John H. Abel

Critical Path Institute

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Emery N. Brown

Massachusetts Institute of Technology

Francis J. Doyle

Harvard University

S. Chakravarty

Alexion Pharmaceuticals (United States)

Harshith Padigela

SignPath Pharma (United States)

Ilan Wapinski

Critical Path Institute

Amaro Taylor‐Weiner

PathAI (United States)

Benyamin Meschede-Krasa

Stanford University

Elizabeth B. Klerman

Massachusetts General Hospital

Marcus A. Badgeley

Tempus Labs (United States)

Andrew H. Beck

Stanford University

Cooperative Institutions

PathAI (United States)

Critical Path Institute

Harvard University

University of Miami

SignPath Pharma (United States)

Colorado State University

International Association for the Study of Lung Cancer

Massachusetts General Hospital

Massachusetts Institute of Technology

Faculty of 1000 (United States)

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Multi stain graph fusion for multimodal integration in pathology

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2022)

Chaitanya Dwivedi Shima Nofallah Maryam Pouryahya Janani S. Iyer Kenneth Leidal

In pathology, tissue samples are assessed using multiple staining techniques to enhance contrast in unique histologic features. In this paper, we introduce a multimodal CNN-GNN based graph fusion approach that leverages complementary information from multiple non-registered histopathology images to predict pathologic scores. We demonstrate this approach in nonalcoholic steatohepatitis (NASH) by predicting CRN fibrosis stage and NAFLD Activity Score (NAS). Primary assessment of NASH typically requires liver biopsy evaluation on two histological stains: Trichrome (TC) and hematoxylin and eosin (H&E). Our multimodal approach learns to extract complementary information from TC and H&E graphs corresponding to each stain while simultaneously learning an optimal policy to combine this information. We report up to 20% improvement in predicting fibrosis stage and NAS component grades over single-stain modeling approaches, measured by computing linearly weighted Cohen's kappa between machine-derived vs. pathologist consensus scores. Broadly, this paper demonstrates the value of leveraging diverse pathology images for improved ML-powered histologic assessment.

Stain

Digital Pathology

Histopathology

Trichrome stain

10.1109/cvprw56347.2022.00200

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Citations (17)

Etiology of Burst Suppression EEG Patterns

Frontiers in Psychology (2021)

Akshay Shanker John H. Abel Gabriel Schamberg Emery N. Brown

Burst-suppression electroencephalography (EEG) patterns of electrical activity, characterized by intermittent high-power broad-spectrum oscillations alternating with isoelectricity, have long been observed in the human brain during general anesthesia, hypothermia, coma and early infantile encephalopathy. Recently, commonalities between conditions associated with burst-suppression patterns have led to new insights into the origin of burst-suppression EEG patterns, their effects on the brain, and their use as a therapeutic tool for protection against deleterious neural states. These insights have been further supported by advances in mechanistic modeling of burst suppression. In this Perspective, we review the origins of burst-suppression patterns and use recent insights to weigh evidence in the controversy regarding the extent to which burst-suppression patterns observed during profound anesthetic-induced brain inactivation are associated with adverse clinical outcomes. Whether the clinical intent is to avoid or maintain the brain in a state producing burst-suppression patterns, monitoring and controlling neural activity presents a technical challenge. We discuss recent advances that enable monitoring and control of burst suppression.

Burst suppression

Bursting

10.3389/fpsyg.2021.673529

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Citations (55)

Correlations between brain catecholamines, neurosecretion, and serum corticoid levels in osmotically stressed mallard ducks (Anas platyrhynchos)

Cell and Tissue Research (1975)

T.H. McNeill John H. Abel G. P. Kozlowski

Reserpine

Median eminence

Corticosterone

Neurophysins

10.1007/bf00220374

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Citations (17)

[Plasma level of etidocaine within the first two hours after axillary plexus block in healthy adults and patients with renal insufficiency (author's transl)].

PubMed (1981)

Kathrin Strasser John H. Abel M. Breulmann I Schumacher Siepmann Hp

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Recent advances in modeling sleep: from the clinic to society and disease

Current Opinion in Physiology (2019)

John H. Abel Kimaya Lecamwasam Melissa A. St. Hilaire Elizabeth B. Klerman

In the past few decades, advances in understanding sleep-wake neurophysiology have occurred hand-in-hand with advances in mathematical modeling of sleep and wake. In this review, we summarize recent updates in modeling the timing and durations of sleep and wake, the underlying neurophysiology of sleep and wake, and the application of these models in understanding cognition and disease. Throughout, we highlight the role modeling has played in developing our understanding of sleep and its underlying mechanisms. We present open questions and controversies in the field and propose the utility of individualized models of sleep for precision sleep medicine.

Sleep

Neurophysiology

Sleep medicine

10.1016/j.cophys.2019.12.001

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Citations (15)

Constructing a control-ready model of EEG signal during general anesthesia in humans.

arXiv (Cornell University) (2019)

John H. Abel Marcus A. Badgeley Taylor E. Baum S. Chakravarty Patrick L. Purdon

Significant effort toward the automation of general anesthesia has been made in the past decade. One open challenge is in the development of control-ready patient models for closed-loop anesthesia delivery. Standard depth-of-anesthesia tracking does not readily capture inter-individual differences in response to anesthetics, especially those due to age, and does not aim to predict a relationship between a control input (infused anesthetic dose) and system state (commonly, a function of electroencephalography (EEG) signal). In this work, we developed a control-ready patient model for closed-loop propofol-induced anesthesia using data recorded during a clinical study of EEG during general anesthesia in ten healthy volunteers. We used principal component analysis to identify the low-dimensional state-space in which EEG signal evolves during anesthesia delivery. We parameterized the response of the EEG signal to changes in propofol target-site concentration using logistic models. We note that inter-individual differences in anesthetic sensitivity may be captured by varying a constant cofactor of the predicted effect-site concentration. We linked the EEG dose-response with the control input using a pharmacokinetic model. Finally, we present a simple nonlinear model predictive control in silico demonstration of how such a closed-loop system would work.

SIGNAL (programming language)

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Pharmacokinetic data analysis of alfentanil after multiple injections and etomidate-infusion in patients undergoing orthopedic surgery.

PubMed (1985)

O. Richter A Klatte John H. Abel Enno Freye W Haag

The pharmacokinetics of Alfentanil, a new short acting analgesic, and Etomidate, a very short acting hypnotic compound, were studied in patients undergoing orthopedic surgery. Anesthesia was induced by bolus i.v. injection of Alfentanil and Etomidate, whereas the maintenance was performed by continuous infusion of Etomidate and repetitive i.v. injections of Alfentanil. The kinetics of the analgesic can be described by a two-compartment model; the elimination half-life has a mean value of 64.8 +/- 38.5 min. No cumulation of the drug was seen in spite of multiple injections. Concerning Etomidate it is not clear whether one or two compartments are needed. The mean elimination half-life is 29.4 +/- 6.2 min. The results suggest a possible shortening of the half-life of Etomidate in the presence of Alfentanil.

Etomidate

Alfentanil

Bolus (digestion)

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A Coupled Stochastic Model Explains Differences in Circadian Behavior of Cry1 and Cry2 Knockouts

arXiv (Cornell University) (2014)

John H. Abel Lukas A. Widmer Jörg Stelling Francis J. Doyle

In the mammalian suprachiasmatic nucleus (SCN), a population of noisy cell-autonomous oscillators synchronizes to generate robust circadian rhythms at the organism-level. Within these cells two isoforms of Cryptochrome, Cry1 and Cry2, participate in a negative feedback loop driving circadian rhythmicity. Previous work has shown that single, dissociated SCN neurons respond differently to Cry1 and Cry2 knockouts: Cry1 knockouts are arrhythmic while Cry2 knockouts display more regular rhythms. These differences have led to speculation that CRY1 and CRY2 may play different functional roles in the oscillator. To address this proposition, we have developed a new coupled, stochastic model focused on the Period (Per) and Cry feedback loop, and incorporating intercellular coupling via vasoactive intestinal peptide (VIP). Due to the stochastic nature of molecular oscillations, we demonstrate that single-cell Cry1 knockout oscillations display partially rhythmic behavior, and cannot be classified as simply rhythmic or arrhythmic. Our model demonstrates that intrinsic molecular noise and differences in relative abundance, rather than differing functions, are sufficient to explain the range of rhythmicity encountered in Cry knockouts in the SCN. Our results further highlight the essential role of stochastic behavior in understanding and accurately modeling the circadian network and its response to perturbation.

Gene knockout

Knockout mouse

10.48550/arxiv.1411.4624

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A simulation-based comparative analysis of PID and LQG control for closed-loop anesthesia delivery

IFAC-PapersOnLine (2020)

S. Chakravarty Ayan S. Waite John H. Abel Emery N. Brown

Closed loop anesthesia delivery (CLAD) systems can help anesthesiologists efficiently achieve and maintain desired anesthetic depth over an extended period of time. A typical CLAD system would use an anesthetic marker, calculated from physiological signals, as real-time feedback to adjust anesthetic dosage towards achieving a desired set-point of the marker. Since control strategies for CLAD vary across the systems reported in recent literature, a comparative analysis of common control strategies can be useful. For a nonlinear plant model based on well-established models of compartmental pharmacokinetics and sigmoid-Emax pharmacodynamics, we numerically analyze the set-point tracking performance of three output-feedback linear control strategies: proportional-integral-derivative (PID) control, linear quadratic Gaussian (LQG) control, and an LQG with integral action (ILQG). Specifically, we numerically simulate multiple CLAD sessions for the scenario where the plant model parameters are unavailable for a patient and the controller is designed based on a nominal model and controller gains are held constant throughout a session. Based on the numerical analyses performed here, conditioned on our choice of model and controllers, we infer that in terms of accuracy and bias PID control performs better than ILQG which in turn performs better than LQG. In the case of noisy observations, ILQG can be tuned to provide a smoother infusion rate while achieving comparable steady state response with respect to PID. The numerical analysis framework and findings reported here can help CLAD developers in their choice of control strategies. This paper may also serve as a tutorial paper for teaching control theory for CLAD.

Optimal projection equations

10.1016/j.ifacol.2020.12.369

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Citations (3)

Constructing a control-ready model of EEG signal during general anesthesia in humans

arXiv (Cornell University) (2019)

John H. Abel Marcus A. Badgeley Taylor E. Baum S. Chakravarty Patrick L. Purdon

SIGNAL (programming language)

10.48550/arxiv.1912.08144

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