Glossary
========
.. currentmodule:: nilearn
The Glossary provides short definitions of neuro-imaging concepts as well
as Nilearn specific vocabulary.
If you wish to add a missing term, please `create a new issue`_ or
`open a Pull Request`_.
.. glossary::
:sorted:
ANOVA
`Analysis of variance`_ is a collection of statistical models and
their associated estimation procedures used to analyze the differences
among means.
AUC
`Area under the curve`_.
BIDS
`Brain Imaging Data Structure`_ is a simple and easy to adopt way
of organizing neuroimaging and behavioral data.
BOLD
Blood oxygenation level dependent. This is the kind of signal measured
by functional Magnetic Resonance Imaging.
CanICA
`Canonical independent component analysis`_.
contrast
A `contrast`_ is a linear combination of variables (parameters or
statistics) whose coefficients add up to zero, allowing comparison
of different treatments.
Decoding
`Decoding`_ consists in predicting, from brain images, the conditions
associated to trial.
EEG
`Electroencephalography`_ is a monitoring method to record electrical
activity of the brain.
EPI
Echo-Planar Imaging. This is the type of sequence used to acquire
functional or diffusion MRI data.
FDR correction
`False discovery rate`_ controlling procedures are designed to control
the expected proportion of "discoveries" (rejected null hypotheses)
that are false (incorrect rejections of the null).
FIR
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.
fMRI
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.
FPR correction
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.
FREM
`FREM`_ 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.
functional connectivity
Functional connectivity is a measure of the similarity of the response
patterns in two or more regions.
functional connectome
A `functional connectome`_ is a set of connections representing brain
interactions between regions.
FWER correction
`Family-wise error rate`_ is the probability of making one or more
false discoveries, or type I errors when performing multiple
hypotheses tests.
GLM
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.
HRF
Haemodynamic response function. This is a temporal filter that converts
neural signals to hemodynamic signals observable with :term:`fMRI`.
ICA
`Independent component analysis`_ is a computational method for separating
a multivariate signal into additive subcomponents.
MEG
`Magnetoencephalography`_ is a functional neuroimaging technique for mapping
brain activity by recording magnetic fields produced by electrical currents
occurring naturally in the brain.
MNI
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.
MVPA
Mutli-Voxel Pattern Analysis. This is the way :term:`supervised learning`
methods are called in the field of brain imaging.
Neurovault
`Neurovault`_ is a public repository of unthresholded statistical maps,
parcellations, and atlases of the human brain.
parcellation
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.
predictive modelling
`Predictive modelling`_ uses statistics to predict outcomes.
ReNA
`Recursive nearest agglomeration`_.
resting-state
`Resting state`_ :term:`fMRI` 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.
ROC
The `receiver operating characteristic curve`_ plots the true positive rate
(TPR) against the false positive rate (FPR) at various threshold settings.
Searchlight
`Searchlight analysis`_ 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.
SpaceNet
`SpaceNet`_ is a decoder implementing spatial penalties which improve brain
decoding power as well as decoder maps.
SPM
`Statistical Parametric Mapping`_ is a statistical technique for examining
differences in brain activity recorded during functional neuroimaging
experiments. It may alternatively refer to a `software`_ created by the Wellcome
Department of Imaging Neuroscience at University College London to carry out
such analyses.
supervised learning
`Supervised learning`_ 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:
.. glossary::
regression
In regression problems, the objective is to predict a continuous
variable, such as participant age, from the data X.
classification
In classification problems, the objective is to predict a binary
variable that splits the observations into two groups, such as
patients versus controls.
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.
SVM
`Support vector machines`_ are a set of :term:`supervised learning` methods used
for :term:`classification`, :term:`regression` and outliers detection.
TR
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.
Unsupervised learning
`Unsupervised learning`_ 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 (:term:`ICA`).
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.
VBM
`Voxel-Based Morphometry`_ measures differences in local concentrations of brain
tissue, through a voxel-wise comparison of multiple brain images.
voxel
A voxel represents a value on a regular grid in 3D space.
Ward clustering
Wardâ€™s algorithm is a hierarchical clustering algorithm: it recursively merges voxels,
then clusters that have similar signal (parameters, measurements or time courses).
.. LINKS
.. _`create a new issue`:
https://github.com/nilearn/nilearn/issues/new/choose
.. _`open a Pull Request`:
https://github.com/nilearn/nilearn/compare
.. _`Analysis of variance`:
https://en.wikipedia.org/wiki/Analysis_of_variance
.. _`Area under the curve`:
https://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics
.. _`Brain Imaging Data Structure`:
https://bids.neuroimaging.io/
.. _`Canonical independent component analysis`:
https://arxiv.org/abs/1006.2300
.. _`contrast`:
https://en.wikipedia.org/wiki/Contrast_(statistics)
.. _`Decoding`:
https://nilearn.github.io/decoding/decoding_intro.html
.. _`Electroencephalography`:
https://en.wikipedia.org/wiki/Electroencephalography
.. _`False discovery rate`:
https://en.wikipedia.org/wiki/False_discovery_rate
.. _`Family-wise error rate`:
https://en.wikipedia.org/wiki/Family-wise_error_rate
.. _`FREM`:
https://www.sciencedirect.com/science/article/abs/pii/S1053811917308182
.. _`functional connectome`:
https://nilearn.github.io/connectivity/functional_connectomes.html
.. _`Independent component analysis`:
https://en.wikipedia.org/wiki/Independent_component_analysis
.. _`Magnetoencephalography`:
https://en.wikipedia.org/wiki/Magnetoencephalography
.. _`Neurovault`:
https://www.neurovault.org/
.. _`Predictive modelling`:
https://en.wikipedia.org/wiki/Predictive_modelling
.. _`Recursive nearest agglomeration`:
https://hal.archives-ouvertes.fr/hal-01366651/
.. _`receiver operating characteristic curve`:
https://en.wikipedia.org/wiki/Receiver_operating_characteristic
.. _`Resting state`:
https://en.wikipedia.org/wiki/Resting_state_fMRI
.. _`Searchlight analysis`:
https://nilearn.github.io/decoding/searchlight.html
.. _`software`:
https://www.fil.ion.ucl.ac.uk/spm/software/
.. _`SpaceNet`:
https://nilearn.github.io/decoding/space_net.html
.. _`Statistical Parametric Mapping`:
https://en.wikipedia.org/wiki/Statistical_parametric_mapping
.. _`Supervised learning`:
https://en.wikipedia.org/wiki/Supervised_learning
.. _`Unsupervised learning`:
https://en.wikipedia.org/wiki/Unsupervised_learning
.. _`Support vector machines`:
https://scikit-learn.org/stable/modules/svm.html
.. _`Voxel-Based Morphometry`:
https://en.wikipedia.org/wiki/Voxel-based_morphometry