Abstract：In order to investigate the characteristics of human brain's responses to different gripping force tests, we proposed a novel numerical approach, combining the independent component analysis (ICA) and the cloud model. Firstly, we obtained ten subjects' fMRI image data from MRI scanner under different experimental conditions, and preprocessed those fMRI raw data, then the ICA was used to determine the brain active regions of different tasks, and the cloud model was applied to explore the numerical distribution characteristics of the activated voxels in activated regions under different grip force tests. The results showed that the activated regions mainly located at the contralateral cerebrum such as Brodmann area 2, 3, 4, 6 and ipsilateral cerebellum, and with the increasement of grip force, the number of the activated voxels in precentral gyrus and postcentral gyrus increased (cluster size: 4 075, 4 218, 4 965). In the activated region, the characteristic parameters of cloud model such as expectation, entropy, hyperentropy (Ex, En, He) showed significant differences between task state and control state of different force conditions. The Ex (P<0.001) and En (P<0.005) were significantly increased while the He (P<0.005) was significantly decreased. But these parameters had no significant differences between different force conditions. In the unactivated region, the Ex、En、He had no significant differences between task and control state. The proposed method can be used to investigate the numerical distribution characteristics of brain's response to different tasks in future.
程思佳 景斌 李海云#*. 人脑对握力刺激响应特征的数值计算分析方法[J]. 中国生物医学工程学报, 2014, 33(3): 266-273.
CHENG Si Jia JING Bin LI Hai Yun #*. A Numerical Approach to Calculation and Analysis of Characteristics of Brain Functional Response to Grip Test. journal1, 2014, 33(3): 266-273.
［1］Yousry TA, Schmid UD, Jassoy AG, et al. Topography of the cortical motor hand area: prospective study with functional MR imaging and direct motor mapping at surgery［J］. Radiology, 1995, 195(1): 23-29.
［2］钟士江，陈静,包春雨，等.功能磁共振成像对正常人脑手运动皮质的定位研究［J］. 武警后勤学院学报(医学版), 2012, 21(11): 851-855.
［3］Coynel D, Marrelec G, Perlbarg V, et al. Dynamics of motorrelated functional integration during motor sequence learning［J］. NeuroImage, 2010, 49(1): 759-766.
［4］Boudrias M, Gonalves CS, Penny WD, et al. Agerelated changes in causal interactions between cortical motor regions during hand grip［J］. NeuroImage, 2012, 59(4): 3398-3405.
［5］Pope P, Wing AM, Praamstra P, et al. Force related activations in rhythmic sequence production［J］. NeuroImage, 2005, 27(4): 909-918.
［6］Jncke L, Specht K, Mirzazade S, et al. A parametric analysis of the ‘rate effect’ in the sensorimotor cortex: a functional magnetic resonance imaging analysis in human subjects［J］. Neuroscience Letters, 1998, 252(1): 37-40.
［7］Jncke L, Peters M, Schlaug G, et al. Differential magnetic resonance signal change in human sensorimotor cortex to finger movements of different rate of the dominant and subdominant hand［J］. Cognitive Brain Research, 1998, 6(4): 279-284.
［8］Lutz K, Koeneke S, Wüstenberg T, et al. Asymmetry of cortical activation during maximum and convenient tapping speed［J］. Neuroscience Letters, 2004, 373(1): 61-66.
［9］Pope P, Wing AM, Praamstra P, et al. Force related activations in rhythmic sequence production［J］. NeuroImage, 2005, 27(4): 909-918.
［10］Noble JW, Eng JJ, Kokotilo KJ, et al. Aging effects on the control of grip force magnitude: An fMRI study［J］. Experimental Gerontology, 2011, 46(6): 453-461.
［11］Mckeown MJ, Makeig S, Brown GG, et al. Analysis of fMRI data by blind separation into independent spatial components［J］. Hum Brain Mapp, 1998, 6(3): 160-188.
［12］Calhoun VD, Adali T, Mcginty VB, et al. fMRI activation in a visualperception task: network of areas detected using the general linear model and independent components analysis［J］. NeuroImage, 2001, 14(5): 1080-1088.
［13］李德毅，孟海军，史雪梅. 隶属云和隶属云发生器［J］. 计算机研究与发展, 1995, 32(6): 15-20.
［14］GonzalezCastillo J, Talavage TM. Reproducibility of fMRI activations associated with auditory sentence comprehension［J］. NeuroImage, 2011, 54(3): 2138-2155.
［15］刘常昱，冯芒，戴晓军，等. 基于云 X 信息的逆向云新算法［J］. 系统仿真学报, 2004, 16 (11): 2417-2420.
［16］Zhao Jing, Li Haiyun. An image fusion algorithm based on multiresolution decomposition for functional magnetic resonance images［J］. Neuroscience Letters, 2011, 487(1): 73-77.
［17］Eickhoff SB, Grefkes C. Approaches for the integrated analysis of structure, function and connectivity of the human brain［J］. Clin EEG Neurosci, 2011, 42(2): 107-121.