Faculty Bibliography

We present a comprehensive probabilistic point process framework to estimate and monitor the instantaneous heartbeat dynamics as related to specific cardiovascular control mechanisms and hemodynamics. Assessment of the model's statistics is established through the Wiener-Volterra theory and a multivariate autoregressive (AR) structure. A variety of instantaneous cardiovascular metrics, such as heart rate (HR), heart rate variability (HRV), respiratory sinus arrhythmia (RSA), and baroreceptor-cardiac reflex (BRS), can be rigorously derived within a parametric framework and instantaneously updated with an adaptive algorithm. Instantaneous metrics of nonlinearity, such as the bispectrum of heartbeat intervals, can also be derived. We have applied the proposed point process framework to experimental recordings from healthy subjects in order to monitor cardiovascular regulation under propofol anesthesia. Results reveal interesting dynamic trends across different pharmacological interventions, confirming the ability of the algorithm to track important changes in cardiorespiratory elicited interactions, and pointing at our mathematical approach as a promising monitoring tool for an accurate, noninvasive assessment of general anesthesia.

Songs of many songbird species consist of variable sequences of a finite number of syllables. A common approach for characterizing the syntax of these complex syllable sequences is to use transition probabilities between the syllables. This is equivalent to the Markov model, in which each syllable is associated with one state, and the transition probabilities between the states do not depend on the state transition history. Here we analyze the song syntax in Bengalese finch. We show that the Markov model fails to capture the statistical properties of the syllable sequences. Instead, a state transition model that accurately describes the statistics of the syllable sequences includes adaptation of the self-transition probabilities when states are revisited consecutively, and allows associations of more than one state to a given syllable. Such a model does not increase the model complexity significantly. Mathematically, the model is a partially observable Markov model with adaptation (POMMA). The success of the POMMA supports the branching chain network model of how syntax is controlled within the premotor song nucleus HVC, but also suggests that adaptation and many-to-one mapping from the syllable-encoding chain networks in HVC to syllables should be included in the network model.

BACKGROUND:Auditory verbal hallucinations (AVHs) are a core symptom of schizophrenia. Previous reports on neural activity patterns associated with AVHs are inconsistent, arguably owing to the lack of an adequate control group (i.e., patients with similar characteristics but without AVHs) and neglect of the potential confounding effects of medication. METHODS:The current study was conducted in a homogeneous group of patients with schizophrenia to assess whether the presence or absence of AVHs was associated with differential regional cerebral glucose metabolic patterns. We investigated differences between patients with commenting AVHs and patients without AVHs among a group of dextral antipsychotic-naive inpatients with acute first-episode schizophrenia examined with [(18)F]fluoro-deoxyglucose positron emission tomography (FDG-PET) at rest. Univariate and multivariate approaches were used to establish between-group differences. RESULTS:We included 9 patients with AVHs and 7 patients without AVHs in this study. Patients experiencing AVHs during FDG uptake had significantly higher metabolic rates in the left superior and middle temporal cortices, bilateral superior medial frontal cortex and left caudate nucleus (cluster level p < 0.005, family wise error-corrected, and bootstrap ratio > 3.3, respectively). Additionally, the multivariate method identified hippocampal-parahippocampal, cerebellar and parietal relative hypoactivity during AVHs in both hemispheres (bootstrap ratio < -3.3). LIMITATIONS/CONCLUSIONS:The FDG-PET imaging technique does not provide information regarding the temporal course of neural activity. The limited sample size may have increased the risk of false-negative findings. CONCLUSION/CONCLUSIONS:Our results indicate that AVHs in patients with schizophrenia may be mediated by an alteration of neural pathways responsible for normal language function. Our findings also point to the potential role of the dominant caudate nucleus and the parahippocampal gyri in the pathophysiology of AVHs. We discuss the relevance of phenomenology-based grouping in the study of AVHs.

The extracellular space (ECS) consists of the narrow channels between brain cells together with their geometrical configuration and contents. Despite being only 20-60 nm in width, the ECS typically occupies 20% of the brain volume. Numerous experiments over the last 50 years have established that molecules moving through the ECS obey the laws of diffusion but with an effective diffusion coefficient reduced by a factor of about 2.6 compared to free diffusion. This review considers the origins of the diffusion barrier arising from the ECS and its properties. The paper presents a brief overview of software for implementing two point-source paradigms for measurements of localized diffusion properties: the real-time iontophoresis or pressure method for small ions and the integrative optical imaging method for macromolecules. Selected results are presented. This is followed by a discussion of the application of the MCell Monte Carlo simulation program to determining the importance of geometrical constraints, especially dead-space microdomains, and the possible role of interaction with the extracellular matrix. It is concluded that we can predict the impediment to diffusion of many molecules of practical importance and also use studies of the diffusion of selected molecular probes to reveal the barrier properties of the ECS.