Tutorial 2: Using neurocaps.analysis.CAP

Performing CAPs on All Subjects

import numpy as np
from neurocaps.extraction import TimeseriesExtractor
from neurocaps.analysis import CAP

# Extracting timseries
parcel_approach = {"Schaefer": {"n_rois": 100, "yeo_networks": 7, "resolution_mm": 2}}

# Simulate data for example
subject_timeseries = {str(x): {f"run-{y}": np.random.rand(100, 100) for y in range(1, 4)} for x in range(1, 11)}

# Initialize CAP class
cap_analysis = CAP()

# Get CAPs
cap_analysis.get_caps(
    subject_timeseries=subject_timeseries,
    n_clusters=range(2, 11),
    cluster_selection_method="elbow",
    show_figs=True,
    step=2,
)

2024-11-02 21:02:28,145 neurocaps.analysis.cap [INFO] [GROUP: All Subjects | METHOD: elbow] - Optimal cluster size is 6.

Performing CAPs on Groups

cap_analysis = CAP(groups={"A": ["1", "2", "3", "5"], "B": ["4", "6", "7", "8", "9", "10"]})

cap_analysis.get_caps(
    subject_timeseries=subject_timeseries,
    n_clusters=range(2, 21),
    cluster_selection_method="silhouette",
    show_figs=True,
    step=2,
)

2024-11-02 21:02:28,322 neurocaps.analysis.cap [INFO] [GROUP: A | METHOD: silhouette] - Optimal cluster size is 2.

2024-11-02 21:02:28,541 neurocaps.analysis.cap [INFO] [GROUP: B | METHOD: silhouette] - Optimal cluster size is 2.

Calculate Metrics

df_dict = cap_analysis.calculate_metrics(
    subject_timeseries=subject_timeseries,
    return_df=True,
    metrics=["temporal_fraction", "counts", "transition_probability"],
    continuous_runs=True,
)

print(df_dict["temporal_fraction"])

Subject_ID	Group	Run	CAP-1	CAP-2
1	A	run-continuous	0.5066666666666667	0.49333333333333335
2	A	run-continuous	0.5333333333333333	0.4666666666666667
3	A	run-continuous	0.6	0.4
5	A	run-continuous	0.54	0.46
4	B	run-continuous	0.41333333333333333	0.5866666666666667
6	B	run-continuous	0.47333333333333333	0.5266666666666666
7	B	run-continuous	0.44	0.56
8	B	run-continuous	0.5	0.5
9	B	run-continuous	0.4866666666666667	0.5133333333333333
10	B	run-continuous	0.46	0.54

Plotting CAPs

import seaborn as sns

cap_analysis = CAP(parcel_approach=extractor.parcel_approach)

cap_analysis.get_caps(subject_timeseries=subject_timeseries, n_clusters=6)

sns.diverging_palette(145, 300, s=60, as_cmap=True)

palette = sns.diverging_palette(260, 10, s=80, l=55, n=256, as_cmap=True)

kwargs = {
    "subplots": True,
    "fontsize": 14,
    "ncol": 3,
    "sharey": True,
    "tight_layout": False,
    "xlabel_rotation": 0,
    "hspace": 0.3,
    "cmap": palette,
}

cap_analysis.caps2plot(visual_scope="regions", plot_options="outer_product", show_figs=True, **kwargs)

cap_analysis.caps2plot(
    visual_scope="nodes", plot_options="heatmap", xticklabels_size=7, yticklabels_size=7, show_figs=True
)

Generate Pearson Correlation Matrix

cap_analysis.caps2corr(annot=True, cmap="viridis", show_figs=True)

corr_dict = cap_analysis.caps2corr(return_df=True)
print(corr_dict["All Subjects"])

	CAP-1	CAP-2	CAP-3	CAP-4	CAP-5	CAP-6
CAP-1	1 (0)***	-0.24 (0.016)*	-0.26 (0.01)*	-0.1 (0.3)	-0.17 (0.087)	-0.17 (0.09)
CAP-2	-0.24 (0.016)*	1 (0)***	-0.11 (0.28)	-0.15 (0.14)	-0.28 (0.0051)**	-0.28 (0.0055)**
CAP-3	-0.26 (0.01)*	-0.11 (0.28)	1 (0)***	-0.3 (0.0021)**	-0.18 (0.075)	-0.19 (0.058)
CAP-4	-0.1 (0.3)	-0.15 (0.14)	-0.3 (0.0021)**	1 (0)***	-0.18 (0.076)	-0.22 (0.028)*
CAP-5	-0.17 (0.087)	-0.28 (0.0051)**	-0.18 (0.075)	-0.18 (0.076)	1 (0)***	-0.17 (0.089)
CAP-6	-0.17 (0.09)	-0.28 (0.0055)**	-0.19 (0.058)	-0.22 (0.028)*	-0.17 (0.089)	1 (0)***

Creating Surface Plots

from matplotlib.colors import LinearSegmentedColormap

# Create the colormap
colors = [
    "#1bfffe",
    "#00ccff",
    "#0099ff",
    "#0066ff",
    "#0033ff",
    "#c4c4c4",
    "#ff6666",
    "#ff3333",
    "#FF0000",
    "#ffcc00",
    "#FFFF00",
]

custom_cmap = LinearSegmentedColormap.from_list("custom_cold_hot", colors, N=256)

# Apply custom cmap to surface plots
cap_analysis.caps2surf(cmap=custom_cmap, size=(500, 100), layout="row")

Plotting CAPs to Radar

radialaxis = {
    "showline": True,
    "linewidth": 2,
    "linecolor": "rgba(0, 0, 0, 0.25)",
    "gridcolor": "rgba(0, 0, 0, 0.25)",
    "ticks": "outside",
    "tickfont": {"size": 14, "color": "black"},
    "range": [0, 0.6],
    "tickvals": [0.1, "", "", 0.4, "", "", 0.6],
}

legend = {
    "yanchor": "top",
    "y": 0.99,
    "x": 0.99,
    "title_font_family": "Times New Roman",
    "font": {"size": 12, "color": "black"},
}

colors = {"High Amplitude": "red", "Low Amplitude": "blue"}


kwargs = {
    "radialaxis": radial,
    "fill": "toself",
    "legend": legend,
    "color_discrete_map": colors,
    "height": 400,
    "width": 600,
}

cap_analysis.caps2radar(**kwargs)