Faculty Bibliography

Astrocytes respond to injury, infection, and inflammation in the central nervous system by acquiring reactive states in which they may become dysfunctional and contribute to disease pathology. A sub-state of reactive astrocytes induced by proinflammatory factors TNF, IL-1α, and C1q ("TIC") has been implicated in many neurodegenerative diseases as a source of neurotoxicity. Here, we used an established human induced pluripotent stem cell (hiPSC) model to investigate the surface marker profile and proteome of TIC-induced reactive astrocytes. We propose VCAM1, BST2, ICOSL, HLA-E, PD-L1, and PDPN as putative, novel markers of this reactive sub-state. We found that several of these markers colocalize with GFAP⁺ cells in post-mortem samples from people with Alzheimer's disease. Moreover, our whole-cells proteomic analysis of TIC-induced reactive astrocytes identified proteins and related pathways primarily linked to potential engagement with peripheral immune cells. Taken together, our findings will serve as new tools to purify reactive astrocyte subtypes and to further explore their involvement in immune responses associated with injury and disease.

Decisions under uncertainty can be differentiated into two classes: risky, which has known probabilistic outcomes, and ambiguous, which has unknown probabilistic outcomes. Across a variety of types of decisions, people find ambiguity extremely aversive, subjectively more aversive than risk. It has been shown that the transient sympathetic arousal response to a choice predicts decisions under ambiguity but not risk, and that lifetime stress uniquely predicts attitudes toward ambiguity. Building on these findings, this study explored whether we could bias ambiguity and risk preferences with an arousal or acute stress manipulation that is incidental to the choice in two independent experiments. One experiment induced sympathetic arousal with an anticipatory threat paradigm, and the other manipulated incidental acute stress via a psychosocial stressor. The efficacy of the manipulations was confirmed via pupil dilation and salivary cortisol, respectively. Participants made choices between a guaranteed $5 option and a lottery with either a known (risky) or unknown (ambiguous) probabilistic outcome. Consistent with previous findings, participants were more averse to a given level of ambiguity than to a numerically equal level of risk. However, in contrast to our hypothesis, we found no evidence that transient arousal or acute stress that is incidental to the choice biases ambiguity preferences.

Despite major anatomical differences with other mammalian sensory systems, olfaction shares with those systems a modulation by sleep/wake states. Sleep modulates odor sensitivity and serves as an important regulator of both perceptual and associative odor memory. In addition, however, olfaction also has an important modulatory impact on sleep. Odors can affect the latency to sleep onset, as well as the quality and duration of sleep. Olfactory modulation of sleep may be mediated by direct synaptic interaction between the olfactory system and sleep control nuclei, and/or indirectly through odor modulation of arousal and respiration. This reciprocal interaction between sleep and olfaction presents novel opportunities for sleep related modulation of memory and perception, as well as development of non-pharmacological olfactory treatments of simple sleep disorders.

Recent advances in magnetic resonance imaging are enabling the efficient creation of high-dimensional, multiparametric images, containing a wealth of potential information about the structure and function of many organs, including the cardiovascular system. However, the sizes of these rich data sets are so large that they are outstripping our ability to adequately visualize and analyze them, thus limiting their clinical impact. While there are some intrinsic limitations of human perception and of conventional display devices which hamper our ability to effectively use these data, newer computational methods for handling the data may aid our ability to extract and visualize the salient components of these high-dimensional data sets.

Spreading depolarization (SD) is a slow-moving wave of neuronal depolarization accompanied by a breakdown of ion concentration homeostasis, followed by long periods of neuronal silence (spreading depression), and associated with several neurological conditions. We developed multiscale (ions to tissue slice) computer models of SD in brain slices using the NEURON simulator: 36,000 neurons (2 voltage-gated ion channels; 3 leak channels; 3 ion exchangers/pumps) in the extracellular space (ECS) of a slice (1 mm sides, varying thickness) with ion (K⁺, Cl^-, Na⁺) and O₂ diffusion and equilibration with a surrounding bath. Glia and neurons cleared K⁺ from the ECS via Na⁺/K⁺ pumps. SD propagated through the slices at realistic speeds of 2-4 mm/min, which increased by as much as 50% in models incorporating the effects of hypoxia or propionate. In both cases, the speedup was mediated principally by ECS shrinkage. Our model allows us to make testable predictions, including: 1. SD can be inhibited by enlarging ECS volume; 2. SD velocity will be greater in areas with greater neuronal density, total neuronal volume, or larger/more dendrites; 3. SD is all-or-none: initiating K⁺ bolus properties have little impact on SD speed; 4. Slice thickness influences SD due to relative hypoxia in the slice core, exacerbated by SD in a pathological cycle; 5. SD and high neuronal spike rates will be observed in the core of the slice. Cells in the periphery of the slice near an oxygenated bath will resist SD.SignificanceSpreading depolarization (SD) is a slow moving wave of electrical and ionic imbalances in brain tissue and is a hallmark of several neurological disorders. We developed a multiscale computer model of brain slices with realistic neuronal densities, ions, and oxygenation. Our model shows that SD is exacerbated by and causes hypoxia, resulting in strong SD dependence on slice thickness. Our model also predicts that the velocity of SD propagation is not dependent on its initiation, but instead on tissue properties, including the amount of extracellular space and the total area of neuronal membrane, suggesting faster SD following ischemic stroke or traumatic brain injury.

Objective/UNASSIGNED:Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls. Methods/UNASSIGNED:This multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve. Results/UNASSIGNED:The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73-0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction. Conclusions/UNASSIGNED:Our results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians to identify high-risk patients and initiate preventive strategies.

We consider the question whether the inferior olive (IO) is required for essential tremor (ET). Much evidence shows that the olivocerebellar system is the main system capable of generating the widespread synchronous oscillatory Purkinje cell (PC) complex spike (CS) activity across the cerebellar cortex that would be capable of generating the type of bursting cerebellar output from the deep cerebellar nuclei (DCN) that could underlie tremor. Normally, synchronous CS activity primarily reflects the effective electrical coupling of IO neurons by gap junctions, and traditionally, ET research has focused on the hypothesis of increased coupling of IO neurons as the cause of hypersynchronous CS activity underlying tremor. However, recent pathology studies of brains from humans with ET and evidence from mutant mice, particularly the hotfoot17 mouse, that largely replicate the pathology of ET, suggest that the abnormal innervation of multiple Purkinje cells (PCs) by climbing fibers (Cfs) is related to tremor. In addition, ET brains show partial PC loss and axon terminal sprouting by surviving PCs. This may provide another mechanism for tremor. It is proposed that in ET, these three mechanisms may promote tremor. They all involve hypersynchronous DCN activity and an intact IO, but the level at which excessive synchronization occurs may be at the IO level (from abnormal afferent activity to this nucleus), the PC level (via aberrant Cfs), or the DCN level (via terminal PC collateral innervation).

The vast majority of extant vertebrate diversity lies within the bony and cartilaginous fish lineages of jawed vertebrates. There is a long history of elegant experimental investigation of development in bony vertebrate model systems (e.g., mouse, chick, frog and zebrafish). However, studies on the development of cartilaginous fishes (sharks, skates and rays) have, until recently, been largely descriptive, owing to the challenges of embryonic manipulation and culture in this group. This, in turn, has hindered understanding of the evolution of developmental mechanisms within cartilaginous fishes and, more broadly, within jawed vertebrates. The little skate (Leucoraja erinacea) is an oviparous cartilaginous fish and has emerged as a powerful and experimentally tractable developmental model system. Here, we discuss the collection, husbandry and management of little skate brood stock and eggs, and we present an overview of key stages of skate embryonic development. We also discuss methods for the manipulation and culture of skate embryos and illustrate the range of tools and approaches available for studying this system. Finally, we summarize a selection of recent studies on skate development that highlight the utility of this system for inferring ancestral anatomical and developmental conditions for jawed vertebrates, as well as unique aspects of cartilaginous fish biology.

The neurophysiological footprint of brain activity after cardiac arrest and during near-death experience (NDE) is not well understood. Although a hypoactive state of brain activity has been assumed, experimental animal studies have shown increased activity after cardiac arrest, particularly in the gamma-band, resulting from hypercapnia prior to and cessation of cerebral blood flow after cardiac arrest. No study has yet investigated this matter in humans. Here, we present continuous electroencephalography (EEG) recording from a dying human brain, obtained from an 87-year-old patient undergoing cardiac arrest after traumatic subdural hematoma. An increase of absolute power in gamma activity in the narrow and broad bands and a decrease in theta power is seen after suppression of bilateral hemispheric responses. After cardiac arrest, delta, beta, alpha and gamma power were decreased but a higher percentage of relative gamma power was observed when compared to the interictal interval. Cross-frequency coupling revealed modulation of left-hemispheric gamma activity by alpha and theta rhythms across all windows, even after cessation of cerebral blood flow. The strongest coupling is observed for narrow- and broad-band gamma activity by the alpha waves during left-sided suppression and after cardiac arrest. Albeit the influence of neuronal injury and swelling, our data provide the first evidence from the dying human brain in a non-experimental, real-life acute care clinical setting and advocate that the human brain may possess the capability to generate coordinated activity during the near-death period.