People: Faculty

Boston University
My major interest is dynamics of the nervous system, especially brain rhythms associated with cognition. The central questions are: what are the networks and physiology that produce these rhythms; how do the physiological properties of those networks affect the use of the dynamics in cognition; can changes in the rhythms in disease give insights into the nature and treatments of diseases? I'm currently working on projects relating to physiology and interaction of rhythms, attention, Parkinson's disease, schizophrenia and anesthesia; with collaborators, the work involves in vivo and in vitro experiments, dynamical systems modeling and simulation, and geometric singular perturbations.

MIT
We are inventing new tools for analyzing and engineering brain circuits. For example, we have devised 'optogenetic' tools, which enable the activation and silencing of neural circuit elements with light, to understand their causal contribution to normal and pathological neural computations, as well as to support the discovery and repair of neural circuit targets in a therapeutic context. We are using our inventions to enable systematic approaches to neuroscience, revealing how neural circuits operate to generate behavior, and empowering new therapeutic strategies for neurological and psychiatric disorders.

Boston University
My research focuses on developing mathematical and statistical methods to analyze neural spiking activity. I have worked to integrate methodologies related to model identification, statistical inference, signal processing, and stochastic estimation and control, and expand these methodologies to incorporate point process observation models, making them more appropriate for modeling the dynamics of neural systems observed through spike train data. This research can be divided into two categories; first, a methodological component, focused on developing a statistical framework for relating neural activity to biological and behavioral signals and developing estimation algorithms, goodness-of-fit analyses, and mathematical theory that can be applied to any neural spiking system; second, an application component, wherein these methods are applied to spiking observations in real neural systems to dynamically model the spiking properties of individual neurons, to characterize how ensembles maintain representations of associated biological and behavioral signals, and to reconstruct these signals in real time.

MGH/Harvard Medical School/Martinos Imaging Center
The Athinoula A. Martinos Center at the Massachusetts General Hospital has a twofold mission to advance the development of imaging technologies and their integration with complementary technologies, and to apply these technologies to support basic science and translational research that is driven by an overarching interest in the continuous long-term improvement of clinical care. Martinos Center investigators are innovating in the areas of anatomical and functional magnetic resonance imaging (MRI) and spectroscopy (MRS), magnetoencephalography (MEG) and electroencephalography (EEG), near infrared spectroscopy (NIRS) and diffuse optical tomography (DOT), and positron emission tomography (PET) as well as cutting-edge tools for computational image analysis. The Center supports over 200 PHS-funded research projects at the MGH and other Boston-areas institutions, as well as other institutions in the United States and abroad. Research activities at the Martinos Center are supported institutionally as well as by Federal and foundation grants. Martinos Center investigators and their broad network of colleagues are at the forefront of developing advanced imaging technologies, integrating those technologies for multimodality acquisition, and deriving novel acquisition and analysis methods for the rich body of imaging data now acquired with these technologies. Funded by a P41 Regional Resource grant, from National Center for Research Resources, the Martinos Center and its Center for Functional Neuroimaging Technologies is a region-wide resource, broadly used by basic and clinical scientists who employ the full range of imaging technologies available at the Center to address questions of fundamental importance in fields ranging from neurovascular, neurological, and psychiatric disorders to cognitive neuroscience to cancer and cardiovascular function.

Brown University
Dr. Jones uses her background in dynamical systems theory mathematics and computational neural modeling to study neural dynamics in health and disease. She is trained in MEG/EEG imaging and currently uses computational modeling techniques to bridge the critical gap between the non-invasive imaging observables and the underlying microscopic cellular and network level mechanisms. Her current projects and interest include: 1) Investigating the neural dynamics underlying normal development in children ages 0-6 as well as neural abnormalities in children with encephalopathy of prematurity (EP). In collaboration with Drs. Ellen Grant and Yoshio Okada at CHB, we are studying development with a powerful combination of techniques including mathematical modeling, MR diffusion tensor imaging, and MEG imaging. 2) Studying the mechanisms and functions of neural rhythms including their role in sensory perception, attentional processes, and healthy aging. We are also investigating the source of disruption in brain rhythms in diseases such as Parkinson's Disease, Obsessive Compulsive Disorder, and Attention Deficit Disorder. 3) Investigating plasticity induced by training in perceptual attention. In collaboration with Dr. Cathy Kerr at HMS we are studying neurodynamics underlying Mindfulness Medidation Practice. 4)Combing computational modeling and optogenetic techniques, in collaboration with Dr. Chris Moore at Brown University, to study neural dynamics. We are currently delineating the role of specific cell types in controlling neocortical rhythmicity and investigating the impact of these rhythms on sensory perception.

MIT
The Miller Lab uses experimental and theoretical approaches to study the neural basis of the 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. They have provided insights into how categories, concepts, and rules are learned, how attention is focused, and how the brain coordinates thought and action. To this end, the Miller Lab has innovated techniques for studying the activity of many neurons in multiple brain areas simultaneously, which has provided insight into how interactions within local and global networks of neurons 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 deficit disorder.

MGH/Harvard Medical School/Martinos Imaging Center
My research concerns spatiotemporal imaging of human brain function. I have applied integrated magnetoencephalography (MEG), electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) to studies of cortical processing of visual information. My research involves development of techniques for the analysis of multimodal biomedical imaging data, including the use of fMRI data to inform the source estimation (inverse problem) of MEG and EEG. Currently I am studying computationally the characteristics of the sensitivity of MEG and EEG sensor arrays as well as the relationship of MEG and EEG signals to the cortical anatomy and physiology. Collaborative work focuses on the application of MEG, EEG, and fMRI techniques to reveal neural activation patterns related to cognitive processing in normal and clinical populations.

Tufts University
Most of my work is on designing and analyzing computational models in neuroscience. Current projects concern the role of different populations of inhibitory cells in gamma oscillations, modeling of the impact of astrocytes on neuronal activity, and synchronization via gap junctions. I also have one current research project unrelated to neuroscience, on numerical methods for linear Boltzmann equations.

MGH
Current research in the lab is, broadly speaking, dedicated to trying to understand normal and abnormal brain activity, particularly oscillations, using multi-modal and multi-scalar approaches. Specifically, we are combining novel microelectrode approaches with non-invasive techniques such as electroencephalography and magnetoencephalography to record directly from both human and animal cortex and subcortical structures. One part of the lab studies the neurophysiology of epilepsy; trying to understand how seizures start and stop and how they might be predicted and terminated. These questions overlap with investigations into the mechanisms of sleep, normal language, auditory, and other cognitive processing. All of these projects are built on a foundation of combined microelectrode, macroelectrode and non-invasive recording techniques that span information from the level of single action potentials to aggregate activity of millions of neurons. Intensive signal processing and computational techniques are employed to analyze these data sets. Collaborative activities involving neural modeling are aimed at relating these multi-scalar data. Ultimately, all of these projects aim toward the creation of both invasive and non-invasive mechanisms for restoring damaged neuronal function.

Brown University
Dr. Cheng is a neurosurgeon whose laboratory studies the neural basis of different cognitive processes that underlie diseases such as Parkinson's and other movement disorders, pain, epilepsy, depression, and other more rare neurological conditions. The goal of his laboratory is neurorestoration, the idea that therapies can be devised to restore the original function of the brain and spinal cord. Our primary techniques include the use of psychopharmacology, electrical stimulation and reversible lesioning to enhance or alter brain, spine and peripheral nerve function. We measure the results in our subjects with behavioral tasks coupled with intraoperative and extraoperative electrophysiology in human subjects: EEG, electrocorticography (ECoG), extracellular field potential recordings, and single unit microelectrode recordings from multiple brain and spine structures (subthalamic nucleus, caudate, globus pallidus, thalamus, nucleus accumbens, substantia nigra, neocortical areas, dorsal columns, the dorsal horn, etc.). We also use these electrophysiological tools to complement molecular techniques in the study of animal models of hydrocephalus and neurodegenerative disease. We are ultimately interested in applying our findings to the creation of open and closed loop stimulatory devices that can help human patients with neurological and psychiatric diseases. Ultimately, the information we learn will help us create brain and spine machine interfaces to fight disease and change human interaction with the external world.

MIT
A complex visual scene will typically contain many different objects, few of which are currently relevant to behavior. Thus, attentional mechanisms are needed to select the relevant objects from the scene and to reject the irrelevant ones. Neurophysiological studies in our own and other labs have identified some of the neural mechanisms of attentional selection within the ventral, 'object recognition', stream of the cortex. At each stage along this stream, attended, or behaviorally relevant, stimuli are processed preferentially compared to irrelevant distracters. In recent years, we have found that the top-down attentional bias is expressed, at least in part, in visual cortex through an increase in high-frequency (gamma) synchronization of neurons carrying critical information about the location or features of the behaviorally relevant stimulus. Increases in gamma synchrony are found during both spatial attention and featural attention engaged during visual search, and the presence of synchrony predicts faster responses in visual tasks. Recent evidence shows that inputs from the frontal eye fields (FEF) in prefrontal cortex initiates coupled gamma-frequency oscillations between FEF and area V4 during attention, and these oscillations are shifted in time across the two areas to allow for maximally effective communication. Cross-area synchrony may be a general mechanism for regulating information flow through the brain and for regulating spike-timing dependent plasticity.

Boston University
The research program of this laboratory is focused on four closely related projects that seek to understand the brain circuity that supports memory. This research is guided by the hypothesis that our ability to remember specific experiences relies on an organization of memories about objects and the events in the context in which they occurred. We believe that associations between objects and context is accomplished through the circuitry of the medial temporal lobe, in which parallel pathways represent information about objects and about context, and these streams of information converge within the hippocampus. A project central to this goal seeks to characterize how neurons in key components of the medial temporal lobe encode these different types of information and how components of this brain system interact with one another. Another project explores how the hippocampus is initially critical to the associations between objects and context but eventually these associations consolidate in cortical areas with which the hippocampus is connected. Another project explores how the prefrontal cortex controls the retrieval of memories as they bear on ongoing cognitive processes. And yet another project explores how hippocampal networks represent objects in the spacial and temporal context in which they occur. Together these projects will provide new insights into how memories are organized within the medial temporal lobe memory system and how memories are retrieved when we recall our daily experiences.

Boston University
We conduct a tightly integrated computational and experimental research program across three sites (BU, NYU, Columbia) to study spoken language recognition from the psychophysical, neurophysiological, and engineering perspectives. The program proceeds in four fronts:1) Psychophysics. We measure and model the results of human performance in tasks designed to gain a better understanding on the interplay between neuronal oscillators in different frequency bands, and between the oscillations and the speech syllabic structure; 2) Human Neuroimaging. We formulate the intra-relationship among theta, beta and gamma oscillations, using MEG and ECoG data recorded while subjects perform intelligibility tasks; 3) Monkey Electrophysiology. If the emerging cortical computation principles are fundamental, they must generalize across mammalian species. We are using high-resolution physiological methods to measure the intra-relationship among oscillations using multi-electrode recordings in monkeys listening to stimuli specifically designed to capture the rhythmic aspects of natural speech and music; 4)Automatic Speech Recognition. We explore a new perspective to the development of ASR systems that incorporates the insights from the behavioral and brain sciences, specifically rhythmic brain activity. We ascertain whether the proposed cortical computation principle could be used as an adjunct to conventional features used in ASR systems, e.g. in lattice re-scoring of n-best lists – and ultimately result in a decrease in word error rate.

Coming Soon

MIT
Ann Graybiel studies the basal ganglia, forebrain structures that are profoundly important for normal brain function but are also implicated in Parkinson's disease, Huntington's disease, obsessive-compulsive disorder, and addiction. Graybiel's work is uncovering neural deficits related to these disorders, as well as the role the basal ganglia play in guiding normal behavior.

Boston University
Brain disorders represent the biggest unmet medical need, with many disorders being untreatable, and most treatments presenting serious side effects. Accordingly, we are discovering design principles for novel neuromodulation therapies. We invent and apply a variety of genetic, molecular, pharmacological, optical, and electrical tools to correct neural circuits that go awry within the brain. As an example, we have pioneered several technologies for silencing specific cells in the brain using pulses of light. We have also recently participated the first pre-clinical testing of a novel neurotechnology, optical neural modulation. Using these novel neurotechnologies and classical ones such as deep brain stimulation (DBS), we modulate the function of neural circuits to establish causal links between neural dynamics and behavioral phenomena (e.g., movement, attention, memory, and decision making). One of our current interests is the investigation of how neural synchrony arises within and across brain regions, and how synchronous activity contributes to normal cognition and pathology.

Boston University
Coming Soon

Brandeis
Coming Soon

Harvard Medical School
The central focus of my research is the subcortical regulation of hippocampal function and is guided by the general hypothesis that the role of this regulation is to build dynamic associations between several limbic structures that are synchronized by oscillatory population activity. Phasic and rhythmic synchronization of neuronal activity is critical to control the concerted action of spatially separated structures in the brain. The general state and background activity of various brain structures determine how these structures will respond to different specific inputs and how they establish dynamical connections to perform complex functions. An important constituent of these states is the pattern of population activity including coherent oscillations in anatomically scattered structures which can establish functional networks during specific behaviors. Theta synchrony provides an excellent model to study these cooperations and the way in which they differ in specific behavioral states, such as waking exploration and REM sleep. Another model we use to study rhythmic synchronization among neural networks is the autonomic nervous system which is capable of generating different patterns of activity that control the response of the cardiovascular system to changes in the environment (e.g. chemoregulation, thermoregulation, etc.) and different behavioral states (e.g. defense reaction, eating, sleep, etc.). Our guiding hypothesis in this research is that sympathetic rhythm is generated by multiple oscillators and we study the changes in the relationship between these oscillators under different conditions of health and disease.

Boston University
We study mathematical neuroscience, with particular emphasis on neural rhythms, brain diseases, dynamical systems, and data analysis. All of the research involves interdisciplinary collaborations with experimentalists and clinicians. We are currently focused on analysis and modeling of multiscale data recorded in vivo from human subjects, and the construction of computational models of multiscale neuronal activity. We are also interested in techniques to infer and analyze functional connectivity networks from multivariate time series data, and how neuroscience can motivate new research questions in mathematics.

Brown University
Christopher Moore studies brain dynamics and how they change can change perception from moment to moment.The brain's ability to shift the way it processes information—to shift its 'state'—is crucial to surviving in an ever-changing world. Dysregulation of these dynamics are a hallmark of neurologic and psychiatric disease. The laboratory is studying the mechanisms responsible for generating brain states, how they impact the representation of a sensory input, and how, ultimately, they change conscious perception.

Mass. Eye and Ear/Harvard Medical School
Our work focuses on the role of sensory experience in the development and maintenance of functional circuits in the auditory cortex. The auditory cortex is powerfully influenced by experience during finite windows of development known as critical periods, after which time significant changes can only be brought about through learned associations between sounds and behaviorally relevant consequences. We study the mechanisms and perceptual correlates of cortical plasticity across the lifespan using a variety of neurophysiological, genetic, behavioral and computational approaches. We also record from subcortical auditory nuclei such as the inferior colliculus and auditory thalamus to understand more about features that are relayed to the cortex versus constructed there de novo. We believe this class of study will contribute towards a richer understanding of normal function, but might also hold the key for remediating abnormal auditory signal following a history of degraded hearing or deafness in early life. A major goal for our group is to apply what we've learned about the dynamic interplay between plasticity and stability in animal models towards improving auditory processing in humans that have been reconnected to the auditory world following a period of prolonged hearing loss.

Boston University
The Ritt lab concentrates on how organisms gather and use information from their environment, through processes of active sensing and sensory decision making. Current projects employ electrophysiological, behavioral, optogenetic and theoretical methods applied to the rodent whisker system, a highly refined tactile sensory system. Experiments combine multi-electrode recording of brain activity; high speed videography of behavior and development of automated image analysis algorithms; and optical stimulation of specific cell types (e.g., excitatory vs. inhibitory neurons) using genetically targeted expression of light sensitive ion channels. Parallel modeling uses tools from dynamical systems, control theory and decision theory. Augmenting experiments with model-driven, real-time feedback forms a basis for development of brain machine interfaces, with an emphasis on sensory neural prosthetics, in addition to providing state of the art tools to address basic questions of neural function.

Brandeis University
Coming soon

Boston University
How do neurons in the brain encode complex natural sounds? What are the neural substrates of selectivity for and discrimination of different categories of natural sounds? Are these substrates innate or shaped by learning? Our laboratory investigates these questions in the model system of the songbird. Electrophysiological techniques are used to record neural responses from hierarchical stages of auditory processing. Theoretical methods from areas such as statistical signal processing, systems theory, probability theory, information theory and pattern recognition are applied to characterize how neurons in the brain encode natural sounds. Computational models are constructed to understand the processing of natural sounds both at the single neuron and the network level, to model neural selectivity and discrimination, and to explore the role of learning in shaping the neural code.

Boston University
How do neurons in the brain encode complex natural sounds? What are the neural substrates of selectivity for and discrimination of different categories of natural sounds? Are these substrates innate or shaped by learning? Our laboratory investigates these questions in the model system of the songbird. Electrophysiological techniques are used to record neural responses from hierarchical stages of auditory processing. Theoretical methods from areas such as statistical signal processing, systems theory, probability theory, information theory and pattern recognition are applied to characterize how neurons in the brain encode natural sounds. Computational models are constructed to understand the processing of natural sounds both at the single neuron and the network level, to model neural selectivity and discrimination, and to explore the role of learning in shaping the neural code.

Boston University
Research in the Auditory Neuroscience Laboratory addresses how listeners communicate and make sense of sounds in everyday settings. We study everything from basic perceptual sensitivity to the ways in which different brain regions coordinate their activity during complex tasks. We use a range of approaches to explore these issues, including human behavioral experiments, human neuroelectric imaging, computational modeling, and, in collaboration with other laboratories, fMRI, animal behavioral experiments, and animal neurophysiology.

Harvard Medical School
Coming soon

MGH/Harvard Medical School/Martinos Imaging Center
Dr. Stufflebeam's goal is to develop and translate advanced technology at the Martinos Center into clinical practice. Currently, he is using MEG/EEG, fMRI, and optical imaging to understand how the brain processes neural information. He applies multiple imaging technologies to understand epilepsy, schizophrenia, and brain neoplasms. He is also setting up a clinical MEG service for New England.

Newcastle Universtity, UK
Dr. Whittington's group has a major interest in mechanisms that generate oscillatory activity with neural networks, how this activity is sustained and how is modulated in various normal and pathological conditions.

MIT
Research in the Wilson laboratory focuses on the study of information representation across large populations of neurons in the mammalian nervous system, as well as on the mechanisms that underlie formation and maintenance of distributed memories in freely behaving animals. To study the basis of these processes, the lab employs a combination of molecular genetic, electrophysiological, pharmacological, behavioral, and computational approaches. Using techniques that allow the simultaneous activity of ensembles of hundreds of single neurons to be examined in freely behaving animals, the lab examines how memories of places and events are encoded across networks of cells within the hippocampus ¬ a region of the brain long implicated in the processes underlying learning and memory. These studies of learning and memory in awake, behaving animals have led to the exploration of the nature of sleep and its role in memory. Previous theories have suggested that sleep states may be involved in the process of memory consolidation, in which memories are transferred from short to longer-term stores and possibly reorganized into more efficient forms. Recent evidence has shown that ensembles of neurons within the hippocampus, which had been activated during behavior are reactivated during periods of dreaming. By reconstructing the content of these states, specific memories can be tracked during the course of the consolidation process. Combining the measurement of ongoing neuronal activity with manipulation of molecular genetic targets has allowed the study of how specific cellular mechanisms regulate neural function to produce learning and memory at the behavioral level. Pharmacological blockage of these receptors has allowed the study of their involvement in the rapid changes that occur during both waking and sleeping states. Simultaneous monitoring of areas in the hippocampus and neocortex have allowed study of the downstream effects of activation. Taken together, these approaches contribute to the overall research objective: to understand the link from cellular/subcellular mechanisms of plasticity, to neural ensemble representations and interactions, to learning, memory, behavior, and cognition.