The "Connections Matter" symposium will bring together Boston-area researchers focused on various aspects of modeling brain connectivity. The symposium is meant to be an informal, brainstorming event. Please do not expect formal, conference-style presentations. We look forward to seeing you on June 5th!
Click on each speaker's name for additional details.
9:00 - Introduction
9:05-10:00 - Seppo Ahlfors
10:00-11:00 - Steve Stufflebeam
11:00-12:00 - Randy Buckner
2:00-3:00 - Polina Golland
3:15-4:15 - Carl-Fredrik Westin
4:15-5:15 - Dae-Shik Kim
Speakers
Technical Director, MEG Core, Martinos Center for Biomedical Imaging, Harvard Medical School
"MEG and Multimodal Imaging of Activity in Hierarchically Organized Networks of Brain Areas"
Modern non-invasive brain imaging methods provide powerful ways to study the human brain to better understand how networks of interacting brain areas process information. In magnetoencephalography (MEG), the magnetic field generated by neural currents is measured outside the head. Similarly to EEG, MEG has a millisecond time resolution, complementing hemodynamics-based techniques like functional MRI, which typically have a better spatial resolution but poorer time resolution. Convergent results in locations of brain activity may help to reduce uncertainties inherent in the techniques. In cases where there are multiple possible interpretations of the MEG field pattern, comparison with fMRI (or EEG) could help to resolve which of the possible solutions is most likely. As an application of multimodal imaging, we discuss the possibility of identifying activity within networks of hierarchically organized cortical areas. Animal studies have shown that feedforward and feedback input projections characteristically differ in their pattern across cortical layers. Direct observation of laminar distribution of activity, however, is currently beyond the resolution of human neuroimaging. We propose that the polarity of a macroscopic current dipole, as measured by MEG and EEG, may depend on whether the measured activation is a result of input to superficial or deep cortical layers. This could provide a basis for a noninvasive method for studying bottom-up and top-down influences in cortical function.
Director of Clinical Magnetoencephalography, Martinos Center for Biomedical Imaging, Harvard Medical School
"Imaging Connections Through Brain Rhythms: History, Current Clinical Applications, Future Directions"
This informal lecture will introduce and review approaches to imaging connections in the brain using high-temporal resolution methods such as magnetoencephalography (MEG) and electroencephalography (EEG). Emphasis will be on differences in structural, functional and effective connectivity using clinical applications such as presurgical mapping of eloquent cortex for epilepsy and brain tumors. Finally, the role of rhythmic brain activity in specific frequency bands, such as in the alpha (around 10 Hz), will be discussed with perspectives on how rhythmic brain activity may coordinate brain activity across the brain.
Department of Psychology and Center for Brain Science, Harvard University
Associate Director, Martinos Center for Biomedical Imaging, Harvard Medical School
"Some informal thoughts on functional correlation analysis with fMRI"
Analysis of brain systems using spontaneous correlations with functional MRI
has recently become a hot topic. I will lead an informal discussion of some
recent findings, how the methods work, and implications.
Relevant papers:
Coherent Spontaneous Activity Identifies a Hippocampal-Parietal Memory Network (Vincent et al, 2006)
Unrest at rest: Default activity and spontaneous network correlations (Buckner & Vincent, 2007)
Cognitive Neuroscience Laboratory
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
"Functional Hierarchy: Representation and Modeling of Spatial Patterns of Activation in fMRI"
In this talk, I will present a novel approach to computational modeling of spatial activation patterns observed through fMRI. Functional connectivity analysis is widely used in fMRI studies for detection and analysis of large networks that co-activate with a user-selected `seed' region of interest. In contrast, our method is based on clustering; it simultaneously identifies interesting seed time courses and associates voxels with the respective networks. This generalization eliminates the sensitivity to the threshold used to classify voxels as members of a network and enables discovery of co-activated networks without user selection of seed regions.
Based on the empirical observation that the detected patterns of co-activation are inherently hierarchical, we propose a new representation for spatial patterns of functional organization. Just like the anatomical hierarchies represent the structure of the brain as a tree of increasingly simple systems, we believe that the functional description of the brain should also be of a hierarchical nature. We introduce Functional Hierarchy, a top-down representation that encapsulates the notion that functionally defined regions should be viewed at different resolutions, as dictated by the observed activation pattern. We construct the functional hierarchy through an iterative decomposition that utilizes clustering for network subdivision at each step.
The experimental results demonstrate that the functional region hierarchy provides a robust and anatomically meaningful model for spatial patterns of co-activation in fMRI. The hierarchical representation leads to insights into the structure of the functional networks that are not immediately apparent from flat representations that segment the brain into a large number of small regions. In addition, subject-specific region hierarchies tend to share common tree structure, further confirming the validity of this representation for modeling group-wise patterns of co-activation.
Director, Laboratory of Mathematics in Imaging, Brigham & Women's Hospital/Harvard Medical School
"White matter anatomy from Diffusion MRI"
In this talk I will present on-going activities in analysis of data from diffusion MRI at the Laboratory of Mathematics in Imaging (LMI), Department of Radiology, Brigham and Women's Hospital and Harvard Medical School.
The overarching goal of our diffusion analysis work is to pursue technological developments that improve the understanding of white matter anatomy. Such understanding, particularly due to the small dimension of the neural pathways relative to current imaging resolution, is vital to developing novel methods and techniques for the analysis of anatomical structures, and for the application of that analysis to understanding neural diseases. This information naturally complements information obtained from standard structural analysis. A driving clinical application for our work is to detect and localize white matter abnormalities in schizophrenia. We will discuss recent developments in spatial normalization of diffusion data (the first step in group analysis of brain imaging data is often to put the data in a common coordinate system), diffusion tractography, and methods fiber tract grouping for atlas generation. Compared to functional MRI analyses, group analysis paradigms for diffusion MRI data is currently a relatively unexplored area.
For details on our diffusion analysis work, see http://lmi.bwh.harvard.edu/papers/all.html
Laboratory of Mathematics in Imaging
Director, Center for Biomedical Imaging, Boston University
"Diffusion MRI: From connectivity to cytoarchitecture"
Functional and diffusion tensor MRI provide important localization and
connectivity information about the brain in vivo. While the logical next
step would be to combine these two imaging modalities, significant technical
and conceptual problems must be overcome first before such combied imaging
can be employed. In this presentation, I will discuss key technical
challenges and potential solutions for such combined fMRI/DTI approaches.
Please contact us at (617) 353-9144 or vaina@bu.edu.