<p>
    Data were analyzed using <a href="https://nilearn.github.io">Nilearn</a>
    (version= {{ version }}; RRID:SCR_001362).
</p>
{% if model_type == "First Level Model" %}
    <p>
        At the subject level, a mass univariate analysis was performed with a linear
        regression at each voxel of the brain, using generalized least squares with a
        global {{ reporting_data.noise_model }} noise model to account for temporal auto-correlation
        {{ reporting_data.drift_model }}.
    </p>
    {% if reporting_data.trial_types %}
        <p>
            Regressors were entered into run-specific design matrices and onsets were
            convolved with a {{ reporting_data.hrf_model }} canonical hemodynamic response function
            for the following conditions:
            <ul>
                {% for trial in reporting_data.trial_types %}<li>{{ trial }}</li>{% endfor %}
            </ul>
        </p>
    {% endif %}
{% else %}
    <p>
        At the group level, a mass univariate analysis was performed with a linear
        regression at each voxel of the brain.
    </p>
{% endif %}
{% if smoothing_fwhm %}
    <p>
        Input images were smoothed with gaussian kernel
        (full-width at half maximum={{ smoothing_fwhm }} mm).
    </p>
{% endif %}
{% if contrasts %}
    <p>
        The following contrasts were computed
        {% if model_type == "First Level Model" %}
            using a fixed-effect approach across runs
        {% endif %}
        :
        <ul>
            {% for c in contrasts %}<li>{{ c }}</li>{% endfor %}
        </ul>
    </p>
{% endif %}
