1 import scipy as sp
   2 """
   3 BrainSync: An Orthogonal Transform for Syncronize the subject fMRI data to the reference fMRI data
   4 Authors: Anand A Joshi, Minqi Chong, Jian Li, Richard M. Leahy
   5 Based on 
   6 AA Joshi, M Chong, RM Leahy, BrainSync: An Orthogonal Transformation for Synchronization of fMRI Data Across Subjects, Proc. MICCAI 2017.
   7 
   8 
   9 """
  10 
  11 def normalizeData(pre_signal):
  12     """
  13      normed_signal, mean_vector, std_vector = normalizeData(pre_signal)
  14      This function normalizes the input signal to have 0 mean and unit
  15      variance in time.
  16      pre_signal: Time x Original Vertices data
  17      normed_signal: Normalized (Time x Vertices) signal
  18      mean_vector: Vertices x 1 mean for each time series
  19      std_vector : Vertices x 1 std dev for each time series
  20     """
  21     if sp.any(sp.isnan(pre_signal)):
  22         print('there are NaNs in the data matrix, synchronization\
  23 may not work')
  24 
  25     pre_signal[sp.isnan(pre_signal)] = 0
  26     mean_vector = sp.mean(pre_signal, axis=0, keepdims=True)
  27     normed_signal = pre_signal - mean_vector
  28     std_vector = sp.std(normed_signal, axis=0, keepdims=True)
  29     std_vector[std_vector == 0] = 1
  30     normed_signal = normed_signal / std_vector
  31 
  32     return normed_signal, mean_vector, std_vector
  33 
  34 
  35 def brainSync(X, Y):
  36     """
  37    Input:
  38        X - Time series of the reference data (Time x Vertex)
  39        Y - Time series of the subject data (Time x Vertex)
  40 
  41    Output:
  42        Y2 - Synced subject data with respect to reference data (Time x Vertex)
  43        R - The orthogonal rotation matrix (Time x Time)
  44 
  45    Please cite the following publication:
  46        AA Joshi, M Chong, RM Leahy, BrainSync: An Orthogonal Transformation
  47        for Synchronization of fMRI Data Across Subjects, Proc. MICCAI 2017,
  48        in press.
  49        """
  50     if X.shape[0] > X.shape[1]:
  51         print('The input is possibly transposed. Please check to make sure \
  52 that the input is time x vertices!')
  53 
  54     C = sp.dot(X, Y.T)
  55     U, _, V = sp.linalg.svd(C)
  56     R = sp.dot(U, V)
  57     Y2 = sp.dot(R, Y)
  58     return Y2, R