Special Lectures: A Unified Approach for Identifying Behavior-Related Neural Processing Alterations and Functional Connections in the Human Brain: A Spatial Modeling Approach for fMRI Data. F. DuBois Bowman Department of Biostatistics and Bioinformatics Center for Biomedical Imaging Statistics The Rollins School of Public Health Emory University Functional magnetic resonance imaging (fMRI) studies attempt to identify spatially localized brain regions that drive the execution of experimental tasks, for example targeting behavior, cognition, or emotion. These studies may also provide neural representations of the associations between task-related brain activity in spatially distinct regions. These objectives have previously been addressed by separate analytic techniques. There are numerous challenges in analyzing functional neuroimaging data. Neuroimaging studies produce massive data sets comprised of serial scans on each subject, with each scan containing hundreds of thousands of spatially localized measurement sites (voxels). Also, human brain function, viewed through fMRI, presents complex patterns of spatial and temporal correlations. In this talk, we consider fMRI data from a study of inhibitory control among cocaine addicts. We present a Bayesian hierarchical model that establishes a unified framework for addressing inferences concerning task-related 1) changes in brain activity, both at the voxel-level and regional level and 2) functional connections, both within and between designated neuroanatomic structures. We apply our model to fMRI data from the inhibitory control study to assess localized (voxel-level) changes in brain activity associated with cocaine addiction and its response to treatment, regional changes in brain activity, and measures of task-related functional connectivity. ______________________________________________________________________ _______________________________________________________________________ Making and Breaking Habits: The Basal Ganglia in Action Dr. Ann M. Graybiel The same brain that can construct language, music and mathematics also lets us develop habits of thought and action. These semi-automatic routines free us to think and attend to the world. But the habit system can also be hijacked by disease and drug exposure. This lecture will focus on the habit system of the brain and our remarkable ability to switch from conscious activity to nearly non-conscious behavior. The lecture will highlight research directed towards understanding how we make and break habits and how the neurobiology of the habit system is helping to advance understanding of human problems ranging from Parkinson¢s disease to obsessive-compulsive spectrum disorders and addiction. Clinical and experimental evidence suggests that our ability to acquire habits depends on the basal ganglia, deep forebrain structures that are interconnected with the frontal cortex in a series of loop circuits. The development of recording techniques for monitoring neural activity in awake behaving animals now makes it feasible to investigate what forms of neuronal representation are built up in the basal ganglia and cortico-basal ganglia loops as habits are acquired. Recordings from the striatum, the largest input side of the basal ganglia, suggest remarkable plasticity in the response properties of striatal neurons as animals learn sequential procedures and also as they undergo bouts of learning, extinction and reacquisition training. Single unit activity changes systematically through these bouts and so does ensemble activity. These results suggest that there is a form of `neural exploration' followed by `neural exploitation" in the basal ganglia as procedures are learned. After learning, the ensemble activity tends to emphasize the beginning and end of such procedures, as though setting up boundary states in higher-order representations. Such action-boundary representations can be found also in multi-electrode chronic recordings from the prefrontal cortex and striatum of monkeys and information about time may be embedded as an infrastructure in such representations. These and other findings support the view that basal ganglia-based circuits can build representations of sequential actions that facilitate their release or inhibition. Evidence suggests that the laying down of such representations involves genes expressed in basal ganglia-related networks, and there are promising molecular approaches to understanding learning mechanisms of the basal ganglia. Disorders of such basal ganglia plasticity could contribute to behavioral fixity and difficulty of initiation of behavior, as in Parkinson's disease, or to the excessive release of behaviors, as in Huntington's disease, or to the repetitive behaviors and thoughts characteristic of many neuropsychiatric disorders. The basal ganglia thus may influence not only motor pattern generators, but also cognitive pattern generators. ____________________________________________________________________ _____________________________________________________________________ Progress and Challenges of Multimodal Imaging using MEG, EEG, MRI, and fMRI Matti S. Hamalainen Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA Independently, electromagnetic and hemodynamic measurements of brain activity offer compromises between spatial and temporal resolution. Functional Magnetic Resonance Imaging (fMRI) is temporally limited by the slow time course of the hemodynamic response, but can provide a spatial sampling on a millimeter scale. Electroencephalography (EEG) and magnetoencephalography (MEG) in turn provide a temporal resolution of milliseconds, but the localization of sources is more complicated because of the ill-posed electromagnetic inverse problem. Elucidating the spatial distribution and temporal orchestration of human brain regions is thus facilitated by combining information provided by both anatomical and functional MRI with EEG/MEG data. It was recognized very early on the source estimation problem in MEG benefits from combination with anatomical MRI data. Presently, this information is used routinely to delineate boundaries between regions of different electrical conductivities for forward field computations, to restrict the locations and orientations of the sources, and in advanced visualization techniques involving three-dimensional renderings of the cortical mantle and other structures. The most obvious companion of MEG is EEG. Presently, many laboratories are collecting MEG and EEG data simultaneously and exploring source models which incorporate both types of data. Both simulations and analyses of actual human data have shown that the combination of these two methods yields more reliable estimates of the sources than using one modality alone. Furthermore, these studies indicate that the improvement is attributable to the different spatial sensitivities of MEG and EEG. The fusion of electromagnetic and hemodynamic data is still in its infancy. In current modeling methods, this is usually accomplished by confining the sources to the cortical gray matter and by computing a distributed current estimate with a stronger a priori weighting at locations with significant fMRI activity. More elaborate methods which attempt to model the two data sets jointly under a common framework are also emerging. Email: msh@nmr.mgh.harvard.edu _______________________________________________________________ _______________________________________________________________ Ruling out and ruling in neural codes Sheila Nirenberg The subject of neural coding has generated much heated debate. A key issue is whether the nervous system uses coarse or fine coding strategies. Each has different advantages and disadvantages and, therefore, different implications for how the brain computes. For example, the advantage to coarse coding is that it's robust to fluctuations in spike arrival times. Downstream neurons don't have to keep track of the details of the spike trains. The disadvantage, though, is that individual cells can't carry much information, so downstream neurons have to pool signals across cells and/or across time to obtain enough information to represent the sensory world and guide behavior. In contrast, the advantage to fine coding is that individual cells can carry a great deal of information; however,downstream neurons have to resolve spike train structure. Here we address the question of what the neural code can and can't be, using the retinal output cells as the model system. We recorded from essentially all the retinal output cells an animal uses to solve a task, evaluated the cells' spike trains for as long as the animal evaluates them, and used optimal, i.e., Bayesian, decoding. This approach makes it possible to obtain an upper bound on the performance of codes and thus eliminate those that are not viable. Our results show that coarse coding strategies are insufficient; finer, more information-rich codes are necessary. Email: shn2010@med.cornell.edu _______________________________________________________________ _______________________________________________________________ Coding and Computation by Neural Ensembles in the Primate Retina Liam Paninski, Columbia University The neural coding problem --- deciding which stimuli will cause a given neuron to spike, and with what probability --- is a fundamental question in systems neuroscience. We apply statistical modeling methods to analyze data recorded from a complete mosaic of macaque parasol retinal ganglion cells in a small region of visual space. We find that a surprisingly simple model with functional coupling between neurons captures both the stimulus dependence and the detailed spatiotemporal correlation structure of multi-neuronal responses; in addition, ongoing network activity in the retina accounts for a significant portion of the trial-to-trial variability in a neuron's response. We assess the significance of correlated spiking by performing optimal Bayesian decoding of the population spike responses; we find that approximately 20% more stimulus-related information is captured when correlations are taken into account. Finally, we discuss work in progress on the following questions: how much temporal precision is necessary to capture the neural code in the retina? How can we adapt our optimal decoding methods to estimate behaviorally relevant signals such as image velocity? How do we perceive stable images when the retina must contend with the constant motion due to small random eye movements? Email: liam@stat.columbia.edu _______________________________________________________________ _______________________________________________________________ Neuronal Oscillations as Instruments of Sensory Selection. Charles E. Schroeder, Ph.D. Cognitive Neuroscience and Schizophrenia Program Nathan S. Kline Institute for Psychiatric Research Professor of Psychiatry Columbia University College of Physicians and Surgeons It has been known for over 75 years that, independent of frequency, neuroelectric oscillations reflect rhythmic shifting of neuronal ensembles between high and low excitability states. Recent findings indicate that neuronal oscillations have a highly structured hierarchical organization across frequency bands. These findings also strongly reinforce the conclusion that neuronal oscillations both enable and constrain the brain's processing of sensory inputs, as well as its generation of motor outputs. I will discuss recent findings concerning the way the brain uses oscillations to process input, and the methods used in these investigations. I will also discuss a conceptual framework that can help to integrate the evidence on the neuronal oscillation as a mechanism of brain operation, with prior findings in traditional vision research paradigms (e.g., vigilance). The discussion will range across several sensory modalities, and will end with a focus on selective attention's manipulation of oscillatory phase as a mechanism of sensory selection in primate V1. Email: schrod@nki.rfmh.org; cs2388@columbia.edu _______________________________________________________________ _______________________________________________________________ Challenges in the analysis and acquisition of 3-D in vivo imaging data Dr. Philbert Tsai Kleinfeld Laboratory - UCSD The analysis of functional and structural brain imaging data poses nontrivial challenges and opportunities for the statistics and applied mathematics communities. Research in our laboratory focuses on imaging vascular and neurovascular dynamics in cortex, using the rodent as a model system and scanning nonlinear optical microscopy as a tool. I will discuss specific issues that arise in the analysis of blood flow dynamics as well as issues that relate to large-scale vectorization of vascular anatomy. Lastly, I will present general challenges for integration of on-line analysis and machine learning with data acquisition and experimental control. Email: ptsai@physics.ucsd.edu _______________________________________________________________ _______________________________________________________________ Waveform based Spike Sorting yields Inconsistent Rate Codes Estimates Valerie Ventura Carnegie Mellon University Extracellular electrodes are commonly used to monitor populations of neurons. The signal collected at an electrode is a mixture of activities from different neurons. Spike sorting consists of clustering the recorded spike waveforms, with aim to determine how many neurons contributed to the recorded data, and which neurons produced which spikes. The spike trains thus obtained are often used to estimate how neural inputs modulate neurons firing rates. This sequence of first spike sorting, then estimating tuning properties, has long been the accepted way to proceed. Here, we prove that tuning function estimates thus obtained are systematically biased, unless spikes can be classified with perfect accuracy. This means, for example, that the commonly used peristimulus time histogram is a biased estimate of the firing rate of a neuron that is not perfectly isolated. We further illustrate that inconsistent tuning curve estimates are more than a statistical inconvenience. Indeed they can lead to erroneous scientific conclusions or loss of efficiency. In particular, substantial efficiency can be lost when decoding neural spike trains to operate neural prostheses. We further propose a new automatic spike sorter, which combines waveform and tuning information. This spike sorter effectively performs spike sorting and tuning function estimation simultaneously rather than sequentially, as is currently done. We prove that this spike sorter yields consistent tuning curve estimates, so that subsequent statistical procedures are fully efficient and scientific inferences valid.