@inproceedings{NickHeinleinSchneideretal.2012, author = {Nick, Christoph and Heinlein, Thorsten and Schneider, J{\"o}rg and Thielemann, Christiane}, title = {3D Carbon Nanotube Electrodes for Sensing of Neural Activity}, series = {Elsevier World Congress on Biosensors}, booktitle = {Elsevier World Congress on Biosensors}, address = {Cancun, Mexiko}, year = {2012}, subject = {Kohlenstoff-Nanor{\"o}hre}, language = {en} } @inproceedings{KoehlerWoelfelBochtleretal.2016, author = {K{\"o}hler, Tim and W{\"o}lfel, Maximilian and Bochtler, Ulrich and Thielemann, Christiane}, title = {TETRA specific long-term exposure of neuronal in vitro networks}, series = {BioEM2016}, booktitle = {BioEM2016}, year = {2016}, subject = {Neuronales Netz}, language = {en} } @article{ThielemannKrstićJuettneretal.2023, author = {Thielemann, Christiane and Krstić, Nenad and J{\"u}ttner, Jens and Giegerich, Lars and Mayer, Margot and Knuth, Monika and M{\"u}ller, Achim}, title = {3D printed biosensor for continuous glucose measurement in cell cultures}, series = {Annals of 3D Printed Medicine}, volume = {2023}, journal = {Annals of 3D Printed Medicine}, number = {-}, doi = {https://doi.org/10.1016/j.stlm.2023.100111}, pages = {- -- -}, year = {2023}, abstract = {A novel 3D-printed glucose sensor is presented for cell culture application. Glucose sensing was performed using a fluorescence resonance energy transfer (FRET)-based assay principle based on ConA and dextran. Both molecules are encapsulated in alginate microspheres and embedded in the UV-curable, stable hydrogel polyvinyl alcohol (PVA). The rheology of the formulation was adapted to obtain good properties for an extrusion-based printing process. The printed sensor structures were tested for their ability to detect glucose in vitro. A proportional increase in fluorescence intensity was observed in a concentration range of 0 - 2 g/L glucose. Tests with HEK cell cultures also showed good cell compatibility and excellent adhesion properties on plasma-treated Petri dishes. The printed sensors were able to detect the glucose decay associated with the metabolic activities of the fast-growing HEK cells in the cell culture medium over ten days. The proof-of-principle study shows that metabolic processes in cell cultures can be monitored with the new printed sensor using a standard fluorescence wide-field microscope.}, subject = {Biosensor}, language = {en} } @techreport{HelmFrankFriessetal.2014, author = {Helm, Alexander and Frank, Simon and Frieß, Johannes and Thielemann, Christiane and Durante, Marco and Ritter, Sylvia}, title = {Electrophysiological response of mouse embryonic stem cell-derived cardiomyocytes after X-ray and C-ion exposure}, series = {GSI SCIENTIFIC REPORT 2014}, journal = {GSI SCIENTIFIC REPORT 2014}, doi = {10.15120/GR-2015-1-APPA-HEALTH-21}, year = {2014}, subject = {Embryonale Stammzelle}, language = {en} } @techreport{FriessHeselichRitteretal.2014, author = {Frieß, Johannes and Heselich, Anja and Ritter, Sylvia and Layer, Paul and Thielemann, Christiane}, title = {Combined effects of ionizing radiation and cardio-active drugs on human iPSC-derived cardiomyocytes}, series = {GSI SCIENTIFIC REPORT 2014}, journal = {GSI SCIENTIFIC REPORT 2014}, doi = {10.15120/GR-2015-1-APPA-HEALTH-37}, year = {2014}, subject = {Herzmuskelzelle}, language = {en} } @techreport{MayerArrizabalagaRitteretal.2015, author = {Mayer, Margot and Arrizabalaga, Onetsine and Ritter, Sylvia and Thielemann, Christiane}, title = {Human embryonic stem cell derived neurospheres form functional networks on microelectrode arrays}, series = {GSI Scientific Report 2015}, volume = {2015}, journal = {GSI Scientific Report 2015}, number = {GSI Report 2016-1}, doi = {10.15120/GR-2016-1}, pages = {205 -- 205}, year = {2015}, subject = {Embryonale Stammzelle}, language = {en} } @article{CibaPetzoldAlvesetal.2025, author = {Ciba, Manuel and Petzold, Marc and Alves, Caroline L. and Rodrigues, Francisco A. and Jimbo, Yasuhiko and Thielemann, Christiane}, title = {Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data}, series = {Scientific Reports}, volume = {15}, journal = {Scientific Reports}, number = {1}, publisher = {Springer Science and Business Media LLC}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-025-99479-7}, year = {2025}, abstract = {Biosensors, such as microelectrode arrays that record in vitro neuronal activity, provide powerful platforms for studying neuroactive substances. This study presents a machine learning workflow to analyze drug-induced changes in neuronal biosensor data using complex network measures from graph theory. Microelectrode array recordings of neuronal networks exposed to bicuculline, a GABA \$\$_A\$\$ receptor antagonist known to induce hypersynchrony, demonstrated the workflow's ability to detect and characterize pharmacological effects. The workflow integrates network-based features with synchrony, optimizing preprocessing parameters, including spike train bin sizes, segmentation window sizes, and correlation methods. It achieved high classification accuracy (AUC up to 90\%) and used Shapley Additive Explanations to interpret feature importance rankings. Significant reductions in network complexity and segregation, hallmarks of epileptiform activity induced by bicuculline, were revealed. While bicuculline's effects are well established, this framework is designed to be broadly applicable for detecting both strong and subtle network alterations induced by neuroactive compounds. The results demonstrate the potential of this methodology for advancing biosensor applications in neuropharmacology and drug discovery.}, subject = {Maschinelles Lernen}, language = {en} } @article{SallumAlvesdeOToutainetal.2025, author = {Sallum, Loriz Francisco and Alves, Caroline L. and de O Toutain, Thaise Graziele L and Porto, Joel Augusto Moura and Thielemann, Christiane and Rodrigues, Francisco A.}, title = {Revealing patterns in major depressive disorder with machine learning and networks}, series = {Chaos, Solitons \& Fractals}, volume = {194}, journal = {Chaos, Solitons \& Fractals}, publisher = {Elsevier BV}, issn = {0960-0779}, doi = {https://doi.org/10.1016/j.chaos.2025.116163}, year = {2025}, abstract = {Major depressive disorder (MDD) is a multifaceted condition that affects millions of people worldwide and is a leading cause of disability. There is an urgent need for an automated and objective method to detect MDD due to the limitations of traditional diagnostic approaches. In this paper, we propose a methodology based on machine and deep learning to classify patients with MDD and identify altered functional connectivity patterns from EEG data. We compare several connectivity metrics and machine learning algorithms. Complex network measures are used to identify structural brain abnormalities in MDD. Using Spearman correlation for network construction and the SVM classifier, we verify that it is possible to identify MDD patients with high accuracy, exceeding literature results. The SHAP (SHAPley Additive Explanations) summary plot highlights the importance of C4-F8 connections and also reveals dysfunction in certain brain areas and hyperconnectivity in others. Despite the lower performance of the complex network measures for the classification problem, assortativity was found to be a promising biomarker. Our findings suggest that understanding and diagnosing MDD may be aided by the use of machine learning methods and complex networks.}, subject = {Depression}, language = {en} } @article{AlvesCibadeOToutainetal.2024, author = {Alves, Caroline L. and Ciba, Manuel and de O Toutain, Thaise Graziele L and Moura Porto, Joel Augusto and de Sena, Eduardo Pond{\´e} and Thielemann, Christiane and Rodrigues, Francisco A.}, title = {On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments}, series = {Journal of Physics: Complexity}, volume = {2024}, journal = {Journal of Physics: Complexity}, number = {1}, doi = {10.1088/2632-072X/ad1c68}, pages = {1 -- 2}, year = {2024}, abstract = {There is a growing interest in the medical use of psychedelic substances, as preliminary studies using them for psychiatric disorders have shown positive results. In particular, one of these substances is N, N-dimethyltryptamine (DMT), an agonist serotonergic psychedelic that can induce profound alterations in the state of consciousness. In this work, we use an exploratory tool to reveal DMT-induced changes in brain activity using EEG data and provide new insights into the mechanisms of action of this psychedelic substance. We used a two-class classification based on (A) the connectivity matrix or (B) complex network measures derived from it as input to a support vector machine. We found that both approaches could detect changes in the brain's automatic activity, with case (B) showing the highest AUC (89\%), indicating that complex network measurements best capture the brain changes that occur due to DMT use. In the second step, we ranked the features that contributed the most to this result. For case (A), we found that differences in the high alpha, low beta, and delta frequency bands were most important in distinguishing between the state before and after DMT inhalation, which is consistent with the results described in the literature. Further, the connection between the temporal (TP8) and central cortex (C3) and between the precentral gyrus (FC5) and the lateral occipital cortex (P8) contributed most to the classification result. The connection between regions TP8 and C3 has been found in the literature associated with finger movements that might have occurred during DMT consumption. However, the connection between cortical areas FC5 and P8 has not been found in the literature and is presumably related to the volunteers' emotional, visual, sensory, perceptual, and mystical experiences during DMT consumption. For case (B), closeness centrality was the most crucial complex network measure. Furthermore, we discovered larger communities and longer average path lengths when DMT was used and the converse when not, showing that the balance between functional segregation and integration had been disrupted. These findings support the idea that cortical brain activity becomes more entropic under psychedelics. Overall, a robust computational workflow has been developed here with interpretability of how DMT (or other psychedelics) modify brain networks and insights into their mechanism of action. Finally, the same methodology applied here may help interpret EEG time series from patients who consumed other psychedelic drugs.}, subject = {Neuronales Netz}, language = {en} } @article{AlvesRodriguesSallumetal.2024, author = {Alves, Caroline L. and Rodrigues, Francisco A. and Sallum, Loriz Francisco and Toutain, Thaise and Porto, Joel and Aguiar, Patricia and Thielemann, Christiane and M{\"o}ckel, Michael}, title = {Multiclass classification of Autism Spectrum Disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis}, series = {plos one}, volume = {2024}, journal = {plos one}, number = {19(10)}, doi = {https://doi.org/10.1371/journal.pone.0305630}, pages = {1 -- 35}, year = {2024}, abstract = {Neurodevelopmental conditions, such as Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), present unique challenges due to overlapping symptoms, making an accurate diagnosis and targeted intervention difficult. Our study employs advanced machine learning techniques to analyze functional magnetic resonance imaging (fMRI) data from individuals with ASD, ADHD, and typically developed (TD) controls, totaling 120 subjects in the study. Leveraging multiclass classification (ML) algorithms, we achieve superior accuracy in distinguishing between ASD, ADHD, and TD groups, surpassing existing benchmarks with an area under the ROC curve near 98\%. Our analysis reveals distinct neural signatures associated with ASD and ADHD: individuals with ADHD exhibit altered connectivity patterns of regions involved in attention and impulse control, whereas those with ASD show disruptions in brain regions critical for social and cognitive functions. The observed connectivity patterns, on which the ML c}, subject = {Aufmerksamkeitsdefizit-Syndrom}, language = {en} } @unpublished{SchickelBenderKaysanetal.2025, author = {Schickel, Esther and Bender, Tamara and Kaysan, Leon and Hufgard, Simone and Mayer, Margot and Grosshans, David R. and Thielemann, Christiane and Schroeder, Insa S.}, title = {Human cerebral organoids model tumor infiltration and migration supported by astrocytes in an autologous setting}, series = {bioRxiv}, volume = {2025}, journal = {bioRxiv}, publisher = {Cold Spring Harbor Laboratory}, doi = {https://doi.org/10.1101/2025.01.29.635456}, pages = {43}, year = {2025}, abstract = {SummaryEfforts to achieve precise and efficient tumor targeting of highly malignant brain tumors are constrained by the dearth of appropriate models to study the effects and potential side effects of radiation, chemotherapy, and immunotherapy on the most complex human organ, the brain. We established a cerebral organoid model of brain tumorigenesis in an autologous setting by overexpressing c-MYC as one of the most common oncogenes in brain tumors. GFP+/c-MYChighcells were isolated from tumor organoids and used in two different culture approaches: assembloids comprising of a normal cerebral organoid with a GFP+/c-MYChightumor sphere and co-culture of cerebral organoid slices at air-liquid interface with GFP+/c-MYChighcells. GFP+/c-MYChighcells used in both approaches exhibited tumor-like properties, including overexpression of the c-MYC oncogene, high proliferative and invasive potential, and an immature phenotype as evidenced by increased expression of Ki-67, VIM, and CD133. Organoids and organoid slices served as suitable scaffolds for infiltrating tumor-like cells. Using our highly reproducible and powerful model system that allows long-term culture, we demonstrated that the migratory and infiltrative potential of tumor-like cells is shaped by the environment in which glia cells provide support to tumor-like cells.}, subject = {Hirntumor}, language = {en} } @article{SchickelBenderKaysanetal.2025, author = {Schickel, Esther and Bender, Tamara and Kaysan, Leon and Hufgard, Simone and Mayer, Margot and Grosshans, David R. and Thielemann, Christiane and Schroeder, Insa S.}, title = {Human cerebral organoids model tumor initiation and infiltration in an autologous astrocyte-supported setting}, series = {iScience}, journal = {iScience}, publisher = {Elsevier BV}, issn = {2589-0042}, doi = {https://doi.org/10.1016/j.isci.2025.113334}, year = {2025}, abstract = {Efforts to efficiently target brain tumors are constrained by the dearth of appropriate models to study tumor behavior towards treatment approaches as well as potential side effects to the surrounding normal tissue. We established a reproducible cerebral organoid model of brain tumorigenesis in an autologous setting by overexpressing c-MYC, a common oncogene in brain tumors. GFP+/c-MYChigh cells were isolated from tumor organoids and used in two different approaches: GFP+/c-MYChigh cells co-cultured with cerebral organoid slices or fused as spheres to whole organoids. GFP+/c-MYChigh cells used in both approaches exhibited tumor-like properties, including an immature phenotype and a highly proliferative and invasive potential. We demonstrate that the latter is influenced by astrocytes supporting the GFP+/c-MYChigh cells while X-ray irradiation significantly kills and impairs tissue infiltration of GFP+/c-MYChigh cells. In summary, the model represents major features of tumorous and adjacent normal tissue and may be used to evaluate appropriate cancer treatments.}, subject = {Hirntumor}, language = {en} }