Matti Hamalainen - US grants

Area; Biological Neural Networks; Brain; CRISP; Causality; Cell Communication and Signaling; Cell Signaling; Communication; Complex; Computer Retrieval of Information on Scientific Projects Database; Data; EEG; Electroencephalography; Encephalon; Encephalons; Etiology; Functional Magnetic Resonance Imaging; Funding; Goals; Grant; Institution; Intracellular Communication and Signaling; Investigators; Location; MRI, Functional; Magnetic Resonance Imaging, Functional; Measures; Modeling; NIH; National Institutes of Health; National Institutes of Health (U.S.); Nervous System, Brain; Research; Research Personnel; Research Resources; Researchers; Resources; Signal Transduction; Signal Transduction Systems; Signaling; Source; Specific qualifier value; Specified; Time; United States National Institutes of Health; base; biological signal transduction; disease causation; disease etiology; disease/disorder etiology; disorder etiology; fMRI; neural network

DESCRIPTION (provided by applicant): Magnetoencephalography (MEG) and electroencephalography (EEG) provide a unique window to the large scale spatiotemporal neural processes that underlie human brain function. However, even with restrictive models, the low SNR, ill-posedness of the inverse problem, and difficulty of differentiating ongoing brain activity and other electrophysiological signals from induced and event-related changes result in unique challenges in data analysis and interpretation. These challenges include accounting for artifacts and noise, accurately forward modeling from cerebral sources to sensor space, defining appropriate source models, computing inverse solutions, and detecting and quantifying interactions. In this grant we will continue the development of our linked MNE-Python and Brainstorm software packages. Emphasis in the current software is on data preprocessing, the formation and statistical analysis of inverse solutions, and advanced, interactive display and interpretation of these solutions. We have established standard workflows for cortical current density mapping, time-frequency analysis, and statistical testing in both MNE and Brainstorm for these procedures. In the next project period in Aim 1 we will build on these procedures adding new dimensions to the data workflows for the interaction measures described in Aim 3. Under this aim we will also continue general software development (including automated testing and documentation), support and dissemination activities for users. In Aim 2, we will expand the use of Python-based scripting to facilitate large-scale batch processing of multiple subjects and/or conditions from an extensive experimental study. We will also add the ability to import locations of intracranial EEG sensors (depth electrodes and cortical grids) for display and interaction analysis using methods from Aim 3 and for cross-validation of MEG/EEG non-invasive source models. To fully realize the potential of EEG/MEG to elucidate the spatio-temporal networks that underlie human perception, cognition, and action, we will also develop tools to investigate the interactions between cortical neuronal populations. These tools should take into account the dynamically nature of these networks, the inherent complexity of causal and inter-frequency interactions amongst neural populations, and the fact that interactions can occur between multiple brain regions. Since no single parsimonious model can account for all such interactions, Aim 3 of this grant will develop a suite of interaction modeling and powerful visualization tools for use by neuroscience and clinical researchers.

"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)."

Support from the National Science Foundation will enable Drs. Ellen Grant and Yoshio Okada at Children's Hospital Boston (CHB) and Dr Matti Hämäläinen at Massachusetts General Hospital (MGH) to develop a novel magnetoencephalography (MEG) system, babyMEG, optimized for the non-invasive study of human brain development from premature babies up to children 3 years old. This new instrument will be developed under the direction of Dr. Okada in collaboration with an R&D company (Tristan Technologies) in San Diego, CA. The software for data analysis will be developed by Dr. Hämäläinen. Once completed, the instrument will be installed in a novel clinical/research facility that CHB has committed to build next to the Neonatal and Pediatric Intensive Care Units (NICU and PICU). The PI of this project (Dr. Grant) will oversee the use of the babyMEG in this facility as its Center Director.

This babyMEG is transformative because it will have a tremendous impact on the understanding of early brain development in humans. Users will be able to measure cortical activity with an unprecedented level of sensitivity and spatial resolution: high-resolution information about regional cortical activity in real time in the developing brain will become available. This information will allow evaluation of developing functional brain connectivity with acquisition of new skills such as language and determination of how developing connectivity is altered by disease.

The new research facility where this work will be carried out will be equipped with other neuroimaging tools such as a state-of-the-art 3 Tesla MR scanner with custom-built 32-channel head coils and a novel Near Infrared Spectroscopy (NIRS) system. Thus, the babyMEG will be part of a multimodal neuroimaging facility. The proximity of the research center to the NICU and PICU is unique in the world. This infrastructure will provide exciting opportunities for understanding human brain development in health and disease and for eventually helping to maximize the potential of babies with various brain disorders.

It is anticipated that the support from the NSF will help to create a unique research center that will become an important hub for research, training and education not only in this country, but also in the world. The center will be used for training of postdoctoral fellows, graduate and undergraduate students as well junior faculty from engineering and neuroscience. The babyMEG in the clinical environment will expose basic scientists to the real world problems and questions of early brain development and clinicians to the relevance of basic science methods. The babyMEG will promote not only interdisciplinary research but also collaboration between multiple Harvard institutions including CHB, MGH, MIT and Harvard Medical School as well as their collaborators worldwide. Specific benefits to graduate education will focus on training students to research scientific questions drawing on advice and guidance from the three co-PIs and senior faculty members who will be using the facility in collaboration with the three key leaders. Use of the babyMEG will be promoted through regular hands-on teaching sessions, seminar series and public lectures.

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The overall goal of Project 3 is to develop methods for spatiotemporal imaging of human brain function, motivated by and validated with experiments. Specifically, we will extend anatomically and functionally constrained MEG/EEG source models to structures outside the cortex. Using the accurate anatomical information obtained in Project 1 and physiological information regarding plausible source strengths we will construct the anatomical components and physiological elements of a source model which covers both the cerebral cortex other structures, including the hippocampus and the basal ganglia. With computer simulations and experiments we will study the feasibility of detecting and estimating activity in extracortical structures. Second, we will continue our work on developing new inverse models which use the hemodynamic and electromagnetic data in conjunction. We will use experiments probing the relationship between electromagnetic (MEG/EEG) and hemodynamic (fMRI/DOT) data as a basis for a combined activity estimation framework. Third, we will extend the methods to calculate functional connectivity metrics to non-stationary data and will develop proper statistical treatment of the connectivity information across individual trials and across subjects.

DESCRIPTION (provided by applicant): Magnetoencephalography (MEG) and electroencephalography (EEG) are the only non-invasive human brain imaging methods measuring neural currents directly thus providing exquisite temporal resolution and allowing detection and precise timing of transient changes in brain activity as well as studies of dynamic interactions between brain regions. With multichannel MEG and EEG recordings it is possible to locate the sources underlying the measurements in best cases with an accuracy of less than 1 cm. Recently, there has been increasing interest in studying weak high-frequency oscillations associated with normal brain function as well as fast signals related to abnormal epileptic events, observed in invasive recordings. Furthermore, it has been demonstrated that under suitable conditions it is feasible to detect activity from deep brain areas on and outside the cortex. For all these studies a fine amplitude resolution and fast sampling rates are a prerequisite. Due to their very real-time nature, MEG and EEG signals are very suitable for applications involving real-time processing of signatures of brain activity such as brain-machine interfaces (BMI) and experiments in which the stimuli presented are controlled by the "brain state". To enable detection of low-amplitude and/or high- frequency signals and real-time data processing, we propose an upgrade to the electronics of the whole-head MEG/EEG at the Martinos Center of Massachusetts General Hospital (MGH), the only state-of-the-art MEG installation in New England area. The new electronics will provide higher sampling rates (up to 8 ksamples/s instead of present 3 ksamples/s), higher amplitude resolution (24 bits instead of 16 bits) than the present electronics, developed in the late 90s and installed with the system in 2001. In addition, the new modern MEG/EEG electronics will allow direct access to the data during recordings for BMI and other real-time applications. The MGH MEG/EEG system is used by a multitude of research groups in the Boston area, all of which will directly benefit from the proposed upgrade. In addition, our clinical MEG service provides multimodal imaging of epileptic activity and presurgical functional mapping for patients from local and regional hospitals.

? DESCRIPTION (provided by applicant): Presently there is no imaging technology capable of detecting neuronal activity in the entire human brain with millisecond and millimeter resolution. We propose to evaluate the possibility of developing a novel noninvasive method, sonoelectric tomography (SET), capable of directly imaging electrophysiological activity in the entire human brain with such resolution. In this method, conventional scalp electroencephalography (EEG) is used to measure the electrical activity. Each location of active tissue giving rise to the EEG signals is determined from the tagged US signature in the EEG signals. This information can be used to noninvasively construct a tomographic image of neuronal currents. In order to develop such a technique, we will evaluate three candidate mechanisms: (1) acousto-electric (AE) modulation of tissue resistivity, (2) mechanical vibration of the equivalent current dipole sources in active tissue, and (3) modulation of membrane properties. At present, it is still unknown which of these mechanisms can be used to implement the SET. We will first evaluate these mechanisms in rats in vivo. In Aim 1, we will apply a focused US to one region of the barrel cortex of the rat and test the sensitivity of the SET based on each mechanism. One barrel column will be activated by single whisker stimulation and the resulting local field potentials (LFPs) on the brain or scalp will be analyzed. The most viable US-encoding scheme will be determined from the US signatures in the LFPs. Aim 2 will be very similar to Aim 1, except a single linear US beam varying in US frequency along the beam will be applied to produce a one-dimensional image of neuronal activity. Aims 1 and 2 will establish the effective and safe mechanism for developing SET for human use. In Aim 3, the five digits of a hand will be stimulated with transcutaneous electrical stimulation to activate the finger areas in areas 3b. A linear US beam will be applied to each projection area in area 3b. The scalp EEG signals in area 3b will be analyzed for presence of EEG signals at the US frequency specific for each projection site. This will identify each active site. We will test to see if this method can identiy multiple active tissues. Alternatively, we will first identify the active tissues using a whole-hea MEG and/or EEG and then use the US to test the presence of activity at each predicted site. These tests will determine the feasibility and the best direction for developing a truly whole-brai US-based Activity mapping (USAmapping) technique.

Abstract We propose to develop a device-independent, real-time software platform (?MNE-CE?) that will significantly increase the ease and efficiency of acquiring, monitoring, analyzing, and integrating various types of clinical electrophysiolog- ical data (electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), and ste- reotactic EEG (SEEG). Data for epilepsy diagnosis are today obtained and analyzed with a variety of software packages, requiring a significant investment of time in training to use these systems. Data integration is difficult and often qualitative. Our unified approach will not only significantly reduce the cost of training, collecting and analyzing the various types of data, but it anticipates changes in clinical practice by enabling seamless integration of all modal- ities and by enabling new approaches in surgical management. MNE-CE is based on the MNE and MNE-X we have developed during the past 15 years. MNE-X has an architecture that enables the acquisition and analysis of MEG data using any existing MEG systems. MNE-CE will format incoming electrographic data from any recording device for EEG, ECoG, SEEG and MEG, store the data, carry out preprocessing for noise rejections and signal condition- ing, display the incoming data in real time, and carry out the data analysis at the source level (active tissues) instead of the sensor level unlike most of the existing software. Its real-time capability will provide immediate feedback to clinicians, enabling them to use this information for improving surgical management, for example by using the esti- mated locations of epileptogenic tissue to guide the insertion of depth or SEEG electrodes. Accurate identification of the propagation pathway may lead to reduction in volume of resection by specifying the propagation initiation site or the fibers in the pathway to be resected. This project will be carried out synergistically at three institutions led by three PIs who have worked together for many years with complementary expertise. The PIs will work with well- established epileptologists, radiologists and neurosurgeons for coordinating the clinical evaluation. Aim 1: The MGH team will design and develop MNE-CE. The PI at Cleveland is one of the authors of another popular software plat- form (?Brainstorm?). They will work together on this MNE-CE development. Aim 2: The Cleveland team will evalu- ate MNE-CE on SEEG and SEEG/MEG data from adult epilepsy patients. The evaluation will be initially done on the archived data, replaying the data and treating them as incoming data from a virtual EEG/ECoG instrument. The re- sults will be feedback to the MGH development team. As MNE-E matures, it will be used as an add-on to the exist- ing hardware for collecting data during actual clinical measurements, without replacing the existing FDA-approved systems. These results will be used to iteratively improve MNE-CE. Aim 3: The same procedure will be carried out at BCH on EEG, ECoG, SEEG and MEG data in pediatric patients. BCH team will also test whether MNE-CE can reveal abnormal propagation patterns of epileptiform activity in patients with a metabolic disorder.

Project Summary During the past three decades non-invasive functional brain imaging has developed immensely in terms of measurement technologies, analysis methods, and innovative paradigms to capture information about brain function both in healthy and diseased individuals. Although functional MRI (fMRI) has become very useful, it only provides indirect information about neuronal activity through the neurovascular coupling with a limited temporal resolution. Magnetoencephalography (MEG) and electroencephalography (EEG) remain the only available noninvasive techniques capable of directly measuring the electrophysiological activity with a millisecond resolution. During the past eight years we have developed, with NIH support, the MNE-Python software, which covers multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. To further extend our software to meet the needs of a growing user base and reflect recent developments in the MEG/EEG field we will pursue three specific Aims. In Aim 1 we will: (i) Create an all-embracing suite of noise cancellation tools incorporating and extending methods present in different MEG systems; (ii) Implement device independent methods for head-movement determination and compensation on the basis of head movement data recorded during a MEG session; (iii) Develop methods for automatic tagging of artifacts using machine learning approaches. In Aim 2 our focus is to extend the software to make modern distributed computing resources easily usable in processing and to allow for remote visualization without the need to move large amounts of data across the network. Finally, in Aim 3, we will continue to develop MNE-Python using best programming practices ensuring multiplatform compatibility, extensive web-based documentation, training and forums, and hands-on training workshops. As a result of these developments the MNE-Python will be able to effectively process large number of subjects and huge amounts data ensuing and from multi-site studies harmoniously across different MEG/EEG systems.

Project Summary There is an increasing interest in the use of non-invasive electromagnetic stimulation for therapeutic interventions as well as understanding of the functioning of the healthy human brain. Most of the tradi- tional protocols involve stimulation of a single target/focus region. However, evidence is mounting that a wide range of neuronal processing tasks rely on large-scale networks and their synchronization, sug- gesting that multi-focal stimulation would be a particularly promising avenue for enhanced neuromodu- lation protocols. Measuring the response of the brain networks to the stimulation is needed to quantify the effects and therefore concurrent brain mapping methodologies are necessary. To this end, both EEG and fMRI have been employed previously. We consider that the key to maximizing the potential of multi-focal scanning stimulation is the integration of the stimulation and imaging recording as it enables on-line analysis of the brain responses and also allows closed-loop paradigms to be developed. In this TRD, we leverage on our unique expertise in electromagnetic brain stimulation, imaging, and computa- tional modeling to provide a set of tools for the scientific community to promote the integration and ap- plication of multifocal brain imaging and stimulation. Naturally, the single-channel stimulation system users will benefit from the developed methods as well. In Aim 1, we will optimize the anatomical and functional MRI acquisition protocols to enable employing our recently published fast and accurate TMS- induced electric field (E-field) modeling approach to be adopted to computational targeting. In Aim 2, we will develop software (MNE-TMS), with an interface between the stimulation and recording devices that enable real-time analysis of the induced activations using our MNE-CPP platform and control of the stimulating devices. In Aim 3, we will incorporate the geometrical relationships of the neuronal ele- ments with respect to the stimulating E-fields need to be determined to understand the activations at mesoscopic and microscopic levels. In particular, we will extend our previously published methods to allow accurate reconstructions of the white matter bundles as they exit/enter the cortical mantle with of 1 mm resolution in vivo. We will couple the cortical surface geometry reconstructions to simulate the effects of the E-field on various neuronal elements that will allow us predicting the likelihood of the stimulus to engage different activation mechanisms/pathways.