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Support for non-circular lasso FC #24

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Support for non-circular lasso FC #24

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1 change: 1 addition & 0 deletions connectivity_estimation/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,3 +6,4 @@
from .partial_corrconn import *
from .combinedFC import *
from .graphicalLassoCV import *
from .lassoCV import *
11 changes: 9 additions & 2 deletions connectivity_estimation/calcconn_parcelwise_noncircular.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@
from .corrcoefconn import *
from .pc_multregconn import *
from .combinedFC import *
from .lassoCV import *
import ActflowToolbox as actflow
from .. import tools

Expand All @@ -26,7 +27,7 @@ def calcconn_parcelwise_noncircular(data, connmethod='multreg', dlabelfile=defau

PARAMETERS:
data : vertex-wise data... vertices x time; default assumes that data is 96k dense array
connmethod : a string indicating what connectivity method to use. Options: 'multreg' (default), 'pearsoncorr', 'pc_multregconn', 'combinedFC'
connmethod : a string indicating what connectivity method to use. Options: 'multreg' (default), 'pearsoncorr', 'pc_multregconn', 'combinedFC', 'lasso'
dlabelfile : parcellation file; each vertex indicates the number corresponding to each parcel. dlabelfile needs to match same vertex dimensions of data
dilated_parcels : If True, will exclude vertices within 10mm of a target parcel's borders when computing mult regression fc (reducing spatial autocorrelation inflation)
precomputedRegularTS: optional input of precomputed 'regular' mean time series with original region set. This might cut down on computation time if provided.
Expand Down Expand Up @@ -160,7 +161,13 @@ def calcconn_parcelwise_noncircular(data, connmethod='multreg', dlabelfile=defau
# The suggested use, however, is to run an additional multiple regression step on the weights validated by combined-FC to add precision to the act-flow predictive model (see below)
net_mask[target_row,source_cols] = actflow.connectivity_estimation.combinedFC(source_parcel_ts,target_parcel_ts,parcelInt,source_cols)
targetDataAll[parcelInt,:] = target_parcel_ts.copy(); # all target data required for 2nd combined-FC step to generate final fc_matrix (see below)

elif connmethod == 'lasso':
# Modify source_parcel_ts to include target_parcel_ts as a first row. i.e merge target with source
# Provide modified source_parcel_ts as a data parameter with the index of target row (i.e. 0) as targetNodesToRun to lassoCV
modified_source_parcel_ts = np.vstack((target_parcel_ts, source_parcel_ts))
lasso, cvResults = lassoCV.lassoCV(modified_source_parcel_ts, targetNodesToRun=[0])
fc_matrix[target_row,source_cols] = lasso[0,1:] # Only take first row of lasso output (i.e. target row) and exclude (0,0) as it is target * target value from the result.

# Multiple regression step for combined-FC (note: do not need to index by target/sources here; it's done above for net_mask):
if connmethod == 'combinedFC':
fc_matrix = actflow.connectivity_estimation.multregconn(targetDataAll,conn_mask=(net_mask!=0))
Expand Down