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)