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PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences. It is especially designed to support the iterative model development process and automates the repetitive training, hyperparameter optimization and evaluation tasks. Importantly, the workflow ensures unbiased performance estimates while still allowing the user to fully customize the machine learning analysis. PHOTONAI extends existing solutions with a novel pipeline implementation supporting more complex data streams, feature combinations, and algorithm selection. Metrics and results can be conveniently visualized using the PHOTONAI Explorer and predictive models are shareable in a standardized format for further external validation or application. A growing add-on ecosystem allows researchers to offer data modality specific algorithms to the community and enhance machine learning in the areas of the life sciences. Its practical utility is demonstrated on an exemplary medical machine learning problem, achieving a state-of-the-art solution in few lines of code. Source code is publicly available on Github, while examples and documentation can be found at www.photon-ai.com .

Acute kidney injury (AKI) is a frequent complication in critically ill patients, affecting up to 50% of patients in the intensive care units. The lack of standardized and open-source tools for applying the Kidney Disease Improving Global Outcomes (KDIGO) criteria to time series, requires researchers to implement classification algorithms of their own which is resource intensive and might impact study quality by introducing different interpretations of edge cases. This project introduces pyAKI, an open-source pipeline addressing this gap by providing a comprehensive solution for consistent KDIGO criteria implementation. The pyAKI pipeline was developed and validated using a subset of the Medical Information Mart for Intensive Care (MIMIC)-IV database, a commonly used database in critical care research. We constructed a standardized data model in order to ensure reproducibility. PyAKI implements the Kidney Disease: Improving Global Outcomes (KDIGO) guideline on AKI diagnosis. After implementation of the diagnostic algorithm, using both serum creatinine and urinary output data, pyAKI was tested on a subset of patients and diagnostic accuracy was compared in a comparative analysis against annotations by physicians. Validation against expert annotations demonstrated pyAKI's robust performance in implementing KDIGO criteria. Comparative analysis revealed its ability to surpass the quality of human labels with an accuracy of 1.0 in all categories. The pyAKI pipeline is the first open-source solution for implementing KDIGO criteria in time series data. It provides a standardized data model and a comprehensive solution for consistent AKI classification in research applications for clinicians and data scientists working with AKI data. The pipeline's high accuracy make it a valuable tool for clinical research and decision support systems. This work introduces pyAKI as an open-source solution for implementing the KDIGO criteria for AKI diagnosis using time series data with high accuracy and performance.

A required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data. The system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application's performance and functionality. The system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects. Medical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.

Background. Controllability is a measure of the brain's ability to orchestrate neural activity which can be quantified in terms of properties of the brain's network connectivity. Evidence from the literature suggests that aging can exert a general effect on whole-brain controllability. Mounting evidence, on the other hand, suggests that parenthood and motherhood in particular lead to long-lasting changes in brain architecture that effectively slow down brain aging. We hypothesize that parenthood might preserve brain controllability properties from aging.Methods. In a sample of 814 healthy individuals (aged 33.9±12.7 years, 522 females), we estimate whole-brain controllability and compare the aging effects in subjects with vs. those without children.We use diffusion tensor imaging (DTI) to estimate the brain structural connectome. The level of brain control is then calculated from the connectomic properties of the brain structure. Specifically, we measure the network control over many low-energy state transitions (average controllability) and the network control over difficult-to-reach states (modal controllability).In nulliparous females, whole-brain average controllability increases, and modal controllability decreases with age, a trend that we do not observe in parous females. Statistical comparison of the controllability metrics shows that modal controllability is higher and average controllability is lower in parous females compared to nulliparous females. In men, we observed the same trend, but the difference between nulliparous and parous males do not reach statistical significance. Our results provide strong evidence that parenthood contradicts aging effects on brain controllability and the effect is stronger in mothers.

Abstract Disclosure: P. Beeken: None. J. Ernsting: None. L. Ogoniak: None. J. Kockwelp: None. T. Hahn: None. B. Risse: None. A.S. Busch: None. Background: The genetic factors influencing male fertility remain elusive. While testicular volume closely mirrors quantitative spermatogenesis, testicular size serves as a reliable proxy for male reproductive capacity. Previous approaches have faced limitations, as large-scale assessment has been hindered by traditional invasive measurement techniques, resulting in biased samples and restricted applicability. Despite its significance, the genetic basis of testicular volume remains largely unexplored. Methods: We applied machine learning to evaluate bi-testicular volume in 22,499 male participants from the UK Biobank using abdominal DIXON MRI scans. A U-Net model, trained on manual segmentations performed by medical experts, generated automated segmentations for the dataset. Following quality control, excluded incomplete segmentations and participants with conditions affecting segmentation, e.g. undescended testis, 18,998 participants were included. A genome-wide association study (GWAS) on bi-testicular volume was conducted using PLINK adjusting for age and the first ten principal components. Results: The U-Net model for MRI segmentation achieved Dice score of 0.87 (median), enabling precise measurement of testicular volume. The mean (SD) bi-testicular volume was 52 (16) mL. The GWAS identified 14 genome-wide significant loci (p<5×10⁻⁸), offering new insights into the genetic determinants of testicular volume. The most significant association was at rs12271187 within the FSHB locus on chromosome 11 (p = 1.33×10⁻⁴¹), with additional findings at the FSHR locus on chromosome 2, emphasizing the role of follicle-stimulating hormone and its receptor in male reproductive health. Conclusion: Circumventing previous challenges in assessing testicular volume, our study offers a large-scale approach, effectively addressing the limitations of traditional methods. By identifying genetic loci associated with testicular volume, we provide a foundation for further research into the genetic determinants of male fertility and reproductive health. Presentation: Monday, July 14, 2025

Many therapeutic interventions in psychiatry can be viewed as attempts to influence the brain’s large-scale, dynamic network state transitions. Building on connectome-based graph analysis and control theory, Network Control Theory is emerging as a powerful tool to quantify network controllability—i.e., the influence of one brain region over others regarding dynamic network state transitions. If and how network controllability is related to mental health remains elusive. Here, from Diffusion Tensor Imaging data, we inferred structural connectivity and inferred calculated network controllability parameters to investigate their association with genetic and familial risk in patients diagnosed with major depressive disorder (MDD, n  = 692) and healthy controls ( n  = 820). First, we establish that controllability measures differ between healthy controls and MDD patients while not varying with current symptom severity or remission status. Second, we show that controllability in MDD patients is associated with polygenic scores for MDD and psychiatric cross-disorder risk. Finally, we provide evidence that controllability varies with familial risk of MDD and bipolar disorder as well as with body mass index. In summary, we show that network controllability is related to genetic, individual, and familial risk in MDD patients. We discuss how these insights into individual variation of network controllability may inform mechanistic models of treatment response prediction and personalized intervention-design in mental health.

Macroscale brain modeling using neural mass models (NMMs) offers a framework for simulating human whole‐brain dynamics. These models are pivotal for investigating the brain as a complex dynamic system, exploring phenomena like bifurcations, oscillatory patterns, and responses to stimuli. While connectome‐based NMMs allow for the creation of personalized NMMs, their utility in capturing individual‐specific neural characteristics remains underexplored, with current studies constrained by small sample sizes and computational inefficiencies. To address these limitations, we employed an algorithmically differentiable version of the reduced Wong Wang (RWW) model, enabling efficient optimization for large datasets. Applying this to resting‐state fMRI data from 1444 samples, we optimized models with varying parameter complexities (n = 4, 658, and 23,875), which were derived from creating biologically plausible model variants. The optimized models achieved 4%, 19%, and 56% variance explanation in empirical functional connectivity (FC), respectively. Subject identification accuracy, based on simulated FC patterns, improved from < 1% (n = 4) to almost 100% (n = 23,875). Despite this precision, individual‐level correlations between model parameters and attributes like age, gender, or intelligence quotient were small (effect sizes: ηpartial2≤0.03 $$ {\\eta}_{\\mathrm{partial}}^2\\le 0.03 $$ , standardized β≤0.234 $$ \\beta \\le 0.234 $$ ). Machine learning analyses confirmed that these parameters lack the granularity to encode personal traits effectively. These findings suggest that, while current implementations of the RWW NMM can robustly replicate resting‐state dynamics, the resulting parameters may lack the granularity required to map onto individual‐specific behavioral metrics. This highlights a critical alignment problem: neural patterns and behavioral constructs such as intelligence may not correspond in a one‐to‐one fashion but instead represent higher‐level ions. Bridging this gap will require the development of new tools capable of uncovering the underlying mapping manifolds, likely situated at the level of functional dynamics rather than isolated parameters. Future efforts should build on individual‐level mechanistic modeling by exploring more expressive model classes and integrating richer sources of data, such as multimodal imaging or task‐based paradigms, to better capture individual variability in both neural dynamics and behavioral traits. Such approaches may ultimately help to bridge the gap between model‐based neural similarity and clinically meaningful personalization. Leveraging a differentiable reduced Wong Wang model, we optimized individual whole‐brain models for a large cohort (n = 1444) using rs‐fMRI, achieving up to 56% variance explanation. However, we found that current neural mass models, despite replicating resting‐state dynamics, lack the granularity to effectively model individual‐specific neural signatures.

Abstract Electroconvulsive Therapy (ECT) is arguably the most effective intervention for treatment-resistant depression. While large interindividual variability exists, a theory capable of explaining individual response to ECT remains elusive. To address this, we posit a quantitative, mechanistic framework of ECT response based on Network Control Theory (NCT). Then, we empirically test our approach and employ it to predict ECT treatment response. To this end, we derive a formal association between Postictal Suppression Index (PSI)—an ECT seizure quality index—and whole-brain modal and average controllability, NCT metrics based on white-matter brain network architecture, respectively. Exploiting the known association of ECT response and PSI, we then hypothesized an association between our controllability metrics and ECT response mediated by PSI. We formally tested this conjecture in N = 50 depressive patients undergoing ECT. We show that whole-brain controllability metrics based on pre-ECT structural connectome data predict ECT response in accordance with our hypotheses. In addition, we show the expected mediation effects via PSI. Importantly, our theoretically motivated metrics are at least on par with extensive machine learning models based on pre-ECT connectome data. In summary, we derived and tested a control-theoretic framework capable of predicting ECT response based on individual brain network architecture. It makes testable, quantitative predictions regarding individual therapeutic response, which are corroborated by strong empirical evidence. Our work might constitute a starting point for a comprehensive, quantitative theory of personalized ECT interventions rooted in control theory.

A series of neutral and cationic methylplatinum(II) complexes with bidentate NN and tridentate NNO ligands has been prepared. The complexes involving tridentate NNO ligands were expected to be easier to handle than those with NN ligands, which has indeed been confirmed by the experiments. Nevertheless, the neutral [Pt(Me)(NNO)] and ionic [Pt(Me)(NNO)] + BF 4 - complexes retain their (non-selective) reactivity to hydrocarbon C-H bonds.

Testis size is known to be one of the main predictors of male fertility, usually assessed in clinical workup via palpation or imaging. Despite its potential, population-level evaluation of testicular volume using imaging remains underexplored. Previous studies, limited by small and biased datasets, have demonstrated the feasibility of machine learning for testis volume segmentation. This paper presents an evaluation of segmentation methods for testicular volume using Magnet Resonance Imaging data from the UKBiobank. The best model achieves a median dice score of \\(0.87\\), compared to median dice score of \\(0.83\\) for human interrater reliability on the same dataset, enabling large-scale annotation on a population scale for the first time. Our overall aim is to provide a trained model, comparative baseline methods, and annotated training data to enhance accessibility and reproducibility in testis MRI segmentation research.