# 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")
