The brain-basis of perception is complex, and recent research suggests that neural processing depends on large-scale oscillation of neuronal units. Oscillatory cortical networks detected with electroencephalography and magnetoencephalography recordings often involve several frequency bands, indicating that a multivariate (multi-frequency) analytic approach would have better sensitivity in detecting neural effects than univariate analysis. However, popular connectivity measures, such as coherence and phase synchrony, typically analyze pairs of spatial locations and take into account a single quantity from each location, such as amplitude or phase within a specified frequency band. With funding from the National Science Foundation, Drs. Dimitrios Pantazis, Richard Leahy, and Jintao Jiang will develop robust multivariate statistical methods for detecting brain interactions in electroencephalography. Given the wealth of information in electroencephalography data, analysis using a single frequency approach requires either prior knowledge of the frequencies at which interactions occur or, conversely, a large number of tests, one for each possible type of interaction. In this project, the researchers are using canonical correlation analysis, which can find the optimal combinations of frequencies in one cortical site that best correlate with frequencies at another cortical site. In contrast to conventional methods of interaction analysis, this project is automating the identification of frequency bands that contribute significantly to cortical networks. The target application focuses on audiovisual speech integration effects. The multivariate methods developed in this proposal are being used to detect multisensory interaction cortical sites and account for different levels of phase-resetting from audiovisual speech stimuli with different stimulus onset asynchronies.
This research will facilitate the detection of oscillatory cortical networks both in the normal and pathologic brain. Changes in oscillatory brain activity have been reported in a wide array of neurological diseases, including epilepsy, schizophrenia, and Alzheimer's disease, and improved methodologies to detect the presence and differences in oscillatory activity and associated networks will in turn advance the understanding of these diseases and facilitate the development and assessment of therapeutic interventions. This effort brings together engineers and neuroscientists to tackle a broad range of scientific and technological problems, and as a result, the project offers opportunities for integrated interdisciplinary research training of doctoral students. Research results will be disseminated broadly to the research community through professional meetings and journals, and the developed research tools will be distributed to the research community through the open source software BrainStorm.
Modern imaging technologies, including magnetoencephalography (MEG), electroencephalography (EEG), and functional magnetic resonance imaging (fMRI), have enabled the study of brain networks formed by the rapid communication of neuronal populations between different cortical areas. Research outcomes from this award have facilitated the detection of oscillatory brain networks both in the normal and pathologic brain. Changes in oscillatory brain activity have been reported in a wide array of neurological diseases, including epilepsy, schizophrenia, and Alzheimer's disease, and efficient methodologies to detect the presence and differences in oscillatory activity and associated networks can in turn advance the understanding of these diseases and facilitate the development and assessment of therapeutic interventions. Over the past 4 years of the award, we have made significant advances in developing theoretical and mathematical tools to partition and statistically analyze brain networks, methods that can now streamline the identification of biomarkers for neurological diseases. These tools have also been incorporated in the popular software tool Brainstorm, an open source toolbox for the analysis of brain electrophysiological data. We have also developed a new method that offers unique spatial and temporal resolution for brain imaging. Until now, scientists have been able to observe the location or timing of human brain activity at high resolution, but not both, because different imaging techniques are not easily combined. fMRI measures changes in blood flow related to brain function, but is too slow to capture the fast brain dynamics. Another imaging technique, MEG, measures neuronal activity using hundreds of sensors encircling the head, offering millisecond resolution, but does not reveal the precise location of these signals. For the first time we combined these two scanners using a computational technique which relies on the fact that two similar objects (such as two human faces) that evoke similar signals in fMRI will also produce similar signals in MEG. This research would not have been possible without the support of NSF, which funded our infrastructure (creation of our MEG laboratory), and supported the investigators who conducted this work. We now have the tools to precisely map brain function both in space and time at unique resolution, opening up tremendous possibilities to study the human brain. For example, even the most advanced machine vision algorithms are hopeless compared to the human visual system. The human brain can teach us how to radically redesign machine vision by replicating human brain function. Using this new method we produced a first-of-its-kind movie illustrating the activation of the ventral visual pathway, an area of the brain involved in visual object recognition, with a combined resolution of millimeters and milliseconds. As a result, we measured the timing of object categorization in the first stages of human vision, and discriminated transient from persistent neural activity during visual object processing.
