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Winter, Nils R. ; Blanke, Julian ; Leenings, Ramona ; Ernsting, Jan ; Fisch, Lukas ; Sarink, Kelvin ; Barkhau, Carlotta ; Thiel, Katharina ; Flinkenflügel, Kira ; Winter, Alexandra ; Goltermann, Janik ; Meinert, Susanne ; Dohm, Katharina ; Repple, Jonathan ; Gruber, Marius ; Leehr, Elisabeth Johanna ; Opel, Nils ; Grotegerd, Dominik ; Redlich, Ronny ; Nitsch, Robert ; Bauer, Jochen ; Heindel, Walter ; Groß, Joachim ; Andlauer, Till ; Forstner, Andreas Josef ; Nöthen, Markus Maria ; Rietschel, Marcella ; Hofmann, Stefan G. ; Pfarr, Julia-Katharina ; Teutenberg, Lea ; Usemann, Paula ; Thomas-Odenthal, Florian ; Wroblewski, Adrian ; Brosch, Katharina ; Stein, Frederike ; Jansen, Andreas ; Jamalabadi, Hamidreza ; Alexander, Nina ; Straube, Benjamin ; Nenadić, Igor ; Kircher, Tilo ; Dannlowski, Udo ; Hahn, Tim
Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
Borsch, Thomas ; Stevens, Albert-Dieter ; Häffner, Eva ; Güntsch, Anton ; Berendsohn, Walter G. ; Appelhans, Marc Sebastian ; Barilaro, Christina ; Beszteri, Bánk ; Blattner, Frank R. ; Bossdorf, Oliver ; Dalitz, Helmut ; Dressler, Stefan ; Duque-Thüs, Rhinaixa ; Esser, Hans-Joachim ; Franzke, Andreas ; Goetze, Dethardt ; Grein, Michaela ; Grünert, Uta ; Hellwig, Frank ; Hentschel, Jörn ; Hörandl, Elvira ; Janßen, Thomas ; Jürgens, Norbert ; Kadereit, Gudrun ; Karisch, Timm ; Koch, Marcus ; Müller, Frank ; Müller, Jochen ; Ober, Dietrich ; Porembski, Stefan ; Poschlod, Peter ; Printzen, Christian ; Röser, Martin ; Sack, Peter ; Schlüter, Philipp M. ; Schmidt, Marco ; Schnittler, Martin ; Scholler, Markus ; Schultz, Matthias ; Seeber, Elke ; Simmel, Josef ; Stiller, Michael ; Thiv, Mike ; Thüs, Holger ; Tkach, Natalia ; Triebel, Dagmar ; Warnke, Ursula ; Weibulat, Tanja ; Wesche, Karsten ; Yurkov, Andrey ; Zizka, Georg
Plants, fungi and algae are important components of global biodiversity and are fundamental to all ecosystems. They are the basis for human well-being, providing food, materials and medicines. Specimens of all three groups of organisms are accommodated in herbaria, where they are commonly referred to as botanical specimens.The large number of specimens in herbaria provides an ample, permanent and continuously improving knowledge base on these organisms and an indispensable source for the analysis of the distribution of species in space and time critical for current and future research relating to global biodiversity. In order to make full use of this resource, a research infrastructure has to be built that grants comprehensive and free access to the information in herbaria and botanical collections in general. This can be achieved through digitization of the botanical objects and associated data.The botanical research community can count on a long-standing tradition of collaboration among institutions and individuals. It agreed on data standards and standard services even before the advent of computerization and information networking, an example being the Index Herbariorum as a global registry of herbaria helping towards the unique identification of specimens cited in the literature.In the spirit of this collaborative history, 51 representatives from 30 institutions advocate to start the digitization of botanical collections with the overall wall-to-wall digitization of the flat objects stored in German herbaria. Germany has 70 herbaria holding almost 23 million specimens according to a national survey carried out in 2019. 87% of these specimens are not yet digitized. Experiences from other countries like France, the Netherlands, Finland, the US and Australia show that herbaria can be comprehensively and cost-efficiently digitized in a relatively short time due to established workflows and protocols for the high-throughput digitization of flat objects.Most of the herbaria are part of a university (34), fewer belong to municipal museums (10) or state museums (8), six herbaria belong to institutions also supported by federal funds such as Leibniz institutes, and four belong to non-governmental organizations. A common data infrastructure must therefore integrate different kinds of institutions.Making full use of the data gained by digitization requires the set-up of a digital infrastructure for storage, archiving, content indexing and networking as well as standardized access for the scientific use of digital objects. A standards-based portfolio of technical components has already been developed and successfully tested by the Biodiversity Informatics Community over the last two decades, comprising among others access protocols, collection databases, portals, tools for semantic enrichment and annotation, international networking, storage and archiving in accordance with international standards. This was achieved through the funding by national and international programs and initiatives, which also paved the road for the German contribution to the Global Biodiversity Information Facility (GBIF).Herbaria constitute a large part of the German botanical collections that also comprise living collections in botanical gardens and seed banks, DNA- and tissue samples, specimens preserved in fluids or on microscope slides and more. Once the herbaria are digitized, these resources can be integrated, adding to the value of the overall research infrastructure. The community has agreed on tasks that are shared between the herbaria, as the German GBIF model already successfully demonstrates.We have compiled nine scientific use cases of immediate societal relevance for an integrated infrastructure of botanical collections. They address accelerated biodiversity discovery and research, biomonitoring and conservation planning, biodiversity modelling, the generation of trait information, automated image recognition by artificial intelligence, automated pathogen detection, contextualization by interlinking objects, enabling provenance research, as well as education, outreach and citizen science.We propose to start this initiative now in order to valorize German botanical collections as a vital part of a worldwide biodiversity data pool.