Brain Mapping seminar abstracts 2012-13
Thomas Nichols, PhD
University of Warwick, UK
Sensitive and interpretable modelling of neuroimaging meta-analysis data
Neuroimaging Meta-analysis is growing in importance as the number of available studies grows. Unfortunately, the only data widely available for meta-analysis are the [x,y,z] coordinates of peak activation foci. Most existing methods convert these coordinate data into images and then apply traditional imaging modelling and inference proceedures. However, these methods are mass-univariate, make inference on individual voxels (or clusters) and require fixed tuning parameters. Such methods do not provide an interpretable fitted model, and cannot produce spatial confidence intervals on location of activation. Using a Bayesian hierarchical point process approach, modelling brain activation as a mixture of latent activation centers, and these activation centers are in turn modelled as off-spring from latent population centers. When our fully Bayesian model fit to multiple groups it is trival to make predictions of the group label for new data and, through an importance sampling trick, leave-one-out cross validation accuracy for the sample studied.
Joint work Jian Kang, Emory Biostatistics, and Tim Johnson, University of Michigan Biostatistics.
Blaise Frederick, PhD
Extracting quantitative cerebral hemodynamic measures and removing physiological noise from BOLD fMRI data using time delay processing
BOLD fMRI data is affected by the oxygenation and volume of blood moving through the circulatory system. Because these properties are carried by the blood itself, fluctuations in these parameters arrive at different parts of the brain at different times. Time delay processing extracts hemodynamic information from BOLD fMRI data by characterizing the time delay between a regressor reflecting these parameters and the BOLD data throughout the brain. Global endogenous random variations of blood volume and oxygenation can be tracked as they pass through the highly perfused tissue of the brain through their effect on the BOLD signal by determining the strength and peak time delay of their cross correlation with regressors generated from near infrared spectroscopy (NIRS) data, or extracted from the BOLD data itself. Blood oxygenation can also be manipulated with a gas challenge to increase the SNR of the technique. Using this technique, BOLD data can be separated into global hemodynamic effects and local hemodynamic variations due to neuronal activity.
Jaakko Nieminen, PhD
Aalto University, Finland
Hybrid ultra-low-field MRI and MEG: brain imaging and more with a multichannel instrument
Magnetic resonance imaging (MRI) and magnetoencephalography (MEG) are well-established modalities for imaging brain anatomy and function, respectively. Traditionally, MRI is performed in high magnetic fields (> 1 T), which has the side effect that the required scanner is massive and imaging loud. On the other hand, MEG operates at the other extreme: in a magnetically shielded environment, the weak magnetic field arising from brain electrical activity is mapped around the head with a multichannel instrument. In this talk, I will describe our instrumentation that combines MRI and MEG into a single silent device and the motivation behind the development. I will also explain how the underlying technique, so-called ultra-low-field (ULF) MRI, where the imaging field is comparable to the Earth's magnetic field, differs from conventional high-field MRI. We have used our hybrid system for MRI of the human brain and for MEG studies. The results are compared with those obtained with a 3-T scanner and a commercial MEG device. In addition, we have performed several ULF-MRI experiments demonstrating, e.g., the suitability of ULF MRI for temperature mapping.
Nicolas Bolo, PhD
Beth Israel Deaconess Medical Center
Dr. Bolo is Director of Neuroimaging in Psychiatry at the Beth Israel Deaconess Medical Center in Boston and Assistant Professor of Psychiatry at Harvard Medical School.
Dr. Bolo did his undergraduate studies at the University of Paris, France, Pierre et Marie Curie, where he obtained his BS and Master’s degrees in Physical Chemistry. He did his graduate studies at the University of California, Berkeley, where he obtained his PhD in Biophysics.
After a short post-doctoral fellowship at the Lawrence Berkeley Laboratory, he returned to France to work at the Magnetic Resonance Unit of a Non-Profit Clinical Research Center associated with the psychiatric hospital in Rouffach (East of France). First as a research scientist and later as director of biological research and development, Dr. Bolo used a 3T scanner - the second 3T to be operational in France - to apply MRS and fMRI to investigations of neuropsychiatric disorders including, schizophrenia, Alzheimer’s disease, depression and epilepsy.
Dr. Bolo joined the Brain Imaging Center at McLean Hospital in 2001 to work as an Associate Biophysicist using its new 4T scanner. There he started collaborations with the Joslin Diabetes Center investigating the effects of diabetes on brain function, structure and neurochemistry. He continued this line of investigation after taking the position of director of neuroimaging in psychiatry at BIDMC about three years ago.
He will be talking about some of this recent work on diabetes and the brain.
Diabetes and the Brain: What Magnetic Resonance Studies Tell Us
In 2011, about 26 million people in the US -- 8.3% of the population -- had diabetes. Peripheral complications such as heart disease, kidney disease and neuropathy are relatively well known, however we have only recently been able to examine the impact of diabetes on the central nervous system using advanced neuroimaging technology. Dr. Bolo will be talking about recent studies of brain metabolism, function and structure and their relationship to emotional and cognitive disorders in type 1 and type 2 diabetes using magnetic resonance spectroscopy and imaging.
Javeria Hashmi, PhD
Predictive Dynamics in Brain Networks of Chronic Pain Patients.
The advent of new imaging methods and data modeling techniques has revolutionized our capacity to understand and utilize brain signals. With these techniques, the dynamic interactions between the complex elements of the brain can be used to effectively decode the neurological substrates of pain perception and modulation. The task to discover how intricately connected brain networks process psychological cues and interpret them into analgesia is an important scientific challenge that can segue into novel clinical tools and improved pain therapeutics. This talk will present evidence that brain network properties can be used to predict treatment outcomes and the time course of pain experiences. We have reported that synchronizations in specific brain networks in particular BOLD frequency bands forecast outcomes of placebo treatment in chronic back pain patients that were tested in clinical trial conditions. More recently we have discovered that computationally characterized whole brain network alignments predict subsequently induced placebo analgesia in a clinical population of knee pain patients. These findings indicate a role of network configurations in facilitating adaptive pain modulation. On the other hand, brain properties also provide several clues into maladaptive pain processing associated with chronic pain. We will report in how brain activity related to the perception of back pain shifts in location from regions involved in acute pain to engage emotion circuitry as the condition persists, thereby providing a percept linked brain signature for the transition to chronic pain.
Kwanghun Chung, PhD
CLARITY and beyond: towards integrative understanding of the brain in health and disease.
Obtaining detailed structural and molecular information from complex biological systems while simultaneously maintaining the global perspective has long been a fundamental challenge in neuroscience and throughout biology. Current pioneering methods, while proven adequate to study small-scale fine structures, fall short of providing integrated system-wide information. To address this challenge, we have developed a novel technology (which we term CLARITY) that rapidly transforms intact tissue into a hydrogel hybrid that is optically-transparent and macromolecule-permeable while retaining structural and molecular information. This unique framework enables effective and complete access of intact tissue using both light microscopy and molecular phenotyping techniques (immunohistochemistry and in situ hybridization). Exogenous macromolecules can rapidly penetrate deep into transmuted tissue for labeling and can be completely removed after imaging, enabling multiple rounds of molecular interrogation in 3D volumes of tissue. The application of CLARITY to the adult mouse brain has enabled imaging of long-range circuits, local circuits, subcellular morphological details, and molecular architectures. Finally, we find that this technique allows fine structural analysis of postmortem human brain, opening the door to the possibility of mapping the human brain at single cellular resolution with integrated molecular information. Taken together, this technology promises to provide high-content system-wide structural and molecular information that may enable integrative understanding of large-scale intact biological systems.
Ruth Rosenholtz, PhD
Rethinking the roles of selective attention and economical encoding in vision.
Considerable research points to a bottleneck in visual processing. According to the traditional view, at any given moment selective attention allows only a small portion of the visual input to get through the bottleneck for further processing. Some processing can occur "preattentively" and guide this selection. Much of the early research on visual search aimed at determining what processing could occur preattentively, and what required selective attention. This work pointed to a need for selective attention in order to bind basic features and locations. While this view of visual processing has held sway for many years, it has also been problematic. How is it that we can have such trouble searching for a simple symbol in a display, and yet be so good at getting the gist of a scene and navigating through our environment? My lab proposes an alternative, in which the visual system's strategy for dealing with limited capacity focuses on compression of the visual input, rather than on selective attention. I will demonstrate that this model can predict not only classic results in visual search, but also phenomena that were problematic for the traditional selective attention story. This suggests that there may be a fundamental, relatively low-level limit to a diverse array of visual tasks, from visual search and object recognition, through perception of our spatial environment and understanding of complex displays.
Connectivity, Pharmacology and Computation: Towards a Mechanistic Understanding Cognitive and Connectivity Deficits in Schizophrenia
Neuropsychiatric diseases such as schizophrenia and bipolar illness alter the structure and function of distributed neural networks and presents a massive health and economic burden. Functional neuroimaging tools have evolved sufficiently to reliably detect systems-level disturbances in neural networks. This two-part presentation first focuses on emerging findings in schizophrenia and bipolar illness using resting-state neuroimaging, an advantageous approach for biomarker development given its ease of data collection and lack of task-based confounds. Findings in schizophrenia suggest that disruptions in sensory-thalamic-prefrontal networks may hold promise as a marker for treatment effects in future clinical studies and might constitute a final common pathway of neural system disturbances in schizophrenia. However, neuroimaging does not yet allow the evaluation of individual neurons within local circuits, where pharmacological treatments ultimately exert their effects. This limitation constitutes an important obstacle to the effort to translate findings from animal research to humans and from healthy humans to patient populations. Integrating new neuroscientific tools may help to bridge some of these gaps. Two complementary approaches are discussed in the context of understanding cognitive deficits in schizophrenia: First, causal pharmacological manipulations in healthy volunteers that transiently mimic some cardinal features of psychiatric conditions. Specific focus is placed on recent neuroimaging studies using the NMDA receptor antagonist, ketamine, to probe glutamate synaptic dysfunction associated with schizophrenia. Second, human pharmacologic imaging is discussed in combination with biophysically-informed computational models developed to guide the interpretation of functional imaging studies and to inform the development of pathophysiologic hypotheses. This approach is illustrated via recent findings showing how computational modeling has guided inferences drawn from studies in healthy subjects administered ketamine and evaluations of schizophrenia patients. In summary, the argument is presented that linking experimental studies in humans with computational models will advance to effort to bridge cellular, systems, and clinical neuroscience approaches to psychiatric disorders.
Dissecting neural circuits underlying behaviors relevant to psychiatric disease in animal models
The ability to differentiate between positive and negative environmental stimuli is critical to an animal's survival. However, the neural circuits that endow the brain with the ability to differentiate positive and negative motivationally significant stimuli have been difficult to disentangle and represent one of the most important fundamental neuroscience questions today. The development and application of optogenetic approaches has allowed us to probe the causal relationships between activity in specific circuit elements and animal behavior relevant to psychiatric disease states such as anxiety, addiction and depression. In this seminar, Kay Tye will discuss her research on the corticolimbic circuits that mediate valence processing.
MGH Martinos Center
Echo-planar imaging of cerebral physiology and metabolism at 7 Tesla
This seminar will summarize our recent efforts in quantifying physiology and metabolism of the brain by means of echo-planar imaging (EPI) at high magnetic field strength (7 Tesla). Quantification of brain function by fMRI techniques based on EPI is hampered by the presence of physiological sources of instability due to motion, heartbeat, and respiration. In our previous research, we investigated novel methods to deal with this limitation, by optimizing physiologic noise removal. In our current research, we exploited this limitation as an opportunity to investigate cerebral physiology, by considering physiological noise as a “signal of interest”. In this seminar we will talk about the effects in EPI time-courses of: paramagnetic oxygen moving inside the chest during respiration; cardiac and respiratory pressure waves traveling in the brain; changes in the cardiac rate and in the respiratory volume rate. We will show the different contribution of these effects to magnitude and phase EPI time-courses, and how they can be used to investigate cerebrovascular compliance and the hemodynamic waveform in the brain.
Dr. Nikola Stikov is a postdoctoral fellow at the Montreal Neurological Institute, currently working with Dr. Bruce Pike on measuring white matter microstructure using quantitative magnetic resonance imaging. He completed his B.S., M.S., and Ph.D. degrees in Electrical Engineering at Stanford University under the supervision of Dr. John Pauly. In collaboration with the Stanford Radiology and Psychology departments, he identified novel magnetic resonance biomarkers for the study of cartilage in the knee and myelin in the brain.
In vivo magnetic resonance imaging of the myelin g-ratio
The myelin g-ratio, defined as the ratio between the inner and the outer diameter of the myelin sheath, has been proposed as an important biomarker for studying normal brain development, as well as brain pathology. Recent studies have suggested that the sexual dimorphism in white matter development is due to a higher g-ratio (thinner myelin) in adolescent boys. Variations in the g-ratio have also been detected in the frontal white matter of patients with schizophrenia, and have been associated with differences in brain connectivity in autism. Finally, in vivo imaging of the myelin thickness in multiple sclerosis can provide a real-time tool for tracking myelination in lesions, facilitating the development and
evaluation of new therapeutic agents that promote remyelination. In this lecture, a unique combination of quantitative magnetic resonance imaging, histology, and numerical simulations will be presented, resulting in a novel method for in vivo measurement of the myelin g-ratio. The ramifications of this method will be discussed in the context of applications and validation in animal models.
The Picower Institute for Learning and Memory and Department of Brain
and Cognitive Sciences, Massachusetts Institute of Technology,
Cambridge, MA 02139
Earl Miller is the Picower Professor of Neuroscience at the Massachusetts Institute of Technology. He has academic appointments in The Picower Institute for Learning and Memory and the Department of rain and Cognitive Sciences at MIT. Professor Miller uses experimental and theoretical approaches to study the neural basis ofhe high-level cognitive functions that underlie complex goal-directed
behavior. The focus is on the frontal lobe, the region of the brain most elaborated in humans and linked to neuropsychiatric disorders. His laboratory has provided insights into how categories, concepts, and rules are learned, how attention is focused, and how the braincoordinates thought and action. They have innovated techniques for
studying the activity of many neurons in multiple brain areas simultaneously, which has provided insight into how different brain structures interact and collaborate. This work has established a foundation upon which to construct more detailed, mechanistic accounts
of how executive control is implemented in the brain and its dysfunction in diseases such as autism, schizophrenia and attention
Cognition is Rhythmic
How are some thoughts favored over others? A wealth of data at the level of single neurons has yielded candidate brain areas and mechanisms for our best understood model: visual attention. Recent work has naturally evolved toward efforts at a more integrative, network, understanding. It suggests that focusing attention arises from interactions between widespread cortical and subcortical networks
that may be regulated via their rhythmic synchronization. This could extend to all cognitive processes, suggesting our brain does not operate continuously, but rather discretely, with pulses of activity routing packets of information. Such discrete cycles would provide a backbone for coordinating computations (and their results) across disparate networks. However, it comes at a cost: it is naturally
limited in bandwidth; only so many things can be computed or carried in a single oscillatory cycle. This can explain the most fundamental property of conscious thought, its limited capacity, which is the reason why we evolved attention in the first place.
Professor, Biomedical Engineering & Diagnostic Radiology
Technical Director, MRRC (mrrc.yale.edu)
Program Director, QNMR Core Center (qnmr.yale.edu)
Energetic Footprint of Cortical Function
Energy demand of neural activity in cerebral cortex is extraordinarily high, a necessity met by glucose oxidation yielding ATP abundantly. My narrative trails through folklore and dogma to discoveries from various independent observations about how brain’s insatiable appetite for glucose and oxygen dynamically tracks neural activity, from regions as small as cortical columns to large cortical swathes across hemispheres. Results show that imaging oxidative energy provides an integrated perspective on brain networks, where oxidative energy demand of cortical workspace is quite uniform. Diagnosis of brain disorders and diseases can be improved by mapping the oxidative energetic footprint of cortical function.
Mass General Hospital
Neurosurgeon at the Massachusetts General Hospital (MGH)
Associate Professor of Surgery at Harvard Medical School.
Basal Ganglia: Learning & Motivation
The Basal Ganglia play a central role in theories of learning, motivation, depression and drug addiction. His group uses microelectrode and electrochemical recordings to evaluate the role of the basal ganglia in both primates and humans performing complex behavioral tasks. The group also uses electrical stimulation to directly modulate neuronal activity during complex behaviors. This is a unique approach in that ideas from the laboratory can quickly be tested in the clinical arena and vice-versa.
In a recent study, they have shown that a part of the brain called the cingulate cortex plays an important role in linking reward information with action selection. Another recent study found that delivering micro-stimulation in one part of the basal ganglia, the caudate nucleus, significantly increases the rate of learning beyond baseline rates. These findings suggest that the caudate plays a critical role in learning, and that learning can be enhanced to promote recovery after brain injury.
Department of Neurobology,
Harvard Medical School
Anatomical, Physiological and Computational Studies of Cortico-cortical Feedback.
Feedback connections are prevalent throughout the cerebral cortex, yet their function remains poorly understood. Previous studies in anesthetized monkeys found that inactivating feedback from extrastriate visual cortex produced effects in striate cortex that were relatively weak, generally suppressive, largest for visual stimuli confined to the receptive field center, and detectable only at low stimulus contrast. We studied the influence of corticocortical feedback in alert monkeys using cortical cooling to reversibly inactivate visual areas 2 (V2) and 3 (V3) while characterizing receptive field properties in primary visual cortex (V1).We show that inactivation of feedback results in both response suppression and facilitation for stimuli restricted to the receptive field center, in most cases leading to a small reduction in the degree of orientation selectivity but no change in orientation preference. For larger-diameter stimuli that engage regions beyond the center of the receptive field, eliminating feedback from V2/V3 results in strong and consistent response facilitation, effectively reducing the strength of surround suppression in V1 for stimuli of both low and high contrast. For extended contours, eliminating feedback had the effect of reducing end stopping. Inactivation effects were largest for neurons that exhibited strong surround suppression before inactivation, and their timing matched the dynamics of surround suppression under control conditions. Our results provide direct evidence that feedback contributes to surround suppression, which is an important source of contextual influences essential to vision.
Associate Professor of Psychology, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School.
Abnormal sleep in schizophrenia: A novel target for improving cognition?
Cognitive deficits are the strongest predictor of functional outcome in schizophrenia, and effective treatments are lacking. Accordingly, treating cognitive deficits is now a priority of the schizophrenia research community and the focus of large-scale studies. A limitation to these efforts is that cognition is measured in cross-section. While this provides a valid snapshot of function, it fails to capture critical aspects of learning and memory that happen offline, over time and with sleep. A burgeoning basic literature suggests that sleep spindles, a defining characteristic of stage 2 sleep, index human intelligence and mediate memory consolidation. Concurrently, our findings in schizophrenia demonstrate dramatically reduced sleep spindles that correlate with marked impairments of sleep-dependent memory consolidation. I will present evidence suggesting that the sleep spindle deficit in schizophrenia is a marker of genetic vulnerability to illness, contributes to impaired memory consolidation and symptoms and can be effectively treated. I will also describe new fMRI/MEG/EEG studies of the contribution of sleep spindles to memory. Given that sleep spindles correlate with a wide range of cognitive measures in health, our findings expand current models of cognitive deficits in schizophrenia and raise intriguing questions about whether pharmacological enhancement of sleep spindles can improve outcomes.
Anastasia Yendiki, PhD
MGH Martinos Center
Anastasia received a Ph.D. in Electrical Engineering: Systems from the University of Michigan at Ann Arbor, where she worked on statistical image reconstruction for nuclear imaging. Her thesis addressed the analytical optimization of reconstruction methods with respect to lesion detectability. Upon graduation she came to the Martinos Center to learn about functional MRI but quickly got distracted by the pretty, colorful strands of diffusion tractography. She has since developed TRACULA, a probabilistic method for the automated reconstruction of white-matter pathways that uses prior information on the anatomical neighborhood of each pathway along its trajectory. She is currently an Assistant Physicist at Massachusetts General Hospital and Assistant Professor in Radiology at Harvard Medical School.
Spurious findings due to head motion, and other exciting news for diffusion MRI group studies
I will present results from two lines of work related to our development of tools for the analysis of diffusion MRI (dMRI) data. First, we have recently investigated the effects of head motion on dMRI group studies, using data from children with autism spectrum disorders (ASD) and typically developing (TD) children. We show that, as the differences in head motion between groups increase, so do the differences in dMRI-derived anisotropy and diffusivity measures. The group that moves more tends to have lower fractional anisotropy, higher radial diffusivity, and lower axial diffusivity. We observe similar patterns when we compare groups of TD children only, as we do when we compare groups of ASD vs. TD children. Our results have implications for any dMRI study where one group is less likely to remain still in the scanner than the other.
Second, we have developed a novel framework for reconstructing white-matter pathways from longitudinal dMRI data. The challenge that conventional streamline tractography faces in the presence of degeneration is that, e.g., it may miss part of the pathway in later time points, making side by side comparisons of anisotropy and diffusivity to earlier time points problematic. Our approach is to reconstruct the pathway jointly using a subject's dMRI and T1 data from all time points at once. This method is unbiased, making no assumptions on the direction of longitudinal change. By design, it allows longitudinal analysis of anisotropy and diffusivity measures to be performed as a function of position along the trajectory of a tract. We demonstrate that the proposed approach improves both specificity and sensitivity compared to the conventional approach of performing tractography in each time point independently.
All analyses that I will describe in this talk can be performed with our publicly available tool for automated probabilistic tractography, TRActs Constrained by UnderLying Anatomy (TRACULA).
Swathi Kiran, PhD
Boston University and MGH
Changes in task based effective connectivity following rehabilitation in individuals with post-stroke aphasia
In this talk, I will first review the latest research into the examination of language recovery and its neural underpinnings. I will review the current neuroimaging evidence for language recovery and then talk about studies that have examined changes in connectivity as a function of recovery in post-stroke aphasia. In particular, I will discuss two studies from our lab that have examined changes in connectivity (using graph-metrics and DCM) following rehabilitation of word retrieval in individuals with post-stroke aphasia. In both these preliminary studies, results show that language rehabilitation is associated with positive behavioral and neural changes in the left language network, and right hemisphere homologues. Connectivity analyses reveal that across patients, LIFG and LMTG are important nodes in the network that are modulated as a function of rehabilitation.
Nathan McDannold, PhD
Brigham and Women's Hospital
I am the Research Director of the Focused Ultrasound Laboratory at The Brigham and Women's Hospital and an Associate Professor in Radiology at Harvard Medical School. I received my B.S. in Physics from the University of Virginia, Charlottesville and a Ph.D. in Physics from Tufts University in Boston, MA in 2001. My work has been primarily concerned with the development and implementation of MRI-based thermometry methods, animal experiments testing MRI and ultrasound related work, and clinical focused ultrasound treatments of breast tumors, uterine fibroids, and brain tumors. In recent years, a main focus of my work has been studying the use of ultrasound for temporary disruption of the blood-brain barrier, which may allow for targeted drug delivery in the brain.
Enabling targeted drug delivery in the brain via ultrasound-induced blood-brain barrier disruption
The physiology of the vasculature in the central nervous system (CNS), which includes the blood-brain barrier (BBB) and other factors, complicates the delivery of most drugs to the brain. Different methods have been used to bypass the BBB, but they have limitations such as being invasive, non-targeted or requiring the formulation of new drugs. Focused ultrasound (FUS), when combined with circulating microbubbles, is a noninvasive method to locally and transiently disrupt the BBB at discrete targets. This presentation will review the current status of this unique drug delivery technique, experience in preclinical models, and potential for clinical translation. If translated to humans, this method would offer a flexible means to target therapeutics to desired points or volumes in the brain, and enable the whole arsenal of drugs in the CNS that are currently prevented by the BBB.