Whatever one might say about it on other fronts, 2016 has been a banner year for athletics. Underdog triumphs and incredible performances marked nail-biting competitions in everything from the English Premier League to this summer's Olympics in Rio to the recent World Series.

Watching all of these great sporting achievements recently brought to mind a less telegenic, but no less inspiring, moment on a playing field. This highlight took place at the 2014 FIFA World Cup tournament in Brazil, but it didn't feature a spectacular bicycle kick or a diving save. In fact, this short video of a man taking a few steps and kicking a ball would be wholly unremarkable were it not for the shiny metal exoskeleton surrounding the star's frame. What's even more amazing is the fact he is paraplegic—a victim of a spinal cord injury (SCI) in his youth—and this feat is only possible thanks to a brain-machine interface (BMI) that used neural signals from his motor cortex to drive this robotic suit.

This moment was the culmination of years of work by a large team of neuroscientists, roboticists, computer scientists, and surgeons. Work on brain-machine interfaces has only been going on in earnest for about two decades now; although in that short timeframe the technology has already advanced from simple laboratory demonstrations to multiple ongoing clinical trials. This rapid scientific progress was on my mind when selecting a Trends in Neurosciences review to highlight for this year's celebration of the 40th anniversary of the Trends journals. This is because one of the best cited articles from the journal in the past decade is a piece co-authored by the leader of the team that built the BMI used in Rio in 2014, Miguel Nicolelis of Duke University and the Edmond and Lily Safra International Neuroscience Institute of Natal.

Written by Mikhael Lebedev and Nicolelis in 2006, the article, entitled "Brain Machine Interfaces: Past, Present, and Future," aims to identify the central obstacles limiting translation of BMI technology from the lab to the clinic and suggests several possible avenues for surmounting these challenges. In addition to various technical roadblocks to engineering realistic robotic effectors—daunting problems in their own right, but of less interest to neuroscientists— Lebedev and Nicolelis highlight three central problems to developing a therapeutic BMI, specifically: (1) obtaining stable, long-term, recordings from large populations of cells distributed across multiple brain regions, (2) developing efficient algorithms to translate neural activity into BMI control signals, and (3) harnessing neural plasticity to incorporate BMIs into the body representation. Surprisingly, in the relatively short time since the review was published, significant progress has been made on each of these fronts

The first challenge that Nicolelis and Lebedev cover—developing methods to record continuously from distributed groups of neurons over long timescales—has been tackled in two basic ways. As they note in their review, recordings can be made either non-invasively (for example, via scalp EEG) or invasively (from subdural electrocorticography arrays or intracortical implants). Spatiotemporal resolution, efficacy, and reliability vary across all of these methods, though invasive options appear to offer more hope for restoring naturalistic movement to patients. However, it should be noted that using the EEG-based BMI designed for the demonstration at the World Cup was sufficient to induce partial recovery in a cohort of eight SCI patients. This suggests that, even if non-invasive BMIs can't be used to control complex effectors, they could still have therapeutic benefit.

The second challenge that Lebedev and Nicolelis highlight is optimization of algorithms translating neural activity into BMI control signals. They point out that this process will require a greater understanding of how motor and executive control result from activity in distributed neural circuits, including where in the brain these signals are generated. Furthermore, a greater understanding of how the brain encodes various motor parameters is necessary if control signals are to take into account various factors such as motor sequences and environmental context. Although these are incredibly difficult problems, efforts in these areas are continuing apace, and enough progress has been made that algorithms controlling ever more complex BMIs continue to be developed.

Finally, Lebedev and Nicolelis stress the importance of developing a better understanding of neural plasticity and using this knowledge to design BMIs that are seamlessly incorporated into neural representations of the body. Work examining how the brain adapts in order to control a BMI as well as the opportunity offered by adaptive updating of BMI control signals in response to this neural plasticity is improving BMI efficacy, for example by yielding performance improvements and skill retention, thus pushing towards greater translational potential.

Like team sports, science is a group effort. Success in both arenas requires collaboration between specialists with different talents and skills. Just as a team requires a great coach to identify areas of strength and weakness and set strategy, critical and integrative reviews such as this piece by Lebedev and Nicolelis—the ideal of what we would like Trends Reviews to be—are critical to ensuring continued scientific progress.