diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
index 2ed9dd15..094620ef 100644
--- a/CODE_OF_CONDUCT.md
+++ b/CODE_OF_CONDUCT.md
@@ -5,7 +5,7 @@
 We as members, contributors, and leaders pledge to make participation in our
 community a harassment-free experience for everyone, regardless of age, body
 size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
+identity and expression, level of experience, education, socioeconomic status,
 nationality, personal appearance, race, caste, color, religion, or sexual
 identity and orientation.
 
diff --git a/narps_open/pipelines/__init__.py b/narps_open/pipelines/__init__.py
index d6af5905..e4f90dfb 100644
--- a/narps_open/pipelines/__init__.py
+++ b/narps_open/pipelines/__init__.py
@@ -26,7 +26,7 @@
     '3TR7': 'PipelineTeam3TR7',
     '43FJ': None,
     '46CD': None,
-    '4SZ2': None,
+    '4SZ2': 'PipelineTeam4SZ2',
     '4TQ6': 'PipelineTeam4TQ6',
     '50GV': None,
     '51PW': 'PipelineTeam51PW',
diff --git a/narps_open/pipelines/team_4SZ2.py b/narps_open/pipelines/team_4SZ2.py
new file mode 100644
index 00000000..76f80825
--- /dev/null
+++ b/narps_open/pipelines/team_4SZ2.py
@@ -0,0 +1,466 @@
+#!/usr/bin/python
+# coding: utf-8
+
+""" Write the work of NARPS team 4SZ2 using Nipype """
+
+from os.path import join
+from itertools import product
+
+from numpy import array
+
+from nipype import Workflow, Node, MapNode
+from nipype.interfaces.utility import IdentityInterface, Function, Split
+from nipype.interfaces.io import SelectFiles, DataSink
+from nipype.interfaces.fsl import (
+    IsotropicSmooth, Level1Design, FEATModel,
+    L2Model, Merge, FLAMEO, FILMGLS, MultipleRegressDesign,
+    FSLCommand, Cluster
+    )
+from nipype.algorithms.modelgen import SpecifyModel
+from nipype.interfaces.fsl.maths import MathsCommand
+
+from narps_open.utils.configuration import Configuration
+from narps_open.pipelines import Pipeline
+from narps_open.data.task import TaskInformation
+from narps_open.data.participants import get_group, get_participants_information
+from narps_open.core.common import list_intersection, elements_in_string, clean_list
+from narps_open.core.interfaces import InterfaceFactory
+
+# Setup FSL
+FSLCommand.set_default_output_type('NIFTI_GZ')
+
+class PipelineTeam4SZ2(Pipeline):
+    """ A class that defines the pipeline of team 4SZ2 """
+
+    def __init__(self):
+        super().__init__()
+        self.fwhm = 5.0
+        self.team_id = '4SZ2'
+        self.contrast_list = ['1', '2']
+        self.run_level_contrasts = [
+            ('effect_of_gain', 'T', ['gain', 'loss'], [1, 0]),
+            ('effect_of_loss', 'T', ['gain', 'loss'], [0, 1])
+            ]
+        self.group_level_contrasts = [
+            ('group_equal_indifference', 'T', ['equalIndifference', 'equalRange'], [1, 0]),
+            ('group_equal_range', 'T', ['equalIndifference', 'equalRange'], [0, 1]),
+            ('group_comparison', 'T', ['equalIndifference', 'equalRange'], [-1, 1])
+            ]
+
+    def get_preprocessing(self):
+        """ No preprocessing has been done by team 4SZ2 """
+        return None
+
+    def get_subject_information(event_file):
+        """
+        Create Bunchs for specifyModel.
+
+        Parameters :
+        - event_file : str, file corresponding to the run and the subject to analyze
+
+        Returns :
+        - subject_info : list of Bunch for 1st level analysis.
+        """
+        from nipype.interfaces.base import Bunch
+
+        onsets = []
+        durations = []
+        amplitudes_gain = []
+        amplitudes_loss = []
+
+        with open(event_file, 'rt') as file:
+            next(file)  # skip the header
+
+            for line in file:
+                info = line.strip().split()
+                onsets.append(float(info[0]))
+                durations.append(float(info[1]))
+                amplitudes_gain.append(float(info[2]))
+                amplitudes_loss.append(float(info[3]))
+
+        return [
+            Bunch(
+                conditions = ['gain', 'loss'],
+                onsets = [onsets] * 2,
+                durations = [durations] * 2,
+                amplitudes = [amplitudes_gain, amplitudes_loss]
+                )
+            ]
+
+    def get_run_level_analysis(self):
+        """
+        Create the run level analysis workflow.
+
+        Returns:
+            - run_level : nipype.WorkFlow
+        """
+        # Create run level analysis workflow and connect its nodes
+        run_level = Workflow(
+            base_dir = self.directories.working_dir,
+            name = 'run_level_analysis'
+            )
+
+        # IdentityInterface Node - Iterate on subject and runs
+        information_source = Node(IdentityInterface(
+            fields = ['subject_id', 'run_id']),
+            name = 'information_source')
+        information_source.iterables = [
+            ('subject_id', self.subject_list),
+            ('run_id', self.run_list)
+            ]
+
+        # SelectFiles - Get necessary files
+        templates = {
+            'func' : join('derivatives', 'fmriprep', 'sub-{subject_id}', 'func',
+                'sub-{subject_id}_task-MGT_run-{run_id}_bold_space-MNI152NLin2009cAsym_preproc.nii.gz'),
+            'events' : join('sub-{subject_id}', 'func',
+                'sub-{subject_id}_task-MGT_run-{run_id}_events.tsv')
+        }
+        select_files = Node(SelectFiles(templates), name = 'select_files')
+        select_files.inputs.base_directory = self.directories.dataset_dir
+        run_level.connect(information_source, 'subject_id', select_files, 'subject_id')
+        run_level.connect(information_source, 'run_id', select_files, 'run_id')
+
+        # IsotropicSmooth Node - Smoothing data
+        smoothing_func = Node(IsotropicSmooth(), name = 'smoothing_func')
+        smoothing_func.inputs.fwhm = self.fwhm
+        run_level.connect(select_files, 'func', smoothing_func, 'in_file')
+
+        # Get Subject Info - get subject specific condition information
+        subject_information = Node(Function(
+            function = self.get_subject_information,
+            input_names = ['event_file'],
+            output_names = ['subject_info']
+            ), name = 'subject_information')
+        run_level.connect(select_files, 'events', subject_information, 'event_file')
+
+        # SpecifyModel Node - Generate run level model
+        specify_model = Node(SpecifyModel(), name = 'specify_model')
+        specify_model.inputs.high_pass_filter_cutoff = 100
+        specify_model.inputs.input_units = 'secs'
+        specify_model.inputs.time_repetition = TaskInformation()['RepetitionTime']
+        run_level.connect(smoothing_func, 'out_file', specify_model, 'functional_runs')
+        run_level.connect(subject_information, 'subject_info', specify_model, 'subject_info')
+
+        # Level1Design Node - Generate files for run level computation
+        model_design = Node(Level1Design(), name = 'model_design')
+        model_design.inputs.bases = {'dgamma' : {'derivs' : True }}
+        model_design.inputs.interscan_interval = TaskInformation()['RepetitionTime']
+        model_design.inputs.model_serial_correlations = True
+        model_design.inputs.contrasts = self.run_level_contrasts
+        run_level.connect(specify_model, 'session_info', model_design, 'session_info')
+
+        # FEATModel Node - Generate run level model
+        model_generation = Node(FEATModel(), name = 'model_generation')
+        run_level.connect(model_design, 'ev_files', model_generation, 'ev_files')
+        run_level.connect(model_design, 'fsf_files', model_generation, 'fsf_file')
+
+        # FILMGLS Node - Estimate first level model
+        model_estimate = Node(FILMGLS(), name='model_estimate')
+        model_estimate.inputs.output_pwdata = True
+        run_level.connect(smoothing_func, 'out_file', model_estimate, 'in_file')
+        run_level.connect(model_generation, 'con_file', model_estimate, 'tcon_file')
+        run_level.connect(model_generation, 'design_file', model_estimate, 'design_file')
+
+        # DataSink Node - store the wanted results in the wanted directory
+        data_sink = Node(DataSink(), name = 'data_sink')
+        data_sink.inputs.base_directory = self.directories.output_dir
+        run_level.connect(model_estimate, 'results_dir', data_sink, 'run_level_analysis.@results')
+        run_level.connect(
+            model_generation, 'design_file', data_sink, 'run_level_analysis.@design_file')
+        run_level.connect(
+            model_generation, 'design_image', data_sink, 'run_level_analysis.@design_img')
+
+        # Remove large files, if requested
+        if Configuration()['pipelines']['remove_unused_data']:
+            remove_smooth = Node(
+                InterfaceFactory.create('remove_parent_directory'),
+                name = 'remove_smooth')
+            run_level.connect(data_sink, 'out_file', remove_smooth, '_')
+            run_level.connect(smoothing_func, 'out_file', remove_smooth, 'file_name')
+
+        return run_level
+
+    def get_run_level_outputs(self):
+        """ Return the names of the files the run level analysis is supposed to generate. """
+
+        parameters = {
+            'run_id' : self.run_list,
+            'subject_id' : self.subject_list,
+            'contrast_id' : self.contrast_list,
+        }
+        parameter_sets = product(*parameters.values())
+        output_dir = join(self.directories.output_dir,
+            'run_level_analysis', '_run_id_{run_id}_subject_id_{subject_id}')
+        templates = [
+                join(output_dir, 'results', 'cope{contrast_id}.nii.gz'),
+                join(output_dir, 'results', 'tstat{contrast_id}.nii.gz'),
+                join(output_dir, 'results', 'varcope{contrast_id}.nii.gz'),
+                join(output_dir, 'results', 'zstat{contrast_id}.nii.gz')
+            ]
+        return [template.format(**dict(zip(parameters.keys(), parameter_values)))\
+            for parameter_values in parameter_sets for template in templates]
+
+    def get_subject_level_analysis(self):
+        """ No subject level analysis has been done by team 4SZ2 """
+        return None
+
+    @staticmethod
+    def get_group_level_regressors(subject_list: list, run_list: list):
+        """
+        Create dictionary of regressors for two sample t-test group analysis.
+
+        Parameters:
+            - subject_list: ids of subject for which to do the analysis
+            - run_list: ids of runs for which to do the analysis
+
+        Returns:
+            - regressors, dict: containing named lists of regressors.
+            - groups, list: group identifiers to distinguish groups in FSL analysis.
+        """
+
+        # Create lists containing regressors for each group (equalRange, equalIndifference)
+        #  * 1 if the participant is on the group
+        #  * 0 otherwise
+        equal_range_group = get_group('equalRange')
+        equal_indif_group = get_group('equalIndifference')
+        equal_range_regressor = [
+            1 if s in equal_range_group else 0 for s in subject_list for _ in run_list]
+        equal_indif_regressor = [
+            1 if s in equal_indif_group else 0 for s in subject_list for _ in run_list]
+
+        # Get gender and age of participants
+        participants_data = get_participants_information()[['participant_id', 'gender', 'age']]
+        participants = participants_data.loc[
+            participants_data['participant_id'].isin([f'sub-{s}' for s in subject_list])
+            ]
+        ages = array(participants['age'])
+        genders = array(participants['gender'])
+
+        # Create regressors output
+        regressors = dict(
+            equalIndifference = equal_indif_regressor,
+            equalRange = equal_range_regressor,
+            age = [int(a) for a in ages for _ in run_list], 
+            gender = [1 if i == 'F' else 0 for i in genders for _ in run_list]
+        )
+
+        return regressors
+
+    def get_group_level_analysis(self):
+        """
+        Return all workflows for the group level analysis.
+
+        Returns;
+            - a list of nipype.WorkFlow
+        """
+        # Compute the number of participants in the analysis
+        nb_subjects = len(self.subject_list)
+
+        # Declare the workflow
+        group_level = Workflow(
+            base_dir = self.directories.working_dir,
+            name = f'group_level_analysis_nsub_{nb_subjects}')
+
+        # Infosource Node - iterate over the contrasts generated by the subject level analysis
+        information_source = Node(IdentityInterface(
+            fields = ['contrast_id']),
+            name = 'information_source')
+        information_source.iterables = [('contrast_id', self.contrast_list)]
+
+        # SelectFiles Node - select necessary files
+        templates = {
+            'cope' : join(self.directories.output_dir,
+                'run_level_analysis', '_run_id_*_subject_id_*', 'results',
+                'cope{contrast_id}.nii.gz'),
+            'varcope' : join(self.directories.output_dir,
+                'run_level_analysis', '_run_id_*_subject_id_*', 'results',
+                'varcope{contrast_id}.nii.gz'),
+            'masks': join('derivatives', 'fmriprep', 'sub-*', 'func',
+                'sub-*_task-MGT_run-*_bold_space-MNI152NLin2009cAsym_brainmask.nii.gz')
+            }
+        select_files = Node(SelectFiles(templates), name = 'select_files')
+        select_files.inputs.base_directory = self.directories.dataset_dir
+        group_level.connect(information_source, 'contrast_id', select_files, 'contrast_id')
+
+        # Create a function to complete the subject ids out from the get_*_subjects node
+        complete_subject_ids = lambda l : [f'_subject_id_{a}' for a in l]
+        complete_sub_ids = lambda l : [f'sub-{a}' for a in l]
+
+        # Function Node elements_in_string
+        #   Get contrast of parameter estimates (cope) for subjects in a given group
+        # Note : using a MapNode with elements_in_string requires using clean_list to remove
+        #   None values from the out_list
+        get_copes = MapNode(Function(
+            function = elements_in_string,
+            input_names = ['input_str', 'elements'],
+            output_names = ['out_list']
+            ),
+            name = 'get_copes', iterfield = 'input_str'
+        )
+        get_copes.inputs.elements = complete_subject_ids(self.subject_list)
+        group_level.connect(select_files, 'cope', get_copes, 'input_str')
+
+        # Function Node elements_in_string
+        #   Get variance of the estimated copes (varcope) for subjects in a given group
+        # Note : using a MapNode with elements_in_string requires using clean_list to remove
+        #   None values from the out_list
+        get_varcopes = MapNode(Function(
+            function = elements_in_string,
+            input_names = ['input_str', 'elements'],
+            output_names = ['out_list']
+            ),
+            name = 'get_varcopes', iterfield = 'input_str'
+        )
+        get_varcopes.inputs.elements = complete_subject_ids(self.subject_list)
+        group_level.connect(select_files, 'varcope', get_varcopes, 'input_str')
+
+        # Function Node elements_in_string
+        #   Get masks for subjects in a given group
+        # Note : using a MapNode with elements_in_string requires using clean_list to remove
+        #   None values from the out_list
+        get_masks = MapNode(Function(
+            function = elements_in_string,
+            input_names = ['input_str', 'elements'],
+            output_names = ['out_list']
+            ),
+            name = 'get_masks', iterfield = 'input_str'
+        )
+        get_masks.inputs.elements = complete_sub_ids(self.subject_list)
+        group_level.connect(select_files, 'masks', get_masks, 'input_str')
+
+        # Merge Node - Merge cope files
+        merge_copes = Node(Merge(), name = 'merge_copes')
+        merge_copes.inputs.dimension = 't'
+        group_level.connect(get_copes, ('out_list', clean_list), merge_copes, 'in_files')
+
+        # Merge Masks - Merge mask files
+        merge_masks = Node(Merge(), name = 'merge_masks')
+        merge_masks.inputs.dimension = 't'
+        group_level.connect(get_masks, ('out_list', clean_list), merge_masks, 'in_files')
+
+        # Merge Node - Merge cope files
+        merge_varcopes = Node(Merge(), name = 'merge_varcopes')
+        merge_varcopes.inputs.dimension = 't'
+        group_level.connect(get_varcopes, ('out_list', clean_list), merge_varcopes, 'in_files')
+
+        # MathsCommand Node - Create a global mask by
+        #   computing the intersection of all run masks.
+        mask_intersection = Node(MathsCommand(), name = 'mask_intersection')
+        mask_intersection.inputs.args = '-Tmin -thr 0.9'
+        group_level.connect(merge_masks, 'merged_file', mask_intersection, 'in_file')
+
+        # Get regressors for the group level analysis
+        regressors = self.get_group_level_regressors(self.subject_list, self.run_list)
+
+        # MultipleRegressDesign Node - Specify model
+        #     NB : no "groups" input is needed because equalRange and equalIndifference groups
+        #     are already modeled in the design
+        specify_model = Node(MultipleRegressDesign(), name = 'specify_model')
+        specify_model.inputs.regressors = regressors
+        specify_model.inputs.contrasts = self.group_level_contrasts
+
+        # FLAMEO Node - Estimate model
+        estimate_model = Node(FLAMEO(), name = 'estimate_model')
+        estimate_model.inputs.run_mode = 'flame1'
+        group_level.connect(mask_intersection, 'out_file', estimate_model, 'mask_file')
+        group_level.connect(merge_copes, 'merged_file', estimate_model, 'cope_file')
+        group_level.connect(merge_varcopes, 'merged_file', estimate_model, 'var_cope_file')
+        group_level.connect(specify_model, 'design_mat', estimate_model, 'design_file')
+        group_level.connect(specify_model, 'design_con', estimate_model, 't_con_file')
+        group_level.connect(specify_model, 'design_grp', estimate_model, 'cov_split_file')
+
+        # Cluster Node - Perform clustering on statistical output
+        cluster = MapNode(
+            Cluster(),
+            name = 'cluster',
+            iterfield = ['in_file', 'cope_file'], 
+            synchronize = True
+            )
+        cluster.inputs.threshold = 2.3
+        cluster.inputs.out_threshold_file = True
+        group_level.connect(estimate_model, 'zstats', cluster, 'in_file')
+        group_level.connect(estimate_model, 'copes', cluster, 'cope_file')
+
+        # Datasink Node - Save important files
+        data_sink = Node(DataSink(), name = 'data_sink')
+        data_sink.inputs.base_directory = self.directories.output_dir
+        group_level.connect(estimate_model, 'zstats', data_sink,
+            f'group_level_analysis_nsub_{nb_subjects}.@zstats')
+        group_level.connect(estimate_model, 'tstats', data_sink,
+            f'group_level_analysis_nsub_{nb_subjects}.@tstats')
+        group_level.connect(cluster,'threshold_file', data_sink,
+            f'group_level_analysis_nsub_{nb_subjects}.@threshold_file')
+
+        return group_level
+
+    def get_group_level_outputs(self):
+        """ Return all names for the files the group level analysis is supposed to generate. """
+
+        parameters = {
+            'contrast_id': self.contrast_list,
+            'file': [
+                '_cluster0/zstat1_threshold.nii.gz',
+                '_cluster1/zstat2_threshold.nii.gz',
+                '_cluster2/zstat3_threshold.nii.gz',
+                'tstat1.nii.gz',
+                'tstat2.nii.gz',
+                'tstat3.nii.gz',
+                'zstat1.nii.gz',
+                'zstat2.nii.gz',
+                'zstat3.nii.gz'
+                ]
+        }
+        parameter_sets = product(*parameters.values())
+        template = join(
+            self.directories.output_dir,
+            'group_level_analysis_nsub_'+f'{len(self.subject_list)}',
+            '_contrast_id_{contrast_id}',
+            '{file}'
+            )
+        return [template.format(**dict(zip(parameters.keys(), parameter_values)))\
+            for parameter_values in parameter_sets]
+
+    def get_hypotheses_outputs(self):
+        """ Return all hypotheses output file names. """
+
+        nb_sub = len(self.subject_list)
+        files = [
+            # Hypothesis 1
+            join(f'group_level_analysis_nsub_{nb_sub}',
+                '_contrast_id_1', '_cluster0', 'zstat1_threshold.nii.gz'),
+            join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_1', 'zstat1.nii.gz'),
+            # Hypothesis 2
+            join(f'group_level_analysis_nsub_{nb_sub}',
+                '_contrast_id_1', '_cluster1', 'zstat2_threshold.nii.gz'),
+            join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_1', 'zstat2.nii.gz'),
+            # Hypothesis 3
+            join(f'group_level_analysis_nsub_{nb_sub}',
+                '_contrast_id_1', '_cluster0', 'zstat1_threshold.nii.gz'),
+            join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_1', 'zstat1.nii.gz'),
+            # Hypothesis 4
+            join(f'group_level_analysis_nsub_{nb_sub}',
+                '_contrast_id_1', '_cluster1', 'zstat2_threshold.nii.gz'),
+            join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_1', 'zstat2.nii.gz'),
+            # Hypothesis 5
+            join(f'group_level_analysis_nsub_{nb_sub}',
+                '_contrast_id_2', '_cluster0', 'zstat1_threshold.nii.gz'),
+            join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_2', 'zstat1.nii.gz'),
+            # Hypothesis 6
+            join(f'group_level_analysis_nsub_{nb_sub}',
+                '_contrast_id_2', '_cluster1', 'zstat2_threshold.nii.gz'),
+            join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_2', 'zstat2.nii.gz'),
+            # Hypothesis 7
+            join(f'group_level_analysis_nsub_{nb_sub}',
+                '_contrast_id_2', '_cluster0', 'zstat1_threshold.nii.gz'),
+            join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_2', 'zstat1.nii.gz'),
+            # Hypothesis 8
+            join(f'group_level_analysis_nsub_{nb_sub}',
+                '_contrast_id_2', '_cluster1', 'zstat2_threshold.nii.gz'),
+            join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_2', 'zstat2.nii.gz'),
+            # Hypothesis 9
+            join(f'group_level_analysis_nsub_{nb_sub}',
+                '_contrast_id_2', '_cluster2', 'zstat3_threshold.nii.gz'),
+            join(f'group_level_analysis_nsub_{nb_sub}', '_contrast_id_2', 'zstat3.nii.gz')
+        ]
+        return [join(self.directories.output_dir, f) for f in files]
diff --git a/narps_open/utils/correlation/__main__.py b/narps_open/utils/correlation/__main__.py
index 36ff4aa0..0ccf80c2 100644
--- a/narps_open/utils/correlation/__main__.py
+++ b/narps_open/utils/correlation/__main__.py
@@ -19,7 +19,7 @@ def main():
     parser = ArgumentParser(description = 'Compare reproduced files to original results.')
     parser.add_argument('-t', '--team', type = str, required = True,
         help = 'the team ID', choices = get_implemented_pipelines())
-    parser.add_argument('-n', '--nsubjects', type=int, required = True,
+    parser.add_argument('-n', '--nsubjects', type = int, required = True,
         help='the number of subjects to be selected')
     arguments = parser.parse_args()
 
diff --git a/tests/pipelines/test_team_4SZ2.py b/tests/pipelines/test_team_4SZ2.py
new file mode 100644
index 00000000..68ebb9c7
--- /dev/null
+++ b/tests/pipelines/test_team_4SZ2.py
@@ -0,0 +1,106 @@
+#!/usr/bin/python
+# coding: utf-8
+
+""" Tests of the 'narps_open.pipelines.team_4SZ2' module.
+
+Launch this test with PyTest
+
+Usage:
+======
+    pytest -q test_team_4SZ2.py
+    pytest -q test_team_4SZ2.py -k <selected_test>
+"""
+from os.path import join, exists, abspath
+from filecmp import cmp
+
+from pytest import helpers, mark
+from nipype import Workflow, Node, Function
+from nipype.interfaces.base import Bunch
+
+from narps_open.utils.configuration import Configuration
+from narps_open.pipelines.team_4SZ2 import PipelineTeam4SZ2
+
+class TestPipelinesTeam4SZ2:
+    """ A class that contains all the unit tests for the PipelineTeam4SZ2 class."""
+
+    @staticmethod
+    @mark.unit_test
+    def test_create():
+        """ Test the creation of a PipelineTeam4SZ2 object """
+
+        pipeline = PipelineTeam4SZ2()
+
+        # 1 - check the parameters
+        assert pipeline.fwhm == 5.0
+        assert pipeline.team_id == '4SZ2'
+
+        # 2 - check workflows
+        assert pipeline.get_preprocessing() is None
+        assert isinstance(pipeline.get_run_level_analysis(), Workflow)
+        assert pipeline.get_subject_level_analysis() is None
+        assert isinstance(pipeline.get_group_level_analysis(), Workflow)
+
+    @staticmethod
+    @mark.unit_test
+    def test_outputs():
+        """ Test the expected outputs of a PipelineTeam4SZ2 object """
+
+        pipeline = PipelineTeam4SZ2()
+
+        # 1 - 1 subject outputs
+        pipeline.subject_list = ['001']
+        helpers.test_pipeline_outputs(pipeline, [0, 2*4*1*4, 0, 2*9, 18])
+
+        # 2 - 4 subjects outputs
+        pipeline.subject_list = ['001', '002', '003', '004']
+        helpers.test_pipeline_outputs(pipeline, [0, 2*4*4*4, 0, 2*9, 18])
+
+    @staticmethod
+    @mark.unit_test
+    def test_subject_information():
+        """ Test the get_subject_information method """
+
+        # Get test files
+        test_file = join(Configuration()['directories']['test_data'], 'pipelines', 'events.tsv')
+
+        # Prepare several scenarii
+        info_missed = PipelineTeam4SZ2.get_subject_information(test_file)
+
+        # Compare bunches to expected
+        bunch = info_missed[0]
+        assert isinstance(bunch, Bunch)
+        assert bunch.conditions == ['gain', 'loss']
+        helpers.compare_float_2d_arrays(bunch.onsets, [
+            [4.071, 11.834, 19.535, 27.535, 36.435],
+            [4.071, 11.834, 19.535, 27.535, 36.435]
+            ])
+        helpers.compare_float_2d_arrays(bunch.durations, [
+            [4.0, 4.0, 4.0, 4.0, 4.0],
+            [4.0, 4.0, 4.0, 4.0, 4.0]
+            ])
+        helpers.compare_float_2d_arrays(bunch.amplitudes, [
+            [14.0, 34.0, 38.0, 10.0, 16.0],
+            [6.0, 14.0, 19.0, 15.0, 17.0]
+            ])
+
+    @staticmethod
+    @mark.unit_test
+    def test_get_group_level_regressors():
+        """ Test the get_group_level_regressors method """
+
+        regressors = PipelineTeam4SZ2.get_group_level_regressors(
+            ['001', '002', '003', '004'],
+            ['01', '02'])
+
+        for k1, k2 in zip(regressors.keys(), ['equalIndifference', 'equalRange', 'age', 'gender']):
+            assert k1 == k2
+        assert regressors['equalIndifference'] == [1, 1, 0, 0, 1, 1, 0, 0]
+        assert regressors['equalRange'] == [0, 0, 1, 1, 0, 0, 1, 1]
+        assert regressors['age'] == [24, 24, 25, 25, 27, 27, 25, 25]
+        assert regressors['gender'] == [0, 0, 0, 0, 1, 1, 0, 0]
+
+    @staticmethod
+    @mark.pipeline_test
+    def test_execution():
+        """ Test the execution of a PipelineTeam4SZ2 and compare results """
+        helpers.test_pipeline_evaluation('4SZ2')