Glossary

The Glossary provides short definitions of neuro-imaging concepts as well as Nidl specific vocabulary.

If you wish to add a missing term, please create an issue or open a Pull Request.

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.

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.

faces

When referring to surface data, a face corresponds to one of the triangles of a triangular mesh.

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.

fMRIPrep

fMRIPrep is a fMRI 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 fMRI, graph theory measures, surface or volume-based statistics, etc.

functional connectivity

Functional connectivity is a measure of the similarity of the response patterns in two or more regions.

functional connectome

functional connectome is a set of connections representing brain interactions between regions.

MEG

Magnetoencephalography is a functional neuroimaging technique for mapping brain activity by recording magnetic fields produced by electrical currents occurring naturally in the brain.

mesh

In the context of brain surface data, a mesh refers to a 3D representation of the brain’s surface geometry. It is a collection of vertices, edges, and faces that define the shape and structure of the brain’s outer surface. Each vertex represents a point in 3D space, and edges connect these vertices to form a network. Faces are then created by connecting three or more vertices to form triangles.

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

Multi-Voxel Pattern Analysis. This is the way 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.

probabilistic atlas

Probabilistic atlases define soft parcellations of the brain in which the regions may overlap. In such atlases, and contrary to deterministic atlases, a voxel can belong to several components. These atlases are represented by 4D images where the 3D components, also called ‘spatial maps’, are stacked along one dimension (usually the 4th dimension). In each 3D component, the value at a given voxel indicates how strongly this voxel is related to this component.

resting-state

Resting state 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.

self-supervised learning

Self-supervised learning is a form of unsupervised learning where the data itself provides the supervision. In particular, it allows to learn the statistical dependencies between the input variables without relying on labels. The idea is to create surrogate tasks from the data that can be used to learn useful representations. For instance, in computer vision, a common self-supervised task is to learn an invariant representation to a set of stochastic data augmentations (like random crops, rotations or blur). The main challenge is to avoid representation collapse where the model learns a trivial solution (e.g. a constant representation). Various methods have been proposed to prevent this collapse, such as contrastive learning, redundancy reduction, or clustering-based methods.

In neuroimaging, self-supervised learning can be used to learn representations of brain images without relying on labeled data, which can be scarce or expensive to obtain. These learned representations can then be fine-tuned for specific downstream tasks, such as disease classification or cognitive state prediction.

SNR

SNR stands for “Signal to Noise Ratio” and is a measure comparing the level of a given signal to the level of the background noise.

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:

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.

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.

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.

vertex

A vertex (plural vertices) represents the coordinate of an angle of face on a triangular mesh in 3D space.

voxel

A voxel represents a value on a regular grid in 3D space.