{ "cells": [ { "cell_type": "markdown", "id": "realistic-photograph", "metadata": {}, "source": [ "# Predict Stop Signal Response Time (SSRT)\n", "\n", "This notebook uses stop signal task fMRI data derived contrasts from the ABCD study to \n", "predict stop signal response time (SSRT). This is an example of a regression type machine learning,\n", "and additionally includes an extra example of how to plot ROIs feature importance on brain surfaces from nilearn. \n", "\n", "## Loading Data\n", "\n", "We will be loading data from is essentially a big csv file with all the different columns, some slightly processed, from the ABCD DEAP rds (saved r dataframe). The benefit of this approach is that while a little slow, we can just load any column of interest easily as a dataframe." ] }, { "cell_type": "code", "execution_count": 1, "id": "shaped-indonesian", "metadata": {}, "outputs": [], "source": [ "import BPt as bp\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "id": "focal-olive", "metadata": {}, "outputs": [], "source": [ "def load_from_rds(names, eventname='baseline_year_1_arm_1'):\n", " \n", " data = pd.read_csv('data/nda_rds_201.csv',\n", " usecols=['src_subject_id', 'eventname'] + names,\n", " na_values=['777', 999, '999', 777])\n", " \n", " data = data.loc[data[data['eventname'] == eventname].index]\n", " data = data.set_index('src_subject_id')\n", " data = data.drop('eventname', axis=1)\n", " \n", " return data" ] }, { "cell_type": "code", "execution_count": 3, "id": "extended-plain", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['subjectid',\n", " 'src_subject_id',\n", " 'eventname',\n", " 'anthro_1_height_in',\n", " 'anthro_2_height_in',\n", " 'anthro_3_height_in',\n", " 'anthro_height_calc',\n", " 'anthro_weight_cast',\n", " 'anthro_weight_a_location',\n", " 'anthro_weight1_lb']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This way we can look at all column available\n", "all_cols = list(pd.read_csv('data/nda_rds_201.csv', nrows=0))\n", "all_cols[:10]" ] }, { "cell_type": "code", "execution_count": 4, "id": "instant-vintage", "metadata": {}, "outputs": [], "source": [ "# Use python list comprehensions to get lists of the column names of interest\n", "contrasts = ['tfmri_sst_all_correct.go.vs.fixation_beta_',\n", " 'tfmri_sst_all_correct.stop.vs.correct.go_beta_',\n", " 'tfmri_sst_all_incorrect.stop.vs.correct.go_beta_']\n", "\n", " \n", "parcs = ['.destrieux', '_subcort.aseg']\n", "\n", "data_cols = [col for col in all_cols\n", " if any([ct for ct in contrasts if ct in col])\n", " and any([p for p in parcs if p in col])] + ['sex']\n", "target_col = ['tfmri_sst_all_beh_total_mean.rt']" ] }, { "cell_type": "code", "execution_count": 5, "id": "atmospheric-mauritius", "metadata": {}, "outputs": [], "source": [ "# Load the actual data from the saved csv\n", "df = load_from_rds(data_cols + target_col)" ] }, { "cell_type": "code", "execution_count": 6, "id": "substantial-angel", "metadata": {}, "outputs": [], "source": [ "# Cast from a dataframe to BPt Dataset class\n", "data = bp.Dataset(df)\n", " \n", "# Obsificate subject ID for public example\n", "data.index = list(range(len(data)))\n", "\n", "# Set optional verbosity of\n", "data.verbose = 1" ] }, { "cell_type": "code", "execution_count": 7, "id": "precise-reggae", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dropped 27 Columns\n" ] } ], "source": [ "data = data.drop_cols(exclusions=['.ventricle', '_csf', '.white.matter'], scope='_subcort.aseg')" ] }, { "cell_type": "markdown", "id": "material-building", "metadata": {}, "source": [ "Data in BPt can have one of three roles, these are 'data' by default, 'target' for variable to predict, and 'non input' for variables which we don't use directly as input features. We set 'sex' as non input in this example." ] }, { "cell_type": "code", "execution_count": 8, "id": "cultural-english", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dropped 6 Rows\n" ] }, { "data": { "text/html": [ "
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\n", "6816 rows × 507 columns - Train Set
1704 rows × 507 columns - Test Set
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1704 rows × 1 columns - Test Set
\n", " | sex | \n", "
---|---|
0 | \n", "0 | \n", "
2 | \n", "1 | \n", "
3 | \n", "1 | \n", "
4 | \n", "1 | \n", "
5 | \n", "1 | \n", "
... | \n", "... | \n", "
11870 | \n", "1 | \n", "
11871 | \n", "0 | \n", "
11872 | \n", "0 | \n", "
11873 | \n", "0 | \n", "
11874 | \n", "0 | \n", "
8520 rows × 1 columns
\n", "6816 rows × 1 columns - Train Set
1704 rows × 1 columns - Test Set
\n", " | mean_scores_r2 | \n", "mean_scores_explained_variance | \n", "mean_scores_neg_mean_squared_error | \n", "std_scores_r2 | \n", "std_scores_explained_variance | \n", "std_scores_neg_mean_squared_error | \n", "mean_timing_fit | \n", "mean_timing_score | \n", "
---|---|---|---|---|---|---|---|---|
subjects | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
M | \n", "0.033538 | \n", "0.035266 | \n", "-6382.616684 | \n", "0.010401 | \n", "0.009289 | \n", "485.944440 | \n", "6.773705 | \n", "0.015207 | \n", "
F | \n", "0.055129 | \n", "0.055459 | \n", "-6093.100561 | \n", "0.011371 | \n", "0.011138 | \n", "488.356616 | \n", "6.585156 | \n", "0.016477 | \n", "
\n", " | index | \n", "0 | \n", "
---|---|---|
0 | \n", "tfmri_sst_all_correct.go.vs.fixation_beta_cort... | \n", "0.096960 | \n", "
1 | \n", "tfmri_sst_all_correct.go.vs.fixation_beta_cort... | \n", "-0.067299 | \n", "
2 | \n", "tfmri_sst_all_correct.go.vs.fixation_beta_cort... | \n", "0.696026 | \n", "
3 | \n", "tfmri_sst_all_correct.go.vs.fixation_beta_cort... | \n", "-0.732170 | \n", "
4 | \n", "tfmri_sst_all_correct.go.vs.fixation_beta_cort... | \n", "-1.197480 | \n", "
... | \n", "... | \n", "... | \n", "
502 | \n", "tfmri_sst_all_incorrect.stop.vs.correct.go_bet... | \n", "3.734274 | \n", "
503 | \n", "tfmri_sst_all_incorrect.stop.vs.correct.go_bet... | \n", "-1.032871 | \n", "
504 | \n", "tfmri_sst_all_incorrect.stop.vs.correct.go_bet... | \n", "-2.059234 | \n", "
505 | \n", "tfmri_sst_all_incorrect.stop.vs.correct.go_bet... | \n", "-1.065423 | \n", "
506 | \n", "tfmri_sst_all_incorrect.stop.vs.correct.go_bet... | \n", "-1.673626 | \n", "
507 rows × 2 columns
\n", "