def get_corrvox_gs(data_ts,head_mask, regions):
# remove GS
cf_rm = ConfoundsRm(data_ts[head_mask].mean(0).reshape(-1,1),data_ts[head_mask].T,intercept=False)
data_ts[head_mask] = cf_rm.transform(data_ts[head_mask].mean(0).reshape(-1,1),data_ts[head_mask].T).T
# extract time series
ts_regions = ts.get_ts(data_ts,regions)
ts_allvox = data_ts[head_mask]
# compute correlations
return ts.corr(ts_regions,ts_allvox)
def get_corrvox_gs(data_ts, head_mask, regions):
# remove GS
cf_rm = ConfoundsRm(data_ts[head_mask].mean(0).reshape(-1, 1), data_ts[head_mask].T, intercept=False)
data_ts[head_mask] = cf_rm.transform(data_ts[head_mask].mean(0).reshape(-1, 1), data_ts[head_mask].T).T
# extract time series
ts_regions = ts.get_ts(data_ts, regions)
ts_allvox = data_ts[head_mask]
# compute correlations
return ts.corr(ts_regions, ts_allvox)
def get_corrvox_std(data_ts,head_mask, regions):
# extract time series std
ts_regions = ts.get_ts(data_ts,regions,metric='std')
ts_allvox = data_ts[head_mask]
# compute correlations
return ts.corr(ts_regions,ts_allvox)
def get_corrvox_std(data_ts, head_mask, regions):
# extract time series std
ts_regions = ts.get_ts(data_ts, regions, metric='std')
ts_allvox = data_ts[head_mask]
# compute correlations
return ts.corr(ts_regions, ts_allvox)