fMRI R

R语言的统计功能如此强大，我在想能不能用R来分析fMRI数据，于是google了一下。

有一个关于用R分析fMRI数据的课程：Neuroimaging Analysis within R

R有一个叫做“fmri”的package。（具体怎么用，以后再说）

R还有一个叫做“neurobase”的package。

library(neurobase)setwd("~/Desktop/validating-fmri/data")data <- readnii("sub-10159/sub-10159_task-rest_bold_space—MNI152NLincAsym_preproc.nii.gz")#The function readnii requires one argument: the name of the file we want to read. #What comes out is an object of type oro.nifti. #Essentially, it is a 4D array, with extra metadata specific to fMRI data. #We can therefore apply functions that we can apply to arrays. Let’s look for example at the shape of our data.dim(data)[1] 65 77 49 152#This shows us that the first three dimensions are 65 x 77 x 49. #These correspond to the x-, y- and z-coordinates of the brain. #The fourth dimension is time, which means there are 152 time points measured.#We can also run a function from neurobase that only works on this type of object:check_nifti(data)NIfTI-1 formatType : niftiData Type : 16 (FLOAT32)Bits per Pixel : 32Slice Code: 0 (Unknown)Intent Code: 0 (None)Qform Code: 2 (Aligned_Anat)Sform Code: 1 (Scanner_Anat)Dimension : 65 x 77 x 49 x 152Pixel Dimension : 3 x 3 x 4 x 2Voxel Units: UnknownTime Units: sec#This shows some information that is stored in the “header” of the file. For example, it tells us the pixel dimensions. #The first three numbers tell us the size of the voxels in space: 3 x 3 x 4 cm.#The last number tells us the dimension in time: a scan was taken every 2 seconds.#Another interesting function is the visualisation of nifti’s. #Note that we only visualise one timepoint, the first one. R will automatically show the first timepoint.orthographic(data)orthographic(data,xyz=c(20,20,30))#The argument xyz allows us to specify where to draw the cross-hairs#To look at the value of one specific point in time and space, we can use indexes.data[20,20,30,1][1] 1148.524#（20,20,30表示坐标，1表示时间点）#What is more interesting is to look at how the value changes over time, which we can do by omitting the index in the 4th dimension:data[20,20,30,][1] 1148.524 1148.461 1142.563 1141.060 1127.099 1131.675 1135.632 1128.941[9] 1160.061 1161.512 1137.481 1116.822 1109.309 1109.323 1093.169 1110.955[17] 1109.561 1116.910 1134.975 1118.357 1132.719 1150.193 1165.766 1173.594[25] 1176.839 1142.286 1145.902 1159.892 1152.587 1136.450 1127.974 1148.936[33] 1152.663 1174.247 1168.272 1147.255 1159.906 1184.832 1145.608 1141.148[41] 1132.086 1137.863 1133.563 1143.711 1141.173 1128.889 1124.897 1132.619[49] 1144.724 1153.767 1133.262 1135.230 1137.793 1165.782 1160.442 1159.418[57] 1165.768 1156.091 1169.558 1182.266 1167.106 1157.483 1163.735 1131.992[65] 1121.514 1111.989 1121.143 1141.684 1122.967 1126.063 1121.025 1138.645[73] 1132.201 1133.171 1132.688 1134.140 1127.046 1127.342 1134.374 1148.151[81] 1147.634 1145.883 1143.286 1142.210 1138.209 1123.161 1148.567 1149.430[89] 1155.253 1115.818 1116.521 1119.263 1136.349 1144.674 1151.027 1158.472[97] 1145.075 1124.284 1120.904 1129.625 1131.517 1120.666 1126.623 1109.636[105] 1093.798 1126.940 1126.045 1120.136 1133.749 1101.970 1098.026 1116.716[113] 1142.895 1143.010 1138.357 1127.057 1102.292 1122.499 1136.995 1126.363[121] 1137.567 1122.647 1142.551 1131.645 1125.780 1120.023 1122.369 1132.125[129] 1104.158 1107.103 1088.539 1116.694 1149.974 1146.161 1140.482 1114.848[137] 1099.089 1121.268 1141.496 1141.877 1129.998 1106.165 1107.426 1105.125[145] 1089.509 1103.882 1114.204 1113.552 1096.908 1104.983 1102.881 1121.604#If we plot this vector, we can see how the measured signal in this voxel changes over time.plot(data[20,20,30,],type='l')

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