code: def t_score_with_covars_and_normalized_design(
      tested_vars, target_vars, covars_orthonormalized=None
  ):
      """t-score in the regression of tested variates against target variates.
  
      Covariates are taken into account (if not None).
      The normalized_design case corresponds to the following assumptions:
      - tested_vars and target_vars are normalized
      - covars_orthonormalized are orthonormalized
      - tested_vars and covars_orthonormalized are orthogonal
        (np.dot(tested_vars.T, covars) == 0)
  
      Parameters
      ----------
      tested_vars : array-like, shape=(n_samples, n_tested_vars)
          Explanatory variates.
  
      target_vars : array-like, shape=(n_samples, n_target_vars)
          Targets variates. F-ordered is better for efficient computation.
  
      covars_orthonormalized : array-like, shape=(n_samples, n_covars) or None, \
              optional
          Confounding variates.
  
      Returns
      -------
      score : numpy.ndarray, shape=(n_target_vars, n_tested_vars)
          t-scores associated with the tests of each explanatory variate against
          each target variate (in the presence of covars).
  
      """
      if covars_orthonormalized is None:
          lost_dof = 0
      else:
          lost_dof = covars_orthonormalized.shape[1]
      # Tested variates are fitted independently,
      # so lost_dof is unrelated to n_tested_vars.
      dof = target_vars.shape[0] - lost_dof
      beta_targetvars_testedvars = np.dot(target_vars.T, tested_vars)
      if covars_orthonormalized is None:
          rss = 1 - beta_targetvars_testedvars**2
      else:
          beta_targetvars_covars = np.dot(
              target_vars.T, covars_orthonormalized
          )
          a2 = np.sum(beta_targetvars_covars**2, 1)
          rss = 1 - a2[:, np.newaxis] - beta_targetvars_testedvars**2
      return beta_targetvars_testedvars * np.sqrt((dof - 1.0) / rss)
  
file_path: nilearn/nilearn/mass_univariate/_utils.py
imports: import numpy
  from scipy import linalg
  from scipy.ndimage import label
label_desc: Use image node_id 6 for calling a global function with example usage: t_score_with_covars_and_normalized_design(tested_vars, target_vars, covars_orthonormalized) and returns: unknown