# input: interscan interval / repetition time in secs (a float): l1design.inputs.interscan_interval = time_repetition # input: Model serial correlations AR(1), FAST or none: l1design.inputs.model_serial_correlations = 'AR(1)' # input: number of time-bins per scan in secs (an integer): l1design.inputs.microtime_resolution = 16 # input: the onset/time-bin in seconds for alignment (a float): l1design.inputs.microtime_onset = 1 # set expected thread and memory usage for the node: l1design.interface.num_threads = 1 l1design.interface.mem_gb = 2 # ====================================================================== # DEFINE NODE: ESTIMATE MODEL (ESTIMATE THE PARAMETERS OF THE MODEL) # ====================================================================== # function: use spm_spm to estimate the parameters of a model l1estimate = Node(EstimateModel(), name="l1estimate") # input: (a dictionary with keys which are 'Classical' or 'Bayesian2' # or 'Bayesian' and with values which are any value) l1estimate.inputs.estimation_method = {'Classical': 1} # set expected thread and memory usage for the node: l1estimate.interface.num_threads = 1 l1estimate.interface.mem_gb = 2 # ====================================================================== # DEFINE NODE: ESTIMATE CONTRASTS (ESTIMATES THE CONTRASTS) # ====================================================================== # function: use spm_contrasts to estimate contrasts of interest l1contrasts = Node(EstimateContrast(), name="l1contrasts") # input: list of contrasts with each contrast being a list of the form: # [('name', 'stat', [condition list], [weight list], [session list])]: # l1contrasts.inputs.contrasts = l1contrasts_list # node input: overwrite previous results:
# input: Model serial correlations AR(1), FAST or none: l1design.inputs.model_serial_correlations = 'AR(1)' # input: number of time-bins per scan in secs (an integer): l1design.inputs.microtime_resolution = 16 # input: the onset/time-bin in seconds for alignment (a float): l1design.inputs.microtime_onset = 1 # set expected thread and memory usage for the node: l1design.interface.num_threads = 1 l1design.interface.estimated_memory_gb = 2 # ====================================================================== # DEFINE NODE: ESTIMATE MODEL (ESTIMATE THE PARAMETERS OF THE MODEL) # ====================================================================== # function: use spm_spm to estimate the parameters of a model # EstimateModel - estimate the parameters of the model level1estimate = Node(EstimateModel(estimation_method={'Classical': 1}), name="level1estimate") l1estimate = Node(EstimateModel(), name="l1estimate") # input: (a dictionary with keys which are 'Classical' or 'Bayesian2' # or 'Bayesian' and with values which are any value) l1estimate.inputs.estimation_method = {'Classical': 1} # set expected thread and memory usage for the node: l1estimate.interface.num_threads = 1 l1estimate.interface.estimated_memory_gb = 2 # ====================================================================== # DEFINE NODE: ESTIMATE CONTRASTS (ESTIMATES THE CONTRASTS) # ====================================================================== # function: use spm_contrasts to estimate contrasts of interest l1contrasts = Node(EstimateContrast(), name="l1contrasts")