NeuroSynth Query in SQUALL Controlled English.

Reproduces the NeuroSynth forward model — P(Activation | Term = ‘auditory’) — using SQUALL controlled natural language instead of the IR builder DSL.

The four logic rules plus an obtain query are expressed as plain English sentences and executed via execute_squall_program(), which returns the query result directly when an obtain clause is present. Compare with NeuroLang Example based Implementing a NeuroSynth Query which writes identical rules using with nl.scope as e:.

The SQUALL program

define as Activation every Peak_reported (?i; ?j; ?k; ?s)
    such that ?s is a Selected_study.

define as Term_association every Term_in_study_tfidf (?s; ?t; ?tfidf)
    such that ?s is a Selected_study.

define as Activation_given_term with probability
    every Activation (?i; ?j; ?k; _)
    conditioned to every Term_association ?t that is 'auditory'.

define as Activation_given_term_image
    every Agg_create_region_overlay of the Activation_given_term (?i; ?j; ?k; ?p).

obtain every Activation_given_term_image (?x).

import warnings

warnings.filterwarnings("ignore")

from pathlib import Path
from typing import Iterable

import nibabel
import nilearn.datasets
import nilearn.image
import nilearn.plotting
import numpy as np
from neurolang import ExplicitVBROverlay, NeurolangPDL
from neurolang.frontend.neurosynth_utils import get_ns_mni_peaks_reported

Set the data directory and load the MNI T1 atlas resampled to 4 mm voxels.

data_dir = Path.home() / "neurolang_data"

mni_t1 = nibabel.load(
    nilearn.datasets.fetch_icbm152_2009(data_dir=str(data_dir / "icbm"))["t1"]
)
mni_t1_4mm = nilearn.image.resample_img(mni_t1, np.eye(3) * 4)

nl = NeurolangPDL()


@nl.add_symbol
def agg_create_region_overlay(
    i: Iterable, j: Iterable, k: Iterable, p: Iterable
) -> ExplicitVBROverlay:
    """Aggregate (i,j,k,probability) rows into a brain overlay image."""
    mni_coords = np.c_[i, j, k]
    return ExplicitVBROverlay(
        mni_coords, mni_t1_4mm.affine, p, image_dim=mni_t1_4mm.shape
    )

Register peaks, study IDs, and term–study associations as extensional facts. Relation names are lowercase so they match SQUALL’s case-folding convention.

peak_data = get_ns_mni_peaks_reported(data_dir)
ijk_positions = np.round(
    nibabel.affines.apply_affine(
        np.linalg.inv(mni_t1_4mm.affine),
        peak_data[["x", "y", "z"]].values.astype(float),
    )
).astype(int)
peak_data["i"] = ijk_positions[:, 0]
peak_data["j"] = ijk_positions[:, 1]
peak_data["k"] = ijk_positions[:, 2]
peak_data = peak_data[["i", "j", "k", "id"]]

nl.add_tuple_set(peak_data, name="peak_reported")
study_ids = nl.load_neurosynth_study_ids(data_dir, "study")
nl.add_uniform_probabilistic_choice_over_set(
    study_ids.value, name="selected_study"
)
nl.load_neurosynth_term_study_associations(
    data_dir, "term_in_study_tfidf", tfidf_threshold=1e-3
)

Four define sentences plus one obtain clause replace the entire with nl.scope as e: block and the separate nl.query() call.

• such that ?s is a Selected_study — existential study filter

• ?t that is 'auditory' — string equality on term

• with probability … conditioned to — MARG conditional probability

• (?i; ?j; ?k; _) — _ drops the study column from the conditioned head (anonymous wildcard)

• of the Activation_given_term (?i;?j;?k;?p) — explicit tuple label so the aggregation functor receives all four columns (i, j, k, probability)

• obtain every Activation_given_term_image (?x) — runs only this query

Note: ?tfidf binds the TF-IDF weight column but is absent from the rule head (projected away). Anonymous wildcards are supported in tuple labels (e.g. (?i; ?j; ?k; _) on lines below — each _ creates a fresh variable that is matched but dropped from the rule head).

squall_program = """
define as Activation every Peak_reported (?i; ?j; ?k; ?s)
    such that ?s is a Selected_study.

define as Term_association every Term_in_study_tfidf (?s; ?t; ?tfidf)
    such that ?s is a Selected_study.

define as Activation_given_term with probability
    every Activation (?i; ?j; ?k; _)
    conditioned to every Term_association (?s; ?t; _) such that ?t is 'auditory'.

define as Activation_given_term_image
    every Agg_create_region_overlay of the Activation_given_term (?i; ?j; ?k; ?p).

obtain every Activation_given_term_image (?x).
"""

result_set = nl.execute_squall_program(squall_program)

The obtain clause executes only the image query; result_set is the single-column result directly from execute_squall_program().

result_image = result_set.as_pandas_dataframe().iloc[0, 0].spatial_image()

img = result_image.get_fdata()
plot = nilearn.plotting.plot_stat_map(
    result_image, threshold=np.percentile(img[img > 0], 95)
)
nilearn.plotting.show()