Biology subjects

Caspers, J.

Publications and source records attributed to Caspers, J..

6 recordsLinked to original sources

bioRxiv2025

Kidney-specific lipid metabolism underlies stable chemical individuality in domestic cats and other felids

Domestic cats uniquely accumulate lipid droplets (LDs) in renal proximal tubular epithelial cells, but their chemical composition and biological functions remain unknown. Here, we identify a distinctive renal specialization in felids: lipid droplets enriched in branched-chain fatty acids (BFAs). These BFAs accumulate as triglyceride components and are excreted in urine as free fatty acids with stable and individually distinctive profiles. Behavioral assays show that cats discriminate urine samples based on BFA composition, even against complex volatile backgrounds, suggesting BFAs as robust, metabolically encoded olfactory signatures for individual recognition. Comparative analyses of non-domesticated felids reveal that BFA production is broadly conserved but exhibits both species-specific and individual variation. These findings demonstrate how a core metabolic pathway has been evolutionarily co-opted into a chemical signature system. This discovery highlights an unusual integration of renal physiology with social communication and expand our understanding of lipid metabolism, organ-level adaptation, and the evolution of chemical individuality in mammals. Significance statementUnique lipid droplets in felid kidneys reveal how organ-specific metabolism can be adapted for social communication.

zoology↗

bioRxiv2024

Voxel-wise or Region-wise Nuisance Regression for Functional Connectivity Analyses: Does it matter?

Removal of nuisance signals (such as motion) from the BOLD time series is an important aspect of preprocessing to obtain meaningful resting-state functional connectivity (rs-FC). The nuisance signals are commonly removed using denoising procedures at the finest resolution, i.e. the voxel time series. Typically the voxel-wise time series are then aggregated into predefined regions or parcels to obtain a rs-FC matrix as the correlation between pairs of regional time series. Computational efficiency can be improved by denoising the aggregated regional time series instead of the voxel time series. However, a comprehensive comparison of the effects of denoising on these two resolutions is missing. In this study, we systematically investigate the effects of denoising at different time series resolutions (voxel- and region-level) in 370 unrelated subjects from the HCP-YA dataset. Alongside the time series resolution, we considered additional factors such as aggregation method (Mean and first eigenvariate [EV]) and parcellation granularity (100, 400, and 1,000 regions). To assess the effect of those choices on the utility of the resulting whole-brain rs-FC, we evaluated the individual specificity (fingerprinting) and the capacity to predict age and three cognitive scores. Our findings show generally equal or better performance for region-level denoising with notable differences depending on the aggregation method. Using mean aggregation yielded equal individual specificity and prediction performance for voxel- and region-level denoising. When EV was employed for aggregation, the individual specificity of voxel-level denoising was reduced compared to region-level denoising. Increasing parcellation granularity generally improved individual specificity. For the prediction of age and cognitive test scores, only fluid intelligence indicated worse performance for voxel-level denoising in the case of aggregating with the EV. Based on these results, we recommend the adoption of region-level denoising for brain-behavior investigations when using mean aggregation. This approach offers equal individual specificity and prediction capacity with reduced computational resources for the analysis of rs-FC patterns.

neuroscience↗

bioRxiv2024

Test Retest Reliability of Meta Analytic Networks During Naturalistic Viewing

Functional connectivity analyses have given considerable insights into human brain function and organization. As research moves towards clinical application, test-retest reliability has become a main focus of the field. So far, the majority of studies have relied on resting-state paradigms to examine brain connectivity, based on its low demand and ease of implementation. However, the reliability of resting-state measures is mostly moderate, potentially due to its unconstrained nature. Recently, naturalistic viewing paradigms have gained popularity because they probe the human brain under more ecologically valid conditions, thereby possibly increasing reliability. Therefore, we here compared the reliability of graph metrics extracted from resting-state and naturalistic viewing in functional networks, across two sessions. We show that naturalistic viewing can increase reliability over resting-state, but that its effect varies between stimuli and networks. Furthermore, we demonstrate that the effect of naturalistic viewing differs between two cohorts with Asian and European cultural backgrounds. Taken together, our study encourages the use of naturalistic viewing to increase reliability, but emphasizes the need to carefully select the appropriate stimulus and network for the respective research question.

neuroscience↗

bioRxiv2024

Simulated brain networks reflecting progression of Parkinson's disease

Neurodegenerative progression of Parkinsons disease affects brain structure and function and, concomitantly, alters topological properties of brain networks. The network alteration accompanied with motor impairment and duration of the disease is not yet clearly demonstrated in the disease progression. In this study, we aim at resolving this problem with a modeling approach based on large-scale brain networks from cross-sectional MRI data. Optimizing whole-brain simulation models allows us to discover brain networks showing unexplored relationships with clinical variables. We observe that simulated brain networks exhibit significant differences between healthy controls (n=51) and patients with Parkinsons disease (n=60) and strongly correlate with disease severity and disease duration of the patients. Moreover, the modeling results outperform the empirical brain networks in these clinical measures. Consequently, this study demonstrates that utilizing simulated brain networks provides an enhanced view on network alterations in the progression of motor impairment and potential biomarkers for clinical indices.

neuroscience↗

bioRxiv2022

Brain-age prediction: a systematic comparison of machine learning workflows

The difference between age predicted using anatomical brain scans and chronological age, i.e., the brain-age delta, provides a proxy for atypical aging. Various data representations and machine learning (ML) algorithms have been used for brain-age estimation. However, how these choices compare on performance criteria important for real-world applications, such as; (1) within-site accuracy, (2) cross-site generalization, (3) test-retest reliability, and (4) longitudinal consistency, remains uncharacterized. We evaluated 128 workflows consisting of 16 feature representations derived from gray matter (GM) images and eight ML algorithms with diverse inductive biases. Using four large neuroimaging databases covering the adult lifespan (total N = 2953, 18-88 years), we followed a systematic model selection procedure by sequentially applying stringent criteria. The 128 workflows showed a within-site mean absolute error (MAE) between 4.73-8.38 years, from which 32 broadly sampled workflows showed a cross-site MAE between 5.23-8.98 years. The test-retest reliability and longitudinal consistency of the top 10 workflows were comparable. The choice of feature representation and the ML algorithm both affected the performance. Specifically, voxel-wise feature spaces (smoothed and resampled), with and without principal components analysis, with non-linear and kernel-based ML algorithms performed well. Strikingly, the correlation of brain-age delta with behavioral measures disagreed between within-site and cross-site predictions. Application of the best-performing workflow on the ADNI sample showed a significantly higher brain-age delta in Alzheimers and mild cognitive impairment patients. However, in the presence of age bias, the delta estimates in the diseased population varied depending on the sample used for bias correction. Taken together, brain-age shows promise, but further evaluation and improvements are needed for its real-world application. HighlightsO_LIThere is an effect of both feature space and ML algorithm on prediction error. C_LIO_LIVoxel-wise features performed better than parcel-wise features. C_LIO_LIGPR, KRR and RVR algorithms performed well. C_LIO_LIThe within-site and cross-site delta-behavior correlations disagree. C_LIO_LIHigher brain-age delta inference in AD depends on data used for bias correction. C_LI

neuroscience↗

bioRxiv2022

Whole-brain dynamical modeling for classification of Parkinson's disease

Simulated whole-brain connectomes demonstrate an enhanced inter-individual variability depending on data processing and modeling approach. By considering the human brain connectome as an individualized attribute, we investigate how empirical and simulated whole-brain connectome-derived features can be utilized to classify patients with Parkinsons disease against healthy controls in light of varying data processing and model validation. To this end, we applied simulated blood oxygenation level-dependent signals derived by a whole-brain dynamical model simulating electrical signals of neuronal populations to reveal differences between patients and controls. In addition to the widely used model validation via fitting the dynamical model to empirical neuroimaging data, we invented a model validation against behavioral data, such as subject classes, which we refer to as behavioral model fitting and show that it can be beneficial for Parkinsonian patient classification. Furthermore, the results of machine-learning reported in this study also demonstrated that performance of the patient classification can be improved when the empirical data are complemented by the simulation results. We also showed that temporal filtering of blood oxygenation level-dependent signals influences the prediction results, where the filtering in the low-frequency band is advisable for Parkinsonian patient classification. In addition, composing the feature space of empirical and simulated data from multiple brain parcellation schemes provided complementary features that improve prediction performance. Based on our findings, we suggest including the simulation results with empirical data is effective for inter-individual research and its clinical application.

neuroscience↗