group fc

.gitignore

@@ -175,3 +175,7 @@ cython_debug/
 .pypirc
 
 /individual_images
+*.xlsx
+*.csv
+*.snirf
+*.json

changelog.md

@@ -1,7 +1,29 @@
+# Version 1.2.1
+
+- Added a requirements.txt file to ensure compatibility
+- Added new options 'Missing Events Bypass' and 'Analysis Clearing Bypass' to the Preferences Menu
+- Missing Events Bypass allows comparing events in the Group Viewers even if not all participants in the group have the event present. Fixes [Issue 28](https://git.research.dezeeuw.ca/tyler/flares/issues/28)
+- Clicking Process after an analysis has been performed will now clear the existing analysis by default with a popup warning that the analysis will be cleared
+- Analysis Clearing Bypass will prevent the popup and will not clear the existing analysis data. Fixes [Issue 41](https://git.research.dezeeuw.ca/tyler/flares/issues/41)
+- Clicking 'Clear' should now actually properly clear all data. Hopefully fixes [Issue 9](https://git.research.dezeeuw.ca/tyler/flares/issues/9) for good
+- Setting SHORT_CHANNEL to False will now grey out SHORT_CHANNEL_REGRESSION, as it is impossible to regress what does not exist. Sets SHORT_CHANNEL_REGRESSION to False under the hood when it is greyed out regardless of what is displayed. Fixes [Issue 47](https://git.research.dezeeuw.ca/tyler/flares/issues/47)
+- Projects can now be saves if files have different parent folders. Fixes [Issue 48](https://git.research.dezeeuw.ca/tyler/flares/issues/48)
+- It is no longer possible to attempt a save before any data has been processed. A popup will now display if a save is attempted with nothing to save
+- Fixed a bug where LONG_CHANNEL_THRESH was not being applied in the processing steps
+- Added a new option in the Analysis window for Group Functional Connectivity. Implements [Issue 50](https://git.research.dezeeuw.ca/tyler/flares/issues/50)
+- Group Functional connectivity is still in development and the results should currently be taken with a grain of salt
+- A warning is displayed when entering the Group Functional Connectivity Viewer disclosing this
+- Fixed a bug when updating optode positions that would prevent .txt files from being selected. Fixes [Issue 54](https://git.research.dezeeuw.ca/tyler/flares/issues/54)
+- Fixed a bug where the secondary download server would never get contacted if the primary failed
+- Automatic downloads will now ignore prerelease versions. Fixes [Issue 52](https://git.research.dezeeuw.ca/tyler/flares/issues/52)
+
+
 # Version 1.2.0
 
+- This is a save-breaking release due to a new save file format. Please update your project files to ensure compatibility. Fixes [Issue 30](https://git.research.dezeeuw.ca/tyler/flares/issues/30)
 - Added new parameters to the right side of the screen
 - These parameters include SHOW_OPTODE_NAMES, SECONDS_TO_STRIP_HR, MAX_LOW_HR, MAX_HIGH_HR, SMOOTHING_WINDOW_HR, HEART_RATE_WINDOW, BAD_CHANNELS_HANDLING, MAX_DIST, MIN_NEIGHBORS, L_TRANS_BANDWIDTH, H_TRANS_BANDWIDTH, RESAMPLE, RESAMPLE_FREQ, STIM_DUR, HRF_MODEL, HIGH_PASS, DRIFT_ORDER, FIR_DELAYS, MIN_ONSET, OVERSAMPLING, SHORT_CHANNEL_REGRESSION, NOISE_MODEL, BINS, and VERBOSITY.
+- Certain parameters now have dependencies on other parameters and will now grey out if they are not used
 - All the new parameters have default values matching the underlying values in version 1.1.7
 - The order of the parameters have changed to match the order that the code runs when the Process button is clicked
 - Moved TIME_WINDOW_START and TIME_WINDOW_END to the 'Other' category
@@ -15,6 +37,20 @@
 - Fixed the User Guide window to properly display information about the 24 stages and added a link to the Git wiki page
 - MAX_WORKERS should now properly repect the value set
 - Added a new CSV export option to be used by other applications
+- Added support for updating optode positions directly from an .xlsx file from a Polhemius system
+- Fixed an issue where the dropdowns in the Viewer windows would immediately open and close when using a trackpad
+- glover and spm hrf models now function as intended without crashing. Currently, group analysis is still only supported by fir. Fixes [Issue 8](https://git.research.dezeeuw.ca/tyler/flares/issues/8)
+- Clicking 'Clear' should now properly clear all data. Fixes [Issue 9](https://git.research.dezeeuw.ca/tyler/flares/issues/9)
+- Revamped the fold channels viewer to not hang the application and to better process multiple participants at once. Fixes [Issue 34](https://git.research.dezeeuw.ca/tyler/flares/issues/34), [Issue 31](https://git.research.dezeeuw.ca/tyler/flares/issues/31)
+- Added a Preferences menu to the navigation bar
+- Two preferences have been added allowing to bypass the warning of 2D data detected and save files being from previous, potentially breaking versions
+- Fixed a typo when saving a CSV that stated a SNIRF was being saved
+- Loading a save file now properly restores AGE, GENDER, and GROUP. Fixes [Issue 40](https://git.research.dezeeuw.ca/tyler/flares/issues/40)
+- Saving a project now no longer makes the main window go not responding. Fixes [Issue 43](https://git.research.dezeeuw.ca/tyler/flares/issues/43)
+- Memory usage should no longer grow when generating lots of images multiple times. Fixes [Issue 36](https://git.research.dezeeuw.ca/tyler/flares/issues/36)
+- Added a new option in the Analysis window for Functional Connectivity
+- Functional connectivity is still in development and the results should currently be taken with a grain of salt
+- A warning is displayed when entering the Functional Connectivity Viewer disclosing this
 
 
 # Version 1.1.7
@@ -105,7 +141,7 @@
 - Added a group option when clicking on a participant's file
 - If no group is specified, the participant will be added to the "Default" group
 - Added option to update the optode positions in a snirf file from the Options menu (F6)
-- Fixed [Issue 3](https://git.research.dezeeuw.ca/tyler/flares/issues/3), [Issue 4](https://git.research.dezeeuw.ca/tyler/flares/issues/4), [Issue 17](https://git.research.dezeeuw.ca/tyler/flares/issues/17), [Issue 21](https://git.research.dezeeuw.ca/tyler/flares/issues/21), [Issue 22](https://git.research.dezeeuw.ca/tyler/flares/issues/22)
+- Fixed [Issue 3](https://git.research.dezeeuw.ca/tyler/flares/issues/3), [Issue 5](https://git.research.dezeeuw.ca/tyler/flares/issues/5), [Issue 17](https://git.research.dezeeuw.ca/tyler/flares/issues/17), [Issue 21](https://git.research.dezeeuw.ca/tyler/flares/issues/21), [Issue 22](https://git.research.dezeeuw.ca/tyler/flares/issues/22)
 
 
 # Version 1.0.1

flares.py

@@ -47,9 +47,9 @@ from nilearn.glm.regression import OLSModel
 import statsmodels.formula.api as smf  # type: ignore
 from statsmodels.stats.multitest import multipletests
 
-from scipy import stats
 from scipy.spatial.distance import cdist
 from scipy.signal import welch, butter, filtfilt # type: ignore
+from scipy.stats import pearsonr, zscore, t
 
 import pywt # type: ignore
 import neurokit2 as nk # type: ignore
@@ -58,6 +58,7 @@ import neurokit2 as nk # type: ignore
 import pyvistaqt # type: ignore
 import vtkmodules.util.data_model
 import vtkmodules.util.execution_model
+import xlrd
 
 # External library imports for mne
 from mne import (
@@ -90,6 +91,9 @@ from mne_nirs.io.fold import fold_channel_specificity  # type: ignore
 from mne_nirs.preprocessing import peak_power  # type: ignore
 from mne_nirs.statistics._glm_level_first import RegressionResults  # type: ignore
 
+from mne_connectivity.viz import plot_connectivity_circle
+from mne_connectivity import envelope_correlation, spectral_connectivity_epochs,  spectral_connectivity_time
+
 # Needs to be set for mne
 os.environ["SUBJECTS_DIR"] = str(data_path()) + "/subjects" # type: ignore
 
@@ -123,8 +127,6 @@ SECONDS_TO_KEEP: float
 OPTODE_PLACEMENT: bool
 SHOW_OPTODE_NAMES: bool
 
-HEART_RATE: bool
-
 SHORT_CHANNEL: bool
 SHORT_CHANNEL_THRESH: float
 LONG_CHANNEL_THRESH: float
@@ -189,9 +191,9 @@ TIME_WINDOW_END: int
 MAX_WORKERS: int
 VERBOSITY: bool
 
-AGE = 25    # Assume 25 if not set from the GUI. This will result in a reasonable PPF
+AGE: int = 25  # Assume 25 if not set from the GUI. This will result in a reasonable PPF
-GENDER = ""
+GENDER: str = ""
-GROUP = "Default"
+GROUP: str = "Default"
 
 # These are parameters that are required for the analysis
 REQUIRED_KEYS: dict[str, Any] = {
@@ -928,11 +930,12 @@ def interpolate_fNIRS_bads_weighted_average(raw, max_dist=0.03, min_neighbors=2)
         raw.info['bads'] = [ch for ch in raw.info['bads'] if ch not in bad_ch_to_remove]
 
     print("\nInterpolation complete.\n")
+    print("Bads cleared:", raw.info['bads'])
+    raw.info['bads'] = []
 
     for ch in raw.info['bads']:
         print(f"Channel {ch} still marked as bad.")
 
-    print("Bads cleared:", raw.info['bads'])
     fig_raw_after = raw.plot(duration=raw.times[-1], n_channels=raw.info['nchan'], title="After interpolation", show=False)
 
     return raw, fig_raw_after
@@ -1333,7 +1336,7 @@ def make_design_matrix(raw_haemo, short_chans):
             drift_model=DRIFT_MODEL,
             high_pass=HIGH_PASS,
             drift_order=DRIFT_ORDER,
-            fir_delays=range(15),
+            fir_delays=FIR_DELAYS,
             add_regs=short_chans.get_data().T,
             add_reg_names=short_chans.ch_names,
             min_onset=MIN_ONSET,
@@ -1347,7 +1350,7 @@ def make_design_matrix(raw_haemo, short_chans):
             drift_model=DRIFT_MODEL,
             high_pass=HIGH_PASS,
             drift_order=DRIFT_ORDER,
-            fir_delays=range(15),
+            fir_delays=FIR_DELAYS,
             min_onset=MIN_ONSET,
             oversampling=OVERSAMPLING
         )
@@ -1659,8 +1662,11 @@ def fold_channels(raw: BaseRaw) -> None:
         landmark_color_map = {landmark: colors[i % len(colors)] for i, landmark in enumerate(landmarks)}
 
     # Iterate over each channel
+    print(len(hbo_channel_names))
+
     for idx, channel_name in enumerate(hbo_channel_names):
 
+        print(idx, channel_name)
         # Run the fOLD on the selected channel
         channel_data = raw.copy().pick(picks=channel_name) # type: ignore
 
@@ -1703,6 +1709,7 @@ def fold_channels(raw: BaseRaw) -> None:
 
             landmark_specificity_data = []
 
+
     # TODO: Fix this
     if True:
         handles = [
@@ -1725,8 +1732,9 @@ def fold_channels(raw: BaseRaw) -> None:
     for ax in axes[len(hbo_channel_names):]:
         ax.axis('off')
     
-    plt.show()
+    #plt.show()
-    return fig, legend_fig
+    fig_dict = {"main": fig, "legend": legend_fig}
+    return convert_fig_dict_to_png_bytes(fig_dict)
 
                 
 
@@ -2246,9 +2254,15 @@ def brain_3d_visualization(raw_haemo, df_cha, selected_event, t_or_theta: Litera
     # Get all activity conditions
     for cond in [f'{selected_event}']:
 
-        if True:
         ch_summary = df_cha.query(f"Condition.str.startswith('{cond}_delay_') and Chroma == 'hbo'", engine='python') # type: ignore
 
+        print(ch_summary)
+        
+        if ch_summary.empty:
+            #not fir model
+            print("No data found for this condition.")
+            ch_summary = df_cha.query(f"Condition in [@cond] and Chroma == 'hbo'", engine='python') 
+
         # Use ordinary least squares (OLS) if only one participant
         # TODO: Fix.
         if True:
@@ -2269,6 +2283,9 @@ def brain_3d_visualization(raw_haemo, df_cha, selected_event, t_or_theta: Litera
         valid_channels = ch_summary["ch_name"].unique().tolist() # type: ignore
         raw_for_plot = raw_haemo.copy().pick(picks=valid_channels) # type: ignore
 
+        print(f"DEBUG: Model DF rows: {len(model_df)}")
+        print(f"DEBUG: Raw channels: {len(raw_for_plot.ch_names)}")
+
         brain = plot_3d_evoked_array(raw_for_plot.pick(picks="hbo"), model_df, view="dorsal", distance=0.02, colorbar=True, clim=clim, mode="weighted", size=(800, 700)) # type: ignore
         
         if show_optodes == 'all' or show_optodes == 'sensors':
@@ -2804,7 +2821,7 @@ def run_second_level_analysis(df_contrasts, raw, p, bounds):
         result = model.fit(Y)
 
         t_val = result.t(0).item()
-        p_val = 2 * stats.t.sf(np.abs(t_val), df=result.df_model)
+        p_val = 2 * t.sf(np.abs(t_val), df=result.df_model)
         mean_beta = np.mean(Y)
 
         group_results.append({
@@ -3299,7 +3316,7 @@ def hr_calc(raw):
     # --- Parameters for PSD ---
     desired_bin_hz = 0.1
     nperseg = int(sfreq / desired_bin_hz)
-    hr_range = (30, 180)
+    hr_range = (30, 180) # TODO: SHould this not use the user defined values?
 
     # --- Function to find strongest local peak ---
     def find_hr_from_psd(ch_data):
@@ -3343,6 +3360,7 @@ def process_participant(file_path, progress_callback=None):
     logger.info("Step 1 Completed.")
     
     # Step 2: Trimming
+    # TODO: Clean this into a method
     if TRIM:
         if hasattr(raw, 'annotations') and len(raw.annotations) > 0:
             # Get time of first event
@@ -3385,7 +3403,7 @@ def process_participant(file_path, progress_callback=None):
         fig_individual["short"] = fig_short_chans
     else:
         short_chans = None
-    get_long_channels(raw, min_dist=SHORT_CHANNEL_THRESH, max_dist=LONG_CHANNEL_THRESH) # Don't update the existing raw
+    raw = get_long_channels(raw, min_dist=0, max_dist=LONG_CHANNEL_THRESH) # keep both short channels and all channels up to the threshold length
     if progress_callback: progress_callback(4)
     logger.info("Step 4 Completed.")
 
@@ -3527,6 +3545,7 @@ def process_participant(file_path, progress_callback=None):
     logger.info("19")
 
     # Step 20: Generate GLM Results
+    if "derivative" not in HRF_MODEL.lower():
         fig_glm_result = plot_glm_results(file_path, raw_haemo, glm_est, design_matrix)
         for name, fig in fig_glm_result:
             fig_individual[f"GLM {name}"] = fig
@@ -3534,6 +3553,7 @@ def process_participant(file_path, progress_callback=None):
     logger.info("20")
 
     # Step 21: Generate Channel Significance
+    if HRF_MODEL == "fir":
         fig_significance = individual_significance(raw_haemo, glm_est)
         for name, fig in fig_significance:
             fig_individual[f"Significance {name}"] = fig
@@ -3568,6 +3588,7 @@ def process_participant(file_path, progress_callback=None):
         [(column, contrast_matrix[i]) for i, column in enumerate(design_matrix.columns)]
     )
 
+    if HRF_MODEL == "fir":
         all_delay_cols = [col for col in design_matrix.columns if "_delay_" in col]
         all_conditions = sorted({col.split("_delay_")[0] for col in all_delay_cols})
 
@@ -3599,6 +3620,8 @@ def process_participant(file_path, progress_callback=None):
     # Step 23: Compute Contrast Results
     contrast_results = {}
 
+    if HRF_MODEL == "fir":
+
         for cond, contrast_vector in contrast_dict.items():
             contrast = glm_est.compute_contrast(contrast_vector)  # type: ignore
             df = contrast.to_dataframe()
@@ -3618,7 +3641,17 @@ def process_participant(file_path, progress_callback=None):
     if progress_callback: progress_callback(25)
     logger.info("25")
 
-    return raw_haemo, epochs, fig_bytes, cha, contrast_results, df_ind, design_matrix, AGE, GENDER, GROUP, True
+    # TODO: Tidy up
+    # Extract the parameters this file was ran with. No need to return age, gender, group?
+    config = {
+        k: globals()[k]
+        for k in __annotations__
+        if k in globals() and k != "REQUIRED_KEYS"
+    }
+
+    print(config)
+
+    return raw_haemo, config, epochs, fig_bytes, cha, contrast_results, df_ind, design_matrix, True
 
 
 def sanitize_paths_for_pickle(raw_haemo, epochs):
@@ -3629,3 +3662,421 @@ def sanitize_paths_for_pickle(raw_haemo, epochs):
     # Fix epochs._raw._filenames
     if hasattr(epochs, '_raw') and hasattr(epochs._raw, '_filenames'):
         epochs._raw._filenames = [str(p) for p in epochs._raw._filenames]
+
+
+def functional_connectivity_spectral_epochs(epochs, n_lines, vmin):
+
+    # will crash without this load
+    epochs.load_data()
+    hbo_epochs = epochs.copy().pick(picks="hbo")
+    data = hbo_epochs.get_data()
+    names = hbo_epochs.ch_names
+    sfreq = hbo_epochs.info["sfreq"]
+    con = spectral_connectivity_epochs(
+        data,
+        method=["coh", "plv"],
+        mode="multitaper",
+        sfreq=sfreq,
+        fmin=0.04,
+        fmax=0.2,
+        faverage=True,
+        verbose=True
+    )
+
+    con_coh, con_plv = con
+
+    coh = con_coh.get_data(output="dense").squeeze()
+    plv = con_plv.get_data(output="dense").squeeze()
+
+    np.fill_diagonal(coh, 0)
+    np.fill_diagonal(plv, 0)
+
+    plot_connectivity_circle(
+        coh,
+        names,
+        title="fNIRS Functional Connectivity (HbO - Coherence)",
+        n_lines=n_lines,
+        vmin=vmin
+    )
+
+
+
+
+
+def functional_connectivity_spectral_time(epochs, n_lines, vmin):
+
+    # will crash without this load
+    epochs.load_data()
+    hbo_epochs = epochs.copy().pick(picks="hbo")
+    data = hbo_epochs.get_data()
+    names = hbo_epochs.ch_names
+    sfreq = hbo_epochs.info["sfreq"]
+
+    freqs = np.linspace(0.04, 0.2, 10)
+    n_cycles = freqs * 2
+
+    con = spectral_connectivity_time(
+        data,
+        freqs=freqs,
+        method=["coh", "plv"],
+        mode="multitaper",
+        sfreq=sfreq,
+        fmin=0.04,
+        fmax=0.2,
+        n_cycles=n_cycles,
+        faverage=True,
+        verbose=True
+    )
+
+    con_coh, con_plv = con
+
+    coh = con_coh.get_data(output="dense").squeeze()
+    plv = con_plv.get_data(output="dense").squeeze()
+
+    np.fill_diagonal(coh, 0)
+    np.fill_diagonal(plv, 0)
+
+    plot_connectivity_circle(
+        coh,
+        names,
+        title="fNIRS Functional Connectivity (HbO - Coherence)",
+        n_lines=n_lines,
+        vmin=vmin
+    )
+
+
+
+
+def functional_connectivity_envelope(epochs, n_lines, vmin):
+    # will crash without this load
+
+    epochs.load_data()
+    hbo_epochs = epochs.copy().pick(picks="hbo")
+    data = hbo_epochs.get_data()
+
+
+    env = envelope_correlation(
+        data,
+        orthogonalize=False,
+        absolute=True
+    )
+    env_data = env.get_data(output="dense")
+
+    env_corr = env_data.mean(axis=0)
+
+    env_corr = np.squeeze(env_corr)
+
+    np.fill_diagonal(env_corr, 0)
+
+    plot_connectivity_circle(
+        env_corr,
+        hbo_epochs.ch_names,
+        title="fNIRS HbO Envelope Correlation (Task Connectivity)",
+        n_lines=n_lines,
+        vmin=vmin
+    )
+
+
+def functional_connectivity_betas(raw_hbo, n_lines, vmin, event_name=None):
+
+    raw_hbo = raw_hbo.copy().pick(picks="hbo")
+    onsets = raw_hbo.annotations.onset
+    
+    # CRITICAL: Update the Raw object's annotations so the GLM sees unique events
+    ann = raw_hbo.annotations
+    new_desc = []
+
+    for i, desc in enumerate(ann.description):
+        new_desc.append(f"{desc}__trial_{i:03d}")
+
+    ann.description = np.array(new_desc)
+
+
+    # shoudl use user defiuned!!!!
+    design_matrix = make_first_level_design_matrix(
+        raw=raw_hbo,
+        hrf_model='fir', 
+        fir_delays=np.arange(0, 12, 1),
+        drift_model='cosine',
+        drift_order=1
+    )
+
+
+    # 3. Run GLM & Extract Betas
+    glm_results = run_glm(raw_hbo, design_matrix)
+    betas = np.array(glm_results.theta()) 
+    reg_names = list(design_matrix.columns)
+    
+
+
+
+
+    n_channels = betas.shape[0]
+
+    # ------------------------------------------------------------------
+    # 5. Find unique trial tags (optionally filtered by event)
+    # ------------------------------------------------------------------
+    trial_tags = sorted({
+        col.split("_delay")[0]
+        for col in reg_names
+        if (
+            ("__trial_" in col)
+            and (event_name is None or col.startswith(event_name + "__"))
+        )
+    })
+
+    if len(trial_tags) == 0:
+        raise ValueError(f"No trials found for event_name={event_name}")
+
+    # ------------------------------------------------------------------
+    # 6. Build beta series (average across FIR delays per trial)
+    # ------------------------------------------------------------------
+    beta_series = np.zeros((n_channels, len(trial_tags)))
+
+    for t, tag in enumerate(trial_tags):
+        idx = [
+            i for i, col in enumerate(reg_names)
+            if col.startswith(f"{tag}_delay")
+        ]
+        beta_series[:, t] = np.mean(betas[:, idx], axis=1).flatten()
+
+
+
+    # n_channels, n_trials = betas.shape[0], len(onsets)
+    # beta_series = np.zeros((n_channels, n_trials))
+
+    # for t in range(n_trials):
+    #     trial_indices = [i for i, col in enumerate(reg_names) if col.startswith(f"trial_{t:03d}_delay")]
+    #     if trial_indices:
+    #         beta_series[:, t] = np.mean(betas[:, trial_indices], axis=1).flatten()
+
+    # Normalize each channel so they are on the same scale
+    # Without this, everything is connected to everything. Apparently this is a big issue in fNIRS?
+    beta_series = zscore(beta_series, axis=1)
+
+    global_signal = np.mean(beta_series, axis=0) 
+    beta_series_clean = np.zeros_like(beta_series)
+    for i in range(n_channels):
+        slope, _ = np.polyfit(global_signal, beta_series[i, :], 1)
+        beta_series_clean[i, :] = beta_series[i, :] - (slope * global_signal)
+
+    # 4. Correlation & Strict Filtering
+    corr_matrix = np.zeros((n_channels, n_channels))
+    p_matrix = np.ones((n_channels, n_channels))
+
+    for i in range(n_channels):
+        for j in range(i + 1, n_channels):
+            r, p = pearsonr(beta_series_clean[i, :], beta_series_clean[j, :])
+            corr_matrix[i, j] = corr_matrix[j, i] = r
+            p_matrix[i, j] = p_matrix[j, i] = p
+
+    # 5. High-Bar Thresholding
+    reject, _ = multipletests(p_matrix[np.triu_indices(n_channels, k=1)], method='fdr_bh', alpha=0.05)[:2]
+    sig_corr_matrix = np.zeros_like(corr_matrix)
+    triu = np.triu_indices(n_channels, k=1)
+
+    for idx, is_sig in enumerate(reject):
+        r_val = corr_matrix[triu[0][idx], triu[1][idx]]
+        # Only keep the absolute strongest connections
+        if is_sig and abs(r_val) > 0.7: 
+            sig_corr_matrix[triu[0][idx], triu[1][idx]] = r_val
+            sig_corr_matrix[triu[1][idx], triu[0][idx]] = r_val
+
+    # 6. Plot
+    plot_connectivity_circle(
+        sig_corr_matrix, 
+        raw_hbo.ch_names,
+        title="Strictly Filtered Connectivity (TDDR + GSR + Z-Score)",
+        n_lines=None, 
+        vmin=0.7, 
+        vmax=1.0,
+        colormap='hot' # Use 'hot' to make positive connections pop
+    )
+
+
+
+
+def get_single_subject_beta_corr(raw_hbo, event_name=None, config=None):
+    """Processes one participant and returns their correlation matrix."""
+    raw_hbo = raw_hbo.copy().pick(picks="hbo")
+    ann = raw_hbo.annotations
+    
+    # Rename for trial-level GLM
+    new_desc = [f"{desc}__trial_{i:03d}" for i, desc in enumerate(ann.description)]
+    ann.description = np.array(new_desc)
+
+    if config == None:
+        print("no config")
+        design_matrix = make_first_level_design_matrix(
+            raw=raw_hbo, hrf_model='fir', 
+            fir_delays=np.arange(0, 12, 1),
+            drift_model='cosine', drift_order=1
+        )
+    else:
+        print("config")
+        if config.get("SHORT_CHANNEL_REGRESSION") == True:
+            short_chans = get_short_channels(raw_hbo, max_dist=config.get("SHORT_CHANNEL_THRESH"))
+
+            design_matrix = make_first_level_design_matrix(
+                raw=raw_hbo,
+                stim_dur=config.get("STIM_DUR"),
+                hrf_model=config.get("HRF_MODEL"),
+                drift_model=config.get("DRIFT_MODEL"),
+                high_pass=config.get("HIGH_PASS"),
+                drift_order=config.get("DRIFT_ORDER"),
+                fir_delays=config.get("FIR_DELAYS"),
+                add_regs=short_chans.get_data().T,
+                add_reg_names=short_chans.ch_names,
+                min_onset=config.get("MIN_ONSET"),
+                oversampling=config.get("OVERSAMPLING")
+            )
+            print("yep")
+        else:
+            design_matrix = make_first_level_design_matrix(
+                raw=raw_hbo,
+                stim_dur=config.get("STIM_DUR"),
+                hrf_model=config.get("HRF_MODEL"),
+                drift_model=config.get("DRIFT_MODEL"),
+                high_pass=config.get("HIGH_PASS"),
+                drift_order=config.get("DRIFT_ORDER"),
+                fir_delays=config.get("FIR_DELAYS"),
+                min_onset=config.get("MIN_ONSET"),
+                oversampling=config.get("OVERSAMPLING")
+            )
+
+
+    glm_results = run_glm(raw_hbo, design_matrix)
+    betas = np.array(glm_results.theta()) 
+    reg_names = list(design_matrix.columns)
+    n_channels = betas.shape[0]
+
+    # Filter trials by event name
+    trial_tags = sorted({
+        col.split("_delay")[0] for col in reg_names 
+        if "__trial_" in col and (event_name is None or col.startswith(event_name + "__"))
+    })
+
+    if not trial_tags:
+        return None, None
+
+    # Build Beta Series
+    beta_series = np.zeros((n_channels, len(trial_tags)))
+    for t, tag in enumerate(trial_tags):
+        idx = [i for i, col in enumerate(reg_names) if col.startswith(f"{tag}_delay")]
+        beta_series[:, t] = np.mean(betas[:, idx], axis=1).flatten()
+        #beta_series[:, t] = np.max(betas[:, idx], axis=1).flatten() #TODO: Figure out which one to use
+
+    # Z-score and GSR (Global Signal Regression)
+    beta_series = zscore(beta_series, axis=1)
+    global_signal = np.mean(beta_series, axis=0)
+    for i in range(n_channels):
+        slope, _ = np.polyfit(global_signal, beta_series[i, :], 1)
+        beta_series[i, :] -= (slope * global_signal)
+
+    # Correlation Matrix
+    corr_matrix = np.corrcoef(beta_series)
+    return corr_matrix, raw_hbo.ch_names
+
+
+def run_group_functional_connectivity(haemo_dict, config_dict, selected_paths, event_name, n_lines, vmin):
+    """Aggregates multiple participants and triggers the plot."""
+    all_z_matrices = []
+    common_names = None
+
+    for path in selected_paths:
+        raw = haemo_dict.get(path)
+        config = config_dict.get(path)
+        if raw is None: continue
+        print(config)
+
+        corr, names = get_single_subject_beta_corr(raw, event_name, config)
+        
+        if corr is not None:
+            # Fisher Z-transform for averaging
+            z_mat = np.arctanh(np.clip(corr, -0.99, 0.99))
+            all_z_matrices.append(z_mat)
+            common_names = names
+
+    from scipy.stats import ttest_1samp
+    # 1. Convert list to 3D array: (Participants, Channels, Channels)
+    group_z_data = np.array(all_z_matrices) 
+    
+    print("1")
+    # 2. Perform a T-Test across the participant dimension (axis 0)
+    # We test if the mean Z-score is different from 0
+    # C:\Users\tyler\Desktop\research\.venv\Lib\site-packages\scipy\stats\_axis_nan_policy.py:611: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.
+    # res = hypotest_fun_out(*samples, axis=axis, **kwds)
+
+    print("--- Variance Check ---")
+
+    # ADD THIS LINE: Define n_channels based on the data shape
+    # group_z_data.shape is (n_participants, n_channels, n_channels)
+    n_channels = group_z_data.shape[1]
+
+    variance_matrix = np.var(group_z_data, axis=0)
+
+    # Find where variance is exactly 0 (or very close to it)
+    zero_var_indices = np.where(variance_matrix < 1e-15)
+    coords = list(zip(zero_var_indices[0], zero_var_indices[1]))
+
+    diag_count = 0
+    non_diag_pairs = []
+
+    for r, c in coords:
+        if r == c:
+            diag_count += 1
+        else:
+            non_diag_pairs.append((r, c))
+
+    print(f"Total pairs with zero variance: {len(coords)}")
+    print(f"Identical diagonals: {diag_count}/{n_channels}")
+
+    if non_diag_pairs:
+        print(f"Warning: {len(non_diag_pairs)} non-diagonal pairs have zero variance!")
+        for r, c in non_diag_pairs[:10]: # Print first 10
+            print(f"  - Pair: Channel {r} & Channel {c}")
+    else:
+        print("Clean! Zero variance only exists on the diagonals.")
+    print("----------------------")
+
+    t_stats, p_values = ttest_1samp(group_z_data, popmean=0, axis=0)
+    print("2")
+
+    # 3. Multiple Comparisons Correction (FDR)
+    # We only care about the upper triangle (unique connections)
+    n_channels = p_values.shape[0]
+    triu_indices = np.triu_indices(n_channels, k=1)
+    flat_p = p_values[triu_indices]
+    
+    reject, corrected_p = multipletests(flat_p, method='fdr_bh', alpha=0.05)[:2]
+
+    # 4. Create the final "Significant" Matrix
+    avg_r = np.tanh(np.mean(group_z_data, axis=0))
+    sig_avg_r = np.zeros_like(avg_r)
+
+    # Only keep connections that are Significant AND above your VMIN (r-threshold)
+    for idx, is_sig in enumerate(reject):
+        row, col = triu_indices[0][idx], triu_indices[1][idx]
+        r_val = avg_r[row, col]
+        
+        if is_sig and abs(r_val) >= vmin:
+            sig_avg_r[row, col] = sig_avg_r[col, row] = r_val
+
+    # 5. Plot the significant results
+
+
+    # if not all_z_matrices:
+    #     return
+
+    # # Average and convert back to R
+    # avg_z = np.mean(all_z_matrices, axis=0)
+    # avg_r = np.tanh(avg_z)
+
+    # # Thresholding
+    # avg_r[np.abs(avg_r) < vmin] = 0
+
+    plot_connectivity_circle(
+        sig_avg_r, common_names, n_lines=n_lines, 
+        title=f"Group Connectivity: {event_name if event_name else 'All Events'}",
+        vmin=vmin, vmax=1.0, colormap='hot'
+    )