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.

Chalas N, Meyer L, Lo CW, Park H, Kluger DS, Abbasi O, Kayser C, Nitsch R, Groß J (in press). Dissociating prosodic from syntactic delta activity during natural speech comprehension. Current Biology (full article)

Abstract:

Decoding human speech requires the brain to segment the incoming acoustic signal into meaningful linguistic units, ranging from syllables and words to phrases. Integrating these linguistic constituents into a coherent percept sets the root of compositional meaning and hence understanding. One important cue for segmentation in natural speech is prosodic cues, such as pauses, but their interplay with higher-level linguistic processing is still unknown. Here, we dissociate the neural tracking of prosodic pauses from the segmentation of multi-word chunks using magnetoencephalography (MEG). We find that manipulating the regularity of pauses disrupts slow speech-brain tracking bilaterally in auditory areas (below 2 Hz) and in turn increases left-lateralized coherence of higher-frequency auditory activity at speech onsets (around 25–45 Hz). Critically, we also find that multi-word chunks—defined as short, coherent bundles of inter-word dependencies—are processed through the rhythmic fluctuations of low-frequency activity (below 2 Hz) bilaterally and independently of prosodic cues. Importantly, low-frequency alignment at chunk onsets increases the accuracy of an encoding model in bilateral auditory and frontal areas while controlling for the effect of acoustics. Our findings provide novel insights into the neural basis of speech perception, demonstrating that both acoustic features (prosodic cues) and abstract linguistic processing at the multi-word timescale are underpinned independently by low-frequency electrophysiological brain activity in the delta frequency range.

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.

BMBF-Validiserungsförderung VIP+