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FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on “20 years of fMRI” we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis.

FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source.

Karl Friston began the SPM project around 1991. The rest is history

The core concept within the field of brain mapping is the use of a standardized, or “stereotaxic”, 3D coordinate frame for data analysis and reporting of findings from neuroimaging experiments. This simple construct allows brain researchers to combine data from many subjects such that group-averaged signals, be they structural or functional, can be detected above the background noise that would swamp subtle signals from any single subject. Where the signal is robust enough to be detected in individuals, it allows for the exploration of inter-individual variance in the location of that signal. From a larger perspective, it provides a powerful medium for comparison and/or combination of brain mapping findings from different imaging modalities and laboratories around the world. Finally, it provides a framework for the creation of large-scale neuroimaging databases or “atlases” that capture the population mean and variance in anatomical or physiological metrics as a function of age or disease. However, while the above benefits are not in question at first order, there are a number of conceptual and practical challenges that introduce second-order incompatibilities among experimental data. Stereotaxic mapping requires two basic components: (i) the specification of the 3D stereotaxic coordinate space, and (ii) a mapping function that transforms a 3D brain image from “native” space, i.e. the coordinate frame of the scanner at data acquisition, to that stereotaxic space. The first component is usually expressed by the choice of a representative 3D MR image that serves as target “template” or atlas. The native image is re-sampled from native to stereotaxic space under the mapping function that may have few or many degrees of freedom, depending upon the experimental design. The optimal choice of atlas template and mapping function depend upon considerations of age, gender, hemispheric asymmetry, anatomical correspondence, spatial normalization methodology and disease-specificity. Accounting, or not, for these various factors in defining stereotaxic space has created the specter of an ever-expanding set of atlases, customized for a particular experiment, that are mutually incompatible. These difficulties continue to plague the brain mapping field. This review article summarizes the evolution of stereotaxic space in term of the basic principles and associated conceptual challenges, the creation of population atlases and the future trends that can be expected in atlas evolution.

In 2001, we published a paper on the representation of faces and objects in ventral temporal cortex that introduced a new method for fMRI analysis, which subsequently came to be called multivariate pattern analysis (MVPA). MVPA now refers to a diverse set of methods that analyze neural responses as patterns of activity that reflect the varying brain states that a cortical field or system can produce. This paper recounts the circumstances and events that led to the original study and later developments and innovations that have greatly expanded this approach to fMRI data analysis, leading to its widespread application.

AFNI is an open source software package for the analysis and display of functional MRI data. It originated in 1994 to meet the specific needs of researchers at the Medical College of Wisconsin, in particular the mapping of activation maps to Talairach–Tournoux space, but has been expanded steadily since then into a wide-ranging set of tool for FMRI data analyses. AFNI was the first platform for real-time 3D functional activation and registration calculations. One of AFNI's main strengths is its flexibility and transparency. In recent years, significant efforts have been made to increase the user-friendliness of AFNI's FMRI processing stream, with the introduction of “super-scripts” to setup the entire analysis, and graphical front-ends for these managers. ► How AFNI came to be and how it is structured. ► Outline of recent usability and statistical improvements. ► Speculations about the future of AFNI and FMRI software.

The opportunity to explore the human connectome using cutting-edge neuroimaging methods has elicited widespread interest. How far will the field be able to progress in deciphering long-distance connectivity patterns and in relating differences in connectivity to phenotypic characteristics in health and disease? We discuss the daunting nature of this challenge in relation to specific complexities of brain circuitry and known limitations of in vivo imaging methods. We also discuss the excellent prospects for continuing improvements in data acquisition and analysis. Accordingly, we are optimistic that major insights will emerge from human connectomics in the coming decade.

Surface-based brain imaging analysis offers the advantages of preserving the topology of cortical activation, increasing statistical power of group-level statistics, estimating cortical thickness, and visualizing with ease the pattern of activation across the whole cortex. SUMA is an open-source suite of programs for performing surface-based analysis and visualization. It was designed since its inception to allow for a fine control over the mapping between volume and surface domains, and for very fast and simultaneous display of multiple surface models and corresponding multitudes of datasets, all while maintaining a direct two-way link to volumetric data from which surface models and data originated. SUMA provides tools for performing spatial operations such as controlled smoothing, clustering, and interactive ROI drawing on folded surfaces in 3D, in addition to the various level-1 and level-2 FMRI statistics including FDR and FWE correction for multiple comparisons. In our contribution to this commemorative issue of Neuroimage we touch on the importance of surface-based analysis and provide a historic backdrop that motivated the creation of SUMA. We also highlight features that are particular to SUMA, notably the standardization procedure of meshes to greatly facilitate group-level analyses, and the ability to control SUMA's graphical interface from external programs making it possible to handle large collections of data with relative ease.

We present a history of the concepts and developments that have led us to focus on the resting state as an object of study. We then discuss resting state research performed in our laboratory since 2005 with an emphasis on papers of particular interest.

Neuroimaging studies began using block design and event-related design experiments. While providing many insights into brain functions, these fMRI design types ignore components of the BOLD signal that can teach us additional elements. The development of the mixed block/event-related fMRI design allowed for a fuller characterization of nonlinear and time-sensitive neuronal responses: for example, the interaction between block and event related factors and the simultaneous extraction of transient activity related to trials and block transitions and sustained activity related to task-level processing. This review traces the origins of the mixed block/event-related design from conceptual precursors to a seminal paper and on to subsequent studies using the method. The review also comments on aspects of the experimental design that must be considered when attempting to use the mixed block/event-related design. When taking into account these considerations, the mixed block/event-related design allows fuller utilization of the BOLD signal allowing deeper interpretation of how regions of the brain function on multiple timescales. ► Mixed fMRI design allows for extraction of transient and sustained BOLD activity. ► Different BOLD timescales suggest different neural functions. ► Mixed design allows for modeling of putative task control signals. ► Use of mixed design requires power considerations prior to implementation.