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The origin of modern science is often located in Europe and the West. This Euro/West-centrism relegates emergent practices elsewhere to the periphery, undergirding analyses of contemporary transnational science and technology with traditional but now untenable hierarchical categories. In this book, Amit Prasad examines features of transnationality in science and technology through a study of MRI research and development in the United States, Britain, and India. In an analysis that is both theoretically nuanced and empirically robust, Prasad unravels the entangled genealogies of MRI research, practice, and culture in these three countries. Prasad follows sociotechnical trails in relation to five aspects of MRI research: invention, industrial development, market, history, and culture. He first examines the well-known dispute between American scientists Paul Lauterbur and Raymond Damadian over the invention of MRI, then describes the post-invention emergence of the technology, as the center of MRI research shifted from Britain to the U.S; the marketing of the MRI and the transformation of MRI research into a corporate-powered \"Big Science\"; and MRI research in India, beginning with work in India's nuclear magnetic resonance (NMR) laboratories in the 1940s. Finally, he explores the different dominant technocultures in each of the three countries, analyzing scientific cultures as shifting products of transnational histories rather than static products of national scientific identities and cultures. Prasad's analysis offers not only an innovative contribution to current debates within science and technology studies but also an original postcolonial perspective on the history of cutting-edge medical technology.

Following early efforts in applying nuclear magnetic resonance (NMR) spectroscopy to study biological processes in intact systems, and particularly since the introduction of 4 T human scanners circa 1990, rapid progress was made in imaging and spectroscopy studies of humans at 4 T and animal models at 9.4 T, leading to the introduction of 7 T and higher magnetic fields for human investigation at about the turn of the century. Work conducted on these platforms has provided numerous technological solutions to challenges posed at these ultrahigh fields, and demonstrated the existence of significant advantages in signal-to-noise ratio and biological information content. Primary difference from lower fields is the deviation from the near field regime at the radiofrequencies (RF) corresponding to hydrogen resonance conditions. At such ultrahigh fields, the RF is characterized by attenuated traveling waves in the human body, which leads to image non-uniformities for a given sample-coil configuration because of destructive and constructive interferences. These non-uniformities were initially considered detrimental to progress of imaging at high field strengths. However, they are advantageous for parallel imaging in signal reception and transmission, two critical technologies that account, to a large extend, for the success of ultrahigh fields. With these technologies and improvements in instrumentation and imaging methods, today ultrahigh fields have provided unprecedented gains in imaging of brain function and anatomy, and started to make inroads into investigation of the human torso and extremities. As extensive as they are, these gains still constitute a prelude to what is to come given the increasingly larger effort committed to ultrahigh field research and development of ever better instrumentation and techniques. •Development of ultrahigh magnetic field MRI for human imaging.•Functional brain imaging (fMRI) at ultrahigh magnetic fields; cortical columns and layers.•Solutions to Ultrahigh field challenges: Power deposition (SAR), transmit RF and static magnetic field inhomogeneities.•Multichannel RF (B1) transmit and transmit pulses.•Highly accelerated imaging for high resolution whole brain coverage; Human Brain Connectome at ultrahigh fields.

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.

Diffusion MRI (or dMRI) came into existence in the mid-1980s. During the last 25years, diffusion MRI has been extraordinarily successful (with more than 300,000 entries on Google Scholar for diffusion MRI). Its main clinical domain of application has been neurological disorders, especially for the management of patients with acute stroke. It is also rapidly becoming a standard for white matter disorders, as diffusion tensor imaging (DTI) can reveal abnormalities in white matter fiber structure and provide outstanding maps of brain connectivity. The ability to visualize anatomical connections between different parts of the brain, non-invasively and on an individual basis, has emerged as a major breakthrough for neurosciences. The driving force of dMRI is to monitor microscopic, natural displacements of water molecules that occur in brain tissues as part of the physical diffusion process. Water molecules are thus used as a probe that can reveal microscopic details about tissue architecture, either normal or in a diseased state.

Radiation therapy (RT) continues to play an important role in the treatment of cancer. Adaptive RT (ART) is a novel method through which RT treatments are evolving. With the ART approach, computed tomography or magnetic resonance (MR) images are obtained as part of the treatment delivery process. This enables the adaptation of the irradiated volume to account for changes in organ and/or tumor position, movement, size, or shape that may occur over the course of treatment. The advantages and challenges of ART maybe somewhat abstract to oncologists and clinicians outside of the specialty of radiation oncology. ART is positioned to affect many different types of cancer. There is a wide spectrum of hypothesized benefits, from small toxicity improvements to meaningful gains in overall survival. The use and application of this novel technology should be understood by the oncologic community at large, such that it can be appropriately contextualized within the landscape of cancer therapies. Likewise, the need to test these advances is pressing. MR-guided ART (MRgART) is an emerging, extended modality of ART that expands upon and further advances the capabilities of ART. MRgART presents unique opportunities to iteratively improve adaptive image guidance. However, although the MRgART adaptive process advances ART to previously unattained levels, it can be more expensive, time-consuming, and complex. In this review, the authors present an overview for clinicians describing the process of ART and specifically MRgART.

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

Functional connectivity between different brain regions can be estimated from MRI data by computing the temporal correlation of low frequency (<0.1Hz) fluctuations in the MRI signal. These correlated fluctuations occur even when the subject is “at rest” (not asked to perform any particular task) and result from spontaneous neuronal activity synchronized within multiple distinct networks of brain regions. This estimate of connectivity, however, can be influenced by physiological noise, such as cardiac and respiratory fluctuations. This brief review looks at the effect of physiological noise on estimates of resting-state functional connectivity, discusses ways to remove physiological noise, and provides a personal recollection of the early developments in these approaches. This review also discusses the importance of physiological noise correction and provides a summary of evidence demonstrating that functional connectivity does have a neuronal underpinning and cannot purely be the result of physiological noise.

The anatomy of language has been investigated with PET or fMRI for more than 20 years. Here I attempt to provide an overview of the brain areas associated with heard speech, speech production and reading. The conclusions of many hundreds of studies were considered, grouped according to the type of processing, and reported in the order that they were published. Many findings have been replicated time and time again leading to some consistent and undisputable conclusions. These are summarised in an anatomical model that indicates the location of the language areas and the most consistent functions that have been assigned to them. The implications for cognitive models of language processing are also considered. In particular, a distinction can be made between processes that are localized to specific structures (e.g. sensory and motor processing) and processes where specialisation arises in the distributed pattern of activation over many different areas that each participate in multiple functions. For example, phonological processing of heard speech is supported by the functional integration of auditory processing and articulation; and orthographic processing is supported by the functional integration of visual processing, articulation and semantics. Future studies will undoubtedly be able to improve the spatial precision with which functional regions can be dissociated but the greatest challenge will be to understand how different brain regions interact with one another in their attempts to comprehend and produce language.

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.