Faculty Bibliography

Metabolic syndrome is a condition that typically includes central obesity, insulin resistance, glucose intolerance, dyslipidemia, and hypertension. Disruption of the hypothalamic-pituitary-adrenal axis, a regulator of corticosterone secretion, occurs in some cases of metabolic syndrome and obesity, and Cushing hypercortisolemia is associated with obesity and metabolic disorders. We therefore assessed anatomic and clinical pathology in C57BL/6NCrl mice to evaluate the effects of chronic corticosterone in the drinking water at doses of 25, 50, and 100 mug/mL for 25 d. Treated mice developed obesity, glucose intolerance, electrolyte aberrations, and dyslipidemia that were dose-dependent and most severe in the 100-mu;g/mL treatment group. To evaluate return to normal function, additional C57BL/6NCrl mice received corticosterone-free water for 2 wk after the 25-d treatment period. According to results of gross examination, mice appeared to recover within days of exogenous corticosterone withdrawal; however, adrenal gland vacuolation and protein, lipid, and electrolyte abnormalities persisted. Together, these findings support chronic corticosterone exposure through the drinking water as a potentially useful, noninvasive method to induce some features of metabolic syndrome.

Hippocampal population codes play an important role in representation of spatial environment and spatial navigation. Uncovering the internal representation of hippocampal population codes will help understand neural mechanisms of the hippocampus. For instance, uncovering the patterns represented by rat hippocampus (CA1) pyramidal cells during periods of either navigation or sleep has been an active research topic over the past decades. However, previous approaches to analyze or decode firing patterns of population neurons all assume the knowledge of the place fields, which are estimated from training data a priori. The question still remains unclear how can we extract information from population neuronal responses either without a priori knowledge or in the presence of finite sampling constraint. Finding the answer to this question would leverage our ability to examine the population neuronal codes under different experimental conditions. Using rat hippocampus as a model system, we attempt to uncover the hidden "spatial topology" represented by the hippocampal population codes. We develop a hidden Markov model (HMM) and a variational Bayesian (VB) inference algorithm to achieve this computational goal, and we apply the analysis to extensive simulation and experimental data. Our empirical results show promising direction for discovering structural patterns of ensemble spike activity during periods of active navigation. This study would also provide useful insights for future exploratory data analysis of population neuronal codes during periods of sleep.

The moving bar experiment is a classic paradigm for characterizing the receptive field (RF) properties of neurons in primary visual cortex (V1). Current approaches for analyzing neural spiking activity recorded from these experiments do not take into account the point-process nature of these data and the circular geometry of the stimulus presentation. We present a novel analysis approach to mapping V1 receptive fields that combines point-process generalized linear models (PPGLM) with tomographic reconstruction computed by filtered-back projection. We use the method to map the RF sizes and orientations of 251 V1 neurons recorded from two macaque monkeys during a moving bar experiment. Our cross-validated goodness-of-fit analyses show that the PPGLM provides a more accurate characterization of spike train data than analyses based on rate functions computed by the methods of spike-triggered averages or first-order Wiener-Volterra kernel. Our analysis leads to a new definition of RF size as the spatial area over which the spiking activity is significantly greater than baseline activity. Our approach yields larger RF sizes and sharper orientation tuning estimates. The tomographic reconstruction paradigm further suggests an efficient approach to choosing the number of directions and the number of trials per direction in designing moving bar experiments. Our results demonstrate that standard tomographic principles for image reconstruction can be adapted to characterize V1 RFs and that two fundamental properties, size and orientation, may be substantially different from what is currently reported.

Brain-machine interfaces (BMIs) rely on decoding neuronal activity from a large number of electrodes. The implantation procedures, however, do not guarantee that all recorded units encode task-relevant information: selection of task-relevant neurons is critical to performance but is typically performed based on heuristics. Here, we describe an algorithm for decoding/classification of volitional actions from multiple spike trains, which automatically selects the relevant neurons. The method is based on sparse decomposition of the high-dimensional neuronal feature space, projecting it onto a low-dimensional space of codes serving as unique class labels. The new method is tested against a range of existing methods using simulations and recordings of the activity of 1592 neurons in 23 neurosurgical patients who performed motor or speech tasks. The parameter estimation algorithm is orders of magnitude faster than existing methods and achieves significantly higher accuracies for both simulations and human data, rendering sparse decoding highly attractive for BMIs.

Amyotrophic lateral sclerosis (ALS) is a devastating disease that progresses from detachment of motor nerve terminals to complete muscle paralysis and lethal respiratory failure within 5 years of diagnosis. Genetic studies have linked mutations in several genes to ALS, and mice bearing mutations in SOD1 recapitulate hallmark features of the disease. We investigated whether disease symptoms can be ameliorated by co-opting the retrograde signaling pathway that promotes attachment of nerve terminals to muscle. We crossed SOD1G93A mice with transgenic mice that express MuSK, a receptor tyrosine kinase that is required for retrograde signaling, and we used histological and behavioral assays to assess motor innervation and behavior. A 3-fold increase in MuSK expression delayed the onset and reduced the extent of muscle denervation, improving motor function for more than a month without altering survival. These findings suggest that increasing MuSK activity by pharmacological means has the potential to improve motor function in ALS.

Foxp3 activity is essential for the normal function of the immune system. Two types of regulatory T (T reg) cells express Foxp3, thymus-generated natural T reg (nT reg) cells, and peripherally generated adaptive T reg (iT reg) cells. These cell types have complementary functions. Until now, it has not been possible to distinguish iT reg from nT reg cells in vivo based solely on surface markers. We report here that Neuropilin 1 (Nrp1) is expressed at high levels by most nT reg cells; in contrast, mucosa-generated iT reg and other noninflammatory iT reg cells express low levels of Nrp1. We found that Nrp1 expression is under the control of TGF-beta. By tracing nT reg and iT reg cells, we could establish that some tumors have a very large proportion of infiltrating iT reg cells. iT reg cells obtained from highly inflammatory environments, such as the spinal cords of mice with spontaneous autoimmune encephalomyelitis (EAE) and the lungs of mice with chronic asthma, express Nrp1. In the same animals, iT reg cells in secondary lymphoid organs remain Nrp1(low). We also determined that, in spontaneous EAE, iT reg cells help to establish a chronic phase of the disease.

Motor axons receive retrograde signals from skeletal muscle that are essential for the differentiation and stabilization of motor nerve terminals. Identification of these retrograde signals has proved elusive, but their production by muscle depends on the receptor tyrosine kinase, MuSK (muscle, skeletal receptor tyrosine-protein kinase), and Lrp4 (low-density lipoprotein receptor (LDLR)-related protein 4), an LDLR family member that forms a complex with MuSK, binds neural agrin and stimulates MuSK kinase activity. Here we show that Lrp4 also functions as a direct muscle-derived retrograde signal for early steps in presynaptic differentiation. We demonstrate that Lrp4 is necessary, independent of MuSK activation, for presynaptic differentiation in vivo, and we show that Lrp4 binds to motor axons and induces clustering of synaptic-vesicle and active-zone proteins. Thus, Lrp4 acts bidirectionally and coordinates synapse formation by binding agrin, activating MuSK and stimulating postsynaptic differentiation, and functioning in turn as a muscle-derived retrograde signal that is necessary and sufficient for presynaptic differentiation.

Sleep is composed of an alternating sequence of REM and non-REM episodes, but their respective roles are not known. We found that the overall firing rates of hippocampal CA1 neurons decreased across sleep concurrent with an increase in the recruitment of neuronal spiking to brief "ripple" episodes, resulting in a net increase in neural synchrony. Unexpectedly, within non-REM episodes, overall firing rates gradually increased together with a decrease in the recruitment of spiking to ripples. The rate increase within non-REM episodes was counteracted by a larger and more rapid decrease of discharge frequency within the interleaved REM episodes. Both the decrease in firing rates and the increase in synchrony during the course of sleep were correlated with the power of theta activity during REM episodes. These findings assign a prominent role of REM sleep in sleep-related neuronal plasticity.

Most of the choices we make have uncertain consequences. In some cases the probabilities for different possible outcomes are precisely known, a condition termed "risky". In other cases when probabilities cannot be estimated, this is a condition described as "ambiguous". While most people are averse to both risk and ambiguity(1,2), the degree of those aversions vary substantially across individuals, such that the subjective value of the same risky or ambiguous option can be very different for different individuals. We combine functional MRI (fMRI) with an experimental economics-based method(3 )to assess the neural representation of the subjective values of risky and ambiguous options(4). This technique can be now used to study these neural representations in different populations, such as different age groups and different patient populations. In our experiment, subjects make consequential choices between two alternatives while their neural activation is tracked using fMRI. On each trial subjects choose between lotteries that vary in their monetary amount and in either the probability of winning that amount or the ambiguity level associated with winning. Our parametric design allows us to use each individual's choice behavior to estimate their attitudes towards risk and ambiguity, and thus to estimate the subjective values that each option held for them. Another important feature of the design is that the outcome of the chosen lottery is not revealed during the experiment, so that no learning can take place, and thus the ambiguous options remain ambiguous and risk attitudes are stable. Instead, at the end of the scanning session one or few trials are randomly selected and played for real money. Since subjects do not know beforehand which trials will be selected, they must treat each and every trial as if it and it alone was the one trial on which they will be paid. This design ensures that we can estimate the true subjective value of each option to each subject. We then look for areas in the brain whose activation is correlated with the subjective value of risky options and for areas whose activation is correlated with the subjective value of ambiguous options.