Summary

Neurological disorders are the leading cause of disabilities and second leading cause of death worldwide. Important discoveries therefor have the potential to save or aid large patient populations. However, valorising this research in a way that leads to this societal impact is not without its obstacles. A lack of resources, a lack of awareness from researchers and institutions, and differences between fundamental and clinical researchers are often mentioned in the literature as bottlenecks limiting the translational activity.

For this study, semi structured interviews were held with researchers from varying backgrounds in either fundamental, translational or clinical neuroscience. Transcripts were coded using iterative coding strategies and analysed for common themes. Based on this analysis, it seems expertise regarding effective translational research is not widely available, collaborators are difficult to find, and resources are sometimes only available for those with interdisciplinary collaborators. There seems to be an interest among researchers in a professional body focussing on translation embedded within the research institute. This body would have a role in helping find collaborators, increasing crosstalk and connections between clinical and fundamental researchers, as well as serving as a source of expertise regarding best translational practises.

These organisations, often called intermediaries, are characterised through three main roles: the knowledge broker, the network manager, and the system transitioner. Based on these three roles and available literature, different intermediary organisations are discussed through a novel framework that builds upon existing models from different fields. Two case studies are used to validate the framework and further explore trade offs and strategic choices made during the founding of the organisation or transitioning to a new strategy. Interviews with experts involved in these organisations were held and used to compare these two organisations based on the proposed framework. Based on their relationship to bridging and bonding social capital, autonomy, and knowledge aggregation, a first proposal is made regarding the best approach for this translational intermediary.

This intermediary tasked with optimizing the translational activity in the neuroscience field should be tasked with collecting and maintaining knowledge, be a centralised organisations with strong ties with different researchers in the network, and function autonomously from any existing institution.

In recent years, the increase in brain research led to the development of large-scale brain imaging techniques. With large-scale brain imaging techniques, such as functional magnetic resonance imaging (fMRI), functional connectivity analyses have shown altered connectivity patterns in humans and mice with neurobiological disorders, such as autism spectrum disorder (ASD). To further investigate different mutations that contribute to ASD, a behavioral neuroscientific experiment has been performed at the neuroscientific department of Erasmus MC. During the trial, brain activity in groups of wild-type and homozygous mice is measured using functional ultrasound (fUS) while allowing for spontaneous behaviors of mice. The homozygous mice lack the SHANK2 protein, resulting in hyperactivity and autistic-like behavioral alterations associated with ASD in humans. Understanding the origin of ASD is key to providing effective treatment. However, with the introduction of novel large-scale brain imaging techniques such as fUS, new methods have to be developed that enable functional connectivity analyses. Furthermore, new insights might also be acquired from dynamic functional connectivity analyses, in which changes in functional connectivity over time are evaluated. Subsequently, the question remains if it is possible to unravel differences in brain dynamics between wild-type and homozygous mice using a dynamic functional connectivity analysis.
First, an fUS data model is developed to model how fUS signals arise from a generative perspective. This model comprises a combination of a convolutive and a state-space model. Subsequently, inference of functional networks and their temporal dynamics can be performed. Also, a pre-processing pipeline for experimental fUS data is designed to reduce problem complexity and data cleaning. The performance of the developed methods is evaluated on the experimental data set, where a difference in brain dynamics between wild-type and homozygous mice is investigated.
It is found that a deconvolution procedure using the non-negative least absolute shrinkage and selection operator (NNLASSO) is necessary to reconstruct the underlying activity of neural populations. After that, using the hidden Markov model (HMM) as a state-space model, it is found that functional networks and their temporal dynamics can be learned from fUS data using expectation maximization (EM). It has been discovered that the developed methods consistently decompose reconstructed neural activity into biologically plausible functional networks from experimental fUS data. Also, with 96% certainty, a difference in brain dynamics between wild-type and homozygous mice is found using this method.
In summary, in this thesis, novel methods are developed to perform a dynamic functional connectivity analysis on experimental fUS data. Also, by performing such dynamic functional connectivity analysis for the first time on fUS data, a consistent decomposition of reconstructed neural activity into biologically plausible functional networks and a possible difference in brain dynamics between wild-type and homozygous mice are found. This research highlights the potential of fUS as a large-scale brain imaging technique in the quest to understand the origin of ASD and other neurobiological disorders.