Brain activation while forming memories of fearful and neutral faces in women and men.

Event-related functional MRI (fMRI) was used to assess brain activity during encoding of fearful and neutral faces in 12 women and 12 men. In a subsequent memory analysis, the authors separated successful from unsuccessful encoding of both types of faces, based on whether they were remembered or forgotten in a later recognition memory test. Overall, women and men recruited overlapping neural circuitries. Both sexes activated right-sided medial-temporal regions during successful encoding of fearful faces. Successful encoding of neutral faces was associated with left-sided lateral prefrontal and right-sided superior frontal activation in both sexes. In women, relatively greater encoding related activity for neutral faces was seen in the superior parietal and parahippocampal cortices. By contrast, men activated the left and right superior/middle frontal cortex more than women during successful encoding of the same neutral faces. These findings suggest that women and men use similar neural networks to encode facial information, with only subtle sex differences observed for neutral faces. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于SVM及其改进算法的fMRI图像分类性能研究

为了提出一种更适用于分析fMRI图像特征的机器学习算法,引入机器学习近年提出的、具有较好的泛化能力、并能够保证极值解是全局最优解的新方法支持向量机（SVM）算法,具体选择了PSVM、SSVM、LPSVM、NSVM 4种SVM改进算法以及基本SVM算法应用于fMRI图像的分类问题,在MATLAB平台上进行了算法仿真实现。在对各种算法的分类计算时间、分类精确度两个方面进行比较和讨论后,得到PSVM算法在fMRI图像的分类问题上,有较好的综合性能。     

基于fMRI的思维数据分析方法研究

利用功能磁共振成像(functional magnetic resonance imaging, fMRI)技术解读思维数据,已经实现大脑活动的功能定位,但是大脑的思维过程具体如何运行还不得而知;利用何种分析方法更能揭示思维过程也待进一步研究.采用解决4×4数独问题和图像情感反应的两种刺激任务获取思维过程数据,来分别解读不同的思维状态,探索适用于不同思维数据的分析方法.实验数据证明t-test的特征选择方法、峰值所在时间点的特征提取的方法和SVM分类算法较适用于分析这两种不同思维状态的fMRI数据,揭示正确的思维状态.     

高级认知过程对人脑信号活动模式的影响及认知系统仿真*

为了帮助解决脑机接口中人脑信号与高级认知活动之间的对接问题,建立了同时模拟高级认知过程与人脑信号模式的认知模型。采用的方法是ACT-R认知仿真实验结合fMRI脑影像实验。以简化四方趣题为实验范式,设计有锚和无锚两类不同复杂度任务。从ACT-R认知仿真实验分析出高级认知过程,从fMRI实验结果分析出感兴趣脑区BOLD信号模式。通过对两实验结果进一步分析得出,不同的高级认知活动过程对脑功能区BOLD信号模式存在多处影响,包括启动值和BOLD的峰值位置、平均变化量、上升变化量以及下降变化量。本研究获得了高级认知     

基于局部空间数据的功能磁共振图像运动校正

在脑功能磁共振实验中,运动校正是数据预处理的关键环节。运动校正的结果对后续的脑区定位、功能连接等分析有着重要的影响。但因数据量较大,常规分析软件对实验数据进行运动校正时做了一些简化处理,校正误差较大。为减少这种误差,提出了一种基于局部空间数据的运动校正方法,首先从数据获取的角度构造功能像各切片的局部空间数据,然后利用修正的Gauss-Newton最优化方法估计各切片相对于参考图像做刚体变换后的空间位置,最后利用Delaunay三角剖分方法重构功能像以实现精确校正。仿真实验及实际的视觉实验数据分析结果表明,该方法具有较高的校正精度,是一种有效的功能磁共振数据运动校正方法。     

脑fMRI特征重建的分层快速聚类方法

在对大脑fMRI感兴趣区域的分析中,利用特征选择所得到的筛选属性进行特征重建问题上,提出了分层快速聚类的分析方法,同已有K-均值聚类方法相比,在聚类有效性得到提高的前提下,总体降低了聚类的时间代价,并为后续的回归分析处理提供了精确保证。     

功能磁共振数据结构性噪声分析

本文提出了一种非参数化功能磁共振数据噪声分析方法.该方法根据噪声时域特性将其分为结构性噪声和非结构性噪声两类,它们对统计检验功效造成的影响在文中分别进行了讨论.使用正则相关分析提取数据中的结构性信号,利用基于降阶自回归模型的surrogate检验从结构性信号中确定出神经响应信号;利用随机化方法在保持非结构性噪声能量不变的情况下消除它们时域中的自相关性,使得非结构性噪声谱低频部分的能量下降.利用神经响应信号和经过随机化处理后的非结构性信号重构数据.重构数据基本满足了多种统计推断方法中白噪声的假设.构造了2种仿真数据,使用基于多窗口谱估计的F检验来验证算法的有效性.最后用此方法处理了20组真实的功能磁共振数据,成功提取到了一些在未降噪数据中检测不到的任务相关脑功能区.     

Is There Reduced Hemodynamic Brain Activation in Multiple Sclerosis Even with Undisturbed Cognition?

Cognitive impairments related to changes in deep gray matter and other brain regions occur in up to 70% of people with multiple sclerosis. But do such brain changes also occur in patients without significant cognitive impairment? Eighteen participants with relapsing-remitting multiple sclerosis (RRMS) and fifteen healthy controls participated in this study. Cognitive status, depression, and fatigue were assessed using the Multiple Sclerosis Inventory of Cognition (MUSIC), Beck’s Depression Inventory (BDI-II), and the Fatigue Severity Scale (FSS). fMRI was recorded while a participant performed the modified attention network test (ANT). The effects of ANT executive attention network on hemodynamic activation of a priori defined regions of interest, including the hippocampus, anterior cingulate cortex (ACC), thalamus, caudate nucleus, pallidum, and putamen were studied. The individual lesion load was estimated. For fMRI data analysis a general linear model with randomization statistics including threshold-free cluster enhancement as implemented in the FSL software was used. Participants with RRMS showed reduced activation of the executive attention network in the hippocampus, pallidum, and ACC. The thalamus was involved in both group activations but did not differ between groups. In summary, functional changes in the brain can also be demonstrated in RRMS patients without cognitive deficits. The affected brain regions can best be assigned to the attention network for executive control. This association could likely serve as a biological indicator of susceptibility to imminent cognitive impairment in MS.