Searchlight analysis: Promise, pitfalls, and potential (2023)


Multivariate pattern analysis (MVPA) of functional MRI (fMRI) data has grown steadily since its beginnings in 2001(Haxby, 2012). Following Raizada and Kriegeskorte (2010), we illustrate the growth of the literature by showing the citation rate for several key MVPA papers in Fig.1. Interest in MVPA spans disciplines. Advances have arisen from synergistic interactions with the machine learning community, which has developed new methods for addressing fMRI datasets and questions, as seen in the proliferation of relevant articles (e.g. Cuingnet et al., 2011, Mitchell et al., 2004, Van De Ville and Lee, 2012) and dedicated conference workshops (e.g. the International Conference on Pattern Recognition, NIPS, Cosyne, etc.). Interest in the cognitive neuroscience applications of MVPA is just as great (e.g. Heinzle et al., 2012, Tong and Pratte, 2012, Yang et al., 2012). The growing popularity of MVPA within neuroimaging has been driven by multiple factors, including: a) suggestions that it provides greater sensitivity and specificity than mass-univariate analyses with generally complementary results (Haynes and Rees, 2005, Jimura and Poldrack, 2012, Kamitani and Tong, 2005); b) the possibility of designing tests to address hypotheses which cannot be addressed with mass-univariate methods (e.g. Knops et al., 2009, Quadflieg et al., 2011, Stokes et al., 2009); and c) the intuitive appeal of a method which incorporates the signal from multiple voxels at once.

Searchlight analysis (also called information mapping) is an MVPA method introduced as a technique for identifying locally informative areas with greater power and flexibility than mass-univariate analyses (Kriegeskorte and Bandettini, 2007a, Kriegeskorte et al., 2006). Searchlight approaches are relatively unique, in that they were developed specifically for fMRI analysis, addressing both the common localization goal (many fMRI studies aim to identify small brain areas) and the spatial structure of the BOLD signal (adjacent voxels tend to have similar activation timecourses). Searchlight analysis produces maps by measuring the information in small spherical subsets (“searchlights”) centered on every voxel; the map value for each voxel thus derives from the information present in its searchlight, not the voxel individually. Note that the word “information” is not used here in its formal sense (as in the field of information theory), but rather following its conventional use in the MVPA application literature. Specifically, we use the word “information” to indicate that the activity in a group of voxels varies consistently with experimental condition: a highly informative voxel cluster can be used to identify experimental condition more accurately than a weakly informative one.

Appealing aspects of searchlight analysis include its whole-brain approach (i.e., a priori region specification is not needed), the ability to pool over subject-specific activation patterns, and its minimization of the extremes of the curse of dimensionality associated with whole-brain MVPA (the “curse” refers to computational difficulties which can occur when there are more voxels than examples, see (Clarke et al., 2008, Jain et al., 2000); it is minimized in searchlight analysis since relatively few voxels are typically included in each searchlight). Additionally, searchlight analysis produces a whole-brain results map that is superficially similar in appearance to the whole-brain significance maps produced by more familiar mass-univariate analyses (based on the general linear model); thus, searchlight analysis results are potentially easier to interpret. These appealing aspects, plus promising early results, have led to a rapid increase in the number of studies using searchlight analyses (note the rapid rise in citations for Kriegeskorte et al., 2006 in Fig.1, particularly in the last few years). Its acceptance as a standard approach is reflected in its inclusion in recent MVPA review and methodology articles (e.g. Bandettini, 2009, Mourao-Miranda et al., 2006, Raizada and Kriegeskorte, 2010, Tong and Pratte, 2012), as well as in the most prominent MVPA software packages (BrainVoyager QX 2.0, the Princeton MVPA Toolbox, PyMVPA).

Reflecting its potential and appeal, variations of the searchlight technique have been developed. In the spatial domain, it has been extended to circular subsets on cortical surfaces (Chen et al., 2011, Oosterhof et al., 2010, Oosterhof et al., 2011), rather than the original volumetric spheres. Efforts have also been made to extend the technique to incorporate the temporal domain (Fogelson et al., 2011, Rao et al., 2011). The first searchlight analyses used the Mahalanobis distance as the similarity measure for information mapping, but a widely adopted variation is to use machine learning algorithms, often support vector machines (SVMs), instead (Haynes et al., 2007, Kriegeskorte and Bandettini, 2007b). In these approaches, generalization accuracy of the classifier is used as a proxy for information content. Group analysis is usually performed by combining individual subject's maps with a binomial or t-test at each voxel (with the null hypothesis that the group classification accuracy is at chance level), creating maps of voxels with significant searchlights. Here we primarily consider classification-based searchlight analysis, but much of the discussion applies regardless of the precise implementation.

Searchlight analysis is a powerful and attractive tool for understanding neuroimaging data. However, it has particular characteristics and limitations that can lead to serious interpretation errors in practice, and so we recommend that straightforward confirmatory and sensitivity tests (analogous to post-hoc tests after an ANOVA), such as the ones described here, be considered a standard part of the searchlight analysis procedure. In the following sections we describe two assumptions that often implicitly underlie the interpretation of searchlight analysis results. Unfortunately, as we illustrate, these assumptions do not always hold, and so may lead to distorted results. We then describe how confirmatory follow-up tests can be used to guard against particularly harmful distortions, using two hypotheses common in cognitive studies as illustrations. This manuscript is accompanied by Supplemental Information containing examples (with code) and technical details.


Information is detected consistently.

A fundamental aspect of fMRI is that information is not distributed uniformly across voxels but rather has a three-dimensional structure: some groups of voxels (e.g. those corresponding to a specific anatomical region) are more informative for a particular task than other groups of the same size. Additionally, neuroimaging data contains information at multiple spatial frequencies (Kriegeskorte et al., 2010, Op de Beeck, 2010). For example, consider a cued finger-tapping task. The finger area of the primary motor cortex will be highly informative at a very small spatial frequency while the premotor and somatosensory cortices may be equally informative, but at a larger spatial frequency. The difference can be imagined as the size of box required to enclose the minimum set of voxels capable of task classification: a larger box is necessary to enclose the pattern in premotor or somatosensory cortices than to enclose the pattern in the primary motor cortex.

The distribution of information is relevant for searchlight analysis because interpretation of any particular map depends on whether the information can be detected equally across spatial frequencies. In a simulation designed with equal power in all spatial frequency bands, Kriegeskorte et al. (2006) showed that detection did not require a close match between the size of the searchlight and the informative area: a 4mm radius consistently performed well. When this finding holds, it simplifies searchlight analysis interpretation: the peak areas of the map are the most informative voxels. However, if information is not present and detected equally at all spatial frequencies, then searchlight analysis results will depend fairly strongly upon the searchlight size; moreover, no single searchlight radius will be universally optimal or sufficient.

Additionally, although the Mahalanobis distance may be consistently sensitive to information across spatial frequency bands (Kriegeskorte et al., 2006), this property does not hold for all information measures used with searchlight analysis, especially the linear SVM. Training a linear SVM algorithm results in a set of weights; its decision function is a weighted linear combination of the voxels (Norman et al., 2006). Two properties of the linear SVM are particularly relevant when used in searchlight analysis: (1) It is sometimes able to correctly classify when the searchlight contains a small minority of highly informative voxels (intermixed with a majority of uninformative voxels), and conversely, (2) It is sometimes able to correctly classify when the searchlight contains a large number of weakly informative voxels.

Since, as described above, linear SVMs are relatively resistant to the curse of dimensionality (Jain et al., 2000), they can sometimes classify a dataset accurately even when only a tiny minority of the voxels are informative. The degree to which this occurs varies depending on dataset properties, but it happens often enough to be relevant in practice. For instance, Supplemental Example 4 shows that introducing just five informative voxels from an actual fMRI dataset into a group of two hundred random (uninformative) voxels is sufficient to shift the median accuracy of an SVM from chance to 0.6. For an extreme example, a dataset containing a single highly informative voxel and 200 random voxels is accurately classified in Supplemental Example 5. Searchlight analysis generally includes fewer than 200 voxels in each searchlight, increasing the likelihood that searchlights containing a single or only a few informative voxels will be detected (see the “Detection of rare informative voxels” section of the Supplemental Information for further discussion).

This behavior can cause distortions in a searchlight map. To illustrate, suppose that a cluster of five highly informative voxels (capable of significant classification whenever included in a searchlight) is surrounded by hundreds of truly uninformative voxels. Any searchlight overlapping the five-voxel cluster will be significant, even if the majority of its voxels are uninformative. As a result, some voxels in the results map will be categorized as significant, not because they themselves are informative, but because they are at the center of a searchlight that contains the informative voxels. Fig.2 (Supplemental Example 7) gives examples of this occurrence in an actual fMRI dataset (see Supplemental Example 6 as well): for instance, the voxel in the lower-left corner (at coordinates 1, 1) changes its mapped classification accuracy from “uninformative” to “informative” when the starred (actually informative) voxel is moved, despite there being no change the properties of the (lower-left) voxel itself.

A second issue is that the number of voxels marked as informative in a searchlight map will tend to grow as the searchlight radius increases, even when the size of the truly informative cluster stays fixed (Fig.3), so long as the curse of dimensionality does not dominate; classifiers will vary in how many uninformative voxels can be added to the fixed informative cluster before performance declines. This phenomenon, which has been termed the “needle-in-the-haystack-effect”, was demonstrated as a formal proof in Viswanathan et al. (2012). As an extreme example, Viswanathan et al. (2012) showed how all 147,000 voxels of a simulated volume would be classified as “informative” in a 3 voxel radius searchlight map when the volume contained just 430 evenly distributed informative voxels.

Another property of linear SVMs relevant for their use in searchlight analysis is that they can pool weak biases across many voxels, with the result that it is possible for a group of voxels to be classified accurately while the individual voxels making up the group do not yield significant classification, either singly or as subsets. This information “pooling” is often a useful characteristic for fMRI data, which is sometimes structured as weak information present in a large number of voxels. However, it can be troublesome for searchlight analysis interpretation. For example, suppose that there is a large cluster of voxels, each with the same small bias (i.e. a uniformly weakly informative voxel cluster). Ten voxels from this cluster (a small searchlight) may not yield significant classification, but thirty voxels (a larger searchlight) could produce a weakly significant classification, and fifty voxels, a highly significant classification (Fig.4 and Supplementary Example 1). This can be thought of as a case of discontinuous detection of information: at the extreme, a voxel cluster can change from “uninformative” to “informative” upon the addition of a single voxel (Supplementary Examples 2 and 3).

Discontinuous detection makes it possible for groups of weakly informative voxels to be partially or entirely missed when mapping information. Continuing the example, with a searchlight encompassing fewer than 30 voxels, the cluster will be classified as uninformative because no single searchlight can include enough voxels to enable accurate classification (Fig.5a). Larger searchlights could detect the cluster, but only when the shape of the searchlight matches the shape of the cluster: a spherical searchlight could miss an elliptical cluster (Fig.5b). An additional complication comes from assigning each searchlight's accuracy to its center voxel: large, weakly informative clusters will appear smaller in the information map if the searchlight radius is less than the cluster diameter, since only searchlights fully overlapping the cluster will be significant (Fig.5c).

Prior reports in the literature have documented the failure of weakly informative areas to be detected in searchlight analysis, mirroring our experience that widespread, weakly informative areas are common in fMRI datasets (see also Gonzalez-Castillo et al., 2012). For example, Eger et al. (2009) found that searchlight analysis (linear SVM, 3-voxel radius) identified no ROI voxels as informative, despite significant classification when using the whole ROI. Likewise, Diedrichsen et al. (in press) report needing to expand their searchlight size to achieve adequate sensitivity in one experimental condition (increasing from 80 to 160 voxels, with regularized linear discriminant analysis as the classification algorithm).


Spatial variation between subjects is small compared to the searchlight radius.

Most applications using searchlight analysis interpret results primarily based on group-level aggregation of single-subject information maps, even though strategies for constructing and interpreting these maps have not been fully explored. Methods for constructing group-level maps often parallel those used in mass-univariate analysis: a t-test (for average accuracy across individuals greater than chance) is conducted at every voxel independently, followed by multiple-comparisons correction (Kriegeskorte and Bandettini, 2007a). Alternatively, the individual maps are statistically thresholded and the group-level map is reported in terms of the proportion of subjects with a significant searchlight at each voxel (Pereira and Botvinick, 2011). Permutation-based tests have also been proposed (Kriegeskorte et al., 2006), with new techniques increasing their interpretability and computational tractability (Gaonkar and Davatzikos, 2012, Stelzer et al., 2013). Some authors perform the searchlight analysis in native space then normalize the individual maps to an atlas, while others normalize the images first and then perform the searchlight analysis in atlas space (both of which can introduce distortions). This proliferation of techniques reflects the importance placed on group information maps in cognitive neuroscience applications of MVPA, and also the lack of agreement regarding the best method for constructing them. All of these techniques rely on a common assumption, however: that spatial variation in the information maps between individuals is minimal compared to the searchlight radius. Group maps may be misleading if this does not hold.

Spatial variation between individuals is not a concern unique to searchlight analysis but a factor in all neuroimaging techniques. For example, smoothing is used during mass-univariate analysis to help reduce the impact of inter-individual variability. However, evaluating results when inter-individual variability is present is particularly complex in searchlight analysis because of distortions that can occur when constructing individual information maps, particularly distortions causing a mismatch between the actual informative voxels and their appearance in the searchlight map (such as those shown in Fig.3, Fig.5). Since all methods of constructing a group information map involve combining some version of the individual maps, distortions in the individual maps are carried to the group level, where their effects may be magnified.

For example, spatial variation in the location of an informative cluster between individuals may cause the cluster to be missed in the group-level map. In Fig.6a, weakly informative clusters overlap in the individual maps, but since the individual searchlight mapping detects only a minority of the informative voxels (as in Fig.5c), the individual information maps do not overlap at the group level (Fig.6b green area), and so the cluster is missing from the group information map.

At the opposite extreme, voxels that are uninformative in each individual when examined separately can be identified as being informative at the group level. To illustrate that this can occur, suppose half of the individuals have a cluster of highly informative voxels towards the left side of a ROI while the rest of the individuals have the same cluster of informative voxels, but shifted towards the right side (Fig.7a). The group-level information map will not identify the voxels corresponding to either cluster as informative but rather the voxels between the two clusters, because this is where the individual maps overlap (Fig.7b). While Fig.7 is a simple illustration contrived to show the problem, such an outcome can occur in many actual situations. Fig.8 (Supplemental Example 9) shows an occurrence in real fMRI data: The most informative voxel in the group information map (starred voxel at left) has the lowest average accuracy when the voxels are tested for classification in a univariate manner (i.e. as single voxels; Fig.8, right).

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What is a SearchLight analysis? ›

SearchLight analysis was introduced in [Kriegeskorte et al. [3]], and consists of scanning the brain with a searchlight. Briefly, 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.

What does multi-voxel pattern analysis rely on? ›

Multi-voxel pattern analysis (MVPA) involves searching for highly reproducible spatial patterns of activity that differentiate across experimental conditions.

What is multivariate pattern analysis? ›

MVPA refers to a set of methods that analyze neural responses as patterns of activity, thus affording investigation of the varying brain states that a cortical field or system can produce, thus increasing the amount of information that can be decoded from brain activity, in contrast to simpler univariate measures that ...

What is the purpose of searchlight? ›

searchlight, high-intensity electric light with a reflector shaped to concentrate the beam, used to illuminate or search for distant objects or as a beacon.

Why is it called searchlight? ›

Admission is free.

Founded in 1898, Searchlight takes its name from the Searchlight Mine.

What factors affect voxel size? ›

Voxel size and detail in MR images is determined by the values selected for the three protocol factors: FOV, matrix size, and slice thickness. The dimension of a voxel in the plan of the image is determined by the ratio of the field of view (FOV) and the size of the matrix.

What are the limitations of fMRI? ›

Limitations of fMRI

It was stated that fMRI does not measure neuron activity directly but rather does so indirectly by measuring blood flow changes to determine active areas.

What is the difference between single voxel and multi voxel spectroscopy? ›

Multi-voxel Chemical Shift Imaging (CSI) techniques offer two potential advantages over SVS: 1) a larger total coverage area (since the size of the entire multivoxel slab is greater), and 2) higher spatial resolution (since the individual voxels are smaller).

What are the 3 categories of multivariate analysis? ›

Multiple linear regression. Multiple logistic regression. Multivariate analysis of variance (MANOVA) Factor analysis.

Which are the two most common multivariate analysis methods? ›

Principal component analysis (PCA) and Factor analysis are two of the common techniques used to perform such a dimension reduction.

What is the most common multivariate analysis? ›

Multiple Regression Analysis

Multiple regression is the most commonly utilized multivariate technique. It examines the relationship between a single metric dependent variable and two or more metric independent variables.

What is most commonly used as searchlights? ›

Concave mirrors are used in search lights and torches so that we have a more focused light beam which will not diverge out and hence help in searching.

Are searchlights still used? ›

Non-military use

Today, searchlights are used in advertising, fairs, festivals and other public events. Their use was once common for movie premieres; the waving searchlight beams can still be seen as a design element in the logos of 20th Century Studios and the Fox television network.

Which is used in searchlight? ›

(b) A concave mirror is used as a search-light reflector to obtain a parallel beam of light.

What is the other term for searchlight? ›

synonyms for searchlight

On this page you'll find 24 synonyms, antonyms, and words related to searchlight, such as: flashlight, light, beacon, gaslight, torch, and gas lamp.

What was searchlight before? ›

On January 17, 2020, it was announced that the "Fox" name would be dropped from several of the Fox assets that were acquired by Disney, shortening the company's name to "Searchlight Pictures", in order to` avoid brand confusion with Fox Corporation.

What is the difference between spotlight and searchlight? ›

Re: Searchlight vs Spotlight

IMHO a "spotlight" is a narrow focus beam only and a "searchlight" is a hard mounted moveable light.

What is the difference between a pixel and a voxel? ›

A voxel is a cube within a 3D model with a position in a 3D grid and a single color value, whereas a pixel is a square inside a 2D image with a location in a 2D grid and a single color value. The fact that pixels are preserved in picture formats like PNG and JPG is another important factor.

What is the difference between pixel size and voxel size? ›

A pixel represents the smallest sampled 2D element in an image. It has dimensions given along two axes in mm, dictating in-plane spatial resolution. Pixel sizes range in clinical MRI from mm (e.g., 1 × 1 mm2) to sub-mm. A voxel is the volume element, defined in three dimensions.

What is a good voxel size? ›

A typical voxel size for a structural MRI is 1 mm x 1mm x 1mm; for fMRI, 3 mm x 3 mm x 3 mm. However, these can vary substantially from study to study.

What is the main weakness of fMRI and why? ›

Strengths and weaknesses of fMRI

Because BOLD contrast derives from the sluggish hemodynamic response to metabolic changes, a significant weakness is its low temporal resolution.

What are two major disadvantages of MRI scans? ›

Drawbacks of MRI scans include their much higher cost, and patient discomfort with the procedure. The MRI scanner subjects the patient to such powerful electromagnets that the scan room must be shielded.

What are potential limitations of brain imaging techniques? ›

Repetition of a high quality test may not produce any findings but increases the financial and psychological strain on the patient. Finally, a major limitation of neuroimaging lies in its inability to detect reliably all neuro-ophthalmic disorders of intracranial and intra- orbital etiology.

What is the smallest voxel size? ›

The small field of view units may use a small voxel size of 0.076 mm, which enables visualization of very small changes to structures. Other voxel sizes available for CBCT units are variable, such as 0.2 mm, 0.3 mm, and 0.4 mm.

What is the minimum voxel size? ›

This is a single value which results in the generation of equal volume, cubic, isotropic voxels. The minimum allowable setting is 0.10 cm and the resolution for incremental increases from this value is 0.01 cm.

Is voxel 2D or 3D? ›

Voxel is an image of a three-dimensional space region limited by given sizes, which has its own nodal point coordinates in an accepted coordinate system, its own form, its own state parameter that indicates its belonging to some modeled object, and has properties of modeled region.

What is multivariate voxel-based lesion symptom mapping? ›

This voxel‐based lesion‐symptom mapping (VLSM) technique produces a statistical map showing the strength of the relationship between damage at any given voxel and performance on a behavioral measure of interest across a group of individuals with brain lesions (Bates et al., 2003).

What is Mvpa in neuroscience? ›

Multi-Voxel Pattern Analysis (MVPA)

What is voxel analysis? ›

Voxel-based analysis involves registration of diffusion maps into a standard space (i.e., normalization) to achieve accurate registration between subjects across voxels and subsequently across anatomical structures.

What is a voxel and what does it measure in the brain? ›

Each pixel corresponds to a three-dimensional square or rectangular chunk of brain tissue called a volume element (or voxel). Each pixel of the image is typically assigned either a level of visual grayness ranging from black to white (Fig. 1) or an arbitrary color that represents a numerical value.

Does MRI use voxels? ›

An MRI image is composed of a number of voxels; the voxel size is the spatial resolution of the image. A voxel is a 3D unit of the image with a single value, just as for digital photographs a pixel is a 2D unit of the image with a single value.

What is the purpose of a voxel? ›

A voxel layer represents multidimensional spatial and temporal information in a 3D volumetric visualization. For example, you can visualize atmospheric or oceanic data, a geological underground model, or space-time cubes as voxel layers.

How many voxels are in an MRI scan? ›

Typically, fMRI machines divide the brain into three-dimensional pixels called voxels, each about five cubic millimeters in size. The complete activity of the brain at any instant can be recorded using a three-dimensional grid of 60 x 60 x 30 voxels.

How do you detect MVPA? ›

HOW DO I KNOW IF MY ACTIVITIES ARE OF MODERATE TO VIGOROUS INTENSITY? To gauge your level of intensity, you can simply measure your heart rate during your activities. Heart rate is measured in beats per minute (bpm) and can vary greatly from person to person depending on factors like age and fitness level.

What are some examples of Mvpa? ›

  • Hiking.
  • Jogging at 6 mph.
  • Shoveling.
  • Carrying heavy loads.
  • Bicycling fast (14-16 mph)
  • Basketball game.
  • Soccer game.
  • Tennis singles.

What are the 4 types of exercise defined by the ACSM? ›

Exercises should involve motor skills (balance, agility, coordination and gait), proprioceptive exercise training, and multifaceted activities (yoga) to improve physical function and prevent falls in older adults.


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