Source code for CPAC.reho.utils



[docs]def getOpString(mean, std_dev): """ Generate the Operand String to be used in workflow nodes to supply mean and std deviation to alff workflow nodes Parameters ---------- mean: string mean value in string format std_dev : string std deviation value in string format Returns ------- op_string : string """ str1 = "-sub %f -div %f" % (float(mean), float(std_dev)) op_string = str1 + " -mas %s" return op_string
[docs]def f_kendall(timeseries_matrix): """ Calculates the Kendall's coefficient of concordance for a number of time-series in the input matrix Parameters ---------- timeseries_matrix : ndarray A matrix of ranks of a subset subject's brain voxels Returns ------- kcc : float Kendall's coefficient of concordance on the given input matrix """ import numpy as np nk = timeseries_matrix.shape n = nk[0] k = nk[1] sr = np.sum(timeseries_matrix, 1) sr_bar = np.mean(sr) s = np.sum(np.power(sr, 2)) - n*np.power(sr_bar, 2) kcc = 12 *s/np.power(k, 2)/(np.power(n, 3) - n) return kcc
[docs]def compute_reho(in_file, mask_file, cluster_size): """ Computes the ReHo Map, by computing tied ranks of the timepoints, followed by computing Kendall's coefficient concordance(KCC) of a timeseries with its neighbours Parameters ---------- in_file : nifti file 4D EPI File mask_file : nifti file Mask of the EPI File(Only Compute ReHo of voxels in the mask) cluster_size : integer for a brain voxel the number of neighbouring brain voxels to use for KCC. Returns ------- out_file : nifti file ReHo map of the input EPI image """ out_file = None res_fname = (in_file) res_mask_fname = (mask_file) CUTNUMBER = 10 if not (cluster_size == 27 or cluster_size == 19 or cluster_size == 7): cluster_size = 27 nvoxel = cluster_size res_img = nb.load(res_fname) res_mask_img = nb.load(res_mask_fname) res_data = res_img.get_data() res_mask_data = res_mask_img.get_data() print(res_data.shape) (n_x, n_y, n_z, n_t) = res_data.shape # "flatten" each volume of the timeseries into one big array instead of # x,y,z - produces (timepoints, N voxels) shaped data array res_data = np.reshape(res_data, (n_x*n_y*n_z, n_t), order='F').T # create a blank array of zeroes of size n_voxels, one for each time point Ranks_res_data = np.tile((np.zeros((1, (res_data.shape)[1]))), [(res_data.shape)[0], 1]) # divide the number of total voxels by the cutnumber (set to 10) # ex. end up with a number in the thousands if there are tens of thousands # of voxels segment_length = np.ceil(float((res_data.shape)[1])/float(CUTNUMBER)) for icut in range(0, CUTNUMBER): segment = None # create a Numpy array of evenly spaced values from the segment # starting point up until the segment_length integer if not (icut == (CUTNUMBER - 1)): segment = np.array(np.arange(icut * segment_length, (icut+1) * segment_length)) else: segment = np.array(np.arange(icut * segment_length, (res_data.shape[1]))) segment = np.int64(segment[np.newaxis]) # res_data_piece is a chunk of the original timeseries in_file, but # aligned with the current segment index spacing res_data_piece = res_data[:, segment[0]] nvoxels_piece = res_data_piece.shape[1] # run a merge sort across the time axis, re-ordering the flattened # volume voxel arrays res_data_sorted = np.sort(res_data_piece, 0, kind='mergesort') sort_index = np.argsort(res_data_piece, axis=0, kind='mergesort') # subtract each volume from each other db = np.diff(res_data_sorted, 1, 0) # convert any zero voxels into "True" flag db = db == 0 # return an n_voxel (n voxels within the current segment) sized array # of values, each value being the sum total of TRUE values in "db" sumdb = np.sum(db, 0) temp_array = np.array(np.arange(0, n_t)) temp_array = temp_array[:, np.newaxis] sorted_ranks = np.tile(temp_array, [1, nvoxels_piece]) if np.any(sumdb[:]): tie_adjust_index = np.flatnonzero(sumdb) for i in range(0, len(tie_adjust_index)): ranks = sorted_ranks[:, tie_adjust_index[i]] ties = db[:, tie_adjust_index[i]] tieloc = np.append(np.flatnonzero(ties), n_t + 2) maxties = len(tieloc) tiecount = 0 while(tiecount < maxties -1): tiestart = tieloc[tiecount] ntied = 2 while(tieloc[tiecount + 1] == (tieloc[tiecount] + 1)): tiecount += 1 ntied += 1 ranks[tiestart:tiestart + ntied] = np.ceil(np.float32(np.sum(ranks[tiestart:tiestart + ntied ]))/np.float32(ntied)) tiecount += 1 sorted_ranks[:, tie_adjust_index[i]] = ranks del db, sumdb sort_index_base = np.tile(np.multiply(np.arange(0, nvoxels_piece), n_t), [n_t, 1]) sort_index += sort_index_base del sort_index_base ranks_piece = np.zeros((n_t, nvoxels_piece)) ranks_piece = ranks_piece.flatten(order='F') sort_index = sort_index.flatten(order='F') sorted_ranks = sorted_ranks.flatten(order='F') ranks_piece[sort_index] = np.array(sorted_ranks) ranks_piece = np.reshape(ranks_piece, (n_t, nvoxels_piece), order='F') del sort_index, sorted_ranks Ranks_res_data[:, segment[0]] = ranks_piece sys.stdout.write('.') Ranks_res_data = np.reshape(Ranks_res_data, (n_t, n_x, n_y, n_z), order='F') K = np.zeros((n_x, n_y, n_z)) mask_cluster = np.ones((3, 3, 3)) if nvoxel == 19: mask_cluster[0, 0, 0] = 0 mask_cluster[0, 2, 0] = 0 mask_cluster[2, 0, 0] = 0 mask_cluster[2, 2, 0] = 0 mask_cluster[0, 0, 2] = 0 mask_cluster[0, 2, 2] = 0 mask_cluster[2, 0, 2] = 0 mask_cluster[2, 2, 2] = 0 elif nvoxel == 7: mask_cluster[0, 0, 0] = 0 mask_cluster[0, 1, 0] = 0 mask_cluster[0, 2, 0] = 0 mask_cluster[0, 0, 1] = 0 mask_cluster[0, 2, 1] = 0 mask_cluster[0, 0, 2] = 0 mask_cluster[0, 1, 2] = 0 mask_cluster[0, 2, 2] = 0 mask_cluster[1, 0, 0] = 0 mask_cluster[1, 2, 0] = 0 mask_cluster[1, 0, 2] = 0 mask_cluster[1, 2, 2] = 0 mask_cluster[2, 0, 0] = 0 mask_cluster[2, 1, 0] = 0 mask_cluster[2, 2, 0] = 0 mask_cluster[2, 0, 1] = 0 mask_cluster[2, 2, 1] = 0 mask_cluster[2, 0, 2] = 0 mask_cluster[2, 1, 2] = 0 mask_cluster[2, 2, 2] = 0 for i in range(1, n_x - 1): for j in range(1, n_y -1): for k in range(1, n_z -1): block = Ranks_res_data[:, i-1:i+2, j-1:j+2, k-1:k+2] mask_block = res_mask_data[i-1:i+2, j-1:j+2, k-1:k+2] if not(int(mask_block[1, 1, 1]) == 0): if nvoxel == 19 or nvoxel == 7: mask_block = np.multiply(mask_block, mask_cluster) R_block = np.reshape(block, (block.shape[0], 27), order='F') mask_R_block = R_block[:, np.argwhere(np.reshape(mask_block, (1, 27), order='F') > 0)[:, 1]] K[i, j, k] = f_kendall(mask_R_block) img = nb.Nifti1Image(K, header=res_img.get_header(), affine=res_img.get_affine()) reho_file = os.path.join(os.getcwd(), 'ReHo.nii.gz') img.to_filename(reho_file) out_file = reho_file return out_file