Larger research projects with heavy data needs may have the resources to exercise more autonomy. But even so, it is imperative that such projects put a qualified person in charge of cybersecurity who can take sole responsibility for keeping up with the fast-moving requirements that security issues present.Large group or small, the ultimate responsibility for protecting data and other resources has to rest with the laboratories that own them. Every lab director must be aware of the risks, and must treat cybersecurity with the same respect as laboratory safety, patient safety and scientific integrity.

This attitude is unhelpful, bordering on reckless. University information-technology administrators do need to manage things with as light and as unobtrusive a hand as possible — for example, by making sure that researchers retain the freedom to use the software they choose. But laboratories, especially the smaller ones, need to avail themselves of the professionals' skills as much as possible.

Worse, from researchers' point of view, is that much — if not all — of their hard-won laboratory data live in that environment, where the information is vulnerable to theft and malicious damage. Computer security has moved up the agenda of universities and other research institutions over the past decade, and most places now have teams of professionals to monitor suspicious traffic and maintain a safe environment.But such structured, centralized efforts can result in controls that raise scientists' hackles and violate their impulse to do things their own way. As a result, too many researchers set up their own computer systems and ignore any security help the university's professionals can give them.

Most scientists, like most Internet users, probably think of cybercrime as a misfortune that happens to others — to banks, say, or to online retailers who are careless with customers' credit-card information, or to individuals who fall for a get-rich-quick e-mail from Nigeria.But the unsettling truth is that academic institutions are among hackers' prime targets. Not only do campuses tend to be richly supplied with personal computers, servers and other computing resources, but they are connected to the world by high-bandwidth networks and populated by inexperienced, casual and sometimes reckless students (see page 1260). This wide-open computational environment is ripe for being co-opted, whether it is to send out spam, run illegal file-sharing sites or launch further cyberattacks.

Still, it is unlikely that Europe’s quantum-technology initiative will take this route. Given the many scientific goals in the manifesto, the authors seem to hope that the plan will have its own quantum properties and be able to address all the goals simultaneously. That looks like a mistake. It would be a missed opportunity if the quantum world that the commission hopes to create is hamstrung by the small steps and endless compromise that haunt other European projects. The initiative needs a clear and a bold goal. This is one project that should not have to be in several places at once.

Two of the five ideas that were presented — an invention that permits quantum computers to be linked, and a start-up that will design quantum machine-learning algorithms — set out to depend on the few companies and groups who have already invested huge sums of money to try to build quantum-computing hardware. Both ideas are betting on being able to sell their products to only a few customers. It sounds like a risky strategy, but it might indicate a way to create and sustain the necessary critical mass of start-ups that the European Quantum Manifesto is aiming for. Focusing investment on one high-risk, high-gain goal — such as a universal quantum computer — could create a string of start-ups that each specialize in one integral component or aspect.

A PhD student from University College London invented a quantum-inspired accelerometer with a relatively safe and clear route to market. And two postdocs from the University of New South Wales in Sydney, Australia, have the ambition to outshine Google and IBM and build a universal quantum computer based on silicon qubits.

Can we peek inside the box to get some insights on how this commercial future might unfold? Nature has designed an experiment to try. The project (see go.nature.com/53iiw6) trained seven young quantum physicists to conceive and evaluate business ideas in quantum technologies. The project culminated in a presentation day last week at Nature’s London office, where the physicists’ ideas were scrutinized by a panel of experienced entrepreneurs and leaders in quantum technologies.

Given that a large majority of start-up firms fail, how is this plan supposed to work in the risky and unproven quantum-technology business? Predicting the likely outcome of the European Commission’s plan is as hard as determining whether Schrödinger’s cat is dead or alive without opening its box.

Revolutions happen through popular uprising and not through carefully directed government investment. At some point, investors, entrepreneurs and academics are supposed to conspire on this revolution without directives from above. Hence the European Quantum Manifesto seeks to mobilize a broad base of quantum technologists. Specifically, it plans an environment in which small, high-potential quantum-tech businesses can thrive.

These are great expectations. Europe is no doubt encouraged by the various quantum technologies that have matured in recent years. Quantum sensors, for example, can achieve high sensitivity and resolution through quantum superposition or entanglement, outperforming classical sensors in various imaging applications. Strategic use of funds could indeed take quantum sensors to market in a few years. But for most quantum technologies, the path to commercialization is much longer and more contrived. The arguable peak of quantum technologies — the construction of a universal quantum computer — is decades, and billions of euros of targeted investment, away. But it promises perhaps the greatest gains: substantially greater power for key computations, such as simulations of chemical reactions and — maybe — machine learning.

Can this widespread ignorance — the puzzlement at how cats can be both alive and dead, or how particles can exist in two places at once — be capitalized on? The European Commission believes that it can. Next week, it will release a plan for a continent-wide drive to turn the mysteries of quantum physics into hard cash. This plan, called the European Quantum Manifesto, will be officially released in the Dutch town of Delft, where the commission hopes a revolution will be born. Eyeing China, Australia, Canada and other countries that have invested huge sums of money in quantum technology, Europe does not want to miss out. With €1 billion (US$1.1 billion) of funding, scientists and businesses will be expected to translate quantum research into quantum products to create “a more sustainable, more productive, more entrepreneurial and more secure European Union”.

Nobody ever went broke by underestimating the intelligence of the American public, goes the famous line by the US editor Henry Louis Mencken. It’s actually a paraphrase, but the meaning is clear: to make money, it is safe to assume that nobody knows anything. Related storiesExpect knowledgeQuantum leapPhysics: Quantum computer questBy rights, then, quantum physics should be extremely profitable. The subject is often used as shorthand for knowledge that is reserved for a small intellectual elite, with everyone else left scratching their heads. As Canadian Prime Minister Justin Trudeau showed last month, the quantum world is so weird that to mount even a half-decent explanation of its basic principles can bring praise and plaudits.

Given the breadth of human brain mapping techniques available in neuroscience research, it is difficult to cover each of these approaches in one journal issue. The methodologies highlighted in this focus issue are by no means intended to define the scope of neuroimaging work considered for publication at Nature Neuroscience. Rather, these pieces were commissioned to inform our readers about exciting advances in the field and to highlight some of the areas in which the field is rapidly developing. It is our hope that the neuroscience community at large will consider these noninvasive approaches as essential tools that provide substantial insights into brain structure and function, when combined with strong research questions, experimental rigor in study design and informed choices in data acquisition and analyses. With this issue, we celebrate the valuable neuroscientific contributions from human brain mapping, and we look forward to working closely with the neuroimaging community to develop and publish new, exciting neuroscience research using these techniques.

Neuroimaging data are often used as 'biomarkers' for particular behavioral traits or disordered processes in the brain. On page 365, Tor Wager and colleagues provide a critical review of translational research in which neuroimaging data are used to predict clinical outcomes. Based on their survey of the published literature, they propose general recommendations for building better neuroimaging biomarkers for health and disease.

Moving from neuroimaging data acquired with magnetic fields of typically 3 Tesla or more to measurements at the femtotesla scale (10−15 Tesla), MEG enables the detection of magnetic inductions that are generated by neuronal activity. On page 327, Sylvain Baillet reviews several aspects of MEG that are advantageous for examining neural processing in humans relative to EEG, fMRI or positron emission tomography (PET). The review also discusses the application of machine-learning techniques to MEG data, developments in making MEG data available on a larger scale ('big data') and some major conceptual advances provided by MEG research so far.The complexity of neuroimaging data sets allows researchers to examine properties of collective neural activity at the level of networks. On page 340, Michael Breakspear provides an essential introduction to models of large-scale brain dynamics for neuroscientists. In his paper, he outlines core theoretical concepts for examining neural activity using this framework, as well as considerations and insights that might arise when this framework is applied to different modalities of neuroimaging data (fMRI, EEG, etc.). On page 353, Danielle Bassett and Olaf Sporns discuss parallel efforts in examining networks at the genetic, molecular, neuronal, regional and behavioral scale, and they encourage the neuroscience community to consider network-level research questions that bridge across scales and species.

MRI also provides an unparalleled opportunity to noninvasively measure brain structure. On page 314, Jason Lerch and colleagues present the next installment of Nature Neuroscience's series promoting data quality. This piece provides an overview of the structural and diffusion MRI methods used to examine neuroanatomy at macroscopic, mesoscopic and microscopic scales, accompanied by important considerations for acquiring, analyzing and interpreting MRI data. The authors also briefly cover studies of human structural neurodevelopment and MRI applications in population neuroscience, a field that examines epidemiological and genetic influences on human brain structure.

fMRI data are acquired in high resolution across three spatial dimensions and time, yet standard analysis methods do not always take advantage of the richness of these data. On page 304, Nicholas Turk-Browne and colleagues discuss advanced fMRI analysis techniques that uncover unique insight into neural computations in humans, enable shared inferences about neural processes across multiple humans and describe a computing infrastructure for performing these cutting-edge analyses.

In light of growing concerns about the robustness and reproducibility of functional MRI (fMRI) research findings, the Organization for Human Brain Mapping has created the Committee on Best Practices in Data Analysis and Sharing (COBIDAS) to delineate standards for reporting MRI methods, analyses and data sharing. On page 299, the COBIDAS comments on reproducibility pertaining to MRI-based research and on the sociological impediments to adopting their suggested practices. (For our editorial stance on recent concerns about fMRI research, please see our accompanying editorial http://dx.doi.org/10.1038/nn.4521.)

Neuroscientists endeavor to understand how the brain develops and controls our perception of the world and our interactions with it. Animal models enable investigations of the genetic, molecular, cellular, circuit-level and neurophysiological mechanisms underlying these processes. Noninvasive technologies such as magnetic resonance imaging (MRI), magnetoencephalography (MEG) and electroencephalography (EEG) complement these approaches by assessing human brain structure and neural responses to complex behaviors. In this issue, Nature Neuroscience presents a series of commissioned pieces that discuss recent progress in several noninvasive techniques and put forth conceptual frameworks under which we can examine neuroimaging data to deepen our understanding of these rich data sets. These advances may help connect findings from various species and achieve a more complete picture of the brain's structure and function.

Computations have limits: they take up space, time and energy. In 2000, IT researcher Seth Lloyd calculated the computing power of the ultimate laptop, which, by miraculous engineering advances, could harness all its energy for information processing (S. Lloyd Nature 406, 1047–1054; 2000). This ultimate machine could perform 1051 operations per second, 40 orders of magnitude more than computers today. That represents 250 years of progress at current rates of improvement. Markov’s message is not to be overly optimistic or pessimistic about further progress. We should focus on the boundaries and push to see where they yield.

On page 147, information technologist Igor Markov argues that we should focus on the fundamental limits in computing, and use those to evaluate future possibilities. This approach has a rich history. Working out the maximum efficiency of steam engines, nineteenth-century physicists discovered thermodynamics. Modern information science was born in 1948 when Claude Shannon at Bell Labs considered what an ideal communication channel would look like.

No emerging technology is likely to be a get-out-jail-free card. Amazing performance in one area is often accompanied by serious limits in another. Computing based on carbon nanotubes or graphene, for example, presents formidable challenges in reliable fabrication.

What emerging technologies promise to displace conventional silicon chips? Future computers could run on graphene, perhaps, or the hidden powers of quantum physics or brain-like synaptic networks. Research on all these options and more is under way as it becomes clear that enhancement of silicon-chip technology is hitting serious practical obstacles: in manufacturing, connectivity and heat generation.

It is the project of Dr. Jay Giedd (pronounced Geed), chief of brain imaging in the child psychiatry branch at the National Institute of Mental Health. Giedd, 43, has devoted the past 13 years to peering inside the heads of 1,800 kids and teenagers using high-powered magnetic resonance imaging (MRI). For each volunteer, he creates a unique photo album, taking MRI snapshots every two years and building a record as the brain morphs and grows. Giedd started out investigating the developmental origins of attention-deficit/hyperactivity disorder (ADHD) and autism ("I was going alphabetically," he jokes) but soon discovered that so little was known about how the brain is supposed to develop that it was impossible to figure out where things might be going wrong. In a way, the vast project that has become his life's work is nothing more than an attempt to establish a gigantic control group. "It turned out that normal brains were so interesting in themselves," he marvels. "And the adolescent studies have been the most surprising of all."

Before the imaging studies by Giedd and his collaborators at UCLA, Harvard, the Montreal Neurological Institute and a dozen other institutions, most scientists believed the brain was largely a finished product by the time a child reached the age of 12. Not only is it full-grown in size, Giedd explains, but "in a lot of psychological literature, traced back to [Swiss psychologist Jean] Piaget, the highest rung in the ladder of cognitive development was about age 12 — formal operations." In the past, children entered initiation rites and started learning trades at about the onset of puberty. Some theorists concluded from this that the idea of adolescence was an artificial construct, a phenomenon invented in the post-Industrial Revolution years. Giedd's scanning studies proved what every parent of a teenager knows: not only is the brain of the adolescent far from mature, but both gray and white matter undergo extensive structural changes well past puberty. "When we started," says Giedd, "we thought we'd follow kids until about 18 or 20. If we had to pick a number now, we'd probably go to age 25."

Five young men in sneakers and jeans troop into a waiting room at the National Institutes of Health Clinical Center in Bethesda, Md., and drape themselves all over the chairs in classic collapsed-teenager mode, trailing backpacks, a CD player and a laptop loaded with computer games. It's midafternoon, and they are, of course, tired, but even so their presence adds a jangly, hormonal buzz to the bland, institutional setting. Fair-haired twins Corey and Skyler Mann, 16, and their burlier big brothers Anthony and Brandon, 18, who are also twins, plus eldest brother Christopher, 22, are here to have their heads examined. Literally. The five brothers from Orem, Utah, are the latest recruits to a giant study that's been going on in this building since 1991. Its goal: to determine how the brain develops from childhood into adolescence and on into early adulthood.

The criminologists who promoted the superpredator theory have acknowledged that their prediction never came to pass, repudiated the theory and expressed regret. They have joined several dozen other criminologists in an amicus brief to the court asking it to strike down life without parole sentences for children convicted of murder. I urge the justices to apply the logic and the wisdom of their earlier decisions and affirm that the best time to decide whether someone should spend his entire life in prison is when he has grown to be an adult, not when he is still a child.

An overwhelming majority of young offenders grow out of crime. But it is impossible at the time of sentencing for mental health professionals to predict which youngsters will fall within that majority and grow up to be productive, law-abiding citizens and which will fall into the small minority that continue to commit crimes. For this reason, the court has previously recognized that children should not be condemned to die in prison without being given a “meaningful opportunity to obtain release based on demonstrated maturity and rehabilitation.”

As a former juvenile court judge, I have seen firsthand the enormous capacity of children to change and turn themselves around. The same malleability that makes them vulnerable to peer pressure also makes them promising candidates for rehabilitation.

The most disturbing part of the superpredator myth is that it presupposed that certain children were hopelessly defective, perhaps genetically so. Today, few believe that criminal genes are inherited, except in the sense that parental abuse and negative home lives can leave children with little hope and limited choices.

Homicide is the worst crime, but in striking down the juvenile death penalty in 2005, the Supreme Court recognized that even in the most serious murder cases, “juvenile offenders cannot with reliability be classified among the worst offenders”: they are less mature, more vulnerable to peer pressure, cannot escape from dangerous environments, and their characters are still in formation. And because they remain unformed, it is impossible to assume that they will always present an unacceptable risk to public safety.

The court has already struck down the death penalty for juveniles and life without parole for young offenders convicted in nonhomicide cases. The rationale for these earlier decisions is simple and equally applicable to the cases to be heard: Young people are biologically different from adults. Brain imaging studies reveal that the regions of the adolescent brain responsible for controlling thoughts, actions and emotions are not fully developed. They cannot be held to the same standards when they commit terrible wrongs.

But the prediction of a generation of superpredators never came to pass. Beginning in the mid-1990s, violent juvenile crime declined, and it has continued to decline through the present day. The laws that were passed to deal with them, however, continue to exist. This month, the United States Supreme Court will hear oral arguments in two cases, Jackson v. Hobbs and Miller v. Alabama, which will decide whether children can be sentenced to life without parole after being convicted of homicide.

Nationwide, 79 young adolescents have been sentenced to die in prison — a sentence not imposed on children anywhere else in the world. These children were told that they could never change and that no one cared what became of them. They were denied access to education and rehabilitation programs and left without help or hope.

At the same time, “tough on crime” rhetoric led some states to enact laws making it easier to impose life without parole sentences on adults. The unintended consequence of these laws was that children as young as 13 and 14 who were charged as adults became subject to life without parole sentences.

IN the late 1980s, a small but influential group of criminologists predicted a coming wave of violent juvenile crime: “superpredators,” as young as 11, committing crimes in “wolf packs.” Politicians soon responded to those fears, and to concerns about the perceived inadequacies of state juvenile justice systems, by lowering the age at which children could be transferred to adult courts. The concern was that offenders prosecuted as juveniles would have to be released at age 18 or 21.