January 3: New cortical anatomy viewer available!
To understand any study of the human cerebral cortex, it is critical to have a good sense of human cortical neuroanatomy. The classical way to learn neuroanantomy is to memorize the patterns and names of sulci and gyri shown in static photos of slices or volumes. We have developed a simple pycortex viewer that provides a more dynamic, interactive atlas of the sulci and gyri of the human cerebral cortex. The cortical surface can also be inflated and flattened so that the relationship between locations in the original 3D volume and on the flattened cortical surface can be visualized easily. You can find the viewer on our brain viewer page or you can get it directly from here.
December 14: Natalia Bilenko is now a Ph.D.!
Our wonderful Natalia Bilenko has turned in her Ph.D. Thesis, “Modeling of natural stimulus representation in the human brain using canonical correlation analysis.” One chapter of her Thesis has already been published and you can find it here. The second chapter should be out soon. Congratulations Natalia!
November 22: New paper published describing the Pyrcca cannonical correlation analysis software
Natalia Bilenko’s first paper, “Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Applications to Neuroimaging” has just been published in Frontiers in Neuroinformatics. This paper describes Pyrcca, an open-source Python package for performing canonical correlation analysis (CCA). CCA is a multivariate analysis method for identifying relationships between sets of variables, and it is a very useful tool for group analysis of high-dimensional fMRI data. Pyrcca supports CCA with or without regularization, and with or without linear, polynomial, or Gaussian kernelization. Get the paper here.
October 12: “Freakonomics” radio interview with Jack Gallant
Stephen Dubner from the Freakonomics podcast does a great job of interviewing Jack Gallant in this podcast titled, “This is your brain on podcasts”. If you are looking for a good, accessible introduction to the philosophy and ongoing work in the Gallant lab, this is a great place to start. Get the podcast here.
September 21: New paper published on decoding semantic information evoked by natural movies
Alex Huth’s latest paper, “Decoding the semantic content of natural movies from human brain activity” has just been published in Frontiers in Systems Neuroscience. Several recent neuroimaging studies have decoded the structure or semantic content of static visual images from human brain activity. This paper presents a decoding algorithm, hierarchical logistic regression (HLR), that makes it possible to decode detailed information about the object and action categories present in natural movies from human brain activity signals measured by functional MRI. The model decodes the present of many object and action categories from fMRI responses with a high degree of accuracy. This framework can also be used to test whether semantic relationships defined in the WordNet taxonomy are represented the same way in the human brain. Hierarchical relationships between general categories and atypical examples, such as organism and plant, did not seem to be reflected in brain representations measured by fMRI. Get the paper here.
July 10: “All In The Mind” radio interview with Jack Gallant and Alex Huth
Lynne Malcolm from the ABC (not that ABC!) radio show, All In The Mind, did a nice interview with Jack Gallant and Alex Huth. The interview covered the basic approach used in our lab, our previous results on decoding and reconstructing natural movies, and our recent results on semantic representation. Listen to the interview here.
June 9: New video posted, “Using Image Processing to improve reconstruction of movies from brain activity”
In 2011 we published a nice paper, Reconstructing visual experiences from brain activity elicited by natural movies, by Shinji Nishimoto and others from our lab. Now Natalia Bilenko and Valkyrie Savage have developed an improved algorithm for reconstructing movies from brain activity, and a new video describes and demonstrates their work. (A detailed explanation of the algorithm is given at the end of the video.)
June 9: Alex Huth wins early career award from the Burroughs Wellcome Fund!
Alex Huth has won a prestigious early career award from the Borroughs Wellcome Fund. Similar to an NIH K award, these funds can be used to support Alex’s post-doctoral research and the work that he will do in his own lab. Read the UC Berkeley Press Release here. Congratulations Alex!
April 27: Nature article on our semantic atlas
Alex Huth’s paper, “Semantic information in natural narrative speech is represented in complex maps that tile human cerebral cortex” has just been published in Nature. The meaning of language is represented in regions of the cerebral cortex known collectively as the “semantic system”. However, little of the semantic system has been mapped comprehensively, and the semantic selectivity of most regions is still unknown. Here we systematically map semantic selectivity across the cerebral cortex using voxel-wise modeling of fMRI data collected while subjects listened to several hours of natural narrative stories. We show that the semantic system is organized into intricate patterns that appear highly consistent across individuals. We then use a novel Bayesian generative model to map these patterns and create a detailed semantic atlas. Our results suggest that most areas within the semantic system represent information about specific semantic domains and our atlas shows which domains are represented in each area. You can find a detailed writeup about the paper here, and you can find a video summary of the paper here. And be sure to check out the new brain viewer! To request a reprint please send an email to <email@example.com>.
April 26: Caseforge team wins the Delta Prize!
James Gao, Alex Huth and Young Park, the founders of Caseforge, have won the Delta Prize! The Delta Prize is a UC Berkeley startup prize aimed at accelerating the growth of early-stage ventures. Caseforge is a company formed to manufacture the headcase, a head stabilization device developed in our laboratory. This device can dramatically increase the quality of fMRI data, and it may also have important medical applications.
About our lab
This is the web home of Professor Jack Gallant’s cognitive, computational and systems neuroscience lab at the University of California, Berkeley. Our lab uses functional MRI, computational modeling and machine learning to map perceptual, language and cognitive functions across the human brain. We also study how these maps are altered by top-down processes such as attention, learning and memory, and how they differ across individuals. The computational modeling framework that we have developed for brain mapping can also be used to decode human brain activity with remarkable fidelity.
Our laboratory is located in the Department of Psychology, University of California at Berkeley. We are also affiliated with the programs in Neuroscience,Bioengineering, Biophysics and Vision Science, and with the Department of Electrical Engineering and Computer Science.
Information about joining us as a graduate student or post-doc can be found here.