目的:优化英文版虚拟数字脑软件平台的操作步骤,增加虚拟数字脑的功能,构建虚拟数字脑中文版软件平台.方法:首先,将菜单栏中的各项和对话框中的控件名称中文化.然后,优化操作步骤,在虚拟数字脑中增加测量脑区神经活动强度和信息处理速度的功能.最后,在VC++12.0开发环境下,优化代码、修改程序中的漏洞,完成虚拟数字脑中文版软件平台的构建.结果:仿真实验证明了该软件的有效性.结论:与英文版虚拟数字脑平台相比,虚拟数字脑中文版软件平台操作步骤更简洁,功能更完善,可用于脑科学研究,并为类脑人工智能的研究提供借鉴.

It remains poorly understood how brain causal connectivity networks change following hearing loss and their effects on cognition. In the current study, we investigated this issue. Twelve patients with long-term bilateral sensorineural hearing loss [mean age, 55.7 ± 2.0; range, 39–63 years; threshold of hearing level (HL): left ear, 49.0 ± 4.1 dB HL, range, 31.25–76.25 dB HL; right ear, 55.1 ± 7.1 dB HL, range, 35–115 dB HL; the duration of hearing loss, 16.67 ± 4.5, range, 3–55 years] and 12 matched normally hearing controls (mean age, 52.3 ± 1.8; range, 42–63 years; threshold of hearing level: left ear, 17.6 ± 1.3 dB HL, range, 11.25–26.25 dB HL; right ear, 19.7 ± 1.3 dB HL, range, 8.75–26.25 dB HL) participated in this experiment. We constructed and analyzed the causal connectivity networks based on functional magnetic resonance imaging data of these participants. Two-sample t-tests revealed significant changes of causal connections and nodal degrees in the right secondary visual cortex, associative visual cortex, right dorsolateral prefrontal cortex, left subgenual cortex, and the left cingulate cortex, as well as the shortest causal connectivity paths from the right secondary visual cortex to Broca’s area in hearing loss patients. Neuropsychological tests indicated that hearing loss patients presented significant cognitive decline. Pearson’s correlation analysis indicated that changes of nodal degrees and the shortest causal connectivity paths were significantly related with poor cognitive performances. We also found a cross-modal reorganization between associative visual cortex and auditory cortex in patients with hearing loss. Additionally, we noted that visual and auditory signals had different effects on neural activities of Broca’s area, respectively. These results suggest that changes in brain causal connectivity network are an important neuroimaging mark of cognitive decline. Our findings provide some implications for rehabilitation of hearing loss patients.

目的:研究听视觉刺激对听力损失患者大脑中与语言处理相关脑区神经活动的影响,为患者的康复治疗提供理论支撑.方法:募集12个听力损失患者和12个性别年龄匹配的正常听力受试者.首先构建每个参与者个体化虚拟数字脑,然后将虚拟视觉刺激信号施加到已构建虚拟数字脑的次级视觉皮层;将虚拟听觉刺激信号施加到已构建虚拟数字脑的初级听觉皮层.最后,观察这些刺激信号在听力损失患者脑皮层中所诱发的神经活动变化.结果:同正常受试者相比,虚拟视觉刺激信号抑制了听力损失患者听觉皮层和布洛卡区的神经活动,并通过最短因果路径削弱了布洛卡区的激活.相反,虚拟听觉刺激信号抑制了听力损失患者视觉皮层的激活,但通过最短因果路径增强了布洛卡区的神经活动.此外,听力损失患者也呈现了减弱的视觉诱发的威尼克区的激活.结论:目前的研究表明,视觉刺激通过削弱听觉皮层和布洛卡区的神经活动抑制了听力损失患者的语言处理.相反,听觉刺激通过抑制视觉皮层的活动,增强布洛卡区的神经活动,从而改善了听力损失患者的语言处理.

目的:改进脑白质纤维束连续跟踪算法,提高纤维束跟踪的连续性和准确性.方法:使用山东第一医科大学第二附属医院影像科和国际人脑连接组共享数据库网站提供的弥散张量图像对算法进行验证.首先,对磁共振弥散张量图像进行中值滤波和高斯平滑滤波,去除噪声对纤维束连续跟踪算法的影响,利用脑模板消除颅骨对后续跟踪的影响.然后,利用最小二乘法获得每一个体素的弥散张量和各向异性指数.最后,从各向异性指数大于阈值的起始体素开始,利用跟踪编辑技术和基于弥散熵的线性跟踪方法完成纤维束连续跟踪.结果:同传统的纤维束连续跟踪算法相比,本研究提出的算法能得到更连续、更准确的跟踪结果,算法具有较好的抗噪声能力和较强的鲁棒性.结论:改进的纤维束跟踪方法可以应用于脑结构网构建以及脑疾病的研究.

目的:为脑科学和类脑人工智能提供一个软件研究平台.方法:首先,基于预处理后的静息态功能磁共振数据和因果连接法构建因果连接网,利用最小均方误差算法获得节点神经活动预测模型中的回归系数.然后,基于因果连接网和预测模型构建虚拟数字脑.最后,在VC++12.0开发环境下,利用C/C++编程语言开发出能实现虚拟数字脑各项功能的软件包.结果:同欧洲的虚拟大脑软件平台相比,该软件包操作简单,可用于脑科学和类脑人工智能的研究.结论:初步的实践结果证明了该软件在脑科学研究中的有效性和实际应用价值.

Various methods for displaying medical images in neuroimaging informatics technology initiative (NIFTI) format,including multi-planar reconstruction,fusion of multi-planar functional and constructional magnetic resonance imaging (MRI) images,and three-dimensional reconstruction based brain effective connection network,were investigated,and their advantages and disadvantages were analyzed.The results of simulation indicate that the multi-planar reconstruction is easily fulfilled to display functional or constructional brain images of different slices and angles.However,the spatial position of functional or constructional brain regions cannot be located using multi-planar reconstruction.The fusion of multi-planar functional and constructional MRI images can be used to locate the functional brain region in high-resolution MRI,which is helpful in the investigation of the function of brain regions,but cannot be used for the analysis of regional causality connectivity.The three-dimensional reconstruction based brain effective connection network,which is limited due to complex computation,can be used to analyze the nodal topological characteristics of causality connectivity network and the regional causality connectivity,and to explore the neuroimaging mechanism of some diseases.These methods mentioned above have their advantages and disadvantages and are used for displaying medical images in NIFTI format,with significant application in the study of brain function and effective connection network.

The objective of the study is to provide some implications for rehabilitation of hearing impairment by investigating changes of neural activities of directional brain networks in patients with long-term bilateral hearing loss. Firstly, we implemented neuropsychological tests of 21 subjects (11 patients with long-term bilateral hearing loss, and 10 subjects with normal hearing), and these tests revealed significant differences between the deaf group and the controls. Then we constructed the individual specific virtual brain based on functional magnetic resonance data of participants by utilizing effective connectivity and multivariate regression methods. We exerted the stimulating signal to the primary auditory cortices of the virtual brain and observed the brain region activations. We found that patients with long-term bilateral hearing loss presented weaker brain region activations in the auditory and language networks, but enhanced neural activities in the default mode network as compared with normally hearing subjects. Especially, the right cerebral hemisphere presented more changes than the left. Additionally, weaker neural activities in the primary auditor cortices were also strongly associated with poorer cognitive performance. Finally, causal analysis revealed several interactional circuits among activated brain regions, and these interregional causal interactions implied that abnormal neural activities of the directional brain networks in the deaf patients impacted cognitive function.

Objective To provide a novel edge detection algorithm which can effectively reduce noise and adaptively extract much edge information by improving traditional Kirsch edge detection method.Methods The background and random noises of images were firstly eliminated using fuzzy mathematical method and median filtering.And then 4 filtering masks were constructed based on the wavelet coefficients of cubic spline wavelet,and the filtering images and local maximum images were obtained.Finally,the edge images were obtained based on the local maximum images and the thresholds which were automatically selected using the maximum entropy algorithm.The edge images were further processed utilizing the edge tracing and noise eliminating methods.Results The improved Kirsch algorithm showed stronger resistance to noise than Kirsch method,with a signal-to-noise ratio increased by 8.67 dB compared with the average value.Conclusion By improving Kirsch algorithm,we introduce a novel edge detection method with stronger resistance to noise and better adaptive ability.

We introduce an improved Canny edge detection algorithm for extracting much more edge information with less noise.Firstly,the background noises of images are eliminated using fuzzy mathematical method,and the random noises of images are removed using median filtering and Gaussian smoothing filter.And then,the gradient vector and gradient magnitude of every pixel point in images are obtained utilizing the difference method,and the extreme points are obtained using non-maximum suppression method.Finally,the edge images are achieved based on the extreme point images and thresholds which are automatically selected based on maximum entropy algorithm,and the edge images were further processed utilizing the edge tracing and noise eliminating methods.The results show that the signal to noise ratio of edge images obtained with improved algorithm are increased by 8 dB,compared with that of edge images obtained with Canny method.The improved Canny algorithm can obtain much more edge information and less noise than Canny algorithm.

研究DICOM格式图像的非线性开窗显示方法,并同目前常用的线性开窗显示方法进行比较.本研究提出了逆线性、指数型、对数型、幂律型、S型、倒S型和基于复杂曲线的伪彩色非线性开窗显示方法,并比较了各种开窗显示方法.仿真结果表明,线性开窗显示适合于较亮背景中的低密度病变细节显示,对低密度病变增强效果较好.逆线性开窗显示刚好相反.对数型开窗显示能够将窗口中较窄范围的低密度值映射到较宽范围的输出密度值来显示,对窗口中的低密度病变增强效果好,而指数型开窗显示相反.幂律型开窗显示根据幂的取值不同分别类似于线性、指数型和对数型开窗显示.S型和倒S型开窗显示对窗口中的中等密度的病变增强效果好.基于复杂曲线的伪彩色显示方法,能够在较宽的窗口中显示较多的病变信息.非线性方法能够显示更多的图像信息,是线性方法的较好补充.