def kundu_selection_v2(comptable, n_echos, n_vols): """ Classify components as "accepted", "rejected", or "ignored" based on relevant metrics. The selection process uses previously calculated parameters listed in comptable for each ICA component such as Kappa (a T2* weighting metric), Rho (an S0 weighting metric), and variance explained. See `Notes` for additional calculated metrics used to classify each component into one of the listed groups. Parameters ---------- comptable : (C x M) :obj:`pandas.DataFrame` Component metric table. One row for each component, with a column for each metric. The index should be the component number. n_echos : :obj:`int` Number of echos in original data n_vols : :obj:`int` Number of volumes in dataset Returns ------- comptable : :obj:`pandas.DataFrame` Updated component table with additional metrics and with classification (accepted, rejected, or ignored) metric_metadata : :obj:`dict` Dictionary with metadata about calculated metrics. Each entry corresponds to a column in ``comptable``. Notes ----- The selection algorithm used in this function was originated in ME-ICA by Prantik Kundu, and his original implementation is available at: https://github.com/ME-ICA/me-ica/blob/b2781dd087ab9de99a2ec3925f04f02ce84f0adc/meica.libs/select_model.py This component selection process uses multiple, previously calculated metrics that include kappa, rho, variance explained, noise and spatial frequency metrics, and measures of spatial overlap across metrics. Prantik began to update these selection criteria to use SVMs to distinguish components, a hypercommented version of this attempt is available at: https://gist.github.com/emdupre/ca92d52d345d08ee85e104093b81482e References ---------- * Kundu, P., Brenowitz, N. D., Voon, V., Worbe, Y., Vértes, P. E., Inati, S. J., ... & Bullmore, E. T. (2013). Integrated strategy for improving functional connectivity mapping using multiecho fMRI. Proceedings of the National Academy of Sciences, 110(40), 16187-16192. """ LGR.info( "Performing ICA component selection with Kundu decision tree v2.5") RepLGR.info("Next, component selection was performed to identify " "BOLD (TE-dependent), non-BOLD (TE-independent), and " "uncertain (low-variance) components using the Kundu " "decision tree (v2.5; Kundu et al., 2013).") RefLGR.info("Kundu, P., Brenowitz, N. D., Voon, V., Worbe, Y., " "Vértes, P. E., Inati, S. J., ... & Bullmore, E. T. " "(2013). Integrated strategy for improving functional " "connectivity mapping using multiecho fMRI. Proceedings " "of the National Academy of Sciences, 110(40), " "16187-16192.") comptable["classification"] = "accepted" comptable["rationale"] = "" # Set knobs LOW_PERC = 25 HIGH_PERC = 90 if n_vols < 100: EXTEND_FACTOR = 3 else: EXTEND_FACTOR = 2 RESTRICT_FACTOR = 2 # Lists of components all_comps = np.arange(comptable.shape[0]) # unclf is a full list that is whittled down over criteria # since the default classification is "accepted", at the end of the tree # the remaining elements in unclf are classified as accepted unclf = all_comps.copy() """ Step 1: Reject anything that's obviously an artifact a. Estimate a null variance """ # Rho is higher than Kappa temp_rej0a = all_comps[(comptable["rho"] > comptable["kappa"])] comptable.loc[temp_rej0a, "classification"] = "rejected" comptable.loc[temp_rej0a, "rationale"] += "I002;" # Number of significant voxels for S0 model is higher than number for T2 # model *and* number for T2 model is greater than zero. temp_rej0b = all_comps[( (comptable["countsigFS0"] > comptable["countsigFT2"]) & (comptable["countsigFT2"] > 0))] comptable.loc[temp_rej0b, "classification"] = "rejected" comptable.loc[temp_rej0b, "rationale"] += "I003;" rej = np.union1d(temp_rej0a, temp_rej0b) # Dice score for S0 maps is higher than Dice score for T2 maps and variance # explained is higher than the median across components. temp_rej1 = all_comps[(comptable["dice_FS0"] > comptable["dice_FT2"]) & (comptable["variance explained"] > np.median( comptable["variance explained"]))] comptable.loc[temp_rej1, "classification"] = "rejected" comptable.loc[temp_rej1, "rationale"] += "I004;" rej = np.union1d(temp_rej1, rej) # T-value is less than zero (noise has higher F-statistics than signal in # map) and variance explained is higher than the median across components. temp_rej2 = unclf[(comptable.loc[unclf, "signal-noise_t"] < 0) & (comptable.loc[unclf, "variance explained"] > np.median(comptable["variance explained"]))] comptable.loc[temp_rej2, "classification"] = "rejected" comptable.loc[temp_rej2, "rationale"] += "I005;" rej = np.union1d(temp_rej2, rej) unclf = np.setdiff1d(unclf, rej) # Quit early if no potentially accepted components remain if len(unclf) == 0: LGR.warning( "No BOLD-like components detected. Ignoring all remaining components." ) ign = sorted(np.setdiff1d(all_comps, rej)) comptable.loc[ign, "classification"] = "ignored" comptable.loc[ign, "rationale"] += "I006;" # Move decision columns to end comptable = clean_dataframe(comptable) metric_metadata = collect.get_metadata(comptable) return comptable, metric_metadata """ Step 2: Make a guess for what the good components are, in order to estimate good component properties a. Not outlier variance b. Kappa>kappa_elbow c. Rho<Rho_elbow d. High T2* dice compared to S0 dice e. Gain of F_T2 in clusters vs noise f. Estimate a low and high variance """ # Step 2a # Upper limit for variance explained is median across components with high # Kappa values. High Kappa is defined as Kappa above Kappa elbow. varex_upper_p = np.median(comptable.loc[ comptable["kappa"] > getelbow(comptable["kappa"], return_val=True), "variance explained", ]) # Sort component table by variance explained and find outlier components by # change in variance explained from one component to the next. # Remove variance-explained outliers from list of components to consider # for acceptance. These components will have another chance to be accepted # later on. # NOTE: We're not sure why this is done this way, nor why it's specifically # done three times. ncls = unclf.copy() for i_loop in range(3): temp_comptable = comptable.loc[ncls].sort_values( by=["variance explained"], ascending=False) diff_vals = temp_comptable["variance explained"].diff(-1) diff_vals = diff_vals.fillna(0) ncls = temp_comptable.loc[diff_vals < varex_upper_p].index.values # Compute elbows from other elbows f05, _, f01 = getfbounds(n_echos) kappas_nonsig = comptable.loc[comptable["kappa"] < f01, "kappa"] if not kappas_nonsig.size: LGR.warning("No nonsignificant kappa values detected. " "Only using elbow calculated from all kappa values.") kappas_nonsig_elbow = np.nan else: kappas_nonsig_elbow = getelbow(kappas_nonsig, return_val=True) kappas_all_elbow = getelbow(comptable["kappa"], return_val=True) # NOTE: Would an elbow from all Kappa values *ever* be lower than one from # a subset of lower (i.e., nonsignificant) values? kappa_elbow = np.nanmin((kappas_all_elbow, kappas_nonsig_elbow)) rhos_ncls_elbow = getelbow(comptable.loc[ncls, "rho"], return_val=True) rhos_all_elbow = getelbow(comptable["rho"], return_val=True) rho_elbow = np.mean((rhos_ncls_elbow, rhos_all_elbow, f05)) # Provisionally accept components based on Kappa and Rho elbows acc_prov = ncls[(comptable.loc[ncls, "kappa"] >= kappa_elbow) & (comptable.loc[ncls, "rho"] < rho_elbow)] # Quit early if no potentially accepted components remain if len(acc_prov) <= 1: LGR.warning( "Too few BOLD-like components detected. Ignoring all remaining.") ign = sorted(np.setdiff1d(all_comps, rej)) comptable.loc[ign, "classification"] = "ignored" comptable.loc[ign, "rationale"] += "I006;" # Move decision columns to end comptable = clean_dataframe(comptable) metric_metadata = collect.get_metadata(comptable) return comptable, metric_metadata # Calculate "rate" for kappa: kappa range divided by variance explained # range, for potentially accepted components # NOTE: What is the logic behind this? kappa_rate = (np.max(comptable.loc[acc_prov, "kappa"]) - np.min(comptable.loc[acc_prov, "kappa"])) / ( np.max(comptable.loc[acc_prov, "variance explained"]) - np.min(comptable.loc[acc_prov, "variance explained"])) comptable["kappa ratio"] = kappa_rate * comptable[ "variance explained"] / comptable["kappa"] # Calculate bounds for variance explained varex_lower = stats.scoreatpercentile( comptable.loc[acc_prov, "variance explained"], LOW_PERC) varex_upper = stats.scoreatpercentile( comptable.loc[acc_prov, "variance explained"], HIGH_PERC) """ Step 3: Get rid of midk components; i.e., those with higher than max decision score and high variance """ max_good_d_score = EXTEND_FACTOR * len(acc_prov) midk = unclf[(comptable.loc[unclf, "d_table_score"] > max_good_d_score) & (comptable.loc[unclf, "variance explained"] > EXTEND_FACTOR * varex_upper)] comptable.loc[midk, "classification"] = "rejected" comptable.loc[midk, "rationale"] += "I007;" unclf = np.setdiff1d(unclf, midk) acc_prov = np.setdiff1d(acc_prov, midk) """ Step 4: Find components to ignore """ # collect high variance unclassified components # and mix of high/low provisionally accepted high_varex = np.union1d( acc_prov, unclf[comptable.loc[unclf, "variance explained"] > varex_lower]) # ignore low variance components ign = np.setdiff1d(unclf, high_varex) # but only if they have bad decision scores ign = np.setdiff1d( ign, ign[comptable.loc[ign, "d_table_score"] < max_good_d_score]) # and low kappa ign = np.setdiff1d(ign, ign[comptable.loc[ign, "kappa"] > kappa_elbow]) comptable.loc[ign, "classification"] = "ignored" comptable.loc[ign, "rationale"] += "I008;" unclf = np.setdiff1d(unclf, ign) """ Step 5: Scrub the set if there are components that haven't been rejected or ignored, but are still not listed in the provisionally accepted group. """ if len(unclf) > len(acc_prov): comptable["d_table_score_scrub"] = np.nan # Recompute the midk steps on the limited set to clean up the tail d_table_rank = np.vstack([ len(unclf) - stats.rankdata(comptable.loc[unclf, "kappa"]), len(unclf) - stats.rankdata(comptable.loc[unclf, "dice_FT2"]), len(unclf) - stats.rankdata(comptable.loc[unclf, "signal-noise_t"]), stats.rankdata(comptable.loc[unclf, "countnoise"]), len(unclf) - stats.rankdata(comptable.loc[unclf, "countsigFT2"]), ]).T comptable.loc[unclf, "d_table_score_scrub"] = d_table_rank.mean(1) num_acc_guess = int( np.mean([ np.sum((comptable.loc[unclf, "kappa"] > kappa_elbow) & (comptable.loc[unclf, "rho"] < rho_elbow)), np.sum(comptable.loc[unclf, "kappa"] > kappa_elbow), ])) # Rejection candidate based on artifact type A: candartA conservative_guess = num_acc_guess / RESTRICT_FACTOR candartA = np.intersect1d( unclf[comptable.loc[unclf, "d_table_score_scrub"] > conservative_guess], unclf[comptable.loc[unclf, "kappa ratio"] > EXTEND_FACTOR * 2], ) candartA = candartA[comptable.loc[candartA, "variance explained"] > varex_upper * EXTEND_FACTOR] comptable.loc[candartA, "classification"] = "rejected" comptable.loc[candartA, "rationale"] += "I009;" midk = np.union1d(midk, candartA) unclf = np.setdiff1d(unclf, midk) # Rejection candidate based on artifact type B: candartB conservative_guess2 = num_acc_guess * HIGH_PERC / 100.0 candartB = unclf[ comptable.loc[unclf, "d_table_score_scrub"] > conservative_guess2] candartB = candartB[comptable.loc[candartB, "variance explained"] > varex_lower * EXTEND_FACTOR] comptable.loc[candartB, "classification"] = "rejected" comptable.loc[candartB, "rationale"] += "I010;" midk = np.union1d(midk, candartB) unclf = np.setdiff1d(unclf, midk) # Find components to ignore # Ignore high variance explained, poor decision tree scored components new_varex_lower = stats.scoreatpercentile( comptable.loc[unclf[:num_acc_guess], "variance explained"], LOW_PERC) candart = unclf[comptable.loc[unclf, "d_table_score_scrub"] > num_acc_guess] ign_add0 = candart[ comptable.loc[candart, "variance explained"] > new_varex_lower] ign_add0 = np.setdiff1d(ign_add0, midk) comptable.loc[ign_add0, "classification"] = "ignored" comptable.loc[ign_add0, "rationale"] += "I011;" ign = np.union1d(ign, ign_add0) unclf = np.setdiff1d(unclf, ign) # Ignore low Kappa, high variance explained components ign_add1 = np.intersect1d( unclf[comptable.loc[unclf, "kappa"] <= kappa_elbow], unclf[comptable.loc[unclf, "variance explained"] > new_varex_lower], ) ign_add1 = np.setdiff1d(ign_add1, midk) comptable.loc[ign_add1, "classification"] = "ignored" comptable.loc[ign_add1, "rationale"] += "I012;" # at this point, unclf is equivalent to accepted # Move decision columns to end comptable = clean_dataframe(comptable) metric_metadata = collect.get_metadata(comptable) return comptable, metric_metadata
def kundu_tedpca(comptable, n_echos, kdaw=10., rdaw=1., stabilize=False): """ Select PCA components using Kundu's decision tree approach. Parameters ---------- comptable : :obj:`pandas.DataFrame` Component table with relevant metrics: kappa, rho, and normalized variance explained. Component number should be the index. n_echos : :obj:`int` Number of echoes in dataset. kdaw : :obj:`float`, optional Kappa dimensionality augmentation weight. Must be a non-negative float, or -1 (a special value). Default is 10. rdaw : :obj:`float`, optional Rho dimensionality augmentation weight. Must be a non-negative float, or -1 (a special value). Default is 1. stabilize : :obj:`bool`, optional Whether to stabilize convergence by reducing dimensionality, for low quality data. Default is False. Returns ------- comptable : :obj:`pandas.DataFrame` Component table with components classified as 'accepted', 'rejected', or 'ignored'. metric_metadata : :obj:`dict` Dictionary with metadata about calculated metrics. Each entry corresponds to a column in ``comptable``. """ LGR.info('Performing PCA component selection with Kundu decision tree') comptable['classification'] = 'accepted' comptable['rationale'] = '' eigenvalue_elbow = getelbow(comptable['normalized variance explained'], return_val=True) diff_varex_norm = np.abs( np.diff(comptable['normalized variance explained'])) lower_diff_varex_norm = diff_varex_norm[(len(diff_varex_norm) // 2):] varex_norm_thr = np.mean( [lower_diff_varex_norm.max(), diff_varex_norm.min()]) varex_norm_min = comptable['normalized variance explained'][ (len(diff_varex_norm) // 2) + np.arange(len(lower_diff_varex_norm))[ lower_diff_varex_norm >= varex_norm_thr][0] + 1] varex_norm_cum = np.cumsum(comptable['normalized variance explained']) fmin, fmid, fmax = getfbounds(n_echos) if int(kdaw) == -1: lim_idx = utils.andb( [comptable['kappa'] < fmid, comptable['kappa'] > fmin]) == 2 kappa_lim = comptable.loc[lim_idx, 'kappa'].values kappa_thr = kappa_lim[getelbow(kappa_lim)] lim_idx = utils.andb( [comptable['rho'] < fmid, comptable['rho'] > fmin]) == 2 rho_lim = comptable.loc[lim_idx, 'rho'].values rho_thr = rho_lim[getelbow(rho_lim)] stabilize = True LGR.info('kdaw set to -1. Switching TEDPCA algorithm to ' 'kundu-stabilize') elif int(rdaw) == -1: lim_idx = utils.andb( [comptable['rho'] < fmid, comptable['rho'] > fmin]) == 2 rho_lim = comptable.loc[lim_idx, 'rho'].values rho_thr = rho_lim[getelbow(rho_lim)] else: kappa_thr = np.average(sorted( [fmin, (getelbow(comptable['kappa'], return_val=True) / 2), fmid]), weights=[kdaw, 1, 1]) rho_thr = np.average(sorted([ fmin, (getelbow_cons(comptable['rho'], return_val=True) / 2), fmid ]), weights=[rdaw, 1, 1]) # Reject if low Kappa, Rho, and variance explained is_lowk = comptable['kappa'] <= kappa_thr is_lowr = comptable['rho'] <= rho_thr is_lowe = comptable['normalized variance explained'] <= eigenvalue_elbow is_lowkre = is_lowk & is_lowr & is_lowe comptable.loc[is_lowkre, 'classification'] = 'rejected' comptable.loc[is_lowkre, 'rationale'] += 'P001;' # Reject if low variance explained is_lows = comptable['normalized variance explained'] <= varex_norm_min comptable.loc[is_lows, 'classification'] = 'rejected' comptable.loc[is_lows, 'rationale'] += 'P002;' # Reject if Kappa over limit is_fmax1 = comptable['kappa'] == F_MAX comptable.loc[is_fmax1, 'classification'] = 'rejected' comptable.loc[is_fmax1, 'rationale'] += 'P003;' # Reject if Rho over limit is_fmax2 = comptable['rho'] == F_MAX comptable.loc[is_fmax2, 'classification'] = 'rejected' comptable.loc[is_fmax2, 'rationale'] += 'P004;' if stabilize: temp7 = varex_norm_cum >= 0.95 comptable.loc[temp7, 'classification'] = 'rejected' comptable.loc[temp7, 'rationale'] += 'P005;' under_fmin1 = comptable['kappa'] <= fmin comptable.loc[under_fmin1, 'classification'] = 'rejected' comptable.loc[under_fmin1, 'rationale'] += 'P006;' under_fmin2 = comptable['rho'] <= fmin comptable.loc[under_fmin2, 'classification'] = 'rejected' comptable.loc[under_fmin2, 'rationale'] += 'P007;' n_components = comptable.loc[comptable['classification'] == 'accepted'].shape[0] LGR.info('Selected {0} components with Kappa threshold: {1:.02f}, Rho ' 'threshold: {2:.02f}'.format(n_components, kappa_thr, rho_thr)) # Move decision columns to end comptable = clean_dataframe(comptable) metric_metadata = collect.get_metadata(comptable) return comptable, metric_metadata
def manual_selection(comptable, acc=None, rej=None): """ Perform manual selection of components. Parameters ---------- comptable : (C x M) :obj:`pandas.DataFrame` Component metric table, where `C` is components and `M` is metrics acc : :obj:`list`, optional List of accepted components. Default is None. rej : :obj:`list`, optional List of rejected components. Default is None. Returns ------- comptable : (C x M) :obj:`pandas.DataFrame` Component metric table with classification. metric_metadata : :obj:`dict` Dictionary with metadata about calculated metrics. Each entry corresponds to a column in ``comptable``. """ LGR.info("Performing manual ICA component selection") RepLGR.info("Next, components were manually classified as " "BOLD (TE-dependent), non-BOLD (TE-independent), or " "uncertain (low-variance).") if ("classification" in comptable.columns and "original_classification" not in comptable.columns): comptable["original_classification"] = comptable["classification"] comptable["original_rationale"] = comptable["rationale"] comptable["classification"] = "accepted" comptable["rationale"] = "" all_comps = comptable.index.values if acc is not None: acc = [int(comp) for comp in acc] if rej is not None: rej = [int(comp) for comp in rej] if acc is not None and rej is None: rej = sorted(np.setdiff1d(all_comps, acc)) elif acc is None and rej is not None: acc = sorted(np.setdiff1d(all_comps, rej)) elif acc is None and rej is None: LGR.info( "No manually accepted or rejected components supplied. Accepting all components." ) # Accept all components if no manual selection provided acc = all_comps[:] rej = [] ign = np.setdiff1d(all_comps, np.union1d(acc, rej)) comptable.loc[acc, "classification"] = "accepted" comptable.loc[rej, "classification"] = "rejected" comptable.loc[rej, "rationale"] += "I001;" comptable.loc[ign, "classification"] = "ignored" comptable.loc[ign, "rationale"] += "I001;" # Move decision columns to end comptable = clean_dataframe(comptable) metric_metadata = collect.get_metadata(comptable) return comptable, metric_metadata