Hippocampal representations for deep learning on Alzheimer’s disease

Deep learning offers a powerful approach for analyzing hippocampal changes in Alzheimer’s disease (AD) without relying on handcrafted features. Nevertheless, an input format needs to be selected to pass the image information to the neural network, which has wide ramifications for the analysis, but has not been evaluated yet. We compare five hippocampal representations (and their respective tailored network architectures) that span from raw images to geometric representations like meshes and point clouds. We performed a thorough evaluation for the prediction of AD diagnosis and time-to-dementia prediction with experiments on an independent test dataset. In addition, we evaluated the ease of interpretability for each representation–network pair. Our results show that choosing an appropriate representation of the hippocampus for predicting Alzheimer’s disease with deep learning is crucial, since it impacts performance and ease of interpretation

Odefy -- From discrete to continuous models

<p>Abstract</p> <p>Background</p> <p>Phenomenological information about regulatory interactions is frequently available and can be readily converted to Boolean models. Fully quantitative models, on the other hand, provide detailed insights into the precise dynamics of the underlying system. In order to connect discrete and continuous modeling approaches, methods for the conversion of Boolean systems into systems of ordinary differential equations have been developed recently. As biological interaction networks have steadily grown in size and complexity, a fully automated framework for the conversion process is desirable.</p> <p>Results</p> <p>We present <it>Odefy</it>, a MATLAB- and Octave-compatible toolbox for the automated transformation of Boolean models into systems of ordinary differential equations. Models can be created from sets of Boolean equations or graph representations of Boolean networks. Alternatively, the user can import Boolean models from the CellNetAnalyzer toolbox, GINSim and the PBN toolbox. The Boolean models are transformed to systems of ordinary differential equations by multivariate polynomial interpolation and optional application of sigmoidal Hill functions. Our toolbox contains basic simulation and visualization functionalities for both, the Boolean as well as the continuous models. For further analyses, models can be exported to SQUAD, GNA, MATLAB script files, the SB toolbox, SBML and R script files. Odefy contains a user-friendly graphical user interface for convenient access to the simulation and exporting functionalities. We illustrate the validity of our transformation approach as well as the usage and benefit of the Odefy toolbox for two biological systems: a mutual inhibitory switch known from stem cell differentiation and a regulatory network giving rise to a specific spatial expression pattern at the mid-hindbrain boundary.</p> <p>Conclusions</p> <p>Odefy provides an easy-to-use toolbox for the automatic conversion of Boolean models to systems of ordinary differential equations. It can be efficiently connected to a variety of input and output formats for further analysis and investigations. The toolbox is open-source and can be downloaded at <url>http://cmb.helmholtz-muenchen.de/odefy</url>.</p

Joint Reconstruction and Parcellation of Cortical Surfaces

The reconstruction of cerebral cortex surfaces from brain MRI scans is instrumental for the analysis of brain morphology and the detection of cortical thinning in neurodegenerative diseases like Alzheimer's disease (AD). Moreover, for a fine-grained analysis of atrophy patterns, the parcellation of the cortical surfaces into individual brain regions is required. For the former task, powerful deep learning approaches, which provide highly accurate brain surfaces of tissue boundaries from input MRI scans in seconds, have recently been proposed. However, these methods do not come with the ability to provide a parcellation of the reconstructed surfaces. Instead, separate brain-parcellation methods have been developed, which typically consider the cortical surfaces as given, often computed beforehand with FreeSurfer. In this work, we propose two options, one based on a graph classification branch and another based on a novel generic 3D reconstruction loss, to augment template-deformation algorithms such that the surface meshes directly come with an atlas-based brain parcellation. By combining both options with two of the latest cortical surface reconstruction algorithms, we attain highly accurate parcellations with a Dice score of 90.2 (graph classification branch) and 90.4 (novel reconstruction loss) together with state-of-the-art surfaces.Comment: accepted at MLCN workshop 202

Local hydrological conditions and spatial connectivity shape invertebrate communities after rewetting in temporary rivers

Temporary rivers (TRs) dominate global river networks and are increasing in occurrence and spatiotemporal extent. However, few studies have investigated the communities that establish after rewetting events (i.e. the end of the dry phase), when local hydrological conditions can shape the communities through species sorting, and the spatial connectivity of sites can also influence colonisation. Here, we analysed the relative importance of both local hydrological conditions and spatial connectivity on the invertebrate communities of seven not impacted Mediterranean TRs after rewetting. We quantified the frequency and duration of drying events and the time since flow resumed. We also quantified spatial connectivity based on each site's position in the river network (i.e. network connectivity) and the presence of nearby disconnected streams. Overall, we found that both hydrological conditions and network connectivity played a significant role in structuring aquatic invertebrate communities after rewetting. Taxonomic richness, functional richness and functional redundancy decreased with the frequency and duration of drying events and increased with time since the most recent rewetting. Network connectivity showed a significant unimodal relationship with taxonomic and functional metrics. In contrast, the presence of nearby disconnected streams was negatively related to functional richness and functional dispersion. Given that flow intermittence in Mediterranean areas is expected to intensify under future global change scenarios, our results can be helpful to guide future conservation and management actions

Navigating through space and time: A methodological approach to quantify spatiotemporal connectivity using stream flow data as a case study

1. The growing interest in combining spatial and temporal patterns in nature has been fostered by the current availability of high-frequency measurements. However, we still lack a methodological framework to process and interpret spatiotemporal datasets into meaningful values, adaptable to different time windows and/or responding to different spatial structures. Here, we developed and tested a framework to evaluate spatiotemporal connectivity using two new measures: the spatiotemporal connectivity (STcon) and the spatiotemporal connectivity matrix (STconmat). 2. To obtain these measures, we consider a set of spatially connected sites within a temporally dynamic network. These measures are calculated from a spatiotemporal matrix where spatial and temporal connections across sites are captured. These connections respond to a determined network structure, assign different values to these connections and generate different scenarios from which we obtain the spatiotemporal connectivity. We developed these measures by using a dataset of stream flow state spanning a 513-day period obtained from data loggers installed in seven temporary streams. These measures allowed us to characterise connectivity among stream reaches and relate spatiotemporal patterns with macroinvertebrate community structure and composition. 3. Spatiotemporal connectivity differed within and among streams, with STcon and STconmat capturing different hydrological patterns. Macroinvertebrate richness and diversity were higher in more spatiotemporally connected sites. Community dissimilarity was related to STconmat showing that more spatiotemporally connected sites had similar communities for active and passive dispersers. Interestingly, both groups were related to spatiotemporal connectivity patterns for some of the analysed scenarios, highlighting the relevance of spatiotemporal connectivity in dynamic systems. 4. As we exemplified, the proposed framework can help to disentangle and quantify spatiotemporal dynamics or be applied in the conservation of dynamic systems such as temporary streams. However, the current framework is not limited to the temporal and spatial features of temporary streams. It can be extended to other ecosystems by including different time windows and/or consider different network structures to assess spatiotemporal patterns. Such spatiotemporal measures are especially relevant in a context of global change, with the spatiotemporal dynamics of ecosystems being heavily disrupted by human activities.info:eu-repo/semantics/publishedVersio