Examples using API

This package computes single-subject networks, hence you may need loop over samples/subjects in your dataset to extract them for all the samples/subjects. Them proceed to your typical subsequent analysis (such as classification etc).

Note:

• The hiwenet.extract could be used to extract advance covariance/connectome features in place of MNE.extract_label_time_course or nilearn.input_data.NiftiLabelsMasker.transform - see here and here.

• However, we plan to add interfaces to tools e.g. via a scikit-learn compatible API/interface is also in the works. Stay tuned.

A rough example of usage when using the hiwenet API can be:

import hiwenet
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
import numpy as np
import nibabel
import os

# ------------------------------------------------------------------------------------
# see docs/example_thickness_hiwenet.py for a concrete example
# ------------------------------------------------------------------------------------

for ss, subject in enumerate(subject_list):
  features = get_features(subject)
  edge_weight_matrix = hiwenet.extract(features, groups,  weight_method = 'kullback_leibler')
  edge_weights_vec[ss,:] = upper_tri_vec(edge_weight_matrix)

  out_file = os.path.join(out_folder, 'hiwenet_{}.txt'.format(subject))
  np.save(out_file, edge_weight_matrix)


# proceed to analysis


# very rough example for training/evaluating a classifier
rf = RandomForestClassifier(oob_score = True)
scores = cross_val_score(rf, edge_weights_vec, subject_labels)

A fuller example (still a bit rough) for cortical thickness applications can be shown below:

#~/usr/bin/env python

import hiwenet
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
import numpy as np
import nibabel
import os

# ----------------------------------------------------
# toy examples - modify for your application/need
my_project = '/data/myproject'
subject_list = ['a1', 'b2', 'c3', 'd4']
subject_labels = [1, 1, -1, -1]

num_subjects = len(subject_list)
# number of features (imaging vertex-wise cortical thickness values over the whole brain)
feature_dimensionality = 1000
num_ROIs = 50
edge_weights = np.empty(num_subjects, num_ROIs*(num_ROIs-1)/2.0)

atlas = 'fsaverage'
# ----------------------------------------------------

def get_parcellation(atlas, parcel_param):
    "Placeholder to insert your own function to return parcellation in reference space."

    parc_path = os.path.join(atlas, 'parcellation_param{}.mgh'.format(parcel_param))
    parcel = nibabel.freesurfer.io.read_geometry(parc_path)

    return parcel


groups = get_parcellation(atlas, feature_dimensionality)

out_folder = os.path.join(my_project, 'hiwenet')

# choose a method from one from among the three groups (metrics, semi-metrics and similarity functions)
metrics = [ 'manhattan', 'minowski', 'euclidean', 'noelle_2', 'noelle_4', 'noelle_5' ]

semi_metric_list = [
    'kullback_leibler', 'cosine_1',
    'jensen_shannon', 'chi_square',
    'chebyshev', 'chebyshev_neg',
    'histogram_intersection_1',
    'relative_deviation', 'relative_bin_deviation',
    'noelle_1', 'noelle_3',
    'correlate_1']
similarity_func = ['correlate', 'cosine', 'cosine_2', 'cosine_alt', 'fidelity_based']


def get_features(subject_id):
    "Placeholder to insert your own function to read subject-wise features."

    features_path = os.path.join(my_project,'base_features', subject_id, 'features.txt')
    feature_vector = np.loadtxt(features_path)

    return feature_vector


def upper_tri_vec(matrix):
    "Returns the vectorized values of upper triangular part of a matrix"

    triu_idx = np.triu_indices_from(matrix, 1)
    return matrix[triu_idx]

num_links = num_ROIs*(num_ROIs-1)/2.0
edge_weights_vec = np.zeros(len(subject_list), num_links)
for ss, subject in enumerate(subject_list):
  features = get_features(subject)
  edge_weight_matrix = hiwenet.extract(features, groups,  weight_method = 'kullback_leibler')
  edge_weights_vec[ss,:] = upper_tri_vec(edge_weight_matrix)

  out_file = os.path.join(out_folder, 'hiwenet_{}.txt'.format(subject))
  np.save(out_file, edge_weight_matrix)


# proceed to analysis

# very rough example for training/evaluating a classifier
rf = RandomForestClassifier(oob_score = True)
scores = cross_val_score(rf, edge_weights_vec, subject_labels)