Workshop 3: Integrating Modalities and Scales in Life Science Imaging

(March 17,2014 - March 21,2014 )

Organizers


Monica Hurdal
Department of Mathematics, Florida State University
Michael Liebling
Electrical and Computer Engineering, University of California Santa Barbara
Rob MacLeod
SCI Institute and Bioengineering, University of Utah
Kristin Swanson
Neurological Surgery, Northwestern University

Merging imaging modalities is increasingly important for biomedical questions related to time and space scales including function and anatomy. Integrating modalities from multiple scales can assist with understanding development and function, disease, diagnosis and treatment. This workshop will bring together researchers who are attempting to combine and integrate different imaging modalities to better understand anatomy, function and disease from the cellular to organ level. Methodologies and challenges in combining imaging data from multiple sources, such as MRI, fMRI, DTI, PET, EEG, MEG, CT, ultrasound, NMR, x-ray diffraction, electron microscopy, proteomic and genomic data will be explored. Merging data from different modality time scales (functional time scales from nanoseconds to minutes; developmental time scales from embryonic to adult) and space scales (from microns to millimeters) present many mathematical questions. Interpretation, analysis and modeling of multi-modality data as it applies to development, disease models and therapies will also be explored. The heterogeneity of the data presents many difficult challenges that are suited for mathematical exploration. The focus will include brain and cardiac imaging related to multiscale and bioscale data collection, merging data, modeling and analysis. This workshop will be of interest to mathematicians working in areas of statistical analysis, PDE modeling, inverse problems, differential geometry, computational visualization and multiscale problems. Biomedical researchers interested in merging imaging modalities to investigate questions related to genomics, gene expression and biomarkers and the role they play in macroscopic function would benefit from this workshop.

Accepted Speakers

Oleg Aslanidi
Biomedical Engineering, Kings College London
Bastiaan Boukens
Biomedical Engineering, Washington University
Dana Brooks
Electrical and Computer Engineering, Northeastern University
Olivier Coulon
LSIS Lab, Aix-Marseille University
Mark Ellisman
National Center for Microscopy, UCSD
Birte Forstmann
Department for Psychology, University of Amsterdam
Jonathan Freund
Mechanical Science & Engineering and Aerospace Engineering, University of Illinois at Urbana-Champaign
Ali Gharaviri
Physiology, Maastricht University
Ali Khan
Robarts Research Institute, Western University
Paul Kinahan
Radiology, University of Washington
Peter Kohl
National Heart and Lung Institute, Imperial College London
Alan Koretsky
Neuroscience, National Institutes of Health
Irina Larina
Molecular Physiology and Biophysics, Baylor College of Medicine
Gabriele Lohmann
Biomedical magnetic resonance, University Clinic Tuebingen
Andrew McCulloch
Bioengineering, University of California, San Diego
Randy McIntosh
Psychology, University of Toronto
Finbarr O’Sullivan
Mathematical Sciences, University College Cork
Gernot Plank
Institute of Biophysics, Medical University of Graz
Rosemary Renaut
School of Mathematical and Statistical Sciences, Arizona State University
Kawal Rhode
Biomedical Engineering, Kings College London
Frank Sachse
Bioengineering, University of Utah
Willy Supatto
Laboratory for Optics and Biosciences, Ecole Polytechnique
Daniel Turnbull
Radiology and Skirball Institute, New York University School of Medicine
Adriaan van Oosterom
Medical Physics, Radboudumc
Lei Wang
Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine
Monday, March 17, 2014
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:15 AM

Greetings and Info from MBI - Marty Golubitsky

09:15 AM
09:30 AM

Introduction by Organizers

09:30 AM
10:15 AM
Peter Kohl - Mechanics matters: macro, micro, nano

When scientists think of the heart, they usually see an electrically-controlled, chemically-driven, mechanical pump. We tend to forget that the heart€™s mechanical environment has a pronounced influence on electrophysiology, calcium handling, and even drug effects. How these effects are mediated between macro-, micro-, and nano-levels depends strongly on the three-dimensional (3D) organisation of tissue, cells and sub-cellular structures. This talk will illustrate cardiac mechano-sensitivity on a number of human €˜test-cases€™, highlight the potential of 3D structure-function mapping as a driver of clinically relevant basic studies, and identify bottlenecks in our present approach to integrating scales and modalities in heart research.

10:15 AM
10:30 AM

Discussion

10:30 AM
11:00 AM

Break

11:00 AM
11:30 AM
Gernot Plank - Anatomically accurate multiscale-multiphysics models of total cardiac function

Despite the overwhelming wealth of data available today, gaining mechanistic insight into cardiac function remains to be a challenging endeavour due to the multiscale/multiphysics nature of cardiac function, where complex interactions of processes arise within and across levels of organization, as well as between electrical, mechanical and fluidic systems. Computer simulation has become a powerful adjunct to experimental studies, but current modeling methodology imposes severe limitations, forcing research to resort to overly simplified modeling assumptions. This talk will highlight recent methodological advances in terms of modeling organ scale cardiac anatomy and electro-mechano-fluidic function at high spatial resolution. The presented methods aim at lifting many of the current modeling restrictions to enable computational studies where model complexity is chosen as a function of the question being addressed, and not based on feasibility constraints. The use of advanced numerical methods is of pivotal importance to reduce execution times, thus facilitating quick simulation-analysis cycles. Application examples will be presented including multiscale arrhythmogenic effects due to mitochondrial dysfunction and calcium handling, as well as clinical modeling studies which aim at optimization and outcome prediction due to interventions such as aortic valve replacement and repair of aortic coarctations.

11:30 AM
11:45 AM

Discussion

11:45 AM
12:15 PM
Frank Sachse - Deriving Macroscopic Parameters for Computational Modeling of Cardiac Tissues from High-Resolution Three-Dimensional Confocal Microscopy

Computational models play an important role in studies of cardiac tissue physiology and pathophysiology. Various types of models have been developed based on histological and electrophysiological studies, for instance, monodomain, bidomain and multidomain models of cardiac conduction. Over the last years, we developed new approaches for deriving model parameters from three-dimensional reconstructions of cardiac tissue at sub-micrometer resolution. We create the reconstructions using image data from fluorescent labeling and scanning confocal microscopy. Here, we provide an overview of our approaches for acquiring and processing of image data as well as extraction of model parameters. We suggest that the developed approaches provide important input for parameterization of models of cardiac tissues, in particular, models for investigations of tissue remodeling in disease and restoration after therapy.

12:15 PM
12:30 PM

Discussion

12:30 PM
02:15 PM

Lunch Break

02:15 PM
02:45 PM
Rosemary Renaut - Biofuel cell polarization estimation: inversion of electrochemical impedance spectroscopic measurements Importantance of Model Formulation

The inverse problem associated with electrochemical impedance spectroscopy requiring the solution of a Fredholm integral equation of the first kind is considered. If the underlying physical model is not clearly determined, the inverse problem needs to be solved using a regularized linear least squares problem that is obtained from the discretization of the integral equation. For this system, it is shown that the model error can be made negligible by a change of variables and by extending the effective range of quadrature. This change of variables serves as a right preconditioner that significantly improves the condition of the system. Still, to obtain feasible solutions the additional constraint of non-negativity is required. Simulations with artificial, but realistic, data demonstrate that the use of non-negatively constrained least squares with a smoothing norm provides higher quality solutions than those obtained without the non-negative constraint. Using higher-order smoothing norms also reduces the error in the solutions. The L-curve and residual periodogram parameter choice criteria, which are used for parameter choice with regularized linear least squares, are successfully adapted to be used for the non-negatively constrained problem. Although these results have been verified within the context of the analysis of electrochemical impedance spectroscopy, there is no reason to suppose that they would not be relevant within the broader framework of solving Fredholm integral equations for other applications.

02:45 PM
03:00 PM

Discussion

03:00 PM
03:45 PM
Daniel Turnbull - MBI COLLOQUIUM: In Vivo Imaging of the Developing Mouse Brain: From Morphology to Molecules

Extensive genetic information and the expanding number of techniques available to manipulate the genome of the mouse have led to its widespread use in studies of brain development and to model human neurodevelopmental diseases. We are developing a combination of ultrasound and magnetic resonance micro-imaging approaches with sufficient resolution and sensitivity to provide noninvasive structural, functional and molecular data on developmental and disease processes in normal and genetically-engineered mice. Our efforts over the past decade have focused on in utero and early postnatal imaging and analysis of the developing brain and cerebral vasculature. The advantages and limitations of both ultrasound and MRI for imaging mouse development will be discussed, and examples provided to illustrate the utility of these approaches for 4D mutant phenotype analysis. Recent advances have also made in the area of molecular imaging, including the generation of novel reporter mice that enable cell-specific imaging with ultrasound and MRI contrast agents. Future directions for molecular imaging of mouse brain development will be discussed.

03:45 PM
04:00 PM

Discussion

04:00 PM
04:30 PM

Break (Refreshments Served)

04:30 PM
05:00 PM

Software Panel I: Membrane and cell simulations Moderator: Michael Leibling

05:00 PM
05:45 PM

Poster Introductions

05:45 PM
07:15 PM

Reception and Poster Session in MBI Lounge

07:15 PM

Shuttle pick-up from MBI

Tuesday, March 18, 2014
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:45 AM
Mark Ellisman - Toward Making the Invisible and Complicated Understandable: Microscopy Across Scales and Modalities

A grand goal in cell biology is to understand how the interplay of structural, chemical and electrical signals in and between cells gives rise to tissue properties, especially for complex tissues like nervous systems. New technologies are hastening progress as biologists make use of an increasingly powerful arsenal of tools and technologies for obtaining data, from the level of molecules to whole organs, and at the same time engage in the arduous and challenging process of adapting and assembling data at all scales of resolution and across disciplines into computerized databases. This talk will highlight projects in which development and application of new contrasting methods and imaging tools have allowed us to observe otherwise hidden relationships between cellular, subcellular and molecular constituents of cells, including those of nervous systems. New chemistries for carrying out correlated light and electron microscopy will be described, as well as recent advances in large-scale high-resolution 3D reconstruction with LM, TEM and SEM based methods. Examples of next generation cell-centric image libraries and web-based multiscale information exploration environments for sharing and exploring these data will also be described.


09:45 AM
10:00 AM

Discussion

10:00 AM
10:30 AM

Break

10:30 AM
11:00 AM
Kristin Swanson - Predictive Patient-Specific Mathematical Neuro-Oncology
Predictive Patient-Specific Mathematical Neuro-Oncology: A Paradigm shift in Giloma Treatment.
11:00 AM
11:15 AM

Discussion

11:15 AM
11:45 AM

Integration Panel I: Neural (across scales and between structure and function) Moderator: Monica Hurdal

11:45 AM
01:15 PM

Boxed Lunch at MBI

01:15 PM
02:00 PM
Willy Supatto - Advanced multiphoton microscopy for high-content in vivo imaging

High-content imaging of biological processes tremendously benefits from recent advances in light microscopy. While light-sheet microscopy has gained widespread recognition in recent years, due to its distinct advantages for imaging live organisms with high acquisition speed, large field-of-view and low phototoxicity, its imaging depth remains a limitation. On the other hand, multiphoton microscopy achieves high imaging depth into scattering tissues and permits multimodal detection, including the combination of fluorescence and harmonic generation as sources of signal. However its acquisition speed is limited and it remains challenging to obtain efficient multicolor excitation. In this context, we report on recent advances in multiphoton microscopy to improve imaging speed and multicolor excitation. We present multiphoton light-sheet microscopy, combining two-photon excited fluorescence with orthogonal illumination and demonstrate its performance in maintaining high spatial resolution deep inside biological tissues, as well as high acquisition speed and low phototoxicity. In addition, we present a strategy based on wavelength mixing to perform optimal and simultaneous two-photon excitation of three chromophores with distinct absorption spectra. These approaches open new opportunities for live, multicolor, multidimensional and multiscale imaging: we illustrate these potentials by imaging Brainbow-labeled tissues, heart dynamic and early fly and zebrafish embryonic development.

02:00 PM
02:15 PM

Discussion

02:15 PM
02:45 PM
Bastiaan Boukens - Optical imaging of the heart

Mathematical modeling is of crucial importance for understanding the complexity of biological systems. Where biological experiments educate us about nature itself, mathematical models allow us to make prediction on the outcome of events based on current theories and hypotheses. In the field of cardiology, modeling has an important role in forward and inverse calculations between local cardiac events and the body surface ECG or the understanding complex atrial or ventricular arrhythmias. To develop and optimize an accurate mathematical model experimental data is required. Optical imaging has enabled the recording of epicardial, and also transmural, activation and repolarization patterns with a high spatial resolution. Also, optical imaging allows to relate the metabolic state and ionic homeostasis with cardiac electrophysiology by measuring simultaneously membrane voltage and Ca2+ and Na+ with fluorescent indicators or NADH, which has its own fluorescent spectrum. In this talk I will discuss several optical imaging modalities as they are applied to study human non-failing and failing hearts.

02:45 PM
03:00 PM

Discussion

03:00 PM
03:30 PM

Break

03:30 PM
04:00 PM
Irina Larina - Studying mammalian embryonic development through optical imaging

Understanding the nature and mechanism of congenital defects of different organ systems in humans has heavily relied on the analysis of the corresponding mutant phenotypes in mouse models. This talk will describe how optical imaging can be used to visualize live developing mouse embryonic structures at different embryonic stages. Two optical imaging approaches, fluorescence microscopy of vital reporters and optical coherence tomography will be highlighted, discussing how these methods can be utilized for structural imaging of early mouse embryos in static culture, 4D cardiodynamic and blood flow analysis, and in utero embryonic imaging at later stages of gestation, demonstrating how these methods can be used to assess structural and functional birth defects in mouse models. Future trends and existing challenges in data interpretation, analysis and modeling will be discussed.

04:00 PM
04:15 PM

Discussion

04:15 PM
04:45 PM
Jonathan Freund - Cellular and Network Simulation of Microcirculatory Flow Dynamics

The time-dependent dynamics of microcirculatory blood flow is well understood to couple dynamically with other mechanisms in developmental, disease processes, and potential therapies. We use advanced simulation techniques to study such flows, both with fully detailed descriptions of the cellular detail of the flowing blood and with reduced models that represent entire vascular networks. This talk will introduce our methods for cellular blood flow simulation in the context of methods available, and discuss results regarding the shear-stress footprints of passing cells and how they might constitute important mechanotriggers, the transport of therapeutic magnetic nanoparticles by a cellular blood flow, and the capacitive role of vascular elasticity at the onset of circulation in the developing zebrafish.


04:45 PM
05:00 PM

Discussion

05:00 PM
05:15 PM
Richard Conroy - NIH Funding

NIH Funding

05:15 PM
05:45 PM

Software Panel II: Tissue and Cell-Cell interactions Moderator: Rob MacLeod

05:45 PM

Shuttle pick-up from MBI

Wednesday, March 19, 2014
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:45 AM
Spencer Sherwin
09:45 AM
10:00 AM

Discussion

10:00 AM
10:30 AM

Break

10:30 AM
11:15 AM
Adriaan van Oosterom - A comparison of electrocardiographic imaging of the epicardial potential field and that of the timing of the EDI source.


The dynamics of the contraction of the cardiac muscle is initiated and accompanied by the flow electric currents. These set up potential fields throughout the myocardium as well as in the body tissues surrounding the heart. The observed time course of the potential differences between any two locations inside the thorax or on its surface is referred to as an electrocardiogram (ECG). In cardiology the set of 12 simultaneously recorded signals, derived from nine strategically chosen locations on the body surface, forms one of the most frequently used diagnostic tools. As such, the interpretation of the ECG can be classed as a non-invasive method. Moreover, it can be classed as a performing an inverse procedure: the interpretation of a state, or phenomenon on the observations at a distances.


Over the period of more than a century following the first recording of an ECG, the diagnostic accuracy of the ECG has increased steadily. Yet, in some types of cardiac disease still needs to be improved. One of the recent developments is the increase the number of electrodes sampling the time course of the potential field on the thorax. The display of a sequence of instantaneous potential fields by means of scalar maps has inspired the search for methods for also displaying the inverse interpretation of the signals in the form of maps, thus emphasizing the spatial character of the cardiac electric sources. This procedure can be classed as imaging.


The nature of the physics involved, the electric volume conduction, demands the formulation of the general nature of these sources from which the transfer between source distributions and observed potential field can be derived. Currently, two such descriptions (models) are developed. In this presentation both models are introduced and their similarities and differences (advantages and disadvantages) are illustrated in an application to the data of one and the same subject.


11:15 AM
11:30 AM

Discussion

11:30 AM
12:00 PM
Dana Brooks - How Can We Use Dynamic Models in Inverse Bioelectric Problems?

Both cardiac and brain bioelectric forward problems can be modeled accurately as quasi-static, implying that torso or scalp surface measurements depend on the spatial distribution of the respective sources independently at each time instant. However in both cases the time courses of the sources are in large part a function of intrinsic electrophysiological dynamics, and so exhibit strong and complex temporal correlations. This suggests that it would be advantageous to incorporate temporal models into inverse methods, especially in light of the ill-posed nature of both problems. This talk will present some ideas, results, and possibilities for dynamic modeling in inverse bioelectric problems, concentrating primarily on electrocardiography. We will review some standard methods and describe how three such approaches are related through their assumptions about spatiotemporal covariance structure. We will then present some recent results with clinically measured data using a new method which incorporates a non-linear temporal model. Finally we will illustrate manifold-inspired, non-linear dynamic structure in measured signals, suggesting the potential for even more powerful dynamic modeling in the near future. If time permits we will also show results of our dynamic modeling of EEG and pose some questions about their implications for brain source localization.

12:00 PM
12:15 PM

Discussion

12:15 PM
12:45 PM

Integration Panel II: Cardiac (across scales and between structure and function) Moderator: Rob MacLeod

12:45 PM
02:15 PM

Pizza Lunch at MBI

02:15 PM
03:00 PM
Paul Kinahan - Forward Modeling of Medical Imaging Systems

In medical imaging, the true underlying property of interest is unknown. A single image provides little to no insight into the impact of confounding factors such as: statistical noise, biological variability, scattered radiation, patient motion, deadtime in detectors and electronics, detector resolution, etc. Some of these physical factors can be quantified by scanning various configurations of test objects, often called phantoms. Physical phantoms, however, can not capture variability due to patient physiology and offer only mean performance characteristics of a limited set of objects.


Forward modeling of imaging systems provides an unparalleled window for examining and improving performance. For example, simulations using Monte Carlo photon tracking can be used to isolate a single factor of interest, for instance multiple i.i.d. realizations of the same imaging scenario can determine the effect of quantum noise or biological variability. Likewise, faster analytical models can be used to elucidate non-linear effects in the imaging chain. Accurate forward models also play a key role in improving iterative estimation of optimal images. Finally, forward models can be integrated into a feedback loop for optimization of a medical imaging tasks (not just images), that are not predictable a priori. We will give examples of the value of forward modeling in which an accurate model of the physics of the medical imaging system is an essential component to solving challenges in imaging research and healthcare.


03:00 PM
03:15 PM

Discussion

03:15 PM
03:45 PM
Finbarr O’Sullivan - Measurement of Cancer Heterogeneity with Positron Emission Tomography (PET) Data

PET/CT has seen widespread clinical use in the diagnosis and monitoring of cancer patients. Data acquired over the past several years has lead to the opportunity to refine the extraction of potential prognostic variables from PET imaging information. Up to now the emphasis has been on the use simple measures such as maximum tracer uptake in the tumor volume. However cancer is a heterogeneous process and the quantitation of this aspect is of particular interest. With imaging data the scales of heterogeneity that can be measured are limited by image resolution. In PET the bandwidth used in reconstruction plays a key role. We describe some alternative approaches to heterogeneity measurement with examples from PET imaging studies in patients with sarcoma, brain and breast cancer. The measures considered include both spatially invariant ones as well as more elaborate approaches based on modeling the spatial distribution of the tracer. Patient follow-up data is used to which measures have greatest prognostic utility.



(Supported by Science Foundation Ireland under 11/PI/1027 and the National Cancer Institute under CA-42045.)


03:45 PM
04:00 PM

Discussion

04:00 PM
04:30 PM

Break

04:30 PM
05:00 PM
Gabriele Lohmann - Methods for detecting and analyzing large-scale networks in the human brain using fMRI data

One of the most challenging problems in neuroscience today is the detection and analysis of functional networks in the human brain. Traditional approaches to the analysis of fMRI data have regarded the brain as a stationary and univariate entity. Even though this was an extremely simplified view, it turned out to be surprisingly successful and yielded many valuable insights into brain function. Nonetheless, more realistic models should now be used as a basis for data analysis. In particular, since the brain operates as a complex and dynamic network, massively multivariate techniques will become increasingly important. In this talk, several new data analysis techniques that aim at understanding complex networks in the brain will be discussed.


05:00 PM
05:15 PM

Discussion

05:15 PM
05:45 PM

Imaging and Modeling Panel I: Multi-scale, multi-modality, multi-resolution imaging: strategies for integrating static and dynamic, low and high resolution, imaging and non-imaging methods Moderator: Kristin Swanson

05:45 PM

Shuttle pick-up from MBI

Thursday, March 20, 2014
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:45 AM
Andrew McCulloch - Multi-Scale Modeling and Imaging of the Heart: From Mouse to Man

Multi-scale models of the heart have been developed that integrate both functionally across biomechanical, electrophysiological and regulatory functions and structurally across physical scales of organization from molecule to organ system. Multi-scale imaging, from high-resolution microscopy to clinical imaging in patients is a critical tool for the development of structurally and biophysically detailed multi-scale models. Here, we illustrate the development and application of these models to improving understanding and management of heart diseases. Electron tomography enables high-resolution reconstructions of subcellular microanatomy. We use novel subcellular models show how the three-dimensional architecture of the transverse tubule system and calcium release units affect the dynamics and heterogeneity of calcium signaling in cardiac myocytes. The mouse is a valuable model organism for studying heart failure because of the availability of genetically engineered strains harboring mutations that lead to heart failure in animals and humans. We illustrate this with results from recent multi-scale models of the mouse heart. Experimental studies in these mice at multiple scales including in-vivo MRI allow us to validate models and identify integrative mechanisms of disease. In humans, image-based patient-specific multi-scale models of ventricular electromechanics in the failing heart are helping improve our understanding of therapeutic strategies, including cardiac resynchronization therapy (CRT) for patients with heart failure that is complicated by an electrical conduction defects and catheter ablation therapy for persistent atrial fibrillation. Three-dimensional models derived from cardiac CT imaging are showing potential for enhancing understanding of therapeutic mechanisms and improving therapeutic outcomes.

09:45 AM
10:00 AM

Discussion

10:00 AM
10:30 AM

Break

10:30 AM
11:00 AM
Kawal Rhode - Multimodal data fusion for cardiac biophysical modelling

Heart disease is a major cause of patient mortality and morbidity. It poses a significant economic burden to healthcare. The use of medical imaging is central to the management of patients with heart disease as are basic physiological measurements, such as blood pressure and ECG. Often a number of different types of imaging and physiological measurements are performed that give complimentary information. These data are then used in all stages of patient management, including diagnosis, treatment selection/planning/guidance and patient follow-up. Despite the availability of these rich data, treatment success rates for many procedures remain sub-optimal. Multi-modality data fusion coupled to biophysical modelling of the heart has a great potential to increase these success rates by providing key input at all stages of patient management. This talk will highlight methods for cardiac multi-modal data fusion and show applications to predictive biophysical modelling of heart disease. Examples will include arrhythmia and heart failure management.

11:00 AM
11:15 AM

Discussion

11:15 AM
11:45 AM
Ali Gharaviri - Three Dimensional Conduction During Atrial Fibrillation (a modeling approach)

Several mechanisms have been suggested to explain the increasing stability of atrial fibrillation (AF) over time. Disruption of electrical coupling between muscle bundles, resulting in narrower and thus more fibrillation waves, is considered as one of the main mechanisms contributing to AF stability in structurally remodeled atria. Also, the anatomy of the atrial wall has been demonstrated to significantly determine conduction patterns during AF. Most of these mechanisms have been studied in several in silico studies. But more than that, there are experimental studies suggesting that the development of the substrate for AF goes along with increasing incidence of conduction from the sub-epicardial layer to the endocardial bundle network and vice versa. While these studies conclusively demonstrate transmural conduction in the atrial wall, they leave open several important conceptual questions. In particular this talk focuses on in silico modeling of three-dimensional conductions during AF and its effect on AF stability.

11:45 AM
12:00 PM

Discussion

12:00 PM
12:30 PM

Imaging and Modeling Panel II: From image to image analysis to physical model (and back): comparing mathematical/physical models with experimental images, designing the modeling/imaging pipeline for validation, prediction Moderator: Kristin Swanson

12:30 PM
02:00 PM

Lunch Break

02:00 PM
02:45 PM
Olivier Coulon - Organization and variability of the human cerebral cortex: global and local quantification and modeling

The cerebral cortex as it can be observed in neuroimaging shows a large apparent variability across subjects. Such variability is a problem for several reasons: 1) it is an obstacle to the inter-subject matching that is necessary for all neuroimaging group studies, and 2) it limits possibilities to define intervals of normality and therefore detect abnormal characteristics associated to pathologies. In the context of neuroimaging, and in particular of Magnetic Resonance Imaging (MRI) acquisitions, macro-anatomy of the cortex can be observed and described in terms of folds, sulci, and gyri, which are highly variable and to date it is still a debate if these landmarks are representative of cortical organisation and architecture. This talk will present here different methods that are used to study cortical organization and variability from a global level to a more local one. In particular it will show how cortical organization can be modeled at the global level, how variability can be quantified at the more local level of individual sulci, how this variability can be related to functional organization, and what we can expect of imaging micro-structure in-vivo.

02:45 PM
03:00 PM

Discussion

03:00 PM
03:30 PM

Break

03:30 PM
04:00 PM
Lei Wang - Multimodal Neuroimaging Biomarkers for Neuropsychiatric Disorders

In this talk, we will describe some of the recent developments of computational anatomy tools for the study of brain structure, function and how they interact with cognitive behavior. Specifically we will touch upon the following 3 areas of work: 1) brain structural shape as biomarkers in schizophrenia and Alzheimer€™s disease; 2) integrating cortical thickness, cortical geometry, and cortical metabolism in AD; and 3) a nuanced model of cortical thinning, functional compensation, cognitive stability of schizophrenia.

04:00 PM
04:15 PM

Discussion

04:15 PM
05:00 PM
Alan Koretsky - Crossing scales in the brain from functional MRI to synaptic function

Functional MRI techniques have found widespread use to measure brain neural circuits that are used for a large number of behaviors. When circuit activity changes due to plasticity, it remains a challenge to identify sites of synaptic changes responsible for the circuit level changes measured. Of particular interest are the cases of long range cortical rearrangements that have been detected in the human brain after injury. Rodent models that mimic some of these cortical rearrangements have been developed and are being used to determine the synaptic basis for the plasticity detected. We have developed a model of adult cortical plasticity due to peripheral somatosensory nerve damage that is being used to develop MRI tools that can pinpoint sites of synaptic changes. Two weeks after peripheral denervation of one side of the forepaw, hindpaw, or whisker pathway there is a large up-regulation of cortical activity from the spared side and a large up-regulation of callosal inputs from the spared cortex to the cortical representation of the denervated area. A combination of functional MRI and laminar specific neural track tracing using manganese enhanced MRI predicted changes in thalamo-cortical inputs to layer IV that contribute to the up-regulation of cortical activity along the spared whisker barrel pathway. Slice electrophysiology confirmed that the thalamic inputs on layer IV stellate cells were strengthened by a post-synaptic mechanism and that there is a re-opening of critical period plasticity. Sites of plasticity that explain the up-regulation of the callosal communication have also been studied with MRI. High temporal-spatial resolution fMRI demonstrates that up-regulation of the communication between the spared and denervated cortices likely occur through callosal inputs. These fMRI results were consistent with manganese enhanced MRI that predicts a strengthening of inputs into layer 2/3 and 5. Taken together these results demonstrate that MRI is positioned to begin to give laminar specific information about mechanisms of cortical plasticity. The challenge of multi-scale modeling in the context of arriving at a quantitative understanding of MRI results will be discussed.


05:00 PM
05:15 PM

Discussion

05:15 PM
05:45 PM

Software Panel III: Whole organ, including volume conductor problems Moderator: Rob MacLeod

05:45 PM

Shuttle pick-up from MBI

06:30 PM
07:15 PM

Cash Bar

07:15 PM
09:15 PM

Banquet in the Fusion Room at the Crowne Plaza

Friday, March 21, 2014
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:45 AM
Randy McIntosh - Building and Interacting with The Virtual Brain

The Virtual Brain (TVB, thevirtualbrain.org) is an international project that uses real neuroimaging data to construct a simulation of the human brain. Anatomical data setup the conduit for communication between different brain regions. The dynamics for each region are generated from a library of nonlinear models, and produce large-scale activity patterns that can be compared directly to empirical functional data, such EEG/MEG or functional MRI. The talk will present the core of the platform and its applications to understanding the structure-function interplay that forms the basis of cognitive architectures. TVB€™s use of real data is also at the heart of a larger social neuroscience initiative, wherein small groups of people interact with TVB through wireless EEG headsets, modifying an immersive audiovisual environment that mimics a dream €“ My Virtual Dream. The goal is to make use of individual brain signals to augment the group experience through TVB. The two avenues of development for TVB will inform neurally-inspired computing architectures and the evolution of interactive devices that can use a person€™s physiology to redesign their experience.

09:45 AM
10:00 AM

Discussion

10:00 AM
10:30 AM

Break

10:30 AM
11:00 AM
Ali Khan - Integrating multi-modal quantitative MRI and histology for improving surgical treatment of epilepsy

Drug-resistant epilepsy occurs in over one third of epilepsy patients, and surgical excision of the affected brain region is often necessary to achieve seizure control. However, precise delineation of the seizure onset zone can be challenging, and can lead to poor surgical outcomes when incorrect. In many of these cases, the underlying pathology consists of subtle architectural abnormalities at the microscopic scale. Improved imaging of these lesions at a macroscopic scale thus requires integration of in-vivo MRI modalities that can probe the tissue microarchitecture, along with rigorous validation against histology. This talk will present our work on correlating quantitative relaxometry and diffusion imaging of temporal lobe epilepsy patients with histology of surgical specimens. I will highlight the challenges faced in spatial alignment of anatomy at vastly different scales, steps taken towards quantitative characterization of pathology in epilepsy, and how intrinsic MRI parameters can be used to improve our understanding of the excitotoxic effects of seizures.

11:00 AM
11:15 AM

Discussion

11:15 AM
11:45 AM

Wrap up panel: Major Challenges and Future Directions Moderator: Monica Hurdal

11:45 AM
12:00 PM

Closing Remarks by Organizers

12:15 PM

Shuttle pick-up from MBI (One to Hotel/One to Airport)

Name Email Affiliation
Acar, Nihan nacar@math.fsu.edu Mathematics, Florida State University
Alessio, Adam aalessio@u.washington.edu Radiology, University of Washington
Aslanidi, Oleg oleg.aslanidi@kcl.ac.uk Biomedical Engineering, Kings College London
Boukens, Bastiaan boukensb@seas.wustl.edu Biomedical Engineering, Washington University
Brooks, Dana brooks@ece.neu.edu Electrical and Computer Engineering, Northeastern University
Camara, Oscar oscar.camara@upf.edu Department of Information and Communication Technologies, Universitat Pompeu Fabra
Cantwell, Chris c.cantwell@imperial.ac.uk National Heart and Lung Institute, Imperial College London
Chien, Aichi aichi@ucla.edu Department of Radiological Sciences, David Geffen School of Medicine at UCLA
Cluitmans, Matthijs m.cluitmans@cardio.unimaas.nl Department of Knowledge Engineering & Department of Cardiology, Maastricht University
Coll-Font, Jaume jcollfont@ece.neu.edu ECE, Northeastern University
Conroy, Richard conroyri@mail.nih.gov NIBIB, National Institutes of Health
Coulon, Olivier olivier.coulon@univ-amu.fr LSIS Lab, Aix-Marseille University
Dean, Delphine finou@clemson.edu Bioengineering, Clemson University
DiBella, Ed ed@ucair.med.utah.edu Radiology, University of Utah
Ellisman, Mark mark@ncmir.ucsd.edu National Center for Microscopy, UCSD
Forstmann, Birte buforstmann@gmail.com Department for Psychology, University of Amsterdam
Freund, Jonathan jbfreund@illinois.edu Mechanical Science & Engineering and Aerospace Engineering, University of Illinois at Urbana-Champaign
Gharaviri, Ali a.gharaviri@maastrichtuniversity.nl Physiology, Maastricht University
Haider, Mansoor m_haider@ncsu.edu Mathematics & Biomathematics Graduate Program, North Carolina State University
Hurdal, Monica mhurdal@math.fsu.edu Department of Mathematics, Florida State University
Jackson, Pamela pamela.r.jackson@gmail.com Neurological Surgery, Northwestern University
Jacquemet, Vincent vincent.jacquemet@umontreal.ca Physiologie, Universite de Montreal
Jeraj, Robert rjeraj@wisc.edu Medical Physics, University of Wisconsin
Khan, Ali alik@robarts.ca Robarts Research Institute, Western University
Kinahan, Paul kinahan@uw.edu Radiology, University of Washington
Kohl, Peter p.kohl@imperial.ac.uk National Heart and Lung Institute, Imperial College London
Koretsky, Alan koretskya@ninds.nih.gov Neuroscience, National Institutes of Health
Larina, Irina larina@bcm.edu Molecular Physiology and Biophysics, Baylor College of Medicine
Lepore, Natasha nlepore@chla.usc.edu radiology, University of Southern California
Liebling, Michael liebling@ece.ucsb.edu Electrical and Computer Engineering, University of California Santa Barbara
Lohmann, Gabriele lohmann@cbs.mpg.de Biomedical magnetic resonance, University Clinic Tuebingen
MacLeod, Rob macleod@sci.utah.edu SCI Institute and Bioengineering, University of Utah
McCulloch, Andrew amcculloch@ucsd.edu Bioengineering, University of California, San Diego
McIntosh, Randy rmcintosh@rotman-baycrest.on.ca Psychology, University of Toronto
O�Sullivan, Finbarr f.osullivan@ucc.ie Mathematical Sciences, University College Cork
Osan, Remus rosan@gsu.edu Mathematics and Statistics, Georgia State University
Plank, Gernot gernot.plank@medunigraz.at Institute of Biophysics, Medical University of Graz
Quinn, T Alexander alex.quinn@dal.ca Physiology & Biophysics, Dalhousie University
Rampersad, Sumientra S.Rampersad@neuro.umcn.nl Neurology, Radboud University Medical Centre
Renaut, Rosemary renaut@asu.edu School of Mathematical and Statistical Sciences, Arizona State University
Rettmann, Maryam Rettmann.Maryam@mayo.edu Department of Physiology and Biomedical Engineering, Mayo Clinic
Rhode, Kawal kawal.rhode@kcl.ac.uk Biomedical Engineering, Kings College London
Rugonyi, Sandra rugonyis@ohsu.edu Biomedical Engineering, Oregon Health & Science University
Sachse, Frank fs@cvrti.utah.edu Bioengineering, University of Utah
Schulze, Walther walther.schulze@kit.edu Institute of Biomedical Engineering, Karlsruhe Institute of Technology
Sen, Anando anandosen@gmail.com Department of Biomedical Engineering, University of Houston
Skwerer, Sean sskwerer@unc.edu Statistics and Operations Research, University of North Carolina, Chapel Hill
Studholme, Colin colin.studholme@ieee.org Pediatrics and Bioengineering, University of Washington
Supatto, Willy willy.supatto@polytechnique.edu Laboratory for Optics and Biosciences, Ecole Polytechnique
Swanson, Kristin kristin.swanson@northwestern.edu Neurological Surgery, Northwestern University
Turnbull, Daniel daniel.turnbull@med.nyu.edu Radiology and Skirball Institute, New York University School of Medicine
van Oosterom, Adriaan avo-linden@home.nl Medical Physics, Radboudumc
Wang, Lei leiwang1@northwestern.edu Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine
Electroanatomical substrate for atrial fibrillation: Insights from multi-scale modelling

Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting over six million people in the US and imposing a huge healthcare burden on modern society. Despite a vast amount of data collected from patients and ex-vivo protein-to-organ scale experiments, complex mechanisms of AF arrhythmogenesis are poorly understood and clinical treatments remain sub-optimal. Biophysical modelling provides a quantitative framework for integrating multi-modal and multi-scale data and simulating the arrhythmogenic atrial dynamics arising across multiple scales. Such an integrative approach is extremely difficult to implement in a purely experimental setting. State-of-the-art 3D models of the atria integrate heterogeneous electrophysiological (ion channel, gap junction, action potential) and structural (atrial morphology, fibre orientation, fibrosis) characteristics and their remodelling during AF progression. Model simulations enable (i) dissecting the key determinants of atrial substrate for re-entrant waves maintaining AF, (ii) exploring how evolution of this substrate due to progressive remodelling self-perpetuates AF, and (iii) quantifying effects of antiarrhythmic drugs on the evolving substrate. Model predictions are validated against available atrial mapping data, and may provide novel insights into AF mechanisms and clinical treatments.

Optical imaging of the heart

Mathematical modeling is of crucial importance for understanding the complexity of biological systems. Where biological experiments educate us about nature itself, mathematical models allow us to make prediction on the outcome of events based on current theories and hypotheses. In the field of cardiology, modeling has an important role in forward and inverse calculations between local cardiac events and the body surface ECG or the understanding complex atrial or ventricular arrhythmias. To develop and optimize an accurate mathematical model experimental data is required. Optical imaging has enabled the recording of epicardial, and also transmural, activation and repolarization patterns with a high spatial resolution. Also, optical imaging allows to relate the metabolic state and ionic homeostasis with cardiac electrophysiology by measuring simultaneously membrane voltage and Ca2+ and Na+ with fluorescent indicators or NADH, which has its own fluorescent spectrum. In this talk I will discuss several optical imaging modalities as they are applied to study human non-failing and failing hearts.

How Can We Use Dynamic Models in Inverse Bioelectric Problems?

Both cardiac and brain bioelectric forward problems can be modeled accurately as quasi-static, implying that torso or scalp surface measurements depend on the spatial distribution of the respective sources independently at each time instant. However in both cases the time courses of the sources are in large part a function of intrinsic electrophysiological dynamics, and so exhibit strong and complex temporal correlations. This suggests that it would be advantageous to incorporate temporal models into inverse methods, especially in light of the ill-posed nature of both problems. This talk will present some ideas, results, and possibilities for dynamic modeling in inverse bioelectric problems, concentrating primarily on electrocardiography. We will review some standard methods and describe how three such approaches are related through their assumptions about spatiotemporal covariance structure. We will then present some recent results with clinically measured data using a new method which incorporates a non-linear temporal model. Finally we will illustrate manifold-inspired, non-linear dynamic structure in measured signals, suggesting the potential for even more powerful dynamic modeling in the near future. If time permits we will also show results of our dynamic modeling of EEG and pose some questions about their implications for brain source localization.

NIH Funding

NIH Funding

Organization and variability of the human cerebral cortex: global and local quantification and modeling

The cerebral cortex as it can be observed in neuroimaging shows a large apparent variability across subjects. Such variability is a problem for several reasons: 1) it is an obstacle to the inter-subject matching that is necessary for all neuroimaging group studies, and 2) it limits possibilities to define intervals of normality and therefore detect abnormal characteristics associated to pathologies. In the context of neuroimaging, and in particular of Magnetic Resonance Imaging (MRI) acquisitions, macro-anatomy of the cortex can be observed and described in terms of folds, sulci, and gyri, which are highly variable and to date it is still a debate if these landmarks are representative of cortical organisation and architecture. This talk will present here different methods that are used to study cortical organization and variability from a global level to a more local one. In particular it will show how cortical organization can be modeled at the global level, how variability can be quantified at the more local level of individual sulci, how this variability can be related to functional organization, and what we can expect of imaging micro-structure in-vivo.

Toward Making the Invisible and Complicated Understandable: Microscopy Across Scales and Modalities

A grand goal in cell biology is to understand how the interplay of structural, chemical and electrical signals in and between cells gives rise to tissue properties, especially for complex tissues like nervous systems. New technologies are hastening progress as biologists make use of an increasingly powerful arsenal of tools and technologies for obtaining data, from the level of molecules to whole organs, and at the same time engage in the arduous and challenging process of adapting and assembling data at all scales of resolution and across disciplines into computerized databases. This talk will highlight projects in which development and application of new contrasting methods and imaging tools have allowed us to observe otherwise hidden relationships between cellular, subcellular and molecular constituents of cells, including those of nervous systems. New chemistries for carrying out correlated light and electron microscopy will be described, as well as recent advances in large-scale high-resolution 3D reconstruction with LM, TEM and SEM based methods. Examples of next generation cell-centric image libraries and web-based multiscale information exploration environments for sharing and exploring these data will also be described.


Functional and structural role of the subthalamic nucleus: A model-based approach

Current academic consensus holds that the subthalamic nucleus (STN) consists of three parts, each anatomically distinct and selectively associated with cognitive, emotional, or motor functioning. In my talk, I will first provide a critical overview challenging the view of a tripartite STN. Next, I will present post-mortem and in-vivo ultra-high resolution structural 7T magnetic resonance imaging (MRI) STN data. Finally, I will show recent results from functional ultra-high resolution MRI data investigating the role of the STN in multi-alternative decision making. The results will be discussed in light of the STN´s functional role in both healthy and clinical populations.

Cellular and Network Simulation of Microcirculatory Flow Dynamics

The time-dependent dynamics of microcirculatory blood flow is well understood to couple dynamically with other mechanisms in developmental, disease processes, and potential therapies. We use advanced simulation techniques to study such flows, both with fully detailed descriptions of the cellular detail of the flowing blood and with reduced models that represent entire vascular networks. This talk will introduce our methods for cellular blood flow simulation in the context of methods available, and discuss results regarding the shear-stress footprints of passing cells and how they might constitute important mechanotriggers, the transport of therapeutic magnetic nanoparticles by a cellular blood flow, and the capacitive role of vascular elasticity at the onset of circulation in the developing zebrafish.


Three Dimensional Conduction During Atrial Fibrillation (a modeling approach)

Several mechanisms have been suggested to explain the increasing stability of atrial fibrillation (AF) over time. Disruption of electrical coupling between muscle bundles, resulting in narrower and thus more fibrillation waves, is considered as one of the main mechanisms contributing to AF stability in structurally remodeled atria. Also, the anatomy of the atrial wall has been demonstrated to significantly determine conduction patterns during AF. Most of these mechanisms have been studied in several in silico studies. But more than that, there are experimental studies suggesting that the development of the substrate for AF goes along with increasing incidence of conduction from the sub-epicardial layer to the endocardial bundle network and vice versa. While these studies conclusively demonstrate transmural conduction in the atrial wall, they leave open several important conceptual questions. In particular this talk focuses on in silico modeling of three-dimensional conductions during AF and its effect on AF stability.

Integrating multi-modal quantitative MRI and histology for improving surgical treatment of epilepsy

Drug-resistant epilepsy occurs in over one third of epilepsy patients, and surgical excision of the affected brain region is often necessary to achieve seizure control. However, precise delineation of the seizure onset zone can be challenging, and can lead to poor surgical outcomes when incorrect. In many of these cases, the underlying pathology consists of subtle architectural abnormalities at the microscopic scale. Improved imaging of these lesions at a macroscopic scale thus requires integration of in-vivo MRI modalities that can probe the tissue microarchitecture, along with rigorous validation against histology. This talk will present our work on correlating quantitative relaxometry and diffusion imaging of temporal lobe epilepsy patients with histology of surgical specimens. I will highlight the challenges faced in spatial alignment of anatomy at vastly different scales, steps taken towards quantitative characterization of pathology in epilepsy, and how intrinsic MRI parameters can be used to improve our understanding of the excitotoxic effects of seizures.

Forward Modeling of Medical Imaging Systems

In medical imaging, the true underlying property of interest is unknown. A single image provides little to no insight into the impact of confounding factors such as: statistical noise, biological variability, scattered radiation, patient motion, deadtime in detectors and electronics, detector resolution, etc. Some of these physical factors can be quantified by scanning various configurations of test objects, often called phantoms. Physical phantoms, however, can not capture variability due to patient physiology and offer only mean performance characteristics of a limited set of objects.


Forward modeling of imaging systems provides an unparalleled window for examining and improving performance. For example, simulations using Monte Carlo photon tracking can be used to isolate a single factor of interest, for instance multiple i.i.d. realizations of the same imaging scenario can determine the effect of quantum noise or biological variability. Likewise, faster analytical models can be used to elucidate non-linear effects in the imaging chain. Accurate forward models also play a key role in improving iterative estimation of optimal images. Finally, forward models can be integrated into a feedback loop for optimization of a medical imaging tasks (not just images), that are not predictable a priori. We will give examples of the value of forward modeling in which an accurate model of the physics of the medical imaging system is an essential component to solving challenges in imaging research and healthcare.


Mechanics matters: macro, micro, nano

When scientists think of the heart, they usually see an electrically-controlled, chemically-driven, mechanical pump. We tend to forget that the heart’s mechanical environment has a pronounced influence on electrophysiology, calcium handling, and even drug effects. How these effects are mediated between macro-, micro-, and nano-levels depends strongly on the three-dimensional (3D) organisation of tissue, cells and sub-cellular structures. This talk will illustrate cardiac mechano-sensitivity on a number of human ‘test-cases’, highlight the potential of 3D structure-function mapping as a driver of clinically relevant basic studies, and identify bottlenecks in our present approach to integrating scales and modalities in heart research.

Crossing scales in the brain from functional MRI to synaptic function

Functional MRI techniques have found widespread use to measure brain neural circuits that are used for a large number of behaviors. When circuit activity changes due to plasticity, it remains a challenge to identify sites of synaptic changes responsible for the circuit level changes measured. Of particular interest are the cases of long range cortical rearrangements that have been detected in the human brain after injury. Rodent models that mimic some of these cortical rearrangements have been developed and are being used to determine the synaptic basis for the plasticity detected. We have developed a model of adult cortical plasticity due to peripheral somatosensory nerve damage that is being used to develop MRI tools that can pinpoint sites of synaptic changes. Two weeks after peripheral denervation of one side of the forepaw, hindpaw, or whisker pathway there is a large up-regulation of cortical activity from the spared side and a large up-regulation of callosal inputs from the spared cortex to the cortical representation of the denervated area. A combination of functional MRI and laminar specific neural track tracing using manganese enhanced MRI predicted changes in thalamo-cortical inputs to layer IV that contribute to the up-regulation of cortical activity along the spared whisker barrel pathway. Slice electrophysiology confirmed that the thalamic inputs on layer IV stellate cells were strengthened by a post-synaptic mechanism and that there is a re-opening of critical period plasticity. Sites of plasticity that explain the up-regulation of the callosal communication have also been studied with MRI. High temporal-spatial resolution fMRI demonstrates that up-regulation of the communication between the spared and denervated cortices likely occur through callosal inputs. These fMRI results were consistent with manganese enhanced MRI that predicts a strengthening of inputs into layer 2/3 and 5. Taken together these results demonstrate that MRI is positioned to begin to give laminar specific information about mechanisms of cortical plasticity. The challenge of multi-scale modeling in the context of arriving at a quantitative understanding of MRI results will be discussed.


Studying mammalian embryonic development through optical imaging

Understanding the nature and mechanism of congenital defects of different organ systems in humans has heavily relied on the analysis of the corresponding mutant phenotypes in mouse models. This talk will describe how optical imaging can be used to visualize live developing mouse embryonic structures at different embryonic stages. Two optical imaging approaches, fluorescence microscopy of vital reporters and optical coherence tomography will be highlighted, discussing how these methods can be utilized for structural imaging of early mouse embryos in static culture, 4D cardiodynamic and blood flow analysis, and in utero embryonic imaging at later stages of gestation, demonstrating how these methods can be used to assess structural and functional birth defects in mouse models. Future trends and existing challenges in data interpretation, analysis and modeling will be discussed.

Methods for detecting and analyzing large-scale networks in the human brain using fMRI data

One of the most challenging problems in neuroscience today is the detection and analysis of functional networks in the human brain. Traditional approaches to the analysis of fMRI data have regarded the brain as a stationary and univariate entity. Even though this was an extremely simplified view, it turned out to be surprisingly successful and yielded many valuable insights into brain function. Nonetheless, more realistic models should now be used as a basis for data analysis. In particular, since the brain operates as a complex and dynamic network, massively multivariate techniques will become increasingly important. In this talk, several new data analysis techniques that aim at understanding complex networks in the brain will be discussed.


Multi-Scale Modeling and Imaging of the Heart: From Mouse to Man

Multi-scale models of the heart have been developed that integrate both functionally across biomechanical, electrophysiological and regulatory functions and structurally across physical scales of organization from molecule to organ system. Multi-scale imaging, from high-resolution microscopy to clinical imaging in patients is a critical tool for the development of structurally and biophysically detailed multi-scale models. Here, we illustrate the development and application of these models to improving understanding and management of heart diseases. Electron tomography enables high-resolution reconstructions of subcellular microanatomy. We use novel subcellular models show how the three-dimensional architecture of the transverse tubule system and calcium release units affect the dynamics and heterogeneity of calcium signaling in cardiac myocytes. The mouse is a valuable model organism for studying heart failure because of the availability of genetically engineered strains harboring mutations that lead to heart failure in animals and humans. We illustrate this with results from recent multi-scale models of the mouse heart. Experimental studies in these mice at multiple scales including in-vivo MRI allow us to validate models and identify integrative mechanisms of disease. In humans, image-based patient-specific multi-scale models of ventricular electromechanics in the failing heart are helping improve our understanding of therapeutic strategies, including cardiac resynchronization therapy (CRT) for patients with heart failure that is complicated by an electrical conduction defects and catheter ablation therapy for persistent atrial fibrillation. Three-dimensional models derived from cardiac CT imaging are showing potential for enhancing understanding of therapeutic mechanisms and improving therapeutic outcomes.

Building and Interacting with The Virtual Brain

The Virtual Brain (TVB, thevirtualbrain.org) is an international project that uses real neuroimaging data to construct a simulation of the human brain. Anatomical data setup the conduit for communication between different brain regions. The dynamics for each region are generated from a library of nonlinear models, and produce large-scale activity patterns that can be compared directly to empirical functional data, such EEG/MEG or functional MRI. The talk will present the core of the platform and its applications to understanding the structure-function interplay that forms the basis of cognitive architectures. TVB’s use of real data is also at the heart of a larger social neuroscience initiative, wherein small groups of people interact with TVB through wireless EEG headsets, modifying an immersive audiovisual environment that mimics a dream – My Virtual Dream. The goal is to make use of individual brain signals to augment the group experience through TVB. The two avenues of development for TVB will inform neurally-inspired computing architectures and the evolution of interactive devices that can use a person’s physiology to redesign their experience.

Measurement of Cancer Heterogeneity with Positron Emission Tomography (PET) Data

PET/CT has seen widespread clinical use in the diagnosis and monitoring of cancer patients. Data acquired over the past several years has lead to the opportunity to refine the extraction of potential prognostic variables from PET imaging information. Up to now the emphasis has been on the use simple measures such as maximum tracer uptake in the tumor volume. However cancer is a heterogeneous process and the quantitation of this aspect is of particular interest. With imaging data the scales of heterogeneity that can be measured are limited by image resolution. In PET the bandwidth used in reconstruction plays a key role. We describe some alternative approaches to heterogeneity measurement with examples from PET imaging studies in patients with sarcoma, brain and breast cancer. The measures considered include both spatially invariant ones as well as more elaborate approaches based on modeling the spatial distribution of the tracer. Patient follow-up data is used to which measures have greatest prognostic utility.



(Supported by Science Foundation Ireland under 11/PI/1027 and the National Cancer Institute under CA-42045.)


Anatomically accurate multiscale-multiphysics models of total cardiac function

Despite the overwhelming wealth of data available today, gaining mechanistic insight into cardiac function remains to be a challenging endeavour due to the multiscale/multiphysics nature of cardiac function, where complex interactions of processes arise within and across levels of organization, as well as between electrical, mechanical and fluidic systems. Computer simulation has become a powerful adjunct to experimental studies, but current modeling methodology imposes severe limitations, forcing research to resort to overly simplified modeling assumptions. This talk will highlight recent methodological advances in terms of modeling organ scale cardiac anatomy and electro-mechano-fluidic function at high spatial resolution. The presented methods aim at lifting many of the current modeling restrictions to enable computational studies where model complexity is chosen as a function of the question being addressed, and not based on feasibility constraints. The use of advanced numerical methods is of pivotal importance to reduce execution times, thus facilitating quick simulation-analysis cycles. Application examples will be presented including multiscale arrhythmogenic effects due to mitochondrial dysfunction and calcium handling, as well as clinical modeling studies which aim at optimization and outcome prediction due to interventions such as aortic valve replacement and repair of aortic coarctations.

Biofuel cell polarization estimation: inversion of electrochemical impedance spectroscopic measurements Importantance of Model Formulation

The inverse problem associated with electrochemical impedance spectroscopy requiring the solution of a Fredholm integral equation of the first kind is considered. If the underlying physical model is not clearly determined, the inverse problem needs to be solved using a regularized linear least squares problem that is obtained from the discretization of the integral equation. For this system, it is shown that the model error can be made negligible by a change of variables and by extending the effective range of quadrature. This change of variables serves as a right preconditioner that significantly improves the condition of the system. Still, to obtain feasible solutions the additional constraint of non-negativity is required. Simulations with artificial, but realistic, data demonstrate that the use of non-negatively constrained least squares with a smoothing norm provides higher quality solutions than those obtained without the non-negative constraint. Using higher-order smoothing norms also reduces the error in the solutions. The L-curve and residual periodogram parameter choice criteria, which are used for parameter choice with regularized linear least squares, are successfully adapted to be used for the non-negatively constrained problem. Although these results have been verified within the context of the analysis of electrochemical impedance spectroscopy, there is no reason to suppose that they would not be relevant within the broader framework of solving Fredholm integral equations for other applications.

Multimodal data fusion for cardiac biophysical modelling

Heart disease is a major cause of patient mortality and morbidity. It poses a significant economic burden to healthcare. The use of medical imaging is central to the management of patients with heart disease as are basic physiological measurements, such as blood pressure and ECG. Often a number of different types of imaging and physiological measurements are performed that give complimentary information. These data are then used in all stages of patient management, including diagnosis, treatment selection/planning/guidance and patient follow-up. Despite the availability of these rich data, treatment success rates for many procedures remain sub-optimal. Multi-modality data fusion coupled to biophysical modelling of the heart has a great potential to increase these success rates by providing key input at all stages of patient management. This talk will highlight methods for cardiac multi-modal data fusion and show applications to predictive biophysical modelling of heart disease. Examples will include arrhythmia and heart failure management.

Deriving Macroscopic Parameters for Computational Modeling of Cardiac Tissues from High-Resolution Three-Dimensional Confocal Microscopy

Computational models play an important role in studies of cardiac tissue physiology and pathophysiology. Various types of models have been developed based on histological and electrophysiological studies, for instance, monodomain, bidomain and multidomain models of cardiac conduction. Over the last years, we developed new approaches for deriving model parameters from three-dimensional reconstructions of cardiac tissue at sub-micrometer resolution. We create the reconstructions using image data from fluorescent labeling and scanning confocal microscopy. Here, we provide an overview of our approaches for acquiring and processing of image data as well as extraction of model parameters. We suggest that the developed approaches provide important input for parameterization of models of cardiac tissues, in particular, models for investigations of tissue remodeling in disease and restoration after therapy.

Advanced multiphoton microscopy for high-content in vivo imaging

High-content imaging of biological processes tremendously benefits from recent advances in light microscopy. While light-sheet microscopy has gained widespread recognition in recent years, due to its distinct advantages for imaging live organisms with high acquisition speed, large field-of-view and low phototoxicity, its imaging depth remains a limitation. On the other hand, multiphoton microscopy achieves high imaging depth into scattering tissues and permits multimodal detection, including the combination of fluorescence and harmonic generation as sources of signal. However its acquisition speed is limited and it remains challenging to obtain efficient multicolor excitation. In this context, we report on recent advances in multiphoton microscopy to improve imaging speed and multicolor excitation. We present multiphoton light-sheet microscopy, combining two-photon excited fluorescence with orthogonal illumination and demonstrate its performance in maintaining high spatial resolution deep inside biological tissues, as well as high acquisition speed and low phototoxicity. In addition, we present a strategy based on wavelength mixing to perform optimal and simultaneous two-photon excitation of three chromophores with distinct absorption spectra. These approaches open new opportunities for live, multicolor, multidimensional and multiscale imaging: we illustrate these potentials by imaging Brainbow-labeled tissues, heart dynamic and early fly and zebrafish embryonic development.

Predictive Patient-Specific Mathematical Neuro-Oncology
Predictive Patient-Specific Mathematical Neuro-Oncology: A Paradigm shift in Giloma Treatment.
MBI COLLOQUIUM: In Vivo Imaging of the Developing Mouse Brain: From Morphology to Molecules

Extensive genetic information and the expanding number of techniques available to manipulate the genome of the mouse have led to its widespread use in studies of brain development and to model human neurodevelopmental diseases. We are developing a combination of ultrasound and magnetic resonance micro-imaging approaches with sufficient resolution and sensitivity to provide noninvasive structural, functional and molecular data on developmental and disease processes in normal and genetically-engineered mice. Our efforts over the past decade have focused on in utero and early postnatal imaging and analysis of the developing brain and cerebral vasculature. The advantages and limitations of both ultrasound and MRI for imaging mouse development will be discussed, and examples provided to illustrate the utility of these approaches for 4D mutant phenotype analysis. Recent advances have also made in the area of molecular imaging, including the generation of novel reporter mice that enable cell-specific imaging with ultrasound and MRI contrast agents. Future directions for molecular imaging of mouse brain development will be discussed.

A comparison of electrocardiographic imaging of the epicardial potential field and that of the timing of the EDI source.


The dynamics of the contraction of the cardiac muscle is initiated and accompanied by the flow electric currents. These set up potential fields throughout the myocardium as well as in the body tissues surrounding the heart. The observed time course of the potential differences between any two locations inside the thorax or on its surface is referred to as an electrocardiogram (ECG). In cardiology the set of 12 simultaneously recorded signals, derived from nine strategically chosen locations on the body surface, forms one of the most frequently used diagnostic tools. As such, the interpretation of the ECG can be classed as a non-invasive method. Moreover, it can be classed as a performing an inverse procedure: the interpretation of a state, or phenomenon on the observations at a distances.


Over the period of more than a century following the first recording of an ECG, the diagnostic accuracy of the ECG has increased steadily. Yet, in some types of cardiac disease still needs to be improved. One of the recent developments is the increase the number of electrodes sampling the time course of the potential field on the thorax. The display of a sequence of instantaneous potential fields by means of scalar maps has inspired the search for methods for also displaying the inverse interpretation of the signals in the form of maps, thus emphasizing the spatial character of the cardiac electric sources. This procedure can be classed as imaging.


The nature of the physics involved, the electric volume conduction, demands the formulation of the general nature of these sources from which the transfer between source distributions and observed potential field can be derived. Currently, two such descriptions (models) are developed. In this presentation both models are introduced and their similarities and differences (advantages and disadvantages) are illustrated in an application to the data of one and the same subject.


Multimodal Neuroimaging Biomarkers for Neuropsychiatric Disorders

In this talk, we will describe some of the recent developments of computational anatomy tools for the study of brain structure, function and how they interact with cognitive behavior. Specifically we will touch upon the following 3 areas of work: 1) brain structural shape as biomarkers in schizophrenia and Alzheimer’s disease; 2) integrating cortical thickness, cortical geometry, and cortical metabolism in AD; and 3) a nuanced model of cortical thinning, functional compensation, cognitive stability of schizophrenia.

Posters

Mathematical Models of Cortical Folding Process of the Human Brain

The mechanism for cortical folding pattern formation is not fully understood. Existing biological hypotheses that try to explain this folding process are in disagreement with one other. Two competing biological hypotheses and corresponding biomathematical models of cortical folding that implement these hypotheses will be presented: (i) a chemical based hypothesis called the Intermediate Progenitor Hypothesis, and (ii) a mechanical based hypothesis called the Axonal Tension Hypothesis. Applications of these biomathematical models to cortical folding development and disease will be shown and directions of future research will be presented.

Comparison of myocardial blood flow models for quantitative perfusion estimation from Dynamic CT

Introduction: Myocardial blood flow (MBF) can be estimated from dynamic contrast enhanced (DCE) cardiac CT acquisitions leading to quantitative assessment of regional perfusion. However, demands for low radiation dose acquisitions and the lack of consensus on MBF estimation methods necessitate refinement of acquisition protocols and models for CT-derived MBF.

Methods: DCE cardiac CT acquisitions were simulated for a range of flow states (MBF = 0.5, 1, 2, 3 ml/g/min, cardiac output = 3, 5, 8 L/min). Patient kinetics were derived from a mathematical model of iodine exchange incorporating numerous physiologic features including heterogenenous microvascular flow, permeability and capillary contrast gradients. CT acquisitions were simulated with a validated CT simulator incorporating beam hardening and realistic x-ray flux levels. CT acquisitions that reduce radiation exposure were varied with 1, 2, and 3 sec sampling as well as tube current reductions with 140, 70, and 25 mAs. Time attenuation curves were extracted for multiple regions around the myocardium to estimate MBF. We compared four MBF estimation methods for all acquisitions: a two-compartment model, an axially-distributed capillary model, the adiabatic approximation to the tissue homogeneous model, and a peak/slope method.

Results: After iodine-based beam hardening correction, the slope method consistently underestimated flow by on average 47.5% and the quantitative models provided estimates with less than 6.5% average bias and increasing variance with increasing dose reductions. The three quantitative models performed equally well, offering estimates with essentially identical root mean squared error (RMSE) for matched acquisitions. MBF estimates using the qualitative slope method were inferior in terms of bias and RMSE compared to the quantitative methods.

Conclusions: Simulations of dynamic cardiac CT suggest that, under realistic beam hardening and noise conditions, radiation dose reductions through tube current reduction or temporal subsampling strategies have comparable effects on the fidelity of model-based myocardial flow estimation. All three two-region models perform better than the slope method under all circumstances. In this evaluation, the two-region models all performed similarly, suggesting that the choice of flow estimation model may be based on other factors such as ease of use or computational speed.

Research by: Michael Bindschadler, Dimple Modgil, Kelley R Branch, Patrick J La Riviere, Adam M Alessio

Patient-specific manifold model of the left atrium using high-order spectral/hp element methods

Computational modelling of cardiac electrophysiology has the potential to revolutionise treatment of arrhythmias through aiding clinical intervention and improving our mechanistic understanding of their initiation and perpetuation. The complexity of full 3D models makes their real-time use in clinical practice currently intractable. The left atrium wall thickness is only a few millimeters and can be reasonably modelled more efficiently as a two-dimensional surface. We describe a novel manifold spectral/hp element discretisation of the left atrium and illustrate the performance benefits of high-order methods over conventional linear finite elements. Imaging data is incorporated into the model to delineate areas of fibrosis and comparison is made with clinical electrophysiology measurements.

Prevention and Monitoring of Stroke based on Multi-modality Medical Images

Brain aneurysm rupture causes a life-threatening type of stroke. One third of patients die before arriving at the hospital and an additional third of those who are hospitalized die as a result of the stroke. Only 20% of these patients will be able to recover to normal. As medical imaging technology has advanced, in clinical practice more and more aneurysms are found before rupture. Using different imaging techniques, we are able to follow the lesion growth, analyze stroke risks and assist treatment decision-making. Collaborating with mathematicians, our group and others have extracted several risk predictors from clinical images based on aneurysm morphology and cerebral vascular hemodynamic factors. Applying these findings to our database of over 1000 longitudinally followed patient cases at the UCLA Medical Center, in our study we found distinct morphology differences between ruptured and unruptured lesions and identified high risk morphology in 95% of patients with aneurysms that ruptured. This information allows clinical care to provide individualized treatment plans for clinical follow-up and stroke prevention for patients.

Inverse reconstruction of epicardial potentials is improved by novel regularization methods and extensive validation

The inverse problem of electrocardiography aims at reconstructing electrical heart activity from measured body-surface potentials. Due to ill-posedness, solutions are very sensitive to noise. In our lab, we have proposed three novel methods to regularize the solution. Thorough validation of these methods will be attained by means of in vivo canine models.

Authors: Matthijs Cluitmans, Pietro Bonizzi, Joël Karel, Ralf Peeters, Paul Volders, Ronald Westra

Computational Model for Optical Coherence Tomography Imaging of the Human Eye

Spectral Domain Optical Coherence Tomography (SDOCT) is a non-invasive technique for high-resolution imaging of internal tissue microstructures in real time. In collaboration with Bioptigen, Inc., manufacturer of the hand-held Envisu SDOIS, we are developing mathematical and computational models for accurate and robust three-dimensional imaging of the eye. The imaging system employs a radial sampling pattern in which each radial cross-section (b-scan) is sampled at many individual data points (a-scans). Initial work has focused on surface imaging of human corneas and contact lenses. The primary goal of the modeling is to develop accurate, efficient and robust algorithms that can translate OCT point cloud data into curvature maps. This process first involves noise filtering and replacement of gap regions in the point cloud data. Next, optimal sampling resolutions and polynomial orders for representation of the corneal surface in terms of orthogonal polynomials on the unit disk (Zernike polynomials) must be determined and utilized. Lastly, the three-dimensional geometric model representation of the corneal surface is used to compute a curvature map. Primary challenges include the high sensitivity of curvature computations to noise/uncertainty in the point cloud data as well as each part of the modeling process, and the limited ability to perform model validation for in vivo imaging applications. (This is joint work with current PhD student Micaela Mendlow).

Automated initiation of multiple spiral wave reentries in reaction-diffusion models of the atria

Phase singularity analysis provides a quantitative description of spiral wave patterns observed in chemical or biological excitable media, for instance a cardiac tissue during an arrhythmia. The configuration of phase singularities (locations and directions of rotation) is easily derived from phase maps in two-dimensional manifolds. The question arises whether one can construct a phase map with a given configuration of phase singularities. We will present a constructive mathematical approach to numerically solve this problem in geometries relevant to atrial anatomy. This tool can notably be used to create initial conditions with controllable spiral wave configuration for cardiac propagation models and thus help in the design of computer experiments in atrial electrophysiology.

Antoine Herline, Elhacene Matene, Vincent Jacquemet

Mechanically-Induced Excitation and Arrhythmias in the Whole Heart

Tight control exists between electrical and mechanical activity in the heart. This involves both feed-forward links of electrical excitation and mechanical contraction, as well as feed-back from the mechanical environment to the origin and spread of excitation. It has been shown that mechanical stimulation of the ventricles can cause a variety of rhythm changes, from the induction of benign ectopic beats to conduction disturbances, and in extreme cases, tachycardia or fibrillation. We have been investigating the electrophysiological effects of mechanical stimulation in the isolated heart by applying controlled, non-traumatic combinations of local epicardial deformation and/or global volume change, while monitoring responses by optical mapping. It has been shown that sufficient local mechanical stimulation in diastole results in electrical activation from the site of deformation, followed by ectopic excitation. This is dependent on regional tissue strain, rather than strain rate, applied force (i.e. stress), or changes in intraventricular pressure associated with local deformation, with threshold affected by ischemia and increased ventricular pressure. As coupling interval is shortened, ectopy induction continues until stimulation is timed with late repolarization, when spatio-temporal overlap of the repolarization wave and mechanically-affected tissue occurs, which can result in re-entry and ventricular fibrillation. This supports a role for regionally heterogeneous excitation in mechanically-induced arrhythmogenesis.

Model-based optimization of bipolar tDCS electrode placement

Results of transcranial direct current stimulation are promising, but reported effects can be small. Modeling studies have shown that with the standard electrode configurations the highest electric field strengths are not found in the targeted areas, suggesting that an increased effect could be achieved with different placement of the electrodes. With this study, we aim to provide experimenters with optimal bipolar configurations. Using simulations with a detailed head model, bipolar electrode placements were optimized for six often-targeted areas. A large number of configurations were evaluated and the configurations resulting in the highest field strength in the target area were selected. For most target areas, the target field strength could be doubled compared to standard configurations. Additionally, the field strength in the presumably most effective direction (perpendicular to the cortical surface) was maximized. Much smaller improvements were obtained by this direction-based optimization. Strength- and direction-based optimization resulted in largely different configurations. By combining the optimization results of several target areas distributed over the brain, we were able to construct general electrode placement recommendations for all targets and subjects. We conclude that the local brain geometry at the location of the target determines which configurations are optimal for a specific target.

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Integrating multi-modal quantitative MRI and histology for improving surgical treatment of epilepsy
Ali Khan

Drug-resistant epilepsy occurs in over one third of epilepsy patients, and surgical excision of the affected brain region is often necessary to achieve seizure control. However, precise delineation of the seizure onset zone can be challengin

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Three Dimensional Conduction During Atrial Fibrillation (a modeling approach)
Ali Gharaviri

Several mechanisms have been suggested to explain the increasing stability of atrial fibrillation (AF) over time. Disruption of electrical coupling between muscle bundles, resulting in narrower and thus more fibrillation waves, is considered

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Forward Modeling of Medical Imaging Systems
Paul Kinahan

In medical imaging, the true underlying property of interest is unknown. A single image provides little to no insight into the impact of confounding factors such as: statistical noise, biological variability, scattered radiation, patient mot

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How Can We Use Dynamic Models in Inverse Bioelectric Problems?
Dana Brooks

Both cardiac and brain bioelectric forward problems can be modeled accurately as quasi-static, implying that torso or scalp surface measurements depend on the spatial distribution of the respective sources independently at each time instant.

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Optical imaging of the heart
Bastiaan Boukens

Mathematical modeling is of crucial importance for understanding the complexity of biological systems. Where biological experiments educate us about nature itself, mathematical models allow us to make prediction on the outcome of events base

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MBI COLLOQUIUM: In Vivo Imaging of the Developing Mouse Brain: From Morphology to Molecules
Daniel Turnbull

Extensive genetic information and the expanding number of techniques available to manipulate the genome of the mouse have led to its widespread use in studies of brain development and to model human neurodevelopmental diseases. We are develo

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Biofuel cell polarization estimation: inversion of electrochemical impedance spectroscopic measurements Importantance of Model Formulation
Rosemary Renaut

The inverse problem associated with electrochemical impedance spectroscopy requiring the solution of a Fredholm integral equation of the first kind is considered. If the underlying physical model is not clearly determined, the inverse proble

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Anatomically accurate multiscale-multiphysics models of total cardiac function
Gernot Plank

Despite the overwhelming wealth of data available today, gaining mechanistic insight into cardiac function remains to be a challenging endeavour due to the multiscale/multiphysics nature of cardiac function, where complex interactions of pro