Theoden I. Netoff

Perfusion-decellularized matrix: using nature's platform to engineer a bioartificial heart

About 3,000 individuals in the United States are awaiting a donor heart; worldwide, 22 million individuals are living with heart failure. A bioartificial heart is a theoretical alternative to transplantation or mechanical left ventricular support. Generating a bioartificial heart requires engineering of cardiac architecture, appropriate cellular constituents and pump function. We decellularized hearts by coronary perfusion with detergents, preserved the underlying extracellular matrix, and produced an acellular, perfusable vascular architecture, competent acellular valves and intact chamber geometry. To mimic cardiac cell composition, we reseeded these constructs with cardiac or endothelial cells. To establish function, we maintained eight constructs for up to 28 d by coronary perfusion in a bioreactor that simulated cardiac physiology. By day 4, we observed macroscopic contractions. By day 8, under physiological load and electrical stimulation, constructs could generate pump function (equivalent to about 2% of adult or 25% of 16-week fetal heart function) in a modified working heart preparation.

Epilepsy in small-world networks.

In hippocampal slice models of epilepsy, two behaviors are seen: short bursts of electrical activity lasting 100 msec and seizure-like electrical activity lasting seconds. The bursts originate from the CA3 region, where there is a high degree of recurrent excitatory connections. Seizures originate from the CA1, where there are fewer recurrent connections. In attempting to explain this behavior, we simulated model networks of excitatory neurons using several types of model neurons. The model neurons were connected in a ring containing predominantly local connections and some long-distance random connections, resulting in a small-world network connectivity pattern. By changing parameters such as the synaptic strengths, number of synapses per neuron, proportion of local versus long-distance connections, we induced “normal,” “seizing,” and “bursting” behaviors. Based on these simulations, we made a simple mathematical description of these networks under well-defined assumptions. This mathematical description explains how specific changes in the topology or synaptic strength in the model cause transitions from normal to seizing and then to bursting. These behaviors appear to be general properties of excitatory networks.

Sniffing controls an adaptive filter of sensory input to the olfactory bulb

Most sensory stimuli are actively sampled, yet the role of sampling behavior in shaping sensory codes is poorly understood. Mammals sample odors by sniffing, a complex behavior that controls odorant access to receptor neurons. Whether sniffing shapes the neural code for odors remains unclear. We addressed this question by imaging receptor input to the olfactory bulb of awake rats performing odor discriminations that elicited different sniffing behaviors. High-frequency sniffing of an odorant attenuated inputs encoding that odorant, whereas lower sniff frequencies caused little attenuation. Odorants encountered later in a sniff bout were encoded as the combination of that odorant and the background odorant during low-frequency sniffing, but were encoded as the difference between the two odorants during high-frequency sniffing. Thus, sniffing controls an adaptive filter for detecting changes in the odor landscape. These data suggest an unexpected functional role for sniffing and show that sensory codes can be transformed by sampling behavior alone.

Early seizure detection.

Summary: For patients with medically intractable epilepsy, there have been few effective alternatives to resective surgery, a destructive, irreversible treatment. A strategy receiving increased attention is using interictal spike patterns and continuous EEG measurements from epileptic patients to predict and ultimately control seizure activity via chemical or electrical control systems. This work compares results of seven linear and nonlinear methods (analysis of power spectra, cross‐correlation, principal components, phase, wavelets, correlation integral, and mutual prediction) in detecting the earliest dynamical changes preceding 12 intracranially‐recorded seizures from 4 patients. A method of counting standard deviations was used to compare across methods, and the earliest departures from thresholds determined from non‐seizure EEG were compared to a neurologists judgement. For these data, the nonlinear methods offered no predictive advantage over the linear methods. All the methods described here were successful in detecting changes leading to a seizure between one and two minutes before the first changes noted by the neurologist, although analysis of phase correlation proved the most robust. The success of phase analysis may be due in part to its complete insensitivity to amplitude, which may provide a significant source of error.

Seizure prediction with spectral power of EEG using cost-sensitive support vector machines

Purpose:  We propose a patient‐specific algorithm for seizure prediction using multiple features of spectral power from electroencephalogram (EEG) and support vector machine (SVM) classification.

Identification of the Hippocampal Input to Medial Prefrontal Cortex In Vitro

To delineate the cellular mechanisms underlying the function of medial prefrontal cortex (mPFC) networks, it is critical to understand how synaptic inputs from various afferents are integrated and drive neuronal activity in this region. Using a newly developed slice preparation, we were able to identify a bundle of axons that contain extraneocortical fibers projecting to neurons in the prelimbic cortex. The anatomical origin and functional connectivity of the identified fiber bundle were probed by in vivo track tracing in combination with optic and whole-cell recordings of neurons in layers 2/3 and 5/6. We demonstrate that the identified bundle contains afferent fibers primarily from the ventral hippocampus but does not include contributions from the mediodorsal nucleus of the thalamus, amygdala, or lateral hypothalamus/medial forebrain bundle. Further, we provide evidence that activation of this fiber bundle results in patterned activity of neurons in the mPFC, which is distinct from that of laminar stimulation of either the deep layers 5/6 or the superficial layer 1. Evoked excitatory postsynaptic potentials are monosynaptic and glutamatergic and exhibit bidirectional changes in synaptic efficacy in response to physiologically relevant induction protocols. These data provide the necessary groundwork for the characterization of the hippocampal pathway projecting to the mPFC.

Beyond Two-Cell Networks: Experimental Measurement of Neuronal Responses to Multiple Synaptic Inputs

Oscillations of large populations of neurons are thought to be important in the normal functioning of the brain. We have used phase response curve (PRC) methods to characterize the dynamics of single neurons and predict population dynamics. Our past experimental work was limited to special circumstances (e.g., 2-cell networks of periodically firing neurons). Here, we explore the feasibility of extending our methods to predict the synchronization properties of stellate cells (SCs) in the rat entorhinal cortex under broader conditions. In particular, we test the hypothesis that PRCs in SCs scale linearly with changes in synaptic amplitude, and measure how well responses to Poisson process-driven inputs can be predicted in terms of PRCs. Although we see nonlinear responses to excitatory and inhibitory inputs, we find that models based on weak coupling account for scaling and Poisson process-driven inputs reasonably accurately.

Chaotic desynchronization as the therapeutic mechanism of deep brain stimulation.

High frequency deep-brain stimulation of the subthalamic nucleus (deep brain stimulation, DBS) relieves many of the symptoms of Parkinsons disease in humans and animal models. Although the treatment has seen widespread use, its therapeutic mechanism remains paradoxical. The subthalamic nucleus is excitatory, so its stimulation at rates higher than its normal firing rate should worsen the disease by increasing subthalamic excitation of the globus pallidus. The therapeutic effectiveness of DBS is also frequency and intensity sensitive, and the stimulation must be periodic; aperiodic stimulation at the same mean rate is ineffective. These requirements are not adequately explained by existing models, whether based on firing rate changes or on reduced bursting. Here we report modeling studies suggesting that high frequency periodic excitation of the subthalamic nucleus may act by desynchronizing the firing of neurons in the globus pallidus, rather than by changing the firing rate or pattern of individual cells. Globus pallidus neurons are normally desynchronized, but their activity becomes correlated in Parkinsons disease. Periodic stimulation may induce chaotic desynchronization by interacting with the intrinsic oscillatory mechanism of globus pallidus neurons. Our modeling results suggest a mechanism of action of DBS and a pathophysiology of Parkinsonism in which synchrony, rather than firing rate, is the critical pathological feature.

Neuromodulation for Brain Disorders: Challenges and Opportunities

The field of neuromodulation encompasses a wide spectrum of interventional technologies that modify pathological activity within the nervous system to achieve a therapeutic effect. Therapies including deep brain stimulation, intracranial cortical stimulation, transcranial direct current stimulation, and transcranial magnetic stimulation have all shown promising results across a range of neurological and neuropsychiatric disorders. While the mechanisms of therapeutic action are invariably different among these approaches, there are several fundamental neuroengineering challenges that are commonly applicable to improving neuromodulation efficacy. This paper reviews the state-of-the-art of neuromodulation for brain disorders and discusses the challenges and opportunities available for clinicians and researchers interested in advancing neuromodulation therapies.

Reconstructing micrometer-scale fiber pathways in the brain: Multi-contrast optical coherence tomography based tractography

Comprehensive understanding of connective neural pathways in the brain has put great challenges on the current imaging techniques, for which three-dimensional (3D) visualization of fiber tracts with high spatiotemporal resolution is desirable. Here we present optical imaging and tractography of rat brain ex-vivo using multi-contrast optical coherence tomography (MC-OCT), which is capable of simultaneously generating depth-resolved images of reflectivity, phase retardance, optic axis orientation and, for in-vivo studies, blood flow images. Using the birefringence property of myelin sheath, nerve fiber tracts as small as a few tens of micrometers can be resolved and neighboring fiber tracts with different orientations can be distinguished in cross-sectional optical slices, 2D en-face images and 3D volumetric images. Combinational contrast of MC-OCT images enables visualization of the spatial architecture and nerve fiber orientations in the brain with unprecedented detail. The results suggest that optical tractography, by virtue of its direct accessibility to nerve fibers, has the potential to validate diffusion magnetic resonance images and investigate structural connections in normal brain and neurological disorders. In addition, an endoscopic MC-OCT may be useful in neurosurgical interventions to aid in placement of deep brain stimulating electrodes.