"""Voxel-Based Morphometry on Oasis dataset
========================================

This example uses Voxel-Based Morphometry (VBM) to study the relationship
between aging, sex and gray matter density.

The data come from the `OASIS <http://www.oasis-brains.org/>`_ project.
If you use it, you need to agree with the data usage agreement available
on the website.

It has been run through a standard VBM pipeline (using SPM8 and
NewSegment) to create VBM maps, which we study here.

VBM analysis of aging
---------------------

We run a standard GLM analysis to study the association between age
and gray matter density from the VBM data. We use only 100 subjects
from the OASIS dataset to limit the memory usage.

Note that more power would be obtained from using a larger sample of subjects.
"""
# Authors: Bertrand Thirion, <bertrand.thirion@inria.fr>, July 2018
#          Elvis Dhomatob, <elvis.dohmatob@inria.fr>, Apr. 2014
#          Virgile Fritsch, <virgile.fritsch@inria.fr>, Apr 2014
#          Gael Varoquaux, Apr 2014


n_subjects = 100  # more subjects requires more memory

############################################################################
# Load Oasis dataset
# ------------------

from nilearn import datasets
oasis_dataset = datasets.fetch_oasis_vbm(n_subjects=n_subjects)
gray_matter_map_filenames = oasis_dataset.gray_matter_maps
age = oasis_dataset.ext_vars['age'].astype(float)

###############################################################################
# Sex is encoded as 'M' or 'F'. Hence, we make it a binary variable.
sex = oasis_dataset.ext_vars['mf'] == b'F'

###############################################################################
# Print basic information on the dataset.
print('First gray-matter anatomy image (3D) is located at: %s' %
      oasis_dataset.gray_matter_maps[0])  # 3D data
print('First white-matter anatomy image (3D) is located at: %s' %
      oasis_dataset.white_matter_maps[0])  # 3D data

###############################################################################
# Get a mask image: A mask of the  cortex of the ICBM template.
gm_mask = datasets.fetch_icbm152_brain_gm_mask()

###############################################################################
# Resample the images, since this mask has a different resolution.
from nilearn.image import resample_to_img
mask_img = resample_to_img(
    gm_mask, gray_matter_map_filenames[0], interpolation='nearest')

#############################################################################
# Analyse data
# ------------
#
# First, we create an adequate design matrix with three columns: 'age',
# 'sex', 'intercept'.
import pandas as pd
import numpy as np
intercept = np.ones(n_subjects)
design_matrix = pd.DataFrame(np.vstack((age, sex, intercept)).T,
                             columns=['age', 'sex', 'intercept'])

#############################################################################
# Let's plot the design matrix.
from nistats.reporting import plot_design_matrix

ax = plot_design_matrix(design_matrix)
ax.set_title('Second level design matrix', fontsize=12)
ax.set_ylabel('maps')

##########################################################################
# Next, we specify and fit the second-level model when loading the data and also
# smooth a little bit to improve statistical behavior.

from nistats.second_level_model import SecondLevelModel
second_level_model = SecondLevelModel(smoothing_fwhm=2.0, mask_img=mask_img)
second_level_model.fit(gray_matter_map_filenames,
                       design_matrix=design_matrix)

##########################################################################
# Estimating the contrast is very simple. We can just provide the column
# name of the design matrix.
z_map = second_level_model.compute_contrast(second_level_contrast=[1, 0, 0],
                                            output_type='z_score')

###########################################################################
# We threshold the second level contrast at uncorrected p < 0.001 and plot it.
from nistats.thresholding import map_threshold
from nilearn import plotting

_, threshold = map_threshold(
    z_map, alpha=.05, height_control='fdr')
print('The FDR=.05-corrected threshold is: %.3g' % threshold)

display = plotting.plot_stat_map(
    z_map, threshold=threshold, colorbar=True, display_mode='z',
    cut_coords=[-4, 26],
    title='age effect on grey matter density (FDR = .05)')
plotting.show()

###########################################################################
# We can also study the effect of sex by computing the contrast, thresholding it
# and plot the resulting map.

z_map = second_level_model.compute_contrast(second_level_contrast='sex',
                                            output_type='z_score')
_, threshold = map_threshold(
    z_map, alpha=.05, height_control='fdr')
plotting.plot_stat_map(
    z_map, threshold=threshold, colorbar=True,
    title='sex effect on grey matter density (FDR = .05)')

###########################################################################
# Note that there does not seem to be any significant effect of sex on
# grey matter density on that dataset.

###########################################################################
# Generating a report
# -------------------
# It can be useful to quickly generate a
# portable, ready-to-view report with most of the pertinent information.
# This is easy to do if you have a fitted model and the list of contrasts,
# which we do here.

from nistats.reporting import make_glm_report

icbm152_2009 = datasets.fetch_icbm152_2009()
report = make_glm_report(model=second_level_model,
                         contrasts=['age', 'sex'],
                         bg_img=icbm152_2009['t1'],
                         )

#########################################################################
# We have several ways to access the report:

# report  # This report can be viewed in a notebook
# report.save_as_html('report.html')
# report.open_in_browser()
