Biology subjects

Lohmann, G.

Publications and source records attributed to Lohmann, G..

9 recordsLinked to original sources

bioRxiv2025

Beyond ROIs: Laminar fMRI Mapping with Cylarim

Laminar fMRI offers a powerful approach to investigate brain function by resolving neural activity across the distinct layers of the cortical gray matter. However, current laminar fMRI analysis methods often rely on predefined regions of interest (ROIs), which introduce subjectivity and bias due to arbitrary spatial delineation. To address this limitation, we present "Cylarim", a novel software package that eliminates dependence on rigid ROI definitions. Cylarim employs small, overlapping cylinders that systematically traverse the cortical ribbon, enabling unbiased, large-scale mapping of laminar-specific hemodynamic activity. This approach supports comprehensive sampling of cortical depth-resolved signals across extensive regions. By circumventing the need for manual or atlas-driven ROI selection, Cylarim facilitates the study of feedforward and feedback pathways in diverse cortical areas without compromising spatial coverage. Preliminary results demonstrate Cylarims ability to accurately localize layer-dependent responses, providing a robust framework for advancing ultrahigh-resolution fMRI research in neuroscience.

neuroscience↗

bioRxiv2024

UltraCortex: Submillimeter Ultra-High Field 9.4 T Brain MR Image Collection and Manual Cortical Segmentations

The UltraCortex repository (https://www.ultracortex.org) houses magnetic resonance imaging data of the human brain obtained at an ultra-high field strength of 9.4 T. It contains 86 structural MR images with spatial resolutions ranging from 0.6 to 0.8 mm. Additionally, the repository includes segmentations of 12 brains into gray and white matter compartments. These segmentations have been independently validated by two expert neuroradiologists, thus establishing them as a reliable gold standard. This resource provides researchers with access to high-quality brain imaging data and validated segmentations, facilitating neuroimaging studies and advancing our understanding of brain structure and function. Existing repositories do not accommodate field strengths beyond 7 T, nor do they offer validated segmentations, underscoring the significance of this new resource.

neuroscience↗

bioRxiv2023

Improving the reliability of fMRI-based predictions of intelligence via semi-blind machine learning

Predicting neuromarkers for cognitive abilities using fMRI has been a major focus of research in the past few years. However, it has recently been reported that many thousands of participants are required to obtain reproducible results (Marek et al (2022)). This appears to be a major impediment to obtaining neuromarkers from fMRI because large sample sizes are typically not available in neuroimaging studies. Here we show that the out-of-sample prediction accuracy can be dramatically improved by supplementing fMRI with readily available non-imaging information so that reliable predictive modeling becomes feasible even for small sample sizes. Specifically, we introduce a novel machine learning method that predicts intelligence from resting-state fMRI data, leveraging educational level as supplementary information. We refer to our approach as "semi-blind machine learning (SML)" because it operates under the assumption that supplementary information, such as educational level, is available for subjects in both the training and test sets. This setup closely mirrors real-world scenarios, especially in clinical contexts, where patient background information typically exists and can be utilized to boost prediction accuracy. However, guarding against bias is crucial. Subjects should not be categorized as more intelligent simply based on their higher education levels. Therefore, our approach contains a component explicitly designed for bias control. We have applied our method to three different data collections and observed marked improvements in prediction accuracies across a wide range of sample sizes. We anticipate that semi-blind machine learning provides a promising approach to fMRI-based predictive modelling with the potential for a wide range of future applications.

neuroscience↗

bioRxiv2022

The 3D Structural Architecture of the Human Hand Area is Non-Topographic

The functional topography of the human primary somatosensory cortex (S1) hand area is a widely studied model system to understand sensory organization and plasticity. It is so far unclear whether or not the underlying 3D structural architecture also shows a topographic organization. We used 7T MRI data to quantify layer-specific myelin, iron and mineralization in relation to population receptive field maps of individual finger representations in Brodman area 3b (BA 3b) of human S1 in female and male younger adults. This 3D description allowed us to identify a characteristic profile of layer-specific myelin and iron deposition in the BA 3b hand area, but revealed an absence of structural differences, an absence of low-myelin borders, and high similarity of 3D microstructure profiles between individual fingers. However, structural differences and borders were detected between the hand and face areas. We conclude that the 3D structural architecture of the human hand area is non-topographic, unlike in some monkey species, which suggests a high degree of flexibility for functional finger organization and a new perspective on human topographic plasticity. Significance StatementUsing ultra-high field MRI, we provide the first comprehensive in vivo description of the 3D structural architecture of the human BA 3b hand area in relation to functional population receptive field (pRF) maps. High similarity of precise finger-specific 3D profiles, together with an absence of structural differences and an absence of low-myelin borders between individual fingers, reveal the 3D structural architecture of the human hand area to be non-topographic. This suggests reduced structural limitations to cortical plasticity and reorganization, and allows for shared representational features across fingers.

neuroscience↗

bioRxiv2021

Evaluation of Machine Learning predictions of a highly resolved long time series of Chlorophyll-a concentration

Pelagic Chlorophyll-a concentrations are key for evaluation of the environmental status and productivity of marine systems. In this study, chlorophyll-a concentrations for the Helgoland Roads Time Series were modeled using a number of measured water and environmental parameters. We chose three common Machine Learning algorithms from the literature: Support Vector Machine Regressor, Neural Networks Multi-layer Perceptron Regressor and Random Forest Regressor. Results showed that Support Vector Machine Regressor slightly outperformed other models. The evaluation with a test dataset and verification with an independent validation dataset for chlorophyll-a concentrations showed a good generalization capacity, evaluated by the root mean squared errors of less than 1 g L-1. Feature selection and engineering are important and improved the models significantly, as measured in performance, improving by a minimum of 48% the adjusted R2. We tested SARIMA in comparison and found that the univariate nature of SARIMA does not allow for better results than the Machine Learning models. Additionally, the computer processing time needed was much higher (prohibitive) for SARIMA.

ecology↗

bioRxiv2021

Predicting intelligence from fMRI data of the human brain in a few minutes of scan time

In recent years, the prediction of individual behaviour from the fMRI-based functional connectome has become a major focus of research. The motivation behind this research is to find generalizable neuromarkers of cognitive functions. However, insufficient prediction accuracies and long scan time requirements are still unsolved issues. Here we propose a new machine learning algorithm for predicting intelligence scores of healthy human subjects from resting state (rsfMRI) or task-based fMRI (tfMRI). In a cohort of 390 unrelated test subjects of the Human Connectome Project, we found correlations between the observed and the predicted general intelligence of more than 50 percent in tfMRI, and of around 59 percent when results from two tasks are combined. Surprisingly, we found that the tfMRI data were significantly more predictive of intelligence than rsfMRI even though they were acquired at much shorter scan times (approximately 10 minutes versus 1 hour). Existing methods that we investigated in a benchmark comparison underperformed on tfMRI data and produced prediction accuracies well below our results. Our proposed algorithm differs from existing methods in that it achieves dimensionality reduction via ensemble learning and partial least squares regression rather than via brain parcellations or ICA decompositions. In addition, it introduces Ricci-Forman curvature as a novel type of edge weight.

neuroscience↗

bioRxiv2020

Jumping over Baselines with New Methods to Predict Activation Maps from Resting-state fMRI

Cognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI). Leveraging this fact, several recent studies have therefore aimed at predicting task activation from rsfMRI using various machine learning methods within a growing literature on connectome fingerprinting. In reviewing these results, we found lack of an evaluation against robust baselines that reliably supports a novelty of predictions for this task. On closer examination to reported methods, we found most underperform against trivial baseline model performances based on massive group averaging when whole-cortex prediction is considered. Here we present a modification to published methods that remedies this problem to large extent. Our proposed modification is based on a single-vertex approach that replaces commonly used brain parcellations. We further provide a summary of this model evaluation by characterizing empirical properties of where prediction for this task appears possible, explaining why some predictions largely fail for certain targets. Finally, with these empirical observations we investigate whether individual prediction scores explain individual behavioral differences in a task.

neuroscience↗

bioRxiv2018

Eigenvector centrality mapping for ultrahigh resolution fMRI data of the human brain

Eigenvector centrality mapping (ECM) is a popular technique for analyzing fMRI data of the human brain. It is used to obtain maps of functional hubs in networks of the brain in a manner similar to Googles PageRank algorithm. ECM attributes a score to the time course of each voxel that reflects its centrality within the network. Voxels that are strongly correlated with many other voxels that are themselves strongly correlated with other voxels receive high scores. Currently, there exist two different implementations ECM, one of which is very fast but limited to one particular type of correlation metric whose interpretation can be problematic. The second implementation supports many different metrics, but it is computationally costly and requires a very large main memory. Here we propose two new implementations of the ECM approach that resolve these issues. The first is based on a new correlation metric that we call \"ReLU correlation (RLC)\". The second method is based on matrix projections. We demonstrate the use of both techniques on standard fMRI data, as well as on high-resolution fMRI data acquired at 9.4 Tesla.

neuroscience↗

bioRxiv2017

Inflated False Negative Rates Undermine Reproducibility In Task-Based fMRI

Reproducibility is generally regarded as a hallmark of scientific validity. It can be undermined by two very different factors, namely inflated false positive rates or inflated false negative rates. Here we investigate the role of the second factor, i.e. the degree to which true effects are not detected reliably. The availability of large public databases and also supercomputing allows us to tackle this problem quantitatively. Specifically, we estimated the reproducibility in task-based fMRI data over different samples randomly drawn from a large cohort of subjects obtained from the Human Connectome Project. We use the full cohort as a standard of reference to approximate true positive effects, and compute the fraction of those effects that was detected reliably using standard software packages at various smaller sample sizes. We found that with standard sample sizes this fraction was less than 25 percent. We conclude that inflated false negative rates are a major factor that undermine reproducibility. We introduce a new statistical inference algorithm based on a novel test statistic and show that it improves reproducibility without inflating false positive rates.

neuroscience↗