同时融合时空先验信息的CICA模型,可有效规避CICA中阈值参数选择的问题。此外,提出一种从多被试fMRI数据中提取本真先验信息来指导fMRI组分析的自适应挖掘算法,从而为CICA获取先验信息提供一种新途径。最后,利用10例模拟数据、5例任务态和23例静息态fMRI数据,验证所提方法的有效性。结果表明:基于多目标优化的CICA(MOPCICA)获得的时空源信号总体上优于ICA、包含时间信息的CICA(CICA-tR)和包含空间信息的CICA(CICA-sR)(P<0.05)(如在模拟数据中,对应的空间AUC和时间相关系数分别0.75±0.05、0.62±0.02、0.72±0.03、0.71±0.06和0.81±0.13、0.67±0.04、0.74±0.09、0.77±0.13),而空间独立性则优于CICA-tR和CICA-sR(P<0.05)(如在任务态数据中,对应的峭度和负熵分别为69.20±23.36、17.60±13.22、36.71±13.43和0.031 2±0.007 7、0.003 7±0.002 1、0.018 4±0.004 5),从而说明它具有更好的源信号恢复性能。同时,在静息态数据中利用fMRI本真先验信息,MOPCICA获得的组成分与每个被试相应成分之间的相关系数平均高于ICA、基于牛顿迭代法的CICA(CICA-nR)和基于不动点迭代法的CICA(CICA-fR)(P<0.05)(分别为0.46±0.08、0.44±0.08、0.45±0.08和0.44±0.08),从而更能代表组中被试的共性。研究表明,所提出的方法对fMRI脑功能连通性检测具有重要意义。     

任务态fMRI对脊髓型颈椎病严重程度及手术预后的评价

文题释义：血氧水平依赖功能磁共振：是一种非侵入性的检测方法。该技术依赖于脑血流和神经元激活的耦合，当大脑的某个区域处于活动状态时，流向该区域的血液会随之增加，超过了代谢需求时，顺磁性去氧血红蛋白浓度会相对下降，这一效应会减弱局部磁场的不均匀性，从而显示出成像信号的差异。   皮质重构：脊髓受到损伤后患者会出现脑皮质功能活动的改变。相对于正常健康人群，这种脑皮质活动的异常，类似于一种特殊的激活区域空间移位，被称之为皮质重构。神经组织的可塑性是脊髓损伤后脑皮质能够发生重构的重要原因。 背景：血氧水平依赖功能磁共振(BOLD-fMRI)通过检测大脑功能区活动时血流变化而获得脑功能改变的客观信息。以往关于脊髓型颈椎病的任务态fMRI研究能够观察到患者感觉和运动皮质功能的重构。 目的：运用任务态fMRI分析脊髓型颈椎病患者皮质激活的变化程度与临床症状严重程度及手术预后之间的关系。 方法：2018年1月至2019年1月收集行颈椎后路减压手术治疗的脊髓型颈椎病患者82例(脊髓型颈椎病组)及健康志愿者45名(正常组)。所有对象均行颅脑fMRI扫描，动作任务均为右手对指任务。采用日本骨科学会(JOA)评分评估患者脊髓功能，术后6个月随访时将JOA评分改善率< 50%定义为手术预后不良。 结果与结论：①脊髓型颈椎病组术后JOA评分较术前显著改善(P < 0.05)；②脊髓型颈椎病组术前左侧中央前回的激活体积值较正常组显著升高(P < 0.05)，左侧中央后回的激活体积值与正常组差异无显著性意义(P > 0.05)；脊髓型颈椎病组术前左侧激活体积比值(中央前回/中央后回)较正常组显著升高(P < 0.05)；③术后6个月随访时，脊髓型颈椎病组左侧中央前回的激活体积值较术前显著减低(P < 0.05)，左侧中央后回的激活体积值与术前差异无显著性意义(P > 0.05)，左侧激活体积比值较术前显著降低(P < 0.05)；④相关性分析显示左侧中央前回、中央后回的激活体积值、激活体积比值与术前JOA评分、术后JOA评分改善率间均具有显著相关性(P < 0.05)。激活体积比值与术前JOA评分、术后改善率的相关系数绝对值最大；⑤受试者工作特征曲线分析发现术前激活体积比值预测预后不良的曲线下面积为0.803，界值为3.621；术前JOA评分预测预后不良的曲线下面积为0.751，界值为8；激活体积比值的预测效能高于JOA评分；⑥结果表明，脊髓型颈椎病患者任务态fMRI皮质左侧中央前回与中央后回的激活体积比值与临床症状严重程度(JOA评分)存在显著负相关。术前激活体积比值可有效预测脊髓型颈椎病患者术后脊髓功能恢复情况。 ORCID: 0000-0002-9156-0702(宋彦澄) 中国组织工程研究杂志出版内容重点：人工关节；骨植入物；脊柱；骨折；内固定；数字化骨科；组织工程     

Primary motor cortex activation by transcranial direct current stimulation in the human brain

Transcranial direct current stimulation (tDCS) can modulate motor cortex excitability in the human brain. We attempted to demonstrate the cortical stimulation effect of tDCS on the primary motor cortex (M1) using functional MRI (fMRI). An fMRI study was performed for 11 right-handed healthy subjects at 1.5 T. Anodal tDCS was applied to the scalp over the central knob of the M1 in the left hemisphere. A constant current with an intensity of 1.0 mA was applied. The total fMRI paradigm consisted of three sessions with a 5-min resting period between each session. Each session consisted of five successive phases (resting-tDCS-tDCS-tDCS-tDCS), and each of the phases was performed for 21s. Our findings revealed that no cortical activation was detected in any of the stimulation phases except the fourth tDCS phase. In the result of group analysis for the fourth tDCS phase, the average map indicated that the central knob of the left primary motor cortex was activated. In addition, there were activations on the left supplementary motor cortex and the right posterior parietal cortex. We demonstrated that tDCS has a direct stimulation effect on the underlying cortex. It seems that tDCS is a useful modality for stimulating a target cortical region.     

Primary visual cortex and memory

The purpose of this study was to identify the functional fields activated in relation to gestural movements. Using functional magnetic resonance imaging (fMRI), we mapped brain activity in ten right-handed, normal volunteers during activation and control tasks. The activation condition consisted of pantomiming tool-use gestures with either the left hand or right hand, whereas the control condition comprised repetitive, oppositional movements between thumb and index finger. Activated cortical regions were highly lateralized to the left hemisphere during pantomiming of tool use regardless of hand used. Praxis with either hand commonly activated the superior parietal lobule, supplementary motor area, premotor area of the left hemisphere, and cerebellar vermis. However, minimal activation occurred in the inferior parietal lobule, which has been known to be a critical area for praxis generation. Compared with left-hand praxis, right-hand praxis exhibited additional activation in the left putamen and posterior part of the left inferior temporal region. Our findings concur with neuropsychological observations that the left hemisphere in right-handers mediates programming and executing skilled movements and that, within the left hemisphere, praxis is predominantly subserved by the parietal lobe, supplementary motor area, and premotor area. However, unlike previous lesion studies, the results of our fMRI study suggested that the superior parietal lobule more likely than the inferior parietal lobule play an important role in gesture production. Electronic Publication     

Hyperactive external awareness against hypoactive internal awareness in disorders of consciousness using resting‐state functional MRI: highlighting the involvement of visuo‐motor modulation

Resting‐state functional MRI (fMRI) has emerged as a valuable tool to characterize the complex states encompassing disorders of consciousness (DOC). Awareness appears to comprise two coexistent, anticorrelated components named the external and internal awareness networks. The present study hypothesizes that DOC interrupts the balance between the internal and external awareness networks. To gain more understanding of this phenomenon, the present study analyzed resting‐state fMRI data from 12 patients with DOC versus 12 healthy age‐matched controls. The data were explored using independent component analysis and amplitude of low‐frequency fluctuation (ALFF) analysis. The results indicated that DOC deactivated midline areas associated with internal awareness. In addition, external awareness was strengthened in DOC because of increased activation in the insula, lingual gyrus, paracentral and supplementary motor area. The activity patterns suggested strengthened external awareness against weakened internal awareness in DOC. In particular, increased activity found in the insula, lingual gyrus, paracentral and supplementary motor area of patients with DOC implied possible involvement of augmented visuo‐motor modulation in these patients. DOC is probably related to hyperactive external awareness opposing hypoactive internal awareness. This unique pattern of brain activity may potentially be a prognostic marker for DOC. Copyright © 2014 John Wiley & Sons, Ltd.     

Changes of Functional MRI Findings in a Patient Whose Pathological Gambling Improved with Fluvoxamine

Legalized gambling is a growing industry, and is probably a factor in the presently increasing prevalence of pathological gambling. We present a case of a 36-year-old pathological gambler who was treated with fluvoxamine, a selective serotonin reuptake inhibitor, and who was assessed by functional MRI before and after drug administration. During activation periods, the pathological gambler was shown cards as stimuli, and fMRI results in several brain regions showed differential effects before and after medication and a maintenance period. This case demonstrates that the treatment response to fluvoxamine in a pathological gambler was observed not only by subjective self-report, but also by objective fMRI results. Therefore, fMRI may be a useful tool in the diagnosis and prediction of treatment response in patients afflicted with pathological gambling.     

Dynamic changes in cerebello-thalamo-cortical motor circuitry during progression of Parkinson's disease

Both the basal ganglia and cerebellum are known to influence cortical motor and motor-associated areas via the thalamus. Whereas striato-thalamo-cortical (STC) motor circuit dysfunction has been implicated clearly in Parkinson's disease (PD), the role of the cerebello-thalamo-cortical (CTC) motor circuit has not been well defined. Functional magnetic resonance imaging (fMRI) is a convenient tool for studying the role of the CTC in vivo in PD patients, but large inter-individual differences in fMRI activation patterns require very large numbers of subjects in order to interpret data from cross-sectional, case control studies. To understand the role of the CTC during PD progression, we obtained longitudinal fMRI 2 years apart from 5 PD (57±8 yr) and five Controls (57±9 yr) performing either externally- (EG) or internally-guided (IG) sequential finger movements. All PD subjects had unilateral motor symptoms at baseline, but developed bilateral symptoms at follow-up. Within-group analyses were performed by comparing fMRI activation patterns between baseline and follow-up scans. Between-group comparisons were made by contrasting fMRI activation patterns generated by the more-affected and less-affected hands of PD subjects with the mean of the dominant and non-dominant hands of Controls. Compared to baseline, Controls showed changes in CTC circuits, but PD subjects had increased recruitment of both cortical motor-associated and cerebellar areas. Compared to Controls, PD subjects demonstrated augmented recruitment of CTC circuits over time that was statistically significant when the IG task was performed by the hand that transitioned from non-symptomatic to symptomatic. This longitudinal fMRI study demonstrates increased recruitment of the CTC motor circuit concomitant with PD progression, suggesting a role of the CTC circuit in accommodation to, or pathophysiology of, PD.