Add continuous correlation decoder examples

examples/04_decoding/02_plot_continuous_decoders.py

@@ -0,0 +1,92 @@
+"""
+
+.. _decode_continuous:
+
+============================
+Continuous functional decoding
+============================
+
+Perform meta-analytic functional decoding on statistical images.
+
+We can use the methods in ``nimare.decode.continuous`` to apply functional
+characterization analysis to statistical images.
+"""
+import os
+
+import nibabel as nib
+import requests
+from nilearn.plotting import plot_stat_map
+
+from nimare.decode.continuous import CorrelationDecoder
+from nimare.extract import fetch_neurosynth
+from nimare.io import convert_neurosynth_to_dataset
+from nimare.meta.cbma.mkda import MKDAChi2
+
+###############################################################################
+# Download Neurosynth with Topics
+# -----------------------------------------------------------------------------
+# Neurosynth's data files are stored at https://github.com/neurosynth/neurosynth-data.
+out_dir = os.path.abspath("../example_data/")
+os.makedirs(out_dir, exist_ok=True)
+
+files = fetch_neurosynth(
+    data_dir=out_dir,
+    version="7",
+    overwrite=False,
+    source="abstract",
+    vocab="LDA50",
+)
+neurosynth_db = files[0]
+
+###############################################################################
+# Convert Neurosynth database to NiMARE dataset file
+# -----------------------------------------------------------------------------
+neurosynth_dset = convert_neurosynth_to_dataset(
+    coordinates_file=neurosynth_db["coordinates"],
+    metadata_file=neurosynth_db["metadata"],
+    annotations_files=neurosynth_db["features"],
+)
+
+###############################################################################
+# Download a continuous map for decoding from NeuroVault
+# -----------------------------------------------------------------------------
+image_id = "3158"  # tfMRI motor left hand from the HCP dataset
+image_info_url = f"https://neurovault.org/api/images/{image_id}/"
+image_info = requests.get(image_info_url).json()
+
+image_url = image_info["file"]
+image_filename = os.path.basename(image_url)
+image_path = os.path.join(out_dir, f"{image_id}_{image_filename}")
+
+response = requests.get(image_url)
+with open(image_path, "wb") as image_file:
+    image_file.write(response.content)
+
+continuous_map = nib.load(image_path)
+
+plot_stat_map(
+    continuous_map,
+    cut_coords=[0, 0, -8],
+    draw_cross=False,
+)
+
+###############################################################################
+#
+# .. _neurosynth-topic-decoder-example:
+#
+# Decode an statistical image using an LDA-Based continuous decoder
+# -----------------------------------------------------------------------------
+
+# Train the decoder
+decoder = CorrelationDecoder(
+    frequency_threshold=0.05,
+    meta_estimator=MKDAChi2(),
+    feature_group="LDA50_abstract_weight",
+    target_image="z_desc-association",
+    n_cores=1,
+)
+decoder.fit(neurosynth_dset)
+
+# Decode the image
+corr_df = decoder.transform(continuous_map)
+corr_df.sort_values(by="r", ascending=False).head(10)