The Institute of Translational Neuroscience is an independent research institution at the Medical Faculty of the University Münster. It is dedicated to provide new options for diagnostic and/or therapeutic strategies for human diseases of the nervous system. This “bench to bedside” approach builds on molecular research pursued during the last 30 years which paved the avenue for novel therapeutic strategies to be assessed in man. It follows the idea that neuroscience is a discipline providing understanding of human disease and eventually its cure, rather than to explain man itself. Translation defines the goal of basic research, the patient. While experimental studies are pursued in close collaboration with the Institute of Anatomy, University of Cologne (Director: Prof. Dr. med. Johannes Vogt), clinical studies are performed in close collaboration with the Institute of Translational Psychiatry, University Münster (Director: Prof. Dr. med. Dr. phil. Udo Dannlowski).

In addition to translational research, philosophical aspects of the mind-body debate are addressed and a focus of the Institute’s teaching activities. The ever-recurring attempts of the neurosciences to explain all mental phenomena in physical terms alone are revised by critical reappraisal of classical concepts, e.g. Wilder Penfield’s “storehouse of memories”. Our analyzes question the idea of a realization of memory in solely naturalistic terms. These studies are performed in part at the Montreal Neurological Institute (Prof. Dr. Richard Leblanc, Prof. Dr. Jack Antel), Quebec, Canada, and in close collaboration with Prof. Dr. med. Frank Stahnisch at the Hotchkiss Brain Institute, University of Calgary, Alberta, Canada.

McWhinney SR, Hlinka J, Bakstein E, Dietze LMF, Corkum ELV, Abé C, Alda M, Alexander N, Benedetti F, Berk M, Bøen E, Bonnekoh LM, Boye B, Brosch K, Canales-Rodríguez EJ, Cannon DM, Dannlowski U, Demro C, Diaz-Zuluaga A, Elvsåshagen T, Eyler LT, Fortea L, Fullerton JM, Goltermann J, Gotlib IH, Grotegerd D, Haarman B, Hahn T, Howells FM, Jamalabadi H, Jansen A, Kircher T, Klahn AL, Kuplicki R, Lahud E, Landén M, Leehr EJ, Lopez-Jaramillo C, Mackey S, Malt U, Martyn F, Mazza E, McDonald C, McPhilemy G, Meier S, Meinert S, Melloni E, Mitchell PB, Nabulsi L, Nenadić I, Nitsch R, Opel N, Ophoff RA, Ortuño M, Overs BJ, Pineda-Zapata J, Pomarol-Clotet E, Radua J, Repple J, Roberts G, Rodriguez-Cano E, Sacchet MD, Salvador R, Savitz J, Scheffler F, Schofield PR, Schürmeyer N, Shen C, Sim K, Sponheim SR, Stein DJ, Stein F, Straube B, Suo C, Temmingh H, Teutenberg L, Thomas-Odenthal F, Thomopoulos SI, Urosevic S, Usemann P, van Haren NEM, Vargas C, Vieta E, Vilajosana E, Vreeker A, Winter NR, Yatham LN, Thompson PM, Andreassen OA, Ching CRK, Hajek T (2024). Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity. Hum Brain Mapp 45(8):e26682. (full article)

Abstract

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables.

Tüscher O, Muthuraman M, Horstmann JP, Horta G, Radyushkin K, Baumgart J, Sigurdsson T, Endle H, Ji H, Kuhnhäuser P, Götz J, Kepser LJ, Lotze M, Grabe HJ, Völzke H, Leehr EJ, Meinert S, Opel N, Richers S, Stroh A, Daun S, Tittgemeyer M, Uphaus T, Steffen F, Zipp F, Groß J, Groppa S, Dannlowski U, Nitsch R, Vogt J (in press). Altered cortical synaptic lipid signaling leads to intermediate phenotypes of mental disorders. Mol Psychiatry. (full article)

Abstract:

Excitation/inhibition (E/I) balance plays important roles in mental disorders. Bioactive phospholipids like lysophosphatidic acid (LPA) are synthesized by the enzyme autotaxin (ATX) at cortical synapses and modulate glutamatergic transmission, and eventually alter E/I balance of cortical networks. Here, we analyzed functional consequences of altered E/I balance in 25 human subjects induced by genetic disruption of the synaptic lipid signaling modifier PRG-1, which were compared to 25 age and sex matched control subjects. Furthermore, we tested therapeutic options targeting ATX in a related mouse line. Using EEG combined with TMS in an instructed fear paradigm, neuropsychological analysis and an fMRI based episodic memory task, we found intermediate phenotypes of mental disorders in human carriers of a loss-of-function single nucleotide polymorphism of PRG-1 (PRG-1^R345T/WT). Prg-1^R346T/WT animals phenocopied human carriers showing increased anxiety, a depressive phenotype and lower stress resilience. Network analysis revealed that coherence and phase-amplitude coupling were altered by PRG-1 deficiency in memory related circuits in humans and mice alike. Brain oscillation phenotypes were restored by inhibtion of ATX in Prg-1 deficient mice indicating an interventional potential for mental disorders.

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