Rationale engineering generates a compact new tool for gene therapy
Researchers redesign a compact RNA-guided enzyme from bacteria, making it an efficient editor of human DNA.
McGovern Institute
MIT’s McGovern Institute is shaping brain science and improving human lives on a global scale
A quarter century after its founding, the McGovern Institute reflects on its discoveries in the areas of neuroscience, neurotechnology, artificial intelligence, brain-body connections, and therapeutics.
An ancient RNA-guided system could simplify delivery of gene editing therapies
The programmable proteins are compact, modular, and can be directed to modify DNA in human cells.
For healthy hearing, timing matters
Machine-learning models let neuroscientists study the impact of auditory processing on real-world hearing.
Four from MIT named 2025 Rhodes Scholars
Yiming Chen ’24, Wilhem Hector, Anushka Nair, and David Oluigbo will start postgraduate studies at Oxford next fall.
Symposium highlights scale of mental health crisis and novel methods of diagnosis and treatment
Co-hosted by the McGovern Institute, MIT Open Learning, and others, the symposium stressed emerging technologies in advancing understanding of mental health and neurological conditions.
Complex, unfamiliar sentences make the brain’s language network work harder
A new study finds that language regions in the left hemisphere light up when reading uncommon sentences, while straightforward sentences elicit little response.
Deep neural networks show promise as models of human hearing
Study shows computational models trained to perform auditory tasks display an internal organization similar to that of the human auditory cortex.
Search algorithm reveals nearly 200 new kinds of CRISPR systems
By analyzing bacterial data, researchers have discovered thousands of rare new CRISPR systems that have a range of functions and could enable gene editing, diagnostics, and more.
The brain may learn about the world the same way some computational models do
Two studies find “self-supervised” models, which learn about their environment from unlabeled data, can show activity patterns similar to those of the mammalian brain.