Electrodes in patients’ temporal lobes carry information that, when analyzed, enables scientists to predict what object patients are seeing.
Using electrodes implanted in the temporal lobes of awake patients, scientists have decoded brain signals at nearly the speed of perception. Further, analysis of patients’ neural responses to two categories of visual stimuli – images of faces and houses – enabled the scientists to subsequently predict which images the patients were viewing, and when, with better than 95 percent accuracy.
The research is published today in PLOS Computational Biology.
University of Washington computational neuroscientist Rajesh Rao and UW Medicine neurosurgeon Jeff Ojemann, working their student Kai Miller and with colleagues in Southern California and New York, conducted the study.
“We were trying to understand, first, how the human brain perceives objects in the temporal lobe, and second, how one could use a computer to extract and predict what someone is seeing in real time?” explained Rao. He is a UW professor of computer science and engineering, and he directs the National Science Foundation’s Center for Sensorimotor Engineering, headquartered at UW.
“Clinically, you could think of our result as a proof of concept toward building a communication mechanism for patients who are paralyzed or have had a stroke and are completely locked-in,” he said.
The study involved seven epilepsy patients receiving care at Harborview Medical Center in Seattle. Each was experiencing epileptic seizures not relieved by medication, Ojemann said, so each had undergone surgery in which their brains’ temporal lobes were implanted – temporarily, for about a week – with electrodes to try to locate the seizures’ focal points.
“They were going to get the electrodes no matter what; we were just giving them additional tasks to do during their hospital stay while they are otherwise just waiting around,” Ojemann said.
Temporal lobes process sensory input and are a common site of epileptic seizures. Situated behind mammals’ eyes and ears, the lobes are also involved in Alzheimer’s and dementias and appear somewhat more vulnerable than other brain structures to head traumas, he said.
In the experiment, the electrodes from multiple temporal-lobe locations were connected to powerful computational software that extracted two characteristic properties of the brain signal: “event-related potentials” and “broadband spectral changes.”
Rao characterized the former as likely arising from “hundreds of thousands of neurons being co-activated when an image is first presented,” and the latter as “continued processing after the initial wave of information.”
The subjects, watching a computer monitor, were shown a random sequence of pictures – brief (400 millisecond) flashes of images of human faces and houses, interspersed with blank gray screens. Their task was to watch for an image of an upside-down house.
“We got different responses from different (electrode) locations; some were sensitive to faces and some were sensitive to houses,” Rao said.
The computational software sampled and digitized the brain signals 1,000 times per second to extract their characteristics. The software also analyzed the data to determine which combination of electrode locations and signal types correlated best with what each subject actually saw.
In that way it yielded highly predictive information.
By training an algorithm on the subjects’ responses to the (known) first two-thirds of the images, the researchers could examine the brain signals representing the final third of the images, whose labels were unknown to them, and predict with 96 percent accuracy whether and when (within 20 milliseconds) the subjects were seeing a house, a face or a gray screen.
This accuracy was attained only when event-related potentials and broadband changes were combined for prediction, which suggests they carry complementary information.
“Traditionally scientists have looked at single neurons,” Rao said. “Our study gives a more global picture, at the level of very large networks of neurons, of how a person who is awake and paying attention perceives a complex visual object.”
The scientists’ technique, he said, is a steppingstone for brain mapping, in that it could be used to identify in real time which locations of the brain are sensitive to types of information.
Lead author of the study is Kai Miller, a neurosurgery resident and physicist at Stanford University who obtained his M.D. and Ph.D. at the UW. Other collaborators were Dora Hermes, a Stanford postdoctoral fellow in neuroscience, and Gerwin Schalk, a neuroscientist at the Wadsworth Institute in New York.
“The computational tools that we developed can be applied to studies of motor function, studies of epilepsy, studies of memory. The math behind it, as applied to the biological, is fundamental to learning,” Ojemann said.
Funding: The study was funded by National Aeronautics and Space Administration GraduateStudent Research Program, National Institutes of Health, National Science Foundation, and the US Army.
Source: Rajesh Rao – UW Medicine
Image Credit: The image is credited to Kai Miller and Brian Donohue
Original Research: Full open access research for “Spontaneous Decoding of the Timing and Content of Human Object Perception from Cortical Surface Recordings Reveals Complementary Information in the Event-Related Potential and Broadband Spectral Change” by Kai J. Miller, Gerwin Schalk, Dora Hermes, Jeffrey G. Ojemann, Rajesh P. N. Rao in PLOS Computational Biology. Published online January 28 2016 doi:10.1371/journal.pcbi.1004660
Abstract
Spontaneous Decoding of the Timing and Content of Human Object Perception from Cortical Surface Recordings Reveals Complementary Information in the Event-Related Potential and Broadband Spectral Change
The link between object perception and neural activity in visual cortical areas is a problem of fundamental importance in neuroscience. Here we show that electrical potentials from the ventral temporal cortical surface in humans contain sufficient information for spontaneous and near-instantaneous identification of a subject’s perceptual state. Electrocorticographic (ECoG) arrays were placed on the subtemporal cortical surface of seven epilepsy patients. Grayscale images of faces and houses were displayed rapidly in random sequence. We developed a template projection approach to decode the continuous ECoG data stream spontaneously, predicting the occurrence, timing and type of visual stimulus. In this setting, we evaluated the independent and joint use of two well-studied features of brain signals, broadband changes in the frequency power spectrum of the potential and deflections in the raw potential trace (event-related potential; ERP). Our ability to predict both the timing of stimulus onset and the type of image was best when we used a combination of both the broadband response and ERP, suggesting that they capture different and complementary aspects of the subject’s perceptual state. Specifically, we were able to predict the timing and type of 96% of all stimuli, with less than 5% false positive rate and a ~20ms error in timing.
“Spontaneous Decoding of the Timing and Content of Human Object Perception from Cortical Surface Recordings Reveals Complementary Information in the Event-Related Potential and Broadband Spectral Change” by Kai J. Miller, Gerwin Schalk, Dora Hermes, Jeffrey G. Ojemann, Rajesh P. N. Rao in PLOS Computational Biology. Published online January 28 2016 doi:10.1371/journal.pcbi.1004660
