Biology subjects

Sager, S.

Publications and source records attributed to Sager, S..

6 recordsLinked to original sources

bioRxiv2026

Machine-Learning-Based spike marking in signal and source space EEG from a patient with focal epilepsy

Accurate detection of interictal epileptiform discharges (IEDs) in electroencephalography (EEG) plays a crucial role in epilepsy diagnosis. Our work investigates the classification of IEDs using Artificial Neural Networks (ANNs) trained on EEG data represented in both signal and source space. Source waveforms were computed using equivalent current dipole models fitted using either a 1-parameter fixed-orientation or a 3-parameter projection approach, both localized to a single best-fit position during the rising flank of the IED. The ANN was trained on raw and feature-extracted versions of signal space and source space data. Feature extraction significantly improved performance across all domains. The highest accuracy (0.98) was achieved in signal space using Katz Fractional Dimension (KFD). In source space analyses, the 1-parameter and 3-parameter models achieved a maximum accuracy of 0.84, with statistical features performing best for the fixed-orientation model and KFD for the free orientation model. Additionally, annotations from three independent expert markers showed considerable variability, with ANN performance falling within the range of inter-expert agreement. These findings support the potential of ANN-based tools to assist expert evaluation in future clinical workflows.

neuroscience↗

bioRxiv2024

Temporal kinetics of brain state effects on visual perception

We investigated the effects of brain states on human perception and early visual response comparing focused wakefulness (ON state) to external inattention (OFF state). In two experiments, we investigated the temporal kinetics of brain states changes during stimulus processing and assessed fluctuations across extended periods of time. We used a classifier to distinguish between these states on a single trial level using theta activity in MEG sensors. We found that participants shifted from an ON to an OFF state as rapidly as two seconds. Visual target discrimination was comparable in both states, but reaction times were slower and more variable during the OFF state. Broad band high-frequency activity (BHA) recorded in MEG sensors covering the occipital cortex tracked target grating orientation. BHA was reduced during the OFF state but participants were still able to distinguish sensory information highlighting the role of BHA in visual perception across cognitive brain states.

neuroscience↗

bioRxiv2024

Data-Driven Discovery of Feedback Mechanisms in Acute Myeloid Leukaemia: Alternatives to classical models using Deep Nonlinear Mixed Effect modeling and Symbolic Regression

In pharmacometrics, developing and selecting models is crucial for quantitatively assessing drug-biological interactions, treatment planning, and gaining insights into underlying processes. These validated models are essential for predictive analytics and strategic decision-making in drug development and clinical practice. Unlike traditional methods, machine learning (ML) offers a data-driven alternative to conventional, first-principle approaches. This paper presents an automatic method to derive unknown or uncertain (sub)models using longitudinal, heterogeneous data. Initially, we employ deep nonlinear mixed effect (DeepNLME) to train a neural network as a universal approximator, which then generates a parameterized representation of the underlying process. Subsequently, we apply symbolic regression to identify a set of potential models expressed as equations. Within the study parameters, the proposed method outperforms the baseline models and demonstrates the validity of both the DeepNLME approach and the symbolic regression approach.

pharmacology and toxicology↗

bioRxiv2020

A Personalized Switched Systems Approach for the Optimal Control of Ventricular Assist Devices based on Atrioventricular Plane Displacement

ObjectiveA promising treatment for congestive heart failure is the implementation of a left ventricular assist device (LVAD) that works as a mechanical pump. Modern LVADs work with adjustable constant rotor speed and provide therefore continuous blood flow; however, recently undertaken efforts try to mimic pulsatile blood flow by oscillating the pump speed. This work proposes an algorithmic framework to construct and evaluate optimal pump speed policies. MethodsWe use a model that captures the atrioventricular plane displacement, which is a physiological indicator for heart failure. We employ mathematical optimization to adapt this model to patient specific data and to find optimal pump speed policies with respect to ventricular unloading and aortic valve opening. To this end, we reformulate the cardiovascular dynamics into a switched system and thereby reduce nonlinearities. We consider system switches that stem from varying the constant pump speed and that are state dependent such as valve opening or closing. ResultsAs a proof of concept study, we personalize the model to a selected patient with respect to ventricular pressure. The model fitting results in a root-mean-square deviation of about 6 mmHg. Optimized constant and piecewise constant rotor speed profiles improve the default initialized solution by 31% and 68% respectively. ConclusionThese in silico findings demon-strate the potential of personalized hemodynamical optimization for the LVAD therapy. SignificanceLVADs and their optimal configuration are active research fields. Mathematical optimization enhances our understanding of how LVADs should provide pulsatility.

bioengineering↗

bioRxiv2019

The genomic underpinnings of oscillatory biomarkers supporting successful memory encoding in humans

In humans, brain oscillations are thought to support critical features of memory formation such as coordination of activity across regions, consolidation, and temporal ordering of events. However, understanding the molecular mechanisms underlining this activity in humans remains a major challenge. Here, we measured memory-sensitive oscillations using direct intracranial electroencephalography recordings from the temporal cortex of patients performing an episodic memory task. By then employing transcriptomics on the resected tissue from the same patients, we linked gene expression with brain oscillations, identifying genes correlated with oscillatory signatures of memory formation across six frequency bands. A co-expression analysis isolated biomarker-specific modules associated with neuropsychiatric disorders as well as ion channel activity. Using single-nuclei transcriptomic data from this resected tissue, we further revealed that biomarker-specific modules are enriched for both excitatory and inhibitory neurons. This unprecedented dataset of patient-specific brain oscillations coupled to genomics unlocks new insights into the genetic mechanisms that support memory encoding. By linking brain expression of these genes to oscillatory patterns, our data help overcome limitations of phenotypic methods to uncover genetic links to memory performance.

genomics↗

bioRxiv2018

Mathematical Models for the Influence of Cytarabine on White Blood Cell Dynamics in Acute Myeloid Leukemia

We investigate the personalisation and prediction accuracy of mathematical models for white blood cell (WBC) count dynamics during consolidation treatment using intermediate or high-dose cytarabine (Ara-C) in acute myeloid leukemia (AML). Ara-C is the clinically most relevant cytotoxic agent for AML treatment.\n\nWe extend the gold-standard model of myelosuppression and a pharmacokinetic model of Ara-C with different hypotheses of Ara-Cs pharmacodynamic effects. We cross-validate 12 mathematical models using dense WBC count measurements from 23 AML patients. Surprisingly, the prediction accuracies are similarly good despite different modelling hypotheses. Therefore, we compare average clinical and calculated WBC recovery times for different Ara-C schedules as a successful methodology for model discrimination. As a result, a new hypothesis of a secondary pharmacodynamic effect on the proliferation rate seems plausible. Furthermore, we demonstrate how personalized predictions of the impact of treatment timing on subsequent nadir values could be used for clinical decision support.\n\nAuthor summaryThe major obstacle in accurately predicting the outcome of a medical therapy is the vast variation in individual response patterns. It concerns both the subjective experience of the patient and the objectively measurable achievement of a clinical remission with restoration of normal blood counts. Here, we address acute myeloid leukemia (AML)-chemotherapy using cytarabine (Ara-C) as this drug is this most important component of AML-treatment. In addition to the wide spectrum of genetic aberrations involved in pathogenesis leading to variations in patient response patterns, another facet of personalised medicine awaits exploration of its full potential: a systematic, mathematical approach to understand and manipulate the dynamics of relevant biomarkers. We use personalised mathematical models to describe and predict white blood cell (WBC) counts during AML consolidation treatment. We analyse why and to what extent low WBC counts, a serious adverse event during therapy, occur. In a comprehensive approach we investigate published models, compare them with our extended models and outline the impact of modelling assumptions and varying chemotherapy schedules on prediction accuracy and model discrimination. Our numerical results confirm the clinical finding that a newly proposed schedule is superior with respect to WBC recovery and shed new light on the reasons why.

systems biology↗