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<div class="section" id="glossary">
<h1>Glossary<a class="headerlink" href="#glossary" title="Permalink to this headline">¶</a></h1>
<p>The Glossary provides short definitions of neuro-imaging concepts as well
as Nilearn specific vocabulary.</p>
<p>If you wish to add a missing term, please <a class="reference external" href="https://github.com/nilearn/nilearn/issues/new/choose">create a new issue</a> or
<a class="reference external" href="https://github.com/nilearn/nilearn/compare">open a Pull Request</a>.</p>
<dl class="glossary">
<dt id="term-ANOVA">ANOVA<a class="headerlink" href="#term-ANOVA" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/Analysis_of_variance">Analysis of variance</a> is a collection of statistical models and
their associated estimation procedures used to analyze the differences
among means.</p>
</dd>
<dt id="term-AUC">AUC<a class="headerlink" href="#term-AUC" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics">Area under the curve</a>.</p>
</dd>
<dt id="term-BIDS">BIDS<a class="headerlink" href="#term-BIDS" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://bids.neuroimaging.io/">Brain Imaging Data Structure</a> is a simple and easy to adopt way
of organizing neuroimaging and behavioral data.</p>
</dd>
<dt id="term-BOLD">BOLD<a class="headerlink" href="#term-BOLD" title="Permalink to this term">¶</a></dt><dd><p>Blood oxygenation level dependent. This is the kind of signal measured
by functional Magnetic Resonance Imaging.</p>
</dd>
<dt id="term-CanICA">CanICA<a class="headerlink" href="#term-CanICA" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://arxiv.org/abs/1006.2300">Canonical independent component analysis</a>.</p>
</dd>
<dt id="term-Closing">Closing<a class="headerlink" href="#term-Closing" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/Closing_(morphology)">Closing</a> is, together with <a class="reference internal" href="#term-Opening"><span class="xref std std-term">opening</span></a>, one of the basic
operations of <a class="reference external" href="https://en.wikipedia.org/wiki/Mathematical_morphology">mathematical morphology</a>. The closing of a binary image
by a structuring element is defined as the <a class="reference internal" href="#term-Erosion"><span class="xref std std-term">erosion</span></a> of
the <a class="reference internal" href="#term-Dilation"><span class="xref std std-term">dilation</span></a> of that set.</p>
</dd>
<dt id="term-contrast">contrast<a class="headerlink" href="#term-contrast" title="Permalink to this term">¶</a></dt><dd><p>A <a class="reference external" href="https://en.wikipedia.org/wiki/Contrast_(statistics)">contrast</a> is a linear combination of variables (parameters or
statistics) whose coefficients add up to zero, allowing comparison
of different treatments.</p>
</dd>
<dt id="term-Decoding">Decoding<a class="headerlink" href="#term-Decoding" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://nilearn.github.io/decoding/decoding_intro.html">Decoding</a> consists in predicting, from brain images, the conditions
associated to trial.</p>
</dd>
<dt id="term-Dictionary-learning">Dictionary learning<a class="headerlink" href="#term-Dictionary-learning" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/Sparse_dictionary_learning">Dictionary learning</a> (or sparse coding) is a representation learning
method aiming at finding a sparse representation of the input data as
a linear combination of basic elements called atoms. The identification
of these atoms composing the dictionary relies on a sparsity principle:
maximally sparse representations of the dataset are sought for. Atoms
are not required to be orthogonal.</p>
</dd>
<dt id="term-Dilation">Dilation<a class="headerlink" href="#term-Dilation" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/Dilation_(morphology)">Dilation</a> is, with <a class="reference internal" href="#term-Erosion"><span class="xref std std-term">erosion</span></a> one of the fundamental
operations of <a class="reference external" href="https://en.wikipedia.org/wiki/Mathematical_morphology">mathematical morphology</a> from which other operations
like <a class="reference internal" href="#term-Opening"><span class="xref std std-term">opening</span></a> or <a class="reference internal" href="#term-Closing"><span class="xref std std-term">closing</span></a> are based.
Dilation uses a structuring element for probing and expanding the
shapes contained in the input image.</p>
</dd>
<dt id="term-EEG">EEG<a class="headerlink" href="#term-EEG" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/Electroencephalography">Electroencephalography</a> is a monitoring method to record electrical
activity of the brain.</p>
</dd>
<dt id="term-EPI">EPI<a class="headerlink" href="#term-EPI" title="Permalink to this term">¶</a></dt><dd><p>Echo-Planar Imaging. This is the type of sequence used to acquire
functional or diffusion MRI data.</p>
</dd>
<dt id="term-Erosion">Erosion<a class="headerlink" href="#term-Erosion" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/Erosion_(morphology)">Erosion</a> is, with <a class="reference internal" href="#term-Dilation"><span class="xref std std-term">dilation</span></a>, one of the fundamental
operations in <a class="reference external" href="https://en.wikipedia.org/wiki/Mathematical_morphology">mathematical morphology</a> from which other operations
like <a class="reference internal" href="#term-Opening"><span class="xref std std-term">opening</span></a> or <a class="reference internal" href="#term-Closing"><span class="xref std std-term">closing</span></a> are based.
Erosion uses a structuring element for probing and reducing the shapes
contained in the input image.</p>
</dd>
<dt id="term-FDR-correction">FDR correction<a class="headerlink" href="#term-FDR-correction" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/False_discovery_rate">False discovery rate</a> controlling procedures are designed to control
the expected proportion of “discoveries” (rejected null hypotheses)
that are false (incorrect rejections of the null).</p>
</dd>
<dt id="term-FIR">FIR<a class="headerlink" href="#term-FIR" title="Permalink to this term">¶</a></dt><dd><p>Finite impulse response. This is a type of free-form temporal filter
that is used to link neural activity with hemodynamic response, when
there is uncertainty on the true model.</p>
</dd>
<dt id="term-fMRI">fMRI<a class="headerlink" href="#term-fMRI" title="Permalink to this term">¶</a></dt><dd><p>Functional magnetic resonance imaging is based on the fact that
when local neural activity increases, increases in metabolism and
blood flow lead to fluctuations of the relative concentrations of
oxyhaemoglobin (the red cells in the blood that carry oxygen) and
deoxyhaemoglobin (the same red cells after they have delivered the
oxygen). Oxyhaemoglobin and deoxyhaemoglobin have different magnetic
properties (diamagnetic and paramagnetic, respectively), and they
affect the local magnetic field in different ways.
The signal picked up by the MRI scanner is sensitive to these
modifications of the local magnetic field.</p>
</dd>
<dt id="term-fMRIPrep">fMRIPrep<a class="headerlink" href="#term-fMRIPrep" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://fmriprep.org/en/stable/">fMRIPrep</a> is a <a class="reference internal" href="#term-fMRI"><span class="xref std std-term">fMRI</span></a> data preprocessing pipeline designed
to provide an interface robust to variations in scan acquisition
protocols with minimal user input. It performs basic processing
steps (coregistration, normalization, unwarping, noise component
extraction, segmentation, skullstripping etc.) providing outputs,
often called confounds or nuisance parameters, that can be easily
submitted to a variety of group level analyses, including task-based
or resting-state <a class="reference internal" href="#term-fMRI"><span class="xref std std-term">fMRI</span></a>, graph theory measures, surface or
volume-based statistics, etc.</p>
</dd>
<dt id="term-FPR-correction">FPR correction<a class="headerlink" href="#term-FPR-correction" title="Permalink to this term">¶</a></dt><dd><p>False positive rate correction. This refers to the methods employed to
correct false positive rates such as the Bonferroni correction which
divides the significance level by the number of comparisons made.</p>
</dd>
<dt id="term-FREM">FREM<a class="headerlink" href="#term-FREM" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://www.sciencedirect.com/science/article/abs/pii/S1053811917308182">FREM</a> means “Fast ensembling of REgularized Models”. It uses an implicit
spatial regularization through fast clustering and aggregates a high
number of estimators trained on various splits of the training set, thus
returning a very robust decoder at a lower computational cost than other
spatially regularized methods.</p>
</dd>
<dt id="term-functional-connectivity">functional connectivity<a class="headerlink" href="#term-functional-connectivity" title="Permalink to this term">¶</a></dt><dd><p>Functional connectivity is a measure of the similarity of the response
patterns in two or more regions.</p>
</dd>
<dt id="term-functional-connectome">functional connectome<a class="headerlink" href="#term-functional-connectome" title="Permalink to this term">¶</a></dt><dd><p>A <a class="reference external" href="https://nilearn.github.io/connectivity/functional_connectomes.html">functional connectome</a> is a set of connections representing brain
interactions between regions.</p>
</dd>
<dt id="term-FWER-correction">FWER correction<a class="headerlink" href="#term-FWER-correction" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/Family-wise_error_rate">Family-wise error rate</a> is the probability of making one or more
false discoveries, or type I errors when performing multiple
hypotheses tests.</p>
</dd>
<dt id="term-GLM">GLM<a class="headerlink" href="#term-GLM" title="Permalink to this term">¶</a></dt><dd><p>General Linear Model. This is the name of the models traditionally fit
to fMRI data, where one linear model is fit to each voxel time course.</p>
</dd>
<dt id="term-HRF">HRF<a class="headerlink" href="#term-HRF" title="Permalink to this term">¶</a></dt><dd><p>Haemodynamic response function. This is a temporal filter that converts
neural signals to hemodynamic signals observable with <a class="reference internal" href="#term-fMRI"><span class="xref std std-term">fMRI</span></a>.</p>
</dd>
<dt id="term-ICA">ICA<a class="headerlink" href="#term-ICA" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/Independent_component_analysis">Independent component analysis</a> is a computational method for separating
a multivariate signal into additive subcomponents.</p>
</dd>
<dt id="term-MEG">MEG<a class="headerlink" href="#term-MEG" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/Magnetoencephalography">Magnetoencephalography</a> is a functional neuroimaging technique for mapping
brain activity by recording magnetic fields produced by electrical currents
occurring naturally in the brain.</p>
</dd>
<dt id="term-MNI">MNI<a class="headerlink" href="#term-MNI" title="Permalink to this term">¶</a></dt><dd><p>MNI stands for “Montreal Neurological Institute”. Usually, this is
used to reference the MNI space/template. The current standard MNI
template is the ICBM152, which is the average of 152 normal MRI scans
that have been matched to the MNI305 using a 9 parameter affine transform.</p>
</dd>
<dt id="term-MVPA">MVPA<a class="headerlink" href="#term-MVPA" title="Permalink to this term">¶</a></dt><dd><p>Mutli-Voxel Pattern Analysis. This is the way <a class="reference internal" href="#term-supervised-learning"><span class="xref std std-term">supervised learning</span></a>
methods are called in the field of brain imaging.</p>
</dd>
<dt id="term-Neurovault">Neurovault<a class="headerlink" href="#term-Neurovault" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://www.neurovault.org/">Neurovault</a> is a public repository of unthresholded statistical maps,
parcellations, and atlases of the human brain.</p>
</dd>
<dt id="term-Opening">Opening<a class="headerlink" href="#term-Opening" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/Opening_(morphology)">Opening</a> is, together with <a class="reference internal" href="#term-Closing"><span class="xref std std-term">closing</span></a>, one of the basic
operations of <a class="reference external" href="https://en.wikipedia.org/wiki/Mathematical_morphology">mathematical morphology</a>. It is defined as the
<a class="reference internal" href="#term-Dilation"><span class="xref std std-term">dilation</span></a> of the <a class="reference internal" href="#term-Erosion"><span class="xref std std-term">erosion</span></a> of a set by a
structuring element.</p>
</dd>
<dt id="term-parcellation">parcellation<a class="headerlink" href="#term-parcellation" title="Permalink to this term">¶</a></dt><dd><p>Act of dividing the brain into smaller regions, i.e. parcels. Parcellations
can be defined by many different criteria including anatomical or functional
characteristics. Parcellations can either be composed of “hard” deterministic
parcels with no overlap between individual regions or “soft” probabilistic
parcels with a non-zero probability of overlap.</p>
</dd>
<dt id="term-predictive-modelling">predictive modelling<a class="headerlink" href="#term-predictive-modelling" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/Predictive_modelling">Predictive modelling</a> uses statistics to predict outcomes.</p>
</dd>
<dt id="term-ReNA">ReNA<a class="headerlink" href="#term-ReNA" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://hal.archives-ouvertes.fr/hal-01366651/">Recursive nearest agglomeration</a>.</p>
</dd>
<dt id="term-resting-state">resting-state<a class="headerlink" href="#term-resting-state" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/Resting_state_fMRI">Resting state</a> <a class="reference internal" href="#term-fMRI"><span class="xref std std-term">fMRI</span></a> is a method of functional magnetic resonance
imaging that is used in brain mapping to evaluate regional interactions that
occur in a resting or task-negative state, when an explicit task is not being
performed.</p>
</dd>
<dt id="term-ROC">ROC<a class="headerlink" href="#term-ROC" title="Permalink to this term">¶</a></dt><dd><p>The <a class="reference external" href="https://en.wikipedia.org/wiki/Receiver_operating_characteristic">receiver operating characteristic curve</a> plots the true positive rate
(TPR) against the false positive rate (FPR) at various threshold settings.</p>
</dd>
<dt id="term-Searchlight">Searchlight<a class="headerlink" href="#term-Searchlight" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://nilearn.github.io/decoding/searchlight.html">Searchlight analysis</a> consists of scanning the brain with a searchlight.
That is, a ball of given radius is scanned across the brain volume and the
prediction accuracy of a classifier trained on the corresponding voxels is measured.</p>
</dd>
<dt id="term-SNR">SNR<a class="headerlink" href="#term-SNR" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/Signal-to-noise_ratio">SNR</a> stands for “Signal to Noise Ratio” and is a measure comparing the level
of a given signal to the level of the background noise.</p>
</dd>
<dt id="term-SpaceNet">SpaceNet<a class="headerlink" href="#term-SpaceNet" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://nilearn.github.io/decoding/space_net.html">SpaceNet</a> is a decoder implementing spatial penalties which improve brain
decoding power as well as decoder maps.</p>
</dd>
<dt id="term-SPM">SPM<a class="headerlink" href="#term-SPM" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/Statistical_parametric_mapping">Statistical Parametric Mapping</a> is a statistical technique for examining
differences in brain activity recorded during functional neuroimaging
experiments. It may alternatively refer to a <a class="reference external" href="https://www.fil.ion.ucl.ac.uk/spm/software/">software</a> created by the Wellcome
Department of Imaging Neuroscience at University College London to carry out
such analyses.</p>
</dd>
<dt id="term-supervised-learning">supervised learning<a class="headerlink" href="#term-supervised-learning" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/Supervised_learning">Supervised learning</a> is interested in predicting an output variable,
or target, y, from data X. Typically, we start from labeled data (the
training set). We need to know the y for each instance of X in order to
train the model. Once learned, this model is then applied to new unlabeled
data (the test set) to predict the labels (although we actually know them).
There are essentially two possible types of problems:</p>
<dl class="glossary simple">
<dt id="term-regression">regression<a class="headerlink" href="#term-regression" title="Permalink to this term">¶</a></dt><dd><p>In regression problems, the objective is to predict a continuous
variable, such as participant age, from the data X.</p>
</dd>
<dt id="term-classification">classification<a class="headerlink" href="#term-classification" title="Permalink to this term">¶</a></dt><dd><p>In classification problems, the objective is to predict a binary
variable that splits the observations into two groups, such as
patients versus controls.</p>
</dd>
</dl>
<p>In neuroimaging research, supervised learning is typically used to derive an
underlying cognitive process (e.g., emotional versus non-emotional theory of
mind), a behavioral variable (e.g., reaction time or IQ), or diagnosis status
(e.g., schizophrenia versus healthy) from brain images.</p>
</dd>
<dt id="term-SVM">SVM<a class="headerlink" href="#term-SVM" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://scikit-learn.org/stable/modules/svm.html">Support vector machines</a> are a set of <a class="reference internal" href="#term-supervised-learning"><span class="xref std std-term">supervised learning</span></a> methods used
for <a class="reference internal" href="#term-classification"><span class="xref std std-term">classification</span></a>, <a class="reference internal" href="#term-regression"><span class="xref std std-term">regression</span></a> and outliers detection.</p>
</dd>
<dt id="term-TR">TR<a class="headerlink" href="#term-TR" title="Permalink to this term">¶</a></dt><dd><p>Repetition time. This is the time in seconds between the beginning of an
acquisition of one volume and the beginning of acquisition of the volume following it.</p>
</dd>
<dt id="term-Unsupervised-learning">Unsupervised learning<a class="headerlink" href="#term-Unsupervised-learning" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/Unsupervised_learning">Unsupervised learning</a> is concerned with data X without any labels. It analyzes
the structure of a dataset to find coherent underlying structure, for instance
using clustering, or to extract latent factors, for instance using independent
components analysis (<a class="reference internal" href="#term-ICA"><span class="xref std std-term">ICA</span></a>).</p>
<p>In neuroimaging research, it is typically used to create functional and anatomical
brain atlases by clustering based on connectivity or to extract the main brain
networks from resting-state correlations. An important option of future research
will be the identification of potential neurobiological subgroups in psychiatric
and neurobiological disorders.</p>
</dd>
<dt id="term-VBM">VBM<a class="headerlink" href="#term-VBM" title="Permalink to this term">¶</a></dt><dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/Voxel-based_morphometry">Voxel-Based Morphometry</a> measures differences in local concentrations of brain
tissue, through a voxel-wise comparison of multiple brain images.</p>
</dd>
<dt id="term-voxel">voxel<a class="headerlink" href="#term-voxel" title="Permalink to this term">¶</a></dt><dd><p>A voxel represents a value on a regular grid in 3D space.</p>
</dd>
<dt id="term-Ward-clustering">Ward clustering<a class="headerlink" href="#term-Ward-clustering" title="Permalink to this term">¶</a></dt><dd><p>Ward’s algorithm is a hierarchical clustering algorithm: it recursively merges voxels,
then clusters that have similar signal (parameters, measurements or time courses).</p>
</dd>
</dl>
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