Neuralink has announced that the U.S. Food and Drug Administration (FDA) has approved the launch of its first clinical study in humans. "We are excited to share that we have received the FDA's approval to launch our first-in-human clinical study!" Neuralink's official Twitter account wrote on Thursday.(opens in a new tab) "This is the result of incredible work by the Neuralink team in close collaboration with the FDA and represents an important first step that will one day allow our technology to help many people." The neurotechnology company isn't recruiting test subjects just yet, and hasn't released any information on exactly what the clinical trial will involve. Even so, fans of Neuralink founder Elon Musk are already chomping(opens in a new tab) at(opens in a new tab) the(opens in a new tab) bit(opens in a new tab) to implant questionable experimental technology in their grey matter. Neuralink aims to develop implantable devices that will let people control computers with their brain, as well as restore vision or mobility to people with disabilities.

Turns out Elon Musk's FDA prediction was only off by about a month. After reportedly denying the company's overtures in March, the FDA approved Neuralink's application to begin human trials of its prototype Link brain-computer interface (BCI) on Thursday. Founded in 2016, Neuralink aims to commercialize BCIs in wide-ranging medical and therapeutic applications -- from stroke and spinal cord injury (SCI) rehabilitation, to neural prosthetic controls, to the capacity "to rewind memories or download them into robots," Neuralink CEO Elon Musk promised in 2020. BCIs essentially translate the analog electrical impulses of your brain (monitoring it using hair-thin electrodes delicately threaded into that grey matter) into the digital 1's and 0's that computers understand. Since that BCI needs to be surgically installed in a patient's noggin, the FDA -- which regulates such technologies -- requires that companies conduct rigorous safety testing before giving its approval for commercial use.

Gert-Jan Oskam, paralyzed for 12 years, is able to walk again thanks to the brain-spine "digital bridge" interface developed at France's Atomic Energy Commission (CEA). A paralyzed man has regained the ability to walk thanks to artificial intelligence-powered implants that re-established communication between the brain and spinal cord, researchers said. "Now I can just do what I want – when I decide to make a step the stimulation will kick in as soon as I think about it," Gert-Jan Oskam said, adding that he now has "freedom that I did not have" and that between the surgeries and therapy, it has been "a long journey to get here." Oskam, a 40-year-old Dutchman, was left paralyzed following a cycling accident 12 years ago. He lost full use of his legs and partial use of his arms due to damage to the spinal cord in his neck.

Chinese scientists claim to have designed a brain implant that allows a monkey to control a robotic arm using just its mind. Researchers at Nankai University shared the announcement on May 5, praising it as a breakthrough that will improve the lives of people with disabilities. The brain-computer transforms electroencephalogram (EEG) signals into the animal's control instructions to navigate the machine with food attached. The research has not been peer-reviewed, and the claims - which cannot be verified independently - are only available in a statement on the university's website. 'The trial was led by the team of Professor Duan Feng of Nankai University and jointly completed with the General Hospital of the Chinese People's Liberation Army (301 Hospital) and Shanghai Xinwei Medical Technology Co., Ltd,' the announcement reads.

It sounds like the stuff of science fiction - but a company in Utah has already implanted brain chips in dozens of patients. Blackrock Neurotech, based in Salt Lake City, has the grand ambition of curing physical paralysis, blindness, deafness and depression. The chip -- known as NeuroPort Array -- allow people to control robotic arms and wheelchairs, play video games and even feel sensations. It works by using nearly 100 microneedles that attach to the brain and read electrical signals produced by someone's thoughts. More than three dozen people have so far received it.

Multi-subject fMRI data is critical for evaluating the generality and validity of findings across subjects, and its effective utilization helps improve analysis sensitivity. We develop a shared response model for aggregating multi-subject fMRI data that accounts for different functional topographies among anatomically aligned datasets. Our model demonstrates improved sensitivity in identifying a shared response for a variety of datasets and anatomical brain regions of interest. Furthermore, by removing the identified shared response, it allows improved detection of group differences. The ability to identify what is shared and what is not shared opens the model to a wide range of multi-subject fMRI studies.

Whole-brain neural connectivity data are now available from viral tracing experiments, which reveal the connections between a source injection site and elsewhere in the brain.

This paper presents Generalized Correspondence-LDA (GC-LDA), a generalization of the Correspondence-LDA model that allows for variable spatial representations to be associated with topics, and increased flexibility in terms of the strength of the correspondence between data types induced by the model. We present three variants of GC-LDA, each of which associates topics with a different spatial representation, and apply them to a corpus of neuroimaging data. In the context of this dataset, each topic corresponds to a functional brain region, where the region's spatial extent is captured by a probability distribution over neural activity, and the region's cognitive function is captured by a probability distribution over linguistic terms. We illustrate the qualitative improvements offered by GC-LDA in terms of the types of topics extracted with alternative spatial representations, as well as the model's ability to incorporate a-priori knowledge from the neuroimaging literature. We furthermore demonstrate that the novel features of GC-LDA improve predictions for missing data.

Stimulus-driven brain-computer interfaces (BCIs), such as the P300 speller, rely on using a sequence of sensory stimuli to elicit specific neural responses as control signals, while a user attends to relevant target stimuli that occur within the sequence. In current BCIs, the stimulus presentation schedule is typically generated in a pseudo-random fashion. Given the non-stationarity of brain electrical signals, a better strategy could be to adapt the stimulus presentation schedule in real-time by selecting the optimal stimuli that will maximize the signal-to-noise ratios of the elicited neural responses and provide the most information about the user's intent based on the uncertainties of the data being measured. However, the high-dimensional stimulus space limits the development of algorithms with tractable solutions for optimized stimulus selection to allow for real-time decision-making within the stringent time requirements of BCI processing. We derive a simple analytical solution of an information-based objective function for BCI stimulus selection by transforming the high-dimensional stimulus space into a one-dimensional space that parameterizes the objective function - the prior probability mass of the stimulus under consideration, irrespective of its contents. We demonstrate the utility of our adaptive stimulus selection algorithm in improving BCI performance with results from simulation and real-time human experiments.

It is an interesting study that tackles one of very important questions in computational neuroscience - how generalisation across stimuli and environments is achieved. It is similar to the concept of schemas, which are thought to primarily rely on frontal cortical areas. In this particular case the focus is on entorhinal grid cells and hippocampal place cells, which authors assert code for the environment and conjunction of environment and stimulus respectively. The authors present a computational model that aims to address the question of whether place cells encode a conjunctive outcome of environment and stimulus representations. It is an interesting hypothesis, which if shown convincingly, would be a major breakthrough in neuroscience.