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The Rey–Osterrieth complex figure (ROCF) test is a neuropsychological task that can be useful for early detection of cognitive decline in the elderly population. Several computer vision systems have been proposed to automate this complex analysis task, but the lack of public benchmarks does not allow a fair comparison of these systems. To advance in that direction, we present a benchmarking framework for the automatic scoring of the ROCF test that provides: the ROCFD528 dataset, which is the first open dataset of ROCF line drawings; and experimental results obtained by several modern deep learning models, which can be used as a baseline for comparing new proposals. We evaluate different state-of-the-art convolutional neural networks (CNNs) under traditional and transfer learning paradigms. Experimental quantitative results (MAE = 3.448) indicate that a CNN specifically designed for sketches outperforms other state of the art CNN architectures when the number of examples available is limited. This benchmark can also be a paradigmatic example within the broad field of machine learning for the development of efficient and robust models for analyzing line drawings and sketches not only in classification but also in regression tasks.

Machine learning (ML) methodologies for detecting Mild Cognitive Impairment (MCI) are progressively gaining prevalence to manage the vast volume of processed information. Nevertheless, the black-box nature of ML algorithms and the heterogeneity within the data may result in varied interpretations across distinct studies. To avoid this, in this proposal, we present the design of a decision support system that integrates a machine learning model represented using the Semantic Web Rule Language (SWRL) in an ontology with specialized knowledge in neuropsychological tests, the NIO ontology. The system’s ability to detect MCI subjects was evaluated on a database of 520 neuropsychological assessments conducted in Spanish and compared with other well-established ML methods. Using the F2 coefficient to minimize false negatives, results indicate that the system performs similarly to other well-established ML methods (F2TE2 = 0.830, only below bagging, F2BAG = 0.832) while exhibiting other significant attributes such as explanation capability and data standardization to a common framework thanks to the ontological part. On the other hand, the system’s versatility and ease of use were demonstrated with three additional use cases: evaluation of new cases even if the acquisition stage is incomplete (the case records have missing values), incorporation of a new database into the integrated system, and use of the ontology capabilities to relate different domains. This makes it a useful tool to support physicians and neuropsychologists in population-based screenings for early detection of MCI.

Dementia disease research encompasses diverse data modalities, including advanced imaging, deep phenotyping, and multi-omics analysis. However, integrating these disparate data sources has historically posed a significant challenge, obstructing the unification and comprehensive analysis of collected information. In recent years, knowledge graphs have emerged as a powerful tool to address such integration issues by enabling the consolidation of heterogeneous data sources into a structured, interconnected network of knowledge. In this context, we introduce DemKG, an open-source framework designed to facilitate the construction of a knowledge graph integrating dementia research data, comprising three core components: a KG-builder that integrates diverse domain ontologies and data annotations, an extensions ontology providing necessary terms tailored for dementia research, and a versatile transformation module for incorporating study data. In contrast with other current solutions, our framework provides a stable foundation by leveraging established ontologies and community standards and simplifies study data integration while delivering solid ontology design patterns, broadening its usability. Furthermore, the modular approach of its components enhances flexibility and scalability. We showcase how DemKG might aid and improve multi-modal data investigations through a series of proof-of-concept scenarios focused on relevant Alzheimer’s disease biomarkers.

Informatics increases the yield from neuroscience due to improved data. Data sharing and accessibility enable joint efforts between different research groups, as well as replication studies, pivotal for progress in the field. Research data archiving solutions are evolving rapidly to address these necessities, however, distributed data integration is still difficult because of the need of explicit agreements for disparate data models. To address these problems, ontologies are widely used in biomedical research to obtain common vocabularies and logical descriptions, but its application may suffer from scalability issues, domain bias, and loss of low-level data access. With the aim of improving the application of semantic models in biobanking systems, an incremental semantic framework that takes advantage of the latest advances in biomedical ontologies and the XNAT platform is designed and implemented. We follow a layered architecture that allows the alignment of multi-domain biomedical ontologies to manage data at different levels of abstraction. To illustrate this approach, the development is integrated in the JPND (EU Joint Program for Neurodegenerative Disease) APGeM project, focused on finding early biomarkers for Alzheimer's and other dementia related diseases.