Multimodal non-invasive functional brain imaging has made a tremendous impact in improving our understanding of the neural correlates of human behavior, and is now an indispensable tool for systems and cognitive neuroscientists. We propose to develop state-of-the-art multimodal functional imaging fusion algorithms for accurate visualization of the brain's dynamic activity and high spatial and temporal resolution. We propose to develop algorithms that combine complementary high spatial resolution of functional MRI (fMRI) and high-temporal resolution of magnetoencephalography (MEG) and electroencephalography (EEG) data for high-fidelity reconstruction of brain activity. In recent years, our research group has developed a suite of novel and powerful algorithms for MEG/EEG imaging superior to existing benchmark algorithms, and we have compared these results with electrocorticography (ECOG). Specifically, our algorithms can solve for many brain sources, including sources located far from the sensors, in the presence of large interference from unrelated brain sources using fast and robust probabilistic inference techniques. Here, we propose to extend this success in M/EEG inverse algorithms into the domain of multimodal imaging data fusion. Our overall goal here is to ultimately produce robust, high fidelity videos of event-related brain activation at a sub-millimeter and sub-millisecond resolution from noisy MEG/EEG and fMRI data using state-of-the-art machine learning algorithms. Specifically, we propose to extend a powerful new algorithm that we have recently developed, called Champagne, into two new fusion algorithms that combine fMRI, MEG and EEG data in different ways. Performance of both algorithms will first be rigorously evaluated in simulations, including performance comparisons with existing benchmark fusion algorithms. Algorithms will then tested for consistency on four fMRI-MEG+EEG datasets from healthy controls obtained for identical paradigms (auditory, motor, picture naming and verb-generation) and two fMRI-EEG datasets (face and motion perception). Additional validation studies will also be performed on fMRI-MEG/EEG datasets obtained from epilepsy patients and compared to electrocorticography (ECoG). Following successful testing and evaluation, all algorithms developed in this grant proposal, as well as example validation datasets, will be distributed using NUTMEG (nutmeg.berkeley.edu), an open-source software toolbox that we have developed.

Public Health Relevance

Multimodal non-invasive functional brain imaging has made a tremendous impact in improving our understanding of the neural correlates of human behavior, and is now an indispensable tool for systems and cognitive neuroscientists. With the development of appropriate analytical tools, multimodal functional brain imaging is in the process of revolutionizing the diagnosis and treatment of a variety of neurological and psychiatric disorders such as autism, schizophrenia, dementia, and epilepsy that affect tens of millions of Americans.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21NS076171-02
Application #
8320120
Study Section
Special Emphasis Panel (ZRG1-NT-L (09))
Program Officer
Babcock, Debra J
Project Start
2011-09-01
Project End
2014-08-31
Budget Start
2012-09-01
Budget End
2014-08-31
Support Year
2
Fiscal Year
2012
Total Cost
$193,125
Indirect Cost
$68,125
Name
University of California San Francisco
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94143
Subramaniam, Karuna; Gill, Jeevit; Fisher, Melissa et al. (2018) White matter microstructure predicts cognitive training-induced improvements in attention and executive functioning in schizophrenia. Schizophr Res 193:276-283
Sekihara, Kensuke; Adachi, Yoshiaki; Kubota, Hiroshi K et al. (2018) Beamspace dual signal space projection (bDSSP): a method for selective detection of deep sources in MEG measurements. J Neural Eng 15:036026
Ranasinghe, Kamalini G; Gill, Jeevit S; Kothare, Hardik et al. (2017) Abnormal vocal behavior predicts executive and memory deficits in Alzheimer's disease. Neurobiol Aging 52:71-80
Ranasinghe, Kamalini G; Hinkley, Leighton B; Beagle, Alexander J et al. (2017) Distinct spatiotemporal patterns of neuronal functional connectivity in primary progressive aphasia variants. Brain 140:2737-2751
Subramaniam, Karuna; Ranasinghe, Kamalini G; Mathalon, Daniel et al. (2017) Neural mechanisms of mood-induced modulation of reality monitoring in schizophrenia. Cortex 91:271-286
Sekihara, Kensuke; Nagarajan, Srikantan S (2017) Subspace-based interference removal methods for a multichannel biomagnetic sensor array. J Neural Eng 14:051001
Subramaniam, Karuna; Gill, Jeevit; Slattery, Patrick et al. (2016) Neural Mechanisms of Positive Mood Induced Modulation of Reality Monitoring. Front Hum Neurosci 10:581
Hinkley, Leighton B N; Marco, Elysa J; Brown, Ethan G et al. (2016) The Contribution of the Corpus Callosum to Language Lateralization. J Neurosci 36:4522-33
Vossel, Keith A; Ranasinghe, Kamalini G; Beagle, Alexander J et al. (2016) Incidence and impact of subclinical epileptiform activity in Alzheimer's disease. Ann Neurol 80:858-870
Dale, Corby L; Brown, Ethan G; Fisher, Melissa et al. (2016) Auditory Cortical Plasticity Drives Training-Induced Cognitive Changes in Schizophrenia. Schizophr Bull 42:220-8

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