The Cognitive Atlas

Download the Cognitive Atlas and extract CogAt terms from text.

import os

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

from nimare import annotate, extract
from nimare.dataset import Dataset
from nimare.utils import get_resource_path

Load dataset with abstracts

dset = Dataset(os.path.join(get_resource_path(), "neurosynth_laird_studies.json"))

Download Cognitive Atlas

ID DataFrame

id name alias length ratio
0 tsk_4a57abb949e35 structured clinical interview for diagnostic a... structured clinical interview for diagnostic a... 96.0 100
1 tsk_4a57abb949e35 structured clinical interview for diagnostic a... dsm-iv structured clinical interview for diagn... 94.0 92
2 tsk_4a57abb949e35 structured clinical interview for diagnostic a... structured clinical interview for diagnostic a... 87.0 95
3 trm_5176cf9d3d512 living/nonliving judgment on mirror-reversed a... living/nonliving judgment on mirror-reversed a... 65.0 100
4 trm_56a2b7c08a279 short penn continuous performance test-number ... short penn continuous performance test-number ... 64.0 100


Relationships DataFrame

input output rel_type
0 trm_4a3fd79d096be ctp_C3 inCategory
1 trm_4a3fd79d096e3 ctp_C3 inCategory
2 trm_4a3fd79d096f0 ctp_C3 inCategory
3 trm_4a3fd79d096fc ctp_C1 inCategory
4 trm_4a3fd79d09707 ctp_C1 inCategory


Extract Cognitive Atlas terms from text

counts_df, rep_text_df = annotate.cogat.extract_cogat(dset.texts, id_df, text_column="abstract")

Expand counts

weights = {"isKindOf": 1, "isPartOf": 1, "inCategory": 1}
expanded_df = annotate.cogat.expand_counts(counts_df, rel_df, weights)
# Sort by total count and reduce for better visualization
series = expanded_df.sum(axis=0)
series = series.sort_values(ascending=False)
series = series[series > 0]
columns = series.index.tolist()

Make some plots

We will reduce the dataframes to only columns with at least one count to make visualization easier.

# Raw counts
fig1, ax1 = plt.subplots(figsize=(16, 8))
pos = ax1.imshow(counts_df[columns].values, aspect="auto", vmin=0, vmax=np.max(expanded_df.values))
fig1.colorbar(pos, ax=ax1)
ax1.set_title("Counts Before Expansion", fontsize=20)
ax1.set_yticks(range(counts_df.shape[0]))
ax1.set_yticklabels(counts_df.index)
ax1.set_ylabel("Study", fontsize=16)
ax1.set_xticks(range(len(columns)))
ax1.set_xticklabels(columns, rotation=90)
ax1.set_xlabel("Cognitive Atlas Term", fontsize=16)
fig1.tight_layout()
fig1.show()

# Expanded counts
fig2, ax2 = plt.subplots(figsize=(16, 8))
pos = ax2.imshow(
    expanded_df[columns].values, aspect="auto", vmin=0, vmax=np.max(expanded_df.values)
)
fig2.colorbar(pos, ax=ax2)
ax2.set_title("Counts After Expansion", fontsize=20)
ax2.set_yticks(range(counts_df.shape[0]))
ax2.set_yticklabels(counts_df.index)
ax2.set_ylabel("Study", fontsize=16)
ax2.set_xticks(range(len(columns)))
ax2.set_xticklabels(columns, rotation=90)
ax2.set_xlabel("Cognitive Atlas Term", fontsize=16)
fig2.tight_layout()
fig2.show()
  • Counts Before Expansion
  • Counts After Expansion

Total running time of the script: ( 0 minutes 10.970 seconds)

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