Big Data in electrophysiology

Nedios, Sotirios; Iliodromitis, Konstantinos; Kowaleski, C.; Bollmann, Andreas; Hindricks, Gerhard; Dagres, Nikolaos; Bogossian, Harilaos

doi:10.1007/s00399-022-00837-z

Cited by 4 publications

(3 citation statements)

References 86 publications

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“…This model matched risk preference with population activity to predict 1) current risk associated with large rewards, and 2) choice between large risky vs small safe rewards. F1-scores — the harmonic mean of precision and recall, and a normalized metric of model performance 69 — were used to compare model performance between risk groups and between safe and risky choice). In both risk-preferring and -averse rats, lOFC activity encoded presence/absence of risk associated with large rewards (Supplemental 2A,B).…”

Section: Resultsmentioning

confidence: 99%

Lateral Orbitofrontal Cortex Encodes Presence of Risk and Subjective Risk Preference During Decision-Making

Gabriel,

Havugimana,

Liley

et al. 2024

Preprint

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Adaptive decision-making requires consideration of objective risks and rewards associated with each option, as well as subjective preference for risky/safe alternatives. Inaccurate risk/reward estimations can engender excessive risk-taking, a central trait in many psychiatric disorders. The lateral orbitofrontal cortex (lOFC) has been linked to many disorders associated with excessively risky behavior and is ideally situated to mediate risky decision-making. Here, we used single-unit electrophysiology to measure neuronal activity from lOFC of freely moving rats performing in a punishment-based risky decision-making task. Subjects chose between a small, safe reward and a large reward associated with either 0% or 50% risk of concurrent punishment. lOFC activity repeatedly encoded current risk in the environment throughout the decision-making sequence, signaling risk before, during, and after a choice. In addition, lOFC encoded reward magnitude, although this information was only evident during action selection. A Random Forest classifier successfully used neural data accurately to predict the risk of punishment in any given trial, and the ability to predict choice via lOFC activity differentiated between and risk-preferring and risk-averse rats. Finally, risk preferring subjects demonstrated reduced lOFC encoding of risk and increased encoding of reward magnitude. These findings suggest lOFC may serve as a central decision-making hub in which external, environmental information converges with internal, subjective information to guide decision-making in the face of punishment risk.

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Section: Resultsmentioning

confidence: 99%

Lateral Orbitofrontal Cortex Encodes Presence of Risk and Subjective Risk Preference During Decision-Making

Gabriel,

Havugimana,

Liley

et al. 2024

Preprint

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“…The article above however, discusses the problem more exhaustively, has been carefully planned and additionally focuses on precision medicine. Other articles present an insight into AI itself [166][167][168][169][170], discuss an application of a "Digital Twin" [171,172] or an AI-application in various medical fields like nuclear medicine [173], genetics of CVDs [174], cardio-oncology [175][176][177][178], oncology [179], electrophysiology [180], assessment of a valvular heart disease [181], chronic diseases [182], and Alzheimer's [183]. This review was not reported and has no official protocol.…”

Section: Limitationsmentioning

confidence: 99%

Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review

Kolaszyńska,

Lorkowski

2024

Applied Bionics and Biomechanics

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Cardiovascular diseases remain the main cause of death worldwide which makes it essential to better understand, diagnose, and treat atherosclerosis. Artificial intelligence (AI) and novel technological solutions offer us new possibilities and enable the practice of individually tailored medicine. The study was performed using the PRISMA protocol. As of January 10, 2023, the analysis has been based on a review of 457 identified articles in PubMed and MEDLINE databases. The search covered reviews, original articles, meta-analyses, comments, and editorials published in the years 2009–2023. In total, 123 articles met inclusion criteria. The results were divided into the subsections presented in the review (genome-wide association studies, radiomics, and other studies). This paper presents actual knowledge concerning atherosclerosis, in silico, and big data analyses in cardiology that affect the way medicine is practiced in order to create an individual approach and adjust the therapy of atherosclerosis.

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“…Im vorliegenden Schwerpunkt dieser Ausgabe der Herzschrittmachertherapie + Elektrophysiologie wird explizit auch auf die Weiterentwicklung in diesen Bereichen eingegangen: In diesem Zusammenhang werden die Themen "Big Data, Machine Learning und künstliche Intelligenz in der Elektrophysiologie" von Isaksen und Kollegen [6] sowie Nedios und Kollegen [7] präsentiert und diskutiert.…”

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